Myopia: Animal Models to Clinical Trials

Myopia

Animal Models to Clinical Trials

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Myopia

Animal Models to Clinical Trials

editors Roger W. Beuerman Seang-Mei Saw Donald T. H. Tan Tien-Yin Wong Singapore Eye Research Institute Singapore National Eye Centre National University of Singapore

World Scientific NEW JERSEY

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LONDON

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SINGAPORE

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BEIJING

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SHANGHAI

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TA I P E I

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CHENNAI

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Published by World Scientific Publishing Co. Pte. Ltd. 5 Toh Tuck Link, Singapore 596224 USA office: 27 Warren Street, Suite 401-402, Hackensack, NJ 07601 UK office: 57 Shelton Street, Covent Garden, London WC2H 9HE

Library of Congress Cataloging-in-Publication Data Myopia : animal models to clinical trials / editors, Roger W. Beuerman ... [et al.]. p. ; cm. Includes bibliographical references and index. ISBN-13: 978-981-283-297-9 (hardcover : alk. paper) ISBN-10: 981-283-297-1 (hardcover : alk. paper) 1. Myopia. I. Beuerman, Roger W., 1942– [DNLM: 1. Myopia. 2. Clinical Trials as Topic--methods. 3. Disease Models, Animal. WW 320 M9962 2010] RE938.M963 2010 617.7'55--dc22 2010010940

British Library Cataloguing-in-Publication Data A catalogue record for this book is available from the British Library.

Copyright © 2010 by World Scientific Publishing Co. Pte. Ltd. All rights reserved. This book, or parts thereof, may not be reproduced in any form or by any means, electronic or mechanical, including photocopying, recording or any information storage and retrieval system now known or to be invented, without written permission from the Publisher.

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Foreword

The field of myopia research is curiously different from research into the etiology of other medical conditions. Whereas for most conditions, research on animals models is universally held to be essential for understanding the etiology and promising treatment modalities, in the case of myopia there is little cross-over from laboratory to clinic, despite the dramatic findings that animals can be made myopic or hyperopic in compensation for defocus imposed by spectacle lenses — a result consistent with the prevalence of other diseases being associated with homeostatic mechanisms. Why this disconnect between animal and clinical studies of myopia? One likely cause is the strong association of the prevalence of myopia with educational level, making it seem to be a uniquely human disorder. This association has led to speculative conjectures about how myopia develops, but has not led to effective prophylaxis. As a result of this lack of therapeutic progress, some have rejected the possibility of arresting myopia by relatively non-invasive visual treatments. Despite several decades of experimental studies of myopia in animals, many clinicians continue to consider myopia as a particularly human conditions, or as a consequence of one’s genetic makeup, and regard the animal studies as only weakly v

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related to why humans become myopic. Given that every few years a new group of animals is added to the myopia zoo, all compensating for defocus imposed by spectacle lenses, how likely is it that humans are different? Were the etiology of human myopia simple this controversy would have been resolved by now. But many human diseases, such as cardiovascular disease and diabetes, have a complex etiology, involving both genetic and behavioral components. How do they differ from myopia? One reason may be that these diseases can be effectively treated without fully understanding their underlying causes, whereas in the case of myopia, understanding the cause of the myopia may well be necessary because simply correcting the myopia may reinstate the conditions that caused the myopia in the first place, thereby setting in motion a positive feedback loop, resulting in an iatrogenic worsening of the myopia. Indeed, some of the possible treatments (scleral reinforcement, daily atropine administration) might be worse than the disease, at least for otherwise healthy children with mild myopia. Only now are we beginning to appreciate and measure in children the parameters likely to be important indicators of the initiation and progression of the myopia development. For example, what is the refractive status of the retinal periphery and how is it affected by visual experience? Does the periphery become hyperopic relative to the fovea as a cause or consequence of the fovea becoming myopic? Does intensive reading first affect the central or peripheral refractions? How much of the variability in peripheral refractions is a function of eye-shape at birth vs the visual surroundings, and how do these interact? If medical students are more likely to become myopic than athletes, is this due to the total amount of reading or to the duration of episodes of reading? Or to the amount of time outdoors? If the latter, is the relevant factor the absence of hyperopic or myopic defocus at the fovea or in the periphery, or perhaps the enhanced stimulation of dopamine by bright light? These are issues that can be studied in both humans and animals, but require experimental manipulations more difficult than those that have been attempted to date. An unfortunate consequence of the complicated, multifactorial, nature of the control of eye growth and development of refractive state is that the field of research has been nearly completely divided into those doing animal research and those doing human research. My dear friend and colleague, the late Sek-Jin Chew, was an exception. He studied muscarinic receptors in different ocular tissues to understand why atropine reduced myopic progression. He studied the blinking of chicks to understand whether brief pulses of increased intraocular pressure would affect ocular

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elongation. And he raised mice wearing a diffuser over one eye under his bed to explore whether mice might be a useful animal model for myopia research because of the variety of genetic manipulations available. When he returned from New York to Singapore he initiated both epidemiological and animal research that led to Singapore becoming one of the world’s leading centers of myopia research. We hope this volume will be a step in the direction of bringing together the fields of animal and epidemiological research into the etiology of myopia. Josh Wallman Department of Biology City University of New York USA

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Professor Sek-Jin Chew

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Dedication to the Late Professor Sek-Jin Chew

The late Professor Sek-Jin Chew was the first Singapore ophthalmologist to be awarded the Fellow College of Surgeons of Edinburgh (FRCS Ed) Gold Medal, as well as MS and PhD degrees. In 1993, he left Singapore to pursue a PhD in the USA in the midst of a promising clinical career in pursuit of his dream in research. As the first full-time medical staff of the Department of Ophthalmology in the National University Hospital (NUH), he had blazed the trail by devoting his career to full-time research, leading by example in showing how research should and must be integral to the future of ophthalmology in Singapore. From the start, he clearly identified that his research focus would primarily be dedicated to myopia, with a goal towards contributing to our knowledge of causation, and the ultimate development of new approaches towards retarding myopia progression. After obtaining his Master’s (in anatomy) from the Louisiana State University Eye Center in the USA, he went on to attain his PhD degree (in neuroscience) at the Rockefeller University in 1996. Upon his return, he immediately set to work in getting the Singapore Eye Research Institute (SERI) underway. In those early days, he worked day and night writing grant proposals to the National Medical Research Council (NMRC), recruiting scientists locally and overseas, and cajoling medical students to participate in projects. At the same time, he was networking with industry and overseas research collaborators to build a name for Singapore as the potential hub for eye research in Asia. It was at one of those overseas meetings that Sek-Jin fell ill, collapsed, and was discovered to have an inoperable brain tumour. Most people would have resigned themselves to fate and would have given up, but not Sek-Jin. He worked even harder and faster, knowing that he was living on borrowed time. Within a very short time, he was able to assemble funds to the tune of ix

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S$20 million as a five-year grant from the NMRC. At the same time, he set up the SERI laboratories in the National University of Singapore (NUS) and built up a team of researchers and support staff from scratch, opening myopia research clinics. On top of that, he was seeing an increasing number of patients, recruiting school children for his myopia trials, as well as establishing successful links and clinical trials with top industry firms such as Bausch and Lomb, CIBA Vision, and others, which continue to this day. However, I believe that it was not his brilliant academic achievements or his lightning speed in getting things done that has touched our lives the most. I believe all of us will best remember Sek-Jin for his fearless courage, his boundless optimism in coping with his brain tumour, his total devotion to his work, in spite of his terminal condition, his genuine friendship, and the interest and concern shown to even the most junior medical students. In recognition of his work in myopia research, Sek-Jin was appointed to a number of international organisations. He was Vice President of the Myopia International Research Foundation and the Director of its AsiaPacific headquarters, based in Singapore. He was also appointed Visiting Professor at the City University of New York and the New York Eye and Ear Infirmary, as well as Visiting Scientist at the Rockefeller University. Sek-Jin was also awarded the SNEC Gold Medal Award in 1997 and the SNEC International Gold Medal posthumous Award in recognition of his achievements internationally. In his short lifespan, Sek-Jin published over 50 papers. He also made more than 150 presentations, mainly in the field of myopia research, in regional and international conferences, and in particular at the world’s foremost research meeting. Sek-Jin played a pivotal role in establishing links between SNEC and top universities in the USA, such as Harvard with its strong emphasis in myopia research, and Johns Hopkins University in a collaborative myopia study on RGP lens. He was constantly bringing in top scientists from the USA, UK, Australia, Japan, Taiwan, and China, just to name a few, to create opportunities to start new areas of collaboration into the 21st century. Donald T.H. Tan FRCSG, FRCSEd, FRCOphth, FAMS Medical Director, Singapore National Eye Centre Chairman, Singapore Eye Research Institute Professor of Ophthalmology Yong Loo Lin School of Medicine National University of Singapore

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“My dear friend and colleague, the late Sek-Jin Chew, was an exception. He studied muscarinic receptors in different ocular tissues to understand why atropine reduced myopic progression. He studied the blinking of chicks to understand whether brief pulses of increased intraocular pressure would affect ocular elongation. And he raised mice wearing a diffuser over one eye under his bed to explore whether mice might be a useful animal model for myopia research because of the variety of genetic manipulations available. When he returned from New York to Singapore, he initiated both epidemiological and animal research that led to Singapore becoming one of the world’s leading centers of myopia research.” Professor Josh Wallman Department of Biology City College, CUNY, USA “Dr Sek-Jin Chew who was also my student, was my friend and a leader of research at the National University of Singapore. We founded the Singapore Eye Research Institute (SERI) of which he was the founding deputy director. Sek-Jin’s intense interest in myopia has made Singapore one of the world’s leading centers for myopic studies.” Professor Arthur S.M. Lim Founding Chairman Singapore Eye Research Institute “I first met Sek-Jin here in Singapore at the WOC in 1988; he had a dream to become the first clinician-scientist in Singapore and to establish an eye research institute here. He achieved both goals in a remarkably short-time and it is a great honor for all of us to recognize that his dream has taken root and grown. Sek and Esther were married while he worked in my lab in the US and they became dear friends. Roger W. Beuerman Singapore Eye Research Institute Duke-NUS SRP Neuroscience and Behavioral Disorders Yong Loo Lin School of Medicine National University of Singapore

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“Despite the onset of a fatal illness, he continued to direct the institute, organize its programmes, and produce a flood of ideas to encourage his team.” Dr J.F. Cullen Royal College of Surgeons of Edinburgh Newsletter No. 55, Singapore 1999 “There are a lot of capable people, but few are both capable and respectable like Sek-Jin…. He was visionary, selfless, and both a good team leader and team player. One of his favorite quotes was, ‘Let’s work together.” ’ Professor Dennis Lam Chairman Department of Ophthalmology and Visual Sciences of the Chinese University of Hong Kong Hong Kong Sek-Jin was a dedicated and highly motivated researcher who did not spare himself even when he knew that a brain tumour threatened his long-term future. He directed SERI up until his life was cruelly cut short. Together in New York, Glasgow and in Singapore, Sek-Jin and I put together a plan for what became the Singapore Eye Research Institute. It is a sadness that he was not spared to see the flowering of the seed that was planted at that time. Wallace S. Foulds, CBE, MD, ChM DSc (Hon), FRCS (Eng & Glasg), FRCOphth Singapore Eye Research Institute Singapore University of Glasgow United Kingdom

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Message

Myopia: Clinical Analysis to Animal Models Dedicated to the late Professor Sek-Jin Chew

Myopia has for some years been of great concern to Singaporeans because of its increased incidence, especially among the Chinese. The prevalence of myopia in Singapore now ranges from 25% to 50% among students, and up to 80% among undergraduates. Paradoxically, this problem also presents ophthalmologists with opportunities to make significant contributions. The problem of myopia is complex. We are still unclear in biological terms how myopia occurs. Is it due to the collagen at the posterior pole? Is this collagen different from that present in the rest of the sclera? What is the role of retinal pigment epithelium? The application of molecular and cell biology may lead to some answers to the numerous questions regarding the etiology of myopia. A worrying medical point is that when myopia is high, e.g. six dioptres or more, degenerative changes may develop in the retina affecting the macula and leading to poor vision in middle age or peripheral retinal degeneration may occur leading to retinal tear and detachment. We have known for many years that myopia tends to run in families and genetic studies will be valuable. What environmental factors aggravate myopia? Is myopia associated with prolonged use of the computer or with prolonged reading? Will eye exercises help? Will genetic therapy help? Do we know of factors that can slow down the progress of myopia? There are numerous unanswered questions. Myopia is more common among the Chinese. “Why?” The late Professor Ida Mann noted that the hunters of Europe like the Germans and the aborigines of Australia were not myopic because if they could see xiii

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far, they would not survive. In contrast, Chinese scholars, craftsmen and artists engaged in near work survived. This might explain why, over the generations, myopia has become prevalent among the Chinese. There are many studies to slow down the progress of myopia — eye exercises, eye massages, avoidance of prolonged reading, use of Atropine, use of large letters on white or black boards in school and a host of other methods. Many have sought alternatives to the use of spectacles. Contact lenses became popular but their use is not without problems. In fact, the use of soft lenses can lead to infection which has caused blindness. When the patient is 20-year or older, his vision can be improved with surgery, the most popular of which is the use of the excimer laser. More recently, LASIK — where a thin layer of the cornea is lifted before the application of laser — has been used. There are now several new lasers and methods of refractive surgery being introduced. This book addresses myopia and I believe that readers will find it useful in their understanding of the condition. I am delighted that this publication is dedicated to one of the world’s most enthusiastic researchers on myopia — the late Dr Sek-Jin Chew who was also my student, my friend and a leader of research at the National University of Singapore. We founded the Singapore Eye Research Institute (SERI) of which he was the founding deputy director. Sek Jin’s intense interest in myopia has made Singapore one of the world’s leading centres for myopic studies. While we wait for research to give us more answers, this book is a useful guide for anyone wishing to learn more about myopia. Professor Arthur Lim, MD (Hon), FRCS Founding Chairman Singapore Eye Research Institute

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Contents

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Foreword

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Dedication

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Message

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About the Editors

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List of Contributors

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Acknowledgments

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Section 1

Epidemiology and Risk Factors

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Chapter 1.1

Epidemiology of Myopia and Myopic Shift in Refraction Barbara E.K. Klein

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Chapter 1.2

Environmental Risk Factors for Myopia in Children Wilson C.J. Low, Tien-Yin Wong and Seang-Mei Saw

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Chapter 1.3

Gene-Environment Interactions in the Aetiology of Myopia Ian G. Morgan and Kathryn A. Rose

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Chapter 1.4

The Economics of Myopia Marcus C.C. Lim and Kevin D. Frick

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Section 2

Clinical Studies and Pathologic Myopia

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Chapter 2.1

Quality of Life and Myopia Ecosse L. Lamoureux and Hwee-Bee Wong

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Chapter 2.2

Ocular Morbidity of Pathological Myopia V. Swetha E. Jeganathan, Seang-Mei Saw and Tien-Yin Wong

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Chapter 2.3

Myopia and Glaucoma Shamira A. Perera and Tin Aung

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Chapter 2.4

The Myopic Retina Shu-Yen Lee

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Chapter 2.5

Retinal Function Chi D. Luu and Audrey W.L. Chia

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Section 3

Genetics of Myopia

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Chapter 3.1

New Approaches in the Genetics of Myopia Liang K. Goh, Ravikanth Metlapally and Terri Young

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Chapter 3.2

Twins Studies and Myopia Maria Schäche and Paul N. Baird

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Chapter 3.3

TIGR, TGFB1, cMET, HGF, Collagen Genes, and Myopia Chiea-Chuen Khor

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Chapter 3.4

Statistical Analysis of Genome-wide Association Studies for Myopia Yi-Ju Li and Qiao Fan

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Section 4

Animal Models and the Biological Basis of Myopia

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Chapter 4.1

The Relevance of Studies in Chicks for Understanding Myopia in Humans Josh Wallman and Debora L. Nickla

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Chapter 4.2

The Mechanisms Regulating Scleral Change in Myopia Neville A. McBrien

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Chapter 4.3

The Mouse Model of Myopia Frank Schaeffel

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Chapter 4.4

Gene Analysis in Experimental Animal Models of Myopia Roger W. Beuerman, Liang K. Goh and Veluchamy A. Barathi

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Section 5

Interventions for Myopia

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Chapter 5.1

Atropine and Other Pharmacological Approaches to Prevent Myopia Louis M.G. Tong, Veluchamy A. Barathi and Roger W. Beuerman

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Chapter 5.2

Physical Factors in Myopia and Potential Therapies Wallace S. Foulds and Chi D. Luu

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Index

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About the Editors

Roger W. Beuerman, PhD Singapore Eye Research Institute Duke-NUS SRP Neuroscience and Behavioral Disorders Ophthalmology Yong Loo Lin School of Medicine National University of Singapore

Roger Beuerman is currently Senior Scientific Director of the Singapore Eye Research Institute; Professor of Neuroscience and Behavioral Disorders at the DUKE-NUS School of Medicine; Adjunct Professor of Ophthalmology at the National University of Singapore; Adjunct Professor of Chemical and Biomedical Engineering at the Nanyang Technological University; and Senior Scientist at the Bioinformatics Institute. He has more than 20 years’ experience in translational research in ophthalmology. He is an ARVO Fellow; a Fellow of the Alcon Research institute, and the Paolo Foundation in Helsinki, Finland. He has been a Visiting Professor in the Department of Ophthalmology and Visual Sciences at the Chinese University of Hong Kong, the Department of Ophthalmology at the University of Helsinki, and the Department of Ophthalmology of Tianjin, China. He has received many awards, including the CIBA-CVO Research Excellence Award, the Everett Kinsey Award (CLAO), the 2nd Chew Sek-Jin Lecture, Bireswar Chakrabarti Memorial Oration of the Indian Eye Research Group, LA Technology Award, and recently the President’s (Singapore) First Science and Technology Award. He has previously edited books on corneal wound healing and dry eye, has more than 220 publications, and sits on several editorial boards, including Cornea, Ocular Surface, and Journal of Ocular Pharmacology and Therapeutics. xix

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About the Editors

Seang-Mei Saw, MBBS, MPH, PhD Department of Epidemiology & Public Health Yong Loo Lin School of Medicine National University of Singapore Singapore Eye Research Institute

Seang-Mei Saw is currently an Associate Professor at the Department of Epidemiology and Public Health, and Vice-Dean (Research Preclinical), Yong Loo Lin School of Medicine, National University of Singapore (NUS). She received her MBBS degree from NUS and both her MPH and PhD from the Johns Hopkins Bloomberg School of Public Health. Her primary research interests are related to the epidemiology, genetics and gene-environment interactions for myopia and other eye diseases. She has published more than 200 peer-reviewed articles in international journals, including the Lancet and Journal of the American Medical Association (JAMA). She is currently the PI and Co-I of grants totaling > $15 million from the BMRC, NMRC, NIH and NHMRC (Australia). She supervises 30 research staff, 3 post-doctoral research fellows and 6 PhD and MSc students. Seang-Mei is an Editorial Board member of Investigative Ophthalmology and Visual Science, Ophthalmic and Physiologic Optics, and the Annals Academy of Medicine (Singapore). She is the recipient of the Garland W. Clay Award (2006); the Great Women of our Times Award, Science and Technology Category, Singapore (2006); the American Academy of Ophthalmology (AAO) Achievement Award (2009); and the NUS School of Medicine Faculty Research Excellence Award (2009). Seang-Mei was the past Chair of the 11th International Myopia Conference held from 16th to 18th August 2006 in Singapore, and is currently Chair of the Program Committee, Clinical/Epidemiologic section, Association for Research in Vision and Ophthalmology (ARVO).

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About the Editors

Donald T.H. Tan, FRCSG, FRCSEd, FRCOphth, FAMS Singapore National Eye Centre Singapore Eye Research Institute Yong Loo Lin School of Medicine National University of Singapore

Donald Tan is the Medical Director of the Singapore National Eye Centre (SNEC), Chairman of the Singapore Eye Research Institute (SERI) and tenured professor at the Department of Ophthalmology at the National University of Singapore. He heads the SNEC Cornea and Refractive Services and is also Medical Director of the Singapore Eye Bank. A corneal and refractive surgeon by training, Professor Tan’s research contributions lie in new forms of lamellar keratoplasty, ocular surface and stem cell transplantation, and artificial cornea surgery, refractive surgery trials, and epidemiological studies on myopia and clinical trials on various approaches to retarding myopia progression. To date, he has published 226 peer-reviewed articles, and contributed 18 book chapters, and has also trained 22 corneal fellows from 13 countries. Professor Tan was awarded the Asia-Pacific Academy of Ophthalmology De Ocampo Award in 2001, a 2006 AAO Distinguished Achievement Award, and a Singapore National Public Health Award for first identifying and stemming the 2006 global outbreak of contact lens solution-related Fusarium keratitis. In 2009, he was awarded the 2009 Casebeer Award by the International Society of Refractive Surgery (ISRS) and the American Academy of Ophthalmology (AAO), for his contributions to research in the field of refractive surgery. He is currently Vice-President/President Elect of The Cornea Society, and the founding President of the Asia Cornea Society (ACS) and the Association of Eye Banks of Asia (AEBA).

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About the Editors

Tien-Yin Wong, MBBS, M.Med(Ophthl), FRCSEd, FRANZCO, MPH, PhD Singapore National Eye Centre Singapore Eye Research Institute Department of Ophthalmology Yong Loo Lin School of Medicine National University of Singapore Centre for Eye Research Australia & Department of Ophthalmology The University of Melbourne Royal Victorian Eye & Ear Hospital Australia Tien-Yin Wong is currently Professor and Director of the Singapore Eye Research Institute, National University of Singapore and Senior Consultant Ophthalmologist at the Singapore National Eye Centre and National University Health System. He is concurrently Professor of Ophthalmology at the Centre for Eye Research Australia, the University of Melbourne. Professor Wong is a retinal specialist and leads a research program on the epidemiology, impact and treatment of retinal diseases, including diabetic retinopathy, age-related macular degeneration, and retinal vein occlusion. A particular focus of his work is on early retinal vascular changes and the use of novel retinal imaging techniques to predict cardiovascular disease. He has published more than 400 peer-reviewed papers, including papers in the New England Journal of Medicine, the Lancet, and the Journal of the American Medical Association and has written 3 books that are widely used in ophthalmology. For his research, Professor Wong has been recognized nationally and internationally with awards not only in ophthalmology, but also in the fields of cardiovascular disease and diabetes. He was the recipient of the Alcon Research Institute Award, the Novartis Prize in Diabetes (Global Young Investigator) award, the Australian Commonwealth Health Minister’s Award for Excellence in Health and Medical Research and the Sandra Doherty Award from the American Heart Association. Prof. Wong is the Executive Editor of the American Journal of Ophthalmology, and on Editorial Board of three other journals, Investigative Ophthalmology and Visual Sciences, Ophthalmic Epidemiology and Diabetes Care. He currently supervises 30 research staff and has previously trained 10 postdoctoral research fellows and PhD students. He is currently supervising 4 PhD students and 4 postdoctoral fellows.

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List of Contributors

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Tin Aung, FRCSEd, FRCOphth, FAMS, PhD Singapore National Eye Centre Singapore Eye Research Institute Yong Loo Lin School of Medicine National University of Singapore Singapore

Audrey W.L. Chia, MBBS(Hons), FRANZCO Singapore National Eye Centre Singapore Eye Research Institute Singapore

Paul N. Baird, PhD Centre for Eye Research Australia University of Melbourne Royal Victorian Eye and Ear Hospital Australia

Qiao Fan, M.S. Department of Epidemiology and Public Health National University of Singapore Singapore

Veluchamy A. Barathi, BVSc, PhD Singapore Eye Research Institute Singapore

Wallace S. Foulds, CBE MD, ChM, DSc (Hon), FRCS (Eng, Glasg), FRCOphth Singapore Eye Research Institute Singapore University of Glasgow United Kingdom

Roger W. Beuerman, PhD Singapore Eye Research Institute Duke-NUS, SRP Neuroscience and Behavioral Disorders Ophthalmology, Yong Loo Lin School of Medicine National University of Singapore Singapore

Kelvin D. Frick, PhD, MA Johns Hopkins Bloomberg School of Public Health USA

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List of Contributors Liang K. Goh, PhD Duke-National University of Singapore Graduate Medical School Singapore

Shu-Yen Lee, FRCSEd(Ophth), FAMS Singapore National Eye Centre Duke-National University of Singapore Graduate Medical School Singapore

V. Swetha E. Jeganathan, MBBS, MAppMgt (Hlth), DSc Centre for Eye Research Australia University of Melbourne Australia

Yi-Ju Li, PhD Department of Biostatistics and Bioinformatics Duke University Medical Center Center for Human Genetics Duke University Medical Center USA

The Tun Hussein Onn National Eye Hospital, Malaysia Singapore Eye Research Institute Singapore Chiea-Chuen Khor, MBBS, D.Phil Division of Infectious Diseases Genome Institute of Singapore Agency for Science Technology and Research Centre for Molecular Epidemiology National University of Singapore Singapore

Marcus C. C. Lim, FRCS (Late) Singapore National Eye Centre Singapore Eye Research Institute Singapore

Barbara E.K. Klein, MD, MPH, FACPM, FACE, FAAO Department of Ophthalmology and Visual Sciences University of Wisconsin School of Medicine and Public Health USA

Wilson C.J. Low, BSc Department of Epidemiology & Public Health Yong Loo Lin School of Medicine National University of Singapore Singapore

Ecosse L. Lamoureux, MSc., PhD Centre for Eye Research Australia The Royal Victorian Eye and Ear Hospital, University of Melbourne Australia

Chi D. Luu, PhD Singapore Eye Research Institute Singapore

Singapore Eye Research Institute Singapore National Eye Centre Singapore

Macular Research Unit, Centre for Eye Research Australia The University of Melbourne Royal Victorian Eye & Ear Hospital Australia

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List of Contributors Neville A. McBrien, BSc, PhD Department of Optometry and Vision Sciences University of Melbourne Australia

Seang-Mei Saw, MBBS, MPH, PhD Department of Epidemiology & Public Health Yong Loo Lin School of Medicine National University of Singapore Singapore

Ravikanth Metlapally, PhD Duke Center for Human Genetics Durham NC USA

Maria Schäche, PhD Centre for Eye Research Australia University of Melbourne Royal Victorian Eye and Ear Hospital Australia

Ian G. Morgan, BSc, PhD ARC Centre of Excellence in Vision Science, Research School of Biology Australian National University Australia

Frank Schaeffel, PhD Section of Neurobiology of the Eye Ophthalmic Research Institute Eberhart-Karls-University Tübingen Germany

Debora L. Nickla, PhD New England College of Optometry Boston Massachusetts USA

Donald T.H. Tan, FRCSG, FRCSEd, FRCOphth, FAMS Singapore National Eye Centre Singapore Eye Research Institute National University of Singapore Singapore

Shamira A. Perera, MBBS(Hons), BSc(Hons) FRCOphth Singapore National Eye Centre Singapore Eye Research Institute Singapore

Louis M.G.Tong, FRCSEd, PhD Singapore National Eye Centre Singapore Eye Research Institute Duke-NUS Graduate Medical School Singapore

Kathryn A. Rose, PhD Discipline of Orthoptics, Faculty of Health Sciences University of Sydney Australia

Josh Wallman, PhD Department of Biology City College, CUNY USA

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List of Contributors Hwee-Bee Wong, MSc Health Services Research and Evaluation Division, Ministry of Health Department of Epidemiology & Public Health Yong Loo Lin School of Medicine National University of Singapore Singapore Tien-Yin Wong, M.Med(Ophthl), FRCSEd, FRANZCO, MPH, PhD Singapore National Eye Centre & Singapore Eye Research Institute & Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore Singapore

Terri L. Young, MD Duke-National University of Singapore Graduate Medical School Duke University Medical CenterDuke Eye Center and the Duke Center for Human Genetics USA

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Acknowledgments

We would like to thank the project team at the Singapore National Eye Centre for their dedication in managing and coordinating the development of this publication. Singapore National Eye Centre Advisor Charity Wai Project Manager Kathy Chen Cover Design Paul Kwang-Yeow Chua Kasi Sandhanam Joanna Peh Wei-Fang We would also like to thank the editorial team at the World Scientific Publishing Singapore for their professionalism and technical support World Scientific Publishing Editor Sook-Cheng Lim Typesetter Stallion Press Proofreader Meng-Wai Chow Artist Hui-Chee Lim xxvii

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Section 1

Epidemiology and Risk Factors

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1.1 Epidemiology of Myopia and Myopic Shift in Refraction Barbara E.K. Klein*

There have been many recent publications concerning myopia. Some are population-based while others reflect specific exposure groups. This chapter includes the findings from many of these publications and describes some of the risk factors/risk indicators for myopia, including personal exposures, family similarities and possible genetic correlates of his or her refractive state.

Introduction Myopia has become a focus of ocular epidemiologic research worldwide for many reasons. There are no current national prevalence estimates of myopia in U.S. children, but the Eye Disease Prevalence Research Group estimated that there were 30,358 cases of myopia of −1.0 D or less (more minus) in U.S. adults 40-years of age or older, of whom 5308 had a refractive error of −5.0 D or less.1 It has been estimated that the costs of correcting myopic refractive errors, either by spectacles or contact lenses, was about 2 billion dollars per year in the U.S. in 1983–87,2 and about 4.6 billion dollars more recently; according to the authors, this is a conservative estimate.3 Current cost estimates would need to include more modern methods of refractive surgery primarily involving the cornea and lensectomy with or without lens implant. These costs are a significant burden for individuals and health systems that are privately or publicly funded. More importantly, myopia, especially higher degrees of myopia, has occupational, medical and quality of life consequences for individuals. *Department of Ophthalmology and Visual Sciences, University of Wisconsin, School of Medicine and Public Health, Madison, 610 North Walnut Street, 4th Floor WARF, Madison, WI, 53726-2336 USA. E-mail: [email protected]

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Myopia has been considered to be a problem with origins in childhood. The estimated prevalence in 6-year-olds is 2% and in 15-year-olds, 15%.4 However, adult onset myopia is not an infrequent occurrence. Furthermore, myopic shifts in refraction can occur across the lifespan, although more common in the first two decades than in older persons, and affects those with hypermetropic refractive errors and emmetropes as well as myopes. Thus, this chapter will describe the distribution of myopia and myopic shifts in refraction as reflected in a (non-scientific) sample of studies worldwide in children and adults. In addition, risk factors that have been evaluated for myopia will be briefly described. The list of risk factors includes a brief description of familiality or family aggregation and in some cases, actual genetic loci. The reader should be aware that a comprehensive review paper of epidemiology of myopia was published in 1996.5 In addition, a description of the literature on refraction in general was published in 2008.6 The material in this chapter overlaps in part the information in these two sources.

Methodologic Issues Studies reported upon in this chapter are of several types, including traditional population-based surveys, studies of special exposure groups such as students, occupationally exposed workers, social or ethnic groups, and rural or urban groups. Some studies were performed on convenience samples, although these were largely avoided. In addition, some studies are cross-sectional, while others incorporate longitudinal follow-up. Some case-control studies have been included as well. Risk factors or risk indicators of myopia are briefly discussed. In addition, no survey of risk factors associated with virtually any condition is complete without at least a brief review of genetic correlates of the condition. To that end, this chapter includes some investigations that have approached the study of myopia using the tools of statistical genetics, with data sources from population-based, family, and twin studies, and combinations of these designs. The field of genetics is progressing rapidly and will be further advanced by the time of the publication of this tome. Nevertheless, although the content will be somewhat dated, the approach to investigating these important determinants will still be relevant. Measurement techniques of refraction varied between studies.7 In most studies in children and in some studies in adults, cycloplegic agents were

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instilled prior to refraction and, among these, different pharmacologic agents were used, likely resulting in some variation in the actual amount of cycloplegia attained. In other studies, no cycloplegic agents were used. The data may have been reported only as continuous data with mean refraction given, while in other cases, the authors reported the data in categories. When the latter was so, the category limits for myopia (and for hyperopia and emmetropia as well) may have differed between studies. Objective refraction was performed in some studies and subjective refraction or refinement in others. In addition, the testing for refraction was based on best corrected distance acuity. This may differ with regard to the use of charts or projection and distances. These details may not have been reported in the publications reviewed. Automated refractors may have been used and these devices differ in design, and consequently could yield systematic differences between results. Lastly, many studies reported the spherical equivalent, while others only gave spherical refraction. All these factors are likely to have led to variation in the reported measure of the refraction, resulting in different estimates of the proportion of persons classified as myopic (and emmetropic and hyperopic). In some cases, the “errors” so induced may not have been unbiased. In addition, there were, no doubt, actual errors and statistical variation around the measurements that were, in general, not reported at all. Despite these concerns, some common findings emerged and some directions for further research have been provided as well. Because the thrust of this volume is on myopia, a refractive state, ocular biometry will not be discussed, although myopia results anatomically and physiologically from the biometry.

Review of Studies (Table 1) For the sake of space and because of the possibility that there have been temporal changes in the distribution of refraction, only the more recent publications have been reviewed for the purpose of this chapter. Epidemiologic studies of refraction have burgeoned and now there are data from studies across the age span and in many different ethnic and cultural groups. For the sake of brevity, a limited amount of descriptive material from each study has been included; however, interested readers could refer to the reference citations. Still, this review is not exhaustive but is merely a (non-systematic) sample of studies of myopia.

Age (Years)

Cycloplegic

CLEERE*,10

No

2583

6–14

Sydney Myopia Study8,76 Hyderabad, India11 < 15 years Oman53 Hong Kong77 Singapore12,43 Jordan39

Yes

1724

Yes No Yes No No

663 2853 7560 1453 1777

Definition of Myopia (SE)

Prevalence (%)

Eye

Yes

≤ −0.5 ≤ −0.75

11.6 10.1

Right Right

5.5–8.4

Yes

≤ −0.5

1.4

Right

< 16–19+ 5–16 7–9 12–17

Yes Yes Yes Yes Not given

< −0.5 ≤ −0.5 ≤ −0.5 ≤ −0.5 < −0.5

4 Not given

Worse Worse Right Right Not given

Studies in Children

29.0 17.6

(Continued)

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Overview of Studies

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Table 1.

Yes Yes Yes Yes Yes Yes Yes No No

1722 3650 5036 5588 4533 4506 4330 559 1518

49–97 40–80+ 40+ 43–86 40–80+ 40–84 40–45 43.2 ± 9.8

Cycloplegic

Definition of Myopia (SE)

Prevalence (%)

Eye

No No No No No No No

≤ −0.5 ≤ −0.5 ≤ −0.5 ≤ −0.5 ≤ −0.5 ≤ −0.5

19 14 25 Not given 26.2 17.0 21.9

Worse Right Right Right Right Right Right

No

≤ −0.5

29.2

Right

Studies in Adults Hyderabad, India11 ≥ 15 years Blue Mountains, Australia18 Baltimore Urban19 LALES20 BDES15 VIP17 Barbados16 Auckland, New Zealand78 Buenos Aires, Argentina79

Abbreviations: SE = spherical equivalent; CLEERE = Collaborative Longitudinal Evaluation of Ethnicity and Refractive Error; LALES = Los Angeles Latino Epidemiologic Study; BDES = Beaver Dam Eye Study; VIP = Visual Impairment Project. * = with refraction data.

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Epidemiology of Myopia

Location/Study

(Continued )

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Table 1.

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The Sydney Myopia Study evaluated refraction in a sample of 6- to 7-year-old school children.8 The mean spherical equivalent refraction in these children was +1.26 D in the right eyes. The boys were slightly more likely to be myopic than the girls, and white children were slightly less likely to be myopic than non-whites. In the 12-year-old children in that study, spherical equivalent was less positive than in the younger children.9 Investigators from six sites in the U.S. pooled their data on refractive errors and ocular biometry in school children ages 6 to 14+ years.10 The students were from different ethnic backgrounds. They found no difference in average spherical equivalent between girls and boys; there was a shift towards myopia with increasing age in both. There were 663 subjects who were 15-years of age or younger in the Andhra Pradesh Eye Disease Study11 (Table 1). Myopia was less common in those 15-years of age and younger (about 4%) than in older persons (19%). The first reported myopes were about 5-years-old. The prevalence of myopia in Chinese school children 7–9-years of age in Singapore was 29%, with successively higher prevalences with increasing age.12 The age when the tendency for increasing myopia levels off is uncertain. Studies of university students suggest that this may continue into the third decade, although there may be the confounding effect of near work activities in these subjects.13,14 The Beaver Dam Eye Study reported that 28.1% of women and 24.0% of men 43–86-years of age were myopic. A difference between men and women was true throughout the age range.15 Overall, the prevalence was 26.2%. Wu reported that the prevalence of myopia decreased with age until 60 years but increased thereafter in a population of AfroCaribbeans.16 The Visual Impairment Project conducted in Victoria, Australia, included urban and rural persons.17 Prevalence decreased with increasing age; the overall prevalence was 17%. Overall, sex was not associated with myopia, but after age correction, women were slightly more likely to have a hyperopic refractive error. The Blue Mountains Eye Study is a population-based study of 3654 persons, 49–97-years of age.18 The overall estimate of prevalent myopia was 14%. The Baltimore Eye Survey reported on refraction in 2200 African Americans and 2659 white Americans in Baltimore.19 Overall, 25% of the population was myopic and whites had higher myopia on average than blacks. Myopia declined with increasing age. The Los Angeles Latino Eye Study found that in 5588 adults 40-years of age or older, the mean spherical equivalent was 0.02 D (+/−1.66) in men and 0.18 D (+/−) in women,20 and that on average there was 0.04 D

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increase in spherical error per year for those 40- to 79-years of age, and −0.07 D per year for those 80-years of age and older. Many of the studies on prevalences or case-control studies with past ocular history available reported on myopic shifts in refraction, i.e. a change in the sphere or spherical equivalent of the refractive correction in a negative direction even within the hyperopic range. In general, in children there is a shift towards more myopic refraction with increasing age. Thorn and colleagues have modeled myopia progression in children using double exponential growth function (Gompertz function).21,22 These investigators estimated that refraction stabilizes in 80% of children by about 19-years of age. This tendency extends past ages that are usually considered to be childhood. In a group of 432 patients being followed up regularly at a clinic, longitudinal measures of refraction were reviewed over about a 10-year course.23 Myopic shifts in refraction occurred in some persons through the seventh decade of life. The mean amount of shift decreased with increasing decade of life, from an average of −0.6 D in the third decade to −0.4 D in the fourth decade and −0.3 D in the fifth decade.

Cohort Effects on Myopia Mutti and Zadnick reported on an apparent birth cohort effect on myopia in three population-based and one family-oriented study of refraction.24 They found that the prevalence of myopia standardized to 44.5- to 49.5-years of age increased in cohorts from about 1900 to about 1940. The most impressive increase in prevalence occurred in those born between 1920 and 1935. Wensor observed, aside from a higher prevalence of myopia in those of younger age in the Visual Impairment Project, that those in younger age groups were more likely to have reported wearing a spectacle correction for distance between 10- and 19-years of age.17 In addition, those who were 40–49-years of age reported wearing a myopic correction at age 40 more often as compared with those who were 70-years of age or older at the time of the survey. Bengtsson and Grodum reported decreased spherical equivalent power in 65- to 74-year-olds in persons with more recent birth year.25 Lee and colleagues found a birth cohort effect in adults participating in the Beaver Dam Eye Study.26 They found that for persons of the same age, those born more recently were more likely to be myopic than those born in earlier years.

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In summary, in adults of largely European background there appears to be a cohort effect on myopia.

Risk Factors for Myopia Risk factors for myopia or myopic shifts in adults are given in Table 2. A description of these and other risk factors in children and in adults is given below. Near work Much of the information on the association of near work with myopia in children is inferred from estimated intensity of school work or reading. A study in Hong Kong examined fishing families and found an association between education and myopia.27 Hepsen and colleagues reported on greater frequency of progression of myopia in children from private schools as compared with apprentices in a skilled labor group.28 Saw and colleagues reported a significant association between the degree of myopia and the

Table 2.

Selected Characteristics* Associated with Myopia in Adults

Location/Study Andhra Pradesh11 Blue Mountains18,80 Baltimore Eye Survey19 BDES15,36,56 VIP17 LALES20 Barbados16,37 Tanjong Pagar33,34 Reykjavik81

SES/ Age Gender Income + + + + + + + Inf. +

0 + + + + + Inf. 0

Near Work/ Education

0

+

0 +

+ + +

+

** +

Nuclear Cataract Occupation + + + + + + +

0 + + +

Abbreviations: SES = socioeconomic status; BDES = Beaver Dam Eye Study; VIP = Visual Impairment Project; LALES = Los Angeles Latino Epidemiologic Study; Inf. = inferred. * Direction not given as associations may vary by strength and direction between categories of some characteristic. ** Near work was associated but not education. + Association found. 0 Association evaluated but not found.

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number of books read per week in a group of Singaporean school children.12 In a study of Los Angeles and Australian 6- and 12-year-olds, parents’ report of children’s near work activity was modestly associated with myopia.9 Recently, Rose and colleagues reported a marked difference in the prevalence of myopia between Australian and Chinese Singaporean 6- to 7-year-old school children. The prevalence in Australians was 3.3% and in Singaporeans, 29.1%, despite the fact that the Australians read more books per week and did more hours of homework per week.29 The possibility that recent increases in years of preschool instruction for Singaporean children may be related to the higher prevalence in these children. Khader and colleagues found that myopic children were likely to spend more time reading and writing and using the computer than their nonmyopic school mates,30 but the analyses were not adjusted for age, which is likely to be an important confounder in these analyses. Rah and colleagues in a study of myopia in parents and children have found that there is an association between near work and myopia, but speculated that the actual strength of the association was probably imprecise because of the inaccuracy of measures of near work. They suggest that better methods of reporting near work activities are needed for future myopia research in children.31 A relationship between near work activity and myopic change in refractive error has been found in adults. Microscopists have been shown to have higher prevalence of myopia than the general population and higher prevalence of adult progression of myopia, but a comparison group was lacking in this report.32 Studies of other specific exposure groups, e.g. medical students,14 suggest that these persons have greater prevalence of myopia than other similarly aged groups. Wu et al. reported that adults who reported near work activities were more likely to be myopic as compared with others in the population.16 Few studies in adults have had careful, precise measures of near work and therefore it is yet to be established whether near work activity is the important exposure and not just a confounder of other important possible causes. Education/Income Education and income are considered together because it is usually not possible to separate the effects of these two exposures. The association of more myopic refractive error with level of educational achievement (and usually with income as well) in children and adults has been found in most

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studies of refraction.9,12,15,18,33,34 It is thought that this reflects near work activities, although there is a dearth of studies that assess the relationship quantitatively and by specific activity as noted above. The education/ refraction association relation may reflect common genetic determinants of intelligence (or educational achievement) and refraction.35 It is noteworthy that education was not associated with change in refraction in two large epidemiologic studies of adults.36,37 Outdoor activity Ip et al. reported a small effect of hours spent outdoors on refraction (more hyperopic) in children in the Sydney Myopia Study.9 This finding was extended by Rose and colleagues, who reported on refraction in a sample of 6- and 12-year-old school children in Sydney, Australia. They found an inverse association of total time outdoors with refraction after adjusting for near work, parental myopia, and ethnicity.38 Hours spent playing sports was inversely associated with myopia in a study of 1777 students aged 12- to 17-years old in Amman, Jordan, but these data were not adjusted for age.39 Jones and colleagues reported that lower amounts of sports and outdoor activities increased the odds of children, with two myopic parents, becoming myopic.40 The chances of children with no myopic parents becoming myopic was the lowest in the children with the greatest amount of sports and outdoor activities. Higher levels of total time spent outdoors, rather than sports per se, were associated with less myopia after adjustment is made for near work, parental myopia, and ethnicity. Rose and colleagues reported that Australian 6- to 7-year-olds spent more hours in outdoor activities than Singaporean children of the same age, the latter having a higher prevalence of myopia.41 Jacobsen and colleagues reported an apparent protective effect of physical activity for development of myopia over a two-year interval in a group of medical students in Copenhagen.42 There is no data to suggest that physical activity or sports has any effect on refraction or change in refraction in adults. Age In childhood, increasing age is associated with increasing prevalence of myopia.43,44 In adults, increasing age is associated with a hyperopic shift36 unless cataract is present when there may then be increasing myopia.45,46 The age effect is further described in the section on “Review of Studies.”

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Race/Ethnicity The comparisons that have been described are based on published data from many different studies and so comparison between the groups described is usually not based on uniform criteria for inclusion, nor are the methods of refraction identical. Nevertheless, the generalizations about the differences between the racial/ethnic groups are correct. Adult Chinese in Singapore have a higher prevalence of myopia than similarly aged European-derived populations34; Mongolians have a lower prevalence of myopia than the Chinese in Singapore and Taiwan and similar prevalences to populations of largely European background47; Eskimos have a lower prevalence of myopia than Whites, Blacks and Chinese48; and Barbadians have higher prevalences of hyperopia and myopia than European-derived populations.16,37 For the last study, the authors suggest that this may be due to higher prevalences of cataract, glaucoma and other ocular conditions in Barbadians. Nuclear cataract Nuclear cataract has been found to be associated with myopia in many studies11,36,46,49,50 (Table 2). This is thought to reflect the increased power of the more sclerotic lens and not a reflection of increased axial length. Family aggregation/Genetics This section will briefly address the genetic epidemiologic evidence to support the notion that myopia has both environmental and genetic determinants. Study designs include population-based and traditional pedigree studies. The primary question involves determining how much of the clustering of myopia in the families reflects common exposures and how much is due to hereditary factors. The section starts with studies based on phenotype and then a few studies that examine genes or genetic markers are briefly described. Siblings The presence of myopia in a sibling was associated with increased odds of myopia in school children in Amman, Jordan.39 In the Framingham Offspring Eye Study, the odds of the subjects having myopia was significantly

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increased when a sibling was myopic (OR varied from 2.50 to 5.13, depending upon the age difference of the siblings).51 In the adult population participating in the Beaver Dam Eye Study, Lee and colleagues reported a sibling correlation of a refractive error of 0.37 (1418 sibling pairs).46 There was no correlation of refraction between spouse pairs. The odds ratios for myopia were similar to those for sistersister, sister-brother and brother-brother pairs, being 4.64, 4.53, and 3.36, respectively. These data in adults suggest strong familial effects on refraction, and, although the relative importance of environment and genetics were not partitioned, the findings are compatible with the existence of important genetic determinants of refraction across its range. In data from the Salisbury Eye Evaluation Study that included 274 older adult sibships, Wojciechowski and colleagues found an OR of 2.65 (95% CI: 1.67-4.19) for myopia threshold of −0.050; neither gender nor race (black or white) had a significant effect on this relationship.48 Eskimo families showed correlations between sibs but not between parents and children, suggesting environmental effects; there was virtually no myopia in grandparents or parents but 58% of children were myopic.52 Parent-child In a case-control study of myopia in 1853 school children in Oman, the presence of myopia in parents was associated with myopia in the children.53 In the study in Jordanian school children, the odds of a child being myopic increased with the number of myopic parents.39 Saw and her colleagues found that the progression of myopia in children was greater for those children whose parents were myopic.54 This finding was also reported by Lam and colleagues who examined the effects of parental myopia on myopia (n = 7560) and myopic shift (n = 2628) over one year in their children.55 Children with a greater number of myopic parents were more myopic and had a greater average myopic shift. Analyses were adjusted for age, gender, parental education and near work performed by the child. Other family members Klein and colleagues evaluated the possibility of a familial effect on refraction for several different sorts of family relationships (sibs, parents and children, avunculars and cousins).56 She found stronger correlations

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between sibling and cousin pairs than between parents and children and avuncular pairs. Segregation analysis did not support the involvement of a single major locus across the range of refractive error but models allowing for polygenic effects provided a better fit. This suggests that several genes of modest effect may influence refraction, possibly in conjunction with environmental factors. Genetics High myopia, sometimes associated with other anomalies, has been associated with several genes.57–60 Some of the regions associated with high myopia have been mapped to chromosomes 18p11.3 (MYP2),61 12q21 to 23 (MYP3),62 17q21 to 22 (MYP5)63 and to other sites.57,59,62–68 Moderate myopia has been mapped to 22q1369 and 8p23.70,71 Simpson and colleagues examined the association of PAX 6 and SOX2 with refraction in a British cohort.72 They found no relationship of these genes to myopia or other spherical refractions, although Hewitt and colleagues60 and Tsai and colleagues73 did find associations with severe myopia. Hammond and his colleagues reported a susceptibility locus for myopia in the PAX6 gene region in their study of twins.70 In contrast, Schache and colleagues did not report linkage to this site in their study of 233 adult dizygotic Australian twin pairs.64 It is possible that the twins in this study are in some way selectively different from the twins in Hammond’s study. Such lack of consistency is not uncommon in genetic studies of complex traits. Klein and colleagues found evidence of linkage of refraction to regions on 22q, previously linked to myopia, and also to novel regions on 1q and 7p.74 These authors interpret their data to confirm the notion that refraction is a complex trait likely influenced by several genetic (and environmental/behavioral) exposures. This brief review of genetic studies of myopia is not meant to be comprehensive nor orderly, but to illustrate the importance of having large enough samples of cases to meaningfully address the potential importance of genes of modest effects that are likely to interact with other genetic and environmental factors to influence the phenotype. Furthermore, epidemiologic studies are essential to address these relationships in general populations as opposed to ascertained groups and twins. These goals are unlikely to be achieved by any one group of investigators; they require judicious and thoughtful harmonizing of phenotype definitions, appropriate stratification, uniform genotyping, and a consistent

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approach to data analysis. Replication of findings in other groups is essential to validate findings.

Comments I have summarized some of the information about the epidemiology of myopia that has appeared in the recent literature. In addition, I have reviewed some of the risk factor data. To “prove” some etiologic and therapeutic hypotheses, randomized controlled clinical trials often provide the best evidence of causal relationships. However, for most questions relating to causes and treatments for myopia, clinical trials cannot be performed. For example, it is unethical and not feasible to randomly assign near work for a period of time in order to determine causal effects of this risk factor. Treatment with drugs to modify accommodative mechanisms has proven to be of limited effect and may not be long-lasting. Treatment with optical approaches has also been of limited success.75 Thus, we must rely largely on well planned and executed epidemiologic (and genetic) studies to enlighten us about the development and course of myopia and other refractive errors, and to search for preventive measures that might alter the refractive status of persons “at risk.”

Acknowledgments The National Institutes of Health grant EY06594 and, in part, the Research to Prevent Blindness (Senior Scientific Investigator Awards), New York, NY, provided funding for the entire study, including collection and analyses of data. The author thanks Mary Kay Aprison for technical support.

References 1. Kempen JH, Mitchell P, Lee KE, et al. (2004) The prevalence of refractive errors among adults in the United States, Western Europe, and Australia. Arch Ophthalmol 122: 495–505. 2. National Eye Advisory Council (US). (1983) Vision research a national plan 1983–87. U.S. Department of Health and Human Services. NIH Publication No. 83–2469. Bethesda, MD.

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3. Javitt JC, Chiang YP. (1994) The socioeconomic aspects of laser refractive surgery. Arch Ophthalmol 112: 1526–1530. 4. Mutti DO, Zadnik K, Adams AJ. (1996) Myopia. The nature versus nurture debate goes on. Invest Ophthalmol Vis Sci 37: 952–957. 5. Saw SM, Katz J, Schein OD, et al. (1996) Epidemiology of myopia. Epidemiol Rev 18: 175–187. 6. Zadnik K, Mutti DO. (2006) In: Benjamin W (ed.), Incidence and Distribution of Refractive Anomalies. Borish’s Clinical Refraction, 2nd ed., pp. 35–55. Elsevier, St. Louis. 7. Goss DA, Grosvenor T. (1996) Reliability of refraction — a literature review. J Am Optom Assoc 67: 619–630. 8. Ojaimi E, Rose KA, Morgan IG, et al. (2005) Distribution of ocular biometric parameters and refraction in a population-based study of Australian children. Invest Ophthalmol Vis Sci 46: 2748–2754. 9. Ip JM, Huynh SC, Kifley A, et al. (2007) Variation of the contribution from axial length and other oculometric parameters to refraction by age and ethnicity. Invest Ophthalmol Vis Sci 48: 4846–4853. 10. Zadnik K, Manny RE, Yu JA, et al. (2003) Ocular component data in schoolchildren as a function of age and gender. Optom Vis Sci 80: 226–236. 11. Dandona R, Dandona L, Naduvilath TJ, et al. (1999) Refractive errors in an urban population in Southern India: the Andhra Pradesh Eye Disease Study. Invest Ophthalmol Vis Sci 40: 2810–2818. 12. Saw SM, Carkeet A, Chia KS, et al. (2002) Component dependent risk factors for ocular parameters in Singapore Chinese children. Ophthalmology 109: 2065–2071. 13. Kinge B, Midelfart A. (1999) Refractive changes among Norwegian university students — a three-year longitudinal study. Acta Ophthalmol Scand 77: 302–305. 14. Lin LL, Shih YF, Lee YC, et al. (1996) Changes in ocular refraction and its components among medical students — a 5-year longitudinal study. Optom Vis Sci 73: 495–498. 15. Wang Q, Klein BE, Klein R, Moss SE. (1994) Refractive status in the Beaver Dam Eye Study. Invest Ophthalmol Vis Sci 35: 4344–4347. 16. Wu SY, Nemesure B, Leske MC. (1999) Refractive errors in a black adult population: the Barbados Eye Study. Invest Ophthalmol Vis Sci 40: 2179–2184. 17. Wensor M, McCarty CA, Taylor HR. (1999) Prevalence and risk factors of myopia in Victoria, Australia. Arch Ophthalmol 117: 658–663. 18. Attebo K, Ivers RQ, Mitchell P. (1999) Refractive errors in an older population: The Blue Mountains Eye Study. Ophthalmology 106: 1066–1072. 19. Katz J, Tielsch JM, Sommer A. (1997) Prevalence and risk factors for refractive errors in an adult inner city population. Invest Ophthalmol Vis Sci 38: 334–340.

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20. Shufelt C, Fraser-Bell S, Ying-Lai M, et al. (2005) Refractive error, ocular biometry, and lens opalescence in an adult population: the Los Angeles Latino Eye Study. Invest Ophthalmol Vis Sci 46: 4450–4460. 21. Thorn F, Gwiazda J, Held R. (2005) Myopia progression is specified by a double exponential growth function. Optom Vis Sci 82: 286–297. 22. Dong L, Gwiazda J, Hyman L, et al., COMET Group. (2007) Myopia stabilization in the Correction of Myopia Evaluation Trial (COMET) cohort. Invest Ophthalmol Vis Sci 48: 2385. 23. Ellingsen KL, Nizam A, Ellingsen BA, Lynn MJ. (1997) Age-related refractive shifts in simple myopia. J Refract Surg 13: 223–228. 24. Mutti DO, Zadnik K. (2000) Age-related decreases in the prevalence of myopia: longitudinal change or cohort effect? Invest Ophthalmol Vis Sci 41: 2103–2107. 25. Bengtsson B, Grodum K. (1999) Refractive changes in the elderly. Acta Ophthalmol Scand 77: 37–39. 26. Lee KE, Klein BE, Klein R, Wong TY. (2002) Changes in refraction over 10 years in an adult population: the Beaver Dam Eye Study. Invest Ophthalmol Vis Sci 43: 2566–2571. 27. Wong L, Coggon D, Cruddas M, Hwang CH. (1993) Education, reading, and familial tendency as risk factors for myopia in Hong Kong fishermen. J Epidemiol Community Health 47: 50–53. 28. Hepsen IF, Evereklioglu C, Bayramlar H. (2001) The effect of reading and near-work on the development of myopia in emmetropic boys: a prospective, controlled, three-year follow-up study. Vision Res 41: 2511–2520. 29. Rose KA, Morgan IG, Smith W, et al. (2008) Myopia, lifestyle, and schooling in students of Chinese Ethnicity in Singapore and Sydney. Arch Ophthalmol 126: 527–530. 30. Mallen EA, Gammoh Y, Al Bdour M, Sayegh FN. (2005) Refractive error and ocular biometry in Jordanian adults. Ophthalmic Physiol Opt 25: 302–309. 31. Rah MJ, Mitchell GL, Mutti DO, Zadnik K. (2002) Levels of agreement between parents’ and children’s reports of near work. Ophthalmic Epidemiol 9: 191–203. 32. Adams DW, McBrien NA. (1992) Prevalence of myopia and myopic progression in a population of clinical microscopists. Optom Vis Sci 69: 467–473. 33. Wong TY, Foster PJ, Johnson GJ, Seah SK. (2002) Education, socioeconomic status, and ocular dimensions in Chinese adults: the Tanjong Pagar Survey. Br J Ophthalmol 86: 963–968. 34. Wong TY, Foster PJ, Hee J, et al. (2000) Prevalence and risk factors for refractive errors in adult Chinese in Singapore. Invest Ophthalmol Vis Sci 41: 2486–2494. 35. Dirani M, Shekar SN, Baird PN. (2008) The role of educational attainment in refraction: the Genes in Myopia (GEM) twin study. Invest Ophthalmol Vis Sci 49: 534–538.

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36. Lee KE, Klein BE, Klein R. (1999) Changes in refractive error over a 5-year interval in the Beaver Dam Eye Study. Invest Ophthalmol Vis Sci 40: 1645–1649. 37. Wu SY, Yoo YJ, Nemesure B, et al. (2005) Nine-year refractive changes in the Barbados Eye Studies. Invest Ophthalmol Vis Sci 46: 4032–4039. 38. Rose KA, Morgan IG, Ip J, et al. (2008) Outdoor activity reduces the prevalence of myopia in children. Ophthalmology 115: 1279–1285. 39. Khader YS, Batayha WQ, Abdul-Aziz SM, Shiekh-Khalil MI. (2006) Prevalence and risk indicators of myopia among schoolchildren in Amman, Jordan. East Mediterr Health J 12: 434–439. 40. Jones LA, Sinnott LT, Mutti DO, et al. (2007) Parental history of myopia, sports and outdoor activities, and future myopia. Invest Ophthalmol Vis Sci 48: 3524–3532. 41. Rose KA, Morgan IG, Smith W, et al. (2008) Myopia, lifestyle, and schooling in students of Chinese ethnicity in Singapore and Sydney. Arch Ophthalmol 126: 527–530. 42. Jacobsen N, Jensen H, Goldschmidt E. (2008) Does the level of physical activity in university students influence development and progression of myopia? — a 2-year prospective cohort study. Invest Ophthalmol Vis Sci 49: 1322–1327. 43. Saw SM, Chua WH, Hong CY, et al. (2002) Height and its relationship to refraction and biometry parameters in Singapore Chinese children. Invest Ophthalmol Vis Sci 43: 1408–1413. 44. Matsumura H, Hirai H. (1999) Prevalence of myopia and refractive changes in students from 3 to 17 years of age. Surv Ophthalmol 44(1): S109–S115. 45. Guzowski M, Wang JJ, Rochtchina E, et al. (2003) Five-year refractive changes in an older population: the Blue Mountains Eye Study. Ophthalmology 110: 1364–1370. 46. Lee KE, Klein BE, Klein R, Fine JP. (2001) Aggregation of refractive error and 5-year changes in refractive error among families in the Beaver Dam Eye Study. Arch Ophthalmol 119: 1679–1685. 47. Wickremasinghe S, Foster PJ, Uranchimeg D, et al. (2004) Ocular biometry and refraction in Mongolian adults. Invest Ophthalmol Vis Sci 45: 776–783. 48. Wojciechowski R, Congdon N, Bowie H, et al. (2005) Heritability of refractive error and familial aggregation of myopia in an elderly American population. Invest Ophthalmol Vis Sci 46: 1588–1592. 49. Brown NA, Hill AR. (1987) Cataract: the relation between myopia and cataract morphology. Br J Ophthalmol 71: 405–414. 50. Kubo E, Kumamoto Y, Tsuzuki S, Akagi Y. (2006) Axial length, myopia, and the severity of lens opacity at the time of cataract surgery. Arch Ophthalmol 124: 1586–1590.

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51. The Framingham Offspring Eye Study Group. (1996) Familial aggregation and prevalence of myopia in the Framingham Offspring Eye Study. The Framingham Offspring Eye Study Group. Arch Ophthalmol 114: 326–332. 52. Young FA, Leary GA, Baldwin WR, et al. (1969) The transmission of refractive errors within eskimo families. Am J Optom Arch Am Acad Optom 46: 676–685. 53. Khandekar R, Al Harby S, Mohammed AJ. (2005) Determinants of myopia among Omani school children: a case-control study. Ophthalmic Epidemiol 12: 207–213. 54. Saw SM, Nieto FJ, Katz J, et al. (2001) Familial clustering and myopia progression in Singapore school children. Ophthalmic Epidemiol 8: 227–236. 55. Lam DS, Fan DS, Lam RF, et al. (2008) The effect of parental history of myopia on children’s eye size and growth: results of a longitudinal study. Invest Ophthalmol Vis Sci 49: 873–876. 56. Klein AP, Duggal P, Lee KE, et al. (2005) Support for polygenic influences on ocular refractive error. Invest Ophthalmol Vis Sci 46: 442–446. 57. Tang WC, Yip SP, Lo KK, et al. (2007) Linkage and association of myocilin (MYOC) polymorphisms with high myopia in a Chinese population. Mol Vis 13: 534–544. 58. Zhang Q, Li S, Xiao X, et al. (2007) Confirmation of a genetic locus for X-linked recessive high myopia outside MYP1. J Hum Genet 52: 469–472. 59. Nallasamy S, Paluru PC, Devoto M, et al. (2007) Genetic linkage study of high-grade myopia in a Hutterite population from South Dakota. Mol Vis 13: 229–236. 60. Hewitt AW, Kearns LS, Jamieson RV, et al. (2007) PAX6 mutations may be associated with high myopia. Ophthalmic Genet 28: 179–182. 61. Young RR. (1998) Diagnosis and medical management of multiple sclerosis. J Spinal Cord Med 21: 109–112. 62. Young TL, Ronan SM, Alvear AB, et al. (1998) A second locus for familial high myopia maps to chromosome 12q. Am J Hum Genet 63: 1419–1424. 63. Paluru P, Ronan SM, Heon E, et al. (2003) New locus for autosomal dominant high myopia maps to the long arm of chromosome 17. Invest Ophthalmol Vis Sci 44: 1830–1836. 64. Schache M, Richardson AJ, Pertile KK, et al. (2007) Genetic mapping of myopia susceptibility loci. Invest Ophthalmol Vis Sci 48: 4924–4929. 65. Paluru PC, Nallasamy S, Devoto M, et al. (2005) Identification of a novel locus on 2q for autosomal dominant high-grade myopia. Invest Ophthalmol Vis Sci 46: 2300–2307. 66. Naiglin L, Gazagne C, Dallongeville F, et al. (2002) A genome wide scan for familial high myopia suggests a novel locus on chromosome 7q36. J Med Genet 39: 118–124. 67. Zhang Q, Guo X, Xiao X, et al. (2005) A new locus for autosomal dominant high myopia maps to 4q22-q27 between D4S1578 and D4S1612. Mol Vis 11: 554–560.

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68. Zhang Q, Guo X, Xiao X, et al. (2006) Novel locus for X linked recessive high myopia maps to Xq23-q25 but outside MYP1. J Med Genet 43: e20. 69. Stambolian D, Ibay G, Reider L, et al. (2004) Genomewide linkage scan for myopia susceptibility loci among Ashkenazi Jewish families shows evidence of linkage on chromosome 22q12. Am J Hum Genet 75: 448–459. 70. Hammond CJ, Andrew T, Mak YT, Spector TD. (2004) A susceptibility locus for myopia in the normal population is linked to the PAX6 gene region on chromosome 11: a genomewide scan of dizygotic twins. Am J Hum Genet 75: 294–304. 71. Stambolian D, Ciner EB, Reider LC, et al. (2005) Genome-wide scan for myopia in the Old Order Amish. Am J Ophthalmol 140: 469–476. 72. Simpson CL, Hysi P, Bhattacharya SS, et al. (2007) The Roles of PAX6 and SOX2 in Myopia: lessons from the 1958 British Birth Cohort. Invest Ophthalmol Vis Sci 48: 4421–4425. 73. Tsai YY, Chiang CC, Lin HJ, et al. (2007) A PAX6 gene polymorphism is associated with genetic predisposition to extreme myopia. Eye 22: 576–581. 74. Klein AP, Duggal P, Lee KE, et al. (2007) Confirmation of linkage to ocular refraction on chromosome 22q and identification of a novel linkage region on 1q. Arch Ophthalmol 125: 80–85. 75. Gwiazda JE, Hyman L, Norton TT, et al. (2004) Accommodation and related risk factors associated with myopia progression and their interaction with treatment in COMET children. Invest Ophthalmol Vis Sci 45: 2143–2151. 76. Ojaimi E, Morgan IG, Robaei D, et al. (2005) Effect of stature and other anthropometric parameters on eye size and refraction in a population-based study of Australian children. Invest Ophthalmol Vis Sci 46: 4424–4429. 77. Fan DS, Lam DS, Lam RF, et al. (2004) Prevalence, incidence, and progression of myopia of school children in Hong Kong. Invest Ophthalmol Vis Sci 45: 1071–1075. 78. Grosvenor T, Skeates PD. (1999) Is there a hyperopic shift in myopic eyes during the presbyopic years? Clin Exp Optom 82: 236–243. 79. Cortinez MF, Chiappe JP, Iribarren R. (2008) Prevalence of refractive errors in a population of office-workers in Buenos Aires, Argentina. Ophthalmic Epidemiol 15: 10–16. 80. Lim R, Mitchell P, Cumming RG. (1999) Refractive associations with cataract: the Blue Mountains Eye Study. Invest Ophthalmol Vis Sci 40: 3021–3026. 81. Gudmundsdottir E, Arnarsson A, Jonasson F. (2005) Five-year refractive changes in an adult population: Reykjavik Eye Study. Ophthalmology 112: 672–677.

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1.2 Environmental Risk Factors for Myopia in Children Wilson C.J. Low*, Tien-Yin Wong*,†,‡,§ and Seang-Mei Saw*,‡

Introduction In Caucasian populations, approximately 20% to 25% of individuals develop myopia.1 In contrast, the prevalence of myopia is much higher, reaching epidemic levels of up to 80% in selected regions of Asian countries, such as Taiwan, Hong Kong, and Singapore.2–4 Myopia is a major public health problem because of under-correction and undiagnosed cases, which can lead to visual impairments and potentially blinding ocular complications.5 Myopia also poses a direct economic burden resulting from the cost of refractive correction through repeat optometry visits and prescription of eyeglasses, contact lenses, and refractive surgery.6 In Singapore, the mean annual direct cost of myopia for each schoolchild aged 7 to 9 years is US$148.7 Myopia is a complex eye disease, in which both genetic and environmental factors contribute to its development.8 Twin heritability, familial aggregation, pedigree segregation, and linkage studies provide evidence to support a major genetic component influencing myopic development.9–12 Additionally, environmental factors such as near work and outdoor activities appear to also play an important role in the development of myopia.13–15 This chapter aims to provide a summary of the known as well as controversial risk factors for myopia and ocular biometry in children, including family

*Department of Epidemiology and Public Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore. E-mail: [email protected] † Centre for Eye Research Australia, University of Melbourne, Australia. ‡ Singapore Eye Research Institute, Singapore. § Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.

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history, near work, outdoor and stature, birth parameters, smoking, and breastfeeding.

Definition of Myopia in Epidemiologic Studies Refractive error is commonly quantified as spherical equivalent (SE) (sphere + half negative cylinder) in diopters (D) on a continuous scale. Most commonly used and acknowledged definitions of myopia in epidemiologic studies include SE of at least –0.5 D, –0.75 D, and –1.0 D.16 The Refractive Error Study in Children (RESC) used the definition of myopia as SE of at least –0.5 D.17 Other definitions include moderate myopia defined as SE of at least –3.0 D, while high myopia is denoted as SE as least –6.0 D, –8.0 D, and –10.0 D respectively. It should be noted that the cutoff values for myopia are arbitrary and serve to dichotomize the presence of myopia, i.e. myopia present or not present. However, setting an arbitrary cutoff of a physiologic range limits the comparison of studies using dissimilar criteria and disregards the elongation of the axial length (AL). To date, there is no universal accepted definition of myopia.

Risk Factors for Myopia and Ocular Biometry Family history of myopia In a population-based cross-sectional study of 2353 Sydney schoolchildren (60% European Caucasian and 15% East Asian) aged 12 years who participated in the Sydney Myopia Study (SMS), children with one and two myopic parents had about two and eight times higher risk respectively (OR = 2.3; 95% confidence interval (CI) = 1.8–2.9 and OR = 7.9; 95% CI = 5–12.4, respectively) of developing myopia (defined as SE at least –0.5 D) compared to those with no myopic parents, after adjusting for age, gender, near work, outdoor activity, and ethnicity (Table 1).18 The level of parental myopia followed a dose-response relationship with children’s myopia onset; increasing severity of parental myopia conferred a greater risk of myopia. The OR for mild myopia (defined as SE from –3 to –0.5 D), moderate myopia (defined as SE at least –6 to –3 D), and high myopia (defined as SE at least –6 D) was 6.4 (95% CI = 1.5–27.8), 10.2 (95% CI = 2.6–40.1),

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and 21.8 (95% CI = 5.3–89.4) respectively. However, in SMS, the AL of premyopic eyes did not associate with parental myopia (defined as SE ≤ –0.75D in this analysis). In a landmark study coordinated by Zadnik and co-workers, (the Orinda Longitudinal Study of Myopia (OLSM) on 716 predominantly Caucasian children aged 6 to 14 years), she demonstrated that the premyopic eyes in children with myopic parents had a longer AL than those without myopic parents, suggesting that the size of the premyopic eyes was already influenced by parental myopia status (Table 1).19 Moreover, she found that children with two myopic parents developed myopia more often (11%) than children with one myopic parent (5%) or children without myopic parents (2%). Myopia was defined as SE at least –0.75 D in this analysis. In a cross-sectional analysis of 1453 Singapore Chinese schoolchildren aged seven to nine years from the Singapore Cohort Study on the Risk factors for Myopia (SCORM), having one myopic parent increased the AL by 0.14 mm (95% CI = 0.00034–0.25), and two myopic parents increased the AL by 0.32 mm (95% CI = 0.02–0.03) compared with no myopic parent after adjusting for age, gender, books read per week, school, and height (Table 1).20 Similarly, after controlling for the same confounders, having one myopic parent lowered the SE by 0.39 D (95% CI = –0.59– –0.18), and one myopic parents reduced the SE by 0.74 D (95% CI = –0.97– –0.51). The odds ratio of myopia for children with two myopic parents compared with those with one myopic parent was 1.53 (95% CI = 1.16–2.01). There were other studies that showed the association of family history of myopia with myopia in children, but these studies suffered from methodological limitations such as small sample size, inappropriate sampling strategies, lack of cycloplegic refraction, and lack of control for major confounders.10,21–27 For example, a school-based cross-sectional analysis of 7560 Chinese children aged 5 to 16 years from Hong Kong showed that the number of myopic parents was associated with SE, vitreous chamber depth, and AL in all children (both myopic and non-myopic children) (Table 1).27 However, this Hong Kong study suffered from sampling problems as only selected schools were sampled. Nevertheless, a previous study demonstrated no significant association of family history with myopia in children.28 In Hong Kong, Fan and coworkers studied 514 Chinese children aged between two and six years but did not find an association of parental myopia status with more myopic refractive error and longer AL (Table 1).28 However, this study is limited by

Cycloplegic

Age (Years)

Definition of Myopia (SE)

Association with SE

Association with AL

Association with Myopia

Sydney Myopia Study18 Orinda Longitudinal Study of Myopia19 Singapore Cohort Study on Risk Factors of Myopia20 Hong Kong27

Population-based, cross-sectional Cross-sectional

2353

Yes

11.1–12.7

≤–0.5 D

+

+

+

716

Yes

6–14

≤–0.75 D

+

+

+

Cross-sectional

1453

Yes

7–9

≤–0.5 D

+

+

+

School-based, cross-sectional School-based, cross-sectional

7560

Yes

5–16

≤–0.5 D

+

+

514

Yes

2.3–6.4

Not given

0

0

Hong Kong28

AL: Axial length, D: Diopters, SE: Spherical equivalent, +: Association found, 0: Association evaluated but not found.

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Table 1.

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the school-based design since the schools recruited may not be representative of the general population. Near work In a population-based cross-sectional study on schoolchildren recruited in the SMS (n = 2339 and aged 11.1 to 14.4 years), near work parameters were associated with myopia after adjusting for age, sex, ethnicity, school type, parental myopia, and outdoor activity (Table 2).29 Specifically, children who read continuously for more than 30 minutes were 1.5-fold (OR = 1.5; 95% CI = 1.05–2.1) more likely to develop myopia when compared to those who read less than 30 minutes continuously. Likewise, children who performed close reading distance of less than 30 cm were 2.5 times (OR = 2.5; 95% CI = 1.7–4.0) more likely to have myopia than those who performed more than this distance. Similarly, children who spent longer time reading for pleasure and read close at less than 30 cm were more likely to be associated with more myopic SE, after adjusting for age, sex, ethnicity, and school type (p trend = 0.02 and p = 0.0003). One thousand and five Singaporean children aged seven to nine years were cross-sectionally analyzed in the SCORM; 72.5%, 19.4%, 5.6%, and 2.5% were Chinese, Malays, Indians, and children of other races respectively (Table 2).13 Saw found that children who read more than two books per week were about three times more likely (OR = 3.05; 95% CI = 1.80–5.18) to have higher myopia (defined as SE at least –3.0 D) compared to those who read less than two books per week, after controlling for age, gender, race, night light, parental myopia, and school. Reading more than two hours per day gave a 1.5 times greater odds (OR = 1.50; 95% CI = 0.87–2.55) of having higher myopia compared to those who read less than this amount, but this was not significant. For every book read per week, the AL elongated by 0.04 mm after adjusting for the same covariates. There was a statistically significant interaction effect of parental history of myopia and books read per week on SE (P < 0.001). For example, children with two myopic parents and who read more than two books per week had an age-gender-race adjusted mean SE of –1.33 D, while children with no myopic parents and who read two or fewer books per week had an adjusted mean SE of –0.19 D. A similar effect was found on AL; mean AL of 23.78 mm when the children had two myopic parents and who read more than two books per week vs. mean AL of 23.2 mm in children with no myopic parents and who read fewer than two books per week.

Age (Years)

Definition of Myopia (SE)

Association with SE

Sydney Myopia Study29 Singapore Cohort Study on Risk Factors for Myopia13 Orinda Longitudinal Study of Myopia24 Xichang Pediatric Refractive Error Study36 Singapore31

Population-based, cross-sectional Cross-sectional

2339

Yes

11.1–14.4

≤ –0.5 D

+

1005

Yes

7–9

≤ –0.5 D

Cross-sectional

366

Yes

Mean: 13.7 ± 0.5

≤ –0.75 D

School-based, cross-sectional

998

Yes

13–17

≤ –0.5 D

Cross-sectional

128

Yes

3–7

≤ –0.50 D

Association with AL

Association with Myopia +

+

++

+

0

0

0

AL: Axial length, D: Diopters, SE: Spherical equivalent, +: Association found, ++: Association found with higher myopia (SE ≤ −3D), 0: Association evaluated but not found.

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Table 2. Summary of Near Work as Risk factor for Myopia and Ocular Biometry

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The OLSM looked at 366 eighth-grade predominantly Caucasian children (mean age of 13.7 ± 0.5 years) and found that the OR of myopia (defined as SE at least –0.75 D) was 1.02 (95% CI = 1.008–1.032) for every diopter-hours spent per week, after controlling for parental myopia, diopter-hours per week and achievement scores (Table 2).24 However, there was no interaction between parental myopia and near work (p = 0.67). Children with myopia were more likely to have parents with myopia. Other studies suffered from methodological limitations such as small sample size, inappropriate sampling strategies, lack of cycloplegic refraction, and lack of control for major confounders.30–35 Near work was also shown to be not associated with myopia.31,36 Lu and co-workers (Table 2)36 analyzed 998 Chinese school children aged 13 to 17 years from the Xichang Pediatric Refractive Error Study (X-PRES) and reported the multivariate adjusted OR of myopia (defined as SE at least –0.5 D) was 1.27 (95% CI = 0.75–2.14) for reading in hours per week and SE was not associated with near work. However, the study subjects may not be representative of the general population since this was a school-based design. In another study, Saw and co-workers recruited 128 children from one kindergarten in Singapore (Table 2).31 The cross-sectional study found that after adjusting for parental history of myopia and age, the OR of myopia was 1.0 (95% CI = 0.8–1.3) for close-up work activity. However, this finding could be due to the small sample size. Outdoor activity There were few prior studies that analyzed outdoor activity as a major environmental factor for myopia.14,15,23,36,37 Jones and co-workers (Table 3)23 conducted a longitudinal study of children in the OLSM in California. 514 children in the third to eighth grade (aged 8 to 13 years) were included. Children who became myopic (defined as SE at least –0.75 D) by the eighth grade were found to perform less sports and outdoor activity (hours per week) at the third grade compared to those who did not become myopic (7.98 ± 6.54 hours vs. 11.65 ± 6.97 hours). In predictive models for future myopia, combined amount of sports and outdoor hours per week conferred a protective effect against future myopia (OR = 0.91; 95% CI = 0.87–0.95) after adjusting for parental myopia, reading hours, and sports and outdoor hours. Significant

Age (Years)

Definition of Myopia (SE)

Association with SE

514

Yes

8–13

≤ –0.75 D

2367

Yes

11.1–14.4

≤–0.5 D

+

1249 143 998

Yes Yes Yes

11–20 Mean: 23 13–17

≤ –0.5 D ≤ –0.5 D ≤ –0.5 D

+ +

Association with AL

Association with Myopia +

AL: Axial length, D: Diopters, SE: Spherical equivalent, +: Association found, 0: Association evaluated but not found.

+ + +

+ + 0

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Summary of Outdoor Activity as Risk Factor for Myopia and Ocular Biometry

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Table 3.

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interaction was found between the number of parents with myopia and hours of sports and outdoor activity on the development of myopia. The SMS (Table 3)14 analyzed 2367 school children aged 11 to 14 years and found a higher level of outdoor activity (>2.8 hours per day) was associated with more hyperopic mean SE refraction (0.54 D) after adjusting for gender, ethnicity, parental myopia, near work activity, maternal and paternal education. Furthermore, in an analysis combining amount of outdoor activity and near work activity spent, children with low outdoor and high near work had the (OR = 2.6; 95% CI = 1.2–6.0) higher odds for myopia compared to those performing low near work and high outdoor (reference group). In Singapore, a cross-sectional analysis of SCORM was conducted to analyze the effect of outdoor activity on myopia in 1249 teenagers aged 11 to 20 years (71.1%, Chinese, 20.7% Malays and 0.8% other ethnicities) (Table 3).15 After adjusting for age, gender, ethnicity, school, number of books read per week, height, parental myopia, father’s education and IQ level, outdoor activity was significantly negatively associated with myopia (OR = 0.90; 95% CI = 0.84–0.96). For each hour increase in outdoor activity per day, the SE refraction increased by 0.17 D (95% CI = 0.10–0.25), and the AL decreased by 0.06 mm (95% CI = −0.1 − −0.03), after adjusting for the same confounders. An analysis on a two-year longitudinal cohort study conducted in 143 Caucasian Danish medical students (mean age = 23 years) was performed to investigate the level of physical activity on myopia.37 The multiple regression showed that time spent reading scientific literature was associated with a refractive change toward myopia (regression coefficient = –0.063; 95% CI = –0.117– –0.008; p = 0.024), while the association was inversed for the level of physical activity (regression coefficient = 0.175; 95% CI = 0.035–0.315; p = 0.015). Although the total amount of time spent on outdoor activity was not recorded, the author postulated that the level of physical activity could parallel that of outdoor activity and thus the protective effect of physical activity on myopia could be attributed in part to outdoor activity. In the X-PRES, Lu and co-workers (Table 3)36 initiated a school-based cross-sectional study of 998 secondary school Chinese children aged 13 to 17 years from Xichang, China. After controlling for age, gender, parental education, homework, reading and TV watching, outdoor activity was not significantly associated with myopia (OR = 1.14; 95% CI = 0.69–1.89). The students were administered a near-work survey to collect information on

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the time spent during the previous week on schoolwork, reading, watching television, video games and computer use, family business related near-work tasks and outdoor activities. Nevertheless, the authors acknowledged the lack of association between outdoor and myopia could be biased by estimating near work and outdoor activities based on self-reported questionnaires and by focusing on a single week rather than the children’s long term experience. In addition, the interpretation of the findings was possibly limited by the school-based design, high refusal (13%), and incomplete near-work survey (19%). Stature In a cross-sectional study of 1449 Singapore Chinese schoolchildren aged seven to nine years from the SCORM, Saw and co-workers (Table 4)38 compared height in the first quartile and fourth quartile (adjusting for age, gender, parental myopia, number of books per week, school, and weight). The analysis showed that the AL was 0.46 mm longer. On the other hand, the SE refraction was more negative by 0.47 D. In multiple linear regression models for AL adjusting for the same factors, each cm increase in height resulted in a 0.032 mm increase in AL (p < 0.001). For each cm in height, the SE refraction decrease by 0.031 D (p = 0.002), while for each kg increase in weight, the SE refraction decreased by 0.027 D (p = 0.01). The SMS conducted a population-based cross-sectional analysis on 1765 six-year-old schoolchildren; 64.5% were Caucasians, 17.2% were East Asians, and 18.3% belonged to other races (Table 4).39 Children in the first quintile for height had AL of 22.39 ± 0.04 mm compared with 22.76 ± 0.04 mm in children in the fifth quintile. After adjusting for age, gender, parental myopia, weight, BMI, body fat percentage and waist circumference, each 10 cm increase in height corresponded to a 0.29 mm (95% CI = 0.19–0.39) increase in AL. However, height was not significantly associated with SE refraction. A population-based cross-sectional study (the Tanjong Pagar survey (TPS)) in Singapore analyzed data of 951 Chinese adults aged between 40 and 80 years, (Table 4)40 and demonstrated that a 10 cm greater height was associated with a longer AL of 0.23 mm (95% CI = 0.1–0.37), after adjusting for age, gender, education, occupation, housing, income, and weight. Adjusting for the same factors, for every 10 kg increase in weight, the SE refraction increased by 0.22 D (95% CI = 0.05–0.39), and every 10 kg/m2

Definition of Myopia (SE)

Association with SE

Association with AL

Cross-sectional

1449

Yes

7–9

≤ –0.5 D

+

+

Population-based, cross-sectional Population-based, cross-sectional

1765

Yes

Mean: 6

≤ –0.5 D

0

+

951

No

40–81

Not given

++

+

AL: Axial length, D: Diopters, SE: Spherical equivalent, +: Association found, ++: Association found in weight and BMI but not height, 0: Association evaluated but not found.

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N

Study Design

Environmental Risk Factors for Myopia in Children

Location/Study

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Table 4. Summary of Stature as Risk Factor for Myopia and Ocular Biometry

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BMI increased the SE refraction by 0.56 D (95% CI = 0.14–0.98). However, height was not significantly associated with SE refraction. Studies in other adult populations such as the Beaver Dam Eye Study (BDES)41 and the Reykjavik Eye Study (RES)42 showed positive associations between height and AL, while the Singapore Malay Eye Study (SiMES) demonstrated that both height and weight were associated with AL.43 The Meiktila Eye Survey (MES) went so far to illustrate an association between AL and height, weight and BMI.44 The MES, however, showed that SE refraction was positively associated with weight and BMI. Birth parameters Few studies had elucidated the association of birth parameters and myopia, SE refraction and ocular biometry, and it is still not clear if birth parameters could influence myopia development in children.45,46 The SMS conducted a population-based stratified random cluster sample of six-year-old school students (n = 1765) of mean age 6.7 years (range = 5.5–8.4 years) (Table 5)45 and birth parameters were obtained from the children’s hospital personal health records. After adjusting for cluster, age, and gender, children with birth weight < 2500 g had a shorter mean AL of 22.46 mm (95% CI = 22.20–22.72) compared with a mean AL of 22.80 mm (CI = 22.70–22.90) for birth weight > 2500 g. The multivariate analysis showed that birth length, but not birth weight, was weakly associated with AL (Regression coefficient = 0.02 mm; 95% CI = 0.00–0.03; p = 0.0472) after controlling for age, gender, birth weight, birth length, head circumference, gestational age, and parental myopia. In Singapore, 1413 Singaporean Chinese children aged seven to nine years with ocular data were included in an analysis on birth parameters obtained from the SCORM (Table 5).46 The study showed that children with birth weights ≥ 4.0 kg had longer AL (adjusted mean 23.65 mm versus 23.16 mm), compared with children with birth weights < 2.5 kg, after adjusting for age, gender, school, height, parental myopia, and gestational age. Multivariate analysis for AL showed that for each kg increase in birth weight, each cm increase in birth head circumference, each cm increase in birth length and each week increase in gestational age, the AL increased by 0.25 mm (p < 0.001), 0.05 mm (p = 0.004), 0.02 mm (p = 0.044), and

Age (Years)

Definition of Myopia (SE)

Population-based, cross-sectional Cross-sectional

1765

Yes

5.5–8.4

1413

Yes

7–9

≤–0.5 D

Cross-sectional

2448

Yes

18–86

≤–0.5 D

Association with SE

Association with AL

0

+

0

+

AL: Axial length, D: Diopters, SE: Spherical equivalent, +: Association found, 0: Association evaluated but not found.

Association with Myopia

0

0

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Study Design

Environmental Risk Factors for Myopia in Children

Location/Study

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Table 5. Summary of Birth Parameters as Risk Factor for Myopia and Ocular Biometry

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0.04 mm (p = 0.028) respectively. However, birth parameters (birth length, weight, head circumference, and gestational age (ORs between 0.91–1.08)) were not significantly associated with myopia (defined as SE at least –0.5 D) or high myopia (SE at least –3.0 D). As far as the adult population was concerned, only one study conducted by Dirani and co-workers (Table 5)47 had attempted to investigate the association between birth parameters and myopia (defined as SE at least –0.5 D) in 1224 twins residing in Victoria, Australia, participating in the Genes in Myopia (GEM) twin study. However, the multivariate analysis showed no significant association between birth weight and myopia (p = 0.26). The finding was difficult to interpret as birth weight was self-reported rather than obtained from hospital records. Smoking history Smoking was recently identified as protective against myopia in three studies; two were children studies48,49 and one was an adult study.50 Saw and co-colleagues conducted a cross-sectional analysis of 1334 Chinese school children aged seven to nine years from the SCORM (Table 6).48 In this study, multivariate analysis adjusting for age, sex, school, parental smoking status, and parent’s education demonstrated that for each year of maternal smoking during the child’s lifetime, the SE refraction rose by 0.15 D (95% CI = 0.041–0.25; p = 0.006). The number of years the father smoked during the child’s lifetime was not significantly associated with myopia (Regression coefficient = 0.008; 95% CI = –0.02–0.036; p = 0.57). After controlling for age, sex, school, parental smoking status, and parental education, for each year the mother smoked during the child’s lifetime, the AL reduced by 0.07 mm (95% CI = –0.12–0.015; p = 0.012). Conversely, the father smoking during the child’s lifetime had no effect on AL (p = 0.96). After adjusting for age, sex, school, mother’s education, and mother’s myopia, children with mothers who had smoked during their lifetimes had more ‘‘positive’’ SE (adjusted mean –0.28 D vs. –1.38 D) compared with children whose mothers did not smoke (p = 0.012). In an United States study, Stone and co-workers (Table 6)49 analyzed 323 outpatients aged 1 to 20 years (mean age of 8.7 ± 4.4 years) from a pediatric ophthalmology clinic at the Children’s Hospital of Philadelphia. They found that if one or both parents had smoked, their children had a

Definition of Myopia (SE)

Association with SE

Association with AL

Cross-sectional

1334

Yes

7–9

≤ –0.5 D

+

+

Cross-sectional Population-based, cross-sectional

323 6491

Yes No

1–20 30–99

≤ –0.5 D ≤ –0.5 D

+

AL: Axial length, D: Diopters, SE: Spherical equivalent, +: Association found.

Association with Myopia

+ +

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N

Study Design

Environmental Risk Factors for Myopia in Children

Location/Study

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Table 6. Summary of Parental Smoking as Risk factor for Myopia and Ocular Biometry

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lower prevalence of myopia (12.4% vs. 25.4%; p = 0.004) and more hyperopic mean SE refractions (1.83 ± 0.24 vs. 0.96 ± 0.27 D; p = 0.02) than those whose parents never smoked. If one or more parents smoked during pregnancy, their children had a lower prevalence of myopia (8.6% vs. 23.9%; p = 0.003) and more hyperopic mean SE refractions (2.19 ± 0.34 vs. 1.07 ± 0.22 D; p = 0.006) than those whose parents never smoked. Multivariate OR for myopia was 0.22 (95% CI = 0.07–0.64; p = 0.008) when either parent currently smoked, 0.15 (95% CI = 0.04–0.53; p = 0.003) when one of the parents smoked during pregnancy, and 0.22 (95% CI = 0.08–0.59; p = 0.003) when either parent smoked during their child’s lifetime, after adjusting for the child’s age, BMI, weighted near work, parental myopia, and parental education. In a population-based cross-sectional study of 6491 Chinese adults aged 30 to 99 years from Handan, China, Liang and co-workers (Table 6)50 found that the multivariate OR for myopia was 0.7 (95% CI = 0.5–0.9; p = 0.003) in adults who currently smoked compared with those who never smoked after controlling for age, history of diabetes, smoking, hours of reading per day, and number of family members with myopia. Breastfeeding In a population-based cross-sectional study of 2639 Chinese preschool children aged 6 to 72 months from the Strabismus, Amblyopia and Refractive Error Study (STARS) in Singapore, Sham and co-workers (Table 7)51 demonstrated that a history of breastfeeding lowered the SE refraction by 0.12 D (standard error = 0.06; p = 0.03) after adjusting for age, gender, history of breastfeeding, outdoor activity, mother’s education, mother’s smoking history, parental myopia, birth weight, maternal age, and child’s height. However, breastfeeding was not associated with myopia (defined as SE at least –0.5 D) after controlling for the same confounders (OR = 0.85; 95% CI = 0.62–1.18). The SCORM investigated 797 school children aged 10 to 12 years and reported that the multivariate OR of myopia (defined as SE at least –0.5 D) for breastfed children was 0.58 (95% CI = 0.39–0.84) after adjusting for the child’s age, sex, race, birth weight, height, number of books read per week, IQ scores, mother’s education, parental myopia, maternal age at delivery, household income, and clustering of siblings within family (Table 7).52 Moreover, the mean SE refraction of breastfed

Age (Years)

Definition of Myopia (SE)

Association with SE

Association with Myopia

Strabismus, Amblyopia and Refractive Error Study in Singapore Children51 Singapore Cohort Study on Risk Factors for Myopia52

Population-based, cross-sectional

2639

Yes

0.5–6

≤ –0.5 D

+

0

Cross-sectional

797

Yes

10–12

≤ –0.5 D

+

+

AL: Axial length, D: Diopters, SE: Spherical equivalent, +: Association found, 0: Association evaluated but not found.

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Study Design

Environmental Risk Factors for Myopia in Children

Location/Study

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Table 7. Evidence Table for Breastfeeding as Risk Factor for Myopia and Ocular Biometry

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children (–1.6 D) was significantly less myopic than non-breastfed children (–2.1 D; p = 0.01).

Conclusion Both genes and environments are known to play important roles in the onset and development of myopia. There is consensus that a family history of myopia is a major risk factor for myopia and ocular biometry and represents a surrogate for genetic or shared environmental factors. However, current evidence also suggests that environmental factors such as near work and outdoor activity are implicated in the development of myopia and longer AL.13–15,23,24,29 Near work is potentially modifiable but given the emphasis on academic excellence in Asian cultures, reducing the time spent on near work activity, particularly reading, may not be acceptable to the parents and is unlikely to be implemented. Other environmental factors for myopia remain controversial. Greater height is associated with longer AL but the relationship remains unclear with myopia. Similarly, birth weight is positively associated with AL but the association with spherical refraction is weak. Evidence suggests that exposure to smoking in pregnancy and childhood may protect against myopia in children. Breastfeeding appears to protect against myopia in Singapore children but this finding requires validation in other populations and ethnic groups. Further studies should be conducted to determine the nature of the association of time spent outdoors, and other possible risk factors such as diet in longitudinal cohort studies that document the temporal sequence of events. The issue of an accurate and precise quantification of “near work activity” remains challenging and represents an area of in-depth study. Particularly, near work “parameters” such as posture while reading, frequency of close reading, breaks during reading and lighting conditions, are modifiable and need to be further evaluated. Portable instruments that record activity over a 24-hour period could document lighting levels, time spent on close work in an objective manner. Randomized clinical trials should be conducted to evaluate the efficacy of time spent outdoors to protect for myopia. The relative contribution of genetic and environmental factors needs to be examined, and well-designed studies will be required to tease out the interactions of genes-environmental mechanisms in the development of myopia and changes in axial dimensions throughout life.53

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References 1. Kempen JH, et al. (2004) The prevalence of refractive errors among adults in the United States, Western Europe, and Australia. Arch Ophthalmol 122(4): 495–505. 2. Lin LL, et al. (2004) Prevalence of myopia in Taiwanese schoolchildren: 1983 to 2000. Ann Acad Med Singapore 33(1): 27–33. 3. Lam CS, Goldschmidt E, Edwards MH. (2004) Prevalence of myopia in local and international schools in Hong Kong. Optom Vis Sci 81(5): 317–322. 4. Wu HM, et al. (2001) Does education explain ethnic differences in myopia prevalence? A population-based study of young adult males in Singapore. Optom Vis Sci 78(4): 234–239. 5. Saw SM, et al. (2005) Myopia and associated pathological complications. Ophthalmic Physiol Opt 25(5): 381–391. 6. Javitt JC, Chiang YP. (1994) The socioeconomic aspects of laser refractive surgery. Arch Ophthalmol 112(12): 1526–1530. 7. Lim MC, et al. (2009) Direct costs of myopia in Singapore. Eye 23(5): 1086–1089. 8. Mutti DO, Zadnik K, Adams AJ. (1996) Myopia. The nature versus nurture debate goes on. Invest Ophthalmol Vis Sci 37(6): 952–957. 9. Dirani M, et al. (2006) Heritability of refractive error and ocular biometrics: the Genes in Myopia (GEM) twin study. Invest Ophthalmol Vis Sci 47(11): 4756–4761. 10. Pacella R, et al. (1999) Role of genetic factors in the etiology of juvenile-onset myopia based on a longitudinal study of refractive error. Optom Vis Sci 76(6): 381–386. 11. Ashton GC. (1985) Segregation analysis of ocular refraction and myopia. Hum Hered 35(4): 232–239. 12. Ciner E, et al. (2009) Genome-wide scan of African-American and white families for linkage to myopia. Am J Ophthalmol 147(3): 512–517 e2. 13. Saw SM, et al. (2002) Nearwork in early-onset myopia. Invest Ophthalmol Vis Sci 43(2): 332–329. 14. Rose KA, et al. (2008) Outdoor activity reduces the prevalence of myopia in children. Ophthalmology 115(8): 1279–1285. 15. Dirani M, et al. (2009) Outdoor activity and myopia in Singapore teenage children. Br J Ophthalmol 93(8): 997–1000. 16. Luo HD, et al. (2006) Defining myopia using refractive error and uncorrected logMAR visual acuity >0.3 from 1334 Singapore school children ages 7–9 years. Br J Ophthalmol 90(3): 362–366. 17. Negrel AD, et al. (2000) Refractive Error Study in Children: sampling and measurement methods for a multi-country survey. Am J Ophthalmol 129(4): 421–426.

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18. Ip JM, et al. (2007) Ethnic differences in the impact of parental myopia: findings from a population-based study of 12-year-old Australian children. Invest Ophthalmol Vis Sci 48(6): 2520–2528. 19. Zadnik K, et al. (1994) The effect of parental history of myopia on children’s eye size. JAMA 271(17): 1323–1327. 20. Saw SM, et al. (2002) Component dependent risk factors for ocular parameters in Singapore Chinese children. Ophthalmology 109(11): 2065–2071. 21. Goss DA, Jackson TW. (1996) Clinical findings before the onset of myopia in youth: 4. Parental history of myopia. Optom Vis Sci 73(4): 279–82. 22. Yap M, et al. (1993) Role of heredity in the genesis of myopia. Ophthalmic Physiol Opt 13(3): 316–319. 23. Jones LA, et al. (2007) Parental history of myopia, sports and outdoor activities, and future myopia. Invest Ophthalmol Vis Sci 48(8): 3524–3532. 24. Mutti DO, et al. (2002) Parental myopia, near work, school achievement, and children’s refractive error. Invest Ophthalmol Vis Sci 43(12): 3633–3640. 25. Hui J, Peck L, Howland HC. (1995) Correlations between familial refractive error and children’s non-cycloplegic refractions. Vision Res 35(9): 1353–1358. 26. Keller JT. (1973) A comparison of the refractive status of myopic children and their parents. Am J Optom Arch Am Acad Optom 50(3): 206–211. 27. Lam DS, et al. (2008) The effect of parental history of myopia on children’s eye size and growth: results of a longitudinal study. Invest Ophthalmol Vis Sci 49(3): 873–876. 28. Fan DS, et al. (2005) The effect of parental history of myopia on eye size of pre-school children: A pilot study. Acta Ophthalmol Scand 83(4): 492–496. 29. Ip JM, et al. (2008) Role of near work in myopia: findings in a sample of Australian school children. Invest Ophthalmol Vis Sci 49(7): 2903–2910. 30. Williams C, et al. (2008) A comparison of measures of reading and intelligence as risk factors for the development of myopia in a UK cohort of children. Br J Ophthalmol 92(8): 1117–1121. 31. Saw SM, et al. (2001) Myopia in Singapore kindergarten children. Optometry 72(5): 286–291. 32. Hepsen IF, Evereklioglu C, Bayramlar H. (2001) The effect of reading and near-work on the development of myopia in emmetropic boys: a prospective, controlled, three-year follow-up study. Vision Res 41(19): 2511–2520. 33. Mohan M, Pakrasi S, Garg SP. (1988) The role of environmental factors and hereditary predisposition in the causation of low myopia. Acta Ophthalmol Suppl 185: 54–57. 34. Richler A, Bear JC. (1980) Refraction, nearwork and education. A population study in Newfoundland. Acta Ophthalmol (Copenh) 58(3): 468–478. 35. Tan GJ, et al. (2000) Cross-sectional study of near-work and myopia in kindergarten children in Singapore. Ann Acad Med Singapore 29(6): 740–744.

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36. Lu B, et al. (2009) Associations between near work, outdoor activity, and myopia among adolescent students in rural China: the Xichang Pediatric Refractive Error Study report no. 2. Arch Ophthalmol 127(6): 769–775. 37. Jacobsen N, Jensen H, Goldschmidt E. (2008) Does the level of physical activity in university students influence development and progression of myopia? — a 2-year prospective cohort study. Invest Ophthalmol Vis Sci 49(4): 1322–1327. 38. Saw SM, et al. (2002) Height and its relationship to refraction and biometry parameters in Singapore Chinese children. Invest Ophthalmol Vis Sci 43(5): 1408–1413. 39. Ojaimi E, et al. (2005) Effect of stature and other anthropometric parameters on eye size and refraction in a population-based study of Australian children. Invest Ophthalmol Vis Sci 46(12): 4424–4429. 40. Wong TY, et al. (2001) The relationship between ocular dimensions and refraction with adult stature: the Tanjong Pagar Survey. Invest Ophthalmol Vis Sci 42(6): 1237–1242. 41. Lee KE, et al. (2009) Association of age, stature, and education with ocular dimensions in an older white population. Arch Ophthalmol 127(1): 88–93. 42. Eysteinsson T, et al. (2005) Relationships between ocular dimensions and adult stature among participants in the Reykjavik Eye Study. Acta Ophthalmol Scand 83(6): 734–738. 43. Lim LS, et al. (2009) Distribution and determinants of ocular biometric parameters in an Asian population: the Singapore Malay Eye Study. Invest Ophthalmol Vis Sci. 44. Wu HM, et al. (2007) Association between stature, ocular biometry and refraction in an adult population in rural Myanmar: the Meiktila eye study. Clin Experiment Ophthalmol 35(9): 834–839. 45. Ojaimi E, et al. (2005) Impact of birth parameters on eye size in a populationbased study of 6-year-old Australian children. Am J Ophthalmol 140(3): 535–537. 46. Saw SM, et al. (2004) The relation between birth size and the results of refractive error and biometry measurements in children. Br J Ophthalmol 88(4): 538–542. 47. Dirani M, Islam FM, Baird PN. (2009) The role of birth weight in myopia — the genes in myopia twin study. Ophthalmic Res 41(3): 154–159. 48. Saw SM, et al. (2004) Childhood myopia and parental smoking. Br J Ophthalmol 88(7): 934–937. 49. Stone RA, et al. (2006) Associations between childhood refraction and parental smoking. Invest Ophthalmol Vis Sci 47(10): 4277–4287. 50. Liang YB, et al. (2009) Refractive errors in a rural Chinese adult population The Handan Eye Study. Ophthalmology.

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51. Sham WK, et al. (2009) Breastfeeding and association with refractive error in young Singapore Chinese children. Eye. 52. Chong YS, et al. (2005) Association between breastfeeding and likelihood of myopia in children. JAMA 293(24): 3001–3002. 53. Wong TY, Hyman L. (2008) Population-based studies in ophthalmology. Am J Ophthalmol 146(5): 656–663.

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1.3 Gene-Environment Interactions in the Aetiology of Myopia Ian G. Morgan* and Kathryn A. Rose†

The term “gene-environment interactions” in statistical genetics refers to the possibility of different genotypes responding differentially to environmental exposures. Myopia is an etiologically heterogeneous disorder, in which there is a low prevalence of clearly genetic myopias, which are generally strongly familial, early in onset and severe. In the last few decades, there has been a marked increase in the prevalence of mild to moderate myopia, particularly in urban East Asia. This increase appears to be strongly associated with changing environmental exposures involving increasingly intensive education and less time spent outdoors. With analysis restricted to this form of acquired or school myopia, there is abundant evidence for environmental impacts, but only limited evidence for genetic contributions. Until the relevant genetic variation has been identified, scientific analysis of gene-environment interactions will not be possible. Currently, it is more parsimonious to interpret school myopia as a disorder caused by environmentally induced excessive axial elongation.

Introduction After a period of flirtation with the idea that myopia was a predominantly genetic disorder (for the early references, see Curtin, 19851), myopia is now

*ARC Centre of Excellence in Vision Science, Research School of Biology, Australian National University, Canberra, Australia. E-mail: [email protected] † Discipline of Orthoptics, Faculty of Health Sciences, University of Sydney, Lidcombe, Australia.

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commonly regarded as a disorder in which both genes and environment are involved, and in which gene-environment interactions may be important.2 At one level, the first part of this statement is trivial, since gene expression must be involved in any biological process, including the development of myopia. Refractive development of the eye involves changes in the cellular composition and interactions of the cornea, lens, retina, choroid and sclera. These changes must involve changes in gene expression. At the same time, genes must operate in an environment, and thus genes and environment, acting together, are essential for the expression of any biological trait. From an epidemiological perspective, however, the important question is not whether genes and environment are involved. They simply must be. The important question is about the relationship between variations in phenotype and variations in genotype and environmental factors. In this context, the term “gene-environment interactions” has a very specific meaning in statistical genetics. Martin3 has stated this quite neatly: “Many people who should know better use the term GxE to denote that both genes and environment are important. Apart from the triviality of such a sentiment (what manifestation of life is not ultimately coded for by genes, and what life is not dependent on the supply of air, water, food from the environment?), a better term is “genotype-environment co-action). We should reserve for the term GxE its statistical sense of different genotypes responding differentially to the same environment; or viewed from the other end, some genotypes being more sensitive to changes in the environment than others….”

From this biological perspective, the cells and molecules involved in growth control pathways, which link detection of relevant variations in visual input at the retinal level to regulation of sclera extracellular matrix remodeling, must ultimately be encoded in the genome. Some of these pathways have now been extensively studied (for a comprehensive review see Ref. 4), and are also reviewed in other sections of this book. Expression of some genes in these pathways must change when visual inputs lead to changes in eye growth. But this is not evidence of gene-environment interactions. This is simply evidence of environmental impacts on gene expression.

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Demonstrating gene-environment interactions in the etiology of myopia and other refractive errors, requires analysis of three specific questions: • • •

Do genetic differences contribute to phenotypic variations in myopia? Do environmental exposures contribute to phenotypic variations in myopia? Is there evidence of differential sensitivity of the different genotypes characterized to environmental changes?

Aetiological Heterogeneity of Myopia Analysis of these questions is complicated by the fact that myopia is clearly heterogeneous in etiology, with relatively rare, clearly genetic severe forms, and a broad category of school-juvenile-onset or “acquired” myopia, which is generally mild to moderate in severity. This has become by far the most common form in many populations.5 Clearly genetic forms of myopia Myopia is a common feature of a number of syndromes, such as Marfan and Stickler syndromes. These are commonly, but not exclusively, associated with mutations that affect connective tissue, and are typically early onset and severe, with clear familial patterns of inheritance. There are also several forms of early onset, severe, familial myopia, where the primary characteristic is excessive axial elongation and myopia, often referred to as the non-syndromic high myopias. The linked characteristics of early onset and severity make these forms of myopia important from a clinical perspective, because of their association with chorioretinal pathologies. Many of these forms of myopia clearly have a major genetic component, and indeed, a number of relevant genes and chromosomal localizations have been identified (for reviews see Refs. 6 and 7). These forms of myopia may be relatively resistant to environmental variations. Candidate gene analysis has supported a role for polymorphisms in collagen isoforms, particularly COL2A1,8 hepatocyte growth factor9 and transforming growth factor beta1.10 However, in total, these forms of myopia account for only few percent of myopia in the population.

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School or acquired myopia The situation is different with regard to school- or acquired myopia, which is generally later in onset and mild to moderate in severity. This form of myopia could be heterogeneous in etiology, but there is, at present, no real evidence for this. This form often appears during the school years; hence its name. In populations with little formal schooling it may hardly be present,11 while it may affect most of the school population in urban East Asia.12,13 In these parts of the world, the prevalence of myopia has increased at a rapid rate over the past few decades, which is incompatible with simple ideas of genetic determinism.5 It has been argued that this form of myopia is genetic in origin, because children with myopic parents are more likely to become myopic,14,15 and there are clear patterns of associated sibling risk.16,17 The problem with this argument is that families share environments, and it is not clear how much of the familial clustering is due to shared genes and shared environments.5,18 There is certainly evidence that when there has been rapid social change, such that the environments in which children and parents grew up were very different, as with the Eskimo and Inuit of North America during the period in which the indigenous populations were moved into settlements, parent-offspring correlations can almost completely disappear, although sibling correlations remain substantial.5,19 This shows that, at least in certain circumstances, environmental variation can overshadow genetic relationships. It should be noted that distinguishing forms of myopia on the basis of severity alone is problematic. The massive change in prevalence of myopia in urban East Asia seems to be associated with earlier onset and more rapid progression, leading to severe myopia by the end of schooling.12 Thus high myopia in a low prevalence country may be predominantly genetic, but in a high prevalence environment such as that of Taiwan, much of the high myopia may itself be acquired, showing the same features of rapid change in prevalence over the past few decades.

Misunderstandings of Heritability and Twin Studies Heritability from twin studies has generally been taken as strong evidence for a genetic basis to variation in myopia and refractive error, but the limitations of such studies are often not appreciated. Modern twin studies examine correlations in refractive status between genetically identical

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monozygotic (MZ) twins, as compared to those for dizygotic (DZ) twins, which share half their genes. For additive genetic effects, the correlations between MZ twins are expected to be around twice those for DZ twins. If the MZ correlations are significantly greater than twice the DZ correlations, then this may be an indication of dominant genetic effects. Studies using this approach have produced consistently high heritability values for myopia as a category or for refractive error as a quantitative trait, of around 80–90%.20,21 This does not mean that 80–90% of myopia is genetically determined, as is sometimes erroneously stated, but that 80–90% of the variance in myopia may be cautiously attributed to genetic variation, provided that certain assumptions, such as the common environment assumption, are met. Unfortunately, the limitation of twin studies, and heritability analysis, have often been ignored, despite the emphasis in textbooks of statistical genetics on the fact that heritability values are specific to particular populations at particular times, and cannot be easily generalized. It is easy to understand why — because in an analysis of the contribution of genetic and environmental effects to phenotypic variation, if the range of relevant environmental variation increases, then the genetic contribution must decrease, and vice versa. To give just one example of the dogmatic interpretation of twin studies, on the basis of one of Sorsby’s early studies of twins,22 the British Medical Research Council concluded that: “It may therefore be taken as established that the dimensions of the optical components, the efficiency of the mechanism co-ordinating the growth of the components and thus the refraction of the eye are all genetically determined. The modes of inheritance and the possibility that environmental factors have a minor modifying influence are the principal problems now awaiting clarification.”

Despite the greater sophistication of current data analysis relative to the simple analysis of Sorsby’s era, heritability analysis still depends on certain assumptions, followed by mathematical modeling, and, as with all modelling, does not, and in principle, cannot prove that the phenotype has a genetic basis. Unfortunately, this sort of claim is often made. It is important to recognize that heritability analysis is about modeling the sources of variation in the phenotype, and in the case of twin studies, the sources of variation within twin pairs. This poses a major barrier to generalization,

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since the characteristic of twin pairs is extremely restricted environmental variation. This probably explains why more extended family studies, where there is almost always greater environmental variation, almost always result in lower estimates of heritability.23–25 Hopper,26 a leading statistical geneticist and Director of the Australian Twin Register, has expressed some of the limitations of twin study analysis in the following way: “Therefore, the typical lack of evidence for shared or common environment effects resulting from textbook application of classical biometrical modeling may not be a proper interpretation of reality. Such statistical analysis, biased towards a genetic explanation and then interpreted as evidence (if not proof!) that genetic factors alone are causing familial aggregation, may be misleading … It is suggested that twin researchers might benefit from taking a more critical approach to model fitting, and in particular, trying to falsify genetic hypotheses.”

One of the critical assumptions of the classical modeling is the common environment assumption, namely that the effects of environmental factors shared by twins are independent of zygosity, and hence that higher correlations within MZ pairs as compared with DZ pairs must be explained in terms of genetic differences. Attempts to test this assumption have rarely been made for any phenotype, and in any case must be trait-specific. However, recently, heritability of one of the common environmental associations of myopia, namely level of educational attainment, has been examined in the context of a study of myopia.27 The authors reported that correlations in educational achievements were lower in DZ twins than in MZ twins. But instead of interpreting this result as a falsification of the common environment assumption in the case of refractive error, the authors suggested that this was “proof ” that educational outcomes were also genetically determined. This provides a logical trap, because this logic means that unless the correlation in a trait is lower in MZ twins than in DZ twins, a highly unlikely outcome, either the common environment assumption will be confirmed, or another source of genetically determined impact will be “discovered.” There is no basis to the common misinterpretations of high heritability, such as the idea that high heritability values imply tight genetic determination and resistance to environmental impacts. There is, in fact, no

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incompatibility between a high heritability of a trait and rapid and massive environmental change.28 Genotypes are expressed within particular environmental conditions, and even with simple Mendelian inheritance, relevant variations in environmental conditions can lead to variations in phenotype, changing the balance of genetic and environmental contributions. This is simply more likely with complex multifactorial diseases, with smaller changes in environmental exposures.

But Heritability has Its Uses A more positive view of heritability is that high heritability values in twin studies do not prove that there is a genetic basis to any condition, but they validate the search for a genetic basis using modern molecular population genetic tools. They also provide a privileged population for genetic studies, DZ twins, where environmental variation is minimized, and average genetic variation is understood. In the absence of a reasonable heritability value from such studies, the highly expensive search for relevant genes would arguably not be justified. The proof of a genetic basis will, however, only come if genetic variation associated with variation in the trait is detected.

Evidence for Genetic Associations of School Myopia So far, there has been little success, with little replication of studies. In a follow-up molecular study on DZ twins from the Twin Eye Study, Hammond and colleagues found a chromosomal localization suggestive of the involvement of Pax6.29 However, subsequent analysis on the British 1958 Birth Cohort did not find any association.30 In addition, most of the genes associated with syndromic and non-syndromic high myopias do not seem to contribute to more moderate myopia.31 Studies on socially isolated populations such as the Old Order Amish and orthodox Ashkenazi Jews have so far given shared chromosomal localizations associated with refractive status in the two populations, despite what would appear to be markedly different prevalence rates in the two populations. As the authors have pointed out,32 this could be explained if the genetic and environmental factors operated independently, with the environmental factors shifting the overall distribution, while the genetic factor determined the position within the distribution — in other words, if

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there were separate genetic and environmental risk factors, but no geneenvironment interactions. It should be noted that as myopia prevalences have changed, not only the mean, but also the shape of the distribution has changed. This could be evidence of differential sensitivity in some individuals, but it could also indicate differential exposure to whatever the environmental effects are. The authors also cautioned that because of their selection criteria (only one parent being myopic and more than one sibling being myopic), they may be looking at autosomal dominant myopia, which may make only a small contribution to the overall prevalence of school myopia. Baird and colleagues33 have reported linkage of common myopia at 2q37.1 in three families from the GEM study. While the families were identified from probands with mild-moderate myopic, 35/49 members of the families were myopic and over 10% were highly myopic, making this a very unusual sample. It is therefore questionable whether these families are representative of most cases of common myopia, and indeed a case-control association study failed to find significant associations between myopia and this locus.34 Polymorphisms in COL2A1 have also been identified as associated with syndromic high myopia.8 Mutti et al.35 reported that COL2A1 polymorphisms were also associated with myopia, but a recent analysis by Metlapally et al.36 suggests that the association is with high, rather than mild to moderate forms of myopia. Two recent reports using candidate gene approaches are also of interest. Analysis of the SCORM data has suggested associations between genetic variation in the hepatocyte growth factor receptor c-Met37 and myopia. In this case, it is possible to calculate the prevalence of myopia in children without, or with the susceptibility allele. In children without the mutation, the prevalence is 61%; in children with one copy of the mutation, the prevalence is 68%; and in children with two copies, the prevalence rate is 66%. None of the progression rates reported in the paper are statistically different between the mutation groups. Thus, myopia develops in children in Singapore irrespective of whether they carry the mutant form of c-Met or not, but those with the mutation in c-Met become slightly more myopic. It should be noted that the variation is associated with different corneal powers, and not with different axial lengths. It will certainly be interesting to see if this variation is replicated in other populations, and what its relationship is to axial myopia. Strong impact on the prevalence myopia of polymorphisms of matrix metalloproteases has also been reported,38 with a strong dose effect.

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These emerging genetic associations with mild to moderate myopia set the scene for proper studies of gene-environment interactions. But because the evidence for such associations is very recent, such studies have not yet been reported. Given this situation, two factors — ethnicity and parental myopia — have been taken as potential surrogates for genetic differences.

Evidence for the Impact of Environmental Factors on Myopia Phenotypes In contrast to the limited evidence for an impact of genetic variation in school myopia, there is abundant evidence that suggests a role for environmental factors in generating variation at the phenotypic level. We have previously reviewed this material in some detail,5 and will therefore concentrate on newer evidence that adds to the picture. One of the long-standing pieces of evidence for environmental impacts has come from the association of higher prevalences of myopia with higher educational achievements, and with near-work-intensive occupations. This has led to a view that education, and the level of near-work and accommodation involved leads to the development of myopia, and has inspired one strand of myopia epidemiology. A number of different measures have been taken in this area, ranging from educational achievements in adults, school grades, IQ, number of books read, and attempts to determine hours spent on near-work or sustained near-work, and taking account of viewing distance, calculations of dioptre, and hours of accommodative effect. Of these various factors, educational achievements stand out as the most consistent measure, while the more quantitative estimates of near-work and accommodation have given less convincing associations. As a result, the relevance of near-work, either as a measure of accommodative effort, or as an estimate of accommodative lag and hyperopic defocus has been questioned.39 Recently, an apparently more powerful factor, time spent outdoors, has been revealed in the Orinda study40 and in the Sydney Myopia Study.41

Gene-Environment Interactions and Ethnicity The rapid increases in prevalence of myopia, particularly in urban East Asia, over the past few decades, possibly associated with the expansion of mass intensive education in those areas, suggest that changes in gene

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pools cannot account for the rate of change that has been seen. However, the concentration of the epidemic of myopia in urban East Asia has been interpreted as indicating the current differences in prevalence of myopia between ethnic groups may be genetic in origin, perhaps because of a concentration of susceptibility genes in those of East Asia origin. The available evidence does not favor this interpretation. First of all, it is clear that East Asian, or even more specifically Chinese, origin, does not necessarily lead to myopia. In addition to the earlier studies covered in our 2005 review,5 which demonstrated major variation in the prevalence of myopia for particular ethnic groups associated with different sites and times, recent work has documented the differences in prevalence of myopia in those of Chinese and Malay ethnicity, as well as Indian ethnicity, in two countries as close together as Singapore and Malaysia,42 and we have shown marked differences in the prevalence of myopia between children of Chinese origin in Singapore and Sydney.43 However, this evidence does not rule out ethnic genetic differences in susceptibility to environmental factors. On this issue, more conclusive evidence comes from studies on migrant populations. The prevalence of myopia is generally low in children and young adults in India, but shows some evidence of an urban-rural difference.44,45 Myopia is higher in the Indian population of Malaysia,42 and even higher again in the Indian population of Singapore,46 which approaches that seen in the Chinese community. This suggests that people of Chinese, Indian and Malay origin respond to the environment of Singapore with an increased prevalence of myopia, which suggests that there is a broadly similar susceptibility to the relevant environmental factors. The fact that, in the environment of Singapore, Chinese are more myopic than Indians, who are in turn more myopic than Malays, could be an indication of differential susceptibility to the environmental factors, but given that this difference in prevalence mirrors patterns of educational success,47 and engagement in outdoor activities,48 it could also be attributed to differential environmental exposures alone. A similar picture is found in the children from ethnic groups in Sydney, where the most myopic groups, those of East Asian and South Asian origin,49 achieve higher educational outcomes, and engage in lesser amount of time in outdoor activities than those of European origin. In collaboration with the SCORM study, we have also compared in detail children of Chinese origin growing up in Sydney with those of

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Chinese origin growing up in Singapore, and found that the prevalence of myopia in Sydney is lower. In the case of this study, ethnicity was controlled for, and the level of parental myopia was very similar in the two parent groups.43 It, therefore, seems likely that the differences can be attributed to environmental differences, of which the most obvious was the higher amount of time spent outdoors by the children of Chinese origin in Sydney than in Singapore. Unexpectedly, the less myopic children of Chinese ethnicity in Sydney apparently performed more near-work. It is always possible to construct an argument as to how these results could be explained in genetic terms. For example, it could be argued that the Indians who migrated to Singapore were more susceptible to the development of myopia than those who stayed at home. But to explain why the children of South Asian origin in Sydney were much less myopic, it would be necessary to postulate that those who migrated were less susceptible to myopia. Similarly, it is possible that the Chinese who migrated south to Singapore were as susceptible to the development of myopia as those who remained behind, whereas those who migrated further south to Australia were less susceptible. Despite some flaws in the study designs, the prevalence of myopia in Chinese Canadians50 also seems quite high, which might suggest that those who went north or east were more susceptible to developing myopia. Overall, a series of ad hoc hypotheses is required to interpret these data in terms of genetic differences, which is a characteristic sign of a theory in crisis. Almost all studies that have examined the issue have given evidence of an association between high educational achievements and myopia.5 While the studies are very limited for some ethnic groups, in all the ethnic groups that have been examined there is evidence of such an association, and there is no evidence that there is any differential susceptibility to education between ethnic groups. One of the few anomalies in the literature concerns the rapid appearance of high myopia prevalence rates in the Inuit during the period of acculturation, when children were brought into settlements and commenced formal education.51 This appears to have happened when educational pressures were much lower than in Singapore, and it is possible that genetic susceptibility might be involved. But given the evidence for light exposures in reducing the prevalence of myopia, it is also possible that the Inuit were particularly susceptible to environmental change because of the pattern of light exposures characteristic of Arctic environments.

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Overall, with the present evidence, there is little evidence for genetic contributions to the current differences in prevalence of myopia by ethnicity, and correspondingly little evidence of a role for gene-environment interactions.

Gene-Environment Interactions and Parental Myopia As a possible surrogate measure of differentially susceptible genomes, parental myopia is a less than satisfactory measure, because it is clear that environmental exposures are important, and that parents who are not myopic may simply have not received the necessary environmental exposures. Despite this limitation, most studies have shown a consistently higher prevalence of myopia amongst those who have myopic parents as compared with those who do not.14,15,52,53 There has been some investigation of whether there is an interaction between parental myopia and measures of education and near-work, with results from the SCORM study suggesting an interaction,52 whereas data from the Orinda study do not.14 Given the uncertainty about whether near-work is a risk factor, further work in the area is required. Recent work on the interaction between time spent outdoors and parental myopia suggests that all children are protected by time spent outdoors, and the risks decline in parallel for children with and without myopic parents.40,41 Thus, there is no real evidence of gene-environment interactions in relation to parental myopia. Attempts have been made to explain the impact of parental myopia in terms of a tendency for myopic parents to create myopigenic environments characterized by intense education and little time spent outdoors. This area deserves further study, but the very limited data available show at most a slight tendency in this direction,40,41 and the effects would need to be highly nonlinear to explain the differences. It should be noted that studies on populations of European origin14,15,53 have shown several-fold differences in the prevalence of myopia depending on parental myopia. A study of myopia across three generations in China54 has, however, suggested that as the prevalence of myopia increases, the impact of parental myopia declines. Consistent with this, studies carried out at sites with characteristically high myopia prevalence such as Singapore52 and Guangzhou (Fan and He, personal communication), the relative risk associated with parental myopia is much more modest.

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Overall, parental myopia appears to be more viable as a surrogate measure of genetic background and susceptibility than ethnicity, but there is currently no convincing evidence of gene-environment interactions.

Conclusion Clearly much more data need to be collected for rigorous testing of hypotheses. But, at this stage, putting to one side the clearly genetic high familial myopias and hyperopia of >2D, there is little evidence for a genetic basis to variations in refractive error, particularly of common myopia. Strong evidence for gene-environment interactions in the statistical genetic sense is lacking, although there is clear evidence for an impact of environmental exposures on refractive outcomes independent of genetic background. Thus, since the evidence for gene-enviroment interactions was reviewed in 2000,55 there has been little progress, and better definition of genetic contributions to school myopia and rigorous analysis of gene-environment interactions are still necessary. When we reviewed the etiology of myopia in 2005,5 we concluded that there was abundant evidence of environmental impacts on the prevalence of myopia, but little evidence of significant genetic contributions to myopia, apart from high myopia. The genetic evidence obtained since then has not substantially changed this conclusion, although there are now a few cases of potential genetic associations with some forms of mild to moderate myopia, which provide the basis for looking at gene-environment interactions in the future. The use of ethnicity or parental myopia as surrogate measures of genetic factors has not provided evidence of geneenvironment interactions. Overall, we conclude that the evidence still favors the idea that myopia is predominantly a disorder caused by abnormal environmental exposures, and that the marked differences in prevalence associated with ethnicity or educational attainments represent cases of simple environmental effects, rather than cases of genetic determination or geneenvironment interactions. We, therefore, suggest that school myopia is not primarily due to some sort of genetically determined failure of emmetropization, but represents a case in which environmental exposures that promote axial elongation, simply push the emmetropization mechanism too far.

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Acknowledgments This work was supported by a grant from the Australian Research Council to the ARC Centre of Excellence in Vision Science (COE956320).

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15. Pacella R, McLellan J, Grice K, et al. (1999) Role of genetic factors in the etiology of juvenile-onset myopia based on a longitudinal study of refractive error. Optom Vis Sci 76: 381–386. 16. The Framingham Offspring Eye Study Group (1996) Familial aggregation and prevalence of myopia in the Framingham Offspring Eye Study. Arch Ophthalmol 114: 326–332. 17. Fotouhi A, Etemadi A, Hashemi H, et al. (2007) Familial aggregation of myopia in the Tehran eye study: estimation of the sibling and parent offspring recurrence risk ratios. Br J Ophthalmol 91: 1440–1444. 18. Guggenheim JA, Pong-Wong R, Haley CS, et al. (2006) Correlations in refractive errors between siblings in the Singapore Cohort Study of Risk factors for Myopia. Br J Ophthalmol 91: 781–784. 19. Guggenheim JA, Kirov G, Hodson SA. (2000) The heritability of high myopia: a reanalysis of Goldschmidt’s data. J Med Genet 37: 227–231. 20. Lyhne N, Sjølie AK, Kyvik KO, Green A. (2001) The importance of genes and environment for ocular refraction and its determiners: a population based study among 20–45 year old twins. Br J Ophthalmol 85: 1470–1476. 21. Hammond CJ, Snieder H, Gilbert CE, Spector TD. (2001) Genes and environment in refractive error: the twin eye study. Invest Ophthalmol Vis Sci 42: 1232–1236. 22. Sorsby A, Sheridan M, Leary GA. (1962) Refraction and its components in twins. MRC Special Report #303, Medical Research Council, London. 23. Chen CY, Scurrah KJ, Stankovich J, et al. (2007) Heritability and shared environment estimates for myopia and associated ocular biometric traits: the Genes in Myopia (GEM) family study. Hum Genet 121: 511–520. 24. Klein AP, Suktitipat B, Duggal P, et al. (2009) Heritability analysis of spherical equivalent, axial length, corneal curvature, and anterior chamber depth in the Beaver Dam Eye Study. Arch Ophthalmol 127: 649–655. 25. Lopes MC, Andrew T, Carbonaro F, et al. (2009) Estimating heritability and shared environmental effects for refractive error in twin and family studies. Invest Ophthalmol Vis Sci 50: 126–131. 26. Hopper JL. (2000) Why “common environment effects” are so uncommon in the literature. In: Spector TD, Snieder H, MacGregor AJ (eds.), Advances in Twin and Sib-pair Analysis, pp. 152–163. Greenwich Medical Media, London. 27. Dirani M, Shekar SN, Baird PN. (2008) The role of educational attainment in refraction: the Genes in Myopia (GEM) twin study. Invest Ophthalmol Vis Sci 49: 534–538. 28. Rose KA, Morgan IG, Smith W, Mitchell P. (2002) High heritability of myopia does not preclude rapid changes in prevalence. Clin Experiment Ophthalmol 30: 168–172. 29. Hammond CJ, Andrew T, Mak YT, Spector TD. (2004) A susceptibility locus for myopia in the normal population is linked to the PAX6 gene region on

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46. Wu HM, Seet B, Yap EP, et al. (2001) Does education explain ethnic differences in myopia prevalence? A population-based study of young adult males in Singapore. Optom Vis Sci 78: 234–239. 47. Ministry of Education, Singapore. Education Statistics Digest 2008. http://www.moe.gov.sg/education/education-statistics-digest/esd-2008.pdf 48. Dirani M, Tong L, Gazzard G, et al. (2009) Outdoor activity and myopia in Singapore teenage children. Br J Ophthalmol 93: 997–1000. 49. Ip JM, Huynh SC, Robaei D, et al. (2008) Ethnic differences in refraction and ocular biometry in a population-based sample of 11-15-year-old Australian children. Eye 22: 649–656. 50. Cheng D, Schmid KL, Woo GC. (2007) Myopia prevalence in ChineseCanadian children in an optometric practice. Optom Vis Sci 84: 21–32. 51. Morgan RW, Speakman JS, Grimshaw SE. (1975) Inuit myopia: an environmentally induced “epidemic”? Can Med Assoc J 8: 112: 575–577. 52. Saw SM, Nieto FJ, Katz J, et al. (2001) Familial clustering and myopia progression in Singapore school children. Ophthalmic Epidemiol 8: 227–236. 53. Ip JM, Huynh SC, Robaei D, et al. (2007) Ethnic differences in the impact of parental myopia: Findings from a population-based study of 12-year-old Australian children. Invest Ophthalmol Vis Sci 48: 2520–2528. 54. Wu MM, Edwards MH. (1999) The effect of having myopic parents: an analysis of myopia in three generations. Optom Vis Sci 76: 387–392. 55. Saw SM, Chua WH, Wu HM, et al. (2000) Myopia: gene-environment interaction. Ann Acad Med Singapore 29: 290–297.

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1.4 The Economics of Myopia Marcus C.C. Lim* and Kevin D. Frick†

The economics of myopia is not well elucidated. Economic evaluation can take several forms. This chapter gives an overview of myopia economics and provides a walkthrough for the calculation of the burden of disease related to worldwide myopia. Directions for further research are suggested.

Introduction Economic science is the study of allocation of scarce resources. Fundamentally, all forms of economic evaluation are undertaken for one reason: Resources are limited and decision makers need guidance (which is all that economic evaluation can provide) for making resource allocation decisions between alternatives. In health economics, resources include time (for the patient and any caregivers), money, medical manpower and other resources to produce health and health services. Economists are interested in the modification of resource allocation or prices that will change the way those resources are allocated and bring about a change in health. With regard to myopia, questions that can be answered at a local or global level by an economic evaluation include: • •

What is the cost of vision correction for myopia? What is the total cost related to myopia, combining vision correction, the costs of treating associated eye disease as well as loss of productivity?

*Singapore National Eye Centre, 11 Third Hospital Avenue, Singapore 168751. † Johns Hopkins Bloomberg School of Public Health, 624 N. Broadway, Rm. 606, Baltimore MD 21205, USA. E-mail: [email protected]

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• • •

What benefits are there from vision correction of myopia? What is the magnitude of future costs that will be offset? Does government subsidization of spectacles for myopes make economic sense? How much could we save if there was a cure for myopia?

Economic evaluations One purpose of economic evaluation is to describe and compare the costs and benefits of health care services. This type of analysis can help us to answer any of the questions above. One methodological step necessary to perform economic analysis is to decide what set of costs and benefits should be measured. The set of costs and benefits considered is defined by the perspective taken, using economic jargon. The perspective could be the hospital, the Ministry of Health, the government as a whole or society. Generally, economists favor analyses at the broadest category, society. From a societal perspective, an economic evaluation of myopia would describe and compare the inputs (costs) and outputs (benefits) of treating myopia for all those who are affected. The costs include the cost of optical correction of myopia, in the form of spectacles, contacts lenses and refractive surgery, regardless of who pays. The benefits include increased productivity and increased quality of life. Since the outcome of every economic evaluation depends on both the action contemplated and the alternative, it is important to specify the alternative. The alternative could be the status quo. If it is, the status quo must be precisely defined. In our example, a course of action might be for government to fund spectacles for myopes. This would be compared with the status quo alternative, which could be to continue with private funding. Full vs partial evaluations Economic evaluations can be classified into full or partial evaluations by the answers to the following two questions1: 1) Does the evaluation look at both costs and benefits? 2) Does it involve two or more alternatives? Translating these answers into a form of economic evaluation is demonstrated in the table below.1

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YES Does the evaluation look at both costs and benefits?

NO

CEA, CUA or CBA

Partial

Partial

A description of costs or benefits

YES

NO

Does it involve two or more alternatives?

The differences between cost-effectiveness analysis (CEA), cost-utility analysis (CUA) or cost-benefit analysis (CBA) are the means of valuing the health benefit. For CEA, the benefit measure is usually stated as $/change in disease-specific measure of health-related benefits. For CUA, the result is stated as $/Quality-Adjusted Life Year (QALY); for CBA, it is stated as “Net benefit” ($(benefit)–$(cost)). In international settings, the result can be expressed in local currency units. Economic evaluation of myopia Why perform an economic evaluation of myopia? Cynics might suggest that this is an academic exercise and nothing more, as the correction of myopia is not funded by the state in most countries. Free markets do not require economic evaluations as it is assumed that individuals will only make decisions that benefit their own interests. Countries that make provincial or national provision and coverage decisions are more likely to use economic evaluations, e.g. the United Kingdom, Canada and Australia and not the United States. For example, in the United States, multiple private insurers fund a significant portion of healthcare (for which the market measures value rather than simulating a market through an economic evaluation) while in the United Kingdom, it is the governmentcontrolled National Health Service, the world’s third largest employer, who funds healthcare. Nevertheless, screening programs for refractive error are becoming increasingly common throughout the world.2 In addition, research has shown that off-the-shelf spectacles are suitable for a large

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proportion of uncorrected refractive error; an economic rationale for governments to provide these is more likely to be justifiable.3,4 Myopia is often given minor priority in public health research, yet the societal costs of myopia can be considerable. In a broad view, costs include not only the costs of optical correction, but also morbidity resulting from eye diseases associated with myopia such as glaucoma, cataract, maculopathy and retinal detachment. As a first step, conducting a study that looks at only costs of the disease and treatment without considering alternatives is relatively simple to do. Studies that examine only costs of treating the disease can be either cost-of-illness or burden-of-disease studies. The former is concerned with costs from time of incidence, while the latter is concerned with the costs of treating prevalent cases regardless of time of incidence. These studies help to quantify the overall burden of a disease and allow comparisons with burdens of other diseases of the costs of the disease. Ultimately, economists prefer evaluations looking at both costs and benefits. With sufficient information, a full economic evaluation would look at costs and benefits of multiple alternatives.

The Economic Cost of Myopia: A Burden-of-Disease Study At the simplest level, one could look into calculating the worldwide cost of correcting myopia, a burden-of-disease evaluation. Initially, the prevalence of myopia, or the number of myopes, must be estimated. The proportion of myopes paying for correction and unit price data are also needed to estimate the economic cost of myopia. Annual cost of myopia = Number of paying myopes × average amount spent per myope = Population × myopia prevalence × proportion of myopes having correction × proportion of myopes paying for correction × average amount spent per myope. (The reader should note that this is the “observed” burden. The “maximum” burden would include treatment of all myopes.)

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Data needed include: i. Prevalence of myopia ii. Proportion of myopes with correction/paying for correction iii. Amount paid for myopic correction i. Prevalence of myopia The data for this comes from the numerous epidemiological studies carried out around the world that have already been covered in detail in Chapter 1.1. However, data for many countries are still lacking. We will estimate the myopia prevalence in these countries based on data from neighboring countries or countries with similar socioeconomic characteristics. China China is country with the largest population in the world; it is important to use accurate figures for myopia prevalence. Numerous studies have been published recently. A population-based study of an urban cohort in Guangzhou, China, showed a prevalence of myopia of 32.3% in subjects aged 50 years and above.5 Urban 15-year-old school children had a myopia prevalence rate of 73%.6 Further, a study showed that 62.3% of 15-year-old Chinese school children in a rural area were myopic.7 Another study of rural school children in Southern China found that 37% of 13-year-olds were myopic, this figure rising to 54% of 17-year-olds, were myopic.8 The Beijing eye study showed that 22.9% of the population aged 40 to 90 years of age in a mixed urban and rural cohort were myopic.9 Table 1 shows an estimation of the number of myopes in China. For adults aged 44 years and above, data from the Beijing Eye Study9 was used because the mixed urban and rural cohort was more representative for the whole of the country than other purely urban or rural cohorts. India India is the country with the second largest population in the world. However, there are relatively few studies on myopia prevalence, which was estimated to be 7% in an urban cohort in 5- to15-year-olds10 and 4.1% of 7- to 15-year-olds in a rural cohort.11 In South India, it was 27% for those above 39-years of age.12 The estimates used are shown in Table 2.

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Age segment 0–15 15–29 30–44 45–59 60–74 75–84 85+

Proportion of Total Population (%) 20.3 22.8 26.7 18.2 9.4 2.3 0.3

Population Number 267,960,000 300,960,000 352,440,000 240,240,000 124,080,000 30,360,000 3,960,000

Total

0–14 15–64 65+ Total

Myopia Prevalence (%)

Source

Number of Myopes

40.0 30.0 26.0 22.9 22.9 22.9 22.9

Guangzhou6 Est. Est. Beijing9 Beijing9 Beijing9 Beijing9

107,184,000 90,288,000 91,634,400 55,014,960 28,414,320 6,952,440 906,840

1,320,000,000

Table 2.

Age

Prevalence of Myopia in China

Population 356,500,000 736,000,000 57,500,000 1,150,000,000

380,394,960

Prevalence of Myopia in India

Proportion (%)

Prevalence (%)

Source

No. of Myopes

31 64 5

6 27 27

New Delhi10 South India12 South India12

21,390,000 198,720,000 15,525,000 235,635,000

Europe The estimated prevalence of myopia in subjects 40 years and above in Western Europe is estimated at 26.6%, similar to that in the USA.13 From this we can assume that the myopia prevalence in those under 40 years of age is similar to that in our calculations, and the prevalence for the whole of Europe is assumed to be similar. The figure of 27% was used in our model and this is reasonable as it is similar to the figure we computed from more data, for the United States (see later). Assumptions such as these are necessary due to lack of data. Singapore There are several studies detailing the prevalence of myopia in Singapore and these are shown in Table 3.

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The Economics of Myopia Table 3.

Age

Number of People

0–4 5–9 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74 75–79 80+ Total

196,500 240,100 261,500 249,900 222,400 257,300 302,700 308,200 331,900 319,300 272,200 219,100 120,900 111,500 80,600 57,600 56,700 3,608,500

Prevalence of Myopia in Singapore

Percentage of Myopes

Source

Number of Myopes

5 29 60 74 79 70 60 60 45 45 25 25 30 30 32 32 32

Estimated Tan et al.14 Estimated Quek et al.15 Wu et al.16 Estimated Estimated Estimated Tanjong Pagar 17 Tanjong Pagar17 Tanjong Pagar17 Tanjong Pagar17 Tanjong Pagar17 Tanjong Pagar17 Tanjong Pagar17 Tanjong Pagar17 Estimated

9825 69,629 156,900 184,926 175,696 180,110 181,620 184,920 149,355 143,685 68,050 54,775 36,270 33,450 25,792 18,432 18,144 1,544,157

Southeast Asia In a predominantly Malay cohort, school-age children in a suburban area near Kuala Lumpur in Malaysia had lower rates of myopia. In children aged 7 years, 10% had myopia, this figure rising to 34% in 15-year-olds.18 In Indonesia, it was 26.2%.19 For our analysis, Indonesia, the fourth largest country in the world, was taken to have a prevalence of myopia of 26%. Africa Myopia prevalence of 15-year-olds has been reported at 10%.20 In Ghanaian school children aged 6 to 22 years, it was 7%.21 For students aged 11–27 years, the prevalence of myopia was 5.6%.22 In general, myopia seems to be less prevalent among those born and raised in Africa. There is little data on myopia prevalence in adults so we use a figure of 10% for Africa. USA Estimates for the prevalence of myopia in the USA are shown in Table 4.

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Prevalence of myopia in the USA

Age

Number of People23

Percentage of Myopes (%)

Source

Number of Myopes

0–4 5–9 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74 75–79 80+

20,417,636 19,709,887 20,627,397 21,324,186 21,111,240 20,709,480 19,706,499 21,185,785 22,481,165 22,797,569 20,480,605 18,224,445 13,362,238 10,375,554 8,541,290 7,381,027 10,962,481

2.7 10.1 10.1 15.0 33.1 33.1 33.1 33.1 33.1 33.1 33.1 33.1 33.1 33.1 33.1 33.1 33.1

Baltimore24 CLEERE25 CLEERE25 Estimated NHANES26 NHANES26 NHANES26 NHANES26 NHANES26 NHANES26 NHANES26 NHANES26 NHANES26 NHANES26 NHANES26 NHANES26 NHANES26

551,276 1,970,989 2,062,740 3,198,628 6,987,820 6,854,838 6,522,851 7,012,495 7,441,266 7,545,995 6,779,080 6,032,291 4,422,901 3,434,308 2,827,167 2,443,120 3,628,581

Total

299,398,484

79,716,347

South America The prevalence of myopia in Brazil27 is similar to that of Europe, with 3.8% in those under 5-years of age and 29.7% in those ranging from 30-to 39-years of age. In Los Angeles Latinos, it is 16.8% in those aged 40 years and above, although this may not be a perfect comparison group as only some Latinos in Los Angeles are first generation immigrants.28 We will assume an overall estimate of population prevalence of myopia in South America of 21%. Bangladesh The prevalence of myopia was 22.1% in the over 30-year-olds.29 We will assume that this is the population prevalence of myopia for the purposes of our estimate.

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ii. Proportion of myopes paying for correction Uncorrected and undercorrected refractive error, spectacle coverage rate and reasons for spectacles nonwear Because myopes who do not wear glasses are not included in our calculation, an estimate of the percentage of myopes who do not wear or no longer wearing glasses is needed to complete the calculations. Studies show that there is a large range of uncorrected or undercorrected refractive error in various populations (Table 5). Uncorrected refractive error refers to individuals with refractive errors who do not use any form of refractive correction, while undercorrected refractive error includes individuals with uncorrected refractive errors and individuals with refractive errors that are undercorrected by their prescriptions. Undercorrected refractive error is also usually defined as improvement of at least 2 lines of visual acuity in the better eye with the best possible refractive correction. From these studies we can estimate the number of myopes who are paying, or not paying for vision correction. It has been estimated that there are 153 million people aged 5-years and above worldwide with uncorrected refractive error.38 Uncorrected refractive error can also be measured using a spectacle coverage rate. This is the proportion of people with refractive error who have glasses and can be expressed as the fraction “met need/(met need + unmet

Table 5.

Uncorrected or Undercorrected Visual Acuity Globally

Group United States general population Mexican Americans Taiwanese Elderly Australians Los Angeles Latinos Singapore Chinese* China school children Singapore Malays* India

Uncorrected or Undercorrected* Visual Acuity (%) 5 6 10 10 15 17 19 29 >66

Study NHANES30 Projecto VER31 Shihpai32 BMES33 LALES34 Tanjong Pagar35 XPRES7 SMES36 Andhra Pradesh37

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need).” This is 66% in Tehran using a 20/40 visual acuity cutoff,39 and 25.2% or 40.5% in Bangladesh, using 20/40 and 20/60 visual acuity cutoffs respectively.3 Even if subjects have glasses, there is evidence from other countries that not all want to wear them. In Mexico, 493 school children received free spectacles through a local program and underwent unannounced examination within 18 months.2 Only 13.4% of them were found to be wearing spectacles. The reasons they gave included appearance of the glasses or fear of being teased. A study of Tanzanian school children showed that at 3 months, only 47% were wearing the free spectacles they had been given.40 Of 580 Chinese children owning spectacles, 17.9% did not wear them at school and a common reason for nonwear was the belief that spectacles weaken the eyes.41 In African school children, barriers to spectacles use included peer pressure, parental concern about the safety of spectacles use and their costs.42 In Southern India, a population-based study found that the prevalence of current spectacles use in those with spherical equivalent +/−3.00 Diopters or worse was 34.2%, and among those who had used spectacles previously, 43.8% discontinued because they felt either the prescription was incorrect or the spectacles were uncomfortable.37 However, there might be differences in cultural attitudes between countries as a study of Australian children found unnecessary or overuse of spectacles.43,44 The proportion of myopes with correction for each region shown in Table 7. iii. Amount paid for myopic correction USA In the USA, burden-of-disease studies have been carried out on myopia. It has been estimated that US$2 billion per year in 198345 and US$4.6 billion per year in 199446 were spent in the USA on correcting myopic refractive errors. The figure in 1990 for vision products (eyeglass frames, lenses and contact lenses) for all refractive errors is higher, at $US8.1 billion.47 After adjustment for inflation (U.S. Department of Labor), this equates to $13.4 billion. A cross-sectional study in the USA showed that 110 million Americans could and do achieve normal vision with refractive correction and the estimated cost of this was US$3.8 billion48 or approximately US$4.9 billion

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today. From these studies we can estimate that the cost of optical correction of myopia lies in the range of US$2–5 billion. Since data are available, we can use them to calculate the cost per myope in order to help us calculate the cost of myopia in other countries with less data. Table 4 shows the calculation for how the number of myopes in the USA is estimated to be approximately 80 million. If we use a conservative estimate of the cost of myopia correction to be $4 billion, then the annual amount spent per myope is US$4 billion divided by 80 million which is US$50. Note that this is the average amount spent annually for all myopes, whether they wear glasses or change them regularly or not. The “true” cost is higher than this as we estimate that 95% of myopes in this population buy optical correction aids.30 The estimate may also be low because some myopes may not change glasses annually. Singapore The cost data for a study of Singaporean school children aged 12–17 were collected using questionnaires; the resulting estimate was that they spent an average of S$222 (US$148) per year or a median of S$125 (US$83) on glasses or contact lenses from opticians.49 Unfortunately no data are available indicating how much is spent by other demographic groups on glasses, laser refractive surgery and other complications resulting from myopia as well as days lost from work. Thus we have to use the data from the single age group to derive a bottom-up estimate for the direct costs of myopia. This bottom-up approach entails estimating the costs of single elements and then extrapolating upwards to the entire population, whereas a topdown approach might look at the global cost of myopia and then working downwards to an estimate for Singapore. Often both approaches are used to determine if the estimates are in the correct magnitude. The result is an annual figure of S$373m, or US$250m. Table 6 summarizes the estimated total annual direct cost of myopia in Singapore. Sensitivity analyses can also be carried out to calculate the upper and lower bounds of our estimate. If we use the median annual cost of S$125 instead, the annual cost is S$180m. It can be argued that myopia correction in older adults (the average age of the LASIK patient in a Singapore tertiary hospital is 33 years of age — unpublished data) may be more costly especially with the advent of laser refractive surgery. However, this does not necessarily result in a substantial

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Age 0–4 5–9 10–14 15–19 20–24 25–29 30–34 35–39 40–44 45–49 50–54 55–59 60–64 65–69 70–74 75–79 80+ Total

Cost of Myopia Correction in Singapore

Percentage Number of Myopes of People (%) 196,500 240,100 261,500 249,900 222,400 257,300 302,700 308,200 331,900 319,300 272,200 219,100 120,900 111,500 80,600 57,600 56,700 3,608,500

5 29 60 74 79 70 60 60 45 45 25 25 30 30 32 32 32

Source of Data for Preceding Column Estimated Tan et al.14 Estimated Quek et al.15 Wu et al.16 Estimated Estimated Estimated Tanjong Pagar17 Tanjong Pagar17 Tanjong Pagar17 Tanjong Pagar17 Tanjong Pagar17 Tanjong Pagar17 Tanjong Pagar17 Tanjong Pagar17 Estimated

Total Annual Cost Annual in Singapore Cost Per Dollars (S$) ≈S$1.50 Number Myope US$1≈ of Myopes ($) ($) 9825 69,629 156,900 184,926 175,696 180,110 181,620 184,920 149,355 143,685 68,050 54,775 36,270 33,450 25,792 18,432 18,144

100 222 222 222 222 222 222 222 222 222 222 222 222 222 222 222 222

982,500 15,388,009 34,674,900 40,868,646 38,828,816 39,804,310 40,138,020 40,867,320 33,007,455 31,754,385 15,039,050 12,105,275 8,015,670 7,392,450 5,700,032 4,073,472 4,009,824 372,650,134

change in the economic cost of myopia as the cost of laser surgery may be offset by future savings in spectacles and contact lenses. Contact lenses have been shown to be more costly than LASIK, which is itself more costly than eyeglasses.50 If we take an average cost of LASIK to be US$2000 for both eyes, then this is approximately the cost of 15 years of spectacles and contact lenses, based on a mean annual cost of US$148. Javitt et al. found that PRK was equivalent to wearing daily-wear soft contact lenses for 10 years.46 The average age of the LASIK patient in Singapore is 33-years, as mentioned earlier. This patient, whom we still classify as “myopic,” has spent US$3000 on LASIK, but does not have to pay for refractive correction for the next 10–15 years, until she becomes presbyopic 10 years later, when she will start spending approximately as much as a myope annually. This is because myopes who have been rendered emmetropic by LASIK will have to purchase reading glasses when they become presbyopic in their mid 40s.

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The Singapore study49 on myopic schooling teenagers found a conservative median annual cost of US$83. This is slightly higher but in the same magnitude as the USA estimate. This is probably because the Lim et al. study was on a prospective cohort of myopes measuring actual expenditures. In contrast, Vitale et al. used cost data based on Centers for Medicare & Medicaid Services fee schedules for 2000, and expenditure data from the Medical Expenditure Panel Survey. Also, the amount that a private individual is willing to pay is larger than the amount the government is willing to subsidize. The difference could be equated to the extra amount private individuals are willing to pay for other factors such as design and esthetics — it is possible to get a cheap pair of spectacles in Singapore for US$35. The burden of myopia Table 7 shows the number of myopes, approximately 900 million, around in the world who pay for vision correction. In the US, the annual cost is approximately $US50. Since the Singapore cost estimate is higher, we used the US cost data as a conservative estimate for the cost of myopia. However, the cost in US dollars in poorer countries like India would be lower than that in the USA, so we can use purchasing power parity to estimate the cost of myopia in these countries. A pair of spectacles in USA is the same as the cost of a pair of spectacles in Russia. In real life, it is not as simple as that, but it will suffice for an approximation for the global cost of myopia. Thus the global burden of myopia would be approximately $US50 x 900 million, or $US45 billion.

Further Directions for Economic Research A major limitation in our analysis is the paucity of cost data from large regions of the world. This necessitated the extrapolation of cost data from the USA, rather than from Singapore, as the USA cost data were more conservative. Instead of just a burden of disease study, we could perform partial and full evaluations examining the benefits of treating myopia as well as the costs, and define possible alternatives of action. The benefits of reducing myopia would include reduction of diseases which myopes are at greater risk, e.g., cataract, glaucoma and retinal detachment, and their attendant direct and indirect costs. Alternative courses of action might include government programs for free sight tests and glasses.

Asia

1.32

1.15

0.23

0.17

0.15

1.01

3.88

0.92

0.30

0.11

0.92

0.03

0.09

0.53

0.38

0.73

0.03

0.38

0.73

0.03

TOTAL

6.47

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Population, billion Total population, billion Myopia prevalence No. of myopes Percentage of myopes with correction No. of myopes having correction

South China India Indonesia Pakistan Bangladesh Others Africa USA Mexico Canada Others America Europe Australia

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Estimation of Number of Myopes with Optical Correction Globally

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Table 7.

0.29

0.21

0.26

0.18

0.20

0.20

0.10

0.27

0.18

0.27

0.27

0.21

0.27

0.07

0.38 80%

0.16 33%

0.06 50%

0.03 33%

0.03 33%

0.2 50%

0.09 20%

0.08 95%

0.02 80%

0.01 90%

0.02 80%

0.08 80%

0.2 90%

0.002 85%

1.44

0.30

0.05

0.03

0.01

0.01

0.10

0.02

0.08

0.02

0.01

0.02

0.06

0.18

0.00

0.89

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References 1. Drummond MF, Schulpher MJ, Torrance GW, et al. (2005) Methods for the Economic Evaluation of Health Care Programmes, 3rd ed. Oxford University Press. 2. Castanon Holguin AM, Congdon N, Patel N, et al. (2006) Factors associated with spectacle-wear compliance in school-aged Mexican children. Invest Ophthalmol Vis Sci 47(3): 925–928. 3. Bourne RR, Dineen BP, Huq DM, et al. (2004) Correction of refractive error in the adult population of Bangladesh: meeting the unmet need. Invest Ophthalmol Vis Sci 45(2): 410–417. 4. Zeng Y, Keay L, He M, et al. (2009) A randomized, clinical trial evaluating ready-made and custom spectacles delivered via a school-based screening program in China. Ophthalmology 116(10): 1839–1845. 5. He M, Huang W, Li Y, et al. (2009) Refractive error and biometry in older Chinese adults: the Liwan Eye Study. Invest Ophthalmol Vis Sci 50(11): 5130–5136. 6. He M, Zeng J, Liu Y, et al. (2004) Refractive error and visual impairment in urban children in southern china. Invest Ophthalmol Vis Sci 45(3): 793–799. 7. Congdon N, Wang Y, Song Y, et al. (2008) Visual disability, visual function, and myopia among rural chinese secondary school children: the Xichang Pediatric Refractive Error Study (X-PRES) — report 1. Invest Ophthalmol Vis Sci 49(7): 2888–2894. 8. He M, Huang W, Zheng Y, et al. (2007) Refractive error and visual impairment in school children in rural southern China. Ophthalmology 114(2): 374–382. 9. Xu L, Li J, Cui T, et al. (2005) Refractive error in urban and rural adult Chinese in Beijing. Ophthalmology 112(10): 1676–1683. 10. Murthy GV, Gupta SK, Ellwein LB, et al. (2002) Refractive error in children in an urban population in New Delhi. Invest Ophthalmol Vis Sci 43(3): 623–631. 11. Dandona R, Dandona L, Srinivas M, et al. (2002) Refractive error in children in a rural population in India. Invest Ophthalmol Vis Sci 43(3): 615–622. 12. Raju P, Ramesh SV, Arvind H, et al. (2004) Prevalence of refractive errors in a rural South Indian population. Invest Ophthalmol Vis Sci 45(12): 4268–4272. 13. Kempen JH, Mitchell P, Lee KE, et al. (2004) The prevalence of refractive errors among adults in the United States, Western Europe, and Australia. Arch Ophthalmol 122(4): 495–505. 14. Tan GJ, Ng YP, Lim YC, et al. (2000) Cross-sectional study of near-work and myopia in kindergarten children in Singapore. Ann Acad Med Singapore 29(6): 740–744. 15. Quek TP, Chua CG, Chong CS, et al. (2004) Prevalence of refractive errors in teenage high school students in Singapore. Ophthalmic Physiol Opt 24(1): 47–55.

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16. Wu HM, Seet B, Yap EP, et al. (2001) Does education explain ethnic differences in myopia prevalence? A population-based study of young adult males in Singapore. Optom Vis Sci 78(4): 234–239. 17. Wong TY, Foster PJ, Hee J, et al. (2000) Prevalence and risk factors for refractive errors in adult Chinese in Singapore. Invest Ophthalmol Vis Sci 41(9): 2486–2494. 18. Goh PP, Abqariyah Y, Pokharel GP, Ellwein LB (2005) Refractive error and visual impairment in school-age children in Gombak District, Malaysia. Ophthalmology 112(4): 678–685. 19. Saw SM, Gazzard G, Koh D, et al. (2002) Prevalence rates of refractive errors in Sumatra, Indonesia. Invest Ophthalmol Vis Sci 43(10): 3174–3180. 20. Naidoo KS, Raghunandan A, Mashige KP, et al. (2003) Refractive error and visual impairment in African children in South Africa. Invest Ophthalmol Vis Sci 44(9): 3764–3770. 21. Ntim-Amponsah CT, Ofosu-Amaah S. (2007) Prevalence of refractive error and other eye diseases in schoolchildren in the Greater Accra region of Ghana. J Pediatr Ophthalmol Strabismus 44(5): 294–297. 22. Wedner SH, Ross DA, Todd J, et al. (2002) Myopia in secondary school students in Mwanza City, Tanzania: the need for a national screening programme. Br J Ophthalmol 86(11): 1200–1206. 23. Table 1: Annual Estimates of the Population by Five-Year Age Groups and Sex for the United States: April 1, 2000 to July 1, 2006 (NC-EST2006-01). Population Division, U.S. Census Bureau; 2007. 24. Giordano L, Friedman DS, Repka MX, et al. (2009) Prevalence of refractive error among preschool children in an urban population: the Baltimore Pediatric Eye Disease Study. Ophthalmology 116(4): 739–746, 46 e1–4. 25. Zadnik K, Manny RE, Yu JA, et al. (2003) Ocular component data in schoolchildren as a function of age and gender. Optom Vis Sci 80(3): 226–236. 26. Vitale S, Ellwein L, Cotch MF, et al. (2008) Prevalence of refractive error in the United States, 1999–2004. Arch Ophthalmol 126(8): 1111–1119. 27. Schellini SA, Durkin SR, Hoyama E, et al. (2009) Prevalence of refractive errors in a Brazilian population: the Botucatu eye study. Ophthalmic Epidemiol 16(2): 90–97. 28. Tarczy-Hornoch K, Ying-Lai M, Varma R. (2006) Myopic refractive error in adult Latinos: the Los Angeles Latino Eye Study. Invest Ophthalmol Vis Sci 47(5): 1845–1852. 29. Bourne RR, Dineen BP, Ali SM, et al. (2004) Prevalence of refractive error in Bangladeshi adults: results of the National Blindness and Low Vision Survey of Bangladesh. Ophthalmology 111(6): 1150–1160. 30. Vitale S, Cotch MF, Sperduto RD. (2006) Prevalence of visual impairment in the United States. JAMA 295(18): 2158–2163.

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31. Munoz B, West SK, Rodriguez J, et al. (2002) Blindness, visual impairment and the problem of uncorrected refractive error in a Mexican-American population: Proyecto VER. Invest Ophthalmol Vis Sci 43(3): 608–614. 32. Kuang TM, Tsai SY, Hsu WM, et al. (2007) Correctable visual impairment in an elderly Chinese population in Taiwan: the Shihpai Eye Study. Invest Ophthalmol Vis Sci 48(3): 1032–1037. 33. Thiagalingam S, Cumming RG, Mitchell P. (2002) Factors associated with undercorrected refractive errors in an older population: the Blue Mountains Eye Study. Br J Ophthalmol 86(9): 1041–1045. 34. Varma R, Wang MY, Ying-Lai M, et al. (2008) The prevalence and risk indicators of uncorrected refractive error and unmet refractive need in Latinos: the Los Angeles Latino Eye Study. Invest Ophthalmol Vis Sci 49(12): 5264–5273. 35. Saw SM, Foster PJ, Gazzard G, et al. (2004) Undercorrected refractive error in Singaporean Chinese adults: the Tanjong Pagar survey. Ophthalmology 111(12): 2168–2174. 36. Rosman M, Wong TY, Tay WT, et al. (2009) Prevalence and risk factors of undercorrected refractive errors among Singaporean Malay adults: the Singapore Malay Eye Study. Invest Ophthalmol Vis Sci 50(8): 3621–3628. 37. Dandona R, Dandona L, Kovai V, et al. (2002) Population-based study of spectacles use in southern India. Indian J Ophthalmol 50(2): 145–155. 38. Resnikoff S, Pascolini D, Mariotti SP, Pokharel GP. (2008) Global magnitude of visual impairment caused by uncorrected refractive errors in 2004. Bull World Health Organ 86(1): 63–70. 39. Fotouhi A, Hashemi H, Raissi B, Mohammad K. (2006) Uncorrected refractive errors and spectacle utilisation rate in Tehran: the unmet need. Br J Ophthalmol 90(5): 534–537. 40. Wedner S, Masanja H, Bowman R, et al. (2008) Two strategies for correcting refractive errors in school students in Tanzania: randomised comparison, with implications for screening programmes. Br J Ophthalmol 92(1): 19–24. 41. Congdon N, Zheng M, Sharma A, et al. (2008) Prevalence and determinants of spectacle nonwear among rural Chinese secondary schoolchildren: The Xichang Pediatric Refractive Error Study Report 3. Arch Ophthalmol 126(12): 1717–1723. 42. Odedra N, Wedner SH, Shigongo ZS, et al. (2008) Barriers to spectacle use in Tanzanian secondary school students. Ophthalmic Epidemiol 15(6): 410–417. 43. Robaei D, Kifley A, Rose KA, Mitchell P. (2006) Refractive error and patterns of spectacle use in 12-year-old Australian children. Ophthalmology 113(9): 1567–1573. 44. Robaei D, Rose K, Kifley A, Mitchell P. (2005) Patterns of spectacle use in young Australian school children: findings from a population-based study. J AAPOS 9(6): 579–583.

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45. Vision Research a national plan 1983–87: National Eye Advisory Council (US) 1983. 46. Javitt JC, Chiang YP. (1994) The socioeconomic aspects of laser refractive surgery. Arch Ophthalmol 112(12): 1526–1530. 47. Levit KR, Lazenby HC, Cowan CA, Letsch SW. (1991) National health expenditures, 1990. Health Care Fin Rev 13: 29–54. 48. Vitale S, Cotch MF, Sperduto R, Ellwein L. (2006) Costs of refractive correction of distance vision impairment in the United States, 1999–2002. Ophthalmology 113(12): 2163–2170. 49. Lim MC, Gazzard G, Sim EL, et al. (2009) Direct costs of myopia in Singapore. Eye 23(5): 1086–1089. 50. Berdeaux G, Alio JL, Martinez JM, et al. (2002) Socioeconomic aspects of laser in situ keratomileusis, eyeglasses, and contact lenses in mild to moderate myopia. J Cataract Refract Surg 28(11): 1914–1923.

Section 2

Clinical Studies and Pathologic Myopia

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2.1 Quality of Life and Myopia Ecosse L. Lamoureux*,†,‡ and Hwee-Bee Wong§,¶

The measurement of the impact of myopia from a patient’s point of view has been advocated in the recent years. We provide a critical assessment of the impact of myopia on vision-specific functioning, generic and visionspecific health-related quality of life in children, adolescents, and adults. We also comment on the important inclusion of modern psychometric methods, particularly Rasch analysis, in future work associated with myopia and the quality of life.

Introduction Ophthalmology has traditionally relied on objective measurements of vision impairment to represent patients’ functional capabilities. Measures of visual acuity and visual field remain the main outcomes of interest.1 However, over the last two decades, patient-centered benefits have become important healthcare outcomes as clinicians, researchers, administrators, and policy makers have concluded that measures such as visual acuity may not capture all important aspects of vision functioning from a patient’s perspective.1–3 Within this new framework, an effective measurement of the impact of vision loss from the patient’s point of view has become

*Corresponding author. Department of Ophthalmology, University of Melbourne, 32 Gisborne Street, East Melbourne, Victoria 3002, Australia. E-mail: [email protected] † Centre for Eye Research Australia, the Royal Victorian Eye and Ear Hospital, University of Melbourne, East Melbourne, Australia. ‡ Singapore Eye Research Institute, Singapore National Eye Centre, Republic of Singapore. § Health Services Research and Evaluation Division, Ministry of Health, 16 College Road, Singapore 169854, Republic of Singapore. ¶ Department of Epidemiology and Public Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.

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essential to determine the effectiveness of controlled clinical trials, clinical audit, or outcomes research. Since the measurement of patient-reported health outcomes aims at understanding the effect of ocular diseases or impairment taken from the patient’s perspective, there has been a plethora of instruments developed to measure these concepts. Confusingly however, many authors refer to instruments that simply measure disability or functioning as quality of life (QoL). Disability is the limitation of a person’s ability to perform activities caused by a medical condition. Visual disability or restricted visual functioning would be more appropriately called vision-related activity limitation as advocated by the World Health Organization (WHO) International Classification of Functioning, Disability and Health (ICF).4 Compared to disability and functioning, health-related quality of life (HRQoL) is a broader concept, which encompasses many issues that impact a person’s life. HRQoL usually refers to the effect of a disease on the way a person enjoys life, including the way the illness affects a person’s ability to live free of pain, to work productively, and to interact with loved ones. These issues are usually grouped into domains such as well-being, symptoms, work/economic concerns, cognition, independence, and social interaction. There have been two common methods of assessing HRQoL. The first involves generic instruments that measure broad aspects of health. Generic HRQoL instruments provide a general sense of the effects of an illness but not a particular medical condition. The Medical Outcomes Study ShortForm Health Survey (SF-36) is one most used generic HRQoL instruments.5 The major limitation of generic HRQoL instruments is that they do not assess potential condition-specific domains of HRQoL. Because of this, they may not be sensitive enough to detect subtle treatment effects. The second approach to measure HRQoL involves the use of instruments that are specific to a disease. Measures geared toward specific diseases or populations are likely to be more sensitive, and therefore, to have greater relevance to practicing clinicians. Vision-specific HRQoL therefore investigates the impact of vision impairment on QoL, examining both the impact and importance of each domain on QoL and allowing for variability in the relevance of specific domains to individual respondents. The measurement of the impact of myopia from a patient’s point of view has been advocated in recent years. In this chapter, we provide a critical assessment of the research associated with the impact of myopia on generic HRQoL, vision-specific functioning, and vision-specific HRQoL.

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Impact of Myopia in Children, Adolescents and Young Adults In spite of the high prevalence rates of myopia in children, adolescents, and young adults, particularly in Asian countries, there is a paucity of research that has investigated myopia’s impact on functioning or HRQoL in these younger populations (Table 1). The Pediatric Quality of Life Inventory Version 4.0A (PedsQL 4.0) was recently utilized to assess the impact of myopia in 1249 Singaporean adolescents aged 11 to 18 years.6 The 23-item PedsQL 4.0 measures the core physical, mental, and social health dimensions as delineated by the World Health Organization, as well as role (school) functioning.7 The scale comprises parallel child self-report and parent-proxy report formats for age ranges of 5 to 7, 8 to 12, and 13 to 18 years. Respondents are asked about the difficulty of performing each item over the past month (e.g. “It is hard for me to run”).8 Responses are made on a five-point Likert scale and scores are transformed to a 0 to 100 scale. Total and two subscale scores, i.e. physical and psychosocial health summary scores, can then be derived, with higher scores indicating better HRQoL. The total scores reported by high and low myopic adolescents were not significantly different when compared to adolescents without myopia (p > 0.05). This study, however, showed that presenting visual impairment (VA [visual acuity] < 6/12) was associated with diminished total HRQoL, psychosocial, and school functioning scores in healthy Table 1. Details of Studies that have Investigated the Impact of Myopia on Generic Health and Vision-specific Functioning in Children, Adolescents, and Young Adults

Author

Country

Age Range

Sample Size

Wong et al., 20096 Saw et al., 200313

Singapore

11 to 18

1249

Singapore

15 to 18

699

Lim et al., 200514

Singapore

18 to 22

120

Cross-sectional, school-based

Congdon et al., 200815

China

13–17

1892

Cross-sectional, school-based

Study Design Cross-sectional, school-based Cross-sectional, school-based

Measure PedsQL 4.0 Generic Core Scales Time trade off and standard gamble for death Utility values: Time trade off and standard gamble for death Vision-specific functioning

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adolescents without any medical problems. Since the best-corrected VA was not assessed, visual impairment attributable to uncorrected refractive error could not be determined in these adolescents. Other generic HRQoL methods have also been used to determine the impact of myopia. Utility values are measures that assess the QoL associated with a health state.9–11 Utility values traditionally range from 1.0, associated with perfect health, to 0.0, associated with death. Scores approximating a value of 1.0 indicate a better QoL associated with a health state. Conversely, those closer to 0.0 suggest poorer levels of QoL.9 Time-Trade-Off (TTO) is also another technique used to help determine the QoL of a patient or group. Similarly, the Standard Gamble (SG) technique is a traditional technique of measuring preferences under uncertainty. It is used to measure utility functions over life-years and health states, as well as the preference weights to be used in the Quality Adjusted Life Years (QALY) calculations.12 Two studies in Singapore have been conducted to examine the utility values in myopic students. The first involved 699 myopic students aged 15 to 18 years who reported that the mean time trade-off (years of life willing to be sacrificed) and standard gamble (risk of blindness from therapy willing to be sacrificed), utility values for treatment of myopia were not related to the severity of myopia.13 They reported that myopic teenagers with better presenting visual acuity (LogMAR [Logarithm of the Minimum Angle of Resolution] <0.3), for those who wore glasses or contact lenses, had a higher total family income, had more “academic” schooling, and were non-Muslim, reported higher utility values. Another Singaporean study of 120 university myopic medical students aged 18 to 22 years examined time trade-off and standard gamble utility values for treatment of myopia. No relationship between utility values and severity of myopia was found. The utility values reported was higher (time trade-off 0.97 and standard gamble for death 0.99)14 than those obtained from other ophthalmic conditions, such as diabetic retinopathy and age-related macular degeneration, suggesting myopia may have less impact compared to other ocular conditions. Also, as the medical students included in this study differed in age, education level, religion, and race from the general adult population in Singapore, these results may not be generalizable to the population. Data on the impact of myopia on vision-specific functioning (VSF) is also very scarce. A visual functioning questionnaire was used to assess the impact of myopia in rural Chinese secondary school children.15 In this cohort of middle school children, myopia was significantly and

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monotonically associated with worse self-reported visual functioning. Myopic refractive error was more strongly associated with self-reported visual function than was presenting vision.15 The findings of this study are substantiated by a recent trial demonstrating a significant improvement in VSF with provision of glasses among school-aged children having modest levels of refractive error in rural Mexico.16 The VSF score in that study was calculated using the Refraction Status Vision Profile (RSVP) scale17 designed specifically to measure the impact of refractive error and its correction on visual functioning.

Impact of Myopia in Adults Compared to younger populations, the impact of myopia and refractive error on QoL in adults has been marginally better evaluated (Table 2). In Japan, 200 pathological myopia patients (refraction exceeding −8.0 D [diopter] in one eye or both eyes, aged 18 years and above) reported poorer scores in eye and life satisfaction after adjusting for their daily life activities compared to control patients who had best corrected visual acuity better than 0.8; refractive error between −3.0 and +3.0 D; and no ocular disease.18 In contrast, the general well-being schedule score, which evaluated the patient’s psychological status over the last month, was not reduced in the pathologic myopia patients. This is the only study assessing QoL of pathological myopia patients and thus comparative analyses are difficult. Generic HRQoL has been also evaluated in patients with correctable visual impairment in different countries. A population-based study of 3153 Australians aged 49 to 98 years, reported that age and sex adjusted HRQoL scores (as measured using the SF-36 questionnaire5), was not affected in participants with unilateral visual impairment correctable by refraction.19 The medical conditions of the participants, such as comorbidity or disabilities, were however not adjusted in these analyses and may have confounded the study findings.20 When participants with bilateral correctable visual impairment of the same Australian cohort (n = 3154) were examined, significant lower scores were reported for physical functioning, social functioning, and the physical component when compared with those with no visual impairment.21 In a smaller population-based Taiwanese study (n = 1361) that included participants aged 65 and above, it was shown that bilateral correctable visual impairment has an impact only in the physical functioning dimension score of SF-36, but no significant

Author

Country

Sample Size 200

18 and above

Chia et al., 200319

Australia

49 to 98

Owsley et al., 200727

55 and above

Chia et al., 200421

United States of America Australia

49 to 98

3154

Kuang et al., 200722

Taiwan

65 and above

1361

Hollands et al., 200923

Canada

15 to 81

193

Lamoureux et al., 200924

Singapore

40 to 80

2912

Chen et al., 200725 Rose et al., 200026 Broman et al., 200228

Australia United Kingdom United States of America Australia

18 and above 18 to 65 40 to 96

195 112 4550

50 and above

892

Chia et al., 200629

3153 142

Cross-sectional, clinic-based

Cross-sectional, population-based Longitudinal, nursing home-based Cross-sectional, population-based Cross-sectional, population-based Cross-sectional, community-based Cross-sectional, population-based Cross-sectional, clinic-based Cross-sectional, clinical-based Cross-sectional, population-based Cross-sectional, population-based

Self-rating questionnaire (Indices of QoL: General well-being schedule, eye satisfaction, life satisfaction) SF-36 NHVQoL, SF-36, VF-14 SF-36 SF-36 SF-12 VF-11 VisQoL VQoL, VF14 NEI-VFQ-25 NEI-VFQ-25

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difference was found in the other seven dimensions.22 Unfortunately, the mean scores for each dimension between participants with correctable visual impairment and those without visual impairment were not reported. Hence, it is difficult to assess if the insignificant difference in social functioning and the physical component was due to the insufficient sample size in this study (127 with correctable visual impairment). Poorer physical component scores of SF-12 were also reported by adults aged 15 to 81 years with visual impairment due to either refractive or pathological causes, selected from the poorest community in Canada.23 Although the study had a small sample size and non-randomly selected study population, their finding also suggested that visual impairment detrimentally affected the physical score of generic QoL. The vision-specific functioning of patients with corrected and uncorrected myopia was assessed with the VF-11 (a modified version of VF-14) in a recent population-based study of Singaporean Malays.24 Uncorrected myopia was found to be independently associated with poorer overall functioning score and vision-related activities (e.g. reading street signs, recognizing friends, and watching television), but not corrected myopia. This finding suggests that myopia alone does not have an effect on daily activities, but myopia that remains uncorrected affects visual functioning. This finding, however, differs from that found in an Australian study, which showed that corrected myopia affects vision-related QoL. Participants with myopia corrected with glasses or contact lenses (best corrected visual acuity >20/20) reported a negative impact on some specific aspects of vision-related QoL, such as having concerns about injuring themselves, difficulties coping with demands in their lives, difficulties fulfilling their work, family, and community roles, and less confidence joining in everyday activities.25 Cultural, study design, and sample size differences may account for the discrepancies between these two studies. Critically though, corrected myopia in the Australian study was more related to the emotional and psychosocial impact of myopia, which was not assessed in the study in Singapore. A study in United Kingdom investigated the relationship between the severity of myopia and vision-related QoL, as measured by Vision Core Measure 1 (VCM1).26 Patients with severe myopia reported poorer quality of life scores when compared to those with moderate and low levels of myopia. Unfortunately, the findings of this study were limited by a low response rate (28%), and the analyses were not stratified for the correction

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of refractive error. Similar to myopic patients, poorer functioning scores were also reported by nursing home residents who had uncorrected refractive error in one or both eyes.27 Although data pertaining to the impact of myopia per se is limited, there is some information about the impact of uncorrected refractive error on vision-specific HRQoL. In 4550 Hispanics adults aged 40 to 96 years, visual impairment due to refractive errors was associated with decrements on several subscales of the National Eye Institute Visual Function Questionnaire (NEI-VFQ-25) scale, including general vision, distance vision, driving, peripheral vision, role difficulties, dependency, social functioning, and mental health when compared to those with no eye disease and without uncorrected refractive error.28 Lower NEI-VFQ-25 scores (eight dimensions, including the composite score) were also reported by Australians participants with bilateral visual impairment due to uncorrected refractive error, but not in those with unilateral visual impairment.29 Both studies using the NEI-VFQ-25 scale, however, did not adjust for general health status or non-ocular comorbidities, which have been shown to impact vision-specific HRQoL.20 This may have confounded their findings. Uncorrected refractive error was also associated with a decrement in the nursing home vision-targeted health-related quality of life (NHVQoL) subscales of general vision, reading, psychological distress, activities and hobbies, and social interaction, reported by nursing home residents.27

Overall Conclusion In spite of myopia being a growing public health problem and its prevalence and severity increasing in different parts of the world, particularly in Asia,30–32 there is remarkably limited data about its impact on HRQoL. In children and younger adults, the limited findings indicate that myopia has little or no impact on general health. On the other hand, and perhaps as anticipated, the meager available data suggest a systematic and positive relationship between worsening levels of myopia and poorer vision-specific functioning. In addition, the available current information seems to indicate that myopia is associated with visual disabilities other than visual acuity measurements, potentially including micropsia and deficits of peripheral vision among children wearing corrective glasses.15 Considering the

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limited available evidence, more work is needed to gain a better understanding of the impact of myopia on vision-specific functioning and HRQoL in children and younger adults not only in Asia but also other countries. In adults, unilateral visual impairment associated with refractive error (including myopia) appears to have a limited impact general health. However, bilateral correctable visual impairment and severe stage myopia negatively impact general and vision-specific HRQoL. Correction of myopia does not impact on vision-specific functioning24 although more information is needed to establish its impact on emotional well-being and social inclusion.25

Future Studies Future work to improve the understanding of the impact of myopia should focus on myopia-specific QoL scales, which include a number of life domains such as well-being, economic concerns, cognition, independence, and social interaction. Our understanding of the impact of myopia in these areas is limited. There is also a need for future investigators to use modern psychometric methods to analyze questionnaire data. With the exception of one study, most studies have used Classical Test Theory methods such as a mean or summary score.24 Summary scoring, termed Likert scoring, allocates an ordinal assignment of a numerical value to a participant’s response and assumes a score based on an interval scale. The validity of such summary scores has been questioned by the Item Response Theory (IRT) methods, namely Rasch analysis.2,33–36 Rasch analysis states that the probability of an individual choosing a response on a particular item depends on both the person’s ability and item difficulty. Thus, Rasch analysis is taken as a criterion for the structure of the responses, which should be satisfied rather than a simple statistical description of the responses commonly evidenced in studies that have investigated the impact of myopia. Once the data fits the Rasch model, estimates of measures on an interval scaling are provided, which can improve the accuracy of scoring and remove measurement noise.35,37–39 The transformed score can then be used in analysis of variance and regression more readily than the raw score, which has floor and ceiling effects. The utilization of some form of IRT in future studies will ensure an improved measurement of the impact of myopia on HRQoL.

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Finally, reports have suggested that there is an “epidemic” of myopia in Asia. Population-based studies in urban Asian cities indicate a high prevalence of myopia compared to European-derived populations.40–42 Paradoxically, there has been very little work undertaken in these countries to better understand the impact of myopia, particularly in adults. Valid vision-specific QoL questionnaires are needed to determine the impact of myopia and refractive error on all aspects of daily living in Asian countries. Several scales have been developed in Western countries, such as the Refractive Status and Vision Profile,17 the National Eye Institute Refractive Quality of Life,43 and the Quality of Life Impact of Refractive Correction.39 These scales either should be validated in Asian cultures or new scales specific to Asian countries need to be developed and validated, preferably using IRT methods.

References 1. Massof RW, Rubin GS. (2001) Visual function assessment questionnaires. Survey of Ophthalmol 45: 531–548. 2. Lamoureux EL, Pallant JF, Pesudovs K, et al. (2006) The Impact of Vision Impairment Questionnaire: an evaluation of its measurement properties using Rasch analysis. Investigative Ophthalmol Vis Sci 47: 4732–4741. 3. Stelmack J. (2001) Quality of life of low-vision patients and outcomes of low-vision rehabilitation. Optom Vis Sci 78: 335–342. 4. World Health Organization. (2001) The international classification of functioning, disability and health (ICF). World Health Organization, Geneva, Switzerland. 5. Ware JE Jr, Sherbourne CD. (1992) The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med Care 30: 473–483. 6. Wong HB, Machin D, Tan SB, et al. (2009) Visual impairment and its impact on health-related quality of life in adolescents. Am J Ophthalmol 147: 505–511 e1. 7. WHO. (1948) Constitution of the World Health Organization basic document, p. 1. World Health Organization, Geneva, Switzerland. 8. Varni JW, Seid M, Kurtin PS. (2001) PedsQL 4.0: Reliability and validity of the Pediatric Quality of Life Inventory version 4.0 generic core scales in healthy and patient populations. Med Care, 39: 800–812. 9. Brown GC, Brown MM, Sharma S, et al. (2001) The reproducibility of ophthalmic utility values. Trans Am Ophthalmol Soc 99: 199–203; discussion 203–204.

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10. Brown MM, Brown GC, Sharma S, Garrett S. (1999) Evidence-based medicine, utilities, and quality of life. Curr Opinion Ophthalmol 10: 221–226. 11. Torrance GW. (1986) Measurement of health state utilities for economic appraisal. J Health Econ 5: 1–30. 12. Gafni A. (1994) The standard gamble method: what is being measured and how it is interpreted. Health Serv Res 29: 207–224. 13. Saw SM, Gazzard G, Au Eong, KG, Koh D. (2003) Utility values and myopia in teenage school students. Br J Ophthalmol 87: 341–345. 14. Lim WY, Saw SM, Singh MK, Au Eong KG (2005) Utility values and myopia in medical students in Singapore. Clin Exp Ophthalmol 33: 598–603. 15. Congdon N, Wang Y, Song Y, et al. (2008) Visual disability, visual function, and myopia among rural chinese secondary school children: the Xichang Pediatric Refractive Error Study (X-PRES) — report 1. Inv Ophthalmol & Vis Sci 49: 2888–2894. 16. Esteso P, Castanon A, Toledo S, et al. (2007) Correction of moderate myopia is associated with improvement in self-reported visual functioning among Mexican school-aged children. Inv Ophthalmol & Vis Sci 48: 4949–4954. 17. Vitale S, Schein OD, Meinert CL and Steinberg EP. (2000) The refractive status and vision profile: a questionnaire to measure vision-related quality of life in persons with refractive error. Ophthalmol 107: 1529–1539. 18. Takashima T, Yokoyama T, Futagami S, et al. (2001) The quality of life in patients with pathologic myopia. Jap J Ophthalmol 45: 84–92. 19. Chia EM, Mitchell P, Rochtchina E, et al. (2003) Unilateral visual impairment and health related quality of life: the Blue Mountains Eye Study. Br J Ophthalmol 87: 392–395. 20. Ahmadian L, Massof R. (2008) Impact of general health status on validity of visual impairment measurement. Ophth Epidemiol 15: 345–355. 21. Chia EM, Wang JJ, Rochtchina E, et al. (2004) Impact of bilateral visual impairment on health-related quality of life: the Blue Mountains Eye Study. Inv Ophthalmol Vis Sci 45: 71–76. 22. Kuang TM, Tsai SY, Hsu WM, et al. (2007) Correctable visual impairment in an elderly Chinese population in Taiwan: the Shihpai Eye Study. Inv Ophthalmol Vis Sci 48: 1032–1037. 23. Hollands H, Brox AC, Chang A, et al. (2009) Correctable visual impairment and its impact on quality of life in a marginalized Canadian neighbourhood. Can J Ophthalmol 44: 42–48. 24. Lamoureux EL, Saw SM, Thumboo J, et al. (2009) The impact of corrected and uncorrected refractive error on visual functioning: the Singapore Malay Eye Study. Inv Ophthalmol Vis Sci 50: 2614–2620. 25. Chen CY, Keeffe JE, Garoufalis P, et al. (2007) Vision-related quality of life comparison for emmetropes, myopes after refractive surgery, and myopes wearing spectacles or contact lenses. J Refract Surg 23: 752–759.

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26. Rose K, Harper R, Tromans C, et al. (2000) Quality of life in myopia. Br J Ophthalmol 84: 1031–1034. 27. Owsley C, McGwin G Jr, Scilley K, et al. (2007) Effect of refractive error correction on health-related quality of life and depression in older nursing home residents. Arch Ophthalmol 125: 1471–1477. 28. Broman AT, Munoz B, Rodriguez J, et al. (2002) The impact of visual impairment and eye disease on vision-related quality of life in a Mexican-American population: proyecto VER. Inv Ophthalmol Vis Sci 43: 3393–3398. 29. Chia EM, Mitchell P, Ojaimi E, et al. (2006) Assessment of vision-related quality of life in an older population subsample: the Blue Mountains Eye Study. Ophth Epidemiol 13: 371–377. 30. Lin LL, Shih YF, Hsiao CK, Chen CJ. (2004) Prevalence of myopia in Taiwanese schoolchildren: 1983 to 2000. Ann Acad Med Singapore 33: 27–33. 31. Wu HM, Seet B, Yap EP, et al. (2001) Does education explain ethnic differences in myopia prevalence? A population-based study of young adult males in Singapore. Optom Vis Sci 78: 234–239. 32. Wong TY, Foster PJ, Hee J, et al. (2000) Prevalence and risk factors for refractive errors in adult Chinese in Singapore. Inv Ophthalmol Vis Sci, 41: 2486–2494. 33. Fisher WP Jr, Eubanks RL, Marier RL. (1997) Equating the MOS SF36 and the LSU HSI Physical Functioning Scales. J Outcome Measure 1: 329–362. 34. Massof RW. (2002) The measurement of vision disability. Optom Vis Sci 79: 516–552. 35. Pesudovs K. (2006) Patient-centered measurement in ophthalmology — a paradigm shift. BMC Ophthalmol 6: 25. 36. Wright BD, Linacre JM. (1989) Observations are always ordinal; measurements, however, must be interval. Arch Phys Med Rehab 70: 857–860. 37. Garamendi E, Pesudovs K, Stevens MJ, Elliott DB. (2006) The Refractive Status and Vision Profile: Evaluation of psychometric properties and comparison of Rasch and summated Likert-scaling. Vis Res 46: 1375–1383. 38. Norquist JM, Fitzpatrick R, Dawson J, Jenkinson C. (2004) Comparing alternative Rasch-based methods vs. raw scores in measuring change in health. Med Care 42: I25–I36. 39. Pesudovs K, Garamendi E, Elliott DB. (2004) The Quality of Life Impact of Refractive Correction (QIRC) Questionnaire: development and validation. Optom Vis Sci 81: 769–777. 40. Seet B, Wong TY, Tan DT, et al. (2001) Myopia in Singapore: taking a public health approach. Br J Ophthalmol 85: 521–526. 41. Saw SM, Katz J, Schein OD, et al. (1996) Epidemiology of myopia. Epidemiol Rev 18: 175–187.

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42. Saw SM, Chan YH, Wong WL, et al. (2008) Prevalence and risk factors for refractive errors in the Singapore Malay Eye Survey. Ophthalmology 115: 1713–1719. 43. Berry S, Mangione CM, Lindblad AS, McDonnell PJ. (2003) Development of the National Eye Institute refractive error correction quality of life questionnaire: Focus groups. Ophthalmology 110: 2285–2291.

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2.2 Ocular Morbidity of Pathological Myopia V. Swetha E. Jeganathan*,†,‡,§, Seang-Mei Saw‡,¶ and Tien-Yin Wong†,‡

Introduction The epidemic of myopia is a public health concern, particularly in East Asia (Singapore, Taiwan, Hong Kong, Japan).1–4 In Singapore, the prevalence of myopia is one of the highest worldwide, affecting 9% to 15% preschool children,5–7 29% primary school children,8 70% of high school students,9 80% in military conscripts,10,11 and almost 90% of medical students.12 The Tanjong Pagar Survey first suggested that the prevalence of myopia (< −0.5 D) in Chinese adults 40 years and older was nearly twice the rates in similarly aged Caucasian populations, including the Melbourne Visual Impairment Project.13,14 Furthermore, compared to ethnic Malays, the Chinese in Singapore have a higher prevalence of myopia (37.8% versus 33.3%).13,15 A large proportion of Singaporeans have pathological myopia (< −6 D), which has been observed across the whole age range spectrum,16 including 15% of Singapore’s military conscript population.11 The prevalence of high myopia is especially significant in parts of East Asia, with rates of 9–21%, compared with 2–4% in Caucasians.10 In the Tanjong Pagar Eye Study, Chinese women had significantly higher rates of high myopia than men, with bimodal age pattern of myopia, higher prevalence in the 40 to 49 and 70 to 81 age groups, and lower prevalence

*Corresponding author. E-mail: [email protected] † Centre for Eye Research Australia, University of Melbourne, Victoria, Australia. ‡ Singapore Eye Research Institute, Yong Loo Lin School of Medicine, National University of Singapore, Singapore. § Tun Hussein Onn National Eye Hospital, Malaysia. ¶ Department of Community, Occupational & Family Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.

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between those age ranges.13 In comparison, the prevalence of pathological myopia (< −7.9 D) is less than 0.4% in most Western countries.17 Of particular concern is that the prevalence and severity of myopia has increased significantly in Singapore over the last two decades across a whole spectrum of ages.3 Serial cross-sectional data from the Singapore Armed Forces, reveal that the prevalence of low vision myopia in military conscripts aged 18 to 25 years has increased from 26% in the late-1970s, to 43% in the 1980s, 66% in the mid-1990s, and 83% by the late 1990s, accompanied by a two-fold rise in the proportion with pathological myopia (< −8 D) from 2% (1993) to 4% (1997).10,18,19 A similar trend of increasing myopia prevalence has been observed in schoolchildren.20 The risk factors for myopia include higher education, urban residential status, higher income, professional occupation, and increased near work.4 However, the underlying explanation for the worsening trend of myopia prevalence and severity is poorly understood and is likely complex and multifactorial, given that East Asian countries with high myopia have similar socioeconomic demographic risk factors as in the West.21 Pathological myopia is the fourth leading cause of blindness in Singapore,22 and may be associated with a myriad of potentially blinding, irreversible conditions such as retinal detachment and myopic macular degeneration.23 Patients with pathological myopia (≤ −10 D) have also been shown to experience impaired quality of life.24 Lower utility values in myopic Singapore high school children are a good example.25 Despite previous studies supporting the numerous associations between pathological myopia and ocular complications,16 limitations in study design prevent inference of causality.

Definition of Pathological Myopia The terms “pathological myopia,” “degenerative myopia,” “malignant myopia,” or “high myopia” are commonly used interchangeably; however, there is no standardized definition of pathological myopia to date. Duke and Elder first defined pathological myopia as myopia accompanied by degenerative changes in the sclera, choroid, and retinal pigment epithelium, associated with compromised visual function.26 Tokoro later defined pathological myopia as myopia caused by excessive and progressive axial elongation.27 Some studies have defined pathological myopia as high myopia (≤ −6 D) and/or axial length of >25.5 mm.10,18,28 Furthermore,

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there is no standardized cut-off for pathological myopia to date. Common definitions of pathological myopia include spherical equivalent of at least −6 D, −8 D, or −10 D. In the Blue Mountains Eye Study (BMES), myopic retinopathy included the presence of staphyloma, lacquer cracks, Fuch’s spot, and/or myopic chorioretinal thinning or atrophy.29 Other signs include β-peripapillary atrophy, cytotorsion (tilting of the optic disc), and the T-sign found in central retinal vessels.30 Shih and co-authors used a grading system by Avila for myopic macular chorioretinopathy.31 MO indicated a normal posterior pole with no tessellation pattern in the macular area; M1 indicated tessellation and choroidal pallor pattern in the macular area; M2 indicated choroidal pallor and tessellation, and the border of an ectasia posteriorly was visualized; M3 indicated pallor and tessellation with several yellowish lacquer cracks in Bruch’s membrane and posterior staphyloma; M4 showed choroidal pallor and tessellation, with lacquer cracks with posterior staphyloma and focal areas of deep choroidal atrophy; M5 indicated choroidal pallor and tessellation with lacquer cracks, posterior staphyloma, geographic areas of atrophy of retinal pigment epithelium and choroids, and choroidal neovasculariation were visualized. M3 or greater was defined as “with maculopathy.”32 Jonas graded peripapillary tessellation from 0 for “no tessellated fundus” to 3 for “very marked tessellated fundus.”33 Thus, the systematic grading of fundus photographs in population-based epidemiologic studies is important in assessing the prevalence and extent of myopia-associated pathology. However, there are few grading systems developed for pathologic myopia that have been consistently used across multiple study populations.

Cataract The association between pathological myopia and cataract is well known.16,34,35 Evidence from epidemiological studies on the relationship of high myopia and cataract are summarized in Table 1.15,35–43 Several population and clinic-based studied have confirmed a strong and consistent association between high myopia and age-related nuclear sclerosis (NS) in adults aged more than 40 years.15,39,40–42,44–47 In the Singapore Malay Eye Study of 3000 Malay adults aged 40 to 80 years, there was a U-shaped relationship between increasing age and the prevalence of myopia, which was partially explained by the age-related increase in the

Place

Study Sample Age Design Size (n) (Years) ≥ 40

Methodology

Definition of Myopia

SR Lens Excluded high photography myopia (cataract) (−12 D)

Summary of Main Findings Crude OR of myopia = 1.06 95% CI (0.6, 1.9).

CCS

220

Lim et al.; Blue Mountains Eye Study36

1999 Sydney, Australia

PBCS

7308

49–97 AR and SR High myopia Lens (≤ −6 D) Photography

OR of cataract prevalence, adjusted for age, sex, smoking, hypertension, diabetes, steroids, sun related skin damage: PSC = 4.9 (2.1, 11.4) CC = 2.9 (1.4, 6.0) NS = 1.4 (0.8, 2.4).

McCarthy et al.; Visual Impairment Project37

1999 Melbourne, Australia

PSC

5147

≥ 40

OR of cataract prevalence: PSC = 3.59 (2.5, 5.15), adjusted for age, rural, diuretics, vitamins A and E, ultraviolet light; CC = 1.76 (1.3, 2.4), adjusted for age, gender, iris, arthritis, diabetes, gout, beta-blockers, ultraviolet light, glaucoma NS = 2.73 (1.9, 3.92), adjusted for age, gender, diabetes, smoking and education.

AR Lens ≤ −1 D photography (cataract)

(Continued )

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Table 1.

Year

Place

Study Sample Age Design Size (n) (Years) 4036

Dandona et al.; the Andhra Pradesh Eye Disease Study39

1999 Hyderabad, India

PBCS

2321

PBCS

4470

Wong et al.; 2001 USA Beaver Dam Eye Study (FU = 5 years)40

40–84 AR

≥16

AR

≤ −0.5 D

A higher prevalence of myopia was positively associated (P < 0.05) with NS, PSC, glaucoma, and ocular hypertension.

≤ −0.75 D

With multivariate analysis, myopia was higher in subjects with NS ≥ 3.5 (OR 9.10; 95% CI, 5.15–16.09), and those with education of class 11 or higher (OR 1.80; 95% CI, 1.18–2.74).

43–84 AR (baseline); ≤ −1 D Lens photography (prevalent cataract at baseline and incident cataract at 5 years)

Myopia not associated with incident cataract. OR of prevalent cataract for myopia, adjusted for age, gender, diabetes, smoking, education: PSC = 1.23 (0.75, 2.03) CC = 0.86 (0.64, 1.16) NS = 1.74 (1.28, 2.37). (Continued )

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Summary of Main Findings

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1999 Barbados

Definition of Myopia

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Wu et al.; Barbados Eye Study38

Methodology

Ocular Morbidity of Pathological Myopia

Source

(Continued )

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Table 1.

Place

PBCS

2334

≥ 49

Methodology

Definition of Myopia

AR and SR ≤ −3.5 D (baseline); Lens photography (cataract at 5 years) ≤ −6.0 D

OR of incident cataract: PSC = 4.4 (1.7, 11.5) adjusted for age, sex, education, obesity, hypertension, NS. CC = 0.5 (0.2, 2.0), adjusted for age, sex, education, alcohol, uv light, diabetes, obesity, stroke, NS. NS = 3.3 (1.5, 7.4) adjusted for age, sex, smoking, education, iris, inhaled steroids. Incident cataract surgery was significantly associated with any myopia (OR 2.1, 95% CI 1.1–4.2), as well as moderate (−3.5 to more than −6 D; OR 2.9, 1.2–7.3) and high myopia (OR 3.4, 95% CI 1.0–11.3). (Continued )

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Younan et al.; 2002 Sydney, Blue Mountains Australia Eye Study (FU = 5 years)41

Study Sample Age Design Size (n) (Years)

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Source

(Continued )

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Table 1.

Place

Study Sample Age Design Size (n) (Years)

Methodology

1232

40–81 SR, if unavailable AR

Chang et al.; Salisbury Eye Evaluation Project43

2005 Salisbury, United Kingdom

PBCS

2520

65–84 AR

−1.35 D vs −0.11 D −1.80 D vs −0.39 D

−0.50 D and −1.99 D −2.00 D and −3.99 D −4.00 D and −5.99 D −6.00 D or more −0.50 D and −1.99 D −2.00 D and −3.99 D

Adjusted for age, gender, education, diabetes, and smoking. NS was associated with myopia (p < 0.001); PSC was associated with myopia (p < 0.001). Adjustment for vitreous chamber depth attenuated the association between PSC (not NS) and myopia by 65.5%. OR for incident cataract: NS = 2.25 (P < 0.001) NS = 3.65 (P < 0.001) NS = 4.54 (P < 0.001) NS = 3.61 (P = 0.002) PSC = 1.59 (P = 0.11), PSC = 3.22 (P = 0.002) (Continued )

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2003 Singapore

Summary of Main Findings

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Wong et al.; Tanjong Pagar Survey42

Definition of Myopia

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Source

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Table 1.

Study Sample Age Design Size (n) (Years)

Methodology

Definition of Myopia

Saw et al.; Singapore Malay Eye Study15

2008 Singapore

PBCS

2974

40–80 SR, if unavailableAR

≤ −0.5 D

PSC = 5.36 (P < 0.001), PSC = 12.34 (P < 0.001). No association was found between myopia and CC. In a multiple logistic regression model, female sex, age, higher educational level, and cataract were associated with myopia.

AR: autorefraction, CI: confidence interval, CC: cortical cataract, CCS: case control study, D: dioptres, NS: nuclear sclerosis, OR: odds ratio, PBSC: population-based cross-sectional study, PSC: posterior subcapsular cataract, SR: subjective refraction.

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prevalence of cataract, increasing density of lens nucleus with age, causing a myopic shift in refraction (i.e. index myopia).15 The Tanjong Pagar Survey of 1200 Chinese adults aged 40 to 80 years further supports this hypothesis; NS was associated with myopia (p < 0.001) without any change to axial length or the biometric components.43 In the BMES of Caucasian adults, there was a statistically significant association between high myopia (≤ −6 D) and incident NS.41 A myopic refractive shift occurred in persons with NS levels 4 or higher, attesting the contribution of NS to the mild myopic shift that neutralizes the age-related hyperopic shift occurring in older persons.48 Furthermore, according to the Beaver Dam Eye Study (BDES) five-year follow up, much of the myopic change after the age of 70 may be attributed to increasing NS.40 The NS is often missed because any increase in refraction is generally attributed to an increase in the pathological myopia. Oxidative lens damage is known to occur early in myopic eyes.49 Furthermore, it is unknown whether myopia is a risk factor for NS because NS is known to affect refraction and cause myopia. The relationship between myopia and posterior subcapsular cataract (PSC) is controversial. In BMES, incident PSC was associated with the presence of myopia (OR 2.1, 95% CI 1.0–4.8), moderate to high myopia (−3.5 D or less, OR 4.4, 95% CI 1.7–11.5).41 Moreover, eyes with the onset of myopia before age 20 years had the greatest risk of PSC (OR 3.9; CI 2.0–7.9), suggesting the possibility of a dose response between the levels of myopia and PSC.36 The Tanjong Pagar Survey showed that PSC was related to deeper anterior chamber, thinner lens, and longer vitreous chamber; and adjusting for these components, especially vitreous chamber depth, attenuated the association of myopia significantly, suggesting that the refractive association of this form of cataract was axial.50 As PSC does not appreciably affect refraction, it was suggested that the relationship between PSC and myopia is causal i.e. myopia may be a risk factor for the development of PSC. In contrast, cortical cataract was not related to myopia, either cross-sectionally or longitudinally in these studies.15,39,40–42,44–47 Incident cataract surgery was significantly associated with myopia, (OR 2.1, 95% CI 1.1–4.2) as well as moderate (−3.5 to more than −6 D; OR 2.9, 1.2–7.3) and high myopia (OR 3.4, 95% CI 1.0–11.3).41 The BDES found an association between myopia and five-year risk of cataract surgery; most likely due to the presence of PSC, which is known to be the most important lens opacity predicting the need for cataract surgery.41

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Cataract extraction in high myopia must be considered carefully because patients with high myopia are at increased risk of retinal breaks and retinal detachment.51,52

Glaucoma An association between high myopia and primary open angle glaucoma (POAG) has been supported by numerous case series, case control, and large population-based studies (Table 2).38,53–61,63 The prevalence of myopia with POAG is 4% and may increase to 6–7% with higher degrees of myopia.62 In the BMES, after adjusting for age, gender and other risk factors, glaucoma was two to three times as frequent as eyes with myopia compared with eyes with emmetropia or hyperopia.55 In the Barbados Eye Study, a myopic refraction was one of several risk factors in adult black people with prevalent POAG.38,63 Both the BMES and Barbados Eye Study confirm a dose-response between the level of myopia and prevalence of glaucoma.38,55,63 Severe myopia (< − 4D), not mild myopia, has been shown to be a significant risk factor for subsequent visual field loss in patients with POAG.64 However, in the Ocular Hypertension Treatment Study, high myopia was not predictive of POAG.65 Moreover, individuals with myopia were not found to have a higher incidence or progression of glaucoma in the Ocular Hypertension Treatment Study or Early Manifest Glaucoma Trial.65,66 High myopia is related with higher intraocular pressure (IOP) and occurs more often in glaucoma patients than in the normal population, particularly amongst the elderly.55 In POAG patients with high IOP, higher myopia is thought to be a factor that threatens their quality of life.67 Interestingly, the visual field of myopic eyes more often improves and less often worsens once the IOP has been lowered therapeutically. There is now evidence that myopia is a risk factor for the development of ocular hypertension, based on data of the screening examination for the Early Manifest Glaucoma Trial and other studies.68–70 Structural changes associated with myopia, such as longer axial length, larger and/or tilted optic disc, thinner lamina cribrosa, peripapillary atrophy, and shared alteration in collagen and other extracellular matrix of the optic nerve is postulated to make the upper maculopapillary bundle (lower cecofield) more susceptible to glaucomatous injury.67 Highly myopic patients are also at higher risk of postoperative hypotony maculopathy.71

Year

Place

Study Sample Age Design Size (n) (Years)

Methodology

Definition of Myopia

Summary of Main Findings

SR OAG-eyes with open angles and VFD

High myopia (≤ −5 D) Low myopia (−0.25 D to −5 D)

OR of OAG = 3.1 (95% CI 1.6, 5.8) OR of OAG = 1.3 (1.0, 1.8). Adjusted for age, IOP, sex, family history, blood pressure, astigmatism, season, health.

Ponte et al.; 1994 Italy Casteldaccia Eye Study54

PBCS

264

≥ 40

Cases: IOP ≥ 24, history of glaucoma, VFD. Controls: IOP ≤20, CDR 0–0.2

≤ −0.5 D

OR of glaucoma prevalence = 5.56 (1.85, 16.67). Adjusted for diabetes, hypertension, steroids, iris texture.

Mitchell et al; Blue Mountains Eye Study55

1999 Sydney, Australia

PBCS

3654

≥ 49

AR and SR OAG defined as CDR ≥0.7 or CD asymmetry ≥0.3

High myopia ≤ −3 D Low myopia (−3 D to ≥ −1 D)

OR of OAG prevalence = 3.3 (1.7, 6.4) OR of OAG prevalence = 2.3 (1.3, 4.1). Adjusted for gender, family history, diabetes, hypertension, migraine, steroid use, psedoexfoliation.

Wu et al; Barbados Eye Study38

1999 Barbados

PBSC

4709

40–84 AR OAG-eyes with open angles and VFD

≤ −0.5 D

OR of OAG prevalence = 1.48 (1.12,1.95). (Continued )

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≥ 40

1:56 AM

953

1981 London, United Kingdom

4/8/2010

CCS

Daubs and Crick53

Ocular Morbidity of Pathological Myopia

Source

Summary of Published Data on Myopia and Glaucoma

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Table 2.

Place

Study Sample Age Design Size (n) (Years)

Methodology

Definition of Myopia

Summary of Main Findings

32,918

57–79 AR OAG defined as 2 repeatable VFD

≤ −1 D

Prevalence of newly diagnosed OAG increased with increasing myopia (p < 0.01), and 1.5% in moderate to high myopia. Adjusted for age, gender, IOP.

Yoshida et al.57 2001 Yokohama, Japan

CCS

64,394

≥ 49

≤ −3 D

Prevalence of POAG is higher in moderate to high myopes (p < 0.001).

Wong et al; 2003 Wisconsin, Beaver Dam USA Eye Study58

PBCS

4670

AR POAG defined as glaucomatous VFD associated with abnormal optic disc and/or disc margin

43–86 AR and SR POAG ≤ −1 D defined as IOP ≥ 22, CDR ≥ 0.8, VFD, history of glaucoma treatment

OR of prevalent POAG for myopia = 1.6 (1.1, 2.3). Adjusted for age and gender.

(Continued )

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Grodum et al.56 2001 Malmo, Sweden

4/8/2010

Year

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Source

(Continued )

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Table 2.

Year

Place

Study Sample Age Design Size (n) (Years)

Methodology

Definition of Myopia

Summary of Main Findings

119

≥ 40

AR POAG diagnosed from IOP, CDR, and VFD

Low myopia OR of prevalent POAG = (−3 D to −1 D) 1.85 (1.03, 3.31) for low Moderate to myopia and 2.60 (1.56, 4.35) high myopia for moderate to high myopia. (≤ −3 D) Adjusted for age, gender, IOP, corneal curvature, central corneal thickness, diabetes, migraine, hypertension, smoking, family history of glaucoma.

Xu. et al; Beijing Eye Study60

2007 Beijing, China

PBCS

4439

≥ 40

AR and SR High myopia OR of prevalent POAG in highly POAG diagnosed (> −8 D) myopic marked myopia = from CDR Marked myopia 2.28 (0.99, 5.25) compared to (photos) and (< −6 D to −8 D) moderate myopia. The OR was IOP Moderate myopia significantly higher than in (< −3 D to −6 D) the group with low myopia (OR 3.5; 1.71, 7.25).

Casson, et al; Meiktila Eye Study61

2007 Meiktila, Burma

PBCS

2076

≥ 40

AR POAG diagnosed from IOP, CDR, and VFD

Myopia < 0.5 D

Myopia (P = 0.049), increasing age, and IOP (P < 0.001) were significant risk factors for POAG.

AR: autorefraction, CDR: cup-disc ratio, CI: confidence interval, CCS: case control study, D: dioptres, IOP: intraocular pressure, OR: odds ratio, PBSC: population-based cross-sectional study, POAG: primary open angle glaucoma, SR: subjective refraction, VFD: visual field defect.

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PBCS

1:56 AM

2006 Tajimi, Japan

4/8/2010

Suzuki et al; Tajimi Eye Study59

Ocular Morbidity of Pathological Myopia

Source

(Continued )

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Table 2.

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Myopic Maculopathy Several population-based studies have not found an association between myopia and AMD.72,73 In contrast, the Beijing Eye Study showed that highly myopic eyes had a significant lower prevalence of early and late AMD, compared to non-highly myopic eyes.74 Macular choroidal neovascularisation (CNV) is the most common vision-threatening complication of high myopia,24 especially in persons younger than 50 years.75,76 The impact of myopic degeneration on visual impairment is important, because it is often bilateral, irreversible, and affects individuals during their productive years.77 Several studies report a prevalence of myopic degeneration at about 1% in the general population in Asia.78 Clinical and histopathological studies have documented CNV in 5% to 10% of eyes with axial length of over 26.5mm.33 CNV has been reported to develop in 12.5% of patients with high myopia after cataract surgery.79 Among myopic patients with preexisting CNV, more than 30% develop CNV in the fellow eye within eight years.31 Myopic degeneration may occur independently of the scleral conus, or it may be caused by enlargement of the temporal conus, involving the macular region. In highly myopic eyes, the Forster-Fuchs’ spot at the macula forms due to the proliferation of pigment epithelium and deposition of blood pigment following choroidal haemorrhage from the neovascular tissue.80 The Forster–Fuchs’ spot has been found in 3.2% to 20% of patients identified with pathological myopia, predominantly in middle age.28,81 Myopic CNV is considered to have a limited course, in contrast to CNV secondary to AMD.31 Some studies report a favorable visual acuity outcome of myopic CNV,31,82 while others report a poorer prognosis.83–85 Imaging modalities including OCT, angiography, and fundus autofluorescence have provided further insights into the in vivo pathology of myopic CNV.86 Therapeutic interventions to date include laser photocoagulation and pharmacologic agents, such as steroids and anti-angiogenic drugs.

Myopic Retinopathy Myopic retinopathy refers to a cluster of signs that indicate degeneration of the chorioretinal tissues associated with the excessive axial elongation of the myopic eye, leading to mechanical stretching and thinning of the choroid and retinal pigment epithelium with concomitant vascular and

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degenerative changes.28,87 Posterior pole changes include posterior staphyloma, lacquer cracks, Fuchs’ spot, and chorioretinal atrophy.29,88 Peripheral retinal features of myopic retinopathy include lattice, paving stone, whitewithout-pressure, and pigmentary degenerations, as well as retinal tears.87,88 In BMES, progression of myopic retinopathy was observed in 17.4% of eyes after five years.29 Posterior pole staphyloma has been reported to be the most common type of staphyloma.89 Lacquer cracks or ruptures in the retinal pigment epithelium-Bruch’s membrane-choriocapillaris complex have been reported in patients with high myopia.90 The prevalence of lacquer cracks has ranged from 0.2% to 9.2% in highly myopic populations,28,81 and may characterize an unfavorable prognosis in patients with pathologic myopia.91 In studies by Pierro et al. and Gozum et al., it was found that longer axial length was associated with increased prevalence of lattice degeneration, pavingstone degeneration, and white-withoutpressure.87,92 Chorioretinal atrophy occurs in the late stage of myopic degeneration.16 Macular hole formation is an important complication of highly myopic eyes, and it is frequently complicated by macular hole retinal detachment.93–95 Studies by Azzolini and Benhamou correlated the biomicroscopic signs of early macular holes (e.g. microcystic appearance, macular striae) with the presence of foveal retinoschisis, as well as prefoveal tractional membranes on OCT images.96,97

Retinal Detachment Retinal breaks and retinal detachment (RD) occur more frequently in eyes with increased axial length as a result of lattice degeneration, increased frequency of posterior vitreous detachment, or macular hole formation.98 Round and multiple retinal breaks characterize myopic retinal detachment. The yearly incidence of retinal detachments has been estimated as 0.015% in patients with less than 4.75 D myopia, increases to 0.07% in patients with ≥5 D myopia and 3.2% in patients’ ≥6 D myopia.99,100 Posterior vitreous detachment tends to occur at an earlier age, in high myopes.101 The prevalence of posterior vitreous detachment was 12.5% in a case series of patients with high myopia, and 60.7% in patients with axial length >30mm.101 Up to 6.3% of highly myopic eyes are reported to develop asymptomatic macular holes, confirmed by ocular coherence tomography.102,103 Macular holes in patients with pathological myopia are caused by traction effects of firm vitreoretinal adhesions.104 Consequently,

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macular holes occur more frequently in highly myopic eyes with advanced posterior staphyloma. Symptomatic retinal tears, or subclinical RD, should be treated.105,106 Asymptomatic lattice degenerations generally do not require prophylactic treatment with absence of other risk factors.107 High myopia also predisposes eyes to RD after cataract surgery, such as phacoemulsification.52,108 Axial length, in addition to myopic pathology, is a factor associated with such retinal detachments.108 Scleral ectasia can make surgical repair of such detachment more difficult as well.109

Optic Disc Abnormalities Globe elongation in myopia and the resulting posterior staphyloma leads to characteristic optic nerve changes, such as increased size and the tilted shape of the optic disc, as well as larger cup-to-disc ratio (CDR).110 The greater the axial length of the eye, the higher the CDR.111 The majority of patients with tilted discs are reported to have a visual field defect112,113; however, in other studies these field defects are not consistently found.114 Previous studies on persons with more severe myopia showed a greater prevalence of complications; and increased peripapillary atrophy with increasing severity of myopia.110,115 Thinning of the retina and RPE leads to the peripapillary thinning crescent observed to surround the optic disc. No myopic crescent was present if the axial length was 21 mm, 75% eyes had myopic crescents if the axial length was 25 mm to 29 mm, and 100% of the eyes had myopic crescents if the axial length was more than 29 mm.116 Peripapillary detachment, an elevated, yellow–orange lesion inferior to the optic disc, is also seen in highly myopic eyes.117 Peripapillary detachment was present in 4.9% of highly myopic eyes and was associated with glaucomatous optic nerve defects in 71% of eyes.117 Glaucoma is difficult to diagnose in high myopia because of the position of the lamina cribrosa and resulting cupping. Moreover, arcuate visual field defects may be secondary to retinochoroidal degenerations.

Conclusion As pathological myopia is among the leading causes of legal blindness, the detection and treatment of potential complications are vital in high-risk subjects. The prevalence of myopia, subsequent high myopia, and

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associated pathology is rising in several countries. Thus, it is important to prevent a possible rise in blindness due to the myopia epidemic. Furthermore, refractive surgical procedures such as laser in situ keratomileusis (LASIK) and photorefractive keratectomy (PRK) have achieved emmetropia in high myopes, but do not eliminate the myriad of posterior segment complications that are potentially incapacitating. Given that the ocular morbidity of myopia may constitute an important clinical, public health, and economic problem, an integrated, pragmatic public health approach with community-based eye screening and research programs, as well as correcting myopia and retarding myopia progression are important. The early detection and management of degenerative eye diseases is central in the care of myopic adults in the prevention of visual impairment and blindness. The prevalence of pathologic myopia is expected to increase with ageing populations and with time due to the cohort effect. Myopic degeneration, macular holes, and retinal detachment are lesions that are potentially blinding. If the risks of pathologic myopia are proportional to the severity of myopia, measures to prevent the early onset and rapid progression of myopia in childhood will eliminate or reduce pathologic myopia later in life.

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43. Chang MA, Congdon NG, Bykhovskaya I, et al. (2005) The association between myopia and various subtypes of lens opacity: SEE (Salisbury Eye Evaluation) project. Ophthalmology 112(8): 1395–1401. 44. Wensor M, McCarty CA, Taylor HR. (1999) Prevalence and risk factors of myopia in Victoria, Australia. Arch Ophthalmol 117(5): 658–663. 45. Praveen MR, Vasavada AR, Jani UD, et al. (2008) Prevalence of cataract type in relation to axial length in subjects with high myopia and emmetropia in an Indian population. Am J Ophthalmol 145(1): 176–181. 46. Gupta A, Casson RJ, Newland HS, et al. (2008) Prevalence of refractive error in rural Myanmar: the Meiktila Eye Study. Ophthalmology 115(1): 26–32. 47. Xu L, Li J, Cui T, et al. (2005) Refractive error in urban and rural adult Chinese in Beijing. Ophthalmology 112(10): 1676–1683. 48. Panchapakesan J, Rochtchina E, Mitchell P. (2003) Myopic refractive shift caused by incident cataract: the Blue Mountains Eye Study. Ophthalmic Epidemiol 10(4): 241–247. 49. Spector A. (1995) Oxidative stress-induced cataract: Mechanism of action. Faseb J 9(12): 1173–1182. 50. Foster PJ, Wong TY, Machin D, et al. (2003) Risk factors for nuclear, cortical and posterior subcapsular cataracts in the Chinese population of Singapore: the Tanjong Pagar Survey. Br J Ophthalmol 87(9): 1112–1120. 51. Fan DS, Lam DS, Li KK. (1999) Retinal complications after cataract extraction in patients with high myopia. Ophthalmology 106(4): 688–691; discussion 91–92. 52. Lois N, Wong D. (2003) Pseudophakic retinal detachment. Surv Ophthalmol 48(5): 467–487. 53. Daubs JG, Crick RP. (1981) Effect of refractive error on the risk of ocular hypertension and open angle glaucoma. Trans Ophthalmol Soc UK 101(1): 121–126. 54. Ponte F, Giuffre G, Giammanco R, Dardanoni G. (1994) Risk factors of ocular hypertension and glaucoma. The Casteldaccia Eye Study. Doc Ophthalmol 85(3): 203–210. 55. Mitchell P, Hourihan F, Sandbach J, Wang JJ. (1999) The relationship between glaucoma and myopia: the Blue Mountains Eye Study. Ophthalmology 106(10): 2010–2015. 56. Grodum K, Heijl A, Bengtsson B. (2001) Refractive error and glaucoma. Acta Ophthalmol Scand 79(6): 560–566. 57. Yoshida M, Okada E, Mizuki N, et al. (2001) Age-specific prevalence of open-angle glaucoma and its relationship to refraction among more than 60,000 asymptomatic Japanese subjects. J Clin Epidemiol 54(11): 1151–1158.

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2.3 Myopia and Glaucoma Shamira A. Perera* and Tin Aung*,†

Introduction Myopia is associated with glaucoma. The evidence in this area stems from large-scale epidemiological studies and clinical studies. The finding that a person is myopic has certain implications for their subsequent investigation and assessment from a glaucoma perspective, and influences the interpretation of tests, clinical assessment, and management. This chapter will summarize the association between myopia and glaucoma, the possible reasons for the associations that have been determined, and the clinicopathological correlation of the sequelae of myopia on glaucoma assessment.

The Association Between Myopia and POAG Information from epidemiological studies The association between refractive error and glaucoma has been the subject of many clinical trials and population-based studies.1–4 Most have suggested that moderate to high myopia is associated with the increased risk of primary open angle glaucoma (POAG),5,6 low-tension glaucoma,7,8 and ocular hypertension.9–12 For a Caucasian population in the Blue Mountains Eye Study (BMES) in Australia, eyes with moderate myopia were two times more likely to have POAG, after adjusting for age, sex, and other risk factors.13

*Singapore Eye Research Institute & Singapore National Eye Center, Singapore. E-mail: [email protected] † Department of Ophthalmology, Yong Loo Lin School of Medicine, National University of Singapore.

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Importantly, a dose-response pattern between the increasing severity of myopia and prevalence of glaucoma was observed. However, this was not appreciated in the other two large epidemiological studies (Barbados and Beaver Dam Eye Studies).14,15 In the Barbados Eye Study, a myopic refraction was one of several risk factors for POAG in adult black people.16 The Beaver Dam Eye study showed that after taking into account the effects of age, sex, and other risk factors, persons with myopia were 60% more likely to have glaucoma than those with emmetropia.15 (Table 1) The Malmö eye survey4 found that the prevalence of glaucoma was dose-related to the level of myopia. This association was particularly strong at lower intraocular pressure levels.17 However, not all studies have found significant relationships; notably no association between myopia and POAG was found in the Ocular Hypertension Treatment Study (OHTS) in an ethnically mixed population of Americans.3 An interesting study looking at inter eye differences in refractive error and the inter eye degree of glaucomatous optic nerve damage showed that the refractive error did not play a major part, at least for eyes not exceeding –8 D.17 Asian populations: Myopia and POAG In Asian populations, myopia is generally more common18 and the incidence is increasing. The Beijing Eye Study from China found a significant relationship between POAG and high myopia <−6 D19 compared to the remaining eyes. In contrast, in hyperopic eyes, emmetropic eyes and eyes with low to moderate myopia (myopic refraction up to −6 diopters or less), the frequency of glaucoma did not vary significantly. The Meiktila Eye Study, conducted in Myanmar, also found an association with myopia, albeit weaker than the other studies.20 Similarly, the population-based study in Singapore Malays (SiMES) showed an association between moderate or higher myopia (worse than –4 D) and POAG. Persons with moderate or higher myopia had an almost three times higher risk of POAG compared to emmetropes. In Singapore Malays, there was an association between increasing axial length (AL) (measured using the IOLMaster, Carl Zeiss Jena, Germany) and POAG. This association of moderate or higher myopia and POAG was no longer significant after controlling for AL, suggesting that axial myopia rather than other

BMES

% with POAG

Age-Sex OR (95% CI)

No. of Eyes

% with POAG

Age-Sex OR (95% CI)

No. of Eyes

% with POAG

Age-Sex OR (95% CI)

Myopia Emmetropia Hyperopia

583 1511 788

3.3 1.7 1.9

1.7 (0.9, 3.2) 1.0 0.9 (0.5, 1.7)

1073 1583 734

3.0 2.2 4.0

1.6 (0.9, 2.6) 1.0 1.1 (0.7, 1.7)

475 1533 1646

4.4 1.8 1.5

2.0 (1.1, 3.7) 1.0 0.6 (0.3, 1.0)

Refraction Less than −3.00 −1.00 to −3.00 −0.75 to +0.75 +1.00 to +2.25 More than +2.25

217 366 1511 651 137

3.7 3.0 1.7 1.8 2.2

2.1 (0.9, 4.8) 1.5 (0.7, 3.2) 1.0 0.9 (0.4, 1.8) 0.9 (0.3, 3.2)

339 734 1583 1256 600

3.0 3.0 2.2 3.7 4.7

1.7 (0.8, 3.5) 1.5 (0.9, 2.6) 1.0 1.1 (0.7, 1.7) 1.1 (0.7, 1.8)

165 310 1533 1029 617

4.2 4.5 1.8 1.2 1.9

2.4 (1.0, 5.7) 1.9 (1.0, 3.7) 1.0 0.5 (0.2, 1.0) 0.6 (0.3, 1.3)

Myopia: −1.00 diopters or less; Emmetropia: between −0.75 and +0.75 diopters; Hyperopia: +1.00 diopters or greater. Abbreviations: SiMES — Singapore Malay Eye Study; BDES — Beaver Dam Eye Study; BMES — Blue Mountains Eye Study.

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Prevalence and Odds Ratio of POAG, by Refractive Status, SiMES, BDES and BMES, Right Eye only

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factors (e.g. corneal curvature or lenticular changes) may be the main biometric constituent that underlies the risk for POAG.21 Myopia in other situations Myopia and ocular hypertension It is not surprising that some weaker associations have been found between myopia and ocular hypertension, as raised intraocular pressure (IOP) is an accepted major risk factor for the development of POAG. The BMES study, whilst showing a strong relationship between myopia and glaucoma, only showed a borderline relationship with ocular hypertension with an odds ratio of 1.8 CI (1.2–2.9). In one cohort study of ocular hypertensives, followed up for a mean of 7.3 years, axial myopia was found to be a significant risk factor for the development of glaucoma.22 This was in direct contrast to the much larger OHTS which showed that myopia was not a predictive factor for the development of glaucoma.3 There is also documented interplay between some glaucoma risk factors and myopia. The risk of steroid induced ocular hypertension is higher in patients with severe myopia, alongside other risk factors such as a history of open angle glaucoma and diabetes mellitus.23 Myopia in angle closure Generally, it is hyperopic eyes, with short axial lengths and shallow anterior chamber depths that are associated with angle closure. However, angle closure can occur in myopic eyes. In a retrospective chart review of 322 cases of primary angle closure, six cases occurred in myopic eyes.24 This shows that eyes with a myopic refraction are not immune from changes that predominate in the angle recess and anterior chamber, which may predispose to angle closure. Myopia in Pigment Dispersion Syndrome (PDS) It is well documented that PDS patients are frequently myopes, and that this could play a role in the pathogenesis of reverse pupil block. It is claimed that higher degrees of myopia lead to glaucomatous optic

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neuropathy earlier,25 and more often in PDS. This was established in a study of 18 patients with PDS and 93 with pigmentary glaucoma, who were analyzed for risk factors.26 Limitations of cross-sectional studies: Methodologic issues There are many difficulties in performing a good population-based study, including achieving a high participation rate and ensuring little missing data. It is important to standardize diagnostic criteria where possible, e.g. by using the International Society for Geographical and Epidemiological Ophthalmology (ISGEO) criteria for the diagnosis of glaucoma based on optic nerve changes and perimetric findings. Unfortunately, where IOP was used to define glaucoma, such as in the Beaver Dam Eye Study, its results are not so comparable. A standardized assessment of refraction, IOP measurement, and glaucoma definitions strengthen the validity of any conclusions. The measurement of AL by optical methods instead of ultrasound methods and the use of subjective refractions by trained optometrists instead of autorefraction are the gold standard methods of each measurement, however, they have rarely been implemented in large studies. A population-based design also minimizes selection bias. Some clinic-based studies are biased by the fact that more myopes attend, thereby increasing the likelihood of detecting POAG in these studies. Although many studies have shown the correlation of central corneal thickness (CCT) with optic disc parameters,27,28 and thinner CCT has been identified as a risk factor for the development of POAG in eyes with ocular hypertension,3 some studies have not controlled for the influence of CCT on myopia and glaucoma development. The potential influence of myopia on POAG has not been derived from longitudinal data. Only a cohort study will elucidate if myopia is associated with a risk of developing POAG, but it may take many years of follow-up, especially considering the onset of POAG is variable and delayed compared to the onset and stabilization of myopia. Much of this population data has been based on single measurements (of refraction, IOP, optic disc, and visual fields) during the course of the study. In addition, many cross-sectional studies were underpowered to elicit any associations with myopia, especially once subdivided into relatively arbitrary myopic categories.

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Theories for a Link Between Myopia and POAG The association between myopia and glaucoma: How is it mediated? It is unclear if the relationship between myopia (as measured by spherical equivalent) and POAG is mediated by AL or other factors (corneal curvature or lenticular changes with age) although there is some supporting evidence from both the Meiktila eye study and the SiMES data that AL is the mediator. Comparatively, the SiMES data is more robust as many studies have not controlled for the effects of central corneal thickness, which is now known to strongly influence the measurement of IOP and is a risk factor for POAG.3 Several theories have been put forward to explain a true link between myopia and POAG. Myopia has been found to influence IOP, with myopes associated with having higher IOP than emmetropes and hyperopes. The Blue Mountains study found 0.45-mmHg IOP difference between myopic and emmetropic eyes.13 This was slightly lower than in some previous reports, which have reported differences ranging from 0.75 to more than 1.0 mmHg.8,29,30 This has failed to be replicated in the Beijing Eye Study, where the mean IOP was not significantly different between the eyes with marked and high myopia compared to the remaining eyes, despite having a significantly higher frequency of glaucoma.31 Other studies have reported higher applanation pressures in children,32 or in subjects with increased axial length.33 This may shed some light on the variation in findings of the populationbased studies.

Glaucoma Assessment in Myopic Eyes Biometric differences Axial length and CCT Recently, there has been some interest in CCT, as it was revealed as a significant risk factor for the development of glaucoma in ocular hypertensives,3 and as its role in accurate IOP measurement became known. Longer AL, however, does not correlate with CCT in myopes34 or in populations of normal or glaucoma patients.35 The relationship between CCT and refractive error favors no correlation. Although in myopic populations,

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Chang et al. reported that the CCT was thinner in the more myopic eyes, Fam et al.36 found no correlation with the degree of myopia with CCT in Singaporean Chinese. Among normal populations, a significant correlation between CCT and refraction was demonstrated in the Japanese.37 However, for the majority of studies in Chinese and Malays, CCT was not correlated with refraction.38–40 Optic disc assessment in myopic eyes The clinical diagnosis of glaucoma in myopic eyes may be difficult. The optic discs of myopes are notoriously difficult to assess, especially those with coexistent tilted discs.41 The discs frequently appear glaucomatous with larger diameters, greater cup disc ratios, and larger and shallower optic cups.42–44 Myopic discs are often obliquely inserted and tilted.45 A possible source of bias for graders in studies is that highly myopic eyes usually show a characteristic appearance of the optic nerve head with an enlarged disc, shallow cupping, a large myopic crescent, and fundus hypopigmentation (Figs. 1 and 2).

Figure 1

Figure 2

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Visual fields in myopic eyes In addition, myopic retinal degeneration, which is common in high myopes, may cause visual field defects that mimic glaucomatous visual field defects. Non-glaucomatous visual field abnormalities, e.g. enlarged blind spots and superotemporal defects, have been reported in both highly myopic eyes and those with tilted discs.46–48 It is important not to overstate the effect of simple myopia on visual field testing. In one study, there appeared to be a low prevalence of visual field defects amongst a population of young healthy myopic males with no myopic degeneration. The observation that myopia affected threshold sensitivity, especially in higher degrees of myopia48 and in relation to AL,49 may be explained by microscopic structural changes in the retina and choroid, axial elongation of the eye with increased spacing of retinal photoreceptors, or distortion of the stimulus by the negative prescription of the lenses. It is possible that such cases of high myopia may have been misclassified as POAG in studies, leading to a spurious association between myopia and POAG. In younger myopic individuals with tilted optic discs, significant focal visual field defects were rare, suggesting that these defects probably do not develop until their later years.50 Abnormal visual fields in myopic eyes have also been reported when tested with other forms of perimetry. Myopic optical defocus has a significant effect on the Humphrey Matrix 30–2 test results, impacting more so in the moderate-myopic group than in low-myopic eyes.51 Imaging tests and variations with myopia Recently, there has been an explosion in new technologies, which aim to give quantitative and objective measurements of optic nerve head (ONH) and retinal nerve fibre layer (RNFL) parameters. Although they have reasonable sensitivity and specificity for detecting glaucoma, each technology has some challenges when assessing myopic eyes. A study using the Stratus-optical coherence tomography (OCT) revealed that overall RNFL thickness decreased 7 microns per 1 mm of axial length, and 3 microns per 1 D sphere. Moderately myopic subjects tended to have thinner peripapillary RNFL, mainly at the superior and inferior poles, which would make judging the RNFL thickness at these critical points less straightforward. This, together with the likelihood of having a thinner RNFL, should be considered when interpreting a glaucoma suspect’s OCT measurements

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compared with the normative database.52 For eyes with a spherical equivalent of <−10.0 D, it has been shown that the average RNFL thickness is 80 µm, as opposed to 100 µm in emmetropes.53 Myopic eyes were more likely to develop abnormal birefringence patterns in both types of scanning laser polarimetry (GDxTM VCC and ECC, Carl Zeiss Meditec, Jena, Germany).54 This could stem from a more spread out RNFL and may lead to anomalous diagnoses if not used together with clinical assessment. In contrast, Heidelberg retinal tomography (Heidelberg Engineering, Heidelberg, Germany) tests do not seem to be as affected by refractive error as they are by optic disc tilt.55 ONH susceptibility to damage Jonas and Budde56 showed that for a given intraocular pressure (IOP) level in eyes with POAG, optic nerve damage appears to be more pronounced in highly myopic eyes with large optic discs than in non-highly myopic eyes. The optic nerve head in myopic eyes may be more susceptible to glaucomatous damage from elevated or normal IOP57,58 than in nonmyopic eyes. Also, the cup-disc-ratio is higher in myopes,60 which may predispose more nerve fibers to damage at any level of IOP60–62 as shearing forces exerted by scleral tension across the lamina cribrosa are heightened in myopia for the same IOP.63 It has been proposed that similar connective tissue changes may also occur in glaucoma and myopia.64,65 The finding that AL largely explains the association between myopia and POAG also supports a theory involving the connective tissue changes associated with longer axial dimensions as a potential mechanism for POAG. It may fit with the finding of a thinner lamina cribrosa in combination with a secondary enlargement of the optic nerve head in highly myopic eyes.66–70 In two cadaveric studies, it was found that in highly myopic eyes, the lamina cribrosa was significantly thinner than in non-highly myopic eyes.71,72 This was postulated to steepen the translaminar pressure gradient for a given intraocular pressure and cause increased susceptibility to glaucoma in highly myopic eyes. A trial combing A-scan ultrasonography with confocal scanning laser ophthalmoscope images of the optic disc showed that increased disc area is associated with longer axial length measurements (and African ancestry) in normal individuals. This may have implications for pathophysiology and risk assessment of glaucoma, as these normal eyes may be misclassified as glaucomatous.73

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It is logical that myopia exhibits its influence via eye size and lamina cribrosa biomechanical properties. The resulting changes in ONH biomechanics may play a role in retinal ganglion cell loss in glaucomatous optic neuropathy.74 Ocular blood flow, which has a role in the pathophysiology of glaucoma, also seems to be affected by the structural changes in myopia. Flow in the ophthalmic75 and retinal arteries76 in very high myopes with glaucoma has been found to be significantly reduced when compared with controls. This reduction may lead to an increased vulnerability to the effects of IOP.

The Influence of Myopia on the Clinical Management of the Glaucoma Patient There are no current differences in the treatment of myopes with glaucoma compared to any other refractive error. Previously, when pilocarpine was commonplace, it was avoided in high myopes because of the risk of retinal detachment. Glaucomatous eyes with high myopia poses some additional problems, as the increased axial length, the thinner sclera, and larger intraocular volume predispose the eyes to choroidal effusions as the IOP rapidly drops post filtration surgery. The thinner sclera of myopes may collapse, leading to hypotony and shallow anterior chamber after both trabeculectomy and post-glaucoma drainage device surgeries.77 Deep sclerectomy has been reported to cause fewer of these complications with a more gradual alleviation of the high IOP, however, this must be balanced against other factors, such as the steep learning curve and the more modest IOP lowering.78 Glaucoma progression and myopia POAG patients with myopia more severe than −6 D had a greater progression of visual field loss as revealed by logistic regression in one study. The incidence of VF loss progression was 15.1% in the group of eyes with myopia less severe than −3 D, 10.5% in the group with −3 D to −6 D, 34.4% in the group with −6 D to −9 D, and 38.9% in the group with myopia more severe than −9 D.79 This is supported by another study in Japan, which found only myopia worse than −4.0 as a major risk for progression in POAG.80 To place these findings in perspective though, it is more likely that other factors, such as elevated initial IOP, wide variations and poor control of IOP, late detection of glaucoma, and non-compliance with therapy, play a more important role in the development of blindness.81

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19. Xu L, Wang Y, Wang S, et al. (2007) High myopia and glaucoma susceptibility the Beijing Eye Study. Ophthalmology 114(2): 216–220. 20. Casson RJ, Gupta A, Newland HS, et al. (2007) Risk factors for primary openangle glaucoma in a Burmese population: The Meiktila Eye Study. Clin Experiment Ophthalmol 35(8): 739–744. 21. Perera SA, Wong TY, Tay WT, et al. (2009) Refractive Error, Axial Dimensions and Primary Open Angle Glaucoma: The Singapore Malay Eye Study. Accepted by Archives of Ophthalmology. 22. Georgopoulos G, Andreanos D, Liokis N, et al. (1997) Risk factors in ocular hypertension. Euro J Ophthalmol 7(4): 357–363. 23. Vetrugno M, Maino A, Quaranta M, Cardia L. (2000) A randomized, comparative open-label study on the efficacy of latanaprost and timolol in steroid induced ocular hypertension after photorefractive keratectomy. Eur J Ophthalmol 10: 205–211. 24. Chakravarti T, Spaeth GL. (2007) The prevalence of myopia in eyes with angle closure. J Glaucoma 16(7): 642–643. 25. Berger A, Ritch R, McDermott JA, Wang RF. (1987) Pigmentary dispersion, refraction and glaucoma. Invest Ophthalmol Vis Sci 28(Suppl): 114–119. 26. Farrar SM, Shields MB, Miller KN, Stoup CM. (1989) Risk factors for the development and severity of glaucoma in the pigment dispersion syndrome. Am J Ophthalmol 108(3): 223–229. 27. Jonas JB, Stroux A, Velten I, et al. (2005) Central corneal thickness correlated with glaucoma damage and rate of progression. Invest Ophthalmol Vis Sci 46: 1269–1274. 28. Henderson PA, Medeiros FA, Zangwill LM, Weinreb RN. (2005) Relationship between central corneal thickness and retinal nerve fiber layer thickness in ocular hypertensive patients. Ophthalmology 112(2): 251–256. 29. Abdalla MI, Hamdi M. (1970) Applanation ocular tension in myopia and emmetropia. Br J Ophthalmol 54: 122–125. 30. Shiose Y, Kitazawa Y, Tsukahara S, et al. (1991) Epidemiology of glaucoma in Japan: A nationwide glaucoma survey. Jpn J Ophthalmol 35: 133–155. 31. Xu L, et al. (2007) High myopia and glaucoma susceptibility the Beijing Eye Study. Ophthalmology 114(2): 216–220. 32. Quinn GE, Berlin JA, Young TL, et al. (1995) Association of intraocular pressure and myopia in children. Ophthalmology 102: 180–185. 33. Tomlinson A, Phillips CI. (1970) Applanation tension and axial length of the eyeball. Br J Ophthalmol 54: 548–553. 34. Al-Mezaine HS, et al. (2008) The relationship between central corneal thickness and degree of myopia among Saudi adults. Int Ophthalmol Epub 28. June 2008. 35. Ventura AC, Bohnke M, Mojon DS. (2001) Central corneal thickness measurements in patients with normal tension glaucoma, primary open angle

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36. 37. 38. 39. 40. 41. 42. 43.

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glaucoma, pseudoexfoliation glaucoma, or ocular hypertension. Br J Ophthalmol 85(7): 792–795. Fam HB, How AC, Baskaran M, et al. (2006) Central corneal thickness and its relationship to myopia in Chinese adults. Br J Ophthalmol 90: 1451–1453. Suzuki S, Suzuki Y, Iwase A, Araie M. (2005) Corneal thickness in an ophthalmologically normal Japanese population. Ophthalmology 112: 1327–1336. Cho P, Lam C. (1999) Factors affecting the central corneal thickness of Hong Kong Chinese. Curr Eye Res 18: 368–374. Chang SW, Tsai IL, Hu FR, Lin LL, Shih YF. (2001) The cornea in young myopic adults. Br J Ophthalmol 85: 961–970. Tong L, Saw SM, Siak JK, et al. (2004) Corneal thickness determination and correlates in Singaporean schoolchildren. Invest Ophthalmol Vis Sci 45: 4004–4009. Nicolela MT, Drance SM. (1996) Various glaucomatous optic nerve appearances. Clinical correlations. Ophthalmology 103: 640–649. Jonas JB, Dichtl A. (1997) Optic disc morphology in myopic primary openangle glaucoma. Graefes Arch Clin Exp Ophthalmol 235(10): 627–633. Dichtl A, Jonas JB, Naumann GO. (1998) Histomorphometry of the optic disc in highly myopic eyes with absolute secondary angle closure glaucoma. Br J Ophthalmol 82(3): 286–289. Jonas JB, Gusek GC, Naumann GO. (1988) Optic disk morphometry in high myopia. Graefes Arch Clin Exp Ophthalmol 226(6): 587–590. How AC, Tan GS, Chan YH, et al. (2009) Population prevalence of tilted and torted optic discs among an adult Chinese population in Singapore: The Tanjong Pagar Study. Arch Ophthalmol 127(7): 894–899. Brazitikos PD, Safran AB, Simona F, Zulauf M. (1990) Threshold perimetry in tilted disc syndrome. Arch Ophthalmol 108: 1698–1700. Ito A, Kawabata H, Fujimoto N, Adachi-Usami E. Effect of myopia on frequency-doubling perimetry. Invest Ophthalmol Vis Sci 42: 1107–1110. Aung T, Foster PJ, Seah SK, et al. (2001) Automated static perimetry: The influence of myopia and its method of correction. Ophthalmology 108: 290–295. Rudnicka AR, Edgar DF. (1995) Automated static perimetry in myopes with peripapillary crescents — part I. Ophthalmic Physiol Opt 15: 409–412. Tay E, Seah SK, Chan SP, et al. (2005) Optic disk ovality as an index of tilt and its relationship to myopia and perimetry. Am J Ophthalmol 139(2): 247–252. Kim JH, Kee C. (2009) The effect of myopic optical defocus on the Humphrey Matrix 30–2. Threshold test. J Glaucoma Aug 5. [Epub ahead of print]. Rauscher FM, Sekhon N, Feuer WJ, Budenz DL. (2009) Myopia affects retinal nerve fiber layer measurements as determined by optical coherence tomography. J Glaucoma 18(7): 501–505. Schweitzer KD, Ehmann D, García R. (2009) Nerve fiber layer changes in highly myopic eyes by optical coherence tomography. Can J Ophthalmol 44(3): e13–6.

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54. Qiu K, Leung CK, Weinreb RN, et al. (2009) Predictors of atypical birefringence pattern in scanning laser polarimetry. Br J Ophthalmol 93(9): 1191–1194. Epub 2009 May 4. 55. Tong L, Chan YH, Gazzard G, et al. (2007) Heidelberg retinal tomography of optic disc and nerve fiber layer in Singapore children: Variations with disc tilt and refractive error. Invest Ophthalmol Vis Sci 48(11): 4939–4944. 56. Jonas JB, Budde WM. (2005) Optic nerve damage in highly myopic eyes with chronic open-angle glaucoma. Eur J Ophthalmol 15: 41–47. 57. Chihara E, et al. (1997) Severe myopia as a risk factor for progressive visual field loss in primary open-angle glaucoma. Ophthalmologica 211(2): 66–71. 58. Lotufo D, et al. (1989) Juvenile glaucoma, race, and refraction. JAMA 261(2): 249–252. 59. Tomlinson A, Phillips CI. (1969) Ratio of optic cup to optic disc. In relation to axial length of eyeball and refraction. Br J Ophthalmol 53: 765–768. 60. Chihara E, Sawada A. (1990) Atypical nerve fiber layer defects in high myopes with high-tension glaucoma. Arch Ophthalmol 108: 228–232. 61. Jonas JB, Dichtl A. (1997) Optic disc morphology in myopic primary openangle glaucoma. Graefes Arch Clin Exp Ophthalmol 235: 627–633. 62. Jonas JB, Gusek GC, Naumann GO. (1988) Optic disk morphometry in high myopia. Graefes Arch Clin Exp Ophthalmol 226: 587–590. 63. Cahane M, Bartov E. (1992) Axial length and scleral thickness effect on susceptibility to glaucomatous damage: A theoretical model implementing Laplace’s law. Ophthalmic Res 24: 280–284. 64. Curtin BJ, Iwamoto T, Renaldo DP. (1979) Normal and staphylomatous sclera of high myopia. An electron microscopic study. Arch Ophthalmol 97: 912–915. 65. Quigley HA. (1987) Reappraisal of the mechanisms of glaucomatous optic nerve damage. Eye 1: 318–322. 66. Jonas JB, Gusek GC, Naumann GO. (1988) Optic disk morphometry in high myopia. Graefes Arch Clin Exp Ophthalmol 226: 587–590. 67. Jonas JB, Berenshtein E, Holbach L. (2004) Lamina cribrosa thickness and spatial relationships between intraocular space and cerebrospinal fluid space in highly myopic eyes. Invest Ophthalmol Vis Sci 45: 2660–2665. 68. Morgan WH, Yu DY, Cooper RL, et al. (1995) The influence of cerebrospinal fluid pressure on the lamina cribrosa tissue pressure gradient. Invest Ophthalmol Vis Sci 36: 1163–1172. 69. Bellezza AJ, Hart RT, Burgoyne CF. (2000) The optic nerve head as a biomechanical structure: Initial finite element modeling. Invest Ophthalmol Vis Sci 41: 2991–3000. 70. Jonas JB, Berenshtein E, Holbach L. (2003) Anatomic relationship between lamina cribrosa, intraocular space, and cerebrospinal fluid space. Invest Ophthalmol Vis Sci 44: 5189–5195.

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71. Jonas JB, Berenshtein E, Holbach L. (2004) Lamina cribrosa thickness and spatial relationships between intraocular space and cerebrospinal fluid space in highly myopic eyes. IOVS 45: 2660–2665. 72. Ren R, Wang N, Li B, et al. (2009) Lamina cribrosa and peripapillary sclera histomorphometry in normal and advanced glaucomatous Chinese eyes with various axial length. Invest Ophthalmol Vis Sci 50(5): 2175–2184. 73. Oliveira C, Harizman N, Girkin CA, et al. (2007) Axial length and optic disc size in normal eyes. Br J Ophthalmol 91(1): 37–39. 74. Sigal A, Flanagan JG, Ethier CR. (2005) Factors Influencing Optic Nerve Head Biomechanics. Invest Ophthalmol Vis Sci 46(11): 4189–4199. 75. Galassi F, Sodi A, Ucci F, et al. Ocular haemodynamics in glaucoma associated with high myopia. Int Ophthalmol 22(5): 299–305. 76. Shimada N, Ohno-Matsui K, Harino S, et al. (2004) Reduction of retinal blood flow in high myopia. Graefes Arch Clin Exp Ophthalmol 242(4): 284–288. 77. Park HY, Lee NY, Park CK. (2009) Risk factors of shallow anterior chamber other than hypotony after Ahmed glaucoma valve implant. J Glaucoma. 18(1): 44–48. 78. Hamel M, Shaarawy T, Mermoud A. (2001) Deep sclerectomy with collagen implant in patients with glaucoma and high myopia. J Cataract Refract Surg. 27: 1410–1417. 79. Lee YA, Shih YF, Lin LL, et al. (2008) Association between high myopia and progression of visual field loss in primary open-angle glaucoma. J Formos Med Assoc. 107(12): 952–957. 80. Chihara E, Liu X, Dong J, et al. (1997) Severe myopia as a risk factor for progressive visual field loss in primary open-angle glaucoma. Ophthalmologica. 211(2): 66–71. 81. Kooner KS, Albdoor M, Cho BJ, Adams-Huet B. (2008) Risk factors for progression to blindness in high tension primary open angle glaucoma: Comparison of blind and non-blind subjects. Clin Ophthalmol. 2(4): 757–762.

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2.4 The Myopic Retina Shu-Yen Lee*,†

A crescent of peripapillary atrophy, chorioretinal degeneration and atrophy, lattice degeneration and posterior staphyloma are very common findings in the posterior segment of the elongated myopic eye. With higher levels of myopia, particularly if the myopia is greater than –6.00 DS or the axial length greater than 26 mm, the retina may be more severely affected. In the presence of myopic macular degeneration and atrophy, the macula is at risk of lacquer cracks and choroidal neovascularization, potentially causing the loss of central vision. The lattice degeneration is prone to retinal breaks, and thus predisposes the eye to retinal detachment. Abnormal vitreoretinal relationships are not uncommon, and can result in macular holes and detachment, myopic foveoschisis and vitreomacular traction.

Posterior Staphyloma The posterior staphyloma is a pathognomonic feature of eyes with pathologic myopia. It is a localized ectasia of the sclera, choroids, and retinal pigment epithelium that can be of variable size and involve different aspects of the posterior fundus. The morphological classification of posterior staphylomatous findings were described by Curtin1 to nasal, maculacentered, disc-centered and tiered staphylomas. These staphylomas are best observed with indirect binocular ophthalmoscopy and B-scan ultrasonography. The posterior staphylomas are usually present from a young age and may progress with age, particularly with high myopias and long axial lengths. Vision progressively deteriorates in eyes with staphylomas that are

*Singapore National Eye Centre, Singapore. E-mail: [email protected] † Duke-National University of Singapore, Graduate Medical School, Singapore.

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macula-centered because of the progressive thinning of the choroids and retinal pigment epithelium in the macula.2

Myopic Chorioretinal Atrophy The terms “tessellated” and “tigroid” fundus appearances have been commonly used to describe the myopic retina. As a consequence of the elongation of the globe and the internal structures within, peripapillary crescent formation and chorioretinal atrophy are common features seen in highly myopic eyes3,4 (Fig. 1). The areas of atrophy may be focal or diffused, defined or irregularly shaped, isolated or composed of multiple pale white areas in the posterior pole. There may be clumps of pigment within these areas. The choroidal vessels are usually easily seen beneath the thinned out retina. Fundus fluorescein angiography would show staining of these atrophic areas while they are hypofluorescent with indocyanine green angiography. The natural history of these areas of atrophy is gradual enlargement and coalescence. As the fovea becomes more extensively involved, central vision becomes progressively affected.

Figure 1. Myopic fundus with peripapillary atrophy and a large area of chorioretinal atrophy within the macula.

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Lacquer Cracks With the thinning of the retinal pigment epithelium and choroid, ruptures of Bruch’s membrane occurs in up to 4.2%.5 These typically occur with a streak of retinal hemorrhage that is not associated with any underlying choroidal neovascularization. Upon resolution of the blood, pale linear or stellate lines are revealed across the macula (Fig. 2), without disruption of the adjacent retina. The lacquer cracks are best defined as linear hyperfluorescence in the early phase of fluorescein angiography and late hypofluorescence on indocyanine angiography. Over time, the cracks may widen and join with areas of atrophy. Central vision may be affected depending on the course of the lacquer crack, especially if there is foveal involvement. It should be noted that the presence of lacquer cracks does put the eye at risk of choroidal neovascularization. Hence, lacquer cracks are not innocuous findings and central visual prognosis may be guarded. In our series of 28 Asian eyes with myopia of greater than –6.0 DS, six eyes (21.4%) were found to have lacquer cracks as the underlying cause. The mean initial visual acuity was 6/36 and progressively improved to 6/21 at 3 months, 6/15 at 6 months and 6/18 at 12 months. In general, the

Figure 2. Myopic macula chorioretinal degeneration with lacquer crack across the fovea.

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prognosis for central vision is good once the hemorrhage has cleared, unless there is foveal involvement. A lacquer crack that courses the fovea will result in persistent metamorphopsia and deterioration of central acuity.

Myopic Choroidal Neovascularization Myopic choroidal neovascularization (mCNV) has been reported to occur in up to 10% in those with myopia6 and up to 40.7% in high myopia.7 The patient would report metamorphopsia or central blurring of vision that occurs acutely. The mCNV typically occurs at the edge of a lacquer crack or an area of atrophy within the macula. The area of hemorrhage is usually small with minimal serous exudation compared to the age-related type of choroidal neovascularization. A welldefined grey subretinal membrane can often be seen adjacent to the hemorrhage (Fig. 3a). The typical term that has been used to describe a pigmented lesion in the myopic macula is a Foerster–Fuchs’ spot.8,9 This represents subretinal or intraretinal migration of RPE accompanying the CNV.

Figure 3a. A myopic CNV which is seen as a greyish subfoveal membrane with surrounding subretinal blood.

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Figure 3b. Fluorescein angiogram showing the classic CNV.

Fluorescein angiography would usually show that the mCNV, in the majority of cases, is located in the subfoveal and juxtafoveal areas (Fig. 3b). It is usually of the classic type of network seen and does not leak as exuberantly as in the age-related type of CNV. The visual prognosis is controversial. It is not unusual for the mCNV to spontaneously involute or to remain in status quo, unlike the age-related CNV. However, despite the involution, there is continued and progressive chorioretinal atrophy around the scar or pigmented mound of the involuted CNV, resulting in progressive loss of central vision. There is also a high recurrence rate. Treatment is however, often considered. Focal laser photocoagulation works well in destroying the vascular network, but because of the potential damage to the photoreceptors, it is now largely reserved for the treatment of extrafoveal lesions. Focal laser for juxtafoveal lesions is controversial, mainly because of the high rate of recurrences. The options currently available for the treatment of subfoveal and juxtafoveal mCNVs are photodynamic therapy with verteporfin (PDT) and anti-vascular endothelial growth factor (VEGF) intravitreal injections. The VIP study

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(a)

(b)

(c)

(d)

(e)

Figure 4. A 53-year-old female with myopia of –8.50 DS, presented with a subfoveal hemorrhage in the right eye with visual acuity 6/15 (4a). The fluorescein anigoram showed the presence of a myopic CNV involving the macula (4b). The OCT scan showed the subretinal fluid space and the subretinal neovascular membrane (4c). (4d) and (4e) She received a single dose of intravitreal bevacizumab 1.25 mg/0.05 ml and the vision improved to 6/9 after one month and this was maintained after 12 months. However there was formation of chorioretinal atrophy around the involuted mCNV (black arrow).

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showed that PDT resulted in fewer retreatments with stabilization or improvement in vision in 76% of eyes up to 12 months, beyond which reading ability starts to deteriorate largely due to the progressive chorioretinal atrophy surrounding the involuted mCNV. Intravitreal injections with anti-VEGF agents, such as ranibizumab and bevacizumab, result in a rapid return of vision with resolution of the hemorrhages and involution of the mCNV. It also potentially causes less damage to the surrounding choroid and retinal pigment epithelium. The SNEC experience is that whilst there is faster visual recovery compared to treatment with PDT with better visual outcome at 12 months (Fig. 4). With PDT, there is initial visual improvement but after 6 months, the visual outcome tends towards that of natural history, probably due to the inevitable development of chorioretinal atrophy. Currently, it is possible to result in involution of the mCNV. The real challenge in the management of mCNV is to halt the progressive chorioretinal atrophy, which at this point in time is not possible.

Myopic Foveoschisis As a consequence of the ectasia secondary to the posterior staphyloma, highly myopic individuals can develop foveoschisis. This is the splitting of the retinal layers in the macula (Fig. 5), which can cause blurring of vision

Figure 5. There is splitting of the retinal layers of the macula in the presence of myopic foveoschisis as seen on this OCT scan.

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and metamorphopsia. It can then progress on to a myopic macular hole formation that may be associated with a retinal detachment. Surgical intervention may be necessary to restore the anatomy and visual function. The surgical procedures that have been performed include vitrectomy with gas tamponade and macular buckling. Myopic macular hole detachments Macular holes can develop in highly myopic eyes. This usually occurs as a consequence of tractional forces from the vitreoretinal interface (Fig. 6). Often, a localized detachment arises within the macula, which over time, can extend peripherally (Fig. 7). Surgery would be warranted for re-establishment of anatomy, and this would involve a vitrectomy with clearance of the posterior cortical vitreous and peeling of the internal limiting membrane with internal gas tamponade and face down posturing postoperation. The surgery is challenging, as the retina is usually thin and atrophic, while the retinal pigment epithelium is also very thin, and not uncommonly, there is only bare sclera. Hence, other adjunctive measures have been proposed such as the macula buckle and cryopexy or photocoagulation of the edge of the

Figure 6. Myopic macular hole.

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Figure 7. A myopic retina with a macular hole with surrounding retinal detachment within the macula.

macula hole to induce permanent adhesion. Silicone oil may not be a good option because of extreme convexity seen in these eyes with posterior staphylomas. The visual prognosis is guarded, even if the macula detachment is fixed because of the damage to the atrophic macula. Lattice degeneration Lattice degeneration is a peripheral vitreoretinal thinning that is clinically important because of the potential risk of developing retinal tears and detachments. It is present in about 10% of the population10 and more commonly so in high myopia. Lattice lesions can vary in their appearance. They can be linear or oval lesions of retinal thinning that can be of various sizes and extents of pigmentation. They are usually anterior to the equator and circumferential in distribution. Some may have round atrophic holes within them. These are not considered to be major risk factors for retinal detachment, however, lattice degeneration has been reported in up to 20% of all detachments, and Byer showed that the risk of developing retinal detachment in the presence of lattice degeneration is about 0.3 to 0.5%.10

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As the margin of lattice degeneration is associated with vitreous adhesions, posterior vitreous detachments can result in a tractional retinal tear along the edges of the lesion. Despite the risk of developing retinal tears and retinal detachments, prophylactic laser of lattice degeneration as a means to prevent retinal detachment is not universally accepted. This is usually recommended if there is a symptomatic retinal tear or a history of retinal detachment in the fellow eye. Retinal tears and detachments Retinal detachments (Fig. 8) occur in about one in 10,000 of the population.11 This increases with increasing degrees of myopia. 67 percent of detachments occur in eyes with myopia. The myopic eyes that develop retinal detachments tend to be younger than those whose detachments occur as a consequence of posterior vitreous detachment. It is also not infrequent that these individuals are found to have bilateral retinal detachments, which may be asymptomatic.

Figure 8. Peripheral lattice degeneration with pigmentation and an atrophic round hole within.

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Figure 9. Retinal detachment involving the macula secondary to multiple retinal breaks superiorly.

Surgery is necessary to re-establish retinal anatomy and to restore visual function. Scleral buckling and vitrectomy procedures when well selected are excellent surgical options for this problem.

References 1. Curtin BJ. (1977) The posterior staphyloma of pathologic myopia. Trans Am Ophthalmol Soc 75: 67–86. 2. Noble KG, Carr RE. (1982) Pathologic myopia. Ophthalmology 89: 1099–1100. 3. Curtin BJ. (1985) The Myopias: Basic Science and Clinical Management. Harper & Row, Philadelphia. 4. Curtin BJ, Karlin DB. (1971) Axial length measurements and fundus changes in the myopic eye. Am J Ophthalmol 71: 42–53. 5. Curtin BJ, Karlin DB. (1971) Axial length measurements and fundus changes in the myopic eye. Am J Ophthalmol 71: 42–53. 6. Curtin BJ. (1963) The pathogenesis of congenital myopia: A study of 66 cases. 69: 166–173. 7. Hotchkiss ML, Fine SL. (1981) Pathologic myopia and choroidal neovascularisation. Am J Ophthalmol 91: 177–183.

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8. Foerster R. (1862) Ophthalmologische Beitrage, Berlin, Enslin. 9. Fuchs E. (1901) Der centrale schwarze Fleck bei Myopie. Z Augenheilkd 5: 171–178. 10. Byer NE. (1989) Long term natural history of lattice degeneration of the retina. Ophthalmology 96(9): 1396–1401. 11. Rosman M, Wong TY, Ong SG, Ang CL. (2001) Retinal detachment in Chinese, Malay and Indian residents in Singapore: a comparative study of risk factors, clinical presentations and surgical outcomes. Int Ophthalmol 84(2): 101–106.

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2.5 Retinal Function Chi D. Luu* and Audrey W.L. Chia†,‡

Introduction Myopia, or short sightedness, is a condition where the refractive apparatus of the eye, the cornea, and lens, focus the image of distant objects in front of the retina. Myopia is often associated with an increase in axial length, however, it is unclear whether anatomical changes as a result of axial elongation have any effect on retinal function. This chapter summarizes the existing literature on the effect of myopia on the function of various retinal components, including the photoreceptors, post-receptoral bipolar cells, and the inner retinal component. The central retinal function in myopia and its potential role for predicting the rate of myopia progression in children will also be discussed. This chapter will cover only those studies involving human subjects and that use electrophysiological techniques for the assessment of retinal function.

Electroretinography Electroretinography is a technique used to study the physiology of both neuronal and non-neuronal cells in the retina. There are a number of electroretinography techniques, but only Ganzfeld electroretinography and the multifocal electroretinography will be described here, because they are the most commonly used as means to assess retinal function in myopic eyes.

*Centre for Eye Research Australia, The University of Melbourne, Royal Victorian Eye and Ear Hospital, Melbourne, Victoria, Australia. E-mail: chi_luu@!yahoo.com.au † Singapore Eye Research Institute, Singapore. ‡ Singapore National Eye Centre, Singapore.

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Ganzfeld electroretinography Ganzfeld or full-field electroretinography (ERG) has been well-recognized as a useful and non-invasive tool for objective assessment of retinal function. The ERG response is a mass electro-retinal potential generated by various retinal cell types whose relative contributions depend on the stimulus properties and background adapting conditions. For example, under dark-adapted conditions, the ERG response to a bright flash stimulus manifests an a- and b-wave that primarily reflects the activity of photoreceptors (a mix of rods and cones) and depolarizing bipolar cells, respectively. The ERG response to a bright flash under light-adapted condition is derived from the activities of only cone photoreceptors and cone bipolar cells. The standard protocol for full-field ERG as defined by the International Society for the Clinical Electrophysiology of Vision (ISCEV) consists of five responses (Fig. 1); scotopic response (derived from the rod system), maximal response (derived from rod and cone receptors and postreceptoral activities), oscillatory potentials (derived from inner retinal and amacrine cells), photopic single flash response (shaped by cone photoreceptors and post-receptoral activities), and response to 30 Hz flicker (cone bipolar cell function).1 Thus, the function of different layers

Figure 1. Five standard responses of the full-field ERG.

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of the retina can be assessed from the responses obtained, and the pattern of abnormality from these five responses enables us to identify the retinal site of the lesion. Because the ERG records a mass retinal potential, it is useful in diseases that affect the retina globally (e.g. retinitis pigmentosa), but it is not sensitive enough to detect those conditions associated with subtle or localized functional changes within the retina (e.g. macular degeneration). Multifocal electroretinography Unlike full-field ERG, multifocal ERG (mfERG) can record responses from more than 100 different retinal locations simultaneously and provide a detailed functional topography of the retina (Fig. 2).2,3 Thus, mfERG is more sensitive than full-field ERG in detecting a localized retinal dysfunction. The mfERG response waveform has three major components, namely N1 (first negative trough), P1 (first positive peak), and N2 (second negative trough). The clinical applications of mfERG have been widely recognized,4 and mfERG has been proven to be a sensitive technique for the early detection of retinal dysfunction in various conditions including diabetic retinopathy5–7 and retinal toxicity.8

Figure 2. Trace array response of the mfERG superimposition on a fundus photograph (left), demonstrating the functional topography feature of mfERG. The mfERG waveform was enlarged (right) to illustrate different components of the mfERG response.

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Assessment of Retinal Function By using our knowledge of the origin of various components of the electroretinography response, we are able to assess the effect of myopia on different retinal layers and regions in turn. Outer retinal (photoreceptor) function The ERG a-wave response amplitude is reduced in adults with myopia,9,10 indicating the presence of abnormal outer retinal (photoreceptors) function in adult myopic eyes. The relationship between ERG amplitude and severity of myopia is best described by a linear function. The amplitude of the ERG a-wave is positively correlated with the severity of myopia9 and inversely correlated with the axial length.10,11 The affect of myopia on the function of each individual cone type was investigated by Yamamoto et al.12 using specialized ERG technique. In their study, the ERG was recorded with chromatic stimuli obtained with various colored filters. The results showed that short- (S), medium- (M), and long-wavelength (L) sensitive cone response amplitudes decreased with increasing myopia, however, a more significant correlation was detected between the L,M-cone response amplitude and refractive error than between the S-cone response amplitude and refractive error.12 These findings suggest that the L,M-cones are possibly more affected than the S-cones in myopia. Post-receptoral (bipolar cell) and retinal transmission function Many studies have shown that the ERG b-wave response amplitude is decreased with increasing myopia.9–11 Similar to that of the ERG a-wave response, the b-wave response amplitude is positively correlated with the severity of myopia9 and inversely associated with the axial length.10,11 The interpretation of the b-wave reduction in myopia is not as straightforward as that of the a-wave. Although the ERG b-wave amplitude has been reported to be consistently reduced in eyes with myopia, this does not necessarily imply that there is a presence of abnormal transmission between the outer and middle retina, or post-receptoral dysfunction. This is because reduction of the a-wave amplitude will cause a proportionate reduction of the b-wave amplitude. In order to examine

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the retinal transmission abnormality, a b-/a-wave amplitude ratio should be investigated. Perlman et al. reported that all myopic eyes in their study had subnormal ERG b-wave amplitude, but normal b-/a-wave amplitude ratios suggest that myopic eyes have normal signal transmission in the retina.10,13 However, Pallin et al.9 reported that the higher the myopia, the smaller the b-/a-wave amplitude ratio, although these ratios were within the normal range. Pallin et al. believed that signal transmission in the retina tends to be somewhat reduced in an eye with high myopia. Inner retinal function Abnormal oscillatory potentials (OPs) and retinal adaptation in eyes with myopia have been reported in a number of studies.11,14 Chen et al.15 investigated retinal adaptation in eyes with myopia using a global flash paradigm of the mfERG. They showed that retinal adaptation varied with the degree of myopia. The abnormal OP and retinal adaptation are thought to be linked to the hypothesis that dopamine may play a role in the development of myopia. It has been shown in animal models of myopia that dopamine, a neurotransmitter released by the inner retina, is associated with the development of myopia.16,17 Dopamine is an important chemical messenger for amacrine cell and retinal ganglion cell processing and is involved in the process of retinal luminance adaptation.18 Retinal amacrine cells have also been shown to play an important role in the modulation and control of ocular growth.19–22 The retinal oscillatory potentials (OPs) of the ERG reflect the amacrine cell function, hence, abnormal OPs in myopia have been suggested to be related to the changes in dopamine level within the inner retina associated with myopia. However, the presence of outer retinal dysfunction, which is commonly seen in eyes with myopia, can cause an apparent abnormal inner retinal ERG parameter, including the OPs. Therefore, the association between abnormal OPs and myopia needs to be interpreted with caution, because it is possible that the abnormal OPs were due to the presence of abnormal outer retinal function. Macular function in myopic retina The macular function of eyes with myopia has been investigated by a number of groups using the multifocal ERG (mfERG).23–27 There was

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a significant correlation between first-order kernel mfERG responses and refractive error. The findings showed that the amplitude of the mfERG decreased as the degree of myopia increased. The mfERG P1 amplitude is negatively correlated with the axial length. The mfERG P1 implicit time increased with an increase in axial length and severity of myopia.

Effect of Long-Term Atropine Usage on Retinal Function To date, only atropine eye drops have been shown to have a consistent effect on the retardation of myopia progression.28,29 There are, however, at least two potential chronic side effects associated with the long-term use of atropine. Firstly, accumulation of atropine over a period of time might be toxic to the neural retina. Secondly, constant pupillary dilatation will increase the amount of light entering the eye and could theoretically cause photic damage to the retina. Luu et al.30 recorded the mfERG responses in children (n = 48) receiving atropine eye drops once daily for two years, and in those receiving placebo eye drops (n = 57) for a similar duration. Their results showed that there was no significant difference in the mfERG response amplitudes and implicit times between the atropine treated and placebo control groups, suggesting that atropine use for two years has no significant effect on retinal function.

Macular Function Associates with Myopia Progression Studies have examined the relationship between macular function changes and the rate of myopia progression. Chen et al.14 studied the multifocal oscillatory potential (mfOP) changes in emmetropes, stable myopes, and progressing myopes. They found that progressing myopes had significantly shorter mfOP implicit times compared to emmetropes and stable myopes. There were, however, no statistically significant differences in OP amplitudes between the groups. Luu et al.31 examined the mfERG responses of 81 children (aged 9–10 years) with myopia (mean spherical equivalent refraction ranging from −1.00 D to −5.88 D), and they showed that the initial mfERG P1 amplitude within the central 5 degrees was significantly associated with the subsequent two-year myopia progression rate, but not with the initial degree of

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myopia. The mfERG P1 amplitude of the central ring was significantly reduced in a high progression group, defined as a progression rate of at least 1 D per two years, compared to a medium progression group (progression rate of >0.25 D and <1 D per two years) or a low/no progression group (progression rate of ≤0.25 D per two years). Responses from rings 2–5 (central 5 to 35 degrees retina) were similar for all the progression groups. No significant differences in mfERG response implicit times were found in any of the progression groups at any of the locations tested.

Factors Associated with ERG Changes in Myopia Although the reduction in ERG response in adults with myopia has been well recognized, the actual mechanisms of ERG reduction in myopia are unclear. It has been suggested that the reduction in ERG amplitudes seen in adults with myopia may be owing to a reduced image size and decreased retinal illumination, known as the optical factor, as a result of axial elongation of the eye.32 By examining the stimulus intensity response function, Kawabata and Adachi-Usami23 claimed that decreased retinal illuminance did not explain the reduced ERG response because the responses of the high-myopic eye had much lower saturated amplitudes than for the emmetropic eye. It has been suggested that the ERG amplitudes are reduced in persons with myopia because of a higher resistance between the source of the current (the retina) and the place where the current is measured (the cornea). It is believed that increased distance between the electrical source and the recording corneal electrode, also referred to as the electrical factor, due to a larger eyeball, caused an increase in the ocular resistance to electric current.9,33 Decreased retinal photoreceptor density, morphological changes in the photoreceptor outer segment, and photoreceptor dysfunction have been postulated as the causes for ERG reduction in myopia.24,34 Altered neural processing could result, in part, from retinal stretching in the enlarged myopic eye, which may produce both increased retinal cell spacing and post-receptoral retinal dysfunction, and lead to a decrease in retinal sampling.34 Another mechanical attribute of the eye in axial myopia might be an increase in the subretinal space and subsequent reduction in photoreceptor response.34

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Luu et al.25 examined the relationship between ERG amplitude and myopia in adults and children with various degrees of myopia. While their results confirm that there is a significant correlation between the refractive error and ERG amplitude in adults with myopia, they also discovered that such a relationship is absent in children with myopia. In light of the results obtained from this study, the optical and electrical factors are unlikely to be the cause of the ERG reduction because of the absence of any relationship between ERG amplitudes and the severity of myopia in children. Similarly, with photoreceptor morphological and functional changes in myopic eyes, a good correlation between ERG amplitude reduction and severity of myopia would be expected irrespective of the subject’s age. These data provide strong evidence that the reduction of ERG response seen in adults with myopia is not directly due to the severity of myopia. The lack of correlation between ERG amplitude and the degree of myopia in children suggest that other mechanisms must be responsible for the reduction in ERG. It is postulated that the reduction in ERG response seen in the adult group may be owing to retinal function modifications that are associated with long-standing myopia.

Conclusion There are demonstrable changes in retinal function in subjects with myopia. In studies involving Ganzfield electroretinography, there is a progressive reduction in both a-wave (photoreceptor) and b-wave (bipolar cell) responses in adult subjects with increasing myopia, and reduction in b-/a-wave amplitude ratio in those with very high myopia. Similar reduction in P1 responses of the mfERG were seen in the macular regions. The exact nature of ERG reduction in myopia remains unknown. Gradual changes do occur in myopic fundi over time with the development of posterior staphyloma, peripalliary atrophy, and myopic macular degeneration, and there may be clinically significant visual loss in some individuals. Future directions include a better understanding on what, when, and where functional changes occur within the myopic retina over time. Of interest is also whether these changes precede or even induce anatomical changes and whether they can be used to identify individuals at greatest risk of developing high myopia. Electroretinography can also be used to

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monitor the safety and efficacy of new drug therapies for myopia as they become available in the future.

References 1. Marmor MF, Fulton AB, Holder GE, et al. (2009) ISCEV Standard for fullfield clinical electroretinography (2008 update). Doc Ophthalmol 118: 69–77. 2. Hood DC. (2000) Assessing retinal function with the multifocal technique. Prog Retin Eye Res 19: 607–646. 3. Sutter EE, Tran D. (1992) The field topography of ERG components in man — I. The photopic luminance response. Vision Res 32: 433–446. 4. Lai TY, Chan WM, Lai RY, et al. (2007) The clinical applications of multifocal electroretinography: A systematic review. Surv Ophthalmol 52: 61–96. 5. Bearse MA Jr, Adams AJ, Han Y, et al. (2006) A multifocal electroretinogram model predicting the development of diabetic retinopathy. Prog Retin Eye Res 25: 425–448. 6. Han Y, Bearse MA Jr, Schneck ME, et al. (2004) Multifocal electroretinogram delays predict sites of subsequent diabetic retinopathy. Invest Ophthalmol Vis Sci 45: 948–954. 7. Han Y, Schneck ME, Bearse MA Jr, et al. (2004) Formulation and evaluation of a predictive model to identify the sites of future diabetic retinopathy. Invest Ophthalmol Vis Sci 45: 4106–4112. 8. Lai TY, Chan WM, Li H, et al. (2005) Multifocal electroretinographic changes in patients receiving hydroxychloroquine therapy. Am J Ophthalmol 140: 794–807. 9. Pallin E. (1969) The influence of the axial size of the eye on the size of the recorded b-potential in the clinical single-flash electroretinogram. Acta Ophthalmol 101(suppl): 1–57. 10. Perlman I, Meyer E, Haim T, Zonis S. (1984) Retinal function in high refractive error assessed electroretinographically. Br J Ophthalmol 68: 79–84. 11. Westall CA, Dhaliwal HS, Panton CM, et al. (2001) Values of electroretinogram responses according to axial length. Doc Ophthalmol 102: 115–130. 12. Yamamoto S, Nitta K, Kamiyama M. (1997) Cone electroretinogram to chromatic stimuli in myopic eyes. Vision Res 37: 2157–2159 13. Perlman I. (1983) Relationship between the amplitudes of the b wave and the a wave as a useful index for evaluating the electroretinogram. Br J Ophthalmol 67: 443–448. 14. Chen JC, Brown B, Schmid KL. (2006) Evaluation of inner retinal function in myopia using oscillatory potentials of the multifocal electroretinogram. Vision Res 46: 4096–4103.

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15. Chen JC, Brown B, Schmid KL. (2006) Retinal adaptation responses revealed by global flash multifocal electroretinogram are dependent on the degree of myopic refractive error. Vision Res 46: 3413–3421. 16. Megaw PL, Morgan IG, Boelen MK. (1997) Dopaminergic behavior in chicken retina and the effect of form deprivation. Aust N Z J Ophthalmol 25(Suppl 1): S76–S78 17. Stone RA, Lin T, Laties AM, Luvone PM. (1989) Retinal dopamine and formdeprivation myopia. Proc Natl Acad Sci USA 86: 704–706. 18. Witkovsky P. (2004) Dopamine and retinal function. Doc Ophthalmol 108: 17–40. 19. Feldkaemper MP, Schaeffel F. (2002) Evidence for a potential role of glucagon during eye growth regulation in chicks. Vis Neurosci 19: 755–766. 20. Laties AM, Stone RA. (1991) Some visual and neurochemical correlates of refractive development. Vis Neurosci 7: 125–128. 21. Pendrak K, Nguyen T, Lin T, et al. (1997) Retinal dopamine in the recovery from experimental myopia. Curr Eye Res 16: 152–157. 22. Raviola E, Wiesel TN. (1990) Neural control of eye growth and experimental myopia in primates. Ciba Found Symp 155: 22–38; discussion 39–44. 23. Kawabata H, Adachi-Usami E. (1997) Multifocal electroretinogram in myopia. Invest Ophthalmol Vis Sci 38: 2844–2851. 24. Chan HL, Mohidin N. (2003) Variation of multifocal electroretinogram with axial length. Ophthalmic Physiol Opt 23: 133–140. 25. Luu CD, Lau AM, Lee SY. (2006) Multifocal electroretinogram in adults and children with myopia. Arch Ophthalmol 124: 328–334. 26. Chen JC, Brown B, Schmid KL. (2006) Delayed mfERG responses in myopia. Vision Res 46: 1221–1229. 27. Wolsley CJ, Saunders KJ, Silvestri G, Anderson RS. (2008) Investigation of changes in the myopic retina using multifocal electroretinograms, optical coherence tomography and peripheral resolution acuity. Vision Res 48: 1554–1561. 28. Chua WH, Balakrishnan V, Chan YH, et al. (2006) Atropine for the treatment of childhood myopia. Ophthalmology 113: 2285–2291. 29. Saw SM, Shih-Yen EC, Koh A, Tan D. (2002) Interventions to retard myopia progression in children: an evidence-based update. Ophthalmology 109: 415–421; discussion 422–414; quiz 425–416, 443. 30. Luu CD, Lau AMI, Koh AHC, et al. (2003) Effects of long-term atropine usage on retinal function. Invest Ophthalmol Vis Sci 44(Suppl 2): U646 (Meeting Abstract 4790). 31. Luu CD, Foulds WS, Tan DT. (2007) Features of the multifocal electroretinogram may predict the rate of myopia progression in children. Ophthalmology 114: 1433–1438.

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32. Palmowski AM, Berninger T, Allgayer R, Andrielis H, Heinemann-Vernaleken B, Rudolph G. (1999) Effects of refractive blur on the multifocal electroretinogram. Doc Ophthalmol 99: 41–54. 33. Lemagne JM, Gagne S, Cortin P. (1982) Resistance in clinical electroretinography: Its role in amplitude variability. Can J Ophthalmol 17: 67–69. 34. Atchison DA, Schmid KL, Pritchard N. (2006) Neural and optical limits to visual performance in myopia. Vision Res 46: 3707–3722.

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Section 3

Genetics of Myopia

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3.1 New Approaches in the Genetics of Myopia Liang K. Goh*, Ravikanth Metlapally† and Terri Young*,†

Introduction Myopia is the most common human ocular disorder, and its public health and economic impact is significant.1–3 The prevalence of myopia varies across different populations and ethnicities. Myopia prevalence in some Asian countries has reached epidemic proportions, e.g. affecting approximately 82% of medical students in Singapore4 and 90% of high school children in Taiwan.5 Chinese and Japanese populations also have high myopia prevalence rates of > 50–70%.6,7 One-third of the U.S. adult population has some degree of myopia.8 Asians in the United States have a higher prevalence than Caucasians or African Americans.9 Ashkenazi Jews, especially Orthodox Jewish males, have shown a higher prevalence than other American Caucasians or European populations.10 Pathologic or high myopia (refractive spherical dioptric power of –5 or worse) affects approximately 2% of the general population,1,9 and is a major cause of legal blindness given the increased risks of associated co-morbidities of choroidal neovascularization (CNV), retinal detachment (RD), and glaucoma.11,12 Multiple studies provide compelling evidence that myopia is inherited. Familial aggregation studies report a positive correlation between parental myopia and myopia in their children, indicating a hereditary influence in myopia susceptibility.13,14 Multiple familial studies also support a definite genetic basis for myopia.15–18 Twin studies estimate a notable high heritability value between 0.5–0.96 (the proportion of the total phenotypic variance that is attributed to genetic variance).19,20 *Duke-National University of Singapore Graduate Medical School, Singapore. E-mail: liang.goh@ duke-nus.edu.sg † Duke Center for Human Genetics, Durham NC.

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Human molecular genetic studies to understand the pathogenesis of non-syndromic myopia have been carried out over the last two decades. These include mapping studies with relatively small number of families affected by pathologic myopia and large family-based/case-control genetic association studies based on positional candidate gene approach. The first study to define a genetic interval for myopia on chromosome Xq28 (MYP1, OMIM 310460) was regarding an X-linked recessive form of high myopia named the Bornholm (Denmark) Eye Disease (BED), the phenotype that also included cone dysfunction.21 Since then, numerous loci have been identified for non-syndromic myopia, a detailed list of which is shown in Table 1. The loci on chromosomal regions Xq28, 2q37, 12q21, 18p11, and 22q12 have been replicated independently22–28 as several new ones are being discovered. The involvement of multiple loci suggests that myopia development perhaps is driven by polygenic influences. This notion was corroborated by Klein et al. in their investigation of familial aggregation and pattern of inheritance of ocular refraction in a large cohort (data from the Beaver Dam Eye Study).16 Single nucleotide polymorphism association studies or genome-wide association studies (GWAS) in myopia genetics research have burgeoned in recent years. The approach has been widely used for the discovery of genetic variants associated with common diseases such as bipolar disorder, type 2 diabetes, Crohn’s disease, and cancer.29–32 This has been made possible by the completion of the Human Genome and HapMap projects as well as development of high-throughput genotyping technology for interrogating thousands of single nucleotide polymorphisms (SNPs). In these studies, SNPs across the genome are genotyped and each marker is analyzed individually for association with the trait. It is based on the rationale that some of these markers that are observable (or genotyped) are in linkage disequilibrium with the quantitative trait locus (QTL) which is often not observable or typed. This simple approach of ‘association by guilt’ has led to the discovery of novel disease susceptible genes. To date, approximately 15 genes have been positively associated with myopia disease status — a list of these is shown in Table 2. In these studies, establishing replication in independent and/or diverse datasets is critical for success in identifying the causative gene/s. While there are studies that have reported lack of association between myopia and some of the listed genes in Table 2,33–40 the reasons for the lack of association can be multifold (sample size, study design, heterogeneity, etc). Since inconsistent and incompatible results can act as a hindrance to gene discovery, we

Location

Reference Study

Xq28 18p11.31

MYP3

603221

12q21 – q23

MYP4 MYP5 MYP6

608367 608474 608908

7q36 17q21 – q22 22q12

Schwartz, et al. 1990 Young, Ronan, Drahozal, et al. 1998 Young, Ronan, Alvear et al. 1998 Naiglin, et al. 2002 Paluru, et al. 2003 Stambolian, et al. 2004

MYP7 MYP8 MYP9 MYP10 MYP11 MYP12

609256 609257 609258 609259 609994 609995

11p13 3q26 4q12 8p23 4q22 – q27 2q37.1

Hammond, et al. 2004 Hammond, et al. 2004 Hammond, et al. 2004 Hammond, et al. 2004 Zhang, Guo, et al. 2005 Paluru, et al. 2005

High: –6.75 D to –11.25 D High: –6 D to –21 D

Early: 1.5 to 5 years Early: 6.8 years (average)

High: –6.25 D to –15 D

Early: 5.9 years (average)

High: –13.05 D (average) High: –5.5 D to –50 D Mild-moderate: –1.00 D or lower –12.12 D to +7.25 D –12.12 D to +7.25 D –12.12 D to +7.25 D –12.12 D to +7.25 D High: –5 D to –20 D High: –7.25 D to −27 D

n/a Early: 8.9 years (average) n/a n/a n/a n/a n/a Early: before school age Early: before 12 years (Continued )

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Locus

List of Genetic Loci for Myopia. OMIM — Online Mendelian Inheritance in Man, D-diopter

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Table 1.

Location

Age of Onset

MYP13 MYP14

300613 610320

Xq23 – q25 1p36

— — — —

1q41 7p21 22q11.23 – 12.3 7p15

Klein, et al. 2007 Klein, et al. 2007 Klein, et al. 2007 Ciner, et al. 2008

MYP? MYP?

— —

3q26 20q11.23 – 13.2

Andrew T, et al. 2008 Ciner, et al. 2009

MYP?

—

9q34.11

Li, et al. 2009

High: –6 D to –20 D Moderate to high: –3.46 D (average) High: –7.04 D (average) High: –7.13 D to –16.86 D (range of averages) Range of refractive errors Range of refractive errors Range of refractive errors Moderate to high: –2.87 D (average) Range: –20 D to +8.75 D Moderate to high: –4.39 D and –4 D, averages High: –5 D or worse

Early: before school age n/a

10q21.1 5p15.33−p15.2

Zhang, Guo, et al. 2006 Wojciechowski R, Moy C, et al. 2006 Nallasamy, et al. 2007 Lam, et al. 2008

MYP15 MYP16

612717 612554

MYP? MYP? MYP? MYP?

Early: 6 to 16 years n/a n/a n/a n/a n/a n/a n/a Early: school age

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Locus

(Continued )

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New Approaches in the Genetics of Myopia Table 2. List of Genetic Association Studies of Genes with Positive Association with Myopic Refractive Error States. PMID — PubMed Unique Identifier. D-diopter

Gene

Study

PMID

Ethnicity

Degree of Myopia

Lin HJ, et al. 2009 Lin HJ, et al. 2009 Chen ZT, et al. 2009 Khor, et al. 2009 Yanovitch, et al. 2009 Metlapally, et al. 2009 Zha Y, et al. 2009

19753311 19643966 19616852 19500853 19471602

Taiwanese Taiwanese Taiwanese Chinese Caucasian

High High High Any Mild to moderate

19387081

Caucasian

19365037

Chinese

Hall NF, et al. 2009 Hall NF, et al. 2009 Hall NF, et al. 2009 Vatavuk Z, et al. 2009 Han W, et al. 2009 Tsai YY, et al. 2008

19279308 19279308 19279308 19260140

Caucasian Caucasian Caucasian Croatia

High (–8.00 D or more) Any Any Any High

19124844 17948041 17896318

High Extreme (–10.00 D or more) High

17653045

Caucasian

17557158

Japanese

17438518

Chinese

17117407

English and Finnish English and Finnish English and Finnish English and Finnish Taiwanese

TGFb1

Hewitt AW, et al. 2007 Mutti DO, et al. 2007 Inamori Y, et al. 2007 Tang WC, et al. 2007 Majava M, et al. 2007 Majava M, et al. 2007 Majava M, et al. 2007 Majava M, et al. 2007 Wang IJ, et al. 2006 Lin HJ, et al. 2006

Han Chinese Chinese Taiwanese Caucasian

HGF

Han W, et al. 2006

CHRM1 Lumican Lumican cMET HGF COL2A1 TGFb1 MMP-1 MMP-3 MMP-9 MYOC PAX6 PAX6 PAX6 COL2A1 COL1A1 MYOC Lumican FMOD PRELP OPTC Lumican

17117407 17117407 17117407 16902402 16807529 16723436

Chinese Taiwanese Han Chinese

High

Any Extreme (–9.25 D or more) High High High High High Extreme (–10.00 D or more) High High

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believe new approaches such as integrating genomic information with quantitative analyses to prioritize loci or genes of interest will streamline the gene discovery process and improve statistical power. Two approaches are reviewed in this chapter: genomic convergence and pathway analysis.

Genomic Convergence Using Genomic Content Genomic convergence refers to the integration of genomic information with quantitative analyses to prioritize loci or genes of interest.41,42 Quantitatively, it gives weight to the role of biological relevance of the loci, taking into consideration the genomic properties, functionality, gene expression as well as evidence of association reported in literature. It is a multi-factorial, multi-step approach toward discovery of candidate susceptibility genes for diseases. The idea was first applied on a linkage study for Parkinson disease (PD).41 In the study, gene expression using serial analysis of gene expression (SAGE) was integrated or mapped with loci from linkage analyses. As fewer than 10% of linkage regions are actually expressed, the approach resulted in significant wet laboratory effort. While GWAS are powerful for identifying susceptible loci associated with diseases, the number of candidate loci presented and the high false positives pose challenges in discovery of genes that are involved in the disease. By complementing this with genomic information available in databases and literature, and converging this information using a quantitative approach, a comprehensive analysis of the significant role candidate loci can play in the biological pathway of the disease can be developed. One of the challenges in genomic convergence is the mapping or integration of different sources of information. The most common mapping framework exists in the genome databases that have been set up by several key genome groups such as the University of California Santa Cruz (UCSC), National Center for Biotechnology Information (NCBI) (http://www.ncbi.nlm.nih.gov/), and European Bioinformatics Institute (EBI) (http://www.ebi.ac.uk/). The genome databases that have been developed to collate information in a structured and easily visualized framework to aid researchers have been instrumental in new discovery. Three most popular genome databases are the UCSC Genome Browser ((http://genome.ucsc.edu/), EBI Ensembl Genome Browser (http://www. ebi.ac.uk/ensembl/), and NCBI MapViewer (http://www.ncbi.nlm.nih.gov/ projects/mapview/). All browsers allow multiple and simultaneous

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display of annotations in a single window. Additionally, they also allow query and retrieval of data from underlying databases that support the browsers. The genomic content in the UCSC Genome Browser is organized into several groups, each consisting of relevant tracks with provision for customized tracks using various established formats such as BED (positions of data items in a standard UCSC Browser format) or WIG (allows the display of continuous-valued data in a track format). There are other tools for further analyses of the data, one of which is the Table Browser. It is a portal to the relational database underlying the browser, allowing query and retrieval of information through structured query. From this portal, relevant genomic information can be elicited for genomic convergence. Information that may be useful for genomic convergence in GWAS is: gene and regulatory functionality, linkage disequilibrium, and allele specific information. In addition, genomic information from other databases specific to the disease can also be included. In our association study for myopia (manuscript submitted), we have converged the myopia loci shown in Table 1 and the EyeSAGE43 database (which contains a rich set of reported loci and corresponding gene expression using SAGE) together with our GWAS to help prioritize the markers. Figure 1 shows an example of the convergence of GWAS p-values with genomic information in the UCSC Genome Browser.

Pathway Analysis Pathway analysis in cancer genomics Pathway analysis, sometimes synonymously known as Gene Set Enrichment Analysis (GSEA), comprises of statistical methods developed in the field of cancer genomics for expression-based annotations.44–50 It utilizes biological pathway information in discovery of candidate genes. Diseases are often regulated by networks of genes or gene sets, each conferring a small effect on the overall phenotype. Traditional data mining or statistical approaches may not capture the small effects of these genes on the disease. Such genetic heterogeneity common in many complex human diseases can lead to the loss of power to detect genetic associations using single marker analyses due to weak marginal effects and multiple testing corrections.51

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Fig. 1. Genomic convergence of GWAS p-values with genomic information on the UCSC Genome Browser. The top track shows the GWAS p-values followed by the other tracks available in the browser such as Genetic Association Studies of Complex Diseases and Disorders (GAD), Human Quantitative Trait Locus (RGD Human QTL), UCSC Genes, Gene Expression Atlas Ratios (GNF Ratio), CpG Islands, TS miRNA sites (TargetScan miRNA Regulatory Sites), 7X Reg Potential (Regulatory Potential), Mammal Cons (PhastCons Conservation), SNPs, Linkage Disequilibrium, Database of Genomic Variants (DGV), and ENCODE regions.

In pathway analyses, statistical methods are used to compute the combined statistics of genes and then assess the significance of these genes in gene sets or pathways. It involves two steps: score statistics for each gene set and assessment of the significance of the gene sets with annotated pathways. For score statistics, Subramanian et al.48 and Mootha et al.44 calculated an enrichment score for each gene set by ranking the genes based on their association with the phenotype. Tian et al.46 and Kim and Volsky47 used two sample statistics such as t-test for each gene and aggregate it for every gene set. A difference in these methods is the treatment of genes that are not in the gene set. One approach is to apply penalties on the non-member genes44,48 and the other is to ignore it.46 To assess significance of the statistics, most of these methods use nonparametric statistics

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such as permutation to measure the significance of overlapped genes with those in annotated gene sets. Pathway analyses and GSEA have been successfully applied in many studies involving basic science, clinical studies, and pathway deregulation in cancer biology.52–54 Pathway analysis in GWAS Single marker-based association tests suffer from low power if each tested marker is in incomplete LD with the unobserved or untyped QTL. This has led to the development of multi-locus analysis, which considers the joint effects of markers simultaneously. It can be performed on the basis of either genotype or haplotype. Instead of single marker-based association with the trait, a group of markers are assessed for association with the trait. For genotype analysis, the approach typically uses multi-linear regression to model the relationship between the traits and a vector of covariates corresponding to the genotypes or similarity between pairs of genotypes. Intuitively, this should provide greater power to detect QTL, but due to the large number of degrees of freedom in multivariate test statistics, simulation studies have shown similar or reduced power compared with single-marker analyses. Several novel statistical approaches (parametric and non-parametric) have been developed to overcome this. One approach is a dimension-reduction procedure, such as principal component analysis55,56 or Fourier transformation57 to reduce the genetic data, while another uses kernel functions to reduce the score statistics to a global statistic,58–61 all resulting in a smaller degree of freedom. An alternative to genotype analysis mentioned above is combining pvalues from single marker-based association tests.62–64 It has two steps, just as in the pathway analysis or GSEA approaches for cancer genomics. The first involves robust methods on combining test statistics from multiple markers or SNPs into meaningful statistics for genes in a pathway. These can then be tested against the null hypothesis that the gene sets are not clustered by chance. Instead of focusing on a few markers with strongest associations with the disease, the biological relevance of the markers as captured in pathways is considered. It is still a relatively new approach for GWAS though encouraging results have been shown in several studies.65–68 Lastly, haplotype association tests offer a higher dimension of analyses and may identify markers with small genetic effects that could otherwise be missed out in single marker tests. However, phase information of haplotypes is not easily available, and though it can be inferred statistically, it

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introduces some uncertainty that leads to an inflated statistics variance and therefore reduces power.69 As in multi-locus analysis, it suffers from a large degree of freedom. Haplotype analysis is also limited to within a chromosome, which does not necessarily make biological sense since genes within a given pathway are often from different chromosomes. As the focus of this review is pathway analysis, we refer readers to a comprehensive review of haplotype analysis by Salem et al.70 A discussion of haplotype and genotype-based analysis can also be found in Clayton et al.69 In the following sections, we review the multi-locus methods that have been developed to enable pathway analysis in GWAS. As highlighted, they can be categorized as non-parametric, parametric, and p-values combining approaches. Non-parametric approaches A non-parametric method was proposed by Schaid et al.58 using Ustatistics71 to compute a global statistic for pair-wise comparison of genotypes between samples using kernel functions that describe the dosage effects of additive, dominant, recessive, quadratic, or allelic models. U global =

∑ i < j ∑ k wk h(g i ,k , g j ,k ) (n2 )

where gi,k and gj,k are the kth genotype of samples i and j, h(gi, gj) is the kernel function and there are N samples and K markers. wk is the weight for k markers, which is estimated from the covariance matrix of U. The statistical test consists of comparing the global statistics between cases and controls. The statistical power of the test is thus dependent on the choice of kernel functions and the resultant is a loss of power when an inappropriate kernel is used. In simulation, the quadratic kernel has been shown to be robust and is thus a reasonable choice when the underlying genetic effect is unknown. Do note that the performance of the method is also influenced by the accumulated noise from an increased number of SNPs. The test statistics may deteriorate when too many SNPs in the K markers are not associated with the trait.57 An alternative U-statistics was proposed by Wei et al.,60 looking at the within and between-group U-statistics instead of the contrast between cases and controls. This allows for qualitative traits of more than two categories and can be extended to quantitative traits as well. Instead of the

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kernel functions used by Schaid et al.,58 a Hamming distance kernel was implemented. wk is SNP-specific and defined as the negative logarithm of the single marker p-value. U within =

∑i < j ∑ k wk I (g i ,k ≠ g j ,k ) (n2 )

i =ncases

U between

j =ncontrol

∑i =1 ∑ j =1 =

∑ k wk I (g i ,k ≠ g j ,k )

ncases ncontrol

(

)

Under the null hypothesis, Ubetween is zero so the test statistics is defined as:T=

U between U within

Simulation shows the U-statistics by Wei et al.60 is comparable with that of Schaid et al.58 for additive and multiplicative models. The more interesting result is the scenario where there are protective and predisposing effects among the multiple markers; Wei et al. shows marked improvement. It should be noted that the comparison with Schaid et al. was implemented using the linear dosage kernel that was acknowledged by the author to suffer from poor power when the minor alleles were both protective and disease predisposing across multiple markers. The quadratic kernel that showed robustness could have been used for a more comprehensive comparison. One of the drawbacks from the above methods is the lack of covariates accommodation, besides the intensive computation sometimes required. Kwee et al.61 proposed a semi-parametric approach that regresses the quantitative trait on a smooth nonparametric function of the genotype, allowing adjustment of any covariates. The nonparametric function is modelled in a reduced-dimension space using kernel function based on identical-by-state (IBS), thereby reducing the degree of freedom. Yi = b X iT + h(g i ) + ei where Yi denotes the trait for sample i, Xi the covariate vector, h(g i ) the nonparametric function of genotype g i , and εi the random sample-specific

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effect. h and β are estimated using least-square kernel machines. The score utilizing kernel function is similar to what we have seen:

∑ k =1wk IBS(g i ,k , g j ,k ) K ∑ k =1wk K

S(g i ,k , g j ,k ) =

Considerations for weights are minor-allele frequency (MAF, q) and p-value of association. The first gives more weights to rare SNPs while the latter intuitively up weights of SNPs with prior evidence of association. Several options were suggested:

1 qk

and

1 qk

for rare SNPs, and −log10 (pk )

for association, which is similar to that used by Wei et al. The test statistics is based on testing the nonparametric function. Simulation results show the approach achieves higher statistical power compared to single locus tests. Non-parametric approaches typically utilize similarity measures prior to setting up the test statistics. Several methods on genomic similarity in multi-locus analysis have been discussed in detail by Wessel et al.59 Various similarity measures designed with weights to accommodate genomic functionality, such as IBS, allele frequency, functional variations, nucleotide conservation, single-locus association, haplotype, and ancestry were proposed. Parametric approaches Multi-locus analysis suffers from large degrees of freedom so one approach is to reduce the dimensionality by exploiting the underlying LD structure. Wang and Elston57 proposed a method using Fourier transformation (FT) to capture the genotype variations across different traits. In the scenario where SNPs are in LD, the genotypic variation among trait groups extends across all the SNPs, and hence could be compressed into low-frequency components of a Fourier transform. To maintain consistency of genotypic variation across the SNPs, the genotype matrix is recoded to obtain positive correlation between the SNPs. For an additive model, this is done by changing the negative correlated SNPs xij by |2 – xij|.

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With the assumption that the genotype affects only the mean of the phenotype measure and not its scale, the score statistics for the kth FT component of sample i is N

U k = ∑Yi (x ik − x k ) i

The variance of Uk is estimated by Vk =

2 1 (Yi − Y ) ∑ (x ik − x k)T (x ik − x k) ∑ n −1 i i

To give weights to low frequency FT components, a weight function [1/(k+1)]2 is added. The global weighted score statistic is then defined as Tw =

w TU w T V0w

which follows an asymptotic normal distribution. V0 is the estimated variance-covariance matrix. Another dimension reduction approach is principal component (PC) analysis. In Gauderman et al.55 and Wang and Abbott,56 PC was computed for multiple SNPs to capture underlying LD structure and then tested for association with the disease. Instead of using SNPs in a logistic regression model, PCs of the sample covariance matrix of SNPs are used. The stronger the LD among SNPs, the fewer the PCs are needed. Given the property of PC analysis that variance of the kth PC is its eigenvalue λ k, a subset of PCs that will explain most of the SNP variation is selected, thereby reducing the degree of freedom. The choice then becomes a trade-off between the amount of variance explained and the degree of freedom penalty. A general rule of thumb is to select PCs that explain at least 80% of the variance. In a similar idea of utilizing LD structure, Li et al.72 proposed a genebased association test by combining optimally weighted markers (ATOM). It basically assigns weights to markers based on LD structure from a reference set such as the HapMap. Suppose M markers are available in the reference set, the score statistic is defined as

si ,k =

1 M

M

∆kj

∑pq j =1

j j

g i,j

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where ∆kj is the LD coefficient between markers k and j, and pj and qj are allele frequencies of marker j. To test for genetic association, the authors proposed using the aggregate score s k = ∑ i si ,k of marker k for all samples in a single marker-based test or PC of the scores in a regression model. Simulation to compare the various methods was done using the dataset of CHI3L2 on chromosome 1 and CDH17 on chromosome 8, a benchmark dataset with complementary gene expression data, where significant evidence was found for cis-acting regulatory elements.73 Based on the results, it is difficult to conclude which method is better. However, it should be noted that SNPTEST — a method that relies on imputation from IMPUTE74 — shows robust performance. P-values combining approaches Methods on p-value combining are a departure from multi-locus analysis, but still adhere to the methodology from pathway analysis in cancer genomics. The approach utilizes single marker-based test statistics for a region of interest (e.g. gene) by selecting the ‘best’ SNP63,65 or combined pvalues of all SNPs using various methods. P-value combining methods are quite established, some of which are utilized in meta-analysis. They include Fisher Information, SIMES, False Discovery Rate, Rank Truncated Product and its various isoforms, Fourier Transform, and Bayesian approach.62,64,66,75,76 Since GWAS test statistics are SNP-based and not genebased, the question is the window for determining the gene region in order to combine the statistics. In Wang et al.,63 the window is centered at each SNP with 500kb up- and down-stream of the SNP. Others are based on genomic intervals elicited from genome databases, some extending the window into promoter regions. In the simulation study by Chapman et al.,62 two separate regions surrounding the genes of interest (24kb for CTLA4 and 48 kb IL21R) were selected. Once the GWAS SNP-based statistics is converted into gene-based statistics, the established approach of gene-set enrichment analysis can be applied. This involves computing an enrichment score similar to that in GSEA. Statistical significance and adjustment for multiple testing was done by permutation. Another approach is to elucidate top ranked SNPs associated with genes and perform functional analyses using tools such as DAVID,77 GoStat,78 or commercial software Ingenuity Pathway Analysis

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(Ingenuity® Systems, www.ingenuity.com). This approach bypasses the underlying enrichment effect of combining test statistics within the gene region and might miss genes that are comprised of SNPs not highly ranked individually because of small effects, but has an overall combined moderate effect on the disease.

Conclusion This chapter presents a review of current approaches in utilizing genomic information in genetics study. Two approaches using genomic convergence and biological pathways are highlighted as complementary methods to potentially improve power in GWAS. Both methods depend on the type and relevance of the genomic information elicited for the analyses, requiring collaborative efforts from different expertise to keep afloat in the deluge of genomic information. As seen, there is still room to explore the similarity measures in pathway analysis and how these measures can be aggregated in a robust manner to allow comprehensive analysis of functionality within a statistical test. At present, each similarity measure is assessed individually. As we move towards integrative analysis in system biology, we need to think beyond our current GWAS approaches and develop methodology that will incorporate genomic information to enhance discovery. As more biological context is included in the analyses, the richer and more relevant the information, the better the outcome. In our study on the genetics of myopia, we have adopted genomic convergence in our GWAS and are currently applying pathway analysis on the genes in Table 2 to help us identify novel genes.

References 1. Curtin B. (1985) The Myopias: Basic Science and Clinical Management. Harper & Row, New York. 2. Javitt JC, Chiang YP. (1994) The socioeconomic aspects of laser refractive surgery. Arch Ophthalmol 112(12): 1526–1530. 3. Vitale S, et al. (2006) Costs of refractive correction of distance vision impairment in the United States, 1999–2002. Ophthalmology 113(12): 2163–2170. 4. Chow YC, et al. (1990) Refractive errors in Singapore medical students. Singapore Med J. 31(5): 472–473.

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5. Lin LL, et al. (1988) Nation-wide survey of myopia among schoolchildren in Taiwan, 1986. Acta Ophthalmol Suppl 185: 29–33. 6. Goss DA, Winkler RL. (1983) Progression of myopia in youth: age of cessation. Am J Optom Physiol Opt 60(8): 651–658. 7. Saw SM, et al. (1996) Epidemiology of myopia. Epidemiol Rev 18(2): 175–187. 8. Vitale S, et al. (2008) Prevalence of refractive error in the United States, 1999–2004. Arch Ophthalmol 126(8): 1111–1119. 9. Kleinstein RN, et al. (2003) Refractive error and ethnicity in children. Arch Ophthalmol 121(8): 1141–1147. 10. Zylbermann R, Landau D, Berson D. (1993) The influence of study habits on myopia in Jewish teenagers. J Pediatr Ophthalmol Strabismus 30(5): 319–322. 11. The Eye Disease Case-Control Study Group. (1993) Risk factors for idiopathic rhegmatogenous retinal detachment. Am J Epidemiol 137(7): 749–757. 12. Perkins ES. (1960) Glaucoma in the younger age groups. Arch Ophthalmol 64: 882–891. 13. Goss DA, Jackson TW. (1996) Clinical findings before the onset of myopia in youth: 4. Parental history of myopia. Optom Vis Sci 73(4): 279–282. 14. Zadnik K, et al. (1994) The effect of parental history of myopia on children’s eye size. JAMA 271(17): 1323–1327. 15. Ashton GC. (1985) Segregation analysis of ocular refraction and myopia. Hum Hered 35(4): 232–239. 16. Klein AP, et al. (2005) Support for polygenic influences on ocular refractive error. Invest Ophthalmol Vis Sci 46(2): 442–446. 17. Naiglin L, et al. (1999) Familial high myopia: evidence of an autosomal dominant mode of inheritance and genetic heterogeneity. Ann Genet 42(3): 140–146. 18. Teikari JM, et al. (1991) Impact of heredity in myopia. Hum Hered 41(3): 151–156. 19. Hammond CJ, et al. (2001) Genes and environment in refractive error: the twin eye study. Invest Ophthalmol Vis Sci 42(6): 1232–1236. 20. Lyhne N, et al. (2001) The importance of genes and environment for ocular refraction and its determiners: a population based study among 20–45 year old twins. Br J Ophthalmol 85(12): 1470–1476. 21. Schwartz M, Haim M, Skarsholm D. (1990) X-linked myopia: Bornholm eye disease. Linkage to DNA markers on the distal part of Xq. Clin Genet 38(4): 281–286. 22. Chen CY, et al. (2007) Linkage replication of the MYP12 locus in common myopia. Invest Ophthalmol Vis Sci 48(10): 4433–4439. 23. Farbrother JE, et al. (2004) Linkage analysis of the genetic loci for high myopia on 18p, 12q, and 17q in 51 U.K. families. Invest Ophthalmol Vis Sci 45(9): 2879–2885.

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24. Klein AP, et al. (2007) Confirmation of linkage to ocular refraction on chromosome 22q and identification of a novel linkage region on 1q. Arch Ophthalmol 125(1): 80–85. 25. Lam DS, et al. (2003) Familial high myopia linkage to chromosome 18p. Ophthalmologica 217(2): 115–118. 26. Li YJ, et al. (2009) An international collaborative family-based wholegenome linkage scan for high-grade myopia. Invest Ophthalmol Vis Sci 50(7): 3116–3127. 27. Nurnberg G, et al. (2008) Refinement of the MYP3 locus on human chromosome 12 in a German family with Mendelian autosomal dominant high-grade myopia by SNP array mapping. Int J Mol Med 21(4): 429–438. 28. Young TL, et al. (2004) X-linked high myopia associated with cone dysfunction. Arch Ophthalmol 122(6): 897–908. 29. Barrett JC, et al. (2008) Genome-wide association defines more than 30 distinct susceptibility loci for Crohn’s disease. Nat Genet 40(8): 955–962. 30. Amos CI, et al. (2008) Genome-wide association scan of tag SNPs identifies a susceptibility locus for lung cancer at 15q25.1. Nat Genet 40(5): 616–622. 31. Ferreira MA, et al. (2008) Collaborative genome-wide association analysis supports a role for ANK3 and CACNA1C in bipolar disorder. Nat Genet. 32. Horikawa Y, et al. (2008) Replication of genome-wide association studies of type 2 diabetes susceptibility in Japan. J Clin Endocrinol Metab 93(8): 3136–3141. 33. Hasumi Y, et al. (2006) Analysis of single nucleotide polymorphisms at 13 loci within the transforming growth factor-induced factor gene shows no association with high myopia in Japanese subjects. Immunogenetics 58(12): 947–953. 34. Liang CL, et al. (2007) Systematic assessment of the tagging polymorphisms of the COL1A1 gene for high myopia. J Hum Genet 52(4): 374–377. 35. Metlapally R, et al. (2009) COL1A1 and COL2A1 genes and myopia susceptibility: evidence of association and suggestive linkage to the COL2A1 locus. Invest Ophthalmol Vis Sci 50(9): 4080–4086. 36. Paluru PC, et al. (2004) Exclusion of lumican and fibromodulin as candidate genes in MYP3 linked high grade myopia. Mol Vis 10: 917–922. 37. Mutti DO, et al. (2007) Candidate gene and locus analysis of myopia. Mol Vis 13: 1012–1019. 38. Scavello GS Jr, et al. (2005) Genomic structure and organization of the high grade Myopia-2 locus (MYP2) critical region: mutation screening of 9 positional candidate genes. Mol Vis 11: 97–110. 39. Simpson CL, et al. (2007) The roles of PAX6 and SOX2 in myopia: lessons from the 1958 British Birth Cohort. Invest Ophthalmol Vis Sci 48(10): 4421–4425.

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40. Zayats T, et al. (2008) Comment on ‘A PAX6 gene polymorphism is associated with genetic predisposition to extreme myopia’. Eye 22(4): 598–599; author reply 599. 41. Hauser MA, et al. (2003) Genomic convergence: identifying candidate genes for Parkinson’s disease by combining serial analysis of gene expression and genetic linkage. Hum Mol Genet 12(6): 671–677. 42. Noureddine MA, et al. (2005) Genomic convergence to identify candidate genes for Parkinson disease: SAGE analysis of the substantia nigra. Mov Disord 20(10): 1299–1309. 43. Bowes Rickman C, et al. (2006) Defining the human macula transcriptome and candidate retinal disease genes using EyeSAGE. Invest Ophthalmol Vis Sci 47(6): 2305–2316. 44. Mootha VK, et al. (2003) PGC-1alpha-responsive genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes. Nat Genet 34(3): 267–273. 45. Edelman E, et al. (2006) Analysis of sample set enrichment scores: assaying the enrichment of sets of genes for individual samples in genome-wide expression profiles. Bioinformatics 22(14): p. e108–e116. 46. Tian L, et al. (2005) Discovering statistically significant pathways in expression profiling studies. Proc Natl Acad Sci USA 102(38): 13544–13549. 47. Kim SY, Volsky DJ. (2005) PAGE: parametric analysis of gene set enrichment. BMC Bioinformatics 6: 144. 48. Subramanian A, et al. (2005) Gene set enrichment analysis: a knowledgebased approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci USA 102(43): 15545–15550. 49. Barry WT, Nobel AB, Wright FA. (2005) Significance analysis of functional categories in gene expression studies: a structured permutation approach. Bioinformatics 21(9): 1943–1949. 50. Tomfohr J, Lu J, Kepler TB. (2005) Pathway level analysis of gene expression using singular value decomposition. BMC Bioinformatics 6: 225. 51. Slager SL, Huang J, Vieland VJ. (2000) Effect of allelic heterogeneity on the power of the transmission disequilibrium test. Genet Epidemiol 18(2): 143–156. 52. Sweet-Cordero A, et al. (2005) An oncogenic KRAS2 expression signature identified by cross-species gene-expression analysis. Nat Genet 37(1): 48–55. 53. Alvarez JV, et al. (2005) Identification of a genetic signature of activated signal transducer and activator of transcription 3 in human tumors. Cancer Res 65(12): 5054–5062. 54. Bild AH, et al. (2006) Oncogenic pathway signatures in human cancers as a guide to targeted therapies. Nature 439(7074): 353–357.

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55. Gauderman WJ, et al. (2007) Testing association between disease and multiple SNPs in a candidate gene. Genet Epidemiol 31(5): 383–395. 56. Wang K, Abbott D. (2008) A principal components regression approach to multilocus genetic association studies. Genet Epidemiol 32(2): 108–118. 57. Wang T, Elston RC. (2007) Improved power by use of a weighted score test for linkage disequilibrium mapping. Am J Hum Genet 80(2): 353–360. 58. Schaid DJ, et al. (2005) Nonparametric tests of association of multiple genes with human disease. Am J Hum Genet 76(5): 780–793. 59. Wessel J, Schork NJ. (2006) Generalized genomic distance-based regression methodology for multilocus association analysis. Am J Hum Genet 79(5): 792–806. 60. Wei Z, et al. (2008) U-Statistics-based tests for multiple genes in Genetic Association Studies. Ann Hum Genet. 61. Kwee LC, et al. (2008) A powerful and flexible multilocus association test for quantitative traits. Am J Hum Genet 82(2): 386–397. 62. Chapman J, Whittaker J. (2008) Analysis of multiple SNPs in a candidate gene or region. Genet Epidemiol 32(6): 560–566. 63. Wang K, Li M, Bucan M. (2007) Pathway-Based Approaches for Analysis of Genomewide Association Studies. Am J Hum Genet 81(6). 64. Zhou H, Wei LJ, Xu X. (2008) Combining association tests across multiple genetic markers in case-control studies. Hum Hered 65(3): 166–174. 65. Torkamani A, Topol EJ, Schork NJ. (2008) Pathway analysis of seven common diseases assessed by genome-wide association. Genomics 92(5): 265–272. 66. Yu K, et al. (2009) Pathway analysis by adaptive combination of P-values. Genet Epidemiol 33(8): 700–709. 67. Dinu V, Miller PL, Zhao H. (2007) Evidence for association between multiple complement pathway genes and AMD. Genet Epidemiol 31(3): 224–237. 68. Pan W. (2008) Network-based model weighting to detect multiple loci influencing complex diseases. Hum Genet 124(3): 225–234. 69. Clayton D, Chapman J, Cooper J. (2004) Use of unphased multilocus genotype data in indirect association studies. Genet Epidemiol 27(4): 415–428. 70. Salem RM, Wessel J, Schork NJ. (2005) A comprehensive literature review of haplotyping software and methods for use with unrelated individuals. Hum Genomics 2(1): 39–66. 71. Hoeffding W. (1948) A class of statistics with asymptotically normal distribution. Annals of Mathematical Statistics 22: 165–179. 72. Li M, et al. (2009) ATOM: a powerful gene-based association test by combining optimally weighted markers. Bioinformatics 25(4): 497–503. 73. Cheung VG, et al. (2005) Mapping determinants of human gene expression by regional and genome-wide association. Nature 437(7063): 1365–1369.

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74. Marchini J, et al. (2007) A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet 39(7): 906–913. 75. Zaykin DV, et al. (2002) Testing association of statistically inferred haplotypes with discrete and continuous traits in samples of unrelated individuals. Hum Hered 53(2): 79–91. 76. Peng G, et al. (2009) Gene and pathway-based second-wave analysis of genome-wide association studies. Eur J Hum Genet. 77. Huang da W, et al. (2007) DAVID Bioinformatics Resources: expanded annotation database and novel algorithms to better extract biology from large gene lists. Nucleic Acids Res. 35(Web Server issue): W169–W175. 78. Beissbarth T, Speed TP. (2004) GOstat: find statistically overrepresented Gene Ontologies within a group of genes. Bioinformatics 20(9): 1464–1465.

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3.2 Twins Studies and Myopia Maria Schäche*,† and Paul N. Baird†

Twins provide a unique resource with which to study the influence of genetic and environmental factors on the development of myopia. In this chapter we review some of the methodological approaches that have been used in twin cohorts to quantitate the extent of involvement of genes in myopia and its underlying determinants such as axial length, anterior chamber depth, and corneal curvature. The use of twins to study the association of myopia with other factors such as birth weight, body stature, and personality will also be discussed.

Introduction Myopia is defined as a complex trait as its underlying causes are both genetic and environmental in origin. Environmental factors such as excessive reading, high educational attainment, and urbanization1–3 have been suggested as risk factors in its etiology, but more recently, studies by Rose et al. (2008) and Dirani et al. (2009) have suggested that increased exposure to outdoor activity may be protective for myopia, but their results remain under debate.3,4 The known environmental factors account for approximately 11.6% of the phenotypic variation seen with myopia, which suggests that there are other, as yet, unidentified factors that also play a major role in its development. Evidence supporting a genetic origin for myopia has come from several sources including familial correlation studies, association studies, and genetic linkage studies. Data suggests that children of myopic parents are *Corresponding author. E-mail: [email protected]. † Centre for Eye Research Australia, University of Melbourne, Royal Victoria Eye and Ear Hospital, 32 Gisborne Street, East Melbourne, Victoria 3002, Australia.

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at four times greater risk of developing myopia than children with no myopic parents.5,6 In support of this, genetic linkage studies in large multigenerational families have suggested over 27 loci are involved in the development of myopia.7 In this chapter, the discussion will focus predominately on twin studies and touch very briefly on family studies only for comparative purposes. A complete understanding of the underlying causes of myopia requires a survey of both the environmental and genetic influences on the condition. This chapter will describe the application of twin studies in measuring the genetic and environmental components of myopia. A description of the most commonly used methodologies will be provided, including the advantages and limitations of each. This chapter is not intended to be a comprehensive review of all the methodological approaches using twins in genetic studies, but will instead focus on those that have been used to study myopia. This chapter begins with some important definitions and a historical perspective on how the use of twins in genetic studies of myopia originated. Only articles written in English or those with English translations have been reviewed.

Definition of Myopia Any genetic study into myopia, or other conditions, must begin with a clear definition of the phenotype (physical presentation). The simplest approach is to define myopia as a type of refractive error that affects visual acuity. In the case of myopia, parallel light rays entering the eye are focussed in front of the retina, resulting in blurred distance vision. Myopia is clinically defined using Spherical Equivalent (SE) measures that are quantitated using units of dioptres (D). Typically, individuals having dioptre readings of −0.5 D or less in one or both eyes are considered to be myopic with further classifications into low (–0.5 D to –2.99 D), moderate (–3.00 D to –5.99 D) and high myopia (<–6.0 D). Whilst these categorical definitions are commonly used and accepted, there is some debate as to the validity of using relatively arbitrary clinical cut-offs. An alternative way to define myopia is to broaden the definition and consider the entire spectrum of refraction measures, from hypermetropia (positive refractive values) to emmetropia and myopia (negative refractive values). This approach considers refraction as a quantitative trait that can influence

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myopia as well as other refractive errors. Both categorical and quantitative definitions have been used with success in myopia genetic studies. In addition to these issues, it is also important to appreciate that the refractive status of the eye has a number of underlying determinants that may be considered as sub-phenotypes. In particular, the refractive status of the eye is largely determined by the coordinated contributions of three principle ocular biometric components: ocular axial length, anterior chamber depth, and corneal curvature. A mismatch between the length of the eye and the combined refractive power of the lens and cornea leads to intercepting light rays falling short of the retina, thus leading to blurred vision.7 The sub-phenotype of axial length, independent of a person’s overall height, is a major trait as it has been reported to explain at least half of the total variance of myopia.8 Therefore, it is important to consider the complexity of the phenotypic definition of myopia when undertaking genetic studies. As will be detailed below, there has been a recent shift in studies for myopia that takes these sub-phenotypes into account. With these definitions in mind, we now turn our attention to twins and their application to the study of the genetics of myopia.

The Classical Twin Model What is the classical twin model? A twin can be defined as one of two offspring produced in the same pregnancy and born during the same birthing procedure. There are two main classes of twins — monozygotic and dizygotic. Monozygotic twins (also known as identical twins) develop when a single ovum (egg) is fertilized and splits into two independent embryos early during development. Monozygotic twins share one hundred per cent of their genetic material, unless there is a de novo mutation during early development. They are very similar in their physical appearance but not always completely identical due to the influence of environmental factors. On the other hand, dizygotic twins (also known as non-identical, biovular, or fraternal twins) develop when two independent ova (eggs) are fertilized at the same time. Dizygotic twins share 50% of their genetic material and from a genetic point of view can be considered as siblings born at the same time. Dizygotic twins can be of the same sex or opposite sexes whereas monozygotic twins are always of the same sex. Dizygotic

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twins are more common than monozygotic twins, accounting for 0.8% of live births in Caucasian populations compared to 0.4% for monozygotic twins. Monozygotic twinning rates are relatively constant in different population, whereas dizygotic twinning rates vary with ethnicity, ranging from 0.4% of live births in populations of Asian descent to as high as 4.5% in Africans. The proportion of genetic material that is shared between monozygotic twins and dizygotic twins can be exploited by genetic studies to allow an assessment of the relative importance of genes and environment in the development of myopia. As monozygotic twins share all their genetic material, this suggests that differences in the clinical expression of myopia between monozygotic twins are likely to be due to non-genetic effects. On the other hand, for dizygotic twins that share up to 50% of their genetic material, the differences in clinical expression will be due to both genetic and environmental effects. In other words, if a trait is influenced by genetic factors then one would expect that monozygotic twins would be phenotypically very similar (concordant), and that dizygotic twins would be less so. This principle is the fundamental basis of the “classical twin model” that is used to determine whether myopia is influenced by hereditary factors or non-hereditary (environmental) factors, and to what extent. Historical perspective The use of twins as a means to understand the aetiology of myopia and refractive errors originated in 1924 when Walter Jablonski published an article in a German journal that translates to English as, “A contribution to the hereditary refraction in human eyes.”9 This study has been buried in the literature for some time with its existence coming to the forefront by the recent publication by Liew that summarizes the original Jablonski publication.10 The Jablonski paper was pioneering for its time in that it was not only the first publication to use the “classical twin model” to analyze refraction, but it was also the first to have applied it to any trait. The Jablonski study consisted of 40 pairs of monozygotic twins and 12 pairs of dizygotic twins, all of which underwent ophthalmic examinations. Refraction measurements were compared within each pair of twins (within-pair differences) and a comparison was made between within-pair differences for monozygotic twins compared to dizygotic twins. It was reported that the within-pair differences were less in monozygotic twin pairs than in dizygotic twin pairs. Thus, the monozygotic twins were more

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concordant for measures of refraction compared to dizygotic twins. These results provided the first clue that there may be an underlying hereditary influence to refraction. In fact, this study was well ahead of its time as it was not until much later that additional quantitative studies of this nature were undertaken. Following the Jablonski study, a series of reports were published that supported the notion that refraction was most likely influenced by genetic factors. Here, we highlight two of them. The first report was published in 1935 by Law and examined eight pairs of what are presumed to be monozygotic twin pairs.11 Refraction measurements were compared between monozygotic twins and their respective siblings born at different times. It was observed that refraction measurements were similar twice as often in monozygotic twins than they were in the siblings. Another report, published in 1948 by Burns, observed a single pair of monozygotic twin pairs with similar levels of myopia.12 This study went on to examine the extended pedigree of these twins and observed a presumed transmission pattern for myopia through the generation of the family of these twins. These observations were taken together to conclude that the influences on myopia are genetic in origin. Early studies such as the ones described above were largely observational in nature and certainly crude when measured against current methodological approaches. However, the fundamental observations are valid and they provided the first hint of evidence to suggest that refraction and myopia are influenced by genetic factors. Statistical approaches In 1962, Sorsby et al. published a study using the classical twin model to analyze refraction.13 The typical approach was taken whereby withinpair refraction measures were compared between monozygotic twin pairs, dizygotic twin pairs, and control pairs. As predicted from previous studies, Sorsby et al. observed a consistently smaller within-pair difference in monozygotic twins compared to dizygotic twins. This finding was not novel or unexpected, but in 1964 this work was extended by additional work from Sorsby and Fraser to include statistical measures.14 This statistical analysis from Sorsby and Fraser involved using correlation coefficients (a measure of the extent to which measures between a

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twin and their co-twin vary) to quantitate the within-pair differences between twin pairs. Correlation coefficients were calculated for monozygotic twins, dizygotic twins, and unrelated age and gender matched controls pairs. Correlation coefficients were calculated for refraction, corneal power, lens power, axial length, and anterior chamber depth, and were in all cases found to be higher in the monozygotic twin pairs. This study was important as it raised the study of twins and myopia to a new level with the introduction of statistical measures to complement previous observational studies for the classical twin model. The statistical measures suggested by Sorsby et al. in 1962 have since been extended and refined into what is now known as the heritability study. Heritability studies aim to provide an absolute quantitation of the extent to which genetic and environmental factors contribute to myopia. Heritability (h2) is broadly defined as the proportion of phenotypic variance attributed to genetic factors. In very simple terms, heritability can be estimated by multiplying the difference between monozygotic and dizygotic twin pair within-pair correlations by a factor of two. Heritability measures range from 1.0 for a trait that is entirely influenced by genes to 0 for a trait that is influenced entirely by environmental factors. The beauty of this type of analysis is that it can be extended to quantify the proportion of phenotypic variance that is due to environmental factors such as unique environment effects (those that affect one twin but not the co-twin) and common environment effects (those that affect both twins). In real terms, the calculation of heritability and the contributions of shared and common environmental effects are more sophisticated than this. Analyzes are able to model the genetic effects to determine if there are multiple contributing genes each with small effects (additive model) or if there is one major gene (dominant model). Additionally, other factors that may affect refraction measures such as age, gender, and height can also be taken into account. The true elegance of heritability studies come from the fact that one can determine the role of hereditary in myopia without any prior knowledge of the exact nature of the contributing genes. Similarly, the role of environmental factors can also be assessed without knowing their exact nature. Hence, twins provide a unique opportunity to study gene-environment effects and are often referred to as the “perfect natural experiment.”15 Next, we describe a selection of published heritability studies that have used twins to study myopia.

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Twins, Myopia and Heritability Studies Heritability studies for myopia using twins Heritability studies ultimately aim to quantify the proportion of phenotypic variance that is due to genetic effects. In the case of myopia, heritability studies using twins as the model have defined myopia in terms of the quantitative measure of refraction. Heritability estimates for myopia have ranged from 0.77 in a cohort of British twins to 0.91 for a cohort of Danish twins.16–18 For the British twins, the heritability study was extended in a genetic linkage study whereby analysis was undertaken in order to determine where the gene or genes contributing to myopia are located in the human genome. This linkage analysis identified four regions in the genome on chromosomes 11p13, 3q26, 8p23, and 4q12 that may contain myopia genes.19 These results have been partly confirmed in two independent studies. The first by the same group confirmed linkage to the chromosome 3q26 region as harboring a myopia gene.20 The second study by Stambolian et al. (2005) in a family-based cohort independently confirmed the locus on chromosome 8p23.21 However, our own study using an Australian twin cohort did not find evidence to suggest that any of these chromosomal regions were linked to myopia.22 Further analysis is clearly warranted in order to identify the precise genetic change in these regions. A heritability study in a cohort of Australian twins suggested there may be a gender effect with heritability estimates of 0.88 reported for male twins and 0.75 for females.23 However, the true extent of this gender effect in other cohorts remains to be further investigated. Furthermore, these heritability studies have also suggested that the environmental component to myopia is due predominately to unique environment effects rather than environmental factors that are shared between a twin and its co-twin. From these heritability estimates it is clear that the major influence on refraction, and hence on myopia, is likely to come from genes. Furthermore, heritability studies have suggested that the gene effects on myopia are likely to be additive, meaning that these are the result of multiple genes, each with small effects that come together to cause myopia.17,23 In conjunction with this, a myopia heritability study by Dirani et al. (2006) in an Australian twin cohort suggested that the genetic effects are even more complex in that there are not only additive genetic effects, but also dominant effects whereby there is one predominant gene that causes

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myopia.23 In other words, there are likely to be multiple genes with varied and complex gene-gene interactions that are contributing to the development of myopia. Although there is a significant amount of evidence from twin studies to suggest that myopia is predominately a genetic trait, not all studies are in agreement. A study by Angi et al. (1993) in an Italian twin cohort suggested that the genetic contribution to refraction was between 8–14%, indicating that the majority of the phenotype is not influenced by genetic effects.24 This appears to be the only heritability study in twins to argue against the involvement of genes in the development of myopia. It is of course possible that this result is true and there is something curiously different about this population in Italy, although this is unlikely to be the case as other studies have been performed on twin cohorts of similar ethnicity. The study by Angi et al. (1993) used a small sample of twins comprising of 29 pairs in total, which differs from the other heritability studies that used between 114 and 2301 twin pairs. Additionally, the Angi study utilized twins that were under the age of 7 years, in which refraction measurements may not yet have stabilized. Other studies have predominately used adult twins, which have more stable and reliable refraction measures provided that age-related changes in refraction (presbyopia) are accounted for. The small sample size and limited age ranges makes the Angi study results likely to be flawed, and until there is another study replicating this finding it must be treated with caution. As we have discussed earlier in this chapter (see section “definitions”), the phenotypic definition of myopia is complicated and extends beyond simple refraction measures. It is influenced by the sub-phenotypes of ocular axial length, anterior chamber depth, and corneal curvature. With these sub-phenotypes in mind, heritability studies for myopia have been extended to ask the question of what is the genetic influence on axial length, corneal curvature, and anterior chamber depth. We will highlight three key twin studies that have undertaken a heritability analysis on these traits. The first study by Lyhne et al. (2001) from Denmark analyzed 53 monozygotic and 61 dizygotic twin pairs using the classical twin model.17 This study reported heritability estimates of 0.94, 0.88–0.94, and 0.90–0.92 for axial length, anterior chamber depth, and corneal curvature respectively. Furthermore, axial length and corneal curvature measurements could largely be explained by additive genetic effects, whereas anterior chamber depth was explained mainly by dominant effects.

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The next study by Dirani et al. (2006) from Australia supported the results from Lyhne et al. (2001) in that it showed that these traits were predominately genetic in origin but there were some differences in the findings.23 For example, Dirani et al. (2006) suggested that axial length was influenced by dominant genetic effects rather than additive effects. Additionally, corneal curvature in the Dirani et al. (2006) study was also suggested as being influenced by dominant genetic effects as opposed to additive effects reported for the Lyhne et al. (2001) study. It is unclear why such a difference existed in the results for axial length, but those for corneal curvature could mostly be explained by discrepancies in the measures used. The Lyhne et al. (2001) study obtained keratometry measures using an autokeratometer and then averaged the values from the two principal meridians to get the final corneal curvature output. On the other hand, the Dirani et al. (2006) study calculated the difference between the two keratometry readings as the final corneal curvature output. The third study by Zhu et al. (2008) focussed on axial length and added further support for a genetic involvement in this trait.25 This study used 131 monozygotic and 302 dizygotic twin pairs and reported a heritability of 0.81 for axial length, which was largely determined by additive genetic effects. This study went further in the analysis and performed a genetic linkage analysis in order to determine where the gene or genes contributing to axial length are located in the human genome. This work suggested that a region on human chromosome 5q spanning 98 cM is likely to contain a gene for axial length. Additionally, a number of other genomic regions on chromosomes 6, 10, and 14 also indicated the likely existence of other genes that might influence axial length. Replication studies are required to establish the consistency of these regions as well as further characterization of the identified genomic regions in order to establish the exact nature of the causative genes. Further extension of the basic heritability analysis also needs to be undertaken in order to determine if there are common genes between myopia and its underlying determinants. To date, two such studies have been undertaken, which performed a statistical analysis to determine the extent to which genetic and environmental effects influencing axial length also influence refraction or anterior chamber depth.8,26 These studies were undertaken by fitting a bivariate Cholesky decomposition model to axial length and refraction or axial length and anterior chamber depth within the heritability analysis. For a more detailed description on this statistical

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methodology, we refer the reader to Loehlin et al.27 The first study by Dirani et al. (2008) suggested that not only were refraction and axial length highly heritable traits, but also that 50% of the genes influencing refraction are common to axial length. The second study by He et al. (2008) went on to suggest that 25% of the genes influencing axial length are also common to anterior chamber depth, suggesting that there are shared genetic influences on myopia and its determinants. Although we have endeavored to convince the reader of the utility of using twins in heritability studies for myopia, a word of caution is required at this point. As we have hinted at earlier, the results from heritability studies may not necessarily always be reliable. Results need to be interpreted with scientific rigor and some scepticism. General flaws that are evident in the myopia twin literature are in the methodologies used in various studies. Some studies used less reliable measures of refraction, such as questionnaires or current eyeglass prescriptions. Given the high number of under- or un-corrected myopic individuals in the community, this measure can in some cases provide an unreliable estimate of true refraction measures.28 Additionally, it is known that age (>50 years) as well as gender can affect refraction measures, so these also need to be taken into account. Caution should also be taken with studies that have small sample sizes, as highlighted above, or those with selection biases, as this may skew statistical calculations. Additionally, using cohorts of older twins poses problems as the effects of age-related hyperopic shift can skew true refraction measures. Having said all this, it should be noted that many of the myopia heritability studies have provided robust and reliable heritability estimates for a number of traits. These have convincingly demonstrated there is a clear and strong genetic involvement in myopia and its underlying sub-phenotypes, such as axial length. Limitations of using twins in heritability studies Heritability studies and the underlying principle of the classical twin model have proven to be a valuable tool for the study of myopia and its determinants. These tools can also be used to study other traits to quantitate the level of involvement of genes and environment in their etiology, but discussion of these studies are beyond the scope of this chapter. Heritability studies for myopia have also been undertaken using extended families rather than twins, which may provide some advantages given that

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the classical twin model relies on a number of assumptions that may skew heritability calculations. One of the major assumptions of the classical twin model is the “equal environment assumption” (EEA). The EEA assumes that monozygotic twins and dizygotic twins are exposed to the same degree of environmental factors. Environmental factors can be considered to be anything that is non-genetic in origin that can influence the development of myopia. If the EEA is true, then any phenotypic differences between monozygotic and dizygotic twin will likely be due to genetic effects. However, it has been argued that monozygotic twins have a greater sharing of environment during their upbringing as they tend to be treated more alike than dizygotic twins.29 If this argument holds (and hence the EEA does not), then one would expect that monozygotic twins will exhibit a greater phenotypic similarly due to greater shared environment rather than due to shared genes. A natural test for the EEA is to observe twins that have mislabeled zygosities, or in other words, monozygotic twins that were thought to be dizygotic by their parents or vice versa. However, the number of mislabeled twins is small, and hence, there is limited data available to test the validity of the EEA with respect to myopia. The classical twin model also makes the assumption that genetic and environmental effects on the trait, myopia in this case, are independent. The model does not account for the possibility of gene-environment interactions (see Chapter 1.3 for further detail). It has been suggested that there is likely to be an interplay between genes and the environment, such that genetically susceptible individuals may develop myopia if they are exposed to environmental risk factors such as excessive near work.17 The classical twin model does not account for the potential effect that environmental factors may have on the phenotypic expression of myopia during a person’s lifetime. These fundamental assumptions of the classical twin model described above generally result in an overestimation of the genetic influences on myopia. This is exemplified by the fact that myopia studies using family designs have calculated lower heritability values of 0.5 and 0.61 for refraction compared to twin based studies.30,31 More recent advances in statistical methodologies for twin analysis have meant that these limitations have less of an impact on the heritability estimates, but nevertheless heritability estimates from twin studies should generally be considered as the upper limit.

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Twins and Myopia — Other Studies Studies using twins have proved invaluable in determining that genes play a role in the development of myopia, but they have also indicated that environmental factors also play a role, albeit to a lesser extent. There have been many studies assessing the role of environmental factors in the development of myopia, as has been described in detail in Chapter 1.3. Here, we will focus on the handful of studies that have been performed in twins. In particular, the role of body stature, birth weight, educational attainment, and personality will be discussed. The possibility that intrauterine factors may influence the development of myopia has been explored in a number of studies. A study by Grijbovski et al. (2005) assessed the role of birth weight in the development of multiple traits, including myopia.32 Using a cohort of 2880 monozygotic twins and 4960 dizygotic twins recruited from Norway, Grijbovski et al. (2005) were able to show that birth weight is negatively associated with myopia. In other words, a lower birth weight increases the risk of developing myopia. This opened up the possibility that intrauterine factors that influence birth weight may also impact of the development of myopia later in life. However, this finding was not replicated in a more recent study by Dirani et al. (2009), which suggested there was no statistically significant association between birth weight and myopia.33 Clearly, the potential role of intrauterine factors in myopia remains unclear. Work has also focussed on physical attributes in adult twins that may influence myopia. This has been undertaken in studies that have tried to define a particular body type (height, weight, body mass index) that may predispose a person to myopia. A study by Teikari et al. (1987) using both twins and singletons suggested that height and body mass index, but not weight, are positively associated with myopia, and that this association is only evident in males.34 In contrast to this, a recent study by Dirani et al. (2009) suggested that height and body mass index are not associated with myopia, but that increased weight is a risk factor for myopia in females.35 The conflicting evidence from these studies suggests that there is unlikely to be a “myopia body type.” In addition to these attempts to find a “myopia body type,” many studies have also attempted to define behavioral characteristics that may predispose a person to the development of myopia. It has long been believed that myopic individuals have a tendency to certain personality traits such as introversion and conscientiousness. However, an important study by

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van de Berg et al. (2008) suggested that this was certainly not the case, with no association found between myopia and these personality traits. What van de Berg et al. (2008) did find was that myopia was associated with a group of traits referred to as “openness.” “Openness” refers to a group of interrelated personality traits, including the desire to try new things, a tendency towards a vivid imagination, the readiness to re-examine traditional values, and the tendency to be intellectually curious. These results suggested that perhaps there may be an association between education levels and myopia. This association has been confirmed in studies by Lyhne et al. (2001) and Dirani et al. (2008), showing that myopia is associated with increased levels of education.17,36 Furthermore, it has been suggested that educational attainment is also a highly heritable trait (heritability measure of 0.66) and that there is a commonality between the genes related to educational attainment and myopia.36 Thus, both environmental as well as genetic components need to be considered when undertaking studies involving traits such as myopia.

The Importance of Twin Registries While our focus in this chapter is exclusively on twins and myopia it should be made clear to the reader that twins can be used to study a myriad of traits limited only by the individual investigator’s research interests. To facilitate scientific studies using twins, a number of twin registries have been established around the world. These registries are a national or sometimes regional register of twins and their families that are willing to participate in scientific studies. A number of such twin registries have been used for the study of myopia, including the Australian Twin Registry (ATR), the Danish Twin Registry (DTR), and the St. Thomas’ UK Adult Twin Registry. These registries have been invaluable in being able to recruit twins for such studies as outlined in this chapter. We will now briefly describe these registries and the myopia studies that they have facilitated. The most utilized twin registry for the study of myopia is by far the ATR. The ATR was established in the 1970’s and is a registry of twins living in Australia.37 The ATR is volunteer based and there is no selection bias in terms of zygosities, ages, or medical history. In essence, any twins born and residing in Australian who are willing to participate in scientific studies are included in the ATR. In total there are over 31,000 twin pairs registered with the ATR, of which 38% are monozygotic, 58% are dizygotic, and

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4% are of unknown zygosities. The ATR has facilitated many major myopia studies including the Genes in Myopia (GEM) Study, which recruited twins from the state of Victoria and the Twins Eye Study in Tasmania, which recruited twins from the state of Tasmania.25,38 A third study, the Brisbane Adolescent Twin Study, also used the ATR as a recruitment source in addition to independent recruitment regionally in the state of Queensland.39 Other twin registries that have proven invaluable in the study of myopia have been the Danish Twin Registry and the St Thomas’ UK Adult Twin Registry.40,41 The Danish Twin Registry is the oldest twin registry in the world, having been established in 1954, and contains at least 75,000 twin pairs. The St Thomas’ UK Adult Twin Registry was established in 1993 and contains at least 10,000 twin pairs. Both these registries have been valuable sources for recruitment of twins for many myopia studies, including heritability studies from Lyhne et al. (2001), Hammond et al. (2001), and Lopes et al. (2009), which we have previously described.16–18

Concluding Comments In this chapter we have summarized the current methodological approaches that have been used to study twins and myopia. This is by no means a comprehensive description of all experimental designs incorporating twins in the study of heritable traits, but it is a focussed discussion on the methods used with respect to myopia. It is clear that twins can provide a valuable insight into the relative contributions of genes and environment to the development of myopia and its underlying determinants. Despite the methodological limitations, particularly with regards to the classical twin model, current twin studies indicate that myopia is likely to be a highly heritable trait as are its underlying determinants such as axial length. The next step in the use of twins to understand the etiology of myopia will be to undertake more detailed genetic studies to pinpoint the exact nature of the hereditary factors influencing myopia. This can now be achieved through the use of gene chip technology, which allows up to one million single nucleotide polymorphisms (SNPs) across the entire genome to be assessed in a single association study. Such studies, termed genome-wide association studies (GWAS), can be used to identify gene variants that are associated with myopia. GWAS analysis is statistically

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demanding and findings will require verification in order to confirm their role in myopia (see Chapter 3.4). Furthermore, gene-environment interaction studies will also be required to understand how identified genetic variants interact with environmental factors. However, these issues are beyond the scope of this chapter, but will be covered in Chapter 1.2. The current chapter has highlighted the power and utility of twins in the study of myopia and indicated that myopia is a complex trait with a polygenic etiology. Twins have therefore provided an extremely useful resource with which we have been able to gain valuable information regarding myopia and it is envisaged that their continued use will allow us to uncover many more facets of myopia in the future.

Acknowledgments The author’s research on myopia is supported by the Australian Federal Government through a National Health and Medical Research Project Grant (#529912), as well by the Angior Family Foundation.

References 1. Saw SM, Chua WH, Hong CY, et al. (2002) Nearwork in early-onset myopia. Invest Ophthalmol Vis Sci 43: 332–339. 2. Morgan I, Rose K. (2005) How genetic is school myopia? Prog Retin Eye Res 24: 1–38. 3. Rose KA, Morgan IG, Smith W, et al. (2008) Myopia, lifestyle, and schooling in students of Chinese ethnicity in Singapore and Sydney. Arch Ophthalmol 126: 527–530. 4. Dirani M, Tong L, Gazzard G, et al. (2009) Outdoor activity and myopia in Singapore teenage children. Br J Ophthalmol 11: 11. 5. Liang CL, Yen E, Liu C, et al. (2004) Impact of family history of high myopia on level and onset of myopia. Invest Ophthalmol Vis Sci 45: 3446–3452. 6. Wallman J. (1994) Parental history and myopia — Taking the long view. JAMA 272: 1255–1256. 7. Young TL, Metlapally R, Shay AE. (2007) Complex trait genetics of refractive error. Arch Ophthalmol 125: 38–48. 8. Dirani M, Shekar SN, Baird PN. (2008) Evidence of shared genes in refraction and axial length: the Genes in Myopia (GEM) twin study. Invest Ophthalmol Vis Sci 49: 4336–4339.

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9. Jablonski W. (1922) Ein Beitrag zur Vererbung der Refraktion menschlicher Augen [A contribution to the hereditary of refraction in human eyes]. Arch Augenheilk 91: 308–328. 10. Liew SH, Elsner H, Spector TD, Hammond CJ. (2005) The first “classical” twin study? Analysis of refractive error using monozygotic and dizygotic twins published in 1922. Twin Res Hum Genet 8: 198–200. 11. Law FW. (1935) The Refractive Error of Twins. Br J Ophthalmol 19: 99–101. 12. Burns RA. (1949) Hereditary myopia in identical twins. Br J Ophthalmol 33: 491–494. 13. Sorsby A, Sheridan M, Leary GA. (1962) Refraction and its components in twins. Her Majesty’s Stationary Office. Medical Research Council Special Report Series, London. 14. Sorsby A, Fraser GR. (1964) Statistical note on the components of ocular refraction in twins. J Med Genet 55: 47–49. 15. Martin N, Boomsma D, Machin G. (1997) A twin-pronged attack on complex traits. Nat Genet 17: 387–392. 16. Lopes MC, Andrew T, Carbonaro F, et al. (2009) Estimating heritability and shared environmental effects for refractive error in twin and family studies. Invest Ophthalmol Vis Sci 50: 126–131. 17. Lyhne N, Sjolie AK, Kyvik KO, Green A. (2001) The importance of genes and environment for ocular refraction and its determiners: a population based study among 20–45 year old twins. Br J Ophthalmol 85: 1470–1476. 18. Hammond CJ, Snieder H, Gilbert CE, Spector TD. (2001) Genes and environment in refractive error: the twin eye study. Invest Ophthalmol Vis Sci 42: 1232–1236. 19. Hammond CJ, Andrew T, Mak YT, Spector TD. (2004) A susceptibility locus for myopia in the normal population is linked to the PAX6 gene region on chromosome 11: a genome-wide scan of dizygotic twins. Am J Hum Genet 75: 294–304. 20. Andrew T, Maniatis N, Carbonaro F, et al. (2008) Identification and replication of three novel myopia common susceptibility gene loci on chromosome 3q26 using linkage and linkage disequilibrium mapping. PLoS Genet 4: e1000220. 21. Stambolian D, Ciner EB, Reider LC, et al. (2005) Genome-wide scan for myopia in the Old Order Amish. Am J Ophthalmol 140: 469–476. 22. Schache M, Richardson AJ, Pertile KK, et al. (2007) Genetic mapping of myopia susceptibility loci. Invest Ophthalmol Vis Sci 48: 4924–4929. 23. Dirani M, Chamberlain M, Shekar SN, et al. (2006) Heritability of refractive error and ocular biometrics: the Genes in Myopia (GEM) twin study. Invest Ophthalmol Vis Sci 47: 4756–4761. 24. Angi MR, Clementi M, Sardei C, et al. (1993) Heritability of myopic refractive errors in identical and fraternal twins. Graefes Archive for Clinic Exper Ophthalmol 231: 580–585.

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25. Zhu G, Hewitt AW, Ruddle JB, et al. (2008) Genetic dissection of myopia: evidence for linkage of ocular axial length to chromosome 5q. Ophthalmology 115: 1053–1057 e1052. 26. He M, Hur YM, Zhang J, et al. (2008) Shared genetic determinant of axial length, anterior chamber depth, and angle opening distance: the Guangzhou Twin Eye Study. Invest Ophthalmol Vis Sci 49: 4790–4794. Epub 2008 Jun 4727. 27. Loehlin JC. (1996) The Cholesky approach: a cautionary note. Behav Genet 26: 65–69. 28. Access Economics Pty Ltd. (2004) Clear Insight: The Economic Impact and Cost of Vision Loss in Australia. Melbourne. 29. Plomin R, DeFries JC, McClean GE, McGuffin P. (2001) Behavioral Genetics. Worth Publishers, New York. 30. Chen CY, Scurrah KJ, Stankovich J, et al. (2007) Heritability and shared environment estimates for myopia and associated ocular biometric traits: the Genes in Myopia (GEM) family study. Hum Genet 112: 541–546. 31. Wojciechowski R, Congdon N, Bowie H, et al. (2005) Heritability of refractive error and familial aggregation of myopia in an elderly American population. Invest Ophthalmol Vis Sci 46: 1588–1592. 32. Grjibovski AM, Harris JR, Magnus P. (2005) Birth weight and adult health in a population-based sample of Norwegian twins. Twin Res Hum Genet 8: 148–155. 33. Dirani M, Islam FM, Baird PN. (2009) The role of birth weight in myopia — the genes in myopia twin study. Ophthalmic Res 41: 154–159. Epub 2009 Mar 2026. 34. Teikari JM. (1987) Myopia and stature. Acta Ophthalmol (Copenh). 65: 673–676. 35. Dirani M, Islam A, Baird PN. (2008) Body stature and myopia — The Genes in Myopia (GEM) twin study. Ophthalmic Epidemiol 15: 135–139. 36. Dirani M, Shekar SN, Baird PN. (2008) The role of educational attainment in refraction: the Genes in Myopia (GEM) twin study. Invest Ophthalmol Vis Sci 49: 534–538. 37. Hopper JL. (2002) The Australian Twin Registry. Twin Res 5: 329–336. 38. Dirani M, Chamberlain M, Garoufalis P, et al. (2008) Testing protocol and recruitment in the genes in myopia twin study. Ophthalmic Epidemiol 15: 140–147. 39. Wright MJ, Martin NG. (2004) Brisbane Adolescent Twin Study: outline of study methods and research projects. Aust J Psych 56: 65–78. 40. Spector TD, MacGregor AJ. (2002) The St. Thomas’ UK adult twin registry. Twin Res 5: 440–443. 41. Skytthe A, Kyvik K, Bathum L, et al. (2006) The Danish Twin Registry in the new millennium. Twin Res Hum Genet 9: 763–771.

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3.3 TIGR, TGFB1, cMET, HGF, Collagen Genes, and Myopia Chiea-Chuen Khor*

The candidate gene approach is a feasible and widely employed method in our search for disease genes. This approach has resulted in the identification of many putative ‘susceptibility genes’ of which a majority could not be subsequently replicated. This chapter summarizes recent findings in the field of refractive error genetics, and proceeds to highlight some of the prominent findings, which include the successful validation of MYP2, MYP3, and COL2A1 as myopia susceptibility loci. The inherent weaknesses of this approach, as well as the caveats to be mindful of in future studies, are touched upon in the discussion.

Introduction Myopia is a very common health problem in today’s developed world. The prevalence varies significantly between ethnic groups, but individuals of Asian descent, especially Chinese, have been shown to display a markedly increased prevalence of myopia compared to Western populations. It affects up to 40% of Chinese between the ages of 40 to 79 years. This phenomenon is despite differing environmental and lifestyle conditions in various ‘predominantly Chinese’ countries such as China, Singapore, and Taiwan.1,3–6 There are two forms of myopia that are distinguishable by aetiology. The commoner ‘axial myopia’ results from a disproportionately rapid increase of eye globe length during an individual’s growth phase.

*Division of Infectious Diseases, Genome Institute of Singapore, 60 Biopolis Street, Genome, Singapore 138672; +65 64788200. E-mail: [email protected].

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‘Refractive myopia’ is less common comparatively, and is primarily due to abnormal light diffraction caused by pathological changes in the refractive elements of the eye: extreme corneal curvature, changes in lens density, or increased refractive index of the ocular media (aqueous and vitreous humor). For all forms of myopia, the image is focused in front of (rather than exactly on) the retina; corrective lenses are thus necessarily convex in nature. The mechanism of disease pathogenesis for myopia has not been clearly defined to date. However, it is generally agreed that whilst environmental factors (e.g. near work, location of homestead) contribute strongly to the disease process, there appears to be a considerable genetic contribution to disease susceptibility and progression. Firstly, high hereditability estimates for myopia related traits, such as spherical equivalent (SE, measured in Diopters) and axial (eye-globe) length (AL, measured in millimeters) have been previously shown.7 Indeed, epidemiologic8 and twin studies have demonstrated a markedly increased risk between related individuals and myopia (an increase in susceptibility of between 2.5 to 5.5 fold).9–13 Secondly, family segregation studies also report a strong association between parental myopia and myopia in their offspring.14–17 Taken together, all these data strongly implicate the contribution of genetic factors to the pathogenesis of myopia. To date, more than 14 genetic loci (with each locus implicating the involvement of a genomic region larger than a million base pairs) have been found to be tightly linked to myopia-related phenotypes via family-based linkage studies. These have been designated as MYP loci and numbered according to the chronological timeline of their discovery.18,19 One of these loci (MYP1, located on Chromosome Xq28) has been implicated in Bornholm’s eye disease. Here, patients exhibit X-linked high myopia, mild cone dysfunction, and color vision defects. Other loci that appear to be highly penetrant are MYP2 (18p11.31) and MYP3 (12q21-q23), both of which exhibit an autosomal-dominant mode of inheritance. Subsequent replication studies of linkage at both MYP2 and MYP3 by independent study groups lend further support to the initial observations. Despite these encouraging findings, many unanswered questions remain; linkage studies have limited absolute resolution, and the majority of linkage signals only point to broad genomic regions. As such, much effort is required in the search for the actual genes and genetic variants directly responsible for altered susceptibility to myopia within these large

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linkage regions. Indeed, a more thorough and finer-scale approach with higher resolution is needed to achieve this aim. Fortunately, the completion of the human genome project (2003) and the wide availability of human haplotype maps (the international HapMap project, completed in 2005; www.hapmap.org) has enabled finescale mapping using single-nucleotide polymorphisms (SNPs). The rapidly declining cost of genotyping has also rendered this approach economically practical, and in the last decade, candidate gene studies using a high density marker set for analysis were performed widely for a broad selection of complex-trait diseases. In addition to this, the advent of genome-wide association studies have ushered in a new dawn for a more ‘unbiased,’ non-hypothesis-based search for disease genes, with encouraging results. Turning to our focus on myopia susceptibility genes, we discuss some current findings with a selection of candidate genes found to be associated with myopia and its related endo-phenotypes.

Candidate Gene Selection Strategies for Myopia Candidate genes for myopia susceptibility are genes that encode for a protein product hypothesized to biologically influence individual susceptibility, severity, or progression of myopia. They are normally chosen if existing biological information or observations suggest their involvement in disease pathogenesis. Variants within the selected gene(s) are then identified, genotyped, and analyzed for the presence of association with the myopia phenotype studied (e.g. spherical equivalent, all-cause myopia, severe myopia, axial length, and changes in these phenotypes over time). The selection of candidate genes will be greatly facilitated if some of the potentially important genes could be first linked to myopia via a genome-wide screen. Thus, the combined ‘positional candidate’ approach offers higher chance of success in identifying disease-causing polymorphisms; a pure candidate gene approach is not without its limitations, as many genes have yet to be identified and researchers employing the candidate gene approach are limited to examining the genes that have been described. More often than not, the candidate gene approach relies on a priori information on the possible pathway for pathogenesis. As our understanding of the molecular mechanisms underlying disease susceptibility and progression of myopia is still limited, novel and crucial genes might well be missed.

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Genes Associated With Myopia-Related Phenotypes The HGF/cMET ligand-receptor axis A widely studied candidate gene for myopia was Hepatocyte Growth Factor (HGF). HGF has been shown to have a biologically relevant role in ocular biology and is orthologous to Eye1, a major quantitative trait locus that contributes to a variable rate of eye growth in the mouse.20 HGF works by binding to its receptor cMET, a member of the tyrosine kinase receptor family.22 Once activated, the system triggers many cellular responses, including cell division, migration, differentiation, and survival of numerous cell types.23 In the eye, HGF and cMET are expressed in the lens, all three layers of the cornea, and in both fetal and adult retinal pigment epithelium.22,24–27 Natural genetic variants within HGF were first observed to be associated with high myopia in a family-based association study comprising 128 nuclear families of Chinese descent. One SNP in particular (HGF rs3735520) showed evidence of association with very high myopia (defined as SE ≤ –10.0 D), where the minor allele was observed to be conferring increased risk of disease (P = 0.003).21 However, a second follow-up study enrolling 288 high myopia cases and 208 controls, also of Chinese descent, failed to confirm association with this SNP.28 A third study, this time involving participants of European descent (the Duke– Cardiff cohort), identified the rs3735520 SNP as associated with mild to moderate myopia (SE from –0.50 to –5.00 D), but not with high (SE ≤ –5.00 D) or very high myopia (SE ≤ –10.0 D). Of note, evidence of association with high and very high myopia was observed with another SNP within HGF (rs2286194) in the Duke–Cardiff cohort. Observed associations with cMET genetic variants and myopiarelated phenotypes were more recent.29 In a longitudinal cohort study, which enrolled school children aged 7–12 years (Singapore cohort study for risk factors of myopia; SCORM), a SNP within cMET (rs2073560) was found to be associated with an increased risk of myopia in general (SE ≤ –0.5 D). Longitudinal analysis showed that the variant allele also was associated with more rapid change in refractive error over time regardless of the initial refractive state. However, due to its location within the non-coding region of cMET, this SNP is unlikely to be the causative genetic variant responsible for the observed associations. Thus, further efforts involving direct sequencing of all coding and

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regulatory regions of cMET are needed to define the biologically relevant mutations. Transforming growth factor-β (TGFB1) Axial myopia is the commoner form of myopia compared to refractive myopia,30 and active scleral remodeling has been shown to play a crucial role in axial (globe) elongation, at least in animal models of myopia.31,32 One such gene that could be involved in the process of scleral remodeling is the one encoding for transforming growth factor-β (TGF-β , encoded by the TGFB1 gene). TGF-β is expressed in ocular tissues33 and its concentrations in the retinal pigmented epithelium, choroid, and the sclera have been found to be significantly reduced in myopic eyes. In addition, TGF-β is known to regulate the proliferation of fibroblasts, as well as the production of collagen, matrix metalloproteinases (MMP), and tissue inhibitors of MMP.34 All these processes contribute to the biology of scleral remodeling and consequent axial length change.35 Given all these prior biological information on the potential influence of TGF-β on axial length, it is unsurprising that natural genetic variation within TGFB1 has long been suspected to modify susceptibility to refractive errors, especially axial myopia. Indeed, a SNP within TGFB1 (rs1800470) has been found to associate with severe myopia in a study involving 201 severe myopia cases (SE ≤ –6.0 D) and 86 controls of Chinese descent from Taiwan. Here, the minor T allele was shown to be at reduced risk of severe myopia (OR = 0.55, 95%CI: 0.37 — 0.80; P = 0.001).36 This association was replicated in a second cohort involving 300 severe myopia cases (SE ≤ –8.0D) and 300 controls of Southern Chinese descent (OR = 0.72, 95%CI: 0.57 — 0.90; P = 0.004).37 However, in this second Chinese study,37 a previous SNP that was not assessed in the original study (rs4803455) was found to be much more strongly associated with decreased susceptibility towards severe myopia (OR = 0.66, 95%CI: 0.52–0.84; P = 4.9 × 10–4) compared with rs1800470. As such, SNP rs4803455 is a closer correlate for severe myopia compared to SNP rs1800470. Notably, a Japanese high myopia study (SE ≤ –9.25), which included 330 cases and 330 controls, failed to detect any association with 10 TGFB1 SNPs. However, neither rs1800470 nor rs4803455 were genotyped in the Japanese study. A second study showing non-replication of TGFB1 genetic variants also could not be adequately assessed, as only 1 SNP was typed; again, neither rs1800470 nor rs4803535 were analyzed.

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In the Singapore-based SCORM cohort, recessive carriage of the minor T allele at rs4803455 was found to be associated with decreased susceptibility to severe myopia (SE ≤ –5.0D)(OR = 0.46, 95%CI: 0.19–1.05; twotailed P = 0.046) (SCORM unpublished findings). These individuals also had on average shorter axial lengths (0.21 mm shorter on average, P = 0.05) compared to wild-type individuals, thus lending further support to the two previous Chinese studies and also supporting a role for TGFB1 and axial myopia. Trabecular-meshwork inducible glucocorticoid response (TIGR) gene TIGR was first identified as a susceptibility gene for primary open angle glaucoma,38 but more recent data suggests that it could also be a susceptibility gene for high myopia.39,40 This is in keeping with clinical observations documenting an increased frequency of open-angle glaucoma in individuals with high-myopia. An increased prevalence of myopia in patients with glaucoma was also observed.39 The protein encoded by TIGR is myocillin (MYOC). It has been found to be expressed widely in ocular tissues (e.g. the choroid, cilliary bodies, sclera, and trabecular meshwork),41 and studies have shown that the expression of myocilin in the trebecular meshwork is affected by TGF-β (see previous section on TGFB1) and mechanical stretch. Findings from previous studies investigating the association between genetic variants at the TIGR locus and severe myopia have yielded slightly inconsistent results. In a family-based association study conducted in Hong Kong involving 162 Chinese nuclear families (overall n = 557), two microsatellite polymorphisms flanking TIGR and two SNPs (rs2421853 and rs235858) within the 3′ untranslated region of the gene were shown to be associated with high myopia (SE ≤ –6.0 D) (lowest P = 4 × 10–6).39 A second, smaller case-control study (n = 70 severe myopia cases and 69 non-myopic controls) from Hong Kong was unable to demonstrate evidence of association with one of the microsatellite markers reported (–339(GT)12–16; NGA17).42 Notably, this second Hong Kong-based study did not assess the remaining three polymorphisms (microsatellite NGA19, and SNPs rs2421853 and rs235858), thus rendering it of limited value in demonstrating lack of association. A subsequent reassessment of these four genetic markers consisting of over 1000 Caucasian subjects (the Duke–Cardiff cohort) also did not

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replicate evidence of association with these four TIGR polymorphisms.40 Instead, the Duke–Cardiff cohort showed that SNP rs1684720 (which was previously not tested in the Hong Kong study but is in linkage disequilibrium (LD) with the other two SNPs in East Asians) was consistently associated with high myopia in both the case control and family-based approach. In the Singaporean SCORM cohort, association was observed for SNP rs1684720 with a more negative population-wide SE (P = 0.032) in general, and severe myopia in particular (SE ≤ –5.0 D)(P = 0.048; SCORM cohort). (SCORM unpublished findings.) Although inconsistent with regards to the exact SNPs involved in the association signal, these findings are nonetheless supportive of a role for TIGR genetic variants and increased susceptibility to severe myopia. The collagen family of genes Collagen makes up about 30 percent of total body protein and is a basic building block for human connective tissue. Colagen provides structure and tensile strength to many important body components (e.g. skin, bone, cartilage, and finer structures, such as blood vessels and the sclera). As 90 percent of the human sclera is primarily an ECM of collagen fibrils (mainly type I fibrils),32 genes encoding for the collagen family of proteins are natural candidates for study in myopia pathogenesis and development. Scleral thinning has been attributed to a general loss of ECM (see previous section on TGFB1),43 and studies in animal models such as tree shrews have revealed that myopic eyes typically suffer from thinned and weakened sclerae, which are less capable of withstanding the expansive forces of a positive intraocular pressure. It is thus hardly surprising that one characteristic feature of axial myopia is an elongated eyeball.44 At the cellular level, amounts of collagen mRNA at steady state before and after monocular visual form-deprivation has been well correlated; deprived-eye scleras are myopic and have less collagen mRNA, whereas recovering eye scleras have more.45 Such findings are in keeping with the observation that visual form-deprivation produces a more extensible sclera, and that recovery from form-deprivation reduces the extensibility of the sclera.46,47 Currently, the two most well-studied collagen genes in myopia pathogenesis are COL1A1 and COL2A1, encoding for the α1 and α2 chains of type I collagen respectively. Rare function-altering genetic mutations in COL1A1 have been found to be associated with two distinct connective tissue diseases: osteogenesis

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imperfecta and Ehlers–Danlos syndrome. For both disorders, the clinical phenotype is a result of compromised type-I collagen structure. Studies assessing the role of genetic polymorphisms in COL1A1 and susceptibility to myopia first appeared with Asian cohorts. Conflicting results were observed; a Taiwan-based study with 471 severe myopia (SE ≤ –6.0 D) cases and 623 controls of Chinese ethnicity could not detect evidence of association with COL1A1 genetic variants after assessing 10 representative ‘tagging’ markers.48 In contrast, a Japanese study with 330 severe myopia cases (SE ≤ –9.25 D) and 330 controls managed to detect association with COL1A1 SNPs rs2075555 and rs2269336.49 However, a second Japanese study using 427 severe myopia cases (SE ≤ –5.0 D) and 420 controls failed to find association with these two SNPs.50 Data from the SCORM cohort involving Singaporean Chinese also failed to confirm evidence of association (SCORM unpublished findings). Thus, the current evidence with COL1A1 is not conclusive of association with myopia. Moving on to COL2A1, where severe gene disruptions during foetal development could result in Stickler’s syndrome, a different picture is observed. Stickler’s syndrome (congenital, progressive arthro-ophthalmopathy) is an autosomal dominant form of collagenopathy (disease due to abnormal synthesis of collagen), whereby affected children have a distinct facial appearance, eye abnormalities, hearing loss, and joint problems. It has been observed that many patients with COL2A1 Stickler syndrome suffer from a mono-genic form of severe ‘nearsightedness’ (described as having high myopia) due to the inherently abnormal shape of the eye. In light of these findings with these highly penetrant genetic mutations, it was hypothesized that commoner and less penetrant genetic variants within COL2A1 could also modify individual susceptibility to less severe forms of myopia. In a family-based study involving 123 nuclear families of mixed (but predominantly Caucasian; 62%) ethnicity, strong evidence of association with common myopia (SE ≤ –0.75 D) was detected for one COL2A1 SNP (rs1635529, P = 7.0 × 10–5).51 A second study (the Duke–Cardiff cohort) also showed supporting evidence of association for this particular SNP with common (SE ≤ –0.5 D; P < 0.05 in Duke) and high-grade myopia (SE ≤ –5.0 D; P < 0.05 in Duke). Further replication was achieved in the Cardiff cohort (P = 0.007 for common myopia, and P = 0.004 for highgrade myopia).52 Compared to COL1A1, the evidence of association for COL2A1 and the myopia trait is more robust. Although SNP rs1635529

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has yet to be assessed in the Singaporean SCORM cohort, the evidence for association observed in the Caucasian cohorts thus far are convincing.

Concluding Remarks In conclusion, some clear patterns are emerging in the field of refractive error genetics. Altered susceptibility to myopia in humans appears to be highly polygenic with many genetic loci initially implicated, but only a minority of these have been convincingly replicated in subsequent studies. Most identified loci have relatively small effects with low odds ratios (< 3), the exceptions being monogenic disorders, which also result in severe myopia (e.g. Stickler’s syndrome). Genome-wide linkage analysis of family members (e.g. twins) has recently shown some success in defining broad susceptibility loci for myopia. Despite this, many follow-up studies leading from genome-wide linkage analysis fail to identify the causative genetic variants underlying the broad linkage peaks. Heterogeneity of genotypic and allelic association findings are frequently observed when comparing results between different study cohorts. The most obvious cause for this is the non-uniform phenotype definition across study populations (e.g. definitions of myopia vary from between SE ≤ –0.5 D to SE ≤ –0.75 D across studies, with some considering SE ≤ –1.0 D; for severe myopia, definitions have varied from SE ≤ –5.0 D to SE ≤ –10.0 D). Other possible reasons include different susceptibility genes for different ethnic groups, as well as undetected epistasis within certain population groups, which may mask true associations. As the contribution of environmental factors to myopia pathogenesis is considerable, future efforts should involve more precise inclusion of environmental covariates within multi-variate modelling frameworks in the attempt to accurately control for them in genetic studies. Finally, the possibility of a considerable number of these results being false positives should not be dismissed. These provisional conclusions are currently being altered by the great promise of genome-wide association studies that could revolutionize our approach to the genetic analysis of myopia. Indeed, platforms that simultaneously genotype a million SNPs in each participating individual are now widely available. The recent availability of genome-wide interrogation of structural variants (e.g. copy number variants) and second generation ‘deep sequencing’ facilities will enable researchers to take into account the ‘total

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individual genetic variation’ beyond single-marker analysis. This will allow for much improved genotype-phenotype correlation using advanced analysis frameworks, and advance our understanding in the pathogenesis mechanisms of myopia development and progression.

Acknowledgments The Agency for Science, Technology and Research (A-STAR), and the National University of Singapore — Genome Institute of Singapore (NUS — GIS) Center for Molecular Epidemiology (CME) provided funding for the SCORM studies. The author is an A-STAR scholar and currently holds an adjunct appointment at the NUS-GIS CME.

References 1. Wong TY, Foster PJ, Hee J, et al. (2000) Prevalence and risk factors for refractive errors in adult Chinese in Singapore. Invest Ophthalmol Vis Sci 41: 2486–2494. 2. Seet B, Wong TY, Tan DT, et al. (2001) Myopia in Singapore: taking a public health approach. Br J Ophthalmol 85: 521–526. 3. Saw SM, Chua WH, Hong CY, et al. (2002) Nearwork in early-onset myopia. Invest Ophthalmol Vis Sci 43: 332–339. 4. Lin LL, Shih YF, Tsai CB, et al. (1999) Epidemiologic study of ocular refraction among schoolchildren in Taiwan in 1995. Optom Vis Sci 76: 275–281. 5. Zhao J, Pan X, Sui R, et al. (2000) Refractive error study in children: results from Shunyi District, China. Am J Ophthalmol 129: 427–435. 6. Wu HM, Seet B, Yap EP, et al. (2001) Does education explain ethnic differences in myopia prevalence? A population-based study of young adult males in Singapore. Optom Vis Sci 78: 234–239. 7. Klein AP, Suktitipat B, Duggal P, et al. (2009) Heritability analysis of spherical equivalent, axial length, corneal curvature, and anterior chamber depth in the Beaver Dam Eye Study. Arch Ophthalmol 127: 649–655. 8. Liang CL, Yen E, Su JY, et al. (2004) Impact of family history of high myopia on level and onset of myopia. Invest Ophthalmol Vis Sci 45: 3446–3452. 9. Lin LL, Chen CJ. (1987) Twin study on myopia. Acta Genet Med Gemellol (Roma). 36: 535–540. 10. Teikari JM, Kaprio J, Koskenvuo MK, Vannas A. (1988) Heritability estimate for refractive errors — a population-based sample of adult twins. Genet Epidemiol 5: 171–181.

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11. Hammond CJ, Snieder H, Gilbert CE, Spector TD. (2001) Genes and environment in refractive error: the twin eye study. Invest Ophthalmol Vis Sci 42: 1232–1236. 12. Lyhne N, Sjolie AK, Kyvik KO, Green A. (2001) The importance of genes and environment for ocular refraction and its determiners: a population based study among 20–45 year old twins. Br J Ophthalmol 85: 1470–1476. 13. Stambolian D, Ibay G, Reider L, et al. (2004) Genome-wide linkage scan for myopia susceptibility loci among Ashkenazi Jewish families shows evidence of linkage on chromosome 22q12. Am J Hum Genet 75: 448–459. 14. Mutti DO, Mitchell GL, Moeschberger ML, et al. (2002) Parental myopia, near work, school achievement, and children’s refractive error. Invest Ophthalmol Vis Sci 43: 3633–3640. 15. Wu MM, Edwards MH. (1999) The effect of having myopic parents: an analysis of myopia in three generations. Optom Vis Sci 76: 387–392. 16. Pacella R, McLellan J, Grice K, et al. (1999) Role of genetic factors in the etiology of juvenile-onset myopia based on a longitudinal study of refractive error. Optom Vis Sci 76: 381–386. 17. Yap M, Wu M, Liu ZM, et al. (1993) Role of heredity in the genesis of myopia. Ophthalmic Physiol Opt 13: 316–319. 18. Chen CY, Stankovich J, Scurrah KJ, et al. (2007) Linkage replication of the MYP12 locus in common myopia. Invest Ophthalmol Vis Sci 48: 4433–4439. 19. Wojciechowski R, Stambolian D, Ciner E, et al. (2009) Genome-wide linkage scans for ocular refraction and meta-analysis of four populations in the Myopia Family Study. Invest Ophthalmol Vis Sci 50: 2024–2032. 20. Zhou G, Williams RW. (1999) Eye1 and Eye2: gene loci that modulate eye size, lens weight, and retinal area in the mouse. Invest Ophthalmol Vis Sci 40: 817–825. 21. Han W, Yap MK, Wang J, Yip SP. (2006) Family-based association analysis of hepatocyte growth factor (HGF) gene polymorphisms in high myopia. Invest Ophthalmol Vis Sci 47: 2291–2299. 22. Wormstone IM, Tamiya S, Marcantonio JM, Reddan JR. (2000) Hepatocyte growth factor function and c-Met expression in human lens epithelial cells. Invest Ophthalmol Vis Sci 41: 4216–4222. 23. Ma PC, Maulik G, Christensen J, Salgia R. (2003) c-Met: structure, functions and potential for therapeutic inhibition. Cancer Metastasis Rev 22: 309–325. 24. Weng J, Liang Q, Mohan RR, et al. (1997) Hepatocyte growth factor, keratinocyte growth factor, and other growth factor-receptor systems in the lens. Invest Ophthalmol Vis Sci 38: 1543–1554. 25. Li Q, Weng J, Mohan RR, et al. (1996) Hepatocyte growth factor and hepatocyte growth factor receptor in the lacrimal gland, tears, and cornea. Invest Ophthalmol Vis Sci 37: 727–739.

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26. Wilson SE, Walker JW, Chwang EL, He YG. (1993) Hepatocyte growth factor, keratinocyte growth factor, their receptors, fibroblast growth factor receptor2, and the cells of the cornea. Invest Ophthalmol Vis Sci 34: 2544–2561. 27. He PM, He S, Garner JA, et al. (1998) Retinal pigment epithelial cells secrete and respond to hepatocyte growth factor. Biochem Biophys Res Commun 249: 253–257. 28. Wang P, Li S, Xiao X, et al. (2009) High myopia is not associated with the SNPs in the TGIF, lumican, TGFB1, and HGF genes. Invest Ophthalmol Vis Sci 50: 1546–1551. 29. Khor CC, Grignani R, Ng DP, et al. (2009) cMET and Refractive Error Progression in Children. Ophthalmology 116: 1469–1474. 30. Zadnik K. (1997) Myopia development in childhood. Optom Vis Sci 74: 603–608. 31. McBrien NA, Gentle A. (2003) Role of the sclera in the development and pathological complications of myopia. Prog Retin Eye Res 22: 307–338. 32. Rada JA, Shelton S, Norton TT. (2006) The sclera and myopia. Exp Eye Res 82: 185–200. 33. Saika S. (2006) TGF-β pathobiology in the eye. Lab Invest 86: 106–115. 34. Overall CM, Wrana JL, Sodek J. (1989) Independent regulation of collagenase, 72-kDa progelatinase, and metalloendoproteinase inhibitor expression in human fibroblasts by transforming growth factor-B. J Biol Chem 264: 1860–1869. 35. Rada JA, Thoft RA, Hassell JR. (1991) Increased aggrecan (cartilage proteoglycan) production in the sclera of myopic chicks. Dev Biol 147: 303–312. 36. Lin HJ, Wan L, Tsai Y, et al. (2006) The TGFbeta1 gene codon 10 polymorphism contributes to the genetic predisposition to high myopia. Mol Vis 12: 698–703. 37. Zha Y, Leung KH, Lo KK, et al. (2009) TGFB1 as a susceptibility gene for high myopia: a replication study with new findings. Arch Ophthalmol 127: 541–548. 38. Stone EM, Fingert JH, Alward WL, et al. (1997) Identification of a gene that causes primary open angle glaucoma. Science 275: 668–670. 39. Tang WC, Yip SP, Lo KK, et al. (2007) Linkage and association of myocilin (MYOC) polymorphisms with high myopia in a Chinese population. Mol Vis 13: 534–544. 40. Zayats T, Yanovitch T, Creer RC, et al. (2009) Myocilin polymorphisms and high myopia in subjects of European origin. Mol Vis 15: 213–222. 41. Adam MF, Belmouden A, Binisti P, et al. (1997) Recurrent mutations in a single exon encoding the evolutionarily conserved olfactomedin-homology domain of TIGR in familial open-angle glaucoma. Hum Mol Genet 6: 2091–2097.

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42. Leung YF, Tam PO, Baum L, et al. (2000) TIGR/MYOC proximal promoter GT-repeat polymorphism is not associated with myopia. Hum Mutat 16: 533. 43. Gentle A, Liu Y, Martin JE, et al. (2003) Collagen gene expression and the altered accumulation of scleral collagen during the development of high myopia. J Biol Chem 278: 16587–16594. 44. Phillips JR, McBrien NA. (1995) Form deprivation myopia: elastic properties of sclera. Ophthalmic Physiol Opt 15: 357–362. 45. Siegwart JT, Jr, Norton TT. (2001) Steady state mRNA levels in tree shrew sclera with form-deprivation myopia and during recovery. Invest Ophthalmol Vis Sci 42: 1153–1159. 46. Siegwart JT, Jr, Norton TT. (1999) Regulation of the mechanical properties of tree shrew sclera by the visual environment. Vision Res 39: 387–407. 47. Phillips JR, Khalaj M, McBrien NA. (2000) Induced myopia associated with increased scleral creep in chick and tree shrew eyes. Invest Ophthalmol Vis Sci 41: 2028–2034. 48. Liang CL, Hung KS, Tsai YY, et al. (2007) Systematic assessment of the tagging polymorphisms of the COL1A1 gene for high myopia. J Hum Genet 52: 374–377. 49. Inamori Y, Ota M, Inoko H, et al. (2007) The COL1A1 gene and high myopia susceptibility in Japanese. Hum Genet 122: 151–157. 50. Nakanishi H, Yamada R, Gotoh N, et al. (2009) Absence of association between COL1A1 polymorphisms and high myopia in the Japanese population. Invest Ophthalmol Vis Sci 50: 544–550. 51. Mutti DO, Cooper ME, O’Brien S, et al. (2007) Candidate gene and locus analysis of myopia. Mol Vis 13: 1012–1019. 52. Metlapally R, Li YJ, Tran-Viet KN, et al. (2009) COL1A1, COL2A1 genes and myopia susceptibility: evidence of association and suggestive linkage to the COL2A1 locus. Invest Ophthalmol Vis Sci [Epub ahead of print].

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3.4 Statistical Analysis of Genome-wide Association Studies for Myopia Yi-Ju Li*,† and Qiao Fan‡

Genome wide association (GWA) studies have become a powerful approach for identifying genetic loci or susceptibility genes for common complex diseases. While the number of susceptibility loci identified by GWA studies is increasing, GWA studies for myopia are lagging behind many complex diseases. However, it is expected that more GWA studies related to myopia phenotypes will be reported in the near future. In this chapter, we describe the aspects of statistical analysis of the GWA study for myopia, including study design, quality control procedures, methods for association tests, and myopia related analysis issues.

Introduction The path of identifying the underlying genetic factors for complex human disease has primarily relied on two study designs: (1) genome wide linkage screens to narrow down the chromosomal regions that are linked to the disease gene(s) or quantitative trait loci (QTL); (2) association studies to detect the genetic variants that may lead to the identification of susceptibility genes or genetic modifiers for the traits of interest. In 1996, Risch and Merikangas1 predicted that “the future of the genetics of complex diseases is likely to require large-scale testing by association analysis.” They demonstrated analytically that family-based association studies could have substantially more power than standard linkage analysis, particularly to *Department of Biostatistics and Bioinformatics, Duke University Medical Center, Durham, NC 27710, USA. E-mail: [email protected]. † Center for Human Genetics, Duke University Medical Center, Durham, NC 27710, USA ‡ Department of Epidemiology and Public Health, National University of Singapore, Singapore.

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detect genes with small to moderate genetic effects on disease risk as expected in complex diseases. The caveat to their conclusion is that a sufficient density of markers must be screened to ensure that the actual disease locus, or one in strong linkage disequilibrium (LD) with the disease locus, will be tested. With the availability of genome wide high-density single-nucleotide polymorphism (SNP) arrays (or SNP chips), genome wide association (GWA) studies have become feasible to achieve “large-scale” association testing. In the past few years, GWA studies have proven to be a powerful approach to uncover new disease genes or genetic loci for several different diseases.2 The genetic basis of myopia is supported by data from familial aggregation, segregation, and twin studies. The review of genetic studies of myopia to date can be found in Chapters 3.1–3.5 [Note: refer to Chapters by Drs. Young, Baird, and Khor]. Almost all studies were based on the framework of genome wide linkage scans, association study for selected candidate genes, or sequencing of the promoter and exons of candidate genes to identify functional variants. Although the number of GWA publications is growing in the past few years, no GWA reports for myopia or its related phenotypes have been published until recently by Nakanishi et al.,3 for which they identified a chromosome 11q24.1 locus for the pathological myopia (high myopia with axial length > 28.0 mm in both eyes), a selected subgroup of high myopia. To our knowledge, several myopia-related GWA studies are underway, particularly using existing epidemiologic cohorts of myopia, at the time of writing this chapter. It is expected that more GWA papers will be published within a year or two. Unlike association studies of candidate genes that are limited to specific biological function or chromosome regions of interest, GWA studies utilize hundreds of thousands of markers across the genome to evaluate the association between markers and disease-related phenotypes on the genomewide scale. The GWA study is considered an unbiased approach to survey most of the genome for susceptible or causal variants since no assumptions are made for any pre-selected regions or genes for association tests. While this approach is more comprehensive than conventional candidate gene association studies, several layers of challenges have arisen due to the significantly increased data and tests that we face for the GWA study. In the following sections, we will provide a general review of conducting a GWA study and relate it to myopia. Through this chapter, examples illustrated were obtained from the GWA data of 929 Chinese samples from Singapore Cohort Study of the Risk factors for Myopia (SCORM), for which

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genotyping was conducted using Illumina HumanHap 550 (http://www. illumina.com/).

Phenotypes for Myopia Genetic Studies The diagnosis of myopia is determined by refractive errors, sphere (SPH), or spherical equivalent (SE = sphere + 1/2(cylinder)). The most frequently studied phenotypes in myopia genetic studies are various dichotomous disease states of myopia (e.g. common myopia, moderate myopia, high myopia) defined by different thresholds of SPH or SE. Among them, high myopia was probably investigated the most, resulting in 10 out of 16 MYP loci reported to link to high myopia.4–13 In contrast, the uses of quantitative refractive errors for myopia genetic studies are much fewer.14 Furthermore, although other ocular biometrics, such as axial length, anterior chamber depth, and corneal curvature, are highly correlated to refraction error, contribute to the determination of refraction, and show high heritability in families,15 they have not been widely investigated for genetic association. Clearly, these ocular biometrics are valuable endophenotypes for searching genes that may affect myopia development. In Table 1, we listed several quantitative ocular biometrics and dichotomous disease states of myopia that can be considered for genetic association studies. Even with the most frequently studied dichotomous phenotypes such as myopia and high myopia, the definition of various myopic states was not standardized in the myopia genetic research community. SPH and SE have been used alternatively in the literature for defining the disease state of myopia. In addition, different thresholds of refraction error (in diopters (D)) have been used for declaring the severity of myopia. For instance, −6.00 D or −5.00 D have been alternatively used as the threshold for defining high myopia. Considering the needs of replication evidence for GWA studies, investigators should be mindful of the consistency in phenotypes across studies. With the lack of a gold standard on defining various degrees of myopia diseases status, one will need to make sure that the same thresholds or definition of myopia cases and controls are consistent across all datasets to be investigated. An additional caveat of myopia related phenotypes is that each biometric measure can be obtained from right and left eyes. An affected status of myopia is mostly defined when at least one eye reaches the given threshold

Heritability

Definition

0.24

—

Spherical Equivalent (SE)

Quantitative

0.578 (0.127)

Axial length

Quantitative

0.674 (0.136)

—

Corneal curvature

Quantitative

0.685 (0.128)

—

Anterior chamber depth

Quantitative

0.779 (0.142)

—

Any myopia

Binary

—

SPH or SE ≤ –0.50 D

SPH + Cylinder/2

SPH or SE ≤ –0.75 D SPH or SE ≤ –1.00 D

Young et al., 200961 Klein et al., 200962

Metlapally et al., 200963; Pertile et al. 200864; Mutti et al. 200765; Stambolian et al. 200466; Ibay et al. 200467

Moderate myopia

Binary

—

SPH or SE ≤ –3.00 D

Heath et al. 200168

High myopia

Binary

—

SPH or SE ≤ –5.00 D or SPH or SE ≤ –6.00 D

Yanovitch et al. 200969; Metlapally et al. 200963; Han et al. 200970; Liang et al. 200771

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of refractive errors, and unaffected status is defined when both eyes do not reach the threshold. However, for quantitative phenotypes, we face the question whether the analysis should be conducted for each eye, independently, or a summary form of the two eyes. Furthermore, how should we interpret results if right and left eyes lead to discordant findings? We will discuss this topic further in the correlated phenotype section.

Study Design Statistical methods have strong influences on the study design of any research projects. This is the same for association studies in the human genetic field. Two primary study designs are case-control and family-based association studies. The former uses unrelated population-based casecontrol samples, and the latter uses familial samples such as parents and full-siblings. Case-control design is known to have a higher statistical power than family-based design under the same sample size, but the results are strongly influenced by sample selection.16 In addition, case-control design tends to be prone to spurious association results due to undetected latent population structure. However, the reservation of using case-control study design has been changed significantly since the development of several sophisticated statistical methods, including GENOMIC CONTROL,17 STRUCTURE,18 and EIGENSTRAT,19 which can minimize the effect of population structure to the association tests of unrelated samples. While these two study designs still possess distinct advantages over each other, sample availability is often the primary driving force for the selection of the study design. The epidemiology study of myopia has a larger and earlier research community than the genetic study of myopia. Several epidemiology cohorts of myopia have specimen collected from participants, which provide a great resource for myopia genetic research. Likewise, similar to most of GWA studies published to date, most GWA studies for myopia will be primarily based on population-based samples due to the sample resources and the total genotyping cost. Although the cost per genotype is decreasing, the total cost of a GWA study is still high. Two-stage or multi-stage design has been proposed for the purpose of retaining statistical power and reducing the genotyping cost.20–22 The idea of two-stage design is to conduct the GWA study in the first set of samples at the reasonable and affordable size (first-stage), and

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follow-up a subset of SNPs in another independent samples (secondstage). Skol et al.20 suggested performing joint analysis for pooled samples from both stages in order to maximize the power of detecting significance, which is different from the conventional view of replication that considers the second stage results as an independent dataset. Regardless which analytical approaches were taken, the best practice of declaring GWA findings is to seek out replication evidence in as many independent datasets as possible.

Genotyping and Quality Controls GWA studies rely on commercial SNP chips, predominantly by Affymetrix (http://www.affymetrix.com/) and Illumina (http://www.illumina.com/). The current available SNP chips (>300 K SNPs) all have the ability to detect copy number variants (CNVs), which refer to the chromosomal deletions or duplications. This makes GWA studies more attractive as one can investigate both SNP and CNV association with phenotypes of interest at the same time. The most commonly used criteria for selecting SNP chips is the global coverage across the genome, that is, the fraction of common SNPs that are tagged by the SNPs on the chips.23–25 The latest products, Affymetrix Human SNP array 6.0 and Illumina HamanOmni1-Quad, are indeed aiming for this goal with the dramatically increased number of SNPs on the chips compared to their earlier products. The Affymetrix 6.0 includes more than 906,600 single nucleotide polymorphisms (SNPs) and more than 946,000 additional probes for the detection of copy number variation (CNVs). The Illumina HumanOmni1-Quad BeadChip, a completely redesigned array of HumanlM-Duo, contains over 1 million markers, including aggressively selected SNPs and probes from all three HapMap phases, the 1000 Genome project (http://www.1000genomes. org/page.php), and published studies. Specifically, it contains ~18 K SNPs targeting four 1 Mb regions known to be associated with human diseases; over 62 K non-synonymous SNPs; and SNPs targeting new coding variants. This chip has a median spacing of 1.5 kb to ensure high resolution for CNV detection. Although studies using these newly marketed SNP chips have not been reported, this level of global coverage will indeed increase the power for GWA studies. Regardless what types of SNP chips are used, a rigorous quality control (QC) procedure is very important to ensuring the success of the study.

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While both Affymetrix and Illumina have their own genotype calling algorithms for raw data analysis, one should make sure that the best practice of genotype calling protocol is applied.26 Several QC check points are often examined in the GWA study, including sample call rate, Hardy–Weinberg equilibrium (HWE) for each marker using control samples, minor allele frequency (MAF), genotype missingness per marker, and population structure. Although there is no gold standard for these QC check points, examples of thresholds that we would recommend are: excluding samples with call rates <96% or <98%,26 and excluding SNPs that are out of HWE ( p < 10–7) in control samples, MAF < 0.01, or genotype missingness > 10%. Population structure is another important QC task to investigate. More details are described in the next section.

Population Structure Early views of the role of population structure in genetic association studies of unrelated individuals focused on the concern that cryptic population substructure would raise the false positive rate of statistical tests above their nominal level. For instance, in a case-control dataset, assume there are two underlying subpopulations with different allele frequencies at the SNP and the number of cases is disproportionally high in one subpopulation. Under this scenario, failure to account for population stratification, a confounding factor of allele frequencies differences, could result in significant false positive association between SNPs and disease status. A conventional approach is to select a homogenous dataset as best as you can at the design stage, such as matching cases and controls to minimize the population stratification effect. However, most studies have subtle stratification or have difficulty matching epidemiological or environmental background. Interestingly, the GWA study from the Wellcome Trust Case Control Consortium (WTCCC) study has demonstrated that as long as cases and controls are well matched for broad ethnic backgrounds and solid exclusion criteria are in place, the impact of residual substructure has minimum effect on type I error.2 Concern over the false positive rates by population-based association studies has led to a number of different approaches to control the presence of population structure, including “genomic control,”17 clustering methods, such as STRUCTURE and STRAT methods,18,27 principle components analysis (PCA), and alternative family-based study designs.

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Genomic control requires data on null SNPs to estimate a variance inflation factor that is used to directly correct the test statistic.28 However, the assumption that the inflation factor is globally consistent across a whole genome might simplify the variations between SNPs. Hence adjustment for the inflation factor is preferred after addressing population substructure in a certain level, such as applying the GC method in a dataset that may have similar background (less heterogeneous). A different approach, implemented in STRUCTURE and STRAT software,18,27 (http://pritch.bsd.uchicago.edu/software.html), uses a multistage approach that first identifies any subpopulations using unlinked markers, assigns individuals to putative subpopulations, and then uses subpopulation clusters as a covariate in tests for association with disease phenotype. The STRUCTURE method is extremely computationally demanding. One assumption that we need to make for the STRUCTURE analysis is the number of potential subpopulations in the dataset. Under the given K subpopulations, detected the probability of the membership within each subpopulation is estimated. Therefore, results may be different for a different given number of K, and a STRUCTURE analysis may need to be run a few times to tune the K parameter. Reich et al.29 proposed a feasible computational approach to detect and correct population stratification. In their approach, PCA is used to model ancestry difference between case and control. The EIGENSTRAT approach identifies ancestry differences among samples along eigenvectors of a covariates matrix. For instance, Fig. 1 depicts the relationship among the first three principle components (PC1, PC2, and PC3) in SCORM dataset, which implies five outliers to be excluded from further association analyses. In addition to excluding these samples, the EIGENSTRAT approach is to adjust the amounts attributable to ancestry for the top eigenvectors (http://genepath.med.harvard.edu/~reich/Software.htm). Patterson et al.30 pointed out that top eigenvectors could be caused by a large set of markers in a high (or complete) LD block. Hence, they recommend pruning the markers in tight LD before performing PCA.

Association Tests As association studies have dominated human genetics in the past decade, many association methods (family-based or case-control based) have been developed (e.g. Refs. 31–33). The analysis strategies for GWA data are generally the same as those for candidate gene association studies,

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Figure 1. The first three principle components (PC1, PC2, and PC3) in SCORM dataset.

determined by the study design and the type of phenotypes to be tested. However, new challenges are forthcoming as well due to the large amount of data derived from a GWA study. One key consideration is whether one can perform efficient association analysis in a reasonable timeframe. A few free and commercial computer programs, such as PLINK (http:// pngu.mgh.harvard.edu/~purcell/plink/), Golden Helix (http://www. helixtree.com/index.html), and Syllego (http://www.rosettabio.com/ products/syllego) were, therefore, developed for processing genome wide SNP data, including data management and analyses. PLINK, in particular, a whole genome association analysis tool set, is a popular and widely used free program, which has evolved fast not just to handle genome wide SNP data but also CNV data. A series of modules for data management, quality control checks, population stratification, association analysis, etc, are implemented in PLINK. Most importantly, PLINK decodes pedigree file (pedigree and genotype information) to the binary format, which significantly decreases the computational time for a genome wide scan and makes PLINK an efficient tool for GWA studies.

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Depending on the phenotype and hypothesis to be tested, different association methods can be applied. For unrelated population-based samples, Fisher exact test, Cochran–Armitage trend test, and logistic regression are commonly used for qualitative traits, and the linear model is used for quantitative traits. All these methods have been implemented in PLINK. Both logistic regression and the linear model have the flexibility of incorporating covariates that may have confounding effects to the phenotype. For family-based data, since the development of the TDT method,31 many extensions of TDT methods or new family-based association methods and computer programs were developed, including PDT,32 FBAT,34 APL,35 QTDT,36–40 just to name a few. Although these computer programs with family-based association tests have been used extensively in association studies, computational time is a concern for the GWA setting. While PLINK is an efficient tool, the family-based association methods implemented in PLINK are limited to TDT, parent-TDT, and parent-oforigin using parent-offspring triad dataset for qualitative traits, and Qfam for quantitative traits. Qfam is an ad hoc procedure analogous to the between/within orthogonal model proposed by Fulker et al.39 and Abecasis et al.40 that was implemented in the QTDT package (http://www.sph. umich.edu/csg/abecasis/QTDT/), with some modifications by using permutation procedure to infer familial relationship (see PLINK website). For a GWA dataset using family other than triads (e.g. discordant sibpairs or nuclear families of multiple siblings with or without parents), one will need to seek out the existing family-based association programs. Regardless what association methods and programs are chosen, it is important to perform proper association tests with proper statistical methods. One should judge their own dataset to determine the data analysis strategies. Association tests are generally performed for a single marker at a time or haplotypes of multiple markers within a feasible window size. So far, most GWA studies focus on single marker association tests first. The computational time and strategy of performing haplotypes association tests are the main concern for the genome-wide haplotype analysis, even though haplotypes association methods and programs have been developed extensively in the past (e.g. Haplo.stat, APL, etc).33,41 The use of sliding windows with a fixed window size is a popular approach, but it does not capture the joint effect of distantly located SNPs. One common practice is to conduct single locus association analyses first to identify target regions (or genes)

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and then follow-up with haplotype association analyses. It is clear that we may miss the haplotypes that do not show single locus main effect but joint effects from multiple loci to the target phenotype.

Correlated Phenotypes Although myopia is clinically determined by refractive errors, as mentioned before, other ocular biometrics also play a role in the development of myopia. Therefore, these ocular biometric are correlated in a certain degree. For instance, in Table 2, we show the squared Pearson correlation coefficient (r 2) for all pairs of SPH, SE, axial length, and cornea curvature based on the right eye data using data from Chinese participants in SCORM study. The correlation among SPH, SE, and axial length are indeed strong (r 2 > 0.56), in which axial length is negatively correlated with SPH and SE. Axial length is correlated at some degree with cornea curvature (r 2 = 0.17) and anterior chamber depth (r 2 = 0.19). If GWA analyses are conducted for these endophentoypes of myopia, an immediately question will be whether one should correct multiple testing based on the number of traits tested on top of the number of markers in the panel. This is an open question without an absolute answer. Our view is that if the traits are highly correlated, we do not consider the needs for correcting multiple testing for the number of traits tested since they are equivalent to a single trait. Another aspect of myopia-related phenotypes is that each biometric is measured for the right and left eye, respectively. Should one analyze data from each eye individually or a summary form of both eyes such as the average? Certainly, the results will vary depending on the degree of the Table 2. Pairwise Squared Pearson Correlation Coefficient (r 2) Across Four Ocular Biometrics

Sphere Sphere Sphere equivalent Axial length Cornea curvature Anterior chamber depth

1

Sphere Equivalent

Axial Length

Cornea Curvature

0.98 1.00

0.56 0.57 1.00 0.00

0.01 0.02 0.17 1.00

Anterior Chamber Depth 0.05 0.05 0.19 0.00 1

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similarity between the measures of both eyes for these approaches. Apart from analyzing single phenotype at a time, there are statistical methods available for analyzing correlated data jointly. For instance, generalized equation (GEE) can take into account correlation within the same strata (same individual in this case), which can serve as an alternative approach. Here, we utilize the GWA data from SCORM to illustrate the association results (−log 10(p-value)) in a region (from 23,555,218 to 24,149,104 bp) of chromosome 11 MYP7 locus using GEE analysis for SE from both eyes, and linear model analyses for SE from the right eye and left eye, respectively, and the average of SE of both eyes. This analysis shows that both GEE and linear model analysis for the average SE revealed intermediate results between those obtained from the right and left eye, respectively, for almost all markers tested (Fig. 2). Although in this example the

3 2 0

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23600000 23700000 23800000 23900000 24000000 24100000 basepair position Figure 2. Association results (−log 10( p-values)) of the linear model analysis using SE of right and left eye, respectively, and the average SE of both eyes from linear model analysis, and GEE analysis using SE from both eyes.

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use of average SE from both eyes seems to provide better p-values (smallest p-values) for the top-hit marker than GEE, this does not dismiss the GEE analysis until more formal evaluation is done. The fact that GEE or the analysis on the average SE from both eyes support the top finding from the right or left eye will enhance the credibility of the conclusion for the study.

Imputation and Meta-Analysis Under the phenomenon of common variants of common diseases, most susceptibility variants have small to moderate genetic effects to the disease. Therefore, without a large sample size, it is hard to detect true positive results in a single association study, which is often constrained by the budget and sample resources.42 Meta-analysis, by combining evidence from comparable independent association studies, thus provides a robust approach to enhance statistics power and effective sample size.43,44 Application of meta-analysis in GWAS society is becoming a standard practice recently to identify loci related to the risk of disease, exemplified by studies for diabetes, Alzheimer, bipolar disorder, etc.45–47 Prior to meta-analysis, as described earlier, one should ensure that the phenotypes are comparable and were measured in similar ways across datasets. In addition, due to the rapid changes of SNP chips, different studies may utilize different versions of SNP chips with different coverage of SNP content. That is, not all SNPs were typed consistently across studies. The development of several imputation methods for inferring genotypes of untyped markers has provided a solution for this problem. The basic idea behind imputation is to utilize the correlation among untyped and typed markers to infer the genotypes of untyped markers in each dataset.48 This correlation mostly relies on the information obtained from the reference panel that has genotypes of both untyped and typed markers. With the availability of more than three million genotype data from the International HapMap Project, most non-overlapping SNPs between SNP chips can now be inferred. It should be noted that imputation is generally computational intensive. IMPUT,49 MACH,50 BEAGLE,51 and BIMBAM52 are the frequently used programs for imputation. Each of them has different strengths and weaknesses, but none of them is optimal for all situations.48,53 Nonetheless, with these imputation programs becoming available, we now can impute untyped markers at the first stage to allow assessing multiple datasets for the same set of SNPs.49

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Meta-analysis in the setting of genetic studies refers to combining summary statistics of overlapping SNPs from multiple genetic association studies. Since combining raw individual genotype and phenotype data across studies to perform pooled analysis is in general difficult, the metaanalysis is a reasonable surrogate to assess the association results across all datasets. Here, we describe a few meta-analysis methods. First, the simplest meta-analysis method is Fisher’s methods Tfisher = –2∗Σ log(pi), where pi is p value of study I, i = 1,…,k. Tfisher follows a χ2 distribution of 2k degrees of freedom, where k is the total number of datasets. Since Fisher’s method takes only information from the p-values, it is important to keep in mind that Fisher’s method should be applied to the markers with the same direction of the effect to the susceptibility of the disease. Second, Mantel–Haenszel methods are commonly used for dichotomous traits if the information on 2 × 2 table can be recoverable from each study.54 In combining the odds ratio, weight is usually given proportionally to the precision of its results in each study. Finally, if a 2 × 2 table is not available in each study, such as if p values were obtained from logistical regression framework in order to adjust for potential confounding covariates, using z-score statistics to compute the meta-p values is the best. Z-score statistics are wildly used in practice for metaanalysis since Z-score could be easily converted in each study and the direction of effect is manifested in itself.55 Combined z-score is calculated as: Zmeta = Σzi xwi, where zi is the z-score from study i and wi is the weight of study i. Once pooled z score is obtained, the corresponding p values for the combined studies can be computed as well. Most widely used weights are the inverse of the variance of the effect estimate for each study. The pooled inverse variance-weighted z-score is calculated as the sum of individual z score using inverse variance as weight. In case the variance is not given in the summary statistics or standard error, SE in the equation below, is not on the same unit (for example, the quantitative trait is not measure on the same unit), z score can then be summed across multiple studies weighting them by study sample size Z meta = ∑

bi × wi SE i

where wi =

Ni N total

.

It is unlikely that every dataset for a meta-analysis is derived from a single homogenous population with the same genetic effect. Therefore, it is important to access the heterogeneity across datasets. Random effects,

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assuming true effect differs among studies, consider two sources of variances: within-study sampling error and between-study heterogeneity. The commonly used method to test between-study heterogeneity is called Cochran’s Q statistics, for which the large values of Cochran’s Q favor the alternative hypothesis of heterogeneity.56 For datasets i = 1, …,k, T1, …,Tk is the study-specific effect size. The Cochram’s Q statistic is computed by k

Q = ∑ wi (T1 − T ), i =1

where T =

Σik=1 wiT1 Σik=1 wi

and wi is the inverse of the estimated variance in dataset i. Q is distributed as a chi-square distribution with k-1 degree of freedom. An alternative form, statistic I 2 (inconsistency), derived from Q, 100% × (Q-degree of freedom), is a measure of the percentage of heterogeneity vs total variation across studies. If I 2 > 50%, it indicates the presence of heterogeneity. If evidence of heterogeneity is demonstrated, measure to identify its possible cause is needed before any explicit conclusion is drawn. In such a case, additional cohort for replication or fine mapping approaches might be required to further investigate on the true genetic variants of interest.

Visualization Tools To synthesize hundreds of thousands of p-values for multiple phenotypes from a GWA study, it often relies on good graphical presentation. Manhattan plots and Quantile-Quantil (Q-Q) plots are the most frequently used figures to present p-values of high density markers across the whole genome. Manhattan plot can be easily generated from Haploview program (hapmap.org), and can provide an overall view of the association evidences in the nearby region of the highly significant variants. Here, we show an example of Manhattan plot using the GWA results for SE from right eye from the SCORM GWA studies of Chinese children (Fig. 3). This figure provides snapshots on the chromosomal regions with promising association evidence. For instance, a region in chromosome 13 revealed the best p-value. Q-Q plots provide a visual summary of the distribution of the observed test statistics (e.g. chi-square test statistic) in the GWA study vs the expected statistic. McCarthy et al. (2008)16 provided a nice illustration of Q-Q plots with interpretation of the pattern (see Box 2 in McCarthy et al.).

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Figure 3. An overview of GWA results for the SE trait (left eye) from the SCORM and SP datasets, respectively.

For instance, when the Q-Q plot line is close to the diagonal line, it indicates that there is very little association evidence in the study. One the other hand, if the observed line is much off from the diagonal line, there may be concerns for the population stratification. If only the tail part of the Q-Q plot line is much off from the diagonal line, it indicates that there is compelling evidence of the disease association in the dataset. Both Manhattan and Q-Q plots are tools for summarizing all p-values from GWA studies, not providing additional bioinformation related to the SNP. WGAviewer, another free program, can annotate the SNPs and their associated p-values in relationship to gene structure, SNP function, gene expression, and other GWA studies (http://www.genome.duke.edu/centers/ pg2/downloads/wgaviewer.php).57 This tool can take us beyond p-values by providing biological information for the loci of interest.

Drawing Conclusions To date, the determination of ‘top-hit’ markers in the GWA setting is mostly p-value driven. The threshold for declaring genome wide significance is widely accepted at 5 × 10–8.1,58,59 However, sample size should be considered even though such a p-value is reached. Regardless what top p-values are observed in the GWA study, it will need to be replicated by other independent datasets. In addition, to judge the p-values from GWA studies, prior genetic research findings can also serve as good references. The genetic research of myopia has a great resource of linkage

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information (e.g. MYP loci) and public expression data, such as EyeSAGE database60 from human retina and retinal pigment epithelium. This information can definitely help investigators to prioritize the GWA results.

Acknowledgments The grant 06/1/21/19/466 from the Singapore BioMedical Research Council (BMRC) and National Institutes of Health grant 1R21-EY-019086 provided funding for the genome wide association study for SCORM.

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11. Nallasamy S, Paluru PC, Devoto M, et al. (2007) Genetic linkage study of high-grade myopia in a Hutterite population from South Dakota. Mol Vis 13: 229–236. 12. Lam CY, Tam PO, Fan DS, et al. (2008) A Genome-wide Scan Maps a Novel High Myopia Locus to 5p15. Invest Ophthalmol Vis Sci 49(9): 3768–3778. 13. Barrett JC, Hansoul S, Nicolae DL, et al. (2008) Genome-wide association defines more than 30 distinct susceptibility loci for Crohn’s disease. Nat Genet 40(8): 955–962. 14. Ciner E, Wojciechowski R, Ibay G, et al. (2008) Genomewide scan of ocular refraction in African-American families shows significant linkage to chromosome 7p15. Genet Epidemiol 32(5): 454–463. 15. Klein AP, Suktitipat B, Duggal P, et al. (2009) Heritability analysis of spherical equivalent, axial length, corneal curvature, and anterior chamber depth in the Beaver Dam Eye Study. Arch Ophthalmol 127(5): 649–655. 16. McCarthy MI, Abecasis GR, Cardon LR, et al. (2008) Genome-wide association studies for complex traits: consensus, uncertainty and challenges. Nat Rev Genet 9(5): 356–369. 17. Devlin B, Roeder K. (1999) Genomic control for association studies. Biometrics 55(4): 997–1004. 18. Pritchard JK, Stephens M, Rosenberg NA, Donnelly P. (2000) Association mapping in structured populations. Am J Hum Genet 67: 170–181. 19. Price AL, Patterson NJ, Plenge RM, et al. (2006) Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet 38(8): 904–909. 20. Skol AD, Scott LJ, Abecasis GR, Boehnke M. (2007) Optimal designs for twostage genome-wide association studies. Genet Epidemiol 31(7): 776–788. 21. Wang H, Thomas DC, Pe’er I, Stram DO. (2006) Optimal two-stage genotyping designs for genome-wide association scans. Genet Epidemiol 30(4): 356–368. 22. Muller HH, Pahl R, Schafer H. (2007) Including sampling and phenotyping costs into the optimization of two stage designs for genomewide association studies. Genet Epidemiol 31(8): 844–852. 23. Barrett JC, Cardon LR (2006) Evaluating coverage of genome-wide association studies. Nat Genet 38(6): 659–662. 24. (2004) Finishing the euchromatic sequence of the human genome. Nature 431(7011): 931–945. 25. Li M, Li C, Guan W. (2008) Evaluation of coverage variation of SNP chips for genome-wide association studies. Eur J Hum Genet 16(5): 635–643. 26. Fellay J, Shianna KV, Ge D, et al. (2007) A whole-genome association study of major determinants for host control of HIV-1. Science 317(5840): 944–947.

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27. Pritchard JK, Donnelly P (2001) Case-control studies of association in structured or admixed populations. Theor Popul Biol 60(3): 227–237. 28. Bacanu SA, Devlin B, Roeder K. (2000) The power of genomic control. Am J Hum Genet 66(6): 1933–1944. 29. Reich M, Liefeld T, Gould J, et al. (2006) GenePattern 2.0. Nat Genet 38(5): 500–501. 30. Patterson N, Price AL, Reich D. (2006) Population Structure and Eigenanalysis. PLoS Genet 2(12): e190. 31. Spielman RS, Ewens WJ. (1996) The TDT and other family-based tests for linkage disequilibrium and association. Am J Hum Genet 59(5): 983–989. 32. Martin ER, Monks SA, Warren LL, Kaplan NL. (2000) A test for linkage and association in general pedigrees: the pedigree disequilibrium test. Am J Hum Genet 67: 146–154. 33. Schaid DJ, Rowland CM, Tines DE, et al. (2002) Score tests for association between traits and haplotypes when linkage phase is ambiguous. Am J Hum Genet 70(2): 425–434. 34. Rabinowitz D, Laird N. (2000) A unified approach to adjusting association tests for population admixture with arbitrary pedigree structure and arbitrary missing marker information. Hum Hered 50: 211–223. 35. Martin ER, Bass MP, Hauser ER, Kaplan NL. (2003) Accounting for linkage in family-based tests of association with missing parental genotypes. Am J Hum Genet 73(5): 1016–1026. 36. Allison DB. (1997) Transmission-disequilibrium tests for quantitative traits. Am J Hum Genet 60: 676–690. 37. Rabinowitz D. (1997) A transmission disequilibrium test for quantitative trait loci. Hum Hered 47: 342–350. 38. Monks SA, Kaplan NL. (2000) Removing the sampling restrictions from family-based tests of association for a quantitative-trait locus. Am J Hum Genet 66(2): 576–592. 39. Fulker DW, Cherny SS, Sham PC, Hewitt JK. (1999) Combined linkage and association sib-pair analysis for quantitative traits. Am J Hum Genet 64(1): 259–267. 40. Abecasis GR, Cardon LR, Cookson WO. (2000) A general test of association for quantitative traits in nuclear families. Am J Hum Genet 66(1): 279–292. 41. Chung RH, Hauser ER, Martin ER. (2006) The APL test: extension to general nuclear families and haplotypes and examination of its robustness. Hum Hered 61(4): 189–199. 42. Lohmueller KE, Pearce CL, Pike M, et al. (2003) Meta-analysis of genetic association studies supports a contribution of common variants to susceptibility to common disease. Nat Genet 33(2): 177–182.

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43. Lau J, Ioannidis JP, Schmid CH. (1997) Quantitative synthesis in systematic reviews. Ann Intern Med 127(9): 820–826. 44. Munafo MR, Flint J. (2004) Meta-analysis of genetic association studies. Trends Genet. 20(9): 439–444. 45. Zeggini E, Scott LJ, Saxena R, et al. (2008) Meta-analysis of genome-wide association data and large-scale replication identifies additional susceptibility loci for type 2 diabetes. Nat Genet 40(5): 638–645. 46. Bertram L, McQueen MB, Mullin K, et al. (2007) Systematic meta-analyses of Alzheimer disease genetic association studies: the AlzGene database. Nat Genet 39(1): 17–23. 47. Baum AE, Hamshere M, Green E, et al. (2008) Meta-analysis of two genomewide association studies of bipolar disorder reveals important points of agreement. Mol Psychiatry 13(5): 466–467. 48. Guan Y, Stephens M. (2008) Practical issues in imputation-based association mapping. PLoS Genet 4(12): e1000279. 49. Marchini J, Howie B, Myers S, et al. (2007) A new multipoint method for genome-wide association studies by imputation of genotypes. Nat Genet 39(7): 906–913. 50. Li Y, Abecasis GR. (2006) Mach 1.0: Rapid Haplotype Reconstruction and Missing Genotype Inference. Am J Hum Genet S79: 2290. 51. Browning SR, Browning BL. (2007) Rapid and accurate haplotype phasing and missing-data inference for whole-genome association studies by use of localized haplotype clustering. Am J Hum Genet 81(5): 1084–1097. 52. Servin B, Stephens M. (2007) Imputation-based analysis of association studies: candidate regions and quantitative traits. PLoS Genet 3(7): e114. 53. Ellinghaus D, Schreiber S, Franke A, Nothnagel M. (2009) Current software for genotype imputation. Hum Genomics 3(4): 371–380. 54. Mantel N, Haenszel W. (1959) Statistical aspects of the analysis of data from retrospective studies of disease. J Natl Cancer Inst 22(4): 719–748. 55. de Bakker PI, Ferreira MA, Jia X, et al. (2008) Practical aspects of imputationdriven meta-analysis of genome-wide association studies. Hum Mol Genet 17(R2): R122–R128. 56. Cochran WG. (1954) The combination of estimates from different experiments. Biometrics 10: 101–129. 57. Ge D, Zhang K, Need AC, et al. (2008) WGAViewer: software for genomic annotation of whole genome association studies. Genome Res 18(4): 640–643. 58. International HapMap Consortium. (2005) A haplotype map of the human genome. Nature 437(7063): 1299–1320. 59. Hoggart CJ, Clark TG, De IM, et al. (2008) Genome-wide significance for dense SNP and resequencing data. Genet Epidemiol 32(2): 179–185.

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60. Bowes Rickman C, Ebright JN, Zavodni ZJ, et al. (2006) Defining the human macula transcriptome and candidate retinal disease genes using EyeSAGE. Invest Ophthalmol Vis Sci 47(6): 2305–2316. 61. Young TL. (2009) Molecular genetics of human myopia: an update. Optom Vis Sci 86(1): E8–E22. 62. Klein R, Klein BE, Tomany SC, Cruickshanks KJ. (2003) The association of cardiovascular disease with the long-term incidence of age-related maculopathy: the Beaver Dam Eye Study. Ophthalmology 110(6): 1273–1280. 63. Metlapally R, Li YJ, Tran-Viet KN, et al. (2009) COL1A1, COL2A1 Genes and Myopia Susceptibility: evidence of Association and Suggestive Linkage to the COL2A1 Locus. Invest Ophthalmol Vis Sci. 64. Pertile KK, Schache M, Islam FM, et al. (2008) Assessment of TGIF as a candidate gene for myopia. Invest Ophthalmol Vis Sci 49(1): 49–54. 65. Mutti DO, Hayes JR, Mitchell GL, et al. (2007) Refractive error, axial length, and relative peripheral refractive error before and after the onset of myopia. Invest Ophthalmol Vis Sci 48(6): 2510–2519. 66. Stambolian D, Ibay G, Reider L, et al. (2004) Genomewide linkage scan for myopia susceptibility loci among Ashkenazi Jewish families shows evidence of linkage on chromosome 22q12. Am J Hum Genet 75(3): 448–459. 67. Ibay G, Doan B, Reider L, et al. (2004) Candidate high myopia loci on chromosomes 18p and 12q do not play a major role in susceptibility to common myopia. BMC Med Genet 5: 20. 68. Heath S, Robledo R, Beggs W, et al. (2001) A novel approach to search for identity by descent in small samples of patients and controls from the same mendelian breeding unit: a pilot study on myopia. Hum Hered 52(4): 183–190. 69. Yanovitch T, Li YJ, Metlapally R, et al.(2009) Hepatocyte growth factor and myopia: genetic association analyses in a Caucasian population. Mol Vis 15: 1028–1035. 70. Han W, Leung KH, Fung WY, et al. (2009) Association of PAX6 polymorphisms with high myopia in Han Chinese nuclear families. Invest Ophthalmol Vis Sci 50(1): 47–56. 71. Liang CL, Hung KS, Tsai YY, et al. (2007) Systematic assessment of the tagging polymorphisms of the COL1A1 gene for high myopia. J Hum Genet 52(4): 374–377.

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Section 4

Animal Models and the Biological Basis of Myopia

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4.1 The Relevance of Studies in Chicks for Understanding Myopia in Humans Josh Wallman*,‡ and Debora L. Nickla†

Introduction Research on the etiology of myopia can be divided into the periods before and after animal research into myopia became prominent. During the earlier period, the predominant opinions were that myopia was either entirely of genetic origin (although there was no strong genetic evidence), or that it was entirely due to excess accommodation (with no plausible evidence linking accommodation to myopia). In addition, a few eccentrics held that myopia was a homeostatic response to a habitual near-viewing distance. The accidental discovery during the 1970’s that obscuring the view of the eye of a monkey, chick, tree shrew, or child made the eye myopic demanded an explanation of how visual experience altered the eye or brain.1–4 Subsequently, this was shown to be true for mice as well.5,6 The most important discovery that followed, showing that eyes would alter their refractive state, compensating for either negative or positive spectacle lenses, in chickens, fish, tree shrews, marmosets, rhesus monkeys, and guinea pigs,7–12 made it inescapable that there existed a homeostatic mechanism that regulated refractive error. This homeostatic mechanism posed the difficult problem that it required that the eye or brain be able to distinguish hyperopic defocus (image behind the photoreceptors) from myopic defocus (image in front of the photoreceptors). Here, human

*City College, City University of New York, New York. † New England College of Optometry, Boston, Massachusetts. ‡ Corresponding author. Department of Biology, City College, CUNY, 160 Convent Ave., New York, NY 10031. E-mail: [email protected].

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intuition fails us. We are only able to focus a microscope or binoculars by trial and error, recalling whether the image is more in focus than it was a fraction of a second earlier; it seems impossible that the eye could use this method, especially as it seems implausible that the eye or brain could recall how sharp an image was days or months before — the time required for eye-growth to cause a detectible change in refractive status. Therefore, the visual system would seem to do better automatically than we can do consciously. These discoveries from experiments on animals have had a curiously ambivalent effect on clinical research on myopia. On the one hand, it has made the entire community very attuned to the possible consequences of blur. Thus, there have been dozens, if not hundreds, of papers evaluating the possibility that blur caused by inadequate accommodation or higher order optical aberrations or transient myopia after long periods of reading might cause myopia. Furthermore, there has been a major, meticulously conducted clinical trial using progressive addition lenses on children to reduce the magnitude of the defocus experienced.13 On the other hand, the most important insight of the animal research — that there was a bidirectional homeostatic control of refractive state — has been largely ignored in the clinical community, so that only occasional studies14 have tested the notion that the way to counteract the effects of hyperopic blur leading to myopia is not with less hyperopic blur but with myopic blur, which may lead the eye away from myopia. It was an accident that proved fortunate for the development of modern biology that Gregor Mendel studied the particular traits of peas that he did, and thereby discovered the simplest form of inheritance. One wonders how many other monks chose to study drought-resistance or plantheight or animal-size and failed to find a tractable experimental system. Similarly, the choice of the mold Neurospora and the fly Drosophila were fortunate choices for the study of genetics and of circadian rhythms, as were the choices of the roundworm C. elegans for cell-lineage studies and of zebra fish for developmental studies. One presumes that none of these fields would be where they are today had the researchers chosen cats or chimpanzees. In myopia research, the accidental choice of chicks and monkeys during the 1970’s, resulting from unrelated studies on effects of experience on brain development, have proven particularly fortunate. Because this volume is largely devoted to studies of myopia in humans and because we were asked to write on “The Pivotal Role of the Chick Eye,” we

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will concentrate on aspects of myopia research on chicks that bear on clinical concerns.

The Search for Error Signals The existence of a homeostatic mechanism that guides the eye towards emmetropia requires one or more error signals that reflect how far the eye is from its “goal” or set-point. Thus, to consider what the possible visual error signals guiding eye growth might be rests on whether or not the eyes can discern the sign of defocus, instead of simply the degree of defocus. If the eye can only use the degree of defocus, many possible visual signals, such as the absence of small features in the environment (high spatial frequencies) might provide that information. Indeed, the issue becomes similar to the psychophysical question of how we can assess that an image is blurred. One could imagine that one simply assesses the amount of visual stimulation, which would be higher in focused images; or perhaps one measures the activity of neurons responsive to high spatial frequencies, which would also be higher in focused images. In the perceptual case, one can dismiss both of these possibilities, because clouds, for example, which contain neither strong contrasts nor high spatial frequencies, do not appear blurred. Instead, two psychophysical theories are in contention at present. One holds that the visual system compares the activity of neurons tuned to higher and lower spatial frequencies, that is, it estimates the slope of the function of “power” versus spatial frequency.15 The other holds that the visual system performs a template match of the edges at different spatial frequencies.16 The blur hypothesis In the case of eye-growth, one could argue that the eye simply elongates in proportion to the amount of blur that elongation is inhibited proportionally by retinal activity (the Blur Hypothesis). This view would equate the myopia caused by wearing negative lenses with that caused by formdeprivation, and it would explain why increasing retinal activity by intense stroboscopic illumination prevents myopia induced by form-deprivation and drives eyes toward hyperopia. According to this view, blur could provide the error-signal guidance for people or animals that mostly view distant objects, assuming that neonates

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were hyperopic and accommodation imperfect, so that the amount of visual detail, and hence the magnitude of visual neural activity, would be low at first and would increase until the eye became emmetropic. This hypothesis would not explain how myopic neonates would emmetropize; instead, it would predict that they would become progressively more myopic. Furthermore, to explain the compensation for positive lenses, this hypothesis would require that neonates mostly view nearby objects, so that the positive lenses would bring most objects into focus, thereby increasing the activity of retinal neurons, and as a result, inhibiting ocular elongation. This conjecture brings on a larger problem: Why would not all young eyes become myopic, thereby maximizing their retinal activity? Perhaps if myopia caused all nearby objects to be in focus, the inhibition of ocular elongation would be so strong that it would drive the eye back in the hyperopic direction (as the cornea and lens continued to flatten), such that only emmetropic eyes would have the level of blur that keeps the refractive error stable. It is not obvious how this optimal level would be calibrated to bring nearly all eyes close to emmetropia. Bidirectional lens-compensation The strongest challenge to the Blur Hypothesis is that at least some animals compensating for defocus imposed by eyeglass lenses can apparently compensate for both myopic and hyperopic defocus. To verify this ability, one must be certain that the eyeglass lenses put on the animal do impose opposite signs of defocus. This requires that the eye respond bidirectionally to a level of defocus greater than its distance from emmetropia. That is, if the eye is 5 D hyperopic and only responds to positive lenses of 4 D or less, one cannot test whether it truly responds to myopic defocus. In the case of chicks, their eyes clearly distinguish myopic defocus (image in front of photoreceptors) from hyperopic defocus (image behind photoreceptors). First, chicks, with or without accomodation, compensate for positive lenses even if restrained from approaching the walls of their chamber, which are placed beyond the far point of their eyes, ensuring that all images are myopically defocused.17,18 Second, when chicks are wearing negative lenses, a few minutes a day of wearing positive lenses negates the whole day of wearing negative lenses. It is difficult to imagine that the sharp vision experienced in those few minutes increases the retinal activity more than the sharp vision resulting from accommodation

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over the course of the day. Third, covering positive lenses with a light diffuser does not decrease the compensation for the positive lenses, even though the same diffuser worn alone would cause myopia.18 Fourth, if eyes are enormously blurred by wearing lenses that are +5 D on one axis and –5 D on the orthogonal axis (Jackson Crossed Cylinders), their refractions go slightly in the hyperopic direction (the opposite of what would be expected by the Blur Hypothesis and the opposite of what occurs with light diffusers), and if weak positive or negative lenses are added to the Jackson Crossed Cylinders, the eyes compensate normally for these lenses.19 The cases of marmosets and fish are similar to that of chicks: The refractions of marmoset eyes change reliably in the direction that compensates for the lenses; because the eyes were emmetropic at the start of the experiments, there is no issue of which side of emmetropia the lenses put them.20,21 In the case of fish, eyes wearing lenses that impose at least 9 D of myopic defocus compensated by 7 D within the two-week observation period, a change almost as great, but in the opposite direction as with negative lenses.10 In other species, the situation is more complicated. In guinea pigs and tree shrews, only positive lenses of less than +4 D are compensated. In guinea pigs, the eyes wearing positive lenses change in the opposite direction as those wearing negative lenses in refraction and ocular elongation, but the eyes wearing positive lenses do not become more hyperopic than the fellow eyes, because all lens-wear causes a small myopic shift in guinea pigs.12 However, the loss of directional compensation with the optic nerve section22 suggests that the intact animals may distinguish hyperopic from myopic defocus. In tree shrews, the eyes also compensate for positive lenses, but because the animals are +10 D hyperopic to begin with, the positive lenses do not impose myopic defocus.11 In monkeys, fitting eyes with progressively increasing powers of negative or positive eyeglass lenses causes eyes to become more myopic or hyperopic, respectively. Even very strong positive lenses prevent the normal loss of hyperopia. Therefore, one can say that the positive and negative lenses cause opposite responses. The problem with this view is that the animals could look at very nearby objects, so that even if the eyes could not discern the sign of defocus, the occasional clear views might be enough to keep their refractions stable. To address this concern, in a meticulous study, Norton et al. had tree shrews wear positive lenses for 45 minutes once a day only when their viewing distance was controlled and their accommodation monitored (wearing

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negative lenses the rest of the time). Of those wearing +5 D lenses (but not those wearing –5 D lenses), some animals grew in the hyperopic direction and others in the myopic direction,23 suggesting that at least some animals were compensating for true myopic defocus. Recovery from ametropia vs. compensation for lenses Although the changes in eye-growth caused by lens-wear can only have a visual origin, the same is not true for recovery from the myopia or hyperopia that results following lens-removal. Because the eye is abnormally long or short, during recovery, the eye is not only compensating for the myopia or hyperopia but is also restoring its natural shape. Thus, eyegrowth can be influenced by visual and shape-restoring mechanisms, which can operate in the same or opposite directions, and they may interact. For example, it requires less daily vision for chicks to recover from 10 D of myopia than for them to compensate for lenses imposing the same defocus.24 The relative potency of the visual and the shape-restoring mechanisms may vary among species, ages, and conditions. Thus, making eyes myopic and then correcting the vision to emmetropia with lenses or putting the animals in darkness can either keep the eyes myopic (if only vision is at work) or can permit some recovery (if the shape-restoring mechanism is stronger than the visual one) in both chicks25,26 and tree shrews.27 The complication of the emmetropization end-point Another complication is that, although one tends to think of emmetropization as a process that leads to emmetropia, probably guided by an estimate of the refractive error, there is evidence from both monkeys and chicks that the initial phase of emmetropization in neonates goes to a stable refractive endpoint that is idiosyncratic for each individual, followed much later by a second phase that goes to actual emmetropia.28,29 The problem that this poses for studying lens-compensation in young animals is that, if one does not know the end-point toward which an animal’s eye is growing, one cannot be certain how to interpret the effect of the eyeglass lens. For example, if an animal is +5 D hyperopic and if the set-point the eye is growing towards is +2 D, imposing +3 D or less of myopic defocus with positive lenses would have no effect, as the lenses would simply help the eye towards its end-point, whereas if the set-point had been more hyperopic or the lens-power stronger, it would have put

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the eye beyond its set-point and made it grow in the hyperopic direction, as one would expect for bidirectional lens-compensation. This individual variation in set-points may account for why some tree shrews became hyperopic and others myopic in the experiment described above in which animals wore positive lenses briefly each day. In this regard, the chick eye is most useful because it emmetropizes to within a few diopters of emmetropia within a week of hatching and compensates for lenses from –10 D to +15 D. Therefore, if one controls the viewing distance and accommodation, one can be certain that the defocus is hyperopic with negative lenses and myopic with positive lenses, and therefore that this end-point complication does not apply. Optical aberrations as error signals The existence of bidirectional lens-compensation raises the question of what error signal could provide the growth-guiding signal. In a perfect optical system, one could not distinguish myopic from hyperopic defocus unless one knew the object being imaged. However, real eyes are not perfect optical systems, and so the issue is what aberrations might provide the signed error signal. Among the monochromatic aberrations (that is, the ones that exist in monochromatic light), it is well known that spherical aberration (the difference in focal length between the center and periphery of the lens) can interact with defocus to give a signal that distinguishes myopic from hyperopic defocus,30 so that the focused image is not necessarily the clearest.31 This may be responsible for the finding in some humans that the refractive error depends on the spatial frequency of the stimuli viewed.32 It may also account for the ability of humans to learn to discriminate whether small letters are defocused in the myopic or hyperopic direction, when presented under carefully controlled conditions.33 Arguing against the use of these aberrations is the fact that individual eyes have different aberrations, and the visual system would seem to have to know the aberrations of the particular eye to use them to distinguish the sign of defocus. A simpler error signal would make use of the longitudinal chromatic aberration of the eye. Because short-wavelength (blue) light is focused more strongly than long-wavelength (red) light, if a black/white edge is in focus, the blue light will be focused in front of the photoreceptors, while the red would be focused behind the photoreceptors. Thus, if the blue aspect of the image were sharper than the red aspect, it would indicate that

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the eye was defocused in the hyperopic direction (image focused behind the photoreceptors), whereas if the red aspect were sharper, it would indicate myopic defocus. The interest in chromatic aberration as an error signal for emmetropization and lens-compensation was diminished by studies some years ago showing that chicks raised in monochromatic illumination could compensate for lenses, although no comparison of wavelengths could be made under these conditions. These findings do not imply that chromatic aberration is not used, but only that other error signals can be used. In fact, because chicks can compensate for as much as 10 D of defocus imposed by eyeglass lenses,34 the existence of other error signals is implied because chromatic aberration would provide a useful error signal only in the range of 1–3 diopters. To make a stronger test of whether chromatic aberration is used, we arranged to have chicks presented with wallpaper that simulated the chromatic contrasts that would be present at black/white edges if the eye were myopic or hyperopic. We found clear evidence that the eye grew in the direction that compensated for the simulated refractive error.35 This result shows that chromatic cues can be used to distinguish myopic from hyperopic defocus because no other cues were available from this simple striped wallpaper. Because there is credible evidence that humans use chromatic aberration to determine in which direction to accommodate,36 it is plausible that humans use chromatic aberration in emmetropization as well. The fact that both chickens and humans have cones sensitive to blue, green, and red light argues that chicks may, in this regard, be more similar to humans than most mammals, which have only two cone-types. Other possible visual error signals The possible error signals are limited by the reader’s imagination, maybe not even by that. The aberration of astigmatism results in light being focused at two different planes, much like chromatic aberration. If the eye knew the sign of its astigmatism, it could sense changes in the sign of its defocus by a change in which lines were sharper. If the eye could compare the image quality in the ventral visual field (which is myopic37) with that in the central visual field, it could determine whether it was myopic or hyperopic. If it could keep track of the changes in image quality with accommodation, this too could yield the sign of defocus, as could

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decoding the fluctuations in retinal position caused by oscillations in blood flow or intraocular pressure. Unfortunately, none of these possible signals have been put to a test. One signal that has been put to a test is that the ON and OFF pathways of the visual system seem differentially able to affect the compensation for positive and negative lenses in chicks.38

How Important is Having a Fovea? One of the attributes of the chicken eye that makes many view it as an inappropriate model for human myopia is the relatively uniform distribution of photoreceptors across the retina, in contrast to the steep gradient of photoreceptor density as a function of retinal eccentricity in primates. This uniformity helps make it believable that local parts of the retina can adjust their own refractive state rather independently from other regions, as shown by experiments with partial diffusers or with lenses covering part of the retina.39,40 Subsequently, it has been found that monkeys also adjust the expansion of parts of the eye locally, very much like chicks.41 The obvious importance of the fovea may be another instance in which human intuition fails. Although our subjective awareness of the visual world is dominated by what we see with the fovea, the foveal area is so small that it contains few neurons. For example, although the parasol retinal ganglion cells are 100 times more concentrated in the foveal region than in the periphery, their total number increases with distance from the fovea, simply because the retinal area increases. Indeed, there is no clear evidence that the fovea has a privileged role in control of eye growth in humans. It has been known since 1931 that individuals differ considerably in the refractions of the peripheral retina42; furthermore, a longitudinal study in 1971 showed that those adolescents with hyperopic refractions in their peripheral retina were much more likely to become myopic than those with myopic refractions in their peripheral retina.43 It is an open question whether peripheral hyperopia causes myopia, and, if it does, whether it is because the hyperopia results from the eye compensating for myopic defocus in the periphery or because the eye has an elongated shape caused by myopia in the central retina.44 One cannot overstate the importance of the finding, first in chickens and then in primates, that the peripheral retina plays an important role in experimental myopia. The implication of this role for human myopia is

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threefold: First, it means that it may be futile to look for the etiology of myopia in the small degrees of defocus that occur at the fovea, for example during reading, when much larger degrees of defocus are present in the periphery. Second, it forces one to accept that any particular point on the retina will experience an alternation of myopic and hyperopic defocus depending on whether the fovea (which largely controls accommodation) is looking at an object closer or further than that particular peripheral point. Third, it means that, as Ian Flitcroft has argued, when one is outdoors the visual world is relatively flat in dioptric terms, in that nearly everything is more than a meter away. Thus, no point on the retina would be more than 1 diopter defocused relative to the point viewed with the fovea. In contrast, the range of focal planes indoors is much greater so that when viewing a nearby object like a book, the visual scene surrounding the book can be several diopters defocused in the myopic direction, or, if one focuses in the distance, the book can be several diopters defocused in the hyperopic direction. This difference is likely to be more important than the difference in light intensity between indoor and outdoor vision as a factor in the etiology of myopia.45 The awkward aspect of accepting the likely importance of peripheral defocus in human myopia is that one cannot easily measure or control the temporal pattern of defocus that would be experienced by each retinal locus. In chickens, it is possible to begin to assess the effect of alternation of myopic and hyperopic defocus by alternating strong positive and negative lenses. We found that, even though positive and negative lenses have approximately equal effects when worn alone, the positive lenses have a much greater effect than the negative lenses, when the lenses are alternated.46 Indeed, even a few minutes of positive lens-wear four times a day can balance out the remainder of the day wearing negative lenses.47 Furthermore, this asymmetry in the efficacy of the positive and negative lenses depends on the frequency of alternation: if myopic and hyperopic defocus is alternated several times a second, the asymmetry disappears.48 To get to the mechanism underlying these alternation effects, we studied chicks with different periods of lens-wear alternating with different periods of darkness. We found that periods of lens-wear less than two minutes were without effect. Because chicks accommodate for only brief periods, this may explain why accommodation seems to have little effect on emmetropization or lens-compensation. Furthermore, we found that there was a great difference in how long the intervals between lenswearing bouts could be before lens-compensation was lost: positive lenses

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inhibited ocular elongation for much longer periods than negative lenses stimulated it.49 This probably explains the stronger effects of positive lenses when they are alternated with negative lenses, but it does not explain the similar effects of brief periods of positive and negative lenses when worn alone. Although there are some temporal similarities among the responses of chicks, tree shrews and monkeys,50 it is likely that other relevant temporal parameters may differ among species. The implication of these complex timing effects is that one might be on the wrong track by looking at the amount of time that children spend reading as a risk-factor for myopia; the more important aspect may be the particular temporal pattern with which a child alternates reading and looking up from reading. Indeed, reading may itself involve an alternation of myopic and hyperopic defocus both in the fovea and the periphery because when a child looks up from reading he or she may experience a transient near-work induced myopia.51 Although of course we cannot impose different temporal patterns of alternation of myopic and hyperopic defocus in children, we may be able to obtain clinically useful information by studying the temporal pattern of reading shown by children that become myopic compared with those who do not.

Mechanisms of Emmetropization The two most prominent changes in the eye that contribute to emmetropization and its laboratory analogue, eyeglass-lens compensation, are changes in ocular length (with associated scleral remodeling) and changes in choroidal thickness. If the eye elongates more than usual or if the choroid thins, the retina is pulled backward, making the eye more myopic. Conversely, if the eye slows its elongation while the cornea and lens continue to grow (thereby increasing their focal length), the eye will become less myopic, as would occur if the choroid thickens. Wearing positive eyeglass lenses, which put the image in front of the photoreceptors, causes the choroid to thicken and the ocular elongation to slow, both acting to bring the image back onto the photoreceptors, as do the opposite changes if negative lenses are worn. To understand how homeostatic control of eye growth brings the normal eye towards emmetropia and how disturbances of this process cause myopia, an understanding of the signaling between the tissues of the eye is important. Although visual control of eye growth can occur within the

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eye, without the influence of the brain,52,53 it appears that, at least in guinea pigs, the detection of the sign of defocus requires the brain to be connected to the eyes.22 Whether or not the control of eye-growth is local or brain-mediated, the retina must signal the defocus, and the choroid must conduct or create the signals reaching the sclera. We will now discuss each of these tissues. Scleral similarities and differences between humans and chickens The sclera of chickens differs from that of humans. The chicken has the classic vertebrate sclera, consisting of a layer of cartilage surrounded by layers of fibrous connective tissue, whereas in most mammals, including primates and rodents, the cartilage has been lost, although molecular traces of its existence persist.54 One can speculate that the loss of the cartilage in early mammalian evolution was innocuous because the precursor of Eutherian mammals had small eyes, which did not require the reinforcement of the cartilage. However, the consequence of the loss of cartilage is that whereas birds can have large eyes despite thin (about 120 µ m) scleras, mammals with large eyes, such as elephants, have grotesquely hypertrophied scleras, as much as 8 mm thick,55 apparently required to maintain the shape of the large eye (reviewed by McBrien and Gentle).56 Despite this difference in scleral anatomy, the fibrous sclera of mammals and the fibrous layer of the avian sclera appear to grow similarly. When ocular elongation accelerates, the fibrous sclera thins and loses material both in mammals57,58 and birds.59,60 The cartilaginous layer of the sclera of birds, however, increases its thickness as the eye elongates, and this is accompanied by an increase in synthesis of proteoglycans.61–63,60 Because the cartilaginous layer dominates biochemical measurements of scleral growth, it can mislead one to conclude that the avian sclera grows oppositely to the mammalian one. Although one might expect that the cartilage-reinforced sclera of birds would affect the shape the eye could take, when chicks64 and monkeys65 have half of their visual field covered by a diffuser, the eyes expand only in the visually deprived half, with the boundary between the visually experienced and visually deprived halves being approximately equally sharp in both species. This is further evidence that the tissue differences between

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the two types of sclera do not imply completely different mechanisms of ocular growth regulation. What then is the relation between the two layers of the sclera? If the fibrous and cartilaginous layers from an eye growing toward myopia (or towards hyperopia) are dissected apart, and each layer is co-cultured with the opposite layer from a normal eye, it is the fibrous layer that determines the rate of growth of the “recipient” cartilaginous layer; the condition of the “donor” cartilaginous layer does not affect the growth of the “recipient” fibrous layer.60 If it is the general condition that the fibrous layer controls the cartilaginous layer, one could suppose that when, in the course of mammalian evolution, the cartilaginous layer was lost, eye growth regulation would not have required a major change in the growth control of the sclera. These results should not be taken to imply that the fibrous sclera completely controls the growth of the cartilaginous sclera. We have recently found that the fibroblast growth factor causes the fibrous layer to increase its synthesis of proteoglycans and causes the cartilaginous layer to do the opposite, when the different layers are cultured separately.66 It is unclear where this growth factor comes from under natural conditions because it is also found in the retina, in the nerve fiber layer and inner plexiform layer,67 and in choroidal microvascular endothelium68 and RPE cells.69 Retinal signals In part because of the ease of doing experiments on chicks, there is more known about possible retinal signals that might be registering the degree and sign of defocus in chicks than in other species. Of particular interest are molecular signals that go in opposite directions when negative vs. positive lenses are worn. Glucagon-insulin The first of these potential signals discovered was glucagon, in that the transcription of the immediate early gene ZENK (also known as Egr1, among other names) increases in glucagonergic amacrine cells, when positive lenses are worn and decreases when negative lenses are worn,70,71 these changes being independent of both illuminance levels and chromatic cues.72 Furthermore, exogenous glucagon blocks both the excessive ocular

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elongation and choroidal thinning caused by negative lenses73 and form deprivation.74 Curiously, in the eye, as in the liver, insulin has effects opposite to those of glucagon: it counters the effect of positive lenses, and even in normal eyes, increases ocular elongation and thins the choroid.75,73 Furthermore, glucagon at concentrations too low to have an effect by itself can attenuate the effects of insulin, and vice versa.73 Injections of glucagon suppress the proliferation of retinal progenitors in peripheral retina, while injections of insulin do the opposite.76 These findings support the hypothesis that glucagon and insulin may be output signals from the retina that decode the sign of defocus and modulate eye growth. The site of action of both may be on retinal pigment epithelial cells, where glucagon and insulin receptors have been found, or on the sclera, which has insulin receptors (glucagon:77,78; insulin:79,80). Interestingly, there is a subclass of glucagonergic amacrine cells in young chicks that are located in the peripheral retina, which send axons to the far peripheral retina. Because these cells are concentrated near the equator of the eye, and because injections of glucagon suppress equatorial eye growth, it may be that the expression of glucagon in these neurons determines the equatorial expansion of the eye,78 whereas neurons in the posterior retina may control axial elongation. At present it is unclear whether there is a primate version of this signaling mechanism. Glucagon has not been found in primate retinal neurons, although the expression of the transcription factors erg-1 and fra-2 was found to be decreased in ON-bipolar cells and a subclass of GABA-ergic cells in primate retinas of eyes wearing diffusers, whereas erg-1 (but not fra-2) increased in eyes wearing +3 D lenses, which corrected the eye’s normal hyperopic refractive error,81 suggesting that in-focus images stimulate expression of these transcription factors more than blurred or diffused images. However, because visual stimulation changes the expression of many transcription factors in many retinal neurons, this difference may reflect the amount of retinal stimulation rather than a specific signal related to defocus. Retinoic acid Retinoic acid is a metabolic product of vitamin A with a myriad of critical roles during development. In the retina, retinoic acid increases if eyes are made to accelerate their elongation by wearing diffusers or negative lenses,

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and decreases if eyes are made to slow their elongation (chicks:82,85, guinea pigs83). Inhibiting synthesis of retinoic acid reduces form-deprivation myopia.84 As will be discussed below, synthesis of retinoic acid by the choroid also depends on the direction of eye-growth, but the synthesis in the retina may be uncoupled from that in the choroid, perhaps because the retina does not secrete much retinoic acid.85 Therefore, retinal retinoic acid may be more of an indication of other retinal functions than of a signal acting on other ocular tissues. Dopamine Dopamine is a neurotransmitter used by specific amacrine cells in both chick and monkey retina. The levels of dopamine are reduced in eyes wearing diffusers86 and negative lenses,87 and increased in eyes recovering from form-deprivation myopia.88 Injections of apomorphine, a nonspecific dopamine agonist, inhibit the development of form-deprivation myopia86,89,90 and lens-induced myopia91 in both chicks and monkeys, suggesting a similar role for both species. The mechanism is presumably mediated via the D2 receptors, as a D2-specific agonist, but not a D1 agonist, is effective in inhibiting deprivation-induced myopia.92 Despite the promising nature of these results, the findings that reducing dopamine action either by haloperidol, an antagonist, or 6-hydroxydopamine, which depletes dopamine, also suppresses myopia, casts doubt on dopamine being a primary growth-inhibitory signal molecule.92 It remains possible, however, that the conflicting evidence obtained from these other studies might be a reflection of actions on dopamine receptors in different tissues, such as the RPE or choroid. Acetylcholine A third potential retinal signal molecule is acetylcholine, muscarinic antagonists of which have been used to prevent myopia in humans for many years.93–95 Although originally thought to act on the ciliary muscle preventing accommodation, it is now clear that this is not the case because atropine and pirenzepine also inhibit the development of lens- and diffuser-induced myopia in chickens,96–99,91 in which accommodation is not mediated by muscarinic receptors. Although the site of action of these drugs is unknown, evidence for its being retinal is weak: neurotoxin depletion of the cholinergic amacrine cells does not alter the eye’s response to

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form deprivation, nor does it alter the effects of atropine.98,100 Muscarinic receptors are found in every tissue of the eye. Choroidal signals Although it is clear that any molecule influencing scleral growth must originate in or pass through the choroid, the phenomenon that brought attention to the physiological state of the choroid was the dramatic thickening that occurred when chick eyes were exposed to myopic defocus either imposed by positive lenses or prior form-deprivation.101,102 This thickening could cause the choroid to increase in thickness by as much as a millimeter, four times the normal thickness, accounting for at least half of the refractive compensation to the lens. This response was also found in rhesus monkeys,103 marmosets104 and guinea pigs,12 but to a much smaller extent, having little refractive effect. Although one is tempted to dismiss the choroidal changes in mammals as insignificant, because they are so small, there is evidence from chicks that the state of the choroid has a profound influence on the state of the sclera, and hence on the growth of the eye. Specifically, if one takes the choroid from an eye with imposed myopia or hyperopia and cultures it with sclera from an untreated eye, the sclera responds in the direction predicted by the choroid from which it came: The rate of synthesis of DNA and proteoglycans in the sclera is increased in scleras incubated with choroids from eyes becoming myopic and decreased in scleras with choroids from eyes becoming hyperopic.60 By the same token, fluid aspirated from the choroid of slower-growing eyes recovering from deprivation myopia inhibited scleral proteoglycan synthesis while fluid from eyes becoming myopic stimulated synthesis.105 This choroidal modulation of scleral growth is likely the result of choroidal secretion of signals to which the sclera is sensitive. Potential molecules include retinoic acid, transforming growth factor-beta (TGF-beta), and ovotransferrin, all of which have their choroidal content changed by whether the eye is growing towards myopia or hyperopia, as will be discussed. In addition, the choroid secretes tissue plasminogen activator, tPA,106 which stimulates the production of metalloproteinases and collagenases, which degrade extracellular matrix components, as would be involved in the remodeling of the sclera. Retinoic acid furnishes a particularly clear example of how this choroidal modulation of scleral growth might work. The chick choroid,

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like that of monkeys, secretes large amounts of retinoic acid.85,107 If chicks wear negative lenses or diffusers, which increase ocular elongation, the synthesis of choroidal retinoic acid falls to barely detectable levels; and with positive lenses or removal of a diffuser, both of which decrease ocular elongation, the synthesis increases four-fold. Furthermore, retinoic acid inhibits scleral proteoglycan synthesis in chick sclera.85 Thus, the increased secretion of retinoic acid by the choroid inhibits the growth of the cartilaginous layer of the chick sclera; under normal circumstances this would be associated with an increase in growth of the fibrous sclera. It is not known whether retinoic acid independently affects the growth of the fibrous sclera in birds. In mammals, however, this is known. In marmosets107 and guinea pigs,108 increased ocular elongation in response to form deprivation (or negative lens wear in guinea pigs) is associated with an increase in choroidal retinoic acid and an inhibition of scleral proteoglycan synthesis; treating marmoset sclera with retinoic acid also inhibits proteoglycan synthesis. Thus, retinoic acid inhibits overall scleral growth in both birds and mammals, although this inhibition is associated with ocular elongation being increased in mammals and decreased in birds because of the difference in scleral structure. Ovotransferrin is also greatly increased in choroids of chick eyes with inhibited elongation caused by removing diffusers, and it too inhibits scleral proteoglycan synthesis in vitro,109 supporting a role as a growth inhibitor. Transforming growth factor-beta (TGF-beta) is synthesized by the choroid of chickens,71 tree shrews110 and humans,111 and has many functions, among them extracellular matrix remodeling.111 TGF-beta has been reported to be increased in ocular tissues of form-deprived chicken eyes112 and antagonizes the growth-inhibitory effects of basic fibroblast growth factor (bFGF) in chicks,113 supporting a role as a growth-stimulator. However, subsequent findings from chicks and tree shrews71,110 do not support this role for TGF-beta being a major choroidal growth regulator.

The Role of the Choroid in the Control of Ocular Growth The overall similarity of the effects of visual experience on eye-growth in birds and mammals suggests that the underlying mechanisms are probably conserved. How, then, is one to reconcile the order-of-magnitude

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differences in the defocus-induced choroidal thickness changes between birds and mammals with the presumed similarity of control of eye growth? One possibility is that the choroid contains two independent tissues: the stroma, which thickens and thins in response to changes in refractive state101 as a result of changes in the volume of the lymphatic lacunae,114 and a secretory tissue, perhaps the lamina fusca, which faces the sclera. If this is so, the choroidal thickening response would be independent of the secretory functions of the choroid. Alternatively, the choroidal thickening and thinning may reflect the physiological state of the choroid, which might determine what growth-modulating molecules are secreted. The evidence in favor of this coupling is that a variety of visual manipulations that inhibit ocular elongation also cause a transient choroidal thickening lasting only a few hours, which would generally not be detected in eye-growth experiments lasting days. For example, wearing negative lenses causes chick eyes to rapidly elongate and become myopic, but removal of the lenses for two hours per day cancels both of these effects and causes a transient choroidal thickening.115 Inhibiting this daily thickening by preventing nitric oxide synthesis causes the eye to continue to elongate as though wearing the negative lenses continuously.116 It would be interesting to see if the same transient changes in choroidal thickness are associated with inhibition of elongation in primates. Diurnal rhythms and control of ocular growth During growth of a tissue, cells generally alternate between dividing and synthesizing. In the case of tissues like the sclera, this means that cells stop synthesizing extracellular matrix when they are dividing. If individual cells divided asynchronously, this alternation would not be evident in any measure of growth, either at the tissue synthetic level or at the organ level. However, it is clear not only that the eye elongates with a daily rhythm, growing more during the day than at night,117–119 but also that the growth of isolated pieces of sclera is controlled by a circadian oscillator.120 This means that there must be substantial synchrony of the chondrocytes within the tissue. There are two lines of evidence that this diurnal growth rhythm is modulated by vision. First, dopamine and melatonin constitute a reciprocal system that mediates ocular diurnal rhythms, with dopamine being the “day” signal and melatonin the “night” signal, controlling rod and cone sensitivity, retinomotor movements, and pigment dispersal in RPE cells (review:121).

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Both molecules seem to be involved in the visual control of eye-growth: day-time, but not night-time, levels of dopamine are reduced by formdeprivation,86 and apomorphine, a non-specific dopamine agonist, inhibits the development of myopia induced by wearing diffusers or negative lenses in both chicks86,90,91 and monkeys.89 Melatonin is a potent modulator of retinal dopamine release,122,123 but also has receptors in the cornea, lens, choroid, and sclera.124 Systemic administration of melatonin at night resulted in a significant increase in vitreous chamber depth in normal chick eyes and choroidal thinning in form-deprived eyes,124 and one of the three types of melatonin receptors is increased in the retina/ RPE/choroid in form-deprived eyes. Second, in growing chick eyes, there are diurnal rhythms in choroidal thickness and in the rate of ocular elongation, the phases of which are nearly opposite, with the choroid being thickest at night and the eye longest during the day.118 Inhibition of ocular elongation by positive lenses shifts the two rhythms into near-synchrony, whereas acceleration of ocular elongation shifts the rhythms into anti-phase.125 The phase difference between the rhythms in axial length and choroid thickness predicts the rate of growth on the following day in individual animals when the sign of defocus is switched from myopic to hyperopic or vice versa. Although equivalent studies have not been done on mammals, there are rhythms in axial length and choroidal thickness in humans126,127 and marmosets.128 When marmosets are young, with rapidly elongating eyes, the two rhythms are in approximate anti-phase, whereas in older adolescents, in whom growth has slowed, the rhythms are closer in phase, analogous to the patterns seen in chicks with different rates of ocular growth. If choroidal thickness is correlated with the molecules that the choroid is releasing, as suggested in the previous section, perhaps these molecules stimulate ocular elongation more at one portion of the cycle than at another, or perhaps modulators of metalloproteinases involved in scleral remodeling, such as tPA, may be more effective at certain points in the daily cycle. Finally, the effect of bright light outdoors in preventing chicken129 and human130 myopia may act by stimulation of dopamine release.

Conclusions The eyes of a wide variety of vertebrates adjust their growth using visual cues. The pervasive similarities in the mechanisms shown to operate in

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chicks and primates suggest that the emmetropization machinery has been highly conserved in evolution. The bidirectional modulation of eye growth by hyperopic and myopic defocus in disparate species suggests that the same may occur in children. This possibility should not be ignored in considering what might make children myopic and what could be done to prevent it. Furthermore, the similar local effects in chicks and monkeys of defocus limited to one region of the retina implies that one must study the peripheral refractions in humans to understand the etiology of myopia. This, in turn, implies that one must consider the effects of alternating periods of myopic and hyperopic defocus, because all regions of the retina are continuously exposed to these alternations with the exception of the fovea, which is kept more-or-less in focus by ocular accommodation. From the animal work, it appears likely that understanding the spatial and temporal distribution of defocus will go a long way to understanding human myopia.

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60. Marzani D, Wallman J. (1997) Growth of the two layers of the chick sclera is modulated reciprocally by visual conditions. Invest Ophthalmol Vis Sci 38: 1726–1739. 61. Rada JA, Thoft RA, Hassell JR. (1991) Increased aggrecan (cartilage proteoglycan) production in the sclera of myopic chicks. Developmental Biology 147: 303–312. 62. Rada JA, McFarland AL, Cornuet PK, Hassell JR. (1992) Proteoglycan synthesis by scleral chondrocytes is modulated by a vision dependent mechanism. Curr Eye Res 11: 767–782. 63. Rada JA, Matthews AL. (1994) Visual deprivation upregulates extracellular matrix synthesis by chick scleral chondrocytes. Invest Ophthalmol Vis Sci 35: 2436–2447. 64. Wallman J, Gottlieb MD, Rajaram V, Fugate-Wentzek LA. (1987) Local retinal regions control local eye growth and myopia. Science 237: 73–77. 65. Smith EL, Huang J, Hung LF, et al. (2009) Hemiretinal form deprivation: evidence for local control of eye growth and refractive development in infant monkeys. Invest Ophthalmol Vis Sci 50: 5057–5069. 66. Zhu X, Garzon A, Wallman J. (2009) FGF has opposite effects on the fibrous and cartilaginous layers of the chick sclera. Invest Ophthalmol Vis Sci E-abstract #3842. 67. de longh R, McAvoy J. (1992) Distribution of acidic and basic fibroblast growth factor (FGF) in the foetal rat eye: Implications for lens development. Growth Factors 6: 159–177. 68. Keithahn M, Aotaki-Keen A, Schneeberger S, et al. (1997) Expression of fibroblast growth factor-5 by bovine choroidal endothelial cells in vitro. Invest Ophthalmol Vis Sci 38: 2073–2080. 69. Schweigerer L, Malerstein B, Neufeld G, Gospodarowicz D. (1987) Basic fibroblast growth factor is synthesized in cultured retinal pigment epithelial cells. Biochem Biophys Res Commun 143: 934–940. 70. Fischer A, McGuire J, Schaeffel F, Stell W. (1999) Light and defocus-dependent expression of the transcription factor ZENK in the chick retina. Nat Neurosci 2: 706–712. 71. Simon P, Feldkaemper M, Bitzer M, et al. (2004) Early transcriptional changes of retinal and choroidal TGFB-2, RALD-2, and ZENK following imposed positive and negative defocus in chickens. Mol Vis 10: 588–597. 72. Bitzer M, Schaeffel F. (2002) Defocus-induced changes in ZENK expression in the chicken retina. Invest Ophthalmol Vis Sci 43: 246–252. 73. Zhu X, Wallman J. (2009) Opposite effects of glucagon and insulin on competition for spectacle lenses in chicks. Invest Ophthalmol Vis Sci 50: 24–36. 74. Vessey KA, Lencses KA, Rushforth DA, et al. (2005) Glucagon receptor agonists and antagonists affect the growth of the chick eye: a role for

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75. 76.

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glucagonergic regulation of emmetropization. Invest Ophthalmol Vis Sci 46: 3922–3931. Feldkaemper M, Neacsu I, Schaeffel F. (2009) Insulin acts as a powerful stimulator of axial myopia in chicks. Invest Ophthalmol Vis Sci 50: 13–23. Fischer A, Omar G, Walton N, et al. (2005) Glucagon-expressing neurons within the retina regulate the proliferation of neural progenitors in the circumferential marginal zone of the avian eye. J Neurosci 25: 10157–10166. Buck L, Schaeffel F, Simon P, Feldkaemker M. (2004) Effects of positive and negative lens treatment on retinal and choroidal glucagon and glucagon recepter mRNA levels in the chicken. Invest Ophthamol 45(2): 402–409. Fischer A, Ritchey E, Scott M, Wynne A. (2008) Bullwhip neurons in the retina regulate the size and shape of the eye. Dev Biol 317: 196–212. Waldbillig R, Arnold D, Fletcher R, Chader G. (1990) Insulin and IGF-1 binding in chick sclera. Invest Ophthalmol Vis Sci 31: 1015–1022. Waldbillig R, Arnold D, Fletcher R, Chader G. (1991) Insulin and IGF-1 binding in developing chick neural retina and pigment epithelium: a characterization of binding and structural differences. Exp Eye Res 53: 13–22. Zhong X, Ge J, Smith EL, Stell W. (2004) Image defocus modulates activity of bipolar and amacrine cells in macaque retina. Invest Ophthalmol Vis Sci 45: 2065–2074. Seko Y, Shimizu M, Tokoro T. (1998) Retinoic acid increases in the retina of the chick with form deprivation myopia. Ophthalmic Res 30: 361–367. McFadden SA, Howlett MH, Mertz JR. (2004) Retinoic acid signals the direction of ocular elongation in the guinea pig eye. Vision Res 44: 643–653. Bitzer M, Feldkaemper M, Schaeffel F. (2000) Visually induced changes in components of the retinoic acid system in fundal layers of the chick. Exp Eye Res 70: 97–106. Mertz JR, Wallman J. (2000) Choroidal retinoic acid synthesis: a possible mediator between refractive error and compensatory eye growth. Exp Eye Res 70: 519–527. Stone RA, Lin T, Laties AM, Iuvone PM. (1989) Retinal dopamine and formdeprivation myopia. Proc Nat Acad Sci 86: 704–706. Guo SS, Sivak JG, Callender MG, Diehljones B. (1995) Retinal dopamine and lens-induced refractive errors in chicks. Curr Eye Res 14: 385–389. Pendrak K, Nguyen T, Lin T, et al. (1997) Retinal dopamine in the recovery from experimental myopia. Curr Eye Res 16: 152–157. Luvone PM, Tigges M, Stone RA, et al. (1991) Effects of apomorphine, a dopamine receptor agonist, on ocular refraction and axial elongation in a primate model of myopia. Invest Ophthalmol Vis Sci 32: 1674–1677. Rohrer B, Spira AW, Stell WK. (1993) Apomorphine blocks form-deprivation myopia in chickens by a dopamine D2-receptor mechanism acting in retina or pigmented epithelium. Vis Neurosci 10: 447–453.

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91. Schmid KL, Wildsoet C. (2004) Inhibitory effects of apomorphine and atropine and their combination on myopia in chicks. Optometry Vis Sci 81: 137–147. 92. Schaeffel F, Bartmann M, Hagel G, Zrenner E. (1995) Studies on the role of the retinal dopamine/melatonin system in experimental refractive errors in chickens. Vision Res 35: 1247–1264. 93. Bedrossian RH. (1971) The effect of atropine on myopia. Ann Ophthalmol 3: 891–897. 94. Shih YF, Chen CH, Chou AC, et al. (1999) Effects of different concentrations of atropine on controlling myopia in myopic children. J Ocular Pharm Therapeut 15: 85–90. 95. Kennedy RH, Dyer JA, Kennedy MA, et al. (2000) Reducing the progression of myopia with atropine: a long term cohort study of Olmsted County students. Binocul Vis Strabismus Q 15: 281–304. 96. Stone RA, Lin T, Laties AM. (1991) Muscarinic antagonist effects on experimental chick myopia. Exp Eye Res 52: 755–7588. 97. McBrien NA, Moghaddam HO, Reeder AP. (1993) Atropine reduces experimental myopia and eye enlargement via a nonaccommodative mechanism. Invest Ophthalmol Vis Sci 34: 205–215. 98. Schwahn HN, Schaeffel F. (1994) Chick eyes under cycloplegia compensate for spectacle lenses despite six-hydroxy dopamine treatment. Invest Ophthalmol Vis Sci 35: 3516–3524. 99. Luft W, Ming Y, Stell W. (2003) Variable effects of previously untested muscarinic receptor antagonists on experimental myopia. Invest Ophthalmol Vis Sci 44: 1330–1338. 100. Fischer A, Miethke P, Morgan I, Stell W. (1998) Cholinergic amacrine cells are not required for the progression and atropine-mediated suppression of form deprivation myopia. Brain Res 794: 48–60. 101. Wallman J, Wildsoet C, Xu A, et al. (1995) Moving the retina: choroidal modulation of refractive state. Vision Res 35: 37–50. 102. Wildsoet C, Wallman J. (1995) Choroidal and scleral mechanisms of compensation for spectacle lenses in chicks. Vision Res 35: 1175–1194. 103. Hung L-F, Wallman J, Smith EI. (2000) Vision-dependent changes in the choroidal thickness of Macaque monkeys. Invest Ophthalmol Vis Sci 41: 1259–1269. 104. Troilo D, Nickla D, Wildsoet C. (2000) Choroidal thickness changes during altered eye growth and refractive state in a primate. Invest Ophthalmol Vis Sci 41: 1249–1258. 105. Rada J, Palmer L. (2007) Choroidal regulation of scleral glycosaminoglycan synthesis during recovery from induced myopia. Invest Ophthalmol Vis Sci 48: 2957–2966.

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106. Wang Y, Gilliles C, Cone RE, O’Rourke J. (1995) Extravascular secretion of t-PA by the intact superfused choroid. Invest Ophthalmol Vis Sci 36: 1625–1632. 107. Troilo D, Nickla D, Mertz J, Summers Rada J. (2006) Change in the synthesis rates of ocular retinoic acid and scleral glycosaminoglycan during experimentally altered eye growth in marmosets. Invest Ophthalmol Vis Sci 47: 1768–1777. 108. McFadden S, Howlett M, Mertz J. (2004) Retinoic acid signals the direction of ocular elongation in the guinea pig eye. Vision Res 44: 643–653. 109. Rada J, Huang Y, Rada K. (2001) Identification of choroidal ovotransferrin as a potential ocular growth regulator. Curr Eye Res 22: 121–132. 110. Jobling AI, Wan R, Gentle A, Bui BV, McBrien N. (2009) Retinal and choroidal TGF-B in the tree shrew model of myopia: Isoform expression, activation and effects on function. Exp Eye Res 88: 458–466. 111. Lutty GA, Merges C, Threlkeld AB, Crone S, McLeod DS. (1993) Heterogeneity in localization of isoforms of TGF-beta in human retina, vitreous, and choroid. Invest Ophthalmol Vis Sci 34: 477–487. 112. Seko Y, Shimokawa H, Tokoro T. (1995) Expression of bFGF and TGF-B2 in experimental myopia in chicks. Invest Ophthalmol Vis Sci 36: 1183–1187. 113. Rohrer B, Stell WK. (1994) Basic Fibroblast Growth-Factor (Bfgf) and Transforming Growth-Factor-Beta (TGF-Beta) Act as Stop and Go signals to modulate postnatal ocular growth in the chick. Exp Eye Res 58: 553–561. 114. Liang H, Crewther S, Crewther D, Junghans B. (2004) Structural and elemental evidence for edema in the retina, retinal pigment epithelium, and choroid during recovery from experimentally induced myopia. Invest Ophthalmol Vis Sci 45: 2463–2474. 115. Nickla D. (2007) Transient increases in choroidal thickness are consistently associated with brief daily visual stimuli that inhibit ocular growth in chicks. Exp Eye Res 84: 951–959. 116. Nickla D, Wilken E, Lytle G, et al. (2006) Inhibiting the transient choroidal thickening response using the nitric oxide synthase inhibitor L-NAME prevents the ameliorative effects of visual experience on ocular growth in two different visual paradigms. Exp Eye Res 83: 456–464. 117. Weiss S, Schaeffel F. (1993) Diurnal growth rhythms in the chicken eye: Relation to myopia development and retinal dopamine levels. J Comp Physiol A 172: 263–270. 118. Nickla DL, Wildsoet C, Wallman J. (1998) Visual influences on diurnal rhythms in ocular length and choroidal thickness in chick eyes. Exp Eye Res 66: 163–181. 119. Papastergiou GI, Schmid GF, Riva CE, et al. (1998) Ocular axial length and choroidal thickness in newly hatched chicks and one-year-old chickens

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fluctuate in a diurnal pattern that is influenced by visual experience and intraocular pressure changes. Exp Eye Res 66: 195–205. Nickla DL, Rada JA, Wallman J. (1999) Isolated chick sclera shows a circadian rhythm in proteoglycan synthesis perhaps associated with the rhythm in ocular elongation. J Comp Physiol [A] 185: 81–90. Besharse JC, Iuvone PM, Pierce ME. (1988) Regulation of rhythmic photoreceptor metabolism: a role for post-receptoral neurons. Prog Retinal Res 7: 21–61. Ribelayga C, Wang Y, Mangel SC. (2004) A circadian clock in the fish retina regulates dopamine release via activation of melatonin receptors. J Physiol 554: 467–482. Lorenc-Duda A, Berezinska M, Urbanska A, et al. (2009) Dopamine in the turkey retina — an impact of environmental light, circadian clock, and melatonin. J Mol Neurosci 38: 12–18. Summers Rada J, Wiechmann A. (2006) Melatonin receptors in chick ocular tissues: implications for a role of melatonin in ocular growth regulation. Invest Ophthalmol Vis Sci 47: 25–33. Nickla D. (2006) The phase relationships between the diurnal rhythm in axial length and choroidal thickness and the association with ocular growth rate in chicks. J Comp Physiol A 192: 399–407. Stone RD, Quinn GE, Francis EL, et al. (2004) Diurnal axial length fluctuations in human eyes. Invest Ophthalmol Vis Sci 45: 63–70. Brown JS, Flitcroft DI, Ying G, et al. (2009) In vivo human choroidal thickness measurements: Evidence for diurnal fluctuations. Invest Ophthalmol Vis Sci 50: 5–12. Nickla D, Wildsoet C, Troilo D. (2002) Diurnal rhythms in intraocular pressure, axial length, and choroidal thickness in a primate model of eye growth, the common marmoset. Invest Ophthalmol Vis Sci 43: 2519–2528. Ashby R, Ohlendorf A, Schaeffel F. (2009) The effect of ambient illuminance on the development of deprivation myopia in chicks. Invest Ophthalmol Vis Sci 50: 5348–5354. Rose K, Morgan I, Ip J, et al. (2008) Outdoor activity reduces the prevalence of myopia in children. Ophthalmology 116: 1229–1230.

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4.2 The Mechanisms Regulating Scleral Change in Myopia Neville A. McBrien*

Myopia is one of the most prevalent ocular conditions and is the result of a mismatch between the power and axial length of the eye. As a result, images of distant objects are brought to a focus in front of the retina, resulting in blurred vision. In the vast majority of cases, the structural cause of myopia is an excessive axial length of the eye, or more specifically, the vitreous chamber depth. In about 3% of the general population in Europe, USA and Austraila, the degree of myopia is above 6 dioptres and is termed high myopia. In South East Asia the figure is closer to 20% of the general population with high myopia. The prevalence of sight-threatening ocular pathology is markedly increased in eyes with high degrees of myopia (> –6D). This results from the excessive axial elongation of the eye, which, by necessity, must involve the outer coat of the eye, the sclera. Consequently, high myopia is reported as a leading cause of registered blindness and partial sight. Current theories of refractive development acknowledge the pivotal role of the sclera in the control of eye size and the development of myopia. This chapter considers the major structural, biochemical, and biomechanical mechanisms that underlie abnormal development of the mammalian sclera in myopia. This chapter will characterize the aberrant mechanisms of scleral remodelling that underlie the development of myopia. In describing these mechanisms, certain critical events in both the early and later stages of myopia development that lead to scleral thinning, the loss of scleral tissue, the weakening of the scleral mechanical properties and, ultimately, to the development of posterior staphyloma will be reviewed. In conclusion, it will be proposed that the prevention of aberrant scleral remodelling must be the

*Corresponding author. Department of Optometry and Vision Sciences, The University of Melbourne, Victoria 3010, Australia. E-mail: [email protected].

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goal of any long-term therapy to reduce the permanent vision loss associated with high myopia.

Introduction Myopia is a common refractive error in which the resultant focal length of the optical components of the eye is incompatible with its overall axial length. In the vast majority of cases of human myopia (>95%), the refractive error develops due to excessive axial eye size, and not through changes in corneal or lens power.1 Indeed, the single structural correlate responsible for this excessive axial eye size in human myopia of either youth-onset or adult-onset is an enlarged vitreous chamber depth.2 Myopia has a high prevalence in the human population, with some degree of myopia present in 20–30% of individuals in North American, European, and Australian populations.3–5 In selected South-East Asian populations the prevalence is reported to be as high 80%.6,7 High degrees of myopia, typically classed as in excess of 6 dioptres (D), are of major concern due to the fact that the incidence of myopia-related pathology, often in the form of chorioretinal degenerations and/or retinal detachments, is significantly increased.8,9 In fact, up to 70% of myopes over 6 D are reported to have sight-threatening ocular pathology.10 Prevalence studies indicate that 12–15% of all myopes have refractive errors over –6 D, resulting in a prevalence of high myopia in the general population of approximately 3%.11 The ocular pathology associated with high myopia is among the leading causes of registered blindness and partial sight in populations of the developed world.12 Given that high myopia is invariably due to increased eye size, the mechanical stresses placed on the retina and choroid during eye movements are greatly increased in larger eyes, implicating the mechanical consequences of increased eye size in the development of chorioretinal pathology.13,14 In conjunction with a pathological weakening of the sclera,15 the above observations demonstrate the importance of the sclera in maintaining eye size. Postnatal eye growth is constrained by the properties of the outer coat of the eye. The sclera comprises by far the major component of the ocular coat. The sclera is a fibrous shell of collagenous, fibroblast maintained connective tissue, which is continuous with the cornea anteriorly forming

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an essentially closed shell around the structures of the anterior, equatorial, and posterior eye. Although historically the sclera has been considered a relatively inert tissue in metabolic terms, more recent research has shown it to undergo constant remodelling during eye growth, continuing throughout life, albeit at a lesser degree.16 In common with other specialized connective tissues, the sclera is highly organized, enabling it to perform its roles. A major functional role of the sclera is the protection of the delicate intra-ocular structures. However, the sclera plays important roles in accommodation, by providing a stable base for the contraction of the ciliary muscle, in promoting accurate eye movements, by providing a stable base for extraocular muscle contractions, and in allowing vascular and neural access to adjacent intra-ocular structures. Most importantly from the viewpoint of this chapter, the sclera, through maintenance of stable ocular dimensions, is critical in determining the absolute size of the eye, and thus plays an important role in determining the absolute refractive error of the eye. Due to the limitations of studies on post-mortem human myopic eyes in elucidating the biological mechanisms underlying myopia development, researchers have developed suitable animal models of the condition. Since the development of the first animal models of myopia in the 1970’s,17 greater understanding of the mechanisms underlying scleral thinning during the development of high myopia has been possible. The context of this review is focussed on the role of the sclera in myopia development and its implications for understanding and treating the human condition. Therefore, most of the discussion of data from experimental models will concentrate on the well-characterized mammalian models of myopia, namely the tree shrew, marmoset, and monkey, whose scleral structure is known to be similar to human. In particular, as the most detailed studies of the role of the sclera in myopia have been conducted on the tree shrew model, results from this model will feature strongly. The tree shrew is a diurnal mammal, close to the primate line, with a cone-dominated retina and normal lifespan of six to eight years in captivity.18 Despite the fact that its eye is smaller than that of humans (≈8 mm), it has been shown to be a reliable model of scleral changes in myopia in that it has the same scleral structure and undergoes similar changes to those found in human myopes.19

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Gross Scleral Anatomy The mature sclera forms a spheroidal shell and accounts for some 85% of the total ocular surface. It is enclosed by the episclera, a loose connective tissue connecting the sclera with the overlying conjunctiva anteriorly and generally continuous with the tissue of Tenon’s capsule elsewhere on the globe. In humans, the sclera gradually thickens from the anterior/equatorial regions towards the posterior to reach a maximum thickness of approximately 1 mm at the posterior pole. Although it is essentially continuous, the sclera undergoes a number of specific regional modifications to its gross structure to facilitate rectus muscle insertions, the exit of the optic nerve fiber bundles at the lamina cribosa, also acting as a conduit for the central retinal artery and vein and a number of other nerves and vessels en route to anterior ocular structures. Post-natal scleral growth displays a characteristic anterior–posterior growth axis, as is the case in the embryonic sclera.20 Structural organization of the sclera The sclera is a typical fibrous connective tissue predominantly consisting of collagen. In mammals, collagen accounts for as much as 90% of the scleral dry weight and the vast majority of this collagen (as much as 99%) has been estimated to be type I collagen.21 However, low levels of other fibrillar collagen subtypes, including type III and V have also been reported in the mammalian sclera, and it is possible to attribute likely roles to each of these subtypes.22,11 Scleral collagen fibrils are largely heterologous. Collagen type V has been found to be important in regulating fibril diameter during fibrillogenesis, as evidenced by the very high collagen type V concentration in the cornea to produce a uniform collagen fibril diameter.23 Other reported collagen subtypes of the sclera include types VI and XII, both of which are considered fibril-associated collagens, and the nonfibril forming collagen types VIII and XIII. Proteoglycans are also a major component of the scleral extracellular matrix. A number of different proteoglycans, all consisting of a genetically distinct core protein and one or more attached glycosaminoglycan side chains, have been reported within the mammalian sclera. The mammalian sclera is rich in hyaluronan, a unique, non-sulphated glycosaminoglycan that does not associate with a core protein of its own. The sclera also contains large amounts of dermatan and chondroitin sulphate-based

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proteoglycans, particularly the small proteoglycans, decorin, and biglycan.16 These small proteoglycans play an important role in regulating collagen fibril assembly and interaction.24 In addition to these proteoglycans, larger proteoglycans, such as aggrecan, are also present in the scleral extracellular matrix. These ‘aggregating’ proteoglycans, with many glycosaminoglycan side chains, are likely to be important in the regulation of scleral hydration. Remodelling of the structural matrix of the sclera has been shown to be mediated by a number of protease enzymes, the most extensively studied of these being the matrix metalloproteinase (MMP) family. Members of the gelatinase (MMP-2 and MMP-9) and stromelysin (MMP-3) families are present in the sclera and are involved in scleral remodelling during growth and development, since these enzymes are all known to be involved in the breakdown of collagen.25–27 Members of the collagenase family, most notably MMP-1, are also present in the sclera, particularly in anterior regions of the primate sclera, where they are thought to play a role in mediating the uveoscleral aqueous outflow pathway.28 At least two of the four natural regulators of MMPs, the tissue inhibitors of matrix metalloproteinase (TIMPs), are also present in the sclera with reports of TIMP-1 and TIMP-2 in mammalian species.29,27 Cellular content of the sclera The structural organization of the sclera is largely reliant on the activity of the major extracellular matrix-producing cell, the fibroblast. Other cells, such as melanocytes and the normal transient population of inflammatory response cells are found in the mammalian sclera and are thought to derive from the choroid.30 The scleral fibroblasts, which reside between the collagen fiber bundle lamellae, are typically described as having a flattened spindle shape with a flattened nucleus. They have long branching processes that reach across relatively long distances. Scleral fibroblasts, like many other cell types, express integrins, such as the α1, α2, and β1 subtypes.31 It is likely that clustering of integrin receptors, mediate scleral fibroblast communication with the extracellular matrix. Cell–cell communication within the scleral extracellular matrix is mediated through a complex cascade of growth factors, and among those currently identified within the scleral extracellular matrix are members of the insulin-like growth factor (IGF-I and IGF-II), transforming growth factor-beta (TGF-β1, 2 and 3), and fibroblast growth factor (FGF-2)

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families.11 In addition, a high-affinity FGF-2 receptor, FGFR-1, has also been found to be expressed.32 More recent studies have demonstrated that, in addition to expressing the expected collagen, MMP and TIMP subtypes, fibroblasts express mRNA for the muscarinic receptor subtypes M1, M2, M3, M4, and M5.33 Another finding of particular interest in terms of the biomechanical strength of the sclera is the fact that many scleral fibroblasts display a myofibroblastic phenotype in that they express α-smooth muscle actin, organized within the cytoskeletal architecture.34–36 The sclera is one of the few structures in the body that has a constant population of myofribroblasts. Mechanical properties of the sclera The biomechanical properties of the sclera are dependent upon a number of aspects of the scleral extracellular matrix, which can broadly be discussed within three categories. The first is the scleral structure itself, namely its thickness, the collagen fibril parameters, namely the organization of the collagen fiber bundles and the rate at which the scleral extracellular matrix is turned over. Another important determinant of the sclera’s mechanical properties is its level of hydration, which, in the absence of a barrier of epithelial or endothelial cells, is likely to be controlled by the hydrophylic carbohydrates, particularly the glycosaminoglycans. The final contributor to the mechanical properties of the sclera is the scleral fibroblasts themselves, which have recently been shown to display a myofibroblastic phenotype, thus endowing them with contractile ability.37 Myofibroblasts are generally defined as highly contractile cells that express the smooth muscle protein, α-SMA.34 Typically arising from fibroblast differentiation, these cells are capable of rapid contractile responses to imposed tissue stress, thus relieving tension within, and limiting expansion of, the surrounding matrix.38,39 These cells also control their local environment through remodelling of the surrounding extracellular matrix, strengthening it and relieving cellular stress. Characterization of the myofibroblast population of the sclera has thus far been limited, although the presence of myofibroblasts in the sclera has to date been demonstrated in all of the mammalian species assessed. Studies in human, monkey, tree shrew, and guinea pig sclera suggest that myofibroblasts comprise a subset of scleral cells, with one study suggesting an age-dependant increase in the proportion of myofibroblasts.34,35 These findings imply that scleral myofibroblasts are less prevalent when the eye is growing most rapidly (the

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juvenile phase), but that they increase in number as eye growth slows and reaches its adult size. Interestingly, unlike the normal transience of these cells in processes like wound healing, the sclera contains a stable population of myofibroblasts.

Structural Changes to the Sclera in Myopia Scleral pathology in high myopia is a major cause, if not the most significant factor in the chorioretinal damage that results in the permanent vision loss experienced by a substantial proportion of high myopes. Thinning of the sclera, particularly at the posterior pole of the eye, has long been known to be an important feature of the development of high myopia in humans. One of the most important clinical consequences of such thinning is the formation of posterior staphyloma, a condition in which the thinned sclera becomes ectasic.40 Staphyloma formation occurs almost exclusively in the region of the posterior pole of the eye, and thus can have a catastrophic affect on vision. When compared with the scleral thickness of age-matched emmetropic eyes, high myopes show a greatly reduced thickness (up to 50% thinner) at the posterior pole of the eye, regardless of the presence of staphyloma. Scleral thinning also occurs in the equatorial and anterior regions of highly myopic human eyes, however, these changes are less marked than those encountered around the posterior pole. Early theories of scleral thinning hypothesized that the existing scleral tissue was redistributed to cover the surface of the eye as the eye enlarged, suggesting that the sclera stretched passively to accommodate the expanding eye.41 However, early histological observations also showed that profound morphological changes, in addition to the thinning, were apparent in the scleral extracellular matrix. For example, scleral collagen fibril morphology was found to be altered, particularly at the posterior pole of highly myopic eyes, with a characteristic shift in the fibril diameter distribution, resulting in an increased number of small diameter collagen fibrils.42 In addition, the appearance of what were reported to be ‘stellate’ shaped fibrils was noted, another scleral feature that is suggestive of pathology as these anomalies were not observed in normal sclerae.42 In further support of observations that the human sclera undergoes active remodelling during myopia development, biochemical assays from highly myopic eyes show markedly reduced amounts of biochemical

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markers for collagen and glycosaminoglycans, when compared with the sclera of emmetropic eyes.15 Tensile testing of the sclera from highly myopic human eyes has confirmed that the thinned sclera is less resistant to deformation than the sclera of emmetropic eyes.15 The major drawback from studies of post-mortem tissue from highly myopic human eyes stems from the fact that it is impossible to establish cause and effect of the scleral pathology in high myopia. Specifically, it is not possible to say whether the biochemical changes encountered in the sclera of human high myopes occur prior to, thus implicating them in the cause of scleral thinning and stretch, or whether they are a consequence of the scleral thinning. Such questions are more directly addressed in studies utilizing animal models of myopia, and the results of these studies have enabled us to answer many important questions raised from observations of the sclera of humans with high myopia. Development of structural and ultrastructural scleral changes in myopia A major feature of scleral thinning in human myopia is that it is largely confined to the posterior pole of the eye (Fig. 1A). Obviously, a major requirement of any appropriate model to elucidate mechanisms in human myopia is that it should demonstrate this regional change.19 The most remarkable feature of this scleral thinning is the fact that it occurs very rapidly in response to the onset of myopia development. Indeed, it has been found that the posterior sclera thins by some 20% over the first 12 days of myopia development in young tree shrews (Fig. 1B). This time period represents the early phase of myopia development, during which some 12 dioptres of relative axial myopia are induced. This reduction in scleral thickness progresses slowly over the next three to eight months, representing the later phase of myopia development, despite the fact that these eyes continue to display evidence of myopia progression (up to 20 dioptres or an increase in axial eye size of ~7%).19 Analysis of the dry weight of the sclera has demonstrated that the cause of scelral thinning in myopia is due to the actual loss of scleral tissue as opposed to passive stretch of the sclera. The rate of loss of scleral tissue corresponds closely with the time course of scleral thickness changes at the posterior pole of the myopic tree shrew eye and demonstrates that posterior scleral tissue is lost rapidly. Significant decreases in scleral dry weight are apparent at the posterior pole (>5%, p < 0.05) after only five days of

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Figure 1. Thinning of the posterior sclera in mammalian eyes with progressive high myopia. A. Light micrographs of toluidine blue-stained transverse-sections of the posterior sclera of a highly myopic and fellow control eye of a tree shrew, following eight months of myopia progression. B. Mean posterior scleral thickness in the myopic, fellow control and age-matched normal eyes of tree shrews following 12 days (n = 2 normal and n = 5 myopic) or 6–20 months (n = 4 each group) of myopia progression. Error bars are 1 SEM. **p < 0.01, *p < 0.05 by paired t-test. (Reproduced with permission from McBrien & Gentle 2003, Copyright Elsevier Science Ltd.)

myopia induction in young tree shrews (Figs. 2A and B), representing the initial stages of myopia development.43 This tissue loss continues to occur rapidly over the initial 12 days of myopia development, accounting for 17% reduction of posterior scleral dry weight (Fig. 2B). Over the next three to eight months of myopia progression the continued loss of scleral

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Figure 2. Scleral tissue loss during the progression of high myopia is most rapid during the early stages of myopia progression. A. Absolute dry weight of the posterior sclera (5 mm button centered on the posterior pole) in myopic, fellow control, and age-matched normal eyes following 12 days (n = 8 normal and n = 15 myopic) or 6–8 months (n = 4 normal and n = 15 myopic) of myopia progression. (Updated from McBrien et al., 2001a © Association for Research in Vision and Ophthalmology.) B. Relative difference in anterior, posterior, and total scleral dry weight between myopic and fellow control eyes of tree shrews following five days (n = 13), 12 days (n = 15) and >6 months (n = 15) of myopia progression. Normal animals (n = 16) were age-matched to five-day animals. C. Interocular differences in the posterior scleral dry weight with length of myopia induction in tree shrews. n’s as for Fig. B. Error bars are 1 SEM. **p < 0.01, *p < 0.05 by paired t-test. (Reproduced with permission from McBrien & Gentle 2003, Copyright Elsevier Science Ltd.)

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tissue is less marked (Fig. 2C).19 These studies demonstrate there is a net tissue loss from the whole sclera of up to 7% of dry weight, unequivocally demonstrating that tissue is lost, rather than just re-distributed, during the development of myopia (Fig. 2B). Such a finding highlights the probability that biochemical changes are a precursor to changes in the material properties of the sclera, and ultimately, to myopia development. In conjunction with the tissue loss observed in the tree shrew model, characteristic changes in collagen fibril diameter are also apparent in the sclera of highly myopic eyes, consistent with the findings in humans and monkeys. However, fibril diameter changes are only detectable a considerable time after the main changes in scleral thickness and tissue loss have occurred. Studies demonstrate that the scleral fibril diameter distribution profiles remain similar in myopic eyes to those in the sclera of normal eyes during the early phases of myopia development (Fig. 3A). This is despite major changes in scleral thickness and dry weight having occurred in those same eyes.19 However, after longer periods of myopia development (three months), a reduction in median collagen fibril diameter is found at the posterior pole of myopic eyes (Fig. 3B). This change is most marked in the outer scleral fiber bundles, which is consistent with the embryological observation that the outer fiber bundles are the last to mature.20 By six to eight months of myopia development there is a highly significant reduction in collagen fibril diameter across the scleral thickness, with the greatest reduction in diameter, around 35% (Fig. 3C), apparent in the outer layers of the sclera.19 As in humans, these changes are mainly localized to the posterior pole of the eye, although changes also occur in equatorial regions of the sclera, however, these are less dramatic. The shift in fibril diameter in myopic eyes results in a reduction in the gradient in fibril diameter across the scleral thickness, and it is interesting to note that this gradient is virtually absent in eyes with longstanding high myopia (Fig. 4).19 Scleral pathology and staphyloma Data collected during the long-term development of myopia suggests that although the sclera thins rapidly and alters its material properties during the early stages, shifts in collagen fibril diameter are not apparent until later in myopia development. Recent data indicates that a shift in the ratio of type V collagen to type I collagen production during the early stages of myopia development may ultimately contribute to

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Figure 3. Median scleral fibril diameter as a function of myopia progression. A. 12 days of myopia progression. n = 3 animals. B. Three months of myopia progression. n = 1 animal. C. Nine months of myopia progression. n = 3 animals. Approximately 1200 fibrils were surveyed per eye in each animal. (Reproduced with permission from McBrien & Gentle 2003, Copyright Elsevier Science Ltd.)

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Figure 4. Reduction in the trans-scleral collagen fibril diameter gradient in eyes with progressive myopia. A. Electron micrographs show transverse sections through collagen fibrils in the defined inner, middle, and outer posterior sclera of highly myopic, fellow control and agematched normal eyes of 9–9.5 month-old tree shrews. B. Graphic representation of the transscleral collagen fibril diameter gradient in highly myopic (n = 3), fellow control (n = 3), and age-matched normal (n = 8) eyes of 9–9.5 month-old tree shrews. (Reproduced with permission from McBrien & Gentle 2003, Copyright Elsevier Science Ltd.)

staphyloma development in later life, through the progressive formation of more small diameter collagen fibrils. Furthermore, these changes are more localized to the posterior region of the eye. In humans, identical fibril diameter changes are reported in staphylomatous eyes. It should be

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noted that staphyloma formation, although also related to eye size, is often a later occurrence in human high myopes.44,40 Our knowledge of the biomechanical properties of the sclera now dictates that these markedly thinned regional areas, with reduced glycosaminoglycan content and small collagen fibrils, have a reduced resistance to intraocular pressure. Furthermore, the relatively annular organization of these bundles of collagen, around the optic nerve insertion and macular area, result in a local area that is particularly susceptible to the expansive force of the normal intraocular pressure. Other scleral regions, where collagen fiber bundle organization is relatively anisotropic, would be relatively protected against such an occurrence. Indeed, the anatomical area described above corresponds remarkably well with the area of formation of by far the most common type of staphyloma, the type I posterior staphyloma.40 The data presented in this review are consistent with the hypothesis that the localized thinning of the sclera and glycosaminoglycan loss, in conjunction with the scleral collagen fibril diameter changes, interact with the localized orientation of fibril bundles to result in the development of staphyloma.

Biochemical Changes in the Sclera of Myopic Eyes The marked changes in structure reported in the sclera of eyes with high myopia, and the evidence that this is occurring due to tissue loss, indicate that changes occur in the biochemistry of the sclera. Studies to test this hypothesis have broadly concentrated on investigating three specific aspects of the biochemical processes in the sclera, namely: i) the biochemistry of the structural components of the sclera; ii) the regulation of the degradative processes in the sclera; and iii) the cellular changes that ultimately regulate the structural and biochemical alterations. Structural biochemistry of the sclera in myopia The majority of investigations into the biochemistry of scleral structure in myopia have concentrated on the main structural components, namely collagen and proteoglycans. The importance of scleral collagen biochemistry in the myopic eye is illustrated by the results of a study that prevented tropocollagen cross-linking in the sclera, through the use of beta-aminopropionitrile, which inhibits lysyl oxidase activity.18 The

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treatment was found to result in a significant increase in myopia development and significantly increased scleral thinning in the posterior region of form-deprived myopic eyes, indicating that altered scleral biochemistry made the eye markedly more susceptible to the normal expansive intraocular forces. However, there was found to be no observable effect on eye growth in normal eyes, indicating the importance of other underlying changes in scleral properties in myopia development. Studies in tree shrews and humans have found a reduced collagen content at the posterior pole of the sclera of highly myopic eyes.15,45 Recently, studies have demonstrated reductions of up to 35% in collagen type I mRNA expression in myopic eyes, also suggesting that collagen accumulation in the sclera is reduced due to a decrease in production.46,47 Furthermore, confirmation that the incorporation of the radiolabelled collagen precursor, [3H]-proline, was reduced by a similar magnitude was demonstrated when data was normalized to the extractable collagen content of the sclera, confirming that collagen synthesis is reduced early in myopia development.47 Subsequent investigations demonstrated that [3H]-proline elimination from the sclera was enhanced in a magnitude consistent with the previously reported scleral dry weight changes.47 The above data further strengthens the argument that scleral dry weight loss in myopia development is primarily a result of reduced collagen accumulation. Collectively, one can conclude from the above data that reduced collagen accumulation in the posterior sclera of myopic eyes is driven by both reduced collagen synthesis and increased collagen degradation (see later). Furthermore, these studies demonstrate that the reduction in collagen accumulation is greatest during the early stages of myopia development, which is consistent with the time dependent response of scleral thinning and scleral tissue (dry weight) loss.19,43 A recent study investigated scleral expression of the quantitatively minor fibrillar collagen subtypes III and V. Type V collagen, in particular, is important in the control of fibril diameter in the cornea, and therefore represents a candidate for the regulation of the collagen diameter changes found in the sclera of myopic eyes. Studies have found that although collagen type I mRNA expression is reduced in eyes developing myopia, there are no changes in collagen types III and V expression between myopic and control eyes.47 The reduced type I collagen production and stable type III and V levels result in a 20% increase in the type V/type I and type III/type I collagen ratio. Thus, in relative terms, newly synthesised fibrils in myopic eyes may contain 20% more type V collagen. Previous reports in the

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literature on cornea suggest that such a magnitude of change is sufficient to bring about a 40% reduction in collagen fibril diameter.48 This reduction is similar to the magnitude of the fibril diameter change encountered in the outer scleral layers of longer-term myopic eyes (around 35%).19 The glycosaminoglycan component of the scleral proteoglycans has been investigated extensively as a marker for changes in scleral biochemistry during myopia development. Glycosaminoglycan synthesis is reduced in the sclera of a number of mammalian models of myopia development (Fig. 5).49,50 This is consistent with findings that overall glycosaminoglycan content is also reduced in human and tree shrew eyes.18,45 Altered synthesis is of significance given the importance of the high-density negative charge on glycosaminoglycans in determining the mechanical properties of a tissue. Studies have shown that reduced GAG synthesis occurs in the earliest stages of myopia development and is sustained as the myopia develops.29,43 Reduced glycosaminoglycan synthesis and content usually implies there is a concomitant reduction in proteoglycan production in a given tissue system. However, investigators have paradoxically found no change in the expression of the core protein mRNA of decorin, which is one of the more important proteoglycans of the mammalian sclera.27 It is argued that this suggests glycosaminoglycan side chains may be shorter, or the occupancy of their sulphation sites reduced, in eyes developing myopia. Regardless of

Figure 5. Glycosaminoglycan synthesis in the sclera of myopic eyes. GAG synthesis is reduced in the anterior, posterior, and total sclera of tree shrews following five days of myopia progression. n = 5 animals in each group. Error bars are 1 SEM. **p < 0.01, *p < 0.05. (Reproduced with permission from McBrien and Gentle, 2003 © Elsevier Science Ltd.)

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whether proteoglycan content is reduced in conjunction with glycosaminoglycan content, the important role that the negative charge density plays in the control of extracellular matrix mechanics51 implicates glycosaminoglycans in the mediation of the earliest biomechanical changes in myopic eyes. These observations are consistent with the hypothesis that scleral glycosaminoglycan content is a major factor underlying the early changes in the visco-elastic properties of the sclera that are characteristic of myopic eyes. Degradative processes in the sclera of myopic eyes Studies have shown a change in the activity of collagen degrading enzymes in the sclera of myopic eyes. Matrix metalloproteinase-2 (MMP-2) has been shown to be important both in the degradation of native collagen fibrils and in the further degradation of their breakdown products.52 The enzyme is secreted in a pro-enzyme, or latent, form and is then activated at the cell membrane by cleavage of the latency-conferring terminus of the pro-enzyme.53 Studies in mammalian models have shown that MMP-2 activity is increased in the sclera of myopic eyes (Fig. 6A).26 Protein analyses of the latent and active forms of the enzyme show a supplementation of the latent pools of the enzyme in the sclera, but the major change during myopia development is a three-fold increase in levels of the active form of the enzyme (Fig. 6B).26 Subsequent studies of MMP-2 confirm a small increase in the mRNA expression of latent MMP-2.27 However, this increase does not match the relative increase in levels of active MMP-2 in the sclera of myopic eyes, indicating the major change is related to activation of latent MMP-2 and not increased production of MMP-2 (Fig. 6C). Findings to date are consistent with the hypothesis that increased degradative activity, and possibly net reduction in the inhibition of this activity, drives the scleral thinning and tissue loss seen in myopic eyes. However, it should be remembered that biochemical data also indicates there is a concomitant decrease in the synthesis of new structural material in the extracellular matrix, which contributes to the reduced accumulation of the scleral matrix. Cellular changes in the sclera in myopia Reduced DNA synthesis accompanies tissue remodelling in the sclera of myopic eyes, implicating cellular changes of the scleral fibroblasts, which

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Figure 6. MMP-2 activity in myopia. Scleral MMP-2 activity is increased during myopia development and decreased during recovery from induced myopia, particularly at the posterior pole of the eye. A. Gelatin zymography showing levels of latent and active MMP-2 in titrations of extracted protein from the posterior sclera of highly myopic, recovering and fellow control eyes of tree shrews, relative to titrations of standards. B. Graphic representation of mean levels of latent and active MMP-2 activity in the posterior sclera of highly myopic, recovering and fellow control eyes of tree shrews, relative to titrations of standards. B. Graphic representation of mean levels of latent and active MMP-2 activity in the posterior sclera of highly myopic, recovering and fellow control eyes of tree shrews following five days of myopia progression or five days of myopia progression followed by three days of recovery. C. Active-latent MMP-2 ratio in the sclera of highly myopic, recovering, and fellow control eyes of tree shrews following five days of myopia progression or five days of myopia progression followed by three days of recovery. n = 6 animals in each group. Error bars are 1 SEM. **p < 0.01, *p < 0.05. (Adapted with permission from Guggenheim and McBrien, 1996 © Association for Research in Vision and Ophthalmology.)

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ultimately regulate the process of scleral remodelling.54 However, these studies also report that total DNA content of the sclera is not significantly altered after periods of myopia development. Thus, although DNA synthesis is reduced in myopic eyes, there is no net change in the number of scleral fibroblasts.45,54 There is, however, found to be an increase in the number of scleral cells per unit dry weight in the sclera of myopic eyes, which might be expected if cell numbers remain the same but matrix tissue is lost. One possible explanation is that the reduced overall metabolic demand on the scleral cells of myopic eyes results in a concurrent reduction in the number of cells committing to apoptosis, thus offsetting the reduced number of mitotic events in the sclera.54

Biomechanical Changes in the Sclera of Myopic Eyes As a major component influencing the mechanical properties of any biological tissue is thickness, one would anticipate changes in the biomechanical strength of the sclera based solely on the reported structural changes in myopia.19 Indeed, studies have shown that strips of scleral tissue from myopic human and tree shrew eyes have a greater initial extensibility (elasticity) in response to an imposed load than control tissues from normal eyes, and this occurs in both the posterior sclera and equatorial sclera.18,38,55 This difference is predominantly a result of the thinner sclera of the myopic eye, rather than a change in the particular properties of the sclera, since the modulus of elasticity was found to be similar between myopic and control eyes. Importantly, however, finite element modelling, using the scleral properties determined in this experiment, suggested that this simple elastic stretch could account for no more than 20% of the increase in eye size in myopia.38 This finding demonstrates that scleral thinning alone cannot account for the majority of ocular enlargement that occurs in myopic eyes and strongly implicates there is a significant contribution from other material properties of the sclera in facilitating changes in eye size during myopia development. More recent studies have investigated the visco-elastic, time-dependent response (creep) of the sclera from myopic tree shrew eyes to a constant load over time. The data demonstrated that scleral creep rates were higher in samples from the posterior sclera of myopic eyes, even when correction is made for the cross-sectional area of the tissue samples (Figs. 7A and B).56

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Findings from equatorial scleral samples in tree shrews also show increased creep rates in myopic eyes.56 This indicates that the visco-elastic properties of the sclera are indeed markedly altered in myopic eyes.55,56 These changes occur as early as four days into the process of myopia development, when the sclera displays a creep rate in excess of 200% that of control eyes.55 Of particular significance is the finding that there is a strong correlation between the actual degree of myopia induced and the creep properties of the mammalian sclera (Figs. 7C and D).56 These findings demonstrate a direct relationship between the degree of myopia and the material properties of the sclera in mammalian eyes. The data also indicates that in an eye with a weakened sclera due to a change in material properties and given sufficient time, physiological intraocular pressures may be sufficient to induce continuing progressive ocular enlargement. The data from ultrastructural and biomechanical studies of the sclera from myopic eyes consistently demonstrate that, in early myopia development, scleral tissue loss rapidly results in scleral thinning. This scleral thinning contributes to, but cannot account for, the majority of the early changes in eye size. Furthermore, collagen fibril diameter, a structural element that influences the elastic modulus of the collagen matrix in a number of tissues, is only found to be reduced after myopia has been present for an extended period. These findings demonstrate that changes in other properties of the scleral matrix, such as glycosaminoglycan charge density and/or tissue hydration, may be more important in the early alterations of scleral biomechanical properties found in myopic eyes. Unlike data from the in vitro studies described earlier, changes in axial length act as a surrogate for the extensibility of the eye, and allow estimates of both the elastic and creep behavior of the eye to be determined in vivo. When intraocular pressure is increased, both avian and mammalian eyes exhibit an initial elastic response to the imposed intraocular pressure rise, followed in the avian eye by a gradual, creep extension (Fig. 8A).35 However, in the mammalian eye, this initial elastic enlargement of the eye is subsequently offset by a gradual shortening of the eye, yielding negative creep values (eye gets shorter). When the intraocular pressure was returned to normal, the eye had become shorter than its original starting value (Fig. 8B). The rapidity of the shortening response (less than one hour) cannot be explained in terms of scleral matrix remodelling, implicating the scleral cells themselves in the physical

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Figure 7. Scleral creep extension curves for samples from myopic, fellow control, and normal eyes, and the relation between scleral creep rates and vitreous chamber elongation and myopia progression. A. Complete scleral creep extension vs. time curves from a highly myopic and fellow control eye of a tree shrew, following 12 days of myopia progression. B. Averaged creep extension vs. time curves from highly myopic, fellow control, and agematched normal eyes in tree shrews following 12 days of myopia development. For each sample, creep extension is the percentage of sample length at 300 seconds after the application of the 5 g load. n = 10 animals in each group. C. Interocular difference in vitreous chamber depth vs. interocular difference in creep extensibility between highly myopic and fellow control eyes of tree shrews following 12 days of myopia progression. r = 0.75, p < 0.05. n = 10 animals. D. Interocular difference in refractive error vs. interocular difference in creep extensibility between highly myopic and fellow control eyes of tree shrews following 12 days of myopia progression. r = 0.79, p < 0.01. n = 10 animals. (Reproduced with permission from Phillips et al., 2000 © Association for Research in Vision and Ophthalmology.)

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Figure 8. The effect of intraocular pressure elevation on axial length. A. Change in the axial eye length of 10 normal chick eyes on raising IOP to 100 mm Hg. Initial values of axial length at 15 mm Hg are shown at time = −5 min. IOP was raised to 100 mm Hg at Time = 0 and remained at 100 mm Hg for one hour (horizontal bar) after which it was returned to 15 mm Hg. In all chick eyes axial length increased during the period of elevated pressure. The mean curve is shown as open circles. B. Change in the axial eye length of 10 normal tree shrew eyes on raising IOP to 100 mm Hg for one hour. Experimental procedures were the same as those for chick eyes. However, in all tree shrew eyes, axial length progressively decreased during the period of elevated pressure. The mean curve is shown as filled circles. (Reproduced with permission from Phillips and McBrien, 2004 © Association for Research in Vision and Ophthalmology.)

process of ocular shortening. Further investigation of scleral cells in the mammalian sclera via immunohistochemistry showed the sclera to contain a subset of cells that express a protein known as alpha-smooth muscle actin (α-SMA), namely myofibroblasts.35 Given that α-SMA is typically found in muscles, this finding has led to the hypothesis that the scleral cells have an active role in the mechanical properties of the mammalian sclera, and that this may be of importance in a number of physiological and pathological ocular functions. TGF-β is of primary importance in the regulation of extracellular matrix turnover and the three mammalian isoforms of TGF-β have been demonstrated to be present in the sclera and to regulate collagen production via fibroblasts.57 Furthermore, TGF-β isoform changes in myopia development occur within 24 hours of the initiation of myopia development and have been linked to the altered regulation of ECM production found in the sclera of eyes developing myopia.57 mRNA expression levels

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Figure 9. Alterations in TGF-b isoform gene expression during myopia development. Monocular deprivation of form vision was used to induce myopia in tree shrews and TGF-b isoform expression was quantified after one (A, C) and five (B, D) days deprivation. Copies of individual isoforms were quantified in scleral samples (n = 6) with reference to an external standard, and were expressed per 1000 copies of the housekeeping gene, HPRT (A, B). Data is also presented as the percentage difference in gene expression (treated eye – control eye) ± SEM (C, D).* indicates a statistically significant result. (Reproduced with permission from Jobling et al., 2004 © The American Society for Biochemistry and Molecular Biology, Inc.)

of the TGF-B isoforms in the sclera are differentially reduced in an isoform- and time-dependent manner possibly reflecting isoform-specific roles in the remodelling of the scleral ECM at different stages of myopia development (see Fig. 9). Regulators of scleral myofibroblast differentiation Fibroblast to myofibroblast differentiation is a complex process, with a number of signalling factors important in the fibroblast moving through the proto-myofibroblast to mature myofibroblast stage.58 However, at a basic level the process is initiated either by induced stress on the cell and matrix, or through stimulation with cell signalling factors, among the

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most important of which is the cytokine transforming growth factor beta (TGF-β).58 The sclera, itself, is under constant and fluctuating stress due to intraocular pressure, while TGF-β is present within the scleral matrix and has been implicated in the remodelling that occurs during myopia development. In vitro cell culture studies using attached or stressed collagen gels have shown that scleral myofibroblasts are readily formed by increasing matrix stress. Similarly, the addition of TGF-β to scleral cell cultures brings about a rapid differentiation of fibroblasts into α-SMAexpressing myofibroblasts (Fig. 10).37 Careful assessment of the structural proteins within the cell cytoplasm shows that ‘stress fibers’ have developed within the cell that typically orient themselves parallel to the imposed stress.37 Myofibroblast-extracellular matrix interactions Myofibroblasts are capable of modifying their extracellular environment both through contraction and the production of new extracellular matrix. Once formed, myofibroblasts produce collagen, proteoglycans and many other constituents and regulators of the extracellular matrix, in order to maintain or repair their extracellular environment. For this reason, myofibroblasts must be continually receiving information on the surrounding matrix. The major significance of this direct cell-matrix interaction is twofold. Firstly, the cell is in a position to immediately sense any changes in the stress experienced by the extracellular matrix, and thus be in a position to change its production and regulation of the extracellular matrix accordingly. Secondly, the cell is in a position to physically respond to any imposed stresses, via contraction of its surrounding matrix. Data from many different tissue systems show that extracellular matrixproducing cells, such as myofibroblasts, are closely related to their matrix through a variety of cell-matrix adhesion molecules. On the outside of the cell these adhesion molecules act as receptors, binding to various aspects of the extracellular matrix, such as collagen.59 These cell adhesion molecules also span the cell membrane and join, internally, to the cytoskeleton of the cell, forming a complete bridge between the extracellular matrix and the actin of the internal framework of the cell.59 The integrin family of receptors are perhaps the most important cell adhesion molecules in extracellular matrices such as the sclera. Collagen-binding integrins have been demonstrated on scleral cells.31 Of further interest, integrin gene expression has been shown to be altered in eyes developing myopia,

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Figure 10. TGF-β regulation of scleral fibroblast differentiation. Cultured scleral fibroblasts were incubated with (C) or without (A) TGF-β for five days. The expression of the myofibroblast-marker, α-SMA was assessed using fluorescent immunocytochemistry (×400). Cells observed at higher magnification (×630; E) show α-SMA-containing stress fibers. The respective negative controls are included in panels B and D, and bars represent 50 µm. (Reproduced with permission from McBrien et al., 2009 © American Academy of Optometry.)

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suggesting that the cell-matrix bond is altered in myopic eyes.31 Such a reduction in cell-matrix contact would have implications for the biomechanical response of the sclera. Cellular and matrix contributions to altered scleral biomechanics and myopia From the above discussion, scleral myofibroblasts must be considered an integral part of the biochemical and biomechanical response of the sclera, both in normal and abnormal eye growth. These cells certainly contribute to the matrix changes widely reported in the sclera of eyes developing myopia,19 and their mechanical interaction with the matrix, together with their contractile capability, indicate a mechanism whereby the sclera may control its elastic response to short term changes in stress, such as during fluctuations in intraocular pressure due to cardiac cycle, respiration, and eye movement. A proposed model for the role of scleral myofibroblasts in myopic eye growth, incorporating the current data, is shown in Fig. 11. A retinoscleral signalling mechanism43 initiates a process of scleral tissue loss, partly due to reduced synthesis of extracellular matrix components and partly a result of accelerated degradation.60 As the sclera thins, a series of gene expression changes are initiated amongst the scleral myofibroblasts, which results in the changes in the collagenous matrix that subsequently manifest in myopia development, such as reduced diameter of collagen fibrils.60 Changes in scleral thickness and the material properties of the sclera increase the capacity of the sclera to creep under normal intraocular pressure, and this process also increases the stresses present within the matrix, and therefore on the myofibroblasts. Downregulation of integrin expression early on in the process of myopia development57 represents a mechanism whereby myofibroblasts disconnect from the scleral matrix, releasing the cells’ mechanical influence on the matrix and enhancing the capacity of the sclera to creep and the eye to grow. Such a response may also reflect a protective mechanism in response to the stresses the fibroblast is experiencing. These scleral myofibroblasts may try to reconnect with the creeping matrix, perhaps enhancing their contractile capabilities in doing so. Similarly, they may remain disconnected from the matrix, de-differentiating to fibroblasts, due to their reduced experience of the stress in the matrix, and allowing further increase in the creep capacity of the sclera (Fig. 11).

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Figure 11. Proposed schematic model of the role of scleral myofibroblast cells in the biochemical and biomechanical remodelling that facilitates the scleral changes that occur during myopia development and progression, based on current evidence. See text for details. (Reproduced with permission from McBrien et al., 2009 © American Academy of Optometry.)

The biomechanical properties of the sclera are critical in maintaining normal ocular development. Alterations in these properties, such as those seen during myopia development, produce concurrent alterations in eye size. While remodelling to the scleral matrix was considered to be the sole determinant of biomechanical change, recent data has highlighted the important role of scleral cells, particularly scleral myofibroblasts. While our current knowledge of the role of scleral myofibroblasts in normal and abnormal eye growth is incomplete, proper identification of the factors involved in scleral weakening and subsequent increased eye size

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will enable more refined treatments to be devised than currently available to reduce the impact of the biomechanical weakening of the sclera in high myopia on visual function.

Scleral Changes in Myopia are Reversible To date we have concentrated on the role of the sclera in the development of myopia and the pathological complications consequent to the axial elongation of the eye. However, although it is has not yet been explicitly discussed, there is substantive evidence to demonstrate the vital role that visual information plays in the control of this scleral remodelling. Indeed, there is strong evidence to show that scleral thinning and loss of tissue in myopia are reversible in paradigms resulting in recovery from axial myopia, and as such, provide insight into potential treatment approaches. Eye growth regulation during recovery from induced myopia Recovery from induced myopia in experimental models of refractive error can be considered a manifestation of the innate emmetropization process. In essence, once myopia is induced, either through monocular deprivation or using a negative lens, removal of the inductive device results in the eye altering its growth pattern to eliminate the induced refractive error. It is assumed that this process occurs through the recognition of myopic blur by the retina, on removal of the occluder or negative lens, and subsequent modification of the ocular growth rate to compensate. This essentially can be considered an emmetropization response as the eye alters its growth rate to reduce the defocus. In the avian model of myopia, recovery occurs initially through thickening of the choroid. This moves the retina physically forward and reduces the amount of defocus by bringing the plane of the photoreceptors closer to the image plane. This is followed by a reduction in the growth rate of the sclera until the axial eye length is again coordinated with the optical power of the eye at which time the choroid returns to its normal thickness as does the scleral growth rate.61 In the mammalian and primate models of myopia, however, recovery involves only a minor contribution from changes in choroidal thickness.43,62 Such findings indicate that scleral changes are the principal factor in the recovery process in mammalian species.

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As discussed earlier, scleral glycosaminoglycan synthesis is reduced during myopia development, however, on removal of the occluder/lens there is a rapid rise in glycosaminoglycan synthesis, such that, within 24 hours of recovery, glycosaminoglycan synthesis levels have returned to control eye levels (Fig. 12A).43 At this stage there is no significant change in the degree of myopia or in the length of the eye, however, after three days of recovery, the eye has started to shorten and reduce its refractive error. By this time there is found to be a significant increase in scleral glycosaminoglycan synthesis relative to the control eye. Between days 3 and 5 of recovery, the most marked reduction in refractive error occurs, primarily through a reduction in the axial length of the eye. Importantly, the period of peak glycosaminoglycan synthesis precedes the most rapid period of eye size change, suggesting that the scleral remodelling may lead to the changes in eye size. Thereafter, the magnitude of increase in glycosaminoglycan synthesis begins to diminish, and by the time the relative refractive error is eliminated (around seven to nine days), glycosaminoglycan synthesis is returning to control eye levels (Fig. 12A). There is also a replacement of the tissue that is lost during the development of myopia and this tissue mass briefly exceeds that of the control eye when recovery is achieved.43 Other studies have shown that DNA synthesis is also upregulated during the recovery process in the tree shrew (Fig. 12B) and there is also a relative reduction in the levels of MMP-2 and TIMP-2 mRNA and protein production (see Fig. 6). This results in an overall reduction in active levels of MMP-2 in the sclera, which is consistent with the increase in scleral dry weight observed.26,29 Such findings are important in demonstrating that the direction of eye growth is specifically dependent on the direction of regulation of scleral remodelling. Biochemical studies show that the major replacement of scleral tissue during recovery occurs at the posterior pole of the eye, and that the major increase in glycosaminoglycan synthesis also occurs in this region. Although this might be expected, as this is the location where most of the scleral tissue is lost during myopia development, there is evidence of some remodelling of the equatorial regions of the sclera during recovery from axial myopia.43 The significance of changes in the equatorial region of the sclera during recovery has not been fully elucidated, however, it is possible that it plays a role in the shortening of the eye that occurs in recovery. Recent studies have confirmed the importance of visual information in the control of scleral remodelling in myopia. Studies have established that accurate correction of induced myopia, simulating correction of myopia

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Figure 12. Changes in anterior and posterior scleral glycosaminoglycan and DNA synthesis during the development of, and recovery from, myopia in tree shrew. A. Interocular difference in glycosaminoglycan synthesis in the anterior and posterior sclera of tree shrew eyes, following five days of myopia development and five days of myopia development followed by one, three, five, seven, or nine days of recovery. (Reproduced with permission from McBrien et al., 2000 © Association for Research in Vision and Ophthalmology.) B. Interocular difference in DNA synthesis in the anterior and posterior sclera of tree shrew eyes, following five days of myopia development and five days of myopia development followed by three days of recovery from induced myopia. (Reproduced with permission from Gentle and McBrien, 1999.) n = 5 animals in each group. Error bars are 1 SEM. **p < 0.01, *p < 0.05.

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Figure 13. Correction of induced myopia with eyeglass lenses results in the prevention of recovery from induced myopia in tree shrews. Corrective lenses also prevent the scleral changes in glycosaminoglycan synthesis that are characteristic of the recovery from induced myopia. A. Interocular difference in refractive error between treated and control eyes of animals following five days of myopia progression or five days of myopia progression followed by five days of recovery from induced myopia, either with or without corrective lenses. Normal eyes were age-matched to the five-day myopia animals. B. Interocular difference in vitreous chamber depth between treated and control eyes of animals following five days of myopia progression or five days of myopia progression followed by five days of recovery from induced myopia, either with or without corrective lenses. Normal eyes were age-matched to the fiveday myopia animals. C. Interocular difference in scleral glycosaminoglycan synthesis between treated and control eyes of animals following five days of myopia progression or five days of myopia progression followed by five days of recovery from induced myopia, either with or without corrective lenses. Normal eyes were age-matched to the five-day myopia animals. n = 5 animals per group. Error bars are 1 SEM. **p < 0.01, *p < 0.05. (Reproduced with permission from McBrien et al., 1999 © American Academy of Optometry.)

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in humans by the wearing of eyeglasses or contact lenses for myopia, prevents the recovery response.49,63 In contrast to the animals allowed to recover from induced myopia, animals wearing lenses that fully corrected the induced myopia did not recover and their sclerae retained a ‘myopic’ phenotype of reduced glycosaminoglycan synthesis (Fig. 13) and reduced thickness.49 This phenotype persisted over an extended period of lens wear and beyond the period during which eye growth was found to stabilize. Despite the fact the visual image is immediately placed in focus on the retina, and that the eye has returned to a stable growth rate, the sclera retains a myopic biochemical phenotype for a substantial period of time. Such a finding has important implications for the correction of human myopia.

Summary and Conclusions This review chapter has interpretated the implications of current research findings in humans and in animal models to highlight the role of the sclera in myopia development and progression. In applying current knowledge of the development, structure, and function of the sclera, the ways in which the scleral ultrastructure, and the related biomechanical properties, are altered in myopia development and ultimately lead to the pathological changes seen in human high myopia have been highlighted. This review has provided an updated model of scleral thinning in highly myopic eyes. From the current research evidence, the feasibility of treatments for myopia-related scleral pathology to prevent the aberrant remodelling process in the sclera, thus preventing scleral tissue loss, thinning, and the long term development of a weakened scleral collagen fibril matrix are now an option to control or ameliorate the pathophysiology associated with high myopia.

Acknowledgments The majority of the data presented in this review came from projects funded by the National Health and Medical Research Council of Australia and the Welcome Trust. I particularly acknowledge my research colleagues Alex Gentle, Andrew Jobling, and John Phillips who have made substantive contributions to data collected on scleral changes in myopia in our laboratory.

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References 1. Zadnik K. (1997) The Glenn A. Fry Award Lecture (1995). Myopia development in childhood. Optom Vis Sci 74: 603–608. 2. McBrien NA, Adams DW. (1997) A longitudinal investigation of adult-onset and adult-progression of myopia in an occupational group — Refractive and biometric findings. Invest Ophthalmol Vis Sci 38: 321–333. 3. Sperduto RD, Seigel D, Roberts J, Rowland M. (1983) Prevalence of myopia in the United States. Arch Ophthalmol 101: 405–407. 4. Fledelius HC. (1988) Myopia prevalence in Scandinavia. A survey, with emphasis on factors of relevance for epidemiological refraction studies in general. Acta Ophthalmol Scand Suppl 185: 44–50. 5. Attebo K, Ivers RQ, Mitchell P. (1999) Refractive errors in an older population: the Blue Mountains Eye Study. Ophthalmology 106: 1066–1072. 6. Goh WSH, Lam CSY. (1994) Changes in refractive trends and optical components of Hong Kong Chinese aged 19–39 years. Ophthalmic Physiol Opt 14: 378–388. 7. Lin LL-K, Shih Y-F, Tsai C-B, et al. (1999) Epidemiologic study of ocular refraction among schoolchildren in Taiwan in 1995. Optom Vis Sci 76: 275–281. 8. Celorio JM, Pruett RC. (1991) Prevalence of lattice degeneration and its relation to axial length in severe myopia. Am J Ophthalmol 111: 20–23. 9. Yannuzzi LA, Sorenson JA, Sobel RS, et al. (1993) Risk-factors for idiopathic rhegmatogenous retinal-detachment. Am J Epidemiol 137: 749–757. 10. Grossniklaus HE, Green WR. (1992) Pathologic findings in pathologic myopia. Retina 12: 127–133. 11. McBrien NA, Gentle A. (2003) Role of the sclera in the development and pathological complications of myopia. Prog Retin Eye Res 22: 307–308. 12. Grey RHB, Burnscox CJ, Hughes A. (1989) Blind and partial sight registration in Avon. Br J Ophthalmol 73: 88–94. 13. David T, Smye S, James T, Dabbs T. (1997) Time-dependent stress and displacement of the eye wall tissue of the human eye. Med Eng Phys 19: 131–139. 14. David T, Smye S, Dabbs T, James T. (1998) A model for the fluid motion of vitreous humour of the human eye during saccadic movement. Phys Med Biol 43: 1385–1399. 15. Avetisov ES, Savitskaya NF, Vinetskaya MI, Iomdina EN. (1984) A study of biochemical and biomechanical qualities of normal and myopic eye sclera in humans of different age groups. Metab Pediatr Syst Ophthalmol 7: 183–188. 16. Rada JA, Achen VR, Penugonda S, et al. (2000) Proteoglycan composition in the human sclera during growth and aging. Invest Ophthalmol Vis Sci 41: 1639–48.

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17. Wiesel TN, Raviola E. (1977) Myopia and eye enlargement after neonatal lid fusion in monkeys. Nature 266: 66–68. 18. McBrien NA, Norton TT. (1994) Prevention of collagen cross-linking increases form-deprivation myopia in tree shrew. Exp Eye Res 59: 475–486. 19. McBrien NA, Cornell LM, Gentle A. (2001) Structural and ultrastructural changes to the sclera in a mammalian model of high myopia. Invest Ophthalmol Vis Sci 42: 2179–2187. 20. Sellheyer K, Spitznas M. (1988) Development of the human sclera. A morphological study. Graefes Arch Clin Exp Ophthalmol 226: 89–100. 21. Norton TT, Miller EJ. (1995) Collagen and protein levels in sclera during normal development, induced myopia, and recovery in tree shrews. Invest Ophthalmol Vis Sci 36 (Suppl): S760. 22. Marshall GE. (1995) Human scleral elastic system: an immunoelectron microscopic study. Br J Ophthalmol 79: 57–64. 23. Birk DE. (2001) Type V collagen: heterotypic type I/V collagen interactions in the regulation of fibril assembly. Micron 32: 223–237. 24. Kuc IM, Scott PG. (1997) Increased diameters of collagen fibrils precipitated in vitro in the presence of decorin from various connective tissues. Connect Tissue Res 36: 287–296. 25. Bedrossian RH. (1971) The effect of atropine on myopia. Ann Ophthalmol. 3: 891–897. 26. Guggenheim JA, McBrien NA. (1996) Form-deprivation myopia induces activation of scleral matrix metalloproteinase-2 in tree shrew. Invest Ophthalmol Vis Sci 37: 1380–1395. 27. Siegwart JT, Norton TT. (2001) Steady state mRNA levels in tree shrew sclera with form-deprivation myopia and during recovery. Invest Ophthalmol Vis Sci 42: 1153–1159. 28. Gaton DD, Sagara T, Lindsey JD, Weinreb RN. (1999) Matrix metalloproteinase-1 localization in the normal human uveoscleral outflow pathway. Invest Ophthalmol Vis Sci 40: 363–369. 29. McBrien NA, Gentle A. (2001) The role of visual information in the control of scleral matrix biology in myopia. Curr Eye Res 23: 313–319. 30. Bron AJ, Tripathi RC, Tripathi B. (1997) The cornea and sclera. In: Wolffs Anatomy of the Eye and Orbit, pp. 233–278. Chapman and Hall Medical, London. 31. McBrien NA, Metlapally R, Jobling AI, Gentle A. (2006) Expression of collagen-binding integrin receptors in the mammalian sclera and their regulation during the development of myopia. Invest Ophthalmol Vis Sci 47: 4674–82. 32. Gentle A, McBrien NA. (2002) Retinoscleral control of scleral remodelling during refractive development: A role for endogenous FGF-2? Cytokine 18: 344–348.

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33. McBrien NA, Jobling AI, Truong HT, et al. (2009) Expression of muscarinic receptor subtypes in tree shrew ocular tissues and their regulation during the development of myopia. Molecular Vis 15: 464–475. 34. Poukens V, Glasgow BJ, Demer JL. (1998) Nonvascular contractile cells in sclera and choroid of humans and monkeys. Invest Ophthalmol Vis Sci 39: 1765–1774. 35. Phillips JR, McBrien NA. (2004) Pressure-induced changes in axial eye length of chick and tree shrew: significance of myofibroblasts in the sclera. Invest Ophthalmol Vis Sci 45: 758–63. 36. Jobling AI, Gentle A, Metlapally R, et al. (2009) Regulation of Scleral Cell Contraction by Transforming Growth Factor-{beta} and Stress: competing Roles in Myopic Eye Growth. J Biol Chem 284: 2072–2079. 37. McBrien NA, Jobling AI, Gentle A. (2009) Biomechanics of the sclera in myopia: extracellular and cellular factors. Opt Vis Sci 86: 23–30. 38. Phillips JR, McBrien NA. (1995) Form deprivation myopia — Elastic properties of sclera. Ophthalmic Physiol Opt 15: 357–362. 39. Puxkandl R, Zizak I, Paris O, et al. (2002) Viscoelastic properties of collagen: synchrotron radiation investigations and structural model. Phil Trans Royal Soc B: Biol Sci 357: 191–197. 40. Curtin BJ. (1977) The posterior staphyloma of pathologic myopia. Trans Am Ophthalmol Soc 75: 67–86. 41. Bell GR. (1978) A review of the sclera and its role in myopia. J Am Optom Assoc 49: 1399–1403. 42. Curtin BJ, Iwamoto T, Renaldo DP. (1979) Normal and staphylomatous sclera of high myopia. Arch Ophthalmol 97: 912–915. 43. McBrien NA, Lawlor P, Gentle A. (2000) Scleral remodelling in the development of and recovery from axial myopia in the tree shrew. Invest Ophthalmol Vis Sci 41: 3713–3719. 44. Curtin BJ, Karlin DB. (1970) Axial length measurements and fundus changes of the myopic eye. I. The posterior fundus. Trans Am Ophthalmol Soc 68: 312–334. 45. Norton TT, Rada JA. (1995) Reduced extracellular-matrix in mammalian sclera with induced myopia. Vision Res 35: 1271–1281. 46. Siegwart JT, Norton TT. (2002) The time course of changes in mRNA levels in tree shrew sclera during induced myopia and recovery. Invest Ophthalmol Vis Sci 43: 2067–2075. 47. Gentle A, Liu Y, Martin JE, et al. (2003) Collagen gene expression and the altered accumulation of scleral collagen during the development of high myopia. J Biol Chem 278: 16587–16594. 48. Birk DE, Fitch JM, Babiarz JP, et al. (1990) Collagen fibrillogenesis in vitro: interaction of types I and V collagen regulates fibril diameter. J Cell Sci 95: 649–657.

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49. McBrien NA, Gentle A, Cottriall C. (1999) Optical correction of induced axial myopia in the tree shrew: implications for emmetropization. Optom Vis Sci 76: 419–427. 50. Rada JA, Nickla DL, Troilo D. (2000) Decreased proteoglycan synthesis associated with form deprivation myopia in mature primate eyes. Invest Ophthalmol Vis Sci 41: 2050–2058. 51. Buschmann MD, Grodzinsky AJ. (1995) A molecular model of proteoglycanassociated electrostatic forces in cartilage mechanics. J Biomech Eng 117: 179–192. 52. Aimes RT, Quigley JP. (1995) Matrix metalloproteinase-2 is an interstitial collagenase — Inhibitor-free enzyme catalyzes the cleavage of collagen fibrils and soluble native type-I collagen generating the specific 3/4-length and 1/4length fragments. J Biol Chem 270: 5872–5876. 53. Woessner JFJ. (1991) Matrix metalloproteinases and their inhibitors in connective tissue remodelling. FASEB J 5: 2145–2154. 54. Gentle A, McBrien NA. (1999) Modulation of scleral DNA synthesis in development of and recovery from induced axial myopia in the tree shrew. Exp Eye Res 68; 155–163. 55. Siegwart JT, Norton TT. (1999) Regulation of the mechanical properties of tree shrew sclera by the visual environment. Vision Res 39: 387–407. 56. Phillips JR, Khalaj M, McBrien NA. (2000) Induced myopia associated with increased scleral creep in chick and tree shrew eyes. Invest Ophthalmol Vis Sci 41: 2028–2034. 57. Jobling AI, Nguyen M, Gentle A, McBrien NA. (2004) Isoform-specific changes in scleral TGF-β expression and the regulation of scleral collagen synthesis during myopia progression. J Biol Chem 279: 18121–18126. 58. Serini G, Gabbiani G. (1999) Mechanisms of myofibroblast activity and phenotypic modulation. Exp Cell Res 250: 273–283. 59. Tomasek JJ, Gabbiani G, Hinz B, et al. (2002) Myofibroblasts and mechanoregulation of connective tissue remodelling. Nat Rev Mol Cell Biol 3: 349–363. 60. Hinz B, Dugina V, Ballestrem C, et al. (2003) Alpha-smooth muscle actin is crucial for focal adhesion maturation in myofibroblasts. Mol Biol Cell 14: 2508–2519. 61. Wallman J, Wildsoet C, Xu AM, et al. (1995) Moving the retina — Choroidal modulation of refractive state. Vision Res 35: 37–50. 62. Troilo D, Nickla DL, Wildsoet CF. (2000) Choroidal thickness changes during altered eye growth and refractive state in a primate. Invest Ophthalmol Vis Sci 41: 1249–1258. 63. Wildsoet CF, Schmid KL. (2000) Optical correction of form deprivation myopia inhibits refractive recovery in chick eyes with intact or sectioned optic nerves. Vision Res 40: 3273–3282.

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4.3 The Mouse Model of Myopia Frank Schaeffel*

Introduction The mouse has recently expanded the spectrum of animal models for studies on myopia,1,2 after chicks,3 tree shrews,4 rhesus monkeys,5 and guinea pigs6 had already been used for a number of years. A driving force for introducing the mouse model was that it offers a number of advantages over the other models. These advantages include the availability of numerous knock-out mutants; most advanced gene microarrays for screening the transcriptome, a completely sequenced genome; the fact that the mouse is the most extensively studied mammalian model for human diseases; the fact that considerable knowledge exists already about biochemical pathways and pharmacology; and finally, that mouse strains can be easily crossed and bred. These advantages are counter-balanced by a number of disadvantages. In particular, compared to chicks and monkeys, mice are not predominantly “visual animals,” and their spatial resolution (around 0.5 cycles/degree) is about 100 times less than in humans, about 80 times less than in the monkey, and 15 times less than in chickens, and still about five times less than in the guinea pig. Furthermore, fast eye growth would be advantageous if the effect of visual input on eye growth is to be studied, and again, the mouse does not seem optimal: its eye grows only 0.15% per day over the first 100 days, which is about 10 times slower than in the chicken (1.1%). Accordingly, significant effects of the deprivation of sharp vision can be observed on eye growth in the chick model

*Section of Neurobiology of the Eye, Ophthalmic Research Institute, Calwerstrasse 7/1, 72076 Tübingen, Germany. E-mail: [email protected].

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already after one to two days, but two to three weeks are necessary in the mouse. Furthermore, treatment of mice with diffusers or lenses is demanding, compared to chicks, and experiments often fail because the mice had removed their eye occluders or lenses. Finally, the small size of the eye of the mouse (little more than 3 mm in diameter in adult mice7; and Fig. 1) requires new technology to measure ocular dimensions and optical properties with sufficient precision. Despite these obstacles, the number of publications on myopia in the mouse model increases continuously, and the results were surprisingly clear in some cases. This chapter will review: (1) the spatial visual performance of the mouse and the optical features of its eye; (2) the techniques that are now available for myopia studies in the mouse, both for induction and its measurement; and (3) examples of results that were recently obtained using the mouse model. This review extends and updates a previous analysis of the mouse as a model of myopia,8 but will still not cover all studies that were published on this topic.

Spatial Visual Performance and Optical Features of the Eye The mouse eye, scaled to body weight, is five times larger than the human eye and therefore cannot be considered vestigial. A basic question is whether it also provides “scaled visual acuity.” In a human eye, one degree of the visual field maps on 0.29 mm linear distance on the retina. In a 28-day-old mouse, the image magnification is only a tenth (0.03 mm/deg). Accordingly, even with the best possible optics, a mouse eye can achieve only a tenth in angular resolution — about 5 cyc/deg — since the “pixels” of the image, the photoreceptors, cannot be made much smaller. Behavioral (see detailed descriptions below) and electrophysiological studies9 show, however, that the spatial resolution of the mouse eye is considerably lower by another factor of 10 — only about 0.5 cyc/deg. Although the optics of the mouse eye are far from diffraction-limited,10 it does not seem to be the final limiting factor in visual acuity. Also, cone photoreceptor diameters do not vary much between human and mouse (mouse >2–3 µm; humans 2–8 µm),11,12 and it remains to be explained why the mouse has such poor spatial resolution. Unexpectedly, there is also no clear evidence for a higher level of convergence of photoreceptor

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signals in the mouse retina. The ratio of optic nerve fibers in human and mouse (about 1,100,000 in human versus 66,000 in mouse)13 is about 16:1, and matches roughly the ratio of the retinal areas (14:1) — definitely different, for instance, from the cat (fiber number for human to cat is about 13:1 vs retinal area ratio 1.4:1), suggesting that a much higher level of photoreceptor convergence exists in the cat retina, compared to mouse or human. These findings suggest that mouse spatial vision is not optimized for low illuminances, unlike in the cat. In fact, Schmucker et al.14 found that the spatial resolution of mice in an optomotor task increased with increasing illuminances (up to 400 lux), but was very poor at 4 lux. Given poor visual acuity, depth of focus should be large and it is possible that emmetropization (the developmental matching of axial eye length to the focal length of the eye optics) may not be as precise as in some birds or primates. To focus an image of the environment onto the retina of a mouse eye, a refractive power of cornea and lens of more than 500 diopters [D] is necessary in air (a third of the power is lost because the vitreous cavity is, like in all vertebrate eyes, filled with water-like fluid). Relative to the 500 diopters of optical power, refractive errors of a few diopters may be negligible, and imperfections in the optics of the mouse eye may have less impact on vision than in humans. However, regarding emmetropization, one should keep in mind that a change in axial length of only about 5 µm in the mouse changes the refractive state already by about one diopter.15 Even if the depth of focus of the mouse eye is as large as 10 diopters (see below), the absolute axial growth, determined by the growth of the scleral tissue, needs to be regulated with a precision of 50 µm in axial direction, which is similar to that in the chicken, where this value converts to about one diopter.16 Remtulla and Hallett7 were the first to present a schematic eye model for the adult C57B1/6J mouse, based on frozen sections of 14 eyes of 20–23-week-old animals. Later, Schmucker and Schaeffel15 developed a paraxial schematic eye model for C57BL/6J mice at different ages, also based on frozen sections. Although frozen sections have limited resolution due to distortions that may occur during freezing and sectioning, it is always possible to take averages from several eyes, and to fit the biometrical data from different ages by polynomials. The averaging procedure reduces the impact of measurement variability, and a few general statements could be made about the optics of the mouse eye, which are now compared to more recent measurements with other techniques.

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Axial eye growth and development of refractive state In line with an observation by Zhou and Williams,17 Schmucker and Schaeffel15 observed that the eyes grew about linearly in C57BL/6 mice over the first 100 days with no signs of saturation. Axial length grew from 3.0 mm at P22 by 4.4 µm per day. Zhou et al.18 used a custom-built low coherence interferometer with focal plane advancement (described in detail in Zhou et al.)19 to measure axial eye growth in another strain of C57BL/6 mice. They found that axial eye growth was most rapid between P22 and P35 (about 17 µm per day) and slowed down to about 3 µm per day between P53 and P100. The average growth rate over the whole period was similar, however (5.9 µm/day). Another developmental study in C57BL/6 mice was recently conducted using high resolution small animal MRI20. These authors also found nonlinear growth functions, with a fast growth phase followed by a slower phase. Axial length increased rapidly from P22 to P40, from 2.95 mm to 3.17 mm, followed by a slower increase to about 3.3 mm until P90. Barathi et al.,21 who studied axial eye growth Balb/cJ albino mice in excised eyes with digital calipers, also observed the most rapid axial eye growth between P1 and P56 (about 21 µm per day), and a slower growth rate of only 1.8 µm thereafter (average: 9.6 µm/day). No saturation of axial eye growth was obvious even beyond 100 days of age in any of these studies. It is interesting that the growth rates were variable in the two studies using C57BL/6 mice, in particular between P22 and P35, but frozen sections and low coherence interferometry may give slightly different results in small and soft eyes. Axial eye growth, as measured in these studies, is shown in Fig. 1A, and development of refractive states in Fig. 1B. While axial eye growth was similar in all studies, there were large differences in refractive development, even though the refractions were determined with copies of the original infrared photorefractor,22 at least in the four studies on black mice. It is a question to be answered in the future, why different C57BL/6 strains show different refractive development, and whether this is genetically determined or due to environmental differences in the animal facilities. Large difference in refractive development were recently also found in guinea pigs: a Chinese tricolor guinea pig strain had a significant proportion of spontaneously myopic animals23 — a condition that was not found in other guinea pig studies (e.g. Ref. 24).

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Figure 1. (A) Axial eye growth in mice with normal visual experience as measured in four studies, using either C57BL/6 mice (Zhou et al.18 — using a custom-built low coherence interferometer; Schmucker and Schaeffel15 — using frozen sections, Tkatchenko et al.20 — using high resolution small animal MRI) or Balb/cJ albino mice (Barathi et al.21 — using digital calipers in freshly excised eyes). (B) Development of refractive states in these four studies (same symbols denote same study) with data added from the study by Pardue et al.25 Infrared photorefraction was used in the studies on black mice and streak retinoscopy in the albino mice (Barathi et al.).21 Note that axial eye growth was similar in all studies, but that there were considerable differences in refractive development.

Lens thickness and vitreous chamber depth Because the lens grew in thickness from 1.74 mm at P22 by 5.5 µm per day, vitreous chamber depth declined from 0.83 mm at 22 days, by 3.2 µm per day15 (illustrated in Fig. 2). In the study by Zhou et al.,18 lens thickness was 1.47 mm at P22 and increased daily by about 7.9 µm until P53, and grew slower (about 1.8 µm/day) thereafter. Again, vitreous chamber depth declined with age by 1.4 µm per day between P22 to P102. Corneal radius of curvature A recent study, using video photokeratometry in Egr1 –/– mice and their wild type littermates27 showed that corneal radius of curvature grows from 1.35 mm to 1.53 mm from day 22 to 100 — an average daily growth rate of 2.3 µm. Zhou et al.18 observed a rapid increase in corneal radius of curvature by 9 µm per day between P22 and P35, and a slower phase with a daily increase of 0.8 µm thereafter (average growth rate over the whole period: 2.8 µm/day, similar to Ref. 27).

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Figure 2. Frozen sections of mouse eyes at the ages of 26 and 44 days (re-plotted from Ref. 15). Note that the lens is growing faster than the globe, reducing vitreous chamber depth with age. Note also that accommodation is very unlikely since the large lens can neither be moved nor deformed. Note also the relative thickness of the retina, which should give rise to a large “small eye artifact” in retinoscopic measurements.26 Scale bars denote millimeters.

Schematic eye data To make a mouse eye more myopic by one diopter, axial length has to increase by 5.4 µm at the age of 22 days and by 6.5 µm at the age of 100 days. Retinal image magnification increased from 0.032 mm/deg at 22 days to 0.034 mm/deg at 100 days in the study by Schmucker and Schaeffel15 — not too much of a difference. Using the data by Zhou et al.,18 an axial length of 2.86 mm at P22 converts into an image magnification of about 0.030 mm/deg, and an axial length of 3.34 mm at P102 into 0.034 mm/deg, similar to Schmucker and Schaeffel.15 As in other animals (e.g. chicken, barn owl),28,29 the retinal image brightness increases with age. Image brightness is proportional to the inverse squared f/number, the ratio of anterior focal length to pupil size. Typical for humans are f/numbers around 5 (for a 3.3 mm pupil), but for the mouse, the f/number is as low as 1.02 at day 22. This produces a retinal image 25 times brighter than in humans. Until the age of 100 days, the f/number declines even further, to about 0.93, providing another 20%

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more brightness. Even owls do not have such a low f/number (1.1329), and it is clear that the mouse has probably one of the brightest retinal images among vertebrates. The thickness of the retina, relative to axial length (which grows from 0.178 mm at 22 days of age by 0.6 µm per day), should give rise to a large difference in the position of the photoreceptor layer and of the light reflecting layer(s) in retinoscopy — resulting in large amounts of apparently measured hyperopia (the “small eye artifact”).26 From the schematic eye, the small eye artifact was calculated to more than 30 D.15 Since neither infrared photorefraction nor streak retinoscopy showed such large amounts of hyperopia, mice must either be myopic (which is not in agreement with behavioral data, see below), or the light reflecting layer(s) is (are) not at the vitreo-retinal interface, but rather deeper in the retina.

Techniques Currently Available for Myopia Studies in the Mouse, Both for Its Induction and Measurement The large depth of field and relatively low visual acuity of the mouse suggest that the retinal image degradation must become quite severe before it is detectable for the retina, and before it can potentially trigger changes in eye growth. Devices to induce refractive errors The first attempts were made by gluing hemispherical frosted plastic goggles over the eye of C57BL/6 mice, using instant glue (Fig. 3, from Schaeffel et al.).22 The problem was that the mice tended to remove these goggles. A plastic collar was necessary to prevent the mice from reaching the goggles. Even then, it was demanding to keep the eye covers in place for two weeks.22 Barathi et al.21 managed to keep velcro rings, glued to the fur in Balb/cJ albino mice, in place from P10 to P56. These rings even carried hard contact lenses. Also, Tkatchenko and Tkatchenko30 managed to keep them in place without a collar. Because of the problems associated with gluing the devices to the fur around the eye, Faulkner et al.31 developed a head-mounted pedestal in which a holder was implanted in the skull and the diffusers were carried by a wire. These devices were well tolerated by the mice, but they were applied rather late at P28. They were kept in place until P84.25 Continuous light rearing, which causes severe flattening of the cornea and large hyperopic refractive errors in chickens32 was also attempted but

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Figure 3. C57BL/6 mouse with a hemispherical plastic diffuser attached in front of the right eye. The plastic collar was attached to prevent the mouse from removing the diffuser (from Ref. 22).

no effect on corneal curvature in C57BL/6 mice was found. After 37 days in continuous white light with about 500 lux ambient illuminance, corneal radius was 1.42 ± 0.04 mm (n = 25 eyes), versus 1.40 ± 0.05 mm (n = 20 eyes) in animals kept under regular 12/12 h light/dark cycle. There were significant differences in refraction (+3.1 ± 3.6, n = 40, versus +6.4 ± 4.3, n = 51, p < 0.001), but these small changes were in the opposite direction as in chickens.8 Finally, lid suture was also used to induce deprivation myopia2,33 in 20 days or four weeks, respectively. Non-visual effects on eye growth cannot be excluded, such as increased mechanical pressure on the globe, which might cause a rebound effect after lid re-opening, changes in the metabolic conditions due to reduced oxygen supply or elevated ocular temperature. Techniques to measure the induced refractive errors and changes in eye growth Refractive state In a number of studies, refractive states were measured by white light streak retinoscopy (e.g. Refs. 2, 7, 33, 34). In streak retinoscopy, a slightly

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defocused light streak is projected onto the eye from the retinoscope, which is held at about an arm’s length from the mouse. A small fraction of this light is reflected from the back of the eye, the fundus, and is visible in the pupil. The movement of the reflection in the pupil must be compared to the movement of the light streak seen on the fur surrounding the eye, while the streak retinoscope is tilted up and down. If the reflection in the pupil appears stationary with no clear direction of movement, the “reversal point” is reached and the eye can be assumed to be in focus with the observer. Otherwise, differently powered trial lenses are held in front of the eye until the reversal point is reached, and the lens power provides the information about refractive state. The procedure works well in animals with large pupils, but it is very difficult to judge the direction of movement of the light bar in small pupils (1 mm in diameter, or even smaller, if the pupil constricts due to the white light emitted by the retinoscope). In a trial carried out by a certified optometrist, no correlation was found between streak retinoscopy and infrared photoretinoscopy in22 alert, non-cyclopleged black mice.8 Streak retinoscopy also provided generally more hyperopic readings than infrared photoretinoscopy (see below). High hyperopia was also found in other studies using streak retinoscopy (+20 D34; +13.5 D2; +15 D33; and >+10 D21 — see Fig. 1B). An interesting case involves albino mice (as used by Barathi et al.).21 In these mice, the iris is scarcely pigmented and light penetrates easily. Therefore, these animals are, in fact, mainly refracted through the iris, mimicking a large pupil — finally limited only by the diameter of the cornea. The movement of the retinoscopic reflection can therefore be judged much more easily than in (non-cyclopleged) black mice. Given that light scatter in the iris should further degrade the retinal image, it is interesting that myopia could still be induced by negative lenses in front of the eye. A perhaps powerful technique for refracting small vertebrates is infrared photoretinoscopy. This technique is video-based, uses infrared light, and has been successfully applied in a variety of vertebrate eyes (e.g. barn owls; toads and tadpoles; frogs and salamanders; water snakes).29,35–37 Since infrared light is used, the animal is not disturbed by the measurement and the pupil does not constrict. To measure a mouse, it is sufficient to slightly restrain it by holding its tail while it rests on a small platform and turning down the room light since the pupil of the mouse is very responsive to light.38 An infrared sensitive video camera is positioned at about 60 cm distance. Attached to the camera lens is an arrangement of

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infrared light emitting diodes (IR LED; see Fig. 4A). A small fraction of this light enters the pupil, is diffusely reflected from the fundus of the eye, and returns to the camera. Because the IR LEDs are positioned directly below the camera aperture, they produce a brightly illuminated pupil — like the “red eye effect” seen with flash cameras. Furthermore, the brightness distribution in the vertical pupil meridian displays a gradient, with more light in the bottom in the case of a myopic eye (a screen dump of the refraction software is shown in Fig. 4B), and more light in the top of the pupil in a hyperopic eye. The brightness distributions in the pupils of mice are not smooth, but bumpy, indicating that the optics has considerable aberrations; furthermore, they are affected by the first Purkinje image. Figure 4B shows the measured brightness profile, together with a linear regression line fit through the pixel brightness values. Refractions can be determined from the slopes of these regression lines. The only unknown variable is then the conversion factor from the slope of the brightness profile in the pupil into refraction. However, this factor can be determined by placing trial lenses of known optical power in front of the mouse eye, inducing known refractive errors, and recording the slopes.22

Figure 4. (A) USB2 monochrome video camera, 50 mm lens with focal length extender and 10 mm extension ring, and custom-built photoretinoscope. The camera and the infrared LEDs of the photoretinoscope can be run through the USB port of the laptop, making additional power supplies unnecessary. (B) Screen dump of the software, developed in Visual C++, designed to measure refraction and pupil size with 62 Hz sampling rate. In addition, light-induced pupil responses can be recorded, which are elicited by a green LED attached to retinoscope (not shown in the version in (A)) and flashed through the USB port.

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The temporal sampling rate of this technique is currently 62 Hz, a typical frame rate for USB2 cameras (Fig. 4A). As soon as the mouse eye appears in the video frame, the image processing software detects the pupil — which is a simple task because it is brightly illuminated over a dark background — and fits a linear regression through the pixel brightness values in the vertical pupil meridian. Even though the measurements are easy to perform, some limitations have to be considered:1 because of the excavation of the optic disc (nicely visible in the frozen section of the mouse eye presented by Remtulla and Hallett7), the eye is more myopic (or less hyperopic), close to the pupillary axis and appears considerably more hyperopic in the periphery due to the thickness of the retina.22 Therefore, for consistent refractions, it is important to align the eye with the camera axis.22 It was observed that mice sometimes change their refractive state for a few seconds and become a few diopters more myopic. The mechanism behind this optical change has not yet been systematically studied but it is clear that it occurs without visual stimulation and does not represent accommodation. It was also observed under cycloplegia with Tropicamide,22 and Woolf 39 was unable to find a ciliary muscle for accommodation in the mouse eye. Also, Smith et al.40 stated that the ciliary muscle in the mouse eye is weak and lacks accommodation. An alternative explanation for this change in refraction is that it is produced by activity of the retractor bulbi muscles,41,42 which can pull the globe back into the orbit, causing a temporary change in intraocular pressure, which, in turn, could affect the refractive state. Therefore, it is important to observe mice for several seconds to ensure that their eyes are in a relaxed condition.22 It was found that mice were measured more hyperopic when they had larger pupils (about 0.9 D more hyperopia per 0.1 mm increase in pupil size).22 This effect could result from negative spherical aberration (more hypopic refractions in the pupil periphery). On the other hand, positive spherical aberration was found in mouse eyes by Hartmann-Shack aberrometry,10 and it is more likely that the increasing hyperopia results from non-linearities in the video system. Larger pupils return more light, proportional to the pupil area, and pixel values are not perfectly log-linear to the absolute brightness. A more extensive calibration with different camera aperture sizes would be necessary to control this factor. The standard deviation typically obtained in the same eyes in repeated measurements was about 2.7 D22 — much less than the optical and behavioral depth of focus (see below).

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Corneal radius of curvature The corneal surface is optically the most powerful surface of the eye; in most vertebrates, it generates more than 60% of the total optical power. Therefore, it is important to measure its radius of curvature. This can be done by infrared photokeratometry in alert mice.15 Mice are placed on a platform (Fig. 5A) and slightly restrained by holding their tails. The platform is positioned at about 15 cm from a metal ring with a diameter of 300 mm that carries eight IR LEDs. The reflections of the IR LEDs on the corneal surface, the first Purkinje images, are also arranged in a circular pattern (Fig. 5B). In a digital video image of the eye, the reflections can be detected by an image processing program and fit by a circle in real time. The diameter of the fitted circle is proportional to the curvature of the cornea — small circles for steep corneas with small radius of curvature, and larger circles for flat corneas. Although the equations to calculate corneal curvature from camera distance, distance of the IR LEDs of the keratometer, distance of the keratometer to the mouse, and camera magnification have been worked out,44 it is easier to measure a ball bearing with known radius of curvature, compare it to the measured radius of curvature, and find the correction factor. Since the system is almost perfectly linear, one factor is sufficient to derive the true corneal radius of curvature of the animal. The standard

Figure 5. Measurement of corneal radius of curvature in alert mice, using IR photokeratometry. (A) The mouse (white arrow) is restrained only by holding its tail, but because its eye needs to be positioned in the small field of view of the video camera with a 135 mm lens at a well defined position (depth of field only about 1 mm), the platform is moved back and forth until the first Purkinje images of eight IR LEDs, arranged in a circle, are in good focus. The software continuously fits circles through the eight Purkinje images and provides their radius.

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deviation of this procedure is about 1%, with the major source of variability the depth of focus of the video camera. Axial length measurements and ocular biometry Perhaps the most important variable in myopia studies is axial length. The type of myopia that is experimentally induced in animal models is almost always axial. Therefore, the first question is about the axial length changes. There were several attempts to measure axial eye lengths in mice: video imaging of freshly enucleated eyes21,22,45; analysis of histological sections of eyes2; highly enlarged photographs of frozen sections15; and eye weight measurements.17 These techniques could be used only post-mortem, and all may have limited resolution to detect experimentally induced axial myopia. Attempts to measure axial eye length in vivo with A-scan ultrasonography (which is typically used in other animal models of myopia) also failed in the small eye of the mouse.22 Major progress was therefore made when a commercial optical low coherence interferometer (OLCI) was adapted to measure short-range optical distances. The initial goal was to measure corneal thickness and anterior chamber depth in humans (the Carl Zeiss “AC Master” (http://www.meditec.zeiss.com/), Jena, Germany). A test showed that this device was also able to measure the intraocular distances in living mouse eyes.46 Unfortunately, the company decided not to market the AC Master so that only prototypes are currently available to a

Figure 6. Measurements of ocular dimensions in a mouse with the “AC Master” (Zeiss-Meditec, Jena, Germany). (A) The slightly anesthetized mouse, positioned on an adjustable platform, which is attached to the chinrest of the device, is encircled. (B) Close-up view used to adjust the eye in the measurement beam. The first Purkinje images of six infrared LEDs, built into the device, are used to align the eye.46

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few laboratories.21,46,47 Therefore, custom-built low coherence interferometers were introduced,19 or are currently under construction. The optical principle of optical low coherence interferometry is based on a Michelson interferometer. A low coherence superluminescent laser diode (SLD) that emits an infrared light with a peak emission at 850 nm and a half-band width of 10 nm serves as light source. Due to the broadened bandwidth, the coherence length is rather short (about 10 µm), compared to standard laser diodes, in which it is about 160 µm. The infrared laser beam emerging from the LED is divided into two perpendicular beams via a semi-silvered mirror. One part is transmitted through the semi-silvered mirror and reaches a stationary mirror. The other part reaches a second mirror that can be moved along the light path with high positional precision. After reflection from both mirrors, the two coaxial beams propagate to the eye, where they are reflected off from the cornea, the lens, and the fundal layers. Interference between both beams can only occur when their optical path lengths are matched with extreme precision, within the coherence length. The occurrence of interference is detected by a photo cell and recorded as a function of the displacement of the movable mirror. Due to the usage of coaxial beams, the measurements are largely insensitive against longitudinal eye movements. The scanning time of the movable mirror is about 0.3 sec. In the human eye, a measurement precision in the range of 2 µm has been achieved in corneal thickness measurements, and of 5 to 10 µm for the anterior chamber depth and lens thickness measurements (R. Bergner, Carl Zeiss Meditec, Jena, personal communication, 2004). In repeated measurements in mouse eyes, a standard deviation of 8 µm was found for axial length — equivalent to less than two diopters.46 It should be kept in mind that optical path lengths are measured with this technique, which need to be converted into geometrical path lengths. This requires that the refractive indices for the ocular media are known. The problem has been analyzed by Schmucker and Schaeffel.46 The errors are generally small even if the refractive indices are not exactly known. Also, in most cases, differences are of interest between the treated and control eyes, rather than absolute axial lengths. Measurements of the optical aberrations of the mouse eye In recent years, new optical techniques have been developed to describe the optical quality of the human eye in vivo. Perhaps the currently

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most successful technique, the Hartmann–Shack aberrometer, has been adapted for measurements in mice.10 For Hartmann–Shack measurements, a superluminescent diode at 676 nm produces a bright spot on the retina. A fraction of the light is reflected from the fundus and returns from the eye through the pupil. This light reaches a microlens array of 65 × 65 square lenslets with a 400 µm aperture and a focal length of 24 mm. The lenslets create a pattern of focal spots on a CCD chip of a video camera. If an eye has no aberrations and is focused at infinity, the spot pattern is perfectly regular and each of the foci is exactly along the optical axes of the lenslets. However, if the wavefront is distorted due to optical aberrations in the cornea and lens, the focal spots are laterally displaced and form irregular patterns (Fig. 7, the left columns show the original HartmannShack images). The displacement of each of the foci is proportional to the

Figure 7. (A) Original Hartmann–Shack images, and reconstructions of the wavefronts recorded from 12 eyes of alert mice. Wave aberration maps are for the third and higher order aberrations, and contour lines are plotted in 0.1 µm steps. (B) Calculations of the average modulation transfer functions of the mouse eyes. Note that the contrast transfer drops off steeply with increasing spatial frequency, but that the contrast transfer is still around 20% at 4 cyc/deg (replotted after de la Cera et al., Ref. 10).

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tilt of the wavefront at the respective position. To reconstruct the threedimensional shape of the wavefront, the centers of the focal spots are detected by image processing software — a demanding task if they are as diffuse, as shown in Fig. 7.10 The shape of the wavefront is typically described as a Polynomial expansion, as proposed by Zernike. The coefficients describe the magnitude of known optical aberrations, like defocus, astigmatism, spherical aberration, and so on. In the measurements in the mouse, defocus was the most dominant term (average hyperopia +10.1 ± 1.4 D), but also astigmatism (3.6 ± 3.7 D) and positive spherical aberration (wavefront error 0.15 ± 0.07 µm for a 1.5 mm pupil) were highly significant. At least, the measurements with the Hartmann–Shack sensor provided quite similar spherical refractive errors to the infrared photorefractor (Fig. 1B). The Zernike coefficients also permit the calculation of how much contrast the optics of the eye transfers at the different spatial frequencies. The transfer function is called modulation transfer function (MTF). It shows that the mouse eye’s optics transfers are still about 20% of the contrast at 4 cyc/deg. Comparing this value to the behavioral limit of spatial resolution of the mouse — around 0.5 cyc/deg, it is unlikely that the optics of the eye is the limiting factor for visual acuity in mice. It turned out that alert mice could be accurately positioned for Hartmann–Shack aberrometry by just holding their tails, moving the platform, and waiting until they calmed down. It was also attempted to perform measurements under anesthesia. However, the optical quality of the eyes was then much poorer.10 This could explain why such low optical quality was described in rodent eyes in a previous study in mice and rats.48 Remtulla and Hallett7 initially estimated the depth of field of the mouse at as large as +56 D, based on photoreceptor diameters and using the equations provided by Green et al.78 But they also stated that this must be an unrealistic number. They noted that behavioral visual acuity may be five times higher as calculated from anatomical variables,49 and finally estimated the depth of field as +11 D. Aberrometric techniques permit a more direct estimation of the optical depth of field. De la Cera et al.10 calculated the contrast transfer (modulation) of the mouse optics for a sine wave grating of 2 cyc/deg, at different amounts of defocus. They found that modulation drops to 50% already at about 5 D of defocus. Since the spatial acuity of the mouse is only about a fourth of this value in behavioral studies, the depth of field should be

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several times larger. Schmucker and Schaeffel50 elicited optomotor responses in mice by drifting 0.03 cyc/deg square wave gratings when mice were wearing trial lenses. Significant responses were found with +10 D of imposed defocus. In summary, at least for lower spatial frequencies, the depth of field of the mouse would exceed ±10 D. Behavioral measurement of grating acuity and contrast sensitivity in the mouse There were several approaches to measuring spatial visual performance in mice behaviorally. These approaches can be divided into two principles, testing forced choice behavior in a swimming task, the “Visual Water Task, VWT”51,52 or measuring the optomotor response to drifting gratings that are either presented as printed on paper and attached to the inner wall of a rotating drum, or more sophisticatedly, presented on computer monitors that are arranged in a square (the “virtual optomotor system, VOS”) that permitted better control of the stimulus variables.53–57 The first approach measures visual acuity for stationary targets, and the second for moving targets. Processing of the two stimulations involves different brain areas. While acuity for stationary targets is largely determined by geniculo-cortical processing, moving targets are processed in the subcortical accessory optic system.55 Prusky and Douglas58 have shown that ablation of the cortex did not change the cut-off spatial frequency measured with the visual water task (VWT) and the virtual optomotor system (VOS), but the contrast sensitivity functions were changed. Contrast sensitivity was increased in the VOS but the range of high contrast sensitivity was found at lower spatial frequencies (contrast sensitivity of about 20 at 0.05 cyc/deg with the VOS, but only about two with the VWT). Another interesting aspect observed in the VOS was that tracking occurred only in the temporal-tonasal direction for each eye, similar to the condition in infants (e.g. Ref. 59). This means that, depending on the direction of motion of the stripes, each eye can be independently tested.55 Different body movements, elicited by the drifting gratings, can be studied: head tracking,53,60 optokinetic nystagmus of the eye,61,62 or whole body optomotor responses.14,27 It could be expected from the very bright retinal images of the mouse (see above — schematic eye data) that mice also have good spatial vision at low ambient illuminances. However, optomotor experiments in an

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Figure 8. Automated optomotor drum. The mouse is placed in a small inner perspex drum in the center of a larger drum, which is covered inside with the square wave stripe pattern (black arrow). The large drum is mechanically rotated by a DC motor. Both the center of mass of the mouse and the angular orientation of its body axis are automatically tracked by a video system (black arrow: small surveillance firewire camera that images the mouse, see also laptop screen). The net angular movement is statistically evaluated and compared to the stripe pattern’s direction of movement.14

automated optomotor drum suggest that this is not the case. Individual mice were placed in a small perspex drum in the center of a larger drum that was rotated with vertical square wave patterns of adjustable fundamental spatial frequency (Fig. 8). Their movements were recorded from above by a little surveillance video camera. Movement analysis was fully automated. Both the angular movement of the center of mass of the mouse and angular changes in the orientation of the body axis were tracked by image processing

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software and automatically statistically analyzed. Even though the mice often ignored the visual stimuli when they cleaned themselves, the objective video tracking procedure produced statistically meaningful results. An advantage of the procedure was that the mice experienced no further behavioral restriction, causing little stress. The disadvantages are that the “whole body optomotor response” is less reliable than the eye61,62 or head63 optomotor response, and that the data is therefore more noisy. The automated version of the “whole body optomotor analysis”14 provided some new results: Grating acuity reached its limit at about 0.4 to 0.5 cyc/deg, similar to other published optomotor experiments in which eye movements were evaluated. Grating acuity declined continuously when the illuminance (or luminance) was reduced: The “relative responses” were 100% at 400 lux (about 30 cd/m²), 76% at 40 lux (about 0.1 cd/m²), and 46% at 4 lux (about 0.005 cd/m²). A similar decline in visual acuity with decreasing illuminances was also described by Abdeljalil et al.63 Mutant mice lacking either rods or cones, or both, showed reduced visual acuity in cone-only models (0.10 cyc/deg in Rho –/– and 0.20 cyc/deg in CNGB1–/– compared to 0.30 cyc/deg in C57BL/6 wild-type mice). The “all-rod-mouse” (CNGA3 –/–) performed similarly in the optomotor test as the wild-type, both under photopic and scotopic conditions. This observation suggests that the rod system is not saturated, even at illuminances of 400 lux (about 30 cd/m²). It should also be kept in mind that rods represent about 95% of the photoreceptors in most vertebrates,64 including the mouse. Since the remaining 5% of cones are not clustered in a fovea but rather more evenly distributed across the retina, they may not reach a sampling density necessary for good spatial vision. In mice without any functional photoreceptors (CNGA3 –/– Rho –/–), no optomotor response could be elicited, suggesting that the light sensitive, melanopsin-containing ganglion cells do not contribute to spatial vision. In summary, the considerable number of behavioral studies have provided surprisingly consistent results: The highest contrast sensitivity of BL57J/6 mice is about 20 (equivalent to a threshold contrast of 5%) or even better (up to 10061), and is reached at high illuminances between 30 cd/m² 14 and 63 cd/m² 56 at spatial frequencies of between 0.1 and 0.2 cyc/deg. The highest detected spatial frequency (denoted as “grating acuity”) is around 0.5 cyc/deg.

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Recent Studies on Myopia in the Mouse Model: Some Examples Magnitudes of experimentally induced refractive errors in wild-type mice The success of experiments to induce “deprivation myopia” (the type of myopia that develops “by default” when the retinal image clarity is experimentally degraded) was surprisingly variable across studies. Schaeffel and Burkhardt,1 using frosted hemispherical diffusers (Fig. 3) obtained a small myopic shift only 3–4 D after two weeks, starting at P24, which reached a significance level of p < 0.00036 in eight mice after one outlyer was excluded. Tejedor and de la Villa,2 using lid suture for up to 20 days, starting at P10-11, induced little more than 6 D of myopia and found an impressively smooth correlation between axial length changes and refraction changes. Barathi et al.,21 using early lid suture in a large number of albino mice (n = 80), starting at P10 and continuing until P56, induced up to 14 D of myopia and an axial elongation of about 200 µm. In experiments by Pardue et al.,25 it took wild-type mice eight weeks, starting at P28, to develop a myopic shift of about 5.5 D. Tkatchenko and Tkatchenko30 induced about 45 µm increase in vitreous chamber depth by treating C57BL/6J mice with frosted diffusers from P24 for a duration of 21 days. These changes were measured with a demanding, small animal MRI. Biometric changes were accompanied by refraction shifts of about 4 D, but the significance levels were low due to the low number of animals (n = 4). The large variability in the diffuser experiments cannot be explained by poor measurement resolution (see description of the resolution of the techniques used above); it must result from low sensitivity of the emmetropization mechanism to changes in visual experience, and the fact that axial eye growth is rather slow in mice (Fig. 1A). Somehow, one might expect poor responsiveness due to the large depth of focus (more than +10 D). But then it remains unclear why the variability of the refractions among untreated animals is considerably less than the depth of focus (about +3 D22; or even less ±1.14 D).10 This poses the question as to what keeps the refractions in such close range when emmetropization is slightly sensitive to visual input? Only a few studies are available in which mice were treated with eyeglass lenses. Barathi et al.21 treated 100 albino mice with –10 D lenses for up to

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46 days, and induced an increase in axial length of up to about 0.37 mm and myopia of 14 D — the largest effect ever observed in mice. These lenses had even stronger effects than lid suture over the same treatment period, but non-visual effects of lid suture on the geometry of the anterior segment of the eye are always possible. Faulkner et al.65 used –10 D lenses in nob mice and induced similar amounts of myopia (3–4 D) as with diffusers. Burkhardt and Schaeffel failed to induce compensatory growth inhibition in response to treatment with positive lenses (unpublished observations, 2006). It remains uncertain whether the mouse eye can distinguish between image diffuse, image degradation and defocus. Further studies, in particular with positive lenses, are necessary. In summary, the literature agrees that deprivation myopia can be induced in mice but that the treatment duration should be several weeks and the goggles need to be applied as early as possible (soon after eye opening). The effects are small in most studies (few diopters and less than 50 µm axial length changes). The few studies also show that –10 D lenses can induce myopia, but it remains to be discovered whether diffuse image blur and image defocus are, in fact, distinguished by the mouse retina. Refractive development in mutant mice A few studies have already appeared in which the effects of permanent know-out of a gene on refractive development were studied. Pardue et al.25 found that the susceptibility to deprivation myopia was greatly enhanced in nob mice with a mutation in the Nyx gene (lacking the ERG b-wave due to a defect in the rod ON pathway), which is linked to congenital stationary night blindness (CSNB) in humans: about 5.5 D of myopia could be induced in two weeks, starting at P28, compared to only about 1 D in the respective wild-type. Nob mice had significantly lower retinal dopamine and DOPAC levels than the wild-type and — in contrast to the wild-type — no changes could be induced by diffuser wear. Schippert et al.27 found that mice lacking a functional gene for the transcription factor Egr-1 were more myopic than their heterozygous and wild-type littermates. The homozygous knock-out animals also had significantly longer eyes between P42 and P56, but approached wild-type dimensions later in development. They showed no differences in corneal curvature or anterior chamber depth. They also had normal optomotor responses. This suggests that the effect of Egr-1 knock-out is surprisingly selective for axial eye growth. That these knock-out animals were more myopic fits the idea that

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the EGR-1 protein appears to be associated with an inhibition signal for axial eye growth: in chickens, the protein is up-regulated when hyperopia is induced and down-regulated when myopia is induced.66,67 More recently, Schippert et al.68 presented an analysis of the retinal gene expression patterns in Egr-1 knock-out mice. Similar to Schippert et al.,27 Zhou et al.69 found that an adenosine A2a receptor knock-out mouse went through a phase of longer axial length and relative myopia, similar to what was observed in the Egr-1 knock-out mice (between P42 and 56), but returned to normal axial lengths later in development. Furthermore, these authors found that myopia was accompanied by reduced collagen fibril diameters in the sclera.69 A more extended screening for refractive errors in mutant mice was presented by Faulkner et al.70 Significant refraction differences were detected between C57Bl/6J mice (which had refractive errors from 6.9 to 8.5 D), and nob and rd1 mice, which were about 2 D more hyperopic, and GABAC null mice, which were about 5 D more myopic than C57 mice. A next step would be to find out which morphological changes determine the changes in refraction. It could even be that retinal thickness changes, related to the degenerations, underlie the changes in measured refractions. Pharmacological studies to inhibit axial eye growth in mice Atropine is known as a potent inhibitor of myopia in humans and animal models. A problem that arises if atropine is unilaterally applied in mice is that the fellow “control” eye may also be contaminated with atropine due to the cleaning behavior of the mice. Barathi et al.71 have therefore used the light-induced pupil response to probe the transfer of atropine to the fellow eye. They found that the contralateral light-induced pupil response was also affected by ipsilateral atropine application, but only to a small extent, making an inter-ocular comparison of atropine effects possible. They also found that daily application of a single drop of 1% atropine induced more hyperopic refractions and shorter axial lengths over time — despite visual experience being normal. This is different from the chicken where the effects of atropine are largely confined to a suppression of myopia that would be induced by diffusers or negative lenses (e.g. Ref. 72). Barathi et al.73 have also studied the expression of muscarinic receptors in both human and mouse sclera and their role in the regulation of scleral fibroblasts. Further studies will show whether the eye growth inhibition exerted by atropine is mediated through these receptors.

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Image processing and regulation of retinal genes and proteins Studies on the regulation of the retinal mRNA of Egr-1 by light and retinal image contrast in mice showed surprisingly high sensitivity to the changes in retinal image contrast — despite their low acuity and large depth of field. Even if the retinal illumination was matched in the fellow eye by using neutral density filters that had similar light attentuation as the diffusers, the minor difference in image contrast had clear effects on Egr-1 mRNA concentrations in the retina.74 Furthermore, a microarray analysis of gene expression under the same visual stimulation conditions showed surprisingly clear transcription changes of a number of genes.75 This shows that even minor differences in image contrast were detected. Beuerman et al.76 studied the role of transglutaminase 2 (TGM-2) during the development of lens-induced myopia in albino mice. They found a significant up-regulation of TGM-2 in the retina after eight weeks of treatment, which was fully suppressed by simultaneous atropine treatment. More recently, Beuerman et al.77 studied retinal protein expression following –10 D lens wear in albino mice, which induced 10.5 D relative myopia and an axial elongation of 0.31 mm. From 200 identified proteins, 18 were significantly up-regulated and 10 were down-regulated. It is interesting that one of them was a Muller cell marker (Vementin). All proteins could be assigned to certain molecular and biological functions. It is clear that such studies may help in identifying potential targets for pharmacological intervention of myopia, and understanding the signalling cascades from retina to sclera.

Summary Despite its lower spatial resolution than would be possible based on the size of the eye, inferior optics, and slow eye growth, the mouse seems on its way to becoming an important model for studies on myopia. The required technology to induce and measure experimentally induced refractive errors is now available, and highly significant changes in those variables could be induced by diffusers and eyeglass lens wear. Knock-out models, lacking presumed elements of the signalling cascade translating the output of retinal image processing into growth commands to the sclera, have been studied and more will be introduced in the future. Microarray analyses of retinal and scleral transcripts following alterations in visual experience, or in knock-out models, have been presented and will

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help to identify new pharmacological targets for inhibition of myopia. Finally, screening of drugs against myopia may work well in mice since the drug can be given as eye drops (not as intravitreal injection as in chickens) and can reach retinal and scleral targets in sufficient concentrations due to the small volume of the globe.

Acknowledgments Supported by the German Research Council DFG-Scha 518/13-1.

References 1. Schaeffel F, Burkhardt E. (2002) Measurement of refractive state and deprivation myopia in the black wild-type mouse. Invest Ophthalmol Vis Sci 43: ARVO e-abstract 182. 2. Tejedor J, de la Villa P. (2003) Refractive changes induced by form deprivation in the mouse eye. Invest Ophthalmol Vis Sci 44: 32–36. 3. Wallman J, Turkel J, Trachtman J. (1978) Extreme myopia produced by modest change in early visual experience. Science 201: 1249–1251. 4. Norton TT. (1990) Experimental myopia in tree shrews. Ciba Found Symp 155: 178–194. 5. Wiesel TN, Raviola E. (1977) Myopia and eye enlargement after neonatal lid fusion in monkeys. Nature 266: 66–68. 6. McFadden S, Wallman J. (1995) Guinea pig eye growth compensates for spectacle lenses. Invest Ophthalmol Vis Sci 36: S758 (ARVO abstract). 7. Remtulla S, Hallett PE. (1985) A schematic eye for the mouse, and comparisons with the rat. Vision Res 25: 21–31. 8. Schaeffel F. (2008) The mouse as a model for myopia, and optics of its eye. In: L Chalupa & RW Williams (eds), Eye, Retina and Visual System of the Mouse, pp 73–87. MIT Press. 9. Porciatti V, T Pizzorusso, L Maffei. (1999) The visual physiology of the wild type mouse determined with pattern VEPs. Vision Res 39: 3071–3081. 10. de la Cera EG, Rodriguez G, Llorente L, et al. (2006). Optical aberrations in the mouse eye. Vision Res 46: 2546–2553. 11. Carter-Dawson LD, LaVail MM. (1979) Rods and cones in the mouse retina. I. Structural analysis using light and electron microscopy. J Comp Neurol 15: 245–262. 12. Oyster CW. (1999) The Human Eye: Structure and Function, chapter 15. Sinauer Associates, Inc. Sunderland, Massachusetts.

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13. Dangata YY, Findlater GS, Kaufman MH. (1995) Morphometric analysis of myelinated fiber composition in the optic nerve of adult C57BL and CBA strain mice and (C57BL x CBA) F1 hybrid: A comparison of interstrain variation. J Anat 186: 343–348. 14. Schmucker C, Seeliger M, Humphries P, et al. (2005) Grating acuity at different luminances in wild-type mice and in mice lacking rod or cone function. Invest Ophthalmol Vis Sci 46: 398–407. 15. Schmucker C, Schaeffel F. (2004) A paraxial schematic eye model for the growing C57BL/6 mouse. Vision Res 44: 1857–1867. 16. Schaeffel F, Howland HC. (1988) Visual optics in normal and ametropic chickens. Clin Vis Sci 3: 83–98. 17. Zhou G, Williams RW. (1999) Eye1 and Eye2: gene loci that modulate eye size, lens weight, and retinal area in the mouse. Invest Ophthalmol Vis Sci 40: 817–825. 18. Zhou X, Shen M, Xie J, et al. (2008) The development of the refractive status and ocular growth in C57BL/6 mice. Invest Ophthalmol Vis Sci 49: 5208–5214 19. Zhou X, Xie J, Shen M, et al. (2008) Biometric measurement of the mouse eye using optical coherence tomography with focal plane advancement. Vis Res 48: 1137–1143. 20. Tkatchenko TV, Shen Y, Tkatchenko AV. (2010) Analysis of postnatal eye development in the mouse with high-resolution animal MRI. Invest Ophthamol Vis Sci 51: 21–27. 21. Barathi VA, Boopathi VG, Yap EPH, Beuerman RW. (2008) Two models of experimental myopia in the mouse. Vision Res 48: 904–916. 22. Schaeffel F, Burkhardt E, Howland HC, Williams RW. (2004) Measurement of refractive state and deprivation myopia in two strains of mice. Optom Vis Sci 81: 99–110. 23. Jiang L, Schaeffel F, Zhou X, et al. (2009) Spontaneous axial myopia and emmetropization in a strain of wild-type guinea pig (Cavia porcellus). Invest Ophthalmol Vis Sci 50: 1013–1019. 24. Howlett MH, McFadden SA. (2006) Form deprivation myopia in the guinea pig (Cavia procellus). Vision Res 46: 267–283. 25. Pardue MT, Faulkner AE, Fernandes A, et al. (2008) High susceptibility to experimental myopia in a mouse model with a retinal ON pathway defect. Invest Ophthalmol Vis Sci 49: 706–712. 26. Glickstein M, Millodot M. (1970) Retinoscopy and eye size. Science 168: 605–606. 27. Schippert R, Burkhardt E, Feldkaemper M, Schaeffel F. (2007) Relative axial myopia in an EGR-1 (ZENK) knock-out mouse. Invest Ophthalmol Vis Sci 48: 11–17. 28. Schaeffel F, Howland HC, Farkas L. (1986) Natural accommodation in the growing chicken. Vision Res 26: 1977–1993.

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29. Schaeffel F, Wagner H. (1996) Emmetropization and optical development in the eye of the barn owl (Tyto alba). J Comp Physiol A 178: 717–734. 30. Tkatchenko AV, Tkatchenko TV. (2009) Analysis of postnatal mouse eye growth and plasticity with high-resolution small animal MRI. Invest Ophthalmol Vis Sci 50: #3938 (ARVO abstract). 31. Faulkner AE, Kim MK, Iuvone PM, Pardue MT. (2007) Head-mounted goggles for murine form deprivation myopia. J Neurosci Methods 161: 96–100. 32. Li T, Howland HC, Troilo D. (2000) Diurnal illumination patterns affect the development of the chick eye. Vision Res 40: 2387–2393. 33. Beuerman RW, Barathi A, Weon SR, Tan D. (2003). Two models of experimental myopia in the mouse. Invest Ophthalmol Vis Sci ARVO e-abstract 4338. 34. Drager UC. (1975) Receptive fields of single cells and topography in mouse visual cortex. J Comp Neurol 160: 269–290. 35. Mathis U, Schaeffel F, Howland HC. (1988) Visual optics in toads (Bufo americanus). J Comp Physiol A 163: 201–213. 36. Schaeffel F, Hagel G, Eikermann J, Collett T. (1994) Lower-field myopia and astigmatism in amphibians and chickens. J Opt Soc Am A 11: 487–495. 37. Schaeffel F, Mathis U. (1991) Underwater vision in semi-aquatic European snakes. Naturwissenschaften 78: 373–375. 38. Pennesi ME, Lyubarsky AL, Jr Pugh EN. (1998) Extreme responsiveness of the pupil of the dark-adapted mouse to steady retinal illumination. Invest Ophthalmol Vis Sci 39: 2148–2156. 39. Woolf D. (1956) A comparative cytological study of the ciliary muscle. Anatomical Record 124: 145–163. 40. Smith SS, Sundberg JP, John SWM. (2002) The anterior segment and ocular adnexae. In: RS Smith (ed), Systematic Evaluation of the Mouse Eye: Anatomy, Pathology and Biomethods, pp 3–24. CRC Press LLC, Boca Raton. 41. Lorente de No R. (1933) The interaction of the corneal reflex and vestibular nystagmus. Am J Physiol CIII: 704–711. 42. Pachter BR, Davidaowitz J, Breinin GM. (1976) Morphological fiber types of retractor bubli muscle in mouse and rat. Invest Ophthalmol 15: 654–657. 44. Howland HC, Sayles N. (1985) Photokeratometric and photorefractive measurements of astigmatism in infants and young children. Vision Res 25: 73–81. 45. Fernandes A, Yin H, Byron EA, et al. (2004) Effects of form deprivation on eye size and refraction in C57BL/6J mouse. Invest Ophthalmol Vis Sci 45: ARVO e-abstract 4280. 46. Schmucker C, Schaeffel F. (2004) In vivo biometry in the mouse eye with low coherence interferometry. Vision Res 44: 2445–2456. 47. Puk O, Dalke C, Favor J, et al. (2006) Variations of eye size parameters among different strains of mice. Mamm Genome 17: 851–857. 48. Artal P, Herreros de Tejada P, Munoz Tedo C, Green DG. (1998) Retinal image quality in the rodent eye. Vis Neurosci 15: 597–605.

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49. Birch D, Jacobs GH. (1979) Spatial contrast sensitivity in albino and pigmented rats. Vision Res 19: 933–938. 50. Schmucker C, Schaeffel F. (2006) Contrast sensitivity of wild-type mice wearing diffusers or spectacle lenses, and the effect of atropine. Vision Res 46: 678–687. 51. Prusky GT, West PW, Douglas RM. (2000) Behavioral assessment of visual acuity in mice and rats. Vision Res 40: 2201–2209. 52. Wong AA, Brown RE. (2006) Visual detection, pattern discrimination and visual acuity in 14 strains of mice. Genes Brain Behav 5: 389–403. 53. Prusky GT, Alam NM, Beekman S, Douglas RM. (2004) Rapid quantification of adult and developing mouse spatial vision using a virtual optomotor system. Invest Ophthalmol Vis Sci 45: 4611–4616. 54. Prusky GT, Alam NM, Douglas RM. (2006) Enhancement of vision by monocular deprivation in adult mice. J Neurosci 26: 11554–11561. 55. Douglas RM, Alam NM, Silver BD, et al. (2005) Independent visual threshold measurements in the two eyes of freely moving rats and mice using a virtualreality optokinetic system. Vis Neurosci 22: 677–684. 56. Umino Y, Solessio E, Barlow RB. (2008) Speed, spatial and temporal tuning of rod and cone vision in mouse. J Neurosci 28: 189–198. 57. Puk O, Dalke C, Hrabé de Angelis M, Graw J. (2008) Variation of the response to the optokinetic drum among various strains of mice. Front Biosci 13: 6269–6275. 58. Prusky GT, Douglas RM. (2004) Characterization of mouse cortical spatial vision. Vision Res 44: 3411–3418. 59. Valmaggia C, A Rütsche, A Baumann, et al. (2004) Age related change of optokinetic nystagmus in healthy subjects: a study from infancy to senescence. Br J Ophthalmol 88: 1577–1581. 60. Thaung C, Arnold K, Jackson IJ, Coffey PJ. (2002) Presence of visual head tracking differentiates normal sighted from retinal degenerate mice. Neurosci Lett 325: 21–24. 61. Alphen van B, Winkelman BHJ, Frens MA. (2009) Age- and sex-related differences in contrast sensitivity in C57BL/6 mice. Invest Ophthalmol Vis Sci 50: 2451–2458. 62. Faulstich M, van Alphen AM, Luo C, et al. (2006) Oculomotor plasticity during vestibular compensation does not depend on cerebellar LTD. J Neurophysiol 96: 1187–1195. 63. Abdeljalil J, Hamid M, Abdel-Mouttalib O, et al. (2005) The optomotor response: a robust first-line visual screening method for mice. Vision Res 45: 1439–1446. 64. Sterling P. (2003) How retinal circuits optimize the transfer of visual information. In: LM Chalupa & JS Werner (eds), The Visual Neurosciences, Vol 1, pp 234–259. MIT press, Cambridge, Massachusetts.

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65. Faulkner AE, Pozdeyev N, Iuvone PM, Pardue MT. (2009) The effect of lens defocus versus form deprivation on refractive status and dopamine in Nob mice. Invest Ophthalmol Vis Sci 50: #3846 (ARVO abstract). 66. Fischer AJ, McGuire JJ, Schaeffel F, Stell WK. (1999) Light- and focus-dependent expression of the transcription factor ZENK in the chick retina. Nat Neurosci 2: 706–712. 67. Bitzer M, Schaeffel F. (2002) Defocus-induced changes in ZENK expression in the chicken retina. Invest Ophthalmol Vis Sci 43: 246–252. 68. Schippert R, Schaeffel F, Feldkaemper M. (2009) Microarray analysis of retinal gene expression in Egr-1 knock-out mice. Mol Vis 15: 2720–2739. 69. Zhou X, Huang Q, An J, et al. (2009) The relative myopia in mice deficient in adenosine A2a receptors is associated with reduced collagen synthesis in sclera. Invest Ophthalmol Vis Sci 50: #3839 (ARVO abstract). 70. Faulkner AE, Choi HY, Kim MK, et al. (2007) Retinal defects influenece unmanipulated refractive development in mice. Invest Ophthalmol Vis Sci 48: #4419 (ARVO abstract). 71. Barathi VA, Beuerman RW, Schaeffel F. (2009) Effect of unilateral topical atropine on binocular pupil responses and eye growth in mice. Vision Res 49: 383–387. 72. Diether S, Schaeffel F, Lambrou GN, et al. (2007) Effects of intravitreally and intraperitoneally injected atropine on two types of experimental myopia in chicken. Exp Eye Res 84: 266–274. 73. Barathi VA, Weon SR, Beuerman RW. (2009) Expression of muscarinic receptors in human and mouse sclera and their role in the regulation of scleral fibroblast proliferation. Mol Vis 15: 1277–1293. 74. Brand C, Burkhardt E, Schaeffel F, et al. (2005) Regulation of Egr-1, VIP, and Shh mRNA and Egr-1 protein in the mouse retina by light and image quality. Mol Vis 11: 309–320. 75. Brand C, Schaeffel F, Feldkaemper M. (2007) A microarray analysis of retinal transcripts that are controlled by retinal image contrast in mice. Mol Vis 13: 920–932. 76. Beuerman RW, Barathi VA, Rhuan WS, Chew J. (2009) Role of transglutaminase 2 (TGM-2) in mouse after induction of experimental myopia. In: McBrien, et al. Myopia: Recent advances. Opt Vis Sci 86: 45–66. 77. Beuerman RW, Barathi A, Zhou L. (2009) Quantitative analysis of the retina proteome in a mouse model of myopia. Invest Ophthalmol Vis Sci 50: #3830 (ARVO abstract). 78. Green DG, Powers MK, Banks MS. (1980) Depth of focus, eye size and visual acuity. Vision Res 20: 827–823.

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4.4 Gene Analysis in Experimental Animal Models of Myopia Roger W. Beuerman*,†,‡,¶, Liang K. Goh§ and Veluchamy A. Barathi*

Progress in understanding the biological basis of myopia has taken advantage of various animal models with the implicit suggestion that some of the results may be passed on to studies of human myopia as a conduit to narrow the search for genes underlying myopia. As the number of genes is not really the differentiating factor, but rather the proteins, we try to answer the question: “Can the results of animal studies add to our understanding of human myopia”? This chapter provides information to support the notion that animal models are valuable for their insights into the mechanisms of myopia, which may facilitate the search for the genetic basis of myopia in humans.

Introduction As discussed in Chapter 3.2 “Twin Studies and Myopia,” the insights from twin studies do suggest that heritability must be considered as an underlying factor in the development of myopic refractive errors. Thus, a number of studies have been published searching for genes so that at present there are at least 18 possible loci on 15 different chromosomes associated with myopia, although confirming evidence has not yet been presented for all loci.1 As animals are not known in general to develop myopia naturally (although some animals are maybe naturally myopic), it is unlikely that there are somatic gene mutations that give rise to the experimental outcomes. *Singapore Eye Research Institute. E-mail: [email protected] † Duke-NUS SRP Neuroscience and Behavioral Disorders. ‡ Ophthalmology, Long Loo Lin School of Medicine. § Duke-NUS SRP Cancer and Stem Cell Biology. ¶ Corresponding author: Singapore Eye Research Institute.

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A Brief Introduction to Comparative Genomics It may not be immediately clear as to what can be accomplished by finding genes in animals associated with experimental myopia. In this “omics” era, the number of genes in a species has been bantered about as something associated with the more advanced the organism, which we normally consider to be humans and their closest relatives, the apes and other nonhuman primates. Indeed, the number of human genes had been variously projected to be from 20,000 to over 100,000. Now it is fairly clear that mammals have about 30,000 protein encoding genes,2 which brings parity to the initial consideration of the genome of apparently diverse species such as the mouse and human. To make full use the mouse genome to uncover homologies, an important goal has been to delineate and develop catalogs of the protein encoding genes of several species. Recently, more interest has developed in the chicken genome due to its prominence in agriculture as well as biological sciences.3 However, it can be said that the chicken genome is smaller than either the mouse or human, but there are 39 chromosome pairs, with 20 in the mouse and 23 in humans.4,5 Functional gene encoding of proteins is, however, important for the value of animal models, as the somatic mutation, unless aided by a specific knock-out, will be missing. With some understanding of what protein similarities can be expected, the animal model becomes more or less useful. By comparison with other species, such as mouse and chicken where the emphasis is on similarities between the species with the human genome, work on the non-human primate genome, and in particular the chimpanzee, has concentrated on differences. The genome is about the same size, but other great apes have an additional pair of chromosomes, 48 compared to 46 for humans, but other monkeys have a variable number of chromosomes. Protein homologs were found to differ by on average only two amino acids. A recent study found that the genome-wide nucleotide divergence between chimpanzee and human was only 1.23%.6

Comparative Expression The foregoing points to the fact that gene numbers are not the source of the phenotypes between mammals but rather differences in their expression at the protein level. The complexities multiply at the protein level; gene protein interactions can control genes, and one to several proteins

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can be produced by adjunct mechanisms such as alternative splicing or post-translational processing. Clearly, these mechanisms that control the eventual final expression are species specific. However, genes act in networks, as the gene products activate other genes, which often remain poorly defined even after a gene is defined, and in experimental models of myopia, the work most often concentrates on particular proteins associated with some specific aspect of the biological process. Therefore, despite the knowledge of all potential gene homologies and numbers, it is an additional task to examine and to correlate the transcriptome between species. Due to the utilitarian value of the mouse, much of the effort has been to examine transcription between these two species. When a panel of 79 human and 61 mouse tissues were subjected to custom expression arrays that included over 44,000 unique human and 36,000 mouse transcripts, the authors found that 52% and 59% of human and mouse transcripts were found in at least one tissue.7 The comparison of the human and mouse genome is not yet finished and a recent innovative approach began with a gene discovery method starting with a statistical analysis of sequence alignment for gene prediction.8 However, with the vast amount of public databases, it has been necessary to examine these for consistency of the representation of genes, transcripts, and proteins between human and mouse. A large-scale network, the collaborative consensus coding sequence (CCDS) project has identified 20,159 human and 17,707 mouse consensus coding regions from approximately the same number of genes.9 This new CCDS database found that at least 77% of mouse genes have a homologous human gene and should be a major resource for those working on myopia and who intend to make available for human gene searches the results from mouse studies. While there are apparently about 2.5–3 billion base pairs distributed among about 30,000 genes in mammals, there are significant phenotype differences between the mammalian species in general and those used for myopia studies. However, there are some differences in the number and homologies of genes, which may also contribute to the inter-species differences along with complexities due to factors such as changes in duplicated genes, alternate splicing, and post-translational modifications, which are not universal across mammals. Therefore, an important origin of these differences must be examined in the makeup of the genes, and the ultimate form their expression-protein products. A triplet base sequence provides the amino acid code or codon that is universal, but for most

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amino acids there is more than one codon. Moreover, mitochondrial coding is different. The strategy then becomes directed toward making information from experimental myopia studies that contribute specific genes of functional interest, which could be passed along to those working with human samples to provide data for more focused candidate gene studies.

Genes in Retina and Sclera in Animal Models of Myopia The finding of Zhou and Williams (1999) that eye weight is a heritable trait in various species of mice was independent of body weight or brain size, but the correlation to the retinal area was high.10 Within the 50 strains of mice that were examined, both overall weight and lens weight, as well as the number of retinal ganglion cells showed strain specific variations. As the mouse eye continues to grow past sexual maturity, a mouse opportunities to examine and modify growth with correlative molecular and cellular data could be presented. A somewhat similar study in three strains of chickens found that eye size did not vary, but induced changes in axial length due to posterior chamber elongation varied between strains.11 However, the strain differences appeared to be overcome by within from species differences.

ZENK (EGR-1) Early gene expression studies in association with experimental myopia were all centered on the chick model as that has been prominent and primarily focused on using mRNA and Northern blots of retina for analysis. However, these papers did uncover interesting candidates such as sonic hedgehog, among a few possible genes that were examined.12,13 Sonic hedgehog continues to be of interest in developmental anomalies of eye and brain. Although this was present in the retina in a few cases, bone morphgenetic protein (BMP4) was more associated with myopia.14 Work on association of ZENK in glucagon containing amacrine cells, the avian homolog of EGR-1 — an early immediate gene and transcription factor whose protein is a member of the C2H2-type zinc-finger proteins — was discovered.15,16 Acting as a nuclear protein, Egr-1 is involved in the activation of genes required for differentiation and mitogenesis. Schaeffel and

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colleagues were examining the retinoic acid system, some of which had a relationship to some aspects of modifying the visual input. In 2008, using the Affymetrix GeneChip Chicken Genome with more than 28,000 entries, Schippert, Schaeffel, and Feldkaemper were able to find 16 candidate genes that were differentially expressed and were further assessed by real-time PCR.17 Of the 16 genes, six were found to respond similarly to either positive or negative lenses, and three genes responded differentially to the presence of positive or negative lenses. In this study, lenses were worn for only a short time,24 but the implication was that novel gene programs were set into motion by short defocus on the retina. It is not clear how these signals could be transmitted to the sclera or if there were other gene programs set into motion in the sclera. However, it was found that six genes mapped to regions that were already known to be associated with families of human myopes. Due to the short time period of the modification of light input, perhaps ZENK would not be expected to appear. ZENK was also found to respond to pharmacological control by atropine (muscarinic antagonist) and a dopamine agonist. These were injected in small amounts intravitreally in the chick eye just prior to fitting diffusers over the eye. These agents reversed the down regulation of ZENK due to diffusers alone.18 Relating the chick results with ZENK to mammals has been shown by examining the development of the eye in an EGR-1 knockout mouse. Eye growth and refractive error were followed by measuring the corneal radius of curvature along with refractive state and ocular dimersions.19 A myopic shift was found in the eyes of the knockout animals, and although changes declined with age, the myopic shift remained. Thus, the situation with animal models of myopia is not substantially better than with the many gene candidates found in human studies. A substantial benefit has been the development of arrays for the chick. This is valuable as the chick model shows rapid myopic changes in response to modification of the visual input, and the eye is larger than the mouse, easing the technical hurdles.

Scleral Gene Expression in a Mouse Model of Myopia The emphasis in all these studies has been on the retina, despite the realization that growth processes producing the posterior chamber elongation

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and myopia are located in the sclera. We have shown that two models of experimental myopia can be developed in Balb/cJ mice20,21 and also from two other groups.22,23 We determined that all five muscarinic receptor subtypes expressed in mouse sclera and RPE similar to human.24,25 In another study, we showed that the M1, M4, and M5 muscarinic receptor knock-out (KO) mice eye grew 200 µ m longer than M2 and 220 µ m longer than M3 knock-out mice.26 These results provide initial evidence that M1, M4, and M5 receptors may contribute more than the M2 and M3 receptor in terms of scleral growth in experimental myopia. We have chosen to examine the sclera despite some technical issues, such as the ability to extract sufficient target from a single mouse sclera so that direct experimental-contra-lateral control comparisons can be made, thus increasing our statistical power. Pregnant Balb/cJ mice (Mus musculus) were obtained from the animal holding unit of the National University of Singapore. Animals gave birth in our animal holding unit. Naive control animals were housed in groups of six, while experimental animals were housed individually in standard mouse cages after 28 days of age at 25°C on a schedule of 12:12 hours of light on and off with mouse pellets and water available ad libidum. Approval was obtained from the SingHealth IACUC and all procedures performed in this study complied with the Association of Research in Vision and Ophthalmology (ARVO) Statement for the Use of Animals in Ophthalmology and Vision Research. Procedures for myopia induction and data analysis for biometry measurements were as previously published.21

RNA, Target cDNA and Microarray Chip Preparation Total RNA was isolated from a single mouse sclera (n = 6 at each time point) using MELTTM Total Nucleic Acid Isolation System (Ambion Inc., Austin, TX) according to the manufacturer’s instructions. RNA concentration and quality were assessed by absorbance at 260 nm and the absorbance ratio of 260/280 respectively using Nanodrop® ND-1000 Spectrophotometer (Nanodrop Technologies, Wilmington, DE). cDNAs were synthesized and labeled with biotin using Genechip® Whole Transcript Sense Target Labeling Assay (Affrymetrix, Inc., Santa Clara, CA). Biotin-labeled cDNA were then hybridized to a Mouse Gene 1.0 ST array for naive samples and MOE gene expression chips for −10 D eyeglass lens treated and control samples (Affrymetrix, Inc., Santa Clara, CA) using

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Genechip® Hybridzation kit (Affrymetrix, Inc., Santa Clara, CA). The microarray chips were washed and stained using Genechip® Hybridization, Wash and Stain kit (Affrymetrix, Inc., Santa Clara, CA). Subsequently, the microarray chips were scanned using Genechip® Scanner 3000 7G (Affrymetrix, Inc., Santa Clara, CA).

Microarray Data Analysis The microarray data (cel.files) were imported into Partek Genomic Suite 6.5 beta (Partek. Inc., Louis, MO) and normalized using GC-RMA. The variability of all samples was assessed using PCA plot (Fig. 1), and a Venn diagram (Fig. 2) was generated to compare the differentially expressed

Figure 1. PCA plot shows clustering of gene expression in mouse sclera at one week (T0), two weeks (T1), and eight weeks (T2) after eyeglass lens induced myopia and control sclera.

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Figure 2.

Venn diagram shows the differentially expressed genes found among the age groups.

genes found among the age groups. Furthermore, the gene profiles of all samples were hierarchically clustered based on gene expression (Fig. 3). ANOVA analysis (P < 0.05) was performed on the data and a set of genes was selected using a two-fold change threshold. The set of genes was then further grouped into a biological process, cellular component, and molecular function using Gene Ontology enrichment. The Gene Ontology enrichment score for each functional group is calculated using the Chisquare test, and a bar chart was generated. In addition, a forest plot showing the gene expression of each functional group was also generated.

Scleral Gene Expression in the Myopic Mouse Three time points for the induction of myopia have been tested thus far: T0 (week 1), T1 (week 2), and T2 (week 8). At each time point, batch

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Figure 3.

Gene profiles of all samples were hierarchically clustered based on gene expression.

Figure 4. Selected up and down regulated gene expression in sclera after six weeks of eyeglass lens myopic induction compared to sclera of the contra-lateral control eye.

removal was applied, and then gene expression from these time points were combined for analyses. Principal component analysis (Fig. 1) showed clustering of gene expression. T0 and the control were clustered together as might be expected. Sources of variation indicated gene expression was the major signal.

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Figure 5.

Expression of TGM-2 in muscarinic receptor mutant mice sclera.

Significant genes that were shown to have significant differential expression between different time points were selected for gene expression validation. Real time PCR primers were designed using ProbeFinder 2.45 (Roche Applied Science, IN). Mouse 18srRNA gene was used as control due to its constant expression level across the age group.

Summary These are early stage results and with human studies will require validation in other laboratories. Microarray studies of mouse myopic sclera showed the significant up-regulation of TGM (transglutaminase) 1, 2, 5, MMP-14 (matrix metalloproteinase), 19, Collagen 5a3, a2, MAPK 8, and down-regulation of TGM 3, FBN1 (fibrillin 1) as compared to control sclera after six weeks induction of spectacle lens induced myopia (Ref. 27; Fig. 4). These are all genes with homologies to human gene expression. Previous studies have shown the expression that TGF-beta 1 is downregulated in the tree shrew sclera, which suggested a contribution to greater extensibility.28 Mice with M1 and M5 receptor knock-out expressed more TGM2 in scleral fibroblasts (SF) than knock-outs for M2, M3 in KO and WT mice SFs mRNA levels (Fig. 5). TGM2, also known as tissue transglutaminase, is expressed ubiquitously. The primary function of TGM2 is transamidation and requires calcium as a cofactor. Interestingly,

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transcription is increased by retinoic acid. Amongst its many supposed functions, it appears to play a role in wound healing, apoptosis and extracellular matrix development. However, for its role in the myopic sclera, TG2 also has GTPase activity and in the presence of GTP it is suggested to function as a G protein participating in signaling processes. These results suggest that M1, M5 KO axial growth was higher than the M2, M3 KO and WT mice. Based on these results, TGM2 and muscarinic receptors interaction could be involved in scleral remodeling.

References 1. Jacobi FK, Pusch CM. (2010) A decade in search of myopia genes. Front Biosci. 15: 359–372. 2. Lander ES, Linton LM, Birren B, et al. (2001) Nature 409: 860–921. 3. Aerts J, Crooijmans R, Cornelissen S, et al. (2003) Integration of chicken genome resources to enable whole-genome sequencing. Cytogenet Genome Res 102: 297–303. 4. Burt DW, Bruley C, Dunn IC, et al. (1999) The dynamics of chromosome evolution in birds and mammals. Nature 402: 411–413. 5. International Chicken Genome Sequencing Consortium (ICGSC). (2004) Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution. Nature 432: 695–716. 6. The Chimpanzee Sequencing and Analysis Consortium. (2005) Initial sequence of the chimpanzee genome and comparison with the human genome. Nature 437: 70–87. 7. Su AI, Wiltshire T, Batalov S, et al. (2004) A gene atlas of the mouse and human protein-encoding transcriptomes. Proc Nat Acad Sci, USA 101: 6062–6067. 8. Guigo R, Dermitzakis ET, Agarwal R, et al. (2003) Comparison of mouse and human genomes followed by experimental verification yields an estimated 1019 additional genes. Proc Nat Acad Sci, USA 100: 1140–1145. 9. Pruitt KD, Harrow J, Harte RA, et al. (2009) The consensus coding sequence (CCDS) project: identifying a common protein-coding gene set for the human and mouse genomes. Genome Res 19(7): 1316–1323. 10. Zhou G, Williams RW. (1999) Mouse models for the analysis of myopia: an analysis of variation in eye size of adult mice. Optom Vis Sci 76: 408–418. 11. Guggenheim JA, Erichsen JT, Hocking PM, et al. (2002) Similar genetic susceptibility to form-deprivation myopia in three strains of chickens. Vis Res 42: 2747–2756. 12. Escano MF, Fuji S, Sekiya Y, et al. (2000) Expression of Sonic hedgehog and retinal opsin genes in experimentally induced myopic chick eyes. Exp Eye Res 7: 459–467.

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13. Akamatsu S, Fuji S, Escano MF, et al. (2001) Altered expression of genes in experimentally induced myopic chick eyes. Jpn J Ophthalmol 45: 137–143. 14. Bakrania P, Efthymiou M, Klein JC, et al. (2008) Mutations in BMP4 cause eye, brain and digit developmental anomalies: overlap between the BMP4 and hedgehog signaling pathways. Am J Genetics 82: 304–319. 15. Fischer AJ, McGuire JJ, Schaeffel F, Stell WK. (1999) Light- and focus-dependent expression of the transcription factor ZENK in the chick retina. Nat Neurosci 2: 706–712. 16. Bitzer M, Feldkaemper M, Schaeffel F. (2000) Visually induced changes in components of the retinoic acid system in fundal layers of the chick. Exp Eye Res 70: 97–106. 17. Schippert R, Schaeffel F, Feldkaemper MP. (2008) Microarray analysis of retinal gene expression in chicks during imposed myopic defocus. Mol Vis 14: 1589–1599. 18. Asby R, McCarthy CS, Maleszka R, et al. (2007) A muscarinic cholinergic antagonist and a dopamine agonist rapidly increase ZENK mRNA expression in the form-deprived chicken retina. Exp Eye Res 85: 15–22. 19. Schippert R, Burkhardt E, Feldkaemper M, Schaeffel F. (2007) Relative axial myopia in EGR-1 (ZENK) Knockout mice. Invest Ophthalmol Vis Sci 48: 11–17. 20. Beuerman RW, Barathi A, Weon SR, Tan D. (2003) Two models of experimental myopia in the mouse. Invest Ophthalmol Vis Sci 44(Suppl): 4338. 21. Barathi VA, Boopathi VG, Yap EPH, Beuerman RW. (2008) Two models of experimental myopia in the mouse. Vis Res 48(7): 904–916. 22. Tejedor J, de la Villa P. (2003) Refractive changes induced by form deprivation in the mouse eye. Invest Ophthalmol Vis Sci 44(1): 32–36. 23. Schaeffel F, Burkhardt E, Howland HC, Williams RW. (2004) Measurement of refractive state and deprivation myopia in two strains of mice. Optom Vis Sci 81(2): 99–110. 24. Barathi VA, Weon SR, Kam JH, et al. (2007) Experimental myopia in muscarinic receptor knockout mice: role of specific muscarinic receptor subtypes. Invest Ophthalmol Vis Sci 48(Suppl): 4418. 25. Barathi VA, Weon SR, Beuerman RW. (2009) Expression of muscarinic receptors (mAChRs) in human and mouse sclera and their role in the regulation of scleral fibroblasts proliferation. Mol Vis 15: 1277–1293. 26. Barathi VA, Weon SR, Rebekah PWY, Beuerman RW. (2008) Muscarinic regulation of epidermal growth factor receptor in mammalian retinal pigment epithelial (RPE) cells. Invest Ophthalmol Vis Sci 49(Suppl): 3535. 27. Beuerman RW, Barathi VA, Weon SR, et al. (2008) Expression of transglutaminase 2 in mouse sclera in experimental myopia. Invest Ophthalmol Vis Sci 49(Suppl): 3718. 28. Jobling AI, Gentle A, Metapally R, et al. (2009) Reulation of scleral cell contraction by transforming growth factor-B and stress. J Biol Chem 284: 2072–2079, 2009.

Section 5

Interventions for Myopia

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5.1 Atropine and Other Pharmacological Approaches to Prevent Myopia Louis M.G. Tong*,†,‡, Veluchamy A. Barathi and Roger W. Beuerman†,§

Introduction An earlier chapter (Chapter 1.1) in this book has mentioned the rising incidence of myopia in many countries, and that myopia may occur at a relatively young age (prior to 10 years) and then stabilizing at 16 years or younger. When myopia stabilizes at higher severities, e.g., at greater than 6 D, there is a risk of potential blinding conditions such as retinal detachment, retinal degeneration and glaucoma (Chapters 2.3, 2.4 and 2.5). Higher severity of myopia induces morbidity by increasing the aberrations and reducing quality of life (Chapter 2.1). For these reasons, it is important to attempt to arrest the progression of myopia, or even stop the onset of myopia. Historically, many forms of treatment to arrest myopia have been attempted,1 as early as in the 19th century.2 Currently, there is no general guideline followed by eye care practitioners for interventions that may decrease myopia in children. Recently, the authors have published review articles on the common modalities that have been advocated for myopia treatment.3,4 Apart from pharmacological therapies, these modalities include optical treatment such as changing the pattern of spectacle wear,5,6 the use of bifocals, multifocals and RGP contact lenses,7,8 and the use *Singapore National Eye Centre. E-mail: [email protected] † Singapore Eye Research Institute, Singapore. ‡ Duke-NUS Graduate Medical School, Singapore. § Duke–NUS, SRP Neuroscience and Behavioral Disorders, Ophthalmology, Yong Loo Lin School of Medicine, National University of Sinagapore, Singapore.

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of orthokeratology9 and visual training.10 Apart from pharmacological therapies, and studies that show the effect of progressives on a subset of myopia,11,12 no other forms of treatment have been shown in randomized controlled studies to have a beneficial effect. For this reason, this chapter will focus only on pharmacological therapies. The aim of this chapter is to summarize the postulated mechanisms, historical aspects, and the current evidence for the efficacy and safety of various pharmacological treatments to arrest myopia progression.

Possible Mechanisms of Pharmacological Treatment The main eyedrops that have been evaluated in the treatment of human myopia include the anti-muscarinic agents and ocular hypotensives. Common anti-muscarinic drugs that have been evaluated include atropine,13 pirenzepine,14 tropicamide15 and cyclopentolate.16 The ocular hypotensives that have been evaluated are the beta-adrenergic blockers labetalol and timolol,17 adrenaline18 and parasympathomimetic pilocarpine.2 Although atropine19,20 and pirenzepine14 have been shown to reduce myopia progression via slowing of axial elongation, the exact mechanism is still unknown. Both atropine and pirenzepine are blocking agents that are effective against muscarinic acetylcholine receptors of which there are five types, all of which are present in ocular tissues in varying amounts.21–23 However, pharmacologically, the use of a blocking agent is contingent on identifying the agonist of the effective pathway. In the case of myopia, the actual agent has not been identified and the use of atropine is historical, based on a hypothesized role of accommodation in myopia, which has turned out not to be the case. The second issue regarding the target of atropine, is the location of the muscarinic cholinergic receptors. Many ocular tissues have these receptors (Fig. 1). As shown in Fig. 2, the goal of current research is to determine the initial site of action of atropine or other muscarinic blockers. Locating the tissue with the critical receptor population will be the first step in developing new therapies with better targeting. The muscarinic cholinergic receptors (mAChRs) are well known members of the G-protein coupled receptor superfamily (GPCRs). The mAChRs have both a neuronal and a non-neuronal presence, and interest in non-neuronal applications has been expanding. It has been established

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Figure 1. Immunohistochemistry of muscarinic receptor subtypes in cultured mouse (a) and human scleral fibroblasts (b). Subtype selective antibodies bound demonstrated the presence of the muscarinic receptors M1–M5. No binding was observed when the primary antibody was omitted (not shown). The M1–M5 receptors were localized to the cell membrane as well as to the cytoplasm. Magnification, 200×.

Figure 2.

Schematic showing the action of topical anti-muscarinic agent on myopia.

that there are five types of muscarinic receptors, M1–M5.24 Muscarinic receptors are linked to proliferation through intracellular pathways involving the mitogen activated protein kinase (MAPK). However, in addition and maybe importantly for myopia, there are also established interactions between the muscarinic receptors and the growth factor receptors such

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as epidermal growth factor.24 These pathways interact through the cytoplasmic components of the pathways through various mechanisms referred to as transactivation. The ocular role of these receptors in accommodation has been well-established, which is the underlying reason for the application of atropine to prevent myopia, thought to be associated with near-work. The two tissues that are intimately linked to myopia, the retina and sclera, are both potential targets of muscarinic blockers. The function of mAChRs M1–M3 receptors appears to dominate. This has been the case for mammalian retina, retinal pigment epithelium and the lens.25–27 The chicken retina has also been explored for the effects of mAChRs. This has been motivated by the finding that pirenzepine slows myopia progression in the experimental model of myopia in the chick, suggesting a role for M1, as pirenzepine has a stronger effect on this muscarinic sub-type.28 However, after an extensive effort it was found that the M1 receptor does not exist in the chick, which puts some doubt on the role of muscarinic action in the control of sclera growth. These studies did find the presence of M2 and M4. It was found that various muscarinic antagonists, when injected into the posterior chamber of the chick eye, changes ZENK expression, a chicken analog of the early-immediate mammalian gene EGR–1.29 Recent studies have demonstrated that multiple mAChRs occur in mammals including humans associated with specific tissues.30 The M3-receptor is the main mAChR in human cornea, iris, ciliary body, and the epithelium of the crystalline lens.26–31 The M2- and M4-receptors have been found in the rat retina.32 The biology of the subtypes of mAChRs in the eye has not been explored in detail, but at both the mRNA and protein levels, all five mAChRs were detected in the human sclera,21,33 tree shrew sclera,34 guinea pig sclera,35 and mouse sclera21 (Fig. 1). However, the functional significance of cholinergic receptors in SFs remains to be studied in detail. Anti-muscarinic agents have been known to influence sclera remodeling in tree shrew myopia.23 Chapter 4.2 describes the changes in the extracellular matrix of the sclera, which are known to be involved in scleral remodeling during myopiagenesis. Drugs such as atropine have been known to affect the release of dopamine, which is a critical retinal neurotransmitter important for control of the growth of the eye.36 Atropine can also influence growth hormone, which may exert an effect on the growth of the eye and hence

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myopia development.37 The rationale for the use of ocular hypotensives was that raised intraocular pressure may exert a passive stretching effect on the sclera of the eye, contributing to axial elongation. However, there is little evidence that supports this in the scientific literature.

Efficacy Studies A parallel group randomized controlled study in Taiwan has shown the efficacy of atropine in the retardation of myopia.16 This study, however, is not masked. Another study in Taiwan has38 evaluated the use of various concentrations of atropine (0.5%, 0.25% and 0.1%) in myopia. A study in Singapore has found 1% atropine to be efficacious in the retardation of childhood myopia.19 Three hundred forty-six (86.5%) children completed the 2-year study. After 2 years, the mean progression of myopia and of axial elongation in the placebo-treated control eyes was −1.20+/−0.69 D and 0.38+/−0.38 mm, respectively. In the atropine-treated eyes, myopia progression was only −0.28+/−0.92 D, whereas the axial length remained essentially unchanged as compared with the baseline (−0.02+/−0.35 mm). The differences in myopia progression and axial elongation between the two groups were −0.92 D (95% confidence interval, −1.10 to −0.77 D; p < 0.001) and 0.40 mm (95% confidence interval, 0.35–0.45 mm; p < 0.001), respectively. Despite the efficacy results of the studies employing atropine eyedrops, numerous questions remain. One aspect that needs to be addressed is the effect of stopping atropine eye drops. The results of the above study in Singapore show that on stopping atropine after two years of administration, the progression rate of myopia increased in the subsequent year, as compared with children who had placebo in the first two years.20 In the year after cessation of drugs, the mean progression rate in the atropine group was –1.14+/−0.80 D, whereas the placebo group only progressed by –0.38+/− 0.39 D (p < 0.0001). However, a beneficial effect was still evident in the atropine group over the course of three years in the clinical trial. At the end of three years, the spherical equivalent in the atropine group was –4.29+/–1.67 D as compared with –5.22+/−1.38 D in the placebo group (p < 0.0001). Importantly, after the cessation of atropine, the amplitude of accommodation and near visual acuity, previously impaired, returned to pretreatment levels.

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In a multicenter Asian study14 designed to evaluate pirenzepine in myopia, the subjects received 2% gel twice daily (gel/gel), 2% gel daily (evening, placebo/gel), or vehicle twice daily (placebo/placebo) in the 2:2:1 ratio, respectively, for 1 year. The main OUTCOME MEASURE, like in most studies, was the spherical equivalent under cycloplegic refraction. At study entry, the mean SE refraction was −2.4+/−0.9 D and at 12 months, there was a mean increase in myopia of 0.47 D, 0.70 D, and 0.84 D in the gel/gel, placebo/gel, and placebo/placebo groups, respectively (p < 0.001 for gel/gel versus placebo/placebo). There was only a single report of a randomized controlled parallel study of tropicamide, which evaluated 26 pairs of twins.39 This study, which had a follow up period of 3.5 years, found no difference in the myopia outcome when the results of using tropicamide 1% combined with bifocals, against single vision glasses, respectively, were compared. There were various other drug studies in myopia that employed cycloplegics.13,40,41 These studies, however, suffer from methodological issues such as the lack of a control or randomization. A study comparing the effect of timolol versus single vision spectacles42 showed no retardation of myopia by timolol. There has been no other controlled trials involving ocular hypotensives in myopia. In summary, an evidence-based recommendation on the use of atropine in myopia was considered level B (moderately important to outcome).4 Among the various types of pharmacological treatment in myopia, atropine appears to be the most promising. However, some questions remain, for example, what is the optimal dose for childhood myopia? Could the frequency of the administration of atropine be reduced, as an alternative to reducing the concentration of atropine. Currently, concentrations of atropine eyedrops below 1% are not available commercially in most countries. Another issue is the overall duration of treatment. Most clinicians will want to limit the use of atropine to as short a duration as possible. This is because theoretical side effects like increased incidence of light-induced maculopathy and cataracts should not be neglected. Ideally, if one can predict the time course of myopic progression before stabilization, one can limit atropine treatment to this period. In reality, however, it may not be possible to predict with complete certainty the point of stabilization of refractive errors. A study is ongoing in Singapore that aims to evaluate the optimal duration of atropine treatment in myopic children.

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Other Issues Related to Drugs Treatment of myopia with bilateral atropine eyedrops has the disadvantage of blurring near vision and so myopic children will require near optical correction for school work and close distance visual activities. A study has evaluated the use of atropine in combination with progressive lens.43 This randomized clinical trial involving 188 subjects showed that 0.5% atropine eyedrops in combination of multifocal glasses was more effective in retarding myopia progression as compared with those wearing multifocal glasses or single vision glasses. The mean progression was 0.41 D per year as compared with 1.19 D and 1.40 D per year, respectively (p < 0.0001). Traditional Chinese medicine, or even folk medicine and other “holistic” practices and routines have been tested in Asian countries. The only randomized controlled study of this nature retrievable from the PUBMED database was the study that used adhesive pressure plaster of Semen impatientis, a garden balsam seed extract, which claimed significant therapeutic benefit relative to control.44 It is difficult to perform further studies or replicate the results in more studies because the composition of plant extracts is highly complex — they frequently contain unknown active ingredients, and possess variable biological activity between batches. Myopia has become a highly emotive public health issue in some communities. Anecdotally, people have adopted various practices that they perceive as beneficial. It is important, however, for the scientific community to explain that new advances and convincing therapeutics can only be developed and funded if they are supported by peer reviewed scientific evidence. In the clinical trial conducted in Singapore,19,20 buccal mucosa DNA was collected for genotyping of five published single nucleotide polymorphism (SNP) loci for the human muscarinic receptor-1 gene.45 Polymerase chain reaction (PCR) was performed to amplify a region of the gene containing the SNP of interest (Table 1), followed by restriction enzyme digest to generate DNA bands of differing molecular weight when ran on gel electrophoresis. For this analysis, the subjects who responded to atropine treatment were defined as those with progression of spherical equivalent (cycloplegic autorefraction) of less than 0.5 D during a period of two years. Treatment was deemed to be ineffective when the myopia increased by more than 0.5 D. The chi square test was used to evaluate 2×2 (in the case where two possible alleles exist at the SNP locus) or 2×3 tables (in the case where three possible alleles exist at

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L.M.G. Tong, V.A. Barathi and R.W. Beuerman Table 1. Primers used for Polymerase Chain Reaction for Restriction Fragment Length Polymorphism or Sequencing Analyses

SNP

Primers

rs542269

F: TTTGCAAAAGGCCTAACCTG R: CCTCTTCCCACAGCACTGTTA F: CCACCTTCTGCAAGGACTGT R: CTGGGAATAGCGAAGTCTGG F: CTGTCAGCCCCAACATCAC R: GCCAGCCAGAGGTCACAA F: TGATCAAGATGCCAATGGTG R: TACGGTGTCCAGGTGAGGAT F: GCTCTACTGGCGCATCTACC R: GTCCACCATTGGCATCTTG F: AGATCCCCCTCAGGAAACTG R: CACCCACCTTGGTTTCTAGC

rs2067480 rs2067477 rs2067478 rs1065431 rs2075748

Expected Annealing Amplicon Temperature Restriction Size (bp) (°C) Enzyme 306

60

BslI

403

62

NlaIV

286

65

Cfr13I

241

62

Alu I

307

65

MspA1I

295

62

Ban I

SNP: single nucleotide polymorphism.

the SNP locus). One hundred and twenty-two of the subjects with DNA collection had been exposed to atropine eye drops uni-ocularly. Two out of 122 subjects did not have two years of refractive data because of withdrawal from the clinical trial (red eyes causing drug intolerance). When each of the five loci was evaluated one at a time, no particular genotype was associated with the effectiveness of atropine treatment (all p > 0.05). However, one combinatory criterion involving the SNPs rs2067480 and rs542269 was able to discriminate between drug responders and nonresponders (Table 2). Table 3 shows how the genotypes at rs2067480 and rs542269 were jointly associated with the response to atropine treatment (p = 0.033 by Fishers exact probability test). The odds ratio of responding to the treatment given a positive test on genotyping was 3.40 (95% CI: 1.07–10.82). The test had a reasonably good specificity of 88% (95% CI: 71–96), but sensitivity of only 32% (95% CI: 22–44), with a positive predictive value of 85% (95% CI: 65–95), and a negative predictive value of 37% (95% CI: 27–49). Because of the limited number of subjects in a clinical trial, this data cannot be sufficiently robust to allow multiple testing corrections and evaluation of SNP–SNP interactions, or explore other potentially relevant

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Atropine and Other Pharmacological Approaches to Prevent Myopia Table 2. Observed Frequency of Subjects with Various Genotypes Stratified by Myopic Progression

Genotype at rs2067480 (First 2 Alphabets) and rs542269 (Third and Fourth Alphabet)*

No of Subjects: SE Worse by at Least 0.5 D (Non-Responders)

No of Subjects: SE Worse by Less Than 0.5 D (Responders)

Total No. of Subjects

CCTT CTTT TTTT CCCC CCCT CTCT NNNCa

25 4 0 0 4 0 0

39 10 1 1 16 2 3

64 14 1 1 20 2 3

Total

33

72

105

*Rows indicate the genotypes at the 2 loci: the first two letters represent the genotype at rs2067480 genotype at rs542269. The genotype at each locus is specified by 2 letters or bases, one for each of the 2 chromosomes. 18 subjects were found not have enough DNA to determine the allele at rs2067480, so only 102 subjects have genotypes examined at rs2067480. a This genotype consists of a “C” at rs542269, which can be heterozygous or homozygous for “C”. The genotype at rs2067480 is irrelevant for this genotype, represented by “NN” in the table. These subjects can be included in the analysis regardless of their genotype at rs2067480, or when the genotype at rs2067480 is not known. As a result of this qualification, 3 children’s data could be included in the analysis, which increased the total number of children analysed to 105. SE: spherical equivalent. This table shows the number of subjects with the specified genotypes (in rows) that were drug responders or otherwise. The last row shows the total number of responders (72) and non-responders (33).

SNPs at the other muscarinic receptor subtypes. Nevertheless, the current data are sufficiently interesting to suggest that SNP evaluation, as part of pharmacogenetic testing, should be incorporated into larger drug trials in myopia, with sample sizes that will allow sophisticated statistical procedures on multi-dimensional data.46

Potential Side Effects The adverse effects of the use of 1% atropine has been reported to include photophobia and reduction of outdoor activities.16 Adverse side effects with 0.5% atropine was relatively reduced, and no apparent photophobia

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L.M.G. Tong, V.A. Barathi and R.W. Beuerman Table 3. Two by Two Contingency Table showing Test Result and Effectiveness of Treatment

Non-Responders: Intervention not Effective [SE Worsened by at Least 0.5 D Over 2 Years] NEGATIVE TEST* (rs2067480: CC or CT) and rs542269 TT POSITIVE TEST*

Responders: Intervention Effective [SE Worsened by Less Than 0.5 D (or any Improved) Over 2 Years]

Totala

29

49

78 (74%)

4

23

27 (26%)

72 (69%)

105

Any genotype not covered above Totalb

33 (31%)

*The data for this 2×2 contingency table were obtained by collapsing or merging the genotypes (rows in Table 2), the first 2 rows in Table 2 were combined to produce the genotypes representing a negative test in Table 3, and the bottom 5 rows in Table 2 have been merged to produce the positive test genotypes. The reason for designating the genotypes in the first 2 rows as a “negative test” is that under these circumstances the proportion of non-responders (defined as having progression of more than 0.5 D) was higher than those of the other genotypes. In the cases with genotype CCTT (Table 2, first row), 39% were non-responders, and the cases with genotype CTTT (Table 2, second row), 29% were non-responders, whereas in all other genotypes (Table 2, subsequent rows), only 20% or less were nonresponders. a Percentages in column indicate the proportion of people with negative and positive test results, not the proportion of responders. More people obtained a negative test compared to a positive test in this trial. b This row indicates the percentages of responders and non-responders to treatment. More people respond to treatment than not.

was observed with 0.25% and 0.1% atropine.38 One possible side effect of unilateral use of atropine is the resulting anisometropia, especially if the untreated eye is rapidly progressing in myopia severity. In clinical practice, there is an option to switch unilateral therapy to the opposite eye, or to commence bilateral atropine treatment. Another side effect that has been noted with 1% atropine eyedrops is the effect of cycloplegia or reduction of near visual acuity. A study in Singapore has shown that this complication is well tolerated largely.19 Nevertheless, atropine eyedrops are not recommended for all myopic children.4 This is because the long-term potential side effects of atropine such

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as cataract formation and retinal toxicity have not been evaluated. The electrophysiological assessment of some patients with atropine treatment showed little long-term effect, with the retina-on response changed more than the retina-off response.47 In the pirenzepine study,14 11% (31/282) of pirenzepine-treated subjects were discontinued from the study for adverse events.14 There were 15 serious adverse events reported in 12 subjects (all in the active groups), but none was ophthalmic in nature; all the subjects recovered, and only 1 (abdominal colic preceded by a flu) incident was possibly related to the treatment. The use of timolol in myopia has been linked to stinging sensations and even bronchial asthma.42 This may be one reason why the scientific community has not pursued the use of ocular hypertensives in myopia.

The Future of Drug Treatment in Myopia The mechanism of atropine in retarding the progression of myopia is not clearly understood. The original rationale for using atropine in myopia was the paralysis of accommodation to achieve slowing of myopia progression. However, atropine can still be effective in animal models following destruction of mid-brain nuclei and paralysis of accommodation.48 There are possible alternative mechanisms or multiple mechanisms, such as the remodeling of scleral connective tissue via a retinal or scleral mechanism. Although atropine and pirenzepine are known muscarinic antagonists and these receptors are present in the eye, including the sclera, it is not known if the anti-myopia effects of these drugs are mediated principally by muscarinic receptors. Even if muscarinic receptors mediate the beneficial effect observed clinically, it is still unclear what subtype of muscarinic receptor or what signaling events are critical for the effect. The future of drug treatment in myopia will be influenced by further development in the understanding of the basis of myopia development as well as the pharmacology, and cell and molecular biology of myopiagenesis. With the increased understanding of these areas, more targeted, safer and more effective treatment can be developed. An attractive idea is the customization of pharmacological treatment to different individuals. Since there is a genetic component to myopia development, there may also be differences in response to drug treatment. Such difference may be

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uncovered, for example, by genetic testing of single nucleotide polymorphisms. In addition, pharmacological treatment may be complementary to other behavioral treatment such as lifestyle modification or increase of outdoor activities. What is the aim of pharmacological treatment in myopia? Most clinical trials in myopia currently evaluate the progression of myopia. This is a logical approach because the sight-threatening complications of myopia such as macular degeneration and retinal breaks increase tremendously with high myopia. If myopia can be arrested at an earlier stage, it may become a purely optical problem that is amenable to glasses or refractive surgical procedures. With increased understanding of myopia pathogenesis, it may be possible to shift the aim of treatment to the prevention of the onset of myopia. This type of preventive treatment, for example, may be employed in susceptible children with a family history of high myopia and other risk factors.

Conclusions Myopia is a common ocular condition affecting many people, and recently attention has focused on the high prevalence and incidence of childhood myopia. Pharmacological treatment of myopia involves the use of various types of eyedrops but clinically, the most effective form of treatment is the use of atropine eyedrops. The aim of treatment with atropine eye drops is to retard the progression of myopia. Although this form of treatment can be judiciously used in suitable children, the optimal regime of administration of atropine eyedrops remains to be shown.

References 1. Goss DA. (1982) Attempts to reduce the rate of increase of myopia in young people — A critical literature review. Am J Optom Physiol Opt 59(10): 828–841. 2. Curtin BJ. (1985) The etiology of myopia. The myopias. Basic science and clinical management. Philadelphia: Harper and Row. 3. Saw SM, et al. (2000) Myopia: attempts to arrest progression. Br J Ophthalmol 86(11): 1306–1311. 4. Saw SM, et al. (2002) Interventions to retard myopia progression in children: an evidence-based update. Ophthalmology 109(3): 415–421.

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5. Horner DG, et al. (1999) Myopia progression in adolescent wearers of soft contact lenses and spectacles. Optom Vis Sci 76(7): 474–479. 6. Ong E, et al. (1999) Effects of spectacle intervention on the progression of myopia in children. Optom Vis Sci 76(6): 363–369. 7. Katz J, et al. (2003) A randomized trial of rigid gas permeable contact lenses to reduce progression of children’s myopia. Am J Ophthalmol 136(1): 82–90. 8. Khoo CY, Chong J, Rajan U. (1999) A 3-year study on the effect of RGP contact lenses on myopic children. Singapore Med J 40(4): 230–237. 9. Polse KA, et al. (1983) Corneal change accompanying orthokeratology. Plastic or elastic? Results of a randomized controlled clinical trial. Arch Ophthalmol 101(12): 1873–1878. 10. Angi MR, et al. (1996) Changes in myopia, visual acuity, and psychological distress after biofeedback visual training. Optom Vis Sci 73(1): 35–42. 11. Yang Z, et al. (2009) The effectiveness of progressive addition lenses on the progression of myopia in Chinese children. Ophthalmic Physiol Opt 29(1): 41–48. 12. Hasebe S, et al. (2008) Effect of progressive addition lenses on myopia progression in Japanese children: a prospective, randomized, double-masked, crossover trial. Invest Ophthalmol Vis Sci 49(7): 2781–2789. 13. Bedrossian RH. (1979) The effect of atropine on myopia. Ophthalmology 86(5): 713–719. 14. Tan DT, et al. (2005) One-year multicenter, double-masked, placebocontrolled, parallel safety and efficacy study of 2% pirenzepine ophthalmic gel in children with myopia. Ophthalmology 112(1): 84–91. 15. Manny RE, et al. (2001) Tropicamide (1%): an effective cycloplegic agent for myopic children. Invest Ophthalmol Vis Sci 42(8): 1728–1735. 16. Yen MY, et al. (1989) Comparison of the effect of atropine and cyclopentolate on myopia. Ann Ophthalmol 21(5): 180–182, 187. 17. Quinn GE, et al. (1995) Association of intraocular pressure and myopia in children. Ophthalmology 102(2): 180–185. 18. Wiener M. (1931) The use of epinephrin in progressive myopia. Am J Ophthalmol 14: 520–522. 19. Chua WH, et al. (2006) Atropine for the treatment of childhood myopia. Ophthalmology 113(12): 2285–2291. 20. Tong L, et al. (2009) Atropine for the treatment of childhood myopia: effect on myopia progression after cessation of atropine. Ophthalmology 116(3): 572–579. 21. Barathi VA, Weon SR, Beuerman RW. (2009) Expression of muscarinic receptors in human and mouse sclera and their role in the regulation of scleral fibroblasts proliferation. Mol Vis 15: 1277–1293. 22. Liu S, et al. (2007) The eyelid margin: a transitional zone for 2 epithelial phenotypes. Arch Ophthalmol 125(4): 523–532.

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23. McBrien NA, Gentle A. (2001) The role of visual information in the control of scleral matrix biology in myopia. Curr Eye Res 23(5): 313–319. 24. Wessler I, Kirkpatrick CJ. (2008) Acetylcholine beyond neurons: the non-neuronal cholinergic system in humans. Br J Pharmacol 154(8): 1558–1571. 25. Borda E, et al. (2005) Correlations between neuronal nitric oxide synthase and muscarinic M3/M1 receptors in the rat retina. Exp Eye Res 80(3): 391–399. 26. Collison DJ, et al. (2000) Characterization of muscarinic receptors in human lens cells by pharmacologic and molecular techniques. Invest Ophthalmol Vis Sci 41(9): 2633–2641. 27. Narayan S, et al. (2003) Endothelin-1 synthesis and secretion in human retinal pigment epithelial cells (ARPE-19): differential regulation by cholinergics and TNF-alpha. Invest Ophthalmol Vis Sci 44(11): 4885–4894. 28. Yin GC, Gentle A, McBrien NA. (2004) Muscarinic antagonist control of myopia: a molecular search for the M1 receptor in chick. Mol Vis 10: 787–793. 29. Bitzer M, et al. (2006) Effects of muscarinic antagonists on ZENK expression in the chicken retina. Exp Eye Res 82(3): 379–388. 30. Wessler I, Kirkpatrick CJ, Racke K. (1998) Non-neuronal acetylcholine, a locally acting molecule, widely distributed in biological systems: expression and function in humans. Pharmacol Ther 77(1): 59–79. 31. Gil DW, et al. (1997) Muscarinic receptor subtypes in human iris-ciliary body measured by immunoprecipitation. Invest Ophthalmol Vis Sci 38(7): 1434–1442. 32. Savinainen JR, Laitinen JT. (2004) Detection of cannabinoid CB1, adenosine A1, muscarinic acetylcholine, and GABA(B) receptor-dependent G protein activity in transducin-deactivated membranes and autoradiography sections of rat retina. Cell Mol Neurobiol 24(2): 243–256. 33. Qu J, et al. (2006) The presence of m1 to m5 receptors in human sclera: evidence of the sclera as a potential site of action for muscarinic receptor antagonists. Curr Eye Res 31(7–8): 587–597. 34. McBrien NA, et al. (2009) Expression of muscarinic receptor subtypes in tree shrew ocular tissues and their regulation during the development of myopia. Mol Vis 15: 464–475. 35. Liu Q, et al. (2007) Changes in muscarinic acetylcholine receptor expression in form deprivation myopia in guinea pigs. Mol Vis 13: 1234–1244. 36. Schwahn HN, Kaymak H, Schaeffel F. (2000) Effects of atropine on refractive development, dopamine release, and slow retinal potentials in the chick. Vis Neurosci 17(2): 165–176. 37. Taylor BJ, Smith PJ, Brook CG. (1985) Inhibition of physiological growth hormone secretion by atropine. Clin Endocrinol (Oxf) 22(4): 497–501. 38. Shih YF, et al. (1999) Effects of different concentrations of atropine on controlling myopia in myopic children. J Ocul Pharmacol Ther 15(1): 85–90.

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39. Schwartz JT. (1981) Results of a monozygotic cotwin control study on a treatment for myopia. Prog Clin Biol Res 69: 249–258. 40. Sampson WG. (1979) Role of cycloplegia in the management of functional myopia. Ophthalmology 86(5): 695–697. 41. Chou AC, et al. (1997) The effectiveness of 0.5% atropine in controlling high myopia in children. J Ocul Pharmacol Ther 13(1): 61–67. 42. Jensen H. (1991) Myopia progression in young school children. A prospective study of myopia progression and the effect of a trial with bifocal lenses and beta blocker eye drops. Acta Ophthalmol Suppl 200: 1–79. 43. Shih YF, et al. (2001) An intervention trial on efficacy of atropine and multifocal glasses in controlling myopic progression. Acta Ophthalmol Scand 79(3): 233–236. 44. Liu H, et al. (1994) Treatment of adolescent myopia by pressure plaster of semen impatientis on otoacupoints. J Tradit Chin Med 14(4): 283–286. 45. Lucas JL, DeYoung JA, Sadee W. (2001) Single nucleotide polymorphisms of the human M1 muscarinic acetylcholine receptor gene. AAPS PharmSci 3(4): E31. 46. Boulesteix AL, et al. (2007) Multiple testing for SNP–SNP interactions. Stat Appl Genet Mol Biol 6: Article37. 47. Luu CD, et al. (2005) Multifocal electroretinogram in children on atropine treatment for myopia. Br J Ophthalmol 89(2): 151–153. 48. McBrien NA, Moghaddam HO, Reeder AP. (1993) Atropine reduces experimental myopia and eye enlargement via a nonaccommodative mechanism. Invest Ophthalmol Vis Sci 34(1): 205–215.

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5.2 Physical Factors in Myopia and Potential Therapies Wallace S. Foulds*,† and Chi D. Luu†

Introduction As is obvious from other contributions to this book, factors leading to the development of myopia in childhood are thought to include both genetic predisposition and environmental factors. Although it has been held that after birth the expression of the genetic program involved in refractive development may be fine-tuned by environmental factors acting through the visual system,1 the interplay between genetic factors and environmental factors remains unknown.2 Among possible environmental factors contributing to the etiology of childhood myopia are a number of physical factors, some well recognised and others not. Among these are factors contributing to the sharpness or lack of sharpness of the retinal image that include the vergence of incident light upon the eye, the curvatures and refractive indices of the transparent media in the eye that are involved in the formation of the retinal image and the nature of the incident light itself including its homogeneity, contrast, spatial and temporal frequency characteristics and its spectral composition. The physical characteristics of the eye in terms of axial length, vitreous chamber length, corneal and lens curvatures and their refractive indices are obvious contributors to the formation of an in-focus retinal image as is the degree of accommodation being exercised. Intraocular pressure (IOP) might be a factor involved in the ocular expansion that underlies axial myopia but raised IOP is not found in lid fused eyes developing myopia,1 and pressure lowering treatment with eye drops such as timolol *Corresponding author. E-mail: [email protected] † Singapore Eye Research Institute, Singapore.

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has failed to influence myopia progression.4 Even the temperature of the eye has been invoked as a possible aetiological factor in the development of experimental myopia.4 As regards therapy, correction of myopia by spectacles, contact lenses or various forms of refractive surgery that include corneal reshaping procedures, clear lens extraction, insertion of piggyback lenses and (mainly in the past) scleral shortening procedures may correct the optical consequences of myopia but these do not address the underlying condition of the eye. Pharmacological treatments such as atropine eye drops can beneficially influence the abnormal growth of the eye that occurs in myopia, but as abnormal growth of the eye is probably a consequence of altered retinal cell signalling in response to an abnormal retinal image, such treatment, although effective to a degree, addresses the result rather than the cause of the condition. Genetic factors that appear to play a role in the predisposition to myopia are almost certainly polygenic rather than monogenic, thus restricting the possible application of genetic engineering. Genetic manipulation is also limited due to an incomplete knowledge of the genes involved in the genesis of myopia. Manipulation of retinal cell signaling is also restricted by an inadequate understanding of the retinal factors involved in the etiology of myopia. It has been stated that the identification of myopia susceptibility genes could provide an insight into the molecular basis of myopia and could lead to therapies to prevent the development of myopia or to slow its progression but to date such information remains elusive.6 As physical factors affecting the visual image appear to play a crucial role in the etiology of myopia, modification of these factors holds promise of future therapies but again this would be dependent upon a better understanding of the role of the various physical factors involved in the genesis of myopia. In this chapter, we explore the known roles of some of these factors and suggest hypotheses to explain others. In 1990, Wallmann6 asked, “What visual stimuli or lack of what visual stimuli provoke myopia?” and although much more is known about the factors involved in the genesis of myopia we are still not in a position to answer Wallman’s question with any confidence. Accommodation In the early 20th century, it was accepted that excessive close work was the main environmental factor involved in the etiology of myopia and as

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close work demanded extended periods of accommodation, it was believed that excessive accommodation was the major contributor not only to the development of myopia but also its progression. Sight-saving schools, involving large print and distance learning rather than close work, were established in the belief that avoidance of prolonged accommodation would prevent or slow childhood myopia. In the event sight-saving schools were a great disappointment for no benefit was found and before long these schools were closed down. There are many reasons why excessive accommodation is unlikely to play a direct role in the etiology of childhood myopia although, as has previously been suggested, the stimulus to accommodation from a blurred retinal image may be the same stimulus that leads to axial ocular growth and myopia.6 Accommodation undergoes constant short-term fluctuations in response to variations in the proximity of objects in the field of view, whereas if the same factors that stimulate accommodation are also those involved in eye growth, in the latter instance, they must act over a much more prolonged time-span. A greater than average degree of retinal image blur over time could be a factor influencing eye growth, carrying with it a greater than average degree of accommodation. Thus, a variably blurred retinal image could result in the short-term stimulation of accommodation and in the longer term an alteration in ocular growth, both being a consequence of excessive retinal image blur but without accommodation in itself having a direct effect on ocular growth or refractive development. Evidence that excessive accommodation does not cause myopia includes the fact that visual deprivation myopia can be induced in the young of many animal species, including primates, when accommodation has been abolished by destruction of the ciliary ganglion or the Edinger–Westphal nucleus in the brain stem or following optic nerve section.7 Recovery from refractive errors induced by the wearing of minus or plus lenses can also occur after accommodation has been surgically abolished.8 Experimentally astigmatic errors of refraction can be induced in chicks by the wearing of cylindrical lenses, an outcome that cannot be related to accommodation.9 It has also been found that steps taken to reduce accommodative effort such as the use of bifocal glasses have had no demonstrable beneficial effect on the progress of childhood myopia.10 The administration of 1% atropine eye drops has been shown to slow the progress of childhood myopia,11–13 and may be effective even in reduced

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concentration,14 but the assumption that atropine acts via paralysis of accommodation is too simplistic. Atropine is a non-specific muscarinic antagonist that has many functions within the eye. There are muscarinic receptors of various types in many ocular tissues, including the retina15 and the sclera,16 in addition to their presence in mammalian ciliary muscle. Atropine has been shown to have an inhibitory effect on scleral fibroblasts,17 preventing their proliferation and reducing their production of collagen, and this may explain the effect of atropine on reducing progressive scleral elongation associated with myopia. Although atropine eye drops can prevent the development of experimental myopia in chicks,18 in this species atropine has no effect on accommodation for the ciliary muscle in chicks is striated muscle, and does not contain the M1 muscarinic receptor19,20 i.e. the receptor most closely related to myopia.21 In mammalian species, the ciliary muscle is non-striated and the ciliary body in mammals, e.g. the tree shrew, demonstrates the presence of all five types of muscarinic receptor, including the M1 receptor.22 Close work Studies investigating the effect of close work in relation to the risk of childhood myopia have produced conflicting results. In some studies, there is a clear correlation between the amount of close work and the risk and severity of myopia development,23,24 while in other studies, the association is absent.25,26 Recently, it has been shown that rather than close work being an etiological factor for myopia, lack of outdoor activity may be the key, for outdoor activity is protective.27–29 Outdoor activity appears to be protective against myopia in its own right and not just as a reciprocal of indoor activity.30 Physical characteristics of the retinal image Visual deprivation As is well known, visual deprivation in early life leads to the development of myopia in many species including humans. Although the elongation of the eye leading to axial myopia appears to be driven by a retinal response to the physical characteristics of a blurred retinal image, the specific physical characteristics of a blurred image causing myopia remain to be identified.

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Compensatory changes in refraction In relation to experimental myopia it has been shown that young animals wearing negative lenses become myopic while those wearing positive lenses become hyperopic9,31 and this includes primates.32 It appears that the retina is able to differentiate between hyperopic blur and myopic blur even when the optic nerve has been sectioned.33 The ability of the retina to detect the sign of a defocused image allows a compensatory change in refraction to occur as a result of an alteration of eye growth in the appropriate direction but at present there is no satisfactory explanation for this. The differing vergences of light in conditions of hyperopic or myopic defocus have been suggested as providing cues for the identification of the sign of defocus in these two conditions.34 Longitudinal chromatic aberration has also been suggested as allowing the eye to differentiate between hyperopic and myopic defocus.35–38 The characteristics of the retinal blur circle induced by defocus has also been advanced as an explanation for the ability of the retina to determine the sign of defocus39 but this has been disputed.40 Intensity and periodicity of light exposure Light intensity and photoperiod are physical factors that appear to affect ocular and refractive development in a very complex fashion. As regards photoperiod, rearing chicks in continuous illumination leads to severe hyperopia, raised intraocular pressure, reduction of corneal curvature and of anterior chamber depth with an increase in axial and vitreous chamber lengths.41,42 The hyperopia that develops results from flattening of the cornea that overcomes the effect of increased length of the eye that would otherwise cause myopia. Continuous illumination also prevents visual deprivation myopia in the chick but not the compensatory myopia induced by negative lenses.43 In experimental animals it has been shown that the intensity of light in which animals are reared can have a significant effect upon ocular and refractive development. The effect of continuous illumination on the chick eye appears to be intensity dependent.44 Chicks exposed to higher intensities of light have longer vitreous chamber lengths but flatter corneas and are hyperopic. Chicks raised in low light conditions are less hyperopic than those raised in bright light.45 This situation of continuous illumination, however, is so abnormal that it has no obvious corollary with human refractive development.

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Spatial frequency One possible feature of a blurred image as compared with a sharp image is a difference in spatial frequency composition. Judge (1990),46 however, was of the opinion that eye growth was unlikely to be influenced by the spatial frequency content of the retinal image as even slight defocus would eliminate all high frequency information. Additionally, it has been noted that young monkeys do not develop a high resolution visual system until around two years of age,47 so would be unaffected by high frequency spatial information in the visual image (or its absence) at the young age when visual deprivation myopia can be induced. Lenses inducing myopia in chicks are those that block the transmission of mid- and high frequencies48 and it has been concluded that emmetropization is tuned to mid-frequency spatial frequencies.41 In another study in chicks, it was also concluded that mid-spatial frequency tuning was necessary for emmetropization although chromatic aberration might have a role as a clue to defocus.49 In some experiments no significant interaction between the spatial frequency characteristics and sign of defocus was demonstrated.38 Others have reported that the inclusion of mid to high spatial frequencies is necessary for refractive compensation to induced defocus.50 It has been suggested that it is not the edge structure of the spatial frequency alignments within the image but the spatial frequency composition itself that controls eye growth51 and the relative energy distribution across spatial distributions that is important. Because reduced luminance shifts contrast sensitivity to lower spatial frequencies, it has been suggested that reduced luminance acting through a reduction in spatial frequency, content of the retinal image may be a factor inducing a myopic shift in refraction.52 Contrast adaptation that is a spatial frequency dependent increase in contrast sensitivity after exposure to low contrast patterns may be another mechanism involved in refractive development for contrast adaptation correlates with various optical manipulations inducing myopia (frosted lenses, negative lens wear, and decreased retinal image sharpness).53 Light periodicity Temporal modulation of light intensity using flickering light of low frequency (1–4 Hz) with luminance levels varying between 1.5 and 180 lux,

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during 12 hours of diurnal light exposure, had a marked myopia inducing effect on chicks wearing negative or positive lenses but not those with no defocus. The effect was greater in conditions of hyperopic blur than myopic blur.54 It is known that a flickering light of low frequency causes marked retinal vasodilatation55 thought to be due to nitric oxide release.56 Although a reduced release of dopamine occurs in visual deprivation myopia and in the myopia occurring in low light rearing conditions, in another study,57 flicker at a variety of frequencies from 2–20 Hz did not affect dopamine release from dopaminergic amacrine cells in the chick retina, suggesting that the cells in the retina thought to be involved in myopigenic signalling do not respond readily to short-term changes in retinal illumination. In chicks, compensation to wearing plus or minus 10 D lenses was also affected by temporal and spatial characteristics of the retinal image. With temporal modulation having either a fast-on or a fast-off luminance gradient, compensation to +10 D lenses was reduced by a fast-on modulation and compensation to −10 D lenses similarly reduced by a fast-off modulation.58 Image clarity In an attempt to see whether there were significant optical differences between out of focus images caused by negative lenses as compared with positive lenses, we carried out a series of experiments. Black and white checkerboard patterns were photographed in-focus and with varying degrees of defocus induced by negative or positive lenses placed in front of the camera lens. Not unexpectedly defocus reduced contrast, induced chromatic dispersion and had a significant effect on spatial frequency. The square-wave pattern of an in-focus checkerboard image was converted to a sign-wave pattern by 2–3 dioptres of defocus. All of these features of defocused images, however, were similar in degree whether the defocus simulated myopic or hyperopic blur and none offered an acceptable explanation for the apparent ability of the retina to differentiate between hyperopic and myopic blur, i.e. the sign of the defocus. Another optical factor that might be a possible contributor to the etiology of myopia is the proportion of in-focus and out of focus information present in the visual image. It has been tacitly accepted that as a blurred retinal image in early life causes myopia, a sharp retinal image is required for emmetropia. In everyday life, however, a totally sharp image

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is as abnormal as a totally blurred image for a normal image contains an admixture of in-focus and out of focus content in varying proportions, according to the degree of accommodation exercised and the number and size of near and distant objects within the field of view. It has been shown,59 that even in adults, prolonged microscopy using infinity focus binocular microscopes is associated with an increased prevalence of myopia, some 39% of initially emmetropic adult microscopists becoming significantly myopic after two years of intensive microscopy, and a larger proportion (48%) of initially myopic adult microscopists becoming significantly more myopic over the same time span. The myopia in both instances was axial myopia with a significant increase in axial and vitreous chamber lengths in those affected. As a focused image viewed through a microscope is totally in-focus across the whole visible field of view, there is at least a possibility that this very abnormal situation might contribute to the reported development of myopia in microscopists undertaking intensive microscopy. In a study in chicks undergoing optical defocus and either preclusion of sharp vision or limited sharp vision, it was found that limited sharp vision was required to compensate for induced myopia.60 During reading with the eyes accommodated at reading distance, a large area of the visual field will be occupied by a uniformly in-focus image, so reproducing to an extent the visual situation experienced by microscopists where the visual field through a microscope is also completely in-focus. It has been shown in chicks with lens induced myopia the viewing of a near target confocal with the retina but on a transparent background so allowing more distant visual information to contribute to the retinal image, that the myopia was reduced or eliminated, but only if accommodation were intact.34 In this situation, with the eye accommodated to the distance of the near target, additional distance visual information would add a proportion of myopic blur to the retinal image. As myopic blur is known to be protective against myopia, the presence of a proportion of myopic blur in the image appears to have been sufficient to overcome the degree of myopia that had previously been induced in these chicks by negative lens wear. Outdoor activity and retinal image blur In general, during outdoor activity, objects on the horizon are of less interest than objects nearer at hand and if visual activity is largely restricted to

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near and mid-distance objects, images from more distant objects will be focused in front of the retina so producing myopic blur that is known to act against the development of myopia in experimental animals. This is an everyday corollary to the experimental work in chicks that included both in-focus near targets and distance information.34 The situation is quite different in close work where images of objects from a distance are largely prevented from reaching the eye, being obscured by the reading material that will occupy a significant proportion of the visual field. In addition to the elimination of the protective myopic blur from distant objects, reading material being in-focus across a significant proportion of the visual field will reduce the out of focus content of the visual image. Currently, however, there is no experimental work assessing whether a totally in-focus image on its own can induce changes in eye growth. Light vergence and photon catch In relation to the ability of the retina to differentiate between myopic and hyperopic blur, as has already been noted, an obvious difference between these two situations is that in hyperopic defocus, light passing through the retina to come to a focus behind it is convergent, whereas in myopic defocus with images of distant objects focused in front of the retina, light passing through the retina is divergent. In considering how the retina might be able to differentiate between the blurred images caused by convergent or divergent light, the effect of these two optical conditions on the distribution of photons along the lengths of the photoreceptor outer segments appears to offer a possible explanation. In conditions of convergent light, photons will become more closely packed as the convergent light nears its point of focus posterior to the photoreceptor outer segments so that in the case of hyperopic blur, as convergent light passes through the retina, photon density and absorption in the photoreceptors will increase towards the tips of the photoreceptor outer segments and decrease in their more proximal bases. Conversely in conditions of myopic blur with focus in front of the retina and divergent light passing through the retina, photon density will decrease along the length of the photoreceptor outer segments so that there will be an increased photon catch at the bases of the photoreceptor outer segments rather than the tips. It may be asked whether the Stiles Crawford effect,61 in which photons are thought preferentially to enter the proximate ends of the outer

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segments and be transmitted axially in the outer segments as a result of their waveguide properties, would prevent any skewed distribution of photons within the receptor outer segments. There is good evidence, however, that photons can be transmitted transversely through the lateral outer segment membrane and induce depolarisation limited to a very localised section of the outer segment.62 In conditions of myopic or hyperopic defocus with an enlarged blur circle in the retina for either condition, very few photons would be travelling in a path normal to the plane of the retina, and as a result, very few would be admitted to the receptor outer segments if only those arriving normal to the inner surface of the outer segment were able to enter the outer segment. Defocus does reduce contrast sensitivity for spatial frequencies in the range 3–38 cycles/degree but not lower frequencies.63 Defocus, however, does not reduce total retinal illuminance. In personal experiments in which there was rapid alternation of viewing an illuminated target with a focused or a defocused eye, it was noted that optical defocus with plus or minus lenses up to ± 10 D had no effect on the perceived brightness of the illuminated target, thus indicating that photons arriving at the retina along convergent or divergent paths were captured by the outer segments. Thus it would be possible, that the differing vergences of light reaching the retina in hyperopic or myopic defocus could be identified in the retina as a result of a skewed distribution of photon catch along the photoreceptor outer segments. Where there was an increased photon catch in the tips of the outer segments, such would occur in hyperopic defocus; elongation of the eye would act to even out the distribution of photon catch along the outer segments by moving the maximal photon absorption towards the mid-points of the photoreceptor outer segments. In visual deprivation myopia in chicks, there is an elongation of the photoreceptor outer segments that has been suggested as a driving force for ocular elongation.64 In the short-term, a skewed distribution of photon catch along the photoreceptor outer segments might be a stimulus to accommodation and in the longer term to eye growth. Thus, it is possible that the young eye may be programmed to elongate in response to hyperopic blur in an attempt to overcome an unequal distribution of photons along the length of photoreceptor outer segments. Conversely, an increased photon catch at the bases of the photoreceptor outer segments in conditions of myopic blur would inhibit ocular axial

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growth and if growth characteristics of the lens or cornea were unaffected, this might account for the development of hyperopia in experimental animals in which myopic blur has been induced by the wearing of positive lenses. It is known that the response to an alteration in the vergence of light reaching the eye is almost immediate65 as would be expected if the compensatory response to positive or negative lens wear were a function of photon distribution in the receptor outer segments. In the short-term a skewed distribution of photon catch along the photoreceptor outer segments might be a stimulus to accommodation and in the longer term to eye growth. Chromaticity The effects of longitudinal and transverse chromatic aberration on the focus and location of the retinal image are well known, longitudinal chromatic aberration forming the basis of the duochrome test, red light being focused in the eye more posteriorly than green or blue light. The retinal effect of chromatic aberration may also be increased by dispersion of shorter wavelengths by the lens.66 In white light where all the colors of the spectrum are present in an image, some of the red wavelengths will be focused behind the photoreceptor layer of the retina, while some of the shorter wavelength blue light will be focused in front of the photoreceptor layer of the retina, a situation that has been identified as a factor involved in the elongation of the eye in the development of myopia.38 In the human eye, there are three types of cones: L-cones that preferentially absorb long wavelength red photons; M-cones absorbing maximally mid-wavelength photons; and S-cones preferentially absorbing short wavelength blue photons. In the chick, there are five colour sensitive cone types and a double cone responding to movement67 but the spectral sensitivity curve of the chick eye is similar to that of the human.68 L-cones and M-cones greatly outnumber S-cones but this may be a reflection of the fact that blue photons are significantly more energetic than green or red photons, so likely to stimulate a photochemical effect in the photoreceptor outer segments in excess of their numbers, the energy of a photon acting as a wave, being proportional to its frequency and its momentum being inversely proportional to its wavelength. As the human (and chick) eye is most sensitive to mid-wavelength yellow/green light in photopic conditions, the probability is that

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accommodation is largely influenced by these wavelengths. If green light absorbed in the M-cones determines accommodation so as to maximize luminance contrast, this would ensure that green wavelengths were focused in the mid-points of the M-cone photoreceptor outer segments and, additionally, that most of the red and blue wavelengths would be accommodated within the lengths of the outer segments so that the whole visible spectrum, with the exception of the longest red wavelengths or the shortest blue wavelengths, could be accomodated within the length of the outer segments with red light absorbed in the tips of L-cones, blue light absorbed in the bases of S-cones and green light in the mid-points of Mcones. In conditions of white light with all the wavelengths of the visible spectrum present, if accommodation were largely determined by M-cones and green light to which the retina is the most sensitive, there would be an equal distribution of photon catch of red photons in the distal tips and of blue photons in the proximal bases of the outer segments (or perhaps an equal photochemical effect allowing for the increased energy of short wavelength photons as compared with longer wavelengths and the reduced number of S-cones). In spite of the fact that there is good evidence that the degree of accommodative effort is unlikely to be directly involved in the etiology of myopia, a number of studies have used assessments of accommodation in relation to chromatic aberration as an indication of myopia risk. It has been shown that the accommodation response is sensitive to the chromatic properties of the stimulus, the degree of accommodation being determined by the relative sensitivities of L- and M-cones.36 It has been suggested that if luminance contrast is maximized by accommodation, the longest red wavelengths will be focused behind the photoreceptor layer of the retina and the shortest blue wavelengths infront ot it. It has also been suggested that in individuals in whom luminance contrast is dominated by L-cones, this would result in increased accommodation, elongation of the eye and myopia.36 It has also been shown that the use of green paper (absorbing longer wavelengths) during reading reduces accommodative effort and may thus protect against myopia.69 In another study35 it was shown that humans and chicks accommodated more in red light and less in blue light in accordance with chromatic aberration and that in chicks, a small compensatory change in refractive error could be demonstrated when chicks were refracted in total darkness but not in white light unless refracted under cycloplegia. The difference between the results of this study and an earlier study,68 where no change in

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accommodative tonus or of refraction was found in chicks raised in red or blue near monochromatic light, was ascribed to the fact that the wavelengths of blue light used in the later study were longer than those used in the earlier study. Longitudinal chromatic aberration in conditions of retinal blur leading to visual deprivation myopia, has been held to provide complex color-coded cues for reflexive accommodation.62 As these chromatic cues are most sensitive to spatial frequencies between 3 and 5 cycles/degree, it has been suggested as a possibility that a change in the spatial frequency composition of the retinal image will reduce the sensitivity to chromatic cues, resulting in inadequate accommodation leading to a hyperopic blur and myopia.70 Although a number of features of the retinal image in lid closure such as reduced luminance, reduced contrast, loss of higher frequency spatial content, the effect of altered chromaticity has received little if any attention. In lid closure, the eyelid with its rich blood supply is effectively a red filter. Additionally, as white light traverses the closed eyelids, shorter wavelengths are more likely to be dispersed than are longer wavelengths. An alteration in the spectral composition of light reaching the retina with a preponderance of longer wavelength red light and a deficit of shorter wavelength blue light would be an expected result. Alterations in the spectral composition in which developing animals are raised can lead to structural changes in the eye. Thus, blue acara fish raised for two years in near monochromatic light of various wavelengths71 showed a marked increase in the length of the photoreceptor outer segments of L-cones and M-cones when raised in shorter wavelength blue light. This was ascribed to a compensatory response to long and medium wavelength deprivation. It had previously been shown that fish reared in light of longer wavelengths had increased ocular nasotemporal diameters as compared with those raised in shorter wavelength light.72 In the enhanced S-cone syndrome,73 it is believed that there is an actual increase in the number of S cones in the retina and that these replace some of the L- and M-cones.74 In this condition, the affected eyes are usually hyperopic as would be expected with a greater photon catch in the bases of the increased number of S-cone photoreceptor outer segments as compared with photon catch in the tips of the photoreceptor outer segments of the reduced number of L-cones. It is interesting that growth in other biological systems can be influenced by the spectral composition of incident light, for in experiments related to the production of food plants for space exploration, it was

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found that although plants (lettuces) could be grown in red and blue light, or in white light, their growth was greatly enhanced beyond that in white light by the addition of 24% green light to red and blue growing conditions.75 A possible explanation for the effects that chromaticity might have on ocular or refractive development would be a differential stimulation of red or blue sensitive cones. This explanation however is rendered highly unlikely as in experiments in which chicks were raised in narrow band near monochromatic red light or narrow band near UV blue light,68 ocular and refractive development was similar in each of the two chromatic conditions. In these experiments, only very restricted red wavelengths of 650–700 nm or blue light of 350–425 nm were used and the probability is that the chicks accommodated to whatever wavelengths were available so that those raised in purely red light would accommodate to the degree necessary to focus the available red light in the mid-points of the red sensitive cone outer segments and, similarly, those raised in blue light would accommodate to ensure the focus of blue light in the mid-points of the blue sensitive cone outer segments. There would thus be no imbalance of photon catch along the length of the outer segments, with most of the photon catch being in the mid-points of the outer segments and very little photon catch in either their tips or the bases. To test whether an unequal distribution of photon catch along the photoreceptor outer segments affects ocular growth and, therefore, refractive development in the young animal, we investigated in chicks the effect of chromatic manipulation designed to increase photon catch in the tips of the outer segments or alternatively in their bases. This would also explain the fact that chicks can emmetropise in monochromatic light. In preliminary experiments carried out to test the hypothesis that ocular growth and refractive development might be influenced by the distribution of photon catch along photoreceptor outer segments, we have raised newborn chicks in lighting conditions that contained either an excess of longer wavelength red light or of shorter wavelength blue light, together with an adequate amount of mid-wavelength green light, to ensure that the focal plane for mid-wavelength green light was focused in the outer segment mid-points. Where chicks were raised in lighting conditions containing red and green wavelengths but little blue, if accommodation were determined by the green wavelengths, there would be a preponderance of red photons in the tips of relevant photoreceptor outer segments with a lack of balancing blue photons in the S-cone photoreceptor outer segment

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bases. Where chicks were raised in combined blue and green light without red wavelengths, the opposite would be the case. Chicks were raised in a light-tight enclosure with a 12 hours on/12 hour off illumination cycle from banks of either red or blue emitting light emitting diodes (LEDs). The emission spectrum of red emitting LEDs contained wavelenghts between 575 nm and 700 nm with a peak emission at 640 nm. The emission spectrum of the blue emitting LEDs ranged between 430 nm and 550 nm with a peak emission at 490 nm. Luminance of red and blue emitting LEDs was equal. The enclosure was lined with high contrast black and white stripes, giving a range of spatial frequencies (depending on location of chicks within the enclosure) of 4–8 cycles/degree. Our initial results support the hypothesis that development of the young eye is influenced by the distribution of photon catch along the photoreceptor outer segments, for those raised in light with a preponderance of longer wavelength red light (with some green) were myopic (–1.50 D to –2. 50 D at 14 days). In contrast those raised in light containing a preponderance of shorter wavelength blue light (and some green) that were hyperopic (+2.50 D to +3.50 D at 14 days). There was a highly significant difference in mean refraction between the two lighting conditions (p < 0.001) and a significant difference in mean vitreous chamber lengths, that in the myopic eyes of chicks raised in red plus green light were significantly longer than in the hyperopic eyes of chicks raised in blue plus green light (p < 0.01). As already indicated an alternative explanation for the effect of chromaticity upon refractive development could be that a preponderance of red or blue light might produce an effect on ocular development by altering the balance between stimulated L-cones and S-cones rather than an imbalance of photon catch along the lengths of the outer segments. The fact that raising chicks in either pure monochromatic red or pure blue light (without the addition of any intermediate wavelenghts) has no effect upon eye growth or refractive development68 makes this explanation unlikely. When the spectral emission characteristics of artificial light (tungsten lamps and the more recently introduced long-life fluorescent lamps) were examined, both types of artificial lighting were found to have a preponderance of red light in their emission spectra, a significant amount of midwavelength green emission and very little blue emission that might explain why indoor activity rather than close work is associated with the development of myopia, for a large proportion of indoor activity will be undertaken in conditions of artificial lighting.

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In contrast, when the spectral characteristics of outdoor scenes were analysed, they were found to contain a preponderance of shorter wavelengths of light in virtue of their predominately blue skies and green foliage. The spectral composition of light experienced during outdoor activity could be one explanation for the protective effect against myopia reported in recent studies. A study comparing time spent in outdoor activity with time spent in artificial light rather than time spent in reading might provide some interesting results. We have investigated the spectral composition of outdoor scenes in various climatic conditions. In cloudy conditions, there is an equal amount of red, green or blue in the average outdoor scene. Not unexpectedly, in sunset scenes there is a preponderance of red light while in the average sunlit outdoor scene the largest contribution to spectral content is of blue wavelengths followed by a significant amount of green and a much reduced contribution of red. Thus, the average daylight scene with a predominantly blue sky and green foliage offers an additional explanation as to why outdoor activity appears to protect against the development of myopia. The studies that identified the protective role of outdoor activity were carried out in Ohio, USA,26 Australia25 and Singapore,27 locations where blue skies are the norm. In a very large study of 3,636 school children aged between 6 and 18 years of age76 it was found that those children whose homes were lit by fluorescent lighting had an increased prevalence of hyperopia as compared to those whose homes were lit by tungsten lighting. The older types of fluorescent lights had a rather discontinuous emission spectrum with strong emission peaks at 450 nm and 550 nm and a broad less intense emission from 500 nm to 700 nm. The strong emission peaks at 450 nm and 550 nm might account for the increased hyperopia that was associated with fluorescent lighting. More recent types of fluorescent lamps have colour temperatures varying from 2700K to 6000K. Each has a different spectral emission depending on the phosphor coating. Those with lower colour temperatures with an excess of longer wavelength emission might be conducive to the development of myopia while those with a high colour temperature with more blue in their emission spectrum might be protective. In the absence of well designed trials the possible effects of different types of fluorescent lighting on refractive development remains speculative. An interesting and as yet unexplained finding is that in more northerly or southerly countries (but not in near equatorial countries) those born in

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late summer months, when examined as adults, have a higher prevalence of myopia than those born in the winter.77,78 For the first few months of life, babies spend a large proportion of the day asleep and it is only by three months of age or so that the eyes are open for a large part of the day. Babies born in August (in northern countries) or in April (in southern latitudes) will be entering winter conditions by the time their eyes are open for most of the day. In winter, with a shortened period of daylight in non-tropical latitudes, babies of three months plus of age will be exposed for a significant part of the day to artificial lighting with a preponderance of longer wavelengths that could provide a ready explanation for the finding of an increased prevalence of myopia in those born in the summer but only in high and low latitudes where short days occur in the winter. In equatorial or near equatorial countries, the length of the day does not vary significantly with the season and this would explain the lack of any correlation between dates of birth and refraction in such countries, for there would be no seasonal variation in the amount of time throughout the year spent indoors or outdoors. As already indicated, during outdoor activity with accommodative effort sufficient to achieve a sharp retinal image of near-to mid-distance objects, the images of distant objects will be focused in front of the retina, so inducing the myopic blur that appears to be protective against myopia. Thus, both the chromaticity of the light reaching the retina as suggested by others79 and its vergence from distant objects when the eye is accommodated for near-to mid-distance objects, are likely to play a role in the protective effect against myopia associated with outdoor activity. If an increased photon catch in the tips of the photoreceptor outer segments as compared with their bases is a factor stimulating ocular elongation and myopia, it might be expected that those with protanopia or protonomaly in whom there is an absent or reduced sensitivity to red light might be more hyperopic (or less myopic) than the average person with normal color vision. A recent extensive study in high school students80 found a lower prevalence of myopia among students with red/green color deficiency than among normal controls with a significant difference in refractive error between the two groups. Additionally, those with protanopia or protanomaly had shorter axial lengths than did color normal students, confirming that ocular growth and refractive development is different among those with red/green color deficiency as compared with those with normal colour vision. This finding is in keeping with the

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hypothesis based on the distribution of photon catch along the length of the photoreceptor outer segments. If chromaticity of incident light is a factor influencing ocular and refractive development, it could also be an explanation for the myopic shift in refraction noted among adult microscopists, for hematoxylin and eosin staining commonly used in stained sections has a large red content but also a sufficient content of other wavelengths to ensure the focus of midlength wavelengths in the mid-points of M-cone photoreceptor outer segments. With a preponderance of red wavelengths, there would be a skewed distribution of photons in the outer segments in favor of the distal portions of the L-cone outer segments that we hypothesise leads to axial elongation of the eye and myopia. Currently, although there is evidence that ocular and refractive development can be influenced by a large number of physical attributes of the light incident upon the eye, including intensity, spatial and temporal frequency, contrast, photoperiodicity, vergence of incident light and chromaticity, the mechanisms underlying the development of myopia as a result of these effects remain largely unexplained. There is a probability that the influence of these various factors on ocular growth and refraction is multifactorial just as the genetic contribution is thought to be polygenic. In relation to chromaticity, we have proposed a hypothesis to explain the role that photon distribution in the photoreceptor outer segments may have in ocular development and the resulting refractive state. Even if the hypothesis, as we believe to be the case, proves to be supportable, the mechanisms by which an abnormal distribution of photons along the outer segments of photoreceptors can influence ocular growth remain to be elucidated. Therapeutic implications The physical factors that have been identified as playing a role in the development of myopia are non-specific blurring of the retinal image, the intensity of light reaching the eye and as yet undetermined factors related to outdoor activity. The influence of spatial frequency of the light reaching the retina and the periodicity of exposure is much less obvious. We advance the hypothesis that ocular development in the young eye is governed by the distribution of photon catch in the photoreceptor outer segments and if this is the case, there are a number of strategies that might be considered in terms of therapeutic intervention. The simplest therapeutic option is to

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ensure that children are involved in as much outdoor activity as possible, and additionally, that the amount of time spent in conditions of artificial lighting is curtailed. In the development of therapeutic interventions that might prevent the onset of myopia or slow its progression, randomised trials of chromatic manipulation of light incident on the eyes by appropriate modification of school or home lighting, the wearing of spectacles with appropriate transmission characteristics and so on, are not only required but are currently underway. The development of potential therapies would be greatly aided by a better understanding of the biological and biochemical events that may be induced by the hypothesised effect of an inappropriate photon catch distribution in photoreceptor outer segments, and to this end, more experimental work is necessary to elucidate the exact roles that specific combinations of differing wavelengths of light may have on the retina. Apart from modification of the chromaticity of light to which the developing eye is exposed, it is theoretically possible to change the pattern of photon catch in photoreceptor outer segments by optical means not involving chromaticity. Thus, under-correction of myopia or the wearing of plus lenses should advance the focal plane of light entering the eye but decrease the vergence of light passing through the retina. This, in turn, should favor an increased photon catch in the proximal ends of the photoreceptor outer segments as compared with the distal ends. The effect of under-correction of myopia or the wearing of plus lenses, however, is likely to be much less effective than appropriate chromatic manipulation. In conditions of white light (daylight) viewing, a proportion of longer wavelength red light will be focused not in the photoreceptor outer segments but behind the retina. As a result under-correction of myopia or the use of low power plus lenses will not affect the relative photon catch in the tips and bases of the outer segments, unless the lenses were strong enough to move the focal plane for longer wavelength red light sufficiently far forward to leave the tips of the outer segments unstimulated. Lenses of sufficient strength to shift focus far enough forwards would cause significant blurring of vision and would not be tolerated for continuous wear. The use of progressive addition lenses has been claimed to slow myopia progression in some children,81 but in uncontrolled trials in Australia and Singapore, the wearing of plus lenses of +3.00 D for a short period during the day instead of their usual correction for myopia, failed to slow the progression of myopia in most myopic children treated. A

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study82 in which one eye of myopic children was corrected for distance and the other uncorrected or under-corrected to keep a refractive imbalance between eyes of 2.00D showed that for reading, children accommodated with the distance corrected eye and the under-corrected eye accommodated to the same extent. The under-corrected eye suffered myopic defocus from the combined effects of an under-corrected refractive error and the imposed accommodation to match that of the fully corrected eye. This myopic defocus of the under-corrected eye was shown to be sufficient to slow the rate of myopia progression in the under-corrected eye. I.e. the under-corrected eye benefitted from the equivalent of wearing a plus two dioptre lens but only when a degree of accommodation was also present sufficient to ensure significant myopic defocus. The myopic defocus would result in divergence of light passing through the retina that among other effects would be likely to alter the distribution of photon catch in the outer segments in favour of an increased photon catch in the bases of the Scones. Of all the factors inducing myopia that might be subject to manipulation to reduce the progression of myopia or prevent its development, optical and chromatic factors would appear to offer some hope of therapeutic application. In activities such as reading, measures to ensure that in addition to sharp focus of the reading material in the plane of interest there is also a proportion of visual content from a further distance, if this could be achieved, might be one approach. As regards chromatic manipulation if the hypothesis we have advanced can be supported by further work, modification of ambient lighting to reduce its red content and increase the relative content of blue wavelengths with the preservation of some mid-wavelength green would be worth investigating as a therapeutic option. As already indicated, current therapies for myopia are mainly aimed at correcting the optical effects of myopia by correcting lenses or by refractive surgery. Although of benefit, they do not address the underlying pathology of the condition that carries with it a number of potentially sight-threatening complications. The use of topically applied muscarinic receptor blocking agents such as atropine is undoubtedly effective in preventing scleral elongation and so reducing the progress of myopia. Atropine, however, carries the disadvantage of paralyzing accommodation and dilating of the pupil, with resultant photophobia in bright light and an unknown and possibly adverse effect on the retina from a long-term increase in light exposure.

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Just as the etiology of myopia is likely to be multifactorial, so too, is it likely that more than one therapeutic strategy may have to be employed in the prevention of myopia or the slowing or arresting of its progression. Thus a combination of optical and chromatic manipulation together with pharmacological measures might offer a better cherapeutic option than any individual intervention alone. A better understanding of the relative importance of the various factors that appear to be involved in the etiology of myopia and its progression would allow a more targeted therapeutic approach to be developed and deployed.

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12. Fan DS, Lam DS, Chan CK, Fan AH, et al. (2007) Topical atropine in retarding myopia progression and axial length growth in children with moderate to severe myopia: a pilot study. Jpn J Ophthalmol 51: 27–33. 13. Chua WH, Balakrishnan V, Chan YH, et al. (2008) Atropine for the treatment of childhood myopia. Ophthalmology 115: 1103–1104. 14. Lee JJ, Fang PC, Yang IH, et al. (2006) Prevention of myopia progression with 0.05% atropine solution. J Ocul Pharmacol Ther 22: 41–46. 15. Hutchins JB. (1987) Review: Acetylcholine as a neurotransmitter in the vertebrate retina. Exp Eye Res 45: 1–38. 16. Barathi VA, Weon SR, Beuerman RW. (2009) Expression of muscarinic receptors in human and mouse sclera and their role in the regulation of sclera fibroblasts proliferation. Mol Vis 15: 1277–1293. 17. Cha LY, Weon SR, Beuerman RW. (2002) Effect of muscarinic agents and the role of sclera fibroblasts in experimental myopia. Invest Ophthalmol Vis Sci 43: E-Abstract 2411 ARVO. 18. McBrien NA, Moghaddam HO, Reeder AP. (1993) Atropine reduces experimental myopia and eye enlargement via a nonaccommodative mechanism. Invest Ophthalmol Vis Sci 34: 205–215. 19. Fischer AJ, McKinnon LA, Nathanson NM, Stell WK. (1998) Identification and localization of muscarinic acetylcholine receptors in the ocular tissues of the chick. J Comp Neurol 392: 273–284. 20. Yin GC, Gentle A, McBrien NA. (2004) Muscarinic antagonist control of myopia: A molecular search for the M1 receptor in chick. Mol Vis 10: 787–793. 21. Lin HJ, Wan L, Tsai Y, Chen WC, et al. (2009) Muscarinic acetylcholine receptor 1 gene polymorphisms associated with high myopia. Mol Vis 15: 1774–1780. 22. McBrien NA, Jobling AI, Truong HT, et al. (2009) Expression of muscarinic receptor subtypes in tree shrew ocular tissues and their regulation during the development of myopia. Mol Vis 15: 464–475. 23. Saw SM, D Hong CY, Chia KS, et al. (2001) Nearwork and myopia in young children Lancet 357 (9253): 390. 24. Ip JM, Saw SM, Rose KA, et al. (2008) Role of near work in myopia: findings in a sample of Australian school children. Invest Ophthalmol Vis Sci 49: 2903–2910. 25. Saw SM, Nieto FJ, Katz J, et al. (2000) Factors related to the progression of myopia in Singaporean children. Optom Vis Sci 77: 549–554. 26. Mutti DO, Mitchell DL, Moeschberger ML, et al. (2002) Parental myopia, near work, school achievement, and children’s refractive error. Invest Ophthalmol Vis Sci 42: 3633–3640. 27. Jones LA, Sinott LT, Mutti DO, et al. (2007) Parental history of myopia, sports and outdoor activities and future myopia. Invest Ophthalmol Vis Sci 48: 3524–3532.

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additive genetic effects 189–191 angle closure 124 anterior chamber depth 183, 185, 188, 190–192 association 202–209 atropine 345, 346, 348–356 Australian Twin Registry (ATR) 195, 196 axial length 24, 26, 28, 30, 33, 35, 37, 39, 122, 124, 126, 128–130, 183, 185, 188, 190–192, 196 Beaver Dam Eye Study (BDES) 7–10, 14, 34 biomechanics 292 bipolar 149, 150, 152, 156 birth head circumference 34 birth length 34, 36 birth parameters 24, 34–36 birth weight 34, 36, 38, 40, 183, 194 BMI 32–34, 38 body mass index 194 breastfeeding 24, 38–40 Brisbane Adolescent Twin Study 196 Burden of disease 63, 66, 72, 75 candidate genes 203 case-control association study 219 cataract 99–106, 110, 112 central corneal thickness 125, 126 387

children 23–25, 27, 29, 31, 32, 34, 36, 38–40 choroids 254, 255 chromatic aberration 245, 246, 365, 366, 371–373 chromaticity 371, 373–375, 377–379 classical twin model 185–188, 190, 192, 193, 196 clinical trials 356 close reading 27, 40 close-up work 29 Cochran-Armitage trend test 224 collagen 270–274, 277, 279–283, 286, 288, 290, 292, 298 contrast sensitivity 319, 321 corneal curvature 124, 126, 183, 185, 190, 191 correlated phenotypes 225 cost 63–66, 72–75 cross-sectional 24–33, 35–39 cycloplegia 354 Danish Twin Registry (DTR) 195, 196 definitions of myopia 24 diopter-hours 29 dizygotic twins (DZ) 185–188, 193, 194 DNA array 336 dominant genetic effects 191

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Economics 63 Eigenstrat 219, 222 electroretinography 149–152, 156 emmetropization 244, 246, 248, 249, 258 environment 45–50, 53, 54, 56, 57 environmental 23, 29, 40 epidemiologic studies 24 epidemiology 3, 4, 16, 99 equal environment assumption (EEA) 193 ERG 150–153, 155, 156 etiology 361–363, 367, 372, 381 family history 24–26, 40 family-based association study 215, 219, 224 fibroblast 268, 271, 272, 289, 291, 292 Fisher’s method 228 Fisher-exact test 224 fovea 247–249, 258 gene-environment interactions 45–47, 53, 56, 57 generalized estimate equation (GEE) 226, 227 genes 46–49, 51, 54, 331–335, 338, 340 Genes in Myopia (GEM) Study 196 Genes in Myopia (GEM) twin study 36 genetic epidemiology 204 Genome-wide Association Studies (GWAS) 164, 168–172, 176, 177, 215, 216, 219, 221, 223, 227, 229, 230 genomic control 219, 221, 222 genomic convergence 168–170, 177 genotype 45–47, 51 gestational age 34, 36 glaucoma 100, 101, 106–109, 112

glucagon 251, 252 glycosaminoglycans 272, 274, 282, 283 Handan Eye Study 37 Hardy-Weinberg Equilibrium (HWE) 221 height 25, 31, 32–34, 38, 40, 185, 188, 194 heritability 48–51, 188–193, 195, 196 high myopia 47, 48, 51, 52, 55–57, 216–218 human genome 332 imputation 227 insulin 251, 252 intensity 365, 366, 378 item response theory 91 keratometry 307, 314 lacquer crack 137, 139, 140 linear model 224, 226 logistic regression 224 longitudinal study 25, 26, 29, 30 low coherence interferometry 306, 316 Meiktila Eye Survey (MES) 34 meta-analysis 227, 228 minor allele frequency (MAF) 221 monozygotic twins (MZ) 185–188, 193, 194 mouse 303–305, 308–325, 332–338, 340 multifocal ERG 151, 153 multi-locus analysis 171, 172, 174, 176 muscarinic receptors 347, 355 mutants 303

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myofibroblast 272, 289–291, 293 myopia 3–16, 23–40, 45–57, 83–92, 97–113, 183–197, 201–210, 215–219, 225, 230, 267–269, 273–290, 292–298, 303, 304, 309–311, 315, 322–326, 331–338, 340, 361–373, 375–381 myopia loci 169 myopia progression 149, 154 myopic choroidal neovascularization 140 myopic macular degeneration 137 myopic maculopathy 110 myopic retinopathy 99, 110, 111 near work 23, 24, 27–29, 31, 32, 38, 40, 53, 55, 56 ocular biometry 23, 24, 26, 28, 30, 33–35, 37, 39, 40 optic disc 125, 127–129 optic nerve head 127–129 optical factors 367 optomotor response 319, 321, 323 Orinda Longitudinal Study of Myopia (OLSM) 25, 29 oscillatory potentials 150, 153 outdoor 23, 24, 27, 29–32, 38, 40, 45, 53, 55, 56 pathology 99, 110, 112, 113 pathway analysis 168, 169, 171, 172, 176, 177 patient-reported outcome measures 84 periodicity 365, 366, 378 personality traits 194, 195 pharmacogenetics 353 pharmacology 355 phenotype 46, 49–51, 53 photon catch 369–375, 377–380

photoreceptor 149, 150, 152, 155, 156 photorefraction 307, 309 physical activity 31 pigment dispersion 124 pirenzepine 346, 348, 350, 355 population structure 219, 221 population-based 24, 26–28, 30, 32–35, 37–39 prevalence 3, 4, 6–9, 11–13, 66–70, 72, 76 primary open angle glaucoma 121 principle component analysis (PCA) 221, 222 public health 201 quality controls (QC) 220, 221 Quantitative Trait Loci (QTL) 215 reading 27, 29, 31, 32, 38, 40 refraction 184, 186–193 refractive error 24, 25, 29, 30, 38, 39 retinal detachment 137, 144–147 retinal function 149, 150, 152–154, 156 retinal nerve fibre layer 128 retinoic acid 252–255 Reykjavik Eye Study (RES) 34 risk factors 3, 4, 10, 23–26, 28, 33, 35, 37, 39, 40 school myopia 45, 51–53, 57 school-based 25–32 sclera 250–252, 254–257, 267–277, 279–286, 288–290, 292–296, 298, 334–337, 339 Singapore Cohort Study of the Risk factors for Myopia (SCORM) 25, 27, 31, 32, 34, 36, 38, 216, 222, 223, 225, 226, 229, 230

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Singapore Malay Eye Study (SiMES) 34 single nucleotide polymorphisms (SNPs) 220–222, 224, 227–229 smoking 24, 36–38, 40 spatial frequency 366, 367, 373, 378 sphere (SPH) 217, 218, 225 spherical equivalent (SE) 24, 26, 28, 30, 33, 35, 37, 39, 217, 218 sports 29, 31 St Thomas’ UK Adult Twin Registry 195, 196 staphyloma 267, 273, 277, 279, 280 stature 32, 33 Strabismus, Amblyopia and Refractive Error Study (STARS) 38 structure 219, 221, 222, 229 Sydney Myopia Study (SMS) 24, 25, 27, 31, 32, 34

Tanjong Pagar Survey (TPS) 32, 33 therapy 362 twin registries 195, 196 Twins Eye Study in Tasmania 196 vergence 361, 365, 369–371, 377–379 vision-specific functioning and quality of life 83 visual acuity 304, 305, 309, 318, 319, 321 visual field 125, 128, 130 visual optics 304 weight 32–34, 36, 38, 40, 183, 194 Xichang Pediatric Refractive Error Study (X-PRES) 29, 31

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The Singapore Eye Research Institute (SERI) is the national research institute for ophthalmology and vision research in Singapore, and is affiliated with the National University of Singapore (NUS). SERI is the focal point of eye research in Singapore, serving as the research arm of the Singapore National Eye Centre (SNEC) and other eye departments, including the National University Health Systems (NUHS), and Tan Tock Seng Hospital. It has close working relationships with the A*STAR Research Institutes, Duke-NUS Graduate Medical School, the Nanyang Technological University and other biomedical institutions and eye centers in Singapore and throughout the world. Founded in 1997, SERI has developed a reputation over the last 13 years, as a leading research center in Asia, conducting broad-based basic, clinical, epidemiology and translational research programmes for various eye diseases, particularly diseases relevant to Asia.

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