The Ageing Brain: The Neurobiology and Neuropsychiatry of Ageing

THE AGEING BRAIN

THE AGEING BRAIN THE NEUROBIOLOGY AND NEUROPSYCHIATRY OF AGEING

Edited by

PERMINDER S. SACHDEV

This edition published in the Taylor & Francis e-Library, 2005. “To purchase your own copy of this or any of Taylor & Francis or Routledge’s collection of thousands of eBooks please go to www.eBookstore.tandf.co.uk.” Copyright © 2003 Swets & Zeitlinger B.V., Lisse, The Netherlands All rights reserved. No part of this publication or the information contained herein may be reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, by photocopying, recording or otherwise, without written prior permission from the publishers. Although all care is taken to ensure the integrity and quality of this publication and the information herein, no responsibility is assumed by the publishers nor the author for any damage to property or persons as a result of operation or use of this publication and/or the information contained herein. Published by: Swets & Zeitlinger Publishers www.szp.swets.nl ISBN 0-203-97097-7 Master e-book ISBN

ISBN 90 265 1943 5 (Print Edition)

Contents

ACKNOWLEDGEMENTS

ix

Section I

1

Introduction

CHAPTER 1: THE AGEING BRAIN Perminder S Sachdev

3

CHAPTER 2

POPULATION AGEING, HUMAN LIFESPAN AND NEURODEGENERATIVE DISORDERS: A FIFTH EPIDEMIOLOGIC TRANSITION

11

G Anthony Broe Section II

Characteristics of the ageing brain

CHAPTER 3 STRUCTURAL Jillian J Kril

CHANGES IN THE AGEING HUMAN BRAIN

CHAPTER 4 STRUCTURAL NEUROIMAGING OF Jeffrey CL Looi and Perminder S Sachdev

THE AGEING BRAIN

NEUROPHYSIOLOGICAL, SENSORY AND MOTOR CHANGES WITH AGEING

33 35 49

CHAPTER 5

63

Stephen R Lord and Rebecca St George CHAPTER 6 COGNITIVE CHANGES AND THE Helen Christensen and Rajeev Kumar

AGEING BRAIN

75

AGEING OF THE HUMAN BRAIN AS STUDIED BY FUNCTIONAL NEUROIMAGING

97

CHAPTER 7

Julian N Trollor and Perminder S Sachdev

vi

CONTENTS

CHAPTER 8 NEUROENDOCRINE George A Smythe

ASPECTS OF BRAIN AGEING

CHAPTER 9 CEREBROVASCULAR SYSTEM AND THE Valendai K Srikanth and Geoffrey A Donnan Section III

AGEING BRAIN

Factors influencing brain ageing

CHAPTER 10 THE

MOLECULAR BASIS OF AND FRONTOTEMPORAL DEMENTIA

139 153

171

ALZHEIMER’S

DISEASE

173

John BJ Kwok and Peter R Schofield CHAPTER 11 OXIDATIVE

AND FREE RADICAL MECHANISMS IN BRAIN

187

AGEING

Judy de Haan, Rocco C Iannello, Peter J Crack, Paul Hertzog and Ismail Kola CHAPTER 12 THE

ROLE OF NUTRITIONAL FACTORS IN COGNITIVE

205

AGEING

Janet Bryan CHAPTER 13 THE BRAIN Peter W Schofield Section IV

223

RESERVE HYPOTHESIS

Clinical interface

CHAPTER 14 WILL Carol Brayne

241

WE ALL DEMENT IF WE LIVE LONG ENOUGH?

CHAPTER 15 DETECTING ALZHEIMER’S PRE-SYMPTOMATIC STAGE Gary W Small

DISEASE AT THE

259

CHAPTER 16 PARKINSONISM AND AGEING John GL Morris, Mariese A Hely and Glenda M Halliday CHAPTER 17 AGE VARIATION IN THE PREVALENCE ARE STUDY FINDINGS MEANINGFUL? John Snowdon CHAPTER 18 VASCULAR DEMENTIA Perminder S Sachdev

243

OF

275

DEPRESSION: 283 299

vii

CONTENTS

CHAPTER 19 CONCLUSION Perminder S Sachdev

323

CONTRIBUTORS

327

SUBJECT AUTHOR

INDEX INDEX

ADDRESS LIST

333

Acknowledgements

The seed for this book was sown with the formation of The Ageing Brain Program at the University of New South Wales in 1998, and the early sprout appeared in 2000 at the International Conference on the Ageing Brain held at the Scientia, University of New South Wales, Sydney. The book is, of course, more than the Conference, and its diverse foliage is the dedicated work of many scientists and scholars. I am extremely grateful to all the authors for that extra effort that made each of the chapters a significant contribution. The editing of a book is a labour of love that demands doggedness and compulsive persistence. The latter qualities were brought to this work with measured good humour by Angela Russell, who undertook the tasks of editing and compiling. If she sometimes annoyed the contributors with her deadlines and diligent proofreading, the final manuscript will more than compensate for it. She was assisted in this task by the quiet and behind-the-scenes contribution of Wanda Schinke. The flair of Joanna Christie was an important determinant of the success of the Conference. The planning of this book, and its intellectual content, were influenced by many colleagues to whom I am extremely grateful. I would like to make particular mention of Sam Aroni, Henry Brodaty, Tony Broe, Felicia Huppert, Jeffrey Looi, Gary Small, Julian Trollor, Michael Valenzuela and Xing Li Wang who were generous with their suggestions and time. I found, in the publishers of this book, a rather indulgent group of professionals, led by Arnout Jacobs, who let many deadlines go past with little more than gentle reminders. For the undisturbed small hours of the morning that the writing took me into, I am in debt of my family — my beautiful wife Jagdeep and our lovely daughters Nupur and Sonal. They have provided the environment which has continued to nurture me through all my academic travails. My research into neuropsychiatric disorders of the elderly has been supported generously by the University of New South Wales and the National Health

x

ACKNOWLEDGEMENTS

and Medical Research Council of Australia. Additional support has been provided by the Rebecca Cooper Foundation, the Brain Foundation, the Fairfax Foundation and Pfizer Inc. None of these organizations has any vested interest in the intellectual content of the book or any commercial interest in it. Perminder S Sachdev

SECTION I INTRODUCTION

Chapter 1 THE AGEING BRAIN Perminder S. Sachdev

According to Hesiod, a Greek philosopher, the history of mankind could be divided into five epochs. The first was the Age of Gold in which mortals never aged and peace and happiness were pervasive. This was followed by the Age of Silver in which childhood lasted a hundred years but adulthood was transient. The third age, in which Hesiod lived, was the Bronze Age, which was a time of greed, corruption, injustice and violence. When this ended, Zeus created the Heroic Age in which the world was populated by demigods. Then came the Iron Age, in which we now live. Hesiod wisely predicted that this would be the age of violence, the love of profit, and an increasingly decadent lifestyle. Hesiod predicted that Zeus would be particularly incensed by the lack of honour shown to the elderly by the young, and by children not repaying their parents for the nurturance they received. Zeus would then create a new and idyllic Age. A hallmark of our Age is also the belief that future Ages are of our own making. Few would disagree that the salient characteristic of a golden age of the future would be eternal youthfulness, or at least youthfulness until the time of delayed but sudden death. This may explain our preoccupation with ageing. The images of ageing we confront on a daily basis are contrasting in nature. For those of us who are in mid-life, ageing represents a relentless erosion of our vitality. There are the obvious reminders in the greying hair, the balding scalp, the slight stiffness in the joints, and the small lapses of memory. The death notices in the newspaper become noticeable. A sudden dread fills our hearts as we witness our parents succumb to the travails of senescence. Yet we still hope to age like fine wine, accumulating Talmudic wisdom with our years. Individuals, like the late Madame Jean Calment of France, remind us that we could be living independently well into the 12th decade of our lives.1 We wonder if the tools of modern biology will uncover the mysteries of ageing and help control, if not reverse, it. We look with wonderment at the genome project and the vast worldwide army of biomedical scientists. Our dread is inter-mixed with awe and expectation.

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More than any other organ of the body, we are concerned with the ageing of the brain. The ageing brain must be considered a special case within the domain of ageing. While age-related changes in the brain in general parallel those of the body, there are important exceptions. Brain diseases that are usually regarded as concomitants of old age, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD) are sometimes seen in the young, and most elderly individuals manage to evade them. It is not uncommon to see a very active mind in a frail old body. Our examination of the ageing of the brain must therefore occur in the context of a neurobiological understanding. The ageing brain is a special case also for social reasons. An epidemic of dementia is upon us, and governments in the developed world are preoccupied with the impact this will have on the society of the future. As Professor Broe states later in this book, we are in the Age of Neurodegenerative Disorders, and insights into the mechanisms of ageing are urgently needed. . In the face of this dread, we must take heart in the pace of neuroscientific research, which has been breathless indeed. Let us consider a few examples. Most studies both in vivo and post mortem, suggest shrinkage of the adult brain as it ages, with a reported reduction of about 5% in brain weight per decade after the age of 40 years.2 This change is not uniform, however, with the prefrontal regions being affected more than the temporal and parietal neocortex. In the subcortical regions, the neostriatum atrophies moderately with age, while the globus pallidum and thalamus are relatively spared.3 What we do not understand are the reasons for this variation. Why is the substantia nigra, for example, more susceptible to age-related degeneration than the thalamus? What are the determinants of hippocampal degeneration seen in ageing brains? Questions such as this may be the keys to understanding the physiological processes involved in brain ageing. It is quite likely that the mechanisms of neural ageing are the same as for the rest of the body. There must also be important differences. A large part of the human genome is involved in brain development, suggesting that a great complexity must be fathomed. It was thought for many years that the changes in brain volume seen in ageing were a consequence of age-related neuronal loss.4 This notion was so well accepted that it had entered lay parlance. Recent studies using better stereological methods have shown that this may not be true, and in fact most brain regions do not suffer an age-related neuronal loss.5 If there is a sparing of the total number of cortical neurones, what is the basis of loss of cortical volume with ageing? The hippocampus has been studied extensively to understand this, and it has been shown that its functional organization is altered with ageing. This is related to alterations in connectivity, because of reductions in dendrites and synapses. In both rodents and humans, changes have been reported in dendritic arbor, spines and synapse morphology that could impact on the function of hippocampal circuits but would not be reflected as neuronal loss.6 This is functionally important as the most cognitively impaired aged rats demonstrate the greatest degree of abnormality.

THE AGEING BRAIN

5

The number of synapses can be judged by the density of receptors in the molecular layer as has been shown for the glutamate NMDA receptor which plays a critical role in mechanisms of plasticity comprising the cellular basis of learning and memory.7 The physiological implications of this change with ageing have been studied from many perspectives, one of which is long-term potentiation (LTP). This is a functional change in synaptic transmission secondary to neuronal stimulation, and has a role in memory functioning. The stimulation necessary to induce peak LTP, and the maximal potentiated response attained, are the same in young and old brains, but LTP decays to the prepotentiated baseline levels more rapidly in aged subjects — a possible reason for the “forgetting” experienced by the elderly. Many aspects of synaptic transmission are unaffected by age and there may even be compensatory changes. Functional imaging studies show that aged brains are less efficient in the processing of information, tending to recruit more extensive networks of neurones.8 This may, however, be a correctable change, since the brain is known to retain much of its plasticity despite age, and presents a potential for intervention. The cognitive changes associated with ageing are the subject of intense research. It is reassuring that crystallised intelligence remains intact with age, although some cognitive abilities do show a gradual reduction. Ageing causes a decline in information-processing resources, such as working memory capacity, attentional regulation and processing speed. Ageing results in greater intra-individual and inter-individual variation in performance. The change in these resources must be understood from a neurobiological perspective. It is interesting that the onset of the decline is relatively early in life, and to some extent parallels the decline in physical and sensory functions. The relationship with structural brain change is far from perfect, and can only be demonstrated in those with a pathological degree of impairment. It may be also be interesting to examine the cognitive decline using computational theories of neuronal function.9 Does the change in resources lead to deficient neuromodulation and increased neural noise, i.e. haphazard activation during neuronal information processing? There is evidence that mental representations in the elderly are less distinctive. Events that happen in the course of a day are less distinctly remembered by older individuals, suggesting that they may be processing information less elaborately. Neuro-imaging studies show that the elderly are more likely to activate both hemispheres for tasks that are lateralised in young individuals.10 One proposed mechanism for the increased neuronal noise with age is reduced neuronal responsivity due to a declining dopaminergic modulation.9 There are obviously other possibilities, which prompt for a multi-level approach to the problem of cognitive ageing. When one examines the risk factors for cognitive decline with age, one is confronted with the question: how much of the change is because of pathology in the brain? This is an issue not easy to settle. An ageing brain accumulates pathology that may be due to cerebrovascular disease or systemic diseases with their secondary brain effects. This may account for some of

6

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the age-related deficits that are likely to be misattributed to ageing-related changes. As an example, brains of elderly individuals frequently show hyperintense signals on T2-weighted magnetic resonance imaging (MRI), which has been the subject of hundreds of studies.11 Are these findings always indicative of pathology, or can they represent normative ageing-related changes? What role do these “lesions” play in the development of cognitive change and psychiatric disorders in late life? It is necessary to pose such questions to understand the nature of the “normal” ageing process. We have seen impressive advances in our understanding of neurodegenerative diseases in the last two decades, and are now at the threshold of effective treatments. Much work however remains to be done. Epidemiological studies have revealed many risk factors that have to be explained in terms of pathophysiological processes, and major gaps in this understanding remain. Genetic factors still explain only a minority of cases of Alzheimer’s disease (AD). The great divide between neurodegeneration and vascular pathology is no longer an unbridgeable gulf, but the physiological basis for this association is yet to be understood. The argument whether AD is an extreme form of ageing is unresolved, and some do believe, as Carol Brayne argues in this book, that if we live long enough, all of us will succumb to AD. We know a great deal about other causes of brain degeneration, but the reasons why one brain with fronto-temporal degeneration produces Pick bodies and not another remain elusive. Why is it that a particular region of the neocortex is preferentially affected in some dementias such as semantic dementia, progressive aphasia, etc.? This is a field in which the contributions of clinicians, epidemiologists and neuroscientists have gone hand in hand, and the future lies in the continued cooperation between disciplines. If we are to influence the ageing process, it is necessary that we understand the underlying molecular mechanisms. The frontier of the biochemistry of ageing, although yet to be conquered, is witness to many raging battles. As a consequence, terms like free radicals, heat shock proteins and nerve growth factors have become household words. Some of these theories have inextricably linked the brain with the rest of the body. As an example, the role of corticosteroids in the stress response, and its influence on brain structures has provided a link between psychology and biology.12 Brain regions that are important for learning and memory processes are particularly sensitive to stress hormones. The hippocampus has a high concentration of adrenal steroid receptors. Stress can thereby impair memory acutely; and chronic or repeated stress can lead to atrophy of dendrites and reduced neuronal connections. If prolonged this change can become irreversible and loss of neurones results. It has also been shown that early stress, such as prolonged separation of rat pups from their mothers, may lead to a chronic over-reactivity to stress in these animals. This may result in accelerated brain ageing. Other hormones such as oestrogen, growth hormone, melatonin, testosterone and dehydroepiandrosterone are being examined for their role in reversing some aspects of ageing. Growth or trophic factors abet these.

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7

The brain is intricately linked with the immune system, and age-related changes to the immune system have been of special interest to neurobiologists. The function of T-cells and their ability to proliferate declines with age. The T-cells produce powerful chemicals known as lymphokines, which mobilise mediators of the immune response. The effects of age on these lymphokines are variable, with rise in some and fall in others. It is not known how this may be linked to neuronal function. In this age of genomics, some of the causes of brain ageing are being sought in genetic factors. Most of the progress in neurogenetics has been in discovering genes for various neurological diseases, including those that affect the elderly. There have been exciting developments in AD, with the discovery of three genes that cause early-onset AD. However, this accounts for the disorder in but a small proportion of AD patients. The discovery of the tau gene in fronto-temporal dementia has raised the question of the relationships between the different genes, and what pathways may be shared in neurodegenerative disorders. The pace of this research is likely to increase as animal models are established. Recent research has shown that the expression levels of many genes related to neuronal signalling, plasticity and structure are altered with ageing.13 For example, the expression of certain proteases, such as prolyl oligopeptidase and caspase-6, is up-regulated in the aged brain. These proteases play essential roles in regulating neuropeptide metabolism, amyloid precursor protein processing, and neuronal apoptosis, and are likely contributors to brain ageing. It is interesting that some of these changes in gene expression can be reversed by environmental enrichment,14 providing hope for intervention. The mapping of the human genome, and the recognition that a large number of genes are involved in brain development, has opened up exciting opportunities for understanding the molecular basis of brain ageing. It would be important to find out if there are a few major genes that determine ageing, or is it the result of the cumulative effect of changes in many genes? Is ageing the result of defects in DNA repair that gradually accumulate? Are there some genetic modifications that can delay, if not stop or reverse the processes of brain ageing? Does dietary restriction delay ageing through genetic factors?15 Attempts have recently been made to apply gene transfer technology to protect neurones from death following neurological insults.16 It is conceivable that gene therapy in the future may be able to protect the nervous system from ageing. Transgenic intervention could be in order to over-express a particular gene to protect against decline of its product in old age, or gene therapy could target a discretely damaging event highly likely to occur in the elderly. Technologies for the delivery of genes into neurones to maintain function and protect against injury are being developed. Genomics is likely to be complemented by the newly developed science of Proteomics,17 as there are many more proteins in the human body than can be accounted for by the number of genes recognised on the genome. The nearly 30,000 genes have a complement of nearly 300,000 proteins, and each

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of the 200 cell types in the body has a different set of proteins. The proteins produced by the cells at any particular time are moderated according to the biological needs of the body, and are influenced by the disease process present. Proteomics therefore permits the identification of proteins associated with particular diseases. This will assist diagnosis, as is already the case in AD, and speed the development of new treatments. It also offers the exciting possibility that drugs may be tailored the to individual patient, opening up an era of personalised medicine. The treatments of neuropsychiatric disorders of the elderly are likely to look very different in the future, as suggested by the above developments. In many respects, the future is already here. Depressive disorders have recently seen the introduction of two novel treatments: transcranial magnetic stimulation and vagus nerve stimulation. Parkinson’s disease patients worldwide are benefiting from deep brain stimulation. Targeted drugs are increasingly being developed for specific receptors. Stem cells promise to open up a new era in therapy. In fact, recent findings suggest that a decline in the numbers and plasticity of stem cells may contribute to ageing itself.18 It is likely that methods will be developed to tweak the stem cells already in the brain, or introduce new ones, to replace lost or dysfunctional cell populations. The above developments reveal the rapidity with which new information is being acquired and old orthodoxies challenged. However, a glorious ageless society is not upon us yet. In the medium-term, our goals as a society must be limited. We can start by emphasising the positive aspects of old age. Some people may find this a difficult concept to grasp, and yet for thousands of years, societies have valued age and even venerated it. The wisdom of old age is difficult to quantify, but recent research showed that on a rational choice task, 70 year old subjects performed much more consistently than those 50 years younger.19 With the inevitable ageing of our populations, we have little choice but to make old age productive, healthy and enjoyable. Much of this will be achieved through social and political change and not medical advances. An increasing number of healthy older people can make a significant contribution to the lives of younger generations. Age can help temper and direct the energy of the young. Medical science does not, in the near future, hope to conquer ageing. It can have a more modest goal, however, in delaying the onset of late-life dysfunction. Old age is characterised by an array of ageing-related diseases, which include cardiovascular disease, dementia, sensory deficits, Parkinson’s disease, diabetes, osteoporosis and incontinence. The mere delaying of the onset of some of these will have a major public health impact. For instance, a delaying of the onset of Alzheimer’s disease by five years will halve the prevalence of the disorder. It is this promise that is prompting a burgeoning industry of health promotion. Low-fat labels, cholesterol free diets, folic acid supplementation, aspirin prophylaxis, anti-oxidants and organic foods are more than passing fads. In this rush toward a healthy old brain, it is difficult to separate established scientific facts from overvalued ideas. A few messages

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do seem to have sufficient empirical basis. We can protect the brain somewhat if we control hypertension early and effectively, and attend to other cerebrovascular risk factors such as diabetes, smoking, high cholesterol and obesity. We should aim for moderation in our use of alcohol, and perhaps try to restrict it to red wine, while we refrain from using illicit substances. Whether we will benefit from using anti-oxidants or anti-inflammatory drugs remains to be established. The use of folic acid supplementation to reduce serum homocysteine levels is again not established as an epidemiological health measure20 but is increasingly popular. Also without sufficient scientific backing, the use of a daily multivitamin tablet that does not exceed the RDA of its components makes sense for most adults, given the greater likelihood of benefit than harm and the low cost.21 The use of vitamin E at 400 IU per day in middle and old age by those at risk of vascular disease can also be recommended.21 Stress, no doubt, is bad for the body and the brain, and has been linked with psychiatric and cognitive disorders, and we should unequivocally recommend stress-reduction strategies to our patients. The action of stress on neurones is most probably through the glucocorticoid cascade. This response can be modified by environmental manipulation as early as in the neonatal period,22 and continuing on into later life. It is interesting that this manipulation of the adrenocortical axis can safely and effectively be brought about by a psychologist.22 The promotion of other hormones, such as growth hormone, melatonin, DHEA, pregnenolone, testosterone, oestrogen and progesterone, as elixirs of youth is without unambiguous scientific basis. A study from Boston23 showed that exercise can strengthen muscles, improve mobility, and reduce frailty even among 90-year-old individuals. The same may be true for the brain, which harbours a significant potential for plastic change well into old age.24 Another analogy to be drawn with muscles is that the brain has a reserve than can be influenced by mental activity, and serves to protect the individual from age-related changes.25 A number of studies have reported that higher educational and occupational levels, mental activity and high intellectual performance are protective factors for dementia. These findings, and those relating to nutritional factors and stress, promote an agenda for the future that is hopeful, and suggest interventions at the population level that should begin now without awaiting breakthroughs in the understanding of molecular processes. It is important to take this message to decision-makers if we are to influence the future of an ageing society. References 1. 2.

Ritchie K. Mental status examination of an exceptional case of longevity — J.C. aged 118 years. Br J Psychiatry. 1995; 166:229–235. Kemper T. Neuroanatomical and neuropathological changes during aging and in dementia. In: Albert M, Knoepfel J, editors. Clinical neurology of aging. New York: Oxford University Press, 1994; 3–67.

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3. Trollor J, Valenzuela M. Brain ageing in the new millenium. ANZ J Psychiatry. 2001; 35:788–805. 4. Brody H. Structural changes in the aging nervous system. Interdiscip Topics Gerontol. 1970; 7:9–21. 5. Wickelgren I. Is hippocampal cell death a myth? Science. 1996; 271:1229– 1230. 6. Hamrick J, Sullivan P, Scheff S. Estimation of possible age-related changes in synaptic density in the hippocampal CA1 stratum radiatum. Soc Neurosci Abstr. 1998; 24:783. 7. Gazzaley AH, Siegel SJ, Kordower JH, Mufson EJ, Morrison JH. Circuit-specific alterations of N-methyl-D-aspartate receptor subunit 1 in the dentate gyrus of aged monkeys. Proc Natl Acad Sci USA. 1996; 93:3121–3125. 8. Almkvist O. Functional brain imaging as a looking glass into the degraded brain: reviewing evidence from Alzheimer disease in relation to normal aging. Acta Psychol. 2000; 105: 255–277. 9. Li S-C, Lindenberger U, Sikstrom S. Aging cognition: from neuromodulation to representation. Trends Cog Sci. 2001; 5:479–486. 10. Cabeza R, McIntosh AR, Tulving E, Nyberg L, Grady CL. Age-related differences in effective neural connectivity during encoding and recall. NeuroReport. 1997; 8:3479–3483. 11. Pantoni L, Garcia J. Pathogenesis of leukoaraiosis: A review. Stroke. 1997; 28: 652–659. 12. Sapolsky R. Stress, the aging brain, and mechanisms of neuronal death. Boston: MIT Press, 1992. 13. Jiang CH, Tsien JZ, Schultz PG, Hu YH. The effects of aging on gene expression in the hypothalamus and cortex of mice. Proc Nat Acad Sci USA. 2001; 98: 1930–1934. 14. Rampon C, Jiang CH, Dong H, Tang YP, Lockart DJ, Schultz PG, Tsien JZ, Hu YH. Effects of environmental enrichment on gene expression in the brain. Pro Nat Acad Sci USA. 2000; 97:12880–12884. 15. Weindruch R, Walford RL. The retardation of aging and disease by dietary restriction. Springfield, IL: Charles C Thomas, 1988. 16. Ogle WO, Sapolsky RM. Gene therapy and the aging nervous system. Mech Ageing Dev. 2001; 122:1555–1563. 17. Banks RE, Dunn MJ, Hochstrasser DF, Sanchez JC, Blackstock W, Pappin DJ, Selby PJ. Proteomics: new perspectives, new biomedical opportunities. Lancet. 2000; 356:1749–1756. 18. Rao MS, Mattson MP. Stem cells and aging: expanding the possibilities. Mech Ageing Dev. 2001; 122:713–734. 19. Tentori K, Osherson D, Hasher L, May C. Wisdom and aging: irrational preference in college students but not older adults. Cognition. 2001; 81:B87–96. 20. Diaz-Arrastia R. Homocysteine and neurologic disease. Arch Neurol. 2000; 57: 1422–1427. 21. Willett WC, Stampfer MJ. Clinical Practice. What vitamins should I be taking, doctor? New Eng J Med. 2001; 345:1819–1824. 22. Seligman M. Learned optimism. New York: Alfred Knopf, 1991. 23. Levine S. Plasma-free corticosteroid response to electric shock in rats stimulated in infancy. Science. 1962; 135:795–798. 24. Anstey K. How important is mental activity in old age? Austr Psychol. 1999; 34: 128–131. 25. Schofield P. Alzheimer’s disease and brain reserve. Australas J Ageing. 1999; 18: 10–14.

Chapter 2 POPULATION AGEING, HUMAN LIFESPAN AND NEURODEGENERATIVE DISORDERS: A FIFTH EPIDEMIOLOGIC TRANSITION G Anthony Broe

Introduction Rapid population ageing, or a rising percentage of older people in the population, was a 20th century phenomenon in developed countries and is now affecting most of the world’s populations, the poor as well as the rich. Countries as disparate as Australia, Iran, Thailand and Tunisia are approaching or achieving below replacement fertility levels.1 The immediate cause of world population ageing was fertility decline; this followed the reduction in infant deaths due to infectious diseases from the 19th century onwards and their substitution by deaths due to adult onset degenerative diseases in the first half of the 20th century. The infectious diarrhoeas, influenza and tuberculosis, in children and young people, were gradually replaced by cardiovascular and lung diseases at older ages. Omran,2 in 1971, referred to this shift in disease patterns as the “Epidemiologic Transition”; and he described three disease transitions occurring in developed countries up to the mid-20th century. This chapter examines population ageing and life span in relation to further disease transitions and changing causes of mortality and morbidity later

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in the 20th century. A progressive delay in age of onset, and a decline in mortality from the systemic degenerative diseases (such as cardiovascular and lung diseases) was described as the fourth transition of “delayed degenerative diseases” by Olshansky and Ault in 1986.3 We are now seeing yet another substitution of mortality due to later onset neurodegenerative disorders, such as dementia and Parkinson’s disease (PD).4;5 It is predicted that the neurodegenerative disorders will gradually replace the systemic degenerative disorders as the major causes of both death and morbidity in the 21st century. Population Ageing Population ageing is the product of three factors: birth rate, infant mortality and life span. During the 19th century, and the first half of the 20th, these three demographic factors were largely determined by major external or environmental assaults due to infectious diseases, malnutrition and trauma. Reductions in these risk factors resulted in improvements in maternal and child health and increased infant survival. This was followed by declining fertility and a decreased birth rate leading towards zero population growth; hence the almost instantaneous ageing of Western populations in the first half of the 20th century. Population ageing is one area of human ageing that cannot be claimed by the geneticists as their responsibility. So far it is primarily environmental. The History of Ageing Group in Cambridge examined birth and death records in five parishes in England between 1541 and 1981 to produce an

1541

1751

1921

1981

Figure 1. The proportion of elderly in the English population, 1541-1981 (adapted from Laslett,6).

AGEING, HUMAN LIFESPAN AND NEURODEGENERATIVE DISORDERS

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accurate projection of the number of over –60s in the English population during a period of 440 years6 (Fig. 1). For almost 400 of those 440 years the over –60s fluctuated at around 8% of the population. A consistent rise above 8% did not occur until the 1920s or a mere 80 years ago when rapid population ageing in developed countries commenced. The Epidemiologic Transition Theory The epidemiologic transition theory 2,3,7 explains these population changes by major shifts in health and disease patterns, and in recorded causes of death, as societies change or “modernise” with resultant improvement in social, economic and health factors. The base or first stage of the epidemiologic transition, graphically described by Omran2 as “The Age of Pestilence and Famine,” was characterised by very high death rates due to pandemic infections, trauma, poverty and malnutrition. The major killers during this long pre-industrial era were the ubiquitous diarrhoeas, influenza, tuberculosis and pneumonia, as well as epidemics such as bubonic plague and small pox. The major death toll occurred in infants and young children, with low average life span and a low and static percentage of older people in the population. The next stage of the epidemiologic transition followed the scientific revolution and the start of the industrial revolution in England and Europe in the 18th and 19th centuries. Concomitant social and economic changes gradually brought greater wealth, better nutrition, less crowding, better education, better hygiene, healthier mothers and stronger infants despite the social upheavals of industrialisation and urbanisation. Omran’s second epidemiologic stage, “The Age of Receding Pandemics,” with decreasing infant mortality and an ongoing high birth rate, resulted in more infant survivors and an overall younger population in England up to the mid-19th century. Declining fertility then lead to progressive population ageing, with a shift in causes of death to the later-onset systemic degenerative diseases (particularly cardiovascular and lung disease) and a shift in mortality from the young to the old. These shifts heralded Omran’s third epidemiologic stage “The Age Table 1.

The Epidemiologic Transition Theory (Western model). • The Age of pestilence and famine • The Age of receding pandemics • The Age of degenerative diseases • The Age of delayed degenerative diseases

Omran (1971)2 and Olshansky & Ault (1986)3

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of Degenerative and Man-made Diseases”. In retrospect, this stage represents a major achievement for the ageing survivors of an era of fatal infectious diseases, rather than a “man-made epidemic” of modern life, as it has often been painted. At the time of publication of his general theory of epidemiologic transition and mortality change in 1971, Omran and other demographers were predicting that average human life span would not progress beyond 70 years, then considered to be the biological as well as the biblical limit. Omran himself believed his third stage of degenerative diseases would be the completion of the epidemiologic transition. However mortality rates at older ages have continued to decline and average life expectancy at birth has continued to increase worldwide. These changes lead to the description of a fourth stage of the epidemiologic transition “The Age of Delayed Degenerative Diseases”, by Olshansky and Ault in 1986.3 This stage recognized the rapid decline in mortality due to chronic systemic diseases from the 1960s, particularly a decline in cardiovascular disease and stroke in developed countries. There was a delay in the ages at which these potentially fatal systemic diseases tended to kill, with rapid improvement in life expectancy concentrated among the population at advanced ages; a phenomenon described as “the ageing of the aged.” This ongoing decline in mortality has been attributed to new public health measures, including changes in major risk factors for systemic degenerative diseases such as smoking, diet and exercise, as well as advances in medical technology and drugs.3,8 However Olshansky3,9 has predicted that increases in life expectancy due to the prevention or delay of the known systemic degenerative diseases would not increase average life expectancy at birth much beyond 85 years. Lifespan and Compression of Morbidity The epidemiologic transition theory has focused on mortality with only implicit reference to morbidity, defined as the length and quality of survival in the presence of age-associated disease or disability.7 Description of the phenomenon of “ageing of the aged”, with recognition of significant increases in average life span beyond seven decades, brought increased attention to the concepts of “healthy ageing” or “successful ageing” with emphasis on the duration of disability-free survival, rather than longevity per se. James Fries10 outlined his theory of “Compression of Morbidity” in 1980. Based on a human life span of around 85 years, Fries predicted that chronic systemic disease, and consequent disability, would be delayed and compressed to the end of life by ongoing changes in life style and risk factors such as reduction in smoking, improved diet and more exercise. Recent data support this association.11 However, with further increases in human life span, it remains possible that morbidity is simply being delayed to later decades of life rather than

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Figure 2. The theory of “compression of morbidity” as outlined by James Fries.10 Fries predicted that chronic systemic disease and associated disability would be delayed and compressed to the end of life by ongoing changes in lifestyle and risk factors.

compressed, i.e. from the “young-old” to the “old-old” or from the 70s to the 80s and 90s. A shift to a new epidemiologic transition of later-onset neurodegenerative disorders would result in additional causes of morbidity as well as mortality at advanced ages. While morbidity related to chronic systemic diseases appears to be declining, or being compressed to the end of life, the morbidity related to neurodegenerative disease, particularly the dementias, is increasing with advanced old age. Health related factors (reduction in smoking, improved diet and more exercise, etc.) reducing mortality and morbidity due to chronic systemic diseases have not been demonstrated to be protective against the chronic neurodegenerative diseases. Other protective factors for these diseases may be found, however, particularly those related to early brain growth and development, and to intellectual ability and education in early life. Average human life span in developed countries is now approaching the “natural limit” of 85 years described by Fries10 and predicted by Olshansky9 on the basis of possible cures for the major (systemic) degenerative diseases: cardiovascular diseases, lung diseases, and cancer. More extreme longevity remains possible, with average human life span going beyond the predicted 85 years and up to 100 or more years, but only if new causes of mortality decline are determined and modified, additional to those responsible for the

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decline in later onset systemic diseases, or new factors determining longevity are identified. Important factors for further increases in longevity are likely to be those related to brain development and to lifelong improvements in cognitive and behavioural capacity. The worldwide nature of rapid population ageing12 and the timing of improvements in old age survival from the 1950s (before major advances in the “new” public health) suggest that general social and biodemographic factors, as well as health factors, are producing the improvement in the survival of the “old-old”. Vaupel’s group13 have demonstrated a substantial increase in human survival, commencing in the 1950s, with mortality data showing unpredicted and unexplained decline in mortality in those over 80 years of age accompanied by a marked rise in absolute numbers of the “old-old”. Their data indicate that the population of centenarians in developed countries has doubled every decade since 1960, mostly as a result of increases in survival after 80 years of age. This improvement in late-life survival is primarily nongenetic. It is largely determined by early life factors and experiences, which influence late life survival attributes, rather than by current conditions or risk factors operating in late life. Individual life span is seen as a product of internal (including genetic) defence mechanisms or survival attributes and external assaults on those defence mechanisms. External assaults, such as childhood infections, malnutrition or trauma, may overwhelm internal defences and lead to rapid or early death, as was common in the 19th century. However, the survivors of these external assaults in early life may improve their internal defence mechanisms (survival attributes) and lengthen their subsequent life spans in old age. Vaupel’s group13 have also shown that death rates decelerate with advanced age in multiple species: humans, medflies, wasps, drosophila, nematodes and yeast cells. From the combined data, they postulate that mortality decline with advancing age is a property of many complex systems. It appears to be related to a cohort “survivor effect” transmitted through individual fixed survival attributes, in an environment with markedly reduced external assaults compared to previous centuries. This late life “survivor effect” will apply to half the population in developed countries, as average survival reaches 80 years of age. Human Lifespan and the Brain In terms of human life span, it is proposed that brain function responsible for the human capacities for learning, cognition, insight and social knowledge, is one determinant of longevity in human populations.13-15 Socio-economic status, educational level, and mental ability or intelligence are closely linked. A cohort effect of increasing fluid intelligence, as measured by psychometric tests of verbal reasoning, spatial orientation and inductive reasoning, has been demonstrated over the 20th century in data that span the period from 1889 to 1966.16 This cohort effect, which has been attributed to improvements in

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education, parallels observed changes in both education and longevity over the same period. Although no causal links have previously been suggested, it is arguable that improvements in education and fluid intelligence are in part responsible for increases in longevity. Socioeconomic status in childhood has been associated with mortality from a number of illnesses17;18 and educational level contributes to differences in mortality.19;20 Higher mental ability on test performance also correlates with better educational and occupational outcomes.21 However, until recent longitudinal observations on Scottish school children,22 there were few well-studied links between mental ability and mortality. These included an Australian Vietnam Veteran study23 and the Canberra24 and Rotterdam25 studies on older populations. The larger Scottish study was carried out on a cohort of 2792 school children in Aberdeen, with mental ability assessed at 11 years of age (in 1932) and survival determined 65 years later on 80% of the sample. It concluded that childhood mental ability was a significant factor among the variables that predicted age at death and hence longevity. The effects of IQ are difficult to separate from the effects of social class and education. It can be argued, however, that better brain development, whether it be in utero or in infancy and childhood, is likely to be an important survival attribute in 21st century society with its greatly reduced level of major external assaults and physical risk factors compared to previous centuries. It can also be argued that average human life span is likely to go beyond the 85 years which Olshansky has predicted on the basis of projected reductions in mortality from chronic systemic diseases.9 The important determinants of both mortality and morbidity in the “old-old”, in the knowledge-based societies of the 21st century, are likely to be better brain function on the one hand, and the neurodegenerative diseases associated with brain ageing on the other. The Neurodegenerative Diseases The important late-onset neurodegenerative diseases, for the determination of mortality and morbidity data in the older population, are dementia and Parkinson’s disease. Despite their very high prevalence in the “old-old”, the neurodegenerative diseases are, in general, poorly defined and diagnosed compared to the common systemic degenerative diseases (heart disease, stroke, chronic lung disease and cancer) and there is a high current level of under-ascertainment of neurodegenerative disease mortality.5 The late-onset neurodegenerative diseases include the dementias (Alzheimer’s disease [AD], dementia with Lewy bodies [DLB] and fronto-temporal dementia [FTD]) as well as Parkinson’s disease [PD] and motor-neuron disease [MND]). The commonest cause of visual loss in older people, age-related macular degeneration (ARMD), can be classified as a neurodegenerative disease, as can the almost universal age-related sensori-neural deafness. MND or amyotrophic

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lateral sclerosis is the third commonest well-defined neurodegenerative disease of ageing. The term is also used for a host of less common familial and/ or sporadic neurological diseases of unknown cause, including progressive supranuclear palsy (PSP), cortico-basal degeneration (CBD) and the spinocerebellar atrophies (SCA), many of which appear to be age-related.26 As a class, the neurodegenerative diseases are primary neuronal disorders, i.e. not secondary to known vascular, malignant or toxic causes. Their defining feature is selective neuronal loss in a pattern that tends to be specific to each disease. Many neurodegenerative diseases (AD, PD, MND) manifest as a more common late onset sporadic form, which increases exponentially in incidence with advancing age over 70 years, and rare early onset dominantly inherited forms of what appear to be the same disease process. A number of neurodegenerative diseases are characterized by the accumulation, over many decades, of abnormal gene products in the brain; these proteins have variable associations with the selective patterns of neuronal loss observed in each disease. They include the accumulation of β-amyloid and tau in Alzheimer’s disease, forms of synuclein in PD and DLB, and forms of tau in FTD and other less common neurodegenerative diseases (PSP, CBD). The role of these proteins remains poorly understood in the pathogenesis of the specific diseases and particularly their late onset forms. Detailed study of the early onset familial forms is, however, providing significant insights into the role of some specific gene products. β-Amyloid in particular clearly plays an important role in the commonest age-related neurodegenerative disease, Alzheimer’s disease, and appears to have an additional role in brain ageing. Furthermore, β-amyloid, in conjunction with the evolution of the apolipoprotein alleles in humans, may have an association with basic evolutionary processes determining ageing and longevity.27 Neurodegenerative Diseases and Mortality The first four stages of the epidemiologic transition have been defined by mortality data using life expectancy and survival curves, combined with mortality data on specific causes of death.2,3 The focus has been on deaths from infectious diseases, and from the rapidly fatal systemic diseases, in particular mortality data for heart disease, stroke, lung disease and cancer,3 which are the commonest recorded causes of death in developed countries. They are diseases that tend to have well defined fatal outcomes, and mortality data for these disease categories is likely to be accurate. Clinical diagnosis is also likely to be accurate for the other systemic diseases listed among the 10 common causes of death in developed countries including endocrine, gastrointestinal and genito-urinary causes. Age-standardised mortality, as well as morbidity, for most of these systemic disease categories has been shown to be declining over the 20th century in developed countries, including Australia, with the most recent decline occurring in cancer deaths.28-30 The major exceptions

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to this pattern of declining mortality from the Australian data are late-onset neurological diseases (over 75 years) and later onset “mental health disorders” (over 85 years). While the quality of mortality data for both infectious and systemic degenerative diseases is relatively good, the same is not true for mortality data for the neurodegenerative diseases, where under-ascertainment is a significant problem.5,31 Although dementia is the commonest neurodegenerative disease, its prevalence and incidence had not been well defined up until the last decades of the 20th century, particularly in the “old-old”. It is now clear that the prevalence and incidence of dementia rise exponentially with age at least up to 90 years.32,33 Alzheimer’s disease is the commonest dementia, and shows a doubling of incidence every five years from 65 to 90 years of age.33 There has been controversy as to whether dementia incidence plateaus off over 90 years of age, with two meta-analyses producing conflicting results.33,34 However a decline in AD incidence over 90 years, in men, is now supported by the recent EURODEM project35 and in both men (over 93 years) and women (over 97 years) by the Cache County study.36 Diagnosis of dementia during life remains difficult in old age and a number of studies demonstrate the lack of recognition of dementia in the community by family informant37 medical practitioners38 and nurses.39 Death certificates tend to record the “acute” systemic cause of death and mortality from death certificate data is estimated to be as low as 15% for dementia.5 Finally, studies suggest that Vascular dementia (VaD), a dementia of systemic cause, does not rise as rapidly with age as AD40,41 and that mixed dementia (AD and VaD) is common.42,43The inclusion of VaD in mortality and morbidity data for neurodegenerative diseases is often difficult to avoid, but should not greatly distort the results.5 Parkinson’s disease (PD), the second commonest neurodegenerative disease, is also poorly defined and diagnosed in older people, in whom atypical forms of the disease are more common.44 Until recently, idiopathic PD was stated to decline in incidence with ageing over 75 years in studies based on inappropriate methodology.45 It is now clear that its prevalence and incidence continue to rise with advanced ageing.26,42,46 This rising age-related prevalence may not be well recorded in mortality data. Only 25% of decedents with PD have the disease listed on their death certificates.5 Finally, in terms of the quality of mortality data, neurodegenerative disorders are commonly mixed in the “old-old”.42,43 Furthermore multiple preclinical syndromes commonly co-exist in older people and have been shown to predict subsequent dementia;47 these include cognitive or memory impairment (not reaching criteria for AD), motor slowing (not reaching criteria for PD) and evidence of vasculopathy. Because of multiple pathology in the “old-old”, the neurodegenerative disorders outlined commonly present as multi-factorial “Geriatric Syndromes”, rather than as specific neurological diseases amenable to specific diagnoses on death certificates. Many of the “Geriatric Syndromes” have a high mortality rate including: “immobility” with underlying parkinsonism and dementia; “instability and falls” with

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underlying impairments of balance gait and vision; “delirium” with underlying frontal system impairments and dementia; and “aspiration pneumonia” due to underlying brain and oesophageal-motility disorders. However, the underlying causal diagnoses rarely appear on death certificates. Despite these potential problems with certification of deaths due to neurodegenerative diseases, a number of recent studies have shown increasing mortality from the three major neurodegenerative diseases: dementia,5,31,48 Parkinson’s disease49,50 and MND.51 Few studies have been able to compare mortality data for these neurodegenerative diseases with mortality data for common systemic diseases. Lilienfeld and Perl5 used US Census Bureau population estimates to project the annual death rate from three neurodegenerative diseases (dementia, PD and MND) and from six comparison systemic diseases (liver cirrhosis, colon cancer, lung cancer, cancer of the female breast, multiple sclerosis, and malignant melanoma) over the period between 1990 and 2040. The US National Center for Health Statistics routinely collects individual death certificates for all US residents. To determine death rates they used data for deaths in which the underlying cause was dementia, PD or MND (and the six comparison diseases) for the years 1985–1988. Assuming that the US disease-age-gender-race-specific death rates for these years remained constant over the period between 1990 and 2040, they found that neurodegenerative disease mortality increased by 119–231%, depending on the population model used. For the “middle” population growth model the increase was 166%, with the major component being deaths due to dementia. The increases in mortality for the six comparison diseases ranged from 52% (multiple sclerosis) to 130% (colon cancer). A number of factors make it likely that these projections for neurodegenerative disease mortality are underestimates including under-ascertainment on death certificates (for the reasons outlined above) and the conservative nature of the US Census Bureau estimates of population ageing. Furthermore the comparison with cancer deaths is with a category of systemic disease in which mortality is either still rising or static or showing the slowest falls, in comparison with other common systemic diseases, such as cardiovascular and lung disease, in which mortality is declining rapidly.28;30 Based on this review of the limited mortality, life expectancy and survival data, deaths from most systemic degenerative diseases continue to decline and are being replaced by deaths from even later onset neurodegenerative diseases, as part of a new disease transition. Neurodegenerative Diseases and Morbidity Overall, the quality of data to examine and compare morbidity and disability by disease cause across populations, has been less accurate than mortality data, with few reliable data on morbidity available.7 However it is increasingly important to define and measure morbidity, taking into account the

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delayed onset, slower course and reduced mortality associated with the chronic systemic diseases over the past 50 years, the concomitant rise of the age-related neurodegenerative diseases, and the current controversies about compression of morbidity. This is particularly the case in comparing the burden of chronic systemic disease with that of chronic neurodegenerative disease, as it is the latter that is likely to rise in the 21st century with further population ageing. Many studies have shown rising age-related incidence, prevalence and morbidity in individual neurodegenerative disorders, such as dementia32,33,52 and PD.26,46,50 More general population based studies using Australian Bureau of Statistics (ABS) or US Census Data53,54 commonly rely on selfreport instruments to identify diseases and compare causes of disability, and have not shown this trend. However such instruments may not be sensitive to the impact of neurodegenerative diseases on disability, since cognition and/or insight are commonly impaired.55 This has been confirmed for the ABS disability instrument in a study comparing different disability measures, given to both respondents and informants.56 Few studies have used detailed clinical assessments in the field or compared incidence, prevalence or morbidity data, or disability rates, due to neurodegenerative diseases as a group with those rates due to the common systemic diseases. The Kilsyth Study in Scotland,40,57,58 completed in early 1970, examined the prevalence of major chronic disorders in the elderly and compared disability and dependence due to systemic and neurological causes. The study involved three community-living random samples, comprising 808 people, 65 years and over, examined by physicians experienced in geriatric medicine.58 It demonstrated that the prevalence of disability for IADL (defined as the inability to live at home without domestic help) increased from 12% at 65–69 years to over 80% at the age of 85 years. It showed that neurological disorders — dementia, balance/gait disorder, stroke and parkinsonism in that order — were the commonest cause of disability in 48% of subjects. Neurological and functional psychiatric disorders together contributed to 70% of disability compared with cardio-respiratory (38%), joint disease (24%) obesity (16%) and vision (11%). Neurological disorders, in 93% of cases (particularly dementia in 77% of cases), were by far the greatest contributor to the more severe category of dependence in ADL (defined as impairment in personal care) followed by joint disease (30%), cardio-respiratory (18%), vision (15%) and obesity (11%). Subsequent studies suggest that chronic systemic diseases have been delayed and compressed over the decades since the Kilsyth Study was completed in 1970,3,9-11 and the morbidity of specific neurodegenerative disorders, such as dementia32,52 and PD,26,46 has risen exponentially with advanced ageing of the population. The only known study in the subsequent two decades comparing prevalence and disability data in older people, for a range of common chronic systemic disorders with neurodegenerative disorders, is the Sydney Older Persons Study (1991 to 2002). This longitudinal study comprised two

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Figure 3. The age distribution, by gender, in the Sydney Older Persons Study, Wave 1 (Waite et al., 1997)42.

random samples of 647 community-living people aged 75 to 98 years, with equal numbers of men and women. 59,42,43,47,56,60-62 The study provides prevalence data, on the same neurological and systemic disorders as the Kilsyth Study, in 522 subjects who agreed to a detailed medical, neurological, psychometric and disability assessment by a physician experienced in geriatric medicine.42 The 392 survivors examined at Wave 2 of the study, three years later, provided incidence data. The numbers assessed were modest, but the prevalence and incidence of both systemic and neurodegenerative disorders at this age range is very high, enabling examination of the data to look at the concept of a new epidemiologic transition in terms of causes of morbidity in an ageing population. The six major chronic systemic disorders measured included five shown to be the causes of disability from the Kilsyth study (heart disease, stroke, respiratory disease, arthritis and obesity), with the addition of peripheral vascular disease. The six neurodegenerative disorders measured included dementia, Parkinson’s disease, visual impairment, and a disorder of gait and balance, which had been identified as significant neurodegenerative causes of disability in the Kilsyth Study. Gait and balance disorder was further divided into two clinical components measured as motor slowing (gait slowing in subjects not reaching standard criteria for Parkinson’s disease) and gait ataxia (impaired heel-toe gait performance). Mild cognitive impairment (in subjects not reaching standard criteria for dementia) was included as the sixth neurodegenerative disorder measured. The data presented in Figures 4 and 5 are given in three- year age bands using smoothed estimates from a logistic regression model.

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Figure 4. The prevalence of chronic systemic diseases, in three year age bands combined for males and females, in the Sydney Older Persons Study, Wave 1 using smoothed estimates from a logistic regression model (Waite et al., 1997)42. (N=522; *p <0.05; ** p<0.01.)

The prevalence data for the chronic systemic diseases were examined for men and women in 20 percentile age bands between 75 and 84 years.42 The data showed the expected high rates of systemic disease in this very old population, ranging from 70% for arthritis and 46% for heart disease to 20% for chronic lung disease, 16% for stroke, 14% for obesity and 11% for peripheral vascular disease. The expected male-to-female differences in prevalence were observed, particularly in chronic lung disease (29% to 12%), peripheral vascular disease (14% to 8%) and obesity (8% to 20%). However the systemic diseases did not increase in prevalence with advancing age between 75 and 90 years, most showing a trend to decline which was significant only for chronic lung disease. The three-year incidence data confirmed this picture and showed the same trend towards static or declining incidence for the systemic diseases with advancing age over 75 years. The neurodegenerative disorders, examined in the same 20 percentile age bands,42 show similarly high overall prevalence rates, however without marked gender differences. These ranged from a prevalence of 50% for gait ataxia and 43% for visual impairment to 38% for cognitive impairment, 19% for gait slowing, 17% for dementia and 5% for Parkinson’s disease. All six neurodegenerative disorders examined showed a marked and highly significant increase in prevalence with advancing age from 75 to 93 years.

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Figure 5. The prevalence of neurodegenerative disorders, in three year age bands combined for males and females, in the Sydney Older Persons Study, Wave 1 using smoothed estimates from a logistic regression model (Waite et al., 1997)42. (N=522; *p <0.05; ** p<0.01.)

The three-year incidence data confirmed this picture, showing rapidly increasing incidence with advancing age over 75 years for all six neurodegenerative disorders. A detailed analysis of disability data was carried out in terms of personal care (ADL), domestic care (IADL) and mobility for all clinical diagnoses using the Kilsyth Disability Scale;57 disability rates were compared between the systemic and neurodegenerative disorders.56 Neurodegenerative disorders were prominent contributors to all three areas of disability; they were the major contributors to both ADL and IADL disability, as measured by the Kilsyth Disability Scale, given independently to both the subjects and to their informants. In contrast to the Kilsyth Disability Scale the traditional ABS disability scale completed by the subject identified arthritis as having a large impact on disability but was not sensitive to the impact of the neurodegenerative disorders. Using the Kilsyth Instrument,57 disability was estimated for each of the disorders identified. A proportion of disability was then attributed to either the group of six systemic degenerative disorders or the group of six neurodegenerative disorders for two waves of the Sydney Older Persons Study. As shown in Figure 6, around 70% of all disability was attributable to the neurodegenerative disorders. Based on this review including the limited morbidity data available, together with the previously discussed mortality, life expectancy and survival data, dis-

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Figure 6. Proportions of disablity attributable to neurodegenerative and systemic disorders in in the Sydney Older Persons Study, Waves and 2 (Waite et al., 1997)56.

ability due to the chronic systemic diseases is being reduced or delayed, as predicted by Fries10 and Olshansky.3 However systemic disease related disability is being replaced by a new wave of disability due to neurodegenerative disorders, which are increasing exponentially in the most rapidly growing sector of the population, the “old-old”, forming a new disease transition. A Fifth Epidemiologic Transition: The Age of Neurodegenerative Disorders Rapid population ageing is now a worldwide phenomenon. The trend to “ageing of the aged”, with the biggest increases in the population occurring in those 80 years and over, is most immediately apparent and relevant to the countries of the European Union, Australia, the USA and Japan and, a little further down the track, to the rapidly ageing populations of Eastern Europe and Southeast Asia. In the developed world, a majority of those who survive to 80 years and over will go on to develop neurodegenerative diseases before death. In contrast, while the concept of a “delayed transition” in poorer countries is of great global importance for other reasons,7 age-related neurodegenerative disorders are not likely to be one of the important issues for these societies in the near future.

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The conclusion, from this review of the literature and comparison of mortality, morbidity and disability data and trends, is that the late-onset neurodegenerative disorders will cause increasing morbidity towards the end of the increasing human life span in developed countries. Further they are gradually replacing the fatal systemic diseases, such as heart disease, stroke and respiratory disease, and ultimately cancer, as the major causes of death. This process parallels the exponential increase in the numbers of those surviving into their 80s and 90s. This is the age at which the neurodegenerative diseases show their greatest increase in prevalence and incidence, and the age at which the chronic systemic diseases show static or falling rates, in part due to the success of current public health measures. While a vast array of social, economic, environmental and demographic factors determine the course of population change, it is clear that population ageing and human lifespan are closely related to risk and preventive factors for disease. The theory of Epidemiologic Transition, as outlined here, is an attempt to explain the origin and complexity of the changing mortality and morbidity patterns towards neurodegenerative diseases in an ageing population, in order to emphasise the enormous impact these diseases will have on future public health programs and on the health care industry, including acute hospital care and community health services, in the coming decades. The success of public health measures over the last 50 years, with changes in risk factors such as smoking, diet, exercise and lifestyle factors, has indeed lead to a delayed onset of systemic degenerative diseases and a probable “compression of morbidity” from those diseases towards the end of life, as Fries predicted in 1980.10 However there is as yet no evidence that attention to risk factors identified for systemic degenerative diseases will reduce the incidence, or delay the onset, of the primary neuronal disorders underlying such neurodegenerative diseases as AD, PD, DLB or MND. The neuronal systems involved in these disorders do not appear, on current evidence, to be susceptible to the same life style or risk factors that devastate other body systems. With further increases in human life span, it remains possible, and indeed likely, that morbidity will simply be delayed to later in the life span, i.e. from the “young-old” to the “old-old” or from the 70s to the 80s and 90s, rather than compressed; unless new risk factors to delay the primary neuronal disorders are identified. The important question for ageing research is not only “is this degenerative process ageing or disease?” but, more significantly, “can this degenerative process be modified, prevented or delayed, without significant risk, by manipulation of environmental and/or genetic risk factors?”. The aim of ageing research remains one of compression of morbidity, in this case from the neurodegenerative disorders, towards the end of life and the prolongation of the period of healthy non-disabled life. This aim may, or may not, be consonant with increased longevity. The challenge facing ageing research is to seek new and modifiable risk factors to delay the onset of neurodegenerative disorders which are reducing quality of life in advanced old age. While the

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study of genetic risk factors has dominated recent research publication in AD, there remains good evidence for associated environmental factors, either causal, or as modifiers of timing of disease onset or rate of progression, both of which mechanisms might alter prevalence or morbidity.36,63,64 There is recent evidence that the commonest neurodegenerative disorder, AD, may decline in incidence with advancing age over 90 years35,36 and factors such as educational level and brain size may modify the onset of AD65–68. There are major current research endeavours examining potential genetic and environmental modifiers for the progression of AD.63 Finally, both Omran2 and Olshansky3 were inaccurate in their prediction that we had come to the end of the Epidemiologic Transition, in 1971 and 1986, respectively. There is good reason to believe that we will have yet another transition, that of delayed neurodegenerative disorders, early in the 21st century. Acknowledgements This work has been supported by a series of National Health and Medical Research Council Grants over the past decade and by Grants from the Department of Veteran Affairs, the Ageing and Alzheimer’s Research Foundation and the Centre for Education and Research on Ageing of the University of Sydney and by the resources of the Prince of Wales Medical Research Institute. I am indebted to the support of Professor Francis Caird for the work carried out in the Kilsyth Study (1970-71). I am also indebted to my PhD students for much of the work carried out in the Sydney Older Persons Study (1982-2002), including Dr Louise Waite, Dr Olivier Piguet, Dr Hayley Bennett, Dr Wayne Reid and Dr Tanya Lye; to my colleagues in that study including Associate Professor Dave Grayson, Dr Helen Creasey, Dr William Brooks, Dr Glenda Halliday and Dr Jillian Kril; and to the administrative support for that study from Sandra Forster, Jill Groth and Jan Koh. References 1. 2. 3. 4. 5. 6. 7.

Eberstadt N. The population implosion. Foreign Policy. 2001; 123:42–53. Omran AR. The epidemiologic transition. A theory of the epidemiology of population change. Milbank Q. 1971; 49:509–538. Olshansky SJ, Ault AB. The fourth stage of the epidemiologic transition: The age of the delayed degenerative diseases. Milbank Q. 1986; 64:355–391. Broe GA, Creasey H. Brain ageing and neurodegenerative disease: A major public health issue of the the twenty first century. Perspect Hum Biol. 1995; 1:53–58. Lilienfeld DE, Perl DP. Projected neurodegenerative disease mortality in the United States, 1990-2040. Neuroepidemiology. 1993; 12:219–228. Laslett P. The significance of the past in the study of ageing. Ageing Soc. 1984; 4:379–389. Phillips DR. Problems and potential of researching epidemiological transition: examples from Southeast Asia. Soc Sci Med. 1991; 33:395–404.

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8. Fries JF. Can preventive gerontology be on the way? Am J Public Health. 1997; 87:1591–1593. 9. Olshansky SJ, Carnes BA, Cassel C. In search of Methuselah: estimating the upper limits to human longevity. Science. 1990; 250:634–640. 10. Fries JF. Aging, natural death, and the compression of morbidity. N Engl J Med. 1980; 303:130–135. 11. Vita AJ, Terry RB, Hubert HB, Fries JF. Aging, health risks, and cumulative disability. N Engl J Med. 1998; 338:1035–1041. 12. Laslett P. Interpreting the demographic changes. Phil Trans R Soc Lond B Biol Sci. 1997; 352:1805–1809. 13. Vaupel JW, Carey JR, Christensen K, Johnson TE, Yashin AI, Holm NV, Iachine IA, Kannisto V, Khazaeli AA, Liedo P, Longo VD, Zeng Y, Manton KG, Curtsinger JW. Biodemographic trajectories of longevity. Science. 1998; 280: 855–860. 14. Kaplan H, Hill K, Lancaster J, Hurtado AM. A theory of human life history evolution: Diet, intelligence, and longevity. Evol Anthropol. 2000; 9:156–185. 15. Rapoport SI. How did the human brain evolve? A proposal based on new evidence from in vivo brain imaging during attention and ideation. Brain Res Bull. 1999; 50:149–165. 16. Schaie KW. Intellectual development in adulthood. In Birren JE, Schaie KW, editors. Handbook of the psychology of aging. San Diego: Academic Press, 1996: 266–286. 17. Joseph KS, Kramer MS. Review of the evidence on fetal and early childhood antecedents of adult chronic disease. Epidemiol Rev. 1996; 18:158–174. 18. Smith GD, Hart C, Blane D, Hole D. Adverse socioeconomic conditions in childhood and cause specific adult mortality: prospective observational study. Brit Med J. 1998; 316:1631–1635. 19. Doornbos G, Kromhout D. Educational level and mortality in a 32-year followup study of 18-year-old men in The Netherlands. Int J Epidemiol. 1990; 19: 374–379. 20. Kunst AE, Mackenbach JP. The size of mortality differences associated with educational level in nine industrialized countries. Am J Public Health. 1994; 84: 932–937. 21. Neisser U, Boodoo G, Bouchard TJJ, Boykin AW, Brody N, Ceci SJ, Halpern DF, Loehlin JC, Perloff R, Sternberg RJ, Urbina S. Intelligence: Knowns and unknowns. Am Psychol. 1996; 51:77–101. 22. Whalley LJ, Deary IJ. Longitudinal cohort study of childhood IQ and survival up to age 76. Brit Med J. 2001; 322:819. 23. O’Toole BI, Stankov L. Ultimate validity of psychological tests. Pers Indiv Differ. 1992; 13:699–716. 24. Korten AE, Jorm AF, Jiao Z, Letenneur L, Jacomb PA, Henderson AS, Christensen H, Rodgers B. Health, cognitive, and psychosocial factors as predictors of mortality in an elderly community sample. J Epidemiol Commun H. 1999; 53: 83–88. 25. Deeg DJ, Hofman A, van Zonneveld RJ. The association between change in cognitive function and longevity in Dutch elderly. Am J Epidemiol. 1990; 132: 973–982. 26. Tanner CM, Aston DA. Epidemiology of Parkinson’s disease and akinetic syndromes. Curr Opin Neurol. 2000; 13:427–430. 27. Finch CE, Sapolsky RM. The evolution of Alzheimer disease, the reproductive schedule, and apoE isoforms. Neurobiol Aging. 1999; 20:407–428. 28. Beaglehole R. International trends in coronary heart disease mortality, morbidity, and risk factors. Epidemiol Rev. 1990; 12:1–15.

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29. Australian Bureau of Statistics. 1989–1990 national health survey. Health status indicators. Catalogue No.4370.0. Canberra: ABS, 1992. 30. d’Espaignet ET, van Ommeren M, Taylor F, Briscoe N, Pentony P. Trends in Australian mortality: 1921–1988. Canberra: Australian Institute of Health and Welfare, 1991. 31. Baldereschi M, Di Carlo A, Maggi S, Grigoletto F, Scarlato G, Amaducci L, Inzitari D. Dementia is a major predictor of death among the Italian elderly. ILSA Working Group. Italian Longitudinal Study on Aging. Neurology. 1999; 52: 709–713. 32. Jorm AF, Korten AE, Henderson AS. The prevalence of dementia: a quantitative integration of the literature. Acta Psychiat Scand. 1987; 76:465–479. 33. Jorm AF, Jolley D. The incidence of dementia: a meta-analysis. Neurology. 1998; 51:728–733. 34. Gao S, Hendrie HC, Hall KS, Hui S. The relationships between age, sex, and the incidence of dementia and Alzheimer disease: a meta-analysis. Arch Gen Psychiat.1998; 55:809–815. 35. Andersen K, Launer LJ, Dewey ME, Letenneur L, Ott A, Copeland JRM, Dartigues JF, Kragh-Sorensen P, Baldereschi M, Brayne C, Lobo A, Martinez-Lage JM, Stijnen T, Hofman A. Gender differences in the incidence of AD and vascular dementia: The EURODEM Studies. The EURODEM Incidence Research Group. Neurology. 1999; 53:1992–7. 36. Miech RA, Breitner JC, Zandi PP, Khachaturian AS, Anthony JC, Mayer L. Incidence of AD may decline in the early 90s for men, later for women: The Cache County study. Neurology. 2002; 58:209–218. 37. Ross GW, Abbott RD, Petrovitch H, Masaki KH, Murdaugh C, Trockman C, Curb JD, White LR. Frequency and characteristics of silent dementia among elderly Japanese-American men. The Honolulu-Asia Aging Study. J Am Med Assoc. 1997; 277:800–805. 38. Callahan CM, Hendrie HC, Tierney WM. Documentation and evaluation of cognitive impairment in elderly primary care patients. Ann Intern Med. 1995; 122: 422–429. 39. Sorensen L, Foldspang A, Gulmann NC, Munk-Jorgensen P. Assessment of dementia in nursing home residents by nurses and assistants: criteria validity and determinants. Int J Geriatr Psych. 2001; 16:615–621. 40. Broe GA, Akhtar AJ, Andrews GR, Caird FI, Gilmore AJ, McLennan WJ. Neurological disorders in the elderly at home. J Neurol Neurosurg Psychiat. 1976; 39: 361–366. 41. Brayne C, Gill C, Huppert FA, Barkley C, Gehlhaar E, Girling DM, O’Connor DW, Paykel ES. Incidence of clinically diagnosed subtypes of dementia in an elderly population. Cambridge Project for Later Life. Brit J Psychiat,. 1995; 167: 255–262. 42. Waite LM, Broe GA, Creasey H, Grayson DA, Cullen JS, O’Toole B, Edelbrock D, Dobson M. Neurodegenerative and other chronic disorders among people aged 75 years and over in the community. Med J Australia. 1997; 167:429–432. 43. Waite LM, Broe GA, Grayson DA, Creasey H. The incidence of dementia in an Australian community population: the Sydney Older Persons Study. Int J Geriatr Psych. 2001; 16:680–689. 44. Broe GA. Parkinson’s disease and related disorders. In: Grimley Evans J, editor. Oxford texbook in geriatric medicine. Oxford: Oxford University Press, 1992: 546–557. 45. Koller W, O’Hara R, Weiner W, Lang A, Nutt J, Agid Y, Bonnet AM, Jankovic J. Relationship of aging to Parkinson’s disease. In: Yahr MD, Bergmann KJ, editors. Parkinson’s disease. New York: Raven Press, 1986: 317–321.

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46. Baldereschi M, Di Carlo A, Rocca WA, Vanni P, Maggi S, Perissinotto E, Grigoletto F, Amaducci L, Inzitari D. Parkinson’s disease and parkinsonism in a longitudinal study: two-fold higher incidence in men. ILSA Working Group. Italian Longitudinal Study on Aging. Neurology. 2000; 55:1358–1363. 47. Waite LM, Broe GA, Grayson DA, Creasey H. Preclinical syndromes predict dementia: the Sydney older persons study. J Neurol Neurosur Ps. 2001; 71: 296–302. 48. Dewey ME, Saz P. Dementia, cognitive impairment and mortality in persons aged 65 and over living in the community: a systematic review of the literature. Int J Geriatr Psych. 2001; 16:751–761. 49. Lilienfeld DE, Chan E, Ehland J, Godbold J, Landrigan PJ, Marsh G, Perl DP. Two decades of increasing mortality from Parkinson’s disease among the US elderly. Arch Neurol. 1990; 47:731–734. 50. Rajput AH, Offord KP, Beard CM, Kurland LT. Epidemiology of parkinsonism: incidence, classification, and mortality. Ann Neurol. 1984; 16:278–282. 51. Lilienfeld DE, Chan E, Ehland J, Godbold J, Landrigan PJ, Marsh G, Perl DP. Rising mortality from motoneuron disease in the USA, 1962-84. Lancet. 1989; 1:710–713. 52. Witthaus E, Ott A, Barendregt JJ, Breteler M, Bonneux L. Burden of mortality and morbidity from dementia. Alzheimer Dis Assoc Disord. 1999; 13:176–181. 53. Manton KG, Stallard E, Corder L. Education-specific estimates of life expectancy and age-specific disability in the U.S. elderly population: 1982 to 1991. J Aging Health. 1997; 9:419–450. 54. Manton KG, Corder L, Stallard E. Chronic disability trends in elderly United States populations: 1982-1994. Proc Nat Acad Sci USA. 1997; 94:2593–2598. 55. Ostbye T, Tyas S, McDowell I, Koval J. Reported activities of daily living: agreement between elderly subjects with and without dementia and their caregivers. Age Ageing. 1997; 26:99–106. 56. Waite LM, Broe GA, Grayson DA, Creasey H, Cullen JS, Casey B, Bennett HP, Brooks WS. Clinical diagnoses and disability among community dwellers aged 75 and over. Australas J Ageing. 2001; 20:67–72. 57. Broe GA, Akhtar AJ. Assessment of activities of daily living in the elderly. In: Israel L, Kozarevic D, Sartorius N, editors. Book of geriatric assessment. Basel: Karge,r 1984: 241–242. 58. Akhtar AJ, Broe GA, Crombie A, McLean WM, Andrews GR, Caird FI. Disability and dependence in the elderly at home. Age Ageing. 1973; 2:102–111. 59. Broe GA, Jorm AF, Creasey H, Casey B, Bennett H, Cullen J, Edelbrock D, Waite L, Grayson D. Carer distress in the general population: results from the Sydney Older Persons Study. Age Ageing. 1999; 28:307–311. 60. Broe GA, Creasey H, Jorm AF, Bennett HP, Casey B, Waite LM, Grayson DA, Cullen J. Health habits and risk of cognitive impairment and dementia in old age: a prospective study on the effects of exercise, smoking and alcohol consumption. Aust NZ J Publ Health. 1998; 22:621–623. 61. Creasey H, Waite LM, Grayson DA, Bennett HP, Dent O, Broe GA. The impact of the neurodegenerative disorders on ageing: An overview of the Sydney Older Persons Study. Australas J Ageing. 2001; 20:11–16. 62. Waite LM, Broe GA, Creasey H, Grayson D, Edelbrock D, O’Toole B. Neurological signs, aging, and the neurodegenerative syndromes. Arch Neurol. 1996; 53: 498–502. 63. Breitner JC. The end of Alzheimer’s disease? Int J Geriatr Psych. 1999; 14: 577–586. 64. Amaducci L, Lippi A. Risk factors for Alzheimer’s disease. Editorial. Int J Geriatr Psych. 1997; 7:383–388.

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65. Graves AB, Mortimer JA, Larson EB, Wenzlow A, Bowen JD, McCormick WC. Head circumference as a measure of cognitive reserve. Association with severity of impairment in Alzheimer’s disease. Brit J Psychiat. 1996; 169:86–92. 66. Snowdon DA, Kemper SJ, Mortimer JA, Greiner LH, Wekstein DR, Markesbery WR. Linguistic ability in early life and cognitive function and Alzheimer’s disease in late life. Findings from the Nun Study. J Am Med Assoc. 1996; 275: 528–532. 67. Snowdon DA. Aging and Alzheimer’s disease: lessons from the Nun Study. Gerontologist. 1997; 37:150–156. 68. Jorm AF, Creasey H, Broe GA, Sulway MR, Kos SC, Dent OF. The advantage of being broad-minded: Brain diameter and neuropsychological test performance in elderly war veterans. Pers Indiv Differ. 1997; 23:371–3777.

SECTION II CHARACTERISTICS OF THE AGEING BRAIN

Chapter 3 STRUCTURAL CHANGES IN THE AGEING HUMAN BRAIN Jillian J. Kril

Introduction The link between ageing and disease in the brain is strong and in many instances the increased prevalence of brain disease in the elderly is taken as evidence that the disease is as a result of increasing age. Indeed, it has been suggested that there is a continuum between age-related pathology and diseases such as Alzheimer’s disease (AD), and that a decline in brain function and an increase in brain pathology are inevitable consequences of ageing. However, these assertions are, in many cases, unfounded and there is increasing evidence that brain pathology is not synonymous with brain ageing. There are a number of obstacles to being able to definitively separate ageing from disease. Some of these can be overcome with methodological improvements to research studies or with advances in technology, while others are inherent to the study of human disease. In addition, there are a number of theoretical issues to be considered when discussing the concept of brain ageing. 1. There is convincing evidence of a long “prodromal” or “preclinical” period for diseases such as AD and, unless there is rigorous clinical and pathological scrutiny of subjects for inclusion in studies of ageing, the results of these studies will be biased towards the finding of an age-related decline. Carefully controlled studies have demonstrated both functional and pathological deficits in individuals who do not meet diagnostic criteria for AD. For example, individuals who go on to develop dementia

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may have a long period with stable deficits which precedes the disease diagnosis.1,2 In addition, it has been shown that there is marked neuronal loss from some brain region in patients with equivocal or early disease (i.e. Clinical Dementia Rating = 0.53)4 suggesting that neuronal loss predates the onset of symptoms. Taken together, these studies highlight the need for: (i) the continual refinement and improvement of diagnostic tools to identify early disease not only for dementia but for other diseases common in the elderly, and (ii) post-mortem verification of cases used in functional studies of ageing and disease to confirm diagnosis, exclude co-existing diagnoses (e.g. other causes of dementia), and perform clinicopathological correlations. 2. Increasing age is a risk factor for most neurodegenerative diseases as well as many systemic disorders which also affect brain function. While this does not mean that age and disease are inseparable, it does pose the question of what is meant by “normal”. On one hand, normal may be interpreted as meaning representative of most of the population, while in another sense it may mean free from abnormality. In the case of brain ageing studies, if these are performed on subjects which have been highly selected to exclude the presence of all pathology, the results obtained may be artificial in that they represent a small subset of aged subjects rather than being indicative of the majority of older people. Conversely, if an unselected group of subjects is studied, one runs the risk of falsely attributing the consequences of some common, age-related disease to ageing per se. Mrak et al.5 state that to be regarded as being due to the ageing process, changes must be both universal and intrinsic. While both these requirements appear plausible, it is difficult to see, given the enormous genetic variability in humans, how universal can be assessed. For example, there is evidence that the relationship between increasing age and increasing disease does not hold for those over 95 years (the oldest old). A number of studies have demonstrated that these subjects are healthier than many people in their eighties6 suggesting resistance or delayed sensitivity in some individuals. Other confounding factors such as differential survival rates will also have an impact on the results of studies on ageing. Such constraints must be considered in the design and interpretation of any study of ageing. 3. A true longitudinal pathological study cannot be performed. In rare cases the brains of individuals are sampled on more than one occasion,7–10 and while these offer the advantage of investigations in the same individual over time, exactly the same tissue cannot be evaluated. In addition, there are potential confounding issues associated with the patient having undergone a surgical procedure and the tissue reactions to this. Advances in technology, especially neuroimaging, have allowed for better identification of brain structures during life, but their resolution is still at the millimeter level, which does not allow for accurate identification of cellular populations in most cases, and few studies have been performed following subjects to autopsy. This mean that the majority of pathological studies

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are restricted to group comparisons and are therefore subject to the limitations such cohort studies impose. Brain Atrophy A decrease in brain size with increasing age is widely accepted in medicine.11,12 It is not uncommon for radiological or pathological reports to dismiss the presence of cerebral atrophy as “consistent with the subject’s age”, a practice which has perpetuated the concept of inevitable senescence. While there is good evidence that the brain size of older individuals is, on average, smaller than that of younger individuals, this represents a cohort effect rather than true atrophy. Several studies have demonstrated a steady increase in brain size for both men and women during the past century.11,13,14 Dekaban and Sadowsky13 reviewed the brain weights of over 4,500 persons of all ages who were free from neurological disease and showed an 11% difference between young and old adults. In comparison with similarly derived data, they also showed that the mean brain weight of men and women aged 20–30 years increased by 109 g (7.5%) and 70 g (5.4%) respectively between the 1890s and mid twentieth century. A similar but smaller increase was also seen in the 70–80 year old groups (4% and 3.5%). Another study14 found an annual increase in brain weight of 0.66 g in men and 0.28 g in women over the period between 1860 and 1940. These studies demonstrate that the majority of the difference in brain weight that is attributed to ageing is due to cohort differences in the populations studied. It is believed that this increase in brain (and body) size is due to improvements in public health and nutrition that have occurred during the past century. Greater than the difference in brain weight between younger and older adults is the difference between males and females. Men, on average, have a mean brain weight approximately 100 g greater than women.11,13,14 Differences in body height accounts for most, but not all, of this difference as brain weight to body height ratios are not identical for the genders.13 The reasons for this gender difference are not clear, but it suggests that data from males and females should be analysed separately in studies of ageing. Numerous radiological investigations of brain atrophy have been performed. In cross-sectional studies, atrophy has been reported of the whole brain15,16 as well as specific anatomical regions.17-24 However these findings have not been supported by longitudinal studies.25-27 In a study of 46 cognitively normal subjects aged 65 and over and followed for between three and eight years, Mueller et al.26 found minimal increase in ventricular and CSF volumes and decrease in hippocampus, but no change in other brain regions examined. The rates of decline were similar for the “young old” (mean age at commencement of study 70 years) compared to “middle old” (mean age 81 years) and “oldest old” (mean age 87 years) and do not support the concept of accelerated atrophy in old age. Similarly, Akiyama et al.27 found a small

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annual increase in the volume of the ventricles and subarachnoid space after the age of 60 years. Few quantitative pathological studies of brain volume in normal subjects have been performed. Miller et al28 reported a decline in both cortical and white matter volumes after the age of 50 years. After correction for the secular increase in brain size, the authors found a decline of approximately 2% per decade. Similarly, a small but significant loss of white matter (approximately 2 mL/y or 1.3 – 2.2% of cerebrum volume per decade) but no loss of cerebral cortex volume was demonstrated in subjects aged 46 to 92 years.29 While the analysis of the cortex in its entirety or by lobe, rather than discrete anatomical regions, may mask subtle decreases in volume, the overall conclusion from these studies is that marked atrophy does not accompany advancing age. A variety of factors, other than age, have been shown to accelerate brain atrophy. Chronic alcohol abuse results in a loss of cerebral white matter30,31 that is partially reversible on abstinence from alcohol. Both cerebral atrophy and perfusion have been shown to be influenced by transient ischaemic attacks, hyperlipidaemia, hypertension, smoking, excessive alcohol consumption and male gender,27 suggesting that much of what is interpreted as agerelated atrophy is indeed due to co-existing risk factors. Neuronal Loss As with brain shrinkage, the loss of neurons during ageing is a controversial issue. Neurons do not regenerate following injury and the exact number of neurons each person has is not known. Thus is it not possible at death to accurately determine the degree of neuron loss from any individual. Rather it is inferred from comparisons with other populations (e.g. younger subjects or those without disease) or by mathematical extrapolation. Furthermore, neuronal loss can result from a number of conditions including hypoxia,32 alcohol abuse33,34 and trauma35 and it is essential to exclude such subjects from studies of ageing. Several older studies described a marked loss of neurons from the elderly brain36–39 which was as much as 40% in specific functional regions such as the frontal cortex.37 In recent years, the methodology employed in these studies has been criticised. In many instances, neuron counts were performed using tissue samples which had been processed in paraffin or celloidin, which causes marked shrinkage. In addition, neuron density was measured, which does not account for any volume loss of the structure. Newer, unbiased quantitative techniques have been developed which estimate total neuron number40,41 and therefore allow more accurate determination of neuronal loss. To date only one study that rigorously applied these procedures in the cerebral cortex of normal subjects over a broad spectrum of ages has been published.42 Interestingly, as with brain size, a difference in neuron number

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with gender which is larger (16%) than any age-effect (10%), and not as a result of differences in body height, was observed42. The authors quantified 94 brains that were screened to exclude pathological abnormalities, and found a decrease in total neuron numbers over the range of 20 and 90 years. However, as this study was performed on the entire cerebral cortex, information concerning age effects on specific functional regions of the cortex could not be derived. Other studies have examined discrete anatomical regions in a restricted spectrum of ages. Both the superior temporal43 and entorhinal4 cortices appear to have preserved neuronal content between the sixth and ninth decades. The latter region is of particular interest as it undergoes early and profound neurodegeneration in AD4 and, along with other parts of the limbic system, might be expected to show some decline. Several studies have examined the hippocampal formation in ageing, although some of these show conflicting results. Neuronal loss from the subiculum,44,45 CA445 and CA146,47 subregions has been described, although other studies have shown no loss of CA1 neurons with age.44,45,48 Harding et al.48 performed a multiple regression analysis and showed that 69% of the variance in CA1 number is due to brain size while only 2% is due to age. This study emphasises the difficulties with cross-sectional studies and reinforces the need to examine other variables in addition to age. An age-related decline in dendrites and synapses may well be more informative functionally than a loss of neurons. Structural remodelling (plasticity) of these is believed to occur throughout life, although it has been shown that this capacity declines in the oldest old.49 In addition, failure of this adaptive response has been suggested to underlie neurodegenerative diseases.5 Functional imaging of the ageing brain has yielded conflicting results. Both global50 and localised51 decline in cerebral perfusion with age has been reported in some studies, while others52 find no decline with age once differences in brain size are corrected for. β-Amyloid (Aβ) Accumulation The accumulation of Aβ plaques is one of the hallmarks of AD. However the high prevalence and density of plaques in many non-demented elderly subjects is one of the principal factors used to support the ageing and disease continuum hypothesis. In the cross-sectional autopsy study of Braak and Braak,53 the proportion of subjects with Aβ plaque deposition ranged from less than 3% in those 36–40 years to over 75% in those older than 85 years. These frequencies are based on the examination of a large collection of brains (N=2,661) from which few exclusions were made (e.g. rare dementing disorders such as progressive supranuclear palsy and corticobasal degeneration). In addition to the increase in frequency of cases showing plaque deposition, the proportion which shows the widespread distribution and high density of plaques usually found in AD (i.e. stage C54) also increases with age. Only

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three cases below 56 years (0.5%) had stage C plaques, while 30% of cases above 85 years reached this stage.53 However, epidemiological studies have found that only around 20% of people over 85 years have dementia55 suggesting that the presence of Aβ plaques is not synonymous with AD. This suggestion is supported by a number of carefully conducted, prospective studies examining the mismatch between AD-type pathology and cognitive impairment. Davis et al.56 found that of 59 elderly subjects (mean age 84 years) without cognitive impairment between 12% and 50% met neuropathological criteria for AD depending on which criterion was used. Other cross-sectional57–59 and longitudinal60,61 studies have shown similar findings. Overall it appears that while Aβ plaque formation is common, it is not inevitable in the ageing brain and, when present, is not necessarily associated with clinical dementia. Part of the controversy associated with the significance of Aβ plaques during ageing may stem from the observation that there are several subtypes of plaque deposits. Four subtypes of plaques have been described.62 Originally it was thought that these represented stages in the progression of plaque formation, but more recent studies have suggested that diffuse plaques may not necessarily progress into neuritic plaques and that neuritic plaques may develop without evidence of diffuse plaque pathology.63 In a study of 402 subjects aged 30 to 92 years and free from dementia or other neurological disease, Mackenzie64 showed that while the proportion of subjects with diffuse plaques increased steeply with age, neither the mean nor the maximum density of plaques increased. This finding suggests that diffuse plaques do not progressively accumulate in normal ageing, rather that once these plaques have developed their number remains constant. An increased proportion of neuritic plaques was found to correlate with age.64 As it is believed that neuritic plaques rather than diffuse plaques play a role in the pathogenesis of AD, neuropathological protocols should be modified to incorporate this to allow the differentiation of normal from pathological ageing. Neurofibrillary Tangle Accumulation Neurofibrillary tangles (NFTs) are intracellular accumulations of the microtubule-associated protein tau. Tau is hyperphosphorylated and forms fibrils in the perikaryon, and later dendrites, of susceptible neurons.65 NFTs are ultimately lethal and as they are highly insoluble, remain in the neuropil as extracellular or ghost NFTs.66 The distribution of NFTs in the brain in AD is markedly different to that of Aβ plaques. NFTs form first in the transentorhinal region, then the entorhinal, hippocampus and finally the neocortex. This topographic spread of NFTs is the basis of the Braak staging system for AD pathology.54 In most cases, the clinical manifestation of dementia corresponds to NFT formation in the neocortex (stages V and VI). Like Aβ plaques, NFTs are also found

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in the brains of non-demented subjects and their prevalence is higher than that of Aβ plaques. In the youngest age group studied by Braak and Braak53 (26–30 years), 82% of subjects had NFTs. These were stages I or II, which correspond to infrequent NFTs in the transentorhinal and entorhinal cortices. By 91–95 years, all subjects have some NFT formation with 24% having grade V or VI (all of whom also had Aβ plaques). This proportion correlates well with the prevalence of dementia in this age group,55 which is one of the reasons why NFTs are considered a better indicator of AD than Aβ plaques. Other studies support these prevalence figures for NFTs.56, 67 The length of time taken for a NFT to form and to kill a neuron remains controversial. In one study, it was calculated that it took between three and five years for a mature NFT to become a ghost NFT, 66 but in another study it was found that neurons in the CA1 region of the hippocampus could survive between 15 and 25 years with NFTs. 68 These studies suggest that the onset of neurofibrillary degeneration may be many years before the onset of AD, and highlight the difficulties in differentiating normal from pathological ageing. The degree of neuron loss due to NFTs has been determined by quantifying extracellular NFTs. As extracellular NFTs are not readily degraded,66,69,70 they are a good marker of neurodegeneration due to NFTs. In AD, neuronal loss has been found to exceed NFT formation in the temporal43 and occipital71 cortices, and hippocampus.72 In contrast, NFT formation has been found to account for all neuronal loss from the nucleus basalis in AD,73 and from the hippocampus in Parkinson-dementia complex of Guam. 74,75 Similar studies are yet to be performed in ageing. Clinicopathological Studies The relationship between brain pathology and functional impairment is one of the greatest challenges facing the study of brain ageing. Studies published to date present conflicting points-of-view with regard to the role of pathology (especially AD-type pathology) in the causation of functional deficits. There are many variables which are likely to have an impact on the relationship. These include factors such as premorbid capacity (brain reserve), genetic susceptibility and co-existing disease, as well as practical issues such as the range and type of functional tests performed, the anatomical regions examined, and the type of pathology identified. In a study of 59 non-demented subjects, individuals with greater pathology (“AD-like” using NIA-RI criteria for AD76) had lower scores on specific tests of immediate and delayed recall than subjects without pathology.77 In addition, a strong negative correlation has been found between NFT number and mini-mental state examination (MMSE78) score.79 These, and other similar studies,80 suggest that the burden of AD-type pathology is closely linked with cognitive decline.

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In contrast, a prospective study of 101 subjects aged 75 years and over revealed a high degree of overlap in the frequency and severity of AD-type pathology in both demented and non-demented individuals.81 In addition, no difference on a composite score of neuropsychological tests was found between individual with and without AD-type pathology.82 Overall these studies highlight the controversy which still exists concerning the role of ADtype pathology in age-associated cognitive decline. Interestingly, the presence of other neurodegenerative pathologies also increases with age. Davis et al.56 found that only 17% of their subjects were free from any pathology. Similarly, Xuereb et al.81 showed pathology in a high proportion of their non-demented subjects including microinfarcts in one third of cases. Conclusions There is little evidence to suggest that major neurodegeneration occurs in relation to ageing. While minor shrinkage of the white matter has been demonstrated, generalised cortical atrophy has not been identified. In addition, widespread neuronal loss does not appear to be a feature of normal ageing, although to date only a small number of functionally-discrete brain regions have been examined using unbiased methodologies. Cohort differences in brain size and neuron number have contributed to a misinterpretation of group comparisons and an over-estimation of age-related decline. Neurofibrillary tangles and Aβ plaques are central to the pathological diagnosis of Alzheimer’s disease (AD), but they are also prevalent in the brains of non-demented elderly subjects. The functional consequences of this age-related accumulation of AD-type pathology is unclear. Controversies exist as to whether the presence of plaques and tangles in the brain represents the earliest manifestations of AD, and whether they are associated with a loss of neurons and of brain function. There is a clear need for well designed longitudinal studies of brain ageing to clarify the relationship between advancing age and neurodegeneration. Acknowledgements The author receives funding from the Medical Foundation of The University of Sydney and the National Health and Medical Research Council of Australia. She is a Medical Foundation Fellow.

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References 1. Rubin EH, Storandt M, Miller JP, Kinscherf DA, Grant EA, Morris JC, Berg L. A prospective study of cognitive function and onset of dementia in cognitively healthy elders. Arch Neurol. 1998; 55:395–401. 2. Small BJ, Fratiglioni L, Viitanen M, Winblad B, Backman L. The course of cognitive impairment in preclinical Alzheimer disease: three- and 6-year follow-up of a population-based sample. Arch Neurol. 2000; 57:839–844. 3. Morris JC, McKeel Jr. DW, Storandt M, Rubin EH, Price JL, Grant EA, Ball MJ, Berg L. Very mild Alzheimer’s disease: informant-based clinical, psychometric and pathologic distinction from normal aging. Neurology. 1991; 41:469–478. 4. Gomez-Isla T, Price JL, McKeel Jr DW, Morris JC, Growdon JH, Hyman BT. Profound loss of layer II entorhinal cortex neurons occurs in very mild Alzheimer’s Disease. J Neurosci. 1996; 16:4491–4500. 5. Mrak RE, Griffin WST, Graham DI. Aging-associated changes in human brain. J Neuropathol Exp Neurol. 1997; 56:1269–1275. 6. Perls TT. The oldest old. Sci Am. 1995; 272:50–55. 7. Di Patre PL, Read SL, Cummings JL, Tomiyasa U, Vartavarian LM, Secor DL, Vinters HL. Progression of clinical deterioration and pathological changes in patients with Alzheimer disease evaluated at biopsy and autopsy. Arch Neurol. 1999; 56:1254–1261. 8. Bennett DA, Cochran EJ, Saper CB, Leverenz JB, Gilley DW, Wilson RS. Pathological changes in frontal cortex from biopsy to autopsy in Alzheimer’s disease. Neurobiol Aging. 1993; 14:589–596. 9. Mann DMA, Marcyniuk B, Yates PO, Neary D, Snowden JS. The progression of the pathological changes of Alzheimer’s disease in frontal and temporal neocortex examined both at biopsy and autopsy. Neuropathol Appl Neurobiol. 1988; 14: 177–195. 10. Martin EM, Wilson RS, Penn RD, Fox JH, Clasen RA, Savoy SM. Cortical biopsy results in Alzheimer’s disease: correlation with cognitive deficits. Neurology. 1987; 37:1201–1204. 11. Samorajski T. How the human brain responds to aging. J Am Geriatr Soc. 1976; 24:4–11. 12. Ho K, Roessmann U, Straunfjord JV, Monroe G. Analysis of brain weight. I. Adult brain weight in relation to sex, race, and age. Arch Path Lab Med. 1980; 104:635–639. 13. Dekaban AS, Sadowsky D. Changes in brain weights during the span of human life: relation of brain weights to body heights and body weights. Ann Neurol. 1978; 4:345–356. 14. Miller AKH, Corsellis JAN. Evidence for a secular increase in human brain weight during the past century. Ann Human Biol. 1977; 4:253–257. 15. Yue NC, Arnold AM, Longstreth Jr. WT, Elster AD, Jungreis CA, O’Leary DH, Poirier VC, Bryan RN. Sulcal, Ventricular and white matter changes at MR imaging in the aging brain: data from the cardiovascular health study. Radiology. 1997; 202:33–39. 16. Murphy DG, de Carli C, Schapiro MB, Rapoport SI, Horwitz B. Age-related differences in volumes of subcortical nuclei, brain matter and cerebrospinal fluid in heathly men as measured with magnetic resonance imaging. Arch Neurol. 1992; 49:839–845. 17. Coffey CE, Wilkinson WE, Parashos IA, Soady SAR, Sullivan RJ, Patetrson LJ, Figiel GS, Webb MC, Spritzer CE, Djang WT. Quantitative cerebral anatomy of the aging human brain: A cross-sectional study using magnetic resonance imaging. Neurology. 1992; 42:527–536.

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18. Coffey CE. Anatomic imaging of the aging human brain: computered tomography and magnetic resonance imaging. In: Coffey CE, Cummings JL, editors. Textbook of Geriatric Neuropsychiatry. Washington, DC: American Psychiatric Press Inc, 1994; 159–194. 19. Coffey CE, Lucke JF, Saxton JA, Ratcliff G, Unitas LJ, Billig B, Bryan RN. Sex differences in brain aging: a quantitative magnetic resonance imaging study. Arch Neurol. 1998; 55:169–179. 20. Convit A, de Leon MJ, Tarshish C, de Santi S, Kluger A, Rusinek H, George AE. Hippocampal volume losses in minimally impaired elderly. Lancet. 1995; 345: 266. 21. Convit A, de Leon M, Hoptman MJ, Tarshish C, De Santi S, Rusinek H. Agerelated changes in brain: I. Magnetic resonance imaging measures of temporal lobe volumes in normal subjects. Psychiat Quart. 1995; 66:343–455. 22. Raz N, Torres IJ, Spencer WD, White K, Acker JD. Age-related regional differences in cerebellar vermis observed in vivo. Arch Neurol. 1992; 49:412–416. 23. Raz N, Gunning FM, Head D, Dupuis JH, McQuain J, Briggs SD, Loken WJ, Thornton AE, Acker JD. Selective aging of the human cerebral cortex observed in vivo: differential vulnerability of the prefrontal gray matter. Cereb Cortex. 1997; 7:268–282. 24. Raz N, Dupuis JH, Briggs SD, McGavran C, Acker JD. Differential effects of age and sex on the cerebellar hemispheres and the vermis: a prospective MR study. AJNR. 1998; 19:65–71. 25. Fox NC, Cousens S, Scahill R, Harvey RJ, Rossor MN. Using serial registered brain magnetic resonance imaging to measure disease progression in Alzheimer disease: power calculations and estimates of sample size to detect treatment effects. Arch Neurol. 2000; 57:339–344. 26. Mueller EA, Moore MM, Kerr DC, Sexton G, Camicioli RM, Howieson DB, Quinn JF, Kaye JA. Brain volume preserved in healthy elderly through the eleventh decade. Neurology. 1998; 51:1555–1562. 27. Akiyama H, Meyer JS, Mortel KF, Terayama Y, Thornby JI, Konno S. Normal human aging: factors contributing to cerebral atrophy. J Neurol Sci. 1997; 152: 39–49. 28. Miller AKH, Alston RL, Corsellis JAN. Variation with age in the volumes of grey and white matter in the cerebral hemispheres of man: measurements with an image analyser. Neuropathol Appl Neurobiol. 1980; 6:119–132. 29. Double KL, Halliday GM, Kril JJ, Harasty JA, Cullen K, Brooks WS, Creasey H, Broe GA. Topography of brain atrophy during normal aging and Alzheimer’s disease. Neurobiol Aging. 1996; 17:513–521. 30. Harper CG, Kril JJ, Holloway RL. Brain shrinkage in chronic alcoholics — a pathological study. Br Med J. 1985; 290:501–504. 31. Kril JJ, Halliday GM. Brain shrinkage in alcoholics: a decade on and what have we learned? Prog Neurobiol. 1999; 58:381–387. 32. Auer RN, Benveniste H. Hypoxia and related conditions. In: Graham DI, Lantos PL, editors. Greenfield’s neuropathology. London: Arnold, 1997; 263–314. 33. Kril JJ. The contribution of alcohol, thiamine deficiency and cirrhosis of the liver to cerebral cortical damage in alcoholics. Metab Brain Dis. 1995; 10:9–16. 34. Kril JJ, Halliday GM, Svoboda MD, Cartwright H. The cerebral cortex is damaged in chronic alcoholics. Neuroscience. 1997; 79:983–998. 35. Graham DI, Gennarelli TA. Trauma. In: Graham DI, Lantos PL, editors. Greenfield’s neuropathology. London: Arnold, 1997; 197–262. 36. Anderson JM, Hubbard BM, Coghill GR, Slidders W. The effect of advancing age on the neurone content of the cerebral cortex. J Neurol Sci. 1983; 58:233–244. 37. Brody H. Organisation of the cerebral cortex III. A study of aging in the human cerebral cortex. J Comp Neurol. 1955; 102:511–556.

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38. Henderson G, Tomlinson BE, Gibson PH. Cell counts in human cerebral cortex in normal adults throughout life using an image analysing computer. J Neurol Sci. 1980; 46:113–136. 39. Terry RD, de Theresa R, Hansen LA. Neocortical cell counts in normal human adult aging. Ann Neurol. 1987; 21:530–539. 40. Gundersen HJG, Bendtsen TF, Korbo L, Marcussen N, Moller A, Nielsen K, Nyengaard JR, Pakkenberg B, Sorensen FB, Vesterby A, West MJ. Some new, simple and efficient stereological methods and their use in pathological research and diagnosis. APMIS. 1988; 96:379–394. 41. Gundersen HJG, Bagger P, Bendtsen TF, Evans SM, Korbo L, Marcussen N, Moller A, Nielsen K, Nyengaard JR, Pakkenberg B, Sorensen FB, Westerby A, West MJ. The new stereological tools: disector, factionator, nucleator and point sampled intercepts and their use in pathological research and diagnosis. APMIS. 1988; 96:857–881. 42. Pakkenberg B, Gundersen HJG. Neocortical neuron number in humans: effects of sex and age. J Comp Neurol. 1997; 384:312–320. 43. Gomez-Isla T, Hollister R, West H, Mui S, Growdon JH, Petersen RC, Parisi JE, Hyman BT. Neuronal loss correlates with but exceeds neurofibrillary tangles in Alzheimer’s disease. Ann Neurol. 1997; 41:17–24. 44. West MJ. Regionally specific loss of neurons in the aging human hippocampus. Neurobiol Aging. 1993; 14:287–293. 45. West MJ, Coleman PD, Flood DG, Troncoso JC. Differences in the pattern of hippocampal neuronal loss in normal ageing and Alzheimer’s disease. Lancet. 1994; 344:769–772. 46. West MJ, Gundersen HJG. Unbiased stereological estimation of the number of neurons in the human hippocampus. J Comp Neurol. 1990; 296:1–22. 47. Simic G, Kostovic I, Winblad B, Bogdanovic N. Volume and number of neurons of the human hippocampal formation in normal aging and Alzheimer’s disease. J Comp Neurol. 1997; 379:482–494. 48. Harding AJ, Halliday GM, Kril JJ. Variation in hippocampal neuron number with age and brain volume. Cereb Cortex. 1998; 8:710–718. 49. Flood DG, Buell SJ, Defiore CH, Horwitz GJ, Coleman PD. Age-related dendritic growth in dentate gyrus of human brain is followed by regression in the “oldest old”. Brain Res. 1985; 345:366–368. 50. Petit-Taboue MC, Landeau B, Desson JF, Desgranges B, Baron JC. Effects of healthy aging on the regional cerebral metabolic rate of glucose assessed with statistical parametric mapping. Neuroimage. 1998; 7:176–184. 51. Schultz SK, O’Leary DS, Boles Ponto LL, Watkins GL, Hichwa RD, Andreasen NC. Age-related changes in regional cerebral blood flow among young to mid-life adults. Neuroreport. 1999; 10:2493–2496. 52. Meltzer CC, Cantwell MN, Greer PJ, Ben-Eliezer D, Smith G, Franj G, Kaye WH, Houck PR, Price JC. Does cerebral blood flow decline in healthy aging? A PET study with partial-volume correction. J Nucl Med. 2000; 41:1849–1848. 53. Braak H, Braak E. Frequency of stages of Alzheimer-related lesions in different age categories. Neurobiol Aging. 1997; 18:351–357. 54. Braak H, Braak E. Neuropathological staging of Alzheimer-related changes. Acta Neuropathol. 1991; 82:239–259. 55. Jorm AF. The epidemiology of Alzheimer’s disease and related disorders. London: Chapman and Hall, 1990. 56. Davis DG, Schmitt FA, Wekstein DR, Markesbery WR. Alzheimer neuropathologic alterations in aged cognitively normal subjects. J Neuropathol Exp Neurol. 1999; 58:376–388. 57. Price JL, Davis PB, Morris JC, White DL. The distribution of tangles, plaques and related immunohistochemical markers in healthy aging and Alzheimer’s disease. Neurobiol Aging. 1991; 12:295–312.

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58. McKee AC, Kosik KS, Kowall NW. Neuritic pathology and dementia in Alzheimer’s disease. Ann Neurol. 1991; 30:156–165. 59. Giannakopoulos P, Hof PR, Mottier S, Michel JP, Bouras C. Neuropathological changes in the cerebral cortex of 1258 cases from a geriatric hospital: retrospective clinicopathological evaluation of a ten year autopsy population. Acta Neuropathol. 1994; 87:456–468. 60. Dickson DW, Crystal HA, Mattiace LA, Masur DM, Blau AD, Davies P, Yen S-H, Aronson MK. Identification of normal and pathological aging in prospectively studied nondemented elderly humans. Neurobiol Aging. 1991; 13:179–189. 61. Price JL, Morris JC. Tangles and plaques in nondemented aging and “Preclinical” Alzheimer’s disease. Ann Neurol. 1999; 45:358–368. 62. Delaere P, Duyckaerts C, Piette F, Hauw JJ. Subtypes and differential laminar distributions of BA4 deposits in Alzheimer’s disease: Relationship with the intellectual status of 26 cases. Acta Neuropathol. 1991; 81:328–335. 63. Armstrong RA. Relationships between morphological types of plaque in Alzheimer’s disease as revealed in silver and immunostained preparations. Neurodegeneration. 1993; 2:259–266. 64. Mackenzie IRA. Senile plaques do not progressively accumulate with normal aging. Acta Neuropathol. 1994; 87:520–525. 65. Bancher C, Brunner C, Lassmann H, Budka H, Jellinger K, Wiche G, Seitelberger F, Grundke-Iqbal I, Iqbal K, Wisniewski HM. Accumulation of abnormally phosphorylated tau precedes the formation of neurofibrillary tangles in Alzheimer’s disease. Brain Res. 1989; 477:90–99. 66. Bobinski M, Wegiel J, Tarnawski M, Bobinski M, De Leon MJ, Reisberg B, Miller DC, Wisniewski HM. Duration of neurofibrillary changes in the hippocampal pyramidal neurons. Brain Res. 1998; 799:156–158. 67. Duyckaerts C, Hauw JJ. Prevalence, incidence and duration of Braak’s stages in the general population: can we know? Neurobiol Aging. 1997; 18:362–369. 68. Morsch R, Simon W, Coleman PD. Neurons may live for decades with neurofibrillary tangles. J Neurol Neurosur Ps. 1999; 58:188–197. 69. Cras P, Smith MA, Richey PL, Siedlak SL, Mulvihill P, Perry G. Extracellular neurofibrillary tangles reflect neuronal loss and provide further evidence of extensive protein cross-linking in Alzheimer disease. Acta Neuropathol. 1995; 89:291–295. 70. Braak H, Braak E. Evolution of neuronal changes in the course of Alzheimer’s disease. J Neural Transm Suppl. 1998; 53:127–140. 71. Leuba G, Kraftsik R. Visual cortex in Alzheimer’s disease: occurrence of neuronal death and glial proliferation, and correlation with pathological hallmarks. Neurobiol Aging. 1994; 15:29–43. 72. Kril JJ, Patel S, Harding AJ, Halliday GM. Neuron loss from the hippocampus of Alzheimer’s disease exceeds extra cellular neurofibrillary tangle formation. Acta Neuropathol. 2002; 103:370–376. 73. Cullen KM, Halliday GM. Neurofibrillary degeneration and cell loss in the nucleus basalis in comparison to cortical Alzheimer pathology. Neurobiol Aging. 1998; 19:297–306. 74. Schwab C, Schulzer M, Steele JC, McGeer PL. On the survival time of a tangled neuron in the hippocampal CA4 region in parkinsonian dementia complex of Guam. Neurobiol Aging. 1999; 20:57–63. 75. Schwab C, Steele JC, McGeer PL. Pyramidal neuron loss is matched by ghost tangle increase in Guam parkinsonism-dementia hippocampus. Acta Neuropathol. 1998; 96:409–416. 76. National Institute on Aging and Reagan Institute Working Group on diagnostic criteria for the neuropathological diagnosis of Alzheimer’s disease. Consensus recommendations for the postmortem diagnosis of Alzheimer’s disease. Neurobiol Aging. 1997; 18(4S):S1–S3.

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77. Schmitt FA, Davis DG, Wekstein DR, Smith CD, Ashford JW, Markesbury WR. “Preclinical” AD revisited: Neuropathology of cognitively normal older adults. Neurology. 2000; 55:370–376. 78. Folstein MF, Folstein SE, McHugh PR. “Mini-mental”: A practical method for upgrading the cognitive state of patients for the clinician. J Psychiat Res. 1975; 12:189–198. 79. Green MS, Kaye JA, Ball MJ. The Oregan brain aging study. Neuropathology accompanying healthy aging in the oldest old. Neurology, 2000; 54:105–113. 80. Morris JC, Storandt M, McKeel Jr. DW, Rubin EH, Price JL, Grant EA, Berg L. Cerebral amyloid deposition and diffuse plaques in “normal” aging: evidence for presymptomatic and very mild Alzheimer’s disease. Neurology. 1996; 46: 707–719. 81. Xuereb JH, Brayne C, Dufouil C, Gertz H, Wischik C, Harrington C, MukaetovaLadinska E, McGee MA, O’Sullivan A, O’Connor D, Paykel ES, Huppert FA. Neuropathological findings in the very old. Results from the first 101 brains of a population-based longitudinal study of dementing disorders. Ann NY Acad Sci. 2000; 903:490–496. 82. Crystal HA, Dickson D, Sliwinski M, Masur D, Blau A, Lipton RB. Associations of status and change measures of neuropsychological function with pathologic changes in elderly, originally nondemented subjects. Arch Neurol. 1996; 53: 82–87.

Chapter 4 STRUCTURAL NEUROIMAGING OF THE AGEING BRAIN Jeffrey C.L. Looi and Perminder S. Sachdev*

Introduction The brain undergoes progressive changes in adult life, with these changes accelerating in the later years. The availability of neuroimaging techniques has opened up the possibility of studying these changes in large representative samples of elderly individuals, something not possible with post-mortem examinations. The introduction of computerised axial tomography (CT) was a major advance in the 1970s, but magnetic resonance imaging (MRI) has enhanced this capacity immeasurably. Structural neuroimaging provides data for structural-functional correlation in both healthy and diseased individuals, and enables a comparison of normative age-related changes with those due to neurodegenerative and other disorders. It also helps us understand which aspects of brain changes in the elderly are truly related to ageing, and which may in fact be due to age-related diseases. A differentiation of these is crucial for developing strategies for intervention. Advances in imaging technology have been paralleled by significant developments in the techniques of qualitative and quantitative analysis of neuroimaging. Similarly, research has progressed from cross-sectional to longitudinal studies, using repeated imaging to characterise the evolving changes in individual subjects. Such research has limitations due to differential selection criteria, sample sizes, imaging modality utilised, measurement methods and duration of longitudinal follow-up. Nonetheless, there are some consistent *To whom correspondence should be addressed.

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findings in gross measures of brain volume/atrophy as well as for individual regions of the brain which will be reviewed in this chapter. These changes concord, in many cases, with known neuropathological and neuropsychological data on the ageing brain. Limitations of the Studies Before we summarise the studies, we would like to outline some limitations of the data currently available. The structural imaging literature spans many different iterations in technology and technique. The focus of our review has been on the more recent rigorously designed CT and MRI studies. The differentiation between normal (i.e., with no overt neurological symptoms) and successful ageing (i.e., with minimal physiologic loss even when compared with younger individuals) is a difficult task. 1,2 Due to survivor effects, studies may contain an over-representation of very healthy individuals.1,2 Most series of neuroimaging studies of the ageing brain describe findings from individuals without overt neurological dysfunction, but do not necessarily analyse all risk factors for impairment or have indices of normal function such as neuropsychological tests or neurological function.1 The degree of ascertainment of neurological status has been extremely variable and not generally satisfactory, varying from use of limited screening to full neurological examination. These assessments have usually not included detailed neuropsychological assessment. When these have been performed, they have depended on patient groups or healthy volunteers, rather than a randomly selected group which might reflect a community-dwelling population.3 Some studies utilise qualitative rating scales of atrophy, white matter change, etc., whilst others utilise semi-automated quantitative methods based upon delineation of the area of interest and calculation of the volume or degree of change occurring. The types of data obtained from these methods are necessarily different. The sample size of individual studies may limit the ability to discern differences across the age range, as the majority of studies comprise groups of 50–100 individuals across the entire age range from youth to old age. Therefore, the subgroup of the aged may contain only 10–25 subjects, reducing the power of the study to demonstrate age effects. We have therefore concentrated on reviewing larger studies and those assessing longitudinal change in aged cohorts. Furthermore, cross-sectional studies may be subject to cohort effects from changes in nutrition, living conditions and health care when older persons are compared with contemporary youth.2 Longitudinal studies may be affected by obsolescence of imaging technology.2 Finally, MRI yields better differentiation between grey and white matter, does not expose the subject to radiation (obviating the concern about serial radiation exposure), allows for facility of image acquisition and manipulation within various anatomical planes, and may be more able to detect pathological change.2

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The Neuroimaging Studies of Normal Ageing Atrophy and ventricular enlargement Computerised tomography studies A large number of studies have used CT to determine changes in brain volume and indices of brain atrophy with age. Using an automated method to count pixels on the CT scan, the ratio of brain to the cranial cavity volume ( the cranio-cerebral index, CCI) was calculated for 228 healthy volunteers.4 The CCI for those aged 20–49 was used as the baseline for comparison as an indicator of brain atrophy, yielding a brain volume index (BVI) which decreased markedly after 50 years.4 However, this may be artefactually related to the cut-off of 49 used for the baseline. The ventricle to brain ratio did not change significantly for a subset of 93 subjects in a study to age 40, nor were changes noted in grey/white matter or total brain density calculated in Hounsfield units (HU).5 Ventricular enlargement was investigated using a semi-automated computer analysis in 123 normal subjects aged 23–88, showing only slight increases in ventricular volume to age 60, but more rapid increase in size thereafter.6 A large study of 980 healthy volunteers aged 10 to 88 years used CT to measure CSF and cranial cavity volumes and calculate a brain atrophy index (BAI) from their ratio.7 Both CSF volume and BAI increased significantly after thirties in men and women, with the rate of decline in males in the 30–40s age range being twice that of the women, slowing after this decade. In a large series of 381 healthy volunteers, the brain atrophy index (BAI) was calculated as above, finding that both CSF space volume and BAI were smallest in the 30s for men and 20s for women, but that significant increases occurred after age 40 in both sexes, with more marked atrophy occurring after 40s in men and 50s in women.8 In a study of 152 asymptomatic subjects aged 17–86 in Japan, the ventricular volume was noted to increase in males from age 40 onwards and in women from 50 onwards, with CSF space volume increasing from the 40s in men and 60s in women.9 A large series of 212 normal elderly persons found that ventricular dilatation increased in a statistically significant manner with increased age, but that cortical atrophy was non-significant.10 Analysing midventricular, high ventricular and supraventricular slices for percent fluid volume and mean CT density for each slice, it was concluded that fluid volume at the ventricular level was stable until the 60s, whilst the high and supraventricular volumes increased in the 50s in 79 men aged 31–87.11 This study was notable for careful screening for health, the subjects representing a subset of a normative ageing study. Magnetic Resonance Imaging studies In a study of 76 healthy adults, increasing age was associated with increasing volume of the third ventricle (2.8% per year) and lateral ventricles (3.2% per year).12 A significant positive correlation of ventricle to brain ratio with

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increasing age was found in a small sample.13 In a group of 142 healthy volunteers aged 21–80, lateral ventricular volume increased significantly with age, ranging from 134% in males and 66% in females in comparison between subgroups 21–30 to 71–80.14 The Helsinki Aging Brain study investigated 128 randomly selected, community-dwelling, neurologically non-diseased elderly (as confirmed by neurological examination), and found that central (enlargement of the lateral and third ventricles) atrophy increased significantly with age after stratifying the cohorts into the young-old (56–72) and the old-old (77–88).3 Decreasing total brain volume, increasing subarachnoid CSF, third and lateral ventricle volume have been observed, with the differences being more pronounced for males, especially in the third ventricle (194 subjects).15 In a study of 69 healthy subjects aged 20–85, there were significant increases with age in ventricular and peripheral CSF volume.16 A long term five-year follow-up study of 24 successfully aged elderly without significant neuropyschometric decline showed a modest increase in volumes of the lateral ventricles and frontal CSF spaces.17 Ventricular enlargement was significantly associated with age and sex in a sample of 3660 community dwelling elderly.18 A sample of 330 healthy elderly was recruited from the multicentric Cardiovascular Health Study and it was found that increased age was associated with increased volumes of the lateral and third ventricles.19 In a study utilising quantitative methods to assess various brain parameters in 116 subjects aged 19 months to 80 years, total CSF brain volume was found to increase linearly with age, whilst as a percentage of intracranial space volume it rated 7–9% up to adolescence, increasing from the 2nd decade to range 20–30% in those 71–80.20 The Baltimore Longitudinal Study of Aging investigated 116 subjects, finding that ventricular volumes and ventricle to brain ratios increased significantly with age and sex (males showing more atrophy).21 Longitudinal analysis of ventricular volume showed a decrease of 1525.6 mm3 in ventricular volume (0.5% of total brain volume) over a year and change in VBR of 0.0016, yet the authors stated that these measures were repeatable and showed stability within there brain measurements.21 Conclusion Studies investigating ventricular enlargement have used qualitative rating scales, semi-automated or automated measurement of ventricular volume and the calculation of ventricle to brain ratio. There is considerable heterogeneity in the findings, yet some firm conclusions can be drawn. Both CT and MRI studies demonstrate ventricular enlargement with increasing age, indicating that enlargement begins as early as the 30s in males and the 40s in females, with males showing earlier and more marked atrophy. Gross brain volume Computerised tomography studies Even in a group of 115 subjects aged up to only 40, increased sulcal widening of the frontal lobes and cerebellar vermis beginning in teenage years contin-

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ued to progress.5 Computerised quantitative estimation of volume percentage of brain to cranial cavity based on Hounsfield values showed that in men, brain atrophy began in the sixth decade and continued to decline through to the eighth decade.22 In women, decline in volume began in the fifth decade and appeared to plateau in fifth to sixth decades, to decline again in the seventh and eighth decades. A large series of 212 normal elderly persons found that, with increased age, increase in cortical atrophy was non-significant with a trend towards greater increase in males than females.10 Brain atrophy is usually a diffuse process and attempts have been made to derive linear parameters that describe the atrophy. This yielded a model with combined measurements of the lateral and third ventricles, Sylvian fissure and pre-pontine cistern to describe atrophy.23 A study of 64 healthy males aged 31–87 demonstrated bilateral, symmetrical atrophy of the cingulate gyrus and sulcus, interhemispheric frontal gyri and parieto-occipital sulcus.24 This study also showed asymetrical widening of the central and postcentral sulcus on the left and the intraparietal sulcus on the right. Magnetic resonance imaging studies In a study of 76 healthy adults, increasing age was associated with decreasing volume of cerebral hemispheres (0.23% per year), frontal lobes, (0.55% per year), temporal lobes (0.28% per year) and amygdala–hippocampal complex (0.30% per year).12 In a group of 142 healthy volunteers aged 21–80, cerebral hemispheric volume decreased significantly with age.14 In a study of 69 healthy subjects aged 20–85, a subgroup of a PET study, showed significant decrements in brain matter volumes of cerebral hemispheres, frontal lobe, the parieto-occipital lobe and parahippocampal gyrus.16 Men had a significantly greater age-related decrease than women in brain volume in the cerebral hemispheres, and frontal and temporal lobes in this study. Left frontal regions decreased more in women and right frontal regions decreased more in men. A sample of 330 healthy elderly individuals was recruited from the multicentric Cardiovascular Health Study and it was found that increased age was associated with decreased cerebral hemispheric, frontal and temporo-parietal volumes.19 The Baltimore Longitudinal Study of Aging investigated 116 subjects, finding that gross brain volumes decreased significantly with age and sex (males showing more atrophy).21 Additionally, younger persons had significantly larger grey than white matter volumes.21 An analysis of regional brain volume in the same study yielded a significant region by hemisphere interaction, reflecting greater right than left frontal, and left compared with right temporal volumes. Investigation of gross brain volume of various regions (331 subjects) standardised for cranial size showed markedly smaller volumes for frontal, temporal lobes, basal ganglia, thalamus, and parietal/parieto-occipital lobes, with sparing of only the basal frontal lobe and left cerebellum with ageing.25 In the posterior right frontal lobe, males demonstrated more atrophy then females. Males demonstrated atrophy in the right temporal, left basal ganglia, parietal lobe and cerebellum in contrast to females.

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In a study utilising quantitative methods to assess various brain parameters in 116 subjects aged 19 months to 80 years, whole brain volume steadily declined from 16–80 years, such that by 71–80 it had declined by 26% smaller than healthy 2–3 year olds.20 Whole brain volumes were 12% smaller in female volunteers.20 Conclusion Gross brain volumes decrease with age. Whilst there has been considerable variation in the brain volumes studied, there is consistent evidence of regional decreases in frontal and temporo-parietal volumes. Again, males show earlier atrophy and greater rates of decline. Cortex Computerised tomography study Quantitation of cerebral compartment densities was made via measurement of Hounsfield unit (HU) values in 81 healthy volunteers across an age range, yielding decline in HU values within all cortical grey matter and fronto-parietal white matter.26 Compartmental volumes for cortical and subcortical grey matter declined with increasing age.26 MRI studies In a study of 76 healthy adults, increasing age was associated with increasing odds of cortical atrophy at the rate of 8.9% per year.12 Assessment of 154 medically and cognitively healthy elderly individuals (55-88) found that 33% of the subjects had evidence of hippocampal atrophy (58% bilateral) and the prevalence for the same increased with age (12.8% in 55–65 to 56.8% in 77–88), with changes more common in males.27 In a study of 3660 community–living elderly, sulcal atrophy independently and significantly increased with age and sex (male), and was associated with ventricular atrophy.18 In a study of 619 healthy volunteers, the volume of the hippocampal formation and the amygdala reduced gradually with increasing age, with some increase in the volume of the temporal horn especially in those over 61.28 In a study of 126 healthy control subjects for an AD study, normalised medial temporal lobe (MTL) volumes were found to decrease with age in a linear fashion, with the greatest decline being in the head of the hippocampus.29 Women had larger normalised MTL volumes than men. The mean volumetric decline (cubic mm) was 45.63 for the total hippocampus, 27.43 for the hippocampal head, 8.484 for the hippocampal body, 9.68 for the hippocampal tail, 46.65 for the parahippocampal gyrus and 20.75 for the amygdala.29 Mean total intracranial volume was 1,393 ± 133 cubic cm. In a group of 24 cognitively normal subjects, the same group found the annual average rate of hippocampal volume loss was 75 ± cubic mm or –1.55 ± 1.38% and was greater for the head then the rest of the hippocampus.30 A low steady rate of hippocampal atrophy (-1.73%) per year over four years

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was found for control subjects with stable cognition in an investigation of hippocampal atrophy as a predictor of mild cognitive impairment or AD.31 In a study utilising quantitative methods to assess various brain parameters in 116 subjects aged 19 months to 80 years, grey matter volume was found to decrease by 5% per decade throughout life.20 One of the problems in analysing for cortical atrophy has been the highly gyrified cerebral cortex in humans that presents problems in quantifying the volume of cortex.32 This is further complicated by the sulcal and gyral changes that may be maturational as opposed to involutional. Some groups have made attempts to characterise the complex structure of the cortical surface by using a surface-rendering program that can generate a surface mask for use as a comparator with study subjects. However, these surface maps are not able to quantify sulcal invaginations filled with CSF, and investigators at the University of Iowa have developed a program (BRAINSURF) to more accurately map the cortical surface and hence calculate volume.32 They describe a sample of 148 healthy elderly in which they noted gyral, sulcal and cortical thickness changes with ageing. The cortical mantle becomes thinner with increasing age, more rapidly in males, and sulcal curvature index becomes larger, reflecting widening of the sulci.32 The comparisons were valid for younger (<20 years) versus older (>40 years) subjects. Conclusion There are technical problems in calculating cortical atrophy related to the complex gyral/sulcal structure and orientation of various cortical regions. To an extent, the superior imaging characteristics of MRI have allowed description and quantitation of changes that were undetectable with earlier imaging technologies. Age-related decreases in hippocampal and parahippocampal volumes have been well–characterised with MRI, yielding rates of decline of 5-10% per decade. Whilst new surface-mapping techniques may assist in detection of atrophy, there remains the problem of measurement error obscuring age-related changes, given the slow rate of atrophy noted. Subcortical structures Deep white matter Computerised tomography study. In a group of 123 normal subjects aged 23 to 88 years, there was a significant negative correlation between Hounsfield Score attenuation values and age, and this effect was enhanced after the effect of cranial size was partialled out.33 Furthermore, no significant age relationship could be found in those under the age of 56 in this sample and no focal or generalised radiolucency was noted in the older subjects.33 MRI studies. Examining 23 formalin-fixed brain specimens of patients 60 years and older, subtle changes of gliosis and demyelination accounted for the majority of hyperintense white matter lesions seen in MRI. 34 A signifi-

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cant increase of lacunar infarction was noted with increasing age in a small sample, with 34% of the lesions occurring in the deep white matter. 13 In a group of 142 healthy volunteers aged 21–80, white matter hyperintensities increased almost linearly with age, ranging from 20% in those 21–30, to 100% in those 71–80 years old.14 The Helsinki Aging Brain study investigated 128 randomly selected, community-dwelling, neurologically non-diseased elderly (as confirmed by neurological examination), stratifying the cohort into the young-old (56–72) and the old-old (77–88). 3 This study found that periventricular hyperintensities were present in 21% and 65% respectively.3 In the same sample, centrum semiovale hyperintensities were 11% and 38% respectively. These findings for hyperintensities were significantly associated with central (third and lateral ventricular) atrophy. There was a significant, non-linear, increase in periventricular hyperintensity with age, but not with gender.3 A T2-weighted MRI series of 61 healthy volunteers aged 30-86 showed that age-related changes emerged after 50, comprising increased signal intensity in white matter, high signal foci and constant high signal foci after the age of 65, whilst the grey matter remained stable.35 The Cardiovascular Health Study investigated white matter changes in 3301 elderly people (65 or older), finding that only 4.4% were free of any findings.36 The majority (80%) of these changes were classed as mild and comprised mainly symmetrical, supratentorial or periventricular changes, but higher grades were significantly associated with greater age. Longstreth et al.36 also noted one third of the group had suffered silent strokes. Another analysis of the same cohort showed the mean white matter grade increased with age and was associated with ventricular grade, and found that 34.7% had little or no white matter changes.18 The predominant distribution of white matter change was periventricular in 72.7%. 18 In a study utilising quantitative methods to assess various brain parameters in 116 subjects aged 19 months to 80 years, white matter volume reached a plateau in the 4th decade with reduction of 13% in those aged 70–80 years. 20 The recent Rotterdam MRI study of 1077 subjects aged 60–90 years showed 8% of all subjects had no subcortical white matter lesion, 20% had no periventricular lesion, and 5% had no white matter lesions in either location. 37 Thus, the prevalence of white matter lesions in the aged was high and the proportion increased with age, with trends toward more subcortical lesions in men in frontal white matter and periventricular lesions in women. 37 A five year follow-up study of 24 successfully aged elderly without significant neuropyschometric decline showed a modest increase in deep white matter hyperintensities with increasing age.17 Similarly, lesion progression was found in 17.9% of 273 healthy elderly volunteers in the Austrian stroke prevention study, with 64.5% of the volunteers (mean age 60+/–6.1 years) having white matter hyperintensities at baseline. 38 At baseline, punctate WMH occurred in 52%, and confluent changes in 12.5%.

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Conclusion White matter hyperintensities in the periventricular regions are predominant in the elderly, with rates approaching 100% in large longitudinal studies, though the majority of changes are mild. Lesion progression has also been demonstrated. Quantitation of the volume of WMHs is needed to assist in characterising the rate of change and differentiating age-related changes from neurodegenerative disease. Subcortical changes are less common but have a prevalence of approximately 20%. Corpus callosum and midline structures MRI studies In a small sub-sample of 56 persons aged 16–60 years, a decrease in size of the pituitary gland and corpus callosum was noticeable in the 51–60 year old group, whilst no such declines were evident in the pons or the cerebellar vermis39. Age negatively correlated with corpus callosum cross-sectional area, and there was a positive relationship between age and callosal T1 relaxation times, suggestive of an increase in callosal water in 36 volunteers aged 26–79 years.40 Cerebellum MRI studies The studies investigating cerebellar volume have mostly suffered from very small sample sizes, thereby failing to demonstrate decreases in cerebellar volume with age.41 In a study of 69 healthy subjects aged 20–85, a subgroup of a PET study showed significant decrements in the cerebellum.16 Larger studies have shown some cerebellar atrophy, which is more pronounced in males.25,42 Midbrain, pons, medulla MRI studies In a study of 36 normal volunteers aged 26–79, there was a highly significant age-related decline in the cross-sectional area of the midbrain, less significant decline in the anterior cerebellar vermis and no significant decline in medulla, pons or fourth ventricle.43 The volume of the midbrain, as measured by anteroposterior and interpeduncular diameters, has been found to decrease with age in an MRI study utilising stereological methods. 44 This correlated with previous functional imaging of the nigrostriatal system. A significant correlation of lacunar infarction increase with increasing age was found in a small sample, with 12% of the lesions in the brain stem. 13 An analysis from the Cardiovascular Health Study found that minimal changes were evident in the brain stem in 15.3% and moderate-marked changes in 3.8%.18

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Basal ganglia MRI studies Thirty healthy adult volunteers aged 20–80 years were studied with regard to deep grey matter hypointensities, finding that the red nucleus, substantia nigra and dentate nucleus were relatively unchanged.45 The globus pallidus and the putamen showed hypointensities in the aged. 45 A significant correlation of lacunar infarction increase with increasing age was found in a small sample, with 54% of the lesions in the basal ganglia.13 In a study of 69 healthy subjects aged 20-85, a subgroup of a PET study, showed significant decrements in brain matter volumes of the amygdala, thalamus, and caudate.16 A long term, five-year follow-up study of 24 successfully aged elderly without significant neuropyschometric decline showed a modest increase in white matter hyperintensities in the basal ganglia.17 Conclusion (Midline, Cerebellum, Midbrain, Basal Ganglia) Involutional changes in midline, basal ganglia and infratentorial compartments are evident, but have not been as well studied as cortical, ventricular and gross brain changes. These studies particularly suffer from low numbers of aged within the cross-sectional studies, thus possibly failing to have sufficient power to detect the extent of change with ageing. Imaging characteristics MRI studies Utilising quantitative MRI, a statistically significant relationship existed between T1 (spin lattice relaxation time) in brain tissue in vivo and age for 10 brain structures investigated in 115 healthy controls aged 4–72.46 Least squares regression analysis showed that T1 varied as a function of age in the pulvinar nucleus, anterior thalamus, caudate, frontal white matter, optic radiation, putamen, genu, occipital white matter and cortical grey matter. T1 declined throughout adolescence and early adulthood, reaching a minimum in the fourth to sixth decade and then beginning to increase. These changes differed in white matter such that the white matter reached minimum and increased sooner than grey matter.46 Synthesis of findings While there are many inconsistencies in the literature summarised above, the following findings can be considered to be consistent features of brain ageing: • Ventricular enlargement • Reduction in gross brain volume • Regional declines in frontal and temporo-parietal brain volume • Increased cortical atrophy, especially in hippocampal and parahippocampal regions • Increased white matter hyperintensities, particularly in periventricular regions

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• Some reduction in midline, basal ganglia and cerebellar volume These changes occur earlier and progress more rapidly in males. The validity and reliability of the above findings is limited by: • Incomplete characterisation of “normal” aged persons • Use of cross-sectional studies which may lack validity and have insufficient power to demonstrate significant change • Variations in imaging technology • Lack of standardisation of rating/measurements utilised to assess change • Lack of longitudinal studies of intra-individual change Given the age-related changes noted, there is considerable evidence of measurable involutional change as assessed by structural neuroimaging. Directions for future research Whilst the majority of neuroimaging research demonstrates measurable changes within the brain in normal ageing, refinement of investigative procedures would yield better data for comparison, especially with regard to differentiation from neurodegenerative processes. We suggest the following considerations for future studies: • Better characterisation of the degree of normality of subjects by physical examination, neuropsychological assessment and agreed upon criteria. • Larger sample sizes to increase power to detect change. • Longitudinal as opposed to cross-sectional studies to assess intraindividual change, which has more relevance than cross-sectional studies of individuals from different age groups. • Use of standardised methods of neuroimaging: slice selection, orientation, parameters etc. Multi-centre collaboration may be necessary for this to happen. • Quantitative assessments of change to allow assessment of degree and rate of change, as well as comparison between studies. • Inclusion of midline, basal ganglia and infratentorial structures. • The combining of functional and structural imaging to understand the implications of the structural changes seen. • The applications of newer technologies such as diffusion tensor imaging, and diffusion and perfusion MRI to better understand the brain changes of ageing. Acknowledgements The writing of this paper was supported in part by an NHMRC research grant.

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References 1. Drayer BP. Imaging of the aging brain. Radiology. 1988; 166:785–796. 2. Coffey CE. Anatomic imaging of the aging human brain. In: Coffey CE, Cummings JL, editors. The American Psychiatric Press textbook of geriatric neuropsychiatry. Washington, DC: American Psychiatric Press, 2000; 181–238. 3. Ylikoski A, Erkinjuntti T, Raininko R, Sarna S, Sulkava R, Tilvis R. White matter hyperintensities on MRI in the neurologically non diseased elderly. Stroke. 1995; 26:1171–1177. 4. Yamaura H, Ito M, Kubota K, Matsuzawa T. Brain atrophy during aging: a quantitative study with computed tomography. J Gerontol. 1980; 35:492–498. 5. Cala LA, Thickbroom GW, Black JL, Collins DWK, Mastaglia FL. Brain density and cerbrospinal fluid space size: CT of normal volunteers. Am J Roentgenol. 1981; 2:41–47. 6. Zatz LM, Jernigan TL, Ahumada AJ. Changes on cranial computed tomography with aging: intracranial fluid volume. Am J Neuroradiol. 1982; 3:1–11. 7. Takeda S, Matsuzawa T. Brain atrophy during aging: a quantitative study using computed tomography. J Am Geriatr Soc. 1984; 32:520–524. 8. Takeda S, Matsuzawa T. Age-related brain atrophy: a study with computed tomography. J Gerontol. 1985; 40:159–163. 9. Takeda S, Matsuzawa T. Age-related changes in volumes of the ventricles, cisternae and sulci: a quantitative study using computed tomography. J Am Geriatr Soc. 1985; 33:264–268. 10. Laffey PA, Peyster RG, Natahan R, Haskin ME, McGinley JA. Computed tomography and aging: results in a normal elderly population. Neuroradiology. 1984; 26:273–278. 11. Stafford JP, Albert MS, Naeser MA, Sandor T, Garvey AJ. Age-related differences in computed tomographic scan measurements. Arch Neurol. 1988; 45:409–415. 12. Coffey CE, Wilkinson WE, Parashos IA, Soady SA, Sullivan RJ, Patterson LJ, Figiel GS, Webb MC, Spritzer CE, Djang WT. Quantitative cerebral anatomy of the aging human brain; a cross-sectional study using magnetic resonance imaging. Neurology. 1992; 42:527–536. 13. Matsubayashi K, Shimada K, Kawamoto A, Ozawa T. Incidental brain lesions on magnetic resonance imaging and neurobehavioral functions in the apparently healthy elderly. Stroke. 1992; 23:175–180. 14. Christiansen P, Larsson HBW, Thomsen C, Wieslander SB, Henriksen O. Age dependent white matter lesions and brain volume changes in healthy volunteers. Acta Radiol. 1994; 35:117–122. 15. Blatter DD, Bigler ED, Gale SD, Johnson SC, Anderson CV, Burnett BM, Parker N, Kurth S, Horn SD. Quantitative volumetric analysis of brain MR: normative database spanning 5 decades of life. Am J Neuroradiol. 1995; 16:241–251. 16. Murphy DG, DeCarli C, McIntosh AR, Daly E, Mentis MJ, Pietrini P, Szczepanik J, Shapiro MB, Grady GL, Horwitz B, Rapoport SI. Sex differences in human brain morphometry and metabolism; an in vivo quantitative magnetic resonance imaging and positron emission tomography study on the effect of aging. Arch Gen Psychiat. 1996; 53:585–594. 17. Wahlund LO, Almkvist O, Basun H, Julin P. MRI in successful aging, a five year follow-up study form the eighth to the ninth decade of life. Mag Reson Imaging. 1996; 14:601–608. 18. Yue NC, Arnold AM, Longstreth WT Jr, Elster AD, Jungreis CA, O’Leary DH, Poirier VC, Bryan RN. Sulcal, ventricular, and white matter changes at MR imaging in the aging brain: data from the cardiovascular health study. Radiology. 1997; 202:33–39.

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19. Coffey CE, Lucke JF, Saxton JA, Ratcliff G, Unitas LJ, Billig B, Bryan RN. Sex differences in brain aging: a quantitaive magnetic resonance imaging study. Arch Neurol. 1998; 55:169–179. 20. Courchesne E, Chisum HJ, Townsend J, Cowles A, Covington J, Egaas B, Harwood M, Hinds S, Press GA. Normal brain development and aging: quanitative analysis at in vivo MR imaging in healthy volunteers. Radiology,. 2000; 216: 672–682. 21. Resnick SM, Goldszal AF, Davazikos C, Golski S, Kraut MA, Metter EJ, Bryan RN, Zonderman AB. One-year age changes in MRI brain volumes in older adults. Cereb Cortex. 2000; 10:464–472. 22. Hatazawa J, Ito M, Yamaura H, Matsuzawa T. Sex differences in brain atrophy during aging: a quantitative study with computed tomography. J Am Geriatr Soc. 1982; 28:235–239. 23. Gomori JM, Steiner I, Melamed E, Cooper G. The assessment of changes in brain volume using combined linear measurements — a CT-scan study. Neuroradiology. 1984; 26:21–24. 24. Sandor T, Albert M, Stafford J, Kemper T. Symmetrical and asymmetrical changes in brain tissue with age as measured on CT scans. Neurobiol Aging. 1990; 11: 21–27. 25. Xu J, Kobayashi S, Yamaguchi S, Iijima K, Okada K, Yamashita K. Gender effects on age related changes in brain structure. Am J Neuroradiol. 2000; 21: 112–118. 26. Meyer JS, Takashima S, Terayama Y, Obara K, Muramatsu K, Weathers S. CT changes associated with nromal aging of the human brain. J Neurol Sci. 1994; 123:200–208. 27. Golomb J, de Leon MJ, Kluger A, George AE, Tarshish C, Ferris SH. Hippocampal atrophy in normal aging: an association with recent memory impairment. Arch Neurol. 1993; 50:967–973. 28. Mu Q, Xie J, Wen Z, Weng Y, Shuyun Z. A quantitative MR study of the hippocampal formation, the amyggdala, and the temporal horn of the lateral ventricle in healthy subjects 40 to 90 years of age. Am J Neuroradiol. 1999; 20:207–211. 29. Jack CR Jr, Petersen RC, Xu YC, Waring SC, O’Brien PC, Tangalos EG, Smith GE, Ivnik RJ, Kokmen E. Medial temporal lobe atrophy on MRI in normal aging and very mild Alzheimer’s disease. Neurology. 1997; 49:786–794. 30. Jack CR, Petersen RC, Xu YC, O’Brien PC, Smith GE, Ivnik RJ, Tangalos EG, Kokmen E. Rate of medial temporal lobe atrophy in typical aging and Alzheimer’s disease. Neurology. 1998; 51:993–999. 31. Jack CR, Petersen RC, Xu YC, O’Brien PC, Smith GE, Ivnik RJ, Boeve BF, Tangalos EG, Kokmen E. Rates of hippocampal atrophy correlate with change in clinical status in aging and AD. Neurology. 2000; 55:484–489. 32. Magnotta VA, Andreason NA, Schultz SK, Harris G, Cizadlo T, Heckel D, Nopoulos P, Flaum M. Quantitative in vivo measurement of gyrification in the human brain: changes associated with aging. Cereb Cortex. 1999; 9:151–160. 33. Zatz LM, Jernigan TL, Ahumada AJ. White matter changes in cerebral computed tomography related to aging. J Comput Assist Tomo. 1982; 6:19–23. 34. Braffman BA, Zimmerman RA, Trojanowski JQ, Gonatas NK, Hickey WF, Schlaepfer WW. Brain MR: patahlogic correlation with gross and histopathology. 2. Hyperintense white-matter foci in the elderly. Am J Roentgenol. 1988; 151:559–566. 35. Salonen O, Autti T, Raininko R, Ylikoski A, Erkinjuntti T. MRI of the brain in neurologically healthy middle-aged and elderly individuals. Neuroradiology. 1997; 39:537–545. 36. Longstreth WT Jr, Manolio TA, Arnold A, Burke GL, Bryan N, Jungreis CA, Enright PL, O’Leary D, Fried L. Clinical correlates of white matter findings on

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cranial magnetic resonance imaging of 3301 elderly people: the cardiovascular health study. Stroke. 1996; 27:1274–1282. de Leeuw F-E, de Groot JC, Achten E, Oudkerk M, Ramos LM, Heijboer R, Hofman A, Jolles J, van Gijn J, Breteler MM. Prevalence of cerebral white matter lesions in elderly people: a population based magnetic resonance imaging study. The Rotterdam Scan Study. J Neurol Neurosur Ps. 2001; 70:9–14. Schmidt R, Roob G, Kapeller P, Schmidt H, Berghold A, Lechner A, Fazekas F. Longitudinal change of white matter abnormalities. J Neural Transm Supp. 2000; 59:9–14. Hayakawa K, Konishi Y, Matsuda T, Kuriyama M, Konishi K, Yamashita K, Okumura R, Hamanaka D. Development and aging of brain midline structures: assessment with MR imaging. Radiology. 1989; 172:171–177. Doraiswamy PM, Figiel GS, Husain MM, McDonald WM, Shah SA, Boyko OB, Ellinwood EH Jr, Krishnan KR. Aging of the human corpus callosum: magnetic resonance imaging in normal volunteers. J Neuropsych Clin N. 1991; 3:392– 397. Escalona PR, McDonald WM, Doraiswamy PM, Boyko OB, Husian MM, Figiel G, Laskowitz D, Ellinwood EH Jr, Krishnan KR. In vivo stereological assessment of human cerebellar volume: effects of gender and age. Am J Neuroradiol. 1991; 12:927–929. Raz N. Age and sex do not affect cerebellar volume in humans. Am J Neuroradiol. 1997; 18:594–595. Shah SA, Doraiswamy PM, Husian MM, Figiel GS, Boyko OB, McDonald WM, Ellinwood EH Jr, Krishnan KR. Assessment of posterior fossa structures with midsagittal MRI: the effects of age. Neurobiol Aging. 1991; 12:371–374. Doraiswamy PM, Na C, Husian MM, Figiel GS, McDonald WM, Ellinwood EH Jr, Boyko OB, Krishnan KR. Morphometric changes of the human midbrain with normal aging: MR and sterelogic findings. Am J Neuroradiol. 1992; 13: 383–386. Milton WJ, Atlas SW, Lexa FJ, Mozley PD, Gur RS. Deep gray matter hypointensity patterns with aging in healthy adults: MR imaging at 1.5 T. Radiology. 1991; 181:715–719. Cho S, Jones D, Reddick WE, Ogg RJ, Steen RG. Establishing norms for agerelated changes in proton T1 of human brain tissue in vivo. Magn Reson Imag. 1997; 15:1133–1143.

Chapter 5 NEUROPSYCHOLOGICAL, SENSORY AND MOTOR CHANGES WITH AGEING Stephen R. Lord* and Rebecca St George

Introduction In this chapter we review the studies that have addressed neuropsychological, sensory and motor changes with ageing. Specifically, we examine documented age changes in reaction time, working memory, selective attention, vision, taste, smell, hearing, vestibular sense, proprioception, tactile sensitivity, vibration sense and muscle strength. We then review age changes in composite factors such as postural control and gait stability that are underpinned by many of these sensory, motor and integrative systems. Factors that contribute to the increased variability in functioning evident with increased age are then considered, and health and lifestyle strategies for maximising functional performance and independence in older age are discussed. Part 1: Age-Changes Neuropsychological factors Reaction Time A ubiquitous finding of ageing is the slowing of mental processes and behaviour.1 There is approximately a 25% increase in simple reaction time from the twenties to the sixties, with further significant slowing beyond this age. *To whom correspondence should be addressed.

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Older people are slower in part because they are more careful and more likely to sacrifice speed than accuracy, but carefulness does not explain all the age-differences observed. Hertzog et al. 2 compared performance in young and old subjects on a mental rotation task by varying instructions to emphasize speed or accuracy. They found that older adults used a more conservative strategy, preferring to sacrifice speed for accuracy but even when this difference was taken into account, age differences remained. 2 Other factors such as amount of practice, the length of the preparatory period, mode of response, health and motivational levels can also only partly account for age changes. In addition to the well-documented increases in reaction time with age outlined above, studies that have examined the relationship between reaction time and ageing involving complex motor tasks have found that there are notable age-related increases in movement time in addition to age-related increases in decision time.3 This is particularly the case for movements of whole limbs and the whole body.4 Working memory and selective attention There is substantial evidence that working memory declines in old age and that this explains observed age decrements in complex cognitive tasks. 5,6 Salthouse7 has used working memory tasks involving simultaneous storage and processing of information such as digit span backwards, computation span and reading and listening span. This work shows that working memory could account for about 50% of the association between age and tests of fluid intelligence. Dual task experiments where one of the tasks involves walking or balance and the other task involves working memory 8 or list learning9 have shown a greater cost of dual tasks in older persons than in young. Neuropsychological tests such as Trails B test have also been shown to explain variance in a choice stepping reaction time task that mimics the step requirement necessary to avoid a fall.10 These studies suggest that there is an increase in the cognitive resources required for postural control with age. Sensory systems Vision As Chapter 6 reviews age-related declines in visual function, only a few key issues are addressed here. The research into the decline of visual function is extensive and it has been reported that ageing affects many vision processes. These include visual acuity, contrast sensitivity, glare sensitivity, dark adaptation, accommodation and depth perception, especially beyond 40 years.11 Reduced vision has a significant impact on the behaviour and lifestyle of older people. An older person with reduced vision is placed at an increased risk of social isolation, postural imbalance and falling, and limited ability to undertake daily activities.11,12

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Taste and smell After the age of about 60 years people experience a gradual loss of taste and smell. Taste thresholds increase as a result of taste buds diminishing in size and number.13 Sweet and salty tastes are particularly affected leaving many older people reporting that they no longer can taste or enjoy food. A related decline in sense of smell has also been reported, with about 40% of people 80 years and older have difficulty identifying common substances by smell.14 Hearing Gradual hearing loss begins at the age of 20 and becomes more accelerated above the age of 70. Approximately one third of women over the age of 65 report a hearing difficulty,15 and it is estimated that by the age of 80, two thirds of people will suffer a significant hearing loss.14 In particular, the ability to discriminate between tones of higher frequencies is reduced, as observed in both cross-sectional16 and longitudinal studies.17 Comprehension of speech becomes gradually more difficult due to increases in frequency discrimination thresholds for the short tones present in human speech.18,19 Another aspect of comprehending speech is auditory temporal discrimination and sound localization, factors that are also adversely affected by age.20,21 Older people also experience difficulty in performing dichotic listening tasks, i.e. tasks requiring them to attend to auditory information presented in one ear while ignoring the information presented in the other.22 Vestibular sense The vestibular system plays an important role in maintaining correct balance and posture. Katsorkas23 studied over 1000 patients over the age of 70 and found that over one third had a disease of vestibular origin. The vestibular system contributes to posture by maintaining the reflex arc, keeping the head and neck in the vertical position and by corrective movements elicited through the vestibulo-ocular and vestibulo-spinal pathways.24 To preserve gaze during movement of the head, a visual-vestibular interaction is required involving the enhancement and suppression of compensatory eye movements (the vestibulo-ocular reflex). Ageing has been found to be associated with diminished ability to enhance and suppress the vestibulo-ocular reflex during horizontal rotation.25 A number of researchers have found a reduced reactivity to caloric and rotational stimulation in subjects over the age of 60.26,27 This decline in vestibular function results in many elderly people experiencing dizziness and unstable posture, increasing their likelihood of a fall. Peripheral sensation Receptors in the skin that respond to touch include Meissner and Pacinian corpuscles, Merkel disks, Ruffini cylinders and free nerve endings.28 Pacinian corpuscles are also sensitive to vibratory stimuli. Meissner and Pacinian corpuscles in particular show considerable age-related losses in numbers and morphological changes. Proprioception or kinaesthesis is served by special-

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ised receptors in muscles, tendons, and joints to provide information on the position and movements of body parts.29 Since the initial work by Pearson in 1928,30 age–related changes in vibration sense have been extensively studied. This work has consistently found agerelated declines in vibration sense to vibration frequencies greater than 50 Hz on various parts of the body.31-37 It has also been found that vibration sense is poorer in the lower limb compared with the upper limb at all ages and shows a greater age-related decline.31-37 Compared with vibration sense, there have been relatively few studies on the effect of age on tactile sensitivity. Like vibration sense, most reports indicate that tactile sensitivity, as measured by aesthesiometers or by two–point discrimination, decreases significantly with age37-41 and is reduced in the lower limb compared with the upper limb.37-39,41 Laidlaw and Hamilton were the first researchers to demonstrate an agerelated decline in joint position sense.42 They found that subjects aged 17 to 35 years had lower thresholds and superior ability to detect direction of joint movements of the hip, knee and ankle than subjects aged 50 to 85 years. Since then, further studies have found significant age-related declines in position sense of the knee joint, 43-46 metacarpophalangeal joint47 and metatarsophalangeal joint.48 However, clinical studies that have investigated whether there is a decline in joint position sense beyond 65 years of age have produced inconsistent results. This may be due at least in part to the imprecision of the tests used, which have been based on subjects’ ability to identify experimenter-induced movements of body parts.48 Motor factors Muscle strength Numerous studies have reported a loss of both isometric and dynamic muscle strength with increased age. In men, muscle strength appears to decrease only marginally between 20 and 40 years, but beyond 40 years declines at an accelerated pace, so that hand grip strength is reduced by 16% and leg strength by 28% in men aged 60–69 compared with men aged 20–29.49,50 In women, muscle strength appears to decline from an earlier age and at a greater rate, so that over the same age range handgrip strength declines by 20% and leg strength by 38%.51 It has also been shown that muscle strength continues to decline significantly beyond the sixties in both sexes.52 In studies that have used both men and women, it has been found that muscle strength in women is about 60-70% of that in men.49-51 Leg extensor power (the product of force and the rate of force generation) appears to decline at an even greater rate with age than does isometric strength. In a cross-sectional study of 100 men and women aged 65–89 years, Skelton et al.53 found a loss of isometric strength of 1–2% per annum, whereas the loss of leg extensor power was around 3.5 percent per annum. Increased age is also associated with a deterioration of muscle elastic behaviour and reflex potentiation.54

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Reduced strength in the lower limbs has serious implications for older people. Vandervoort and Hayes55 found impaired ankle plantarflexor muscle force and power in residents of geriatric care facilities who were capable of independently performing activities of daily living. Reduced strength is also reflected in a difficulty in rising from a chair without the use of the hands, and it has been found that an inability to undertake this task is associated with subsequent disability, falls and fractures in older people.56–58 Postural stability Standing Normal standing is a dynamic process and characterized by small amounts of postural sway. Sway is controlled by continual muscle activity (primarily of the calf muscles) and requires an integrated reflex response to visual, vestibular and somatosensory input59 that acts to inform the brain of the position and movement of the body in three-dimensional space. The musculoskeletal component of postural stability encompasses the biomechanical properties of body segments, muscles and joints. Linking the sensory and neuromuscular components are higher-level neurological processes that enable anticipatory mechanisms for planning a movement, and adaptive mechanisms for reacting in a controlled manner to the changing demands of the particular task.60 The contribution of age-related declines in sensorimotor functioning to increased postural sway in old age has been widely evaluated in the literature.61 Factors found to be correlated with increased sway include reduced lower extremity calf muscle strength,62,63 reduced peripheral sensation,64,65 poor vision62,66 and slow reaction time.62,67 Smaller associations between vestibular function and sway have been reported.62,68 Although the extent to which one input can compensate for the loss of another is unclear, there is some evidence that peripheral sensation is the most important sensory system in the regulation of standing balance in older adults.59,62 Therefore, due to its composite nature, standing instability is an indicator of sensory loss and overall functional decline. Gait Many older people demonstrate a slow and laboured gait69,70 as a result of shorter step length69–71 and more time spent in double limb support.69,70,72 Our group has found that older people with slow and variable gait demonstrate reduced sensory acuity, lower limb muscle weakness, impaired vestibular function and slow reaction time. As with the sensorimotor factors that underpin gait stability, slow and variable gait has been found to be a predictor of falls and fractures in older people.69,73–76 Thus as with standing balance, walking patterns provide an overall index of the extent of underlying sensory, motor and central processing declines that occur with age.

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Part 2: Variability and Age Changes

Vestibular sense

Quadriceps strength (kg)

Figure 1 presents findings from the Randwick Falls and Fractures Study40 that show the relationships between a range of sensorimotor factors and age. Although it is apparent that there are considerable mean age-related declines, it is evident that with increased age, there is also increased variability in performance for each measure. For example, while there is an exponential increase in reaction time with age, some older subjects perform similarly to young subjects whereas others have scores indicating a two-fold slowing in performance. Understanding the causes of this variability may have important implications for health and quality of life in older people. Figure 2 shows a theoretical representation of the “normal” age-related decline in function of a

Age group

Visual aculty

Vibration sense

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Sway - foam

Reaction time (ms)

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Age group

Figure 1. Age—related changes in various sensory and motor functions—summary findings of 550 women from the Randwick Falls and Fractures Study.40 The solid line shows the mean change with age and the grey band indicates the inter-quartile range.

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Figure 2. Theoretical representation of the “normal” age-related decline in function of a sensorimotor system that contributes to stability (grey-shaded area represents the upper and lower bounds). The black line indicates the onset of a disease such as a stroke which can rapidly change function and the dashed line indicates a criterion level for adequate performance.

sensorimotor system that contributes to stability. The figure shows that up until age 55 there is little change in function, but beyond this age there is a progressive decline. This decline occurs in all people, however the variability in function becomes progressively greater as age increases. Persons on the lower band reach the criterion level for a critical loss of functioning (for example a fall) by the age of 65 whereas those toward the upper band are still above the criterion level at age 80. The figure also depicts a situation in which the onset of significant disease such as a stroke can rapidly change functional performance and result in performance levels below the criterion level at any age. Part 3: Intervention Strategies for Maximising Function The role of exercise and good health habits Good health habits have an impact on both the length of life and its quality in old age. For example, it has been found that people who followed seven healthy lifestyle habits: never smoked, moderate consumption of alcohol, daily breakfast, no snacking, seven to eight hours of sleep per night, regular exercise and ideal weight, lived on average nine years longer than those who had none of the good health habits. Those with healthy habits also had greater functional ability and suffered less from debilitating illnesses.77 The role of regular exercise, in particular is likely to be important for maximizing functional abilities, as age-related changes in many sensorimotor and

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balance systems also occur with inactivity.14 Research has consistently shown that older people who are active and highly conditioned are able to maintain a higher functional ability, and are able to prolong their lifespan and delay the onset and progression of chronic diseases14. Cross-sectional studies have found that older people actively engaged in exercise perform better in a range of sensori-motor function tests, including reaction time, strength, flexibility and balance compared with matched groups of older non-exercisers.78 Furthermore, randomised controlled trials have now shown conclusively that exercise can improve performance in these sensori-motor parameters.79In a large, randomised, controlled trial of the effect of exercise in elderly women, we found the exercisers showed significant improvements in lower limb strength, simple reaction time, neuromuscular control, standing and leaning balance and gait stability. 80–82 It appears that older individuals adapt to resistive and endurance exercise training in a similar fashion to younger people.14 This not only results in improved functional ability but also beneficial effects on age-associated diseases such as Type II diabetes, coronary heart disease, hypertension, osteoporosis, and obesity. 83 Thus, the development of exercise programs that are enjoyable and easily accessible, so as to maximise long-term participation, may prove a valuable strategy for maximizing sensorimotor functioning, mobility, independence and quality of life in older people. Conclusions In summary, ageing is associated with significant declines in neuropsychological, sensorimotor and balance function. These declines are exponential in nature, with noticeable changes evident in the 40s and 50s and functional limitations common in those aged 80 years and over. Concomitant with age-changes is an increase in variability in functional performance for most measures. This variability may be due in part to genetic differences, although factors such as disease and inactivity are likely to influence functional performance. The maintenance of a “healthy lifestyle”, and in particular physical activity, appears to be an important strategy for maximizing sensorimotor functioning, mobility, independence and quality of life in older age. References 1. 2. 3.

Salthouse TA. Speed and age: multiple rates of age decline. Exp Aging Res. 1976; 2:349–359. Hertzog C, Vernon MC, Rypma B. Age differences in mental rotation task performance: the influence of speed/accuracy tradeoffs. J Gerontol. 1993; 48: 150–160. Spirduso, WW. Reaction and movement time as a function of age and physical activity level. J Gerontol. 1975; 30:435–40.

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4. Grabiner MD, Jahnigen DW. Modeling recovery from stumbles: preliminary data on variable selection and classification efficacy. J Am Geriatr Soc. 1992; 40: 910–913. 5. Salthouse TA. Influence of processing speed on adult age differences in working memory. Acta Psychol. 1992; 79:155–70. 6. Salthouse TA. Mechanisms of age-cognition relations in adulthood. Hillsdale: Lawrence Erlbaum Ass, 1992. 7. Salthouse TA. Working-memory mediation of adult age differences in integrative reasoning. Mem Cognition. 1992; 20:413–423. 8. Maylor EA, Wing AM. Age differences in postural stability are increased by additional cognitive demands. J Gerontol. 1996; 51:143–54. 9. Lindenberger U, Marsiske M, Baltes PB. Memorizing while walking: increase in dual-task costs from young adulthood to old age. Psychol Aging. 2000; 15: 417–36. 10. Lord SR, Fitzpatrick RD. Choice stepping reaction time: a composite measure of falls risk in older people. J Gerontol. [In press]. 11. Pitts DG. The effects of aging on selected visual functions: dark adaptation, visual acuity, stereopsis, brightness contrast. In: Sekuler R, Kline DW, Dismukes K, editors. Aging in human visual functions. New York: Liss, 1982. 12. Lord SR, Dayhew J. Visual risk factors for falls. J Am Geriatr Soc. 2001; 49: 508–515. 13. Bartoshuk LM. Rifkin B. Marks LE. Bars P. Taste and aging. J Gerontol. 1986; 41(1):51–57. 14. Neiman DC. Fitness and sports medicine, 3rd ed. Mountain View, CA: Mayfield Pub, 1995. 15. Ward JA, Lord SR, Williams P, Anstey K. Hearing impairment and hearing aid use in women over 65 years of age. Med J Australia. 1993; 159. 16. Gates GA, Cooper JC, Kannel WB, Miller NJ. Hearing in the elderly: The Framingam cohort, 1983-1985: Part I. Basic audiometric test results. Ear Hearing. 1990; 11:247–256. 17. Brant LJ, Fozard JL. Age changes in pure-tone hearing thresholds in a longitudinal study of normal human aging. J Acoust Soc Am. 1990; 88:813–820. 18. Cranford JL, Stream RW. Discrimination of short duration tones by elderly subjects. J Gerontol. 1991; 46:37–41. 19. Matschke RG. Frequency selectivity and psychoacoustic tuning curves in old age. Acta Oto-Laryngol. 1990; 476 (Suppl.):114–119. 20. Schmitt JF, Carroll MR. Older listeners’ ability to comprehend speaker-generated rate alteration of passages. J Speech Hear Res. 1985; 28:309–312. 21. Rastatter M, Watson M, Strauss-Simmons D. Effects of time-compression on feature and frequency discrimination in aged listeners. Percept Motor Skill. 1989; 68:367–372. 22. Noffsinger D, Martinez CD, Andrews M. Dichotic listening to spech: VA-CD data from elderly subjects. J Am Acad Audiol. 1996; 7:49–56. 23. Katsarkas A. Dizziness in ageing: a retrospective study of 1194 cases. Otolaryng Head Neck. 1994; 110:296–301. 24. Stelmach GE, Worringham CJ. Sensorimotor deficits related to postural stability. Implications for falling in the elderly. Clin Geriatr Med. 1985; 1:679–694. 25. Baloh RW, Jacobsen KM, Socotch TM. The effect of aging on visual-vestbuloocular responses. Exp Brain Res. 1993; 95:509–16. 26. Karlsen EA, Hassanein RM, Goetzinger CP. The effects of age, sex, hearing loss and water temperature on caloric nystagmus. Laryngoscope. 1981; 91: 620–627. 27. Ghosh P: Aging and auditory vestibular response. Ear Nose Throat J. 1985; 64: 264–266.

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28. Kenshalo DR. Age changes in touch, vibration, temperature, kinesthesis and pain sensitivity. In Birren JE, Schaie KW, editors. Handbook of the psychology of aging. New York: Van Nostrand Reinhold, 1977. 29. Howard IP, Templeton WB. Human spatial orientation. London: John Wiley and Sons, 1966. 30. Pearson GHJ. Effect of age on vibratory sensibility. Arch Neuro Psychiatr. 1928; 20:482–496. 31. Laidlaw RW, Hamilton MA. Thresholds of vibratory sensibility as determined by the pallesthesiometer. Bull Neurol Inst NY. 1937; 6:494–503. 32. Cosh JA. Studies on the nature of vibration sense. Clin Sci. 1953; 12:131–151. 33. Mirsky IA, Futterman P, Broh-Kahn RH. The quantitative measurement of vibratory perception in subjects with and without diabetes mellitus. J Lab Clin Med. 1953; 41:221–235. 34. Steiness IB. Vibratory perception in normal subjects. Acta Med Scandi. 1957; 158: 315–325. 35. Rosenberg G. Effect of age on peripheral vibratory perception. J Am Geriatr Assoc. 1958; 6:471–481. 36. Perret E, Regli F. Age and the perceptual thresholds for vibratory stimuli. Eur Neurol. 1970; 4:65–76. 37. Kenshalo DR. Somesthetic sensitivity in young and elderly humans. J Gerontol. 1986; 41:732–742. 38. Dyck PJ, Schultz PW, O’Brien PC. Quantification of touch-pressure sensation. Arch Neurol. 1972; 26:465–473. 39. Bolton CF, Winkelmann RK, Dyck PJ. A quantitative study of Meissner’s corpuscles in man. Neurology. 1966; 16:1–9. 40. Lord SR, Ward JA. Age-associated differences in sensori-motor function and balance in community dwelling women. Age Ageing. 1994; 23:452–460. 41. Halar EM, Hammond MC, LaCava EC, Camann C, Ward J. Sensory perception threshold measurement: an evaluation of semiobjective testing devices. Arch Phys Med Rehab. 1987; 68:499–507. 42. Laidlaw RW, Hamilton NA. A study of thresholds in appreciation of passive movement among normal control subjects. Bull Neurol Inst. 1937; 6:268–273. 43. Skinner HB, Barrack RL, Cook SD. Age-related decline in proprioception. Clin Orthop Relat Res. 1984; 184:208–211. 44. Kaplan FS, Nixon JE, Reitz M, Rindfleish L, Tucker J. Age-related changes in proprioception and sensation of joint position. Acta Orthop Scand. 1985; 56: 72–74. 45. Petrella RJ, Lattanzio PJ, Nelson MG. Effect of age and activity on knee joint proprioception. Am J Phys Med Rehab. 1997; 76:235–241. 46. Hurley MV, Rees J, Newham DJ. Quadriceps function, proprioceptive acuity and functional performance in healthy young, middle-aged and elderly subjects. Age Ageing. 1998; 27:55–62. 47. Kokmen E, Bossemeyer RW, Williams WJ. Quantitative evaluation of joint motion sensation in an aging population. J Gerontol. 1978; 33:62–67. 48. MacLennan WJ, Timothy JI, Hall MRP. Vibration sense, proprioception and ankle reflexes in old age. J Clin Exp Gerontol. 1980; 2:159–171. 49. Petrovsky JS, Burse RL, Lind AR. Comparison of physiological responses of men and women to isometric exercise. J Appl Physiol. 1975; 38:863–868. 50. Murray MP, Gardner GM, Mollinger LA, Sepic SB. Strength of isometric and isokinetic contractions. Knee muscles of men aged 20 to 86. Phys Ther. 1980; 60:412–419. 51. Murray MP, Duthie EH, Gambert SR, Sepic SB, Mollinger LA. Age related differences in knee muscle strength in normal women. J Gerontol. 1985; 40: 275–280.

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52. MacLennan WJ, Hall MRP, Timothy JI, Robinson M. Is weakness in old age due to muscle wasting ? Age Ageing. 1980; 9:188–192. 53. Skelton DA, Greig CA, Davies JM, Young A. Strength, power and related functional ability of healthy people aged 65-89 years. Age Ageing. 1994; 23: 371–377. 54. Bosco C, Komi PV. Influence of aging on the mechanical behaviour of leg extensor muscles. Eur J Appl Physiol. 1980; 45:209–219. 55. Vandervoort AA, Hayes KC. Plantarflexor muscle function in young and elderly women. Eur J Appl Physiol. 1989; 58:389–394. 56. Nevitt M, Cummings S, Kidd S, Black D. Risk factors for recurrent nonsyncopal falls. J Amer Med Assoc. 1989; 261:2663–2668. 57. Campbell AJ, Borrie MJ, Spears GF. Risk factors for falls in a community-based prospective study of people 70 years and older. J Gerontol. 1989; 44:M112– 117. 58. Lipsitz LA, Jonsson PV, Kelley MM, Koestner JS. Causes and correlates of recurrent falls in ambulatory frail elderly. J Gerontol. 1991; 46:M114–122. 59. Fitzpatrick R, Rogers DK, McCloskey DI. Stable human standing with lowerlimb muscle afferents providing the only sensory input. J Physiol. London, 1994; 480(2): 395–403. 60. Shumway-Cook A, Woollacott M. Motor control: Theory and practical applications. Baltimore: Williams and Wilkins, 1995. 61. Lord SR, Sherrington C, Menz HB. Falls in older people: Risk factors and strategies for prevention. Cambridge: Cambridge University Press, 2001. 62. Lord SR, Clark RD, Webster IW. Postural stability and associated physiological factors in a population of aged persons. J Gerontol. 1991; 46(3):M69–76. 63. Satariano WA, DeLorenze GN, Reed D, Schneider EL. Imbalance in an older population: an epidemiological analysis. J Aging Health. 1996; 8(3):334–358. 64. MacLennan WJ, Timothy JI, Hall MRP. Vibration sense, proprioception and ankle reflexes in old age. J Clin Exp Gerontol. 1980; 2: 159–171. 65. Duncan G, Wilson JA, MacLennan WJ, Lewis S. Clinical correlates of sway in elderly people living at home. Gerontology. 1992; 38: 160–166. 66. Lichtenstein MJ, Shields SL, Shiavi RG, Burger MC. Clinical determinants of biomechanis platform measures of balance in aged women. J Am Geriatr Soc. 1988; 36:996–1002. 67. Stelmach G, Phillips J, DiFabio R, Teasdale N. Age, functional postural reflexes, and voluntary sway. J Gerontol. 1989; 44(4):B100–106. 68. Cohen H, Heaton LG, Congdon SL, Jenkins HA. Changes in sensory organisation test scores with age. Age Ageing. 1996; 25:39–44. 69. Lord SR, Lloyd DG, Li SK. Sensori-motor function, gait patterns and falls in community dwelling women. Age Ageing. 1996; 25:292–299. 70. Finley FR, Cody KA, Finizie RV. Locomotion patterns in elderly women. Arch Phys Med Rehab. 1969; 50:140–146. 71. Bohannon RW. Comfortable and maximum walking speed of adults aged 20-79 years: reference values and determinants. Age Ageing. 1997; 26:15–19. 72. Winter DA, Patla AE, Frank JS, Walt SE. Biomechanical walking patterns in the fit and healthy elderly. Phys Ther,. 1990; 70(6):340–347. 73. Woolley SM, Czaja SJ, Drury CG. An assessment of falls in elderly men and women. J Gerontol. 1997; 52A(2):M80–87. 74. Cho C-Y, Kamen G. Detecting balance deficits in frequent fallers using clinical and quantitative evaluation tools. J Am Ger Soc. 1998; 46:426–430. 75. Wolfson L, Whipple R, Amerman P, Tobin JN. Gait assessment in the elderly: a gait abnormality rating scale and its relation to falls. J Gerontol. 1990; 45(1): M12–19.

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76. Luukinen H, Koski K, Laippala P, Kivela S-L. Risk factors for recurrent falls in the elderly in long-term institutional care. Public Health. 1995; 109:57–65. 77. Breslow L, Breslow N. Health practices and disability: Some evidence from Alameda county. Pre Med. 1993; 22:86–95. 78. Wagner EH, LaCroix AZ, Buchner DM, Larson EB. Effects of physical activity on health status in older adults. I: Observational studies. Annu Rev Publ Health. 1992; 13:451–68. 79. Buchner DM, Beresford SA, Larson EB, La Croix AZ, Wagner EH. Effects of physical activity on health status in older adults II: Intervention studies. Annu Rev Publ Health. 1992; 13:469–488. 80. Lord SR, Ward JA, Williams P, Strudwick M. The effect of a 12 month exercise program on balance, strength and falls in older women: a randomised controlled trial. J Am Geriatr Soc. 1995; 43:1198–1206. 81. Lord SR, Ward JA, Williams P. The effect of exercise on dynamic stability in older women: a randomised controlled trial. Arch Phys Med Rehab. 1996; 77: 232–236. 82. Lord SR, Lloyd DG, Nirui M, Raymond J, Williams P, Stewart RA. The effect of exercise on gait patterns in older women: a randomised controlled trial. J Gerontol. 1996; 51A:M64–M70. 83. Rogers MA, Evans WJ. Changes in skeletal muscle with aging: Effects of exercise training. Exercise Sport Sci R. 1993; 21:65–102.

Chapter 6 COGNITIVE CHANGES AND THE AGEING BRAIN Helen Christensen* and Rajeev Kumar

Introduction There is still debate about the specific types of changes in cognitive and intellectual functioning that occur over the lifespan, although there is also agreement that cognitive change is not unitary and that some abilities decline more rapidly than others.1–3 There is considerable interest and speculation about the age of onset of cognitive deterioration and the brain mechanisms responsible for it. More recently there has been greater appreciation of the way in which individuals differ in their rates of cognitive change (i.e., interindividual variability), and in the possible importance of inconsistency in cognitive performance (intra-individual variability) as a predictor of cognitive deterioration. Two areas of intense investigation in cognitive ageing at the present time include the hypothesis that there is a common factor that is responsible for changes in both cognitive abilities and non-cognitive variables over the lifespan, and the question of which brain structures are associated with cognitive change. This chapter summarizes recent evidence on the nature of cognitive decline, the variability in individual responses to ageing and on the newer research questions in cognitive ageing. Many previous reviews describe the relationship between cognitive performance and brain structures from a neuropsychological perspective rather than the individual differences perspective taken in the present chapter. The neuropsychological approach emphasizes the relationship between cognitive functions and brain locations and systems, and is informed by clinical neuropsychological methods, where lesions *To whom correspondence should be addressed.

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and patient behaviour are examined. An example of a neuropsychological approach to ageing is the investigation of the frontal lobe hypothesis. This hypothesis states changes in cognitive functioning are due to shrinkage or other changes in the frontal lobes, and hence deficits should be specific to tests which reflect frontal rather than other lobe functioning.4 In contrast to this approach, the individual differences approach of this chapter examines cognitive domains that have been derived from psychometric studies of the structure of intelligence. The chapter is divided into three sections. First, data are summarized to demonstrate that cognitive ageing is not unitary but rather some abilities decline more rapidly than others. Second, the evidence is examined which suggests that there is greater variability in test scores as a function of age, a finding which may indicate the existence of a number of sub-groups which age at different rates within the same population group. Finally, the evidence is examined for the common cause hypothesis and for the association of various brain structures with change in major areas of cognitive function. Constraints, limitations and definitions In this chapter, cognitive ageing refers to the cognitive performance that is observed in longitudinal and cross-sectional studies of community-dwelling elderly people. Methodologically, longitudinal studies provide information that cross-sectional studies cannot provide such as: estimates of individual rates of decline, risk factors for such decline, data on correlations between changes in cognitive ability and changes in other cognitive and non-cognitive domains. However, longitudinal studies underestimate change because of practice effects and selective attrition. In particular, individuals who later go on to develop dementia are often excluded from community longitudinal studies since they no longer live in the community, and individuals who die before follow-up are known to have lower scores at baseline. Conversely, cross-sectional studies tend to over-estimate cognitive decline. One reason is that there are established cohort effects in intelligence, with an average increase in intelligence being reported for successive generations of individuals. Flynn5 concluded that the average IQ had increased by 14 points (corresponding to approximately one standard deviation of intelligence measures) from the period 1932 to 1978. Although there are many methods used to describe or examine cognitive change we characterize cognitive ability as consisting of three major abilities: crystallized intelligence, memory and cognitive speed. The distinction between crystallized intelligence and cognitive speed or fluid intelligence was not invoked to describe the nature of decline in intellectual abilities, but arose from factor analytic studies of the structure of intelligence.6 Crystallized intelligence is “assumed to be the cumulative end-product of information acquired” by an individual7 and is demonstrated on tests of vocabulary, information accumulation and other knowledge-based activities. Memory is commonly divided into short-term and long-term, with both types further

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Box 1. Common measures of intelligence and memory Ability type

Common measures and tests

Crystallized intelligence

Vocabulary Information Similarities National Adult Reading Test Wechsler Adult Intelligence Scale (Verbal Scale) Raven’s Progressive Matrices Block Design Digit Symbol Substitution Reaction Time Wechsler Adult Intelligence Scale (Performance Scale) Priming Reversed mirror reading Classical conditioning Logical Memory and Paired Associates Buschke Selective Reminding Test

Fluid intelligence

Procedural memory Declarative memory

fractionated into declarative and procedural memory. Declarative memory refers to conscious recollection and recall, while procedural memory (which is memory that does not require the intentional or conscious recollection of an experience) includes priming, classical conditioning and skill-based learning. Cognitive speed refers to performance on perceptual-motor tasks which are timed, and overlaps with more traditional constructs such as fluid intelligence. Examples of such tasks are the Digit Symbol Substitution test from the Wechsler Adult Intelligence Scale8 or simple and choice reaction time tasks. There is enormous debate as to the nature of speed and speed tasks, and whether it is in fact possible to develop a core speed task.9 Box 1 shows typical cognitive tests and their assignment to these key concepts. Cognitive Change is not Unitary The existence of different developmental trajectories according to ability type has been well recognized for decades.6,10 Data from meta-analyses of cross-sectional studies7 suggest that crystallized intelligence remains relatively stable in old age, dropping in late old age. In contrast, cognitive speed drops by approximately 20% at age 40 and by 40–60% at age 80. Other large cross-sectional data sets, including those from the Wechsler Adult Intelligence Scale-Revised (WAIS-R) have been summarized. 11 Results from the WAIS performance measures, such as Digit Symbol Substitution and Block Design, show steep gradients while WAIS-R Verbal tests such as Information,

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Vocabulary, and Comprehension, indicate shallower declines across the age range examined (20–74 years). Data from many longitudinal studies confirm these findings (see Box 2 for a summary of longitudinal studies worldwide examining cognitive change). Figure 1 illustrates changes in crystallized intelligence (vocabulary), perceptual speed (identical pictures), and memory (immediate recall) using data that have been derived from the Seattle Longitudinal Study, which is one of the most influential longitudinal studies ever undertaken. This figure, adapted from Schaie2 illustrates that changes in speed and verbal memory appear to accelerate after 60 years of age, while verbal ability is more robust and may only drop in very late old age. Participants from the study were drawn from an age/sex stratification of members of a health maintenance organization. Schaie2 reported that cohort effects were evident for the baby boomer generation, with deterioration abating in these newer age cohorts. More specific data on age changes after the age of 70 years are illustrated in community-based surveys of older samples, such as the sample reported by us in the Canberra Longitudinal Study12 (Figure 2). These data clearly show that crystallized intelligence does not change significantly, even into late old age, in those surviving to follow-up. The findings from our study showing significant deterioration in memory performance have been reported in many other longitudinal data sets: the Einstein ageing study13, Victorian Longitudinal Study,3 Duke Longitudinal Study and other Australian longitudinal studies (Australian Longitudinal Study of Aging), to name just a few. Similar results have been reported for processing speed.2,13 There is some evidence that the rate of ageing in these cognitive domains may accelerate once individuals reach 70, although this is as yet unclear. Cognitive decline is a predictor of mortality14 with cognitive test scores on many measures predicting mortality even when demographic and health variables have been controlled statistically. 15,16 While sensory, speed and cognitive tests predict mortality, performance on certain tests of pre-morbid ability such as National Adult Reading Test17 is not a predictor. Inter-Individual and Intra-Individual Variability associated with Ageing Older age is associated with greater inter-individual and intra-individual differences. Thus, there is greater diversity in the cognitive trajectories of older individuals (a between-individuals effect) and greater inconsistency in individual responses to task performance (a within-individuals effect). Inter-individual differences (diversity) Although the average performance on most cognitive tasks declines with age, studies have suggested that many older individuals change very little, whereas others deteriorate dramatically. At older ages there is a greater diversity of

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Box 2: Longitudinal Studies of Psychological Ageing Current Longitudinal Studies Australian Longitudinal Study of Aging (ALSA; 1992– ) Baltimore Longitudinal Study of Aging (BLSA; 1958– ) Berkeley Older Generation Study Berlin Aging Study (BASE; 1990– ) The Betula Project (1988– ) Canberra Longitudinal Study (1991– ) Einstein Aging Studies Gender Study of Unlike-Sex DZ Twins (GENDER) Georgia Centenarian Study (1988– ) Groningen Longitudinal Aging Study (GLAS; 1993– ) The Gerontological and Geriatric Population Studies in Göteborg, Sweden (H-70; 1971– ) Health and Retirement Study (HRS; 1992– ) and Asset and Health Dynamics Among the Oldest Old (AHEAD; 1993– ) Interdisciplinary Longitudinal Study of Adult Development (ILSE; 1996– ) Italian Longitudinal Study of Ageing (ILSA) The Kungsholmen Project (1987– ) Long Beach Longitudinal Study (1978– ) Longitudinal Aging Study Amsterdam (LASA; 1991– ) Lund 80+ Study (1988– ) Maastricht Aging Study (MAAS; 1992– ) Manchester and Newcastle Longitudinal Studies of Aging McArthur Successful Aging Studies Mills Longitudinal Study of Women (1956– ) National Growth and Change Study (NGCS) New England Centenarian Study Normative Aging Study (1963– ) Nordic Research on Aging (NORA; 1989– ) The Nun Study (1986– ) Octogenarian Twin Study (OCTO-Twin; 1990– ) San Antonio Longitudinal Study of Aging (SALSA) Seattle Longitudinal Study (1956– ) The Swedish Adoption/Twin Study of Aging (SATSA; 1984– ) The Victoria Longitudinal Study (VLS; 1986– ) WAIS Longitudinal Study Database (NGCS 2000) Completed Longitudinal Studies AT&T Longitudinal Studies of Managers The Bonn Longitudinal Study (BOLSA; 1965–1984, mortality follow-up) First Duke Longitudinal Study (1955–1976 ) Second Duke Longitudinal Study (1968–1976) Intergenerational Growth Study (IGS; 1932–1982) New York State Psychiatric Institute Study of Aging Twins (1946–1973) New York Longitudinal Study (1956–1988) NIMH Study (1955) Iowa State Study (1919–1976) Terman-Stanford Study () Source: http://www.personal.psu.edu/faculty/s/m/smh21/index.htm76

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Figure 1. Age changes in cognitive test scores over age range of 25 to 88 based on two data points (1984 and 1991). Each age segment is based on a single sample followed over seven years. T-score points are scaled to have a mean of 50 and a standard deviation of 10. Data are taken from Schaie.2

(a)

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81

(b)

(c) Figures 2. Mean scores on measures of crystallized intelligence (a), memory (b), and speed (c) for four age groups over three occasions from the Canberra Longitudinal Study.

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cognitive scores.18–21 In a review of six longitudinal and 48 cross-sectional studies, Nelson and Dannefer18 reported increased variability with age, not only for cognitive variables but also for personality and biological indices. There have also been recent studies reporting greater individual differences in cognitive change,3,12 findings that indicate greater diversity in the rates of change at older ages. Most, but not all studies, suggest that the individual differences in speed and memory increase with increasing age. These findings are consistent with the possibility that a number of sub-groups exist with different rates of cognitive change. Data illustrating increased individual differences in change from the Canberra Longitudinal Study12 are displayed in Figure 3. Changes on a verbal measure (crystallized intelligence) and a measure of memory are shown. These data illustrate individual paths for all participants over the three occasions of measurement. Measures were taken in 1991, 1994 and 1998. A measure of individual diversity was calculated by regressing cognitive change scores on age, determining the absolute residuals of all change scores and then correlating these residual scores with age. We found a positive correlation between residuals scores and age both for speed and memory, indicating greater diversity in these cognitive abilities at older ages. This association was not found for crystallized intelligence. Many factors have been suggested as sources of these individual differences in cognitive trajectories. Following Kraemer et al.22 these can be conveniently identified as either marker variables or risk factor variables. Marker variables are those that are essentially unchangeable, such as education (schooling), ApoE ε4 allele and gender. Risk factor variables, such as physical activity and age, are those that develop and change with the individual. Because there are data from cross-sectional studies on the risk factors of education, ApoE (a marker variable) and health these are examined below. Education has often been reported to be a protective factor in cognitive ageing with a number of studies reporting that the rate of decline is less rapid in the highly educated.23–25 Other studies report that the rates do not differ as a function of educational,3 or that the effects are restricted to sub-groups or only observed on verbal intelligence measures.26 To account for the association between education and the rate of cognitive decline, a number of hypotheses have been advanced. Education has been suggested to be a proxy variable for “brain reserve”, i.e. increased brain capacity across the lifespan or pre-morbid intelligence (see Chapter 13). Low intelligence has been found to predict decline on memory tests.27 There is evidence23 that the effect of education on levels of performance may depend on the type of cognitive outcome measure that is investigated. For example, the findings of a recent review23 were that studies that measured outcome with a screening test, such as the MMSE, reported a protective effect of education. Five of seven studies that used a memory test reported faster decline in those with poor education, and three of four studies found faster decline for measures of crystallized intelligence. In comparison, three studies that used a measure of cognitive

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(a)

(b)

Figure 3. Longitudinal individual trajectories for (a) crystallized intelligence and (b) memory from the Canberra Longitudinal Study.

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speed failed to find an effect for education. These findings suggest that the protective effects of higher education may be restricted to crystallized intelligence, memory and mental state measures. The relationship between cognitive change and ApoE ε4 and ApoE ε2 has also been investigated in a number of studies. With respect to ApoE ε4, Anstey and Christensen23 reported that ApoE ε4 predicted cognitive decline in five studies,28–32 three studies found that the effect was present but observed only on certain tests33–35 and two studies reported that the rate of change did not differ.36,37 Three more studies have recently been reported. One study indicated faster decline in ApoE ε4 individuals,38 one suggested the association was only present in those with MMSE below a score of 2739 and one demonstrated no association in a very old sample.40 Overall, these findings support the view that the rate of cognitive decline is greater in those with ApoE ε4. The effect of ApoE ε4 was found primarily on measures of memory, MMSE and processing speed, suggesting the effect is related to memory and fluid abilities. Only one study reported results for a measure of crystallized intelligence and this was non-significant.33 There is a consensus amongst researchers that serious health problems may be detrimental to cognitive functioning but that less serious and self-reported health problems do not account for the bulk of cognitive ageing. To quote Arbuckle et al.26 “Cross-sectional and longitudinal studies of the relation between health and cognitive functioning have generally shown that poorer physical health is associated with poorer cognitive functioning … but that age-associated illnesses explain only part of the age-related variance in cognition.” Anstey and Christensen23 summarized findings from eight longitudinal studies reporting data on health and disease measures as predictors of cognitive change. The measures of health included self-ratings, disease status, lung function and depressive symptoms. Using a global self-report measure, three studies found no relationship between self-reported poor health and cognitive change, while two studies found relationships in the predicted direction (lowratings were associated with greater change). Objective measures of health status including lung function and glucose tolerance were all related to cognitive decline as was atrial fibrillation and cardiovascular disease. These findings suggest that poor health is associated with faster cognitive decline. However, as noted above, poor health may account for only a part of the deterioration. Salthouse et al.41 have conduced analyses investigating the role of health by comparing the magnitude of age-related effects on measures of functioning, mostly speed, before and after statistical control of measures of health. Between 15% and 20% of the age-related variance in cognitive scores was accounted for by self-rated health, suggesting that health factors do not mediate the great proportion of age-related cognitive decline.

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Although there are relatively few studies for each predictor, these findings suggest that higher education, absence of the ApoE ε4 allele, and good health are protective of subsequent cognitive decline. An interesting finding is that risk factors may be specific to the type of cognitive task, with, for example, education exerting an effect on crystallized and memory measures, and ApoE ε4 allele affecting memory and speed to a greater extent than crystallized intelligence. Intra-individual differences (short-term and long-term individual difference) Greater intra-individual change with older age has also been reported. Nesselroade42 made a distinction between individual change that is more or less durable or systematic (developmental change) and individual change that is transient and short term (the “wobble” about the developmental change). Both these types of within-person change have been examined in a variety of studies.43-45 In the original research of this kind, Hertzog et al46 tested memory for sentences fortnightly in seven women over two years, testing performance on equivalent forms of test. Individuals varied in the extent to which they were consistent in their level of remembering. Individuals also had different trajectories, with either steady improvement or consistent decline over the two-year period. Recent studies of intra-individual variability show that the wobble increases with age for memory,44 reaction time,43 and sensori-motor tasks.44 The extent of deviation from the average is also a predictor of poorer levels of performance.45 Li and Lindenberger44 have suggested that transient intra-individual variability indicates impaired neurobiological functioning. This variability seems to occur across different tasks (such as sensori-motor and reaction time) and, hence, may be a relatively stable characteristic of people. Individuals diagnosed with dementia show approximately twice as much wobble as those not so diagnosed.46 The Common Cause Hypothesis One of the major issues concerning cognitive researchers has been whether cognitive decline in various cognitive domains (for example memory and fluid intelligence) have independent rates of decline and independent predictors of decline. In a famous paper,20 Patrick Rabbitt asked “Does it all go together when it goes?”, and recent findings suggest that there may not be independence in the rate of change. Speed of processing (i.e. the rate at which individuals could process incoming stimuli) was suggested by many as a possible common mechanism. Evidence from a number of sources47 indicated that much of the age-related variance among speeded tasks and other cognitive tasks was shared. In an influential development, Lindenberger and Baltes48 reported that sensory functioning, measured by vision and hearing assessments, also shared age-related variance with cognitive tasks, suggesting that a common mechanism might need to account for sensory changes. Even more

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interesting have been reports indicating that grip strength and lung capacity correlate with changes in cognitive functioning.43 These observations have given rise to the “common cause” hypothesis48 — the hypothesis that a single common mechanism may be responsible for the decline in physical, sensory and cognitive processes. Evidence for the common cause hypothesis in cognitive ageing relies on a number of observations made from cross-sectional studies of age-heterogeneous samples. These include the observation that “cognitive primitives”, such as sensory functioning and speed of processing, and non-cognitive factors such as respiratory efficiency, low blood pressure and physical strength are significantly intercorrelated in cross-sectional samples and decline conjointly with age. A second observation is that age has only a small direct role in predicting cognitive performance over its effect via sensory functioning or speed of processing.14,47,48 That is, memory performance can be accounted for almost entirely by performance on speed or sensory tasks, and that the additional effect of age as a variable predicting cognitive performance once these other variables are considered is minimal. A number of studies have indicated that a common factor can be modelled in cross-sectional data sets. For example, Salthouse et al.47 estimated a common factor directly, with the effect of age being regressed onto both the factor and on to its indicators. Fitting this model to five datasets, they found that speed, cognitive functioning, grip strength, and visual tests loaded on a common factor. Age had an (additional) independent relationship with sensory functioning but not with the other indicators of the common factor. To explain the large proportion of shared age-related variance, researchers have proposed that a common physiological process is or processes are responsible, for example, the “ageing brain”,48 changes in the central nervous system and general fitness of the organism,49 and the ageing-related changes to the physiology of the entire organism.50 More recently, Salthouse and Czaja51 have suggested that the common cause may be of two types, which may not be mutually exclusive. The first type of broad influence is cognitive, either a particular type of process or a property of processing, such as speed of processing or the involvement of working memory. A second is neuroanatomical or neurophysiological (either an area of processing, such as the dorso-lateral prefrontal cortex) or a specific process (such as the dopaminergic system). Salthouse and Czaja51 note that “... the results of … analyses impose clear constraints on the nature of plausible explanations for cognitive aging phenomena. That is, because many age-related effects seem to operate at relatively broad levels, which affect a wide variety of cognitive variables, researchers must apparently postulate some general or nonspecific explanatory mechanisms.”

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Data from cross-sectional research43,52 showing associations among cognitive scores and other non-cognitive variables such as grip strength and respiratory function suggest that the common process may be even more broad than brain processes as identified by Salthouse and Czaja.51 The common cause hypothesis needs evaluation in longitudinal studies where the associations among longitudinal changes in sensory, physical and cognitive processes can be evaluated for their independence. To advance this area, researchers need to examine longitudinal relationships among biological, cognitive and sensory indicators preferably in large samples to maximize power. As noted by Deary,9 “There is lack of tractable measures of the brain’s information processing capabilities that will bridge behaviour and biology with an explanation.” In the following section, we review the association between MRI indices of brain structure and cognitive performance. This represents one immediate area of investigation that could help to understand what mediates changes in cognitive performance across the lifespan. Longitudinal Studies of MRI, CT and Cognitive Change Tables 1 and 2 outline longitudinal studies53–74 examining the association between various areas of the brain (as shown by MRI and CT scans) and measures of cognitive performance. Three groups of individuals were examined: those with Alzheimer’s disease (AD) or probable AD, those with Cognitive Decline, Mild Cognitive Impairment (MCI), or described as being “at risk” and those described as “normal”. Information is summarized in these tables about the number of participants in each of the studies, the length of the study follow-up, the age of the participants, the brain area under study, whether the study found significant changes in brain area volumes and whether the study reported change in cognitive performance across a range of measures. If significant volume change or cognitive change was found in each study, a “Yes” was entered into the appropriate column. The final column of each of the tables summarizes the findings from those studies where the correlation between a cognitive measure and a brain volume measure was examined explicitly. A “Yes” in this column indicates that a positive association was found. A “No” indicates that the correlation was not significant. The MRI studies (Table 1) suggest that over time there are changes in brain volumes for normal individuals, those at risk of AD, those with MCI and for those with AD. The degree of volume loss is greater in those with AD than in those with other conditions59,62 and for those at older ages.58 The longitudinal evidence also suggests that cognitive change is particularly evident for individuals who were diagnosed with MCI and AD at baseline. Over the range of follow-ups, normal individuals also show cognitive change, a finding that is consistent with evidence presented earlier in this chapter. At shorter follow-up intervals, there is less clear evidence for cognitive decline in normal

88 Table 1.

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Longitudinal studies reporting MRI changes and their associations with cognitive change.

Study

Number Length Age of of (years) subjects study (years)

Alzheimer’s Disease (AD) or Probable AD Fox 199654 11 1 53.8 Jack 199858 24 1 80.42 Fox 199959 29 6 58.1 Fox 200062 18 1 65 Jack 200064 28 3 73.8

Brain areaa

Change Change Correlationb in brain in area cognitionb

? ? Yes Yes Yes

? ? Yes ? ?

Cognitive decline, Mild Cognitive Impairment (MCI), or “at risk” Kaye 199755 12 3.6 84 B,H,TL Yes Yes Fox 199857 63 6 44.7 H Yes ? Jack 200064 43 3 77 H Yes Yes

Yes ? ?

Normal individuals Wahlund 199653 Fox 199654 Kaye 199755 Mueller 199856 Jack 199858 Fox 199959 Schmidt 199960 Ylikoski 200061 Fox 200062 Jack 200064 Garde 200063 Moffat 200065

24 11 30 46 24 15 273 35 18 58 68 26

B H B B H

Yes Yes Yes Yes Yes

5

79

B,WML

Yes

1 3.6 5 1 6 3 5 1 3 16 2.7

51.3 84 >65 81.04 55.3 60 55-70 65 80.4 80.7 68

B B,H,TL B H B WML WML,TL B H WML H

? Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes

Psychomotor but not other No No ? ? ? ? Yes No Yes Yes ?

No ? ? ? ? ? No No ? ? ? ?

aWML

= White matter lesions, B = Whole brain or other brain regions, H = Hippocampal area, TL = Temporal lobe area. bEvidence not provided.

individuals. Although volume loss and cognitive decline were present in the normal groups, none of the studies found a positive correlation between MRI volume changes and cognitive change. This lack of association cannot be readily attributed to length of followup since the measures were taken over long intervals (up to and including five years). However, one explanation is that the age range of the subjects was restricted (i.e. there was only a small range of ages in those examined), thereby reducing the correlation. Similarly, it is possible that floor or ceiling

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Table 2.

Longitudinal studies reporting CT changes and their associations with cognitive change.

Study

Number Length Age of of (years) subjects study (years)

Alzheimer’s Disease Luxenberg 198770 18 De Leon 198968 50 Burns 199167 Wippold 199166 De Carli 199271 Jobst 199472 Shear 199573 Meyer 200074

63 60 20 61 41 19

5 3

62.8 71.2

1 4 4.5 4 2.1 5.8

79.3 72.6 66 73.1 70.7 59.5

Brain areaa

V VBR, V, WML V, CA V, CSF V TL CSF CA, V

Change Change Correlationb in brain in area cognitionb

Yes

?

Yes

Yes Yes Yes Yes Yes Yes ?

? Yes ? ? Yes ? Yes

? Yes No* Yes ? Yes ?

Cognitive decline, Mild Cognitive Impairment (MCI), or “at risk” Bird 198669 5 4 >60 VBR,CA ? ? Meyer 200074 22 5.8 59.5 CA, V ? Yes Normal Individuals Bird 198669

50

4

Luxenberg 198770 12 De Leon 198968 45

5 3

65.1 68.9

4 17 1.7 2.6 5.8

72.9 62 62 67.4 59.5

Burns 199167 Wippold 199166 De Carli 199271 Jobst 199472 Shear 199573 Meyer 200074

58 17 47 35 224

>60

VBR, V, CA V VBR, V, WML V,CSF V TL CSF CA, V

Yes ? Yes

No Yes

No ?

? ?

Yes

?

?

No Yes Yes ? Yes Yes

No ? No No ?

? ? ? ? ?

aVBR=

Ventricular brain ratio, V= Ventricular volume, CSF= Cerebrospinal volume, TL= Temporal lobe, CA= Cerebral atrophy, WML= White matter lesions. *Cognitive test here was the Clinical Dementia Rating Scale and this may not be a valid measure of cognitive change. bEvidence not provided.

effects in the tests result in a restricted range of test scores and this reduced the size of the correlation. Since many of the studies used screening tests that are known to have ceiling effects, this is a strong possibility. Another explanation is that reliable tracing of brain structures is only possible once these structures show significant involution or shrinkage. A major problem with the reviewed studies is that the measures of both brain area and cognitive

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domain are rather crude. The relationships between brain structural changes and cognitive performance are best examined using tests that reflect specific cognitive domains. This type of analysis has been undertaken in a recent cross-sectional analysis. Raz et al.75 examined the relationship between brain substrates and measures of working memory, explicit memory, priming and executive functioning. Contrary to expectations, they found no relationship between explicit memory and limbic brain volumes in a large sample of 113 individuals who ranged in age from 18–77 years. They suggested that explicit memory might only be affected once extensive damage is incurred in relevant brain structures, a finding consistent with the data in Table 1. If this were the case, however, it leaves unanswered the question of which brain structures or processes mediate the changes in cognitive performance that are observed in “healthy” older adults. Loss of hippocampal volume may not be responsible. Research examining brain volumes and cognitive test scores in large community samples can be expected to clarify these relationships, especially if these investigations include a range of specific cognitive tests and examine limbic areas in addition to the hippocampus 75 and areas of the pre-frontal cortex. Table 2 outlines the results from longitudinal studies using computed tomography (CT) and measuring cognitive change. Although CT imaging is less specific than MRI, the findings in Table 2 are highly consistent with the MRI data. Table 2 demonstrates that there is loss of volume in brain areas as assessed by CT in normal individuals, those with AD and those with mild cognitive decline. As is expected, a change in cognition is less likely to be detected in normal samples than those with dementia or mild cognitive impairment. Almost all studies show an association between change in CT measures and deterioration in cognitive performance for AD and MCD samples. The single failure to find this association66 in the AD samples may well be due to the insensitivity of the Clinical Dementia Rating Scale which was the “cognitive test” used in the study. Consistent with the MRI findings shown in Table 1, the association between changes in cognitive performance and changes in brain volumes as measured by CT scans in non-clinical populations was not established. However, for the CT findings as opposed to the MRI studies, this is likely to be due to the lack of cognitive change observed in the individuals in the normal groups who were followed up. It was argued earlier that structural neuroimaging provides the opportunity to examine relationships between cognitive and brain changes. In particular, this area of investigation may inform us about the causes of cognitive ageing and the possible biological substrate of the “common factor”. At this stage, these investigations have not provided clear evidence of the structures which underlie memory or cognitive change in normal individuals. These early longitudinal studies (Tables 1 and 2) have methodological limitations. However, if these studies were indicative of the underlying associations, they suggest that brain volume losses occur independently of cognitive change. In particular, brain volume loss is apparent without corresponding cognitive

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change55,62,73 and where both cognitive and brain volume loss is observed there is no correlation between the two.,53,61 Conclusions Cognitive ageing is an exciting area of investigation. We know that on average older people perform more poorly in areas of memory and speed than younger people. There is evidence that the elderly are less reliable and less consistent than younger people, that there is greater diversity of cognitive responses in older age, and a number of important risk factors for decline have been identified, in particular ApoE. Much is known about predictors of cognitive change, but the new challenge is to identify the causes of cognitive deterioration. This process will be facilitated by longitudinal research. In recent years there has been a renewed attempt to relate changes in biological processes, indexed by measures of forced expiratory volume (FEV) and grip strength, changes in brain structure as measured by white matter lesions, brain and hippocampal volume and changes across cognitive domains. Further research will involve the examination of a greater range of associations among changes in cognitive and non-cognitive variables, including changes in indices of brain function, cell metabolism, health and activities of daily living.

Acknowledgements This chapter was supported by grant 973302 from the National Health and Medical Research Council and by the Australian Rotary Health Research Fund. Thanks are due to AF Jorm, AE Korten, AS Henderson, PA Jacomb and B Rodgers for their contribution to the survey design, management and analysis of the Canberra Longitudinal Study, and to S Hofer, Department of Human Development and Family Studies,The Pennsylvania State University and AJ Mackinnon, The Mental Health Institute of Victoria, and Department of Psychological Medicine, Monash University, for their assistance with data analysis. References 1. 2. 3. 4.

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Chapter 7 AGEING OF THE HUMAN BRAIN AS STUDIED BY FUNCTIONAL NEUROIMAGING Julian N. Trollor* and Perminder S. Sachdev

Introduction A number of key molecular and structural changes occur in the brain as it ages. The relationship between such changes and function of the aged brain are poorly understood. Ageing brings with it preferential decline in fluid cognitive abilities, with preservation of crystallised intellectual abilities1 (Chapter 6). In particular, age-related decline is seen in speed of information processing, working memory and complex attention. The underlying mechanisms of the decline in some cognitive domains remain obscure. Although structural correlates of this phenomenon such as preferential decline in frontal lobe volumes have been noted2 (Chapter 4), a less than robust relationship between cognitive function and frontal lobe atrophy has been observed.3,4 Such discrepancy emphasises the importance of exploring the functional correlates of age-related neuropsychological decline. Modern imaging techniques are appropriate tools to probe the interrelationship between molecular, structural and functional changes that occur during the ageing process. One of the salient issues to be addressed is that of resting cerebral metabolic and blood flow correlates of senescent cognitive change. To this end, this chapter reviews relevant neuroimaging *To whom correspondence should be addressed.

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studies of ageing, as well as discussing the limitations of the literature. Functional imaging correlates in special groups such as those with hypertension, those at risk for cerebrovascular disease and those with known risk factors for Alzheimer’s disease (AD) are explored. Not only are the neuroimaging parameters at rest of importance, these techniques also enable us to compare the effects of cognitive activation in young and old subjects. Observations during activation assist in developing functional maps of normal and compromised brain function. The study of the effects of age on the functional integrity of networks subserving certain cognitive functions, especially memory, enables the integration of functional neuroanatomy and cognitive theories of ageing. In addition to age-related decline in cognition, the ageing process brings with it vulnerability to age-related neuropsychiatric disorders. The application of functional imaging techniques provides an opportunity to study key metabolic, blood flow and biochemical correlates mediating this vulnerability. Functional Imaging Techniques SPECT & PET Both positron emission tomography (PET) and single photon emission computed tomography (SPECT) have been used to evaluate changes associated with ageing and age-related neuropsychiatric disorders. These techniques allow observation of a number of parameters related to cerebral function. Regional cerebral blood flow (rCBF) can be measured with both techniques, and cerebral metabolic rate of oxygen (rCMRO2) and cerebral metabolic rate of glucose (rCMRglu) with PET. Single photon emission computed tomography SPECT has been used extensively to evaluate changes associated with ageing and age-related neuropsychiatric disorders. Contemporary brain SPECT uses a rotating gamma camera to perform semiquantitative measurement of rCBF by evaluating the relative distribution of a radiopharmaceutical agent in the brain. SPECT detects single gamma rays (photons) and determines the places of their origin based solely on their trajectories. SPECT is based on the principle that some compounds will distribute in the body (in this case the brain) in a way that reflects underlying physiologic function of a particular region. Radiopharmaceuticals used to image the brain in SPECT have differing characteristics, but most have relatively a long half life (T/2) and are readily available commercially, a characteristic which makes SPECT more widely available than PET. The most common of these in modern use are 123I-IMP (iodine 123 N-isopropyl-p-iodoamphetamine), 99mTc-HMPAO (technetium 99m hexamethylpropylamine oxime), 123I HIPDM (123I N,N N-trimethyl-N-2-hydroxyl-3-methyl-5-iodobenzyl-1,3-propanediamine) and 133Xe (xenon 133).

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99

Positron emission tomography The fundamental principle of PET is the use of unstable isotopes that contain an excess of protons in their nuclei and decay rapidly, emitting a positron, which then travels a short distance before its annihilation by collision with an electron. The collision results in the release of two gamma rays travelling at 180o to each other. Coincident detection is registered when these gamma rays strike a circular array of crystal detectors, from which the plane of the annihilation event is determined. Information so gathered permits the mathematical conversion of data into quantitative measures reflecting the amount of isotope present and its spatial distribution in tissue at a given time. As the majority of radioisotopes used have a short T/2, on-site cyclotron production is usually required. Isotopes used are typically biologically relevant, such as 15O (oxygen 15), 11C (carbon 11), 13N (nitrogen 13) and 18F (fluorine 18). These isotopes can be used to label common biological matter (for example H215O using 15O and fluorodeoxyglucose (FDG) using 18F), and can be used to label drugs and receptor analogues. The need of a cyclotron and a special camera makes PET an expensive technology with only limited availability. 5 However, it has a number of advantages over SPECT scanning. PET uses isotopes with short T/2 which allow multiple scans in one individual, making it possible to perform activation studies using multiple variations of a task. While activation studies are possible with SPECT, these are limited by the fact that more than two SPECT scans (in a split-dose design) are not usually possible within a short period because of the radiation exposure involved. PET offers superior spatial resolution of the order of about 3–4 mm, while state-of-the-art SPECT does no better than 7–8mm, but the latter is steadily improving with innovations in technology. A limitation of SPECT is that quantitation of absolute blood-flow values are not easily derived unless more invasive measures are undertaken, e.g. sampling of carotid artery blood to assess absolute radio-activity tracer counts. PET is also more versatile in its application, providing rCMRglu (which SPECT does not), and being more readily adaptable to the study of neurotransmitters and drugs. Because of their poorer resolution in comparison with MRI, specific regions on SPECT and PET images are generally delineated after co-registering them with MRI scans, or by warping images to fit a standard MRI template. Functional magnetic resonance imaging (fMRI) fMRI exploits the changes in microvascular oxygenation in response to changes in blood flow to a region to estimate rCBF by the blood oxygen level dependent (BOLD) technique. This is based on the inherent contrasting magnetic properties of oxygenated versus deoxygenated haemoglobin. In its oxygenated state, the iron in haemoglobin is diamagnetic, having a small magnetic susceptibility effect. In the deoxygenated state, the iron in haemoglobin has a large magnetic susceptibility effect and significantly disturbs the surrounding magnetic field. At rest, a T2 gradient exists locally across

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THE AGEING BRAIN

microvasculature, from a predominantly diamagnetic oxyhaemoglobin rich environment to a paramagnetic deoxyhaemoglobin rich environment. In a time-dependant manner following neuronal activation, there is an initial brief decrease in oxygenated haemoglobin in the microvasculature, followed by a more sustained increase. Thus, during brain activation (whether by motoric, cognitive or pharmacological means), the relative excess of oxygenated haemoglobin produced by activation produces a T2 signal of greater intensity compared with the resting or unactivated state. The measurement of the signal change represents the fundamental principle of BOLD fMRI. Magnetic resonance spectroscopy (MRS) Proton magnetic spectroscopy (1H-MRS) is a tool with a largely untapped potential in the study of brain ageing. This non-invasive technique allows in vivo estimations of specific brain chemical profiles, which can be compared and contrasted across a variety of neuropsychiatric disorders. 1H-MRS can detect N-acetyl containing compounds such as N-acetylaspartate (NAA), which are putative markers of neuronal viability and density. Measurement of choline containing compounds (Cho) (such as free choline, phosphocholine and glycerophosphocholine) and creatine and phosphocreatine (Cr) allows assessment of membrane synthesis and metabolism. Other metabolites such as myoinositol (mI) are considered possible glial cell or intracellular toxin markers. It is also possible to determine spectra for neurotransmitters such as glutamate and GABA. Studies of Cerebral Blood Flow and Metabolism in Healthy Ageing Historical perspective The history of modern techniques for measuring CBF can be traced to the early work of Kety and Schmidt6, who first measured global CBF using an inhaled nitrous oxide technique. This technique was invasive, requiring measurement of arterio-venous difference in oxygen saturation, and allowed measurement of average CBF per minute and derivation of oxygen consumption and cerebrovascular resistance. In reviewing the series of sixteen early studies in which this technique was used to measure CBF in ageing, it was concluded, “There appears to be a rapid fall in over-all cerebral circulation about the time of puberty, which continues through adolescence. From the third decade onward, there is a more gradual decline in this function through middle and old age,” and further that “cerebral oxygen consumption…shows similarly a rapid and then a more gradual fall with advancing age”.7 The early studies were, by today’s standards, seriously flawed methodologically. Nearly one-third of these studies included hospitalised patients. Only four studies specifically noted an absence of vascular disease and three studies noted absence of neurological disease. In one study, some of the aged subjects were not considered to be “mentally alert”.7 Although Kety himself concluded that the observed

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101

changes may have in part been “the result of some age-dependant artifact”,7 these studies formed the basis for early conclusions that reduced cerebral blood flow and oxygen consumption were a natural correlate of the ageing process. Xenon studies of ageing For the decade from the late 1970’s onward, the Xenon (133Xe) steady state inhalation technique was commonly used to measure CBF. This technique relied on detection of cerebral radioactivity using variable numbers of detectors placed around the subject’s head after inhalation of 133Xe via a mask or mouthpiece. Studies of ageing using 133Xe are summarised in Table 1. The majority of these studies noted reduction in global CBF with age.8–13 An exception was the negative study by Iwata and Harano,14 which may in part be explained on the basis of the relatively small sample size and smaller age range of the subjects. Despite their poor spatial resolution, regional variation in CBF was also noted in some of these studies. A number of studies reported anterior or middle cerebral artery territory predilection for this age-related reduction in CBF.8,10,12,15 Others were not so specific, with one study reporting decline in rCBF in all regions except the brainstem,15 and another reporting preservation of rCBF only in the occipital regions.13 Only a single report contradicts this general pattern of anterior preference on rCBF reduction.16 Laterality of effect was rarely explored, with one study demonstrating preserved Right – Left asymmetry with age, and another suggesting accentuation of this affect in the frontal lobes with age.8 As can be seen from Table 1, a number of methodological issues are immediately appreciable in relation to these 133Xe studies. Firstly, sample selection was sub-optimal in several studies, with some subjects drawn from clinical populations.8,9,11 Secondly, screening of individuals for inclusion was performed with varying degrees of thoroughness, ranging from taking of a medical history only in some of the earlier studies14–16 to thorough screening13 in more recent studies. Thirdly, factors known to influence CBF such as visual or auditory stimulation and cognitive processing were rarely standardised between studies. Other factors such as the subject’s anxiety were not acknowledged. Fourthly, only one of these early studies attempted to account for the effects of cerebral atrophy on rCBF, noting a correlation between CBF and an automated measure of atrophy derived from CT scan in subjects over the age of 70 years. Finally, the spatial resolution of these earlier methods was poor, making observed regional effects difficult to interpret. Taken together, the comparability and generalisability of this collection of studies was poor. However, there was some continued support of the notion of an age- dependant decline in global CBF, with a suggestion of an anterior predominance of this effect. Technetium-HMPAO studies The majority of recent SPECT studies of ageing have used 99mTc-HMPAO. It is a highly lipophilic agent which crosses the blood-brain barrier and becomes

102 Table 1.

THE AGEING BRAIN

Early Nitrous Oxide and Xenon Inhalation Studies of Cerebral Blood Flow in Ageing.

Study

Sample Screening

N

Age

Tracer

Eye/Ear

Equipment/ Procedure

Kety (1956) (Review)7

s

179

Mean age varied in 16 studies from 5–93 years

NO

NS/NS

NO technique

32

2 groups: Ra: 18–36 Ra: 38–79

NO

NS/NS

NO technique

60

3 Groups: 1. mean 28.5 2. mean 51.6 3. mean 56.6

Xe

NS/NS

16 fixed detectors Res: NS

44

Ra: 19-79

Xe

EO/EP

16 fixed detectors Res: NS

38

Ra: 23-65

Xe

NS/NS

Tomomatic 64 Res: 17mm

Yamaguchi s et al. (1983)9 uu

102

Ra: 26-81

Xe

EC/EU

Aloka RRG 526 14 fixed detectors Res: NS

Matsuda sss et al. (1984)10 uuu

105

Ra: 19-80

Xe

EC/NS

32 fixed detectors Res: NS

Zemcov sss et al, (1984)16 u

44

Ra: 19-94

Xe

EO/EU

32 fixed detectors Res: NS

u Scheinberg (1953)99

s u

Naritomi et al. (1979)15

s u

Melamed et al. (1980)8

s uu

Iwata & Harano (1986)14

ss u

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CORRELATES OF BRAIN AGEING

Method of Analysis

Results

Kety & Schmidt technique of arterial & venous sampling

Decline in CBFand oxygen consumption with age, rapidly through adolescence then more gradual from third decade. Kety considered that the changes may have been part of a “the result of some age dependant artifact”

Kety & Schmidt technique of arterial & venous sampling

CBF reduced with age. No change in CMRO2 Arterial-venous O2 greater in aged Cerebral vascular resistance greater in elderly Middle aged v’s elderly comparison similar except CMRO2 greater in middle aged

Peak counts per minute from individual detectors over lobar regions.

3 groups: 1. Young healthy group 28 subjects aged <40 years 2. Older healthy group of 18 subjects> 40 years 3. Risk factor group of 14 subjects over 40 years with 1 or more risk for stroke: Group 1 & 2: negative correlation of rCBF & age all regions except brainstem cerebellum, most marked in MCA territory. Group 3: same pattern as groups 1&2. No demonstrable effect of vascular risk factors.

ISI used for rCBF measure

Reduction in global and hemispherir CBF with age. Reduction in rCBF in most regions, with anterior regions more affected in left hemisphere.

ROI: method poorly described. Large ROI’s defined representing cerebral vascular territories.

Non-significant decrease in rCBF with age.

ISI used for rCBF measure. Automated analysis of CT scans allowed calculation of craniocerebral index (CCI) and brain volume index (BVI)

Global CBF reduced with age In old subjects 70+, correlation was seen between CBF and atrophy index. This was not observed for younger subjects.

ISI used for rCBF measure.

Global CBF globally decreased with age Greatest decrease for MCA territory

ISI used for rCBF measure.

Regional CBF decreased with age in bilateral temporal, parietal and occipital corticies. Decrease in those with vascular risks more than others in some regions, but differences failed to reach significance. Table 1 continues

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THE AGEING BRAIN

Table 1. Continued. N

Age

Tracer

Eye/Ear

Equipment/ Procedure

Takeda ss et al. (1988)11 uuu

277

Ra: 19-88

Xe

EC/EU

Aloka RRG-526 14 fixed detectors Res: NS

Tsuda & Hartmann (1989)12

sss

51

Ra: 19-77

Xe

EC/EU

28 fixed detectors Res: NS

Hagstadius & Risberg (1989)13

sss

97

Ra: 19-68

Xe

EC/EU

NDS inhalation cerebrograph. 32 fixed detectors Res: NS

Study

Sample Screening

uu

uuu

s, ss, sss = sample drawn from increasingly desirable source u, uu, uuu = Increasingly detailed screening for optimal health ISI: Initial Slope Index NS: not specified EO: eyes open; EC: eyes closed; EU: ears unplugged; EP: ears plugged tracers: NO: nitrous oxide; Xe: Xenon inhalation; HMPAO: 99mTc-HMPAO Res: Spatial Resolution at Full-width half-maximum (FWHM)

CORRELATES OF BRAIN AGEING

105

Method of Analysis

Results

ISI used for rCBF . measure. Automated analysis of CT scans allowed calculation of craniocerebral index index (CCI) and brain volumeindex (BVI)

Decrease in global CBF with age. Regional specificity not explored. Decrease in BVI noted in elderly, but not entered as covariate in CBF analysis.

ISI used for rCBF measure

Reduction in rCBF with age, with trend toward more reduction in anterior regions. Reduction also observed in vascular CO reactivity in aged subjects

Calculated grey matter flow and initial slope index (ISI).

Decreased global CBF with age. Regional decline in CBF in all areas except occipital region. Hyperfrontality decreased with age. Mean CBF higher in right hemisphere.

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THE AGEING BRAIN

Table 2. Summary of Modern Resting SPECT Studies of Ageing. Study

Sample Screening

N

Age

Tracer

Eye/Ear

Equipment/ Procedure

Waldemar et al. (1991)18

ss

53

Ra: 21-83

HMPAO and Xe

EC/EU

Th45 Tomomatic 64 Res: NS

Markus et al. (1993)100

sss

20

Ra: 21-76

HMPAO

EO/EU

GE/CGR Neurocam. Res: NS

Swartz et al. (1995)20

sss

47 for x 27 for ceretec

Ra: 47-82

HMPAO and Xe

EO/EU

Headtome II Res: NS

Catafau et al. (1996)19

sss

68

2 groups: young mean 29.5 old mean 71.2

HMPAO

EO/ EU

Elscint SP4HR. Res: NS.

Mozley et al. (1997)21

sss

44

Ra: 20-73

HMPAO

EO/EU

Picker 300s, (triple headed camera) Res: NS

Krausz et al. (1998)17 u

sss

27

Ra: 26-71

HMPAO

EO/EU

APEX ECT415, (single headed) Res: 11mm

uuu

uu

uu

uuu

uuu

s, ss, sss = sample drawn from increasingly desirable source u, uu, uuu = Increasingly detailed screening for optimal health ISI: Initial Slope Index NS: not specified EO: eyes open; EC: eyes closed; EU: ears unplugged; EP: ears plugged tracers: NO: nitrous oxide; Xe: Xenon inhalation; HMPAO: 99mTc-HMPAO Res: Spatial Resolution at Full-width half-maximum (FWHM)

CORRELATES OF BRAIN AGEING

107

Method of Analysis

Results

Manual ROIs by reference to atlas. CT scan ratings of atrophy used for analysis in 33 subjects

Cortical atrophy but not age was a significant determinant of global CBF, accounting for 27% of variance. Regional CBF in frontal & frontotemporal regions decreased significantly with age.

Positioning of square ROI with reference to atlas. Normalised to injected dose and cerebellum.

Increased assymetry of ROI uptake in elderly, reaching significance in temporal and temporoparietal regions only. Uptake in both cortical and subcortical regions was less in elderly, but not significant. Non-significant reduction in frontal CBF.

Positioning of circular ROI 1.9cm diameter. Tc-HMAPO regions normalised to ROI containing maximal counts.

Xe: Global CBF stable with age. Decline in occipital CBF noted with age. In men only, global, frontal, temporal, occip & right hemishpere declines. Tc-HMPAO: no age related declines in global of regional CBF.

Manual ROIs drawn directly onto slice. Normalisation to both cerebellum and whole brain counts.

Highest relative CBF in cerebellum & occipital cortex. Effect of age on global CBF not reported. Decreased CBF in anterior regions, L frontal/temporal region with age. Preservation of R>L assymetry with age.

Standardised ROI template. Counts normalised to whole brain.

Regional CBF decreased with age in most cortical regions; minimally so in ocipital and cerebellar ROI’s, maximally in frontal ROI’s. Regression analysis indicated non-linear model; broken stick model with breakpoint median 36.6 yrs across regions. Increase in white matter CBF noted.

Standardised ROI template. Counts normalised to cerebellum and whole brain.

Subjects divided into young <50; old >50 Cerebellum normalisation: global decrease in rCBF with age. Regional decrease in left superior temporal, left basal ganglia, and bilateral frontal, occipital and parietal ROI’s. Whole slice normalisation: regional decrease in occipital, right frontal and increases in right thalamus, right anterior cingulate and bilateral posterior cingulate. Hemispheric perfusion difference R>L unaffected by age.

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trapped intraneuronally by metabolic reduction, thus being distributed in the brain in proportion to rCBF. One of the main advantages of 99mTcHMPAO over other SPECT radiotracers is that it remains intraneuronally for at least six hours after intravenous administration, thus allowing images to be acquired for some hours after the injection, without loss of resolution. 99mTc-HMPAO has favourable dosimetry, allowing improvement in image quality at higher doses. An important development in SPECT technology has been the availability of high-resolution dedicated neuro-SPECT scanners, significantly narrowing the gap between SPECT and PET in achievable spatial resolution. Modern SPECT studies using 99mTc-HMPAO are listed in Table 2. These studies contrast in some respects to the earlier Xenon studies. With the exception of the study by Krausz et al.,17 in which detail was omitted, assessment of subjects has been sufficiently detailed to exclude confounding illnesses. Improved spatial resolution has enabled more detailed region of interest (ROI) analysis. Approaches to this analysis have involved either manually tracing individualised regions onto subjects’ scans18,19 or overlaying of templates with predefined anatomical or geometric regions.17,20,21 The technological and methodological advances are reflected in results which challenge the notion of age-dependent decline in global CBF, with a number of studies reporting a negative finding.18,20 Where age effects on laterality of CBF have been explored, preservation of R–L asymmetry has been reported.17,19 The most consistent regional effect of age has been the demonstration of a reduction in frontal CBF.17-19,21 Decline in temporal CBF18–20 has also been noted as an age effect. An interesting finding, reported in one study, was that age-dependent decline in rCBF was non-linear, with more reduction occurring up until middle age, and negligible further reduction thereafter.21 If this were confirmed, there would be significant implications for the age range included in future studies. A significant limitation of these studies is that SPECT measures represent relative rather than absolute CBF values. In obtaining this relative value, normalisation is frequently obtained to either whole brain or cerebellum. Discrepancies in the way in which the ageing process affects the cerebellum compared to other brain regions may introduce some variability in findings, and this has been noted in one study in which normalisation to both denominators was used.17 Resting 15O PET studies of ageing The methodological details and results of key 15O-PET studies of normal ageing are summarised in Table 3. PET investigations of cerebral correlates of brain ageing share similar problems to that of SPECT. Earlier studies featured samples drawn from hospital populations 22,23 or suffered from poorly described or inadequate screening processes.22,24 Early studies were performed with equipment offering poor spatial resolution.22–26 Despite recent improvement in a number of these methodological issues, the heterogeneity of the methods of analysis makes comparison of studies

CORRELATES OF BRAIN AGEING

109

difficult, and limits the conclusions able to be drawn. The majority of studies have used regions of interest (ROI) analysis, relying on poorly represented anatomical definition on PET scans themselves to allow placement of standardised or geometric ROIs. Supprisingly, only a small number of studies27,28 have sought to co-register the MRI and PET images. In the study by Bentourkia et al.,27 however, the co-registration method (visual interpolation of the two images) lacked the sophistication of other contemporary ROI analyses. Only one 15O PET study has used a semiautomated statistical analysis package — Statistical Parametric Mapping (SPM; Wellcome Department of Behavioural Neurology, Institute of Neurology, London) — for analysis. The issue of correction of CBF and CMRO2 measurements for partial volume effects has not been addressed in the majority of studies. Two studies have used CT scan based methods to correct CBF and CMRO2 measures for the effect of global atrophy.25,29 One study utilised an MRI-based method to correct for partial volume effects for individual regions,28 offering a more rigorous approach to partial volume correction. Studies of global CBF and CMRO2 Although there are inconsistencies in the results from these studies with respect to global changes in CMRO2, CBF and OEF with age, this is not unexpected given the methodological disparities. A number of studies document a global decline in CMRO2 with age.23,24,26,29–31 The magnitude of this effect is relatively small, with various estimates of 0.3%23 0.37%,27 0.5%26 and 0.6% per annum.29 The failure of one study to find evidence of reduction in CMRO2 with age deserves mention. Itoh et al25, showed linear decline with age in a measure of cerebral atrophy (cerebrocranial index) but no decline in either CBF or CMRO2. However, the age range of subjects was narrow, and weighted toward middle age and older subjects between 50 and 85 years. In view of the proposed non-linear decline in CBF with age proposed by Mozley et al.,21 it is possible that the failure of this study to detect global reduction in CBF or CMRO2 was related to the restricted age range of subjects included in this study. Modest decline in global CBF with age has been a common but less consistent finding (see Table 3). In addition to the negative finding by Itoh et al.25 with respect to both CBF and CMRO2, two studies demonstrated a discrepancy between age effects on global CMRO2 and CBF.30,31 In these two studies, CBF reduction was not seen with age, raising the possibility of an uncoupling between CBF and CMRO2 as a factor of age.31 However, as hypothesised by Yamaguchi et al.,30 such a discrepancy may be artifactual and reflective of other age-associated changes known to affect CBF measurement such as reduction in haematocrit and increase in PaCO2 with age. A less consistent finding in 15O-PET studies is that of changes in oxygen extraction ratio (OER) with age. A number of studies have failed to find such a relationship.22,29,30 In the study by Pantano et al.,23 a small but statistically insignificant increase in grey matter OER of 7% was found between young

110

THE AGEING BRAIN

Table 3. Summary of Major Resting

15O

PET Studies of Ageing.

Author

Sample Screening

N (sex ratio)

Age Range (years)

Eye/Ear

Equipment/ Technique

LebrunGrandie et al. (1983)22

s

19 (14M, 5F)

19–76

EC/EU

ECAT, measured attenuation. 15O Steady State Inhalation Res: n

Lenzi et al. (1981)24

sss

27 (NS)

Age not specified. Divided into 2 groups: Young < 50 (n=16) Old >50 (n=11)

NS/NS

Equipment NS 15O Steady State Inhalation Res: NS

Pantano et al. (1984)23

s

27 (19M, 8F)

19–76 Divided into 2 groups: Young: av age 38 Old : av age 63

EC/EU

ECAT II 15O Steady State Inhalation Res: n

Yamaguchi et al. (1986)30

sss

22 (17M, 5F)

26–64 Divided into 2 groups: Young: av age 35.7 Old: av age 57.6

EO/EU

HEADTOME III 15O Steady State Inhalation Res: n n n

Itoh et al. (1990)25

sss

28 50–85 (17M, 11F)

NS/NS

ECAT II 15O Steady State Inhalation Res: n

Leenders et al. (1990)26

sss

30 22–82 (18M, 16F)

EC/EO

ECAT II 15O Steady State Inhalation Res: n

Burns & Tyrrell (1992)101

sss

14 (6M, 8F)

51–85

NS/NS

ECAT/931/08/12 15O Steady State Inhalation Res: NS

Takada et al. (1992)31

sss

32 27–67 (15M, 17F)

EC/EU

Equipment: NS 15O Steady State Inhalation Res: NS

Martin et al. (1991)32

sss

30 30–85 (15M, 15F)

EC/EU

ECAT/931/08/12 15O Steady State Inhalation Res: n n n

u

u

uu

u

u

uu

uu

uuu

uu

111

CORRELATES OF BRAIN AGEING

Method of Analysis Procedure

Results

Circular ROI’s 10mm diamater manually placed over CBF image at point of highest CBF in lobe.

Selected rCBF decrease with age temporosylvian, medial frontal, medial occipital regions. No correlation between regional CMRO2 or OER and age.

NS

rCBF & CMRO2 decline with age, most pronounced in visual cortex & insula. Increased OER with age.

Circular ROI’s traced on CBF image and copied onto CMRO2 & OEF images Normalised to mean of all ROI’s

rCBF and CMRO2 declined by 18% and 17% respectively in grey matter. Largest decrease in frontal, parieto-occipital and temporosylvian regions. Non-significant increase in OEF in grey matter. No difference in white matter between 2 groups.

Circular ROI’s placed with CT superimposed for guidance

Mean left hemisphere CMRO2 significantly lower in older age group. Significant correlation between CMRO2 and age demonstrated. No correlation between age and rCBF, or OEF.

ROI measuring 3 x 7 pixels CT brain used to calculate cerebrocranial index (CCI)

CCI decreased linearly with age (Correlation –0.23) CBF and CMRO2 unchanged with age and not influenced by CCI

Geometric ROI (rectangular for cortical regions, circular for other regions), individually placed.

Decrease in CMRO2, CBF and CBV with age (approx 0.5% per year), both in grey and white matter. Mostly non-significant increase in OER with age.

Stereotactic co-ordinates based on Talairach atlas used to obtain CMRO2 values

Decrease in CMRO2 in parietal lobe. In other regions, this failed to reach significance.

ROI, method unstated

Decrease in mean CMRO2 with age and in specific ROI’s (bilateral putame, left supratemporal, left infrafrontal and left parietal corticies). Decreased CBF in left superior temporal cortex only.

SPM

Mean CBF unchanged with age. RegionaL decrease in CBF in cingulate, parahippocampal gyri, superior temporal gyri, medial frontal gyri, parietal cortex bilaterally and in left insular and inferior frontal gyrus. Table 3 continues

112

THE AGEING BRAIN

Table 3. Continued. Author

Sample Screening

N (sex ratio)

Age Range (years)

Eye/Ear

Equipment/ Technique

Marchal et al. (1992)29

sss

25 (NS)

20–68

EC/EU

LETI TTV03 15O Steady State Inhalation Res n n n

Eustache et al. (1995)102

sss

25 20–68 (14M, 11F)

EC/EU

LETI TTV03 15O Steady State Inhalation Res: n n n

Bentourkia et al. (2000)27

sss

20 (13M, 7F)

21–75 Divided into 2 groups: Young: aged 21-36 Old: aged 55–75

EC/EU ECAT EXACT-HR Subjects Both 15O H2O and asked to 18FDG injection “avoid Res: n n n focussing their mind on anything”

Meltzer et al. (2000)28

sss

27 (9M, 18F)

19–76

EC/EU

uuu

uuu

uuu

uu

Siemens 951R/31 15O H O injection 2 Res: n n.

s, ss, sss sample drawn from increasingly optimal source (s= hospitalised subject; ss outpatient clinic; sss volunteer) u, uu, uuu increasingly rigorous screening (u history only, or unstated; uu history & physical examination +/- laboratory tests; uuu history, physical examination & CT/MRI brain or neuropsychological testing) Res: Spatial Resolution at Full-width half-maximum (FWHM) n = spatial resolution ≥ 15mm; n n = spatial resolution 8.6-14 mm; n n n = spatial resolution ≤ 8.5mm NS: not specified EO: eyes open; EC: eyes closed; EU: ears unplugged; EP: ears plugged

CORRELATES OF BRAIN AGEING

113

Method of Analysis Procedure

Results

ROI, circular, 14 pixel diameter CT scans given atropy rating on 4 point scale

Decrease in CMRO2 whole cortex and in multiple cortical gyri (24/31) with age (approx –6% per decade). Effect on whole cortex independent of cerebral atrophy. Decrease in CBF in 10/31 cortical gyri with age. No change in OEF with age. No decrease in CMRO2 or CBF in white matter or deep grey matter structures.

CT & PET images co-registered (method undefined). Circular ROI 14mm diameter manually placed. Normalisation of ROI values to cerebellum.

Global decline in CMRO2 with age. rCMRO2 values negatively correlated with age in all neocortical regions and left thalamus.

MRI and PET images anatomically matched by visual interpolation ROI’s manually drawn on MRI and adjusted for FDG study. Transferred onto 15O study

Global decline in CBF with age (0.37% per year) Decline in CBF was greatest in frontal regions and least in Occipital cortex. Preserved coupling of rCBF and rCMRGlu with age

MRI & PET coregistered using using automated computerised algorithm. ROI’s traced on MRI & transferred to PET. MRI based partial volume correction method.

Prior to partial volume correction: negative correlation of mean CBF with age. Regional statistically significant results in medial orbitofrontal, lateral orbitofrontal, lateral temporal regions. After partial volume correction: mean CBF no longer significantly correlated with age. Loss of regionally significant results except for medial orbitofrontal region.

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subjects (mean age 38 years) and older subjects (mean age 63). A non-significant increase of 0.35% per year in grey matter OEF was observed by Leenders et al.26 The reported increase in OER by Lenzi et al.24 was modest and not examined for statistical significance. In general, these results suggest that OER may increase slightly with age, and may simply be a reflection of reciprocal decreases in CBF. Regional changes in CMRO2 and CBF with age have been examined in a number of studies. An obvious discrepancy between white matter regions and grey matter regions has emerged, with white matter CMRO2 and CBF being largely unaffected by age.22,23,29,30 The most consistent regional effect of age in cortical grey matter is that of decline in CBF and CMRO2 in selected frontal and temporal regions.22,23,27,29,31,32 Reports of regional declines in cortical CBF or CMRO2 have been predominantly bilateral, although some studies have noted a left-sided31,32 or right-sided emphasis29 for particular regions. An asymmetric left hemispheric decline in CMRO2 with age was noted in one study.30 Resting FDG studies of ageing Evaluation of the effect of ageing on global CMRglu has been undertaken in a number of studies, with conflicting results (see Table 4). In an early study in which the assessment methods for subject selection were not detailed, Kuhl et al.,33 reported a decline in global CMRglu of 0.43% per year. A number of studies have subsequently reported a global decrease in CMRglu ranging from 0.21%34 to 0.6% per year.35 However, the reported decline in CMRglu is uncertain. The screening of aged subjects appears to have been inadequate in a number of studies in which global declines in CMRglu has been described.33,34 Although most studies excluded those with established hypertension, other cerebrovascular risk factors were often not assessed. Several studies have used either CT scan36,37 or MRI scan27 as part of the screening process, but this represents the exception rather than the rule. The effect of cerebral atrophy on global CMRglu has been evaluated in a small number of studies. Schlageter et al.37 employed an automated segmentation technique to measure CSF volume on CT scan of the brains of their subjects. Cerebral atrophy so measured was negatively correlated with CMRglu but only explained 13% of the variance. No age effect on CMRglu was apparent in this study. Another study initially revealed a decline in CMRglu with age, which was no longer statistically significant after the effect of cerebral atrophy (as measured by semiquantitative ratings of MRI scans) was partialled out38. In summary, there are methodologically sound studies demonstrating both decline in CMRglu with age35,39 and no change with age.36,37 The variability of these findings indicates that perhaps a modest effect of age on global CMRglu is being inconsistently determined as a result of methodological and statistical differences between studies. The comparison of FDG studies examining regional changes in CMRglu with age is difficult owing to the different methods used to examine regional

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effects. The majority of studies have employed ROI analysis. The spectrum of ROI methods employed in these studies include: the poorly standardised procedure of tracing irregular regions directly onto the FDG study;40 the use of the subject’s CT scan of the brain36,37 or an atlas41 as an anatomical guide for ROI definition; the placing of standard geometric ROIs38,39,42 directly onto the PET image and the tracing of regions onto individual’s MRI scan of the brain and transferring onto the PET image.27 The recent use of standardised packages such as SPM has enabled exploratory comparison across the whole brain and enables easy comparison of results across studies using reference to Talairach co-ordinates. Despite the difference in methods of analysis for regional effects, only a small number of studies have failed to find a significant regional effect of age on CMRglu.36,40–42 A number of factors including poor resolution of the older scanners and less sophisticated ROI analyses may explain many of these negative findings. A distinct pattern of age related CMRglu decline has emerged which is reminiscent of CBF and CMRO2 studies in ageing. The most consistent pattern of reduction in CMRglu with age is that of frontal reduction27,33,34,35,38,43–45 including that of anterior cingulate. 35,43 Other regions showing reduction in CMRglu with age include specific anterior, posterior and lateral temporal regions35,39,43,45 and parietal cortex.33,34,39,45 The effect of ageing on deep grey matter nuclei is occasionally reported as being a decline, and in the study by Bentourkia et al., 27 the greatest CMRglu reduction was observed in the right striatum. The effect of ageing on CMRglu in the cerebellum, occipital cortex and white matter appears to be minimal. Methodological Issues in Resting Studies Defining healthy ageing One of the key difficulties encountered in any study of the effects of ageing is that of the confounding effects of pre-existing disease. Reducing the likelihood that such confounding factors will influence results requires adoption of a narrow definition of “normal ageing”, thereby excluding those with risk factors for cerebral disease. Although for the sake of sample uniformity conservative inclusion criteria may be desirable, there is a risk that findings from any such study will reflect those of “elite ageing” rather than “normal ageing”. Such results are less likely to be generalisable to the majority of the aged population. Until more is known about the impact of common agerelated conditions on functional imaging findings, it would seem prudent to select a conservative sample to minimise potential confounding effects of systemic disorders on the results. The adequacy of the screening process prior to enrolment of subjects can be questioned in many of the studies reviewed in Tables 1–4. Appropriate clinician review including detailed history, physical examination, laboratory evaluation and neuropsychological assessment,

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Table 4. Summary of Major Resting FDG Studies of Ageing. Author

Sample Screening

N (sex ratio)

Kuhl et al. (1982)33

sss

Eye/Ear

Equipment/ Technique

40 18–78 (17M, 23F)

EO/EU

ECAT II Res: n

De Leon et al. (1984)42

NS

37 (NS)

Divided into 2 groups: Young: av age 26.1 Old: av age 66.6

EC/EU

PET III Res: n

Hawkins et al.

NS

8 (7M, 1F)

18–68

NS/NS

NeuroECAT Res: n n

Duara et al (1983)41

sss

21M

21–83

EC/EP

ECAT II Res: n

Duara et al. (1984)36

sss

40M

21–83

EC/EP

ECAT II Res: n

Horwitz et al. (1986)103

sss

30M

Divided into 2 groups Young: 20–32 Old: 64–83

EC/EP

ECAT II Res: n

Schlageter et al. (1987)37

sss

49M

21–83

EC/EP

ECAT II Res: n

Yoshii et al. (1988)38

sss

76 21–84 (39M, 37F)

EC/EU

PETT V Res: n

Hoffman et al. (1988)45

sss

36 21–74 (22M, 14F)

NS/NS

NS Res: NS

NS

NS

Age Range (years)

uu

uuu

NS

uuu

uu

uu

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Method of Analysis Procedure

Results

Not stated

Global decline in rCMRGlu with age (0.43% per year). Regional decline in rCMRGlu evidenced by reduction in metabolic ration of superior frontal cortex to parietal cortex.

Fudicial markers on PET and CT brain used to match CT and PET slices. Geometric ROI’s placed on CT and transferred to PET image.

No difference in global or regional rCMRGlu between young and elderly subjects.

Irregular individual ROI’s Method unspecified.

No change in rCMRGlu with age.

Individual ROI’s defined on PET using atlas guide.

No significant change in global or regional rCMRGlu for white or grey matter with age.

Individual ROI’s defined on PET using atlas and patient’s CT as a guide.

Study was an extension of above study (Duara et al 1983). Replicated above results.

Individual ROI’s defined on PET image with reference to brain atlas. Partial correlation coefficiants determined between each pair of 59 regions.

No change in mean CMRGlu between groups. Elderly subjects demonstrated a reduced number of significant correlations between pairs of regions noted. This reduction was especially noted in frontal-parietal and parietal-parietal correlations.

Individual ROI’s defined on PET using individual’s CT as a guide. CSF volume measured using automated segmentation on CT scan.

CSF volume, ie cerebral atrophy was negatively correlated with CMRGlu but accounted for no more than 13% of variance. No effect of age on CMRGlu, even after correction for cerebral atrophy.

Geometric ROI’s placed automatically over PET image and manually adjusted to overlay specific regions. MRI scans of 58 subjects rated semiquantiatively for atrophy.

Divided into those with or without 1 or more risk factors for thromboembolic stroke. Significant lower mean CMRGlu in aged subjects compared to young. Age effect non-significant when effects of cerebral volume and atrophy partialed out. Atrophy accounted for 8.3% of variance of mean CMRGlu. CMRGlu not affected by presence of risk factors for stroke.

ROI analysis, method unspecified.

RCMRGlu reduction with age in gyrus rectus, orbital gyri, inferior frontal gyri, medial prefrontal cortex, insula, superior parietal lobule & globus pallidus. Table 4 continues

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Table 4. Continued. Salmon et al. (1991)104

sss

DeSanti et al. (1995)105

sss

Moeller et al. (1996)34

sss

25 (NS)

Divided into 2 groups: Young: av age 25.8 Old: av age 60.1

NS/NS

NeuroEcat Res: n n

72M

Divided into 2 groups: Young: av age 27.5 Old: av age 67.6

EO/EU

Siemens CTI-931 Res: n n n

uu

uu

NS

Group 1: Group 1: 21–90 130 (62M, 68F) Group 2: Group 2: 24–77 20 (10M, 10F)

Murphy et al. (1996)39

Group 1: Group 1: EC/EP Scanditronix PC 1024-7B Res: n n n Group 2: Group 2: EO/EU Scanditronix Superpett 3000 Res: n n n

120 21–91 (55M, 65F)

EC/EP

Scanditronix PC 1024 7B Res: n n n

Petitsss Taboué et al. (1998)35 uuu

24 (15M, 9F)

EC/EP

LETI TTV03 Res: n n n

Garraux et al. (1999)43

sss

EC/EU

uu

43 19–75 (25M, 18F) Divided into 2 groups: Young: 19–28 Old: 47-75

Siemens CTI 951 R 16/31 Res: n n n

Bentourkia et al. (2000)27

sss

20 (13M, 7F)

EC/EU ECAT EXACT-HR Subjects Res: n n n asked to “avoid focussing their mind on anything”

sss uu

uuu

20–70

21–75 Divided into 2 groups: Young 21–36 Old 55-75

s, ss, sss sample drawn from increasingly optimal source (s= hospitalised subject; ss outpatient clinic; sss volunteer) u, uu, uuu increasingly rigorous screening (u history only, or unstated; uu history & physical examination +/- laboratory tests;

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ROI analysis, method unspecified. Absolute values & ratios normalised to mean cortical values both examined.

Global CMRGlu unchanged with age. Absolute rCMRGlu values unchanged with age. Normalised rCMRGlu reduced in frontal lobes and increased in cerebellum with age.

Manually traced ROIs, some anatomical & some geometric.

Absolute rCMRGlu values reduced with age in frontal and temporal lobes. Dorsolateral frontal region demonstrated stronger relationship with age than orbitofrontal region, decreasing 2.6 and 2.2umoles/100g/min per decade respectively.

ROI method not described. Used scaled Subprofle Model (SSM) to examine regional covariation with age. Group 2:

Both groups, global CMRGlu decreased significantly with age (0.21% per year). Regional declines in frontal regions observed in group 1 only. In group 1, relative decrease in frontal rCMRGlu was associated with covariate relative increases in parietooccipital association areas, basal ganglia, brainstem and cerebellum.

Circular ROI template superimposed on each patients PET.

Mean CMRGlu decreased with age. Regional CMRGlu declined in frontal, temporal and parietal ROI’s, with assymetry of this decline noted (parietal L>R decline, frontal R>L decline). L>R assymetry in frontal rCMRGlu was significantly less in women compared with men.

SPM analysis.

Global decline in rCMRGlu with age (6% per decade). Regional decline in rCMRGlu in most areas except occipital cortex and right cerebellum. Most marked age related decline seen bilaterally in perisylvian temporoparietal and anterior temporal regions, insula, inferior and postero-lateral frontal region, anterior cingulate, head of caudate, anterior thalamus.

SPM analysis.

Frontal CMRGlu decreased in elderly subjects in bilateral dorsolateral prefrontal and medial prefrontal areas including anterior cingulate, left lateral premotor area, Broca’s area and left insular, right superior temporal gyrus.

MRI and PET images anatomically matched by visual interpolation. ROI’s manually drawn on MRI and adjusted for FDG study. Transferred onto 15O study.

Global decline in rCMRGlc (0.34% per year). Decline in rCMRGlc greatest in right striatum and least in cerebellum. Preserved coupling of rCBF and rCMRGlc with age.

uuu history, physical examination & CT/MRI brain or neuropsychological testing). Res: Spatial Resolution at Full-width half-maximum (FWHM) n = spatial resolution ≥ 15mm; n n = spatial resolution 8.6-14 mm; n n n = spatial resolution ≤ 8.5mm NS: not specified. EO: eyes open; EC: eyes closed; EU: ears unplugged; EP: ears plugged.

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is required to minimise the chance of inclusion of subjects with disorders affecting cerebral function. The resting state The majority of SPECT and PET studies of ageing rely on evaluation of “resting” rCBF or rCMRglu. The notion of what represents an adequate “resting state” has varied significantly across studies. The common measures of ensuring reduced sensory input by plugging ears and patching eyes are not always adopted. Limited attempt has been made to reduce the effects of cognitive processing. Mental processing occurring during scanning may vary substantially between subjects and is likely to be influenced by factors such as anxiety provoked by the procedure, mood at time of scanning, etc. This uncontrolled mental activity may in turn significantly influence rCBF or rCMRglu. An approach to potentially overcome this confound and minimise inter-subject variability is the introduction of a standardised cognitive task. Accounting for cerebral atrophy The comparison of ageing and youthful brains brings with it the challenge of taking account of the effects of structural changes of ageing on functional data. The most important issue is the effect of brain atrophy. As the resolution of both PET and SPECT fall significantly short of structural imaging techniques such as MRI, functional data will be influenced by the effect of atrophy due to partial-volume averaging. If such an effect is not taken into account, functional data can considerably underestimate rCBF and rCMRglu in the elderly, resulting in misinterpretation of results. This effect may account for some of the positive findings regarding reductions in rCBF and rGMRgl in aged compared to young subjects. There have been several different methods used to attempt to correct for this source of error. Simple techniques include correction using an atrophy score derived from visual ratings of structural scans, correction by using global measures of atrophy such as VBR and more complicated correction by using a MRI-based segmentation of images and generation of regional correction factors. Meltzer et al.28 attempted to address this issue with surprising results. Their study used manually traced regions of interest on MRI that were transferred to PET images using an automated image registration algorithm. An MRI-based partial volume correction coefficient was applied to the CBF data. This correction coefficient was derived for individual regions after segmentation of the MRI into brain and cerebrospinal fluid, creation of a binary data set and smoothing of MRI data to approximate resolution of the PET scan. After corrections were applied, most age-associated changes in cerebral blood flow failed to reach significance. The exception was that CBF continued to demonstrate a statistically significant negative correlation with age in the mesial orbitofrontal region. Such a finding suggests that previous studies may have overestimated age-related reduction in CBF and CMRO2. However, as prefrontal atrophy is about twice that found in the temporal or parietal neo-

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cortex44 atrophy effects are unlikely to account for all age effects on CBF or CMRO2 noted in previous studies, uncorrected for atrophy or partial volume effects. These findings challenge the notion of a large age-specific decrease in brain blood flow and metabolism, and underscore the importance of correction for possible confounding factors. Summary of resting SPECT and PET studies Methodological discrepancies between studies limit the conclusions that can be drawn from this body of literature. However, a general pattern is appreciable which both SPECT and PET studies of CBF and PET studies of CMRglu share. With respect to global effects of the ageing process, a modest effect is demonstrable in some studies. Regional effects appear to mirror known patterns of age-related pathological change, including regional effects of atrophy. Although atrophy may partially account for regional CBF, CMRO2 and CMRglu effects observed with age, it does not appear to be the sole arbiter of these changes. Regional effects demonstrated by resting functional imaging studies add support to theories of cognitive ageing which espouse decline in frontal lobe function as a mediator of age-related change. Resting PET and SPECT Studies in “At Risk” Groups The major focus so far has been on defining the changes associated with “healthy ageing”. The rigorous screening of subjects and exclusion of those with risk factors for conditions such as cerebrovascular disease means that such studies may be poorly representative of the general population. A limited but expanding literature is exploring influence of age-associated phenomena such as cerebrovascular disease and mild cognitive impairment on functional imaging parameters. Vascular risk factors A few studies of healthy ageing have evaluated the effects of cerebrovascular risk factors on CBF. Measuring with the nitrous oxide technique, earlier studies46 failed to demonstrate an effect of hypertension alone on CBF or CMRO2. However, in the presence of systemic arteriosclerosis or frank cerebrovascular disease, both parameters declined and cerebrovascular resistance increased. Using 133Xe inhalation SPECT, Naritomi et al.15 divided their elderly group into those with and those without risk factors for stroke. Although an agerelated effect was observed on rCBF, the presence of cerebrovascular risk factors did not alter rCBF. In an FDG PET study of ageing, Yoshii et al.38 divided their sample of 76 subjects into those with and without risk factors for thrombo-embolic stroke. No appreciable effect on CMRglu was noted in the presence of stroke risk factors. In a study of 60 aged individuals ranging from 65 to 84 years, Claus et al.47 evaluated the effect of cerebrovascular risk factors, quantified indicators of atherosclerosis and cerebral atrophy on

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CBF as measured by 99mTc-HMPAO. An age-related decline in rCBF in temporal and parietal cortex was observed with age, and this relationship was preserved after adjustment for the effect of cerebrovascular risk factors. The relationship between age and rCBF disappeared after correction for fibrinogen levels and measures of carotid atherosclerosis. The authors proposed that age-related declines in CBF might be related to atherosclerosis rather than to vascular risk factors per se. Hypertension 133Xe inhalation was used in a 36-month prospective study evaluating the effect of antihypertensive treatment on CBF in 12 individuals with mild hypertension at baseline.48 As a group, an overall increase in CBF was demonstrable at 6, 12 and 24 months after initiation of antihypertensive treatment. This difference was no longer appreciable by 36 months. Four subjects developed overt signs of cerebrovascular disease over the course of follow-up. The CBF values of these four individuals showed decline from the 24-month evaluation onward, whereas the asymptomatic group continued to demonstrate improved CBF values throughout the study period. In a cross-sectional study, Nobili et al.49 showed that hypertensives who were neurologically asymptomatic, especially when untreated, had focal or diffuse cerebral hypometabolism. More recently, utilising FDG PET, Salerno et al.50 compared a group of 17 elderly hypertensives compared with 25 age-matched non-hypertensive controls. In the hypertensive group, a significant reduction in FGD uptake was demonstrable in regions supplied by basal ganglia perforating arteries and at the middle cerebral/anterior cerebral artery watershed. Although data are limited, it appears that the presence of hypertension alone has a modest but appreciable influence on CBF and CMRglu. This effect may be regional, affecting areas most vulnerable to the effects of ischaemia such as long perforating vessels and “watershed” regions. White matter hyperintensities (WMHs) Hyperintensities on T2-weighted MRI brain imaging are common in the white matter of elderly individuals. The functional significance of these has been a matter of dispute and extensive investigation. In a study of 51 healthy individuals between 19 and 91 years, De Carli et al.51 found that when WMHs comprised >0.5% of intracranial volume, they were associated with cognitive deficits and reduced frontal lobe blood flow on PET scanning. In another study52 it was shown that cortical metabolic dysfunction was related to ischemic subcortical lesions, both lacunar infarcts and non-infarction WMHs, in patients with vascular dementia. Metabolism in the frontal cortex may be particularly dependent on pathologic alterations of subcortical nuclei. In a more recent study of 231 individuals53 without overt neurological disease, the most striking relationship was that observed between periventricular white matter hyperintensities (PVHs) measured by semiquantitative analysis and CMRglu measured by region of interest analysis. CMRglu values showed

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a progressive reduction with increasing grades of PVHs. The relationship between CMRglu and severity of PVHs was significant for multiple cortical and subcortical regions. A less strong relationship was observed between CMRglu and deep white matter or basal ganglia hyperintensities. The relationships between MRI hyperintensities, cerebrovascular risk factors and functional imaging measures awaits further exploration, but promises to be important in understanding the determinants of age-related decline in CBF and CMRglu. Groups at risk of dementia Functional imaging techniques have been used to evaluate individuals who are at risk of age-related disease processes such as AD. The most informative findings have come from those studies in which asymptomatic subjects at risk of AD are compared with healthy age-matched controls. In a study of 24 asymptomatic first degree relatives from familial AD pedigrees, Kennedy et al.54 demonstrated deficits in global and regional CMRglu compared with age matched control subjects not at risk of AD. The regional pattern of CMRglu abnormality was similar to, but less severe than that of affected familial AD controls. In a PET study of cognitively normal subjects with family history of AD,55 the effect of apolipoprotein A (ApoE ε4) was determined by comparing 22 ApoE ε4-negative individuals with 11 subjects homozygous for ApoE ε4. Subjects in both groups performed equally well on neuropsychological measures. Global CMRglu was not different between groups. Those subjects who were homozygous for ApoE ε4 demonstrated reduced rCMRglu in posterior cingulate, parietal, temporal and prefrontal regions compared to ApoE ε4-negative controls. A further study from the same group56 evaluated 10 cognitively normal individuals who were heterozygous for ApoE ε4 and 15 ApoE ε4-negative individuals with a family history of AD. Subjects were followed longitudinally and underwent FDG PET at baseline and two-year follow-up. Cognitive deterioration was not observed over the study period. However, greater CMRglu declines were observed in the ApoE ε4 heterozygous subjects in temporal, posterior cingulate and prefrontal cortex as well as parahippocampal gyrus and thalamus, in comparison to ApoE ε4-negative subjects. Taken together, these preliminary studies of ApoE ε4 positive individuals suggest that functional imaging changes are seen which parallel those of AD rather than normal ageing. These results underscore the potential of ApoE status as a confounding factor in studies of normal ageing. The screening of aged individuals for ApoE status prior to inclusion in functional imaging studies of healthy ageing is therefore highly recommended. This topic is described in greater detailed in Chapter 15. Cognitive impairment Those with mild cognitive impairment (MCI) may be at risk of developing AD, with estimates of conversion varying from 10% to 15% per year.57 The degree to which functional imaging may assist in the prediction of those at

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risk of conversion, has yet to be properly explored. In preliminary reports, significant temporo-parietal abnormalities have been noted with SPECT in those with MCI.58,59 In a PET study, Small et al.60 evaluated 12 cognitively normal subjects with age associated memory impairment (AAMI) and positive family history of AD, who were heterozygous for ApoE ε4 genotype and compared them to a similar group of 19 ApoE ε4-negative individuals. CMRglu was significantly lower in both right and left parietal regions in the ApoE ε4-positive group. In addition, left-right parietal asymmetry was significantly higher in those at risk of AD and who were ApoE ε4-positive, than those without ApoE ε4. These results suggest that functional imaging may be of future utility in identification of subjects at risk of later cognitive decline. However, at present, the positive predictive value of SPECT and PET in those with MCI or AAMI is questionable.59 Activation Studies in Ageing Introduction In healthy young subjects, motor, cognitive and pharmacological challenges have been shown to result in discrete changes in rCBF (using SPECT), rCMRglu or rCMRO2 (using PET) and BOLD signal using fMRI. These techniques are now being applied to ageing and age-associated dysfunction, with a particular focus on cognitive activation. A range of cognitive and non-cognitive tasks has proved suitable for probing functional integrity in aged subjects. Activation procedures have included motoric,61 photic,62 working memory,63 visual attention,64 frontal/executive,65 word identification,66 spatial orientation,67 visual encoding and retrieval68 and verbal encoding and retrieval69 tasks. Intuitively, many of these tasks have been chosen on the assumption that some age-dependent decline occurs within that particular cognitive domain. There are several aims of this exercise. Firstly, there is a possibility that the activation process may unmask changes not appreciable at rest. Secondly, there is a desire to study the functional neuroanatomy of the ageing brain, and to examine functional neuroimaging correlates of age-related neuropsychological changes. Thirdly, the studies aim to identify individuals at increased risk of age-related neuropsychiatric diseases. Potential advantages of activation tasks One advantage of introducing a cognitive task during scanning is the reduction in intra-subject variability of functional data. As mental activity and sensory stimulation have cerebral metabolic and blood flow correlates, standardisation of sensory, motor and cognitive activity via an activation procedure will minimise these potential confounds. The influence of these variables over reliability of cerebral metabolic measurements has been demonstrated by Duara et al.70 who re-scanned nine normal subjects at rest and seven normal subjects during an activation task. Resting scans were performed with eyes

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closed and covered and ears open. Activation scans involved viewing a series of coloured pictures projected onto a screen, and depressing a foot pedal with the right or left foot, depending on whether the subject did, or did not, like a picture. Within subject variability for normal subjects for the repeated scans performed at rest was marked, with correlation coefficient for mean rCMRglu of 0.001, indicating virtually no correlation. However, in repeated activated scans in normal subjects, there was a correlation coefficient of 0.703. Metabolic ratios for specific regions also varied considerably between resting scans, but less so between activated scans. A study by Skolnick et al. 71 used 133Xe inhalation to measure rCBF in elderly normal subjects at rest and during verbal and spatial tasks during two scans, separated by an average of nine weeks. Global CBF was reduced in the repeated baseline scans, but this reduction was not evident in the repeated activated scans. Regional CBF was consistent between the two activated scans. These results suggest that measurement of CBF and CMRglu is reproducible and reliable during activated states and may be less so at rest. Motor and sensory stimulation In a simple reaction time task with BOLD fMRI, D’Esposito et al.72 compared 32 young subjects (mean age 22.9 years) and 20 elderly subjects (mean age 71.3 years). They found a reduced number of suprathreshold voxels and lower signal-to-noise ratio (SNR) in the elderly, but no significant group differences in the shape of the haemodynamic response. The authors suggested that an age effect on the haemodynamic coupling between neural activity and BOLD signal change may need to be taken into account in interpretation of age effects demonstrated by functional imaging techniques. Calautti et al.61 used 15O PET to investigate age effects in a cued thumb-to-index finger opposition task. An over-activation in the superior frontal cortex in aged relative to young subjects was noted, which, in the authors’ opinion, could not be fully explained by differences in resting CBF in this area between young and old subjects. In a BOLD contrast fMRI experiment, Ross et al.62 examined the response to photic stimulation in a group of 9 healthy elderly subjects (mean age 71 years) and healthy young subjects (mean age 24 years). The mean BOLD signal response to photic stimulation in the visual cortex was significantly reduced in aged subjects. There was a trend for elderly subjects, without atrophy, to demonstrate less profound reduction than those with atrophy. During presentation of checkerboard stimuli to 11 young subjects (mean age 23 years) and 11 elderly subjects (mean age 66 years), Huettel et al.73 noted an earlier peak in the haemodynamic response, smaller spatial extent of activation and lower SNR in the elderly subjects. The amplitude, form and refractory properties of the haemodynamic response were similar across groups, and the authors concluded that the smaller spatial extent of activation was attributable to the lower SNR in the elderly subjects. In a study of 12 young and 14 older subjects, Levine et al.74 examined CBF correlates during passive viewing of

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black and white formed and unformed textures. Subjects were scanned in two states: during viewing of random (formed and unformed) textures or formed textures. Random versus formed texture viewing was associated with activation outside the ventral occipitotemporal pathway (predominantly in left anterior cingulate, medial, middle and superior frontal gyri) in the older subjects. Comparison with the younger group demonstrated reduced activation in the older subjects of left precuneus, middle temporal and posterior cingulate and of the right parahippocampal gyrus. Taken together, these results suggest an age-related change in processing of visual stimuli, which may represent a decrement in the efficiency of visual processing with age. Grady et al. have examined age-related changes in object and spatial visual processing in a series of 15O PET experiments. An initial study contrasted a face-matching task with a spatial location-matching task.75 Results from this study suggested that functional separation of dorsal (occipitoparietal) versus ventral (occipitotemporal) pathways subserving spatial relations and object discrimination respectively was apparent in both aged and young subjects. However, functional separation of these two systems appeared less distinct in aged individuals. A further study replicated these findings,76 and in addition demonstrated greater activation by older adults during face matching in bilateral dorsolateral prefrontal cortex, fusiform gyrus, inferior frontal gyrus and left insula as well as left middle temporal gyrus. During location matching, older subjects again activated a more widespread network in bilateral prefrontal cortex, bilateral fusiform gyri as well as left occipitotemporal cortex and inferior parietal cortex. Such changes were taken to represent reduced processing efficiency of prestriate occipital cortex with age and hence increased utilisation of additional networks in order to compensate this reduction in efficiency. Similar results were obtained in a 15O PET study evaluating the effects of age on selective and divided visual attention by Madden et al.64 During the divided attention task only, subjects activated a network of regions including occipitotemporal, occipitoparietal and prefrontal regions. Activation patterns in younger subjects were relatively greater in the occipitotemporal pathway and for the older subjects greater in prefrontal regions. A further study by Grady et al.77 evaluated the effect of age on a task of degraded and nondegraded face perception. Results similar to the earlier study76 were obtained for the non-degraded face matching. Analysis of the activation patterns from the degraded face-matching task revealed that different regions were positively correlated with task performance in the old compared with the young subjects. In the old subjects, activity in the posterior occipital cortex, thalamus and hippocampus showed positive correlation with task performance. In the younger subjects this correlation occurred in the fusiform gyrus, suggesting that brain networks subserving success in this task differed between young and old subjects. Using a short-term visual memory task in which subjects discriminated pairs of vertical sinusoidal gratings of differing spatial frequency, McIntosh

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et al.78 determined age-related differences in activated networks. Older and younger subjects performed equally well on the task. Although a common pattern of activation was seen in many regions (occipital, temporal and inferior prefrontal cortex and caudate), older subjects were observed to activate additional distinct regions (medial temporal and dorsolateral prefrontal cortices). Activity in these additional regions was related to task performance in the older subjects, suggesting a role for these additionally activated networks in maintenance of performance. Working memory In the first report of ageing effects on visual working memory, Grady et al.63 used 15O PET to evaluate rCBF during a delayed match to sample task. Independent of age, a common pattern of activation was noted with delay, including increased rCBF in left anterior prefrontal and decreased rCBF in the ventral extrastriate cortex. Less activation was seen in the right ventrolateral prefrontal cortex, and greater activation was seen in left dorsolateral prefrontal cortex in the aged group. A subsequent 15O PET study utilising two verbal working memory tasks79 demonstrated similar networks of activation in older and younger subjects. However, younger subjects showed more right dorsolateral prefrontal activation during one task and older subjects demonstrated greater left dorsolateral prefrontal activation during another working memory task. Taken together, these two studies support the notion that increased activation observed in the older subjects is a reflection of compensatory strategies required to overcome cognitive inefficiency in working memory occurring with age. Complex tasks Age effects have been examined during more complex tasks such as card sorting. An 15O PET study by Nagahama et al.80 utilised a modified card sorting task. This study revealed age effects of reduced ability to activate left dorsolateral prefrontal cortex, left inferior parietal lobule, left striate and prestriate cortex, bilateral precuneus, left occipital cortex and left cerebellum. In aged subjects a negative correlation between perseverative errors and activation was noted in several of these regions including left dorsolateral prefrontal cortex. A subsequent 15O PET utilising the Wisconsin Card Sort Test (WCST) and Raven’s Progressive Matrices (RPM) for activation65 has demonstrated age specific reductions in activated networks. Age-related reduction in activation was seen in the dorsolateral prefrontal cortex with WCST and in the inferolateral temporal cortex with RPM. In addition, aged subjects activated areas that were normally suppressed in younger individuals. These areas included right parahippocampal gyrus with WCST and polar and medial portions of the prefrontal cortex in both WCST and RPM. Reduced activation in key areas normally subserving these more complex neuropsychological functions appears linked with decrements on task performance. However, it remains unclear whether enhanced activation in regions normally suppressed

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by younger individuals represents use of alternative cognitive strategies or inefficiency of operating networks subserving these cognitive tasks.65 Madden et al.66 investigated the effect of age on a visual word identification task using 15O PET in 10 young and 10 older subjects. Activity representing passive encoding of letter strings was observed in left frontal, striate and inferior temporal cortex, with more prominent activations observed in frontal and temporal regions in younger subjects. Several decreases in rCBF were also observed, with these being greater in the older subjects in the right superior frontal region and greater in the younger subjects for the right anterior thalamus, right posterior cingulate and insula. The activity associated with the semantic retrieval component of the task (distinguishing word from pronounceable non-word) also revealed an age effect. More prominent left occipital activation was observed in younger subjects, and a more prominent deactivation was observed in left superior frontal, anterior cingulate and lateral aspect of inferior temporal gyrus. Memory tasks Encoding Correlates of age-related declines in encoding ability for visual material has been examined in a study by Grady et al.68 using 15O PET in 10 young subjects (mean age 25 and 10 older subjects (mean age 69). In this study, older individuals activated the left ventral temporal cortex with encoding of faces but failed to activate the network seen in younger individuals (anterior cingulate, left prefrontal cortex, left temporal cortex and right medial temporal lobe including hippocampus). The poor activation in older subjects was attributed to failure of the encoding process. In a more complex task, encoding of face/name pairs was examined in a small 15O PET study.81 Activation during encoding was seen in bilateral occipital association areas, extending into parietal lobes bilaterally. No activation was seen in the hippocampus on either side. Reduced rCBF was observed in right temporal, frontal and anterior cingulate regions and in the left superior temporal gyrus. No age effect was identified, although this may have been attributable to the small sample size. Studies examining encoding of verbal material have also shown some age-effects. During the encoding phase of a word pair task, Cabeza et al.69 demonstrated that younger subjects had higher activation in the left prefrontal and occipito-temporal regions than older subjects. Somewhat in contrast to this study are the activation effects noted by Madden et al.82 During an encoding task involving a living/non-living judgement of nouns, no significant activation was observed in the younger subjects relative to a baseline task. However, in the aged group, activation was observed in bilateral prefrontal cortex, left thalamus, fusiform gyrus and parahippocampal gyrus. Direct contrast between young and older subjects revealed a significant difference for thalamus only. Reductions during the encoding task were not seen in young

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subjects but were seen in several regions in the elderly group, including left anterior cingulate and right inferior parietal lobule. Direct comparison of the two age groups failed to reveal significant differences in deactivation patterns. The demonstration of age-related contrasts in patterns of activation during encoding in this study stands in some contrast to the studies of Grady et al.68 and Cabeza et al.,69 and raises the possibility that age related differences in rCBF during encoding may vary across tasks rather than as a function of age per se.82 Retrieval A number of studies have examined the effects of age on functional imaging correlates of recall of verbal material. However, the comparability of results is limited by the widely differing paradigms employed. Cabeza et al.69 used a word pair task to study recognition and cued recall with 15O PET. Effects of these “retrieval” tasks revealed that whilst younger subjects primarily demonstrated a unilateral (right) sided effect for retrieval of verbal material, older subjects demonstrated a more bilateral pattern of frontal activation. Retrieval was examined in the noun recognition experiment undertaken by Madden et al.82 Young subjects appeared to activate a network including right prefrontal cortex, left middle frontal gyrus and left thalamus, whereas older subjects activated bilateral prefrontal cortex, inferior parietal lobule, left inferior temporal cortex and left cerebellum. Direct comparisons revealed statistically significant increase in activation for younger subjects in the thalamus only, and in prefrontal regions in the elderly. Deactivation was also examined and, although deactivated regions were different in young and older subjects, no statistically significant differences were apparent. Using a word stem completion paradigm, Backman et al.83 explored 15O PET correlates of a word stem completion task, incorporating baseline, priming and recall components. Activity attributed to the recall component of the task in both young and elderly included bilateral increases in prefrontal cortex and anterior cingulate. Somewhat unexpectedly, older subjects activated perirhinal cortex bilaterally. The two explanations posited for this medial temporal lobe activity were that either optimal cued recall in elderly subjects involved use of strategic search strategies not utilised by younger subjects or that older subjects were continuing to encode and consolidate the information even during the cued recall task. The medial temporal lobe activation nevertheless stands in some contrast to the majority of studies of recall in both aged and young subjects. In a large 18FDG study of 70 subjects, Hazlett et al.84 examined cerebral metabolic activity during performance of a serial verbal learning task, without reference to a resting condition. Good performance was associated with higher metabolic activity in the frontal lobes for in the younger subjects and in the occipital lobes for the older subjects. An age related decrement in cerebral metabolism in the frontal lobes was observed, which remained significant after correction for cerebral atrophy. The shift in metabolic activity away

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from anterior patterns of activation in the elderly was taken to indicate reallocation of networks invoked by the task. Comparisons of these results with 15O PET studies are made difficult by the combination of encoding and recall components in this experiment. Finally, Cabeza et al.85 examined age effects on activation during a verbal retrieval task, evaluating both ability to recognise a previously studied word from a distractor (content recognition), and ability to determine which of two words had been presented more recently in the study list (recall of temporal order). For content recognition, activation was observed in ventromedial temporal regions in both groups, suggesting a limited effect of age on strategic retrieval processes. However, age effects included reduced activation of right prefrontal regions and enhanced activation of left prefrontal cortex. This effect was thought to represent a compensatory process enacting semantic processing in the elderly. An alternative explanation, that of increasing left prefrontal activity with increasing demands of the task, was not supported by performance data (which indicated equal performance across age groups) or by comparison of hard versus easy blocks included in the experiment. For the temporal order task, an increase was seen in the right prefrontal cortex of young, but not older subjects, an effect that may reflect general age effects on frontal function or regionally specific effects of age on temporal-order retrieval. Summary of activation studies A number of studies have evaluated the effect of age on cerebral activation. There is evidence, from studies employing simple motor and sensory paradigms, of an age-related decline in cerebral activation, an effect that may have its basis in dysfunction in pathways subserving sensory and motor functions. For cognitive processes, the effect of ageing on patterns of activation is partly dependent on the task under consideration. A unifying observation is that during many cognitive tasks, elderly subjects activate brain networks that are similar to those activated by young subjects. However, the extent of this activation is reduced in elderly subjects. With some exceptions, this statement can be made of tasks involving visual processing, short-term visual memory, visual and verbal working memory, verbal recall and complex cognitive tasks such as card sorting. Reduced ability to activate certain brain regions has also been noted as a feature of ageing, and is often seen in association with reduced performance of the task under consideration. Activation of a number of regions additional to those seen in younger subjects has also been observed. In encoding and recall tasks, a more bilateral pattern of prefrontal activation has generally been observed. Alterations in the pattern of prefrontal activation are of interest, given the propensity of these regions to structural change with age. Enhanced activation in prefrontal regions may be an attempt to compensate for reduced functionality of this brain region with age, but this is a simple explanation that requires more study. In particular, the relationship between prefrontal activation and

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success during task performance requires further evaluation. Activation of additional networks outside the prefrontal cortex has also been observed. At times, this extra-frontal activation has been associated with maintenance of performance. This suggests that functional activation of additional regions may reflect engagement of different cognitive strategies, perhaps by way of compensation for age related inefficiencies in processing. In addition to age differences in activation, an age effect on deactivation of various regions has been observed, with older subjects showing less strong deactivation in usually deactivated networks, along with additional deactivation in other areas. Deactivation may be a way in which optimisation of cognitive performance occurs in health, thus suggesting inability to streamline performance with age. A more detailed analysis of deactivation with respect to task performance will allow the potential implication of this age-related change in deactivation pattern to be elucidated. Magnetic Resonance Spectroscopy (MRS) Much of the work related to the effect of ageing on MRS-defined brain metabolites has focused on the neurodevelopmental period 86,87 or young adulthood.87 The studies of the elderly are limited by: small numbers, crosssectional design, limited sampling of brain tissue, different methods and discrepant findings that warrant further studies before definitive conclusions can be made. The relative concentration of N-acetylaspartate (NAA), which is the major marker of neurones, has been the focus of some investigations. Results have been variable, with reports of a reduction in NAA with age in the basal ganglia88 and the grey and white matter89–91 in some, but not all92,93 earlier studies. More recent studies94–96 have reported no reduction in NAA in the elderly, although this is still an inconsistent finding.97 These discrepancies could be related to methodological differences and the age range studied. Since MRS does not yield absolute values, metabolites are quantified in reference to an internal standard. The commonly used standard is total creatine (Cr), but this is not invariant with age, thereby reducing the value of the NAA/Cr ratio as a measure of age-related change. The other reference used is MRI-visible water content, which accounts for >95% of tissue water in the brain.98 While one study reported no change in the water content of the brain with ageing,98 another study reported a significant reduction in brain water with ageing.94 Total creatine (Cr), a marker of the energetics of neurones and glia, has been reported to decrease with age in the basal ganglia88 and increase with age in the frontal white matter,93 frontal gray matter,94 parietal white matter96 and both grey and white,95 but some studies have reported no change.93,96 Choline-containing compounds (Cho) have been shown to increase with age in some studies,89,94,95 decrease according to others88,93,97 and remain

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unchanged in some studies.90,96 The heterogeneity of the above findings suggests that this field is still in its infancy and much work remains to be done. The potential of MRS has therefore not been fully exploited in this field. MRS studies in the healthy elderly are important if a normative database is to be developed for MRS to be used as a diagnostic investigation for neuropsychiatric disorders in the elderly, in particular dementia. Conclusions The increasing sophistication of functional imaging studies, particularly those involving activation techniques, is extending our understanding of how the brain is affected by the ageing process. Although many methodological pitfalls exist, this body of literature has enabled an appreciation of more subtle age effects than were initially thought to exist. At rest, age results in small, regionally specific declines in CBF and CMRglu, maximal in the frontal regions, which mirrors structural and neuropsychological alterations observed with advancing years. The differing patterns of prefrontal activation observed with age across a variety of cognitive activation tasks further highlights a key role for the frontal lobes in mediation of the age related cognitive changes seen in health. Specific but contrasting changes in functional integrity are beginning to be demonstrated in association with risk factors for age-related diseases such as AD and cerebrovascular disease. There are many areas in which functional imaging techniques may contribute to the further unravelling of secrets of the ageing brain. A potential role exists for these technologies in assessing the effects of interventions designed to modify or delay the ageing process. Early identification of those at risk of age-related neuropsychiatric disease might also be a realistic future role for these tools. References 1. 2.

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76. Grady CL, Maisog JM, Horwitz B, Ungerleider LG, Mentis MJ, Salerno JA, Pietrini P, Wagner E, Haxby JV. Age-related changes in cortical blood flow activation during visual processing of faces and location. J Neurosci. 1994; 14: 1450–1462. 77. Grady CL, McIntosh AR, Horwitz B, Rapoport SI. Age-related changes in the neural correlates of degraded and non-degraded face processing. Cognitive Neuropsych. 2000; 17:165–186. 78. McIntosh AR, Sekuler AB, Penpeci C, Rajah MN, Grady CL, Sekuler R, Bennett PJ. Recruitment of unique neural systems to suport visual memory in normal aging. Curr Biol. 1999; 9:1275–1278. 79. Haut MW, Kuwabara H, Leach S, Callahan T. Age-related changes in neural activation during working memory performance. Aging Neuropsychol C. 2000; 7:119–129. 80. Nagahama Y, Fukuyama H, Yamauchi H, Katsumi Y, Magata Y, Shibasaki H, Kimura J. Age-related changes in cerebral blood flow activation during a Card Sorting Test. Exp Brain Res. 1997; 114:571–577. 81. Herholz K, Ehlen P, Kessler J, Strotmann T, Kalbe E, Markowitsch HJ. Learning face-name associations and the effect of age and performance: a PET activation study. Neuropsychologia. 2001; 39:643–650. 82. Madden DJ, Turkington TG, Provenzale JM, Denny LL, Hawk TC, Gottlob LR, Coleman RE. Adult age differences in the functional neuroanatomy of verbal recognition memory. Hum Brain Mapp. 1999; 7:115–135. 83. Backman L, Almkvist O, Andersson J, Nordberg A, Winblad B, Reineck R, Langstrom B. Brain activation in young and older adults during implicit and explicit retrieval. J Cognitive Neurosci. 1997; 9:391. 84. Hazlett EA, Buchsbaum MS, Mohs RC, Spiegel-Cohen J, Wei TC, Azueta R, Haznedar MM, Singer MB, Shihabuddin L, Luu-Hsia C, Harvey PD. Age-related shift in brain region activity during successful memory performance. Neurobiol Aging. 1998; 19:437–445. 85. Cabeza R, Anderson ND, Houle S, Mangels JA, Nyberg L. Age-related differences in neural activity during item and temporal-order memory retrieval: a positron emission tomography study. J Cognitive Neurosci. 2000; 12:197–206. 86. Grachev ID, Apkarian AV. Aging alters regional multichemical profile of the human brain: an in vivo 1H-MRS study of young versus middle-aged subjects. J Neurochem. 2001; 76:582–593. 87. Kadota T, Horinouchi T, Kuroda C. Development and aging of the cerebrum: assessment with proton MR spectroscopy. Amer J Neuroradiol. 2001; 22:128–135. 88. Charles HC, Lazeyras F, Krishman KR, Boyko OB, Patterson LJ, Doraiswamy PM, McDonald WM. Proton Spectroscopy of human brain: effects of age and sex. Prog Neuro-Psychoph. 1994; 18:995–1004. 89. Bruhn H, Stoppe G, Merboldt KD, Michaelis T, Hanicke W, Frahm J. Cerebral metabolite alterations in normal aging and Alzheimer’s dementia (Abstract). Proc Soc Magn Reson Med. 1992; 1:752. 90. Christiansen P, Toft P, Larsson HBW, Stubgaard M, Henriksen O. The Concentration of N-acetyl aspartate, creatine + phosphocreatine, and choline in different parts of the brain in adulthood and senium. Magn Reson Imaging. 1993; 11: 799–806. 91. Lim KO, Spielman DM. NAA in cortical gray matter with applications for measuring changes due to aging. Magn Reson Med. 1997; 37:372–377. 92. Kreis R, Ernst T, Ross BD. Absolute quantitation of water and metabolites in the human brain. II. Metabolite concentratons. J Magn Reson Imaging. 1993; 102: 9–19. 93. Soher BJ, van Zijl PCM, Duyn JH, Barker PB. Quantitative proton MR spectroscopic imaging of the human brain. Magn Reson Med. 1996; 35:356–363.

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94. Chang L, Ernst T, Poland RE, Jenden DJ. In vivo proton magnetic resonance spectroscopy of the normal aging human brain. Life Sci. 1996; 58:2049–2056. 95. Pfefferbaum A, Adalsteinsson E, Spielman D, Sullivan EV, Lim KO. In Vivo Spectroscopic Quantification of the N-acetyl moiety, creatine, and choline from large volumes of brain gray and white matter: Effects of normal aging. Magn Reson Med. 1999; 41:276–284. 96. Saunders DE, Howe FA, van den Boogaart A, Griffiths JR, Brown MM. Aging of the adult human brain: in vivo quantitation of metabolite content with proton magnetic resonance spectroscopy. J Magn Reson Imaging. 1999; 9:711–716. 97. Angelie E, Bonmartin A, Boudraa A, Gonnaud P-M, Mallet J-J, Sappey-Marinier D. Regional differences and metabolic changes in normal aging of the human brain: proton MR spectroscopic imaging study. Amer J Neuroradiol. 2001; 22: 119–127. 98. Christiansen PB, Toft P, Gideon ER, Danielsen PR, Henriksen O. MR-visible water content in human brain: A proton MRS study. Magn Reson Imaging. 1994; 12:1237–1244.

Chapter 8 NEUROENDOCRINE ASPECTS OF BRAIN AGEING George A Smythe

Introduction Regulatory function of the human (and animal) body depends on the integrated and co-ordinated activity of two major control systems: the endocrine system and the nervous system. That there is a close interrelationship between the function of the mind and endocrine hormone secretion is an old concept of western medicine that came from findings such as the association of depression with dysfunction of the hypothalamic–pituitary–adrenal (HPA) axis.1 The field of neuroendocrinology arose out of observations pointing to significant influences being exerted by hormones, and related peptides, on the brain and vice versa. Research into this “neuroendocrine” hypothesis accelerated from the early 1970s as the technologies became increasingly available for the isolation, characterization and measurement of neurotransmitters, hypothalamic peptides, pituitary and other endocrine hormones. Emerging techniques are opening up new ways of examining brain chemistry; proton magnetic spectroscopy, for example, has recently been used to show the marked reorganization of brain chemical networks that occurs with normal ageing.2 Neuroendocrine interactions are critically important in normal human development. The role of the brain, especially its neurotransmitters and hypothalamic peptides, in control of the pituitary, thyroid, thymus, adrenals, pancreas and gonads has been extensively documented from early to adult development phases. Changes to these neuroendocrine systems post-maturity and in the elderly are less well defined. The question arises as to whether there are neuroendocrine factors which have roles in maintaining “healthy ageing,”

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dysfunction of which may result in premature ageing and neurone loss. With ageing there are notable declines in both mental and physical function that may be mediated, in part at least, by known age-related changes in endocrine function and feedback to the brain. These include: • Cognitive function • Reproductive function • Muscle mass and strength • Cardiac performance • Immune function Ageing and Endocrine Relationships The major age-related changes in endocrine hormones include declines in circulating levels and responses of pituitary growth hormone3–6 and gonadal steroid hormones7–16, adrenal dehydroepiandrostenedione (DHEA) 17-24, and insulin-like growth factor-1 (IGF-1). 13,25–28 On the other hand, it is significant that secretion of pituitary ACTH and adrenal glucocorticoids (in contrast to DHEA) trend upward with ageing.24,29-39 These latter findings are consistent with significant changes to HPA function with ageing; evidence

Figure 1. Neuoendocrine aspects of ageing: Brain–body neuroendocrine feedback and putative age-related changes to selected brain and endocrine hormones.

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of cortisol excess raises questions about the role, in ageing, of stress and its effect on cognition and hippocampal neurons40-46. In Figure 1 feedback of neural signals to the periphery and from the peripheral target hormones (and products such as growth hormone-derived IGF) to the pituitary and brain are indicated by the large shaded arrows. The putative direction of change of neuroendocrine effectors with ageing are indicated by the small arrows inside the boxes. Note 1, hypothalamic corticotropin releasing hormone (CRH)42,47 and somatotropin release-inhibiting factor (somatostatin, SRIF) increase;48 growth hormone releasing hormone (GHRH) is decreased49,50 but downtrends in gonadotropin releasing hormone (GnRH) show sexual dimorphism with changes being more evident in females (data from animal studies — see below). Note 2, consistent with the hypothalamic changes, there is evidence that pituitary secretion of ACTH is increased and growth hormone (GH) is reduced but the data is less clear-cut in the case of the pituitary gonadotropins where, again, there is evidence of sexual dimorphism and significant variation between changes in luteinizing hormone (LH) versus those in follicle stimulating hormone (FSH).51 Note 3, as a consequence of reduced GH secretion, the product of its action on the liver and other tissues,52 insulin-like growth factor-1 (IGF-1) is reduced. Note 4, the bulk of evidence is consistent with increased adrenal glucocorticoid production whereas the adrenal androgenic steroid DHEA and its sulphate is reduced.44 Here there is sexual dimorphism with women showing greater changes than men with ageing.53 Note 5, estrogen levels decline following menopause in women and testosterone levels are reduced in men (and women) with ageing.51 Ageing, Stress, and the Hypothalamic–Pituitary–Adrenal (HPA) Axis Evidence of altered HPA function with ageing comes from both animal and human studies. Sapolsky and co-workers showed in the rat there is a significant age-related increase in corticosterone production and that the ability of animals to “turn off” stress-induced glucocorticoid release is impaired.54,55 The apparent age-related failure of glucocorticoid excess to exert normal feedback inhibition on central, hypothalamic, or pituitary receptors has attracted considerable research.24,33,34,40,42, 46,56–60 Normally, a principle brain response to stress is markedly increased activity of noradrenergic neuronal activity. 61 This increased noradrenergic drive acts on CRH neurons at the level of the paraventricular nucleus of the hypothalamus and CRH is released into the pituitary portal circulation to stimulate pituitary ACTH release (see Figure 2). Figure 2 also indicates the feedback of glucocorticoids at the level of the anterior pituitary and certain brain centres to inhibit further CRF and ACTH release. Figure 2 summarizes neuroendocrine control of the HPA axis and glucocorticoid feedback. Included in this diagram are inputs to and from the hippocampus, amygdala and the bed nucleus of the stria terminalis (BNST)

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Figure 2. The hypothalamic-pituitary-adrenal-axis. Neuroendocrine control of ACTH release and central sites of glucocorticoid feedback.

all of which can mediate inhibition or stimulation of CRH release.42,62,63 Using modern analytical methods, the hypothalamic neuronal activity of norepinephrine (NE) and serotonin (5-HT) at terminals arising from cells in the brainstem (locus coeruleus) and midbrain (Raphe) can be assessed by measuring the transmitters and their primary neuronal metabolites (DHPG and 5-HIAA, respectively).61,64 The “glucocorticoid cascade” hypothesis of ageing and hippocampal damage A number of animal studies have shown an age-related chronic increase in corticosterone and an apparent failure of glucocorticoid negative feedback. These data, taken with evidence that excess glucocorticoid levels caused damage to hippocampal neurones that are involved in cognition, led Sapolsky et al. to propose the glucocorticoid cascade hypothesis of ageing and hippocampal damage.65 In ageing man there is a decline in cognitive function and several groups have proposed that this may be due to cortisol excess in accord with Sapolsky’s hypothesis.40,41,57,66,67 Not all studies support the hypothesis in man68,69 and Angelucci, in questioning earlier conclusions, has suggested that changes seen represent an adaptive response to a CNS neurodegenerative inflammatory process.69 It should be noted that, in man, age-related eleva-

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Figure 3. Effects of age on the HPA axis in male Wistar rats. Means± SEM are shown, n=6 per group.

tions of cortisol are not always significant and there are sex differences.44,68 However most evidence does point to a failure of glucocorticoid feedback with ageing but the level at which this failure occurs is not clear.29,31,35,36,70 The author has investigated the status of hypothalamic noradrenergic neuronal activity, circulating ACTH and corticosterone in young (2 months old) compared with old (20–24 months old) rats. The results of this unpublished study are shown in Figure 3. Consistent with published results47 the data show a significant increase in serum concentrations of ACTH and a nonsignificant increase in serum corticosterone. The novel finding here is that of a significant decrease in hypothalamic neuronal activity (DHPG/NE)61 in the old rats. This is discordant with the HPA relationships in normal animals subjected to stress where there is a positive relationship between ACTH and medial basal hypothalamus (mbh) DHPG/NE ratio.61 These data indicate that in the rat, with respect to the HPA axis, ageing is not akin to stress and that the failure of feedback inhibition does not seem to occur at the level of the hypothalamus. Neuroendocrine Changes with the Menopause Ovulatory failure is one of the earliest events of ageing and is one that clearly involves neuroendocrine mechanisms. The central nervous system has been described as the pacemaker of reproductive senescence.71 The consequences of menopause for women relate not only to the loss of reproductive ability but the loss of ovarian follicular estrogen and the decline of circulating estrogen levels. In a major review, Wise et al.72 have highlighted many findings with respect to neurotrophic and neuroprotective properties of estrogen. These findings reinforce the ideas underlying estrogen replacement therapy,73,74 for which there are negative as well as positive issues to be considered.75 The reported benefits of peri- and post-menopausal estrogen replacement include maintenance of normal bone and mineral metabolism,76,77 memory and cognition,78–82 decreased sympathetic nervous system activity, 83,84

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decreased cortisol and HPA axis response.85-87 Improved IGF-1 production is seen with transdermal estrogen administration compared with the oral route;25 the transdermal route of estrogen administration is also associated with improved insulin sensitivity and glucose metabolism.88 Improved memory and attention in postmenopausal women with Alzheimer’s disease has been reported following estrogen administration.79 However, not all investigations have demonstrated a protective effect of estrogen treatment against declines in cognitive function or stress reponses in older non-demented women.89,90 Two recent randomized, double-blind trials of estrogen treatment in women with Alzheimer’s disease (AD) failed to demonstrate any cognitive or functional improvement following estogen.91,92 While it is clear from animal and clinical studies that estrogen acts in the brain via a number of established estrogen receptors as a neuromodulatory and neuroprotective hormone,93-95 it does not appear to act to restore existing neural damage. The Age-Related Decline in Growth Hormone and IGF-1 Human growth hormone secretion (hGH) and IGF-1 production undergo significant declines with ageing.96 The age-related decline in hGH with ageing is exemplified by the comprehensive studies of van Cauter and colleagues into sleep-related hGH release.39 Which neuroendocrine factors either mediate or are affected by the age-related changes have not been established, but several possibilities do arise (cf. Figure 1). These include: i) a primary pituitary defect, ii) increased hypothalamic release of SRIF to inhibit pituitary GH release, iii) reduced hypothalamic release of GHRH, and iv) altered activity of the hypothalamic neurotransmitters that mediate release of SRIF and/or GHRH — either primarily or as a consequence of reduced negative feedback. The bulk of research data from both man and animals mitigates against a primary pituitary defect and favours reduced GHRH activity being a major contributor to the age-related decline in GH.6,49,97–102 Some evidence also supports the possibility that there may also be increased SRIF activity with ageing.49,50,103 Alterations in the activities of these peptides may, in turn, be mediated by their controlling neurotransmitters. The monoaminergic control of GHRH and SRIF has been a contentious issue for many years with both catecholamines and serotonin being proposed as the primary mediators of hypothalamic GHRH release.104–107 Direct measurement of hypothalamic monoamine neurotransmitter activity in the rat is consistent with serotonin neuronal activity being a major activator of GHRH release.105,106,108 When considered with the reduced activity of GHRH noted above, it is conceivable that there may be an age-related decline in central serotonin function and support for this is found in human studies demonstrating reduced brain serotonin activity and binding sites.109–112 If serotoninergic systems in the brain are reduced with ageing then this would not appear to be due to reduced GH

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negative feedback. In the rat at least, GH lack (hypophysectomy) is associated with increased hypothalamic serotonin neuronal activity which is reduced to normal by exogenous GH administration.105 GH and IGF-1 decline with ageing and, taken with the ready availability of recombinant GH, there has been considerable interest in the role of GH replacement therapy to treat this so-called “somatopause”.113–119 This concept is not without controversy and Morley120 has questioned whether, in this context, “GH is a fountain of youth or a death hormone?” The general consensus points to the need for carefully controlled studies.113,115,119,121–123 Androgens and Aging: the “Adrenopause” LH secretion in men is regulated by release of GnRH from the pituitary and negative feedback of circulating testosterone. With ageing, this neuroendocrine control is altered as LH levels increase at the rate of 1.9% per year,124 serum testosterone levels decline at the rate of about 0.4% per year123 and DHEA decreases at a faster rate of 3.1%.124 On the basis of a relative “hypogonadism of ageing” in older men, hormone replacement with testosterone and DHEA have been investigated. In general, testosterone replacement in older hypogonadal males have indicated positive effects on muscle mass, bone mineral density and fat mass but it is too early to establish its true efficacy51 or whether it has any effect in improving neural function. In developing humans, DHEA and its sulphate (DHEAS) are the most abundant steroids but their levels decline after adrenarche and with ageing. DHEAS treatment in animals has been reported to have memory-enhancing properties and replacement in ageing has been proposed.125, 126 Longitudinal studies in elderly men and women have failed to show any association between DHEA levels and cognitive performance127 and caution has been advised in relation to its use as a hormone replacement in ageing.123, 128 Summary Neuroendocrine systems change with ageing. The HPA axis is altered, particularly with respect to glucocorticoid responses and feedback to the pituitary and brain. These changes are proposed to alter hippocampal neurons and cognition. Menopause is associated with marked changes as the decline in estrogen levels reaching the brain take effect. The case for estrogen replacement is strong. Estrogen replacement appears able to prevent (but not repair) brain neuronal damage but specific brain actions at the hippocampus and other regions require further study. The ageing male undergoes a so-called “adrenopause” with falling androgens and brain feedback but effects of exogenous androgen treatment is less well studied than that of the estrogen deficient aged female. Age-related declines of GH and IGF-1 are associated with

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physical and metabolic deterioration and, at the level of the brain, declining serotoninergic systems. The change in this neurotransmitter may reflect a primary failure in GHRH stimulation with ageing. While many of the neuroendocrine changes seen with ageing are reminiscent of those seen with chronic stress, there is no clear evidence that ageing equates with stress. No doubt, however, stress can accelerate or exacerbate the effects of ageing. Acknowledgements The author wishes to express his sincere thanks to Mr Ray Williams for his inspirational support and for generously providing equipment for our work. I also wish to thank all of my colleagues in the BMSF for their constructive assistance. References 1. Carroll B, Mendels J. Neuroendocrine regulation in affective disorder. In: Sachar E, editor. Hormones, behavior, and psychopathology. New York: Raven Press, 1976:193–224. 2. Grachev ID, Apkarian AV. Chemical network of the living human brain. Evidence of reorganization with aging. Brain Res Cogn Brain Res. 2001; 11:185–197. 3. Gil-Ad I, Gurewitz R, Marcovici O, Rosenfeld J, Laron Z. Effect of aging on human plasma growth hormone response to clonidine. Mech Ageing Dev. 1984; 27:97–100. 4. Muggeo M, Fedele D, Tiengo A, Molinari M, Crepaldi G. Human growth hormone and cortisol response to insulin stimulation in aging. J Gerontol. 1975; 30: 546–551. 5. Rudman D, Kutner MH, Rogers CM, Lubin MF, Fleming GA, Bain RP. Impaired growth hormone secretion in the adult population: relation to age and adiposity. J Clin Invest. 1981; 67:1361–1369. 6. Russell-Aulet M, Dimaraki EV, Jaffe CA, DeMott-Friberg R, Barkan AL. Agingrelated growth hormone (GH) decrease is a selective hypothalamic GH-releasing hormone pulse amplitude mediated phenomenon. J Gerontol A Biol Sci Med Sci. 2001; 56:M124–129. 7. Wise PM, Smith MJ, Dubal DB, Wilson ME, Krajnak KM, Rosewell KL. Neuroendocrine influences and repercussions of the menopause. Endocr Rev. 1999; 20:243–248. 8. Jiroutek MR, Chen MH, Johnston CC, Longcope C. Changes in reproductive hormones and sex hormone-binding globulin in a group of postmenopausal women measured over 10 years. Menopause. 1998; 5:90–94. 9. Santoro N, Banwell T, Tortoriello D, Lieman H, Adel T, Skurnick J. Effects of aging and gonadal failure on the hypothalamic-pituitary axis in women. Am J Obstet Gynecol. 1998; 178:732–741. 10. Coulam CB. Age, Estrogens, and the psyche. Clin Obstet Gynecol. 1981; 24: 219–229. 11. Basaria S, Dobs AS. Hypogonadism and androgen replacement therapy in elderly men. Am J Med. 2001; 110:563–572.

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52. Le Roith D, Scavo L, Butler A. What is the role of circulating IGF-I? Trends Endocrin Met. 2001; 12:48–52. 53. Laughlin GA, Barrett-Connor E. Sexual dimorphism in the influence of advanced aging on adrenal hormone levels: the Rancho Bernardo Study. J Clin Endocr Met. 2000; 85:3561–3568. 54. Sapolsky RM, Krey LC, McEwen BS. The adrenocortical stress-response in the aged male rat: impairment of recovery from stress. Exp Gerontol. 1983; 18: 55–64. 55. Sapolsky RM. Glucocorticoids, stress, and their adverse neurological effects: relevance to aging. Exp Gerontol. 1999; 34:721–732. 56. Cizza G, Gold PW, Chrousos GP. Aging is associated in the 344/N Fischer rat with decreased stress responsivity of central and peripheral catecholaminergic systems and impairment of the hypothalamic-pituitary-adrenal axis. Ann NY Acad Sci. 1995; 771:491–511. 57. Wang PS, Lo MJ, Kau MM. Glucocorticoids and aging. J Formos Med Assoc. 1997; 96:792–801. 58. De Kloet ER, Sutanto W, Rots N, et al. Plasticity and function of brain corticosteroid receptors during aging. Acta Endocrinol–(Cop.) 1991; 125:65–72. 59. Heuser IJ, Gotthardt U, Schweiger U, et al. Age-associated changes of pituitaryadrenocortical hormone regulation in humans: importance of gender. Neurobiol Aging. 1994; 15:227–231. 60. Dodt C, Theine KJ, Uthgenannt D, Born J, Fehm HL. Basal secretory activity of the hypothalamo-pituitary-adrenocortical axis is enhanced in healthy elderly. An assessment during undisturbed night-time sleep. Eur J Endocrinol. 1994; 131: 443–450. 61. Smythe GA, Bradshaw JE, Vining RF. Hypothalamic monoamine control of stress-induced adrenocorticotropin release in the rat. Endocrinology. 1983; 113: 1062–1071. 62. Feldman S, Conforti N, Weidenfeld J. Limbic pathways and hypothalamic neurotransmitters mediating adrenocortical responses to neural stimuli. Neurosci Biobehav Rev. 1995; 19:235–240. 63. Lee Y, Davis M. Role of the hippocampus, the bed nucleus of the stria terminalis, and the amygdala in the excitatory effect of corticotropin-releasing hormone on the acoustic startle reflex. J Neurosci. 1997; 17:6434–6446. 64. Smythe GA, Brandstater JF, Lazarus L. Serotoninergic control of rat growth hormone secretion. Neuroendocrinology. 1975; 17:245–257. 65. Sapolsky RM, Krey LC, McEwen BS. The neuroendocrinology of stress and aging: the glucocorticoid cascade hypothesis. Endocrinol Rev. 1986; 7:284–301. 66. Martignoni E, Costa A, Sinforiani E, et al. The brain as a target for adrenocortical steroids: cognitive implications. Psychoneuroendocrinology. 1992; 17:343–354. 67. O’Brien JT. The ‘glucocorticoid cascade’ hypothesis in man: prolonged stress may cause permanent brain damage. Br J Psychiat. 1997; 170:199–201. 68. Kudielka BM, Schmidt-Reinwald AK, Hellhammer DH, Schurmeyer T, Kirschbaum C. Psychosocial stress and HPA functioning: no evidence for a reduced resilience in healthy elderly men. Stress. 2000; 3:229–240. 69. Angelucci L. The glucocorticoid hormone: from pedestal to dust and back. Eur J Pharmacol. 2000; 405:139–147. 70. Veldhuis JD, Iranmanesh A, Samojlik E, Urban RJ. Differential sex steroid negative feedback regulation of pulsatile follicle-stimulating hormone secretion in healthy older men: deconvolution analysis and steady-state sex-steroid hormone infusions in frequently sampled healthy older individuals. J Clin Endocr Met. 1997; 82:1248–1254. 71. Wise PM. Neuroendocrine modulation of the “menopause”: insights into the aging brain. Am J Physiol. 1999; 277:E965–970.

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72. Wise PM, Dubal DB, Wilson ME, Rau SW, Liu Y. Estrogens: trophic and protective factors in the adult brain. Front Neuroendocrin. 2001; 22:33–66. 73. Lichtman R. Perimenopausal hormone replacement therapy. Review of the literature. J Nurse Midwifery. 1991; 36:30–48. 74. Bjorntorp P. Neuroendocrine ageing. J Intern Med. 1995; 238:401–404. 75. Nerhood RC. Making a decision about ERT/HRT. Evidence to consider in initiating and continuing protective therapy. Postgrad Med J. 2001; 109:167–170, 173–174, 178. 76. Kulak CA, Bilezikian JP. Osteoporosis: preventive strategies. Int J Fertil Womens M. 1998; 43:56–64. 77. Shiflett S, Cooke CE. Osteoporosis: a focus on treatment. Maryland State Med J. 1997; 46:303–307. 78. Verghese J, Kuslansky G, Katz MJ, et al. Cognitive performance in surgically menopausal women on estrogen. Neurology. 2000; 55:872–874. 79. Erkkola R. Female menopause, hormone replacement therapy, and cognitive processes. Maturitas. 1996; 23:S27–30. 80. Asthana S, Craft S, Baker LD, et al. Cognitive and neuroendocrine response to transdermal estrogen in postmenopausal women with Alzheimer’s disease: results of a placebo-controlled, double-blind, pilot study. Psychoneuroendocrinology. 1999; 24:657–677. 81. LeBlanc ES, Janowsky J, Chan BK, Nelson HD. Hormone replacement therapy and cognition: systematic review and meta-analysis. J Amer Med Assoc. 2001; 285:1489–1499. 82. Rice MM, Graves AB, McCurry SM, et al. Postmenopausal estrogen and estrogenprogestin use and 2-year rate of cognitive change in a cohort of older Japanese American women: The Kame Project. Arch Intern Med. 2000; 160:1641–1649. 83. Menozzi R, Cagnacci A, Zanni AL, Bondi M, Volpe A, Del Rio G. Sympathoadrenal response of postmenopausal women prior and during prolonged administration of estradiol. Maturitas. 2000; 34:275–281. 84. Ceresini G, Freddi M, Izzo S, et al. Post-menopausal estrogen supplementation only partially blunts the sympathoadrenal response to mental stress. J Endocrinol Invest. 1999; 22:72–73. 85. Prinz P, Bailey S, Moe K, Wilkinson C, Scanlan J. Urinary free cortisol and sleep under baseline and stressed conditions in healthy senior women: effects of estrogen replacement therapy. J Sleep Res. 2001; 10:19–26. 86. Lindheim SR, Legro RS, Bernstein L, et al. Behavioral stress responses in premenopausal and postmenopausal women and the effects of estrogen. Am J Obstet Gynecol. 1992; 167:1831–1836. 87. Kudielka BM, Schmidt-Reinwald AK, Hellhammer DH, Kirschbaum C. Psychological and endocrine responses to psychosocial stress and dexamethasone/ corticotropin-releasing hormone in healthy postmenopausal women and young controls: the impact of age and a two-week estradiol treatment. Neuroendocrinology. 1999; 70:422–430. 88. O’Sullivan AJ, Ho KK. A comparison of the effects of oral and transdermal estrogen replacement on insulin sensitivity in postmenopausal women. J Clin Endocr Met. 1995; 80:1783–1788. 89. Matthews KA, Flory JD, Owens JF, Harris KF, Berga SL. Influence of estrogen replacement therapy on cardiovascular responses to stress of healthy postmenopausal women. Psychophysiology. 2001; 38:391–398. 90. Matthews K, Cauley J, Yaffe K, Zmuda JM. Estrogen replacement therapy and cognitive decline in older community women. J Am Geriatr Soc. 1999; 47:518–523. 91. Mulnard RA, Cotman CW, Kawas C, et al. Estrogen replacement therapy for treatment of mild to moderate Alzheimer disease: a randomized controlled trial. Alzheimer’s Disease Cooperative Study. J Amer Med Assoc. 2000; 283:1007– 1015.

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92. Henderson VW, Paganini-Hill A, Miller BL, et al. Estrogen for Alzheimer’s disease in women: randomized, double-blind, placebo-controlled trial. Neurology. 2000; 54:295–301. 93. Garcia-Segura LM, Azcoitia I, DonCarlos LL. Neuroprotection by estradiol. Prog Neurobiol. 2001; 63:29–60. 94. Shughrue PJ, Merchenthaler I. Evidence for novel estrogen binding sites in the rat hippocampus. Neuroscience. 2000; 99:605–612. 95. Dubal DB, Zhu H, Yu J, et al. Estrogen receptor alpha, not beta, is a critical link in estradiol-mediated protection against brain injury. Proc Natl Acad Sci USA. 2001; 98:1952–1957. 96. Corpas E, Harman SM, Blackman MR. Human growth hormone and human aging. Endocr Rev. 1993; 14:20–39. 97. Corpas E, Harman SM, Pineyro MA, Roberson R, Blackman MR. Growth hormone (GH)-releasing hormone-(1–29) twice daily reverses the decreased GH and insulin-like growth factor-I levels in old men. J Clin Endocrinol Metab. 1992; 75:530–535. 98. Merriam GR, Buchner DM, Prinz PN, Schwartz RS, Vitiello MV. Potential applications of GH secretagogs in the evaluation and treatment of the agerelated decline in growth hormone secretion. Endocrine. 1997; 7:49–52. 99. Guldner J, Schier T, Friess E, Colla M, Holsboer F, Steiger A. Reduced efficacy of growth hormone-releasing hormone in modulating sleep endocrine activity in the elderly. Neurobiol Aging. 1997; 18:491–495. 100. degli Uberti EC, Ambrosio MR, Cella SG, et al. Defective hypothalamic growth hormone (GH)-releasing hormone activity may contribute to declining GH secretion with age in man. J Clin Endocrin Metab. 1997; 82:2885–2888. 101. Mulligan T, Jaen-Vinuales A, Godschalk M, Iranmanesh A, Veldhuis JD. Synthetic somatostatin analog (octreotide) suppresses daytime growth hormone secretion equivalently in young and older men: preserved pituitary responsiveness to somatostatin’s inhibition in aging. J Am Geriatr Soc. 1999; 47: 1422–1424. 102. Thorner MO, Chapman IM, Gaylinn BD, Pezzoli SS, Hartman ML. Growth hormone-releasing hormone and growth hormone-releasing peptide as therapeutic agents to enhance growth hormone secretion in disease and aging. Recent Prog Horm Res. 1997; 52:215–244; (discussion) 244–246. 103. Marcell TJ, Wiswell RA, Hawkins SA, Tarpenning KM. Age-related blunting of growth hormone secretion during exercise may not be soley due to increased somatostatin tone. Metabolism. 1999; 48:665–670. 104. Muller EE. Some aspects of the neurotransmitter control of anterior pituitary function. Pharmacol Res. 1989; 21:75–85. 105. Smythe GA, Duncan MW, Bradshaw JE, Cai WY. Serotoninergic control of growth hormone secretion: hypothalamic dopamine, norepinephrine, and serotonin levels and metabolism in three hyposomatotropic rat models and in normal rats. Endocrinology. 1982; 110:376–383. 106. Smythe GA, Gleeson RM, Stead BH. Stimulation of the hypothalamic-pituitaryadrenal axis and inhibition of growth hormone release via increased central noradrenaline neuronal activity by urethane anaesthesia in the rat: blockade by clonidine. Aust J Biol Sci. 1987; 40:91–96. 107. Conway S, Richardson L, Speciale S, Moherek R, Mauceri H, Krulich L. Interaction between norepinephrine and serotonin in the neuroendocrine control of growth hormone release in the rat. Endocrinology. 1990; 126:1022–1030. 108. Gotoh M, Hirooka Y, Tajima T, Iguchi A, Smythe GA. Adrenocorticotropin and growth hormone secretions after intracerebroventricular administration of neostigmine in rats: their relationships to hypothalamic monoaminergic neuronal activities. Brain Res. 1994; 659:259–262.

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109. Lerer B, Gelfin Y, Shapira B. Neuroendocrine evidence for age-related decline in central serotonergic function. Neuropsychopharmacology. 1999; 21:321–322. 110. Kakiuchi T, Nishiyama S, Sato K, Ohba H, Nakanishi S, Tsukada H. Age-related reduction of [11C]MDL100,907 binding to central 5-HT(2A) receptors: PET study in the conscious monkey brain. Brain Res. 2000; 883:135–142. 111. van Dyck CH, Malison RT, Seibyl JP, et al. Age-related decline in central serotonin transporter availability with [(123)I]beta-CIT SPECT. Neurobiol Aging. 2000; 21:497–501. 112. Meltzer CC, Smith G, DeKosky ST, et al. Serotonin in aging, late-life depression, and Alzheimer’s disease: the emerging role of functional imaging. Neuropsychopharmacology. 1998; 18:407–430. 113. Lamberts SW. The somatopause: to treat or not to treat? Horm Res. 2000; 53: 42–43. 114. Savine R, Sonksen P. Growth hormone – hormone replacement for the somatopause? Horm Res. 2000; 53:37–41. 115. Cummings DE, Merriam GR. Age-related changes in growth hormone secretion: should the somatopause be treated? Semin Reprod Endocr 1999; 17:311–325. 116. Hoffman AR, Ceda GP. Should we treat the somatopause? J Endocr Invest. 1999; 22:4–6. 117. Lieberman SA, Hoffman AR. The somatopause: should growth hormone deficiency in older people Be treated? Clin Geriatr Med. 1997; 13:671–684. 118. Hoffman AR, Lieberman SA, Butterfield G, et al. Functional consequences of the somatopause and its treatment. Endocrine. 1997; 7:73–76. 119. Sonsken P. Growth hormone and the somatopause. Growth Horm IGF Res. 1999; 9:1–2. 120. Morley JE. Growth hormone: fountain of youth or death hormone? J Am Geriatr Soc. 1999; 47:1475–1476. 121. von Werder K. The somatopause is no indication for growth hormone therapy. J Endocrinol Invest. 1999; 22:137–141. 122. Toogood AA, Shalet SM. Conflicts with the somatopause. Growth Horm IGF Res. 1998; 8:47–54. 123. Janssens H, Vanderschueren DM. Endocrinological aspects of aging in men: is hormone replacement of benefit? Eur J Obstet Gyn R B. 2000; 92:7–12. 124. Gray A, Feldman HA, McKinlay JB, Longcope C. Age, disease, and changing sex hormone levels in middle-aged men: results of the Massachusetts Male Aging Study. J Clin Endocr Metab. 1991; 73:1016–1025. 125. Baulieu EE. Dehydroepiandrosterone (DHEA): a fountain of youth? J Clin Endocrin Metab. 1996; 81:3147–3151. 126. Majewska MD. Neuronal actions of dehydroepiandrosterone. Possible roles in brain development, aging, memory, and affect. Ann NY Acad Sci. 1995; 774: 111–120. 127. Carlson LE, Sherwin BB. Relationships among cortisol (CRT), dehydroepiandrosterone-sulfate (DHEAS), and memory in a longitudinal study of healthy elderly men and women. Neurobiol Aging. 1999; 20:315–324. 128. Steel N. Dehydro-epiandrosterone and ageing. Age Ageing. 1999; 28:89–91.

Chapter 9 CEREBROVASCULAR SYSTEM AND THE AGEING BRAIN Velandai K. Srikanth and Geoffrey A. Donnan*

Introduction It is inevitable that the vascular system of the brain undergoes change with increasing age. The primary purpose of this chapter is to review the existing literature with respect to the impact of ageing on anatomical and physiological aspects of the cerebrovascular system. The links and postulated mechanisms between ageing, cerebrovascular changes and disease states will also be discussed, as this is of primary interest to the clinician. Ageing and Cerebrovascular Anatomy Ageing and cerebral macrovasculature Ageing and Arterial Changes The arterial tree supplying the brain is comprised of the extracranial large arteries and the intracranial anastomotic network (The Circle of Willis), together with the progressively smaller vessels that penetrate brain tissue and supply specific areas of the brain. The defining feature of these groups of blood vessels is that they contain smooth muscle and are considered to be responsible for maintaining adequate cerebral blood flow. *To whom correspondence should be addressed.

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Arterial changes in ageing closely resemble changes seen in the vascular tree elsewhere. The changes most often observed with ageing include intimal thickening, medial fibrosis and loss of elasticity for the larger arterioles and arteries. These changes may occur at a slower rate in cerebral arteries than in peripheral arteries such as the radial artery or the coronary artery.1 The magnitude of these changes is observed to increase with each decade from the age of 55.2 Smaller intraparenchymal vessels often tend to show tortuosity, loops and kinks.3 It has been postulated that these loops and kinks may have been mistaken for the so-called Charcot-Bouchard aneurysms, given that current pathological techniques are superior in delineating their characteristics. Ageing, disease and arterial changes Atherosclerosis is best considered as an ageing-related disease state that is almost ubiquitously present in older humans. The longer one lives, the chance of developing atherosclerotic disease increases. However, it is unclear as to how much the pathogenesis of atherosclerosis is dependent on a biological ageing process. Age-related cellular changes in the arterial wall might contribute in part to the development of the atherosclerotic plaque over time. A number of putative factors may lead to this including decreased ability of the endothelium to repair in the presence of injury, altered control of vascular smooth muscle proliferation and the interaction between smooth muscle and circulating lipoproteins. However, increasing age is only one of many important risk factors for the development of atherosclerosis. Changes in other cardiovascular risk factors invariably occur with ageing, and make a major contribution to the accelerated formation of atherosclerotic plaque. Occlusive disease due to atherosclerosis is more frequent at the origin of the internal carotid artery, the carotid siphon, the proximal middle cerebral artery, the proximal anterior carotid artery and the proximal basilar artery. Artery-to-artery embolization occurs as an end result of severe atherosclerosis of these vessels leading to well-known stroke syndromes. The increasing incidence of stroke with age is thus a function of an ageing vascular system with both intrinsic and extrinsic biological mechanisms at play. Another important effect of age-related arterial changes and disease such as atherosclerosis is a loss of elasticity and distensibility of vessels. This may lead to decreased arterial compliance predisposing the affected individual to systolic hypertensive disease. The potentially serious effects of systolic hypertension on the elderly brain include stroke, white matter disease and cognitive decline. Ageing and cerebral microvasculature Although the larger vessels described previously are responsible for maintaining overall cerebral blood flow, the cerebral microvasculature is chiefly responsible for the important function of providing active nutrient substrate for the neuron. There is a good correlation between the extent of this capillary network and the activity of functioning neurons in the cerebral cortex.4

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These microvessels have structural properties that provide the basis for the Blood–Brain Barrier (BBB). The BBB is comprised of a continuous endothelium lacking fenestrations, thus acting as a very selective barrier to bloodborne substances reaching the central nervous system. Pinocytotic vesicles are found in very low numbers in the endothelium indicating that the BBB is comparatively less permeable than other organ endothelium. The BBB allows the transport of lipid-soluble and water-soluble substances either by passive (diffusion) or active transport. The cellular components of the BBB possess specialized carrier processes to provide adequate transport of nutrients, hormones and neurotransmitter peptides. Ageing and blood–brain barrier A number of changes in the Blood–Brain Barrier with the ageing process in animal and human models have been described. Thickening of the basement membrane in the ageing rat 5 together with other changes including loss of capillary endothelial cells and elongation of remaining endothelial cells 6 have been observed. A reduction of mitochondrial numbers has also been described in the endothelial cells of the BBB in the monkey but not the rat, leading the authors to postulate that some BBB changes with ageing may be species specific.6,7 In the human brain, examination of biopsy material showed thinning of white matter capillaries with age possibly related to loss of pericytes and thinning of endothelial cytoplasm.8 There is no evidence to date in humans that endothelial mitochondrial numbers decline with age or that significant changes occur in membrane permeability characteristics such as junctional gaps and pinocytotic vesicle density. In the rat model, alterations in BBB transport function include a decrease in BBB choline transport with ageing and decreased brain glucose influx.6 However there is no conclusive evidence in the literature that age-related permeability of the BBB is altered significantly in the absence of neurological or vascular disease. Although most reports have concentrated on changes in the afferent microvasculature (arterioles and capillaries) in ageing, few have described specific age-related changes in the efferent microvasculature (venules). These include the observation of a non-inflammatory mural thickening of the venular system due to collagenosis, predominantly in the periventricular region.9 Ageing, disease and cerebral microvasculature The cerebral microvasculature has been implicated in diseases such as Alzheimer’s disease (AD), cerebral amyloid angiopathy and potential disease states such as leukoaraiosis (deep white matter change). However, the role of the microvessels and the BBB in these disorders is a matter of ongoing research and controversy. It remains difficult to separate out the effects of intrinsic ageing and disease on microvascular changes. Alzheimer’s disease increases in prevalence with every decade of life in the elderly population. Intense debate and research continues in the search for

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the aetiological factors responsible for the development of the disease. It is tempting to consider that age-related changes to cerebral microvessels may have some role to play in the pathogenesis of AD. It has been proposed that a breakdown of BBB may be an essential step in the pathogenesis of AD.10 Several investigators have hypothesised that microvascular injury secondary to amyloid deposition leads to leakage of substances that may cause neuronal injury.11–14 However, other investigators have demonstrated a lack of BBB alteration in either AD or non-demented controls by immunohistochemical.15 Similarly, in a post-mortem immunohistochemical study of AD compared to vascular dementia, age-matched controls, other neurodegenerative disorders and young controls, albumin leakage in the neuropil was demonstrated in all groups with no statistically significant difference between groups.16 These authors were also unable to consistently detect significant amounts of other serum proteins in the neuropil such as IgG and complement C3c. They concluded that alteration in BBB was neither a primary nor a consistent event in AD. In summary, in spite of ongoing interest in the field, it is still undecided whether BBB alterations are causally related to AD or merely an epiphenomenon. Amyloid Angiopathy: The microvasculature of the ageing brain is susceptible to the development of a specific disease termed cerebral amyloid angiopathy (CAA). This is characterized by the deposition of fibrillar amyloid material in the arteriolar media, gradually replacing the smooth muscle component of the arteriole. This leads to a weakened arterial wall that is susceptible to BBB dysfunction, as well as rupture leading to lobar haemorrhages. The deposition of vascular amyloid is extensive in cases of Alzheimer’s dementia, but is also associated with other conditions such as radiation injury and vasculitis. Amyloid deposition in CAA may lead to small brain infarcts due to microvascular occlusion, and thus may contribute to a dementing syndrome.17 The study of CAA in familial forms of the disease may provide important insights into the mechanistic links between ageing, microvascular pathology and disease states of the brain. Leukoaraiosis is the radiological finding of white matter changes that are commonly visible in CT scans or MRI of brains of elderly people. These changes may contribute to a small extent to age-related decline in intellectual function.18 Some researchers have proposed that these changes may be related to disease processes such as chronic hypertension, diabetes mellitus or even AD.19 They hypothesize that these disease processes may lead to disruption of the BBB, leading to protein leakage into white matter. Others implicate “periventricular venous collagenosis” in the pathogenesis of this condition on finding that venous changes were correlated with the presence of white matter hyperintensity on magnetic resonance imaging, whereas only mild agerelated change was observed in the afferent micro-vessels.20 Their findings led them to hypothesise that deep venous occlusion as an age-related process may lead to a long-term increase in vascular resistance and insidious oedema in surrounding tissue, contributing in part to the development of white matter

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lesions. However, these findings have not yet been duplicated in other studies. Investigators who have used gadolinium-MRI and diffusion MRI techniques have provided conflicting results regarding a role for the BBB in pathogenesis of white matter lesions, although the methods used are qualitatively different.21,22 Ageing and Cerebrovascular Physiology Cerebral Blood Flow and metabolism — physiological concepts Cerebral Blood Flow (CBF) Great advances have been made into the research of CBF due to refinement in existing techniques of measurement including positron emission tomography (PET), 133Xenon inhalation computed tomography (Xenon-CT) and single positron emission computed tomography (SPECT), and the development of new techniques such as perfusion/diffusion magnetic resonance imaging (DWI/PWI MR), magnetic resonance spectroscopy (MRS) and transcranial Doppler. Determinants of Cerebral Blood Flow Average CBF is around 60 ml/min/100 g of brain tissue in the resting adult under physiological conditions with the cerebral grey matter receiving the bulk of supply. Gender differences have been described in global CBF,

Intracranial Pressure

artery

Arteriolar bed

Auto-regulation

vein

Synaptic Activity

Figure 1. Schematic summary of factors involved in regulating Cerebral Blood Flow (CBF).

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with women of pre-menopausal age showing higher levels than similar aged men.23,24 This difference begins to disappear after the fourth decade of life, suggesting a possible role for oestrogen in augmenting CBF. CBF is directly related to cerebral perfusion pressure and inversely related to cerebral vascular resistance (Figure 1). Regional CBF is also determined by the level of synaptic activity and consequently regional cerebral metabolism (refer section on cerebral metabolism). Other factors playing an important role in regulation of CBF include intracranial pressure (ICP) and blood viscosity.25 Cerebral perfusion pressure Cerebral perfusion pressure is the difference between arterial inflow pressure and venous outflow pressure and represents the driving pressure for CBF. The mean perfusion pressure in humans varies between 50 to 150 mm Hg (autoregulatory range) without affecting cerebral blood flow due to the phenomenon of cerebral autoregulation (Figure 2).

CBF (ml/100 g/min)

Cerebral vascular resistance Cerebral vascular resistance (CVR) is primarily a function of the vessel radius such that even small changes in luminal diameter (via constriction or dilatation) can have major effects on resistance. The larger extracranial vessels and the intracranial pial vessels contribute to about half the total cerebrovascular resistance. CVR is controlled largely by cerebral autoregulatory mechanisms such that an increase in CVR leads to a reduction in CBF. Vascular resistance also depends to a certain extent on the viscosity of blood. The factors

Figure 2. Idealized cerebral pressure–flow curve.

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that may play an important role in determining blood viscosity include shear (flow) rate, red cell rheology, platelet aggregability, plasma protein (fibrinogen) levels. An increase in blood viscosity leads to reduction in CBF.26 Reduction in blood flow may lead to further increase in viscosity leading to a vicious cycle of events. Cerebral autoregulation Cerebral autoregulation is achieved by the tight coupling of cerebral perfusion pressure and the diameter responses of the arteriolar system in an attempt to stabilise CBF in the presence of fluctuations in systemic arterial pressure. Within the autoregulatory range, CBF remains constant due to cerebral vasoconstriction when perfusion pressure increases as a result of systemic arterial hypertension. Neural, metabolic, myogenic and endothelial mechanisms have been postulated to explain the phenomenon of autoregulation.27 It appears that the autoregulation of pial vessels may be predominantly due to neurogenic control whereas the intracerebral vessels may well be under the influence of local metabolic phenomena such as changes in arterial CO 2 tension. Autoregulatory control of cerebral pial vessels may be under the influence of adrenergic and trigeminovascular systems although this is the subject of further study.28,29 Parasympathetic innervation of cerebral blood vessels has assumed greater importance in the last decade, contributing to neural vasodilation by means of nitric oxide, acetylcholine, glutamate and other agonists. However, investigators using animal models have not provided evidence for a major role for this system in resting and autoregulatory control of CBF. Smaller intracerebral vessels are extremely sensitive to metabolic influences such as arterial pCO2 tension. CBF may change by up to 5% with every mmHg change in arterial pCO2 within physiological ranges (30–60 mmHg). Arterial response to CO2 may be lessened in situations such as hypotension or cerebral ischaemia.30 The effect of CO2 is mediated by the autonomic supply of the vessels and perivascular cerebrospinal fluid pH. Arterial pO2 and Hydrogen ion also play a role in CBF regulation. The myogenic theory holds that changes in transmural pressure may directly affect the tone of the vascular smooth muscle leading to vasoconstriction or vasodilation in response to increase or decrease in systemic arterial pressure respectively.27,31 More recently, investigators have shown that such smooth muscle response to pressure changes may be dependent on the presence of intact endothelium.32 This may be linked to the release of contractile substances from the endothelium rather than a tonic inhibition of endothelium-derived relaxing factor (EDRF or Nitric Oxide).33 Intracranial pressure Intracranial pressure (ICP) is an important determinant of cerebral perfusion. A rise in ICP leads to compression of intracerebral vessels causing a decrease in perfusion. Conversely, a fall in ICP leads to an increase in cerebral per-

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fusion. This mechanism appears to be important in maintaining relatively constant cerebral perfusion in states of positive and negative “g” and physiological states of straining. Extreme rises in ICP (> 30 mm Hg) can lead to stimulation of the brainstem vasomotor centre, leading to reflex increase in systemic arterial pressure in order to maintain adequate cerebral perfusion. Cerebral metabolism The brain has a high demand for oxygen and glucose in order to maintain optimal neuronal function even at rest. There are a number of unique features regarding the activity and metabolism of the brain. More than a century ago, it was postulated that cerebral metabolism was closely coupled with the level of pre-synaptic activity in the brain.34 Results of studies using various techniques appear largely to support this hypothesis.35–38 This coupling may (hypothetically) occur as a result of a glutamate-mediated uptake of glucose into peri-synaptic astrocytes leading to lactate production. Lactate can then be used as energy substrate in the pre-synaptic vesicle. Cerebral metabolism and CBF are also closely coupled with functional neuronal activity as an intermediate link, suggesting that cerebral metabolism may be an important determinant of CBF.34,39,40 This coupling may be mediated by a number of vasodilatory metabolites produced by the neuron (Vasoactive Intestinal Peptide, Nitric Oxide etc) which serve to increase regional CBF by acting on local small vessels as well as upstream resistance vessels.41,42 The coupling mechanism between neuronal activity and cerebral metabolism may be altered in disease states such as cerebral ischaemia. Ageing and cerebrovascular haemodynamics Controversy exists regarding the effect of “normal ageing” on physiological parameters such as CBF and cerebral metabolism. As with other aspects of “normal ageing” and disease, the difficulty in establishing a clear relationship between ageing and cerebrovascular physiology has been partly due to the lack of a clear definition of “normal ageing”. Most studies examining this relationship have been cross-sectional in nature (rather than longitudinal) comparing younger to older age groups. Cross-sectional approaches lead to difficulty in differentiating between genuine age-related change and cohort effects unrelated to age. A great deal of uncertainty still exists about the actual mechanisms involved in the perceived changes in CBF and cerebral metabolism. Ageing and CBF Studies of ageing and CBF have been mostly cross-sectional in nature. “Normal ageing” in most of these studies refers to subjects who are relatively free of vascular disease or risk factors for vascular disease. The results of most of these studies, utilizing different techniques, seem to point towards a reduction in mean CBF values with increasing age. These reductions have not only been demonstrated in older age groups and may begin in the third or fourth dec-

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Table 1.

161

Cross-sectional studies of cerebral blood flow and perfusion in human ageing.

Investigators

Year

Technique

Reduction of CBF with Ageing Kety44 1956 Wang et al.46 1975 Obrist48 1979 De Koninck et al.50 1977 Meyer et al.52 1978 Yamaguchi et al.54 1979 Naritomi et al.56 1979 Thomas et al.58 1979 Yamamoto et al.60 1980 Melamed et al.62 1980 Davis et al.64 1983 Iwata et al.66 1986 Zemcov et al.67 1984 Gur et al.23 1987 Hagstadius et al.68 1989 Leenders et al.69 1990 Martin et al.70 1991 Markus et al.71 1993 Krausz et al.72 1998 Bentourkia et al.73 2000 Scheel et al.74 2000

Nitrous oxide 133 Xenon inhalation 133 Xenon inhalation 133 Xenon intracarotid 133 Xenon inhalation 133 Xenon inhalation 133 Xenon inhalation 133 Xenon intravenous 133 Xenon inhalation 133 Xenon inhalation 133 Xenon inhalation 131 Xenon CT 133 Xenon inhalation 133 Xenon inhalation 133 Xenon inhalation P.E.T. P.E.T. 99mTc-HMPAO SPECT 99mTc-HMPAO SPECT P.E.T. Colour duplex sonography

No Reduction of CBF with Ageing Shieve and Wilson45 1953 Shenkin et al.47 1953 Gordan et al.49 1956 Lassen et al.51 1960 Dastur et al.53 1963 Sokoloff et al.55 1966 Aizawa et al.57 1961 Gottstein et al.59 1979 Waldemar61 1991 Takada et al.63 1992 Meltzer et al.65 2000

Nitrous oxide Nitrous oxide Nitrous oxide 85Kr Nitrous oxide Nitrous oxide Nitrous oxide Nitrous oxide 99mTc-HMPAO SPECT P.E.T. P.E.T.

P.E.T. – positron emission tomography; 99mTc-HMPAO SPECT – 99mTechnetiumhexamethylpropyleneamine oxime single photon emission computed tomography.

ade of life. However there are a significant number of cross-sectional studies with contradictory reports, with some evidence to support either a lack of or a non-significant decline in CBF with increasing age (Table 1). This controversy probably highlights the difficulty with inherent selection bias in using cross-sectional designs, different subject populations and differing techniques of measurement of CBF. In studies where CBF reduction with age has been demonstrated, the reductions are more likely to involve cerebral grey matter

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with an annual estimated decline of approximately 0.5 ml/min/100 g of brain tissue. Evidence from current literature does not seem to support reduction in white matter flow with healthy ageing. Few longitudinal data exist with regards to CBF in ageing, presumably due to the complexity and labour-intensive nature of CBF studies. A limited analysis of CBF data in eight subjects over a 11-year period showed some reduction in CBF in the absence of significant change in mean arterial blood pressure, with a concomitant increase in cerebrovascular resistance.43 In a large cohort of healthy volunteers, volunteers with vascular risks, and patients with stroke or TIA, both cross-sectional and longitudinal analyses revealed significant reduction in gray matter flow with age in all groups, with more decline demonstrated in groups with risk factors or stroke/TIA.24 The estimated decline in cerebral gray matter in this study was slightly higher than in cross-sectional studies, estimated at approximately 1.0 ml/min/100 g of brain tissue. Ageing and cerebral metabolism Data regarding changes in the rate of cerebral metabolism with human ageing are even less conclusive. Most of these data have been derived from positron emission tomography studies (PET) measuring cerebral metabolic rate for oxygen (CMRO2) and cerebral metabolic rate of glucose (CMRG) in resting state (Table 2). Some investigators hold that CMRO2 declines in healthy ageing in parallel with decline in CBF. However a number of researchers have failed to demonstrate reduction in mean CMRO2 levels with age. Similar controversy exists for CMRG, with contradictory reports in the literature regarding changes in glucose metabolism in “normal” ageing. The physiological state of ‘coupling’ of cerebral glucose metabolism and CBF is thought to be largely maintained with increasing age in the absence of disease. Ageing, cerebrovascular reactivity and autoregulation Cerebrovascular reactivity in response to changes in systemic arterial pressure or to other parameters such as arterial CO2 and arterial O2 levels has been investigated in only a few studies. Researchers examining CBF change with posture showed some increase in the “dysautoregulation index” (defined as CBF decrease per unit fall in effective perfusion pressure on head-up tilt). 88,89 Others have demonstrated a decline in CO2 reactivity with ageing using quantitative CBF techniques.45,54,60 However, other groups using either quantitative CBF techniques64 or transcranial doppler methods90–92 have failed to demonstrate significant change in cerebral autoregulation with healthy ageing. Ageing, disease and cerebrovascular haemodynamics The more “common” form of ageing is one that is accompanied by the development of disease as compared to the less common “healthy” (supranormal) form of ageing. Studies of cerebrovascular haemodynamics have mostly been performed in the latter group in order to gain insight into “normal”processes. However, it is the study of the former group that is more relevant to the

CEREBROVASCULAR SYSTEM AND THE AGEING BRAIN

Table 2.

163

Cross-sectional studies of cerebral metabolism in human ageing.

Investigator

Year

Method

Comment

Reduced Cerebral Metabolism with Ageing Sokoloff et al.75 Kuhl et al.76 Pantano et al.77

1975 1984 1984

Dastur78

1985

Yamaguchi et al.54 1986 Leenders et al.69

1990

Marchal et al.79

1992

Takada et al.63 De Santi et al.80

1992 1995

Eberling et al.81 1995 Bentourkia et al.73 2000

Nitrous oxide Reduction in CMRO2, CMRG and CBF 18FDG PET Reduction in whole brain mean CMRG H215O_PET Non-linear reduction in mean gray CBF and CMRO2; white matter unchanged 18FDG PET Reduction in CMRG; no reduction in CBF and CMRO2 H215O_PET Reduction in CMRO2; CBF variable and less age-dependent H215O_PET Coupled reduction in CMRO2 and CBF in pure gray and white matter H215O_PET Reduction in CMRO2 and CBV in gray matter; CBF variable H215O_PET Reduction in CMRO2 but not CBF 18FDG PET Reduction in CMRG in frontal and temporal lobes 18FDG PET Reduction in CMRG in temporal cortex 18FDG PET Coupled reduction in CBF and CMRG

Cerebral Metabolism Unchanged with Ageing Duara et al.82

1984

18FDG

Cutler et al.83 De Leon et al.84

1986 1987

18FDG PET 11CDG PET

Horwitz et al.85

1987

18FDG

Schlageter et al.86 1987 Burns et al.87 1992

PET

PET

18FDG PET H215O_PET

No reduction in mean or regional CMRG; resting CMRG poorly correlated with cognitive tests No reduction in mean CMRG No reduction in absolute regional CMRG No reduction in mean CMRG; age associated reduction in regional cerebral functional interaction No reduction of global CMRG Trend for reduction in CMRO2 only in parietal lobe; effect less significant with advancing age

CMRO2: cerebral metabolic rate for oxygen; PET: positron emission tomography; 18FDG: 18-Fluoro-deoxyglucose; CMRG: cerebral metabolic rate for glucose; H215O: 15-oxygen labelled water; CBF: cerebral blood flow; 11CDG: 11-carbondeoxyglucose; CBV: cerebral blood volume.

majority of the ageing population and provides the opportunity to examine the link between pathological vascular changes and a variety of disease states. Certain vascular risk factors such as hypertension, diabetes, atrial fibrillation and hypercholesterolaemia are common in the elderly population. The incidence of disease states such as stroke and dementia increases with age. The questions then arise as to whether a causal link may exist between vascular

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risk and disease, and whether age plays a part in determining the expression of such disease. Dementia (especially Alzheimer’s dementia) is a prototype disease of the elderly that is the subject of intense interest and study with regards to putative vascular aetiologies. Epidemiological evidence exists from population-based studies describing associations of vascular risk factors with prevalent dementia.93,94 The results of studies of cerebral blood flow and metabolism performed in people with dementia have indicated reductions in regional cerebral metabolism with concomitant reduction in regional CBF.24,78,95–97 These observations have led to the debate whether the reduction in cerebral perfusion (putatively as a result of vascular risk) plays an aetiological role in the development of dementia,98 or whether CBF reduction is a consequence of neuronal damage and the vascular disease merely an epiphenomenon.96 Age by itself remains a crucial risk factor in the development of dementia.99 Neuronal loss, oxidative stress, reduction in vascular reserve and impaired repair mechanisms may all play a role in reducing the reserve of the ageing brain thus leaving it vulnerable to injury and disease. Summary A wide variety of changes are seen in the human cerebrovascular system on a structural and functional basis as one gets older. However, there is a significant amount of disagreement about the true nature of age-related vascular changes in the brain. This is partly due to the increasing complexity in defining the boundaries of ‘normal’ ageing. The limits of human ageing are being pushed further with every passing decade of medical scientific progress. As people get to live longer, they become exposed to a larger number of vascular risks, predisposing them to accrue more vascular change. A more fruitful approach towards unravelling this problem may be to identify the links between such vascular risks, ageing and disease. This is likely to become a reality with the increasingly sophisticated research methods now available. The ultimate goal of such research is to develop ways to prevent and treat important age-related neurological disorders. References 1. 2. 3.

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24. Shaw TG, Mortel KF, Meyer JS, Rogers RL, Hardenberg J, Cutaia MM. Cerebral blood flow changes in benign aging and cerebrovascular disease. Neurology. 1984; 34:855–862. 25. Thomas DJ. Whole blood viscosity and cerebral blood flow. Stroke. 1982; 13: 285–287. 26. Humphrey PR, Du Boulay GH, Marshall J, Pearson TC, Russell RW, Symon L, Wetherley-Mein G, Zilkha E. Cerebral blood-flow and viscosity in relative polycythaemia. Lancet. 1979; 2(8148):873–877. 27. Paulson OB, Strandgaard S, Edvinsson L. Cerebral autoregulation. Cerebrovas Brain Met. 1990; 2:161–192. 28. Edvinsson L, Owman C, Siesjo B. Physiological role of cerebrovascular sympathetic nerves in the autoregulation of cerebral blood flow. Brain Res. 1976; 117: 519–523. 29. Edvinsson L, Degueurce A, Duverger D, MacKenzie ET, Scatton B. Central serotonergic nerves project to the pial vessels of the brain. Nature. 1983; 306(5938):55–57. 30. Hossmann KA. Treatment of experimental cerebral ischemia. J Cerebr Blood Met. 1982; 2:275–297. 31. Bayliss WM. On the local reaction of the arterial wall to changes of internal pressure. J Physiol. 1902:220–231. 32. Harder DR. Pressure-induced myogenic activation of cat cerebral arteries is dependent on intact endothelium. Circ Res. 1987; 60:102–107. 33. Harder DR, Sanchez-Ferrer C, Kauser K, Stekiel WJ, Rubanyi GM. Pressure releases a transferable endothelial contractile factor in cat cerebral arteries. Circ Res. 1989; 65:193–198. 34. Roy CS, Sherrington, C.S. On the regulation of the blood supply of the brain. J Physiol .1890; 11:85–108. 35. Swanson RA, Morton MM, Sagar SM, Sharp FR. Sensory stimulation induces local cerebral glycogenolysis: demonstration by autoradiography. Neuroscience. 1992; 51:451-461. 36. Jueptner M, Weiller C. Review: does measurement of regional cerebral blood flow reflect synaptic activity? Implications for PET and fMRI. Neuroimage. 1995; 2: 148–156. 37. Sibson NR, Dhankhar A, Mason GF, Rothman DL, Behar KL, Shulman RG. Stoichiometric coupling of brain glucose metabolism and glutamatergic neuronal activity. Proc Natl Acad Sci USA. 1998; 95:316-321. 38. Gerrits RJ, Raczynski C, Greene AS, Stein EA. Regional cerebral blood flow responses to variable frequency whisker stimulation: an autoradiographic analysis. Brain Res. 2000; 864:205–212. 39. Baron JC, Lebrun-Grandie P, Collard P, Crouzel C, Mestelan G, Bousser MG. Noninvasive measurement of blood flow, oxygen consumption, and glucose utilization in the same brain regions in man by positron emission tomography: concise communication. J Nucl Med. 1982; 23:391–399. 40. Baron JC, Rougemont D, Soussaline F, Bustany P, Crouzel C, Bousser MG, Comar D. Local interrelationships of cerebral oxygen consumption and glucose utilization in normal subjects and in ischemic stroke patients: a positron tomography study. J Cerebr Blood F Met. 1984; 4:140–149. 41. Akgoren N, Dalgaard P, Lauritzen M. Cerebral blood flow increases evoked by electrical stimulation of rat cerebellar cortex: relation to excitatory synaptic activity and nitric oxide synthesis. Brain Res. 1996; 710:204–214. 42. Ngai AC, Ko KR, Morii S, Winn HR. Effect of sciatic nerve stimulation on pial arterioles in rats. Am J Physiol. 1988; 254:H133–139. 43. Libow LS. Cerebral and electroencephalographic changes in elderly men. Rockville, US Dep. of Health, Education and Welfare, National Institute of Mental Health, 1971. Report No: (HSM) 71–9037.

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44. Kety SS. Human cerebral blood flow and oxygen consumption as related to aging. Res Publ Assoc Res N. 1956; 35:31–35. 45. Schieve JF, Wilson, WP. The influence of age, anesthesia and cerebral arteriosclerosis on cerebral vascular reactivity to CO2. Am J Med. 1953; 15:171–174. 46. Wang HS, Busse EW. Correlates of regional blood flow in elderly community residents. In: Harper M, Jennett, B, Miller, D, Rowan, J, editors. Blood flow and metabolism in the brain. London: Churchill Livingstone, 1975; 17–18. 47. Shenkin HA, Novak, P., Golobuff, B., Soffe, A.M., Bortin, L. The effects of aging arteriosclerosis, and hypertension upon the cerebral circulation. J Clin Invest. 1953; 32:459–465. 48. Obrist WD. Cerebral circulatory changes in normal aging and dementia. In: Bayer Symposium VII;Brain function in old age. New York: Springer-Verlag, 1979; 278–287. 49. Gordan GS. Influence of steroids on cerebral metabolism in man. Recent Prog Horm Res. 1956; 12:153–174. 50. De Koninck WJ, Calay, R., Hongne, JC. CBF in elderly with chronic cerebral involvement. Acta Neurol Scand. 1977; (Suppl) 64:412–413. 51. Lassen NA, Feinberg, I., Lane, M.H. Bilateral studies of cerebral oxygen uptake in young and aged normal subjects and in patients with organic dementia. J Clin Invest. 1960; 39:491–500. 52. Meyer JS, Ishihara N, Deshmukh VD, Naritomi H, Sakai F, Hsu MC, Pollack P. Improved method for noninvasive measurement of regional cerebral blood flow by 133Xenon inhalation. Part I: description of method and normal values obtained in healthy volunteers. Stroke. 1978; 9:195–205. 53. Dastur DK, Lane MH, Hansen, DB. Effects of aging on cerebral circulation and metabolism in man. Washington DC: US Government Printing Office, 1963. USPHS publication no. 986. 54. Yamaguchi T, Kanno I, Uemura K, Shishido F, Inugami A, Ogawa T, Murakami M, Suzuki K. Reduction in regional cerebral metabolic rate of oxygen during human aging. Stroke. 1986; 17:1220–1228. 55. Sokoloff L. Cerebral circulatory and metabolic changes associated with aging. Res Publ Assoc Res N. 1966; 41:237–254. 56. Naritomi H, Meyer JS, Sakai F, Yamaguchi F, Shaw T. Effects of advancing age on regional cerebral blood flow. Studies in normal subjects and subjects with risk factors for atherothrombotic stroke. Arch Neurol. 1979; 36:410–416. 57. Aizawa T, Tazaki, Y., Gotoh, F. Cerebral circulation in cerebrovascular disease. World Neurol. 1961; 2:635–45. 58. Thomas DJ, Zilkha E, Redmond S, Du Boulay GH, Marshall J, Russell RW, Symon L. An intravenous 133xenon clearance technique for measuring cerebral blood flow. J Neurol Sci. 1979; 40:53–63. 59. Gottstein U, Held, K. Effects of aging on cerebral circulation and metabolism in man. Acta Neurol Scand. 1979; (Suppl) 72:54–55. 60. Yamamoto M, Meyer JS, Sakai F, Yamaguchi F. Aging and cerebral vasodilator responses to hypercarbia: responses in normal aging and in persons with risk factors for stroke. Arch Neurol. 1980; 37:489–496. 61. Waldemar G, Hasselbalch SG, Andersen AR, Delecluse F, Petersen P, Johnsen A, Paulson OB. 99mTc-d,l-HMPAO and SPECT of the brain in normal aging. J Cerebr Blood F Met. 1991; 11:508–521. 62. Melamed E, Lavy S, Bentin S, Cooper G, Rinot Y. Reduction in regional cerebral blood flow during normal aging in man. Stroke. 1980; 11:31–35. 63. Takada H, Nagata K, Hirata Y, Satoh Y, Watahiki Y, Sugawara J, Yokoyama E, Kondoh Y, Shishido F, Inugami A. Age-related decline of cerebral oxygen metabolism in normal population detected with positron emission tomography. Neurol Res. 1992; 14:128–131.

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64. Davis SM, Ackerman RH, Correia JA, Alpert NM, Chang J, Buonanno F, Kelley RE, Rosner B, Taveras JM. Cerebral blood flow and cerebrovascular CO2 reactivity in stroke-age normal controls. Neurology. 1983; 33:391–399. 65. Meltzer CC, Cantwell MN, Greer PJ, Ben-Eliezer D, Smith G, Frank G, Kaye WH, Houck PR, Price JC. Does cerebral blood flow decline in healthy aging? A PET study with partial-volume correction. J Nucl Med. 2000; 41:1842–1848. 66. Iwata K, Harano H. Regional cerebral blood flow changes in aging. Acta Radiol Suppl. 1986; 369:440–443. 67. Zemcov A, Barclay L, Blass JP. Regional decline of cerebral blood flow with age in cognitively intact subjects. Neurobiol Aging. 1984; 5:1–6. 68. Hagstadius S, Risberg J. Regional cerebral blood flow characteristics and variations with age in resting normal subjects. Brain Cogn. 1989; 10:28–43. 69. Leenders KL, Perani D, Lammertsma AA, Heather JD, Buckingham P, Healy MJ, Gibbs JM, Wise RJ, Hatazawa J, Herold S. Cerebral blood flow, blood volume and oxygen utilization. Normal values and effect of age. Brain. 1990; 113:27–47. 70. Martin AJ, Friston KJ, Colebatch JG, Frackowiak RS. Decreases in regional cerebral blood flow with normal aging. J Cerebr Blood F Met. 1991; 11:684–689. 71. Markus HS, Ring H, Kouris K, Costa DC. Alterations in regional cerebral blood flow, with increased temporal interhemispheric asymmetries, in the normal elderly: an HMPAO SPECT study. Nucl Med Commun. 1993; 14:628–633. 72. Krausz Y, Bonne O, Gorfine M, Karger H, Lerer B, Chisin R. Age-related changes in brain perfusion of normal subjects detected by 99mTc-HMPAO SPECT. Neuroradiology. 1998; 40):428–434. 73. Bentourkia M, Bol A, Ivanoiu A, Labar D, Sibomana M, Coppens A, Michel C, Cosnard G, De Volder AG. Comparison of regional cerebral blood flow and glucose metabolism in the normal brain: effect of aging. J Neurol Sci. 2000; 181: 19–28. 74. Scheel P, Ruge C, Petruch UR, Schoning M. Color duplex measurement of cerebral blood flow volume in healthy adults. Stroke. 2000; 31:147–150. 75. Sokoloff L. Cerebral circulation and metabolism in the aged. Psychopharmacol Bull. 1975; 11:45-46. 76. Kuhl DE, Metter EJ, Riege WH, Hawkins RA. The effect of normal aging on patterns of local cerebral glucose utilization. Ann Neurol. 1984; 15(Suppl): S133–137. 77. Pantano P, Baron JC, Lebrun-Grandie P, Duquesnoy N, Bousser MG, Comar D. Regional cerebral blood flow and oxygen consumption in human aging. Stroke. 1984; 15:635–641. 78. Dastur DK. Cerebral blood flow and metabolism in normal human aging, pathological aging, and senile dementia. J Cerebr Blood F Met. 1985; 5:1–9. 79. Marchal G, Rioux P, Petit-Taboue MC, Sette G, Travere JM, Le Poec C, Courtheoux P, Derlon JM, Baron JC. Regional cerebral oxygen consumption, blood flow, and blood volume in healthy human aging. Arch Neurol. 1992; 49:1013–1020. 80. De Santi S, de Leon MJ, Convit A, Tarshish C, Rusinek H, Tsui WH, Sinaiko E, Wang GJ, Bartlet E, Volkow N. Age-related changes in brain: II. Positron emission tomography of frontal and temporal lobe glucose metabolism in normal subjects. Psychiatr Quart. 1995; 66:357–370. 81. Eberling JL, Nordahl TE, Kusubov N, Reed BR, Budinger TF, Jagust WJ. Reduced temporal lobe glucose metabolism in aging. J Neuroimaging. 1995; 5:178–182. 82. Duara R, Grady C, Haxby J, Ingvar D, Sokoloff L, Margolin RA, Manning RG, Cutler NR, Rapoport SI. Human brain glucose utilization and cognitive function in relation to age. Ann Neurol. 1984; 16:703–713. 83. Cutler NR. Cerebral metabolism as measured with positron emission tomography (PET) and [18F] 2-deoxy-D-glucose: healthy aging, Alzheimer’s disease and Down syndrome. Prog Neuro-psychoph. 1986; 10:309–321.

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SECTION III FACTORS INFLUENCING BRAIN AGEING

Chapter 10 THE MOLECULAR BASIS OF ALZHEIMER’S DISEASE AND FRONTOTEMPORAL DEMENTIA John B.J. Kwok and Peter R. Schofield*

Introduction Alzheimer’s disease (AD) was thought to be an intractable disorder. Yet, genetic analyses have successfully uncovered three genes, the amyloid precursor protein (APP) gene, presenilin-1 (PS-1) and the presenilin-2 (PS-2) gene which can cause early-onset Alzheimer’s disease. Functional analysis of these genes and gene mutations has highlighted the importance of the amyloid cascade hypothesis to our understanding of the disease process. Moreover, the correlation of mutations in the AD genes with specific clinical outcomes and variant neuropathology has allowed us to detect the existence of modifying factors which alter the course of the disease. More recently, the tau gene has been identified as the causative agent for another form of dementia, fronto-temporal dementia. The challenge now is to determine the enigmatic relationship between tau and the AD genes and to determine whether there is a common neurodegenerative mechanism.

*To whom correspondence should be addressed.

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A Common Affliction Alzheimer’s disease (AD) is a devastating affliction of the brain. A patient will suffer an irreversible deterioration of intellectual abilities involving memory loss, impairment of judgement and reasoning, as well as personality changes in later stages. Ultimately, the condition is fatal due to failure of physical function.1 AD is the most common cause of senile dementia, accounting for 50% of dementia cases. The disease will strike an estimated one in ten persons over the age of 65 years and increases to nearly one in two of those over 80.2 Despite intense basic scientific and pharmacological investigations, there are still no truly effective therapeutic drugs for AD. With such an emotional and financial cost to patients, and to a rapidly ageing society, there is a pressing need for greater understanding of this disease. AD is distinct from other forms of dementia by key pathological features in the brain. Firstly, there are a large number of senile plaques in the extracellular spaces between neurones. The plaques are spherical deposits that consist of central cores of amyloid beta (Aβ) fibrils, surrounded by degenerated neurites and glial cells.2 The Aβ peptide consists of a sequence of hydrophobic amino acids of 39 to 43 amino acids in length.3,4 The peptide is derived from proteolytic cleavage of a larger multidomain glycoprotein, the amyloid precursor protein (APP).5 Secondly, there are neurofibrillary tangles (NFTs) found within neurones. The major components of NFTs are paired helical filaments, which in turn are composed of hyperphosphorylated form of tau, a microtubule associated protein2. Finally, there is extensive neuronal loss in the cerebral cortex and hippocampus, which is directly responsible for the cognitive decline.2 Amyloid Cascade The exact contribution of each neuropathological feature to the clinical symptoms of AD is unclear.6 However, several studies have suggested that the Aβ deposits can be directly neurotoxic, in part through the generation of free radicals6 whose effects can be attenuated by the addition of antioxidants.6 Other studies suggest that Aβ can disrupt ionic homeostasis and lead to severe effects on cellular processes and induction of neuronal cell death6. The amyloid cascade hypothesis postulates that the deposition of Aβ is the central causative event in AD and that the NFTs, cell death and dementia follow as a direct result of this deposition7 as shown in Figure 1. The amyloid cascade hypothesis predicts that mutations in genes, which lead to the overproduction of APP or subsequent mismetabolism, would underlie the genetic basis of AD.

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Figure 1. Amyloid Cascade hypothesis suggests the aberrant metabolism of APP molecule to form the longer peptide isoform, Aβ1-42 and greater deposition of senile plaques. This may arise as a result of age-related factors or genetic mutations. Neurofibrillary tangles and neuronal cell death are secondary events to the initial amyloid production and deposition.

Amyloid Precursor Protein (APP) Gene The Aβ peptide is cleaved from APP via a series of proteolytic steps mediated by enzymes called secretases (Figure 2). Cleavage of APP with the β- and γ-secretase will generate an intact Aβ peptide. Cleavage by the α-secretase within the peptide sequence will prevent the formation of Aβ.5 All the mutations appear to cluster within or adjacent to the sequence which encode Aβ peptide as shown in Figure 2. Each mutation has an effect, either on the metabolism of APP or the nature of the Aβ sequence itself, but ultimately all mutations increase the rate of amyloid deposition.8 For example, the Swedish double mutation results in increased secretion of the normal 40 amino-acid peptide (Aβ1-40) and a longer 42 amino-acid isoform of Aβ (Aβ1-42), most probably by enhancing β-secretase activity. The cerebral angiopathy with amyloidosis (CAA) mutation has been shown to diminish α-secretase activity, thus increasing the secretion of both forms of intact Aβs. Finally, there are a series of mutations which cluster around the γ-secretase site (Figure 2). These mutations include the London mutation at codon 717 (Val to Ile),

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Figure 2. Schematic diagram of APP. The protein is a multi-domain cell-surface molecule. The Aβ region (grey box and circles) contains part of the transmembrane domain and part of the extracellular domain of APP. Secretase cleavage sites are indicated by open arrows. Familial mutations that cause AD are indicated.

which alters the conformation of the γ-secretase recognition site so that APP is preferentially cleaved to produce the Aβ1-42 isoform.9 Presenilin Genes A major locus responsible for early onset AD (EOAD), presenilin-1 (PS-1), was shown to map to the long arm of chromosome 14.10 Together with its homologue, presenilin-2 (PS-2), on chromosome 1,11 pathogenic mutations in

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Figure 3. Schematic diagram of PS-1. The protein has eight hydrophobic transmembrane spanning domains and a large hydrophilic loop. The protein is cleaved within the hydrophilic loop by an unknown protease (presenilinase) and a caspase. The majority of mutations detected in EOAD pedigrees and cases are missense mutations (mutant amino indicated in grey). One special class of mutations which deletes exon 9 (boundaries indicated by open arrows) is associated with variable neuropathology and differing clinical presentations.

these genes account for approximately 50% of EOAD cases.12 As shown in Figure 3, presenilin-1 is predicted to code for a novel transmembrane protein with up to nine potential hydrophobic domains.5 Over 70 point mutations, and splice-site mutations which results in the deletion of a small portion of the protein, have been identified in the presenilin genes.13 These mutations span all putative domains of the presenilin protein, including every potential transmembrane domain as shown in Figure 3. Biochemical analyses indicate all mutations result in the elevated secretion of the amyloidogenic Aβ1–42 isoform.14 Transgenic mice and clonal cell lines which express mutant forms of the presenilin genes have elevated production of Aβ1–42 compared with wildtype constructs.15 Moreover, the elevated secretion of Aβ1-42 peptide was also found in plasma and fibroblasts cultured from AD patients with known PS-1 mutations.16 The mechanism of presenilin-induced Aβ1-42 elevation is still being debated.17 Several lines of evidence suggest that PS-1 is a diaspartyl

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protease which serves as the γ-secretase in the processing of APP. Mutation of two conserved aspartate residues at position 257 and 385 of PS-1, which face the predicted catalytic site, abrogated γ-secretase actvity.18 Moreover, both photoactivated and transition state analogues of γ-secretase inhibitors appear to bind directly to PS-1.19,20 AD Variants — Cotton Wool Plaques and Spastic Paraparesis The hallmark feature of AD is the presence of a large number of neuritic plaques and neurofibrillary tangles (NFTs). Mutations in the PS-1 gene are normally associated with severe neuropathology. Typically, there are large numbers of diffuse as well as cored, neuritic plaques, which are deposited in the cerebral cortex.21 Moreover, there is intense tau pathology, with neuritic dystrophy and NFTs.22,23 However, variations exist in AD neuropathology which include the morphology of senile plaques and the level of tau pathology. Three mutations in PS-1, a deletion of exon 9 (PS-1Δexon9),24,25 a double amino acid deletion (ΔI83/ΔM84),26 and a misssense mutation (P436Q)27 have been associated with a variant, “cotton wool” plaque pathology. As shown in Figure 4, a brain from an affected individual carrying the (PS1Δexon9) mutations had extensive deposition of large, spherical plaques that lacked distinctive cores and neuritic dystrophy. Biochemical analysis of cells transfected with PS-1 cDNAs carrying either of the three mutations secrete exceptionally high levels of Aβ1-4227 and this was suggested to be the molecular basis for PS-1 induced variant plaques. The main clinical symptoms of AD are initial memory deficits and progressive loss of higher cognitive function. However, variant forms of AD have been reported which manifest other neurological disorders. Spastic paraparesis (SP), or progressive spasticity of the lower limbs, frequently occurs on a hereditary background and a number of reports have described SP in families with dementia.13,24,27,28 Mutations reported in familial AD with SP have been confined to PS-1 with the majority of mutations consisting of deletions of exon 9.25,27,29 In the Finn2 pedigree, which has a PS-1Δexon9 mutation,24 10 of 14 individuals with dementia also had SP. Examination of the brains of three subjects, two with and one without SP, revealed many cotton wool plaques in all three cases, together with NFTs and pronounced congophilic angiopathy.24 This led to the suggestion there was an association between the variant plaques and SP clinical presentation. Further investigations into the pattern of inheritance of the dementia and SP phenotypes within another branch of the Finnish pedigree,30 and in an Australian pedigree25 (Figure 4a), suggests that the SP phenotype is due to the inheritance of an unlinked genetic locus acting in concert with the PS-1 mutation. A characteristic of the EOAD pedigrees with PS-1 mutations is that the mutation is usually fully penetrant and that there is a narrow range of age of onset of the disease within family members.31 However, there are excep-

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Figure 4. Spastic paraparesis and AD in an Australian EOAD pedigree. (A) Variable presentation of clinical phenotypes. Left-half black symbols indicate dementia without spastic paraparesis, right-half black symbols indicate spastic paraparesis without apparant dementia and filled symbols indicates spastic paraparesis with dementia. Haplotype analysis using four microsatellite markers flanking the PS-1 gene reveals a common disease haplotype (open box) detected in the affected individuals. (B) Variable neuropathology in EOAD pedigree as detected by antibodies against Aβ (upper panel) or tau (lower panel). Cotton wool plaques are distinguished by their lack of a distinctive amyloid core and neuritic dystrophy which were mostly detected in the individual with SP (III:9).

tions. A pedigree with the H163T mutation has been reported which has a non-penetrant individual as well as a wide range of age of onset.32 This supports the existence of genes and/or environmental factors that may modulate the expression of the AD phenotype. Analysis of large numbers of sib pairs affected with AD33 and ascertainment of the risk of relatives of aged, nondemented probands to develop AD,34 have indicated that there are genetic factors which are protective against AD or modify the age of onset of the disease. One such factor is the apolipoprotein E (ApoE) gene on chromosome 19. Inheritance of the ApoE ε4 allele has been shown to increase the risk of late-onset AD (LOAD) and results in an earlier age of onset in EOAD pedigrees with heritable mutations in the APP gene.35 Conversely, inheritance of the ApoE ε2 allele delays the onset of EOAD and LOAD cases.36 Another pos-

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sible factor is the butyrylcholinesterase (BChE) gene on chromosome 3, which may be protective against LOAD.37 The clinical phenotype in our subjects with SP is unusual in that dementia onset appears to be delayed compared to affected individuals who presented with dementia only, since three of the four individuals who developed SP remained dementia-free for up to ten years.25 Thus, the study of variant forms of AD has been important in suggesting the existence of phenotypic modifier gene(s) act in concert with specific PS-1 mutations to alter the clinical presentation of the disease. Frontotemporal Dementia and the tau Gene Frontotemporal dementias (FTD) represent a significant group of degenerative disorders that overlap in their clinical and neuropathological descriptions. These include seemingly separate disorders such as pallido-ponto-nigral

Figure 5. Mutations in tau detected in FTDP-17. The largest tau isoform is shown within residues from alternatively spliced exons 2,3 and 10. Gray boxes represent each of the four microtubule binding domains. The stem loop structure of the 5’ splice donor site of exon 10 is drawn above the tau isoform (not to scale). Exonic sequence is given in upper case and intronic sequence is in lower case letters.

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Figure 6. Exon trapping analysis of tau mutations. (A) Schematic diagram showing the exon trap procedure. Genomic DNA containing exon 10 of the tau gene is subcloned into the pSPL3 exon trap vector between two vector splice sites. The recombinant construct is transfected into cells whereby the vector promoter drives expression of chimaric RNA. In vivo splicing will generate either mRNA which includes or excludes exon 10 sequence. The exon trap products are then detected by RT-PCR. (B) Gel electrophoresis of RT-PCR products from exon trapping assays in COS-7 cells. The splicing in of exon 10 yeilds a 270 bp product, while the absence results in a 177bp product. The normal tau exon 10 (wt) shows the presence of both exon 10-positive (E10+) and exon 10-negative (E10-) RT-PCR products. The +16 and S305S mutations result in a marked increase in E10+ products. The +19 and 29 mutations result in a marked decrease in E10+ products.

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degeneration, familial multiple system tauopathy, familial progressive subcortical gliosis, progressive supranuclear palsy and disinhibition-dementiaparkinsonism-amyotrophy complex. However, as a group, these disorders share many clinical and neuropathological similarities and the tau gene has been genetically linked to a number of such pedigrees which have now been defined as Frontotemporal Dementia with Parkinsonism (FTDP-17).38 Tau is a microtubule-associated protein that is involved in the neuronal cytoskeleton, in particular, the assembly and stability of microtubules. Alternative splicing of exon 10 in the carboxy-terminal half of the protein result in tau protein which contains either three or four of the microtubule-binding repeat motifs (3 repeat tau and 4 repeat tau respectively). In 1998, tau gene mutations were shown to be causal for FTDP-17.39–41 Tau mutations can be functionally divided into two groups as shown in Figure 5. Firstly, missense mutations such as P301L and V337M have been shown to result in mutant forms of tau with decreased affinity for binding to mictotubules. The second group of exonic or intronic mutations alter the efficiency of splicing of exon 10.42 Intronic mutations (+3, +11, +12, +13, +14 and +16) identified in the 5’ splice donor site of intron 10 cause an increase in splicing of exon 10 by disrupting a stem-loop structure (Figure 5). The effect of a mutation on splicing can be assayed using the in vitro exon trapping assay which analyses the ability of the splice sites flanking an exon of interest to be spliced onto an exon trap vector’s reporter splice donor and reporter site43 (Figure 6A). As shown in Figure 6B, the presence of the +16 mutation results in an increase in exon trap products which contains the spliced exon 10 sequence compared with wildtype sequence. A Common Biochemical Pathway? One of the most crucial questions in AD research is the enigmatic relationship between senile plaques and NFTs. Studies have shown a direct causal relationship between the two diagnostic features of AD, in which Aβ appears to induce the hyperphosphorylation of tau. For example, when transgenic mice, which overexpress mutant tau (Pro301Leu) are injected with Aβ1-42 fibrils, NFTs are induced.44 Moreover, there are mutations in the tau gene that causes tau polymerisation and NFTs, but are associated with FTD rather than AD. Conversely, all mutations in the AD genes give rise to the development of tau pathology. These studies support the amyloid cascade hypothesis which states that the production and deposition of Aβ is the primary event in AD aetiology, which then cause the secondary formation of NFTs. There has been a spirited debate within the AD research field as to the relevance of Aβ1-42 and the amyloid cascade hypothesis to AD aetiology. Yet, it is still possible that there is a more fundamental neurodegenerative mechanism at work. There have been tantalising studies which suggest that there is a common cytotoxic mechanism involved in both AD and FTD. A recent study

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has shown that the apoptotic pathway involving the caspases is activated in AD brains.45 In vitro studies using cells transfected with mutant presenilin genes or exposed to Aβ peptides demonstrated that the cells undergo apoptosis.46,47 Similar experiments have shown that cells transfected with mutant tau cDNAs undergo the same process.48 It is hoped that the insights into the fundamental pathological mechanisms of the AD and FTD genes can be applied to the design of an effective prophylactic or therapeutic strategy for all forms of dementia. Knowing the specific molecules involved in this process, which has been achieved through genetic studies, is a crucial first step in the process. Acknowledgements Supported by a Department of Veterans Affairs Research Grant 9937441 and the National Health and Medical Research Council Block Grant and Research Fellowship 993050 and Unit Grant 983302. References 1. Iqbal K, ed. Research advances in Alzheimer’s Disease and related disorders. Chicester: John Wiley and Sons, 1995. 2. Clark RF, Goate AM. Molecular genetics of Alzheimer’s disease. Arch Neurol. 1993; 50:1164–1172. 3. Masters CL, Simms G, Weinman NA, Multhaup G, McDonald BL, Beyreuther K. Amyloid plaque core protein in Alzheimer disease and Down syndrome. Proc Natl Acad Sci USA. 1985; 82:4245–4249. 4. Selkoe DJ, Abraham CR, Podlisny MB, Duffy LK. Isolation of low-molecularweight proteins from amyloid plaque fibers in Alzheimer’s disease. J Neurochem. 1986; 146:1820–1834. 5. Price DL, Sisodia SS. Mutant genes in familial Alzheimer’s disease and transgenic models. Annu Rev Neurosci. 1998; 21:479–505. 6. Drouet B, Pincon-Raymond M, Chambaz J, Pillot T. Molecular basis of Alzheimer’s disease. Cell Mol Life Sci. 2000; 57:705–715. 7. Hardy JA, Higgins GA. Alzheimer’s disease: the amyloid cascade hypothesis. Science. 1992; 256:184–185. 8. Hardy J. Amyloid, the presenilins and Alzheimer’s disease. Trends Neurosci. 1997; 20:154–159. 9. Lichtenthaler SF, Wang R, Grimm H, Uljon SN, Masters CL, Beyreuther K. Mechanism of the cleavage specificity of Alzheimer’s disease gamma-secretase identified by phenylalanine-scanning mutagenesis of the transmembrane domain of the amyloid precursor protein. Proc Natl Acad Sci USA. 1999; 96:3053–3058. 10. Sherrington R, Rogaev EI, Liang Y, et al. Cloning of a gene bearing missense mutations in early-onset familial Alzheimer’s disease. Nature. 1995; 375:754– 760. 11. Levy-Lahad E, Wijsman EM, Nemens E, et al. A familial Alzheimer’s disease locus on chromosome 1. Science. 1995; 269:970–973. 12. Kwok JBJ, Taddei K, Hallupp M, et al. Martins RN. Two novel (M233T and R278T) presenilin-1 mutations in early-onset Alzheimer’s disease pedigrees and preliminary evidence for association of presenilin-1 mutations with a novel phenotype. NeuroReport. 1997; 8:1537–1542.

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13. Hutton M. Presenilin mutation database. Available from: URL: http:// www.alzforum.org. 14. Murayama O, Tomita T, Nihonmatsu N, et al. Enhancement of amyloid beta 42 secretion by 28 different presenilin 1 mutations of familial Alzheimer’s disease. Neurosci Lett. 1999; 265: 61–63. 15. Citron M, Westaway D, Xia W, et al. Mutant presenilins of Alzheimer’s disease increase production of 42-residue amyloid beta-protein in both transfected cells and transgenic mice. Nat Med. 1997; 3:67–72. 16. Scheuner D, Eckman C, Jensen M, et al. Secreted amyloid beta-protein similar to that in the senile plaques of Alzheimer’s disease is increased in vivo by the presenilin 1 and 2 and APP mutations linked to familial Alzheimer’s disease. Nat Med. 1996; 2:864–870. 17. Sisodia SS, Annaert W, Kim S-H, De Strooper B. Gamma-secretase:never more enigmatic. Trends Neurosci. 2001; 24 (Suppl): 2–6. 18. Wolfe MS, Xia W, Ostaszewski BL, Diehl TS, Kimberly WT, Selkoe DJ. Two transmembrane aspartates in presenilin-1 required for presenilin endoproteolysis and gamma-secretase activity. Nature. 1999; 398:513–517. 19. Li YM, Xu M, Lai MT, et al. Photoactivated gamma-secretase inhibitors directed to the active site covalently label presenilin 1. Nature. 2000; 405:689–694. 20. Esler WP, Kimberly WT, Ostaszewski BL et al. Transition-state analogue inhibitors of gamma-secretase bind directly to presenilin-1. Nat Cell Biol. 2000; 2: 428–434. 21. Lemere CA, Lopera F, Kosik KS, et al. The E280A presenilin 1 Alzheimer mutation produces increased A beta 42 deposition and severe cerebellar pathology. Nat Med. 1996; 2:1146–1150. 22. Smith MJ, Gardner RJ, Knight MA, et al. Early-onset Alzheimer’s disease caused by a novel mutation at codon 219 of the presenilin-1 gene. NeuroReport. 1999; 10: 503–507. 23. Singleton AB, Hall R, Ballard CG, et al. Pathology of early-onset Alzheimer’s disease cases bearing the Thr113-114ins presenilin-1 mutation. Brain. 2000; 123: 2467–2474. 24. Crook R, Verkkoniemi A, Perez-Tur J, et al. A variant of Alzheimer’s disease with spastic paraparesis and unusual plaques due to deletion of exon 9 of presenilin 1. Nat Med. 1998; 4:452–455. 25. Smith MJ, Kwok JBJ, McLean CA, et al. Variable phenotype of Alzheimer’s disease with spastic paraparesis. Ann Neurol. 2001; 49:125–129. 26. Steiner H, Revesz T, Neumann M, et al. A pathogenic presenilin-1 deletion causes abberrant Abeta 42 production in the absence of congophilic amyloid plaques. J Biol Chem. 2001; 276:7233–7239. 27. Houlden H, Baker M, McGowan E, et al. Variant Alzheimer’s disease with spastic paraparesis and cotton wool plaques is caused by PS-1 mutations that lead to exceptionally high amyloid-beta concentrations. Ann Neurol. 2000; 48: 806–808. 28. Sato S, Kamino K, Miki T, et al. Splicing mutation of presenilin-1 gene for earlyonset familial Alzheimer’s disease. Hum Mutat. 1998; (Suppl 1): 91–94. 29. Prihar G, Verkkoniem A, Perez-Tur J, et al. Alzheimer disease PS-1 exon 9 deletion defined. Nat Med. 1999; 5:1090. 30. Hiltunen M, Helisalmi S, Mannermaa A, et al. Identification of a novel 4.6-kb genomic deletion in presenilin-1 gene which results in exclusion of exon 9 in a Finnish early onset Alzheimer’s disease family: an Alu core sequence-stimulated recombination. Eur J Hum Genet. 2000; 8:259–266. 31. Rohan de Silva HA, Patel AJ. Presenilins and early-onset familial Alzheimer’s disease. Neuroreport. 1997; 8:i–xii.

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32. Axelman K, Basun H, Lannfelt L. Wide range of disease onset in a family with Alzheimer disease and a His163Tyr mutation in the presenilin-1 gene. Arch Neurol. 1998; 55:698–702. 33. Tunstall N, Owen MJ, Williams J, et al. Familial influence on variation in age of onset and behavioural phenotype in Alzheimer’s disease. Brit J Psychiat. 2000; 176:156–159. 34. Silverman JM, Smith CJ, Marin DB, et al. Identifying families with likely genetic protective factors against Alzheimer disease. Am J Hum Genet. 1999; 64:832– 838. 35. Chartier-Harlin MC, Parfitt M, Legrain S, et al. Apolipoprotein E, epsilon 4 allele as a major risk factor for sporadic early and late-onset forms of Alzheimer’s disease: analysis of the 19q13.2 chromosomal region. Hum Mol Genet. 1994; 3: 569–574. 36. Corder EH, Saunders AM, Risch NJ, et al. Protective effect of apolipoprotein E type 2 allele for late onset Alzheimer disease. Nat Genet. 1994; 7:180–184. 37. Laws SM, Taddei K, Fisher C, et al. Evidence that the butylcholinesterase K variant can protect against late-onset Alzheimer’s disease. Alzheimers Rep. 1999; 2: 219–223. 38. Foster NL, Wilhelmsen K, Sima AA, Jones MZ, D’Amato CJ, Gilman S. Frontotemporal dementia and parkinsonism linked to chromosome 17: a consensus conference. Conference Participants. Ann Neurol. 1997; 41:706–715. 39. Spillantini MG, Goedert M. Tau mutations in familial frontotemporal dementia. Brain. 2000; 123:857–859. 40. Hutton M, Lendon CL, Rizzu P, et al. Association of missense and 5’-splicesite mutations in tau with the inherited dementia FTDP-17. Nature. 1998; 393: 702–705. 41. Poorkaj P, Bird TD, Wijsman E, et al. Tau is a candidate gene for chromosome 17 frontotemporal dementia. Ann Neurol. 1998; 43:815–825. 42. Spillantini MG, Murrell JR, Goedert M, Farlow MR, Klug A, Ghetti B. Mutation in the tau gene in familial multiple system tauopathy with presenile dementia. Proc Natl Acad Sci USA. 1998; 95:7737–7741. 43. Stanford PM, Halliday GM, Brooks WS, et al. Progressive supranuclear palsy pathology caused by a novel silent mutation in exon 10 of the tau gene: expansion of the disease phenotype caused by tau gene mutations. Brain. 2000; 123: 880–893. 44. Gotz J, Chen F, van Dorpe J, Nitsch RM. Formation of neurofibrillary tangles in P301l tau transgenic mice induced by Abeta 42 fibrils. Science. 2001; 293: 1491–1495. 45. Marx J. Neuroscience. New leads on the ‘how’ of Alzheimer’s. Science. 2001; 293: 2192–2194. 46. Wolozin B, Iwasaki K, Vito P, et al. Participation of presenilin 2 in apoptosis: enhanced basal activity conferred by an Alzheimer mutation. Science. 1996; 274: 1710–1713. 47. Guo Q, Sopher BL, Furukawa K, et al. Alzheimer’s presenilin mutation sensitizes neural cells to apoptosis induced by trophic factor withdrawal and amyloid beta-peptide: involvement of calcium and oxyradicals. J Neurosci. 1997; 17: 4212–4222. 48. Furukawa K, D’Souza I, Crudder CH, et al. Pro-apoptotic effects of tau mutations in chromosome 17 frontotemporal dementia and parkinsonism. NeuroReport. 2000; 11:57–60.

Chapter 11 OXIDATIVE AND FREE RADICAL MECHANISMS IN BRAIN AGEING Judy B. de Haan*, Rocco C. Iannello, Peter J. Crack, Paul Hertzog, and Ismail Kola

Introduction In this chapter, we discuss the relationship between increased oxidative stress and cellular ageing, with particular emphasis on the physiological and pathological changes associated with ageing of the brain. In this regard, focus will be on the role of the antioxidant enzymes during cellular ageing, and the consequences of an altered antioxidant balance. We will highlight the role that antioxidant genes play in the regulation of senescent-like changes both in in vitro and in vivo models, and how perturbations of the antioxidant pathways may lead to clinical outcomes associated with ageing, e.g. neurodegenerative diseases such as Parkinson’s and Alzheimer’s disease. Furthermore, this chapter will illustrate how an altered antioxidant ratio as a direct consequence of the over-expression of the antioxidant gene, Sod1, leads to ageing changes associated with the Down syndrome (DS) phenotype. In this manner it is hoped to emphasize the importance of the antioxidant genes in the regulation of redox status during cellular ageing, and how perturbations of redox balance may have pathological consequences associated with ageing. Molecular oxygen: a paradox for aerobic organisms The existence of reactive oxygen species (ROS) within cells is an unavoidable consequence of both oxidative metabolism and exposure to environmental *To whom correspondence should be addressed.

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stresses such as radiation, air pollutants and herbicides. During oxidative metabolism, oxygen is reduced to water via reactive intermediates that include the superoxide radical (.O2-), hydrogen peroxide (H2O2), and hydroxyl radical (.OH). Irrespective of the mode of radical generation, ROS cause cellular damage through interactions with macromolecules, resulting in mutations in DNA (both mitochondrial1 and nuclear DNA), destruction of protein structure and function, and peroxidation of membrane lipids.2 Together with non-enzymatic antioxidants (e.g. ascorbate, glutathione, αtocopherol), aerobic organisms have evolved highly efficient enzymatic antioxidant defences to overcome the problems of oxidative stress. These include the superoxide dismutases (Sod), glutathione peroxidase (Gpx) and catalase enzymes. Superoxide dismutases function in the first step of the antioxidant pathway (Figure 1) where .O2- is converted to H2O2, while Gpx and Cat are independently involved in the neutralisation of H2O2 to water in a second step. This is a finely tuned process and a balance exists within cells between the first (Sod) and second steps (Gpx and/or Cat) of the antioxidant pathway. Indeed it has been postulated that perturbations of this balance could affect cell function, since a shift in favour of H2O2 (the intermediate product) could result in Fenton-type reactions with transition metals, resulting in even more noxious .OH radicals.3 Once formed, these species quickly interact with molecules in their immediate vicinity, particularly lipids, causing large-scale macromolecular damage.4

Figure 1. Two-step Antioxidant Pathway. Superoxide radicals generated during oxidative metabolism, are neutralised to water via a two-step process involving superoxide dismutase (Sod) in a first step, and both or either glutathione peroxidase (Gpx) and catalase (Cat) in a second step. Fenton-type reactions occur when an imbalance in this pathway favors the build-up of hydrogen peroxide (H2O2), resulting in peroxidation of molecules such as lipids. Adapted from Groner et al.4

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Compared with other organs, the brain is most vulnerable to ROS-induced damage. This is in part due to: (1) the large generation of ROS during oxidative phosphorylation as a consequence of the high rate of oxygen consumption by the brain; (2) high levels of iron in some brain regions which catalyse the generation of ROS via Fenton-type reactions; and (3) the brain is lipid rich, particularly in polyunsaturated fatty acids that are known targets of lipid peroxidation. Thus the levels of cellular antioxidant enzymes become important during the ageing process, since antioxidants are able to limit ROS reactions that would otherwise damage macromolecules in an unabated fashion. Indeed, it is conceivable that continued damage to macromolecules could translate at a higher level into cellular and/or organ dysfunction and ultimately cellular ageing and/or death. Antioxidants and Lipid Peroxidation during Brain Ageing To gain a better understanding of the role played by antioxidants in limiting damage during brain ageing, a number of studies have focussed on the activity of these antioxidants during cellular ageing. However, the literature is controversial with respect to the activities of the major antioxidants, often due to limitations in the methodologies. Often no distinction is made between the different isoforms of the superoxide dismutases, such that Sod1 (the cytosolic and most abundant isoform) and Sod2 (the mitochondrial isoform) are assayed as one. This has resulted in reports of either an increase,5 a decrease6 or no change7 in the Sod activity in ageing murine or rat brains. Even when Sod1 and Sod2 are assayed independently, Sod1 has been reported to increase,8 remain constant,9 or decrease10,11 with ageing in mouse or rat brains. Likewise, Gpx1 (the most abundant cytosolic and mitochondrial isoform) activity has been reported to increase5,11 or remain relatively unchanged6,7 in ageing murine or rat brains. As a marker of oxidative damage in the ageing brain, the levels of lipid peroxidation are often assayed. Here again, the results have been controversial, with reports of either an increase11–13 or a decrease14 occurring. Indeed, very few studies simultaneously investigate the activities of Sod1, Gpx1, catalase and the lipid peroxidation status, which becomes important if discrepancies such as differences in sex, species, strain and age are to be eliminated. We have examined the levels of the three major antioxidant enzymes (Sod1, Gpx1 and Cat) and the levels of lipid peroxidation in a range of ageing murine brains. We show that Sod1 mRNA levels and activity are significantly increased in murine brains during the ageing process. Similarly, Sod1 mRNA levels and activity are increased in other murine organs with age. These include the ageing liver, lung, kidney, heart, ovary, and bone.15–17 An analysis of the Gpx1 profile showed that most organs adapt to the increased Sod1 activity by upregulating Gpx1 and/or Cat, at both the mRNA and activity level with advanced age. However and most importantly, the brain failed

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to show an increase in either Gpx1 or Cat mRNA or activity with increasing age. This implies that the ageing brain has an altered Sod1 to Gpx1 and Cat ratio. As already suggested, an altered ratio can lead to increased H2O2 and .OH, which in turn can damage macromolecules such as lipids. Indeed the ageing brain showed significantly increased levels of lipid peroxidation, while those organs that adapted to the increased Sod1 levels by upregulating Gpx1 and/or catalase showed reduced peroxidative damage.15,17 Our data therefore suggest that an altered antioxidant balance may result in peroxidative damage to biologically important molecules in the ageing brain. Regulation of the antioxidant genes during ageing becomes important, since we are able to show that upregulation of the Sod1 gene in murine brains is transcriptionally regulated. Why the Gpx1 gene is not upregulated during murine brain ageing, but is upregulated in other ageing murine organs, is not yet understood. It may be the need for increased levels of the intermediate H2O2, to perform other regulatory functions in the brain during the lifetime of the individual, that out-ways the damaging peroxidative changes seen in ageing brains. In a recent study, providing the first profile of gene regulation at the molecular level in ageing murine brains, Lee et al. 18 analysed the expression patterns of 6,347 genes using oligonucleotide array analysis. They were able to show that ageing of the murine neocortex and cerebellum resulted in a gene-expression profile indicative of increased oxidative stress, an altered inflammatory response, and reduced neurotrophic support. These data provide good evidence at a global level that oxidative stress and inflammatory processes, the latter known to involve ROS, are increased in the ageing brain and that these processes are regulated at the gene level during brain ageing. Lessons from Gene Targeting Experiments The above studies, which imply a role for an altered antioxidant balance, increased oxidative stress and peroxidative damage during cellular ageing, although informative, are still limited since they are of a correlative nature. To overcome these limitations, and to directly prove a role for ROS in cellular ageing, researchers have used molecular techniques to address these issues. In vitro models of cellular senescence Various studies have shown that over-expression of the human Sod1 gene in human Hela cells, mouse-L cells and rat PC12 cells, leads to increased lipid peroxidation as well as structural and functional alterations of lipid membranes.4,19 We have extended these studies by investigating the effects of Sod1 overexpression in murine NIH 3T3 cells with respect to cellular senescence.20 Two types of cell clones were isolated; those overexpressing both Sod1 and Gpx1 which were termed “adapted cell clones”, and those that failed

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to upregulate Gpx1 in response to increased Sod1 levels, which were termed “non-adapted cell clones”. The adapted cells were morphologically and biochemically indistinguishable from the parental lines from which they were derived. An important observation was that the non-adapted cells looked and behaved like senescent cells in culture. They exhibited a greater cell-surface area and had a larger nuclear and cellular volume. These cells also grew more slowly, as assessed by both growth rate and 3H-dThymidine incorporation. Furthermore, expression levels of Cip1, which is a well-characterized marker of cellular senescence, were elevated in all non-adapted cells investigated.20 Since H2O2 is the intermediate between the two steps of the antioxidant pathway, we measured both intra-and extracellular H2O2 levels of parental, adapted and non-adapted cells. Hydrogen peroxide levels were not significantly different in adapted and parental cells. However, both intra- and extracellular H2O2 levels were significantly increased in non-adapted cells compared with both adapted and parental cells. Thus by altering the Sod1 to Gpx1 ratio in favour of H2O2 formation, we were able to demonstrate senescence-like changes, and we postulated that it was the overproduction of H2O2, either directly or via Fenton-like reactions to produce .OH radicals, that was mediating the senescence-like effects. Indeed, we were able to mimic these changes in both NIH 3T3 cells and primary cultures after the addition of H2O2.20,21 We addressed the issue of an altered redox balance and senescence-like changes in two further culture systems where ROS production is elevated as a consequence of a change in antioxidant gene expression. First we analysed cultured fibroblasts derived from mice that were genetically manipulated to have a null mutation for the Gpx1 gene. Indeed, fibroblasts lacking any functional Gpx1 (Gpx1-/- cells) exhibited features of senescence when compared with control cells, and were more susceptible to H2O2 -induced apoptosis than controls. Furthermore, Gpx1-/- neurons demonstrated decreased viability after exposure to H2O2 compared with controls.22 Second, we showed that cell lines derived from Down syndrome aborted conceptuses (where the Sod1 to Gpx1 ratio is increased as a consequence of three copies of the Sod1 gene) exhibit senescence-like characteristics, namely they grew more slowly, incorporated less 3H-dThymidine and expressed higher levels of the senescence marker, Cip1. Indeed, premature ageing is one of the phenotypes associated with individuals with DS (see Down syndrome section below). From these studies, it is evident that an altered antioxidant balance, either as a consequence of the overexpression of Sod1 or a reduction in the activity of Gpx1, leads to senescence-like changes in cultured cells. In vivo models of cellular senescence The most striking data that an altered antioxidant ratio is involved in the genesis of ageing comes from Yarom et al.,23 who show that mice transgenic for Sod1, develop morphological and biochemical changes at neuromus-

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cular junctions of tongue muscles (namely, withdrawal and destruction of some terminal axons and the development of multiple small terminals), which are similar to those seen in tongue muscles of ageing mice and rats.24 Furthermore, these changes are similar to those seen in tongue muscles of individuals with DS.25,26 A subsequent study has also demonstrated premature ageing changes in the incidence, length and number of nerve branch-points in Sod1 transgenic hind-limb motor-neuron terminals. Again these changes are analogous to those seen in ageing mice and rat muscles of the hindlimb.27 The results may also explain how over-expression of Sod1 affects motor neurons in individuals with DS, resulting in the impairment of central motoric coordination and generalized hypotonia of joints. These authors also report ageing changes in thymocyte populations isolated from Sod1-transgenic animals,28 and the accumulation of the age-related pigment, lipofuscin, in the myofibers of Sod1transgenic animals.27 Importantly, Ceballos et al.29 demonstrate increased lipid peroxidation in the brains of Sod1-transgenic mice. Taken together, the above data strongly suggest that altered redox status, as a consequence of Sod1 over-expression, leads to accelerated ageing changes in these models. The role of Free Radical Mechanisms in Diseases of the Ageing Brain Parkinson’s disease Parkinson’s disease (PD) is characterized by the progressive loss of pigmented dopamine-containing neurons in the substantia nigra pars compacta of the brain. The mechanisms involved in the specific targeting of these cells have received much attention over the past number of years, since an understanding of these process(es) may facilitate drug design to either reduce or limit such damage. One theory is that individuals with PD have a defective antioxidant system that is incapable of removing harmful ROS generated during the oxidation of dopamine. Monoamineoxidase catalyses the oxidation of dopamine via the reactive intermediate, 6-hydroxy-dopamine.30 It is during this process that .O2and H2O2 are generated. The Sod1 activity has been reported to increase in the dopamine-containing neurons of Parkinsonian brains, possibly as a consequence of the increased .O - flux.31 Interestingly, it is within these same cell-types that Ceballos et al.32 2 demonstrate increased Sod1 activity in aged brains (with a mean age of 83 yrs). Cellular damage would be limited if second-step antioxidants were increased concomitantly. However, Gpx1 activity has been reported to remain unchanged or is reduced in the substantia nigra of individuals with PD.33 Furthermore, no difference in either catalase or glutathione reductase is seen in the substantia nigra or basal nucleus of Parkinsonian brains compared with normal brains.34 Interestingly, Sian et al.35 have shown that levels of reduced glutathione are decreased by approximately 40–50% in the substantia nigra, thus limiting the efficient removal of hydrogen peroxide by Gpx in these Parkinsonian

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brains (reduced glutathione is required as a cofactor by Gpx). Furthermore, the potential for the formation of harmful .OH radicals also exists, since this region exhibits an increase in iron content.36 Thus from in vivo studies it would appear that there is a significant shift in the balance of antioxidant enzymes in favour of increased H2O2 and .OH production in these brains, which may account for the increased cell damage seen in this region of Parkinsonian brains. Indeed lipid peroxidation is elevated in nigral tissue of post mortem brains from Parkinsonian individuals compared with control brains.37,38 Further strong evidence for an altered antioxidant balance contributing to PD pathology comes from recent data of Klivenyi et al.39 who demonstrate that Gpx1 knockout mice are more susceptible to 1-methyl-4-phenyl1,2,5,6-tetrahydropyridine (MPTP)–induced PD. Administration of MPTP, a mitochondrial toxin and known inducer of oxidative stress, resulted in significantly greater depletions of dopamine in Gpx1 deficient mice compared with control mice. These data strongly suggest a neuroprotective role for Gpx1 in the prevention or reduction of PD-like symptoms after challenge with mitochondrial toxins. In support of this notion, Bensadoun et al.40 have shown that mice transgenic for Gpx1 are less susceptible to the toxic effects of 6hydroxydopamine, which is known to produce H2O2 and superoxide radicals. Furthermore, Klivenyi et al.39 suggest that Gpx1 protects against neurotoxicity by detoxifying harmful peroxynitrite radicals (the latter are formed through the reaction of nitric oxide and superoxide radicals), since inhibitors of neuronal nitric oxide synthase, enzymes that generate NO, block MPTP neurotoxicity. However, it should be emphasised that any genetic defect(s) in free radical scavenging enzymes appear to be compensated for under physiological conditions, since we22 and others39,41 show no pathology of Gpx1 knockout mice under physiological conditions. These defects translate into PD pathology only when Gpx1 knockout mice are exposed to certain environmental factors or toxins. These toxins may even be produced endogenously. Naoi and Maruyama42 demonstrate degeneration of dopaminergic neurons by NM(R)Sal, an endogenous MPTP-like neurotoxin. This mechanism may also hold true for individuals susceptible to PD, i.e. an altered antioxidant balance, possibly due to a reduction in second-step antioxidants such as Gpx1, and exposure to an environmental/endogenous toxin. A possible therapeutic target for PD is therefore the upregulation of the glutathione/glutathione peroxidase system, and in this regard, glutathione precusors such as N-acetyl-cysteine (NAC) have been considered.43 Stroke Stroke is the leading cause of long-term disability in adults and ranks as the third leading cause of death after heart disease and cancer. Approximately 80% of all strokes are ischaemic, that is, due to a reduction of blood flow to certain brain regions caused by blockage of a vessel. This results in oxygen deprivation to those regions normally supplied by the occluded blood vessel. Blood flow back into the occluded region (reperfusion) is accompanied by

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the production of ROS at an enhanced rate. The increased ROS production is thought to trigger certain molecular pathways leading to necrosis, apoptosis and neuroinflammation, resulting in subsequent neuronal loss and serious cognitive and/or motor disturbances.44 Thus the role played by antioxidants in the removal of these harmful species becomes extremely important in limiting the neuronal damage post-ischemia. Use has been made of transgenic and knockout mice to address the issue of the role of antioxidant genes in the pathogenesis of stroke. For example, following mid cerebral artery occlusion and subsequent reperfusion, the infarct volumes of Sod1 knockout mice are significantly increased, 45,46 implying a protective role for Sod1 against ROS such as .O2-. Conversely, the infarct volume of Sod1 transgenic mice are reduced compared with control mice.47 This is somewhat surprising since overexpression of Sod1 should lead to increased levels of H2O2 and in the absence of an adaptive rise by second-step antioxidants, increased H2O2 levels are known to be cytotoxic. Thus the mechanism by which Sod1 over-expression confers protection in these mice is unclear. It may well be that the Gpx1 gene is upregulated in stroke-related situations in an adaptive response to the increased levels of H2O2. In further trying to tease out the role of the various antioxidants in neuroprotection, Weisbrot-Lefkowitz et al. 48 were able to show that mice transgenic for Gpx1 show a greater level of protection against ischaemiareperfusion damage than Sod1 transgenic mice. These results imply that H2O2 and/or hydroxyl radicals (formed from H2O2 in the Fenton reaction) are more neurotoxic than superoxide radicals and therefore second-step antioxidants such as Gpx1 play a far greater neuroprotective role than the superoxide dismutases. This becomes particularly important when designing strategies for drug therapy. In agreement with this notion, recent studies in our laboratory have shown that Gpx1 knockout mice are more susceptible to ischaemia-reperfusion injury than controls. We show that the severity of the infarct volume is significantly increased in Gpx1-/- mice compared with controls. This increase also correlated with an increase in caspase-3 activation and an elevation in the level of apoptosis in neural cells. 49 Our results suggest that Gpx1 plays an important role in the protection of neural cells against the elevated oxidative stress that accompanies ischemia/reperfusion injury. Amyotrophic lateral sclerosis (AML) Amyotrophic lateral sclerosis (AML) is a progressive disorder of motor neurons found in the cortical regions of the brain, brain stem and spinal cord. Muscular wasting, weakness and fasciculations, spasticity and hyperreflexia characterise the disease. From the time of onset, patients with ALS survive a mean period of 3–4 years. Ninety percent of ALS cases occur as a sporadic event, while the remaining 10% are inherited as an autosomal dominant trait, with high penetrance after the sixth decade. Whether the disease occurs sporadically or is inherited, the clinical features in most instances are similar.

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The Sod1 gene has been identified as one of the key players associated with familial forms of amyotrophic lateral sclerosis (FALS).50 The study of Rosen et al.50 demonstrated that single base mutations in the coding region of the Sod1 gene are associated with FALS (different mutations are detected in different families). These mutations resulted in amino-acid substitutions in regions of the Sod1 enzyme that are highly conserved amongst organisms, suggesting that these sites are important for Sod1 function. Transgenic mice expressing mutated forms of Sod1 have provided some of the most informative data regarding the mechanisms involved in human ALS. Notably, Gurney et al.51 were able to demonstrate ALS-like symptoms in mice that over-express a human Sod1 mutation, namely progressive paralysis and motor neuron loss in the spinal cord and brain stem, thus providing proof that altered Sod1 function can lead to neurodegenerative changes.52 Furthermore, it has been shown that mutations in Sod1 result in a dominant gain-of-function that is peroxidase-like, resulting in the increased formation of .OH radicals.53,54 Recently, Cha et al.55 have shown that neuronal nitric oxide synthetase (nNOS) expression is enhanced in mutant Sod1 transgenic mice, particularly within astrocytes, implying a role for NO in the genesis of neurotoxic injury. NO together with superoxide radicals are known to generate highly noxious peroxynitrate radicals. Furthermore, mutant Sod1 has been shown to facilitate peroxynitrite-mediated nitration of proteins in mutant Sod1 transgenic mice.56 Pathogenesis in the transgenic mouse model of FALS has recently been proposed to occur via a two-step sequential process, in which damage is mediated by free radicals which accumulate to a threshold, triggering catastrophic motor neuron loss through glutamate-mediated excitotoxic mechanisms.57 Evidence in support of this hypothesis comes from therapeutic studies with antioxidants and inhibitors of glutamatergic neurotransmission. Feeding of mutant Sod1 transgenic mice with vitamin E and selenium (selenium is an essential cofactor of Gpx1) delayed onset of the disease, and strength and mobility were transiently improved compared with non-supplemented Sod1 mutant mice. Administering riluzole and gabapentin, two drugs that reduce presynaptic glutamate release or biosynthsis, improved survival of the mutant Sod1 mice.58 It is probably correct to assume that both sporadic and familial forms of ALS trigger a common ROSmediated pathway of motor neuron death that is .OH and/or peroxynitratemediated. Alzheimer’s disease Alzheimer’s disease (AD) affects 7% of the population over 65 years of age and is characterized by slow progressive intellectual decline and personality deterioration. Autopsy studies show intra-neuronal fibrillary tangles and neuritic plaques. The latter are spherical extracellular cores of β-amyloid protein surrounded by degenerating nerve-cell processes. Both plaques and tangles occur throughout the cerebral cortex of the brain and consist of bundles of uniform proteins that appear as paired helical filaments on electron microscopic examination.59

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There is now growing evidence that amyloid β-peptide (Aβ) and oxidative stress are implicated in the pathogenesis of AD.60,61 It has been shown that Aß is over-produced in the brains of patients with AD and that Aβ peptides can be toxic to neuronal cells through a mechanism that involves H2O2.62 Indeed, oxidative stress actually exacerbates Aβ aggregation,63 while the deposition of Aβ in turn, increases intraneuronal generation of ROS.64 In this manner, a cyclical response is established with continued deposition of Aβ. In vitro studies have also shown marked oxidative injury, including lipid peroxidation, protein carbonyl formation, mitochondrial DNA damage and the induction of stress related alterations that include ubiquination of cytoskeletal proteins.65 Furthermore, the amino acid hydroxyproline that is not normally a constituent of cytoplasmic protein in the brain, was identified as an integral part of paired helical filament proteins in AD brains. This led Zemlan et al.59 to propose that these modified amino acids arise due to non-enzymatic hydroxylation of proline residues, presumably arising from .OH radicals. If the hypothesis that oxidative stress contributes to the pathology of AD is correct, then H2O2 and/or .OH could be elevated in Alzheimer’s disease as a consequence of an alteration in antioxidant balance. Indeed, Sod1 levels are elevated in AD brains. In particular, post-mortem analysis of AD brains showed that large pyramidal neurons of the hippocampus contained higher amounts of Sod1 mRNA and protein than control brains.66 It is these cells that are particularly vulnerable to degenerative processes in AD. In addition, Sod1 activity is increased by 30% in fibroblasts of familial AD patients compared with normal controls.59 Also lending support to this notion are the data from amyloid precursor protein (APP)-transgenic mice that show elevated Sod1 levels in brain regions, especially around Aβ deposits.65 These authors also demonstrate an increase in heme-oxygenase1, a marker of oxidative stress, around most amyloid deposits in their APPtransgenic mice. Further evidence that oxidative damage may contribute to cellular damage in AD brains comes from an analysis of lipid peroxidation in these brains. Indeed, the two regions most susceptible to neurodegeneration in AD, the temporal and parietal cortex, showed elevated levels of lipid peroxidation, whilst the least affected areas, namely the occipital cortex and cerebellum, showed no elevation when compared with control brains.67 Overexpression of the Antioxidant Gene Sod1, Leads to Ageing Changes Associated with the Down Syndrome Phenotype Accelerated ageing changes have been observed in individuals with Down syndrome. In particular, the rapid or early onset of ageing is evident visually as premature greying or loss of hair. Detailed biochemical analysis has revealed that individuals with DS show a decline in immune responsiveness similar to that seen in older people. Furthermore, alterations in cyclic nucleotide metabolism have been noted in lymphocytes from individuals with DS, which

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is comparable with what occurs in ageing human lymphocytes. Granulovacuolar degeneration of neurons and the appearance of Alzheimer’s disease pathology, amyloidosis, hypogonadism and degenerative vascular disease have also been noted in DS individuals.68,69 A perturbation in the ratio of Sod1 relative to Gpx1 and/or catalase is of particular relevance in DS. Importantly, the Sod1 gene is located on human Ch21, and in 95% of cases the entire chromosome is triplicated in DS individuals. Indeed, elevated Sod1 activity has been observed in tissues such as red blood cells, platelets, fibroblasts and lymphocytes of DS individuals.70 Furthermore, we show that the Sod1 to Gpx1 ratio is increased in five fetal DS organs.16 These include the fetal brain, liver, thymus, lung and heart. Our results are in agreement with Brooksbank and Balazs71 who demonstrate increased Sod1 activity, unaltered Gpx activity, and increased lipid peroxidation in DS fetal brains. However an altered antioxidant balance may not occur in all DS organs and tissues, since a compensatory increase in Gpx1 activity occurs in erythrocytes and lymphoid cells of DS individuals.72 Interestingly, catalase activity has been shown to be unaffected in DS erythrocytes.73 Thus it appears that the ratio of the major antioxidant enzymes is shifted in favour of H2O2 production in the majority of organs, and may in this manner contribute to the age-related pathologies associated with DS.74 Evidence in favour of this hypothesis comes from data of Buscioglio and Yankner75 who demonstrate increased ROS formation in primary cultures of DS fetal neurons. Glycoxidation and in particular, the accumulation of advanced glycation end products (AGEs), are also enhanced in DS fetal brains.76 These results suggest that brain oxidative stress occurs very early in the life of DS individuals, and may contribute to some of the pathologies associated with DS, such as the premature ageing and neurodegenerative AD-like pathology. Of particular interest has been the age-associated neuritic plaque formation analogous to that found in AD.77 Post-mortem examination of DS brains over the age of 35 years, almost invariably show pathology similar to that seen in AD. Interestingly, the areas most affected in DS brains parallel those affected in individuals with AD. Evidence has already been presented that ROS may contribute to the pathology of AD. Indeed, the gene dosage increase in Sod1 activity in DS may contribute in a similar fashion to the ADlike pathology. However, in this instance it likely to be the concordant effect of increased Sod1 activity and increased Aβ production that is responsible for the AD-like pathology in DS, since the gene coding for APP is also localized to human chromosome 21 and is over-expressed in DS.78 Conclusion This chapter has described the role of the antioxidant genes and their gene-products in the regulation of cellular ageing. It has focussed primarily on their role in brain ageing since this organ is particularly vulnerable to peroxidative insults.

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Figure 2. An altered antioxidant balance may have pathological consequences. An imbalance in the major antioxidants can result in the build-up of noxious radicals, predisposing and/or contributing to various pathologies, e.g. (i) neuropathological outcomes such as stroke, Familial Lateral Sclerosis (FALS), Parkinson’s and Alzheimer’s disease, (ii) Down syndrome as a consequence of the overexpression of SOD1, a chromosome 21 gene, and (iii) the ageing process per se, which in turn may predispose to various pathologies. Furthermore inflammation often accompanies these pathologies e.g. AD, PD and Down syndrome, often as a consequence of radical-mediated induction of transcription factors such as NF-κB, leading to upregulation of TFN-α, IL-1 and IL-6. This in turn generates more radicals. An increase in both SOD1 and APP may contribute to AD-like pathology in DS.

It has shown that an altered Sod1 to Gpx1 and catalase ratio exists in ageing murine brains and that this altered ratio is accompanied by an increase in lipid peroxidation. It has highlighted the importance of maintaining a redox balance in cells and that a perturbation in first to second step antioxidant enzymes can affect cell function, leading to senescence-like changes. Furthermore, evidence was presented that altered redox states exist in various pathologies associated with ageing (Figure 2). Cumulative evidence now strongly suggests the existence of a molecular basis for ageing, with the regulation of the antioxidant genes playing an important role in this regard. Current thinking supports the hypothesis of Sohal and Allen79 who extended the free radical theory of ageing (which was based on the production of ROS in an uncontrolled fashion during aerobic metabolism), to

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include controlled genetic changes induced by ROS during ageing. It is only by clearly defining the molecular basis of senescence-like changes during normal cellular ageing and in pathologies associated with ageing, that one can hope to design drugs to reduce or ameliorate these detrimental ROS-induced effects. References 1. Richter C. Oxidative damage to mitochondrial DNA and its relationship to ageing. Int J Biochem Cell Biol. 1995; 27:647–653. 2. Halliwell B, Gutteridge JMC. In: Free radicals in biology and medicine, Oxford, Clarendon Press, 1985. 3. Imlay JA, Chin SM, Linn S. Toxic DNA damage by hydrogen peroxide through the Fenton reaction in vivo and in vitro. Science. 1988; 240:640–642. 4. Groner Y, Elroy-Stein O, Avraham KB, Yarom R, Schickler M, Knobler H, Rotman G. Down syndrome clinical symptoms are manifested in transfected cells and transgenic mice overexpressing the human Cu/Zn-superoxide dismutase gene. J Physiology-Paris. 1990; 84:53–77. 5. Tayarani I, Cloez I, Clement M, Bourre JM. Antioxidant enzymes and related trace elements in ageing brain capillaries and choroid plexus. J Neurochem. 1989; 53:817–824. 6. Benzi G, Pastoris O, Villa RF. Changes induced by ageing and drug treatment on cerebral enzymatic antioxidant system. Neurochem Res. 1988; 13:467–478. 7. Cand F, Verdetti J. Superoxide dismutase, glutathione peroxidase, catalase, and lipid peroxidation in the major organs of the ageing rats. Free Radical Bio Med. 1989; 7:59–63. 8. Bracco F, Burlina AP, Malesani R, Rigo A, Battistin L. Free-radical related enzymes in the ageing brain. In: Bes A, et al. editors. Senile dementias: Early detection, Libbey, 1986; 293–297. 9. Kurobe N, Suzuki F, Kato K, Sato T. Sensitive immunoassay of rat Cu/Zn superoxide dismutase: concentrations in the brain, liver, and kidney are not affected by ageing. Biomed Res. 1990; 11:187–194. 10. Mariucci G, Ambrosini MV, Colarieti L, Bruschelli G. Differential changes in Cu, Zn and Mn superoxide dismutase activity in developing rat brain and liver. Experientia 1990; 46:753–755. 11. Sahoo A, Chainy GBN. Alterations in the activities of cerebral antioxidant enzymes of rat are related to ageing. Int J Dev Neurosci. 1997; 15:939–948. 12. Mizuno Y, Ohta K. Regional distributions of thiobarbituric acid-reactive products, activities of enzymes regulating the metabolism of oxygen free radicals, and some of the related enzymes in adult and aged rat brains. J Neurochem. 1986; 46:1344–1352. 13. Sawada M, Carlson JC. Changes in superoxide radical and lipid peroxide formation in the brain, heart and liver during the lifetime of the rat. Mech Ageing Dev. 1987; 41:125–137. 14. Boehme DH, Kosecki R, Stern F, Marks N. Lipoperoxidation in human and rat brain tissue: development and regional studies. Brain Res. 1977; 136:11–21. 15. de Haan JB, Newman JD, Kola I. Cu/Zn superoxide dismutase mRNA and enzyme activity, and susceptibility to lipid peroxidation, increases with ageing in murine brains. Mol Brain Res. 1992; 13:179–186. 16. de Haan JB, Wolvetang E, Cristiano F, Iannello R, Kelner M, Kola I. Reactive oxygen species and their contribution to pathology in Down syndrome. Adv Pharmacol. 1997; 38:379–402.

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51. Gurney ME, Pu H, Chiu AY, Dal Canto MC, Polchow CY, Alexander DD, Caliendo J, Hentati A, Kwon YW, Deng HX. Motor neuron degeneration in mice that express a human Cu, Zn superoxide dismutase mutation. Science. 1994; 264: 1772–1775. 52. Dal Canto MC, Gurney ME. A low expressor line of transgenic mice carrying a mutant human Cu,Zn superoxide dismutase (SOD1) gene develops pathological changes that most closely resemble those in human amyotrophic lateral sclerosis. Acta Neuropathol. 1997; 93:537–550. 53. Gurney ME, Cutting FB, Zhai P, Andrus PK, Hall ED. Pathogenic mechanisms in Familial Amyotrophic Lateral Sclerosis due to mutation of Cu, Zn superoxide dismutase. Pathol Biol. 1996; 44:51–56. 54. Liu R, Althaus JS, Ellerbrock BR, Becker DA, Gurney ME. Enhanced oxygen radical production in a transgenic mouse model of familial amyotrophic lateral sclerosis. Ann Neurol. 1998; 44:763–770. 55. Cha CI, Kim JM, Shin DH, Kim YS, Kim J, Gurney ME, Lee KW. Reactive astrocytes express nitric oxide synthetase in the spinal cord of transgenic mice expressing a human Cu/Zn SOD mutation. NeuroReport. 1998; 9:1503–1506. 56. Ferrante RJ, Shinobu LA, Schulz JB, Matthews RT, Thomas CE, Kowall NW, Gurney ME, Beal MF. Increased 3-nitrotyrosine and oxidative damage in mice with a human copper/zinc superoxide dismutase mutation. Ann Neurol. 1997; 42:326–334. 57. Gurney ME. Transgenic animal models of familial amyotrophic lateral sclerosis. J Neurol. 1997; 244:S15–20. 58. Gurney ME, Cutting FB, Zhai P. Benefit of vitamin E, riluzole and gabapentin in a transgenic model of familial amyotrophic lateral sclerosis. Ann Neurol. 1996; 39:147–157. 59. Zemlan FP, Thienhaus OJ, Bosmann HB. Superoxide dismutase activity in Alzheimer’s disease: possible mechanism for paired helical filament formation. Brain Res. 1989; 476:160–162. 60. Hensley K, Carney JM, Mattson MP, Aksenova M, Harris M, Wu JF, Floyd RA, Betterfield DA. A model for beta-amyloid aggregation and neurotoxicity based on free radical generation by the peptide: relevance to Alzheimer disease. Proc Natl Acad Sci USA. 1994; 91:3270–3274. 61. Iannello RC, Crack PJ, de Haan JB, Kola I. Oxidative stress and neural dysfunction in Down syndrome. J Neural Transm. 1999; 57:257–267. 62. Behl C, Davis JB, Lesley R, Schubert D. Hydrogen peroxide mediates amyloid beta protein toxicity. Cell. 1994; 77:817–827. 63. Multhaup G, Ruppert T, Schlicksupp A, Hesse L, Beher D, Masters CL, Beyreuther K. Reactive oxygen species and Alheimer’s disease. Biochem Pharmacol. 1997; 54:533–559. 64. Yan SD, Yan SF, Chen X, Fu J, Chen M, Kuppusamy P, Smith MA, Perry G, Godman GC, Nawroth P, et al. Non-enzymatically glycated tau in Alzheimer’s disease induces neuronal oxidant stress resulting in cytokine gene expression and release of amyloid beta-peptide. Nat Med. 1995; 1:693–699. 65. Pappolla MA, Chyan YJ, Omar RA, Hsiao K, Perry G, Smith MA, Bozner P. Evidence of oxidative stress and in vivo neurotoxicity of beta-amyloid in a transgenic mouse model of Alzheimer’s disease: a chronic oxidative paradigm for testing antioxidant therapies in vivo. Am J Pathol. 1998; 152:871–877. 66. Furuta A, Price DL, Pardo CA, Troncoso JC, Xu ZS, Taniguchi N, Martin LJ. Localization of superoxide dismutases in Alzheimer’s Disease and Down’s syndrome neocortex and hippocampus. Am J Pathol. 1995; 146:357–367. 67. Hajimohammadreza I, Brammer M. Brain membrane fluidity and lipid peroxidation in Alzheimer’s disease. Neurosci Lett. 1990; 112:333–337.

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68. Kola I, Cristiano F, de Haan JB, Sumarsono S, Thomas R, Corrick C, Tymms M. Genes, Embryogenesis and Down syndrome. In: Moeloek F, Affandi B, Trounson AO, editors. Advances in human reproduction. Casterton: Parthanon Publishing, 1995; 309–320. 69. Kola I, Hertzog PJ. Animal models in the study of the biological function of genes on human chromosome 21 and their role in the pathophysiology of Down syndrome. Hum Mol Genet. 1997; 6:1713–1727. 70. Anneren KG, Epstein CJ. Lipid peroxidation and superoxide dismutase-1 and glutathione peroxidase activities in trisomy 16 fetal mice and human trisomy 21 fibroblasts. Pediatr Res. 1987; 21:88–92. 71. Brooksbank BWL, Balazs R. Superoxide dismutase, glutathione peroxidase and lipoperoxidation in Down’s Syndrome fetal brain. Dev Brain Res. 1984; 16: 37–44. 72. Sinet PM, Michelson AM, Bazin A, Lejeune J, Jerome H. Increase in glutathione peroxidase activity in erythrocytes from trisomy 21 subjects. Biochem Biophys Res Com. 1975; 67:910–915. 73. Percy ME, Dalton AJ, Markovic VD, Crapper McLachlan DR, Hummel JT, Rusk ACM, Andrews DF. Red cell superoxide dismutase, glutathione peroxidase and catalase in Down syndrome patients with and without manifestations of Alzheimer’s disease. Am J Med Genet. 1990; 35:469–467. 74. Bladier C, de Haan JB, Kola I. Antioxidant genes and reactive oxygen species in Down syndrome. In: Sen CK, Sies H, Baeuerle PA, editors. Antioxidant and redox regulation of genes. San Diego: Academic Press, 2000; 425–449. 75. Busciglio J, Yankner BA. Apoptosis and increased generation of reactive oxygen species in Down’s syndrome neurons in vitro. Nature. 1995; 378:776–779. 76. Odetti P, Angelini G, Dapino D, Zaccheo D, Garibaldi S, Dagna-Bricarelli F, Piombo G, Perry G, Smith M, Traverso N, Tabaton M. Early glyoxidation damage in brains from Down’s syndrome. Biochem Biophys Res Com. 1998; 243: 849–851. 77. Mann DMA, Esiri MM. The pattern of acquisition of plaques and tangles in the brains of patients under 50 years of age with Down’s Syndrome. J Neurol Sci. 1989; 89:169–179. 78. Neve RL, Finch EA, Dawes LR. Expression of the Alzheimer amyloid precursor gene transcripts in the human brain. Neuron 1988; 1:669–677. 79. Sohal RS, Allen RG. Oxidative stress as a causal factor in differentiation and ageing: a unifying hypothesis. Exp Geront. 1990; 25:499–522.

Chapter 12 THE ROLE OF NUTRITIONAL FACTORS IN COGNITIVE AGEING Janet Bryan

Introduction The association between nutrition and cognitive performance has become a topic of increasing scientific and public interest. 1 Nutrition may be an important, modifiable, life-style factor in age-related cognitive decline and there is a growing interest in the development of nutritional supplements as therapeutic agents aimed at enhancing or maintaining cognitive function or delaying cognitive decline.2 The role that food and nutrition has in the course of age-related cognitive change is yet to be clearly defined. However, early findings from epidemiological and experimental studies suggest that there may be a role for dietary components such as: folate and vitamins B-12 and B-6, antioxidants, omega 3 fatty acids, and herbal supplements like Ginkgo biloba. The assumption that food and its components can impact on cognitive performance and age-related cognitive change is based on the knowledge that the central nervous system (CNS) depends heavily for efficient functioning on a constant supply of almost all of the essential nutrients as well as glucose and oxygen.3 If brain function is optimised, one would expect cognitive function to be optimised as well. Understanding this link between brain function and cognitive function is important in the formulation of hypotheses about diet-cognition links and how we might test them. We need to base research in the area of diet and cognition on hypotheses about the mechanisms by which nutrients or other aspects of the diet might impact on the brain, and how this in turn affects cognitive performance. Nutrients may impact on the brain in

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a number of ways such as: on neurotransmitter synthesis, on the structure of neurons, and on the vasculature of the brain. The effects of nutrients on the brain may be short-term and acute, or more longer-term due to dietary habits over an extended period of time. Importantly, if we understand the mechanisms by which nutrients affect the brain, we can more accurately predict the role that nutrients may have in cognitive ageing and which aspects of cognition are likely to be affected by nutritional factors. At a very basic level, we might expect nutritional factors to impact on fluid, rather than crystallised, abilities among older adults.4 Fluid ability is thought to reflect innate information processing that is determined by genetic and physiological factors such as the integrity of the CNS. Fluid ability is demonstrated in the capacity to process novel information, that is, to apply mental processes to situations that require no previous knowledge. Crystallised ability refers to the application of learned information and cultural experience acquired over the lifetime and therefore depends on our store of knowledge, education and cultural background. Due to its reliance on the integrity of the CNS, we might expect nutritional factors to have more of an impact on fluid, rather than crystallised, ability. Furthermore, there are some aspects of cognition that show marked agerelated decline, such as memory performance and cognitive resources, that may also be sensitive to nutritional factors. Cognitive resources refer to our mental capacity to perform cognitive tasks. The three identified cognitive resources are: speed of information processing (how fast we can think); working memory capacity (how much information we can simultaneously store and work on); and attentional capacity (how long we can concentrate for). Because these resources are so important for the efficient working of the cognitive system,5 any investigation of the links between nutrition and cognitive performance should include measures of cognitive resources.4 In summary, because cognition is multidimensional, it is crucial that pertinent tests of cognitive performance be selected in order to test hypotheses that specify the impact that nutritional factors might have on the cognitive system. Folate, and Vitamins B-12 and B-6 Recent research has focussed on the role of B vitamins, especially folate, B-12 and B-6, in cognitive ageing.6 Although most of the evidence for the link between B vitamins and cognitive performance is based on studies involving participants with clinical vitamin deficiencies, the effect of these vitamins might also be important for a broader population.3, 7 In particular, a mild to moderate folate deficiency is thought to be relatively common in the general population,8 while the incidence of clinical and subclinical levels of folate and B-12 deficiency has been found to be higher in the elderly.8–12 Stabler et al.13 estimate that low serum B-12 concentrations might be evident in 5–15% of the elderly population. They suggest multiple causes of B-12 deficiency in

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older adults such as: pernicious anaemia, atrophic gastritis causing B-12 malabsorption, or previous gastric or intestinal surgery. Evidence is continuing to mount that higher intakes and serum concentrations of certain vitamins, particularly folate and B-12, might be beneficial for cognitive performance.14 Research to date indicates two inter-linked neurochemical mechanisms by which these B vitamins influence cognitive performance through their role in methylation in the CNS.15–17 The first mechanism (the hypomethylation hypothesis) posits that folate, with B-12 or B-6 as catalysing cofactors, may have a direct and possibly acute effect on the CNS via hypomethylation, which inhibits the synthesis of methionine and the formation of S-adenosylmethionine (SAMe). This in turn inhibits methylation reactions throughout the CNS involving proteins, membrane phospholipids, DNA, the metabolism of neurotransmitters such as the monoamines (e.g. dopamine, norepinephrine, serotonin), and melatonin, all of which are crucial to neurological and psychological status.16–19 The second mechanism (the homocysteine hypothesis) proposes that there is an indirect and possibly longer-term effect of folate, B-12 and B-6 on the functioning of the brain via the cerebrovasculature. Studies have demonstrated that high levels of homocysteine, largely attributable to low levels of folate, and B-12 or B-6, are associated with increased risk of vascular disease14,15,20–22 due to toxic effects on vascular tissue, or excessive production of excitotoxic sulphur amino acids, homocysteic acid and cystein sulphinic acid.11 Thus, these B vitamins may function to preserve the integrity of the CNS via their role in the prevention of vascular disease, which is crucial to cognitive function.15,16,23–25 Cross-sectional studies Most studies investigating the links between the B vitamins and cognitive performance among older adults have employed correlational designs. Goodwin et al.26 found significant associations between lower dietary intakes of folate and reduced abstract thinking and problem solving performance, but found no effects for B-12 or B-6. Ortega et al.10, 27 found that those with higher scores on the Mini Mental State Examination (MMSE) had higher blood levels and dietary intake of folate, but again found no effects for B-12 or B-6. Other studies have investigated the individual and combined relationships of folate and B-12 with cognition. Bell et al.28 found that individuals with less than median values of serum folate and B-12 had lower scores on the MMSE than those above the median value for either folate or B-12 or both. Wahlin et al.29 found that those with low serum folate or a combination of low folate and B-12 performed more poorly on tests of episodic memory than did those with normal folate and B-12 levels. Hassing et al.30 found that individuals with low serum folate levels performed more poorly on tests of episodic memory. Furthermore, there is some evidence to suggest that homocysteine levels mediate the relationship between the B vitamins and cognitive performance. Riggs et al.31 found that higher homocysteine, lower folate and B-6 blood levels were associated with poorer spatial copying performance, and that lower

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B-6 was also associated with backward Digit Span performance. Interestingly, homocysteine levels were found to mediate the relationships between folate and B-6 and cognitive performance suggesting that these vitamins impact on cognition via homocysteine status. The results from these studies examining cross-sectional associations between B vitamins and cognitive performance suggest that low folate intake and/or status emerges as the most reliable associate of cognitive performance, either alone or in combination with low B-12. Many aspects of cognition appear to be related to B vitamin status, especially memory performance and measures of abstract reasoning. In addition, the relationship between the B vitamins and cognition may be mediated by homocysteine levels since homocysteine uniquely predicted cognitive performance after controlling for B vitamin status in the study by Riggs et al.31 Longitudinal studies The findings of cross-sectional studies have been supported by longitudinal studies. In a six-year follow up of a healthy subsample of the original Goodwin et al.26 study, La Rue et al.32 found a positive association between past intake of B-12 and B-6 and current cognitive status. Ebly et al.33 found that those in lowest serum folate quartiles were more likely to have cognitive loss and to be depressed, and that they were more likely to be demented, institutionalised and to have a higher mortality rate at two-year follow-up. Results from longitudinal studies allow for the examination of possible long-term effects of B vitamin intake and status on cognition at a later date, or the impact on cognitive change. The results of these studies suggest that prior intake of B vitamins is a predictor of cognitive performance at a later date. The findings of Ebly et al.33 suggest that low folate status may be a predictor of cognitive decline among older adults. Experimental studies Very few studies have manipulated B vitamin intake experimentally and assessed its affects on cognitive performance, and only three studies have used a placebo-controlled design. Tolonen et al.34 investigated B-6 status among older Finnish and Dutch adults aged between 64 to 96 years, and younger Dutch adults aged from 22 to 55 years. In addition, they gave daily oral doses of 2 mg of B-6 for one year to 20 Finnish older adults, while 24 matched participants received a placebo. Biochemical results clearly indicated that both Finnish and Dutch older adults had lower B-6 levels than the younger adults. Clock drawing performance was improved by supplementation relative to controls but there were no significant effects of supplementation on memory or Digit Span performance. Deijen et al.35 also investigated the effects of B-6 supplementation on cognitive performance and mood among 76 men aged between 70 and 79 years. They gave the men 20 mg of B-6 or placebo daily for three months. Significant positive effects of B-6 supplementation, compared with placebo, were found on measures of the amount of information retained in long-term memory,

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but there were no effects for iconic or short-term memory. The authors concluded that B-6 supplementation might have a modest but significant effect in improving the storage of information, thereby reducing age-related memory loss. Fioravanti et al.36 used a double-blind, placebo-controlled study to assess the effects of folate supplementation (15 mg daily for 60 days, a dose well above the Recommended Daily Intake) on the memory performance of 30 community or aged-care dwelling participants, aged 70 to 90 years. Participants were selected for low folate levels (<3 ng/ml) and mild to moderate memory complaints. Learning, memory and attention was tested at pre- and post-supplementation. There were significant differences between the treatment and placebo groups on measures of attention and memory. Moreover, although there was no significant relationship between folate and cognitive performance at baseline, the sensitivity of the cognitive measures to treatment with folate was related to the initial level of the folate deficiency, such that the greater the deficiency at the start of the treatment, the greater the cognitive improvement at the end. In our own laboratory, we recently completed two studies assessing the effects of short-term folate, B-12 and B-6 supplementation on cognitive performance among healthy, community dwelling women. In the first study,37 a daily capsule of folate (750 micrograms), B-12 (15 micrograms), B-6 (75 milligrams) or placebo was taken for five weeks by 211 women aged 20–92 years. A battery of cognitive tests assessing speed of information processing, working memory, recall and recognition, executive function and verbal ability was administered before and after supplementation. Very few effects of supplementation were observed. However, there was a trend for recall performance among younger women receiving folate and B-12, and among middle-aged women receiving B-6 to improve from pre- to post-treatment relative to placebo. Recognition memory performance among older women receiving folate supplementation improved after treatment relative to placebo. These effects of supplementation on memory performance were replicated in a follow-up study38 in which 40 women aged 65 – 84 years received daily doses of 750 micrograms folate and 15 micrograms B-12 or placebo for five weeks. The immediate and delayed recall performance of those in the vitamin supplement group improved after treatment while that of the placebo group did not. Results from well-conducted placebo-controlled experiments would provide the most compelling evidence of the effects of B vitamins on cognition. So far, very few have been conducted, but results for the few studies that have been done suggest that folate, B-12 and B-6 supplementation appear to have positive effects on the memory performance of older adults. These findings require replication with an investigation of dose-response relationships. B vitamins and dementia There is also an interest in the possible therapeutic role of B vitamins in the prevention and treatment of Alzheimer’s disease (AD) and other dementias.39

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Research to date, although equivocal,40 has resulted in recommendations by the United States National Institute of Aging to conduct a blood chemistry profile for folate and B-12 and by the Quality Standards Subcommittee of the American Academy of Neurology41 to screen for B-12 deficiency as part of the diagnostic process of dementia to rule out preventable causes.42 The links between the B vitamins, particularly folate and B-12, and dementia are largely based on findings that individuals with dementia, especially those with AD, have lower serum folate and B-12 concentrations (3). Furthermore, serum levels of folate and homocysteine have been found to be related to the severity of cognitive impairment in dementia. Several studies have investigated the link between B-12 deficiency and the incidence of dementia. Karnaze and Carmel 43 analysed serum B-12 levels in 17 people with primary dementia and 11 with secondary dementia and found that those with primary dementia (20%) had a higher incidence of B-12 deficiency than did those with secondary dementia (0%). Ikeda et al.44 also found evidence of B-12 deficiency among those with AD compared to individuals with other dementias. In this study the deficiency was evident in cerebrospinal fluid but not plasma concentrations of B-12. In contrast, Crystal et al.45 found that the incidence of dementia did not appear to be related to B-12 deficiency and that treatment with B-12 did not benefit performance on measures of cognitive impairment. However, in this study, it was likely that B-12 deficient participants were present in both the groups with and without dementia, making differences between the groups difficult to detect.13 Folate status has also been linked with the incidence of dementia, cognitive impairment and brain atrophy. Sneath et al.46 examined serum folate levels among 113 patients of a geriatric ward and found that the 14 with dementia had levels lower than those of the group as a whole. In addition, they found a positive correlation between red cell folate levels and cognitive performance scores among those with low folate levels. In support, Sommer and Wolkowitz47 reported a positive correlation between red cell folate and scores on the MMSE among 13 patients with dementia, 10 of whom had a diagnosis of AD. Most recently, Snowdon et al.25 conducted neuropathological examinations of the brains of 30 participants in the Nun Study who had died and for whom blood measures were also available. Based on findings that high homocysteine levels are associated with progressive atrophy of the medial temporal lobe in those with AD,23 they set out to determine whether serum folate was inversely related to the severity of atrophy of the neocortex. They found that serum folate was significantly related to atrophy of the cerebral cortex but only among those participants with a significant number of AD lesions. However, since folate was negatively correlated with atrophy even in participants without brain infarcts and minimal atherosclerosis, the authors suggest that folate may exert an influence in maintaining CNS integrity in older age through nonvascular mechanisms. As discussed earlier, folate and B-12 status may also be more accurately marked by homocysteine levels and some studies have found associations

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between homocysteine levels and cognitive impairment. Nilsson et al. 48 found increased plasma homocysteine levels in cognitively impaired older adults with normal blood levels of folate and serum B-12. McCadden et al.49 conducted a prospective case-controlled study of 30 individuals with AD and found that they had significantly elevated total serum homocysteine levels compared with a cognitively intact control group. In addition, homocysteine levels and serum B-12 were correlated with cognitive scores in the AD but not the control group. Levitt and Karlinsky50 propose two hypotheses to account for the relationship between B vitamin deficiency and cognitive impairment associated with dementia. They label the first the “low intake” hypothesis, which proposes that cognitively impaired individuals have a reduced capacity for self-care and nutrition and as a consequence develop vitamin deficiency. If this is the case, then cognitive impairment should be associated with deficiencies in a wide range of nutritional indices. The second hypothesis is the “etiologic” hypothesis, which proposes that the B vitamins play a specific role in the development and severity of cognitive deficits. If this is the case, then the relationship between B vitamins and cognitive performance should exist for all types of dementias. Levitt and Karlinsky50 aimed to test these hypotheses by examining the relationship between indices of nutrition status and the severity of cognitive impairment among people with AD and those with other forms of cognitive impairment. Serum B-12 concentration correlated with MMSE scores only for the group with AD even after controlling for age, education and duration of illness. There were no relationships between serum and red cell folate and cognition scores. The authors concluded that the results did not support the low intake hypothesis since B-12 was related to cognitive impairment, but none of the other nutritional indices (folate, magnesium, calcium, protein, globulin, glucose) was. However, the etiologic hypothesis was not supported either since the relationship between B-12 and cognition was found only for those with AD. The authors suggest a third hypothesis which may account for the association; i.e. the disease process in AD, which results in neuronal death, may also affect cellular function resulting in a decrease in the absorption, storage, utilisation and/or excretion of B-12. B-12 levels may therefore be a marker for the progress of AD. If this is the case, then we might expect that even though B-12 supplementation may improve the nutritional status of deficient individuals, it may not impact on cognitive performance. Indeed, Levitt and Karlinsky50 report that two participants in their study, with low B-12 and possible AD, who subsequently received B-12 replacement therapy, continued to show decline in cognitive function despite a return to normal blood levels of B-12. The few intervention studies that have been conducted provide some support for Levitt and Karlinsky’s50 third hypothesis. Teunisse et al.51 gave B-12 supplementation for six months to 26 individuals with dementia (all but one had a diagnosis of probable AD) and subnormal B-12 serum levels.

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The results indicated no effect of supplementation on the severity of cognitive decline among the treated group compared with an untreated group of individuals with AD. Carmel et al. 52 evaluated B-12, neuropsychological and electrophysiological indices among 13 individuals with dementia and low serum B-12 levels, before and after treatment with B-12 supplementation. Improvements were found for homocysteine and haemoglobin levels, neuropathological symptoms, electroencephalographic abnormalities, visual evoked and somatosensory abnormalities, but not on the performance on neuropsychological tests. Martin et al.53 set out to examine the effects of B12 supplementation on cognitive performance in a group of 18 participants who had low serum cobalamin and evidence of cognitive dysfunction. The participants received 1000 micrograms of cyanocobalamin intramuscularly daily for one week, weekly for one month and then monthly for six months. Post-supplementation scores from the Mattis Dementia Rating Scale for 11 of the 18 participants showed improvement. However, only those participants whose impairment on mental status testing had been in the mild range and who had been symptomatic for less than one year showed improvement. Most notably, those who had been symptomatic for less than six months responded best to supplementation. The authors concluded that age-related cognitive losses in early B-12 deficiency might be reversible if the supplementation is initiated early in the process. The findings from these intervention studies generally support Levitt and Karlinsky’s50 suggestion that AD may be associated with a decrease in the ability to utilise vitamin B-12. Studies in which B-12 was given as a supplement to those with cognitive impairment resulted in no improvement in the performance of cognitive tasks, with the exception of the Martin et al. 53 study in which those who were in the earliest stages of cognitive decline and whose impairment was in the mild category improved. Thus, it could be argued that supplementation might be critical in the early stages of AD and that the window of opportunity for effective intervention with supplementation might be time limited, since the structural changes in the CNS occurring later in the disease process will preclude amelioration of cognition.53 Alternatively, studies in which SAMe, a metabolite of folate and B-12, was given as a supplement54 resulted in improvements in cognition. This may indicate that the utilisation of SAMe bypasses some crucial metabolic step that is compromised during the disease. Clearly, more intervention studies are required to determine if and how B-12 metabolism may be compromised in those with AD. Antioxidants The oxidation model of cognitive ageing proposes that there are cumulative effects of a lifetime of oxidative damage to neuronal tissue in normal ageing.32 Neuronal tissue may be particularly vulnerable to oxidative damage for a number of reasons. First, there is a high content of polyunsaturated

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fatty acids in brain cell membranes,55,56 the relative proportion of which rises with increasing age, which renders them increasingly sensitive to oxidative damage with increasing age.57 Second, the brain contains high concentrations of iron which catalyses the production of oxygen free radical species.55,56 Third, the brain contains limited levels of protective antioxidant enzymes and compounds.55 In addition, the efficiency of mitochondrial function decreases with increasing age and defects in mitochondrial energy metabolism have been linked with oxidative damage. In particular, impaired electron transport activity in mitochondria is thought to generate free radicals 57 which may cause oxidative damage to DNA, proteins and lipid membranes. Furthermore, oxidative modification of low-density lipoprotein is now seen as contributing to the process of atherogenesis58–60 and there is a link between cerebrovascular lesions and cognitive impairments.31,58,61 Oxidation in the brain may impact more on some regions than others. La Rue et al.32 present evidence that shows that with normal ageing there is an increase in monoamine oxidase B which is involved in the breakdown in catecholamines. This results in a decrease in dopamine in the striatum and norepinephrine in the locus ceruleus, spetum and substantia nigra.62 Age-related cognitive changes are consistent with mild impairment of these frontal/subcortical systems. Such changes include psychomotor slowing, a decrease in performance of effortful memory tasks and a reduced flexibility of thought and action. The oxidation model of cognitive ageing therefore suggests a role for antioxidant nutrients, such as vitamins C, E and beta-carotene, in reducing the effects of oxidative stress in the brain. Cross-sectional studies Studies investigating associations between antioxidants and cognitive performance provide mixed support for a role for dietary antioxidants in the maintenance of cognitive function or reduced cognitive impairment with increasing age. Jama et al.62 examined the relation between dietary intake of vitamins C and E and beta-carotene and cognitive function, assessed by the MMSE, in a population-based sample of 5182 participants aged 55–95. Results showed that after controlling for sociodemographic variables, total energy intake and incidence of cardiovascular disease, there was an association between beta-carotene intake and cognitive function. There was no relation between vitamin C or E intake and cognitive performance. Ortega et al.27 also investigated associations between dietary intake of antioxidants and cognitive performance among 260 cognitively unimpaired participants aged 65–90 years. Those with higher MMSE and Pfeiffer’s Mental Status Exam scores had higher intakes of fruit, vitamin C and beta-carotene compared with those with lower scores. Mendelsohn et al.63 studied the use of antioxidant supplements among 1059 rural, noninstitutionalised elderly residents of Pennsylvania. Current use of nutritional supplements containing vitamins A, C, E, beta-carotene, zinc or selenium were measured via self-report. Antioxidant use was significantly and positively associated with delayed recall

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of words and story only, out of an extensive battery of neuropsychological tests, but this association was no longer significant after controlling for age, education and sex. The researchers concluded that there was little support for the hypothesis that antioxidant supplements were associated with cognitive function. Perrig et al.64 examined the relationship between plasma status of vitamins C and E, and beta-carotene and memory performance among 442 participants aged between 65 and 94 years. After controlling for age and education, plasma levels of vitamin C and beta-carotene were correlated with recognition and vocabulary performance but not with recall and working memory. Vitamin E status was not associated with cognitive performance. Berr et al.65 also investigated associations between blood levels of antioxidants and cognitive performance among a larger sample of 1,389 community-dwelling volunteers aged 59 to 71 years. Lower plasma carotenoid status was associated with poorer performance on part B of the Trail Making test and the Digit Symbol Substitution Test, both of which tap processing speed, but not with memory performance. As with the findings from Perrig et al.,64 vitamin E status was not associated with cognitive performance. Perkins et al.66 investigated associations between serum levels of a wide range of antioxidants (vitamins A, C, E, alpha-carotene, beta-carotene, beta-cryptoxanthin, lutein/zeaxanthin, lycopene and selenium) and delayed recall performance among 4,809 community-dwelling adults aged over 60 years. After controlling for age, education, income, vascular risk factors, and folate, calcium and iron status, lower serum vitamin E status was associated with a higher odds ratio of having memory difficulties. There were no other significant associations between blood levels of any other antioxidant and memory performance. Longitudinal studies The results from longitudinal studies examining associations between antioxidants and cognitive performance among older adults provide stronger support for the link than do cross-sectional results. La Rue et al.32 re-examined data collected by Goodwin et al.26 and found that plasma concentrations of vitamin C and use of vitamin C supplements were related to copying, but not recall, of Rey figure among 137 elderly community residents aged 66–90 years. Other cognitive functions (abstract reasoning, verbal and non-verbal memory) were not related. Associations between cognition and dietary and supplementary intake of vitamins A and E were stronger for past (1980) than current (1986) intake. The researchers suggested that vitamin A stored in the liver and vitamin E stored in adipose tissue may be available for antioxidant function at a later time, or antioxidant protection provided by these vitamins (localized in the lipid-soluble environments in the brain) may prevent slow oxidative changes that would manifest as poorer cognition later, may account for these findings. Paleogos et al. 67 conducted a small (N = 117) cohort study in a retirement community in Sydney, Australia. Vitamin C intake was assessed in 1991 with a semiquantitative food frequency questionnaire

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with cognitive function assessed 4 years later. After adjustment for age, sex, smoking, education, total energy intake, and use of psychotropic medications, higher consumption of vitamin C supplements was associated with lower prevalence of more severe cognitive impairment measured by MMSE. However, there were no associations between vitamin C intake and the performance on tests of verbal or category fluency. Antioxidants and Alzheimer’s disease Recently there has been an emerging interest in the role of antioxidants in the treatment of AD. Oxidative damage of cellular lipid, protein and DNA due to an increase in β-amyloid formation may be central to the neurodegenerative process of AD.68 The hippocampal neurons (the loss of which is a key feature in AD) seem particularly vulnerable to the deleterious effects of free radicals on the muscarinic cholinergic receptors.69 Therefore antioxidants, which tend to act as free-radical scavengers, may have a role in delaying the course or prevention of AD. Vitamin E in particular has been the focus of research into the role of antioxidants in AD. There is some evidence that vitamin E status is altered in AD. Some studies have found a decrease in plasma concentrations of vitamin E among those with AD70 while another found increased concentrations of vitamin E in the brains of those with AD,71 which is possibly indicative of a compensatory response to oxidative damage. Animal studies using dogs and rats have demonstrated that an increase in vitamin E concentrations in the brain can be achieved with supplementation.68 However, to date there has been very few studies that have investigated the effects of vitamin E on the course of AD. One such study72 investigated the effects of vitamin E and/or selegiline supplementation among 341 people with moderate AD using a double-blind, placebo-controlled design. The risk of institutionalisation, loss of basic activities of daily living, severe dementia, or death was significantly reduced among those who received vitamin E and/or selegiline compared with placebo. There was no effect of supplementation on cognitive test performance but this could have been due to floor effects or other methodological problems. This study suggests a possible role for vitamin E and other antioxidants in the prevention or treatment of AD. Polyunsaturated Fatty Acids There has been recent interest in the role of omega-3 polyunsaturated fatty acids (PUFAs) in brain function. The brain is extremely rich in PUFAs, especially docosahexaenoic acid (DHA; omega-3) and arachidonic acid (AA; omega-6), which play a critical role in the regulation of membrane permeability and fluidity, as well as in the actions of membrane-bound enzymes and neurotransmitter (dopamine, serotonin) mechanisms. 73 Fatty fish and flaxseed oil are major dietary sources of omega-3 PUFAs, and foods high

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in omega-6 PUFAs include vegetable oils, lean meats, margarines, and eggs. Upon consumption, the PUFAs compete metabolically in their utilization of the enzyme delta-6-desaturase to produce their longer-chain derivatives. Therefore, a diet richer in one PUFA and poorer in the other will result in a net imbalance within the membrane structure. Typically, the Western diet is high in omega-6 PUFAs and low in omega-3 PUFAs.74 In addition, increased levels of omega-6 have been associated with increased production of precursors to cardiovascular disease,75 whereas increasing omega-3 consumption has been found to attenuate production of these precursors.76 Specifically, omega-3 PUFAs may down-regulate omega-6 eicosanoids, which tend to be prothrombotic, vasoconstrictory and proinflammatory, as well as reducing blood viscosity and improving arterial compliance.75 These effects may have implications for the oxygen supply to the brain thereby affecting cognitive function. Further, the net imbalance of omega-3:omega-6 ratio, or omega-3 deficiency, has been associated with psychological consequences. Omega-3 PUFA deficiency has been linked with changes in cortical dopamine function77 that may have implications for cognitive functions associated with the frontal lobes of the brain. Rats fed an omega-3 deficient diet were found to be impaired on a working memory sensitive version of the Morris water maze.78 Findings from other animal studies suggest that omega-3 PUFAs may be important for cognitive function with ageing. A decrease in both DHA and AA has been found in the brain lipids of older rats and it has been proposed that these changes in fatty acid composition are correlated with an age-related decline in the functions of the CNS.79 Further, Lim and Suzuki80 found that diets rich in DHA and phosphatidylcholine improved maze learning ability in both younger and older mice. In humans, there have been some studies assessing the importance of cognitive development in infants,81 but there is limited work on the effects of PUFA intake on cognitive performance among adults. To date there has been only one investigation,58 that found a link between fish consumption and rate of cognitive impairment across a three-year time span among older adults, with higher fish consumption relating to lower rates of decline, assessed solely by the MMSE. Clearly, further work investigating the role of PUFAs in cognitive ageing is warranted. Herbal Supplements — Ginkgo biloba Lately, attention has been focussed on the potential role of herbal supplements, such as Ginkgo biloba, in the enhancement of cognitive function. Use of such supplements, freely available in “health” shops and supermarkets, may be particularly attractive to older adults seeking to improve cognitive performance. The therapeutic benefits of Ginkgo biloba are thought to be due to the action of flavonoids (ginkgo-flavone glycosides) and/or terpenoids (ginkgolides and bilobalide) which are contained in the extract produced

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from the dried leaves of the plant (Ginkgo biloba, maidenhair tree). According to a review by Kleijnen and Knipschild,82 Ginkgo biloba is among the most commonly prescribed drugs in France and Germany and is thought to relieve many neurological conditions common in ageing through a number of potential mechanisms: increased blood flow by the vasoregulating activity of arteries, capillaries and veins; platelet activating factor antagonism by ginkgolides which improve cerebral metabolism and protect the brain against hypoxic damage; metabolic changes as demonstrated by a reduction in EEG theta proportion among older adults; and prevention of cell membrane damage caused by free radicals due to the antioxidant properties of ginkgo flavonoids. Ginkgo biloba is thought to help poor concentration or memory, absent-mindedness, confusion, lack of energy, tiredness, decreased physical performance, depressive mood, anxiety, dizziness, tinnitus and headache. These deficits and symptoms are thought to be associated with impaired cerebral circulation and may be early indicators of dementia.82 Of a total of over forty published studies on the efficacy of Ginkgo biloba, only four84–87 have used standardised neuropsychological tests of cognitive performance. Most of the studies have used subjective reports of cognitive performance from participants, doctors or caregivers and most have employed clinical, elderly samples. Even so, findings from this research suggest that Ginkgo biloba may be important for optimal cognitive function in older adults. Only one study has found no effect of Ginkgo biloba on cognition.85 The findings from this study may be compelling because much effort was made to produce a placebo that could not be distinguished in terms of aftertaste from the Ginkgo biloba supplement. However another interpretation of these findings was that the supplement group contained a heterogeneous group of participants in terms of cognitive impairment which may have served to weaken any effects. Further research on the therapeutic effects and mechanisms by which Ginkgo biloba acts on cognitive performance is needed. Summary There is supporting evidence that dietary intake and nutrients can indeed affect cognitive performance of older adults. Well-conducted research is emerging and there is a growing interest in important methodological considerations.1 So far most of the conclusions are based on cross-sectional studies and there have been very few dietary intervention studies using randomised, placebo-controlled designs. Studies employing such designs are time-consuming and expensive but are essential if we are to discover the mechanisms by which food components impact on cognitive performance. Further, the effects of nutrition on cognition are likely to be subtle and complex, with small effect sizes. Therefore future research needs to be guided by clear hypotheses about the possible mechanisms by which specific nutrients might affect the brain and thus cognitive performance so that pertinent and sensitive cogni-

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tive outcome measures can be selected. The commonly used tests of cognitive impairment, such as the MMSE, may not be suitable for detecting the effects of nutrition among healthy, unimpaired older adults due to ceiling effects and consequent low variability in performance. Adequate sample sizes are also an important consideration due to the likely subtle effects of nutrition on cognitive performance. Research on the role of nutrition in cognitive ageing is in its infancy, but there is growing evidence that nutrition may be an important factor in altering the course and even preventing age-related cognitive decline. References 1. Bellisle F, Blundell JE, Dye L, Fantino M, Fern E, Fletcher RJ, Lambert J, Roberfroid M, Specter S, Westenhofer J, Westerterp-Plantenga MS. Functional food science and behaviour and psychological functions. Br J Nutr.1998; 80 (Suppl 1): S173–193. 2. Riedel WJ, Jorissen BL. Nutrients, age and cognitive function. Curr Opin Clin Nutr. 1998; 1:579–5865. 3. Selhub J, Bagley LC, Miller J, Rosenberg IH. B vitamins, homocysteine, and neurocognitive function in the elderly. Am J Clin Nutr. 2000; 71 (Suppl): 614S– 620S. 4. Bryan J. Cognitive function and its links with nutrition. Proc Nutr Soc Aust. 1998; 22:211–215. 5. Salthouse TA. Theoretical perspectives on cognitive aging. Hillsdale NJ: Erlbaum, 1991. 6. Calvaresi E, Bryan J. B vitamins, cognition and ageing: A review. J Gerontol Psychol Sci. 2001 7. Rosenberg IH, Miller JW. Nutritional factors in physical and cognitive functions of elderly people. Am J Clin Nutr. 1992; 55 (Suppl 6); 1237S–1243S. 8. Mazza G. Functional foods: Biochemical and processing aspects. Lancaster PA: Technomic, 1998. 9. Joosten E, van den Berg A, Riezler R, Naurath JJ, Lindenbaum J, Stabler SP, Allen RH. Metabolic evidence that deficiencies of vitamin B-12 (cobalamin), folate and vitamin B-6 occur commonly in elderly people. Am J Clin Nutr. 1993; 58: 468–476. 10. Ortega RM, Manas LR, Andres P, Gaspar MJ, Agudo RR, Jiminez A, Pascual T. Functional and psychic deterioration in elderly people may be aggravated by folate deficiency. J Nutr. 1996; 126:1192-1199. 11. Parnetti L, Bottiglieri T, Lowenthal D. Role of homocysteine in age-related vascular and non-vascular diseases. Aging Clin Exp Res. 1997; 9:241-257. 12. Sauberlich HE. Relationship of vitamin B-6, vitamin B-12, and folate to neurological and neuropsychiatric disorders. In: Bendich A, Butterworth CE editors. Micronutrients in health and in disease prevention. New York: Marcel Dekker, 1991; 187–218 13. Stabler SP, Lindenbaum J, Allen RH. Vitamin B-12 deficiency in the elderly: current dilemmas. Am J Clin Nutr. 1997; 66:741-749. 14. Lindeman RD, Romero LJ, Koehler KM, Liang HC, LaRue A, Baumgartner RN, Garry PJ. Serum vitamin B12, C and folate concentrations in the New Mexico Elder Health Survey: Correlations with cognitive and affective functions. J Am Coll Nutr. 2000; 19:68-76. 15. Hankey GJ, Eikelboom JW. Homocysteine and vascular disease. Lancet. 1999; 354:407–413.

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16. Bottiglieri T, Crellin RF, Reynolds EH. Folate and neuropsychiatry. In: Bailey, KB, editor. Folate in health and disease. New York: Marcel Dekker, 1995; 435–462 17. Alpert JE, Fava M. Nutrition and depression: The role of folate. Nutr Rev. 1997; 55:145–149. 18. Bottiglieri T. Folate, vitamin B12, and neuropsychiatric disorders. Nutr Rev. 1996; 54:382–390. 19. Fenech M, Aitken C, Rinaldi J. Folate, vitamin B12, homocysteine status and DNA damage in young Australian adults. Carcinogenesis. 1998; 19:1163–1171. 20. Pancharuniti N, Lewis CA, Sauberlick HE, Perkins LL, Go, RCP, Alvarez JO, Macaluso M, Acton RT, Copeland RB, Cousins AL, Gore TB, Cornwell PE, Roseman JM. Plasma homocysteine, folate, and vitamin B-12 concentrations and risk for early-onset coronary artery disease. Am J Clin Nutr. 1994; 59:940–948. 21. Selhub J, Jacques PF, Bostom AG, D’Agostino RN, Wilson PWF, Belanger AJ, O’Leary DH, Wolf PA, Shcaefer EJ, Rosenberg IH. Association between plasma homocysteine concentrations and extracranial carotid-artery stenosis. New Eng J Med. 1995; 331:286–291. 22. Ueland PM, Refsum H. Plasma homocysteine, a risk factor for vascular disease: Plasma levels in health, disease, and drug therapy. J Lab Clin Med. 1989; 114: 473–499. 23. Clark R, Smith AD, Jobst KA, Refsum H, Sutton L, Ueland PM. Folate, vitamin B12 and serum total homocysteine levels in confirmed Alzheimer disease. Arch Neurol. 1998; 55:1449–1455. 24. Homocysteine Lowering Triallists’ Collaboration. Lowering blood homocysteine with folic acid based supplements: Meta-analysis of randomised trials. Brit Med J. 1998; 346:894–898. 25. Snowdon DA, Tully CL, Smith CD, Riley KP, Markesbery WR. Serum folate and the severity of atrophy of the neocortex in Alzheimer disease: Findings from the Nun Study. Am J Clin Nutr. 2000; 71:993–998. 26. Goodwin JS, Goodwin JM, Garry PJ. Association between nutritional status and cognitive functioning in a healthy elderly population. J Am Med Assoc. 1983; 249:2917-2921. 27. Ortega RM, Requejo AM, Andres P, Lopez-Sobaler AM, Quintas ME, Redondo MR, Navaia B, Rivas T. Dietary intake and cognitive function in a group of elderly people. Am J Clin Nutr. 1997; 66:803–809. 28. Bell IR, Edman JS, Marby DW, Satlin A, Dreier T, Liptzin B, Cole JO. Vitamin B12 and folate status in acute geropsychiatric inpatients: Affective and cognitive characteristics of a vitamin nondeficient population. Biol Psychiat. 1990; 27: 125–137. 29. Wahlin A, Hill RD, Winblad B, Bäckman L. Effects of serum vitamin B12 and folate status on episodic memory performance in very old age: A population based study. Psychol Aging. 1996; 11:487–496. 30. Hassing L, Wahlin A, Winblad D, Bäckman L. Further evidence on the effects of vitamin B12 and folate levels on episodic memory functioning: A populationbased study on healthy very old adults. Biol Psychiat. 1999; 45:1472–1480. 31. Riggs KM, Spiro A, Tucker K, Rush D. Relations of vitamin B-12, folate, and homocysteine to cognitive performance in the Normative Aging Study. Am J Clin Nutr. 1996; 63:306–314. 32. La Rue A, Koehler KM, Wayne SJ, Chiulli SJ, Haaland KY, Garry PJ. Nutritional status and cognitive functioning in a normally aging sample: A 6-year reassessment. Am J Clin Nutr. 1997; 65:20–29. 33. Ebly EM, Schaefer JP, Campbell NRC, Hogan DB. Folate status, vascular disease and cognition in elderly Canadians. Age Ageing. 1998; 27:485–491.

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34. Tolonen M, Schrijver J, Westermarck T, Halme M, Touminen SEJ, Frilander A, Keinonen M, Sarna S. Vitamin B-6 status of Finish elderly. Comparison with Dutch younger adults and elderly. The effect of supplementation. Int J Vitam Nutr Res. 1988; 58:73–77. 35. Deijen JB, van der Beek EJ, Orlebeke JF, van den Berg H. Vitamin B-6 supplementation in elderly men: effects on mood, memory, performance and mental effort. Psychopharmacology. 1992; 109:489–496. 36. Fioravanti M, Ferrario E, Massaia M, Cappa G, Rivolta G, Grossi E, Buckley AE. Low folate levels in the cognitive decline of elderly patients and the efficacy of folate as a treatment for improving memory deficits. Arch Gerontol Geriat. 1997; 26:1–13. 37. Bryan J, Calvaresi E, Hughes D. The effects of short-term folate, B-12 and B-6 supplementation and dietary intake on cognition and mood in women. J Nutr. (under revision) 38. Rundle B. The effect of vitamin B-12 and folate on cognitive performance and psychological well-being. Unpublished Honours thesis. University of Adelaide, 2000. 39. Nourhashémi F, Gillet-Guyonnet S, Andrieu S, Ghisolfi A, Ousset PJ, Grandjean H, Grand A, Pous J, Vellas B, Albarede JL. Alzheimer’s disease: protective factors. Am J Clin Nutr. 2000; 71 (Suppl):643S–649S. 40. Piccini C, Bracco L, Amaducci L. Treatable and reversible dementias: and update. J Neurol Sci. 1998; 153:172–181. 41. Knopman DS, De Kosky ST, Cummings JL, Chui H, Corey-Bloom J, Relkin N, Small GW, Miller B, Stevens JC. Practice parameter: Diagnosis of dementia (an evidence-based review). Report of the Quality Standards Subcommittee of the American Academy of Neurology. Neurology. 2001; 56:1143–1153. 42. Pary R, Tobias CR, Lippmann S. Dementia: What to do. Southern Med J. 1990; 83:1182–1189. 43. Karnaze DS, Carmel R. Low serum cobalamin levels in primary degenerative dementia. Arch Int Med. 1987; 147:429–431. 44. Ikeda T, Furukawa Y, Mashimoto S, Takahashi K, Yamada M. Vitamin B 12 levels in serum and cerebrospinal fluid of people with Alzheimer’s disease. Acta Psychiat Scand. 1990; 82:327–329. 45. Crystal HA, Ortof E, Frishman WH. Serum vitamin B 12 levels and incidence of dementia in a health elderly population: a report from the Bronx longitudinal aging study. J Am Geriatric Soc. 1994; 42:933–936. 46. Sneath P, Chanarin I, Hodkinson HM, McPherson CK, Reynolds EH. Folate status in a geriatric population and its relation to dementia. Age Ageing. 1973; 2:177–182. 47. Sommer BR, Wolkowitz OM. RBC folic acid levels and cognitive performance in elderly patients: a preliminary report. Biol Psychiat. 1988; 24:352–354. 48. Nilsson K, Gustafson L, Faldt R, Andersson A, Hultberg B. Plasma homocysteine in relation to serum cobalamin and blood folate in a psychogeriatric population. Eur J Clin Invest. 1994; 24:600–606. 49. McCaddon A, Davies G, Hudson P, Tandy S, Cattell H. Total serum homocysteine in senile dementia of Alzheimer type. Int J Geriatr Psych. 1998; 13:235–239. 50. Levitt AJ, Karlinsky H. Folate, vitamin B 12 and cognitive impairment in patients with Alzheimer’s disease. Acta Psychiat Scand. 1992; 86:301–305. 51. Teunisse AEB, von Gool WA, Walstra GJM. Dementia and subnormal levels of vitamin B 12: effects of replacement therapy on dementia. J Neurol. 1996; 243: 522–529. 52. Carmel R, Gott PS, Waters CH. The frequently low cobalamin levels in dementia usually signify treatable metabolic, neurologic and electrophysiologic abnormalities. Eur J Haematol. 1995; 54:245–253.

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53. Martin DC, Francis J, Protetch J, Huff FJ. Time dependency of cognitive recovery with cobalamin replacement: Report of a pilot study. J Am Geront Soc. 1992; 40: 168–172. 54. Fontanari D, Di Plama C, Giorgetti F, Violante F, Voltolina M. Effects of SAdenosyl-l-methionine on cognitive and vigilance functions in the elderly. Current Therapeutic Res. 1994; 55:682–689. 55. Carney JM, Starke-Reed PE, Oliver CN, Landum RW, Cheng MS, Wu JF, Floyd RA. Reversal of age-related increase in brain protein oxidation, decrease in enzyme activity, and loss in temporal and spatial memory by chronic administration of the spin-trapping compound N-ert-butyl-α-phenylnitrone. Proc Natl Acad Sci USA. 1991; 88:3633–3636. 56. O’Donnell E, Lynch MA. Dietary antioxidant supplementation reverses agerelated neuronal changes. Neurobiol Aging. 1998; 19:461-467. 57. Cassarino DS, Bennett JP. An evaluation of the role of mitochondria in neurodegenerative diseases: Mitochondrial mutations and oxidative pathology, protective nuclear responses, and cell death neurodegeneration. Brain Res Rev. 1999; 29: 1–25. 58. Kalmijn S, Feskens EJM, Launer LJ, Kromhout D. Polyunsaturated fatty acids, antioxidants, and cognitive function in very old men. Am J Epidemiol. 1997; 145: 33–41. 59. Sack MN, Rader DJ, Cannon RO. Oestrogen and inhibition of oxidation of lowdensity lipoproteins in postmenopausal women. Lancet. 1994; 343:269-270. 59. Witztum JL. The oxidation hypothesis of atherosclerosis. Lancet. 1994; 244: 793–795. 60. Rosenberg RN. The aging brain: Limitations in our knowledge and future approaches. Arch Neurol. 1997; 54:1201–1205. 61. Rogers J, Bloom FE. Neurotransmitter metabolism and function in the aging nervous system. In Finch CE, Schneider EL. editors. Handbook of the biology of ageing. New York: Van Nostrand Reinhold, 1995; 645–691. 62. Jama JW, Launer LJ, Witteman JCM, Breeijen JH, Grobbee DE, Hofman A. Dietary antioxidants and cognitive function in a population-based sample of older persons. Am J Epidemiol. 1996; 144:275–280. 63. Mendelsohn AB, Belle SH, Stoeher GP, Ganguli M. Use of antioxidant supplements and its association with cognitive function in a rural elderly cohort. Am J Epidemiol. 1998; 148:38–44. 64. Perrig WJ, Perrig P, Stahelin HB. The relation between antioxidants and memory performance in the old and very old. J Am Geriatr Soc. 1997; 45:718–724. 65. Berr C, Richard MJ, Roussel AM, Bonithon-Kopp C. Systemic oxidative stress and cognitive performance in the population-based EVA study. Free Radical Biol Med. 1998; 24:1202–1208. 66. Perkins AJ, Hendrie HC, Callahan CM, Gao S, Unverzagt FW, Xu Y, Hall KS, Hui SL. Association of antioxidants with memory in a multiethnic elderly sample using the Third National Health and Nutrition Examination Survey. Am J Epidemiol. 1999; 150:37–44. 67. Paleogos M, Cumming RG, Lazarus R. Cohort study of vitamin C intake and cognitive impairment. Am J Epidemiol. 1998; 148:45–50. 68. Grundman M. Vitamin E and Alzheimer’s disease: the basis for additional clinical trials. Am J Clin Nutr. 2000; 71(Suppl): 630S–636S. 69. Lethem R, Orrell M. Antioxidants and dementia. Lancet. 1997; 349:1189– 1190. 70. Zaman Z, Roche S, Fielden P, Frost PG, Niriella DC, Cayley AC. Plasma concentrations of vitamins A and E and carotenoids in Alzheimer’s disease. Age Aging. 1992; 21:91–94.

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Chapter 13 THE BRAIN RESERVE HYPOTHESIS Peter W Schofield*

Introduction Anatomical and functional changes are fundamental to the process of ageing. In the brain, as elsewhere in the body, changes may reflect ageing alone or the consequences of age-related diseases.1 The relationship between brain structure and function and the factors that may influence that relationship are the principal concerns of this chapter. “Brain reserve” evolved as a model that helped to explain some otherwise puzzling disjunctions between brain pathology and brain function. My goal in this chapter is to critically review the concept of brain reserve, particularly with respect to cognition and dementia in the elderly. Background and Definitions In 1937, Rothschild2 drew attention to the “the inconsistency between clinical and neuropathological phenomena in the field of senile conditions.” Tomlinson et al. extended this observation using careful quantitative techniques, correlating the cognitive performance of elderly individuals just prior to their death to indices of brain pathology found at autopsy.3,4 Using this approach, they found that modest brain changes, e.g. senile plaque counts below 15 per low power field, or brain softening due to stroke of up to 50 ml, were associated with normal premortem cognition. Only when senile plaque count, or stroke volume, exceeded a certain threshold was cognitive impairment a consistent premortem finding. *To whom correspondence should be addressed.

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The notion that the brain exhibits a degree of redundancy, which permits limited damage to be tolerated without apparent functional consequence, came to be expressed in the term reserve.5,6 Reserve has been likened to a buffer that is progressively eroded by accumulating pathology. Brain reserve theory has most often been invoked in the setting of progressive dementing diseases, particularly Alzheimer’s disease (AD), although it does have relevance for other conditions such as Parkinson’s disease, stroke, and HIV infection. Some ambiguity exists with regard to the term “reserve”, as it is used in relation to cognition and brain diseases. For example, some authors invite a distinction between “brain reserve” and “cognitive reserve”.7 Brain reserve, it is suggested, represents a model in which some structural characteristic of the brain affords protection against the functional consequences of damage or disease. By contrast, “cognitive reserve” is represented as a more dynamic model of compensation for brain damage for which no structural correlate is identified; the distinction between the two models is likened to that between hardware and software. While the distinction may be in part one of scale — even software has its (micro) structural correlates — the predominant focus of this chapter will be “brain reserve” as characterised above. The concept of “threshold” is closely related to that of brain reserve. For example, Satz, in his comprehensive review of brain reserve theory,6 introduces the term “functional impairment cut-off” to correspond to a threshold level of preserved brain necessary for intact function. Of course, the distinction between intact function and dysfunction may be difficult to make and many cases may fall into a “grey” zone. There may also be conceptual ambiguities. Thus, according to the hierarchical framework suggested by the World Health Organization,8 dysfunction may be characterised as “impairment”, “disability” and/or “handicap”. According to this model, poor or declining cognition would represent an impairment, with or without disability. Disability is defined as a restricted ability to perform a daily activity normally. When disadvantaged by impairment or disability, an individual is regarded as handicapped. The form and meaning of “brain reserve” may differ in important ways, depending upon what outcome is chosen as the measure of dysfunction. One key distinction is that between abnormality and decline, each acceptable as an index of impairment. Thus, cognitive impairment might be diagnosed on the basis of a performance more than some specified amount (e.g. two standard deviations) below accepted norms on cognitive tests. Such an approach is perhaps more commonly adopted in large epidemiological studies. In other circumstances — particularly clinic-based studies — cognitive decline, measured or inferred, forms the basis for identifying cognitive dysfunction (impairment). Implicit in this latter approach is a greater attention to premorbid cognition. Dementia represents a form of cognitive impairment with associated disability that has the same two contrasting approaches to diagnosis, reflecting a relative emphasis either on the level of cognitive abnormality

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Figure 1. Dementia diagnosis: impairment or decline?

Figure 2. Brain reserve: two models.

or on the degree of cognitive decline.9,10 Figure 1 depicts these two differing approaches to dementia diagnosis. In parallel with these two contrasting ways of defining cognitive dysfunction are two distinct concepts of “reserve”. I have characterized them as the “further to fall” and the “resistance to change” versions,11 and a graphic depiction of the two differing concepts is represented in Figure 2. As reflected in Figure 2 panel A, individuals endowed with modest cognitive abilities, just in excess of criteria scores for cognitive abnormality, are clearly at greater risk of dipping below the threshold for “cognitive impairment” following a

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small decline in their performance than are individuals whose baseline performance is superior. In terms of risk of becoming “cognitively impaired” due to some specific brain lesion, the individual of low pre-morbid cognitive performance would appear to be considerably disadvantaged: that they have low “brain reserve” would seem obvious. By contrast, if cognitive decline is used as the index of cognitive dysfunction Figure 2, panel B, it becomes much less obvious whether, for example, premorbid cognitive ability would offer an advantage as far as the impact of a specific brain lesion is concerned. These particular definitional issues can be set aside in relation to what might be termed a “weak” version of brain reserve theory, which, I suggest, is non-contentious, but nevertheless worthwhile. The weak version simply contends that brain reserve is lowered by brain damage, while the strong version states that brain reserve may also differ systematically among individuals for reasons other than previously acquired brain damage. Some of the evidence that has been advanced in support of the strong version — particularly that which comes from epidemiological studies — can be reinterpreted using the weak version, so it will be important to consider the evidence for that first. Brain Reserve: The Weak Version While the assertion that brain damage lowers brain reserve may seem self-evident and trivial, there are several corollaries that are of considerable potential significance. First, neurological lesions may be asymptomatic and apparently without clinical consequence. For example, in studies of patients with atrial fibrillation or those presenting with a recent ischaemic episode, radiological evidence of a previous clinically silent stroke has been found in at least 10%, compatible with the notion that limited amounts of damage can be tolerated without impairment.12,13 Autopsy studies have furnished estimates of the likelihood that the microscopic changes of AD are present at any age. For virtually all ages, the proportion of autopsied brains with some senile plaques or neurofibrillary tangles far exceeds the proportion of age-matched individuals alive with clinical disease,14,15 and, clearly, dementia due to AD supervenes after a protracted “preclinical phase”.16 Second, pre-existing neurological damage, even when asymptomatic, may exaggerate the clinical impact of a subsequent brain insult. For example, in a study of head injury, twenty young adults who had previously sustained a concussion recovered cognitive function more slowly after a second head injury than did control subjects following their first head injury.17 In studies of stroke patients, the risk for cognitive impairment and/or the degree of cognitive impairment increases according to infarct number and infarct size.18 Among individuals with pre-existing cognitive impairment, there is an increased risk of delirium following relatively minor stress.19

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Third, different conditions may be additive or synergistic in their affect on brain function. For example, in one recent study, the clinical impact of AD pathology was significantly accentuated by coexistent vascular disease. In subjects with subcortical infarcts, fewer neuropathologic lesions of AD resulted in dementia than in those without infarction.20 A high prevalence of dementia has been observed in people with learning disabilities even when not attributable to Down’s syndrome21. Fourth, ageing itself is associated with loss of brain reserve, even in the absence of specific age-related diseases, as reflected in the greater functional impact that well-characterised neurological insults have on the aged compared with younger individuals.22,23 As an aside, it is well to remember that small, critically located lesions may produce major neurobehavioural syndromes and that not all regions of the brain contribute equally to the integrity of cognitive function.24 Brain Reserve: The Strong Version In several early studies, some individuals with apparently intact cognition prior to their death had abundant brain changes at autopsy.3, 25 Such observations led to a search for individual characteristics or attributes associated with a heightened capacity to tolerate brain damage. A number of factors have been identified, consistent with a strong version of brain reserve theory, which contends that there are non-pathological correlates of reserve. In general, the evidence bearing on this question has come from three types of studies. It is worth emphasising that, independent of the major methodological differences outlined below, the studies differ also in the strategies employed to detect impairment — in particular either “abnormality” or “decline” — as discussed earlier. Clinicpathological studies Here, investigators have examined the relationship between the extent of brain pathology at autopsy and premortem cognition, seeking systematic differences that might be accounted for by certain characteristics of the patients. One caveat for the interpretation of such studies relates to the uncertain neuropathological basis for cognitive changes in dementing diseases. Thus, in the case of AD, the visible pathology of plaques and tangles have been used as an index of the extent of pathology, while there is evidence to suggest that synaptic loss may be the proximate cause of cognitive decline.26 Imaging studies Brain atrophy or functional brain imaging abnormalities in life can be used to provide a proxy for pathology and related to cognition, or some other measure of brain function. Once again, investigators seek systematic variation in

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the relationship between “pathology” and cognition/brain function according to some defining characteristic of the subjects. Epidemiological studies These offer a means of identifying risk or protective factors for cognitive impairment, cognitive decline, or dementia. Because the common causes of dementia — AD, stroke — are strongly associated with age,27 it is plausible to suggest that protective factors may have their effect by delaying the clinical onset of disease by virtue of an association with increased brain reserve. It is possible, of course that “protective” factors may not operate via a mechanism of “reserve”, but by some other means, as will be discussed below. From these various investigative approaches, a number of factors have been suggested as possible correlates of brain reserve. Low educational attainment has emerged in numerous epidemiological studies as a risk factor for AD28–40 and in some for the development of stroke-related dementia.41–44 The same studies can be interpreted to mean that higher educational attainment is protective. Other studies employing different methods have added weight to these findings, but not all the relevant studies have been in agreement. Other factors that have been advanced as correlates of increased brain reserve include higher “intelligence”, 45–47 increased brain size,25,48–51 certain occupational types33,41,52 and increased mental activity.53–57 I propose to review some of this evidence below. Education and Occupation Low educational attainment has been examined as a potential risk factor for the development of dementia in the context of several common conditions affecting the elderly including stroke, Parkinson’s disease, and AD. In one case control study of stroke, subjects with less than eight years of formal schooling were more than 40 times more likely to have dementia than other stroke subjects with more education, after adjusting for other risk factors, including hypertension, recent smoking, obesity, and proteinuria.42 Other studies have obtained more modest estimates of the effect size.43,44 Education has generally not been found to be a risk factor for dementia associated with Parkinson’s disease although borderline associations have been reported. In one prospective cohort study of community-dwelling patients with Parkinson’s disease, subjects who developed dementia during a 3.5 year follow-up period had an average of two years less formal education than subjects who remained non-demented.58 Numerous studies have reported an association between low educational attainment and frequency of AD.28–41 In an early, important study, the prevalence of dementia (most due to AD) was estimated among individuals over 65 years in Shanghai, China.29 In the initial screening phase of this study, a cognitive test with education-dependent cutoffs was used and, during the

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subsequent diagnostic phase, neuropsychological tests were used that were relatively invariant to education. Clinical evaluations were performed and a history of functional decline was required for the diagnosis of dementia. Age, female gender, and low education were all highly significantly, independently associated with increased prevalence of AD. The association of AD with education was particularly striking, in view of the investigators’ efforts to minimize or eliminate bias with respect to education in the diagnostic process. A study in New York City found that educational attainment and occupational status were both associated with differential risk for incident AD. Subjects with low education and low occupational status were at greatest risk for dementia at follow-up.33 Similar findings were reported from a study conducted in East Boston.36 More recent epidemiological studies have produced new twists. In both the Rotterdam study,38 and in the EURODEM pooled analysis,59 the association between low education and AD was present in women only. Among men, there was no association between low educational attainment and risk of AD. The authors concluded that unmeasured confounding might be the explanation for previous findings. In another study, low educational attainment was associated with increased risk of dementia among individuals with a childhood history of rural but not urban residence.35 And in an Italian study, having no education was associated with increased risk for dementia, but individuals with little education (less than three years) were no more likely than better- educated individuals to be demented.37 In support of the hypothesis that individuals with higher educational attainment become demented later in the course of the underlying disease, several groups have noted that their subsequent mortality may be increased,60,61 or their decline on neuropsychological tests more rapid,62 relative to comparably demented individuals with less education. Several studies have found no association between education and AD. In a case-control study that used record-linkage data from Rochester, no difference was found between the educational level of cases with AD and controls, most of whom had more than nine years of education.63 In a study of incident dementia and AD from Framingham, low educational attainment was not a risk factor for AD, although it was a risk factor for non-AD dementia.64 This study sample also included relatively few individuals with low educational attainment: more than 60% reported at least a high school education. A British study also found no differences in incidence of dementia in relation to educational attainment, after adjustment for age.65 Lower educational attainment has been identified as a risk factor for cognitive decline with ageing,66,67 as well as cognitive impairment (as distinct from a measured decline) without dementia in a number of report.68,69 In several studies, SPECT scanning has been used to provide an index of the extent of pathology in patients with AD. Stern and colleagues showed that, for a given level of cognitive performance on testing, individuals with

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greater educational attainment had greater flow deficits on SPECT scans.70 This result suggests that, in better-educated individuals with AD, cognition is preserved longer into the illness than in those with less education. A later study employing similar methodology provided evidence to suggest that more intellectually challenging occupations confer similar benefits for individuals with AD.71 There are few clinicopathological studies relevant to the possible role of education in early life as a determinant of brain reserve. However, in one fascinating study, the brains of 20 subjects who had been free of cognitive disorders prior to death were examined at autopsy for measures of dendritic complexity, and these measures were correlated with sociodemographic characteristics.72 Educational attainment in life was strongly, positively correlated with dendritic complexity. This finding is consistent with animal data that suggests that both neuronal development and connectivity are promoted by a stimulating environment during early life.73 Of course, it was not possible for the investigators to determine whether educational attainment caused or was a consequence of the favourable brain anatomy, but it does at least suggest a possible structural correlate of reserve. A recent autopsy study of patients attending a memory disorders clinic has been advanced as evidence against the brain reserve theory74 at least in the form I refer to as the “strong version”. In this study, 87 patients who had been followed in a memory disorders clinic and who had come to autopsy were compared with respect to sociodemographic, clinical and autopsy data. Less-educated patients with dementia were on average older at presentation and had more cerebrovascular pathology than better-educated demented patients. The authors of this study proposed that the apparent association between low education and higher frequency of AD suggested by numerous epidemiological studies might be due to a relative excess of cerebrovascular disease among less educated individuals. Other investigators have demonstrated an excess of cardiovascular risk factors75 and risk for stroke76 among individuals with less educational attainment, and asymptomatic stroke may be higher in this group as well. It is certainly plausible that lowered brain reserve, secondary to subclinical stroke-related brain damage, might account for some of the epidemiological findings outlined above. Future epidemiological studies in which participants undergo routine brain imaging will allow this possibility to be carefully evaluated. To summarize, there is evidence from many but not all studies indicating that increased educational attainment is associated with lower frequencies of dementia, cognitive abnormality without dementia, and cognitive decline. There is some support for a threshold effect, such that protection is achieved with relatively little education. A proposed mechanism for these findings is that education in some way leads to increased brain reserve, delaying the clinical onset of disease — and of AD in particular. Katzman77 has suggested that education may act by enhancing the development synapses, citing the evidence from animal

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studies indicating benefits for early brain development of a “stimulating environment”, referred to above. Because educational attainment also correlates strongly with intellectual ability as measured formally by IQ tests,78 it is possible that greater innate intellectual capacity underlies the findings reviewed above. Proponents of “cognitive reserve” theory suggest that those individuals with higher educational attainment or occupational status may have more cognitive flexibility and a wider repertoire of cognitive responses which enables them to better tolerate brain damage or disease.7 Alternative interpretations of the data essentially invoke what I have referred to above as the “weak form” of brain reserve theory. For instance, it is possible that limited educational opportunity is a proxy for early exposures to potentially harmful agents that either impair brain development or more directly lead to cognitive decline later in life.79 The possibility that clinically silent stroke may underlie some of the increase in dementia among the poorer-educated has been touched on already. Intelligence The role of premorbid intellect as a determinant of outcome following brain injury or disease has been examined by a number of investigators. In one study, Vietnam veterans who had sustained penetrating brain injuries were assessed on a range of cognitive measures.80 An estimate of pre-injury intelligence was available for all individuals from their performance on cognitive testing at induction. The volumes of brain tissue loss and lesion location were significant predictors of post injury cognition but, overall, pre-injury intelligence and education were the strongest predictors. In another study, scores on cognitive tests administered to young individuals in the 1940s were compared with the results of cognitive testing conducted via telephone some 50 years later.81 A strong correlation existed between the two test performances, indicating that “premorbid” ability in youth is a strong predictor of cognitive performance at later ages, when the brain is subject to age-related change or dementing diseases. In a large population-based study of elderly individuals, Schmand et al. used the Dutch Adult Reading Test (DART) as an index of premorbid intelligence at base-line assessment and followed initially non-demented subjects for four years for the development of dementia.45 In this study, a low performance on the DART at baseline predicted incident dementia at follow-up better than did a low level of education. Several studies have cleverly used pre-existing data to provide estimates of premorbid intelligence. In a well-known study of elderly nuns, an index of premorbid cognitive ability was obtained by reviewing diary entries that the nuns (then novices) had made when in their late teens or early twenties.46 The entries were “scored” for linguistic competence and these measures were related to the risk for subsequent dementia and to the extent of pathological changes in the brains of those nuns who came to autopsy. Poorer linguistic

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aptitude was associated with increased risk of dementia, a finding consistent with reserve theory. An unexpected finding of this study was a positive association between poorer linguistic aptitude in youth and the presence of AD-like brain pathology at autopsy: nuns whose diaries indicated poorer linguistic performance in youth had on average more brain changes of AD, whether or not they had clinical dementia. This latter finding is not one that would be predicted by reserve theory and led the authors to suggest the intriguing possibility that AD might have its earliest manifestations at a much earlier age than previously thought. According to this hypothesis, the relatively poor linguistic performance of nuns destined to develop AD may have represented its earliest cognitive effects. In a recent study from Scotland, cognitive ability in early life was also found to be associated with risk of subsequent dementia.47 Demented and non-demented individuals, who were all born in 1921, were compared for their performance on routine cognitive testing conducted in 1932 as part of the “Scottish Mental Survey”. Compared with controls, demented individuals had performed more poorly on the cognitive testing in 1932. The effect was restricted to cases who had become demented after the age of 64: early onset dementia cases did not differ from controls with respect to their early life cognition. The results from these studies are open to a number of possible interpretations including those deriving from “reserve theory”. Brighter individuals may manifest enhanced cognitive flexibility with benefits along lines already suggested in relation to the effects of education and occupation. Alternatively, premorbid intellect might be associated with greater levels of mental activity as a more fundamental mechanism of protection, to be addressed in greater detail below. Intellectually brighter individuals may adopt more healthy lifestyles, protecting themselves from negative health outcomes including cerebrovascular disease. Finally, the possibility has been raised that some of the variability in intellectual capacity in early and middle life reflects the consequences of pathological processes, AD particularly, for which there is little currently identifiable neuropathological correlate. According to this speculation, “premorbid” intellect might represent a consequence of, rather than an antecedent for, AD.82 Brain Size In a clinicopathological study of 137 nursing home residents, 10 individuals with the neuropathological changes of AD at autopsy had never been demented.25 These individuals had bigger brains than did the remaining subjects (either with or without dementia). The authors of this study suggested that the larger brains might have afforded increased reserve, delaying the onset of clinical disease. In a study of 28 women attending a memory disorders clinic, each with a diagnosis of probable AD, a cross-sectional intra-

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cranial area measurement was obtained from CT brain scans as an index of premorbid brain size.48 This measure correlated significantly with the age at onset of symptoms, estimated from informant reports. Individuals with larger intracranial areas had a later onset of symptoms, consistent with the hypothesis that brain size mediates reserve, delaying onset of clinical features. A study by Mori and colleagues,50 while differing in important methodological respects, offered partial support for these findings. A more recent study found no evidence for an association between intracranial volume (a reliable index of premorbid brain volume) and age at onset of AD, but this study included many with early onset disease in which mechanisms of brain reserve may be relatively unimportant.83 In two published studies, head circumference has been used as a proxy for premorbid brain size, and evaluated as a risk factor for AD. In a large study of Japanese Americans, 83 individuals were identified with AD, of whom those with smaller head circumference were more severely affected, after adjusting for age and education.49 In a multiethnic, population-based study in New York City, individuals in the lowest quintile of head circumference for gender were at increased risk of having AD, compared with those in the upper four quintiles, even after adjusting for education, age, and ethnicity.51 Height, weight and APOE genotype were not confounders in this association. Again, these results are consistent with the notion that brain size mediates reserve, allowing function to be preserved longer in those with underlying AD. Interestingly, brain size has been shown to correlate with IQ in methodologically rigorous studies84,85 and superior premorbid intellect may therefore underlie the apparent protective effect of larger brains, according to mechanisms already discussed. Conversely, it is possible that the protective effects of higher IQ for AD might be mediated by larger brain size.84,85 The microstructural correlates of bigger brains that might account for greater reserve are uncertain. Increased levels of premorbid synaptic density, neuronal count or neuronal connectivity might optimise the potential for functional adaptation to neurodegenerative processes. Alternative and highly speculative suggestions are that increases in the glial:neuron ratio or synaptic density may have neuroprotective consequence.86,87 Mental and other Activity In a cross-sectional study of nearly 300 elderly volunteers who were questioned about intellectual activities from which a “total intellectual activity” score was obtained, this emerged as the second most powerful positive predictor (after education) of objective memory performance. 88 In several case control studies, a history of inactivity has been associated with increased risk of AD. Kondo et al. reported increased risk of AD associated with a reduction in a variety of “psychosocial behaviours” and “uses of leisure time” in the fifth and sixth decades of life. 89 In a study by Broe et

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al., individuals with AD were significantly more likely than controls to be described as “physically underactive” in the ten years previously, as well as more than ten years ago.90 In a prospective study, a history of doing odd jobs, knitting, or gardening was associated with significantly reduced risk of subsequent dementia in the one to three years of follow-up. 91 In another longitudinal study, subjects who “participated in novel information processing activities such as learning a language or playing bridge” were less likely to show cognitive decline during the follow-up period. 92 More recently, evidence that physical activity may reduce risk for cognitive impairment or dementia has also been advanced.93 Friedland has suggested that chronic neuronal activation might enhance neuronal integrity via favorable secondary changes in cerebral blood flow, calcium homeostasis, amyloid precursor protein turnover, levels of stress, and DNA repair.94,95 More recently, Gould and others96,97 have demonstrated in experimental animals that, contrary to longstanding dogma, new neurons may develop in adult brains. Other investigators have shown that the survival of these neurons may be enhanced by exposure to an enriched environment.98,99 These revolutionary findings suggest additional means by which intellectual activity in humans might lead to enhanced reserve and delay in the clinical onset of dementing diseases. Summary and Conclusions I have stressed that “the brain reserve hypothesis” can be viewed in somewhat different ways, depending upon varying notions of dysfunction. Independent of those distinctions, a weak form of brain reserve theory acknowledges the impact of brain damage, even when this is subclinical, and a strong form of brain reserve theory proposes that individual characteristics also influence the functional outcome of brain damage or disease. Evidence in support of both the weak and strong versions has been reviewed. What are the implications of the brain reserve hypothesis, as I have presented it? One key area of concern relates to the interaction between vascular pathology and AD, with the potential for incipient AD to be “unmasked” by relatively minor vascular events. The potential benefits of scrupulous attention to cerebrovascular risk factors are obvious. Individuals who display evidence of “lowered reserve” perhaps by developing confusion in the context of relatively minor stresses warrant careful evaluation for possible incipient AD, and in the future might be candidates for empiric treatment with disease modifying agents when these become available. At a global perspective, improved infant welfare to optimise brain growth100 and early development may have benefits in the reduction of cognitive disorders later in life. Education may promote brain development, but its benefits in later life might also reflect the fostering of habits of increased mental activity. Hopefully, education also promotes the development of critical faculties leading to avoidance

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54. Broe GA, Henderson AS, Creasey H, McCusker E, Korten AE, Jorm AF, Longley W, Anthony JC. A case-control study of Alzheimer’s disease in Australia. Neurology. 1990; 40:1698–1707. 55. Kondo K, Niino M, Shido K. A case-control study of Alzheimer’s disease in Japan – significance of life-styles. Dementia. 1994; 5:314–326. 56. Fabrigoule C, Letenneur L, Dartigues JF, Zarrouk M, Commenges D, BarbergerGateau P. Social and leisure activities and risk of dementia: a prospective longitudinal study. J Am Geriatr Soc. 1995; 43:485–490. 57. Gold DP, Andres D, Etezadi J, Arbuckle T, Schwartzman A, Chaikelson J. Structural equation model of intellectual change and continuity and predictors of intelligence in older men. Psychol Aging. 1995 ;10:294–303. 58. Marder K, Tang M-X, Cote L, Stern Y, Mayeux R. The frequency and associated risk factors for dementia in patients with Parkinson’s disease. Arch Neurol. 1995; 52:695–701. 59. Letenneur L, Launer LJ, Andersen K, Dewey ME, Ott A, Copeland JR, Dartigues JF, Kragh-Sorensen P, Baldereschi M, Brayne C, Lobo A, Martinez-Lage JM, Stijnen T, Hofman A. Education and the risk for Alzheimer’s disease: sex makes a difference. Am J Epidemiol. 2000; 151:1064–1071. 60. Stern Y, Tang M-X, Denaro J, Mayeux R. Increased risk of mortality in Alzheimer’s disease with more advanced educational and occupational attainment. Ann Neurol. 1995; 37:590–595. 61. Geerlings MI, Deeg DJH, Pennix BWJH, Schmand B, Jonter C, Bouter LM, van Tilburg W. Cognitive reserve and mortality in dementia: the role of cognition, functional ability and depression. Psychol Med. 1999; 29:1219–1226. 62. Stern Y, Albert S, Tang M-X, Tsai W-Y. Rate of memory decline in AD is related to education and occupation: cognitive reserve? Neurology. 1999; 53:1942–1947. 63. Beard CM, Kokmen E, Offord KP, Kurkland LT. Lack of association between Alzheimer’s disease and education, ocupation, marital status or living arrangement. Neurology. 1992; 42:2063–2069. 64. Cobb JL, Wolf PA, Au R, White R, D’Agostino RB. The effect of education on the incidence of dementia and Alzheimer’s disease in the Framingham Study. Neurology. 1995; 45:1707–1712. 65. Paykel ES, Brayne C, Huppert FA, Gill C, Barkley C, Gehlhaar E, Beardsall L, Girling DM, Pollitt P, O’Connor D. Incidence of dementia in a population older than 75 years in the United Kingdom. Arch Gen Psychiat. 1994; 51:325–332. 66. Farmer ME, Kittner SJ, Rae DS, Bartko JJ, Regier DA. Education and change in cognitive function:The epidemiologic catchment area study. Ann Epidemiol. 1995; 5:1–7. 67. Evans DA, Beckett LA, Albert MS, Herbert LE, Scherr PA, Funkenstein HH, Taylor JO. Level of education and change in cognitive function in a community population of older persons. Ann Epidemiol. 1993; 3:71–77. 68. Ebly EM, Hogan DB, Parhad IM. Cognitive impairment in the nondemented elderly; results from the Canadian Study of Health and Aging. Arch Neurol. 1995; 52:612–619. 69. Di Carlo A, Baldereschi M, Amaducci L, Maggi S, Grigoletto F, Scarlato G, Inzitari D. Cognitive impairment without dementia in older people: prevalence, vascular risk factors, impact on disability. The Italian Longitudinal Study on Aging. J Am Geriatr Soc. 2000; 48:775–782. 70. Stern Y, Alexander GE, Prohovnik I, Mayeux R. Inverse relationship between education and parietotemporal perfusion deficit in Alzheimer’s disease. Ann Neurol. 1992; 32:371–375. 71. Stern Y, Alexander GE, Stricks L, Link B, Mayeux R. Relationship between lifetime occupation and parietal flow: implications for a reserve against Alzheimer’s disease pathology. Neurology. 1995; 45:55–60.

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72. Jacobs B. Schall M, Scheibel AB. A quantitiative dendritic analysis of Wernicke’s area in humans.II Gender, hemispheric, and environmental factors. J Comp Neurol. 1993; 327:97–111. 73. Diamond MC. Enriching hereditary: the impact of the environment on the anatomy of the brain. New York: Free Press; London: Collier MacMillan, 1988. 74. Del Ser T, Hachinski V, Merskey H, Munoz DG. An autopsy-verified study of the effect of education on degenerative dementia. Brain. 1999; 122:2309–2319. 75. Shea S, Stein AD, Basch CE, Langtigua R, Maylahn C, Strogatz DS, Novick L. Independent associations of educational attainment and ethnicity with behavioral risk factors for cardiovascular disease. Am J Epidemiol. 1991; 134:567–582. 76. Lindenstrom E, Boysen G, Nyboe J. Risk factors for stroke in Copenhagen, Denmark.I. Basic demographic and social factors. Neuroepidemiology. 1993; 12:37–42. 77. Katzman R. Education and the prevalence of dementia and Alzheimer’s disease. Neurology. 1993; 43:13–20. 78. Chastain RL, Joe GW. Multidimensional relations between intellectual abilities and demographic variables. J Educ Psychol. 1987; 79:323–325. 79. Mortimer JA, Graves A. Education and other socioeconomic determinants of dementia and Alzheimer’s disease. Neurology. 1993; 43 (Suppl. 4):39–44. 80. Grafman J, Salazar A, Weingartner H, Vance S, Amin D. The relationship of brain tissue loss volume and lesion location to cognitive deficit. J Neurosci. 1986; 6: 301–307. 81. Plassman BL, Welch KA, Helms M, Brandt J, Page WF, Breitner JCS. Intelligence and education as predictors of cognitive function in late life: a 50-year follow-up. Neurology. 1995; 45:1446–1450. 82. Mayeux R. Evil forces and vulnerable brains. Neurology. 2000; 55:1428–1429. 83. Jenkins R, Fox NC, Rossor AM, Harvey RJ, Rossor MN. Intracranial volume and Alzheimer’s disease. Evidence against the cerebral reserve hypothesis. Arch Neurol. 2000; 57:220–224. 84. Andreasen NC, Flaum M, Swayze V 2nd, O’Leary DS, Aliger R, Cohen G, Ehrhardt J, Yuh WT. Intelligence and brain structure in normal individuals. Am J Psychiat. 1993; 150:130–134. 85. Wickett JC, Vernon PA, Lee DH. In vivo brain size, head perimeter, and intelligence in a sample of healthy adult females. Pers Indiv Differ. 1994; 16:831–838. 86. Reichenbach A. Glia:Neuron Index: Review and hypothesis to account for different values in various mammals. Glia. 1989a; 2:71–77. 87. Segal M. Dentritic spines for neuroprotection: a hypothesis. Trends Neurosci. 1995; 18:468–71. 88. Arbuckle TY, Gold D, Andres D. Cognitive functioning of older people in relation to social and personality variables. J Psychol Aging. 1986; 1:55–62. 89. Kondo K, Niino M, Shido K. A case-control study of Alzheimer’s disease in Japan – significance of life-styles. Dementia. 1994; 5:314–326. 90. Broe GA, Henderson AS, Creasey H, McCusker E, Korten AE, Jorm AF, Longley W, Anthony JC. A case-control study of Alzheimer’s disease in Australia. Neurology 1990; 40:1698–1707. 91. Fabrigoule C, Letenneur L, Dartigues JF, Zarrouk M, Commenges D, BarbergerGateau P. Social and leisure activities and risk of dementia: a prospective longitudinal study. J Am Geriatr Soc. 1995; 43:485–490. 92. Gold DP, Andres D, Etezadi J, Arbuckle T, Schwartzman A, Chaikelson J. Structural equation model of intellectual change and continuity and predictors of intelligence in older men. Psychol Aging. 1995; 10:294–303. 93. Laurin D, Verreault R, Lindsay J, MacPherson K, Rockwood K. Physical activity and risk of cognitive impairement and dementia in elderly persons. Arch Neurol. 2001; 58:498–504.

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94. Friedland RP. Epidemiology, education, and the ecology of Alzheimer’s disease. Neurology. 1993; 43:246–249. 95. Friedland RP. Epidemiology and neurobiology of the multiple determinants of Alzheimer’s disease. Neurobiol Aging. 1994; 15:239–241. 96. Gould E, Reeves AJ, Graziano MSA, Gross CG. Neurogenesis in the neocortex of adult primates. Science. 1999; 286:548–552. 97. Gould E, Reeves AJ, Fallah M, Tanapat P, Gross CG. Hippocampal neurogenesis in adult Old World primates. Proc Natl Acad Sci USA. 1999; 96:5263–5267. 98. Kempermann G, Kuhn HG, Gage FH. Experience-induced neurogenesis in the senescent dentate gyrus. J Neurosci. 1998; 18:3206–3212. 99. Van Praag H, Kempermann G, Gage FH. Running increases cell proliferation and neurogenesis in the adult mouse dentate gyrus. Nat Neurosci. 1999; 2: 266–270. 100. Lynn R. A nutrition theory of the secular increases in intelligence;positive correlations between height, head size and IQ. Br J Educ Psychol. 1989; 59: 372–377. 101. Bartley AJ, Jones DW, Weinberger. Genetic variability of human brain size and cortical gyral patterns. Brain. 1997; 120:257–269. 102. Plomin R, Neiderhiser JM. Quantitative genetics, molecular genetics, and intelligence. Intelligence. 1991; 15:369–387.

SECTION IV CLINICAL INTERFACE

Chapter 14 WILL WE ALL DEMENT IF WE LIVE LONG ENOUGH? Carol Brayne

Introduction Would we all succumb to dementia if we lived long enough? This is a question posed frequently by the general public to the scientific community. It is of importance both to society, as the proportion of the population made up by older people grows, and to individuals who fear the consequences of prolonged lives with associated frailty. The answer, if it is answerable, will depend on a multitude of factors. This chapter cannot review even a small fraction of the diverse literature relevant to this question, including evidence from biomedical research on ageing, clinical and biomedical research on dementias and also the relevant literature on chronic diseases and causes of death in earlier life. Instead the chapter looks at the types of evidence that have been used to address the question and explores the question itself. Types of Evidence There are many different types of scientific evidence upon which to draw to attempt to answer the question. The underlying question is whether a distinction can be made between changes observed in processes considered to be “normal” for ageing and those that are associated with dementia. Clinical and neuropathological studies There are many papers in which the claim is made that data are provided supporting a clear differentiation of dementia, usually of the Alzheimer’s type,

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Figure 1. How studies which are population-based can be biased.

from normal or usual ageing. The most common of these is a cross-sectional comparison of clinical measures or investigations, or neuropathological measures where cases of disease are compared with controls. The data often show a very different pattern for the selected controls when compared with the demented individuals. However, this does not take account of the selection processes, which are different for cases and controls. This kind of study does not really provide evidence either for or against the question because of this unknown selection bias. When similar measurements on unselected population-based samples are made there is usually a considerable degree of overlap of results, even when the distributions are significantly different.1 Extreme old age provides a different type of evidence. Individuals at the extreme of old age can be compared with either younger controls or demented individuals. Given the lack of understanding about the determinants of survival in these atypical outliers of the population, their characteristics cannot be extrapolated to the general population. Lack of association between neuropathological lesions and ageing in a population-based clinico-pathological study has been taken as evidence that dementia is not inevitable with age, but such a conclusion cannot be drawn when there is selective sampling for post mortem based upon age and dementia status.2 Studies on an unselected series of post mortems have shown steady increases in the presence of Alzheimer type pathology in the brain with age as well as other changes such as reduction in frontal and temporal lobe cortical thickness, lower brain weight and change in the pattern and size of neurons and supporting cells.3,4 Prevalence and incidence studies Combined analysis of population-based studies has been conducted to investigate the clinical expression of dementia in extreme old age. The difficulties with standardization of criteria across age groups are discussed

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Figure. 2. The raltionship between prevalence and incidence in old-age dementia.

below. Notwithstanding these problems, such reviews have suggested that the prevalence estimates of dementia do not rise inexorably with age in the late ninth and tenth decades, but show a levelling off. 5 This has not been demonstrated for incidence6 and the force of mortality at these ages is so strong that it has been shown, using deterministic modelling, that the incidence must continue to rise in the oldest age groups in order to create the prevalence estimates seen.7,8 Volunteer and selected population studies Evidence that volunteer cohorts show relatively little cognitive decline is cited against the inevitability of dementia with age.9 These tend to be highly selective and exclude the majority of the population. In Starr’s Edinburgh study9 603 out of 10,000 individuals aged 70 and over whose notes in general practice were scrutinized, had no health problems and no medication. Of these only 195 fulfilled the same criteria a median of 4.2 years later, i.e. under 2%. These individuals were found to have very limited cognitive decline on clinical measures such as Mini Mental State Examination (MMSE). However changes have been demonstrated with more sensitive tests even after accounting for methodological issues such as practice effects on repeated testing. 10 The expression of dementia at extremes of age has not been adequately covered by these cohort studies and so we do not know whether these individuals are at considerably less risk of dementing, or whether it is merely delayed in onset. Such studies are of great interest in tracking cognitive decline in able groups, but cannot inform public policy issues related to the majority of the population.

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Longitudinal studies of representative population samples Many studies of population-based older people have reported cognitive decline and dementia incidence. Most of these studies have concentrated on risk factors for incident dementia, identifying a range of risks mainly associated with vascular pathology, which confer significantly increased risk although not extreme relative risks. Fewer have examined actual cognitive decline and have almost universally shown that the majority of ordinary populations show decline in measures of cognition over time, with these being greatest in the oldest age groups.11,12 Some variation in patterns of decline according to educational achievement and IQ has been reported,13 but whether education truly protects from the clinical and dementing processes remains controversial.14 This is associated, in studies with neuropathological follow-up, with increasing expression of a variety of features mentioned above: atrophy, loss of synapses, vascular lesions, and lesions associated with Lewy Body Dementia and Alzheimer’s Disease (AD).15–20 Animal models Some natural animal models are held to be helpful in throwing light on this question, but animals do not reach equivalent extreme old age nor suffer the same co-morbidity as humans. Some species do develop aspects of Alzheimer type pathology. Chimpanzees develop plaques.21 Dogs develop plaques and tangles, and show some of the vascular changes seen in humans.22–24 But most animals do not show the same changes. Most Alzheimer research in animals has been conducted in manipulated animals, either genetically or with specific lesions, and does not begin to tackle the complexity of dementia in the aged. Some combined animal models are attempting to reproduce more realistic models.25 The Question The denominator and the desire for absolute answers (would we all dement if we lived long enough?) When people ask about the inevitability of the dementia process, the question can arise from a desire to know about personal risk if they live to some assumed maximum life span. This assumed maximum lifespan has not remained constant over time26 and with confirmation of survival well beyond a hundred years has made more systematic investigation of the physical, mental and psychological state of extreme age possible.27 However, in most cultures the maximum life span experienced would be lower and median life span much lower. The population to which the question is applied influences the answer. The “all” in the question above implies an assumption of certainty. Such terms are shunned by scientists but beloved by journalists. At a personal level, people prefer not to have to deal with uncertainty and there is acknowledged

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Figure 3. Life expectancy. Reproduced with permission from the World Bank.

difficulty in understanding risk in the population. Presenting absolute risk for any individual requires very robust data relevant to the populations and cohorts studied.28 In dementia, there is a substantial literature on risk, which at some levels looks strikingly like the literature on any chronic disease and ageing itself, with examination of processes including inflammation, apoptosis, immune dysfunction, chronic infection, toxic exposures, DNA and protein damage and antioxidant effects. These diverse mechanisms, which are about pathophysiological processes rather than causative risk, have led some to suggest that ageing itself does not exist — “there is no such thing as ageing”.29 At the population level we can examine the proportion of individuals who suffer from particular pathologies and disorders at different ages, but without longitudinal information on specific individuals we cannot assume with absolute certainty that one age group will have the same patterns as another generation at the same age. We know that disability, although not specifically cognitive disability, appears to show cohort effects in studies from the US.30 Two major influences on expression of a disorder at a given age are the risk patterns for that disorder in the ages leading up to the given age, and the forces of mortality in that community. This brings us back to the “we” of the question. At every age, even within a population and certainly across populations, there are different influences on who has survived to that age. In most populations there are few very old men. The demographic profile of the British population for men has been influenced over the last hundred years by the First World War slaughter of young men. This has consequences for the health profile of the survivors. Those that died were the young and more educated, leaving behind a population of survivors with different over-

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all characteristics from those from the generations on either side of them.31 In developing countries survival patterns and consequent genetic patterns are heavily influenced by morbidity at young ages, and now HIV infections. In these societies, given that average life span does not yet reach the ages where dementia is common, and individuals with extreme life spans are exceedingly rare, the chances of dementing are not zero, but low, and those who survive into old age are highly selected. Even in a condition where the genetic predisposition to dementia is known, such as Huntington’s Chorea in which inheritance is dominant, there is variation in the nature and timing of onset leading to uncertainty in predicting age of onset. If life expectancy were as short as it was in early periods of human development, only a minority of the individuals carrying the Huntington’s Chorea mutations would have developed dementia, and it therefore would not have been inevitable. Specific interrelationships might be seen in genetic profiles in developed countries. An example of this is the reported relationship between butyrylcholinesterase and apolipoprotein E alleles as a risk for AD in men in a meta-analysis.32 This pattern would not necessarily be replicable in other populations. Sex differences in the expression of dementia can also be influenced by survival patterns.33 Men tend to live shorter lives than women, even without the world war experiences noted above, and older women are known to suffer from more disability at a given age than older men in most populations where this has been examined.19 Thus, at present, men in the oldest age groups are healthier than surviving women and may well be at lower risk from dementia. To be recognized as demented, particularly with AD, we have to survive through earlier cognitive decline to fulfil diagnostic criteria. But those people who dement die at a faster rate than those who are not demented, even within the same age group.34 Cognitive decline is also associated with increased mortality.19 Thus at all ages, and particularly in the oldest age groups (where the general force of mortality is very great), the survivors from any given age are less likely to be those who are demented and those who are soon to become demented. The cultures where dementia has, through painstaking research using clinically validated comparative methods,35 been shown to be less common are India and Nigeria when compared with population contemporaries in the United States.36,37 In these cultures the pattern and genetic risk for AD appear to be different, and suggest either survival effects or different geneenvironment risk interaction or possibly both. A study of a locality in India revealed the much lower proportions of individuals with the known risk gene apolipoprotein ε4 than in the western populations in whom the risk was originally identified. However the risk associated with this allele was similar to that observed in the west.38 In Nigeria the risk allele was more common, but there was no associated risk of dementia.36 Attenuation with age of the effects of the known risk factor for AD apolipoprotein ε4 supports the need for examination of the impact of risk at different ages.39,40

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Even when longitudinal studies are done, and incidence is found to be lower, the possibility remains that the majority of the population has not reached the age of maximum risk for late onset dementing conditions as their life expectancies are not sufficiently long.7,8 In those countries where life expectancy has increased dramatically over the past decades, such as Japan, the prevalence of dementia is remarkably similar to Western societies. The relative proportions of different clinically diagnosed subtypes of dementia may vary as might be predicted given different levels of vascular risk across populations.41 The disorder itself — what is dementia? Implicit in the “would we all dement?” is the differentiation of usual from successful and unsuccessful. What is usual with age and what unusual, what normal and what abnormal? Basic epidemiological and statistical textbooks on the distributions deal well with the semantic issues raised here. Last42 describes “normal” as having three distinct meanings and points out that conceptual difficulties may arise if these different meanings are not specified or if the area of overlap is not clearly understood (see Box). 1. Within the usual range of variation in a given population or population group, or frequently occurring in a given population or group. In this sense, “normal” is frequently defined as, “within a range extending from two standard deviations below the mean to two standard deviations above the mean”, or “between specified (e.g., the 10th and 90th percentiles of the distribution). 2. In good health, indicative or predictive of good health, or conducive to good health. For a diagnostic or screening test, a “normal” result in one in a range within which the probability of a specific disease is low. 3. Gaussian distribution [i.e. bell curve]. From Last .42 These are rarely specified in discussions about dementia, and even less in discussions about the cognitive impairment seen in many older people that can precede dementia. The dementia process has been described in western literature over the ages, at its most basic as a loss of mind and more recently as ‘a chronic or persistent disorder of the mental processes marked by memory disorders, personality changes, impaired reasoning etc., due to brain disease or injury’ (Concise Oxford Dictionary of Current English, 1990). Frailty associated with ageing is acknowledged in many cultures, but dementia is

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not necessarily recognised. The definition of dementia has varied with time even within western societies, but has now been operationalised within the International Classification of Diseases of the World Health Organisation43 and the Diagnostic and Statistical Manual of the American Psychiatric Association.44 To make a diagnosis of the syndrome of dementia during life requires information from several different axes, the most important are given in the dictionary definition and include cognition, function, behaviour and mood. To recognize abnormal function requires personal, community and societal expectations and norms. These axes themselves are not independent and one will predict decline on another. For example functional ability as measured by activities of daily living can predict the onset of dementia45 despite the fact that functional decline is supposed to be a consequence of the cognitive impairment of dementia. Once the diagnosis of dementia is made, there is usually an attempt to make a specific subtype diagnosis. To do this requires a further combination of history, examination and investigation to identify a range of possible underlying pathologies, of which there are many46 with vascular47 and Alzheimer’s being dominant. Its onset, type of progression, comorbid conditions and particular findings on investigation, which might eventually include the brain itself, can then characterise the dementia. The meaning of the word and the diagnosis “dementia” can thus mean very different things according to whether it has been based on a single measure, such as cognition, or a combination such as cognition and function, or the whole range including detailed investigations and exclusions. It is clear that applying different methods identifies different individuals,48,49 and that subtype diagnosis in ordinary settings is not necessarily good at identifying the true underlying pathologies.50 It is also clear that diagnostic criteria are varied according to the age at which they are applied.51,52 Patterns considered to be abnormal in younger age groups are allowable in older age groups. The assessment of centenarians for dementia would be less stringent than that for people in their sixties. Relaxation of criteria with age is illustrated by the methods used for the French study of extreme old age where to be assumed non-demented an individual had to have a reported meaningful verbal exchange recently — such as “that’s a nice dress you are wearing”.27 Illustration of this principle is illustrated by the NINCDS ADRDA neuropathological criteria for AD in which the absolute pathologies shown were given different cutpoints, with more Alzheimer type pathology allowable in the older age groups.53 The Consortium to Establish a Register for Alzheimer’s Disease group (CERAD) produced consensus criteria for definite Alzheimer’s disease which require a clinical diagnosis of dementia and a sufficient level of Alzheimer type pathology to warrant a diagnosis of AD, based on the demonstration of plaques.54 This demonstrates the dissociation of clinical evidence from biological evidence in that one could be discounted in the presence or absence of the other. Thus, if the question “Will we all dement if we lived long enough?”

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was rephrased to “Will we all demonstrate neuropathological changes in our brains associated with dementia if we live long enough?” the answer would be a categorical yes. All brains over a certain age will show some atrophy, probably some vascular lesions and some Alzheimer type pathology,2,3 with some studies reporting on the added risk of experiencing dementia during life with a combination of pathologies,55,56 and others reporting on the close similarity of the Alzheimer type pathology observed in the very old non-demented when compared with established cases of AD.57 The criteria for dementia and its subtypes are dominated by western clinical thinking and methods of assessment, and these methods and assessment have been adopted and adapted by many different cultures as described above, within the model of westernised medical training and cultural expectations. Where the methods have been carefully adapted and cross-validated across cultures, systematic differences have been demonstrated in the proportion of people diagnosed at different ages and developing dementia.36,37 The question of whether this is related to survival to the ages of risk, and mortality differentials for those with incipient dementia is not yet fully answered. It does not appear to be due to lack of risk for the basic neuropathological changes, since, despite earlier reports to the contrary, we now know that there are Alzheimer type pathological changes in the brains of older Africans.58 Despite the vast evidence collected and published to date, there is still uncertainty about which brain pathology or pathologies underlie clinical dementia seen during life.59–61 The potential mechanisms such as inflammation are mentioned above, but neuropathological criteria for the different dementias include a variety of specific changes. These range from neuronal loss, Alzheimer type pathologies to white matter pallor, sclerosis62 vascular abnormalities from accumulation of proteins in the cell walls63 to thrombosis and ischaemic damage. The issue of what is normal and abnormal ageing is important because most of these changes are found with increasing frequency in ageing brains. Some have suggested integrated hypotheses with many triggering factors leading to common final pathways.64 How these processes might be staged remains uncertain. It has been suggested that pure neuronal loss can be associated with dementia and thus in the absence of all preventable pathologies, this would be the likely dementia of extreme old age.65 Whether individuals exhibiting some, or all, of these pathologies but without having expressed dementia are at an early and unrecognised stage of dementia remains a question. Many think they are66,67 and some suggest that the in vivo clinical measures have been too insensitive to recognise abnormality. However we have shown above that sensitive measures demonstrate change in most older people. Thus the argument becomes circular as this would bring much greater proportions of the population into the abnormal range. There is substantial cognitive impairment which does not reach diagnostic criteria for dementia, and this increases with age even after accounting for co-morbidity and sensorimotor problems.11,68,69 Mild cognitive impairment has been developed into a medicalised category, with its own criteria.

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This appears to be preparing the ground for trials of interventions which could be aimed at substantial proportions of the population, potentially from young ages, and could be seen as part of a more general trend of medicalisation of what used to be considered normal processes.70 What is “old”? There is continuing, unresolved, debate about the nature of ageing itself and what might be the best measure of biological age, if indeed such a thing exists.29 Individuals who survive past a century have been described as having been youthful in their young old age.27 This suggests that, at least for some aged individuals, there are mechanisms at play that are protecting them from the usual ageing experienced by the vast majority of older people. Older people themselves have diverse views about the reasons for their successful old age in our own local population studies, these tend to emphasise moderation in lifestyle, with continuing engagement in social and physical activities. As mentioned above, there are other aspects of ageing, beyond the biological, that might be considered such as societal, chronological and psychological. Enforced retirement of certain careers is based on the assumption that certain powers diminish with age in a way that could jeopardize the particular job. If a society expects very high functioning from all individuals, those with cognitive decline who started low will arrive at the dementia threshold earlier than others, and at certain ages a very high proportion will reach fixed criteria. An illustration of this is the fact that insurance companies tend to use scale measures to define functional and cognitive disability. If a threshold score of 23/24 on the Mini Mental State Examination (MMSE) were to be used, the majority of women aged 85 and over would be declared to have dementia in England and Wales.69 Longevity and dementia How long would be long enough? This has been discussed above in relation to variation in life expectancy in the world. There is a hint of consideration of quality in the term “enough”. Enough may be too much. The question here might be an implicit challenge to the medical model of extension of life at all cost, on the basis that quality of life is not necessarily associated with quantity. This is a truism, but in practical application of western medicine many individuals have their lives extended without quality,71 and possibly with greater risk of acquiring a dementing condition. It has been said “seeking the biological basis of ageing is to continue to pursue technological solutions to age old problems, without addressing the fundamental question of how to improve the experience of the end of life”.72 Consequently the likelihood of extension of life has made the chance of impairing quality of life for some people greater. What would we want for ourselves? Those individuals diagnosed at the early stage of the dementia process, along with their carers, seek support and interventions that at best reverse or, at

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least, slow its progression and manifestations. Those diagnosed at later stages may not wish for reversal of progression unless it were guaranteed to proceed to a sufficient level to improve quality of life. It is possible to argue that there are situations in which informed choice for carers and sufferers might include a rejection of some types of treatment. There might not be agreement between the carers and patients.73 Examples might be if the improvement were to be associated with greater insight into impairments and longer life expectancy, or to improve specific cognitive impairment without improving behavioural difficulties. Relatively little discussion and research has been carried out on public attitudes to treatment of dementia and effects of treatment, partly because trials only assess relatively limited timescales, specific types of intervention and limited aspects of treatment effect. The concentration on identifying single types of dementia, with single causes and simple remedies, mostly in the form of medication74 has led to an expectation of cure. The media support this emphasis which, while entirely understandable, may not provide enough information on which to base informed policies for prevention and care in whole populations. In view of the increasing debate about euthanasia, the right to refuse intervention and active rejection of maintenance of life without quality are likely to be a focus for future debate. Conclusion This chapter has explored the assumptions implicit in the debate about the relationship of ageing to dementia. Examination of the strength of the evidence and the assumptions inherent in the question are of relevance to policy makers, clinicians and researchers. However, these issues are possibly of greater significance to societies than the original question itself: the impact of changing patterns of ageing with more or less fit individuals reaching old age; the high likelihood of having brain pathologies at extreme old age along with the limitations of a binary approach (you either have it or you don’t) 75,76; the observation that the majority of older people do experience some cognitive decline with age paralleled by increasing changes in the brain; and the overall impact, including health, social and economic, on society of any preventive treatments which may be generated. References 1. 2. 3.

Brayne C, Calloway P. Is Alzheimer’s disease distinct from normal ageing? Lancet. 1988; 1:1265–1267. MRC CFAS Neuropathology Group: Ince P, Matthews F, Brayne C, Esiri M. Pathological correlates of late-onset dementia in a multi-centre, community-based population in England and Wales. Lancet. 2001; 357:169–175. Miller FD, Hicks SP, D’Amato CH, Landis JR. A descriptive study of neuritic plaques and neurofibrillary tangles in an autopsy population. Am J Epidemiol. 1984; 3:331–341.

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4. Terry RD, De Teresa R, Hansen LA. Neocortical cell counts in normal human adult aging. Ann Neurol. 1987; 21:530–539. 5. Ritchie K, Kildea D. Is senile dementia “age related” or “ageing related”? Evidence from meta-analysis of dementia prevalence in the oldest old. Lancet. 1995; 346:931–934. 6. Jorm AF, Jolley D. The incidence of dementia: a meta-analysis. Neurology. 1998; 51:728–733. 7. McGee MA, Brayne C. The impact on prevalence of dementia in the oldest age groups of differential mortality patterns: a deterministic approach. Int J Epidemiol. 1998; 27:87–90. 8. McGee MA, Brayne C. Exploring the impact of prevalence and mortality on incidence of dementia in the oldest old: the sensitivity of a deterministic approach. Neuroepidemiology. 2001; 20:221–224. 9. Starr JM, Deary IJ, Inch S, Cross S, MacLennan WJ. Age-associated cognitive decline in healthy old people. Age Ageing. 1997; 26:295–300. 10. Rabbitt P, Lowe C. Patterns of cognitive ageing. Psychol Res. 2000; 63: 308– 316. 11. Brayne C, Spiegelhalter DJ, Dufouil C, Chi LY, Dening TR, Paykel ES, O’Connor DW, Ahmed A, McGee MA, Huppert FA. Estimating the true extent of cognitive decline in the old old. J Am Geriatr Soc. 1999; 47:1283–1288. 12. Cullum S, Huppert FA, McGee MA, Denning T, Ahmed A, Paykel ES, Brayne C. Decline across different domains of cognitive function in normal ageing: results of a longitudinal population based study using CAMCOG. Int J Ger Psychiat. 2000; 15:853–862. 13. Leibovici D, Ritchie K, Ledesert B, Touchon J. Does education level determine the course of cognitive decline? Age Ageing. 1996; 25:392–397. 14. Christensen H, Hofer SM, Mackinnon AJ, Korten AE, Jorm AF, Henderson AS. Age is not kinder to the better educated: absence of an association investigated using latent growth techniques in a community sample. Psychol Med. 2001; 31: 15–28. 15. Masliah E, Mallory M, Hansen L, De Teresa R, Terry RD. Quantitative synaptic alterations in the human neocortex during normal aging. Neurology. 1993; 43: 192–197. 16. Hansen LA, Terry RD. Plaque-only Alzheimer disease is usually the lewy body variant and vice versa. J Neuropath Exp Neurol. 1993; 52:648–654. 17. Xuereb JH, Brayne C, Dufouil C. Neuropathological findings in the very old. Results from the first 101 brains of a population-based longitudinal study of dementing disorders. Ann NY Acad Sci. 2000; 47:490–496. 18. Green MS, Kaye JA, Ball MJ. The Oregon brain ageing study: neuropathology accompanying healthy ageing in the oldest old. Neurology. 2000; 54:105–113. 19. MRC CFAS: Neale R, Brayne C, Johnson A. Cognition and survival: an exploration in a large multicentre study of the population aged 65 years and over. Int J Epidemiol. 2001; 30:1383–1388. 20. Kalaria RN, Ballard CG, Ince PG, Kenny RA, McKeith IG, Morris CM, Brien JT, Parry EK, Perry RH, Edwardson JA. Multiple substrates of late-onset dementia: implication for brain protection. Novart Fdn Symp. 2001; 235:49–60. 21. Gearing M, Rebeck GW, Hyman BT, Tigges J, Mirra SS. Neuropathology and apolipoprotein E profile of aged chimpanzees: implications for Alzheimer disease. Proc Natl Acad Sci USA. 1994; 91:9382–9386. 22. Cummings BJ, Head E, Ruehl W, Milgram NW, Cotman CW. The canine as an animal model of human aging and dementia. Neurobiol Aging. 1996; 17: 259–268. 23. Papaioannou N, Tooten PC, van Ederen AM, Bohl JR, Rofina J, Tsangaris T, Gruys E. Immunohistochemical investigation of the brain of aged dogs. I. Detec-

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44. American Psychiatric Assocation: Diagnostic and statistical manual of mental disorders, 4th ed. Wastinghton, DC: American Psychiatric Association, 1994. 45. Barberger-Gateau P, Dartigues JF, Letenneur L. Four instrumental activities of daily living score as a predictor of one-year incident dementia. Age Ageing. 1993; 22:457–463. 46. Ince PG, McArthur FK, Bjertness E, Torvik A, Candy JM, Edwardson JA. Neuropathological diagnoses in elderly patients in Oslo: Alzheimer’s disease, Lewy Body disease, Vascular lesions. Dementia. 1995; 6:162–168. 47. Roman GC, Tatemichi TK, Erkinjuntti T, Cummings JL, Masdeu JC, Garcia JH, Amaducci L, Orgogozo JM, Brun A, Hofman A. Vascular dementia: diagnostic criteria for research studies: report of the NINDS-AIREN International Workshop. Neurology. 1993; 43:250–260. 48. Erkinjuntti T, Ostbye T, Steenhuis R, Hachinski V. The effect of different diagnostic criteria on the prevalence of dementia. N Engl J Med. 1997; 337: 1667–1674. 49. Thomas VS, Darvesh S, MacKnight C, Rockwood K. Estimating the prevalence of dementia in elderly people: a comparison of the Canadian Study of Health and Aging and National Population Health Survey approaches. Int Psychogeriatr. 2001; 13 (Suppl 1): 169–175. 50. Gilleard CJ, Kellett JM, Coles JA, Millard PH, Honavar M, Lantos PL. The St Georges dementia bed investigation study: a comparison of clinical and pathological diagnoses. Acta Psychiat Scand. 1992; 85:264–269. 51. Brayne C. Clinicopathological studies of the dementias from an epidemiological viewpoint. Br J Psychiat. 1993; 162:439–446. 52. Hansen LA, Terry RD. Position paper on diagnostic criteria for Alzheimer disease. Neurobiol Aging. 1997; 18:S71–73. 53. McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology. 1984; 34:939–944. 54. Mirra SS, Heyman A, McKeel D, Sumi SM, Crain BJ, Brownlee LM, Vogel FS, Hughes JP, van Belle G, Berg L. The Consortium to Establish a Registry for Alzheimer’s disease (CERAD). Part II. Standardization of the neuropathologic assessment of Alzheimer’s disease. Neurology. 1991; 41:479–486. 55. Tomlinson BE, Blessed G, Roth M. Observations on the brains of demented old people. J Neurol Sci. 1970; 11:205–242. 56. Snowdon DA, Greiner LH, Mortimer JA, Riley KP, Greiner PA, Markesbery WR. Brain infarction and the clinical expression of Alzheimer’s Disease. The Nun study. J Amer Med Assoc. 1997; 277:813–817. 57. Arriagada PV, Marzloff K, Hyman BT. Distribution of Alzheimer-type pathological changes in nondemented elderly individuals matches the pattern in Alzheimer’s disease. Neurology. 1992;4 2:1681–1688. 58. Ogeng’o JA, Cohen DL, Sayi JG, Matuja WB, Chande HM, Kitinya JN, Kimani JK, Freidland RP, Mori H, Kalaria RN . Cerebral amyloid beta protein deposits and other Alzheimer lesions in non-demented elderly east Africans. Brain Pathol. 1996; 6:101–108. 59. Arriagada PV, Growdon JH, Hedley Whyte T, Hyman BT. Neurofibrillary tangles but not senile plaques parallel duration and severity of Alzheimer’s disease. Neurology. 1992; 42:631–639. 60. Bancher C, Jellinger KA. Neurofibrillary tangle predominant form of senile dementia of Alzheimer type: a rare subtype in very old subjects. Acta Neuropath. 1994; 88:565–570. 61. Mackenzie IRA, McLachlan RS, Kubin CS, Miller LA. Prospective neuropsychological assessment of nondemented patients with biopsy proven senile plaques. Neurology. 1996; 46:425–429.

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62. Dickson DW, Davies P, Bevona C, Van Hoeven KH, Factor SM, Grober E, Aronson MK, Crystal HA. Hippocampal scelosis: a common pathological feature of dementia in the very old (> or = 80 years of age) humans. Acta Neuropath. 1994; 88:212–221. 63. Ellis RJ, Olichney JM, Thal LJ, Mirra SS, Morris JC, Beekly D, Heyman A. Cerebral amyloid angiopathy in the brains of patients with Alzheimer’s disease: the CERAD experience part XV. Neurology. 1996; 46:1592–1596. 64. Hardy JA, Mann DMA, Wesler P, Winblad B. An integrative hypothesis concerning the pathogenesis and progression of Alzheimer’s disease. Neurobiol Aging. 1986; 7:489–502. 65. Terry RD, Katzman R. Lifespan and synapses: will there be a primary senile dementia? Neurobiol Aging. 2001; 22:347–248. 66. Linn RT, Wolf PA, Bachman DL, Knoefel JE, Cobb JL, Belanger AJ, Kaplan EF, D’Agostino RB. The preclinical phase of probable Alzheimer’s disease. A 13-year prospective study of the Framingham cohort. Arch Neurol. 1995; 52:485–490. 67. Morris JC, Storandt M, Miller JP, McKeel DW, Price JL, Rubin EH, Berg L. Mild cognitive impairment represents early-stage Alzheimer disease. Arch Neurol. 2001; 58:397–405. 68. Ebly EM, Hogan DB, Parhad IM. Cognitive imairment in the nondemented elderly. Results from the Canadian Study of Health and Aging. Arch Neurol. 1995; 52:612–619. 69. Brayne C, Nickson J, Johnson A. Cognitive function and dementia in six areas of England and Wales: the distribution of MMS and the prevalence of GMS organicity level in the MRC CFA Study. Psychol Med. 1998; 28:319–335. 70. Moynihan R, and Smith R. (2002) Too much medicine? Br Med J. 2002; 324: 859–860. 71. Shapin S, Martyn C. How to live forever: lessons of history. Br Med J. 2000; 321: 1580–1582. 72. Tallis RC. Brains and minds: a brief history of neuromythology. J Roy Coll Phys Lond. 2000; 34:563–567. 73. Novella JL, Jochum C, Jolly D, Morrone I, Ankri J, Bureau F, Blanchard F. Agreement between patients’ and proxies’ reports of quality of life in Alzheimer’s disease. Qual Life Res. 2001; 10:443–452. 74. Gallagher M, Gill M, Baxter MG, Bucci DJ. Semin Neurosci. 1994; 6:351–358. 75. Wainwright NWH, Surtees PG, Gilks WR. Diagnostic boundaries, reasoning and depressive disorder. I Development of a Probabilistic model for public health psychology. Psychol Med. 1997; 27:835–845. 76. 76. Surtees PG, Wainwright NWH, Gilks WR. Diagnostic boundaries, reasoning and depressive disorder. II. Diagnostic complexity and depression: time to allow for uncertainty. Psychol Med. 1997; 27:847–860.

Chapter 15 DETECTING ALZHEIMER’S DISEASE AT THE PRE-SYMPTOMATIC STAGES Gary W Small

Introduction As people liver longer, the risk for developing Alzheimer’s disease (AD) increases dramatically. In fact, the incidence appears to double every five years after age 60 years, suggesting that if people lived long enough, they would all develop the disease by a certain age. Although AD is the most common cause of late-life dementia, other causes, particularly vascular disease, do contribute to the occurrence of dementia. In fact, the burden of such vascular decline appears to contribute to a greater portion of dementia cases in the upper age groups. Whether it is pure AD, pure vascular dementia, or something along the continuum, these cases of dementia progress with time. The underlying lesions reach a threshold such that they lead to cognitive decline that interferes with daily life. With Alzheimer’s dementia, this slow insidious decline represents an accumulation of pathological features and declining neurotransmitter functions that begin well before the clinician can confirm a clinical diagnosis in practice. Adapted in large part from: Small GW. Structural and functional imaging of Alzheimer’s disease. In: Davis KL, Charney D, Coyle JT, Nemeroff C, editors. Neuropsychopharmacology: the fifth generation of progress. Philadelphia: Lippincott, Williams and Wilkins, 2002; 1231–1242.

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Focus on Early Detection With the realization that the neuropathological changes of AD begin to accumulate perhaps decades before the disease is obvious clinically, studies have emphasized recruitment of subjects at time points years or decades before a physician begins to focus on early detection of AD at clinical stages. These studies might confirm a clinical diagnosis of probable AD.1 The ultimate goal is to develop tools to identify pre-symptomatic candidates for beginning preventive pharmacological treatments before extensive neuronal damage develops. Brain imaging has become an important tool for the development of surrogate markers that will effectively identify people with only mild cognitive losses who are likely to progress in their cognitive loss and eventually develop the full dementia syndrome of AD. As novel, disease-modifying agents emerge, these surrogate brain-imaging markers will be critical in determining drug efficacy and facilitating drug development in both animal models and human studies. Diagnostic Categories Clinical investigators have developed definitions for categorical pre-symptomatic stages that assist in clinical trials and communicating staging levels. Several diagnostic entities have been described in efforts to better characterize age-related cognitive decline. The mildest form of age-related memory decline is known as age-associated memory impairment (AAMI),2 characterized by self-perception of memory loss and a standardized memory test score > 1SD below the aged norms. In people 65 years of age or older, its estimated prevalence is 40%, afflicting approximately 16 million people in the United States.3 Only about 1% of such cases will develop dementia each year. The term AAMI has generated controversy since many people question the specific criteria and whether they define a stable or declining entity. A more severe form of memory loss is mild cognitive impairment (MCI), often defined by significant memory deficits without functional impairments. People with MCI show memory impairment that is > 1.5 SDs below aged norms on such memory tasks as delayed paragraph recall.4 Approximately 10% of people 65 years or older suffer from MCI, and nearly 15% develop AD each year.4,5 This condition also has generated controversy. Some experts consider MCI a risk state rather than a diagnostic entity. Despite such controversy, MCI appears to be a useful concept and may respond favorably to current symptomatic treatments. Brain imaging studies of pre-symptomatic AD focus on both these forms of age-related memory decline.

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Evidence for Pre-Symptomatic Changes Several areas of research have contributed to the idea that pre-symptomatic conditions do exist, including neuropathological, neuroimaging, and clinical investigations. Taken together, this work supports the notion that the dementing process leading to AD begins years before a clinical diagnosis of probable AD can be confirmed.6 Post-mortem studies of non-demented older people7 indicate that tangle density in healthy ageing correlates with age, but that some cases demonstrate widely distributed neuritic and diffuse plaques throughout neocortical and limbic structures. Other studies8 have found that neurofibrillary tangle density increases in some individuals, presumably those who will eventually develop AD very early in adult life, perhaps even by the fourth decade. The diffuse amyloid deposits in middle-aged non-demented subjects are consistent with an early or pre-symptomatic stage of AD and suggest that the pathological process progresses gradually, taking 20 to 30 years to proceed to the clinical manifestation of dementia.9 Other supportive evidence includes findings that linguistic ability in early life predicts cognitive decline in late life.10 High diffuse plaque density in non-demented older persons has been observed in the entorhinal cortex and inferior temporal gyrus, in association with acetylcholinesterase fibre density.11 Evidence from animal models also supports compromised hippocampal cholinergic transmission during ageing.12 Studies of glucose metabolic rates using positron emission tomography (PET)6,13,14 indicate lower regional brain metabolism in middleaged and older persons with a genetic risk (apolipoprotein E [ApoE ε4]) +ve status, lending further support for a prolonged pre-symptomatic AD stage. Structural Imaging Computerized tomography and magnetic resonance imaging The largest body of data comes from studies of structural imaging modalities in that clinical investigators have had the greatest access to these technologies. Studies of early detection logically follow from initial work demonstrating the differential diagnostic utility of a brain-imaging marker. For structural imaging, particularly magnetic resonance imaging (MRI), data have emerged on the use of regional atrophy patterns for the positive diagnosis of AD and other neurodegenerative disorders. Studies without neuropathological confirmation report the utility of medial temporal lobe atrophy, particularly hippocampal atrophy, on computerized tomography (CT) or MRI for the clinical diagnosis of AD.15 Some but not all quantitative MRI studies indicate that white matter hyperintensities correlate with neuropsychological functioning in both healthy elderly persons and demented patients.16,17 Other studies indicate loss of cerebral gray matter,18 hippocampal and parahippocampal atrophy,19 and lower left amygdala and entorhinal cortex volumes20 in patients with AD. In differentiating AD from older normal controls, the sensitivity of various

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medial temporal atrophy measures ranges from 77% to 92%, with specificities ranging from 49% to 95%.21–23 In older MCI patients, hippocampal atrophy predicts subsequent conversion to AD.24 Of various analytic methods, computerized volumetric techniques are most accurate, but are currently labor-intensive and not widely available. A modified negative-angle axial view designed to cut parallel to the anterior-posterior plane of the hippocampus has been used to assess hippocampal volume using CT or MRI.15 Such hippocampal atrophy is a sensitive and specific predictor of future AD in patients with MCI. Baseline hippocampal ratings accurately predicted decliners with an overall accuracy of 91%. Neuropathological studies find that the sites of maximal neuronal loss for both AD and MCI are in the CA1, subiculum, and entorhinal cortex.15 Hippocampal atrophy also has been found to predict future cognitive decline in older persons without cognitive impairment followed for nearly four years. Visual assessments of medial temporal lobe atrophy on coronal MRI sections show significant correlations between estimated and stereologically measured volumes.25 Because the latter is much more labor-intensive, visual readings may be an alternative approach with greater efficiency. The hippocampus and the temporal horn of the lateral ventricles also may serve as antemortem AD markers in mildly impaired patients (mean MMSE score of 24).26 While hippocampal atrophy may distinguish AD from normal ageing, such atrophy may be non-specific, occurring in other dementing disorders.27 Magnetic resonance imaging hippocampal atrophy measures are not as sensitive as PET glucose metabolism measures, which begin decreasing before memory decline onset.28 The presence of MRI white matter hyperintensities does not improve diagnostic accuracy since they occur both in AD and healthy normal elderly.29,30 The entorhinal cortex (EC), a region involved in recent memory performance, is one of the earliest areas to accumulate NFTs.8 Histological boundaries of the EC from autopsy-confirmed AD patients and controls have been used to validate a method for measurement of EC size relying on gyral and sulcal landmarks visible on MRI.31 Such measures may be additional early AD detection markers. Several studies have addressed the interaction between regional atrophy and ApoE genotype. Increasing dose of ApoE ε4 allele was associated with smaller hippocampal, entorhinal cortical, and anterior temporal lobe volumes in already demented patients.32 A study of non-demented older persons found an association between ApoE ε4 dose and a larger left than right hippocampus.33 Combining medial temporal measures with other functional neuroimaging34 (34) or ApoE genotyping may improve the ability of any of these measures alone to predict cognitive decline.35 In vivo imaging of amyloid plaques and neurofibrillary tangles This is the most innovative structural imaging approach currently under development. Although not yet widely available, the technology offers con-

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siderable promise in studies of new drug development and eventually in differential diagnosis and early detection of dementia. The evidence for neuritic plaque (NP) and neurofibrillary tangle (NFT) accumulation years prior to clinical AD diagnosis suggests that in vivo methods that directly image these pathognomic lesions would be useful presymptomatic detection technologies. Current methods for measuring brain amyloid, such as histochemical stains, require tissue fixation on post-mortem or biopsy material. Available in vivo methods for measuring NPs or NFTs are indirect (e.g., cerebrospinal fluid measures).36 Studies that may lead to direct in vivo human A imaging include various radio-labelled probes using small organic and organometallic molecules capable of detecting differences in amyloid fibril structure or amyloid protein sequences.37 Investigators also have used chrysamine-G, a carboxylic acid analogue of Congo red; an amyloid-staining histologic dye; 38 serum amyloid P component, a normal plasma glycoprotein that binds to amyloid deposit fibrils;39 or monoclonal antibodies.40 Methodological difficulties that hinder progress with these techniques include poor blood–brain barrier crossing and limited specificity and sensitivity. In addition, most approaches do not measure both NPs and NFTs. In a recent study, Barrio et al. 41 used a hydrophobic radiofluorinated derivative of 1,1-dicyano-2-[6-(dimethylamino)naphthalen-2-yl]propene (FDDNP)42 with PET to measure the cerebral localization and load of NFTs and SPs in AD patients (N=7) and controls (N=3). The FDDNP was injected intravenously and found to readily cross the blood-brain barrier in proportion to blood flow, as expected from highly hydrophobic compounds with high membrane permeability. Greater accumulation and slower clearance of FDDNP was observed in brain regions with high concentrations of NPs and NFTs, particularly the hippocampus, amygdala, and entorhinal cortex. The FDDNP residence time in these regions showed significant correlations with immediate and delayed memory performance measures,43 and areas of low glucose metabolism correlated with high FDDNP activity retention. The probe showed visualization of NFTs, NPs and diffuse amyloid in AD brain specimens using in vitro fluorescence microscropy, which matched results using conventional stains (e.g. thioflavin S) in the same tissue specimens. Thus, FDDNP-PET imaging is a promising non-invasive approach to longitudinal evaluation of NP and NFT deposition in preclinical AD. Magnetic resonance spectroscopy This approach is another innovative technique that is only recently been studied in the context of dementia. Initial studies of MRS as a preclinical AD detection technique found significantly lower NAA concentrations in AD and AAMI subjects compared with controls.44 Mean inositol concentration was significantly higher in AD than in controls, whereas AAMI subjects had intermediate values. Another study focused on patients with Down’s syndrome because they invariably develop Alzheimer-type pathology by the time they reach their thirties or forties. Concentrations of myoinositol and

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choline-containing compounds using 1H MRS were significantly higher in the occipital and parietal regions in 19 non-demented adults with Down’s syndrome and 17 age- and sex-matched healthy controls.45 Moreover, older Down’s syndrome subjects (42–62 years) had higher myo-inositol levels than younger subjects (28–39 years) suggesting that this approach may eventually be useful as a preclinical AD marker. Functional Imaging Positron emission tomography In recent years, clinicians have shown greater interest in the use of PET scanning for dementia diagnosis as access to PET centers has increased and mounting evidence of its diagnostic accuracy has come to light. Using fluorodeoxyglucose PET (FDG-PET), our group reported that parietal hypometabolism predicted future AD in people with questionable dementia46 and that even people with very mild age-related memory complaints have baseline PET patterns predicting cognitive decline after three years.47 These initial studies using PET for early AD detection emphasized family history of AD as a risk factor for future cognitive decline. A change in focus came with the discovery of the ApoE genetic risk for AD. The first report combining PET imaging and ApoE genetic risk in people with a family history of AD included 12 non-demented relatives with ApoE ε4, 19 relatives without ApoE ε4, and compared them to seven probable AD patients.14 “At-risk” subjects had mild memory complaints, normal cognitive performance, and at least two relatives with AD. Subjects with ApoE ε4 did not differ from those without ApoE ε4 in mean age (56.4 vs. 55.5 years) or in neuropsychological performance. Parietal metabolism was significantly lower and left-right parietal asymmetry higher in at-risk subjects with ApoE ε4 compared to those without ApoE ε4. Patients with dementia had significantly lower parietal metabolism than did at-risk subjects with ApoE ε4. The following year, Reiman et al.6 replicated these results and extended them to other brain regions. They found hypometabolism in temporal, prefrontal and posterior cingulate regions in a study of 11 non-demented ApoE ε4 homozygotes (4/4 genotype) and 22 ApoE ε3 homozygotes (3/3 genotype) of similar ages, i.e. in their mid-fifties, to those in our own initial study. They also applied an automated image analysis method, wherein metabolic reductions were standardized using three-dimensional stereotactic surface projections from FDG PET scans of AD patients compared with controls.48 The results from these two studies6,14 provided independent confirmation of an association between genetic risk and regional cerebral glucose hypometabolism. Our group recently confirmed these two initial reports in a study that included none of the subjects participating in our previous report on ApoE and PET.14 We studied 65 subjects in the 50 to 84 year age range (mean+SD

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= 67.3+9.4 years), with or without a family history of AD.49 Of the 65 subjects, 54 were non-demented (27 ApoE ε4 carriers and 27 were subjects without ApoE ε4), and 11 were demented and diagnosed with probable AD.49 The non-demented subjects were aware of a gradual onset of mild memory complaints (e.g. misplacing familiar objects, difficulty remembering names) but had memory performance scores within the norms for cognitively intact persons of the same age and educational level. The ApoE ε4 carriers had a small and non-significant but consistent reduction in cognitive performance. As predicted, baseline comparisons among the three subject groups indicated the lowest metabolic rates for the AD group, intermediate rates for the nondemented ApoE ε4 carriers, and highest rates for the non-demented group without ApoE ε4 in several cortical regions, including inferior parietal, lateral temporal, and posterior cingulate (Figure 1). Additional studies using FDG-PET have focused on older patients with Down’s syndrome who are at risk for AD.50 The investigators hypothesized that an audiovisual stimulation paradigm would serve as a stress test and reveal abnormalities in parietal and temporal cerebral glucose metabolism before dementia developed. At mental rest, younger and older patients with Down’s syndrome did not differ in glucose metabolic patterns. During audiovisual stimulation, however, the older patients showed significantly lower parietal and temporal metabolism. Families with familial AD linked to chromosome 14 or APP mutations have been studied with FDG-PET as well.51 In such families with early-onset AD, approximately half of relatives who live to the age at risk will develop AD. While pedigree members with AD show typical parietal and temporal hypometabolism, asymptomatic relatives at risk for AD show a similar but less severe hypometabolic pattern. Single photon emission computed tomography Johnson et al.52 used SPECT with a 99mTc-HMPAO to study longitudinal cerebral perfusion of patients with questionable AD (Clinical Dementia Rating [CDR] = 0.5)53 and controls. Regional decreases in perfusion in patients whose diagnosis converted to AD were most prominent in the hippocampalamygdaloid complex, the anterior and posterior cingulate, and the anterior thalamus. Including ApoE status did not influence results. A direct comparison of FDG-PET and HMPAO-SPECT in their ability to differentiate AD from vascular dementia indicated higher diagnostic accuracy for PET regardless of dementia severity.54 Using ROC curves, PET diagnostic accuracy was better than SPECT for MMSE > 20 (87.2% vs. 62.9%) and for MMSE < 20 (100% vs. 81.2%). Other studies have confirmed a lower sensitivity for even high-resolution SPECT compared with PET.55 Moreover, the parietal hypoperfusion observed using SPECT in AD patients has been observed in such other conditions as normal ageing, vascular dementia, post-hypoxic dementia, and sleep apnea.56

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Figure 1. Examples of PET images (comparable parietal lobe levels) co-registered to each subject’s baseline MRI scan for an 81-year-old non-demented woman (ApoE 3/3 genotype; upper images), a 76-year-old non-demented woman (ApoE 3/4 genotype; middle images), and 79-year-old woman with AD (ApoE 3/4 genotype; lower images). The last column shows two-year follow-up scans for the non-demented women. Compared with the nondemented subject without ApoE ε4, the non-demented ApoE ε4 carrier had 18% (right) and 12% (left) lower inferior parietal cortical metabolism, while the demented woman’s parietal cortical metabolism was 20% (right) and 22% (left) lower, as well as more widespread metabolic dysfunction due to disease progression. Two-year follow-up scans showed minimal parietal cortical decline for the woman without ApoE ε4, but bilateral parietal cortical decline for the non-demented woman with ApoE ε4, who also met clinical criteria for mild AD at follow-up. MRI scans were within normal limits.49

Functional MRI Two recent studies have combined ApoE genotyping and fMRI in persons at risk for AD. Bookheimer at al.57 performed fMRI studies while 30 cognitively intact middle-aged and older persons (mean age 63 years) memorized and retrieved unrelated word pairs. The 16 ApoE ε4 carriers did not differ

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Figure 2. These statistical parametric maps of recall vs. control blocks for ApoE ε4 carriers and non-carriers were standardized into a common coordinate system. Both groups showed significant MRI signal intensity increases in frontal, temporal and parietal regions, and the ApoE ε4 group had greater extent and intensity of activation. The ApoE ε4 group showed additional activation in the left parahippocampal region, left dorsal prefrontal cortex, and other regions in the inferior and superior parietal lobes, and anterior cingulate.57

significantly from the 14 subjects without ApoE ε4 in age, prior educational achievement, or rates of AD family history. Brain activation patterns were determined during both learning and retrieval task periods and analyzed using between-group and within-subject approaches. Memory performance was reassessed on 12 subjects after two years of follow-up. The ApoE ε4 carriers had significantly greater magnitude and spatial extent of MRI signal intensity during memory performance in regions affected by AD, including bilateral hippocampal and left parietal and prefrontal regions (Figure 2). This pattern of activation was greater in the left hemisphere, consistent with the verbal nature of the task, and during the retrieval rather than the learning condition. Longitudinal data indicated that greater baseline brain activation correlated with verbal memory decline assessed two years later. The greater signal in

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subjects with the ApoE ε4 genetic risk suggests that the brain may recruit additional neurons to compensate for subtle deficits. Moreover, the longitudinal data are encouraging that functional MRI may be a useful approach to prediction of future cognitive decline and early AD detection. By contrast, other kinds of memory tasks may produce different patterns of brain activation. In another study of persons at risk for AD, visual naming and letter fluency tasks were used to activate brain areas involved in object and face recognition during functional MRI scanning.58 Subjects in the highrisk group had at least one first-degree relative with AD and one ApoE ε4 allele. The low risk group was matched for age, education, and cognitive performance. The high-risk group showed reduced activation in the midand posterior inferotemporal regions bilaterally. Such decreased activation patterns could result from subclinical neuropathology in the inferotemporal region or in the inputs to that region. Longitudinal studies of glucose metabolism of persons at risk for dementia Two research groups — UCLA and University of Arizona — have reported their longitudinal FDG-PET follow-up data on non-demented persons at risk for AD. At UCLA, a total of 20 non-demented subjects (10 ApoE ε4 carriers and 10 without ApoE ε4) have received repeat PET and neuropsychological testing two years after baseline assessment (mean+SD for follow-up was 27.9+1.7 months).49 The 10 ApoE ε4 carriers available for longitudinal study were similar to the 10 non-carriers in mean+SD age (67.9+8.9 vs. 69.6+8.1 years) and educational achievement (14.4+1.8 vs. 16.4+2.8 years). Memory performance scores did not differ significantly according to genetic risk either at baseline or follow-up and the ApoE ε4 carriers and non-carriers did not differ significantly in cognitive change after two years. The ROI analysis of PET scans performed after two years showed significant glucose metabolic decline (4%) in the left posterior cingulate region in ApoE ε4 carriers. The SPM analysis showed significant metabolic decline in the inferior parietal and lateral temporal cortices with the greatest magnitude (5%) of metabolic decline in the temporal cortex (Figure 3). After correction for multiple comparisons, this decline remained significant for the ApoE ε4 group, wherein a decrease in metabolism was documented for every subject. Based upon these data from only 10 subjects, the estimated power of PET under the most conservative scenario is 0.9 to detect a 1-unit decline from baseline to follow-up using a one-tailed test. Such findings suggest that combining PET and AD genetic risk measures will allow investigators to use relatively small sample sizes when testing anti-dementia treatments in preclinical AD stages. The University of Arizona group also found that ApoE ε4 heterozygotes have significant two-year declines in regional brain activity, the largest of which is in temporal cortex, and that these reductions are significantly greater than those in ApoE ε4 non-carriers. Their findings suggest that as few as 22 cognitively normal, middle-aged ApoE ε4 heterozygotes

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Figure 3. Regions showing the greatest metabolic decline after two years of longitudinal follow-up in non-demented subjects with ApoE ε4 (SPM analysis) included the right lateral temporal and inferior parietal cortex (brain on the left side of figure). Voxels undergoing metabolic decline (p<0.001, before correction) are displayed in color, with peak significance (z=4.35) occurring in Brodmann’s area 21 of the right middle temporal gyrus.49

would be needed in each treatment arm (i.e. active drug and placebo) to test a prevention therapy over a two-year period.59 Clinical trials of pre-symptomatic patients using neuroimaging surrogate markers The longitudinal findings of significant parietal and temporal metabolic decline in asymptomatic persons at risk for AD because of age or genetic risk or both have now been confirmed at two centers in separate subject cohorts. Together these studies indicate that combining PET imaging of glucose metabolism and genetic risk may be useful outcome markers in AD prevention trials. Functional brain imaging techniques could be used to track pre-clinical cognitive decline and test candidate prevention therapies without having to perform prolonged multi-site studies using incipient AD as the primary outcome measure. The consistency and extent of the metabolic decline in these well-screened populations indicate that the PET measures provide adequate power to observe such decline in relatively small subject groups. A similar but less striking metabolic decline pattern was noted in subjects without ApoE ε4 such that larger groups per treatment arm would be needed. These observations provide an opportunity for pre-symptomatic treatment trials not previously available. Until now, such trials involved studies of preclinical subjects with more severe memory impairments consistent with MCI, wherein approximately 50% of subjects actually develop dementia over a 3–4 year period. The MCI trials have required hundreds of subjects for adequate power. These trials use a categorical variable, incipient dementia, as the pri-

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mary outcome measure. The introduction of FDG-PET imaging combined with ApoE ε4 genetic risk increases efficiency and reduces costs by addressing the research questions with fewer subjects. Our group is currently performing two such placebo-controlled trials, one using the cyclooxygenase-2 inhibitor celecoxib and the other using the cholinesterase inhibitor donepezil. Acknowledgements Supported in part by the Alzheimer’s Association; the Fran and Ray Stark Foundation Fund for Alzheimer’s Disease Research, Los Angeles, Calif; and NIH grants MH52453, AG10123, and AG13308. The views expressed are those of the author and do not necessarily represent those of the Department of Veterans Affairs. References 1. McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer’s disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer’s Disease. Neurology. 1984; 34:939–944. 2. Crook T, Bartus RT, Ferris SH, Whitehouse P, Cohen GD, Gershon S. Age-associated memory impairment: proposed diagnostic criteria and measures of clinical change — Report of a National Institute of Mental Health Work Group. Dev Neuropsychol. 1986; 2:261–276. 3. Larrabee GJ, Crook TH. Estimated prevalence of age-associated memory impairment derived from standardized tests of memory function. Int Psychogeriatr. 1994; 6:95–104. 4. Petersen RC, Smith GE, Waring SC, Ivnik RJ, Tangalos EG, Kokmen E. Mild cognitive impairment: clinical characterization and outcome. Arch Neurol. 1999; 56:303–308. 5. Andersen K, Nielsen H, Lolk A, Andersen J, Becker I, Kragh-Sørensen P. Incidence of very mild to severe dementia and Alzheimer’s disease in Denmark: the Odense Study. Neurology. 1999; 52:85–90. 6. Reiman EM, Caselli RJ, Yun LS, Chen K, Bandy D, Minoshima S, Thibodeau SN, Osborne D. Preclinical evidence of Alzheimer’s disease in persons homozygous for the epsilon 4 allele for apolipoprotein E [see comments]. N Engl J Med. 1996; 334:752–758. 7. Price JL, Morris JC. Tangles and plaques in nondemented aging and “preclinical” Alzheimer’s disease. Ann Neurol. 1999; 45:358–368. 8. Braak H, Braak E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 1991; 82:239–259. 9. Arai T, Ikeda K, Akiyama H, Haga C, Usami M, Sahara N, Iritani S, Mori H. A high incidence of apolipoprotein E epsilon4 allele in middle-aged non-demented subjects with cerebral amyloid beta protein deposits. Acta Neuropathol. 1999; 97:82–84. 10. Snowdon DA, Kemper SJ, Mortimer JA, Greiner LH, Wekstein DR, Markesbery WR. Linguistic ability in early life and cognitive function and Alzheimer’s disease in late life. Findings from the Nun Study. J Amer Med Assoc. 1996; 275: 528–532.

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11. Beach TG, Honer WG, Hughes LH. Cholinergic fibre loss associated with diffuse plaques in the non-demented elderly: the preclinical stage of Alzheimer’s disease? Acta Neuropathol. 1997; 93:146–153. 12. Shen J, Barnes CA. Age-related decrease in cholinergic synaptic transmission in three hippocampal subfields. Neurobiol Aging. 1996; 17:439–451. 13. Small GW, Ercoli LM, Huang S-C, Komo S, Bookheimer SY, Saxena S, Silverman DHS, Mega MS, Mazziotta JC, Wu HM, Cummings JL, Phelps ME. PET and genetic risk for Alzheimer disease. J Nucl Med. 1999;40 (Suppl.):70. 14. Small GW, Mazziotta JC, Collins MT, Baxter LR, Phelps ME, Mandelkern MA, Kaplan A, La Rue A, Adamson CF, Chang L. Apolipoprotein E type 4 allele and cerebral glucose metabolism in relatives at risk for familial Alzheimer disease. J Amer Med Assoc. 1995; 273:942–947. 15. de Leon MJ, George AE, Golomb JC, Convit A, Kluger A, De Santi S, McRae T, Ferris SH, Reisberg B, Ince C, Rusinek H, Bobinski M, Quinn B, Miller DC, Wisniewski HM. Frequency of hippocampal formation atrophy in normal aging and Alzheimer’s disease. Neurobiol Aging. 1997; 18:1–11. 16. Boone KB, Miller BL, Lesser IM, Mehringer CM, Hill-Gutierrez E, Goldberg MA, Berman NG. Neuropsychological correlates of white-matter lesions in healthy elderly subjects. A threshold effect. Arch Neurol. 1992; 49:549–554. 17. Lopez OL, Becker JT, D R, Wess J, Boller F, Reynolds CF 3d, Panisset M. Neuropsychiatric correlates of cerebral white-matter radiolucencies in probable Alzheimer’s disease. Arch Neurol. 1992; 49:828–834. 18. Rusinek H, de Leon MJ, George AE, Stylopoulos LA, Chandra R, Smith G, Rand T, Mourino M, Kowalski H. Alzheimer disease: measuring loss of cerebral gray matter with MR imaging. Neuroradiology. 1991; 178:109–114. 19. Kesslak JP, Nalcioglu O, Cotman CW. Quantification of magnetic resonance scans for hippocampal and parahippocampal atrophy in Alzheimer’s disease. Neurology. 1991; 41:51–54. 20. Pearlson GD, Harris GJ, Powers RE, Barta PE, Camargo EE, Chase GA, Noga JT, Tune LE. Quantitative changes in mesial temporal volume, regional cerebral blood flow, and cognition in Alzheimer’s disease. Arch Gen Psychiat. 1992; 49: 402–408. 21. Laakso MP, Soininen H, Partanen K, Lehtovirta M, Helkala EL, Hallikainen M, Hanninen T, Vainio P, Soininen H. MRI of the hippocampus in Alzheimer’s disease: sensitivity, specificity, and analysis of the incorrectly classified subjects. Neurobiol Aging. 1998; 19:23–31. 22. Pasquier F, Lavenu I, Lebert F, Jacob B, Steinling M, Petit H. The use of SPECT in a multidisciplinary memory clinic. Dement Geriatr Cogn. 1997; 8:85–91. 23. Pucci E, Belardinelli N, Regnicolo L, Nolfe G, Signorino M, Salvolini U, Angeleri F. Hippocampus and parahippocampal gyrus linear measurements based on magnetic resonance in Alzheimer’s disease. Eur Neurol. 1998; 39:16–25. 24. Jack CR, Petersen RC, Xu YC, O’Brien PC, Smith GE, Ivnik RJ, Boeve BF, Waring SC, Tangalos EG, Kokmen E. Prediction of AD with MRI-based hippocampal volume in mild cognitive impairment. Neurology. 1999; 52:1397–1403. 25. Wahlund L-O, Julin P, Lindqvist J, Scheltens P. Visual assessment of medial temporal atrophy in demented and healthy control subjects: correlation with volumetry. Psychiat Res Neuroim Section. 1999; 90 :193–199. 26. Killiany RJ, Moss MB, Albert MS, Sandor T, Tieman J, Jolesz F. Temporal lobe regions on magnetic resonance imaging identify patients with early Alzheimer’s disease. Arch Neurol. 1993; 50:949–954. 27. Laakso MP, Partanen K, Riekkinen P, Lehtovirta M, Helkala EL, Hallikainen M, Hanninen T, Vainio P, Soininen H. Hippocampal volumes in Alzheimer’s disease, Parkinson’s disease, with and without dementia, and in vascular dementia: An MRI study. Neurology. 1996; 46:678–681.

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28. Reiman EM, Uecker A, Caselli RJ, Lewis S, Bandy D, de Leon MJ, De Santi S, Convit A, Osborne D, Weaver A, Thibodeau SN. Hippocampal volumes in cognitively normal persons at genetic risk for Alzheimer’s disease. Ann Neurol. 1998; 44:288–291. 29. Erkinjuntti T, Gao F, Lee DH, Eliasziw M, Merskey H, Hachinski CV. Lack of difference in brain hyperintensities between patients with early Alzheimer’s disease and control subjects. Arch Neurol. 1994; 51:260–268. 30. Mauri M, Sibilla L, Bono G, Carlesimo GA, Sinforiani E, Martelli A. The role of morpho-volumetric and memory correlations in the diagnosis of early Alzheimer dementia. J Neurol. 1998; 245:525–530. 31. Bobinski M, de Leon MJ, Convit A, De Santi S, Wegiel J, Tarshish CY, Saint Louis LA, Wisniewski HM. MRI of entorhinal cortex in mild Alzheimer’s disease. Lancet. 1999;353:38–40. 32. Geroldi C, Pihlajamaki M, Laakso MP, DeCarli C, Beltramello A, Bianchetti A, Soininen H, Trabucchi M, Frisoni GB. APOE-ε4 is associated with less frontal and more medial temporal lobe atrophy in AD. Neurology. 1999; 53:1825–1832. 33. Soininen H, Partanen K, Pitkanen A, Hallikainen M, Hänninen T, Helisalmi S, Mannermaa A, Ryynänen M, Koivisto K, Riekkinen P. Decreased hippocampal volume asymmetry on MRIs in nondemented elderly subjects carrying the apolipoprotein E ε4 allele. Neurology. 1995; 45:1467–1472. 34. Mattman A, Feldman H, Forster B, Li D, Szasz I, Beattie BL, Schulzer M. Regional HmPAO SPECT and CT measurements in the diagnosis of Alzheimer’s disease. Can J Neurol Sci. 1997; 24:22–28. 35. Jack CR, Petersen RC, Xu YC, O’Brien PC, Waring SC, Tangalos EG, Smith GE, Ivnik RJ, Thibodeau SN, Kokmen E. Hippocampal atrophy and apolipoprotein E genotype are independently associated with Alzheimer’s disease. Ann Neurol. 1998; 43:303–310. 36. Motter R, Vigo-Pelfrey C, Kholodenko D, Barbour R, Johnson-Wood K, Galasko D, Chang L, Miller B, Clark C, Green R. Reduction of beta-amyloid peptide42 in the cerebrospinal fluid of patients with Alzheimer’s disease. Ann Neurol. 1995; 38:643–648. 37. Ashburn TT, Han H, McGuinness BF, Lansbury PT. Amyloid probes based on Congo Red distinguish between fibrils comprising different peptides. Chem Biol. 1996; 3:351–358. 38. Klunk WE, Debnath ML, Pettegrew JW. Chrysamine-G binding to Alzheimer and control brain: autopsy study of a new amyloid probe. Neurobiol Aging. 1995; 16: 541–548. 39. Lovat LB, O’Brien AA, Armstrong SF, Madhoo S, Bulpitt CJ, Rossor MN, Pepys MB, Hawkins PN. Scintigraphy with 123I-serum amyloid P component in Alzheimer disease. Alz Dis Assoc Dis. 1998; 12:208–210. 40. Majocha RE, Reno JM, Friedland RP, VanHaight C, Lyle LR, Marotta CA. Development of a monoclonal antibody specific for beta/A4 amyloid in Alzheimer’s disease brain for application to in vivo imaging of amyloid angiopathy. J Nucl Med. 1992; 33:2184–2189. 41. Barrio JR, Huang S-C, Cole GM, Satyamurthy N, Petric A, Small GW. PET imaging of tangles and plaques in Alzheimer disease. J Nucl Med. 1999;40 (Suppl.): 70P–71P. 42. Jacobson A, Petric A, Hogenkamp D, Sinur A, Barrio JR. 1,1-dicyano-2-(6dimethylamino)naphthalen-2-yl)propene (DDNP): a solvent polarity and viscosity sensitive fluorophore for fluorescence microscopy. J Amer Chem Soc. 1996; 118:5572–5579. 43. Wechsler D. Wechsler Memory Scale-Revised Manual. San Antonio: The Psychological Corp – Harcourt Brace Jovanovich; 1987.

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44. Parnetti L, Lowenthal DT, Presciutti O, Pelliccioli GP, Palumbo R, Gobbi G, Chiarini P, Palumbo B, Tarducci R, Senin U. 1H-MRS, MRI-based hippocampal volumetry, and 99mTc-HMPAO-SPECT in normal aging, age-associated memory impairment, and probable Alzheimer’s disease. J Amer Geriatr Soc. 1996; 44: 133–138. 45. Huang W, Alexander GE, Daly EM, Shetty HU, Krasuski JS, Rapoport SI, Schapiro MB. High brain myo-inositol levels in the predementia phase of Alzheimer’s disease in adults with Down’s syndrome: a 1H MRS study. Am J Psychiat. 1999; 156:1879–1886. 46. Kuhl DE, Small GW, Riege WH, Fujikawa DG, Metter EJ, Benson DF, Ashford JW, Mazziotta JC, Maltese A, Dorsey DA. Cerebral metabolic patterns before diagnosis of probable Alzheimer’s disease. J Cerebr Blood F Met. 1987; 7 (Suppl 1):S406. 47. Small GW, La Rue A, Komo S, Kaplan A, Mandelkern MA. Predictors of cognitive change in middle-aged and older adults with memory loss. Am J Psychiat. 1995; 152:1757–1764. 48. Minoshima S, Frey KA, Koeppe RA, Foster NL, Kuhl DE. A diagnostic approach in Alzheimer’s disease using three-dimensional stereotactic surface projections of fluorine-18-FDG PET. J Nucl Med. 1995; 36:1238–1248. 49. Small GW, Ercoli LM, Silverman DHS, Huang S-C, Komo S, Bookheimer SY, Lavretsky H, Miller K, Siddarth P, Mazziotta JC, Saxena S, Wu HM, Mega MS, Cummings JL, Saunders AM, Pericak-Vance MA, Roses AD, Barrio JR, Phelps ME. Cerebral metabolic and cognitive decline in persons at genetic risk for Alzheimer’s disease. Proc Natl Acad Sci USA. 2000; 97:6037–6042. 50. Pietrini P, Dani A, Furey ML, Alexander GE, Freo U, Grady CL, Mentis MJ, Mangot D, Simon EW, Horwitz B, Haxby JV, Schapiro MB. Low glucose metabolism during brain stimulation in older Down’s syndrome subjects at risk for Alzheimer’s disease prior to dementia. Am J Psychiat. 1997; 154:1063–1069. 51. Rossor MN, Kennedy AM, Frackowiak RS. Clinical and neuroimaging features of familial Alzheimer’s disease. Ann NY Acad Sci. 1996; 777:49–56. 52. Johnson KA, Jones K, Holman BL, Becker JA, Spiers PA, Satlin A, Albert MS. Preclinical prediction of Alzheimer’s disease using SPECT. Neurology. 1998; 50: 1563–1571. 53. Hughes CP, Berg L, Danziger WL, Coben LA, Martin RL. A new clinical scale for the staging of dementia. Br J Psychiat. 1982; 140:566–572. 54. Mielke R, Heiss W-D. Positron emission tomography for diagnosis of Alzheimer’s disease and vascular dementia. J Neural Transm. 1998; 53 (Suppl):237–250. 55. Messa C, Perani D, Lucignani G, Zenorini A, Zito F, Rizzo G, Grassi F, Del Sole A, Franceschi M, Gilardi MC. High-resolution technetium-99m-HMPAO SPECT in patients with probable Alzheimer’s disease: comparison with fluorine-18-FDG PET. J Nucl Med. 1994; 35:210–216. 56. Miller BL, Mena S, Daly J, Giombetti RJ, Goldbergt MA, Lesser I, Garrett K, Villanueva-Meyer J, Liu C-K. Temporal-parietal hypoperfusion with single-photon emission computerized tomography in conditions other than Alzheimer’s disease. Dementia. 1990; 1:41–45. 57. Bookheimer SY, Strojwas MH, Cohen MS, Saunders AM, Pericak-Vance MA, Mazziotta JC, Small GW. Brain activation in older people at genetic risk for Alzheimer’s disease. N Engl J Med. 2000; 343:450–456. 58. Smith CD, Andersen AH, Kryscio RJ, Schmitt FA, Kindy MS, Blonder LX, Avison MJ. Altered brain activation in cognitively intact individuals at high risk for Alzhiemer’s disease. Neurology. 1999; 53:1391–1396. 59. Reiman EM, Uecker A, Gonzalez-Lima F, Minear D, Chen K, Callaway NL, Berndt JD, Games D. Tracking Alzheimer’s disease in transgenic mice using fluorodeoxyglucose autoradiography. NeuroReport. 2000 11:987–991.

Chapter 16 PARKINSONISM AND AGEING John GL Morris*, Mariese A Hely, and Glenda M Halliday

Introduction It is a matter of everyday experience that, in the journey from childhood to old age, the way in which we move, speak, hold ourselves and walk gradually changes. A movement or gesture by a skilled actor can, in a moment, leave his audience in no doubt as to the age of the person being depicted; our posture and movements denote our age. The changes from childhood to adulthood reflect maturation of the brain and body. But what determines the changes from middle to old age? Is this normal “wear and tear” of the bones, joints and muscles, or are the changes we see due to disease? Friends who have not seen a patient with early Parkinsonism for a while may be struck at how they have “aged”: they have become stooped and slow. Can Parkinsonism be considered a form of accelerated ageing or, conversely, how much of “normal” ageing is due to incipient Parkinson’s disease? In this chapter, we compare and contrast ageing and Parkinsonism. Posture Perhaps the most telling sign of advancing years is the emergence of a stoop. The spine becomes flexed, the head drops forward. Parkinson’s disease (PD) also causes flexion of the neck and spine but here, the elbows, knees and hips are often also flexed causing the so-called simian (“ape-like”) posture. The *To whom correspondence should be addressed.

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flexed posture of Parkinson’s disease reflects the distribution of tone within the flexor and extensor muscles of the spine and limbs*. The question arises: is the posture of old age due to factors such as wedging of the vertebrae due to osteoporosis, reduced mobility associated with osteo-arthritis and muscle weakness and wasting secondary to disuse? Or are there also changes in the brain which contribute to this? With advancing age there is progressive atrophy of the brain1 due to shrinkage2 of neurones and supporting cells but the association between these changes in the brain and posture is uncertain. Lesions of the white matter of the cerebral hemispheres become increasingly common with advancing age. These are due to lacunes, perivascular spaces and gliosis probably secondary to infarction.3 Flexion of the trunk is unlikely to be due to such vascular disease for in patients with severe subcortical arteriosclerotic encephalopathy (Binswanger’s disease), the posture is upright.4 The underlying abnormality in Parkinson’s disease is degeneration of the dopaminergic nigro-striatal tract. Unilateral nigrostriatal lesions in animal models of Parkinson’s disease produce postural abnormalities.5 Yet treatment with the precursor of dopamine, levodopa, while improving most aspects of the disease, does not usually improve posture to a great extent. This suggests that the abnormality of posture in Parkinson’s disease results from involvement of non-dopaminergic systems. More recently, the combination of levodopa and bilateral subthalamic stimulation6 has been shown to improve posture. The mechanism of this aspect of the disease remains uncertain. Gait and Balance The gait of the very old lacks the speed, confidence and spring of the young. In many older people, the gait also appears to reflect a fear of falling. It is slower, the stride length is shortened, the base widened a little and turning is executed in several steps rather than in a single smooth movement. This is the so-called “cautious” gait7 which we all adopt on a ship’s deck in rough conditions or when walking on slippery ice. Factors which contribute to such a gait in the elderly include impaired eyesight (cataract, macular degeneration) and osteo-arthritis, especially of the knees and hips. Sudden pain may cause the leg to give way resulting in a fall. Painful arthritis may further modify the gait producing the so-called “antalgic” or hobbling gait, where the normal leg swings faster than the painful leg, thereby minimising the time that the painful limb has to bear weight. Many older patients have or have had diseases which impair other principal components necessary for normal gait * The posture in some of the variants of Parkinson’s disease is rather different. In anterocollis, a feature of multiple system atrophy and sometimes dementia with Lewy bodies, the overall posture of the trunk is upright but the neck is markedly flexed. The posture of the trunk usually remains upright in progressive supranuclear palsy.

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and balance: the vestibular system (vestibular neuronitis, Meniere’s disease, antibiotic ototoxicity), proprioception (diabetic and other forms of peripheral neuropathy8), cerebellum (alcohol) and pyramidal system (cervical myelopathy, stroke). Apart from diseases, there are age related changes in measures such as peripheral nerve conduction velocity,9 and vestibular function10 which may have a bearing on movement and balance. The cause of changes in gait with ageing may thus be seen as resulting from the cumulative effect of minor impairment in a number of modalities, each of which is insufficient, on its own, to impair gait. An association has been found between impairment in balance in older people and the findings of white matter lesions in the cerebral hemispheres11 and frontal atrophy12 on MRI scanning. Many of these patients have the socalled marche a petit pas with slow, shuffling steps, a broad-based stance, difficulty initiating gait (“start-hesitation”) but with well preserved hand function, arm swing and facial expression and usually no festination (see below)4. This “Parkinsonian-ataxic gait”4 is also seen in patients with low pressure hydrocephalus and frontal tumours. Thompson and Marsden4 have pointed out that the thalamo-cortical fibres destined for the supplementary motor area (SMA) from the basal ganglia, and the thalamo-cortical fibres from the leg area of the cerebellum destined for the motor cortex, are situated in the periventricular white matter. Fibres destined for the upper limbs are more superficial, run a shorter course and may thus be less susceptible to disease in the deep white matter.11,13 The gait of Parkinson’s disease is rather different. The earliest feature is loss of arm swing, usually on only one side to begin with. Later, the stride shortens and the posture becomes flexed. As the righting reflex is lost, falls occur but this does not occur in idiopathic Parkinson’s disease in the early years*. Notably, however bad the balance becomes, the gait remains narrow based in Parkinson’s disease. A particular feature of the Parkinsonian gait in advanced disease is its hurrying (“festinant”) quality where small steps are taken rapidly as if the feet are trying to catch up with the stooped trunk preceding them. In its most marked form, patients break involuntarily into a run (“propulsion”), continuing until they fall or bump into something. A firm pull from behind will cause such a patient to run backwards (“retropulsion”). Associated with festination is “freezing” where the gait is suddenly arrested often as the patient negotiates an obstacle such as a doorway, sometimes the patient appears to “run on the spot” (“clutch slip”). The start-hesitation and freezing of Parkinson’s disease may be overcome by visual cues such as lines on the floor and steps; by contrast freezing in marche a petit pas is not influenced by such cues.4,13 The gait in other patients with Parkinsonism is characterised by extreme slowness and stiffness. What determines why one patient hurries while another is slow is poorly understood. * By contrast, falls occur early in the variants of Parkinson’s disease such as multiple system atrophy and progressive supranuclear palsy.

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The hallmark of the gait disturbance in Parkinson’s disease is the failure to generate an appropriate stride length.14 This is thought to result from disruption of the normal interaction between the basal ganglia and the SMA, as a result of loss of the dopaminergic neurones of the nigro-striatal tract.15 Gait is improved by visual cues and made worse by distraction, something that has also been observed in healthy elderly subjects.16 Gait in idiopathic Parkinson’s disease is improved by levodopa therapy: distances are covered more quickly, arm swing increases, and stride lengthens. By contrast, balance is not improved and, paradoxically, patients may fall more frequently as their mobility is enhanced by the effect of levodopa. The pathophysiology of loss of balance in Parkinson’s disease is not well understood. It was associated with frontal atrophy in one study of non-demented parkinsonian patients.17 Attention is now being directed to other structures such as the pedunculopontine nucleus18 in an attempt to understand these aspects of Parkinson’s disease which are unresponsive to levodopa therapy. Freezing often also persists while levodopa is working. A surprising recent finding was that deprenyl, a selective monoamine oxidase ‘B’ inhibitor which otherwise causes only minor improvement in Parkinson’s disease, may help to prevent freezing;19 time will tell if this finding holds up in clinical practice. Eye Movements Some restriction of upward gaze is apparent in many elderly otherwise normal patients.20 The basis of this is not known though it may be speculated that it reflects ischaemia or degeneration of vertical gaze pathways in the rostral brainstem. In Parkinson’s disease, there is reduction in the amplitude of voluntary saccades, comparable to the shortening of stride length in gait. The velocity of the saccades is not affected*. When asked to look from one object to another, the patient’s eyes are seen to move in a series of small “bunny hops” rather than in one clean leap. Levodopa does not improve this abnormality but high frequency stimulation of the subthalamic nucleus has been shown to improve some aspects of saccadic function.21 Smooth pursuit is commonly impaired in both Parkinson’s disease and the elderly. Speech As people get older their speech changes. Often this can be attributed to illfitting dentures causing distortion of consonants. More saliva is retained in the mouth due, presumably, to reduced frequency of swallowing. There is also a change in the timbre of the voice and some loss of volume. Loss of volume * By contrast, in progressive supranuclear palsy, the most striking finding is of slowing of saccades, and, later, impairment of downward gaze.

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(hypophonia) is the commonest speech abnormality in Parkinson’s disease and may be attributed to reduced amplitude of movement (or akinesia) of the respiratory muscles. More characteristic of Parkinson’s disease is the loss of normal fluctuations of pitch and volume thereby producing a monotonous voice. Like the gait, speech may be hurried, one sound running into the next and with pauses where the power of speech is momentarily lost. Sounds may be repeated, causing a stammer known as pallilalia. The pathological basis of these problems in speech in Parkinson’s disease is uncertain. Speech is not usually improved by levodopa therapy. Pallidotomy significantly worsens speech abnormalities in some patients with Parkinson’s disease,22,23 though this probably varies with the site and size of the lesion. Improvement in the tempo and timbre of speech has been reported with subthalamic stimulation.24 Facial Expression The lines on our face deepen as we get older yet our faces lose some of the animation of our youth. This is rarely marked and probably reflects changes in mood and energy rather than any disorder of our motor system. By contrast, loss of facial expression in Parkinsonism may be profound. Unlike the actor of whom it was said that “Changes in mood crossed his face like small weather fronts” (Daily Telegraph critic on Warren Clarke’s performance in A Respectable Trade on BBC1) these patients’ faces are frozen and unblinking, apparently impervious to the feelings of their owners until, on occasions, a feeling is strong enough to break the ice and a smile slowly dawns. These features reflect akinesia and rigidity and often improve with levodopa only to be replaced in some cases with writhing of the facial features due to levodopa induced dyskinesia. Alternating Fine Movements While some loss of manual dexterity is measurable in normal patients as they get older,25 this is not usually detectable by routine clinical testing. Every patient with Parkinsonism has difficulty making alternating, piano-playing movements of the index and middle fingers and most have difficulty toe tapping. As previously discussed, the amplitude of most movements is reduced, usually lessening further as the task continues. This feature of akinesia is also seen in writing which becomes smaller as it goes across the page. Levodopa markedly improves manual dexterity in idiopathic Parkinson’s disease. Similar improvement is seen with subthalamic stimulation.

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Muscle Tone This is often hard to test in elderly patients for it depends on their ability to voluntarily relax and not actively assist or resist what the examiner is doing to them. Asking such a patient to relax their legs induces a state of extreme stiffness: the harder the examiner pulls, the more the patient resists. Sometimes the problem is due to tenderness of the skin or muscles or painful arthritis, and re-positioning of the examiner’s hands may help. This “voluntary” contraction of the muscles is sometimes called paratonia or gegenhalten. It is commonly seen in the setting of advanced dementia where the patient resumes the flexed posture of the foetus; in time, with contracture of the muscles, the posture becomes permanent. Rigidity Parkinsonian rigidity is rather different. Here, the limbs can usually be moved through their normal range but a resistance is felt which is constant throughout that range (unlike spasticity, where a sudden increase in resistance or “catch” may be felt, particularly if the examiner suddenly increases the rate at which he moves the limb). Rigidity palpably increases in the upper limb, when the patient is asked to slowly raise and lower the other arm at the shoulder (“tone reinforcement”). Rigidity, like akinesia, is improved by levodopa therapy and is presumably a consequence of the loss of the dopaminergic nigro-striatal neurones. Resting Tremor Many elderly patients have a low amplitude tremor of the outstretched hands or occasional flurries of quivering of individual fingers. The presence of a coarse resting tremor, usually in one hand more than the other, which persists during walking and is temporarily abolished by voluntary movement of the affected limb, usually signifies Parkinson’s disease and is not a feature of normal ageing.26 Prevalence of Parkinsonism in the Elderly As we age, our movements become slower. Bradykinesia without other signs of Parkinsonism was commonly found in a study of normal subjects aged 75 years or more.20 Some features of Parkinsonism including slowness, stiffness, gait disorder and tremor were found in 14.9% of people aged 65–74 years, 29.5% of people aged 75–84 years and 52.4% of people aged 85 or more in a study of non-institutionalized elderly Boston residents.26 This is probably

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an overestimate for other studies have found a much lower prevalence of the disease. In the Rotterdam study of 6969 subjects, each individually examined, the prevalence of Parkinson’s disease was 1.0% for those aged 65–74, 3.1% for those aged 75–84 and 4.3% for those aged 85–94 years.27 Similar findings occurred in Sicilian and French community studies.28,29 In conclusion, though old age and Parkinson’s disease appear to share many features, there are subtle differences between the two which point to differing underlying mechanisms.1 The changes in Parkinson’s disease are mainly due to degeneration of the nigro-striatal tracts. In old age, the changes reflect a more generalized disturbance of neuronal function. Significantly, the pathological changes in the substantia nigra in Parkinson’s disease, differ from those due to ageing.30 References 1. Mahant PR, Stacy MA. Movement disorders and normal ageing. Neurol Clin. 2001; 19:553–563, vi. 2. Terry RD, DeTeresa R, Hansen LA. Neocortical cell counts in normal human adult ageing. Ann Neurol. 1987; 21:530–539. 3. Baloh RW, Vintners HV. White matter lesions and disequilibrium in older people. II Clinicopathologic correlation. Arch Neurol. 1995; 52:975–981. 4. Thompson PD, Marsden CD. Gait disorder of subcortical arteriosclerotic encephalopathy: Binswanger’s disease. Movement Disord. 1987; 2:1–8. 5. Johnson RE, Schallert T, Becker JB. Akinesia and postural abnormality after unilateral dopamine depletion. Behav Brain Res. 1999; 104:189–196. 6. Bejjani BP, Gervais D, Arnulf I, Papadopoulos S, Demeret S, Bonnet AM Cornu P, Dmier P, Agid Y. Axial parkinsonian symptoms can be improved: the role of levodopa and bilateral subthalamic stimulation. J Neurol Neurosur Ps. 2000; 68: 595–600. 7. Nutt JG, Marsden CD, Thompson PD. Human walking and higher-level gait disorders, particularly in the elderly. Neurology. 1993; 43:268–279. 8. Bergin PS, Bronstein AM, Murray NM, Sancovic S, Zeppenfeld DK. Body sway and vibration perception thresholds in normal ageing and in patients with polyneuropathy. J Neurol Neurosur Ps. 1995; 58:335–340. 9. Rivner MH, Swift TR, Malik K. Influence of age and height on nerve conduction. Muscle Nerve. 2001; 24:1134–1141. 10. Hajioff D, Barr-Hamilton RM, Colledge NR, Lewis SJ, Wilson JA. Re-evaluation of normative electronystagmography data in healthy ageing. Clin Otolaryngol. 2000; 25:249–252. 11. Baloh RW, Yue Q, Socotch TM, Jacobson KM. White matter lesions and disequilibrium in older people. I. Case-control comparison. Arch Neurol. 1995; 52: 970–974. 12. Kerber KA, Enrietto JA, Jacobson KM, Baloh RW. Disequilibrium in older people: a prospective study. Neurology. 1998; 51:574–580. 13. Curran T, Lang AE. Parkinsonian syndromes associated with hydrocephalus: case reports, a review of the literature, and pathophysiological hypotheses. Movement Disord. 1994; 9:508–520. 14. Morris ME, Iansek R, Matyas TA, Summers JJ. Ability to modulate walking cadence remains intact in Parkinson’s disease. J Neurol Neurosur Ps. 1994; 57: 1532–1534.

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15. Morris ME, Iansek R, Matyas TA, Summers JJ. Stride length regulation in Parkinson’s disease. Normalization strategies and underlying mechanisms. Brain. 1996; 119:551–568. 16. Camicioli R, Howieson D, Lehman S, Kaye J. Talking while walking: the effect of a dual task in aging and Alzheimer’s disease. Neurology. 1997; 48:955–958. 17. Durif F, Pollak P, Hommel M, Ardouin C, Le Bas JF, Crouzet C, Perret J. Relationship between levodopa-independent symptoms and central atrophy evaluated by magnetic resonance imaging in Parkinson’s disease. Eur Neurol. 1992; 32: 32–36. 18. Pahapill PA, Lozano AM. The pedunculopontine nucleus and Parkinson’s disease. Brain. 2000; 123:1767–1783. 19. Giladi N, McDermott MP, Fahn S, Przedborski S, Jankovic J, Stern M, Tanner C. Freezing of gait in PD: prospective assessment in the DATATOP cohort. Neurology. 2001; 56:1712–1721. 20. Waite LM, Broe GA, Creasey H, Grayson D, Edelbrock D, O’Toole B. Neurological signs, aging, and the neurodegenerative syndromes. Arch Neurol. 1996; 53: 498–502. 21. Rivaud-Pechoux S, Vermersch AI, Gaymard B, Ploner CJ, Bejjani BP, Damier P, Demeret S, Agid Y, Pierrot-Deseilligny C. Improvement of memory guided saccades in parkinsonian patients by high frequency subthalamic nucleus stimulation. J Neurol Neurosur Ps. 2000; 68:381–384. 22. Scott R, Gregory R, Hines N, Carroll C, Hyman N, Papanasstasiou V, Leather C, Rowe J, Silburn P, Aziz T. Neuropsychological, neurological and functional outcome following pallidotomy for Parkinson’s disease. A consecutive series of eight simultaneous bilateral and twelve unilateral procedures. Brain. 1998; 121: 659–675. 23. Merello M, Starkstein S, Nouzeilles MI, Kuzis G, Leiguarda R. Bilateral pallidotomy for treatment of Parkinson’s disease induced corticobulbar syndrome and psychic akinesia avoidable by globus pallidus lesion combined with contralateral stimulation. J Neurol Neurosur Ps. 2001; 71:611–614. 24. Gentil M, Chauvin P, Pinto S, Pollak P, Benabid AL. Effect of bilateral stimulation of the subthalamic nucleus on parkinsonian voice. Brain Lang. 2001; 78: 233–240. 25. Nutt JG, Lea ES, Van Houten L, Schuff RA, Sexton GJ. Determinants of tapping speed in normal control subjects and subjects with Parkinson’s disease: differing effects of brief and continued practice. Movement Disord. 2000; 15:843–849. 26. Bennett DA, Beckett LA, Murray AM, Shannon KM, Goetz CG, Pilgrim DM, Evans DA. Prevalence of parkinsonian signs and associated mortality in a community population of older people. N Engl J Med. 1996; 334:71–76. 27. de Rijk MC, Breteler MM, Graveland GA, Ott A, Grobbee DE, van der Meche FG, Hofman A. Prevalence of Parkinson’s disease in the elderly: the Rotterdam Study. Neurology. 1995; 45:2143–2146. 28. Morgante L, Rocca WA, Di Rosa AE, De Domenico P, Grigoletto F, Meneghini F, Reggio A, Savettieri G, Castiglione MB, Patti F. Prevalence of Parkinson’s disease and other types of parkinsonism: a door-to-door survey in three Sicilian municipalities. The Sicilian Neuro-Epidemiologic Study (SNES) Group. Neurology. 1992; 42:1901–1907. 29. Tison F, Dartigues JF, Dubes L, Zuber M, Alperovitch A, Henry P. Prevalence of Parkinson’s disease in the elderly: a population study in Gironde, France. Acta Neurol Scand. 1994; 90:111–115. 30. Fearnley JM, Lees AJ. Ageing and Parkinson’s disease: substantia nigra regional selectivity. Brain. 1991; 114:2283–2301.

Chapter 17 AGE VARIATION IN THE PREVALENCE OF DEPRESSION: ARE STUDY FINDINGS MEANINGFUL? John Snowdon

Introduction Depression in old age has been said to be widespread, common and disabling,1,2 yet the rates of treatment of depression among elderly persons are markedly lower than among younger adults. 3 Underdiagnosis and undertreatment of depression in old age have been attributed to a belief, shared by doctors and patients, that the depressions experienced by elderly persons are usually a “normal” consequence of the many physical illnesses and social and economic problems that they endure. Both clinicians and patients may incorrectly attribute depressive symptoms to the ageing process.3 Blazer4 concluded that the prevalence of clinically significant depressive symptoms in community samples of older adults is approximately 8% to 15%, while the prevalence of current major depression in such samples is only 1%. He commented5 that divergence between results of studies in the last two decades concerning the prevalence of late life depression largely derives from variations in the definitions of “caseness” of depression. There has been controversy regarding the prevalence of depression in different age groups. Is it higher in young, middle-aged or older persons? Blazer5 suggested that the debate is often meaningless because the varying findings are based on differing understanding of what is meant by “depression”. Some used categorical definitions, some recorded whether subjects “scored” as depressed on dimensional scales, and others developed age-appropriate

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THE AGEING BRAIN

schedules for eliciting evidence of depressive features. Blazer4 stated that “although DSM-III-R is not “age biased”, it fails to accommodate correlates of age such as comorbid cognitive impairment and comorbid physical illness”. Caine et al.6 commented that affective disturbances often are not expressed symptomatically among elderly patients in the same stereotypic fashion that is encountered among younger patient populations, and “current use of rigorous, stereotypic criteria for establishing psychiatric diagnoses may prove to be highly reliable but not especially valid”. It could be meaningful to discuss differences between age groups in the prevalence of a particular type of depression if there is good reason to believe that a treatment is effective for that type and not for others. It might be appropriate to investigate whether particular factors (for example, brain changes) are associated with that type of depression and not with others, and to consider whether those factors are age-associated. For example, research on reliably defined psychotic depression or melancholia could provide useful guides to prevention, treatment and prognosis. Such studies have been conducted.7–10 Contrastingly, reports of differences in prevalence between age groups that confine consideration to cases that fulfil criteria for major depression, without considering the clinical context (aetiology, comorbidity, function and effects), could be considered relatively meaningless. It is possible that older people fulfilling four rather than five relevant clinical criteria typically have more serious depressions than younger subjects fulfilling five criteria. We do not know. Having “major” depression does not necessarily mean having a more severe or disabling depression. What matters far more to the clinician is whether the patient has a distressing, disabling condition that could respond to treatment. Various experts have emphasised their conclusion that depression (not just major depression) is less common in old age than among younger age groups. One big danger in airing such views is that they will lead to reduced incentives for doctors to be on the lookout for depression among their older patients. Especially this may apply to patients with comorbid physical illnesses. Another danger is that administrators may be more inclined to fund services aimed at preventing, treating and educating about depression in non-elderly rather than in elderly age groups. The debate may often be meaningless5 but there is good reason to re-examine the evidence, to ensure that doctors and administrators are properly informed on what the prevalence studies really have demonstrated. Cross-Age Studies Studies that allow comparisons of rates of depression in different age groups have led to inconsistent findings. Jorm11 referred to 14 reports of age-associated variations in scores on depressive symptom scales. Five showed an

AGE VARIATION IN THE PREVALENCE OF DEPRESSION

285

increase with age, four showed no age-group differences, two reported a fall followed by a rise, and three showed a decrease. A further study12 (n=2622), showed a negative correlation between age and scores on two depression scales, even though certain items (e.g. “Feeling so miserable this interfered with sleep”, “Feelings of not caring if never woke up”, “Hopelessness”, “Loss of interest”, “Suicidal thoughts”) were endorsed more often by older people. The authors suggested that the depression picture for elderly persons may be characterised by a diminished or reduced evaluation of the future, and that the nature of the depression experienced by younger and older people may be qualitatively different. Table 1.

Differences between studies in the age-group recorded as showing peak prevalence rates of depressive disorders.

Study

Date of Publication

Country

Age-group recording peak prevalence rate

Regier et al.13

1993

U.S.

6.4% MDE + dysthymia at 25–44 years

Murphy et al.24

2000

U.S.

3.7% at <45, 1.9% 45–64, 0.9% at 65+

Australian Bureau of Statistics19

1998

Australia

Men 35–44, Women 18–24

Lindeman et al.21

2000

Finland

11.8% one-year MDE at 45–54, men 9.3%, women 13.6% (7.6% at 55–64, 6.7% 65–75)

Bland et al.14

1988

Canada

55–64

Fichter et al.15

1996

Germany

55–64

Lehtinen et al.16

1990

Finland

60–69

Sandanger et al.22

1999

Norway

60–79

Hwu et al.18

1996

Taiwan

1.0% at 65+ (0.7% at 30–65)

Various

(1) (2) no significant age difference (3) (4) males no age difference, female peak 45–64 (5) 30–69 steady, then decrease (6) 45–64 (7) male 60–69, female 45–64

  

Jorm’s review of seven other studies11

286

THE AGEING BRAIN

Jorm11 highlighted inconsistency, too, in the results of research examining the prevalence of depressive disorders (Table 1). Several groups reported no age difference. The Epidemiologic Catchment Area (ECA) study13 showed a peak depression rate (6.4%) at age 25-44 years, though only cases of major depressive episode and dysthymia were included. Two studies14,15 reported peak rates at age 55–64 years, and another16 showed a peak at 60–69 years. The latter study showed a progressive rise in General Health Questionnaire17 scores beyond 65 years, even though there was a reduced prevalence of neurotic depression. A Taiwan study18 reported a prevalence of 0.7% at 30–65 years, and 1.0% at over 65 years. An Australian study19 of 10,000 adults, which used the Composite International Diagnostic Interview20 (CIDI) and ICD-10, reported the highest rate of affective disorders among men was at 35–44 years, but among women it was at 18–24 years. Since Jorm’s review, research from Finland21, using a short form of the CIDI, has shown 1-year prevalence rates of major depressive episode to be highest at age 45–54 years in both males (9.3%) and females (13.6%), the rate for the total sample in the 65–75 years age-group being considerably lower (6.7%). However, using the CIDI in Norway, the 2-week prevalence of ICD-10 depression was found to be higher at age 60–79 years (4.2%) than at age 20-39 (2.1%) or 40–59 (2.5%).22.The prevalence was higher among women in all three age groups (7.9% versus 1.2% at 60–79 years). Romanovski et al.23 provided an important additional analysis from one of the ECA sites. In Eastern Baltimore (n=3481, 923 being over 65 years), 1.2% were diagnosed as having DSM-III major depression at 25–44 years, 2.0% at 45–64 years and 0.5% at over 65 years. In contrast, 3.7% were diagnosed as having DSM-III depressive disorders other than major depression at 25–44 years, with a lower percentage (2.9%) at 45–64 years, and a rise to 5.0% at over 65 years. These authors23 concluded that the total prevalence of depression increased with age (p<.005) in each sex, though the rates in all age groups were higher in females. Jorm11 did not include results from the Stirling County Study24 in his review. Murphy et al.24 in 1992 used the Diagnostic Interview Schedule25 (DIS), also used in the ECA study, and reported an age-associated decrease in the one-month prevalence of major depressive episode (MDE) from 3.7% (<45 years), through 1.9% (45–64) to 0.9% (65+). The prevalence of MDE plus dysthymia showed, as in the ECA study, a decrease with age in both sexes. When in 1952, 1970 and 1992 they used criteria other than DSM to diagnose depression, they found an age-associated increase in the prevalence among males, but a decrease among females. Finally, while considering studies that compared age groups, those that showed differences between middle-aged, “young old” and late life groups should receive attention (Table 2). In Eastern Baltimore 26 there was an increase in the major depression rate from 0.7% at 65–74 to 1.3% at over 75 years. Kay et al.27 reported 6.3% at 70–79 years and 15.5% at over 80 years. Beekman et al.28 reported an increase in prevalence of major depres-

287

AGE VARIATION IN THE PREVALENCE OF DEPRESSION

sion from 1.3% at 55-59 years to 2.7% at 80-84 years, and a corresponding increase in minor depression from 9.4% to 16.7%. Roberts et al.29 showed an increase in the prevalence of major depressive episode from 8.1% at age 50–59 years and 6.9% at 60–69 years, to 10.4% at 70–79 and 12.7% at 80 years. By contrast, Prince et al,30 analysing data from subjects aged over 65 years in 14 centres in Europe, showed no overall tendency for the prevalence of depressive ‘caseness’ to rise or fall with age. Palsson et al.31 used the Comprehensive Psychopathological Rating Scale32 in a longitudinal study of people initially aged 70 years and residing in their own homes. The one-month prevalence of DSM-III-R depressive disorder

Table 2.

Studies showing differences in prevalence of depressive disorders between “young old” and older samples.

Study

Year of publication

Country

Type of depressive disorder

Prevalence of depressive disorder in different age-groups

Kramer et al.26

1985

U.S.

Major

0.7% 65–74, 1.3% 75+

Kay et al.27

1985

Australia

Major

6.3% 70–79, 15.5% 80+

Beekman et al.28

1995

Netherlands

Major

1.8% 65–69, 2.7% 80–84

Beekman et al.28

1995

Netherlands

Minor

11.4% 65–69, 16.7% 80–84

Roberts et al.29

1997

U.S.

Major

6.9% 60–69, 10.4% 70-79, 12.7% 80+

Palsson et al.31

2001

Sweden

Depressive disorder

Longitudinal study: 5.6% at 70, 11.2% at 79, 13% at 85

Prince et al.30

1999

Europe (14 centres)

“Caseness”

No overall tendency to change with age 65–74 yrs 75–84 yrs 85+yrs

Kennedy et al.57

1989

U.S.

Blazer et al.58

1991

U.S.

Newman et al.56

1998

Canada

CESD “cases” CESD “cases” GMSAGECAT

14.1%

18.7%

23.9%

8.1% 10%

10.3% 12.8%

12.3% 15.3%

288 Table 3.

THE AGEING BRAIN

The prevalence in old age of major depression and other depressive disorders (fulfilling DSM criteria): Results from representative studies.

Study

Date

Place

Instrument

Age

No. of subjects

1980

North Carolina

OARS Depression scale

65+

997

3.7

11

Hobart

GMS DSM-III

70–79 80+

274

6.3 15.5

16.5 22.4

1985

United States

DIS DSM-III

65-74 75+

3481

0.7 1.3

1.0 1.1

Madianos et al.35

1992

Athens

CESD + psychiatric evaluation

65+

251

1.6

7.9

Skoog36

1993

Goteborg

Psychiatric interview DSM-III-R

85

347

13.0

6.6

Henderson et al.33

1993

Canberra

CIE DSM-III-R

70+

945

0.4

0.6

Lobo et al.37

1995

Zaragoza

GMS Psychiatric interview

65+

1080

1.0

3.8

Beekman et al.28

1995

Netherlands CESD + DIS

55–85

3056

2.0

12.9

Fichter et al.38

1995

Munich

GMS + HDRS

85+

358

1.4

5.1

Pahkala et al.39

1995

Ahtari (Finland)

Zung + GP interview

65+

1022

2.2

14.3

Liu et al.40

1997

Rural China

Psychiatric interview DSM-III-R

65+

1313

6.1

6.9

Gallo et al.41

1997

Baltimore

DIS

50+

1612

1.7

15.8

Forsell et al.42

1998

Stockholm

DSM-IV

70+

1101

7.2

3.5

Newman et al.43

1998

Edmonton

GMS + clinician interviews

65+

1119

0.9

3.6

Blazer & Williams34   

Kay et al.27 1985   

Kramer et al.26

Major Other DSM depression depressive (%) disorders (%)

AGE VARIATION IN THE PREVALENCE OF DEPRESSION

289

(including major, dysthymia and depression NOS) was found to rise from 5.6% at age 70 years (n=392; men 1.2%, women 8.8%), 5.9% at age 75 years, and 11.2% at age 79 years (n=206), to 13.8% at age 83 years (n = 116; men 5.6%, women 17.5%) and 13.0% at age 85 years. The incidence of depression increased from 17 to 44 per 1000 person-years between the ages of 70–79 and 79–85. Prevalence Studies of Late Life Depression In addition to those already mentioned,24,26–29 studies of the prevalence of DSM major depression in community samples of older people have been reported from various countries. Inconsistency between results has been remarkable (Table 3). For example, in contrast to a major depression rate of 11% among a sample aged over 70 years in California,29 Henderson et al.33 reported that 0.4% of a same-age sample in Canberra had major depression and another 0.6% had current dysthymia. Results from a number of studies that reported rates of major depression and other DSM depressive disorders are shown in Table 3.26–28,33–43 Although not exhaustive, the list is believed to be representative. Only one study from each centre is reported. A more complete list is provided by Beekman et al.,44 who referred to 34 epidemiological studies of old age depression. Palsson and Skoog45 noted that total prevalence rates of DSM depressive disorders reported in a number of studies ranged from less than 2% to 26.8%. Beekman et al.44 listed 28 studies that reported rates of major plus minor depression. Twenty recorded rates in the range 9–18%. Four each recorded rates above and below this range. Palsson and Skoog45 commented that epidemiological studies of depression among elderly people tended to be heavily weighted towards people aged 65–75 years, thus over-shadowing an increased prevalence after age 75 years. Less inconsistency is evident between the findings of researchers who reported rates of clinically significant depression in community samples of older people, using structured interview schedules. Copeland et al.46 referred to data from nine centres in Europe when reporting a prevalence rate of 12.3%, and various other studies (Table 4)43,46–55 found rates around the range 10.3%54 to 13.5%.50 Newman et al.,56 using GMS-AGECAT, reported a rise in prevalence of depression from 10% at 65–74 years to 12.8% at 75–84 and 15.3% at over 85 years. Two studies57,58 using the CESD also noted increased rates of depression across the same three age-groups. The percentages reported by Kennedy et al.57 were 14.1, 18.7 and 23.9, respectively, and by Blazer et al.58 were 8.1, 10.3 and 12.3.

290 Table 4.

THE AGEING BRAIN

The prevalence of depressive symptoms and clinically significant depression identified during structured interviews.

Study

Date

Gurland et a.47

Place

Instrument

Age

No. of subjects

%

1983 London and New York

CARE

65+

841

12.7

Copeland et al.48

1987 Liverpool

GMS and AGECAT

65+

1070

11.3

Ben-Arie et al.49

1987 Capetown

PSE and CATEGO

65+

139

13.0

Lindesay et al.50

1989 London

Short-CARE

65+

890

13.5

Livingston et al.51

1990 London

Short-CARE

65+

779

17.3

Van Ojen et al.52

1995 Amsterdam

AGECAT

65–84

4051

11.7

Kua et al.53

1996 Singapore

GMS and AGECAT

65+

1062

6.0

Kirby et al.54

1997 Dublin

GMS and AGECAT

65+

1232

10.3

Newman et al.43

1998 Edmonton

GMS and AGECAT

65+

1119

11.4

Copeland et al.46

1999 Nine centres in Europe

GMS and AGECAT

65+

13,808

12.3

Chong et al.55

2001 Taiwan

GMS and AGECAT

65+

1350

21.3

Conclusions from these Prevalence Studies A more than tenfold difference between centres in the prevalence of depressive disorders in old age (Table 3) seems unlikely, though it should be noted that reported suicide rates of some countries differ by more than tenfold.59 Prevalence rates in the non-elderly populations of different countries also vary to an astonishing extent.22,60 Possible reasons include the fact that researchers differ in their use of diagnostic criteria, and in the methodology used to establish those criteria. Palsson and Skoog’s45 comment about differences between samples in the percentages of “young old” and “old old” included in a study is pertinent. Flaws in methodology (as discussed61 in relation to the large Australian sur-

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vey19) may affect results, as may differences in response rate between young and old samples. Older persons may respond differently from younger people to being questioned about symptoms and feelings. Older people with depressive disorders are less likely to acknowledge being sad, down or depressed in mood.62 They are less likely to admit to feelings of hopelessness or anhedonia.63 Difficulties with hearing or understanding questions may be more frequent in old age, but it could be that elderly people are more inclined to misrepresent their feelings in order to fend off perceived threats to their self-esteem. Studies that rely on subject-reported symptoms may underestimate the prevalence of disorders that fulfil DSM criteria. Diagnostic interview schedules used in cross-age studies have been those primarily developed for assessing mental disorders among physically well, young or middle-aged adults. They were not designed to facilitate recognition of depression precipitated by or associated with physical, cognitive and environmental changes that become more common in late life. Jorm11 noted that the DIS and CIDI may discount symptoms that may be attributable to physical illness. Unless interviewers are enabled to use clinical judgement (which is not feasible if they are lay interviewers) somatic symptoms may be mistakenly attributed to physical disorders rather than depression. It is desirable to give consideration to the clinical context of all symptoms that could be depressive even if feelings of depression are denied, and to seek additional information from those who know the subjects well. A further source of bias, when examining variation with age in the prevalence of depression, is that older persons may be more likely to decline involvement in surveys. Most cross-age studies have not reported whether response rates differed between age groups. The recent large Australian study19 did not report differential response rates. Kramer et al.26 noted in their study that interviews were completed in only 60% of those aged over 65 years, but from over 80% of the non-elderly sample. Henderson et al.64 recorded a response rate of 54% among people aged 60–79 but 70% among those aged 18-–59 years. Response tendencies can be influenced by cognitive status and the presence of chronic disease65 and disabilities (including deafness). There is certainly evidence that persons with age-associated brain disease may be excluded from such surveys: the Australian study19 excluded persons with moderate or severe dementia and those living in residential care. Although response bias may partly account for differences between young and old in reported prevalence rates of depression, it is also possible that older people with depression do not fulfil DSM criteria for major depression so frequently as younger people. This maybe should be the crux of this discussion. Are the data on prevalence of major depression clinically meaningful? Is it not more meaningful to ascertain the prevalence of “cases” of depression for whom clinical interventions may be beneficial, whether or not they fulfil DSM criteria for major depression?

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Some experts66 have suggested that “depression” means “major depression”. Various studies of depression prevalence have reported rates only of subjects with major depression and dysthymia. It is true that people with at least five of the listed DSM-IV criterion symptoms of depression are likely to have more severe functional and mental problems than those with only 2–4 such symptoms. However, there is good evidence that many patients with ‘subsyndromal’ depressive symptoms (i.e. who do not report symptoms severe or persistent enough to fulfil DSM-IV criteria for major depression or dysthymia) are functionally disabled to a degree comparable to patients with major depression or dysthymia.67 Such patients experience psychosocial dysfunction which improves when they are treated with an antidepressant.68 Their quality of life is worse and their dysfunction and disability are greater than is true of hypertension or diabetes.69 Although the morbidity associated with subsyndromal depression may be less than that attributable to major depression, the prevalence rates are greater and the total attributable burden to the community is larger for the “subclinical” depressions.70 The number of days lost from work was similar for people with minor and those with major depression.71 Beekman et al.72 found the incidence and course of minor or “subsyndromal” depression were closely related to impaired physical health, whereas major depression was more frequently associated with long-standing vulnerability factors, such as family and personal histories of depression. If this is so, it would suggest that different management strategies are likely to be used, but it does not mean that one is more treatable than the other. Having surveyed over 4000 community-dwelling elderly persons, Hybels et al.73 concluded that depression appears to exist along a continuum. Slater and Katz70 recommended that clinical care and public policy be designed to address the needs of those with the wider spectrum of disorders, rather than concentrating on those with major depression. Gurland et al.47 reported that 13% of elderly people have “pervasive depression” to a degree warranting clinical intervention. Copeland74 regarded it as inappropriate to use the term “subsyndromal” when referring to depressions that could be relieved by clinical interventions. Looking at depression in its broader sense, it was found that handicap due to disability is associated with a sixfold higher prevalence rate of depression.75 Depression is common among residents of aged care facilities76 and hospital inpatients77, and in association with dementia,78 strokes,79 and Parkinson’s disease (PD).80 There is evidence of associations between ageing-related (possibly vascular) changes in the brain and development of depression.81 Given these findings, and providing the definition of depression is not restricted to DSM “major depression”, it does not make sense to suggest that depression is much less common in old age. Most of the studies listed in Tables 3 and 4, together with cross-age studies that examined rates of clinically significant depression rather than of just major depression, point to a high prevalence of depression in late life.

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As well as losses of health and physical ability in old age, it has been suggested that late life is commonly a time of loss in other ways. Certainly, loss situations (such as bereavements, loss of job or change of accommodation) are relatively common, and loss of role or loss of self-esteem may have particular relevance in old age. The losses and stressors are largely different from those of young people,82 among whom relationship problems are common precipitants of depressive features. It cannot be stated with certainty that older people experience negative life-events more often than the young, but providing that ongoing loss or impairment of physical well-being and function are taken into consideration, surely older people are at least as likely as younger persons to experience depression-inducing loss situations. This does not mean they are at least as likely to develop depression. Indeed, some might argue that “survivors” to old age develop increased resilience as they age. Summary The main conclusion to be derived from this discussion is that clinically significant depression is common in old age, and it would be inappropriate to use data concerning the prevalence of only major depression and dysthymia when planning clinical services. Studies of age-related differences in the prevalence of depression commonly have not examined the prevalence of “subsyndromal” depressions in different age groups, even though they may be just as distressing and disabling as major depressions. What matters, when determining allocation of mental health resources for management of depression, is whether those resources, together with other services available in the community, can help reduce distress, improve quality of life, and have a beneficial effect on the functional abilities of depressed people. Findings from cross-age studies have been inconsistent. Errors in case ascertainment could explain why some of these studies have found the prevalence of major depression to be much lower in old age. It is also possible that major depression (i.e. the prevalence of cases fulfilling the DSM criteria) is indeed less common in old age. However, there is a need for recognition that severe, distressing but treatable late life depression commonly does not fulfil DSM-IV criteria for major depression or dysthymia. It is inappropriate to imply to doctors and others that depression is far less prevalent in old age and therefore that there is less need to look out for it. By pointing to the high prevalence we may provide incentives to improve rates of recognition and treatment of depression in late life. References 1. 2.

Katona CLE. Depression in old age. Chichester: Wiley, 1994. Lebowitz BD. Depression in late life: directions for intervention research. In: Maj M, Sartorius N, editors. Depressive disorders. WPA series. Evidence and experience in psychiatry, Vol. 1. Chichester: Wiley, 1999; 382–384.

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3. Friedhoff AJ. Consensus development conference statement: diagnosis and treatment of depression in late life. In: Schneider LS, Reynolds CF, Lebowitz BD, Friedhoff AJ, editors. Diagnosis and treatment of depression in late life. Washington DC:American Psychiatric Press, 1994; 493–511. 4. Blazer DG. Epidemiology of late-life depression. In: Schneider LS, Reynolds CF, Lebowitz BD, Friedhoff AJ, editors. Diagnosis and treatment of depression in late life. Washington DC: American Psychiatric Press, 1994; 9–19. 5. Blazer D. Filling in the gaps about depression in the elderly. In: Maj M, Sartorius N, editors. Depressive disorders. WPA series. Evidence and experience in psychiatry. Chichester: Wiley, 1999; 376–377. 6. Caine ED, Lyness JM, King DA, Connors L. Clinical and etiological heterogeneity of mood disorders in elderly patients. In: Schneider LS, Reynolds CF, Lebowitz BD, Friedhoff AJ, editors. Diagnosis and treatment of depression in late life. Washington DC: American Psychiatric Press, 1994; 21–53. 7. Nelson JC, Conwell Y, Kim K, Mazure C. Age at onset in late-life delusional depression. Am J Psychiat. 1989; 146; 785–786. 8. Nelson JC, Mazure CM, Jatlow PI. Does melancholia predict response in major depression? J Affect Disorders. 1990; 18: 157–165. 9. Brodaty H, Luscombe G, Parker G, Wilhelm K, Hickie I, Austin MP, Mitchell P. Increased rate of psychosis and psychomotor change in depression with age. Psychol Med. 1997; 27:1205–1213. 10. Parker G, Roy K, Hadzi-Pavlovic D, Wilhelm K, Mitchell P. The differential impact of age on the phenomenology of melancholia. Psychol Med. 2001; 31: 1231–1236. 11. Jorm AF. Does old age reduce the risk of anxiety and depression? A review of epidemiological studies across the adult life span. Psychol Med. 2000; 30:11–22. 12. Christensen H, Jorm AF, Mackinnon AJ, Korten AE, Jacomb PA, Henderson AS, Rodgers B. Age differences in depression and anxiety symptoms: a structural equation modelling analysis of data from a general population sample. Psychol Med. 1999; 29:325–339. 13. Regier DA, Farmer ME, Rae DS, Myers JK, Kramer M, Robins LN, George LK, Karno M, Locke BZ. One-month prevalence of mental disorders in the United States and sociodemographic characteristics: the Epidemiologic Catchment Area study. Acta Psychiat Scand. 1993; 88:35–47. 14. Bland RC, Newman SC, Orn H. Period prevalence of psychiatric disorders in Edmonton. Acta Psychiat Scand. 1988; 78:33–42. 15. Fichter MM, Narrow WE, Roper MT, Rehm J, Elton M, Rae DS, Locke BZ, Regier DA. Prevalence of mental illness in Germany and the United States. Comparison of the Upper Bavarian study and the Epidemiologic Catchment Area program. J Nerv Ment Dis. 1996; 184:598–606. 16. Lehtinen V, Joukamaa M, Lahtela K, Raitasalo R, Jyrkinen E, Maatela J, Aromaa A. Prevalence of mental disorders among adults in Finland: basic results from the Mini Finland Health Survey. Acta Psychiat Scand. 1990; 81:418–425. 17. Goldberg DP. The detection of psychiatric illness by questionnaire. Maudsley Monograph 21. London: Oxford University Press, 1972. 18. Hwu H-G, Chang I-H, Yeh E-K, Chang C-J, Yeh L-L. Major depressive disorder in Taiwan defined by the Chinese diagnostic interview schedule. J Nerv Ment Dis. 1996; 184:497–502. 19. Australian Bureau of Statistics. Mental health and wellbeing: Profile of adults, Australia. 1997. Canberra: Australian Bureau of Statistics, 1998. 20. Robins LN, Wing J, Wittchen HU, Helzer JE, Babor TF, Burke J, Farmer A, Jablenski A, Pickens R, Regier DA. The Composite International Diagnostic Interview. Arch Gen Psychiat. 1988; 45:1069–1077.

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21. Lindeman S, Hämäläinen J, Isometsä E, Kapiro J, Poikolainen K, Heikkinen M, Aro H. The 12-month prevalence and risk factors for major depressive episode in Finland: representative sample of 5993 adults. An epidemiologic instrument suitable for use in conjunction with different diagnostic systems and in different cultures. Acta Psychiat Scand. 2000; 102:178–184. 22. Sandanger I, Nygård JF, Ingebrigtsen G, Sørensen T, Dalgard OS. Prevalence, incidence and age at onset of psychiatric disorders in Norway. Soc Psych Psych Epid. 1999; 34:570–579. 23. Romanovski AJ, Folstein MF, Nestadt G, Chahal R, Merchant A, Brown CH, Gruenberg EM, McHugh PR. The epidemiology of psychiatrist-ascertained depression and DSM-III depressive disorders. Results from the Eastern Baltimore Mental Health Survey Clinical Reappraisal. Psychol Med. 1992; 22:629–655. 24. Murphy JM, Laird NM, Monson RR, Sobol AM, Leighton AH. A 40-year perspective on the prevalence of depression. Arch Gen Psychiat. 2000; 57: 209–215. 25. Robins LN, Helzer JE, Croughan J, Ratcliff KS. National Institute of Mental Health Diagnostic Interview Schedule: its history, characteristics and validity. Arch Gen Psychiat. 1981; 38:381–389. 26. Kramer M, German PS, Anthony JC, Von Korff M, Skinner EA. Patterns of mental disorders among the elderly residents of Eastern Baltimore. J Am Geriatric Soc. 1985; 33:236–245. 27. Kay DWK, Henderson AS, Scott R, Wilson J, Rickwood D, Grayson DA. The prevalence of dementia and depression among the elderly living in the Hobart community: the effect of the diagnostic criteria on the prevalence rates. Psychol Med. 1985; 15:771–778. 28. Beekman ATF, Deeg DJH, van Tilburg T, Smit JH, Hooijer C, van Tilburg W. Major and minor depression in later life: a study of prevalence and risk factors. J Affect Disorders. 1995; 36:65–75. 29. Roberts RE, Kaplan GA, Shema SJ, Strawbridge WJ. Does growing old increase the risk of depression? Am J Psychiat. 1997; 154:1384–1390. 30. Prince MJ, Beekman AT, Deeg DJ, Fuhrer R, Kivela SL, Lawlor BA, Lobo A, Magnusson H, Meller I, van Oyen H, Reischies F, Roelands M, Skoog I, Turrina C, Copeland JF. Depression symptoms in late life assessed using the EURO-D scale. Effect of age, gender and marital status in 14 European centres. Br J Psychiat. 1999; 174:339–345. 31. Palsson SP, Östling S, Skoog I. The incidence of first-onset depression in a population followed from the age of 70 to 85. Psychol Med. 2001; 31:1159–1168. 32. Asberg M, Perris C, Schalling D, Sedvall G. The CPRS – development and applications of a psychiatric rating scale. Acta Psychiat Scand. 1978; suppl 271. 33. Henderson AS, Jorm AF, Mackinnon A, Christensen H, Scott LF, Korten AE, Doyle C. The prevalence of depressive disorders and the distribution of depressive symptoms in later life: a survey using Draft ICD-10 and DSM-III-R. Psychol Med. 1993; 23:719–729. 34. Blazer D, Williams CD. Epidemiology of dysphoria and depression in an elderly population. Am J Psychiat. 1980; 137:439–444. 35. Madianos MG, Gournas G, Stefanis CN. Depressive symptoms and depression among elderly people in Athens. Acta Psychiat Scand. 1992; 86:320–326. 36. Skoog I. The prevalence of psychotic, depressive and anxiety syndromes in demented and non-demented 85-year-olds. Int J Geriatr Psych. 1993; 8:247– 253. 37. Lobo A, Saz P, Marcos G, Dia J-L, De-la-Cámara C. The prevalence of dementia and depression in the elderly community in a Southern European population. Arch Gen Psychiat. 1995; 52:497–506.

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38. Fichter MM, Bruce ML, Schroppel H, Meller I, Merikangas K. Cognitive impairment and depression in the oldest old in a German and in U.S. communities. Eur Arch Psychiat Clin N. 1995; 245:319–325. 39. Pahkala K, Kesti E, Köngäs-Saviaro P, Laippala P, Kivelä S-L. Prevalence of depression in an aged population in Finland. Soc Psych Psych Epid. 1995; 30: 99–106. 40. Liu CY, Wang SJ, Teng EL, Fuh JL, Lin CC, Lin KN, Chen HM, Lin CH, Wang PN, Yang YY, Larson EB, Chou P, Liu HC. Depressive disorders among older residents in a Chinese rural community. Psychol Med. 1997; 27:943–949. 41. Gallo JJ, Rabins PV, Lyketsos CG. Depression without sadness: functional outcomes of nondysphoric depression in later life. J Am Geriatr Soc. 1997; 45:570–578. 42. Forsell Y, Jorm AF, Winblad B. The outcome of depression and dysthymia in a very elderly population: results from a three-year follow-up study. Aging Ment Health. 1998; 2:100–104. 43. Newman SC, Sheldon CT, Bland RC. Prevalence of depression in an elderly community sample: a comparison of GMS-AGECAT and DSM-IV diagnostic criteria. Psychol Med. 1998; 28:1339–1345. 44. Beekman ATF, Copeland JRM, Prince MJ. Review of community prevalence of depression in later life. Br J Psychiat. 1999; 174:307–311. 45. Palsson S, Skoog I. The epidemiology of affective disorders in the elderly: a review. Int Clin Psychopharm. 1997; 12: (Suppl 7): S3–S13. 46. Copeland JR, Beekman AT, Dewey ME, Hooijer C, Jordan A, Lawlor BA, Lobo A, Magnusson H, Mann AH, Meller I, Prince MJ, Reischies F, Turrina C, de Vries MW, Wilson KC. Depression in Europe. Geographical distribution among older people. Br J Psychiat. 1999; 174:312–321. 47. Gurland B, Copeland J, Kuriansky, J, Kelleher M, Sharpe L, Dean LL. The mind and mood of aging. London: Croom Helm, 1983. 48. Copeland JRM, Dewey ME, Wood N, Searle R, Davidson IA, McWilliam C. Range of mental illness among the elderly in the community. Prevalence in Liverpool using the GMS-AGECAT package. Br J Psychiat. 1987; 150:169–174. 49. Ben-Arie O, Swartz L, Dickman BJ. Depression in the elderly living in the community: its presentation and features. Br J Psychiat. 1987; 150:169–174. 50. Lindesay J, Briggs K, Murphy E. The Guy’s / Age Concern Survey. Prevalence rates of cognitive impairment, depression and anxiety in an urban elderly community. Br J Psychiat. 1989; 155:317–329. 51. Livingston G, Hawkins A, Graham N, Blizard B, Mann A. The Gospel Oak Study: prevalence rates of dementia, depression and activity limitation among elderly residents in Inner London. Psychol Med. 1990; 20:137–146. 52. Van Ojen R, Hooijer C, Jonker C, Lindeboom J, van Tilburg W. Late-life depressive disorder in the community, early onset and the decrease of vulnerability with increasing age. J Affect Disorders. 1995; 33:159–166. 53. Kua EH, Ko SM, Fones C, Tan SL. Comorbidity of depression in the elderly – an epidemiological study in a Chinese community. Int J Geriatr Psychiat. 1996; 11: 699–704. 54. Kirby M, Bruce I, Radic A, Coakley D, Lawlor BA. Mental disorders among the community-dwelling elderly in Dublin. Br J Psychiat. 1997; 171:369–372. 55. Chong MY, Chen CS, Tsang HY, Chen CC, Yeh TL, Lee YH, Lo HY. Community study of depression in old age in Taiwan. Br J Psychiat. 2001; 178:29–35. 56. Newman SC, Bland RC, Orn HT. The prevalence of mental disorders in the elderly in Edmonton: a community survey using GMS-AGECAT. Can J Psychiat. 1998; 43:910–914. 57. Kennedy GJ, Kelman HR, Thomas C, Wisniewski W, Metz H, Bijur PE. Hierarchy of characteristics associated with depressive symptoms in an urban elderly sample. Am J Psychiat. 1989; 146:220–225.

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Chapter 18 VASCULAR DEMENTIA Perminder Sachdev

Introduction The importance of vascular factors in the aetiology of cognitive impairment was recognised over a century ago 1, but there was debate regarding the mechanisms by which this occurred. Alzheimer 2 described “arteriosclerotic cerebral atrophy” as a cause of senility around the same time as Binswanger’s3 paper on “encephalitis subcorticalis chronica progressiva” was published in 1884. A close examination of Binswanger’s report suggests that the diagnosis was not certain. The patient, a man in his mid-fifties with a history of syphilis, presented with a progressive decline in speech and memory, as well as depression and personality change. On post-mortem examination, there was minimal atherosclerosis, enlargement of lateral ventricles, marked atrophy of the cerebral white matter, granular deposits on basal dura mater, and multiple ependymal thickenings. 3 It was Alzheimer4 who attributed this white matter change to atherosclerosis when he described an analogous case in 1902. Modern reviewers consider Binswanger’s case to have been one of neurosyphilis,5,6 but the concept of extensive atherosclerotic white matter disease associated with cognitive decline became known as Binswanger’s disease. Alzheimer’s7 description of plaques and tangles as a basis of dementia in the first decade of this century did not greatly reduce the emphasis on vascular factors that were still considered to be the more common cause of dementia. Half a century later, the idea that critical atherosclerotic narrowing of cerebral arteries commonly led to progressive ischaemia, neuronal loss and dementia still held sway although Alzheimer’s disease (AD) had by now found its way into most neurological texts. A number of developments in the last three decades have led to a radical rethinking of the association between cerebrovascular disease (CVD) and dementia, and set the stage for a reconceptualisation of vascular dementia.

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First, the classic studies of Tomlinson et al.8 in the 1960s clearly distinguished Alzheimer’s disease from dementia due to vascular causes, and demonstrated the importance of cerebral infarction for the latter. They also reported a relationship between the volume of infarcted cerebral tissue and the likelihood of clinical dementia. Volume loss of up to 100 ml was frequently found in control subjects without dementia, and those with atherosclerotic dementia (n=6) had a mean volume of infarction of 186 ml (range 101–412 ml). Second, work in the mid-1970s9,10 emphasized that dementia from vascular causes was due to discrete and multiple lesions, most of which resulted from thromboembolism originating in the extracranial arteries and the heart. Dementia due to vascular causes became equated with multi-infarct dementia (MID) although the term was used infrequently. An Ischaemic Score, 10 which incorporated risk factors for multiple stroke was proposed to differentiate MID from AD. Third, the ageing of the population increased the public health importance of dementia, and epidemiological studies established vascular causes as the second most common aetiology of dementia after AD in Western countries and, in fact, the most common cause in some Asian countries.11 Fourth, recent developments in neuroimaging, in particular computed tomography (CT) and magnetic resonance imaging (MRI), have permitted the examination of patients’ brains for lesions hitherto possible only at autopsy. It became obvious that cognitive impairment could be caused by subcortical basal ganglia and white matter lesions, often detected as hyperintensities on T2-weighted MRI, and even by strategically placed single infarcts. Not all patients with white matter lesions (WML) had Binswanger’s disease, and the term “leukoaraiosis” (LA)12 (literally, thinning of the white matter) was proposed to describe these brain abnormalities on neuroimaging. It thus became accepted that MID was but one form of the dementias of vascular aetiology. Fifth, the term Vascular Dementia (VaD) emerged as the preferred term for this group of disorders, and some international efforts were made to standardise its diagnostic criteria.13–17 These efforts resulted in a reconceptualisation of the pathogenetic mechanisms to VaD. Sixth, the potential for modifying the risk factors for VaD had become apparent, raising the possibility of influencing the incidence and prevalence of dementia in the elderly population. VaD was hailed as a preventable dementia, which should have much greater recognition than it has hitherto received. Seventh, post-mortem studies established that many patients with dementia had a mixed pathology. It also emerged that factors traditionally recognised as those increasing the risk of cerebrovascular disease, such as hypertension, diabetes mellitus, cholesterol and homocysteine, were also possibly risk factors for AD.

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The Definition of Vascular Dementia There are two obvious steps in the diagnosis of VaD — the diagnosis of dementia and the establishment of its vascular aetiology — both of which are controversial and lack consensus. i) Defining dementia: Most modern diagnostic systems define dementia as a multifaceted decline in cognitive functioning from a previous higher level which impairs functioning in daily life, but is not necessarily progressive. Impairment of memory is usually considered necessary for dementia according to these criteria, with rare exceptions. 18 The criteria differ in whether one, two or more other cognitive domains must have impairments in order to qualify for a diagnosis of dementia. The two most commonly used criteria for dementia are from the Diagnostic and Statistical Manual of Mental Disorders–Fourth Edition (DSM-IV) 17 and the tenth revision of the International Classification of Diseases (ICD-10). 16 The two systems broadly concur in their emphasis on a decline from a previously higher level of cognitive function that impairs daily functioning in occupational or social dimensions. However, there is a point of difference in that DSMIV accepts impairment of memory plus one other area of higher cortical function, whereas ICD-10 requires impairment of memory and two other areas of cognitive function. Moreover, these criteria do not operationalise the definition of impairment, so that deficits that that have little impact on the functioning of a retiree who does not engage in much social activity may prove to be devastating for a middle-aged man who runs his own business or manages a company. ii) Defining Vascular Dementia: That a particular dementia is primarily due to vascular factors is not easy to establish, and different approaches have been proposed. One popular approach to diagnose VaD in the past has been to use a quantitative rating such as the Ischaemia Scale score. Hachinski et al.10 were the first to propose such a scale comprising 13 items (maximum possible score 18) based on history and clinical examination (Table 1), and reported that a score of 7 or more suggested MID and that of 4 or less AD. This approach has been validated in its ability to distinguish a relatively pure MID from AD or mixed dementia.19 However it has limitations in that it focuses on MID to the exclusion of other subtypes of VaD and uses only some of the relevant clinical information, disregarding neuroimaging and other laboratory data. Modifications of the Hachinski Ischaemia Scale have been presented to include neuroimaging data.13 The specific items of the Scale were recently examined using logistic regression models.20 The items that best distinguished MID from AD were: stepwise deterioration (odds ratio = 7.8), fluctuating course (6.1), hypertension (4.2), atherosclerosis (2.6), and focal neurological symptoms (5.4). MID was distinguished from mixed dementia by stepwise deterioration (4.6) alone, and fluctuating course (0.2) and history of stroke (0.08) distinguished AD from mixed dementia. Nocturnal confusion, preservation of personality, depression, and somatic complaints had no

302 Table 1.

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Hachinski’s Ischaemia Scale.10 Item Abrupt onset Stepwise deterioration Fluctuating course Nocturnal confusion Relative preservation of personality Depression Somatic complaints Emotional incontinence History of presence of hypertension History of strokes Evidence of associated arteriosclerosis Focal neurological symptoms Focal neurological signs

Score 2 1 2 1 1 1 1 1 1 2 1 2 2

discriminating value in this study. Loeb13 included CT brain scan abnormalities on his modified ischaemic score. The DSM-IV criteria for VaD require that criteria for dementia be met and the patient have focal neurological signs and symptoms or laboratory evidence indicative of cerebrovascular disease (e.g., multiple infarctions involving cortex and underlying white matter) that are judged to be aetiologically related to the disturbance. Neuroimaging is therefore not an absolute requirement for the DSM-IV diagnosis of VaD. The ICD-10 criteria require unequal distribution of deficits in higher cognitive functions, evidence of focal brain damage (unilateral spastic weakness of the limbs, unilaterally increased tendon reflexes, an extensor plantar response, pseudobulbar palsy) and significant cerebrovascular disease judged to be aetiologically related to the dementia, and again do not specify neuroimaging requirements. Of particular interest are two recent proposals to develop research diagnostic criteria. The Alzheimer’s Disease Diagnostic and Treatment Centers (ADDTC) of the State of California criteria for VaD14 are notable for not specifically requiring memory impairment for the diagnosis of dementia; disturbance in any two or more cognitive domains is sufficient for this purpose. The AADTC criteria only deal with ischaemic lesions, and require that the patient have evidence of two or more ischaemic strokes, and in case of a single stroke, a clearly documented temporal relationship. There is a requirement of at least one infarct outside the cerebellum on neuroimaging. Non-infarction, ischaemic white matter lesions are insufficient for a diagnosis of Probable VaD, but may support Possible VaD. The currently most commonly used criteria in research are the NINDSAIREN criteria15 which were developed by a group of 54 neurologists and neuroscientists as a starting point for international research and discussion. Like the ICD-10 approach,16 the definition of dementia requires dysfunction

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Table 2. Summary of the NINDS-AIREN diagnostic criteria for Vascular Dementia (VaD).15 I.

2.

3.

II.

Probable VaD: All of the following: 1. Dementia: (a) Cognitive decline from higher level, and (b) Impairment of memory, and (c) Impairment in 2 or more cognitive domains, and (d) Impairment interferes with daily living. Exclude if disturbance of consciousness, delirium, psychosis, severe aphasia, or sensorimotor impairment preclude neuropathological testing, or other brain disease exists (e.g. AD). Cerebrovascular disease (CVD) (a) Focal signs on neurological examination, and (b) Brain imaging (CT/MRI) evidence — multiple or strategic single infarcts, multiple lacunes, extensive white matter lesions, or combinations. Relationship between CVD and dementia: one or more of the following: (a) Onset of dementia within three months of stroke, or (b) Abrupt deterioration, or (c) Fluctuating, stepwise progression. Possible VaD: Criteria I-1 and I-2a above, but I-2b lacking, or I-3a, b or c lacking.

III. (a) (b) (c)

Definite VaD: Clinical criteria of Probable VaD, and Histopathological evidence of CVD (autopsy or biopsy), and Absence of neuritic plaques and neurofibrillary tangles exceeding those expected for age, and (d) Absence of other pathology possible causing dementia. IV. AD with CVD (instead of “mixed dementia”) Patients meeting criteria for AD with clinical or imaging evidence of CVD.

of memory and at least two other cognitive domains. The criteria recognise that the lesions could be ischaemic of haemorrhagic. The presence of cerebrovascular disease is recognised by the presence of focal signs on neurological examination consistent with stroke, and evidence on neuroimaging. The latter could be multiple large-vessel strokes, single strategically placed stroke, lacunar infarcts, extensive WMLs, or a combination of these. The establishment of a relationship between CVD and dementia was considered to be problematic — it required either the onset of dementia within three months of a stroke, or a history of abrupt deterioration in cognitive function, or fluctuating, step-wise progression of deficits. The criteria acknowledged that a diagnosis of Definitive VaD could only be made on histopathological confirmation of significant CVD in the absence of Alzheimer-type pathology. Clinically, the diagnosis was either Probable or Possible depending upon the level of certainty of the CVD and its relationship to dementia. These criteria also argue against the use of “mixed” dementia as a diagnosis, suggesting

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instead the appellation of “AD with CVD”, thus shifting the causality of dementia to AD in such cases, a proposal which has been criticised as being premature21. The criteria do not include haemorrhagic lesions in the aetiology of dementia even though the workshop recognised the importance of such lesions. For the diagnosis of dementia, disturbance of consciousness is exclusionary, but the proposal not to make a diagnosis of dementia in the presence of psychosis, severe aphasia or severe sensorimotor impairment precluding neuropsychological testing in the NINDS-AIREN criteria is controversial as intellectual decline in these patients can often be demonstrated by methods other than formal neuropsychological testing. A summary of the NINDSAIREN criteria is presented in Table 2. The sensitivity and specificity of the criteria vary considerably. In a follow-up study of 148 stroke patients, Desmond et al.2 reported impairment in one cognitive domain in 19.7%, two domains in 17.0%, and three or more domains in 15.7%, underscoring the importance of the operational definition. How cognitive impairment is quantified remains highly variable, with the sophistication of the neuropsychological battery being an important determinant of the range of deficits recorded. Obviously, the use of mental status tests, such as the Mini-metal State Examination (MMSE),23 is not considered to be adequate. The different criteria do not overlap, and prevalence rates therefore would differ depending upon which criteria set is used. The DSM-IV criteria are the least restrictive, the ADDTC more sensitive and the NINDSAIREN more specific.24,25 The Concept of Vascular Cognitive Impairment (VCI) We have already examined some of the limitations of the concept of dementia. In addition to the difficulties in operationalising deficits, deciding which cognitive domains are critical, and what level of impairment is necessary, the diagnosis may well be too late in the course of the disorder. If VaD is to be prevented, its recognition should be at a much earlier stage. Hence the proposal26 to broaden the concept to Vascular Cognitive Impairment (VCI) which would include the whole range of individuals from those at high risk of CVD (brain-at-risk) to those meeting strict criteria for VaD. The syndrome is defined clinically, based on neuropsychological performance, and vascular and interaction is documented. It is a heterogeneous concept with varied aetiology, and its specific criteria need development and refinement. Epidemiology VaD is generally considered to be the second most common cause of dementia, accounting for 10–50% of all dementia cases, depending upon the criteria used and the population examined. Prevalence rates have varied across studies owing to methodological differences, and range from 1.2 to 4.2% in

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persons aged 65 years and over. In the Canadian Health and Ageing Study,27 the prevalence of VaD in persons over 65 years was 1.5%. Another 0.9% had mixed VaD and AD, and 2.4% were noted to have cognitive impairment of vascular origin but did not meet criteria for dementia, thereby giving an overall prevalence of about 5% for VCI. This compared with a prevalence of 5.1% for AD without a vascular component. Data from the European collaborative study28 put the prevalence of VaD in individuals aged 65 years and over at 1.6%, which accounted for 15.8% of all dementia cases. Overall, the studies vary considerably in the prevalence rates, from 0.0 to 0.8% at age 65 years to 2% to 8.3% at age 90 years. The prevalence was higher in men up to the age of 85 years, after which it was higher in women. There is a crossnational effect with AD being more common in Western countries, and VaD being much more common in Japan, China and Russia. Hagnell et al.,29 in a Swedish study, estimated the lifetime risk of VaD as 34.5% for men and 19.4% for women. Autopsy data from Western countries confirm VaD to be the second commonest form of dementia after AD.8,30 In community based studies, the incidence of MID29,31 or “arteriosclerotic psychosis”32 has ranged from 0.17 to 0.71/ 100 person-years. In a hospitalised ischaemic stroke sample, 33 the incidence of VaD was 8.4/100 person-years. The incidence increases with age, and in the European collaborative study,26 the rates were 0.5%, 0.8%, 1.9%, 2.4%, 2.4% and 3.0% in the various five-year age bands between 65 and 69 years and 90+ years. Dementia after stroke Since there is a close relationship between stroke and VaD, many studies have examined the rates of dementia in stroke cohorts. The rates at three months after stroke in two studies were 26.3% (New York study)34 and 25% (Helsinki study).35 Rates from our Sydney Stroke Study were comparable (24%), with another 36% having VCI that did not meet criteria for dementia (unpublished data). Stroke may thus be considered to be the most important risk factor for VaD. In the New York study, the rates of VaD in stroke patients over 60 years were nine times that in controls, with an odds ratio of 31.2 in the 70–79 years age group. Of all the cases of dementia in this group, stroke was considered to be the underlying cause in 56.1% cases, and in 36.4% there was considered to be a combined effect of stroke and Alzheimer’s disease. The incidence of new-onset dementia also increases after an index stroke. In the New York study, the risk of new-onset dementia one year after stroke was 5.4% in patients over 60 years, and 10.9% in those over 90 years. In a community-based study from Rochester, Minnesota (USA),36 the rate for new-onset dementia in the first year after a stroke was 8.6%. In this study, the incidence of AD also doubled after a stroke, raising issues about the interaction of the two pathologies. The risk if dementia is also increased after lacunar stroke, which was 23.1% in the four years after such infarction in one study,37 a 4-–12 fold increase over controls.

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Risk factors for Vascular Dementia Factors related to increased risk of stroke Since stroke is one of the major determinants of VaD, it is reasonable to expect that risk factors for stroke would also increase the risk of VaD. It has been demonstrated38,39 that hypertension, diabetes mellitus, coronary artery disease, atrial fibrillation, cigarette smoking and dyslipidemia are the major risk factors for VaD. The role of other factors such as various cardiac factors, obesity, fibrinogen, antiphospholipid antibodies, etc. remain to be fully investigated. Sociodemographic factors relevant to stroke risk are age, male sex and race/ethnicity (e.g., Asians, Afro-Americans). Hypertension is the most important modifiable risk factor for stroke and VaD, as it accounts for about one-quarter to one-half of all strokes.40 Hypertension in early life has been associated with the cognitive impairment and dementia later in life in a number of studies. In the Honolulu-Asia Aging Study,41 systolic hypertension in mid-life increased the risk of cognitive deficits later in life. For every 10 mm Hg increase in systolic blood pressure, there was an increase in risk of 7% for intermediate cognitive function and 5% for poor cognitive function. The association between hypertension and poor cognitive function was also found in a number of other studies, both cross-sectional and longitudinal.42–44 There is also some evidence that treatment of hypertension has a protective effect on cognition.45 The association appears to be not simply for VaD but also for AD.42 Moreover, lower blood pressure (<130 mm Hg systolic and <75 mm Hg diastolic) may be associated with cognitive impairment in the elderly.46 While hypertension increases the risk of VaD, once dementia is set in, high systolic blood pressure may serve a protective role.39 There may therefore be a J-shaped association between hypertension and cognition.47 There are many mechanisms by which hypertension may cause cognitive impairment. There is an increase in atherosclerotic disease, leading to more strokes. There is also an increase in arteriosclerotic disease, which is a major factor in the development of white matter pathology.48 Factors related to increased white matter lesions (WMLs) The main risk factor for WMLs is recognised to be hypertension, presumably through lipohyalinosis and thickening of the walls of the small perforating arterioles, thereby causing ischaemic damage to the myelin sheath of axons.49 However, hypoperfusion and hypoxic-ischaemic episodes can also produce similar lesions. Other risk factors for WMLs include general vascular disorders, diabetes mellitus, and other risk factors for atherosclerosis, high plasma viscosity and indications of blood-brain barrier damage. Genetic factors Genetic factors for CVD, and consequently VaD, are not well understood. Exceptions are rare disorders such as cerebral autosomal dominant arteriopathy with subcortical infarct and leukoencephalopathy (CADASIL)50 and

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autosomal dominant hereditary cerebral haemorrhage with amyloidosis — Dutch type.51 CADASIL is an arteriopathy due to Notch3 gene mutations on chromosome 19 leading to confluent WMLs and multiple strokes by the age of 40–50 years.50 Other rare hereditary vascular conditions unlinked to Notch3 gene mutation have been identified,52 as have non-CADASIL Binswanger-like syndromes without arterial hypertension.53 Several families with autosomal recessive leukoencephalopathy and hereditary cerebral amyloid angiopathy have been described. The role of apolipoprotein E polymorphism in VaD is unclear, with published studies producing conflicting evidence for a link with ε4 allele.3,54 Other factors Stroke is not the only mechanism for the development of VaD, and many stroke patients do not go on to develop dementia, suggesting that the nature and extent of stroke and some host factors may be important. Age has consistently been reported as a substantial risk factor for VaD,55 and VaD is more common in men.29 Certain race/ethnic factors seem to be relevant, with the risk for VaD being higher in Japan, China and Russia when compared with Western societies.55 As for AD, education has been suggested as a protective factor against the development of VaD.33,38,39 It is not known whether this is because of a threshold effect in the more educated, or education in fact has a positive effect on brain development, and whether early education or a more complex and challenging occupation is the key variable. Clinical-Pathological Correlates and Pathogenesis Brain parenchymal lesions may be produced through ischaemia, haemorrhage or oedema because of the involvement of small and/or large arteries resulting in cortical and/or subcortical infarcts or non-infarct lesions. The major vascular mechanisms involved are listed in Table 3, and the resulting neuropathology will vary according to the dominant mechanisms. Since the classic studies of Tomlinson et al.,8 the major focus has been on cerebral infarcts due to the occlusion of large or medium-sized arteries. The earlier suggestion was that the volume of cerebral tissue infarcted was critical, with dementia most likely to occur when a threshold of 100 ml was reached and the brain’s compensatory capabilities overwhelmed. Other authors came to a similar conclusion, although infarct volumes of 50 ml or higher were found to result in VaD by one group.56 Multiple small strokes were suggested to have an additive or multiplicative effect,9 encapsulated in the notion of multi-infarct dementia. This is understandable, given their propensity to disrupt multiple pathways. There is now a recognition that the location of the infarct is an important factor, with strategically placed single-infarcts (e.g. cortical infarcts involving the angular gyrus, inferomedial temporal lobe and medial frontal lobe, and subcortical infarcts of the thalamus, left-sided capsular genu, caudate nuclei

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Table 3. Risk factors for vascular dementia. Sociodemographic Age Race/ethnic Sex Education Atherogenic Hypertension Coronary artery disease Diabetes mellitus Cigarette smoking Hypercholesterolaemia Hyperhomocystinemia ment Fibrinogen, obesity

Increasing incidence with age, especially after 60 years Higher rates in Asian and black populations Higher rates in men May have a protective effect Major risk factor Increases stroke risk Risk factor for stroke Risk factor for stroke Risk factor for stroke Increased risk of stroke, and cognitive impairEvidence lacking

Other cardiovascular Atrial fibrillation Mitral valve prolapse Peripheral vascular disease

Risk of cerebral embolism Cerebral embolism Inconsistent evidence

Other factors Genetic Apolipoprotein E polymorphism Anticardiolipin antibodies Alcoholism

Weak; CADASIL an exception Evidence inconsistent Evidence inconsistent Evidence inconsistent

Stroke related Number, volume, location of stroke Pre-existent silent infarcts Presence of abnormal periventricular signal on magnetic resonance imaging, or (especially) on computed tomography Note: CADASIL = cerebral autosomal dominant arteriopathy with subcortical infarct and leukoencephalopathy.

and white matter pathways) being capable of producing severe dysfunction. It is likely that some strategically placed infarcts lead to a disproportionate effect on metabolism in remote areas of the brain.57 In addition to vascular territory infarcts, infarction may occur in watershed areas. Lacunar infarcts, usually seen in the white matter, basal ganglia, thalamus and pons, are also recognised to produce dementia.15 The role of ischaemic white matter lesions (WMLs) in cognitive impairment has received much recent attention. They are commonly seen on computed tomography (CT) and especially on T2-weighted magnetic resonance imaging (MRI) in patients with VaD, AD and normal healthy elderly individuals. Most

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authors distinguish between periventricular WMLs occurring at the margins of the lateral ventricles (“rims” and “caps”) and deep subcortical white matter lesions sparing the U-fibres. The widespread reporting of WMLs on MRI prompted the introduction of a new term “leukoaraiosis” (rarefaction or thinning of the white matter).58 Their pathological significance has been greatly debated, but when their severity had been considered, periventricular WMLs were reported in MID to be 11.6 times higher than AD and 3.5 times higher than normals, and subcortical WMLs were 2.6 and 13.5 times higher respectively.59 A ‘threshold’ effect has been suggested, with cognitive impairment resulting when WMLs reach a certain severity. Many of the risk factors for WMLs are similar to those for stroke, but empirical evidence for factors other than age and hypertension has been equivocal.60 It is debatable whether leukoaraiosis must indeed be distinguished from Binswanger’s disease3 which has been described as a clinicopathological entity characterised by a slow progression of a dementia associated with psychiatric features, gait disturbance, Parkinsonism, corticobulbar features and incontinence. It usually begins in the fifth or sixth decade and is associated with hypertension. The pathological correlate is extensive white matter softening involving the centrum semiovale sparing U-fibres, internal capsule and corpus callosum, usually associated with moderate cortical atrophy and small infarcts. There is thickening and medial lipohyalinosis of the medullary arteries of the white matter and perforators of the deep grey matter, with hypoperfusion being hypothesised as the pathogenetic mechanism. In addition to the arterial territory and lacunar infarcts, distal field or watershed infarcts occur in the cortical border zones because of hypoperfusion. When multiple small infarcts and focal gliosis are the pathological feature, the term granular cortical atrophy is used. Incomplete ischaemic necrosis leads to focal areas of cell loss with reactive changes (focal gliosis) or selective neuronal loss in cortical segments (laminar necrosis). Cortical and deep infarcts also produce remote changes in cortical functioning. There may be a disconnection of critical functional circuits, e.g. the frontolimbic or thalamocortical circuits, or changes in cholinergic, noradrenergic or other neurotransmitter functions leading to profound cognitive changes.61 CVD may lead to dementia by a process of subdural, subarachnoid or intracerebral haemorrhage, although most authors do not include these under the rubric of VaD.14,15 The same holds for hypoxaemic hypoxia (e.g. pure asphyxia or respiratory failure) and histotoxic hypoxia (e.g. carbon monoxide or cyanide poisoning). Hypoxic-ischaemic events (e.g. seizures, cardiac arrhythmias, pneumonia, congestive heart failure) do, however, place stroke patients at an increased risk of dementia. The nature of the vascular pathology is varied, with atherosclerosis, arteriosclerosis, lipohyalinosis, amyloid angiopathy, senile arteriolar sclerosis and other angiopathies having been described.15 Systemic causes of thromboembolism are important in some cases: inflammatory diseases (e.g. systemic lupus erythematosis, polyarteritis nodosa, sarcoidosis, etc.), hyperviscosisty

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syndromes (e.g. polycythemia vera, sickle cell anaemia), and embolic disorders (e.g. atrial fibrillation, myocardial infarction with mural thrombus, congential heart disease, septic, air or fat emboli). CVD and Alzheimer-type changes not uncommonly co-occur, and 10% to 20% of patients with dementia are classified clinically and pathologically as having both dementias.62 In spite of this, there is no agreement on how best to characterise a “mixed” dementia or “AD with CVD”.14,15 CVD is known to promote the clinical expression of AD.63 Vascular factors have been implicated in the pathogenesis of WMLs and cortical neuronal loss in AD. Cerebral amyloid angiopathy involving pial, leptomeningeal and superficial cortical arterioles and venules can be demonstrated in up to 90% of AD cases. Not only can these cause severe intracranial haemorrhage, they also increase the permeability of the blood brain barrier to serum proteins, thus interfering with neuronal metabolism. A disturbance of vascular autoregulatory mechanisms may also contribute to selective ischaemia of the white matter. The importance of these mechanisms is controversial, and the relationship between CVD and AD needs further study. The pathophysiological mechanisms involved in VaD are summarised in Table 4. Subtypes of VaD and VCI In view of the varied pathogenetic mechanisms involved, the following classification for VaD is proposed: 1. Ischaemic VaD: 1.1. Multi-infarct dementia (MID): This dementia is the consequence of multiple cortical and cortico-subcortical infarcts in arterial territories and distal field or watershed regions, and may be related to thrombo-

Table 4

Pathogenetic mechanisms of vascular dementia. I.

II.

III.

Infarct (single or multiple) A. Arterial territory infarct • Multiple infarcts • Single strategic infarcts B. Watershed infarction C. Lacunar infarction Non-infarction ischaemia A. Subcortical leukoencephalopathy (Binswanger’s) B. Laminar necrosis C. Granular atrophy D. Gliosis or sclerosis Haemorrhage A. Subdural B. Subarachnoid C. Intracerebral

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sis in large arteries, embolic events or hypoperfusion. The presentation of this MID fits the classic description of acute onset and stepwise progression with repeated strokes and the gradual accumulation of pathology. The neuropsychological deficits are consistent with the brain regions affected by infarction. 1.2. Strategic infarct dementia (SID): The clinical picture of dementia is produced by a single strategically placed infarct. Cortical regions that may produce this picture are hippocampal formation, angular gyrus and cingulate gyrus. Subcortical regions include the thalamus, caudate and globus pallidus, genu of the anterior capsule, fornix and basal forebrain. The clinical picture will vary depending upon the lesion. 1.3. Subcortical vascular dementia (SVaD): This is characterised by lesions in the subcortical nuclei and the white matter. Two subtypes can be described: a) Lacunar state, with multiple lacunar infarctions, and b) Binswanger’s disease, predominated by white matter disease which may be associated with small infarcts. Lacunar infarcts result from the occlusion of unbranched endarteries, usually 100-400 µm in diameter, that result in deep cerebral infarctions, 0.2 to 15 mm3 in size, with a mean of about 2 mm3. The arteries generally show lipohyalinosis, often associated with fibrinoid necrosis and microatheroma. The focal and diffuse white matter lesions are ischaemic in nature, and are associated with fibrohyaline thickening of penetrating arteries, and ischaemic injury of the periventricular white matter. The regions particularly affected are frontal white matter, centrum semiovale, and the periventricular regions (rims and caps). Pathology is characterised by demyelination and incomplete infarction. Accordingly, a prefrontal syndrome dominates the clinical picture with deficits in frontal-executive functioning, bradyphrenia, mood disturbance, and behavioral change. Motor and Parkinsonian symptoms are often present. The SVaD has been suggested as a more homogeneous subtype of VaD that may be a candidate for drug trials. 1.4. Mixed cortical and subcortical VaD: It would not be unusual for patients with cortical infarcts to also have extensive white matter lesions which arguably make a major contribution to their cognitive deficits. This is not surprising considering that the risk factors for ischaemic leukoencephalopathy are similar to those for stroke. 2. Haemorrhagic VaD: 2.1. Intracerebral haemorrhage: ICH can lead to considerable disruption of cerebral function and consequently dementia. It may be associated with an infarction in many cases. 2.2. Subdural haemorrhage: In chronic cases, there may be insidious development of cognitive deficits resulting in a picture of dementia. This is often associated with a fluctuating course of drowsiness and mental confusion.64

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2.3. Subarachnoid haemorrhage: Dementia that develops acutely following a subarachnoid haemorrhage is usually associated with other evidence of residual brain damage.65 The delayed onset of dementia may owe its origin to normal pressure hydrocephalus. 3. VaD with AD: The co-occurrence of vascular and Alzheimer type changes in the brain is a relatively common finding in dementia patients, and has been referred to as “mixed dementia”. Since both disorders are common in the elderly, their coincidental overlap is to be expected. However, there are important interactions between the two pathologies. Some risk factors (e.g. hypertension, cholesterol, diabetes, homocysteine) appear to be common for the two disorders. The presence of infarction has been shown to greatly increase the likelihood of expression of subclinical Alzheimer pathology.66,67 About 40% of patients meeting pathological criteria for VaD have concomitant AD pathology, and a similar proportion of AD patients has vascular brain lesions usually considered to be coincidental.68 There may be shared pathogenetic mechanisms such as delayed neuronal death and apoptosis.69 This overlap has clinical as well as research implications. In the clinical setting, the overlap may present in different ways. The cognitive impairment may show a gradual onset and slow progression, which has been generally considered to be typical of AD. However, we now recognise that subcortical VaD may have a similar longitudinal profile. Quite often, such a presentation is punctuated by a cerebrovascular event that is insufficient to explain all the deficits. Neuroimaging may be of assistance, as a predominance of infarcts and WMLs sways the diagnosis in favour of VaD. Early and progressive medial temporal atrophy would argue for a diagnosis of VaD. It has recently been shown, however, that hippocampal atrophy may be seen in VaD patients with no AD pathology and in the absence of hippocampal infarction, possibly due to progressive ischaemic necrosis.70 Functional imaging, such as SPECT, PET or fMRI may be of assistance in the differentiation, with the typical bilateral temporo-parietal hypometabolism or hypoperfusion being the characteristic abnormality in AD. Neuropsychological profile may also be of some discriminating value.71 Clinical diagnosis and prognosis The presentation of VaD varies with the underlying pathophysiology. The onset of MID is often sudden with a transient ischaemic attack or a stroke, after which the clinical course may be static, remitting or progressive, with often a fluctuating or stepwise deterioration. Predominantly subcortical lesions may produce cognitive impairment of gradual onset and slow progression. Some other features that are more often seen in VaD, especially in contrast with AD, are nocturnal confusion and wandering, relative preservation of emotional responsiveness and personality until the later stages of the

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disease, and the presence of depression, emotional lability and incontinence and somatic symptoms.15 A history of risk factors for CVD should alert the clinician to the possibility of VaD, and the presence of neurological symptoms (visual disturbances, brain stem abnormalities, sensory or motor symptoms, etc.) and signs (hemiparesis, visual field defects, pseudobulbar palsy, extrapyramidal signs) should provide further support. These features are encapsulated in the Ischaemic Index,10 a score of 7 or more on which supports the diagnosis. The cognitive deficits in VaD are multifocal and therefore more varied than generally seen in AD. Memory deficit, though necessary for some diagnostic criteria, may not be as marked as that seen in AD, and verbal-performance discrepancies are often notable. Looi and Sachdev71 surveyed the literature on neuropsychological studies that compared VaD and AD. Of the 45 studies, 18 were excluded because of inadequacies and the remaining systematically analysed. There were a number of similarities of dysfunction between VaD and AD. However, when matched for age, education and severity of dementia, VaD patients had relatively superior function in verbal long-term memory, and more impairment in frontal-executive functioning compared to AD patients. Interpretation of the results is limited by uncertainty in diagnostic criteria for VaD, possible inclusion bias due to use of clinical diagnosis alone, possible overlap of AD and VaD, and the methodological shortcomings of some studies. The emphasis on frontal-executive dysfunction in VaD, even when the diagnosis is not one of subcortical VaD is noteworthy, and has attracted much discussion, an should arguably be incorporated in the criteria for VaD. Other common neuropsychological deficits in VaD are visuospatial dysfunction, dysarthria with relative preservation of language and cognitive slowing.15 Mini-mental state examination (MMSE),23 the most commonly used screening instrument for dementia, has a number of deficiencies when applied to VaD: it emphasises cortical functions (language and memory) at the expense of subcortical ones; it does not test psychomotor speed and frontal function; it does not test for recognition memory; and it is insensitive to mild cognitive impairment. The assessment is geared toward identifying the extent of the disabilities and all possible contributing factors. CT and/or MRI are central to the diagnosis and since CVD is common, guidelines are available for the topography and severity of lesions to be considered significant. Lesions generally regarded as trivial, such as one or two lacunes and frontal horn capping, would not meet these criteria. At least a quarter of all white matter would need to be involved for the lesions to be clearly significant. Functional imaging, such as single photon emission (SPECT) and positron emission tomography (PET) may provide further information on the functional significance of any observed lesions. Prognosis While not being totally consistent, longitudinal studies of VaD suggest mortality rates higher than those for AD and rates of admission to nursing homes

314 Table 5. • • • •

• • •

•

•

•

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Clinical assessment for vascular dementia.

History should include onset, course and nature of cognitive deficits, and information from the carer or other person close to the patient on subtle personality and behavioural changes that may have been noticed. Full neuropsychological evaluation is required at some stage, although the Mini-Mental State Examination, supplemented by clock-drawing and clinical assessment of frontal lobe functioning, may be useful for screening. Assessment of functional losses. This may be aided by administration of scales for activities of daily living, and instrumental activities of daily living, and assessment at home by an occupational therapist. Psychiatric evaluation is important, as depressive disorder is common in patients with cerebrovascular disease and depression may produce a syndrome resembling dementia. Anxiety disorders and psychotic symptoms may also occur in people with vascular dementia. General physical examination, including pulse irregularity, cardiovascular status, carotid bruits, fundus examination, peripheral vascular disease and hypertension (multiple blood pressure measurements). Examination for focal neurological signs, in particular gait abnormality, visual field defects, pseudobulbar palsy (dysarthria, dysphagia, spastic tongue, brisk jaw jerk), brisk reflexes, extensor-plantar responses and spasticity in the limbs. Routine investigations, including full blood counts, erythrocyte sedimentation rate, blood glucose, serum cholesterol and triglyceride level, syphilis serology, electrocardiogram, and chest x-ray. Investigations are directed towards providing evidence for CVD and its risk factors. Structural brain imaging, (computed tomography or magnetic resonance imaging) is essential to provide information on the extent, type and distribution of vascular lesions and to exclude other potential causes of dementia, such as subdural haematoma or tumour. Functional imaging, such as single photon emission tomography, positron emission tomography and functional magnetic resonance imaging, may provide further information on the functional significance of any observed lesions or detected abnormalities not apparent on structural imaging. Other specialised investigations may include echocardiography, carotid Doppler, antinuclear antibodies, antiphospholipid antibodies, lupus anticoagulant, serum protein electrophoresis and cerbrospinal fluid examination.

comparable in the two. Brodaty et al.72 reported a five-year mortality rate of 63.6% (cf. 31.8% for AD) and nursing home admission rate of 31.8% (cf. 20.6% for AD). Skoog et al.73 found significantly higher three-year mortality rates in VaD (66.6%) compared to AD (42.2%) in >85 years old subjects. Katzman et al.74 performed a year follow-up on 320 subjects with dementia, and using a Cox proportional hazard model, reported a mortality risk ratio in 65–74 years old subjects of 5.4 in AD and 7.2 in VaD, compared to 2.5 in the entire cohort. These rates may be improved by better treatment and preventative strategies. Treatment and prevention The management of risk factors for VaD offers the opportunity to significantly reduce its incidence or, if dementia has already been diagnosed, halt

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315

Some strategies for primary prevention of vascular dementia.87

Target high-risk groups. These include elderly people; people with hypertension, diabetes, atrial fibrillation, or past transient ischaemic attack or stroke, and smokers. 1. Treat hypertension, optimally. 2. Treat diabetes. 3. Control hyperlipidaemia. 4. Persuade patients to cease smoking and decrease alcohol intake. 5. Prescribe anticoagulants for atrial fibrillation. 6. Provide antiplatelet therapy for high risk patients. 7. Perform carotid endarterectomy for severe (>70%) carotid stenosis. 8. Use dietary control for diabetes, obesity and hyperlipidaemia. 9. Reduce homocysteine levels in those with high levels, by folate supplementation. 10. Recommend lifestyle changes (e.g., weight loss, exercise, reduce stress, decrease salt intake). 11. Intervene early for stroke and transient ischaemic attacks with neuroprotective agents (e.g., propentofylline, calcium channel antagonists, N-methyl-D-aspartate receptor antagonists, antioxidants). 12. Provide intensive rehabilitation after stroke.

its progression and sometimes achieve partial improvement. The various strategies that could potentially be used to prevent dementia are summarised in Table 6. Empirical data on most of these are relatively few. The impact of control of hypertension is important and was examined in a European study (Syst-Eur).75,76 In a follow-up over two years in patients with systolic hypertension, active treatment was found to reduce the incidence of dementia by 50%, from 7.7 to 3.8 cases per 1000 patient-years, which was of borderline significance. Of the 32 cases of dementia, only two had VaD, and 23 were diagnosed with AD. The authors estimated that treatment of 1000 hypertensives for five years could prevent 19 cases of dementia. In the SHEP trial however,77 there was no effect of the treatment of hypertension on the incidence of dementia, even though the incidence of stroke and myocardial infarction was reduced. In a recent double-blind comparison of losartan, an angiotension II receptor blocker, and hydrochlorothiazide, there was a significant improvement in cognitive function with the former but not the latter. It was uncertain whether this was due to the direct pharmacological effects of losartan, or because of its more effective control of blood pressure. More work clearly needs to be done in this field to determine the effect of control of hypertension on dementia. However, since control of hypertension is the most important step in the prevention of strokes, its beneficial effects on health are not in dispute. The control should start early, often decades before the anticipated time of emergence of cognitive deficits. In controlling hypertension, a vigorous approach to avoid hypotension is advocated as poor autoregulation in VaD patients increases its deleterious effects on cerebral blood flow. Cessation of smoking has been noted to have a beneficial effect in one study. The control of other risk factors (hyperlipidemia, platelet aggregation,

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carotid disease) may also have a stabilising or even a beneficial effect. The literature on the use of antiplatelet agents for the prevention of strokes is extensive but inconclusive, and much less certain than that for heart disease. The evidence for the efficacy of high dose aspirin (≥ 975 mg/day) in reducing the risk for stroke is convincing, but that for low dose (≤ 325 mg/day) is equivocal, even though experimental evidence suggests that a dose as low as 40 mg/day should be an effective anti-platelet dose. Considering the gastrointestinal side effects of high dose aspirin, most clinicians prescribe 300–500 mg/day to VaD patients. For those “failing” aspirin therapy, other anti-platelet agents, such as ticlopidine, may be indicated. Anticoagulant therapy is advocated for patients with underlying embolic diseases such as non-valvular atrial fibrillation. Hypoxic ischaemic events, e.g. myocardial infarction, cardiac arrhythmias, seizures, pneumonia, etc. increase the risk of dementia in stroke patients, and should be promptly attended to.78 There is an increasing emphasis on the protection of the brain to minimise the effect of a stroke. Most importantly, treatment strategies have been developed for early intervention, and neuroprotective agents under investigation appear to hold great promise.79 These include NMDA receptor antagonists such as selfotel, aptiganel and memantine, agonists at the GABA receptors, NO-pathway inhibitors and Ca channel antagonists. Many drugs have been investigated for the treatment of VaD but with limited success, and interest in this field is burgeoning. Vasodilators (e.g. hydergine and other alkaloids, cyclandelate) have some positive effects, and modest gains in cognition have been reported with an orally active haemorheological agent (pentoxifylline). Other drugs that have been tried include the vinca alkaloids (vincamine, vinburnine, vinpocetine), calcium channel antagonists (nimodipine, nifedipine, cinnarizine, flunarizine), nootropics (piracetam and its analogues), extracts of Ginkgo biloba and many others (buflomedil, naftidrofuryl, idebenone, etc.), with no spectacular successes. Some of the drugs that improve memory in some AD patients, e.g. acetyl cholinesterase inhibitors (donepezil, rivastigmine, galantamine), may find a role in VaD as well. Other drugs may serve a neuroprotective role, e.g. propentofylline, calcium channel antagonists, N-methyl-D-aspartate receptor antagonists, etc. Nimodipine, a calcium channel antagonist, has attracted some attention as a treatment of VaD. In animal models, it has been demonstrated to reduce infarct size when administered soon after the occurrence of ischaemia. The mechanism was suggested to be the dilatation of small and collateral cerebral vessels,80 and reduced influx of calcium ions into depolarised neurons. 81 Two studies examining its efficacy in MID have been negative,82,83 but a subanalysis of the latter study83 suggested a favourable response in patients with subcortical VaD, a finding which has previously been reported84 and which needs further examination. Memantine, a non-competitive antagonist of NMDA receptors, has been shown in a phase III double-blind trial to be beneficial in AD, VaD and mixed dementia.85 Propentofylline, a selective

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phosphodiesterase and adenosine reuptake inhibitor, has also shown some promise in VaD86 that is worthy of further examination. In the absence of any drug that will unequivocally treat VaD or reverse the cognitive decline, the mainstay of treatment is preventative and supportive. Supportive measures should include a rigorous treatment of psychiatric complications such as depression, measures to facilitate independence and community or institutional care, and support for the carer. Specific neuropsychological treatment may have a role. Conclusion The pace of research into VaD has quickened recently but many questions remain unanswered. In almost all areas of VaD, which include its definition, clinical diagnosis, associated psychiatric disorders, identification of risk factors, and efficacy of interventions, more work is necessary. Since the disorder is common, of great public health importance and potentially preventable, such research may prove to be very cost-effective. References 1. Dening TR, Berrios GE. The vascular dementias. In: Berrios GE, Freeman HL, editors. Alzheimer and the dementias. London: Royal Society of Medicine Services, 1991; 69–76. 2. Alzheimer A. Die arteriosklerotische atrophie des gehirns. Allg Z Psych Psychisch-Gerichtlich Med. 1885; 51:809–812. 3. Binswanger O. Die Abgrenzung der allgemeine progressiven Paralyse, I–III. Berl Klin Wochensohr. 1884; 48:1103–1105, 1137–1139, 1180–1186. 4. Alzheimer A. Die Seelenstörungen auf arteriosklerotischer Grundlage. Allg Z Psych Psychisch-Gerichtlich Med. 1902; 59:695–701. 5. Olszewski J. Subcortical arteriosclerotic encephalopathy. World Neurol. 1962; 3:359–375. 6. Pantoni L, Garcia JH. The significance of cerebral white matter abnormalities 100 years after Binswanger’s resport: A review. Stroke. 1995; 26:1293–1301. 7. Alzheimer A. Über eine eigenartige Erkrankung der Hirnrinde. Allg Z Psych Psychisch-Gerichtlich Med. 1907; 64:146–148. 8. Tomlinson BE, Blessed G, Roth M. Observations on the brains of demented old people. J Neurol Sci. 1970; 11:205–242. 9. Hachinski VC, Lassen NA, Marshall J. Multi-infarct dementia: a cause of mental deterioration in the elderly. Lancet. 1974; 2:207–210. 10. Hachinski VC, Iliff LD, Zilkha E, De Boulay GH, McAllister VL, Marshall J, Russell RW, Symon L. Cerebral blood flow in dementia. Arch Neurol. 1975; 32: 632–637. 11. Tatemichi TK, Desmond DW. Epidemiology of vascular dementia. In: Prohovnik I, Wade J, Knezevic S, Tatemichi T, editors. Vascular dementia: Current concepts. Chichester, UK: John Wiley & Sons, 1996: 42–71. 12. Hachinski VC, Potter P, Merskey H. Leuko-araiosis. Arch Neurol. 1987; 44: 21–23.

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Chapter 19 CONCLUDING COMMENTS — THE AGEING BRAIN Perminder S Sachdev

Conclusion When confronted with a neurological or psychiatric disorder in an elderly individual, a clinician is likely to ask how the processes of ageing have influenced the aetiology and presentation of the disorder, and will impact on its efficient management. The clinician then seeks information about the ageing of the brain to make informed judgements and choices. There are many urban myths about ageing, and some of these apply to the brain. The reviews included in this book are an attempt to flush out these myths, and arm the clinician and general researcher with the empirical facts that can be mustered to substantiate claims about ageing. There are many salient questions: Is cognitive change to be expected in an elderly individual? Is this change progressive, relentless and unselective, or is it focal and constrained? Would every person who lived long enough develop Alzheimer’s disease (AD)? Do our neurones die as we grow old? What happens to the size of the brain and its metabolic activity? How do our hormones change with age? Can anti-oxidants slow or even stop the process of ageing? Are genes important for the ageing brain, or is it all in the environment? How much of what we are is due to what we eat? This book has addressed some of these questions in a language simple enough for a general reader to understand. Does this book also lay out a guide map for the future researcher of brain ageing? The field is too diverse to recommend any one road to the traveller. A cognitive neuroscientist, for example, has a very different perspective from that of an endocrinologist when dealing with age-related processes. Genuine

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breakthroughs will depend, however, on the successful marriage of such diverse outlooks. The emerging role of glucocorticoids in stress, depression, memory dysfunction, brain atrophy and ageing is one example of the power of cross-fertilization. The research on oxidative stress in ageing has led to the growth of a large industry extolling the virtues of antioxidants. The observations on older women have led to the examination of the role of oestrogen in the regulation of neural plasticity. Cross-disciplinary research is more than a mere fad in relation to the future development of this field. Much of the progress in the future is likely to come from predictable quarters. The hunt is on for the genes that regulate the development and senescence of the brain. It is inevitable that this will one-day lead to their manipulation to modify these processes, fraught though this will be with ethical dilemmas. New technology will continue to challenge and astound us. In the last two decades, the introduction of neuroimaging techniques has changed the way we look at the brain. These technologies continue to evolve, and are being complemented by developments in histopathology, such as confocal laser microscopy, which enable us to examine single cells in great detail. Advances in mass spectrometry have opened up the study of proteins and led to the development of proteomics to complement the revolution in genomics. In medical science, many advances have come from the study of disease processes, which may be considered nature’s joints that a researcher can hope to carve. The quintessential disorder of ageing is AD, and its study holds the promise of many insights about nature’s ways. Solving the riddle of plaques and tangles may not solve the problem of ageing, but has the potential of altering the landscape considerably. No other disorder has given more impetus to the study of degeneration of the brain, but it may be only the beginning. There is a limited number of ways in which brain cells lose their vitality or viability in the aged, and some of these leave footprints such as neuronal inclusions or inter-cellular deposits. We do not know why Lewy bodies develop in the cortices of some individuals. We have little understanding of tauopathies, and why some degeneration begins in the temporal lobes, while others may start in the frontal, parietal or even the occipital lobes. More than a decade after the discovery of the Huntington’s gene, we do not fully understand the process of neuronal loss in this disease, and have no method of stopping or modifying it. The researcher of the future will be well served by following the footprints of nature in discovering some insights. This book can be criticized for under-emphasizing the sociological aspects of brain ageing. The aged brain possesses a wealth of knowledge and experience, which it holds on to even in the presence of other devastation, and passes onto future generations. The brain also has an elegant ability to adapt to changing circumstances. It has a built-in reserve that protects it in adversity, and education, mental activity and healthy lifestyle can increase the reserve. Nutritional and lifestyle factors may also retard the disease processes of old age. These factors hold the potential for living well in old age, and to

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stay engaged in family and society. As the demographic shift to old age occurs in our society, there are increasing opportunities for the elderly individual and his or her brain. While we cannot protect the brain forever, we can strive to maximize its quality well into old age if we are to build a happy and productive future. While tabloid science abounds on this issue, it is hoped that this book will bring empirical science to bear on any such quest.

CONTRIBUTORS ADDRESS LIST

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CONTRIBUTORS ADDRESS LIST

Carol Brayne Department of Public Health and Primary Care, Institute of Public Health, University of Cambridge, Forvie Site, Robinson Way, Cambridge CB2 2SR UK Tel: 44-1223-330 334; Fax: 44-1223-330 330 Email: [email protected]. uk G. Anthony Broe Senior Research Scientist, Prince of Wales Medical Research Institute; and Clinical Director, Programme of Community Health and Aged Care, Prince of Wales Hospital; and Professor of Geriatric Medicine, University of New South Wales, Australia Tel: +61-2-9382 4252; Fax: +61-2-9382 4241 Email: [email protected] Janet Bryan, PhD Research Scientist, Consumer Science Program, CSIRO Health Sciences and Nutrition, Adelaide BC, South Australia 5000, Australia Tel: 61-8-8303 8936; Fax: 61-8-8303 8899 Email: [email protected] Helen Christensen, PhD The Centre for Mental Health Research, The Australian National University, Canberra ACT 0200, Australia Tel: +61-2-6125 2741; Fax: +61-2-6125 0733 Email: [email protected]

Peter J. Crack Centre for Functional Genomics and Human Disease, Monash Institute of Reproduction and Development, Monash University, 246 Clayton Road, Clayton VIC 3168, Australia Judy B. de Haan Centre for Functional Genomics and Human Disease, Monash Institute of Reproduction and Development, Monash University, 246 Clayton Road, Clayton VIC 3168, Australia Tel: +61-3-9594 720; Fax: +61-3-9594 7211 Email: [email protected]. edu.au Geoffrey A. Donnan, MD, FRACP Director, National Stroke Research Institute, Neurosciences Building, Repat Campus, Austin & Repatriation Medical Centre, Heidelberg West, Melbourne VIC 3081, Australia Tel: +61-3-9496 2699; Fax: +61-3-9496 2650 Email: [email protected]. au Glenda M Halliday Prince of Wales Medical Research Institute; and the University of New South Wales, Sydney NSW, Australia Mariese A Hely Department of Neurology, Westmead Hospital, Westmead, Sydney, Australia

330 Paul Hertzog Centre for Functional Genomics and Human Disease, Monash Institute of Reproduction and Development, Monash University, 246 Clayton Road, Clayton VIC 3168, Australia Rocco C. Iannello Centre for Functional Genomics and Human Disease, Monash Institute of Reproduction and Development, Monash University, 246 Clayton Road, Clayton VIC 3168, Australia Ismail Kola Pharmacia Corp., 4901 Searle Parkway, Skokie, Ill 60077, USA Jillian J. Kril, PhD Associate Professor (Geriatric Medicine), Centre for Education and Research on Ageing, The University of Sydney; Tel: +61-2-9767 7109; Fax: +61-2-9767 5419 Email: [email protected] Rajeev Kumar, MD, FRANZCP Lecturer and Consultant Psychiatrist, The Canberra Clinical School, University of Sydney, PO Box 11, Woden ACT 2606, Australia Email: [email protected]

THE AGEING BRAIN

John B.J. Kwok Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, Sydney NSW 2010, Australia Jeffrey C.L. Looi, MBBS FRANZCP MFPOA Senior Specialist Older Persons Mental Healt Service, 6 Gaunt Place, Gerral ACT 2005; and Senior Lecturer, Department of Psychological Medicine, Canberra Clinical School, University of Sydney; and Research Fellow, Neuropsychiatric Institute, Prince of Wales Hospital, Randwick, Sydney NSW, Australia Tel: +61-2-6205 1957; Fax: +61-2-6205 1533 Email: [email protected] Stephen R. Lord Associate Professor, Prince of Wales Medical Research Institute, Sydney NSW 2031, Australia Tel: +61-2-9382 2721; Fax: +61-2-9382 2722 Email: [email protected] John G.L. Morris Department of Neurology, Westmead Hospital, Westmead, Sydney NSW 2145, Australia Tel: 61-2-9845 6793; Fax: 61-2-9635 6684 Email: [email protected]

331

CONTRIBUTORS ADDRESS LIST

Perminder S. Sachdev, MD, PhD, FRANZCP Professor of Neuropsychiatry, School of Psychiatry, University of New South Wales, Sydney, Australia; and Neuropsychiatric Institute, The Prince of Wales Hospital, Randwick NSW 2031, Sydney, Australia Tel: +61-2-9382 3763; Fax: +61-2-9382 3773/3774 Email: [email protected] Peter R. Schofield, PhD, DSc Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, Sydney NSW 2010, Australia Tel +61-2-9295 8285; Fax: +61-2-9295 8281 Email: [email protected] Peter W. Schofield Clinical Director, Neuropsychiatry Service, Hunter Area Health, PO Box 833, Newcastle NSW 2300, Australia; and Conjoint Associate Professor of Psychiatry, University of Newcastle, Australia Tel: 61+2+4924 6857; Fax: 61+2+4924 6849 Email: [email protected] Gary W. Small, MD Department of Psychiatry and Biobehavioral Sciences,

The Neuropsychiatric Institute, The Alzheimer’s Disease Center and The Center on Aging, University of California, Rm. 88-201, 760 Westwood Plaza, Los Angeles, CA, USA; and The VA Greater Los Angeles Healthcare System, Los Angeles, CA, USA Tel: 310-825-0291; Fax: 310-825-3910 Email: [email protected] George A. Smythe Biomedical Mass Spectrometry Facility, Faculty of Medicine, University of New South Wales, Sydney NSW 2052, Australia Tel: +61-2-9385 2952; Fax: +61-2-9662 4469 Email: [email protected] John Snowdon Clinical Associate Professor, University of Sydney, PO Box 1, Rozelle NSW 2039, Australia Tel: 61-2-9556 9666; Fax: 61-2-9818 5712 Email: [email protected] Velandai K. Srikanth, MBBS FRACP Research Fellow, The Menzies Centre for Population Health Research (University of Tasmania), 17 Liverpool Street, Hobart TAS 7000, Australia and

332 Staff Specialist Geriatrician, Royal Hobart Hospital, Liverpool Street, Hobart TAS 7000, Australia Tel: +61-3-6226 7700; Fax: +61-3-6226 7704 Email: [email protected] Rebecca St George Prince of Wales Medical Research Institute, Sydney NSW 2031, Australia Tel: +61-2-9382 7916; Fax: +61-2-9382 2722 Email: [email protected]

THE AGEING BRAIN

Julian N. Trollor Conjoint Lecturer, School of Psychiatry, University of New South Wales, Sydney NSW, Australia; and Staff Specialist, Neuropsychiatric Institute, The Prince of Wales Hospital, Sydney NSW, Australia Tel: +61-2-9382 3755; Fax: +61-2-9382 3774 Email: [email protected]

SUBJECT INDEX

335

SUBJECT INDEX

[Numbers in italics refer to Figures and Tables] Activation studies advantages, 124–125 Alzheimer’s disease, 266–268 brain patterns, 267–268 complex tasks, 127–128 deactivation of regions, 131 encoding, 128–129 frontal/executive tasks, 124 motor and sensory stimulation, 125–127 motoric tasks, 124 photic tasks, 124 purpose, 124 retrieval tasks, 129–130 spatial orientation tasks, 124, 126 summary, 130–131 verbal encoding and retrieval, 124, 129–130 visual attention tasks, 124 visual encoding and retrieval, 124 word identification tasks, 124 working memory tasks, 124 Age-associated memory impairment (AAMI), 260 Age-related macular degeneration (ARMD) neurodegenerative disease, 17 Ageing alternating fine movements, 279 balance, 276–278 brain, 4, 35 theoretical issues to consider, 35–36 brain reserve, loss of, 227 CBF and, 160–162, 161 cerebral metabolism and, 162, 163 common cause hypothesis, 85–87 depression see Depression endocrine relationships, 140–141 eye movement, 278 facial expression, 279 gait, 276–278 health promotion industry, 8–9, 26 hormones see Hormones intervention strategies, 69–70 longevity, increased, 15–16 longitudinal studies, 36–37, 79, 87–91, 88, 89 motor factors, 66–67 multiple pre-clinical syndromes, 19–20

muscle tone, 280 normal, 69, 115, 160, 249 successful, distinguished, 50 old, definition, 252 perceptions of, 3, 8 populations, 11, 12, 26, 36 demographic factors, 12 postural stability, 67, 275–276 proprioception or kinaesthesis, 65–66 regional variations in brain utilisation, 126 senses, 64–66 sensorimotor factors, relationship with, 68 speech, 278–279 utilisation of additional networks, 126, 130 vitamin deficiency, 206–207 Ages of Mankind, 3 Alzheimer’s disease (AD) accumulation of abnormal gene products, 18 activation studies, 267–268 age-associated memory impairment, 260 ageing and, 259, 324 Alzheimer’s diagnoses and reports, 299 Alzheimer’s Disease Diagnostic and Treatment Centers (ADDTC), 302 amyloid cascade hypothesis, 174, 175, 182 amyloid precursor protein (APP) gene, 173, 174, 265 antioxidants, 215 altering the balance of, 196, 198 apolipoprotein E (ApoE), effect of, 179, 248, 262, 267, 269 association with ageing, 4, 6, 17, 18, 35 amyloid beta (Aβ) see Amyloid beta (Aß) B vitamins and, 209–212 blood-brain barrier (BBB), 156 brain reserve theory, 224, 226 butyrycholinesterase (BChE) gene, effect of, 180, 248 cerebral microvasculature, 155–156 cerebrovascular disease, co-occurrence with, 310

336 clinical symptoms, 174, 178, 195 Consortium to Establish a Register for Alzheimer’s Disease (CERAD), 250 cotton wool plaques, 178 definition, 249 delayed onset, 8, 26 diagnosing, 250, 259 diagnostic categories, 260 early detection, 260 environmental factors, 27 estrogen treatment, 144 genetic research, 6, 7, 27, 173 genetic risk, 264 glucose metabolism studies, 268– 269 increasing incidence, 19, 174 intelligence, 232–233 longitudinal studies of cognitive change CT, 89, 90 MRI, 87, 88 low educational attainment, 228–231 mental activity, 233–234 multi-infarct dementia (MID), distinguished, 300, 301 neurological evaluation of at risk groups, 123 NFT accumulation, 41, 42, 174, 178, 182, 195 NMDA receptor antagonists, use of, 316 Memantine, 316 occupational status, 229 pathologies, 251 pedigrees, 178–180, 179 perturbations of antioxidant pathways, 187 pre-symptomatic changes, 261 clinical trials to detect, 269–270 presenilin-1 (PS-1) gene, 173 presenilin-2 (PS-2) gene, 173 risk factors, 98 risk groups, 268, 269 spastic paraparepsis (SP), 178–180, 179 tau gene, 173, 174 vascular dementia distinguished, 300, 304, 313–314 VaD with AD, 312 white matter lesions, 309 Amygdala reduction in volume, 54 Alzheimer’s disease, 261–263

THE AGEING BRAIN

Amyloid angiopathy Alzheimer’s disease, 310 cerebral microvasculature and, 155, 156 vascular pathology, 309 Amyloid beta (Aβ) see also ß-amyloid Alzheimer’s disease, 18, 39–40, 42, 182–183, 196 formation, 175 neurotoxic deposits, 174 tau, relationship with, 182–183 Amyloid cascade hypothesis, 174, 175, 182 Amyloid precursor protein (APP) gene Alzheimer’s disease, 173, 174 chronic neuronal activation, 234 formation of Aß peptides, 175 mutations, 175–176 schematic diagram, 176 Amyotrophic lateral sclerosis (AML) familial forms (FALS), 195 accumulation of free radicals, 195 antioxidant balance, altered, 195, 198 neurodegenerative disease, 17–18, 194–195 Sod1 gene, role of, 195 Angular gyrus infarcts, 307 Antioxidants altering the balance, 187, 190, 191, 196, 198 Alzheimer’s disease, 196, 215 cognitive ageing, 212–213 cross-sectional studies, 213–214 longitudinal studies, 214–215 lipid peroxidation in ageing, 189– 190 Parkinson’s disease, 192 pathway, 188, 188 regulation of redox status, 187 Apolipoprotein E (ApoE) cognitive change, 84–85 dementia, subjects at risk of, 123 late onset Alzheimer’s disease (LOAD), 179 mild cognitive impairment, predicting, 123–124 Arachidonic acid (AA) brain function and, 215–216 Arteriosclerosis vascular pathology, 309

SUBJECT INDEX

Atherogenesis antioxidants and, 213 Atherosclerosis ageing and arterial changes, 154 vascular pathology, 309 white matter disease and cognitive decline, 299, 306 Atrophy ageing, 4, 37–38, 42, 58, 251 B vitamins, 210 brain atrophy index (BAI), 51 brain volume index (BVI), 51 CMRglu, effect on, 114 CT studies, 51 gender differences, 52 granular cortical, 309 Helsinki Aging Brain study, 52, 56 homocysteine levels, 210 longitudinal studies, 246 MRI studies, 51–52 posture, impact on, 276 resting studies, impact on, 120–121 Attention attentional capacity, 206 nutrition and, 206 selective, 64 B vitamins cognitive ageing and, 206–207 cross-sectional studies, 207–208 experimental studies, 208–209 longitudinal studies, 208 dementia, 209–212 memory, impact on, 208–209 Basal ganglia infarcts, 308 lesions, 300 MRI studies, 58 N-acetylaspartate (NAA), concentration of, 131 Parkinson’s disease, 278 volume and atrophy, 53 β-amyloid see also Amyloid beta (Aß) accumulation, 39–40, 42, 174, 195 subtypes of plaque deposits, 40 Binswanger disease, 276, 299, 300, 309 report on neurological degeneration, 299 Blood–brain barrier (BBB) ageing and, 155 Alzheimer’s disease, 156

337 Blood oxygen level dependent (BOLD) technique activation studies, 124 fMRI scans, 99–100 Brain ageing process, 4, 35, 58–59 theoretical issues to consider, 35–36 atrophy see Atrophy clinicopathological studies, 41–42 exercise, 9, 84 gender differences, 37–38 gross volume, 52–54 size and neuropathological changes, 232–233 neuronal loss, age-related, 4, 38–39, 42 nutritional factors, 9 pathology, impact of, 5–6, 41–42, 251 polyunsaturated fatty acids (PUFAs), 215–216 regional variations in utilisation by different age groups, 126 research, current status of, 6–7 ROS-induced damage, 189 structural remodelling, 39 ventricular enlargement see Ventricular enlargement weight, 37–38 Brain reserve hypothesis Alzheimer’s disease, 224, 226 brain damage and, 226–227 brain reserve, definition, 224 brain size, 232–233 clinicopathological studies, 227 cognitive reserve, distinguished, 224 dementia, 224–226, 225 educational attainment, 228–231 epidemiological studies, 228 function and pathology distinguished, 223 imaging studies, 227–228 intelligence, 231–232 mental activity, 233–234 non-pathological correlates, 227 threshold concept, 224 BRAINSURF, 55 Capsular genu infarcts, 307–308 Catalase (Cat), 188 upregulation and alteration of ratios, 189–190

338 Caudate nuclei infarcts, 307–308 Cellular senescence in vitro models, 190–191 in vivo models, 191–192 Cerebellum MRI studies, 57 Cerebral autosomal dominant arteriopathy with subcortical infarct and leukoencephalopathy (CADASIL), 306–307, 308 Cerebral autosomal dominant hereditary cerebral haemorrhage with amyloidosis – Dutch type, 307 Cerebral blood flow (CBF) ageing, studies of, 160–162, 161 autoregulation, 159, 162 cerebral perfusion pressure, 158, 158 cerebral vascular resistance, 158– 159 chronic neuronal activation, 234 dementia and, 164 determinants, 157–158, 157 diet and, 206 dysautoregulation index, 162 global decline, age-related, 109 homocysteine hypothesis, 207 neuroimaging techniques, 98–100 oxygen extraction ratio (OER) changes, 109, 114 partial volume, effect of, 109, 120– 121 regional decline, 132 regions of interest (ROI) analysis, 109 research, 157 historical background, 100–101 Technetium-HMPAO (Tc-HMPAO) studies, 101, 106–107, 108, 121 Xenon studies, 101, 102–105, 121, 122, 157 vascular risk factors, 121–122 viscosity, 159 Cerebral metabolic rate of glucose (CMRglu) Alzheimer’s disease, 268–269 cerebral atrophy, 114 dementia, evaluation of at risk groups, 123 effect of ageing, 114–115, 162 neuroimaging techniques, 98, 99, 261 regional decline, 132

THE AGEING BRAIN

resting FDG studies, 114–115, 116– 119 vascular risk factors, 121–122 Cerebral metabolic rate of oxygen (rCMRO2) global decline, age-related, 109, 162 neuroimaging techniques, 98 partial volume, effect of, 109 Cerebral metabolism ageing and, 162, 163 CBF and, 160 features, 160 glucose, 160 lactate, production of, 160 normal ageing, effect of, 160 vasodilatory metabolites, 160 Cerebral perfusion pressure CBF and, 158 idealized curve, 158 intracranial pressure (ICP), 159–160 Cerebral vascular resistance (CVR) CBF, reduction in, 158–159 factors determining blood viscosity, 159 Cerebrovascular disease (CVD) Alzheimer’s disease, co-occurrence with, 310 dementia, association with, 299, 303, 309 diagnosing, 303 risk factors, 300, 313 Cerebrovascular system ageing and arterial changes, 153–154, 162–164, 251 blood–brain barrier (BBB), 155 Atherosclerosis, 154, 299, 306 cerebral microvasculature, 154–155 Alzheimer’s disease, 155–156 disease and pathological vascular changes, 163 focal gliosis, 309 laminar necrosis, 309 occlusive disease, 154 reactivity, 162 risk factors CBF, effect on, 121–122 systolic hypertension, 154, 315 Cingulate activation intensity, 267 atrophy, 53 decreased activation, 126 Cognitive ability see Cognitive speed; Crystallised intelligence; Memory Cognitive ageing

SUBJECT INDEX

antioxidants, 212–213 cross-sectional studies, 213–214 longitudinal studies, 214–215 definition, 76 Cognitive change, 75–76 ApoE ε2 subjects, 84–85 ApoE ε4 subjects, 84–85, 123–124 Canberra Longitudinal Study, 78–82, 81, 83 common cause hypothesis, 85–87 definition, 76 education, role of, 82 health, role of, 85 inter-individual variability, 75, 78–85 intra-individual variability, 76, 78–85 longitudinal studies, 76, 79–81 CT, 89, 90 MRI, 87, 88 marker variables, 82 neurophysiological approach, 75–76 non-unitary nature, 77–78 normal individuals CT changes, 89, 90 MRI changes, 87, 88 physiological change, 85–86, 90–91 risk factor variables, 82 Seattle Longitudinal Study, 78, 80 Cognitive impairment B vitamins, 210 homocysteine levels, 210–211 longitudinal studies of cognitive change CT, 89, 90 MRI, 87, 90 low educational attainment, 228– 231 mild see Mild cognitive impairment (MCI) synapse reduction, 4–5 Cognitive resources definition, 206 nutrition, impact of, 206 Cognitive speed age-related change, 79 crystallised intelligence, distinguished, 76 definition, 76, 206 inter-individual differences, 78–85 nutrition, impact of, 206 seven-year longitudinal change, 81 Computerised axial tomography (CT) atrophy, 51, 53

339 Alzheimer’s disease, 261–262 cortex, 54 development of, 49 gross brain volume, 52–53 subcortical structures, 55 vascular dementia, cases of, 300, 308–309 ventricular enlargement, 51 Corpus callosum MRI studies, 57 Cortex CT studies, 54 MRI studies, 54–55 neuronal loss and Alzheimer’s disease, 174 problems with analysis, 55 retrieval tasks, use in, 129–130 task performance, correlation with, 126 utilisation of additional networks in older subjects, 126, 130 Cortico-basal degeneration (CBD) accumulation of abnormal gene products, 18 neurodegenerative disease, 18 Crystallised intelligence age-related change, 78 cognitive speed, distinguished, 76 definition, 76, 206 education, protective role of, 84 inter-individual differences, 78–85 longitude individual trajectories, 83 measures and tests, 78 seven-year longitudinal change, 80 Dementia ageing, association with, 21, 163– 164, 243, 246, 253 animal models, 246 clinical and neuropathological studies, 243–244 incidence studies, 244–245, 245 longitudinal studies, 246 population studies, 244, 245 prevalence studies, 244–245, 245 volunteer studies, 245 B vitamins, 209–212 brain exercise, 9 brain reserve and, 224–226, 225 care and support for sufferers, 252– 253 CBF and, 164 definition, 301

340 diagnosing, 250 relaxation of diagnostic criteria, 250 epidemic, 4, 12 fronto-temporal see Fronto-temporal dementia (FTD) genetic profiles, 248 increasing incidence, 19–20 intelligence and, 231–232 lengthy prodromal or preclinical periods, 35–36 longevity and, 252 multiple pre-clinical syndromes as predictors, 19–20 neurological evaluation of at risk groups, 123 personal risk, assessment of, 246 regional variations within the brain, 6 Scottish Mental Survey, 232 sex differences, 248 spastic paraparepsis (SP), 178–180, 179 stroke, following, 305 tau gene, 7 vascular see Vascular dementia Dementia with Lewy bodies (DLB) accumulation of abnormal gene products, 18, 324 neurodegenerative disease, 17, 26 Dendrites educational attainment and complexity, 230 reduction in number, 4, 39 Depression age-bias, 284, 291 ageing and, 283, 292–293 decreasing episodes (MDEs), 286 prevalence, 284, 285, 286, 289 Composite International Diagnostic Review (CIDI), 285 Comprehensive Psychopathological Rating Scale, 287 cross-age studies, 284–289, 287 definition, 291–292 Diagnostic Interview Schedule, 286 DSM-III disorders, 284, 286 criterion symptoms, 292 prevalence, 288, 289–290, 290 reasons for varied survey results, 290–293 meaning, 283–284 Depressive disorders transcranial magnetic stimulation, 8

THE AGEING BRAIN

vagus nerve stimulation, 8 Diet see Nutrition Digit span performance vitamin intake and, 208 Disability ADL – impairment in personal care, 21, 24 IADL – inability to live at home without domestic help, 21, 24 Kilsyth Study, 21, 22, 24 Sydney Older Persons Study, 21–25, 22 Diseases death rates, comparison of, 20, 26 epidemiologic transition, 11–12 lengthy prodromal or preclinical periods, 35–36 systemic see Systemic diseases Disinhibition-dementia-parkinsonismamyotrophy complex, 182 Docosahexaenoic acid (DHA) brain function and, 215–216 Dopaminergic nigro-spatial tract Parkinson’s disease, 276, 280 Down syndrome (DS) antioxidants, overexpression of, 187, 191, 192, 198 Sod1, 196–197 AD-like pathology, 197 Alzheimer’s pathology, 263–265 oxidative stress in early life, 197 Dutch Adult Reading Test (DART) index of premorbid intelligence, 231 Education protective role of, 84, 228–231, 234–235 Embolic disorders vascular pathology, 310 Endocrine system see also Neuroendocrinology ageing, 140–141 androgens, 145 human growth hormone secretion (hGH), 144–145 hypothalamic-pituitary-adrenal (HPA) axis, 141–142, 142, 143, 145 IGF-1, 144–145 menopause, 143–144, 145 Entorhinal cortex Alzheimer’s disease, 39, 261–262 NFTs, accumulation of, 40, 263 preservation of neuronal content, 39 Epidemiologic transition theory

SUBJECT INDEX

Age of degenerative diseases, 14 Age of delayed degenerative diseases, 14 Age of neurodegenerative disorders, 25–27 Age of pestilence and famine, 13 Age of receding pandemics, 13 lifespan, 14–16 meaning, 11–12 population change, 13–14 systemic diseases, decline in, 18–19, 26 Exercise brain, 9, 84 intervention strategies, 69–70 Familial multiple system tauopathy, 182 Familial progressive subcortical gliosis, 182 Fenton reaction, 188, 194 15O PET studies complex tasks, 127–128 encoding tasks, 128–129 resting studies, 108–109, 110–113 retrieval tasks, 129–130 visual attention tests, 126 working memory, 127 Fluid intelligence cognitive speed, distinguished, 76 definition, 76, 206 measures and tests, 77 Folate cognitive ageing and, 206–207 cross-sectional studies, 207–208 experimental studies, 208–209 longitudinal studies, 208 memory, impact on, 209 Fronto-temporal dementia (FTD) accumulation of abnormal gene products, 18 neurodegenerative disease, 17 Parkinsonism, with (FTDP-17), 182 tau gene, 180–182 Functional magnetic resonance imaging (fMRI) Alzheimer’s disease and vascular dementia, distinguishing, 312 blood flow, changes in, 99–100 nature of, 99–100, 266 Gait age-related postural stability, 67 Parkinson’s disease, 276–278

341 freezing, 277 stride length, 278 Gene targeting experiments future research, 324 in vitro models of cellular senescence, 190–191 in vivo models of cellular senescence, 191–192 Genomics see also Human genome project gene transfer technology, 7 Ginkgo biloba cognitive function and, 205, 216– 217 treatment strategies for VaD, 316 Globus palladium ageing, 4 Glucocorticoid cascade hypothesis failure of negative feedback, 142– 143, 145 Glutamate NMDA receptor synapses, 5 treatment strategies, 316 Glutathione peroxidase (Gpx), 188 Parkinson’s disease, 192 strokes, role in, 194 upregulation and alteration of ratios, 189–190 Gross brain volume ageing, 58 CT studies, 52–53 MRI studies, 53–54 stroke volume and cognitive impairment, 223 Haemorrhage intracerebral (ICH), 311 subarachnoid, 312 subdural, 311 Hearing decline in function, 65 speech comprehension, 65 Hippocampus degeneration, 4, 39, 54 functional alteration, 4–5 neuronal loss and Alzheimer’s disease, 174, 215, 261–262 NFTs, accumulation of, 40, 263 sclerosis, 251 task performance, correlation with, 126 volume, decreasing, 54, 90 Histopathology developments in, 324

342 Histoxic hypoxia dementia and, 309 Homocysteine hypothesis cognitive performance and vitamin deficiency, 207, 210–211 cross-sectional studies, 207–208 Hormones see also Neuroendocrinology ageing, impact on, 6, 9 androgens, 145 estrogen treatment, 144 glucocorticoid cascade hypothesis, 142–143 human growth hormone secretion (hGH), 144–145 IGF-1, 144–145 menopause, 143–144, 145 Hounsfield unit (HU), 54, 55 Human genome project, 3, 4, 7 Hyperintensities CBF, effect on, 122 CMRglu values, declining, 122–123 cognitive deficits, 122 increase in number, 56–57, 58 periventricular white matter (PVHs), 122 white matter (WMHs), 122–123, 261 Hypertension Biswanger’s disease, 309 CBF, effect on, 122 stroke, 306 systolic, 154, 315 vascular dementia risk factor, 306, 312 controlling, 315 Hyperviscosisty syndromes vascular pathology, 309–310 Hypointensities basal ganglia, 58 Hypomethylation hypothesis cognitive performance and vitamin deficiency, 207 Hypothalamic–pituitary–adrenal (HPA) axis altered function, 141–142, 142, 143, 145 Hypoxaemic hypoxia dementia and, 309 Immune system link with brain, 7 lymphokines, 7 Inferomedial temporal lobe

THE AGEING BRAIN

infarcts, 307 Information processing decline in resources, 5 Intelligence brain reserve theory, 231–232 Interhemispheric frontal gyri atrophy, 53 Intracranial pressure (ICP) cerebral perfusion, 159–160 Ischaemia Scale dementia, diagnosing, 300, 301, 302 Joints age-related changes in sensation, 66 Leukoaraiosis cerebral microvasculature and, 155, 156–157 Lewy body dementia longitudinal studies, 246 Lifespan average human, 15, 247 compression of morbidity, 14–16 demographic profile, 247–248 factors influencing, 16 longevity and dementia, 252 risk of dementia, 246–249 “survivor effect’’, 16–17, 50 Lipohyalinosis vascular pathology, 309 Long-term potentation (LTP) functional alteration and, 5 Longevity education, impact of, 16–17 “survivor effect’’, 16–17, 50 Magnetic resonance imaging (MRI) atrophy, 51–52 Alzheimer’s disease, 261–262 basal ganglia, 58 cerebellum, 57 characteristics, 58 cognitive change studies, 75 corpus callosum, 57 cortex, 54–55 development of, 49, 50 functional see Functional magnetic resonance imaging (fMRI) gross brain volume, 53–54 longitudinal studies of cognitive change, 87, 90 medulla, 57 midbrain, 57 pituitary gland, 57

343

SUBJECT INDEX

pons, 57 subcortical structures, 55–57 T1 – spin lattice relaxation time, 58 vascular dementia, cases of, 300, 308–309 ventricular enlargement, 51–52 Magnetic resonance spectroscopy (MRS) Alzheimer’s disease, 263–264 limitations of studies, 131–132 N-acetylaspartate (NAA), concentration of, 100, 131 nature of, 100 Medial temporal lobe (MTL) decreasing volume, 54 infarcts, 307 Medulla MRI studies, 57 Meissner corpuscles age-related changes, 65 Memory age-associated memory impairment (AAMI), 260 age-changes, 75 antioxidants and, 213–214 brain substrates, relationship to, 90 cognitive ability, 76 declarative, 77 measures and tests, 77 dementia, role in diagnosis of, 301 education, protective role of, 82 encoding, 128–129 Ginkgo biloba and, 217 inter-individual differences, 78–85 intra-individual differences, 85 longitude individual trajectories, 83 nutrition, impact of, 206 procedural, 77 measures and tests, 78 rCBF, activation and, 127 retrieval tasks, 129–130 seven-year longitudinal change, 81 short-term visual, 126–127 stress hormones, impact of, 6 synaptic transmission changes, 5 vitamin supplements and, 208–209 working see Working memory Mental activity brain reserve theory, 233–234 Mental state education, protective role of, 82 Merkel disks age-related changes, 65

Midbrain MRI studies, 57 Mild Cognitive Impairment (MCI) diagnostic criteria, 251–252, 260 longitudinal studies of cognitive change CT, 89, 90 MRI, 87, 88 predicting, 123–124 Mini Mental State Examination (MMSE), 218, 245 antioxidant levels, 213 diagnosing dementia, 252 problems with test, 304, 313 folate levels, 207, 210 polyunsaturated fatty acid intake, 216 Morbidity compression of, 14–16, 15, 26–27 neurodegenerative diseases, 17, 20–25 Motor-neuron disease (MND) increasing mortality rates, 20 late-onset, 18, 26 neurodegenerative disease, 17 Motor stimulation haemodynamic response, 125 neural activity and BOLD signal change, 125 signal-to-noise ratio (SNR), 125 Multi-infarct dementia (MID) Alzheimer’s disease, distinguished, 300, 301 calcium channel antagonists, use of, 316 Nimodipine, 316 ischaemic vascular dementia, as, 310–311, 312 onset, 312 white matter lesions, 309 Muscles gait, 67, 276–278 quadriceps strength, 68 standing, 67 strength, 66–67 Neocortex NFTs, accumulation of, 40 Neostriatum atrophy, 4 Neural noise increased, 5 Neurodegenerative diseases, 17–18 see also by name

344 accumulation of abnormal gene products, 18 disability statistics, 21 features, 18 gender differences, 23 Kilsyth Study, 21, 22, 24 morbidity, 17, 20–25 prevalence data, 23–24, 24 Sydney Older Persons Study, 21–25, 22 systemic diseases, replacement of, 26 under-ascertainment, 19–20 Neuroendocrinology ageing, 140, 141–142 androgens, 145 development of field, 139 human growth hormone secretion (hGH), 144–145 IGF-1, 144–145 menopause, 143–144, 145 proton magnetic spectroscopy, 139 Neurofibrillary tangles (NFTs) accumulation, 40–41, 42, 174, 178, 195, 262–263 nature, 40–41 pre-symptomatic changes in Alzheimer’s disease, 261 timing of formation, 41 Neuroimaging see also Computerised axial tomography (CT); Magnetic resonance imaging (MRI); Magnetic resonance spectroscopy (MRS); Positron emission tomography (PET); Single photon emission computed tomography (SPECT) Alzheimer’s disease and vascular dementia, distinguishing, 312 analysis of results, 49–50 brain reserve theory, 227–228 cognitive change studies, 75, 90 dementia, evaluation of at risk groups, 123 FDDNP, 263 future research directions, 59, 132, 324 in vivo, 262–263 mild cognitive impairment, predicting, 123–124 molecular, structural and functional changes, 97 parameters at rest, importance of, 98 surrogate markers, using, 269–270

THE AGEING BRAIN

synthesis of findings, 58–59 technological advances, 49 validity of findings, 59 vascular dementia, cases of, 300, 308–309, 312 Neuromodulation deficiencies, 5 Neuronal loss cortical volume and, 4 dementia, link to, 251 Neuronal structure diet and, 206 Neurotransmitter synthesis diet and, 206 Nutrition antioxidants see Antioxidants cognitive performance and, 205–206, 324 herbal supplements, 205, 216–217 neurotransmitter synthesis, 206 vitamins see B vitamins; Folate Omega-3 brain function and, 215–216 Pacinian corpuscles age-related changes, 65 Pallido-ponto-nigral degeneration, 180 Parahippocampal gyrus reduced activation, 126 volumetric decline, 54 Parieto-occipital sulcus atrophy, 53 Parkinson’s disease (PD) accumulation of abnormal gene products, 18 alternating fine movements, 279 antioxidants, 192–193, 198 association with ageing, 4, 12, 17, 18, 21, 26 balance, 276–278 brain reserve theory, 224 characteristics, 192 deep brain stimulation, 8 eye movement, 278 facial expression, 279 festination, 277 gait, 276–278 freezing, 277 stride length, 278 Gpx upregulation, effect of, 193 increasing mortality rates, 20 levodopa therapy, 278 low educational attainment, 228–

SUBJECT INDEX

231 muscle tone, 280 perturbations of antioxidant pathways, 187 posture, 275–276 brain atrophy and, 276 prevalence in the elderly, 280–281 resting tremor, 280 rigidity, 280 speech, 278–279 under-ascertainment, 19 Peripheral sensation decline in function, 65–66 Photic stimulation haemodynamic response, 125 neural activity and BOLD signal change, 125 signal-to-noise ratio (SNR), 125 Pituitary gland MRI studies, 57 Plaques diffuse, 40 neuritic, 40, 262–263 pre-symptomatic changes in Alzheimer’s disease, 261 senile count and cognitive impairment, 223 Polyunsaturated fatty acids (PUFAs) imbalance, 216 role in brain function, 215–216 Pons infarcts, 308 MRI studies, 57 Positron emission tomography (PET) Alzheimer’s disease predicting, 264 vascular dementia, distinguishing, 312 basal ganglia, 58 cerebellum, 57 cerebral atrophy, accounting for, 120–121 cerebral metabolic rate of glucose (rCMRglu), 98, 99, 114, 261–262 cerebral metabolic rate of oxygen (rCMRO2), 98 diagnostic accuracy, 265, 266 15O studies see 15O PET studies limitations, 99 nature of, 99 oxygen extraction ratio (OER) changes, 109, 114 regional cerebral blood flow (rCBF), 98, 157

345 regions of interest (ROI) analysis, 268–269 resting studies, 121 “at risk’’ groups, 121 Precuneus decreased activation in left, 126 Presenilin genes Alzheimer’s disease, 173 early onset (EOAD), 176–179 elevated secretion of Aβ peptides, 177 mutation, 176–178 PS-1, 173, 177 PS-2, 173 Progressive supranuclear palsy (PSP) accumulation of abnormal gene products, 18 neurodegenerative disease, 18, 182 Proteases up-regulation, 7 Proteomics, 7–8 Raven’s Progressive Matrices (RPM) complex tasks, evaluation of, 127 Reaction time age-changes, 63–64, 68 Reactive oxygen species (ROS) creation of, 187–188, 198 damage caused by, 188 brain, 189 stroke, 193–194 Research, 6–7 Alzheimer’s disease, 6, 7, 27, 324 cross-disciplinary, 324 dementia and ageing, 243, 323 animal models, 246 clinical and neuropathological studies, 243–244 incidence studies, 244–245, 245 longitudinal studies, 246 population studies, 244, 245 prevalence studies, 244–245, 245 volunteer studies, 245 limitations, 50 neuroimaging see Neuroimaging nutrition and cognitive function, 217–218 sample sizes, 50 stem cell, 8 Resting studies see also Positron emission tomography (PET); Single photon emission computed tomography (SPECT) “at risk’’ groups, 121

346 cerebral atrophy, accounting for, 120–121 defining healthy ageing, 115, 120 resting state, definition, 120 results, 121 screening of subjects, 115 uncontrolled mental activity, 120 Ruffini cylinders age-related changes, 65 Single photon emission computed tomography (SPECT) Alzheimer’s disease, 265 vascular dementia, distinguishing, 312 cerebral atrophy, accounting for, 120–121 diagnostic accuracy, 265 index of pathology in Alzheimer’s disease patients, 229–230 limitations, 99 nature of, 98 regional cerebral blood flow (rCBF), 98, 157 resting studies of ageing, 106–107, 121 “at risk’’ groups, 121 Technetium-HMPAO (Tc-HMPAO) studies, 101, 106–107, 108 Smell decline in function, 65 Spastic paraparepsis (SP) Alzheimer’s disease, 178–180, 179 PS-1 mutations, 178 Spino-cerebellar altrophies (SCA) neurodegenerative disease, 18 Standing age-related postural stability, 67 sway, 67, 68 Stem cell research, 8 Strategic infarct dementia (SID) ischaemic vascular dementia, as, 311 Stress brain, impact on, 6, 9 environmental manipulation therapy, 9 hypothalamic-pituitary-adrenal (HPA) axis, 141–142, 142 Stroke antioxidant balance, altered, 194, 198 brain reserve theory, 223–224 characteristics, 193–194 dementia following, 305, 309 Gpx1, role of, 194

THE AGEING BRAIN

hypertension, 306 increased risk of vascular dementia, 306, 315–316 low educational attainment, 228– 231 ROS production, 194 Subcortical structures CT studies, 55 MRI studies, 55–57 Subcortical vascular dementia (SVaD) ischaemic vascular dementia, as, 311 Substantia nigra Parkinson’s disease, 192 susceptibility to age-related disorders, 4 Sulcus atrophy, 53, 55 Superoxide dismutase (Sod) different isoforms, distinguishing, 189 function, 188 increased levels and alteration of ratios, 189–190 Sod1 gene overexpression, 190–191, 196 mutations, 195 Synapses cognitive impairment, 4–5 long-term potentation, 5 longitudinal studies, 246 reduction in number, 4, 39 Systemic diseases decline in, 18–19 gender differences, 23 neurodegenerative disorders, replacement by, 26 prevalence data, 22–23, 23 public health measures, success of, 26 Sydney Older Persons Study, 21–25 Taste decline in function, 65 Tau gene Aβ, relationship with, 182–183 Alzheimer’s disease, 173, 174, 324 dementia, 7 fronto-temporal dementia (FTD), 180–182 function, 182 mutations, 180 exon trapping analysis, 181 neurofibrillary tangles (NFTs), 40– 41, 178

SUBJECT INDEX

T-cells, 7 Temporal cortex preservation of neuronal content, 39 Temporal horn increasing volume, 54 neuronal loss and Alzheimer’s disease, 262 Thalamus ageing, 4 retrieval tasks, use in, 129–130 subcortical infarcts, 307–308 task performance, correlation with, 126 volume and atrophy, 53 Total creatine (Cr) decreasing levels, 131 Transentorhinal region NFTs, accumulation of, 40 Vascular cognitive impairment (VCI) concept of, 304 Vascular dementia (VaD), 19% Alzheimer’s disease distinguished, 300, 304, 313–314 VaD with AD, 312 anticoagulant therapy, 316 calcium channel antagonists, use of, 316 Nimodipine, 316 cerebral infarcts, 307 location, importance of, 307 cognitive deficits, 313 definition, 301–302 diagnosing, 301–304 ADDTC criteria, 302 clinical diagnosis, 312–313, 314 DSM-IV criteria, 302 ICD-10 criteria, 302 NINDS-AIREN criteria, 302–304, 303 sensitivity and specificity of criteria, 304 education as preventative, 307 epidemiology, 304–305 haemorrhagic VaD, 311–312 intracerebral (ICH), 311 subarachnoid, 312 subdural, 311 history of research, 299–300 Ischaemia Scale, 300, 301, 302 ischaemic VaD, 310–311 mixed cortical and subcortical VaD, 311 NMDA receptor antagonists, use of, 316

347 modification of risk factors, 300 multi-infarct dementia (MID), 300, 310–311, 312 neuroimaging, 300 NMDA receptor antagonists, use of, 316 Memantine, 316 nootropics, use of, 316 pathogenetic mechanisms, 309–310, 310 pathology, mixed, 300 phosphodiesterase and adenosine reuptake inhibitor, use of, 317 prognosis, 313–314 risk factors, 121–122 age, 307, 308 gender, 307, 308 genetic factors, 306–307, 308 increased risk of stroke, 306, 308, 315–316 increased white matter lesions, 306, 308, 308–309 management of, 314–315 race, 307, 308 strategic infarct dementia (SID), 311 subcortical vascular dementia (SVaD), 311 thromboembolism, 300, 309 treatment and prevention, 314–317 strategies, 315 vasodilators, use of, 316 vinca alkaloids, use of, 316 Ventricular enlargement ageing, 58 CT studies, 51 gender differences, 52 MRI studies, 51–52 Vestibular sense decline in function, 65, 68 gait and, 276–278 Vibration sense decline in function, 65–66, 68 Vision decline in function, 64, 68, 126 spatial processing activation studies, 126 Vitamins see B vitamins Wechsler Adult Intelligence ScaleRevised (WAIS-R), 77 White matter ageing, 58 CT studies, 55 hyperintensities (WMHs), 122–123, 300

348 infarcts, 308 lesions (WML), 277, 300 Alzheimer’s disease, 309 hypertension, 306, 312, 315 increased risk of vascular dementia, 306, 308–309 multi-infarct dementia, 309 Leukoaraiosis, 156–157, 300, 309 MRI studies, 55–57 N-acetylaspartate (NAA), concentration of, 131 pallor, 251 younger persons, 53 Wisconsin Card Sort Test (WCST) complex tasks, evaluation of, 127– 128 Working memory, 65, 127 nutrition, impact of, 206

THE AGEING BRAIN