Obesity and Metabolism (Frontiers of Hormone Research)

Obesity and Metabolism

Frontiers of Hormone Research Vol. 36

Series Editor

Ashley B. Grossman

London

Obesity and Metabolism Volume Editor

Márta Korbonits

London

43 figures, 25 in color, and 11 tables, 2008

Basel · Freiburg · Paris · London · New York · Bangalore · Bangkok · Singapore · Tokyo · Sydney

Márta Korbonits Department of Endocrinology Barts and the London, Queen Mary’s School of Medicine and Dentistry University of London London, UK

Library of Congress Cataloging-in-Publication Data Obesity and metabolism / volume editor, M. Korbonits. p. ; cm. – (Frontiers of hormone research, ISSN 0301–3073 ; v. 36) Includes bibliographical references and indexes. ISBN 978-3-8055-8429-6 (hard cover : alk. paper) 1. Obesity. 2. Metabolism. I. Korbonits, M. (Márta) II. Series. [DNLM: 1. Obesity. W1 FR946F v.36 2008 / WD 210 0112182 2008] RC628.02272 2008 362.196⬘398–dc22 2007047338

Bibliographic Indices. This publication is listed in bibliographic services, including Current Contents® PubMed/MEDLINE. Disclaimer. The statements, options and data contained in this publication are solely those of the individual authors and contributors and not of the publisher and the editor(s). The appearance of advertisements in the book is not a warranty, endorsement, or approval of the products or services advertised or of their effectiveness, quality or safety. The publisher and the editor(s) disclaim responsibility for any injury to persons or property resulting from any ideas, methods, instructions or products referred to in the content or advertisements. Drug Dosage. The authors and the publisher have exerted every effort to ensure that drug selection and dosage set forth in this text are in accord with current recommendations and practice at the time of publication. However, in view of ongoing research, changes in government regulations, and the constant flow of information relating to drug therapy and drug reactions, the reader is urged to check the package insert for each drug for any change in indications and dosage and for added warnings and precautions. This is particularly important when the recommended agent is a new and/or infrequently employed drug. All rights reserved. No part of this publication may be translated into other languages, reproduced or utilized in any form or by any means electronic or mechanical, including photocopying, recording, microcopying, or by any information storage and retrieval system, without permission in writing from the publisher. © Copyright 2008 by S. Karger AG, P.O. Box, CH–4009 Basel (Switzerland) www.karger.com Printed in Switzerland on acid-free and non-aging paper (ISO 9706) by Reinhardt Druck, Basel ISSN 0301–3073 ISBN 978–3–8055–8429–6

Contents

VII IX

1 12

37 61

73

85 97 107 118

Foreword Grossman, A.B. (London) Preface Korbonits, M. (London) Monogenic Human Obesity Farooqi, I.S. (Cambridge) Polygenic Contribution to Obesity: Genome-Wide Strategies Reveal New Targets Körner, A.; Kiess, W.; Stumvoll, M.; Kovacs, P. (Leipzig) Genetic Obesity Syndromes Goldstone, A.P.; Beales, P.L. (London) Fetal and Neonatal Pathways to Obesity Gluckman, P.D. (Auckland); Hanson, M.A. (Southampton); Beedle, A.S.; Raubenheimer, D. (Auckland) Developmental Origins of Obesity and the Metabolic Syndrome: The Role of Maternal Obesity Armitage, J.A. (Clayton/Melbourne); Poston, L.; Taylor, P.D. (London) Childhood Obesity Sabin, M.A. (Melbourne/Bristol); Shield, J.P.H. (Bristol) Obesity in Old Age McPhee Chapman, I. (Adelaide) Models of ‘Obesity’ in Large Animals and Birds Clarke, I.J. (Melbourne) The ␤-Cell in Type 2 Diabetes and in Obesity Rutter, G.A. (London); Parton, L.E. (Boston, Mass.)

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146 165 182 198 212 229 260 271

287 288

VI

Role of the Endocannabinoid System in Energy Balance Regulation and Obesity Cota, D. (Cincinnati, Ohio) 11␤-Hydroxysteroid Dehydrogenase Type 1 and Obesity Morton, N.M.; Seckl, J.R. (Edinburgh) Gut and Hormones and Obesity Wren, A.M. (London) Adipokines in Obesity Ahima, R.S.; Osei, S.Y. (Philadelphia, Pa.) The Role of AMP-Activated Protein Kinase in Obesity Kola, B.; Grossman, A.B.; Korbonits, M. (London) Classical Endocrine Diseases Causing Obesity Weaver, J.U. (Newcastle upon Tyne) Emerging Concepts in the Medical and Surgical Treatment of Obesity Aylwin, S.; Al-Zaman, Y. (London) The Sociology of Obesity Rosengren, A.; Lissner, L. (Göteborg) Obesity in Art - A Brief Overview Woodhouse, R. (London) Author Index Subject Index

Contents

Foreword

It has now become a truism that obesity is one of the most serious medical problems of the developed world, and however unsettling this may be in the presence of famine and starvation in parts of the developing world, it is nevertheless a situation which must be faced. It has become difficult for endocrinologists to access all of the recent literature on the genetics, metabolic phenotype and treatment of obesity, and Dr. Korbonits has been stunningly successful in putting together an array of chapters from many of the foremost authorities and researchers in this field. With recent data indicating that the surgical therapy of gross obesity is associated with increased longevity, it is now an ideal time to reveal what we know about the causes, metabolic disturbances and treatment of this so common condition. I recommend this volume to all who are beginning research in this area or are responsible for the clinical care of obese patients: this means the volume will be relevant to almost all practising clinicians in 2008. Ashley B. Grossman, London

Preface

It was the discovery of leptin in 1994 that completely changed endocrinology’s attitude to obesity. What had until then been a niche subject with little known pathophysiological pathways, was, by the discovery of a hormone so profoundly affecting weight, suddenly launched into the fast lane of scientific research. In the current volume we have attempted to summarise key advances in many facets of obesity: in the field of genetics, from the spectacular monogenic and syndromal causes to the less dramatic but more common susceptibility genes, which have only been recently identified; we have explored the effects of obesity in the pregnant mother, in foetal life, in childhood and in old age, and have attempted to draw conclusions from studies of periodic increased weight in animals. We have also mapped out the biochemical and physiological background of the abnormal metabolism in obesity, by scrutinizing the hormones and enzymes most recently implicated in the development, maintenance and consequences of obesity. The ‘traditional’ hormonal causes of obesity are discussed, as they may occasionally cause a differential diagnostic challenge, and we offer a practical update on clinical approach and treatment of obesity. Finally, we have attempted to reflect the social aspects of obesity in society, and the view of the obese body in art throughout the centuries. While a completely comprehensive overview of the metabolism of obesity is beyond the scope of this book, we have aimed for a timely and wide-ranging update which we believe will be both interesting and pertinent to all endocrine clinicians and researchers. Márta Korbonits, London

Korbonits M (ed): Obesity and Metabolism. Front Horm Res. Basel, Karger, 2008, vol 36, pp 1–11

Monogenic Human Obesity I. Sadaf Farooqi Metabolic Research Laboratories, Institute of Metabolic Science, Addenbrooke’s Hospital, University of Cambridge, Cambridge, UK

Abstract We and others have identified several single gene defects that disrupt the molecules in the leptinmelanocortin pathway causing severe obesity in humans. In this review, we consider these human monogenic obesity syndromes and discuss how far the characterisation of these patients has informed our understanding of the physiological role of leptin and the melanocortins in the regulation of human Copyright © 2008 S. Karger AG, Basel body weight and neuroendocrine function.

The genetic contribution to body weight has been established through family studies, investigating parent-offspring relationships, the study of twins and adopted children [1, 2]. These studies consistently report heritability estimates of 40–70% [3]. As is the case for height, where nutritional changes in the last 50 years have contributed to substantial increases in mean final height in many populations, environmentally-driven changes in body weight in the population occur against a background of susceptibility to weight gain that is determined by genetic factors. Thus, genetic approaches can be applied to understand both the molecular and physiological mechanisms involved in human obesity. We have explored the genetic basis of severe childhood obesity where we considered that major and more highly penetrant genetic effects were likely to be found. In 1997, we established the Genetics of Obesity Study (GOOS) to recruit patients with severe obesity (body mass index standard deviation score, BMI SDS, ⬎3) of early onset (⬍10 years). We were particularly interested in children with a strong family history of obesity and those from consanguineous families. Our intention was to use a candidate gene approach to look for mutations in genes thought to play a role in the regulation of body weight based on evidence primarily from rodent models at the time. With the help of colleagues throughout the world, we have to date recruited over 2,500 patients to the GOOS cohort. In the past 9 years, several human disorders of energy balance that arise from genetic defects have been described by ourselves

and others [4]. All of these are in molecules identical or similar to those known to cause obesity in genetic and experimental syndromes of obesity in rodents and all have been identified using a candidate gene approach. These mutations all result in severe obesity in childhood without developmental pleiotropic features.

Mutations in Genes Encoding Leptin and the Leptin Receptor

In 1997, we reported two severely obese cousins from a highly consanguineous family of Pakistani origin [5]. Both children had undetectable levels of serum leptin and were found to be homozygous for a frameshift mutation in the LEP gene (⌬G133), which resulted in a truncated protein that was not secreted. We have since identified 5 further affected individuals from four other families [6, 7; unpubl. obs.] who are also homozygous for the same mutation in the leptin gene. All the families are of Pakistani origin but not known to be related over five generations. A large Turkish family in which 3 adults carry a homozygous missense mutation (C→T substitution at codon 105 resulting in Arg→Trp) in the LEP gene have also been described [8]. The first mutation in the leptin receptor gene was published in 1998 [9]. The mutation was found in homozygous form in 3 severely obese adult siblings from a consanguineous family of Algerian origin. This mutation results in abnormal splicing of leptin receptor transcripts and generates a mutant leptin receptor that lacks both transmembrane and intracellular domains. The mutant receptor circulates at high concentrations bound to leptin, resulting in very elevated serum leptin concentrations [10]. We recently sequenced the leptin receptor gene in a cohort of patients with severe, early onset obesity in the absence of developmental delay and identified 8 unrelated probands with homozygous or compound heterozygous loss of function mutations in the leptin receptor gene [11]. The prevalence of pathogenic leptin receptor mutations in this cohort was 3%. Six of the probands were from consanguineous families but 2 probands (including the compound heterozygote) were UK Caucasians whose parents were unrelated. Although the prevalence of leptin receptor mutations is likely to be higher amongst ethnic groups where consanguinity is common, leptin receptor deficiency should be considered in all patients with hyperphagic obesity of early onset.

Clinical Phenotypes Associated with Leptin and Leptin Receptor Deficiency

The clinical phenotypes associated with congenital leptin and leptin receptor deficiencies are similar. Leptin and leptin receptor deficient subjects are of normal birth weight but exhibit rapid weight gain in the first few months of life resulting in severe obesity [6]. Body composition measurements show that leptin deficiency is characterised by the preferential deposition of fat mass giving a distinct clinical appearance with excessive amounts of subcutaneous fat over the trunk and limbs [6]. All patients

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were hyperinsulinaemic, consistent with the severity of obesity and some adults have developed type 2 diabetes in the 3rd–4th decade [6]. All subjects in these families are characterised by intense hyperphagia with food seeking behaviour and aggressive behaviour when food is denied [6] and energy intake at an ad libitum meal is markedly elevated [11]. In leptin deficient humans we found no detectable changes in resting metabolic rate using indirect calorimetry or total energy expenditure using chamber calorimetry [12]. However, Ozata et al. [13] reported abnormalities of sympathetic nerve function in leptin deficient adults consistent with defects in the efferent sympathetic limb of thermogenesis. Leptin and leptin receptor deficiency are associated with hypothalamic hypothyroidism and hypogonadotropic hypogonadism. Complete leptin deficiency is associated with a moderate degree of hypothalamic hypothyroidism characterised by low free thyroxine and high serum thyroid-stimulating hormone (TSH) which is bioinactive. In leptin deficient children, plasma free thyroxine concentrations are within the normal range, but 4 children had significantly elevated TSH levels [6] and the pulsatility of TSH secretion, studied in a single adult with congenital leptin deficiency, was characterised by a markedly disorganised secretory pattern [14]. Two subjects homozygous for a non-sense mutation in the leptin receptor were diagnosed with hypothyroidism in childhood and thyroid hormone replacement therapy commenced [9]. Normal pubertal development does not occur in adults with leptin or leptin receptor deficiency, with biochemical evidence of hypogonadotropic hypogonadism [8]. However, there is some evidence for the delayed but spontaneous onset of menses in leptin and leptin receptor deficient adults [11, 13]. Leptin and leptin receptor deficient children have normal linear growth in childhood and normal IGF-1 levels. However, because of the absence of a pubertal growth spurt the final height of adult subjects is reduced. In the first reported leptin receptor deficient family, short stature and abnormal serum levels of GH, IGFBP3 were noted in childhood. However, assessment of the GH/IGF axis is difficult in obese children and adults as obesity itself is associated with abnormalities in basal and dynamic tests of the GH/IGF axis. We conclude that while impaired linear growth has been reported in some cases of LEPR deficiency, this does not appear to be a common characteristic of this disease [11]. We demonstrated that children with leptin deficiency had profound abnormalities of T cell number and function [6], consistent with high rates of childhood infection and a high reported rate of childhood mortality from infection in obese Turkish subjects [13].

Response to Leptin Administration in Leptin Deficiency

We have reported the dramatic and beneficial effects of daily subcutaneous injections of recombinant human leptin leading to a reduction in body weight and fat mass in

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a

b Fig. 1. Effects of recombinant human leptin treatment in leptin deficiency. a A 3-year-old boy weighing 42 kg. b The same boy at 7 years of age weighing 32 kg.

3 congenitally leptin deficient children [6, 12] (fig. 1). All children showed a response to initial leptin doses that were designed to produce plasma leptin levels at only 10% of those predicted by height and weight (i.e. approximately 0.01 mg/kg of lean body mass). Leptin therapy has also been successfully used in the 3 Turkish leptin deficient adults [15]. The major effect of leptin was on appetite with normalisation of hyperphagia. Leptin therapy reduced energy intake during an 18MJ ad libitum test meal by up to 84% [6]. Leptin treatment was associated with reduced hunger scores with no change in satiety in adults with leptin deficiency [15]. We were unable to demonstrate a major effect of leptin on basal metabolic rate or free-living energy expenditure, but, as weight loss by other means is associated with a decrease in basal metabolic rate, the fact that energy expenditure did not fall in our leptin deficient subjects is notable. The administration of leptin permitted progression of appropriately timed pubertal development in the single child of appropriate age and did not cause the early onset of puberty in the younger children [6]. In adults with leptin deficiency, leptin induced the development of secondary sexual characteristics and pulsatile gonadotrophin secretion.

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In the 3 previously reported children there were small, but sustained, increases in free T4, free T3, and TSH that occurred within 1 month of leptin therapy. These observations are fully consistent with an effect of leptin at the hypothalamic level. A 4th patient had substantial elevation of TSH before treatment, such that thyroxine therapy was commenced [7]. However, replacement therapy was stopped when thyroid function tests normalised after leptin treatment.

Complete POMC Deficiency

In 1998, Krude et al. [16] provided the first description of humans congenitally lacking POMC gene products. One proband was a compound heterozygote for two nonsense mutations and a 2nd patient was homozygous for a mutation in the 5⬘-untranslated region that introduced an additional out-of-frame start site, thus interfering with POMC translational initiation. Subsequently, Krude et al. [16] have reported 3 additional unrelated European children with congenital POMC deficiency who were either homozygous or compound heterozygous for POMC mutations. We have recently identified a 6th patient with complete POMC deficiency, being homozygous for a complete loss of function mutation which results in the loss of all POMC-derived peptides [17]. These patients all presented in early life with features of hypocortisolaemia secondary to ACTH deficiency, leading to hypoglycaemia, prolonged jaundice, susceptibility to the effects of infection and in one case, neonatal death. The children responded well to physiological replacement with glucocorticoids but all subsequently developed marked obesity in association with hyperphagia. Notably, all children thus far reported have pale skin and red hair, features consistent with the known role of POMC-derived peptides in the determination of the phaeomelanin to eumelanin ratio in melanocytes. Our Turkish proband is the first reported patient with POMC deficiency who does not have red hair [17]. It is likely this can be explained by his differing genetic background as the other reported patients were all white Caucasian subjects of European ancestry. The retention of dark hair in this child and his similarly affected deceased sibling indicates that the synthesis of eumelanin in humans is not absolutely dependent on the presence of melanocortin peptides.

POMC Haploinsufficiency

Krude et al. [18] have previously attempted to assess the impact of loss of one POMC allele in the parents and heterozygous relatives of their probands. They estimated the maximum lifetime BMI SDS in adult POMC heterozygotes and suggested that most had a maximum lifetime BMI SDS of 1, which is at the upper end of the normal range.

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We had the opportunity to study a large Turkish consanguineous pedigree with 12 heterozygote carriers and 7 wild-type subjects [17]. The significantly higher prevalence of obesity/overweight in the carriers provides compelling support for the idea that loss of one copy of POMC is sufficient to markedly predispose to obesity. This is particularly relevant as we and others have described a variety of heterozygous point mutations in POMC, including mutations in ␣- and ␤-MSH, which significantly increase obesity risk but are not invariably associated with obesity.

POMC Mutations Affecting Specific Melanocortin Peptides

In order to determine whether missense/non-sense mutations within the melanocortin peptides might predispose to obesity, we screened the coding regions of the POMC gene for mutations in over 600 UK Caucasian subjects with severe early-onset obesity. We identified a number of sequence variants in POMC in severely obese children. Three of these missense mutations directly affect regions of the POMC gene that encode melanocortin peptides. R236G was identified in 3 patients but also two controls. We have previously shown that this mutation disrupts a di-basic cleavage site between ␤-MSH and ␤-endorphin, resulting in a ␤-MSH/␤-endorphin fusion protein that binds to MC4R but has reduced ability to activate the receptor [19]. Its presence in both obese probands and controls reflects previous studies that show that this is not a highly penetrant cause of inherited obesity but may increase the risk of obesity in carriers. We identified 5 unrelated probands who were heterozygous for a rare missense variant in the region encoding ␤-MSH, Tyr221Cys [20]. This frequency was significantly increased (p ⬍ 0.001) compared to the general UK Caucasian population and the variant co-segregated with obesity/overweight in affected family members. The overrepresentation of this mutation in obese subjects is supported by independent studies in a German population [21]. Compared to wild-type ␤-MSH, the variant peptide was impaired in its ability to bind to and activate signalling from the MC4R [20]. Obese children carrying the Tyr221Cys variant were hyperphagic and showed increased linear growth, both of which are features of MC4R deficiency. These studies support a role for ␤-MSH in the control of human energy homeostasis.

Mutations in Prohormone Convertase 1

Many biologically inactive prohormones and neuropeptides are cleaved by serine endoproteases to release biologically active peptides. The prohormone convertases (PC1 and 2) are expressed in neuroendocrine tissues and act upon a range of substrates including proinsulin, proglucagon and pro-opiomelanocortin (POMC) [22]. PC1 is itself synthesised as an inactive precursor, then undergoes two autocatalytic

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events, firstly within the endoplasmic reticulum and then within the secretory vesicles of the regulated secretory pathway to generate a fully active 66-kDa isoform that is stored in mature secretory granules. We have previously reported an adult female with severe early-onset obesity, hypogonadotropic hypogonadism, postprandial hypoglycaemia, hypocortisolaemia, and evidence of impaired processing of POMC and proinsulin [23]. She was found to be a compound heterozygote for PC1 mutations [24]. We have described the second case of congenital PC1 deficiency, in a patient who was a compound heterozygote for two loss of function mutations [25]. Intriguingly, this patient suffered from severe small intestinal absorptive dysfunction as well as the characteristic severe early-onset obesity, impaired prohormone processing, and hypocortisolaemia. We hypothesised that the small intestinal dysfunction seen in this patient and, to a lesser extent, in the 1st patient we described may be the result of a failure of maturation of propeptides within the enteroendocrine cells and nerves that express PC1 throughout the gut. The finding of elevated levels of progastrin and proglucagon provided in vivo evidence that prohormone processing in enteroendocrine cells was abnormal [25].

Human MC4R Deficiency

In 1998, two groups reported heterozygous mutations in the MC4 receptor in humans which were associated with dominantly inherited obesity [26, 27]. Since then, heterozygous mutations in MC4R have been reported in obese humans from various ethnic groups [28–30]. We have studied over 2,000 severely obese probands and found that approximately 5–6% have pathogenic MC4R mutations that are non-conservative in nature, not found in control subjects from the background population and co-segregate with obesity in families [31]. The prevalence of MC4R mutations has varied from 0.5% of obese adults to 6% in patients with severe childhood obesity [31, 32]. Recent studies provide an important indication of the true population prevalence of this disorder in UK [33] and European populations [32]. While we found a 100% penetrance of early-onset obesity in heterozygous probands, others have described obligate carriers who were not obese [29]. Given the large number of potential influences on body weight, it is perhaps not surprising that both genetic and environmental modifiers will have important effects in some pedigrees. Indeed we have now studied 6 families in whom the probands were homozygotes and in all of these, the homozygotes were more obese than heterozygotes [31]. Interestingly, in these families, some heterozygous carriers were not obese. Taking account of all of these observations, co-dominance, with modulation of expressivity and penetrance of the phenotype, is the most appropriate descriptor for the mode of inheritance. This finding is supported by the pattern of inheritance of obesity seen in heterozygous and homozygous MC4R knockout mice [34].

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Mean ad libitum energy intake (kcal/kg lean mass)

70 60 50 40 30 20 10 0

Leptin deficiency

Inactive Partial MC4R mutations

Treated leptin deficiency

Controls

Fig. 2. Genotype-phenotype correlations in human MC4R deficiency. Ad libitum food intake at an 18MJ test meal for patients with leptin deficiency and complete and partial loss of function MC4R mutations.

We have now studied over 150 MC4R deficient subjects in our Clinical Research Facility. The clinical features of MC4R deficiency include hyperphagia, which invariably starts in the 1st year of life [31]. Alongside the increase in fat mass, MC4R-deficient subjects also have an increase in lean mass and a marked increase in bone mineral density, thus they often appear ‘big-boned’. They exhibit accelerated linear growth in early childhood, which does not appear to be due to dysfunction of the GH axis and may be a consequence of the disproportionate early hyperinsulinaemia seen in these patients [31]. The accelerated linear growth and the disproportionate early hyperinsulinaemia are consistent with observations in the MC4R KO mouse [35]. Although affected subjects are objectively hyperphagic, ad libitum energy intake at a test meal is not as large as that seen with leptin deficiency [31]. Of particular note is the finding that the severity of receptor dysfunction seen in in vitro assays can predict the amount of food ingested at a test meal by the subject harbouring that particular mutation (fig. 2). We have studied in detail the signalling properties of many of these mutant receptors and this information should help to advance the understanding of structure/function relationships within the receptor [36]. Importantly, we have been unable to demonstrate evidence for dominant negativity associated with these mutants, which suggests that MC4R mutations are more likely to result in a phenotype through haplo-insufficiency [36]. About 70% of missense mutations in MC4R are retained intracellularly [37].

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While, at present, there is no specific therapy for MC4R deficiency, it is highly likely that these subjects would respond well to pharmacotherapy that overcame the reduction in the hypothalamic melanocortinergic tone that exists in these patients. As most patients are heterozygotes with one functional allele intact, it is possible that small molecule MC4R agonists might, in future, be excellent treatments for this disorder [38].

Mutations in the Neurotrophin Receptor Tropomyosin-Related Kinase B

Recently, the concept that hypothalamic neuronal networks involved in energy homeostasis are ‘hardwired’ has been challenged. In mice, hypothalamic neurones projecting from the arcuate nucleus to the paraventricular nucleus develop after birth and their development is regulated by leptin [39]. In addition, synaptic plasticity in the mature rodent brain has been identified as a component of the neuronal regulation of energy homeostasis as leptin has been shown to acutely modulate excitatory and inhibitory synaptic inputs at the level of first-order arcuate neurones [40]. However, it is difficult to establish whether synaptic plasticity plays a role in the physiological regulation of energy homeostasis in humans and whether under pathological conditions, hypothalamic neuronal networks and plasticity may be impaired and contribute to human obesity. Brain-derived neurotrophic factor (BDNF) regulates the development, survival and differentiation of neurons through its high-affinity receptor, tropomyosin-related kinase B (TrkB). Recently, BDNF has been implicated in the regulation of body weight, as its expression is reduced by fasting [41] and BDNF administration causes weight loss in wild-type mice through a reduction in food intake. BDNF has also been implicated in memory and a range of behaviours using a number of conditional knockout models [42]. We previously reported a child with severe obesity, impaired short-term memory and developmental delay who had a de novo missense mutation impairing the function of TrkB, the tyrosine kinase receptor that mediates the effects of both BDNF and the neurotrophin, NT4/5 [43]. We have also identified a patient with severe hyperphagia and obesity and a complex neurobehavioural phenotype including impaired cognitive function and memory as well as distinctive hyperactive behaviour. Interestingly, this patient has a de novo paracentric inversion, 46,XX,inv(11)(p13p15.3), which encompasses the BDNF locus and disrupts BDNF expression [44]. Although to date only 2 such patients have been identified, understanding the mechanisms whereby BDNF regulates hypothalamic neuronal circuits may have potential therapeutic benefits for the treatment of more common forms of human obesity.

Conclusions

In practical terms the discovery of these genetic disorders has helped de-stigmatise human obesity and allow it to be seen as a medical condition. The genetic defects

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found to date all affect the drive to eat resulting in hyperphagia in affected subjects. Thus human food intake should not be considered as an entirely voluntarily controllable phenomenon but one driven by powerful biological signals. It is likely that further discovery of causative genetic defects in humans and experimental animals will continue to highlight other molecular elements of the pathways involved in the regulation of body weight.

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13 Ozata M, Ozdemir IC, Licinio J: Human leptin deficiency caused by a missense mutation: multiple endocrine defects, decreased sympathetic tone, and immune system dysfunction indicate new targets for leptin action, greater central than peripheral resistance to the effects of leptin, and spontaneous correction of leptin-mediated defects. J Clin Endocrinol Metab 1999;84:3686–3695. 14 Mantzoros CS, Ozata M, Negrao AB, et al: Synchronicity of frequently sampled thyrotropin (TSH) and leptin concentrations in healthy adults and leptin-deficient subjects: evidence for possible partial TSH regulation by leptin in humans. J Clin Endocrinol Metab 2001;86:3284–3291. 15 Licinio J, Caglayan S, Ozata M, et al: Phenotypic effects of leptin replacement on morbid obesity, diabetes mellitus, hypogonadism, and behavior in leptin-deficient adults. Proc Natl Acad Sci USA 2004;101: 4531–4536. 16 Krude H, Biebermann H, Luck W, Horn R, Brabant G, Gruters A: Severe early-onset obesity, adrenal insufficiency and red hair pigmentation caused by POMC mutations in humans. Nat Genet 1998;19: 155–157. 17 Farooqi IS, Drop S, Clements A, et al: Heterozygosity for a POMC-null mutation and increased obesity risk in humans. Diabetes 2006;55:2549–2553. 18 Krude H, Biebermann H, Schnabel D, et al: Obesity due to proopiomelanocortin deficiency: three new cases and treatment trials with thyroid hormone and ACTH4–10. J Clin Endocrinol Metab 2003;88: 4633–4640. 19 Challis BG, Pritchard LE, Creemers JW, et al: A missense mutation disrupting a dibasic prohormone processing site in pro-opiomelanocortin (POMC) increases susceptibility to early-onset obesity through a novel molecular mechanism. Hum Mol Genet 2002; 11:1997–2004. 20 Lee YS, Challis BG, Thompson DA, et al: A POMC variant implicates beta-melanocyte-stimulating hormone in the control of human energy balance. Cell Metab 2006;3:135–140.

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21 Biebermann H, Castaneda TR, van Landeghem F, et al: A role for beta-melanocyte-stimulating hormone in human body-weight regulation. Cell Metab 2006;3:141–146. 22 Seidah NG, Chretien M: Proprotein and prohormone convertases: a family of subtilases generating diverse bioactive polypeptides. Brain Res 1999;848:45–62. 23 O’Rahilly S, Gray H, Humphreys PJ, et al: Brief report: impaired processing of prohormones associated with abnormalities of glucose homeostasis and adrenal function. N Engl J Med 1995;333:1386–1390. 24 Jackson RS, Creemers JW, Ohagi S, et al: Obesity and impaired prohormone processing associated with mutations in the human prohormone convertase 1 gene [see comments]. Nat Genet 1997;16:303–306. 25 Jackson RS, Creemers JW, Farooqi IS, et al: Smallintestinal dysfunction accompanies the complex endocrinopathy of human proprotein convertase 1 deficiency. J Clin Invest 2003;112:1550–1560. 26 Yeo GS, Farooqi IS, Aminian S, Halsall DJ, Stanhope RG, O’Rahilly S: A frameshift mutation in MC4R associated with dominantly inherited human obesity [letter]. Nat Genet 1998;20:111–112. 27 Vaisse C, Clement K, Guy-Grand B, Froguel P: A frameshift mutation in human MC4R is associated with a dominant form of obesity. Nat Genet 1998;20: 113–114. 28 Farooqi IS, Yeo GS, Keogh JM, et al: Dominant and recessive inheritance of morbid obesity associated with melanocortin 4 receptor deficiency. J Clin Invest 2000;106:271–279. 29 Vaisse C, Clement K, Durand E, Hercberg S, GuyGrand B, Froguel P: Melanocortin-4 receptor mutations are a frequent and heterogeneous cause of morbid obesity. J Clin Invest 2000;106:253–262. 30 Hinney A, Schmidt A, Nottebom K, et al: Several mutations in the melanocortin-4 receptor gene including a nonsense and a frameshift mutation associated with dominantly inherited obesity in humans. J Clin Endocrinol Metab 1999;84:1483–1486. 31 Farooqi IS, Keogh JM, Yeo GS, Lank EJ, Cheetham T, O’Rahilly S: Clinical spectrum of obesity and mutations in the melanocortin 4 receptor gene. N Engl J Med 2003;348:1085–1095. 32 Larsen LH, Echwald SM, Sorensen TI, Andersen T, Wulff BS, Pedersen O: Prevalence of mutations and functional analyses of melanocortin 4 receptor variants identified among 750 men with juvenile-onset obesity. J Clin Endocrinol Metab 2005;90:219–224.

33 Alharbi KK, Spanakis E, Tan K, et al: Prevalence and functionality of paucimorphic and private MC4R mutations in a large, unselected European British population, scanned by meltMADGE. Hum Mutat 2007;28:294–302. 34 Huszar D, Lynch CA, Fairchild-Huntress V, et al: Targeted disruption of the melanocortin-4 receptor results in obesity in mice. Cell 1997;88:131–141. 35 Fan W, Dinulescu DM, Butler AA, Zhou J, Marks DL, Cone RD: The central melanocortin system can directly regulate serum insulin levels. Endocrinology 2000;141:3072–3079. 36 Yeo GS, Lank EJ, Farooqi IS, Keogh J, Challis BG, O’Rahilly S: Mutations in the human melanocortin-4 receptor gene associated with severe familial obesity disrupts receptor function through multiple molecular mechanisms. Hum Mol Genet 2003;12: 561–574. 37 Lubrano-Berthelier C, Durand E, Dubern B, et al: Intracellular retention is a common characteristic of childhood obesity-associated MC4R mutations. Hum Mol Genet 2003;12:145–153. 38 Nargund RP, Strack AM, Fong TM: Melanocortin-4 receptor (MC4R) agonists for the treatment of obesity. J Med Chem 2006;49:4035–4043. 39 Bouret SG, Draper SJ, Simerly RB: Trophic action of leptin on hypothalamic neurons that regulate feeding. Science 2004;304:108–110. 40 Pinto S, Roseberry AG, Liu H, et al: Rapid rewiring of arcuate nucleus feeding circuits by leptin. Science 2004;304:110–115. 41 Xu B, Goulding EH, Zang K, et al: Brain-derived neurotrophic factor regulates energy balance downstream of melanocortin-4 receptor. Nat Neurosci 2003;6:736–742. 42 Snider WD: Functions of the neurotrophins during nervous system development: what the knockouts are teaching us. Cell 1994;77:627–638. 43 Yeo GS, Connie Hung CC, Rochford J, et al: A de novo mutation affecting human TrkB associated with severe obesity and developmental delay. Nat Neurosci 2004;7:1187–1189. 44 Gray J, Yeo GS, Cox JJ, et al: Hyperphagia, severe obesity, impaired cognitive function, and hyperactivity associated with functional loss of one copy of the brain-derived neurotrophic factor (BDNF) gene. Diabetes 2006;55:3366–3371.

Dr. I. Sadaf Farooqi Wellcome Trust Senior Clinical Fellow and Honorary Consultant Physician Metabolic Research Laboratories, Level 4, Institute of Metabolic Science University of Cambridge, Box 289, Addenbrooke’s Hospital Cambridge CB2 2QQ (UK) Tel. ⫹44 1223 762 634, Fax ⫹44 1223 762 657, E-Mail [email protected]

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Korbonits M (ed): Obesity and Metabolism. Front Horm Res. Basel, Karger, 2008, vol 36, pp 12–36

Polygenic Contribution to Obesity: Genome-Wide Strategies Reveal New Targets Antje Körnera ⭈ Wieland Kiessa ⭈ Michael Stumvollb ⭈ Peter Kovacsc a

University Hospital for Children and Adolescents, bDepartment of Internal Medicine III, and cInterdisciplinary Center for Clinical Research, University of Leipzig, Leipzig, Germany

Abstract Obesity results from the complex interaction of environmental factors that act on a genetic background that determines the susceptibility to obesity. The identification of such obesity susceptibility genes can provide important insights into the mechanism underlying this condition. While candidate gene approaches have not been tremendously successful in identifying relevant genetic contributors to obesity, except PPAR γ , the advent of genome-wide strategies has recently revealed novel and unexpected genetic factors with strong associations with obesity and/or diabetes, i.e. FTO, TCF7L2, INSIG2, ENPP1, or FASN (reviewed herein), although some of them are not undebated. Considering the function of the encoded proteins, it will now be of interest to investigate the cellular and molecular mechanisms, how these genetic variations affect body weight, energy metabolism and/or obesity-associated morbidity. Copyright © 2008 S. Karger AG, Basel

The ‘Thrifty’ Heritability of Obesity?

Obesity results from an imbalance of energy expenditure and energy intake. There is a great variety of factors affecting this fragile balance. It is obvious that major environmental factors such as the accessibility to food and the degree of physical activity, but also the psychosocial environment, the perinatal environment and many other factors affect an individual’s body weight. The pandemic increase in obesity prevalence had been attributed to this increasingly urbanized and sedentary lifestyle with convenient access to food, increased calorie intake and a reduction of energy expenditure in the industrialized world. Nevertheless, over the last two decades, it has become clear that genetic factors play an important role in the determination of body weight. First evidence for the

heritability of obesity came from early twin studies that observed a heritability for body weight of 0.78–0.81% in monozygous twins [1–3] and similar values have been obtained in subsequent studies analyzing the impact of the genetic background [4–7]. However, this high degree of heritability is rarely attributed to monogenic forms of obesity [see the chapter by Farooqi, this vol., pp. 1–11], which usually result in extreme and early-onset obesity and are usually accompanied by additional phenotypic and endocrine abnormalities. Even though the discovery of monogenic forms of obesity has allowed important insights into some of these mechanisms by revealing a highly conserved pathway regulating mammalian body weight, it is obvious that the pathology of obesity is far more complex. Considering that the genetic pool has not dramatically changed over the last decades, the secular trend of increased obesity prevalence is now regarded as the interaction of the modern lifestyle factors that act on a genetic background that determines an individuals susceptibility to weight gain and obesity. According to this ‘thrifty gene’ hypothesis [8] individuals with a genetic disposition to accumulate ample energy stores in times of good food availability were evolutionary more likely to survive times of nutrient scarcity and to pass these genotypes to successive generations. For example, if feast and famine cycles characterized early human life, the ‘thrifty genotype’ was more likely to survive periods of food scarcity. This ancient genetic selection to deposit fat efficiently is maladaptive in our modern obesogenic environment with excess calorie intake and sedentary life style, and hence the same genes now contribute to obesity. It is now well acknowledged that a multitude of genetic polymorphisms and candidate regions scattered all over the genome regulate an individual’s susceptibility to weight gain. Evolutionary concepts together with extensive population genetics to characterize geographical and haplotypic structures of newly emerging biological as well positional candidate genes will be inevitable to reveal whether these new genes could indeed represent the ‘thrifty’ genes. These genetic studies provide valuable insights as well as promoting new concepts into the mechanisms from the identification of previously unsuspected genetic factors.

Overview of Tools for Identifying Genes Relevant to Human Obesity

Genetic Dissection of Complex Diseases Complex diseases such as diabetes or obesity have genetic components, which due to their polygenic nature can not easily be identified. Two basic approaches have been used to identify susceptibility genes for complex diseases: candidate gene approach and genomic approach (fig. 1). However, only limited success has been seen so far.

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Human

Candidate genes

Experimental model

Genome-wide strategies

Linkage studies

Association

Linkage

QTL

Chromosomal region Synteny

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Disease gene Fig. 1. Identifying genetic factors that underlie complex diseases. QTL ⫽ Quantitative trait locus.

Candidate Gene Approach Selection of candidate genes for obesity is usually based on their known physiological role in pathways related to energy expenditure, food intake but also glucose and/or lipid metabolism and hence requires some a priori knowledge of the pathophysiology of a disease. In addition, candidate genes are selected on the basis of previous evidence on association with obesity and/or diabetes in other populations or experimental animal models. These genes are then analyzed for sequence variation that is associated or linked with the disease. Even though advances in genotyping technology allied with our knowledge of the human genome’s structure will lead to novel common gene variants involved in susceptibility to human obesity, the candidate gene approach may still be very powerful when it comes to identifying rare variants predisposing to obesity. This is given by the fact that whereas coverage of common variation in genes by commercially available single nucleotide polymorphism (SNP) panels (provided by Affymetrix and Illumina) is generally comparable to the rest of the genome (see association studies below), other focused classes of functional variants are captured poorly by SNP sets aimed at common variation [9]. A large number of genes have been associated with the development of obesity; they have been reviewed recently elsewhere [10, 11].

Genome-Wide Strategies Alternatively, susceptibility genes can be identified by genome-wide linkage or association scans, which are followed by positional cloning (fig. 2). Positional cloning requires no knowledge and/or judgment of the ‘biologically plausible’ genetic candidates. Instead, a disease gene is discovered because it resides on a chromosomal region that segregates

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Case

Control A

T

G

T C

C

Association studies: based on comparisons of the variants’ frequencies between cases and controls – analyze if subjects with a particular condition consistently have a particular mutation pattern and if matched controls do not share this pattern

p = 0.00000001

Disease allele: strong association with obesity Linkage equilibrium

Lower linkage disequilibrium

p = 0.9 p = 0.01

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Linkage studies are being replaced by genome-wide association studies.

Genomic distance ( bp) http://www.affymetrix.com

Fig. 2. Genome-wide association studies.

with a phenotype [12]. In the last two decades, genetic studies have focused on the technique of genetic linkage. This study design proved to be efficient for identifying rare, high-risk alleles, i.e. alleles that have a large population attributable fraction (PAF) in rare single-gene mendelian diseases (cystic fibrosis) [13, 14] but do not appear to have large PAF in common diseases. Using linkage studies, researchers attempt to find regions of the genome with a higher than expected number of shared alleles among affected individuals within a family. Since the design is based on closely related individuals within a family, and these individuals share larger regions of the genome, genotyping of a relatively small number of polymorphic markers is sufficient to detect region of linkage. Using positional cloning one may attempt to identify the gene(s), which reside within the linkage region and segregate with the phenotype. Usually, genetic markers (e.g. SNPs) are being selected to provide a high-density map (e.g. every 2 kb) within the region of linkage. The SNPs are then being genotyped in study subjects and analyzed for association with the disease. SNPs with strongest associations may be the causal disease risk variants (direct association) or are in a close proximity and thus in high linkage disequilibrium (non-random correlation between alleles at a pair of SNPs) and possibly indirectly associated with the disease variants [12].

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In contrast to linkage approaches, association analyses are expected to be more powerful in identifying alleles that confer modest risk for developing a complex disease. Common modest risk variants account for a larger PAF than do rare highrisk alleles and this is often referred to as disease-common variant hypothesis [15]. The advantage of association analyses is based on the fact that for modest-risk alleles the patterns of sharing between related affected individuals are less striking than patterns of sharing between unrelated affected individuals [16]. This may partially explain the limited success of linkage studies in identifying genes for common diseases such as obesity. Another advantage of association analyses is that it is much easier to recruit large cohorts of unrelated individuals than collecting large pedigrees. However, since the shared region among unrelated individuals is much smaller than among the family members, association analyses require higher marker densities than linkage analyses [16]. This seems to be given by recent advances in the field of high throughput genotyping of SNPs, enabling a high-density coverage of the genome. Besides the marker coverage, another point that requires consideration in association analyses based on comparisons of the variants’ frequencies between cases and controls is the power. Most studies are underpowered, especially when considering that in order to detect associations with an odds ratio of 1.2 one would need 1,000 cases and 1,000 controls [17]. Therefore, large collaborating networks enabling replications of initial findings are crucial for successful association analyses. This was impressively represented by a very recent genome-wide analysis involving research centers from Europe and including more than 35,000 study subjects, which led to discovering FTO as the strongest predictor of human polygenic obesity seen so far [18, 19].

Isolated Populations Along with the polygenic nature and pathophysiologic complexity of diseases such as obesity or diabetes, another major problem is the genetic heterogeneity of modern populations. This means that healthy and afflicted subjects are likely to have very different sequences throughout the genome, not only in the area with the disease gene(s). To reduce genetic heterogeneity, one can use crossing studies with experimental models, which are inbred and so genetically uniform. Furthermore, their environment can be standardized to overcome gene-environment interaction. Linkage or quantitative trait analyses using experimental crosses may lead to chromosomal regions associated with a phenotype of interest, which may then point to syntenic regions in humans and so suggest novel human candidate genes. However, very often it proves to be extremely difficult to find an experimental model completely resembling pathophysiological aspects/patterns of human diseases. Therefore, one may attempt to reduce genetic heterogeneity by studying populations with limited genetic variability [20]. Isolated populations have already contributed to identification of mendelian variants of complex diseases, such as Hirschsprung disease in

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the Amish or nonsyndromic hearing loss in Beduins [21–23]. There are several reasons to believe that studying genetics in isolated populations will result in genes involved in susceptibility for complex diseases: (a) genetic homogeneity makes genetic differences between healthy and afflicted individuals more pronounced; (b) the number of disease-causing mutations is much smaller because it can be traced back to very few ancestral carriers (‘founders’); (c) more uniform environment (reducing the effects of gene-environment interactions); (d) higher prevalence for some diseases; (e) good genealogical records. Indeed, current genetic projects on isolated populations are providing a good reason to share this optimism. The project receiving the most attention is the deCODE project, founded in 1996, which investigates the genetically isolated population of Iceland. The country of ca. 275,000 Icelanders has an extensive Icelandic genealogical database that can be traced back over 1,000 years. This unique resource together with the extensive high throughput genotyping led to the discovery of several genes controlling complex diseases such as prostate cancer [24] or stroke [25]. The Icelandic cohort was also one of the first populations in which type 2 diabetes (T2D) susceptibility genes have been reported from genome-wide association studies [26]. Another promising population in the field of metabolic disorders seems to be the population of the Island of Kosrae in Micronesia, in which a comprehensive epidemiological and genetic study has been undertaken [27, 28].

Studies in Children

Children represent an interesting population for identifying such primary genetic determinants involved in the susceptibility to complex polygenic diseases, since unlike in adults, phenotypes are less influenced by co-morbidities, their treatment, and environmental factors. In addition, the detailed evaluation of parameters of glucose and insulin metabolism at early stages of metabolic impairment may help to understand the sequence of events leading to overt pathology and diabetes.

Fatty Acid Synthase and Pima Indians

To identify genetic determinants of human polygenic obesity, researchers at the National Institutes of Health in Phoenix have focused on the relatively genetically and environmentally homogeneous Pima Indian population of Southern Arizona. The Pima Indians of Arizona are one of the most obese populations in the world and also have the highest reported prevalence of T2D [29]. Their diabetes is characterized by obesity, insulin resistance, insulin secretory dysfunction and increased rates of endogenous glucose production [30, 31]. To search for obesity susceptibility genes, a genome-wide linkage scan in Pima Indians was previously completed [32, 33]. The

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strongest evidence for linkage with body mass index (BMI) was on chromosome 11q23–24 (LOD ⫽ 3.6) [32], while the strongest evidence for linkage with percentage of body fat was on chromosome 17q25 (LOD ⫽ 1.9) in a multipoint sibling-based variance component analysis [33]. The region on chromosome 17q35 seemed to be particularly interesting since the human fatty acid synthase (FAS) gene was positioned at 135 cM, within 7 cM of the peak of linkage to percentage of body fat [33]. The FAS enzyme is necessary for the de novo synthesis of long-chain fatty acids from acetylCoA, malonyl-CoA and NADPH [34]. Recent physiologic studies have shown that inhibition of the FAS gene induces a rapid decline in fat stores in mice, suggesting a role for FAS in energy homeostasis [35, 36]. Based on the chromosomal location and known physiology of FAS, the FAS was investigated as a candidate gene for determining body weight and percentage of body fat in Pima Indians. In these studies, a novel Val1483Ile polymorphism was identified, which was associated with percentage of body fat and 24-hour substrate oxidation rates measured in a respiratory chamber [37]. These findings indicate that the Val1483Ile substitution in FAS is protective against obesity in Pima Indians, an effect possibly explained by the role of this gene in the regulation of substrate oxidation. The effects of Val1483 on obesity have been recently also investigated in Caucasian children and adolescents. In a cohort of 738 Caucasian children and adolescents and 205 obese children from Leipzig, Germany, a significant interaction effect between gender and genotype was observed [38]. The findings in Caucasian children suggest a gender specific protective effect of the Val1483Ile polymorphism in FAS for obesity and lipid phenotypes in Caucasian boys. The story of FAS is an example of how combining different approaches may ultimately lead to novel genetic targets possibly involved in the pathophysiology of human obesity. FAS is not only an excellent candidate gene based on its known biological function, but also a positional candidate mapped within a region of linkage to percentage of body fat. In addition, studies in Pima Indians prove that genetically isolated populations may definitely help in the search for new obesity risk candidate genes.

Candidate Genes

Peroxisome Proliferator-Activated Receptor Gene The peroxisome proliferator-activated receptor (PPAR)-␥ is a transcription factor with a key role in adipocyte differentiation, susceptibility to obesity and insulin sensitivity. The common Pro12Ala polymorphism in PPAR␥ is caused by a missense mutation in exon B of the adipocyte-specific ␥2 isoform. It was identified in 1997 and is thought to confer reduced transcriptional activity. The Ala allele is associated with a reduced risk for T2D. The prevalence of the Ala allele varies from about 4% in Asian populations [39] to about 28% in Caucasians [40]. Several more genetic variants in PPAR␥ are known but are much less frequent. For example a very rare gain of function mutation (Pro115Gln) associated with obesity but not insulin resistance and a

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loss of function mutation (Val290Met, Pro467Leu) is reported in 3 individuals with severe insulin resistance but normal body weight [40]. Subsequently, association with T2D was examined and two meta-analyses strongly suggested that the Pro variant is a risk-allele [41, 42] and is now considered to be one of the best replicated genetic risk factors of common T2D. Altshuler et al. [41] evaluated 16 published genetic associations of Pro12Ala and T2D in a family-based design. A modest (1.25-fold) but significant effect on diabetes risk was reported for the Pro allele. Also the effect size is similar to other common genetic variants implicated in susceptibility for T2D (4841T/C in CAPN10/OR 1.2, E23K in KCNJ11/OR 1.2, G972R in IRS1/OR 1.25), but due to the high prevalence of the Pro allele the resulting population attributable risk of about 25% is much higher for Pro12Ala [43]. Interestingly, so far none of the genomewide scans produced linkage with T2D at the PPAR␥ locus on chromosome 3. A recent meta-analysis of 57 suitable studies containing data related to the insulin resistance of cohorts with normal or impaired glucose tolerance revealed no significant effect of the Pro12Ala polymorphism on diabetes-related traits across all studies [44]. However, in the Caucasian subgroup BMI was greater in X/Ala compared with Pro/Pro (p ⫽ 0.015) and HOMA-IR was significantly higher in Pro/Pro compared with X/Ala in the obese subjects, indicating greater insulin sensitivity in obese carriers of the Ala allele (standardized effect size 0.148, p ⫽ 0.0025), which was supported by the meta-analysis of Ala/Ala homozygotes. Masud and Ye [45] found a greater BMI in Ala carriers in obese subjects, but this study included patients with diabetes in contrast to the meta-analysis of Tönjes et al. [44]. Furthermore, the Pro12Ala genotype of the PPAR␥2 gene predicted the conversion from impaired glucose tolerance to T2D in the STOP-NIDDM trial [46] and in the Diabetes Prevention Program [47]. Also in NGT subjects, the PPAR␥ Ala allele showed a relatively strong protective effect on the development of hyperglycemia and hyperinsulinemia during a 6-year period [48]. One study even suggested that already in childhood the effect of the Pro12Ala on insulin sensitivity may become detectable [49]. Since PPAR␥2 is exclusively expressed in adipocytes, the mechanism(s) through which the Pro12Ala influences the risk of developing T2D must originate in adipose tissue [50]. The Ala allele has repeatedly been associated with lower insulin concentrations, a crude indication of greater insulin sensitivity. In a comprehensive study looking at insulin to C-peptide ratios in a variety of clinical experimental tests, the Pro12Ala polymorphism in PPAR␥2 was found to be associated with a significantly greater insulin removal from the circulation and significantly lower FFA concentrations during hyperinsulinemia [51]. Increased insulin clearance could reflect increased hepatic insulin removal and sensitivity. Both, hepatic insulin clearance and sensitivity could well be secondary to decreased FFA delivery which has been shown to strongly interfere with hepatic insulin removal [52]. It is thus possible that the main regulatory effect of the Ala allele is the more efficient suppression of lipolysis. Using a stepwise hyperinsulinemic clamp in combination with a glycerol tracer lipolysis was directly measured and found to be more insulin sensitive in carriers of the Ala allele [53]. It is also possible that

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PPAR␥-mediated adipocytokine release is involved. Adiponectin levels are higher in subjects with the Ala/Ala genotype compared to the Pro/Ala and the Pro/Pro genotype [54]. However, studies are not entirely coherent and an interaction of genetic and environmental factors in the regulation of serum adiponectin concentrations is assumed [40]. Adiponectin renders the liver more sensitive to the suppressive effect of insulin on glucose production [40]. Since adiponectin release is under transcriptional control of PPAR␥ [55] it represents a plausible candidate for mediating the effect of the Pro12Ala polymorphism on hepatic insulin sensitivity and clearance. In support of this scenario, in Pima Indians insulin suppression of glucose production was 40% more efficient in carriers of the Ala allele while insulin-stimulated glucose uptake was not different [56]. In macrophages, PPAR␥ regulates production of inflammatory mediators, which modifies vascular inflammation and endothelial dysfunction [50] providing an alternative scenario of how the polymorphism exerts its effects.

Genes Identified by Genome-Wide Strategies With the improved accessibility and availability of genome-wide scan platforms and gene chips, this technique has increasingly been applied to identify new candidate genes for obesity and diabetes in a relatively short time period over the last 2 years.

INSIG2 – Obesity or Cholesterolemia? The identification of INSIG2 as an obesity risk gene in the well-characterized Framingham cohort by a dense whole-genome scan had gained much attention [57]. Even though the authors investigated several independent cohorts of various ancestries – and confirmed their finding in 4 out of 5 cohorts – with the obesity-predisposing genotype present in 10% of obese individuals [57], a number of subsequent and similarly well-performed studies failed to replicate these initial findings [58–61]. It was concluded that the effect of INSIG2 polymorphisms on obesity may be restricted to those individuals already predisposed to at least moderate obesity as a result of environmental factors and other predisposing genotypes. Nevertheless, in a recent study aimed to validate the original findings, the rs7566605 was investigated in nine large cohorts from eight populations across multiple ethnicities in a total of almost 17,000 individuals applying several study designs [62]. The study confirmed a significant but small risk conferred by this allele that could easily be masked by insufficient sample size, population stratification, or other confounders. Hence, the original association is less likely to be incidental, but the failure to observe an association in every data set suggested that the effect of SNP rs7566605 in INSIG2 on BMI may be heterogeneous across population samples [62]. Besides the results and associations of INSIG2 with obesity, the gene and its encoded protein may have a role in cholesterol metabolism. INSIG2 was identified as

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a susceptibility gene for cholesterol levels in mice applying quantitative trait loci analyses [63]. Mice with a germline mutation of INSIG2 and a targeted disruption of INSIG1 failed to repress cholesterol and fatty acid synthesis in response to cholesterol feeding and subsequent cholesterol accumulation [64]. The underlying mechanism may be derived from its function as a protein of the endoplasmic reticulum that blocks the processing of sterol regulatory element-binding proteins (SREBPs), thereby preventing the proteolytic processing of SREBPs by Golgi enzymes and consequently blocking cholesterol synthesis [65]. Hence, the role of INSIG2 as an obesity risk gene remains a matter of debate, but the functional interaction with cholesterol synthesis may still impose a relevance for this gene in obesity-related disease.

ENPP1/PC-1 – Interaction with the Insulin Receptor? Significant linkage for T2D had been identified on chromosome 6q16.3-q24.2 that includes the gene locus of the ENPP1 gene [66]. The encoded protein ENPP1/PC-1 (ectonucleotide pyrophosphate phosphodiesterase/plasma cell glycoprotein-1) binds to the ␣-subunit of the insulin receptor and can inhibit the insulin-induced conformational changes and autophosphorylation and the tyrosine kinase activation [67]. Pizzuti et al. [68] identified a nonsynonymous K121Q polymorphism in exon 4 of ENPP1, which was strongly associated with insulin resistance in healthy nonobese, nondiabetic Caucasians in Sicily. Subsequently, effects of the K121Q variant on insulin resistance and T2D have been demonstrated in a number of association studies in adults [69–74]. Further evidence for ENPP1/PC-1 as a candidate in the development of T2D was derived from in vitro studies showing that the exonic K121Q amino acid substitution directly inhibited insulin receptor signaling by inhibiting insulin receptor autophosphorylation [68, 71]. In vivo, overexpression of PC-1/ENPP1 with the q allele of PC-1 induces insulin resistance and hyperglycemia in transgenic mice [67]. Considering the direct interaction with the insulin receptor and the association of the gene variant (K121Q) with T2D in adults, ENPP1 had hence been proposed as a diabetes risk gene. In addition, positional cloning studies in French families revealed an association between a three allele ENPP1 haplotype (K121Q, IVS20delT-11, A/G⫹1044TGA) and childhood obesity [75]. Already at young age, the risk alleles were associated with a higher risk of glucose intolerance in obese children as well as increased serum levels of soluble ENPP1 protein in children [75]. Applying a case control design of obese children and lean controls, the association with childhood obesity was confirmed, while in a normal population of Caucasian children evaluating BMI as a quantitative trait there was no association with the risk allele [76]. Nevertheless, the [Q-delT-G] haplotype was furthermore associated with increased 2-hour plasma glucose concentrations in obese children [76]. Still, the possibility that the Q121 PC-1 variant and obesity itself have independent but additive effects in causing insulin resistance, as has been shown in adults [70], should also be considered.

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In contrast to the studies confirming the association of ENPP1 variants with T2D and/or obesity [69–74], metabolic syndrome [77] or T2D and coronary artery disease [78], there are a number of studies that failed to find these associations [79–83], or even reported contrary results of the 121Q variant being associated with decreased BMI or risk for T2D [84, 85] in different populations. However, in meta-analyses that also included these negative studies, the effect of the 121Q risk allele on T2D was retained [80, 83]. These divergences may be attributed to ethnic differences as has been shown in US minority groups [86], although the comparison of ethnic populations did not reveal such differences [69, 84]. Overall, there is evidence from genome-wide studies, from individual association studies, from an animal model and from in vitro studies for a role of ENPP1 as a risk factor for T2D and potentially obesity, but the lack of reproducibility in a considerable number of studies raises some caution regarding this hypothesis.

FTO: The Strongest Predictor of Obesity The FTO gene (fat mass and obesity associated) is currently the strongest and most promising candidate for the association with obesity in adults and children. Two international consortia independently discovered this association applying different approaches [18, 19]. In a setup aimed to verify population stratification by testing intergenic regions, Dina et al. [18] discovered a strong association of a putatively intergenic SNP (rs1121980) with obesity in a French case-control cohort. This SNP is localized in intron 1 of the FTO gene on chromosome 16q12.2. Of 23 (intronic) tagging SNPs, 3 were of potential functional relevance estimated by biomathematical and computational tools. Two of these SNPs (rs1421085 and rs17817449) with high association with the initially identified SNP were subsequently genotyped in several adult and childhood obesity cohorts and revealed a strong association with earlyonset and severe adult obesity with p values of 1.67 ⫻ 10⫺26 and 1.07 ⫻ 10⫺24, and ORs of 1.47 and 1.46, respectively. The risk haplotype yielded a population attributable risk of 22%, representing disease incidence that would be eliminated if exposure (risk haplotype) was eliminated. Frayling et al. [19] set out to identify diabetes risk alleles using a 500K chip and identified a significant association of an intronic SNP (rs9939609) in the FTO gene with an OR of 1.37 for diabetes. This association was, however, abolished when they controlled for BMI (OR ⫽ 1.03). The subsequent analyses for association with obesity in almost 40,000 individuals revealed a strong association with obesity (p ⫽ 3 ⫻ 10⫺35) and an OR of 1.67 for obesity in homozygous carriers. Similarly, the population attributable risk was estimated as 20.4%. There was no association with birth weight or adult height and no significant association with waist circumference or skinfolds as crude indicators of visceral and subcutaneous obesity, respectively. Although there are no replication studies (due to the immediacy of the finding) to date, the finding was indirectly confirmed by recent genome-wide association studies aimed to identify diabetes risk genes [87, 88].

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Hence, these studies, empowered by high case numbers and several independent populations, provide evidence for a strong association of FTO with BMI in Caucasian populations with the strongest association ever observed so far. It will now be of interest to investigate potential mechanisms underlying this association. The FTO gene encodes for a putative 58-kDa protein with as yet unknown function that is translated from a 410-kb mRNA with 9 predicted exons [89] and has no identified structural domain and no network link to any other protein. The presence of a bipartite nuclear localization signal at the N-terminus may, however, suggest a role in the nucleus [89]. The expression pattern is ubiquitous [18, 19, 89], including white adipose tissue, pancreatic ␤-cells, muscle and, particularly, the brain [18, 19] with a predominant expression in the cerebral cortex [19]. Of interest is also the high expression level in fetal tissues in humans [19] and at early embryonic development in mice [90, 91], again both predominantly in the brain. Further evidence for a potential role for the transcript in brain development is derived from studies of the fused toes (Ft) mouse strain that has a deletion of a genomic region on chromosome 8 including the homologous region to the human chromosome 16q [91, 92] and hence the FTO locus. These mice present with severe defects in brain development [91] and in the patterning of the neural tube [90] that may be mediated by interaction with sonic hedgehog, Wnt signaling, bone morphogenic proteins pathway and many other transcription factors (Hesx1, Six3, Pax6, Fgf8) [90, 91]. In addition, heterozygous Ft mice show a phenotype with growth retardation, deformation of craniofacial structures, syn- and polydactyly of the limbs, left-right asymmetry (heart), and apoptosis of immature thymocytes [92, 93]. From these characteristics, the Ft locus was hypothesized as a candidate gene for programmed cell death, limb and craniofacial development or left-right asymmetry. There is, however, no report on obesity or potential metabolic consequences in these mice and hence the mouse model does not provide explanation for the obese phenotype seen in humans. It also needs to be considered that the Ft locus includes 6 genes: 3 of the Iroquois gene family (Irx3, Irx5, Irx6) and 3 genes with unknown function (Fts, Ftm, Fto) [94]. Of interest are, however, some similarities in the Ft mouse phenotype with some features of the human Bardet-Biedl syndrome (BBS) [95]. These patients present with early-onset obesity, retinal defects, polydactyly, developmental delay and kidney problems. Of the 11 chromosomal regions for BBS, one (BBS2) locates to 16q21 and variants in the BBS2 gene have been associated with adult-onset obesity [96]. So far, there has been one patient reported in the literature with a deletion of the 16q and FTO locus, who suffered from obesity, mental retardation, dysmorphic facies, digital anomalies and anisomastia [97]. In conclusion, the FTO gene is so far the strongest candidate for a genetic predisposition for obesity from genome-wide association studies. Since there is as yet no information on the function of the protein and the animal studies and human monogenic forms are not conclusive, the mechanism by which FTO mediates the obese phenotype warrants further functional studies.

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From Obesity to Diabetes: TCF7L2 – The Strongest Predictor of T2D The TCF7L2 gene encodes for a transcription factor (Tcf-4) that is involved in the regulation of cellular proliferation and differentiation [98]. Variants in the TCF7L2 gene have recently been associated with an increased risk for T2D in a genome-wide analysis of the isolate population of Iceland [26]. The relative risk for diabetes of 1.45 for heterozygous to 2.41 for homozygous carriers of the gene variants was appreciably greater than for most identified genetic factors so far, accounting for a population attributable risk of 21% [26]. The strongest associations with T2D were reported for the rs7903146 variant with a clear gene dose effect [99]. Subsequent independent studies in several distinct ethnic populations, all in adults, convincingly confirmed the initial findings, which was finally evidenced by a large global meta-analysis [100]. This knowledge is extended by showing that these risk alleles actually predict the progression from impaired glucose tolerance to diabetes prospectively [101] and an increased severity of the disease [102] in adults. Considering these data that suggest TCF7L2 as a major candidate gene for the predisposition to T2D, one may also hypothesize that carriers of those at-risk variants have an earlier age of onset than noncarriers. In our obese childhood cohort, three risk variants (rs7901695, rs7903146, rs1225572) were significantly associated with higher fasting and 120-min blood glucose levels. Although not statistically significant, fasting and peak insulin levels and HOMA-IR appeared with a similar tendency [103]. These data indicate that TCF7L2 variants confer a higher risk for early impairment of glucose metabolism emerging as soon as in childhood and adolescence, although we cannot directly predict the progression to overt diabetes from these data. Studies in nondiabetic subjects may, however, help to understand the mechanisms involved in this progression. While most studies in adults did not identify an association of TCF7L2 variants with measures of insulin resistance [101, 104–106], several studies did observe a defect in insulin secretion [101, 105, 107–109], possibly by impaired ␤-cell proinsulin processing [110], or a trend for decreased fasting insulinemia [109] in subjects carrying the risk alleles. The insulin secretion in type 1 diabetes or MODY and neonatal diabetes forms are, however, not affected by TCF7L2 polymorphisms [111, 112]. The conclusion from these data was that the polymorphisms affect the capacity of pancreatic ␤-cells to secrete insulin rather than aggravating insulin resistance. This was further supported by expression data suggesting a putative role of TCF7L2 in ␤-cell differentiation [109]. A most recent implication exemplifies the potential applicability of genetic variants of TCF7L2 in the prediction of drug response in the sense of pharmacogenomics. Pearson et al. [113] observed that carriers of the rs12255372 risk allele were less likely to respond to sulfonylureas. Considering that TCF7L2 as a transcription factor regulates genes involved in proliferation and differentiation, one may hypothesize that early-onset obesity through increased or dysregulated adipogenesis may constitute one underlying mechanism [114, 115]. It has been shown that the TCF7L2 interacts with the Wnt/␤-catenin pathway and may hence constitute a mediator of inhibition of adipogenesis by TNF␣

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[116]. Comparing lean and obese children, the risk alleles of rs11196205 and rs7895340 were less prevalent in the obese childhood cohort, which would be indicative for a decreased obesity risk [103]. Studies in adults did not find an association of TCF7L2 variants with obesity [105] or reported a tendency for negative association with BMI [101, 109]. A direct functional mechanism of the five TCF7L2 risk alleles, all of which are intronic, has as yet not been shown. Tcf-4, the product of TCF7L2, has been studied as a transcription factor involved in differentiation of certain tissues and cell types, particularly intestinal cells. Tcf-4 closely interacts with the Wnt/␤-catenin pathway [117] and as such has implications for the normal development of endocrine tissues as well as the development of tumors. A downregulation of Tcf-4 results in the promotion of more differentiated cell type in colonic epithelial cells [118] and decreased cell proliferation of prostate cancer cells [119]. Indeed, variants in the TCF7L2 gene have been correlated with breast cancer risk [120]. Several colorectal cell lines have mutations in the Tcf-4/TCF7L2 gene resulting in an abrogation of the ␤-catenin binding domain, or more often in a decrease in the proportion of the C-terminal isoform with the consequence of reduced transcriptional activity [121, 122]. This appears to translate to the situation in humans where about 40% of colorectal tumors with microsatellite instability also show alterations in Tcf-4/TCF7L2 gene [122]. An animal model of TCF7L2 disruption appears to confirm the role for Tcf4/TCF7L2 in the differentiation and proliferation of intestinal epithelial cells, but does not provide any information as to the putatively functional role with respect to the development of diabetes [123]. In summary, the role of TCF7L2 as a risk marker for diabetes is strong and stably replicated in many populations worldwide encompassing thousands of individuals. Some clinical data point to impaired insulin secretion from ␤-cells as the potential underlying clinical pathomechanism. The precise molecular mechanism underlying these defects has not been elucidated, although the function of TCF7L2 as a transcription factor is promising for cellular studies in unraveling the underlying mechanisms and potentially intervention strategies, if successful.

New Genes on Stage for Diabetes Identified by Genome-Wide Scans The power of genome-wide scans involving not thousands but tens of thousands of individuals facilitated by a high-throughput chip technology impressively demonstrates how new genes can be discovered by this approach. In addition, the studies replicated associations of the genes HHEX, SLC30A8, TCF7L2, FTO, PPARG, and KCNJ11. In a large scale study, the gene SLC30A8, encoding for a recently newly identified zinc transporter exclusively expressed in ␤-cells, and two linkage disequilibrium blocks encompassing the genes IDE-KIF11-HHEX and EXT2-ALX4, both also potentially involved in ␤-cell development and function, were associated with T2D [124].

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Notably, three major and most recent studies were intrigued by a high congruency in the genes identified with an association with T2D. CDKAL1 is a gene of yet unknown function that shares homology with a CDK5 regulatory subunit-associated protein, which is supposed to inhibit the activation of cyclin-dependent kinase 5 (CDK5). An association of CDKAL1 with T2D was previously unknown but similarly identified by all three of these studies [87, 125, 126]. Another chromosomal region associated with T2D was identified near a cluster involving CDKN2A (cyclin-dependent kinase inhibitor-2A) [87, 125, 126]. This gene, also known as p16INK4a, was initially implicated in tumor susceptibility, particularly for melanoma [127]. However, recently mice lacking p16INK4a were reported to show enhanced islet proliferation and prolonged survival after ␤-cell ablation [128]. The third new candidate with an association with T2D is IGF2BP2 [87, 125, 126]. IGF2BP2, also known as IMP2, is an IGF-II gene mRNA-binding protein that attaches to the 5⬘ UTR from the translationally regulated IGF-II leader 3 mRNA and causes a dose-dependent translational repression of IGF-II leader 3-luciferase mRNA [129]. IMPs are expressed in developing epithelia, muscle, and placenta in both mouse and human embryos [129]. Hence, this gene is interesting with respect to the fundamental role of IGF-II in embryonic development. Another large-scale genome-wide association study also confirmed an association of TCF7L2, KCNJ11, and PPARG with T2D [88]. Of particular note is that many of these genes, i.e. HHEX, SLC30A3, CDKN2A, potentially TCF7L2, and others recently identified [124] can be implicated in the physiology of pancreatic ␤-cells through effects on embryonic development, regeneration or insulin processing. Certainly, replication in more cohorts and the elucidation of the underlying mechanism remain mandatory. Nevertheless, the discovery of previously unsuspected genes opens up new directions and potentially important insights into the pathogenesis of common diseases.

Perspectives: Interactions

Even though some recent approaches have been successful in identifying important genetic contributors to obesity, these new genes are obviously not much more than just a few pieces of the puzzle in the complex framework of pathogenic factors contributing to obesity and it is obvious that many more genes await discovery. The successful identification of genetically and clinically significant and relevant genetic factors will require study setups comprising of a large number of individuals with a defined and well-characterized phenotype, the appropriate controls, the technological platforms for high throughput analyses, and finally the statistical tools. For this, increasingly, consortia that comprise a number of cohorts of distinct origin are being established to identify genetic factors in genome-wide association studies. In order to separate true

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associations from false-positive associations, the credibility of genotype-phenotype association must be confirmed in subsequent replicative studies [130]. If such genetic markers are then identified, more emphasis should be put on the investigation of underlying mechanisms, i.e. identifying potential causative SNPs and identifying the cellular and molecular mechanisms, how genetic variation affects individual’s body weight, energy metabolism and/or obesity-associated morbidity. These efforts should not be restricted to SNPs that cause an alteration in the amino acid sequence (nonsynomynous), but should also include the synonymous SNPs. These formerly disregarded (‘silent’) modifications may be relevant for protein function by affecting folding of the protein, ribosome trafficking, or alternative splicing [131]. Even more relevant may be the effects of SNPs in the 5⬘ prime region of genes that may affect promoter activity and hence transcription efficacy. It has also to be considered, that genetic predisposition is not restricted to a limited number of genes but rather a complex network of a great variety of genes. These genes may directly or indirectly interact at several levels, such as direct gene-gene interactions (for example transcription factor and its binding site), functional interaction of genes that are (sequential) components of a common pathway, or functional interactions of genes affecting similar biological, cellular, or developmental processes of metabolism or signaling. Hence several genes may operate synergistically or behave as modifiers affecting the phenotype or the severity of disease. Such a multiple hit model has been proposed for several other complex diseases as for instance holoprosencephaly [132]. The concept of synergistic heterozygosity similarly hypothesizes concurrent partial defects in more than one pathway and the compound effects of these defects finally determine the severity of the phenotype [133]. Besides gene-gene interactions, it is also acknowledged that the genetic background can interact with environmental factors, such as habitual dietary fat composition or physical activity and thereby affect the predisposition to obesity through defining an individuals responsiveness to dietary fat intake. Such gene-nutrient interaction has been proposed for the Ala12-polymorphism in the PPARg gene [134] which is supposed to be protective for the development of obesity. However, this beneficial effect is lost in some individuals with a diet rich in saturated fatty acids [135]. As a potential mechanism, a direct interaction of saturated fatty acids and trans fatty acids on activating the steatosis pathways resulting in insulin resistance has been suggested [136], even though this notion is not undebated. Finally, other environmental factors may similarly act on the basis of the genetic predisposition. As such, it has been suggested that in carriers of the Ala allele the association of dietary fat intake and insulin resistance was modified by the level of physical activity [137]. Epigenetic modifications may provide a plausible link between the environment and alterations in gene expression finally affecting the phenotype [138]. Epigenetics is now generally regarded as a change in gene expression not explained by changes in DNA sequence. Mechanisms include DNA methylation, microRNAs, or histone modifications.

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Given the polygenic etiology of obesity, it has to be considered that the contribution of the single genes is small. This challenge necessitates sensitive and powerful tools to detect the significant but sometimes subtle effects of the genes. For future analyses, an approach that combines the advantages of genome-wide association studies with the rationale of candidate gene approaches, and further refined by adding information from microarray and proteomic techniques may be promising for identifying novel pathways and molecules as well as selecting the significant and relevant genes [139]. Combining the information from several risk factors can allow the identification of high-risk groups of individuals and may hence have an important role in the initiation of preventative measures for complex (polygenic) diseases such as obesity. Similarly to what has recently been stated for diabetes, the genetics of obesity is still a puzzle but no longer a nightmare [17]. Today we know that even the strongest genetic predictors of obesity are not likely to exceed an odds ratio of 2.0. But we also know that these predictors combined between themselves and/or with environment are likely to explain most genetic factors controlling obesity. Identifying these mechanisms remains to be a very exciting challenge.

Acknowledgments This work was supported by grants from the Deutsche Forschungsgemeinschaft KFO 152: ‘Atherobesity’, projects KO 3512/1-1 (TP 1) and BE 1264/10-1 (TP5), the European Community integrated project grant ‘PIONEER’, and by the Interdisciplinary Centre for Clinical Research Leipzig at the Faculty of Medicine of the University of Leipzig (B27 and N06).

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Kavalier F, Kirk C, Lalloo F, Langman C, Locke I, Longmuir M, Mackay J, Magee A, Mansour S, Miedzybrodzka Z, Miller J, Morrison P, Murday V, Paterson J, Pichert G: Replication of genome-wide association signals in UK samples reveals risk loci for type 2 diabetes. Science 2007;316:1336–1341. Saxena R, Voight BF, Lyssenko V, Burtt NP, de Bakker PI, Chen H, Roix JJ, Kathiresan S, Hirschhorn JN, Daly MJ, Hughes TE, Groop L, Altshuler D, Almgren P, Florez JC, Meyer J, Ardlie K, Bengtsson BK, Isomaa B, Lettre G, Lindblad U, Lyon HN, Melander O, Newton-Cheh C, Nilsson P, Orho-Melander M, Rastam L, Speliotes EK, Taskinen MR, Tuomi T, Guiducci C, Berglund A, Carlson J, Gianniny L, Hackett R, Hall L, Holmkvist J, Laurila E, Sjogren M, Sterner M, Surti A, Svensson M, Svensson M, Tewhey R, Blumenstiel B, Parkin M, DeFelice M, Barry R, Brodeur W, Camarata J, Chia N, Fava M, Gibbons J, Handsaker B, Healy C, Nguyen K, Gates C, Sougnez C, Gage D, Nizzari M, Gabriel SB, Chirn GW, Ma Q, Parikh H, Richardson D, Ricke D, Purcell S: Genome-wide association analysis identifies loci for type 2 diabetes and triglyceride levels. Science 2007;316: 1331–1336. Foulkes WD, Flanders TY, Pollock PM, Hayward NK: The CDKN2A (p16) gene and human cancer. Mol Med 1997;3:5–20. Krishnamurthy J, Ramsey MR, Ligon KL, Torrice C, Koh A, Bonner-Weir S, Sharpless NE: p16INK4a induces an age-dependent decline in islet regenerative potential. Nature 2006;443:453–457. Nielsen J, Christiansen J, Lykke-Andersen J, Johnsen AH, Wewer UM, Nielsen FC: A family of insulin-like growth factor II mRNA-binding proteins represses translation in late development. Mol Cell Biol 1999;19:1262–1270. Chanock SJ, Manolio T, Boehnke M, Boerwinkle E, Hunter DJ, Thomas G, Hirschhorn JN, Abecasis G, Altshuler D, Bailey-Wilson JE, Brooks LD, Cardon LR, Daly M, Donnelly P, Fraumeni JF, Jr., Freimer NB, Gerhard DS, Gunter C, Guttmacher AE, Guyer MS, Harris EL, Hoh J, Hoover R, Kong CA, Merikangas KR, Morton CC, Palmer LJ, Phimister EG, Rice JP, Roberts J, Rotimi C, Tucker MA, Vogan KJ, Wacholder S, Wijsman EM, Winn DM, Collins FS: Replicating genotype-phenotype associations. Nature 2007;447:655–660. Komar AA: Genetics. SNPs, silent but not invisible. Science 2007;315:466–467. Ming JE, Muenke M: Multiple hits during early embryonic development: digenic diseases and holoprosencephaly. Am J Hum Genet 2002;71:1017–1032.

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133 Vockley J, Rinaldo P, Bennett MJ, Matern D, Vladutiu GD: Synergistic heterozygosity: disease resulting from multiple partial defects in one or more metabolic pathways. Mol Genet Metab 2000; 71:10–18. 134 Phillips C, Lopez-Miranda J, Perez-Jimenez F, McManus R, Roche HM: Genetic and nutrient determinants of the metabolic syndrome. Curr Opin Cardiol 2006;21:185–193. 135 Luan J, Browne PO, Harding AH, Halsall DJ, O’Rahilly S, Chatterjee VK, Wareham NJ: Evidence for gene-nutrient interaction at the PPARgamma locus. Diabetes 2001;50:686–689. 136 Pisabarro RE, Sanguinetti C, Stoll M, Prendez D: High incidence of type 2 diabetes in peroxisome proliferator-activated receptor gamma2 Pro12Ala carriers exposed to a high chronic intake of trans fatty acids and saturated fatty acids. Diabetes Care 2004;27:2251–2252.

137 Franks PW, Luan J, Browne PO, Harding AH, O’Rahilly S, Chatterjee VK, Wareham NJ: Does peroxisome proliferator-activated receptor gamma genotype (Pro12ala) modify the association of physical activity and dietary fat with fasting insulin level? Metabolism 2004;53:11–16. 138 Jirtle RL, Skinner MK: Environmental epigenomics and disease susceptibility. Nat Rev Genet 2007;8: 253–262. 139 Challis BG, Yeo GS: Past, present and future strategies to study the genetics of body weight regulation. Brief Funct Genomic Proteomic 2002;1:290–304.

Note added in proof In a recent study, the putative role of the Fto protein has been unraveled. By means of bioinformatic analysis [1, 2] and confirmed [1] by functional in vitro experiments, Fto has been shown to act as an Fe(II) and 2-oxoglutarate oxygenase. With these enzymatic properties, Fto may be involved in DNA repair, fatty acid metabolism and posttranslational modifications. Further support that this enzymatic function may be relevant to the regulation of body weight derives from the observation that Fto mRNA levels were reduced by fasting and not rescued by leptin supplementation. 1 Gerken T, Girard CA, Tung YC, Webby CJ, Saudek V, Hewitson KS, Yeo GS, McDonough MA, Cunliffe S, McNeill LA, Galvanovskis J, Rorsman P, Robins P, Prieur X, Coll AP, Ma M, Jovanovic Z, Farooqi IS, Sedgwick B, Barroso I, Lindahl T, Ponting CP, Ashcroft FM, O’Rahilly S, Schofield CJ: The obesity-associated FTO gene encodes a 2-oxoglutarate dependent nucleic acid demethylase. Science 2007; Epub ahead of print.

2 Sanchez-Pulido L, Andrade-Navarro MA: The FTO (fat mass and obesity associated) gene codes for a novel member of the non-heme dioxygenase superfamily. BMC Biochem 2007;8:23.

Dr. Antje Körner Research Laboratory, University Hospital for Children and Adolescents University of Leipzig, Liebigstrasse 20a DE–04103 Leipzig (Germany) Tel. ⫹49 341 97 26854, Fax ⫹49 341 97 26009, E-Mail [email protected]

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Körner ⭈ Kiess ⭈ Stumvoll ⭈ Kovacs

Korbonits M (ed): Obesity and Metabolism. Front Horm Res. Basel, Karger, 2008, vol 36, pp 37–60

Genetic Obesity Syndromes Anthony P. Goldstonea
Philip L. Bealesb a

MRC Clinical Sciences Centre, Hammersmith Hospital, Imperial College London Molecular Medicine Unit, UCL Institute of Child Health, London, UK

b

Abstract There are numerous reports of multi-system genetic disorders with obesity. Many have a characteristic presentation and several, an overlapping phenotype indicating the likelihood of a shared common underlying mechanism or pathway. By understanding the genetic causes and functional perturbations of such syndromes we stand to gain tremendous insight into obesogenic pathways. In this review we focus particularly on Bardet-Biedl syndrome, whose molecular genetics and cell biology has been elucidated recently, and Prader-Willi syndrome, the commonest obesity syndrome due to loss of imprinted genes on 15q11–13. We also discuss highlights of other genetic obesity syndromes including Alstrom syndrome, Cohen syndrome, Albright’s hereditary osteodystrophy (pseudohypoparathyroidism), Carpenter syndrome, MOMO syndrome, Rubinstein-Taybi syndrome, cases with deletions of 6q16, 1p36, 2q37 and 9q34, maternal uniparental disomy of chromosome 14, fragile X syndrome and Börjeson-Forssman-Lehman syndrome. Copyright © 2008 S. Karger AG, Basel

Monogenic Obesity Syndromes

Bardet-Biedl Syndrome Bardet-Biedl syndrome (BBS, OMIM 209900) is a highly heterogeneous disorder inherited in a mainly recessive manner. Clinical features include retinal degeneration, cognitive impairment, obesity, renal cystic disease, polydactyly and genital hypoplasia/malformation. There are numerous secondary craniofacial, endocrine, neurological and behavioural features which can assist in early diagnosis (fig. 1) [1]. Although most infants with BBS are born with normal birth weight, by 1 year most show signs of significant weight gain. There may be few other signs of the syndrome during infancy as up to one third of cases do not have polydactyly and signs of visual impairment do not typically emerge until 6–8 years of age (night blindness). The majority of adults have a body mass index (BMI) 30 often accompanied by hypertension, dyslipidaemia and type 2 diabetes mellitus [1].

a

c

b

d

e

Fig. 1. BBS. a, b Post-axial polydactyly in hands and feet of the same child. c Brachydactyly in hands of an adult. Note the postaxial scars. d High arched palate is common in BBS. e Dental anomalies frequently include crowded dentition with hypodontia and short roots.

Twelve genes have now been identified for which there is little evidence of any phenotype-genotype correlation. The BBS genes (BBS1–12) have few sequence similarities to each other or other protein groups. Three, BBS6, BBS10 and BBS12 have strong homology with the type II group of chaperones and account for around 30% of all mutations [2–4]. Only BBS3/ARL6 (a member of the Ras superfamily of small GTP-binding proteins) and BBS11/TRIM32 (an E3 ubiquitin ligase) encode known proteins [5–7]. Recent evidence suggests that BBS is probably caused by dysfunction of primary cilia and the intraflagellar transport (IFT) process. All BBS proteins studied thus far localise to the cilium/basal body/centrosome complex (fig. 2). In mammalian cultured cells several BBS proteins localise either to the basal body and pericentriolar region or the ciliary axoneme [7–13]. Studies indicate that many BBS proteins function in microtubular processes such as IFT as demonstrated in several Bbs mouse mutants, in which each develops severe retinal degeneration similar to patients [11, 14–17]. In photoreceptors, rhodopsin relies on IFT for transport to the outer segment – in Bbs mutants rhodopsin accumulates in the cell body triggering apoptosis [11, 17]. Anosmia was recently reported in Bbs1 and Bbs4 mutants arising from depletion of olfactory proteins in the ciliary layer of olfactory neurones [15]. Subsequently, anosmia was demonstrated in BBS patients,

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BBS4

BBS7

BBS8

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PCM-1 CHE-11 OSM-5 (polaris) IFT particle Transition fibres Cargo Dynein/dynactin Kinesin

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Transition zone (basal body)

BBS2 BBS5

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Centriolar satellites

Microtubules

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Centrosome Centrioles

Fig. 2. BBS proteins. BBS proteins are found in the basal body, centrosome and occasionally the ciliary axoneme. Many are directly involved with IFT, a process dependent on the molecular motors, dynein and kinesin. BBS4 is thought to behave as an adaptor protein, facilitating loading of cargo prior to dynein (retrograde) transport.

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a novel feature of the syndrome. Finally, repression of Bbs proteins in zebrafish delays IFT-dependent movement of melanosomes [18].

Obesity and BBS Obesity is a cardinal aspect of the BBS phenotype, beginning in early childhood and progressing with age; it is usually associated with the trunk and proximal limbs. A survey of UK BBS patients identified 72% of adults as overweight (BMI 25) and 52% defined as obese (BMI 30) [1]. At present, the physiological and biochemical abnormalities underlying obesity in BBS are poorly understood. A case-control study showed no significant differences between resting metabolic rate between obese BBS and controls suggesting no underlying defect in metabolism [19]. Bbs-deficient mouse models (Bbs4 and Bbs6) are initially runty at birth but display progressive weight gain associated with increased food intake, culminating in obesity at ⬃12 weeks [11, 20].

BBS Association Studies A study by Croft et al. [21, 22] first suggested that heterozygous carriers were at risk of obesity. Attempts to show that BBS gene sequence variants may be associated with general non-syndromic obesity have met with mixed success. Reed et al. [23] investigated 17 genetic markers spanning chromosomal regions implicated in five different obesity syndromes including BBS and BMI in 44 families segregating for non-syndromic morbid obesity. Sib-pair analyses failed to reveal evidence of linkage between any of the markers and obesity in these families. Amongst 60 Danish white men with juvenile-onset obesity who were screened for five variants in MKKS/BBS6, no significant association was found [24]. Another study did not find any association of the common M390R mutation in BBS1 with obesity among Newfoundlanders [25]. A recent large population study however, suggests that variations at BBS genes are associated with risk of common obesity. Benzinou et al. [26] genotyped 12 variants from the coding and conserved regions of BBS1, BBS2, BBS4, and BBS6 in 1,943 FrenchCaucasian obese subjects and 1,299 French-Caucasian non-obese, non-diabetic controls. A BBS2 polymorphism (SNP) was associated with common adult obesity whereas the BBS4 and BBS6 SNPs were associated with common early-onset childhood obesity and common adult morbid obesity, respectively.

Alström Syndrome Alström syndrome (ALS, OMIM 203800) is a rare recessive disorder typically presenting with early-onset obesity, hyperinsulinaemia (often with acanthosis nigricans) and type 2 diabetes mellitus, dilated cardiomyopathy, short stature and male hypogonadism. Infants usually display nystagmus and photophobia, eventually progressing

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to cone and rod photoreceptor degeneration making it a key differential diagnosis with the BBS [27, 28]. It may be associated with hepatic dysfunction, hepatic steatosis and hyperlipidaemia. In addition, ALS patients develop variable sensorineural hearing loss owing to cochlear neuronal degeneration [28, 29]. Rapid weight gain occurs during infancy but tends to plateau in adolescence with a truncal distribution. Despite the improvement in adiposity and BMI with age, the insulin resistance continues to increase and thus ALS may represent a monogenic model of the metabolic syndrome [29]. There are numerous associated endocrine disturbances including growth hormone (GH) deficiency and hyper- or hypogonadotrophic hypogonadism in males [30, 31]. Amongst females with ALS, hirsutism, precocious puberty, and amenorrhoea have been reported. Like BBS up to 50% will have renal and/or urological dysfunction. The underlying gene, ALMS1 was discovered in 2002 on 2p13 [32, 33]. Neither the predicted gene nor protein sequence has similarity to any other genes, although there are several conserved sequence motifs of limited functional significance. Of interest are the presence of a large 8-kb exon containing a tandem-repeat domain and in exon 1, a polyglutamic acid/polyalanine tract, the length of which does not appear to impact on the AS phenotype. There do not appear to be any phenotype-genotype correlations. ALMS1 is ubiquitously expressed throughout all organ tissues [32], and is a component protein of the centrosome with basal body localisation suggesting involvement in ciliary function and perhaps explaining the phenotypic overlap with BBS [33]. Common variations in the ALMS1 gene were not associated with type 2 diabetes mellitus in two studies of a Dutch and UK population [34, 35].

Cohen Syndrome Cohen syndrome (CS, OMIM 216550) patients characteristically have a history of developmental delay, severe cognitive impairment, and maladaptive behaviour in addition to a typical facial appearance. They usually have down-sloping palpebral fissures, mild maxillary hypoplasia, a prominent nasal root, micrognathia, high arching palate, thick hair and an open mouth expression where the upper lip barely covers the upper incisors giving the appearance of incisoral prominence [36–38]. Many have microcephaly at birth [39]. CS babies often have low birth weights and failure to thrive owing to feeding difficulties [36, 37]. Sometime during mid-childhood, patients gain weight and develop truncal adiposity, although it is rarely severe. Short stature is common. Delayed puberty is commonly encountered and cases with GH, testosterone deficiency, hypogonadotrophic hypogonadism, and insulin resistance were reported [36, 40]. CS patients typically develop a progressive retinopathy with early-onset myopia [36,41]. There is a wide range of additional ocular defects with pigmentary changes around the macular (giving rise to the typical ‘bull’s eye’ maculopathy) occurring as young as 3 years of age [36]. A chorioretinal dystrophy with accompanying

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electroretinographic changes is usually evident by 5 years [42]. Progressive visual field loss with night blindness is present by 10 years. Neutropenia has a variable but characteristic association with CS. Despite resting low neutrophil counts, perhaps related to increased neutrophil adhesion, patients appear to be able to mount relevant responses to bacterial infection and bone marrow analysis has shown normal cellularity [43, 44]. Mutations in the responsible gene, VPS13B (originally named COH1) were first identified in Finnish families in whom CS is most commonly found [45]. VPS13B is a large gene spanning 864 kb of genome with a transcript of 14 kb and an open-reading frame of 4,022 codons. Although common founder mutations have been observed in the Finnish and Amish CS population, the positions of mutations in other cases are variable and without any phenotype correlation. Most of the 70 plus mutations described so far are non-sense. The function of VPS13B remains unknown although homologues such as Vps13p are involved in intracellular vesicular trafficking [46]. Expression of the Vps13B in the mouse is widespread amongst neurons of the postnatal brain, but has low level embryonic expression suggesting a role in neuronal differentiation, but not in proliferation [47]. This may explain the postnatal microcephaly seen in CS patients.

Carpenter Syndrome Carpenter syndrome (acrocephalopolysyndactyly type II, OMIM 201000), most often presents with pre-axial polydactyly of the feet, craniosynostosis and progressive generalised or truncal obesity [48, 49]. Patients often have brachydactyly and syndactyly of the hands. It is autosomal recessively inherited. Only some 40 cases have been described. Additional clinical signs include prolonged retention of primary teeth and hypodontia. It is therefore one of the differential diagnoses to consider with polydactyly, obesity and hypodontia, overlapping with BBS. It is likely that the three sibs reported as cases of BBS by McLoughlin et al. [50] have Carpenter syndrome. Recently mutations (truncating and missense) were reported in RAB23, encoding a RAB/GTPase involved in vesicle transport. RAB23 is a negative regulator of sonic hedgehog signalling and is purported to act with other intermediaries in the cilium [51]. Therefore, like BBS and ALS, Carpenter syndrome supports a link between ciliary function and obesity.

Albright’s Hereditary Osteodystrophy In the original report of Albright’s hereditary osteodystrophy (AHO, OMIM 103580), the authors described a child with a short stocky build, round face, short metacarpals and metatarsals, and numerous areas of soft tissue ossification [52]. She also had

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hypoparathyroidism secondary to end organ resistance to parathyroid hormone, a component of which they termed ‘pseudo-hypoparathyroidism’ (PHP). Since then several cases of AHO without end-organ resistance have also be reported and termed ‘pseudo-pseudohypoparathyroidism’ (PPHP). PHP is further subdivided into types Ia, Ib, Ic and type II. The PHP type Ia and PPHP forms of AHO are caused by inactivating mutations in the tissue-specifically imprinted gene GNAS1 resulting in reduction of the encoded Gs protein [53]. Despite normal or even low birth weights, 50–65% of AHO patients develop generalised obesity. The aetiology of the obesity is far from clear but there are a number of possible mechanisms. The melanocortin receptor (MC4R), mutations in which are one of the most common causes of genetic obesity, is transduced by Gs, as are many of the other G-protein-coupled seven transmembrane receptors that mediate anorexigenic signals from hormones and other neurotransmitters. Loss of such anorexigenic signals such as through MC4R should produce hyperphagia, but this has not been widely studied in obese AHO individuals [54]. The observations that Gs represses differentiation of fibroblasts (3T3L1 preadipocytes) into adipocytes, and patients with PHP1a may have reduced cAMP responses to -adrenergic stimulation in fat cell membranes, reduced basal and adrenergic-stimulated glycerol production and reduced circulating levels of noradrenaline suggest that increased adipogenesis and reduced lipolysis and sympathetic activity may also contribute to obesity in AHO [53].

Rubinstein-Taybi Syndrome Affected patients have a characteristic appearance which includes microcephaly, down slanting and widely placed eyes, long eyelashes, mild ptosis, posteriorly rotated ears and a convex nose with the columella protruding below the alae nasi on lateral view (OMIM 180849). The thumbs and halluces are typically broad and occasionally bifid. Many patients develop central obesity for which the cause is unknown. Recently mutations have been found in the CBP gene [55] which encodes a protein that binds the phosphorylated form of the CREB transcription factor culminating in increased expression of genes containing cAMP-responsive elements.

Obesity Syndromes with Chromosomal and Imprinting Anomalies

Prader-Willi Syndrome Prader-Willi syndrome (PWS, OMIM 176270) is the commonest human genetic obesity syndrome with a lower estimated birth incidence of 1 in 25,000, and population prevalence of 1 in 50,000 [56]. Characteristic phenotypes, including several

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Fig. 3. PWS: from genes to phenotype. a A 17-year-old female with PWS. With permission from Goldstone [59]. b PWS chromosomal region on 15q11-q13 (not to scale) showing the genetic map of the 2 Mb PWS region. Imprinted genes are in blue (paternal allele expressed) and red (maternal allele expressed). Non-imprinted genes are in green. Orange arrows indicate the area of regional imprint control through the IC at the 5 end of the bicistronic SNURF-SNRPN locus. Vertical bars indicate snoRNA transcripts and horizontal bars, the relative positions of identified exons and other transcripts within the SNURF-SNRPN locus. Also indicated are the overlapping sense and anti-sense transcripts of the Angelman syndrome (AS) gene, UBE3A, which is located adjacent to the PWS locus. The black crosses indicate common breakpoint (BP) regions for deletions. Adapted with permission from Goldstone [59].

a

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GABR 3 5 3

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HBII-85 (27 copies) HBII-52 (47 copies)

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suggestive of hypothalamic dysfunction, are reduced foetal movement, increased prematurity, neonatal and infantile hypotonia with poor suck and subsequent improvement with age; genital hypoplasia at birth and cryptorchidism; temporary feeding problems with poor suck and poor weight gain in infancy often needing gavage or other special feeding techniques; subsequent childhood development of obesity and then profound hyperphagia (between ages of 1 and 6 years) leading to progressive morbid obesity into adulthood (fig. 3a); short stature due to GH deficiency and (predominantly hypothalamic) hypogonadism with incomplete delayed puberty and infertility; characteristic facial features of narrow bi-frontal diameter, almond-shaped palpebral fissures and down-turned mouth; small feet and hands with straight ulnar border; developmental delay with mild to moderate mental retardation; characteristic

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behavioural problem with temper tantrums, obsessive compulsive behaviours, skin picking, stubbornness, rigidity, stealing and lying; central and obstructive sleep apnoea; eye abnormalities such as esotropis and myopia; thick viscous saliva and speech articulation defect; high pain threshold; decreased vomiting; altered temperature sensitivity; scoliosis or kyphosis [57–60].

PWS Genetics PWS arises from the lack of expression of genes within the paternally derived chromosome 15q11-q13 which are silenced (imprinted) on the maternally derived chromosome [see 59, 61 for references]. These include NDN, MAGEL2, MKRN3 (previously called ZFP127), the SNURF-SNRPN locus which extends over 460 kb encoding at least 148 exons and several repeating intronic C/D box small nucleolar RNAs (snoRNAs) including HBII-52 and HBII-85 (fig. 3b). The finding of patients with smaller microdeletions and balanced translocations has permitted narrowing of the critical PWS region from 4.5 MB to less than 4.3 kb, spanning the promoter and exon 1 of the SNRPN gene and demonstrated the importance of some snoRNAs in the PWS phenotype [61]. The promotor and first exon of the SNURF-SNRPN gene locus is an integral part of the imprinting centre (IC) in the PWS chromosomal region (fig. 3b). The mouse PWS gene homologues are particularly expressed throughout the developing brain, particularly the hypothalamus, and there is also embryonic and post-natal expression of Ndn and Magel2 outside the brain [62]. The molecular biology of the genes within the PWS critical region has not been fully established. The SmN product of the SNRPN locus (exons 4–10) is involved in RNA alternative splicing, but the role of other transcripts from the SNRPN locus, including IPW, PAR-1, -4 and -7, are poorly defined [61] (fig. 3b). The locus also encodes an anti-sense transcript for the paternally-imprinted UBE3A gene involved in Angelman syndrome. Necdin has a pivotal role in neuronal differentiation and survival, prevention of apoptosis and axonal growth, and may interact with several neurotrophic and cell cycle-regulatory transcription factors and hence proapoptotic genes such as TrkA, p75, E2F1, Cdc2, p53, hnRNP U, and NEFA [59, 63]. Necdin and Magel2 proteins can both bind to and prevent proteasomal degradation of Fez1, a fasciculation and elongation protein implicated in axonal outgrowth and kinesin-mediated transport, which also binds to the BBS protein BBS4 at or near centrosomes [64]. Ndndeficient mice exhibit neonatal lethality with respiratory distress; an abnormal respiratory rhythm-generating centre in the medulla; increased skin-scraping activity; improved spatial learning; hypothalamic structural abnormalities with reduced oxytocin and LHRH cell number (but preserved fertility); abnormal axonal outgrowth and fasciculation in embryos, including serotoninergic, noradrenergic, sympathetic (e.g. diaphragmatic and superior cervical ganglion), retinal ganglion cell,

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and thalamocortical neurons; defective cytoarchitecture of the cuneate/gracile nuclei; increased apoptosis in spinal cord sensory neurons; and high tolerance to thermal pain [64–68]. The HBII-52 snoRNA and its mouse homologue MBII-52 have recently been found to change methylation editing and alternative splicing of the serotonin 5HT2cR receptor pre-mRNA [69, 70]. The functions of the other snoRNAs are currently unknown. In 75% of cases, there is a 15q11-q13 paternal deletion, in 22% maternal uniparental disomy (UPD), in 1–3% imprinting errors due in around 15% of cases to a sporadic or inherited microdeletion in the IC and, in 1% there is a paternal chromosomal translocation [61]. Imprinting is achieved partly through parent-of-origin allele-specific methylation of CpG residues, established during or after fertilisation and maintained throughout embryogenesis. The IC not only plays a role in erasure of the grandmaternal imprint during spermatogenesis, but also has a role in the postzygotic maintenance of the maternal imprint. While there are a number of phenotype-genotype correlations between those PWS patients with deletions versus UPD, particularly in the severity of several neurological, cognitive and behavioural phenotypes (including an increased risk of psychosis in UPD), hyperphagia and obesity do not seem to differ significantly between genotypes [59, 71]. Due to loss of expression of the non-imprinted P gene, involved in oculocutaneous albinism, there is a higher frequency of hypopigmentation of skin, hair, and eyes in subjects with deletions [61] (fig. 3b). Between the two common proximal breakpoints (BP1 and 2) are four recently identified genes NIPA1 (mutations in which cause spastic paraplegia), NIPA2, CYFIP1 (whose protein product interacts with the fragile X protein FMRI) and GCP5 (-tubulin complex compenent-5), whose loss might cause phenotypic differences in those with the larger type 1 versus shorter type 2 deletions [72, 73] (fig. 3b).

Obesity and PWS Birth weight is slightly reduced in PWS [74]. The initial post-natal hypotonia, poor suck and feeding difficulties (often needing special feeding strategies for weeks to months to prevent failure-to-thrive) in babies with PWS have usually improved significantly by 6 months of age. Between the ages of 1 and 6 years, there is initially development of mild obesity (from around 1 year of age), and subsequent hyperphagia and more severe obesity (usually developing between the ages of 2 and 6 years of age). Without appropriate dietary restriction, environmental control and behavioural input obesity becomes progressive into adulthood, leading to obesity-related morbidity, such as cardiopulmonary disease, type 2 diabetes mellitus, thrombophlebitis, chronic leg oedema, and mortality under the age of 35 (fig. 3a). Deaths from choking and gastric necrosis after overeating have been reported [75, 76]. Obesity-related sleep apnoea is common and responds to weight loss. The reason for the later onset development of

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hyperphagia and severe obesity in PWS compared to monogenic causes of obesity such as leptin deficiency or melanocortin-4 receptor mutations is unknown. Obesity management involves early institution of a low-calorie, well-balanced diet, with regular exercise, rigorous supervision, restriction of access to food and money with appreciation of legal and ethical obligations, appropriate psychological and behavioural counselling of the patient and family [77]. Group homes specifically designed for individuals with PWS, where available, have been particularly successful in management of these problems during adulthood. Anecdotally, pharmacological treatment, including available anorexigenic agents, has not been of benefit in treating hyperphagia, though there are few published control studies. Restrictive bariatric surgery, such as gastric banding or bypass, have not been shown to reduce hyperphagia or achieve long-term weight reduction and are associated with unacceptable morbidity and mortality, but some of the reports using biliopancreatic diversion which produces intestinal malabsorption have reported successful weight loss though with frequent complications [78]. Body composition studies show both increased body fat and reduced muscle in PWS [79]. Magnetic resonance imaging has found that PWS adults of both sexes have less visceral adiposity than expected for their overall adiposity [80, 81] (fig. 4a). This may explain the relative hypoinsulinaemia and lower triglyceride levels with preservation of insulin sensitivity and protective elevation in adiponectin levels in patients with PWS despite their overall obesity [80, 82, 83] (fig. 4b, c). Obesity, hypersomnolence and persistent poor muscle strength contribute to reduced physical activity in PWS. Resting metabolic rate is reduced relative to body size, as a result of the abnormal body composition, which further contributes to a reduction in 24-hour energy expenditure [79]. Increased physical activity and exercise programs improve body composition in PWS. Spontaneous or pharmacologically stimulated GH secretion and IGF-I levels are reduced in PWS children and adults, and the GH deficiency is independent of obesity [60]. In PWS children, GH therapy is now licensed and significantly improves height velocity and final height [60, 84]. GH significantly decreases total body fat, increases lean body mass, lipolysis and resting energy expenditure, and improves physical strength and agility in children and infants with PWS, and may also have neurodevelopmental benefits [60, 84, 85]. There may also be a potential benefit of lower GH doses to improve body composition in PWS adults.

PWS and Peripheral Appetite Signals The abnormal feeding behaviour in PWS includes a morbid obsession about food, food stealing, money stealing to buy food, hording and foraging, pica behaviour, reduced satiety and earlier return of hunger after the previous meal [86]. Given free access to food, PWS subjects will consume approximately three times that of control subjects. The reduced satiation in PWS occurs despite delayed gastric emptying which would be expected to produce the opposite effect [87].

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a

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5 b b

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a, b

OB CRHO PWS

Fig. 4. Reduced visceral adiposity, preserved insulin sensitivity and hyperghrelinaemia in PWS. a A T1-weighted MRI scan at the level of the lower abdomen, showing less visceral adiposity in a PWS adult female (total adipose tissue, AT, volume 86.9 l, MRI total body fat 54.4%, visceral AT 4.4% of total AT, visceral AT:subcutaneous AT ratio 0.048) compared to a similarly obese control adult female (total AT volume 86.4 l, MRI total body fat 50.5%, visceral AT 9.3% of total AT, visceral AT:subcutaneous AT ratio 0.111). With permission from Goldstone et al. [80]. b–d Mean SEM values for percent body fat (b), homeostasis model insulin resistance index (HOMA-IR; c) and fasting plasma ghrelin levels (d) in non-obese (NO, n 15) and obese (OB, n 16) controls, craniopharyngioma subjects with hypothalamic obesity (CRHO, n 9) and subjects with PWS (n 26). a p  0.01 vs. PWS, b p  0.01 vs. NO. Despite similar degrees of obesity, subjects with PWS have increased fasting plasma ghrelin and preserved insulin sensitivity, compared to the other two obese groups. With permission from Goldstone et al. [82].

Subjects with PWS have marked elevations in the stomach-derived orexigenic hormone ghrelin for their obesity, and increased density of ghrelin immunostaining in the stomach, though plasma levels do fall appropriately after food [82, 88, 89] (fig. 4d). This may be explained at least partly by their relative hypoinsulinaemia (fig. 4). A primary importance for hyperghrelinaemia is questioned by the overlap of ghrelin levels in PWS with lean subjects despite the former’s near ubiquitous hyperphagia, and the inability of acute normalisation of ghrelin levels in PWS using somatostatin to reduce appetite [90]. However somatostatin will also suppress secretion of a wide variety of anorexigenic gut hormones that might counteract any beneficial effect of lowering orexigenic ghrelin [90]. Fasting and post-prandial levels of the anorexigenic

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hormone pancreatic polypeptide are also reduced in PWS children and adults which may also contribute to hyperphagia [91, 92]. It seems likely that in addition to these hormonal abnormalities in PWS, there are overriding brain defects, including hypothalamic, which lead to resistance to peripheral satiety signals [59]. Infusion of PP to subjects with PWS has only a small anorexigenic effect [93]. The possibility of therapeutic avenues for reducing hyperphagia in PWS may depend on the existence of relative rather than absolute resistance to peripheral satiety signals.

PWS and Hypothalamic Abnormalities Quantitative neuroanatomical studies of available post-mortem human hypothalamic tissue from subjects with PWS in the Netherlands Brain Bank have yet to find any pathological abnormalities of orexigenic neuropeptide Y or agouti-related protein, anorexigenic POMC neurons or GH-releasing hormone neurons in the infundibular nucleus, or orexin/hypocretin neurons in the lateral hypothalamus, though interpretation may be complicated by small numbers and effects of pre-mortem illness [59, 94–96]. However, appropriate neuropeptide Y, agouti-related protein and GH-releasing hormone changes in illness, obesity and exogenous GH therapy were found in PWS subjects, suggesting normal neuronal function in their response to alterations in peripheral signals. Nevertheless, cerebrospinal fluid orexin concentrations have been reported to be low in cases of PWS with hypersomnia [97]. There is a reduction in total and oxytocin cell number in the hypothalamic paraventricular nucleus (PVN) of PWS adults, which may play a primary causative role in hyperphagia [98]. Reduced immunostaining of processed vasopressin, its processing enzyme, prohormone convertase 2, and its molecular chaperone polypeptide 7B2, have also been found in the PVN and supraoptic nucleus of hypothalami from subjects with PWS, though diabetes insipidus is not a recognised clinical problem [99, 100]. Oxytocin and the PVN have anorexigenic roles in rodents. A 29% reduction in PVN oxytocin neurons is also seen in ndn knockout mice, though these mice are not obese [65].

PWS and Brain Abnormalities Detailed MR scanning including techniques such as diffusion tensor imaging are revealing neuroanatomical abnormalities within extra-hypothalamic brain structures in PWS, such as ventriculomegaly, hypoplastic or displaced pituitary gland, incomplete Sylvian fissure/insula closure, Sylvian fissure polymicrogyria, decreased parietal-occipital grey matter and white matter lesions [101–105]. These may play a role not only in cognitive, behavioural and neuroendocrine defects in PWS, but also hyperphagia. Recent functional neuroimaging techniques such as positron emission tomography and functional magnetic resonance imaging in PWS have revealed abnormal brain activation patterns in corticolimbic structures, such as the amygdala, pre-frontal,

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orbitofrontal and insula cortex in response to food stimuli, after ingestion of oral glucose or a meal [106–109]. These suggest abnormal reward and motivational responses to food that may also contribute to the hyperphagia in PWS.

PWS Association Studies Individual gene mutations or segmental deletions or duplications across the PWS chr15q region have not yet been reported in patients with PWS-like or specific PWS phenotypes, including severe early-onset morbid obesity, but negative PWS methylation testing [61, 101, 110]. No linkage of the PWS chromosomal region with obesity in sibling studies, nor any association of polymorphisms in NDN or MAGEL2 with obesity in children or adolescents has been found [23, 111, 112]. A genome-wide scan found linkage of childhood-onset severe obesity in French Caucasian families to ch15q12–15q15.1, although finer mapping has yet to be reported [113], while weak linkage of BMI to ch15q13.3 was found in a genome-wide scan from the National Heart, Lung, and Blood Institute Family Heart Study [114].

Deletion 6q16 Childhood-onset obesity and hyperphagia has been reported in 5 individuals with deletions involving chromosome 6q16 [see 115 for references]. These patients have also been reported as having almond-shaped eyes, strabismus, thin upper lip, microretrognathia, small hands and feet, hypogonadism, learning disabilities, developmental delay, behavioural problems, cerebellar signs, hypotonia, neonatal feeding difficulties, providing overlap with features seen in PWS. Interestingly, the obesity may result from haploinsufficiency for a transcription factor involved in neurogenesis, SIM1, since obesity (but not the other syndromal features) has been seen in a subject with a balanced translocation at chr6q16.2 [116]. The sim1 heterozygote knockout mouse is hyperphagic and obese and has a nonselective loss of hypothalamic PVN neurons [117, 118]. Sim1 is also expressed in some non-hypothalamic brain regions involved in appetite, sim1 overexpression protects against diet-induced obesity and sim1 may mediate some of the anorexigenic action of the melanocortin pathways [119, 120].

Deletion 1p36 Obesity and/or hyperphagia has also been reported in 23% of 14 evaluated patients with monosomy 1p36, in whom developmental delay, hypotonia, growth delay, feeding difficulties in infancy, epilepsy, hearing loss, hypermetropia, orofacial clefting abnormalities, structural heart defects and dilated cardiomyopathy, micro- and

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brachycephaly, deep set eyes, flat nasal bridge and nose, pointed chin, thickened ear helices, asymmetric ears and short fifth finger are also reported [121].

Deletion 2q37 In mid-1990s, several patients with an AHO-like phenotype and a deletion of 2q37 were reported [122, 123]. Characteristically they have a round face with deep set eyes, a bulbous nasal tip, thin border of the lips, and sparse hair. Many have seizures with mild cognitive impairment and obesity is occasionally present. Cytogenetic analysis of further patients has narrowed the deleted interval down to a region including the G-protein-coupled receptor 35, glypican 1, and serine/threonine protein kinase 25 genes; the importance of each remains to be investigated [124, 125].

Deletion 9q34.3 A de novo terminal deletion of chromosome 9q34.3 has been reported in 2 unrelated children with early-onset obesity with hyperphagia (between 2 and 3 years old) and mental retardation, severe developmental delay, neonatal hypotonia, distinctive facial features (brachycephaly, synophrys, anteverted nostrils, prognathism, thin upper lip), short neck and extremities, syndactyly of toes, abnormal genitalia with cryptorchidism, micropenis, and hypospadias, sleep disturbances with repeated night awakenings, stereotypic hand movements, short attention span and intolerance to frustration [126]. However obesity was only seen in 2 of 13 other patients with 9q34.3 deletions, though 3 did die as infants with congenital heart abnormalities [127, 128]. The deleted region encompasses at least 20 genes.

Maternal UPD of Chromosome 14 Maternal UPD 14 (when both of a chromosome 14 pair are inherited exclusively from mother) is associated with muscular hypotonia, feeding problems, hypercholesterolaemia, characteristic rib anomalies (referred to as the ‘coat-hanger’ sign), motor delay, small hands and feet, precocious puberty and truncal obesity [129, 130]. Patients with UPD(14)mat show features overlapping with PWS and are probably underdiagnosed. In a recent study of 33 patients with low birth weight, feeding difficulties and subsequent obesity in whom PWS had been excluded by methylation analysis of SNRPN, 12% were found to have UPD(14)mat [131]. Facially, patients display a prominent forehead, prominent supra-orbital ridges, short philtrum and down-turned corners of the mouth. The cause of the obesity is unknown.

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X-Linked Obesity Syndromes

Fragile X Syndrome Fragile X syndrome (OMIM 300624), a common cause of mental retardation, is caused by an unstable expansion of a triplet repeat in the FMR1 gene. A sub-phenotype, resembling PWS has been reported with extreme obesity, a round face, small, broad hands/feet, and regional skin hyperpigmentation [132, 133].

Börjeson-Forssman-Lehmann Syndrome Börjeson-Forssman-Lehmann syndrome (BFLS, OMIM 301900) is a rare X-linked disorder characterised by severe cognitive impairment, obesity with gynaecomastia, hypogonadism, a course facial appearance and large fleshy ears [134]. BFLS individuals are usually born with a normal birth weight but by late childhood have developed significant truncal obesity. Although there is considerable variability in the degree of obesity, those with less generalised adiposity have a tendency to a female-type fat distribution around the lower abdomen and hips. Almost all BFLS patients develop gynaecomastia often in childhood but significant enlargement occurs at or after puberty, both from obesity and breast tissue. Multiple pituitary hormone deficiencies have been reported including GH, TSH, ACTH and gonadotrophin deficiency, and optic nerve hypoplasia [135]. These features suggest that the BFL gene product may play an important role in midline neuro-development including the hypothalamo-pituitary axis. The facial appearance in post-pubertal patients is striking with deep-set eyes, prominent supraorbital ridges, narrow palpebral fissures features which coarsen with age. The majority also have hyperextensible tapering fingers which like their toes are shortened. Positional cloning led to the identification of PHF6 as the genetic cause of BFLS [136]. Its expression is ubiquitous, suggesting an important cellular role. Studies show that the PHF6 protein is localised in the cell nucleus and in the nucleolus, and it has been speculated that PHF6 might have a role in cell growth and proliferation, via its participation in ribosome biogenesis [137]. To date, 19 unrelated cases of BFLS with confirmed PHF6 gene mutations have been reported [137]. Amongst these, twelve different mutations have been found of which five are recurrent thus aiding molecular diagnosis for this syndrome. Several manifesting female carriers have been reported with mild to moderate intellectual impairment, the characteristic facial phenotype, large ears, obesity and short stature. Nonetheless, many female carriers have a normal phenotype. Although it is more likely for a PHF6 mutation carrier female to have skewed X inactivation, there are also families where the X inactivation is random [138]. There is no obvious correlation between X inactivation skewing and the variability of clinical presentation of the BFLS phenotype in carrier females.

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Obesity Syndromes without Identified Genetic Cause

Macrosomia, Obesity, Macrocephaly, and Ocular Abnormalities Moretti-Ferreira et al. [139] reported 2 unrelated children with similar clinical features comprising truncal obesity, mental retardation and retinal coloboma and nystagmus (MOMO, OMIM 157980). Facially, features were unremarkable with hypertelorism, downslanting palpebral fissures, a prominent forehead, and a broad nasal root. A potential third case was reported by Zannolli et al. [140] describing a 5year-old girl with mild learning impairment, morbid obesity, macrocephaly, right optic disc coloboma and left choroidal coloboma, and recurvation of the femur. The cause is unknown.

Conclusions

Although in many of the aforementioned obesity syndromes the underlying genetic cause has not yet been identified, in others these are providing potential insights into biochemical or developmental pathways involved. For example, in BBS, AS and CS there is considerable phenotypic overlap. In BBS, AS and possibly Carpenter syndrome, ciliary dysfunction has been implicated and recently the function of primary cilia has been directly linked with obesity [141]. Study of PWS has identified mechanisms of genetic imprinting, genes involved in neuronal development and growth, and hormonal, hypothalamic and cortical circuits that may be important in appetite and body weight control. By defining the genetic cause of all obesity syndromes we should enrich our understanding of obesogenic pathways in common obesity. It is anticipated that the use of array-based comparative genomic hybridisation in patients with morbid obesity syndromes, often associated with mental retardation, developmental delay and dysmorphic features, will identify novel chromosomal regions and genes involved in body weight regulation, for example ch7q22.1–22.3 deletion and ch19q12q13.2 trisomy [142, 143]. However, care will need to be taken with assigning causality given the frequent presence of gene copy number polymorphisms between individuals.

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56 Whittington JE, Holland AJ, Webb T, Butler J, Clarke D, Boer H: Population prevalence and estimated birth incidence and mortality rate for people with Prader-Willi syndrome in one UK Health Region. J Med Genet 2001;38:792–798. 57 Prader A, Labhart A, Willi H: Ein syndrome von adipositas, kleinwuchs, kryptorchismus und oligophrenie nach myotonieartigem zustand im neugeborenenalter. Schweiz Med Wochenschr 1956;86: 1260–1261. 58 Holm VA, Cassidy SB, Butler MG, Hanchett JM, Greenswag LR, Whitman BY, Greenberg F: PraderWilli syndrome: consensus diagnostic criteria. Pediatrics 1993;91:398–402. 59 Goldstone AP: Prader-Willi syndrome: advances in its genetics, pathophysiology and treatment. Trends Endocrinol Metab 2004;15:12–20. 60 Burman P, Ritzen EM, Lindgren AC: Endocrine dysfunction in Prader-Willi syndrome: a review with special reference to GH. Endocr Rev 2001;22: 787–799. 61 Nicholls RD, Knepper JL: Genome organization, function, and imprinting in Prader-Willi and Angelman syndromes. Annu Rev Genomics Hum Genet 2002;2:153–175. 62 Lee S, Walker CL, Wevrick R: Prader-Willi syndrome transcripts are expressed in phenotypically significant regions of the developing mouse brain. Gene Expr Patterns 2003;3:599–609. 63 Kuwako K, Hosokawa A, Nishimura I, Uetsuki T, Yamada M, Nada S, Okada M, Yoshikawa K: Disruption of the paternal necdin gene diminishes TrkA signaling for sensory neuron survival. J Neurosci 2005; 25:7090–7099. 64 Lee S, Walker CL, Karten B, Kuny SL, Tennese AA, O’Neill MA, Wevrick R: Essential role for the PraderWilli syndrome protein necdin in axonal outgrowth. Hum Mol Genet 2005;14:627–637. 65 Muscatelli F, Abrous DN, Massacrier A, Boccaccio I, Le Moal M, Cau P, Cremer H: Disruption of the mouse Necdin gene results in hypothalamic and behavioral alterations reminiscent of the human Prader-Willi syndrome. Hum Mol Genet 2000;9: 3101–3110. 66 Ren J, Lee S, Pagliardini S, Gerard M, Stewart CL, Greer JJ, Wevrick R: Absence of Ndn, encoding the Prader-Willi syndrome-deleted gene necdin, results in congenital deficiency of central respiratory drive in neonatal mice. J Neurosci 2003;23:1569–1573. 67 Pagliardini S, Ren J, Wevrick R, Greer JJ: Developmental abnormalities of neuronal structure and function in prenatal mice lacking the prader-willi syndrome gene necdin. Am J Pathol 2005;167: 175–191.

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68 Andrieu D, Meziane H, Marly F, Angelats C, Fernandez PA, Muscatelli F: Sensory defects in Necdin deficient mice result from a loss of sensory neurons correlated within an increase of developmental programmed cell death. BMC Dev Biol 2006; 6:56. 69 Kishore S, Stamm S: The snoRNA HBII-52 regulates alternative splicing of the serotonin receptor 2C. Science 2006;311:230–232. 70 Vitali P, Basyuk E, Le Meur E, Bertrand E, Muscatelli F, Cavaille J, Huttenhofer A: ADAR2-mediated editing of RNA substrates in the nucleolus is inhibited by C/D small nucleolar RNAs. J Cell Biol 2005;169: 745–753. 71 Boer H, Holland A, Whittington J, Butler J, Webb T, Clarke D: Psychotic illness in people with Prader Willi syndrome due to chromosome 15 maternal uniparental disomy. Lancet 2002;359:135–136. 72 Chai JH, Locke DP, Greally JM, Knoll JH, Ohta T, Dunai J, Yavor A, Eichler EE, Nicholls RD: Identification of four highly conserved genes between breakpoint hotspots BP1 and BP2 of the Prader-Willi/ Angelman syndromes deletion region that have undergone evolutionary transposition mediated by flanking duplicons. Am J Hum Genet 2003;73: 898–925. 73 Butler MG, Bittel DC, Kibiryeva N, Talebizadeh Z, Thompson T: Behavioral differences among subjects with Prader-Willi syndrome and type I or type II deletion and maternal disomy. Pediatrics 2004; 113:565–573. 74 Dudley O, Muscatelli F: Clinical evidence of intrauterine disturbance in Prader-Willi syndrome, a genetically imprinted neurodevelopmental disorder. Early Hum Dev 7 A.D.;83:471–478. 75 Stevenson DA, Heinemann J, Angulo M, Butler MG, Loker J, Rupe N, Kendell P, Clericuzio CL, Scheimann AO: Deaths due to choking in Prader-Willi syndrome. Am J Med Genet A 2007;143: 484–487. 76 Wharton R, Wang T, GraemeCook F, Briggs S, Cole R: Acute idiopathic gastric dilatation with gastric necrosis in individuals with Prader-Willi syndrome. Am J Med Genet 1997;73:437–441. 77 Eiholzer U: A comprehensive approach to limiting weight gain and to normalising body composition in Prader-Willi syndrome; in Eiholzer U, l’Allemand D, Zipf W (eds): Prader-Willi syndrome as a Model for Obesity. Basel, Karger, 2003, pp 211–221. 78 Papavramidis ST, Kotidis EV, Gamvros O: PraderWilli syndrome-associated obesity treated by biliopancreatic diversion with duodenal switch. Case report and literature review. J Pediatr Surg 2006;41: 1153–1158.

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79 Goldstone AP, Brynes AE, Thomas EL, Bell JD, Frost G, Holland A, Ghatei MA, Bloom SR: Resting metabolic rate, plasma leptin concentrations, leptin receptor expression, and adipose tissue measured by whole-body magnetic resonance imaging in women with Prader-Willi syndrome. Am J Clin Nutr 2002; 75:468–475. 80 Goldstone AP, Thomas EL, Brynes AE, Bell JD, Frost G, Saeed N, Hajnal JV, Howard JK, Holland A, Bloom SR: Visceral adipose tissue and metabolic complications of obesity are reduced in Prader-Willi syndrome female adults: evidence for novel influences on body fat distribution. J Clin Endocrinol Metab 2001;86:4330–4338. 81 Goldstone AP, Unmehopa UA, Thomas EL et al: Hypothalamic neuropeptides and regulation of fat mass in Prader-Willi syndrome; in Eiholzer U, l’Allemand D, Zipf W (eds): Prader-Willi Syndrome as a Model for Obesity. Basel, Karger, 2003, pp 31–43. 82 Goldstone AP, Patterson M, Kalingag N, Ghatei MA, Brynes AE, Bloom SR, Grossman AB, Korbonits M: Fasting and post-prandial hyperghrelinemia in Prader-Willi syndrome is partially explained by hypoinsulinemia, and is not due to peptide YY 3–36 deficiency or seen in hypothalamic obesity due to craniopharyngioma. J Clin Endocrinol Metab 2005; 90:2681–2690. 83 Kennedy L, Bittel DC, Kibiryeva N, Kalra SP, Torto R, Butler MG: Circulating adiponectin levels, body composition and obesity-related variables in PraderWilli syndrome: comparison with obese subjects. Int J Obes (Lond) 2006;30:382–387. 84 Allen DB, Carrel AL: Growth hormone therapy for Prader-Willi syndrome: a critical appraisal. J Pediatr Endocrinol Metab 2004;17(suppl 4):1297–1306. 85 Myers SE, Whitman BY, Carrel AL, Moerchen V, Bekx MT, Allen DB: Two years of growth hormone therapy in young children with Prader-Willi syndrome: Physical and neurodevelopmental benefits. Am J Med Genet 2007;143:443–448. 86 Holland AJ, Treasure J, Coskeran P, Dallow J, Milton N, Hillhouse E: Measurement of excessive appetite and metabolic changes in Prader-Willi syndrome. Int J Obes 1993;17:527–532. 87 Choe YH, Jin DK, Kim SE, Song SY, Paik KH, Park HY, Oh YJ, Kim AH, Kim JS, Kim CW, Chu SH, Kwon EK, Lee KH: Hyperghrelinemia does not accelerate gastric emptying in Prader-Willi syndrome patients. J Clin Endocrinol Metab 2005;90: 3367–3370. 88 Cummings DE, Clement K, Purnell JQ, Vaisse C, Foster KE, Frayo RS, Schwartz MW, Basdevant A, Weigle DS: Elevated plasma ghrelin levels in Praderh Willi syndrome. Nat Med 2002;8:643–644.

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89 Choe YH, Song SY, Paik KH, Oh YJ, Chu SH, Yeo SH, Kwon EK, Kim EM, Rha MY, Jin DK: Increased density of ghrelin expressing cells in the gastric fundus and body in Prader-Willi syndrome. J Clin Endocrinol Metab 2005;90:5441–5445. 90 Tan TM, Vanderpump M, Khoo B, Patterson M, Ghatei MA, Goldstone AP: Somatostatin infusion lowers plasma ghrelin without reducing appetite in adults with Prader-Willi syndrome. J Clin Endocrinol Metab 2004;89:4162–4165. 91 Zipf WB, O’Dorisio TM, Cataland S, Dixon K: Pancreatic polypeptide responses to protein meal challenges in obese but otherwise normal children and obese children with Prader-Willi syndrome. J Clin Endocrinol Metab 1983;57:1074–1080. 92 Goldstone AP: The hypothalamus, hormones, and hunger: alterations in human obesity and illness. Prog Brain Res 2006;153:57–73. 93 Berntson GG, Zipf WB, O’Dorisio TM, Hoffman JA, Chance RE: Pancreatic polypeptide infusions reduce food intake in Prader-Willi syndrome. Peptides 1993;14:497–503. 94 Goldstone AP, Unmehopa UA, Bloom SR, Swaab DF: Hypothalamic NPY and agouti-related protein are increased in human illness but not in PraderWilli syndrome and other obese subjects. J Clin Endocrinol Metab 2002;87:927–937. 95 Goldstone AP, Unmehopa UA, Swaab DF: Hypothalamic growth hormone-releasing hormone (GHRH) cell number is increased in human illness, but is not reduced in Prader-Willi syndrome or obesity (erratum in Clin Endocrinol 59, 266, 2003). Clin Endocrinol (Oxf) 2003;58:743–755. 96 Fronczek R, Lammers GJ, Balesar R, Unmehopa UA, Swaab DF: The number of hypothalamic hypocretin (orexin) neurons is not affected in Prader-Willi Syndrome. J Clin Endocrinol Metab 2005;90: 5466–5470. 97 Nevsimalova S, Vankova J, Stepanova I, Seemanova E, Mignot E, Nishino S: Hypocretin deficiency in Prader-Willi syndrome. Eur J Neurol 2005;12:70–72. 98 Swaab DF, Purba JS, Hofman MA: Alterations in the hypothalamic paraventricular nucleus and its oxytocin neurons (putative satiety cells) in Prader-Willi syndrome: a study of five cases. J Clin Endocrinol Metab 1995;80:573–579. 99 Gabreëls BATF, Swaab DF, deKleijn DPV, Seidah NG, Van de Loo JW, Van de Ven WJM, Martens GJM, van Leeuwen FW: Attenuation of the polypeptide 7B2, prohormone convertase PC2, and vasopressin in the hypothalamus of some Prader-Willi patients: Indications for a processing defect. J Clin Endocrinol Metab 1998;83:591–599. 100 Swaab DF: Prader-Willi syndrome and the hypothalamus. Acta Paediatr Suppl 1997;423:50–54.

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101 Miller J, Kranzler J, Liu Y, Schmalfuss I, Theriaque DW, Shuster JJ, Hatfield A, Mueller OT, Goldstone AP, Sahoo T, Beaudet AL, Driscoll DJ: Neurocognitive findings in Prader-Willi syndrome and early-onset morbid obesity. J Pediatr 2006;149:192–198. 102 Leonard CM, Williams CA, Nicholls RD, Agee OF, Voeller KK, Honeyman JC, Staab EV: Angelman and Prader-Willi syndrome: a magnetic resonance imaging study of differences in cerebral structure. Am J Med Genet 1993;46:26–33. 103 Yamada K, Matsuzawa H, Uchiyama M, Kwee IL, Nakada T: Brain developmental abnormalities in Prader-Willi syndrome detected by diffusion tensor imaging. Pediatrics 2006;118:E442–E448. 104 Miller JL, Couch JA, Schmalfuss I, He G, Liu Y, Driscoll DJ: Intracranial abnormalities detected by three-dimensional magnetic resonance imaging in Prader-Willi syndrome. Am J Med Genet A 2007; 143:476–483. 105 Miller JL, Goldstone AP, Couch JA, Shuster J, He G, Driscoll DJ, Liu Y, Schmalfuss IM: Pituitary abnormalities in Prader-Willi syndrome and early-onset morbid obesity. Am J Med Genet 2007, Part A, in press. 106 Shapira NA, Lessig MC, He GA, James GA, Driscoll DJ, Liu Y: Satiety dysfunction in Prader-Willi syndrome demonstrated by fMRI. J Neurol Neurosurg Psych 2005;76:260–262. 107 Hinton EC, Holland AJ, Gellatly MS, Soni S, Patterson M, Ghatei MA, Owen AM: Neural representations of hunger and satiety in Prader-Willi syndrome. Int J Obes 2005;30:313–321. 108 Holsen LM, Zarcone JR, Brooks WM, Butler MG, Thompson TI, Ahluwalia JS, Nollen NL, Savage CR: Neural mechanisms underlying hyperphagia in Prader-Willi Syndrome. Obesity 2006;14: 1028–1037. 109 Miller JL, James GA, Goldstone AP, Couch JA, He G, Driscoll DJ, Liu Y: Enhanced activation of reward-mediating prefrontal regions in response to food stimuli in Prader-Willi syndrome. J Neurol Neurosurg Psychiatry 2007;78:615–619. 110 Maina EN, Webb T, Soni S, Whittington J, Boer H, Clarke D, Holland A: Analysis of candidate imprinted genes in PWS subjects with atypical genetics: a possible inactivating mutation in the SNURF/SNRPN minimal promoter. J Hum Genet 2007;52:297–307. 111 Oeffner F, Korn T, Roth H, Ziegler A, Hinney A, Goldschmidt H, Siegfried W, Hebebrand J, Grzeschik KH: Systematic screening for mutations in the human necdin gene (NDN): identification of two naturally occurring polymorphisms and association analysis in body weight regulation. Int J Obes Relat Metab Disord 2001;25:767–769.

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112 O’Neill MA, Farooqi IS, Wevrick R: Evaluation of Prader-Willi Syndrome gene MAGEL2 in severe childhood-onset obesity. Obes Res 2005;13: 1841–1842. 113 Meyre D, Lecoeur C, Delplanque J, Francke S, Vatin V, Durand E, Weill J, Dina C, Froguel P: A genomewide scan for childhood obesity-associated traits in French families shows significant linkage on chromosome 6q22.31-q23.2. Diabetes 2004;53:803–811. 114 Feitosa MF, Borecki IB, Rich SS, Arnett DK, Sholinsky P, Myers RH, Leppert M, Province MA: Quantitative-trait loci influencing body-mass index reside on chromosomes 7 and 13: the National Heart, Lung, and Blood Institute Family Heart Study. Am J Hum Genet 2002;70:72–82. 115 Varela MC, Simoes-Sato AY, Kim CA, Bertola DR, de Castro CI, Koiffmann CP: A new case of interstitial 6q16.2 deletion in a patient with Prader-Willilike phenotype and investigation of SIM1 gene deletion in 87 patients with syndromic obesity. Eur J Med Genet 2006;49:298–305. 116 Holder JLJ, Butte NF, Zinn AR: Profound obesity associated with a balanced translocation that disrupts the SIM1 gene. Hum Mol Genet 2000;9: 101–108. 117 Michaud JL, Boucher F, Melnyk A, Gauthier F, Goshu E, Levy E, Mitchell GA, Himms-Hagen J, Fan CM: Sim1 haploinsufficiency causes hyperphagia, obesity and reduction of the paraventricular nucleus of the hypothalamus. Hum Mol Genet 2001;10: 1465–1473. 118 Holder Jr JL, Zhang L, Kublaoui BM, DiLeone RJ, Oz OK, Bair CH, Lee YH, Zinn AR: Sim1 gene dosage modulates the homeostatic feeding response to increased dietary fat in mice. Am J Physiol Endocrinol Metab 2004;287:E105–E113. 119 Kublaoui BM, Holder JL Jr, Tolson KP, Gemelli T, Zinn AR: SIM1 overexpression partially rescues agouti yellow and diet-induced obesity by normalizing food intake. Endocrinol 2006;147:4542–4549. 120 Kublaoui BM, Holder JL Jr, Gemelli T, Zinn AR: Sim1 haploinsufficiency impairs melanocortin-mediated anorexia and activation of paraventricular nucleus neurons. Mol Endocrinol 2006;20: 2483–2492. 121 D’Angelo CS, Da Paz JA, Kim CA, Bertola DR, Castro CI, Varela MC, Koiffmann CP: Prader-Willilike phenotype: investigation of 1p36 deletion in 41 patients with delayed psychomotor development, hypotonia, obesity and/or hyperphagia, learning disabilities and behavioral problems. Eur J Med Genet 2006;49:451–460. 122 Phelan MC, Rogers RC, Clarkson KB, Bowyer FP, Levine MA, Estabrooks LL, Severson MC, Dobyns WB: Albright hereditary osteodystrophy and del(2) (q37.3) in four unrelated individuals. Am J Med Genet 1995;58:1–7.

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123 Wilson LC, Leverton K, Oude Luttikhuis ME, Oley CA, Flint J, Wolstenholme J, Duckett DP, Barrow MA, Leonard JV, Read AP: Brachydactyly and mental retardation: an Albright hereditary osteodystrophy-like syndrome localized to 2q37. Am J Hum Genet 1995;56:400–407. 124 Shrimpton AE, Braddock BR, Thomson LL, Stein CK, Hoo JJ: Molecular delineation of deletions on 2q37.3 in three cases with an Albright hereditary osteodystrophy-like phenotype. Clin Genet 2004; 66:537–544. 125 Polityko A, Maltseva O, Rumyantseva N, Khurs O, Seidel J, Claussen U, Weise A, Liehr T, Starke H: Two further AHO-like syndrome patients with deletion of glypican 1 gene region in 2q37.2-q37.3. Int J Mol Med 2004;14:977–979. 126 Cormier-Daire V, Molinari F, Rio M, Raoul O, De Blois MC, Romana S, Vekemans M, Munnich A, Colleaux L: Cryptic terminal deletion of chromosome 9q34: a novel cause of syndromic obesity in childhood? J Med Genet 2003;40:300–303. 127 Neas KR, Smith JM, Chia N, Huseyin S, St Heaps L, Peters G, Sholler G, Tzioumi D, Sillence DO, Mowat D: Three patients with terminal deletions within the subtelomeric region of chromosome 9q. Am J Med Genet A 2005;132:425–430. 128 Harada N, Visser R, Dawson A, Fukamachi M, Iwakoshi M, Okamoto N, Kishino T, Niikawa N, Matsumoto N: A 1-Mb critical region in six patients with 9q34.3 terminal deletion syndrome. J Hum Genet 2004;49:440–444. 129 Cotter PD, Kaffe S, McCurdy LD, Jhaveri M, Willner JP, Hirschhorn K: Paternal uniparental disomy for chromosome 14: a case report and review. Am J Med Genet 1997;70:74–79. 130 Kurosawa K, Sasaki H, Sato Y, Yamanaka M, Shimizu M, Ito Y, Okuyama T, Matsuo M, Imaizumi K, Kuroki Y, Nishimura G: Paternal UPD14 is responsible for a distinctive malformation complex. Am J Med Genet 2002;110:268–272. 131 Mitter D, Buiting K, von Eggeling F, Kuechler A, Liehr T, Mau-Holzmann UA, Prott EC, Wieczorek D, Gillessen-Kaesbach G: Is there a higher incidence of maternal uniparental disomy 14 [upd(14) mat]? Detection of 10 new patients by methylation-specific PCR. Am J Med Genet A 2006;140: 2039–2049. 132 Fryns JP, Haspeslagh M, Dereymaeker AM, Volcke P, van den BH: A peculiar subphenotype in the fra(X) syndrome: extreme obesity-short stature-stubby hands and feet-diffuse hyperpigmentation. Further evidence of disturbed hypothalamic function in the fra(X) syndrome? Clin Genet 1987;32: 388–392. 133 de Vries BB, Fryns JP, Butler MG, Canziani F, Wesby-van Swaay E, van Hemel JO, Oostra BA, Halley DJ, Niermeijer MF: Clinical and molecular studies in fragile X patients with a Prader-Willi-like phenotype. J Med Genet 1993;30:761–766.

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134 Borjeson M, Forssman H, Lehmann O: An X-linked, recessively inherited syndrome characterized by grave mental deficiency, epilepsy, and endocrine disorder. Acta Med Scand 1962;171: 13–21. 135 Birrell G, Lampe A, Richmond S, Bruce SN, Gecz J, Lower K, Wright M, Cheetham TD: BorjesonForssman-Lehmann syndrome and multiple pituitary hormone deficiency. J Pediatr Endocrinol Metab 2003;16:1295–1300. 136 Lower KM, Turner G, Kerr BA, Mathews KD, Shaw MA, Gedeon AK, Schelley S, Hoyme HE, White SM, Delatycki MB, Lampe AK, Clayton-Smith J, Stewart H, van Ravenswaay CM, de Vries BB, Cox B, Grompe M, Ross S, Thomas P, Mulley JC, Gecz J: Mutations in PHF6 are associated with BorjesonForssman-Lehmann syndrome. Nat Genet 2002;32: 661–665. 137 Gecz J, Turner G, Nelson J, Partington M: The Borjeson-Forssman-Lehman syndrome (BFLS, MIM #301900). Eur J Hum Genet 2006;14: 1233–1237. 138 Crawford J, Lower KM, Hennekam RC, Van Esch H, Megarbane A, Lynch SA, Turner G, Gecz J: Mutation screening in Borjeson-Forssman-Lehmann syndrome: identification of a novel de novo PHF6 mutation in a female patient. J Med Genet 2006;43: 238–243.

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Anthony P. Goldstone Senior Clinician Scientist and Consultant Endocrinologist MRC Clinical Sciences Centre, Imperial College London Hammersmith Hospital Campus Du Cane Road, London W12 0NN (UK) Tel.  44 20 8383 1029, Fax 44 20 8743 5409, E-Mail [email protected]

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Korbonits M (ed): Obesity and Metabolism. Front Horm Res. Basel, Karger, 2008, vol 36, pp 61–72

Fetal and Neonatal Pathways to Obesity Peter D. Gluckmana,c ⭈ Mark A. Hansond ⭈ Alan S. Beedlea ⭈ David Raubenheimera,b a

Centre for Human Evolution, Adaptation and Disease, Liggins Institute, bSchool of Biological Sciences, University of Auckland, and cNational Research Centre for Growth and Development, Auckland, New Zealand; dInstitute of Developmental Sciences, University of Southampton, Southampton, UK

Abstract Evolutionary and developmental perspectives add considerably to our understanding of the aetiology of obesity and its related disorders. One pathway to obesity represents the maladaptive consequences of an evolutionarily preserved mechanism by which the developing mammal monitors nutritional cues from its mother and adjusts its developmental trajectory accordingly. Prediction of a nutritionally sparse environment leads to a phenotype that promotes metabolic parsimony by favouring fat deposition, insulin resistance, sarcopenia and low energy expenditure. But this adaptive mechanism evolved to accommodate gradual changes in nutritional environment; rapid transition to a situation of high energy density results in a mismatch between predicted and actual environments and increased susceptibility to metabolic disease. This pathway may also explain why breast and bottle feeding confer different risks of obesity. We discuss how early environmental signals act through epigenetic mechanisms to alter metabolic partitioning, glucocorticoid action and neuroendocrine control of appetite. A second pathway involves alterations in fetal insulin levels, as seen in gestational diabetes, leading to increased prenatal fat mass which is subsequently amplified by postnatal factors. Both classes of pathway may coexist in an individual. This developmental approach to obesity suggests that potential interventions will vary according to the target population. Copyright © 2008 S. Karger AG, Basel

A large body of research has focused on the environmental, physiological and biochemical mechanisms that underlie the susceptibility of humans to obesity [1]. At the most general level, obesity results from an imbalanced energy budget, where energy intake chronically exceeds energy expenditure. This may result from poor nutritional environment (imbalanced diet), weak or deranged satiety signals (overeating), efficient energy storage or reduced energy output (disinclination to exercise or reduced adaptive thermogenesis [2]).

Over recent years, there has been a growing trend towards considering obesity and related medical problems in the context of human evolutionary ecology. In an early application of this approach – the ‘thrifty genotype hypothesis’ – Neel [3] proposed that regional differences in the propensity of human populations to store ingested energy are a consequence of evolutionary adaptation to different ancestral environments. In this hypothesis, obesity results from a gene-environment interaction, such that in hypocaloric ancestral environments humans accumulated ‘thrifty’ alleles of genes associated with energy partitioning and in modern hypercaloric environments these alleles cause excess energy storage. Although individual variation in obesity does have a strong genetic component [4], several lines of evidence now suggest that the thrifty genotype hypothesis cannot account for the temporal and spatial variation in the incidence of human obesity [5, 6]. Current information thus suggests that there is a substantial developmental component to obesity, such that humans are genetically predisposed to develop susceptible phenotypes in response to some developmental circumstances but not others. Those with a developmentally determined susceptible phenotype will develop obesity if exposed to hypercaloric environments, whereas those with a non-susceptible phenotype will not become obese in the same environments. Such explanations do not diminish the explanatory role for evolution, but refine it to consider also those aspects of human development that favour energy accumulation and disfavour energy expenditure. Two major developmental pathways can lead to obesity. First is an adaptive pathway by which signals in early life act through the processes of developmental plasticity to increase the adipogenic potential of exposure of the organism to a high postnatal nutritional load. We term this the ‘mismatch pathway’, and it may have multiple forms. Second is a pathway that is likely to be pathogenic in its origin, namely the effects of fetal hyperinsulinaemia leading to increased fetal fat mass which is then amplified after birth. This is most typically seen in infants of diabetic mothers. It is less clear whether maternal adiposity without diabetes induces a similar biology. These pathways may coexist in individuals and across generations. In this chapter, we primarily focus on the mismatch pathway and explore the concept that obesity and metabolic syndrome are the maladaptive consequences of an evolutionarily preserved mechanism by which environmental influences early in development set the life course of the organism. We will focus on how such influences can programme contributors to obesity such as altered energy partitioning and altered neuroendocrine control of appetite. We will consider environmental signals arising from the fetal environment (both undernutrition and overnutrition) and from the neonatal environment.

Patterns of Fat Deposition in Humans

Although humans are the fattest mammals at birth, averaging about 15% fat [7], there is substantial variability that is related to intrauterine nutrition. Importantly, the

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variability appears to be in subcutaneous fat, while visceral fat, which represents a separate metabolic compartment characterised by portal drainage and heightened susceptibility to lipolysis, is preserved in growth-restricted infants [8]. Fat deposition continues during the first 6 months of life, with maximum adiposity at about the age of weaning. Post-weaning growth is characterised by a relative increase in lean body mass and minimum adiposity is attained in late childhood; fat deposition then increases (the ‘adiposity rebound’) prior to puberty [7]. Adolescence in both sexes is associated with a further relative increase in lean mass, but sexual dimorphism in body composition is accentuated during pubertal development. Infant, childhood and pre-pubertal fatness are correlated, and adolescent body mass index predicts that in the adult, but the links between childhood and adult adiposity are much weaker [9]. Although the thrifty genotype hypothesis postulated that the human ability to deposit fat as an energy buffer arose as a result of feast and famine cycles during long periods as hunter-gatherers, recent evidence suggests that the hunter-gatherer nutritional supply was relatively assured [10] and that major selective pressure arising from such cycles may only have arisen relatively recently during the seasonal fluctuations associated with agriculture [6]. Indeed, the opposing cycles of energy expenditure and energy supply associated with modern subsistence agriculture result in marked fluctuations of body mass [11]. Explanations for the extreme early adiposity of human infants invoke protection of the energy supply of the large human brain from nutritional stress caused by postweaning infections [7]. Adiposity declines towards mid-childhood, at a time when the relative demand of brain metabolism has decreased, again arguing that its function is not to act as a buffer against nutritional uncertainty.

Effects of Adverse Fetal Environment

There is much epidemiological support for the ‘developmental origins of disease’ hypothesis linking an adverse fetal environment, often but not necessarily manifested as low birth weight, with later risk of metabolic and cardiovascular disease [12]. Within that evidence are specific observations linking impaired fetal nutrition with obesity [13, 14] and insulin resistance [14, 15] in later life, and with relative central adiposity at birth [8] and in late adulthood [16]. Hales and Barker [17] proposed that the link between intrauterine insult and later pathology could be explained by the fetus adopting a ‘thrifty phenotype’, making immediate adaptations (such as muscle insulin resistance) that result in more efficient energy utilisation and ensure survival in utero and the postnatal period but that have deleterious consequences later in life. However, several observations do not fit easily within this hypothesis. First, there is a continuous variation of disease risk with birth weight even within the normal range [18], implying that the processes involved represent some aspect of normative physiology

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rather than survival responses triggered by extremes of intrauterine environment. Second, alterations in developmental trajectory induced by prenatal cues are not necessarily deleterious: children born after in vitro fertilisation show reduced adiposity and heightened insulin sensitivity compared with their normally conceived siblings, implying that developmental responses need not be ‘thrifty’ [19]. Third, growthrestricted infants fail to demonstrate insulin resistance at birth, with impaired insulin sensitivity only appearing later in life [20, 21], an observation that is difficult to reconcile with the mechanistic argument. Numerous animal models have shown that manipulation of fetal nutrition, for example by maternal global undernutrition or protein restriction, can lead to outcomes that mirror those observed in epidemiological studies and are typical of those seen in human obesity [22, 23]. Such outcomes include central and peripheral components such as insulin resistance, obesity, sarcopenia, hyperphagia, altered food preferences and reduced locomotor activity [24–26]. Such manipulations in animals of uniform genetic background provide little support for a primarily genetic origin of later metabolic disease. The ‘thrifty phenotype’ model is adaptive in that it implies survival advantage for the induced trajectory. We have recently extended this model to overcome the limitations suggested above by incorporating developmental plasticity, a wellestablished concept in comparative biology. Plasticity implies that the developmental trajectory is sensitive to the environment and that a given genotype can produce a number of alternative phenotypes in different environments [27]. We note the numerous examples in animal physiology where developmental choices in response to environmental cues have advantages for the individual’s survival or fitness not immediately, as would follow from the ‘thrifty phenotype’ model, but at some later time [28]. We have termed such processes ‘predictive adaptive responses’ [29]. Our model suggests that the mammalian fetus is able to ‘predict’ its future environment from nutritional cues provided by the mother and adjusts its developmental trajectory accordingly to best match its physiology to that environment. For example, if it predicts that the environment will be nutritionally sparse, then the adult phenotype will be adjusted in the direction of metabolic parsimony by, for example, favouring fat deposition, insulin resistance, sarcopenia and low energy expenditure [30]. This predictive model can be enhanced in a number of ways. The cues that the developing mammal receives from the mother may reflect not only nutritional conditions during the pregnancy but also those at conception and during lactation. Maternal nutritional status at conception integrates her own life history and nutritional status in the years prior to conception, and possibly those of her own mother [31, 32], whereas responsiveness to cues during lactation will extend the period of plasticity beyond the uterus [33]. The prediction may affect not only the metabolic phenotype but can also extend to other life history traits such as reproduction [34]. Moreover, the fidelity of the predictive cue need not necessarily be high as long as the

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costs of a wrong prediction are relatively low [35]. In an evolutionary environment of nutritional uncertainty, with a restricted lifespan in which age-related metabolic disease would have been rare, a ‘fail-safe’ prediction of a sparse environment would not have been disadvantageous. But in a modern environment of high energy density and low energy expenditure, with a lifespan extended by improved public health, a mismatch between predicted and actual nutritional environments will result in metabolic disease. This effect will be much more apparent in societies undergoing rapid nutritional transition, because the large disparity between the predicted (customary) nutritional environment and the realised (hypercaloric) environment would impact on a single generation. Even in a historically well-nourished society, such disparity may arise from maternal constraint [36]. Mammalian fetal size is constrained in all pregnancies by two factors: first, by the operation of parent-offspring conflict [37], through which the fetus seeks to maximise and the mother to limit maternal investment in any one pregnancy cycle, and secondly by the need for birth through the pelvic canal. A large head size and the anatomical constraints placed on the pelvic canal by bipedality make the latter factor particularly important in humans [38]. Constraint may act through imprinted genes regulating processes associated with fetal size, such as growth factors or placental nutrient transporters [39], or more directly by limitations on uterine size or blood flow; these latter factors are strongest in primiparous or adolescent pregnancies and in those of smaller mothers [36]. But the fetus will interpret reduced nutrient supply arising from limitation of placental transport or perfusion as a signal of a sparse environment and alter its developmental trajectory towards metabolic parsimony, again resulting in a mismatch between the predicted and actual nutritional environments. Other spurious signals can arise from processes such as maternal smoking that restrict nutrient transfer across the placenta [40], thus ‘misinforming’ the fetus that it will be born into a nutritionally lean environment. The consequences of constraint could be tolerated within the range of environments experienced through our evolutionary history. But the recent shift in nutritional exposure may be entirely novel in evolutionary terms; that is, we are exposed to energy load well beyond our evolutionary experience. Neel [3] posited that such evolutionary novelty would generate disease risk because of the repertoire of genes we have been selected for; maternal constraint implies a limit on the environment that can be predicted and creates an epigenetic rather than genetic explanation of the consequences of encountering this environmental novelty [30]. Although we are here emphasising the importance of developmental processes in the aetiology of chronic non-communicable disease, it is clear that the correlation between environment and phenotype can be modified by genetic factors. For example, polymorphisms in the peroxisome proliferator-activated receptor-␥2 gene interact with the prenatal environment, as reflected by birth weight, to influence later risk of insulin resistance and type 2 diabetes [41].

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Effects of Fetal Overnutrition

Infants of diabetic mothers are born relatively obese as a result of maternal hyperglycaemia, which causes fetal hyperglycaemia and in turn fetal hyperinsulinaemia which then promotes fat deposition in the fetus and macrosomia at birth. This overweight tracks through into childhood and is exacerbated by maternal obesity [42]. The resulting metabolic derangements persist into adulthood and are transmitted to the next generation via disturbance of the intrauterine environment. There is experimental and epidemiological evidence for such transgenerational effects [43–45]. The mismatch pathway may well lead to offspring who, because of the inherent insulin resistance of pregnancy, are predisposed to develop gestational diabetes, and in turn their own offspring may have obesity because of the hyperinsulinaemia pathway [46]. The two pathways can coexist in such circumstances, and this appears to be frequent in the Indian subcontinent where small maternal size drives maternal constraint but with better gestational nutrition and rising rates of gestational diabetes. The consequence is that maternal diabetes is associated with functional fetal macrosomia at birth weights considered normal in Caucasian populations [47, 48].

Effect of Infant Nutrition

Epidemiological evidence suggests that both excessive weight gain early in life in infants of normal birth weight [49–51] and accelerated weight gain (so-called ‘catchup’ or ‘compensatory’ growth) in infants of low birth weight [52] may predispose to later obesity. Such accelerated weight gain represents an increment in central adiposity rather than musculoskeletal growth [53]; the rise in central adiposity continues in early childhood even after normalisation of weight-for-age in late infancy and is associated with the development of insulin resistance [21]. The apparent protective effect of breastfeeding against obesity in later life [54] may reflect the lower energy density of human breast milk compared with formulations based on cows’ milk. These phenomena may represent additional forms of the mismatch pathway – in which the postnatal environment is inappropriate for the environment determined by the level of maternal constraint and where the level of nutritional exposure is in excess of that predicted in utero.

Mechanistic Considerations

Basic Mechanisms There is considerable evidence that the mechanisms of developmental plasticity are underpinned by developmental epigenetic processes, as reviewed in depth elsewhere [55, 56]. These are the processes by which access of transcription factors to DNA is altered by

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chemical modification of DNA or histones in precisely controlled processes such as methylation of CpG islands. Experimentally, the mismatch pathway has been shown to be associated with a number of specific epigenetic changes [57, 58]. As the mismatch pathway is adaptive in origin, although maladaptive in consequences, it involves multiple systems [30]. In contrast, it seems likely that the hyperinsulinaemia pathway operates through the pleiotropic effect of insulin to drive adipose tissue development [59].

Metabolic Partitioning The correlation between an adverse fetal environment and the subsequent development of insulin resistance is well established [60], and hyperinsulinaemia subsequent to insulin resistance will promote fat deposition. In turn, the marked susceptibility of central adipose tissue to lipolysis will result in release of free fatty acids, which increases insulin resistance [61]. A parallel study in low birth weight humans and growth-restricted rats showed reduced muscle expression of specific insulin-signalling proteins, including protein kinase C-␨ and the glucose transporter GLUT4, in affected individuals [62], providing a potential mechanism for the insulin resistance seen in this setting. Dulloo [53] has proposed that preferential deposition of ‘catch-up fat’ is an evolved mechanism for rebuilding adipose stores after nutritional restriction and that such redistribution of glucose from skeletal muscle to adipose tissue is mediated by suppression of muscle thermogenesis. Experimentally, prevention of early catch-up growth reversed the development of glucose intolerance and obesity in a mouse model of diabetes caused by maternal undernutrition [63].

Glucocorticoids Resetting of the hypothalamic-pituitary-adrenal axis has been proposed to underlie many of the phenomena caused by an adverse fetal environment [64], and indeed alterations in basal and stimulated cortisol secretion can be correlated with birth weight in humans [65]. Although adipose tissue is responsive to glucocorticoids and excessive glucocorticoid exposure causes central obesity, dyslipidaemia and insulin resistance, circulating glucocorticoid levels are often not significantly raised in obesity [66]. However, tissue exposure to glucocorticoids is controlled not only by circulating hormone levels but also by tissue levels of the glucocorticoid receptor and tissue activity of the two enzymes that interconvert active and inactive glucocorticoids, namely 11␤hydroxysteroid dehydrogenase (11␤HSD) types 1 and 2, respectively. Visceral adipose tissue has increased levels of 11␤HSD1, suggesting that local glucocorticoid action may be amplified in this tissue, and experimental manipulation of levels of 11␤HSD1 affects propensity to diet-induced obesity [66]. These effects are mimicked by nutritional restriction during gestation, which increases levels of the glucocorticoid receptor and of 11␤HSD1 and decreases levels of 11␤HSD2 in the offspring [67], enhancing adipose tissue sensitivity to glucocorticoids in association with greater adiposity.

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Neuroendocrine Control of Appetite Numerous experimental studies have found that derangement of early nutrition, either prenatally or postnatally, can have effects on central components of appetite control, food preference and locomotor activity. In humans, leptin levels are higher in infants [68] and adults [69] born small, indicating leptin resistance, and there is evidence that infant overnutrition can result in increased leptin levels later in life [70], suggesting that early nutritional experience can affect leptin synthesis or secretion by adipocytes. The adipoinsular axis of insulin and leptin secretion links body fat mass to the function of pancreatic ␤-cells [71], but is disturbed in animals subjected to maternal undernutrition [72] which develop hyperphagia, leptin resistance and reduced locomotor activity, especially if subjected to high-fat feeding after weaning [24, 25]. Exposure in utero to a low-protein diet affects locomotor activity and feeding behaviour in ageing rats [73]. At least in rodents, neural connections involved in regulation of appetite control are not completed until shortly before weaning, and both prenatal overnutrition of the maternal-fetal unit, as imposed by gestational diabetes [74] or maternal overfeeding [75], and postnatal overfeeding, achieved by reducing litter number [76], alter the hypothalamic neuronal circuitry and levels of various appetite-regulating neuropeptides. These effects of the early nutritional environment on central aspects of appetite control may underpin some of the associations reported in humans between early nutrition and later obesity [49, 54].

An Evolutionary Coda

Environmental change, resulting from natural events or migration, was a key driver of human evolution, and there is paleoclimatic evidence that such change would have been felt across several generations rather than over a short time-frame [77]. In such an environment, ‘adaptive versatility’ [77] or ‘forecasting’ [78] using well-established mechanisms of developmental plasticity would have been valuable in allowing more rapid adaptation to changing nutritional conditions than possible by genetic means alone, and integration of the cue across several generations [31] would tend to increase the fidelity of the prediction. An adaptive mechanism to counter a predicted sparse environment would be likely to have effects not only on metabolism, for example a tendency to deposit fat and reduce energy expenditure, but also on other aspects of the individual’s life history, such as reproductive strategy and longevity, and such effects can be observed [30]. But if the prediction is faulty and the individual cannot adjust, as would occur if there is a major shift in the environment between early childhood and adulthood, then the mismatch between phenotype and environment will lead to disease. Such effects are seen most clearly when individuals move rapidly between environments [14, 79] or when their society undergoes rapid nutritional transition [80]. If the later environment does match the prediction, even at a low plane of adult nutrition [81], then the risk of disease is less. But a continued high

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plane of energy-dense nutrition coupled with low energy expenditure, a situation which would have been encountered only rarely if at all during human evolution, will also have deleterious effects via the cycle of maternal hyperglycaemia and increased fetal adiposity. In contrast, gestational diabetes is likely to be a novel phenomenon over evolutionary time; its emergence is dependent on better nutrition and better medical care, and does not fit an adaptationist model. Thus, both adaptive and nonadaptive pathways to developmental obesity coexist in modern populations. In summary, a developmental and evolutionary perspective explains several aspects of the human obesity epidemic. We propose that adaptive mechanisms that have evolved to accommodate gradual changes in nutritional environment are unable to adjust to a rapid transition to a situation of high energy density. This knowledge provides us with another way of thinking about individual and population health interventions to prevent obesity, particularly in the developing world where optimisation of maternal health before and during pregnancy may help to reduce the mismatch between intrauterine and adult environments. In the developed world, interventions to reduce energy intake and increase energy expenditure remain paramount, and again a focus on maternal nutrition during pregnancy may be of value to reduce intergenerational transmission of metabolic imbalance. Finally, the demonstration of reversibility of intrauterine metabolic programming in an animal model by neonatal leptin treatment [82] suggests the possibility of pharmacological or nutritional interventions during the plastic phase of early life.

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Dr. A.S. Beedle Centre for Human Evolution, Adaptation and Disease Liggins Institute, University of Auckland Private Bag 92019 Auckland (New Zealand) Tel. ⫹64 9 373 7599 (ext. 84416), Fax ⫹64 9 373 7497, E-Mail [email protected]

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Korbonits M (ed): Obesity and Metabolism. Front Horm Res. Basel, Karger, 2008, vol 36, pp 73–84

Developmental Origins of Obesity and the Metabolic Syndrome: The Role of Maternal Obesity James Andrew Armitagea,b ⭈ Lucilla Postonc ⭈ Paul David Taylorc a

Department of Anatomy and Developmental Biology, Monash University, Clayton, and Division of Cardiovascular Neuroscience, Baker Heart Research Institute, Melbourne, Australia; cMaternal and Fetal Research Unit, Division of Reproductive Health, Endocrinology and Development, King’s College London, London, UK

b

Abstract Obesity and its sequelae may prove to be the greatest threat to human lifestyle and health in the developed world this century. The so called obesity epidemic has seen the incidence of obesity and overweight almost double in Western societies and the trend is mirrored in nations that are transitioning to first world economies. There is no doubt that much of the rise in obesity can be attributed to lifestyle factors such as the excess consumption of energy-dense foods and the decline in physical activity. However, the ‘fetal origins’ hypothesis, first proposed by Barker and colleagues and elaborated by several groups over the past 15 years to be termed the ‘Developmental Origins of Adult Health and Disease’ (DOHaD), provides an alternative explanation for the rising rates of obesity. The DOHaD hypothesis states that exposure to an unfavourable environment during development (either in utero or in the early postnatal period) programmes changes in fetal or neonatal development such that the individual is then at greater risk of developing adulthood disease. This chapter discusses the effects of maternal obesity on fetal development and birth outcomes as well as the manner in which DOHaD may contribute to the Copyright © 2008 S. Karger AG, Basel obesity epidemic.

The Obesity Epidemic

Health care systems around the globe are beginning to recognise the risk that obesity poses to human health and many programmes are now being put into place in an effort to reduce the burden of obesity and its related diseases. Current definitions of obesity are based on the ratio of bodyweight (in kg) and height squared (in m2) and expressed as body mass index (BMI) with a normal BMI defined as 20–24.9, moderate overweight between 25–29.9 and obesity as ⱖ30. In 2000, the World Health

Organisation released the following statement: ‘Obesity is a chronic disease, prevalent in both developed and developing countries, and affecting children as well as adults. Indeed it is now so common that it is replacing the more traditional public health concerns, including under-nutrition and infectious disease as one of the most significant contributors to ill health’ [1]. At the turn of the millennium and the time of publication of the WHO report, the incidence of obesity in the United States was 30.5% (compared with 22.9% in 1994) and 64.5% of the population were overweight (compared with 55.9% in 1994) [2]. More recent statistics suggest that the incidence of obesity and overweight is rising, not falling, in spite of the apparent efforts of governments and health care agencies. This shift in body mass has occurred over the past one to two generations and as such it is unlikely that genetic drift is the cause of the current obesity epidemic. Rather, a change in lifestyle, compounded by epigenetic or developmental programming of an obese phenotype are the likely causative factors. Obesity statistics from the United States are most often quoted, perhaps because they give the greatest impact; however, scientific studies conducted in other nations emphasise the fact that obesity is a worldwide problem. A study of cause of death in South Korea illustrates this fact. In 1938, cardiovascular disease accounted for approximately 1% of deaths in South Korea whilst infectious diseases were the cause of approximately 23% of deaths. By 1993, this trend had reversed; approximately 30% of deaths were attributable to cardiovascular disease whereas only 3% of deaths were caused by infection. Certainly such statistics are affected both by the vast improvements in anti-microbial medication and sanitation in that 60-year period; however, the fact remains that obesity-related illness is the next public health hurdle. Obesity may not, in itself, be a great risk to human health. Indeed, there are some individuals who are overweight or obese but do not show any other signs of disease or ill health. However, for the vast majority, increased body fat is associated with a range of other, more serious conditions. These include increased blood pressure, insulin resistance and diabetes mellitus, atherogenic plasma lipid profiles, and increased levels of vascular inflammatory markers. Collectively, this spectrum of conditions is termed the ‘metabolic syndrome’ and clinical diagnosis is based on the presence of 3 or more of the above signs. Endothelial dysfunction and leptin resistance are also likely to contribute to the metabolic syndrome [3]. The rise of obesity is certainly due to the increased availability of food, and the preponderance of energy dense (high fat and simple carbohydrate) foods that are regularly consumed in developing and developed societies. Moreover, the industrial era has produced all manner of labour saving devices that has ultimately seen a reduction in the physical activity quotient over time [4]. However, despite the obvious importance of food intake and energy expenditure during adulthood, there is now evidence that adult lifestyle may not be the only factor at play in determining obesity [5]. The environment encountered during the in utero and early postnatal periods may also act to ‘programme’ an individual to have a greater risk of developing obesity and the metabolic syndrome.

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The Developmental Origins of Adult Health and Disease

The developing fetus and neonate orchestrates its growth and development to best meet the environmental conditions encountered at any given period. Where environmental challenges or stimuli span a period of organogensis or developmental plasticity, the adaptations made may be permanent. Epidemiological data from Norway provide the first of many reports that that environment encountered in early life may affect later health outcomes with regard to cardiovascular disease [6]. The concept of ‘programming’ was introduced by Lucas [7] and provides a conceptual framework for the observations made by Barker and Osmond [8] with regard to an individual’s birth weight and the later risk of disease in United Kingdom cohort studies. These early Barker studies focused on the relationship between the weight at birth, and subsequently fetal nutrition, with death from coronary heart disease [9, 10]. The direct relationship between maternal nutrition and later offspring obesity was revealed in a study of conditions encountered by a discrete population during the Dutch Hunger Winter of 1944–1945. During the World War II military operations by Allied forces to liberate The Netherlands, the occupying Nazi forces blockaded areas of Holland over the winter of 1944–1945 and official rations were cut to 300–500 kcal per day. Later study of adults (at 50 years of age) who were in utero during this defined period of famine indicate that exposure to famine during the first half of pregnancy predisposed individuals to the development of obesity [11] and coronary heart disease [12] and that famine exposure later in gestation resulted in glucose intolerance and insulin resistance [13]. This direct evidence for the role of maternal nutrition in the programming of adulthood obesity and disease in the offspring was followed by studies from UK cohorts with several studies showing an inverse association between birth weight and BMI in adulthood [14] as well as insulin resistance. BMI is used as an indicator of obesity because of the simplicity by which it can be measured in large trials or retrieved retrospectively from records. However, use of the BMI parameter as an indicator of obesity has been challenged, and there are suggestions that it is a measure of heaviness rather than obesity per se. Consistent with this, studies of monozygotic twin pairs [15] found that lower birth weight was associated with increased waist–hip ratio, skin fold thickness and reduced muscle mass, but not necessarily BMI. Nonetheless, body fat measurement by dual energy X-ray absorptiometry in adult men born of low birth weight shows these individuals to have a 5% increase in fat mass compared with subjects born of normal birth weight [16]. These studies formed the basis for various experimental animal models of maternal undernutrition in which to study the Developmental Origins of Adult Health and Disease (DOHaD) hypothesis and these models support the hypothesis that fetal or neonatal undernutrition results in aberrant development of the endocrine pancreas, liver, kidney and cardiovascular systems, such that the offspring born from protein or

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calorie deprived dams demonstrate many facets of the metabolic syndrome in adulthood [reviewed by 3, 17]. In the face of an obesity epidemic, it may be more useful to examine the maternal factors that result in the developmental programming of obesity [18]. Although maternal undernutrition is not manifest in many of the societies currently experiencing the increase in obesity rates, one factor that may result in offspring programming is maternal diabetes. Indeed, insulin resistance and alterations in the structure and function of the endocrine pancreas is seen in offspring born to calorie or proteindeprived mothers [reviewed by 3]. This is observed in humans, as evidenced by the finding of insulin resistance in those that were exposed to the effects of the Dutch Hunger Winter in the second half of gestation [13], as well as in experiential animal models. Provision of a low protein diet (50% reduction in protein content but isocaloric) to rats during pregnancy resulted in a reduction in pancreatic insulin content, reduced islet size and vascularisation and a reduction in cell proliferation in term rat fetuses [19]. These animals develop frank insulin resistance later in life [19]. Uteroplacental deficiency, induced by ligation of the uterine artery, produced a similar phenotype of insulin resistance in adult offspring [20]. Consistent with the developmental programming hypothesis, maternal diabetes appears to programme a similar offspring phenotype of insulin resistance and type 2 diabetes mellitus. This is observed in both human populations and in experimental animal studies. One of the earliest, but most striking, studies of programming effects of maternal diabetes was carried out in Pima Indian women. After accounting for confounds such as paternal diabetes, age of diabetes onset in the parents and offspring BMI, Pettitt et al. [21] observed that in this discrete population 45% of offspring born to diabetic mothers went on to develop type 2 diabetes mellitus themselves by the age of 24. Only 1.4% of those born to non-diabetic mothers showed signs of type 2 diabetes mellitus themselves at the same age. The authors conclude that those findings suggested that the intrauterine milieu was an important determinant in the development of diabetes in the offspring, and that the effects were additive with any genetic factors [21]. Macrosomia is often observed in offspring of diabetic mothers, and there is supportive evidence for a ‘U-shaped’ relationship between birth weight and the development of adult obesity [22]. Thus high birth weight may prove to be just as deleterious to later health and well-being as low birth weight. Data from a Swedish birth cohort suggest that within the past decade there has been a 25% increase in the incidence of large for gestational age babies and regression analysis suggests that this increase is attributable to a 25–36% increase in maternal BMI [23]. The association between maternal obesity and offspring macrosomia may be partially influenced by genetic factors; however, a recent study of 150,000 women suggests that weight gain between successive pregnancies is associated with macrosomia in the second pregnancy [24]. Given the same maternal genetic transmission, this study suggests that, at least in part, large for gestational age birth weight is attributable to maternal obesity. Given the link between maternal obesity and maternal diabetes, a range of animal models of diabetes in pregnancy have been developed.

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Experimental animal models of diabetes induced by streptozotocin (STZ, which results in pancreatic ␤-cell death) have been used to dissect the contribution of intrauterine environment from genetic factors. Aerts et al. [25] studied offspring of STZ diabetic rats and found increased insulin secretion, plasma insulin concentration and decreased renal insulin uptake in these rats when compared with controls [25]. A subsequent study by Holemans et al. [26] utilised euglycaemic hyperinsulinaemic clamp in the adult offspring of STZ diabetic rat dams to assess the developmental programming of insulin resistance. This insulin resistance was characterised by a reduction in insulin sensitivity in peripheral tissue and a reduction in hepatic insulin sensitivity and responsivity [26]. Offspring of rats made mildly diabetic by injection of STZ are born macrosomic, and develop insulin resistance and diabetes later in life [27]. Moreover when those first-generation females (that are diabetic as a result of maternal diabetes) are mated, they produce offspring that are also diabetic [27]. Thus evidence from human and experimental animal models supports the hypothesis that fetal undernutrition may result in programming of adulthood obesity and metabolic syndrome. Fetal undernutrition is, in general, not manifest given the current dietary status of many women of child-bearing age in westernised societies. In an era where many women become pregnant whilst obese or overweight, consideration of the effects of maternal obesity on pregnancy outcome and developmental programming of offspring health are of great relevance and maternal diabetes may prove to be a condition of great importance when considering the manner in which the developmental programming of obesity may occur.

Pregnancy Outcomes Associated with Maternal Obesity and Gestational Diabetes

Given the overall rates of obesity within the general population of many societies, it is not surprising that rates of maternal obesity are rising. A retrospective analysis of 287,213 pregnancies in the United Kingdom reports that 27.5% of pregnant women in the cohort were moderately obese (BMI 25–29.5) and a further 10.9% were very obese (BMI ⱖ30) [28]. This trend is also seen in the US population, where recent studies estimate that between 18 and 35% of pregnant women are obese [29]. Maternal obesity is associated with a range of adverse effects that directly affect maternal health and pregnancy outcome. These include, pre-eclampsia, post-partum haemorrhage, hypertensive disorders and complications of delivery [30]. The financial cost of obesity is also an important consideration. As health systems have finite, and often, constrained budgets with which to provide care, any increase in the burden of cost will further limit the abilities of health care systems to cope with the obesity epidemic. In the year 2000, a French study estimated that complications arising from obesity in pregnancy (pre-gravid BMI ⬎30) resulted in a 5.4- to 16.2-fold increase in

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the financial cost of prenatal care as well as a 4.4-day increase in the postnatal care period than that required for women with a BMI of 18–25 [30]. In the context of developmental programming of adult disease, gestational diabetes mellitus (GDM) may prove to be of long-lasting detriment to the fetus. GDM manifests in approximately 8.8% of pregnancies in the developed world and the risk of GDM is directly proportional to maternal BMI. This risk of developing GDM is reported to be 2.9-fold (95% CI 2.2–3.9) for women with a BMI ⱖ30 compared with women with a BMI ⱕ20 [31], but may be as high as 20-fold [30]. In addition to the risk of obese individuals developing GDM, it is also established that being overweight predisposes the development of type 2 diabetes, characterised by whole body insulin resistance and higher plasma insulin concentrations. A study of New Zealand women, by Cundy et al. [32] observed the rate of fetal or neonatal death in offspring of women with type 2 diabetes or GDM to be greater than that seen in non-diabetic controls. This was mainly due to late fetal death. The authors highlight a strong relationship, observed in their study as well as others, between the perinatal mortality rate and maternal obesity in pregnancy women with type 2 diabetes [32]. This relationship between obesity and complications of labour is also observed in a recent study of Jewish and Beduin populations in Southern Israel [33]. Major complications in obese women (pre-gravid BMI ⱖ30, compared with controls pre-gravid BMI ⱕ30) were labour induction (OR 2.3, 95% CI 2.1–2.6), failure to progress to 1st stage of labour (OR 4.0, 95% CI 3.2–4.9), meconium-stained amniotic fluid (OR 1.9, 95% CI 1.2–1.6) and malpresentation (breech or shoulder dystocia) of the fetus (OR 1.6, 95% CI 1.3–1.9). The overall odds ratio for caesarean delivery in that population was 3.2 (95% CI 2.9–3.5) [33]. In addition to the acute complications of delivery, maternal obesity and diabetes mellitus (either GDM or existing type 2 diabetes) are associated with long-term risks to fetal and neonatal health. A Spanish study by Garcia-Patterson et al. [34], of 2,060 infants born to mothers with GDM assessed the incidence of serious (life-limiting: requiring surgery, or resulting in significant functional impairment) congenital abnormalities involving the heart, renal/urinary or skeletal systems. By multiple logistic regression analysis, these authors observed that pre-gravid obesity and the severity of GDM (also related to BMI) predicted the increase in congenital malformations observed in the offspring [34]. Neural tube defects are also more common in offspring of obese mothers (OR 1.9, 95% CI 1.1–3.4 for a maternal BMI ⬎29) [35]. Notwithstanding the devastating impact of such congenital abnormalities, these are present in only 4% of births [34]. The programming of obesity and metabolic syndrome in the offspring of such pregnancies has the potential to exact an even greater burden on future generations.

Programming Vectors – Factors That May Result in DOHaD

The factors present in the maternal or fetoplacental milieu that may instigate alterations in organogenesis or induce morphometric changes in the fetus and thus result

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in developmental programming of obesity or other diseases may be termed ‘programming vectors’. Despite the strong association between maternal obesity, GDM and subsequent offspring developmental programming of obesity and its related sequellae, there is an incomplete understanding as to which vectors present in the maternal milieu may be most deleterious to fetal growth and development. A comprehensive assessment of the maternal milieu in a small cohort of otherwise healthy obese (n ⫽ 23, median BMI ⫽ 31) and lean (n ⫽ 24, median BMI ⫽ 22.1) pregnant women in the UK found that maternal obesity was associated with statistically significant elevation of plasma concentrations of triglycerides, very-low density lipoprotein cholesterols, insulin, leptin and inflammatory markers (interleukin-6, Creactive protein) as well as decreased plasma high-density lipoprotein cholesterols. Additionally, the obese group demonstrated a statistically significant elevation in systolic blood pressure and a reduction in endothelium-dependent and -independent vasodilatation in the microvasculature [36]. This milieu, consistent with that of the metabolic syndrome, is also observed in experimental animal models of maternal obesity. Holemans et al. [37] fed rats a highly palatable cafeteria-style diet for 4 weeks to induce obesity and then mated these obese rats, maintaining the same cafeteriastyle diet regimen. These rats, when pregnant, were insulin resistant (measured by euglycaemic, hyperinsulinaemic clamp), and demonstrated elevated plasma leptin concentrations compared with control-fed pregnant rats [37]. Taylor and Poston [38] fed rats a lard-rich diet for 10 days prior to mating and during pregnancy. Interestingly, consumption of the lard-rich diet did not change plasma leptin or lipid concentrations in these animals; however, this may reflect the relatively short period of time that the rats were obese. Despite the short period of fat feeding, when compared with control-fed rats, fat-fed rats demonstrated significant increases in plasma insulin and corticosterone concentrations, and blunted endothelium-dependent vasodilatation in mesenteric but not uterine arteries [38]. Thus, the developing embryo and fetus are exposed to an altered maternal environment in the presence of maternal obesity. These alterations are marked, and despite the capacity that the placenta has to buffer these changes, it is quite likely that the intra-uterine environment is altered. Interestingly, thus far unidentified culture conditions used for embryo transfer and cloning also seem to programme the development of obesity in the offspring, further highlighting the importance of understanding what factors are most important for ideal fetal or embryonic development [reviewed in 39]. The mechanisms by which these programming vectors may drive changes in fetal development are not established unequivocally; however, an emerging hypothesis of alteration of DNA methylation status appears promising. The promoter regions of many genes contain long repeat sequences of cytosine and guanidine residues and these regions are prone to methylation. Simplistically stated, high levels of methylation in the promoter region decrease transcription of a gene [40]. Maternal diet and the early post-weaning diet have both been shown to alter the methylation status of

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several genes in the fetus and neonate, including the insulin-like growth factor [41]. Although the hypothesis is in its infancy, and there are no reports of aberrant gene methylation status in offspring of obese women, this remains an attractive candidate mechanism.

The Role of Maternal Obesity and Fat Intake in DOHaD

The previous sections of this chapter have focussed on the effect of maternal obesity and offspring exposure to obesity in the in utero period, and the following chapter considers in detail the determinants of childhood obesity. Nonetheless, when considering the developmental origins of obesity, it is important to consider both in utero and early neonatal environments as the hypothalamic appetite centres ultimately responsible for the maintenance and determination of body weight set-points mature in early life [5, 42]. This section will consider the role of maternal obesity and fat intake in both the in utero and postnatal suckling periods on the developmental programming of obesity. The current dietary intakes in many Western populations are high in saturated fatty acids, and clearly the intake of energy-dense and potentially pro-inflammatory fats (those that are prone to becoming oxidised low-density lipoprotein cholesterols) impacts on adult obesity. However, fat intake and obesity may also be of great importance in the developmental programming of disease. Despite the obvious reflection on the food intake in Western societies, there is still a paucity of experimental animal studies considering the role of maternal overnutrition in the developmental programming of adult disease. Early studies in baboons showed that overnutrition in the suckling period resulted in permanent increases in plasma cholesterol concentrations [43] and adipocyte size [44]. Rats exposed to a lard-rich diet during gestation and suckling (via maternal diet), then weaned onto a control diet, are obese, hypertensive, insulin resistant, dyslipidaemic, and demonstrate blunted endothelium-dependent vasodilatation [45–47]. Interestingly, the type of fatty acid predominating in the maternal diet impacts on the programming of offspring disease. High-fat diets that are also rich in ␻–3 polyunsaturated fatty acids have been shown to result in a largely normal offspring phenotype despite the increased total fat and caloric load of the diet [48, 49]. Moreover, neonatal rat pups suckled by dams fed fat-rich diets supplemented with ␻–3 and ␻–6 essential fatty acids show reduced adipose tissue weight and reduced plasma leptin concentrations [50]. Therefore, it appears that exposure to maternal diets that are rich in saturated fatty acids is deleterious to later offspring health, whereas even relatively high essential ␻–3 and ␻–6 fatty acid intake in the diet results in a normal offspring phenotype. There is also emerging evidence in humans that lowering total fat intake whilst increasing the proportion of ␻–3 and ␻–6 essential fatty acid may have beneficial effects on offspring and maternal health [51].

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Experiments utilising cross-fostering of pups between control and fat-fed dams suggest that exposure to the maternal fat-rich diet during either the in utero or suckling periods results in a metabolic syndrome-like phenotype [52]. Combined with other studies of postnatal overfeeding [53] there is clear evidence that the first 21 days of rodent life is a developmental critical period. Moreover, overfeeding during this period can override genetic predisposition; obesity-resistant rats cross-fostered to obese mothers during the suckling period develop diet-induced obesity later in life [54]. As previously discussed, maternal diabetes often accompanies obesity. Pups suckled by dams that are obese or diabetic demonstrate permanent alterations in the manner by which hypothalamic appetite circuits respond to the peripheral signals that normally instigate hunger and satiety. This is a bourgeoning area of research and warrants review in its own right [see reviews 5, 55]. However, briefly summarising a complex neural circuitry model, it appears that exposure to the obese or diabetic milieu during suckling programmes a selective neural leptin resistance at the level of the hypothalamic arcuate nucleus [56]. The mechanism for such resistance is seen in a reduction in synaptic integrity of neurons that normally inhibit appetite but not those that stimulate appetite [56]. The net result of such synaptic plasticity is that normal satiety signals are not attended and animals develop hyperphagia that persists through life. Thus, exposure to a saturated fat, diabetic or energy-rich environment in early life may programme alterations in the hypothalamic neural circuitry that is ultimately responsible for the establishment of growth trajectory and energy balance in young humans and animals. It is most likely that alterations in the plasma concentrations of peripheral appetite signals such as insulin, leptin and ghrelin are altered in the fetus or neonate carried or suckled by obese or diabetic mothers. The developing neural networks in the hypothalamus are then affected and because these changes occur during a critical period of development, the changes to appetite and energy expenditure circuits become permanent, thereby setting the scene for the subsequent development of obesity in the next generation.

Conclusions

There are a myriad of socioeconomic, lifestyle and dietary behavioural factors that have contributed and continue to add to the worldwide obesity epidemic. Complications of maternal obesity or type 2 diabetes are summarised in table 1. The developmental programming of adult disease appears to be one more factor that warrants serious consideration and one that should be tackled by public health initiatives. Because of the alarming rate of obesity in young children across many societies, obesity-related disease will not be eradicated in the near future; however, it is vital that not only is the population informed of the risks and consequences of obesity to maternal health, but also of the potential risks to the health of future generations.

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Table 1. Complications of maternal obesity or gestational diabetes

Pre-eclampsia Post-partum haemorrhage Hypertensive disorders Complications of delivery Labour induction Failure to progress to 1st stage of labour Meconium-stained amniotic fluid Malpresentation Financial cost Fetal or neonatal death Congenital malformations Heart Renal/urinary system Skeletal systems Neural tube defects Developmental programming of adult health and disease of the fetus Obesity Type 2 diabetes Hypertension Atherosclerosis Altered hypothalamic appetite circuitry

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27 Oh W, Gelardi NL, Cha CJ: The cross-generation effect of neonatal macrosomia in rat pups of streptozotocininduced diabetes. Pediatr Res 1991; 29:606–610. 28 Sebire NJ, Jolly M, Harris JP, Wadsworth J, Joffe M, Beard RW, Regan L, Robinson S: Maternal obesity and pregnancy outcome: a study of 287,213 pregnancies in London. Int J Obes Relat Metab Disord 2001; 25:1175–1182. 29 Ehrenberg HM, Dierker L, Milluzzi C, Mercer BM: Prevalence of maternal obesity in an urban center. Am J Obstet Gynecol 2002;187:1189–1193. 30 Galtier-Dereure F, Boegner C, Bringer J: Obesity and pregnancy: complications and cost. Am J Clin Nutr 2000;71:S1242–S1248. 31 Solomon CG, Willett WC, Carey VJ, Rich-Edwards J, Hunter DJ, Colditz GA, Stampfer MJ, Speizer FE, Spiegelman D, Manson JE: A prospective study of pregravid determinants of gestational diabetes mellitus. JAMA 1997;278:1078–1083. 32 Cundy T, Gamble G, Towend K, Henley P, MacPherson P, Roberts A: Perinatal mortality in type 2 diabetes mellitus. Diab Med 1999;17:33–39. 33 Sheiner E, Levy A, Menes T, Silverberg D, Katz M, Mazor M: Maternal obesity as an independent risk factor for caesarian delivery. Pediatr Perin Epidemiol 2004;18:196–201. 34 Garcia-Patterson A, Erdozan L, Ginovart G, Adelantado J, Cubero J, Gallo G, de Leiva A, Corcoy R: In human gestational diabetes mellitus congenital malformations are related to pre-pregnancy body mass index and to severity of diabetes. Diabetologia 2004;47:509–514. 35 Watkins M, Scanlon K, Mulinare J, Khoury S: Is maternal obesity a risk factor for anencephaly and spina bifida? Epidemiology 1996;7:507–512. 36 Ramsay JE, Ferrell WR, Crawford L, Wallace AM, Greer IA, Sattar N: Maternal obesity is associated with dysregulation of metabolic, vascular, and inflammatory pathways. J Clin Endocrinol Metab 2002;87:4231–4237. 37 Holemans K, Caluwaerts S, Poston L, Van Assche FA: Diet-induced obesity in the rat: a model for gestational diabetes mellitus. Am J Obstet Gynecol 2004;190:858–865. 38 Taylor PD, Khan IY, Lakasing L, Dekou V, O’BrienCoker I, Mallet AI, Hanson MA, Poston L: Uterine artery function in pregnant rats fed a diet supplemented with animal lard. Exp Physiol 2003;88: 389–398. 39 Taylor P, Poston L: Developmental programming of obesity. Exp Physiol 2006, in press, DOI: 2005/ 032854. 40 Wolff GL, Kodell RL, Moore SR, Cooney CA: Maternal epigenetics and methyl supplements affect agouti gene expression in Avy/a mice. FASEB J 1998; 12:949–957.

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41 Fowden AL, Sibley C, Reik W, Constancia M: Imprinted genes, placental development and fetal growth. Horm Res 2006;65(suppl 3):50–58. 42 Elmquist JK, Flier JS: Neuroscience. The fat-brain axis enters a new dimension. Science 2004;304:63–64. 43 Mott GE, Jackson EM, McMahan CA, McGill HC Jr: Cholesterol metabolism in adult baboons is influenced by infant diet. J Nutr 1990;120:243–251. 44 Lewis DS, Bertrand HA, McMahan CA, McGill HC Jr, Carey KD, Masoro EJ: Preweaning food intake influences the adiposity of young adult baboons. J Clin Invest 1986;78:899–905. 45 Khan IY, Taylor PD, Dekou V, Seed PT, Lakasing L, Graham D, Dominiczak AF, Hanson MA, Poston L: Gender-linked hypertension in offspring of lard-fed pregnant rats. Hypertension 2003;41:168–175. 46 Taylor PD, McConnell J, Khan IY, Holemans K, Lawrence KM, Asare-Anane H, Persaud SJ, Jones PM, Petrie L, Hanson MA, Poston L: Impaired glucose homeostasis and mitochondrial abnormalities in offspring of rats fed a fat-rich diet in pregnancy. Am J Physiol Regul Integr Comp Physiol 2005;288: R134–R139. 47 Armitage JA, Lakasing L, Taylor PD, Balachandran AA, Jensen RI, Dekou V, Ashton N, Nyengaard JR, Poston L: Developmental programming of aortic and renal structure in offspring of rats fed fat-rich diets in pregnancy. J Physiol 2005;565:171–184. 48 Weisinger HS, Armitage JA, Sinclair AJ, Vingrys AJ, Burns PL, Weisinger RS: Perinatal omega-3 fatty acid deficiency affects blood pressure later in life. Nat Med 2001;7:258–259. 49 Siemelink M, Verhoef A, Dormans JA, Span PN, Piersma AH: Dietary fatty acid composition during pregnancy and lactation in the rat programs growth and glucose metabolism in the offspring. Diabetologia 2002;45:1397–1403.

50 Korotkova M, Gabrielsson BG, Holmang A, Larsson BM, Hanson LA, Strandvik B: Gender-related longterm effects in adult rats by perinatal dietary ratio of n-6/n-3 fatty acids. Am J Physiol Regul Integr Comp Physiol 2005;288:R575–R579. 51 Hachey D: Benefits and risks od modifying maternal fat intake in pregnancy and lactation. Am J Clin Nutr 2006;59:S454–S464. 52 Khan IY, Dekou V, Douglas G, Jensen R, Hanson MA, Poston L, Taylor PD: A high-fat diet during rat pregnancy or suckling induces cardiovascular dysfunction in adult offspring. Am J Physiol Regul Integr Comp Physiol 2005;288:R127–R133. 53 Plagemann A, Harder T, Rake A, Voits M, Fink H, Rohde W, Dorner G: Perinatal elevation of hypothalamic insulin, acquired malformation of hypothalamic galaninergic neurons, and syndrome x-like alterations in adulthood of neonatally overfed rats. Brain Res 1999;836:146–155. 54 Gorski JN, Dunn-Meynell AA, Hartman TG, Levin BE: Postnatal environment overrides genetic and prenatal factors influencing offspring obesity and insulin resistance. Am J Physiol Regul Integr Comp Physiol 2006;291:R768–R778. 55 Plagemann A: Perinatal nutrition and hormonedependent programming of food intake. Horm Res 2006;65(suppl 3):83–89. 56 Horvath TL, Bruning JC: Developmental programming of the hypothalamus: a matter of fat. Nat Med 2006;12 (discussion 53):52–53.

James Andrew Armitage, MOptom, PhD Department of Anatomy and Developmental Biology, Monash University Building 13C Clayton Campus, Wellington Road Clayton, Victoria 3800 (Australia) Tel. ⫹61 3 9905 2761, Fax ⫹61 3 9905 2766, E-Mail [email protected]

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Childhood Obesity M.A. Sabina,b ⭈ J.P.H. Shieldb a

Royal Children’s Hospital and Murdoch Childrens Research Institute, Melbourne, Australia; bUniversity of Bristol, Bristol, UK

Abstract The prevalence of childhood obesity continues to increase worldwide. Its presence is associated with significant adverse effects on health including an increased propensity to type II diabetes, cardiovascular, respiratory, and liver disease. In the vast majority of children, obesity is lifestyle-related, yet there is a dearth of evidence on how to best develop effective prevention and treatment strategies. This review outlines the importance of childhood and adolescent growth on long-term health, the definitions used to define obesity in children (along with up-to-date prevalence data), causes and consequences, and Copyright © 2008 S. Karger AG, Basel aspects of prevention and management.

Most countries throughout the world, excepting certain areas of Sub-Saharan Africa have witnessed a continued increase in the prevalence of obesity over the last two decades [1]. This carries major public health implications as there is little doubt that obese children are at increased risk of developing long-term morbidity and eventual mortality secondary to increasing adiposity. Despite this, there remain very few effective prevention or treatment strategies with which to halt this escalating epidemic. Understanding the factors that regulate adiposity in childhood, and the development of co-morbidities, would allow us to focus prevention and treatment strategies to those most at risk.

The Importance of Childhood and Adolescent Growth on Long-Term Health

Growth throughout childhood has profound implications for later health. There is now considerable evidence indicating that size at birth, and growth in the first years of life, affect the long-term risk of obesity and insulin resistance [2]. The risk associated with birth weight has been defined as a ‘U-shaped curve’ with infants at both ends of the birth weight spectrum being at increased risk. Infants born small for gestational age or with lower ponderal indices [(weight in g/length in cm3) ⫻100], who subsequently show

rapid growth, are at greater risk of type 2 diabetes (T2DM) and cardiovascular disease in later life, with evidence earlier in life of increased insulin resistance. Children born large for gestational age are also at risk of subsequent obesity and T2DM, although the degree to which maternal gestational diabetes accounts for these associations is still poorly characterised. There is evidence that rapid growth in small for gestational age babies is specifically associated with an increased propensity to abdominal obesity. The transition from childhood to adolescence is fundamentally important in the development of obesity, as puberty has a profound effect on regional body composition. During puberty, females accumulate a higher proportion of their total adult fat mass, than their total adult lean tissue mass. The increased subcutaneous fat that is seen in women develops during puberty, suggesting that oestrogen may preferentially promote subcutaneous adipose deposition. During adolescence, boys characteristically maintain a relatively fixed absolute fat mass, whereas girls display an increase in fat mass, accruing fat at ⬎1 kg per year [3]. The remodelling of fat mass and distribution appears to be central to the development of the physiological insulin resistance that develops during pubertal growth [4].

The Definition of Childhood Obesity

Childhood adiposity can be measured in numerous ways although body mass index (BMI) remains the most commonly used. Whilst BMI is a relatively simple tool with which to assess body mass, it is a relatively poor predictor of actual body composition in both adults and children. Due to its ease of determination however, along with a good correlation with body fat, it has remained the accepted method to define obesity in children based on current expert opinion [5]. Another important measure is waist circumference which has been validated as a surrogate marker of visceral adiposity in children [6]. In adults, a BMI ⬎25 and ⬍30 corresponds to ‘overweight’, whereas a BMI ⬎30 identifies those with obesity. These cut-off points correspond to an increased risk of cardiovascular disease and diabetes in adults. In children, however, BMI changes with normal longitudinal growth, as shown in figure 1. Therefore, it is inappropriate to simply express raw BMI in children, without adjusting for age and sex. Instead, BMI standard deviation scores or z-scores (BMI SDS – representing increases or decreases around the 50th centile for age and sex) are used to determine which children are relatively ‘overweight’ or ‘obese’. For national statistics, BMI levels of ⬎95th, ⬎97th, or ⬎98th percentile have been used to identify the ‘fattest’ children within different populations. These limits have high specificity and moderate sensitivity, and allow temporal changes within countries to be assessed. For across-country comparisons, international cut-off points must be used and these have been based on the extrapolation of adult cut-off points back into childhood [7]. The international cut-off points tend to greatly underestimate obesity prevalence when used for determining prevalence rates within a specific country [8]. However, BMI SDS may not be the best tool with which to

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Fig. 1. Boys BMI chart, demonstrating changes in BMI with age. Similar charts for girls are also available. Reproduced with permission from the Child Growth Foundation. Printed copies and further information are available from www.healthforallchildren.co.uk.

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assess longitudinal changes in adiposity in children enrolled into weight management programs, as the within-child variability over time depends upon the child’s levels of adiposity. Under these circumstances, age-adjusted BMI (calculated by subtracting the sexand age-specific median BMI) may be a better tool [9]. However, the normative data necessary to make these calculations are currently unavailable so most workers continue to use BMI SDS when reporting their results. The Prevalence of Childhood Obesity

There has been a dramatic rise in the number of children who fulfil the criteria necessary for the diagnosis of obesity [10]. Data from the CDC in the USA (http://www.cdc.gov) demonstrate an increase in the prevalence of children aged 6–19 years old who were considered to be overweight (ⱖ95th percentile) from 4–5% in 1963–1970 to 15% in 1999–2000. Using similar criteria in the majority of cases, the International Obesity Task Force have inspected the prevalence of obesity in children aged around 10 years old from data derived from 21 European countries between 1992 and 2001 and found levels to vary between 10 and 36% [11]. Data from the Health Survey for England in 2002 indicated that 8.5% of all 6-year-olds and 15% of all 15year-olds satisfied the criteria for obesity, and similar data from 2003 found that the prevalence of obesity in children aged 2–10 had increased from 9.9 to 13.7% from 1995 (www.dh.gov.uk). In the non-Westernised world, there is also evidence that obesity in general is increasing, especially amongst urban populations [12]. For example, China (a country previously defined as one of the world’s leanest populations) has witnessed a dramatic recent rise in childhood overweight and obesity prevalence [13]. Causes of Obesity in Childhood

Endocrine and single gene disorders causing obesity in childhood are rare, accounting for 1–2% of obese children seen in a tertiary care setting. Nevertheless, an understanding of these disorders is required to recognise rare but treatable causes of childhood obesity. A thorough description of these conditions is beyond the scope of this review and can be found elsewhere [14]. The majority of cases, however, arise from a simple interaction between host factors that enhance susceptibility and environmental factors which increase food intake and decrease energy expenditure [15]. Factors causing the imbalance in energy intake and energy expenditure are numerous, simply reflecting the components of the obesogenic environment in which we live. Factors important in excessive energy intake include the consumption of energy-dense foods, increased portion sizes, between-meal snacking and regular intake of sugar-sweetened beverages and fruit juices. Decreased energy expenditure is often due to the coupling of increased sedentary activities, such as TV and computer games, alongside decreased physical activity. There are also very significant

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parental [16] and socioeconomic [1, 17] contributions to obesity risk as demonstrated by a recent study which showed that while 89% of parents of overweight 5- to 6-year-olds were unaware that their child was overweight, 71% were not concerned, with less educated parents being less likely to take action [18]. Ethnicity also significantly impacts upon obesity risk and the development of co-morbidities. For example, data from several countries show that black children have a higher prevalence of obesity than white children [17] while obese children from certain ethnic groups (e.g. South Asia) appear to exhibit higher rates of complications like T2DM for a given level of obesity [19]. Addressing these complex demographic and lifestyle interactions remains central to the development of effective prevention and treatment programs for childhood obesity.

Consequences of Childhood Obesity

Childhood obesity tracks into adult life with a high degree of predictability – 68% of children with a BMI consistently 85th percentile grow up to be obese adults [20]. However, childhood obesity independently has its own short- and long-term adverse effects on health.

Endocrine Complications The major endocrine consequence of obesity is impaired glucose tolerance and subsequent T2DM – a process that develops in individuals with insulin resistance and relative (rather than absolute) insulin deficiency. Significant numbers of children and adolescents with obesity seen within specialised clinics have impaired glucose tolerance when challenged with an oral glucose load. The figures vary across centres but have been put as high as 25% in the USA, lower in the UK with levels around 10% and possibly even lower in mainland Europe [21]. Predictions from the USA imply that obesity-associated T2DM is likely to become the commonest form of newly diagnosed diabetes in adolescent youth with evidence suggesting a global spread in the condition (although actual incidence data are currently sparse [22]). Various centres in the USA have recorded dramatic increases in the number of children diagnosed with T2DM. For example, a 10-fold increase was recorded from a centre in New York from 1990 to 2000 with 50% of all new cases of diabetes having T2DM [23]. In Japan, researchers have documented a rise in the annual incidence of T2DM in children from 1.73/100,000 to 2.76/100,000 over 20 years [24], whilst evidence is also emerging of an increase in urban South-Asian children. Data from Europe are scarce: a population based study in Austria established an incidence of 0.25/100,000 children [25], whilst a report from France indicated relatively low but increasing numbers of children presenting with T2DM [26]. In the UK, Ehtisham et al. [27] estimated a crude prevalence of T2DM in patients under 16 years of 0.21/100,000, whilst a recent report reviewing first hospital admissions with a diagnosis of T2DM in patients under 18 years indicated a significant rise between 1996–7 to 2003–4 [28].

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The emergence of T2DM in adolescence has important implications for both the health of the individual and health service resources. Treatment compliance [29] and psychological health [30] is often poor in childhood T2DM. Various studies have implied an accelerated risk of nephropathy [31] and retinopathy [32] compared to young people with type 1 diabetes, whilst recent data indicate early signs of cardiovascular disease in youth with T2DM [33]. The only currently available longitudinal data make for worrying reading: of 79 children re-contacted up to 15 years after the diagnosis of T2DM, 9% had died and 6% were on dialysis [34].

Cardiovascular Complications and the Metabolic Syndrome Obesity is associated with a significant risk for cardiovascular disease independent of diabetes-related disease. Tounian et al. [35] investigated normotensive, obese, 12year-old children comparing them with normal-weight controls. The obese children had significantly higher arterial wall stiffness and evidence of endothelial dysfunction. Berenson et al. [36] were able to demonstrate that obesity with attendant abnormalities, such as higher LDL cholesterol levels and systolic hypertension, was associated with greater coronary artery atheromatous plaque formation in childhood. Furthermore, recent publications indicate that carotid artery intima-media thickness in adult life, considered predictive of stroke and myocardial infarction, can be directly related to childhood BMI (independent of adult weight status) [37]. The metabolic syndrome, a clustering of cardiovascular risk factors, is defined in adults when central obesity (determined by sex and ethnic waist circumference cut-off points) is present along with 2 of the following: raised triglycerides, reduced HDL cholesterol, hypertension or raised fasting plasma glucose/T2DM (www.idf.org). However, in children this definition needs adapting and there remains some debate as to the most appropriate definition and cut-off points that should be used [38]. Cook et al. [39] estimated the prevalence of the metabolic syndrome in adolescents enrolled in NHANES 1988–1994, using age-modified standards of the Adult Treatment Panel III criteria (a combination of three of the following factors: raised fasting plasma glucose, abdominal obesity, dyslipidaemia with a reduced HDL or elevated triglyceride level, and hypertension) [40]. They found a prevalence rate for the metabolic syndrome in overweight (ⱖ95th BMI percentile) adolescents aged 12–19 years of 28.7%, which by 1999–2000 had increased to 32.1% [41]. Others have reported higher prevalence in cohorts of obese children, with the most severely obese children having levels as high as 50% [42]. In our cohort of obese children, we identified a prevalence of 25% [43].

Respiratory Complications Obesity has long been associated with sleep disorders, now termed ‘obstructive sleep apnoea syndrome’ [44]. This causes decreased overnight oxygen saturation and repetitive arousal prohibiting qualitative REM sleep, leading to daytime lethargy and

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somnolence. This not only affects physical activity levels (further compounding obesity), but also school performance and concentration [45]. Interestingly, several studies have also now identified that reduced sleep duration may be associated with an increased propensity to obesity, probably through changes in orexegenic hormone levels, suggesting that encouraging longer sleep in childhood may be a useful public health tool [46].

Gastrointestinal Complications Obesity causes non-alcoholic fatty liver disease (NAFLD) in childhood [47]. NAFLD refers to a spectrum of fatty infiltration of the liver, without alcohol consumption, that encompasses simple fatty deposits seen in the liver, fatty liver with necro-inflammation (termed non-alcoholic steatohepatitis) and ‘cryptogenic’ cirrhosis. Significant numbers of obese children, particularly those with associated insulin resistance, have been identified as suffering from this condition. It has a male preponderance and is intimately linked to insulin resistance. The classical feature is of raised serum aminotransferases in the absence of significant alcohol ingestion. Weight management remains the mainstay of therapy for this condition [48], although alternative treatments undergoing investigation in childhood include metformin and vitamin E.

Other Systems Other complications of childhood obesity include slipped femoral capital epiphysis, renal abnormalities in a condition now termed ‘obesity-related glomerulopathy’ and idiopathic intracranial hypertension. The psychological effects of obesity should not be forgotten with many obese children seen in the clinical setting, lacking self-esteem, with reduced quality of life scores comparable to those of children diagnosed with cancer. Interestingly, however, some workers have found that while obese children may have higher levels of body dissatisfaction than their normal-weight peers, there is little evidence to suggest that they have significantly reduced self-esteem or higher levels of depression [49].

Prevention of Obesity in Childhood

Obesity prevention is ‘the only feasible solution for developed and developing countries’ [1], although no prevention strategies have yet been developed which show long-term efficacy. It is likely that numerous strategies have to be established ranging from population-based initiatives to targeted interventions aimed at those most at risk. A recent report by the British Medical Association stated that ‘prevention strategies will require a coordinated effort between the medical community, health administrators, teachers, parents, food producers and processors, retailers and caterers, advertisers and the

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media, recreation and sport planners, urban architects, city planners, politicians and legislators’ (www.bma.org.uk/ap.nsf/content/childhoodobesity), highlighting the complexity involved in developing effective prevention programs for childhood obesity. A comprehensive overview of prevention trials for childhood obesity is outside the scope of this review and the reader is directed to other sources on the subject [50]. In brief, different prevention programs have included school-based initiatives, individual and family-based interventions, and antenatal/postnatal/preschool approaches. None has proven to be particularly effective and it is likely that a combination of strategies (within a wider context of governmental policy change, changes in healthcare and service provision to children, and less child-centred advertising) is required to have a demonstrable effect. It is clear that the development of well-designed studies to assess which interventions are most effective is now critical if we are to overcome the mismatch between the prevalence of childhood obesity and the knowledge base from which to inform preventative activity [51].

Management Considerations

The most recent Cochrane review of obesity therapy in childhood concludes that ‘there is a limited amount of quality data on the components of programs to treat childhood obesity that favour one program over another’ [52]. In general, it appears that behaviour modification centred on parent education, by which parents take on the primary responsibility for change in lifestyle, might be of some benefit [53–55]. This appears to be most likely to work in younger children rather than adolescents, as shown in figure 2 [56]. Furthermore reducing sedentary behaviour and increasing exercise might prove useful although the evidence is still very limited [57, 58]. Given the limited resources and ill-defined framework for obesity therapy in childhood, it is sensible to consider who is most likely to benefit if weight management is successful. There is evidence that some racial groups such as Black, AfricanAmericans and Europeans and South Asians have both an increased prevalence of obesity and risk for T2DM with some authors suggesting that these groups should be targeted as a priority [59]. Another priority group should be those with a family history of T2DM as there is good evidence that obese children from these families are a greater risk. Given these considerations, it is still important to establish when referral for obesity management should be undertaken, given that other medical consequences of profound obesity may not be entirely centred on the above high risk groups. The most important consideration is the need for the family of the child concerned to want to address the issue of obesity. Without an acceptance that changes in lifestyle are required, it is highly unlikely that any progress can be made. It has been demonstrated that many parents underestimate their child’s weight and the health care risks associated with obesity. It is therefore important within any consultation in primary

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1.0

Change in BMI SDS

0.5 0.0 ⫺0.5 ⫺1.0 ⫺1.5

2

4

10 12 14 6 8 Age at entry to clinic (years)

16

18

Fig. 2. Change in BMI SDS versus age at entry to a hospital-based multidisciplinary paediatric weight management clinic, demonstrating that younger children achieve better results with this type of intervention. Reproduced from reference [56].

care or a community setting that the issue of a child’s weight is addressed, but this should never be at the expense of embarrassing either the child or family. If after consultation, the family accepts that changes need to be made, then an assessment and plan for action can be developed. In older children, where simple modification of lifestyle tends to prove more difficult, there is now some evidence for the use of pharmacological treatments in resistant individuals. Orlistat (a gastrointestinal lipase inhibitor) and sibutramine (a selective serotonin reuptake inhibitor) are the 2 main anti-obesity medications used in adults and evidence from the USA suggests that each may be associated with beneficial changes in BMI within the context of appropriate lifestyle modification programmes. Both orlistat and sibutramine have been tested in randomised controlled trials in adolescence with some apparent benefit [60, 61]. It is still unclear which adolescents require pharmacological treatment, although it would seem that the best approach is to stratify treatment by the severity of obesity/co-morbid conditions and reserve drug therapy for those in whom behavioural therapy has failed [62]. Current evidence suggests, however, that both medications should only be prescribed from specialist adolescent weight management centres and as part of an overall lifestyle modification programme [63]. Rimonabant, an endocannabinoid receptor antagonist, is a new anti-obesity medication that has recently been licensed for use in adults. Although there have been no studies to date that have examined its use in adolescents, data from adult studies do not suggest any significant side effects that would necessarily preclude its use in this age group in the future [64].

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In those in whom lifestyle change and pharmacotherapy have failed, especially where severe obesity is associated with co-morbid conditions in childhood, then surgery remains an alternative treatment approach [65]. Surgical procedures have involved either gastric restrictive or malabsorptive methods, although the laparoscopic placement of an adjustable gastric band has become the most widely adopted procedure. Results of a large randomised controlled trial assessing this surgical approach versus a modified lifestyle approach in adolescents with severe obesity is keenly awaited from the Centre for Obesity Research and Education in Australia.

Conclusions

Childhood obesity now affects at least 10% of the world’s children. It is associated with numerous health consequences that will have a significant impact on healthcare provision over the next few decades. Despite this, we have very little evidence on the most appropriate prevention and treatment strategies needed to tackle the problem. A recent report assessing the cost effectiveness of interventions in childhood obesity concluded that ‘the greatest health benefit is likely to be achieved by the ‘‘reduction of TV advertising of high fat and/or high sugar foods and drinks to children’’, ‘‘laparoscopic adjustable gastric banding’’ and the ‘‘multi-faceted school-based programme with an active physical education component’’’ [66].

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29 Kawahara R, Amemiya T, Yoshino M, Miyamae M, Sasamoto K, Omori Y: Dropout of young noninsulin-dependent diabetics from diabetic care. Diabetes Res Clin Pract 1994;24:181–185. 30 Katz LSS, Abraham M, Murphy KM, Jawad AF, McKnight-Menci H, Berkowitz R: Neuropsychiatric disorders at the presentation of type 2 diabetes mellitus in children. Pediatr Diabetes 2005;6:79–83. 31 Yokoyama H, Okudaira M, Otani T, Sato A, Miura J, Takaike H, Yamada H, Muto K, Uchigata Y, Ohashi Y, Iwamoto Y: Higher incidence of diabetic nephropathy in type 2 than in type 1 diabetes in early-onset diabetes in Japan. Kidney Int 2000;58: 302–311. 32 Yoshida Y, Hagura R, Hara Y, Sugasawa G, Akanuma Y: Risk factors for the development of diabetic retinopathy in Japanese type 2 diabetic patients. Diabetes Res Clin Pract 2001;51:195–203. 33 Gungor N, Thompson T, Sutton-Tyrrell K, Janosky J, Arslanian S: Early signs of cardiovascular disease in youth with obesity and type 2 diabetes. Diabetes Care 2005;28:1219–1221. 34 Dean H, Flett B: Natural history of Type 2 diabetes diagnsoed in childhood: long term follow up in young adult years (abstract). Diabetes 2002; 51:A24. 35 Tounian P, Aggoun Y, Dubern B, Varille V, GuyGrand B, Sidi D, Girardet JP, Bonnet D: Presence of increased stiffness of the common carotid artery and endothelial dysfunction in severely obese children: a prospective study. Lancet 2001;358: 1400–1404. 36 Berenson GS, Srinivasan SR, Bao W, Newman WP, 3rd, Tracy RE, Wattigney WA: Association between multiple cardiovascular risk factors and atherosclerosis in children and young adults. The Bogalusa Heart Study. N Engl J Med 1998;338: 1650–1656. 37 Freedman DS, Dietz WH, Tang R, Mensah GA, Bond MG, Urbina EM, Srinivasan S, Berenson GS: The relation of obesity throughout life to carotid intima-media thickness in adulthood: the Bogalusa Heart Study. Int J Obes Relat Metab Disord 2004;28: 159–166. 38 Golley RK, Magarey AM, Steinbeck KS, Baur LA, Daniels LA: Comparison of metabolic syndrome prevalence using six different definitions in overweight pre-pubertal children enrolled in a weight management study. Int J Obes (Lond) 2006;30: 853–860. 39 Cook S, Weitzman M, Auinger P, Nguyen M, Dietz WH: Prevalence of a metabolic syndrome phenotype in adolescents: findings from the third National Health and Nutrition Examination Survey, 1988–1994. Arch Pediatr Adolesc Med 2003;157: 821–827. 40 Adult Treatment Panel I: Third report of the national cholesterol education program (NCEP) expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (Adult Treatment Panel III) final report. Circulation 2002;106:3143–3421.

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41 Duncan GE, Li SM, Zhou XH: Prevalence and trends of a metabolic syndrome phenotype among U.S. adolescents, 1999–2000. Diabetes Care 2004;27: 2438–2443. 42 Weiss R, Dziura J, Burgert TS, Tamborlane WV, Taksali SE, Yeckel CW, Allen K, Lopes M, Savoye M, Morrison J, Sherwin RS, Caprio S: Obesity and the metabolic syndrome in children and adolescents. N Engl J Med 2004;350:2362–2374. 43 Sabin MA, Ford AL, Holly JM, Hunt LP, Crowne EC, Shield JP: Characterisation of morbidity in a UK, hospital based, obesity clinic. Arch Dis Child 2006;91:126–130. 44 Dietz WH: Health consequences of obesity in youth: childhood predictors of adult disease. Pediatrics 1998;101:518–525. 45 Wing YK, Hui SH, Pak WM, Ho CK, Cheung A, Li AM, Fok TF: A controlled study of sleep related disordered breathing in obese children. Arch Dis Child 2003;88:1043–1047. 46 Chaput JP, Brunet M, Tremblay A: Relationship between short sleeping hours and childhood overweight/obesity: results from the ‘Quebec en Forme’ Project. Int J Obes (Lond) 2006;30:1080–1085. 47 Roberts EA: Non-alcoholic fatty liver disease (NAFLD) in children. Front Biosci 2005;10: 2306–2318. 48 Marion AW, Baker AJ, Dhawan A: Fatty liver disease in children. Arch Dis Child 2004;89:648–652. 49 Wardle J, Cooke L: The impact of obesity on psychological well-being. Best Pract Res Clin Endocrinol Metab 2005;19:421–440. 50 Ells LJ, Campbell K, Lidstone J, Kelly S, Lang R, Summerbell C: Prevention of childhood obesity. Best Pract Res Clin Endocrinol Metab 2005;19:441–454. 51 Campbell K, Waters E, O’Meara S, Kelly S, Summerbell C: Interventions for preventing obesity in children. Cochrane Database Syst Rev:CD001871, 2002. 52 Summerbell CD, Ashton V, Campbell KJ, Edmunds L, Kelly S, Waters E: Interventions for treating obesity in children. Cochrane Database Syst Rev: CD001872, 2003. 53 Golan M, Fainaru M, Weizman A: Role of behaviour modification in the treatment of childhood obesity with the parents as the exclusive agents of change. Int J Obes Relat Metab Disord 1998;22:1217–1224. 54 Golan M, Crow S: Parents are key players in the prevention and treatment of weight-related problems. Nutr Rev 2004;62:39–50.

55 Golan M, Kaufman V, Shahar DR: Childhood obesity treatment: targeting parents exclusively v. parents and children. Br J Nutr 2006;95:1008–1015. 56 Sabin M, Ford A, Hunt L, Jamal R, Crowne E, Shield J: Which factors are associated with a successful outcome in a weight management programme for obese children? J Eval Clin Prac 2007;13:364–368. 57 Epstein LH, Valoski AM, Vara LS, McCurley J, Wisniewski L, Kalarchian MA, Klein KR, Shrager LR: Effects of decreasing sedentary behavior and increasing activity on weight change in obese children. Health Psychol 1995;14:109–115. 58 Epstein LH, Goldfield GS: Physical activity in the treatment of childhood overweight and obesity: current evidence and research issues. Med Sci Sports Exerc 1999;31:S553–S559. 59 Freedman DS, Khan LK, Serdula MK, Ogden CL, Dietz WH: Racial and ethnic differences in secular trends for childhood BMI, weight, and height. Obesity (Silver Spring) 2006;14:301–308. 60 Chanoine JP, Hampl S, Jensen C, Boldrin M, Hauptman J: Effect of orlistat on weight and body composition in obese adolescents: a randomized controlled trial. JAMA 2005;293:2873–2883. 61 Berkowitz RI, Fujioka K, Daniels SR, Hoppin AG, Owen S, Perry AC, Sothern MS, Renz CL, Pirner MA, Walch JK, Jasinsky O, Hewkin AC, Blakesley VA: Effects of sibutramine treatment in obese adolescents: a randomized trial. Ann Intern Med 2006;145: 81–90. 62 Dietz WH: What constitutes successful weight management in adolescents? Ann Intern Med 2006;145: 145–146. 63 Joffe A: Pharmacotherapy for adolescent obesity: a weighty issue. JAMA 2005;293:2932–2934. 64 Padwal RS, Majumdar SR: Drug treatments for obesity: orlistat, sibutramine, and rimonabant. Lancet 2007;369:71–77. 65 Barnett SJ, Stanley C, Hanlon M, Acton R, Saltzman DA, Ikramuddin S, Buchwald H: Long-term followup and the role of surgery in adolescents with morbid obesity. Surg Obes Relat Dis 2005;1:394–398. 66 Haby MM, Vos T, Carter R, Moodie M, Markwick A, Magnus A, Tay-Teo KS, Swinburn B: A new approach to assessing the health benefit from obesity interventions in children and adolescents: the assessing costeffectiveness in obesity project. Int J Obes (Lond) 2006;30:1463–1475.

Dr. M.A. Sabin Department of Endocrinology and Diabetes Royal Children’s Hospital Flemington Road, Parkville, Victoria 3052 (Australia) Tel. ⫹61 3 9345 5951, Fax ⫹61 3 9347 7763, E-Mail [email protected]

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Obesity in Old Age Ian McPhee Chapman Department of Medicine, Royal Adelaide Hospital, University of Adelaide, Adelaide, Australia

Abstract Many older people in developed countries are overweight or obese. The prevalence is increasing as more people reach old age already overweight. Obesity in old age is associated with increased morbidity and a reduction in quality of life. The relative increase in mortality is less in older than young adults and the body weight associated with maximal survival increases with advancing age. Although intentional weight loss by overweight older people is probably safe and beneficial, caution should be exercised in recommending weight loss to overweight older people on the basis of body weight alone. Methods of achieving weight loss in older adults are the same as in younger adults. Weight loss diets should be combined with an exercise program to preserve muscle mass, as dieting results in loss of muscle as well as fat, and older people have reduced skeletal muscle mass in any case. Weight loss drugs have not been extensively studied in older people, and there is the potential for drug side effects and interactions. Weight loss surgery appears to be safe and effective, albeit slightly less so than in younger adults, but little is known about the outcomes of such surgery in those over 65 years. Copyright © 2008 S. Karger AG, Basel

Overnutrition and Obesity in Older People

Prevalence In most developed countries a substantial minority, and in some countries the majority, of older people are overweight according to standard body weight criteria. According to recent large surveys [1, 2], approximately 71% of Americans 60 years or older and 60% of those 65 years or older were overweight (body mass index, BMI ⱖ25 kg/m2), while approximately 32% of those 60 years or older and 20% of those 65 years or older were obese (BMI ⱖ30). Similarly, 29% of 55- to 64-year-olds in England were obese [3], while 43% of Australians over 65 were overweight and 25% of Australians aged 65–74 years and 14.4% over 75 years were obese [4]. Not only are many older people overweight or obese, but the rates are increasing rapidly, in parallel with the dramatic increase over recent years in rates in younger

adults. For example, the prevalence of obesity (BMI ⱖ30) among people in the USA over 60 years increased from 20 to 32% between 1988–1994 and 1999–2000 [5] and among those over 70 years from 11.4 to 15.5% between 1991 and 2000 [6]. There have been similar increases in other countries [4]. An understanding of the causes and consequences of excess weight in older people is aided by an understanding of the changes in appetite, food intake, energy expenditure and body composition that occur with ageing.

Changes in Appetite and Food Intake with Increasing Age

On average, adults become less hungry and eat less as they get older, even if healthy [7]. This physiological, age-related reduction in appetite and energy intake has been termed ‘the anorexia of aging’ [8] and appears to have many causes [9]. Average daily energy intake decreases by up to 30% between 20 and 80 years [7]. Most of the agerelated decrease in energy is probably a response to the decline in overall energy expenditure that also occurs as people get older. Changes in body weight and body composition reflect the balance of these two declines. As indicated below, body weight tends to increase through early adult life into middle age, suggesting a more rapid decline in energy expenditure than in food intake during this time. In contrast, body weight tends to decrease in older people, suggesting a faster decline in food intake than in energy expenditure in later life.

Changes in Body Weight and Body Composition with Increasing Age

Body Weight Population studies show that on average people in westernised countries gain weight until they are about 50–60 years old and after that tend to lose weight [10]. Although some of the decline in mean body weight after age 50–60 years detected in cross-sectional studies is due to the premature death of obese people, the decline in body weight among older people has also been demonstrated in longitudinal studies. For example, in one 2-year prospective study, community-dwelling American men over 65 years lost on average 0.5% of their body weight per year and 13.1% of the group had weight loss of 4% per annum or more [11]. As a result of this weight loss in older people, and the premature death of obese people at younger ages, the prevalence of overweight and obesity, as defined by standard BMI criteria (BMI ⬎25 and ⱖ30, respectively) peaks around age 50–60 years. It then remains fairly stable until about age 70–75 years, before decreasing. A substantial minority of older people have quite marked weight changes over time. In one study [12], 17% of home-dwelling people in the USA over 65 years lost

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5% or more of their initial body weight over 3 years, while 13% gained 5% or more. Other studies provide similar numbers [11]. There is evidence for interactive effects on health of body weight category and change in body weight, particularly of adverse effects in already underweight people who lose weight and in already overweight people who gain weight [13].

Body Composition Enlargement and Redistribution of Fat Stores With normal aging there is a progressive increase in fat and decrease in fat-free mass, the latter mainly due to loss of skeletal muscle. Consequently, at any given weight, older people, on average, have substantially more body fat than young adults. In one study, the mean body fat of 75-year-old men weighing 80 kg was 29%, compared to 15% in 20-year-old men of the same weight [14]. The increase in body fat with aging is multifactorial in origin, with decreased physical activity as a major cause, and contributions from reduced growth hormone secretion, declining sex hormone action and reduced resting metabolic rate and thermic effect of food. Not only do older adults have more body fat than young adults, but it is distributed differently. A greater proportion of body fat in older than young people is intrahepatic, intramuscular, and intra-abdominal (versus subcutaneous) [15], changes that in both young and older adults are associated with increased insulin resistance [16]. For example, in one study intramuscular fat stores were 50% greater, intrahepatic stores four times greater and insulin resistance two times greater in older (65–74 years) than young adults (20–32 years) [16]. In younger adults, such changes to body fat stores and increases in insulin resistance are associated with adverse metabolic outcomes, including increased rates of diabetes mellitus and cardiovascular disease. It might, therefore, be predicted that the age-related changes in body fat stores would lead to particularly bad metabolic outcomes in older people. This is not proven, however. Given that the body weight compatible with longest survival increases with increasing age (see below), and much if not all of the increase in body weight is due to increased fat stores, it may be that advancing age blunts in some way the harmful effects of increasing body fat. This possibility warrants further study. Loss of Skeletal Muscle (Sarcopaenia) Ageing is associated with a decrease in muscle mass and strength, with loss of up to 3 kg of lean body mass per decade after age 50 years. After age 60 years, loss of body weight is disproportionately of lean body tissue, predominantly skeletal muscle. The causes of age-related skeletal muscle loss are multiple and not fully understood, but probably similar to those leading to fat gain, including reduced exercise and anabolic hormone action. When excessive, this leads to sarcopaenia (from the Greek meaning

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‘poverty of flesh’), which can be defined in various ways, such as a skeletal mass more than 2 standard deviations below the young adult sex-specific mean [17]. The prevalence of sarcopaenia in older people varies according to the population studied and diagnostic criteria used, but is in the order of 6–15% in people over 65 years [17]. The reduction of skeletal muscle mass and strength in sarcopaenia is so severe that it is often associated with marked functional impairment. The presence of sarcopaenia is an independent predictor of poor gait, balance, falls, and fractures. In the NHANES III study, for example, older people with marked sarcopaenia (less than 5.75 kg skeletal muscle/m2) were 3.3 times (women) to 4.7 times (men) more likely to have physical disability than those with low-risk skeletal muscle mass (more than 6.75 kg/m2) [18]. In young adults, obesity tends to be associated with increased skeletal muscle, acquired to support the extra weight. In contrast, in older people, excess weight, to the point of obesity, can co-exist with muscle loss and even sarcopaenia. Hence the possibly counter-intuitive entity of sarcopaenic obesity or the ‘skinny fat’ elderly. This combination of an excess of (probably) metabolically bad fat tissue and a deficiency of beneficial muscle is associated with particularly adverse effects [19, 20]. In one prospective study of elderly people followed for up to 8 years, those with sarcopaenic obesity (skeletal muscle mass more than 2 standard deviations below young adult mean and percentage body fat above the 60th percentile) at baseline were two to three times more likely to develop disabilities in activities of daily living than were lean sarcopaenic, non-sarcopaenic obese or normal body composition subjects [21]. The agerelated loss of skeletal muscle and its adverse effects help explain the consistently demonstrated benefits of exercise programs in elderly people, particularly those that increase muscle mass and function.

Causes of Overweight and Obesity in Older People

As indicated above, people do not usually gain weight in old age. From this, it follows that the high and increasing proportion of overweight and obesity in older people is mainly due to the high and increasing proportion of adults who reach old age already overweight. The causes of obesity in the elderly are therefore largely those of obesity in younger adults. These have been addressed elsewhere in this book. Older people are often not weight stable and a substantial minority of older people have quite marked weight changes over time [11]. There are therefore some people who only become overweight or whose weight increases dramatically in later life. This can be due to medical illnesses, which restrict mobility and hence alter the food intake-energy expenditure balance in favour of the former. Most common among these is musculoskeletal disease, such as osteoarthritis of the lower limbs or back, while cardiac failure and depression are also important causes, as is increased food intake due to corticosteroid use.

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Table 1. Obesity-related co-morbidities in older people

Reduced mobility Reduced quality of life Type 2 diabetes mellitus Hypertension Cardiovascular disease Sleep apnoea Increased rate of malignancies Breast Colon Prostate Uterus Fatty liver Thromboembolic disease

Consequences of Obesity in Older People

As in younger adults, obesity in older adults is associated with absolute and relative increases in both mortality and morbidity. Obesity-related co-morbidities are summarised in table 1. Most of these conditions become more common and severe with increasing age even in those who are not overweight. These effects of age are exacerbated by excess weight.

Mortality The relative increase in the risk of death associated with being obese compared to being of normal weight is not as great in older as in young adults. An assessment of thirteen observational, prospective studies in which non-hospitalised people over 65 years were followed for at least 3 years [22] found an association between mortality and increased BMI in only a few, and then only above a BMI of 27–28.5, with little or no increase in mortality at any BMI for people over 75 years. Where an optimum BMI could be identified, it was usually in the range 27–30. Consistent with this, a combined analysis of the American NHANES I–III (1974–2000) study results found no significant increase in mortality with any degree of overweight in people over 70 years, and an increased death rate only in the ‘morbidly’ obese (BMI ⱖ35) among those 60–69 years [23]. Nevertheless, the relative risk of mortality is still increased at high BMIs until about the age of 75 years, and because of the greater background death rate in older people the absolute increase in death rate attributable to obesity is substantial; 25% of the excess deaths attributed to obesity in that NHANES analysis occurred in people over 70 years of age [5]. The causes of increased mortality are essentially the same as those in younger adults: diabetes, hypertension, sleep apnoea, cardiovascular disease and an increased risk with obesity of developing certain cancers, including breast, uterus, colon and prostate.

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Morbidity Obesity in older people is associated with increased rates of cataracts, mechanical urinary and bladder problems, sleep apnoea and other respiratory problems [reviewed in 10]. After the age of 65 years, over 60% of people have symptomatic osteoarthritis [24] commonly affecting the hip and knee, and this is a major cause of disability. Excess weight hastens the development of knee osteoarthritis [25], exacerbates the symptoms of lower limb osteoarthritis, and makes surgical treatment more hazardous. Functional capacity and mobility are significantly reduced in obese compared to lean older adults [26]. Obesity at any age is associated with a reduced quality of life [27], but this is particularly so in older adults, in whom it is associated with reduced muscle mass and strength and hence with physical frailty [20]. Compared to their non-obese counterparts, obese older people are less likely to be pain free, have greater limitations of physical function and are more likely to be homebound. Obesity is predictive of a greater rate of future disability, declines in functional status [10], particularly when associated with loss of skeletal mass [21], and an increased admission rate to nursing homes. Among women age 65 years or more in the Nurses Health Study, a weight gain of 20 lb (⬇9 kg) or more over 4 years was associated with a reduction in reported physical functioning [13]. Increased fat mass appears to be the factor specifically responsible for obesity-related disability [26]. A beneficial effect of obesity in older people is that it is associated with increased bone density in both weight bearing and non-weight bearing bones. This combines with cushioning of falls provided by the extra fat stores, particularly around the hips (‘endogenous’ hip protectors) to reduce fracture rates in older people [28]. Most studies show that when overweight adults, young or older, intentionally lose weight they also lose bone [reviewed in 10, 29]. Substantial unintentional weight loss in older people is associated with an increased risk of hip fracture [30]. This is probably due to a combination of reduced bone mass and the effects of whatever illness caused the weight loss, for example by increasing the likelihood of falls. Although it is reasonable to assume that there is at least some increase in fracture risk due to the weight loss-associated bone loss, in overweight older people who intentionally lose weight this has not been established.

Management of Obesity in Older People

Should Overweight Older People Be Advised to Lose Weight? This is a controversial question. Obesity in older people is associated with increased morbidity and reduced life expectancy at very high BMIs up to at least 70 years. There is evidence that weight loss by overweight older people is associated with improved quality of life; in the Nurses Health Study weight loss in initially overweight women was associated with improved physical function and vitality as well as decreased

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bodily pain [13]. This must be balanced against the detrimental effects of weight loss on muscle mass and bone density and the association repeatedly detected in large population studies between all-cause weight loss and increased mortality in older people, even those who are initially overweight. Studies demonstrating an association between weight loss and increased mortality in older people have largely examined all-cause weight loss, whether intentional or unintentional. There is little doubt that unintentional weight loss is not good for the elderly. Although some study results have been interpreted to show increased mortality after even intentional weight loss in older people [12], it is difficult to determine what proportion of weight loss labelled intentional was instead unintentional. On balance, it appears that intentional weight loss by initially overweight older people has either no significant effect [31, 32] or even beneficial effects on mortality. In the US National Health Interview Survey, for example, which followed 20,847 adults with mean age 54 years for 9 years, all-cause weight loss was associated with a significant increase in mortality, as in other studies [33]. Reported attempted weight loss, however, even if unsuccessful, was associated with a 24% reduction in mortality [RR 0.76 (0.64–0.9)], as was successful intentional weight loss [RR 0.76 (0.6–0.97)]. There was no interaction between weight loss intention and age in the effect on mortality, consistent with a prolongation of life by intended weight loss in older as well as young adults. Studies involving predominantly younger adults have found that intentional weight loss can reduce mortality in those with obesity-related health problems, such as type 2 diabetes, ischaemic heart disease and hypertension [34]. Although such studies have not been done in older people, available evidence provides no evidence of harm and suggests that it is safe to recommend weight loss to overweight older people with obesity-related morbidities, particularly reduced mobility and function. This is the group that appears to have the most to gain. There are few if any indications for recommending weight loss to older people based on their weight alone.

Weight Loss Measures in Overweight Older People

Treatment options available to achieve weight loss in older people are the same as those in younger adults; changes in diet and exercise, medications and surgery [10]. There is limited information about the effectiveness and safety of weight loss treatments, particularly medications and surgery, in older people.

Lifestyle Measures Lifestyle interventions, combining a reduced energy diet (reduction of about 500–750 kcal/day) and exercise are at least as effective in producing weight loss in

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people over 60 years as in younger adults [10], and possibly more so [35]. The incorporation of an exercise component, particularly weight-bearing aerobic and resistance exercise, is very important, as low muscle mass (even sarcopaenia) is often present even before deliberate weight loss, and is usually exacerbated by it. In the absence of an exercise program, up to 20–30% of diet-induced weight loss is accounted for by loss of skeletal muscle [36]. A meta-analysis of the results of twenty-eight studies, not specifically performed in older adults, found that the addition of an exercise program to diet reduces the diet-induced loss of muscle significantly, from 29 to 17% of weight lost in men and from 22 to 17% in women [37]. Similar benefits are likely to occur with older people, in whom exercise also inhibits the regional loss of bone density that accompanies weight loss, improves physical function by increasing muscle mass and fitness [38, 39] and reduces the risk of falls [40]. Older people should be assessed prior to starting an exercise program, to determine whether there is a need for formal stress testing to exclude significant underlying coronary artery disease [41] and the exercise intensity should be gradually increased. Multivitamin supplements together with calcium (1,000–1,500 mg/day) and vitamin D (800–1,000 IU/day) for bone protection should also be taken.

Medications and Surgery There is little reported experience with weight loss drugs such as sibutramine, which reduces appetite and food intake, and orlistat, which inhibits lipase activity and thus fat absorption, in older people. Such drugs should be used with caution in older people, because of limited efficacy data, the possibility of interactions with other (multiple) medications, and potentially worse side effects. Orlistat appears to be as effective in older as in young adults [10], with a weight loss over 1 year approximately 3 kg more than with placebo, but can cause diarrhoea and other gastrointestinal side effects such as faecal incontinence. Side effects of sibutramine can include insomnia, constipation and increased blood pressure. Only a few studies so far have examined the outcomes of weight loss surgery in overweight people over 60 years. The results are encouraging. In three studies comparing the effects of gastric bypass surgery in 115 people over 60 years to those in 3,470 people less than 60 years [42–44], there was one peri-operative death in the older group (0.86%) and a possible but not definite slight increase in operative complication rate compared to the younger patients. The surgery resulted in a marked mean weight loss of 39–43 kg at 1 year in the older subjects and a significant reduction in the number of obesity-related morbidities and number of medications needed to treat these. Both reductions were slightly, but significantly less in older than young adults. The mean age of the ‘older’ subjects in these studies was quite young, however, at 63–65 years, and the results of such surgery in even older people and also of other forms of obesity surgery in these age groups remain to be determined.

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Bariatric surgery is thus an effective weight loss option in older people, particularly for selected older people substantially disabled by complications of obesity, but greater caution should be exercised in undertaking these procedures in older than young adults.

Conclusions

Many older people are overweight or obese and this is associated with increased morbidity and mortality. Weight loss is achievable and is beneficial in these people, particularly when the excess weight is associated with functional impairments. Weight loss should probably not be recommended to older people on the basis of their body weight alone, rather than co-existent morbidity, as the relative disadvantages of excess weight per se are less in older than young adults.

References 1 Li F, Fisher KJ, Harmer P: Prevalence of overweight and obesity in older U.S. adults: estimates from the 2003 Behavioral Risk Factor Surveillance System survey. J Am Geriatr Soc 2005;53:737–739. 2 Ogden CL, et al: Prevalence of overweight and obesity in the United States, 1999–2004. JAMA 2006;295: 1549–1555. 3 Banks J, Marmot M, Oldfield Z, Smith JP: Disease and disadvantage in the United States and in England. JAMA 2006;295:2037–2045. 4 Cameron AJ, et al: Overweight and obesity in Australia: the 1999–2000 Australian Diabetes, Obesity and Lifestyle Study (AusDiab). Med J Aust 2003;178:427–432. 5 Flegal KM, Carroll MD, Ogden CL, Johnson CL: Prevalence and trends in obesity among US adults, 1999–2000. JAMA 2002;288:1723–1727. 6 Mokdad AH, et al: The continuing epidemics of obesity and diabetes in the United States. JAMA 2001; 286:1195–1200. 7 Wurtman JJ, Lieberman H, Tsay R, Nader T, Chew B: Calorie and nutrient intakes of elderly and young subjects measured under identical conditions. J Gerontol 1988;43:B174–B180. 8 Morley JE: Anorexia of aging: physiologic and pathologic. Am J Clin Nutr 1997;66:760–773. 9 Chapman IM: Endocrinology of anorexia of ageing. Best Pract Res Clin Endocrinol Metab 2004;18: 437–452.

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10 Villareal DT, Apovian CM, Kushner RF, Klein S: Obesity in older adults: technical review and position statement of the American Society for Nutrition and NAASO, The Obesity Society. Am J Clin Nutr 2005;82:923–934. 11 Wallace JI, Schwartz RS, LaCroix AZ, Uhlmann RF, Pearlman RA: Involuntary weight loss in older outpatients: incidence and clinical significance. J Am Geriatr Soc 1995;43:329–337. 12 Newman AB, Arnold AM, Burke GL, O’Leary DH, Manolio TA: Cardiovascular disease and mortality in older adults with small abdominal aortic aneurysms detected by ultrasonography: the cardiovascular health study. Ann Intern Med 2001;134: 182–190. 13 Fine JT, et al: A prospective study of weight change and health-related quality of life in women. JAMA 1999;282:2136–2142. 14 Prentice AM, Jebb SA: Beyond body mass index. Obes Rev 2001;2:141–147. 15 Beaufrere B, Morio B: Fat and protein redistribution with aging: metabolic considerations. Eur J Clin Nutr 2000;54(suppl 3):S48–S53. 16 Cree MG, et al: Intramuscular and liver triglycerides are increased in the elderly. J Clin Endocrinol Metab 2004;89:3864–3871. 17 Melton LJ, 3rd, Khosla S, Riggs BL: Epidemiology of sarcopenia. Mayo Clin Proc 2000;75(suppl): S10–S12; discussion S12–S13. 18 Janssen I, Baumgartner RN, Ross R, Rosenberg IH, Roubenoff R: Skeletal muscle cutpoints associated with elevated physical disability risk in older men and women. Am J Epidemiol 2004;159:413–421.

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19 Roubenoff R: Sarcopenic obesity: the confluence of two epidemics. Obes Res 2004;12:887–888. 20 Villareal DT, Banks M, Siener C, Sinacore DR, Klein S: Physical frailty and body composition in obese elderly men and women. Obes Res 2004;12: 913–920. 21 Baumgartner RN, et al: Sarcopenic obesity predicts instrumental activities of daily living disability in the elderly. Obes Res 2004;12:1995–2004. 22 Heiat A, Vaccarino V, Krumholz HM: An evidencebased assessment of federal guidelines for overweight and obesity as they apply to elderly persons. Arch Intern Med 2001;161:1194–1203. 23 Flegal KM, Graubard BI, Williamson DF, Gail MH: Excess deaths associated with underweight, overweight, and obesity. JAMA 2005;293:1861–1867. 24 Cicuttini FM, Spector TD: Osteoarthritis in the aged. Epidemiological issues and optimal management. Drugs Aging 1995;6:409–420. 25 Cicuttini FM, Baker JR, Spector TD: The association of obesity with osteoarthritis of the hand and knee in women: a twin study. J Rheumatol 1996;23: 1221–1226. 26 Jensen GL: Obesity and functional decline: epidemiology and geriatric consequences. Clin Geriatr Med 2005;21:677–687. 27 Kortt MA, Clarke PM: Estimating utility values for health states of overweight and obese individuals using the SF-36. Qual Life Res 2005;14:2177–2185. 28 Schott AM, et al: How hip and whole-body bone mineral density predict hip fracture in elderly women: the EPIDOS Prospective Study. Osteoporos Int 1998;8:247–254. 29 Ensrud KE, et al: Voluntary weight reduction in older men increases hip bone loss: the osteoporotic fractures in men study. J Clin Endocrinol Metab 2005;90:1998–2004. 30 Langlois JA, Harris T, Looker AC, Madans J: Weight change between age 50 years and old age is associated with risk of hip fracture in white women aged 67 years and older. Arch Intern Med 1996;156:989–994. 31 Yaari S, Goldbourt U: Voluntary and involuntary weight loss: associations with long term mortality in 9,228 middle-aged and elderly men. Am J Epidemiol 1998;148:546–555. 32 Wannamethee SG, Shaper AG, Lennon L: Reasons for intentional weight loss, unintentional weight loss, and mortality in older men. Arch Intern Med 2005;165:1035–1040.

33 Gregg EW, Gerzoff RB, Thompson TJ, Williamson DF: Intentional weight loss and death in overweight and obese U.S. adults 35 years of age and older. Ann Intern Med 2003;138:383–389. 34 Fontaine KR, Allison DB: Does intentional weight loss affect mortality rate? Eat Behav 2001;2:87–95. 35 Wing RR, et al: Achieving weight and activity goals among diabetes prevention program lifestyle participants. Obes Res 2004;12:1426–1434. 36 Ryan AS, Nicklas BJ, Dennis KE: Aerobic exercise maintains regional bone mineral density during weight loss in postmenopausal women. J Appl Physiol 1998;84:1305–1310. 37 Garrow JS, Summerbell CD: Meta-analysis: effect of exercise, with or without dieting, on the body composition of overweight subjects. Eur J Clin Nutr 1995; 49:1–10. 38 Binder EF, et al: Effects of exercise training on frailty in community-dwelling older adults: results of a randomized, controlled trial. J Am Geriatr Soc 2002;50: 1921–1928. 39 Seguin R, Nelson ME: The benefits of strength training for older adults. Am J Prev Med 2003;25: 141–149. 40 Chang JT, et al: Interventions for the prevention of falls in older adults: systematic review and metaanalysis of randomised clinical trials. BMJ 2004; 328:680. 41 Gibbons RJ, et al: ACC/AHA 2002 guideline update for exercise testing: summary article. A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines (Committee to Update the 1997 Exercise Testing Guidelines). J Am Coll Cardiol 2002;40:1531–1540. 42 St Peter SD, Craft RO, Tiede JL, Swain JM: Impact of advanced age on weight loss and health benefits after laparoscopic gastric bypass. Arch Surg 2005;140: 165–168. 43 Sosa JL, Pombo H, Pallavicini H, Ruiz-Rodriguez M: Laparoscopic gastric bypass beyond age 60. Obes Surg 2004;14:1398–1401. 44 Sugerman HJ, et al: Effects of bariatric surgery in older patients. Ann Surg 2004;240:243–247.

Ian McPhee Chapman, MBBS, PhD Department of Medicine, Royal Adelaide Hospital University of Adelaide, Level 6 Eleanor Harrald Building North Terrace, Adelaide 5000 (Australia) Tel. ⫹61 8 82224162, Fax ⫹61 8 82233870, E-Mail [email protected]

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Models of ‘Obesity’ in Large Animals and Birds Iain J. Clarke Department of Physiology, Monash University, Melbourne, Australia

Abstract Most laboratory-based research on obesity is carried out in rodents, but there are a number of other interesting models in the animal kingdom that are instructive. This includes domesticated animal species such as pigs and sheep, as well as wild, migrating and hibernating species. Larger animals allow particular experimental manipulations that are not possible in smaller animals and especially useful models have been developed to address issues such as manipulation of fetal development. Although some of the most well-studied models are ruminants, with metabolic control that differs from monogastrics, the general principles of metabolic regulation still pertain. It is possible to obtain much more accurate endocrine profiles in larger animals and this has provided important data in relation to leptin and ghrelin physiology. Genetic models have been created in domesticated animals through selection and these complement those of the laboratory rodent. This short review highlights particular areas of research in domesticated and wild species that expand our knowledge of systems that are important for our underCopyright © 2008 S. Karger AG, Basel standing of obesity and metabolism.

Obesity is clearly a major issue for humans and there is a very substantial research effort directed towards a better understanding of the causes and consequences of the problem. Basic and clinical researchers seek ways to prevent or treat obesity, and most laboratory-based research is carried out in rodents such as rats and mice, since these species have obvious advantages. Nevertheless, a range of other species offer special attributes that allow lines of investigation different to those achievable with small laboratory rodents. In particular, domesticated animal species allow some experimental procedures that are not possible in smaller animals. A good example of this is the methodology for the sampling of hypothalamic secretions into the hypophysial portal blood in a sentient state; this was established as a technique 25 years ago and is clearly the method of choice if one wishes to measure the output of hypothalamic hormones. Advantages in using livestock and poultry for studies of metabolic function and body composition are as follows:

– Farmers have been selecting for various traits related to millennia which has segregated genes in relation to breed. Genomic analysis is well advanced for a number of breeds and gene chips are available for species such as cattle, pigs and poultry. – Most farm animals are relatively easy to keep and are low cost, without the requirement for special housing. Their ‘natural’ habitat and social structure is also somewhat different to that of laboratory rodents in cages. – Larger animals allow serial sampling of blood, other body fluids and tissues. Serial sampling of cerebrospinal fluid can provide an index of neuronal activity. Multiple sampling of fat and muscle tissues allows longitudinal analysis of metabolism and different compartments of fat can be studied by serial sampling. – Domesticated animals are not nocturnal, as are rodents, so that important diurnal patterns of behaviour and physiology of the former are more similar to humans. Environmental effects on food intake, energy expenditure and metabolic function may be studied and the role of factors such as photoperiod, are well described. Disadvantages in using livestock and poultry are that the technology for genetic manipulation is not as well developed as in small laboratory species such as mice. Nevertheless, cloning is clearly possible in sheep (e.g. Dolly) and is now a relatively straightforward procedure in cattle. The creation of transgenic animals is also somewhat more difficult, although this has been achieved; it is only a matter of time before this becomes more straightforward. Mice are obviously the species of choice for gene knock-out and knock-in studies. Genetic selection studies can be done in domesticated animals, but the longer generation times, compared to those of smaller laboratory species, make this somewhat arduous. Nevertheless, important models of obesity can be generated and should provide important insights into the genetics of obesity and related physiology. This chapter will discuss how non-rodent models of metabolic function, appetite and energy expenditure contribute to our understanding of the mechanisms involved in the development of obesity. Predisposing genetic and epigenetic factors will be examined and endocrine factors will be discussed. Natural models of appetite drive and energy expenditure will be explored, including those that are found in migrating species and those in animals adapted to extreme environments.

Metabolic Regulation in Large Animals and Birds

In the broadest sense, the model of metabolic control that pertains to laboratory rats and mice as well as humans also relates to larger animals and birds. There are, however, some differences that are mentioned below. In animals subjected to ‘natural’ environment, especially in non-domesticated species, seasonal patterns of energy intake and expenditure are evident and can be quite profound. Some widely

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studied species, such as sheep and cattle, are ruminants and this has significant implications in regard to metabolic control. Ruminants have 4 stomachs, which are specialised for the digestion of cellulose, and the predominant source of energy from nutrient is absorbed as volatile fatty acids. Accordingly, the livers of ruminants have exceptional capacity for gluconeogenesis and low hexokinase activity, favouring glucose rather than glucose-6-phosphate which is a substrate for complex carbohydrates [1]. This means that the animal is able to ‘buffer’ blood glucose levels very effectively with minimal changes over extreme body conditions. Thus, in sheep, profound alteration of body weight and adiposity alters insulin levels but does not change plasma glucose levels and a 72-hour fast has minimal effect on plasma glucose levels [2]. On the other hand, short-term fasting elevates plasma non-esterified fatty acid levels, indicating mobilisation of fat reserves to provide substrate for gluconeogenesis. The study of ruminants is valuable because it allows us to compare and contrast control of energy balance in animals that have different metabolic substrates. In terms of appetite control by central mechanisms, there is also widespread conformity across the species, although some species differences are apparent. Accordingly, the most important appetite regulators such as neuropeptide Y (NPY) and agouti-related peptide (AgRP) are found in all species, including avians. In sheep, as in small laboratory rodents, leptin reduces food intake when administered intracerebroventricularly or peripherally [3, 4], even though these animals are ruminants and do not experience short-term emptying of their gastrointestinal tract. The acute response to leptin treatment in the ruminant, in terms of food intake, is similar to that observed in rats [5] such that, in both, the anorectic effect is not sustained with continued treatment. In rats given continuous treatment, body weight is reduced and remains low, even though food intake returns to normal with continued leptin treatment; this seems most likely due to an effect of leptin on energy expenditure. Intracerebroventricular infusion of leptin into sheep for 3 months did not cause profound loss of body weight [6], even though other effects (such as on bone morphology and function) were manifest. In birds (quail), leptin treatment caused a transient reduction in food intake and body weight, but this lasted for only 2 days [7]. Thus, sustained loss of body weight occurs in rats treated with leptin, but this does not occur in sheep or birds. Extensive studies in cattle and in pigs have led investigators in these fields to conclude that the predominant role of leptin is to act as a ‘barometer’ of energy reserve, such that low levels of circulating leptin indicate energy deficiency. In states of negative energy balance, the neuroendocrine system of the brain responds in a predicable manner. Another ‘appetite regulator’ that has received widespread attention is ghrelin, produced by the stomach. In laboratory species and in humans, this hormone stimulates food intake [reviewed in 8], but knockout mice show minimal phenotype [9], raising the question as to the extent that ghrelin is involved in normal metabolic regulation. Given the precedent that NPY knockout mice also have only minor

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perturbation in relation to metabolic function, the genetic models may not be as important as the observations which show that ghrelin administration has effect. Certainly, it is clear in sheep [10] that ghrelin levels rise pre-prandially and fall post-prandially, suggesting an important regulatory role in metabolic process. Interestingly, exhaustive attempts to demonstrate a central role for ghrelin in the regulation of food intake of sheep consistently showed no effect [11], but recent studies have shown that peripherally infused ghrelin did stimulate food intake [12] in this species. Other peripheral effects of ghrelin have been shown in sheep, such as enhancement of glucose-induced insulin secretion. The expression of the ghrelin receptor gene is altered with changing adiposity in sheep [13], increasing in lean condition, suggesting that correction in the ghrelin signalling to the CNS corrects with altered metabolic state. Growth hormone (GH) secretion is elevated in lean sheep, so an increase in ghrelin signalling may be permissive of this since ghrelin is a GH secretagogue. In contrast to the situation in mammals, centrally delivered or peripherally administered ghrelin reduces food intake in the chicken [14]. On the other hand, the lack of an effect on heat production is similar to the effect in mammals. Extensive studies of appetite regulators have been made in various domestic species, with few surprises, compared to the effects observed in laboratory rodents. NPY is a potent orexigen in laboratory species and the same is true for large animals, such as sheep [15] as well as birds [16]. In particular, production in the arcuate nucleus of the hypothalamus is upregulated in lean sheep [17], which may be an adaptive mechanism to stimulate food intake. In this species, the orexigenic effect is mediated through the Y1 subtype receptors [18]. Y2 subtype-specific agonists had no effect on food intake in sheep when delivered intracerebroventricularly, in spite of an effect on the reproductive axis. Thus, whereas PYY3–36 may act via the Y2 receptor to inhibit food intake in rats and humans [19], this mechanism does not appear to be operative in sheep. Leptin reduces NPY expression in the arcuate nucleus of normally fed sheep, as in other species, but this effect is not seen in animals of lean condition. Presumably, the upregulated NPY expression and the appetite drive is such that it does not respond to satiety signals. The role of the melanocortin system in control of homeostasis is well documented for smaller laboratory animals; this is not the case for domestic animals. Melanocortins, produced by the pro-opiomelanocortin (POMC)-expressing cells of the arcuate nucleus, are anorexigenic in rodents [reviewed in 20], although there are no published data to show an effect in species such as sheep, pigs or birds. Unpublished work by Henry and Clarke showed a negative effect of melanocortin agonist on food intake in sheep. Studies in rodents show that reduction in body weight reduces POMC expression in the arcuate nucleus, but this does not appear to be true for the sheep [21]. Even so, the melanocortin system appears to be important in appetite regulation in this species, since AgRP (an endogenous melanocortin receptor antagonist) stimulates food intake [22].

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Fig. 1. Insulin resistance develops with increasing adiposity in sheep. Hyper-insulinaemic-euglycaemic clamps were performed in ovariectomised female ewes of 3 different levels of body weight and adiposity as indicated in the upper two panels. Reduction in body weight was achieved by dietary restriction and elevated body weight was obtained by high-energy supplementation. Glucose utilisation rates (GURs) were calculated for infusion (i.v.) of insulin at the rate of 2 or 6 mU/kg/min. Significance level ⬍0.01 [Clarke and Clarke, unpubl. data].

Insulin Dynamics in Ruminants Are Similar to Those in Other Species

In sheep [10] and in cattle [1], insulin levels rise after a meal and glucose levels fall, in spite of the fact that these animals absorb most of their energy from substrates other than sugar. The rise in insulin levels leads to a fall in plasma glucose levels. It is well known that the obese state leads to a state of insulin resistance in humans and in laboratory animals. Even though plasma glucose levels remain relatively stable over a wide range of body weight in sheep (vide supra), insulin resistance develops with increasing adiposity. This is exemplified in figure 1. In spite of this, obese ruminants do not progress to fully developed type 1 diabetes, but the reason for this is not known.

Endocrine and Reproductive Effects of Leptin (Leptin as a Barometer)

The notion that leptin informs the brain of metabolic status has been expounded by various writers in the field and this is well demonstrated in a recent human study

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[23], showing reproductive function is compromised by a 3-day fast, but can be fully restored by leptin replacement. The central system (gonadotropin-releasing hormone, GnRH, neurons) controlling reproductive function is a sensitive barometer of energy status, even thought the GnRH neurons do not express leptin receptors. At the other end of the spectrum, obesity compromises reproductive function in humans. This may relate to so-called ‘leptin resistance’ but does not appear to occur in large animal species, which do not show reproductive compromise.

Genetic Models of Obesity

There is a wide variety of genetic models of obesity in rodents, due to the ease with which transgenics can be created. In domesticated animals, genetic models have relied on selection or identification of naturally occurring mutations. A flock of sheep in New Zealand was screened for backfat thickness and then the extreme phenotypes were backcrossed through a number of generations [24]. The selection process created flocks of fat, lean and normal (unselected) groups. Interestingly, the three groups have similar body weights and food intake, but differ profoundly in the level of adiposity. Predictable differences are seen in circulating GH concentrations, with lean animals having high levels and fat animals having low levels. No single mutation has been identified in these animals and the genetic selection was probably polygenic. This is consistent with observations made in humans, lending support to the notion that body conformation has a high heritability. Studies are in progress to ascertain the levels of gene expression for appetite-regulating peptides in the hypothalami of these animals, but analysis of a range of body tissues may be required in order to understand the genetic and/or phenotypic character of these animals. Polymorphisms have been identified in the leptin gene of the pig and more extensively in dairy cattle [reviewed in 25]. Various polymorphisms in the bovine gene appear to be important for production (such as milk yield) and fertility. In some cases, polymorphisms have been linked to food intake and relative adiposity, but further studies are probably required before this can be regarded as conclusive. Perhaps the most interesting allelic variant is the C to T transition in exon 2 of the leptin gene, which causes an Arg25Cys transition in the mature protein [26]. The T allele is associated with fatter carcasses. It has been speculated that the extra Cys in the leptin sequence destabilises the molecule, which normally has a single Cys-Cys disulphide bridge. Another explanation offered by these researchers is that the transition affects the A helix of the leptin molecule which affects binding to the leptin receptor.

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Date Fig. 2. Natural variation in the propensity to gain weight with high-energy diet. Mature ovariectomised ewes were run on pasture with supplementation of pasture hay. Over the period indicated by the bars, the animals were given a dietary supplement of 500 g lupin grain per week as a highenergy source. The animals were managed so that there was no competition for the supplement and were weighed at regular intervals. As can be seen from the heavy lines representing two animals of similar starting weight, particular animals have a propensity to gain much more weight than others in a repeatable manner [Clarke, unpubl. data].

Predisposition to Obesity

The rodent model of diet-induced obesity that is unmasked by feeding a high fat diet is one which has particular pertinence to the human condition of obesity and has been extensively studied by Levin and colleagues. This model is predicated on the propensity of a subset of animals in a population becoming obese (diet-induced obese) when fed a high-fat diet as opposed to another subset that do not develop obesity (diet-resistant) on the same diet. The model unmasks genetic variants in the population. Such a model can also be applied to sheep even though these animals are ruminants and do not eat high-fat foodstuffs. Figure 2 shows data from a group of animals that were given a high-energy dietary supplement (lupin grain) for various periods over a 5-year period. Although the animals were of similar body weight at the start of the study, it is clear that some animals gained more weight on the high-energy supplement than others. The data from this relatively small sample of animals suggest that the relative propensity to become obese with high-energy intake is genetically determined in sheep as in other species.

Natural Models of Changes in Adiposity

Non-domesticated, semi-domesticated and feral animals provide especially useful models since they respond to environmental cues to control of appetite, energy

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expenditure and metabolic function. An extreme example of such a model is found in reindeer, which adapt to an especially harsh environment. In the wild state, these animals live in snow-covered territory for up to 200 days/year, when the only food available is lichen, for which they must dig in the snow. It is understandable that they possess inbuilt adaptive mechanisms that promote food intake to increase body reserves in the summer months. An example of the seasonal change in voluntary food intake of these beasts is provided by Mesteig et al. [27], who studied an animal that ate 33.8 g dry matter/kg body weight/day in mid-summer and 14.5 g dry matter/kg body weight/day in mid-winter. The study of appetite-driving mechanisms of these animals and seasonality of different adipose stores could prove extremely informative. Given that reindeer are probably somewhat difficult to procure for scientific study, alternatives for the examination of seasonality and metabolic function are sheep and other smaller species. The Soay breed is derived from animals that became feral on the Island of St Kilda in the Atlantic Ocean, when people departed. Some of this breed are now found on the British mainland and represent a valuable research resource. Soay sheep display marked seasonal cycles of food intake and energy expenditure due to photoperiodic regulation. Thus, under laboratory conditions, food intake is high in long-day photoperiod and low in short-day photoperiod. This photoperiodically controlled cycle is driven by alterations in the expression of NPY, AgRP and POMC genes in the arcuate nucleus of the hypothalamus and melanin-concentrating hormone in the lateral hypothalamus [28, 29]. Interestingly, the pattern of response to short-day photoperiod is different in gonad-intact and gonadectomised animals (fig. 2), indicating an intimate interaction between the seasonal reproductive cycle and the appetite cycle. Another recent paper [3] showed that there is marked seasonality in the extent to which leptin is transferred from blood to the cerebrospinal fluid in Soay sheep, but this transfer was greater under long-day photoperiod, when food intake is maximal. Migrating birds provide another very interesting example of programmed storage of energy in fat. In such species that migrate south for the winter, a number of stopovers are made to restore energy reserves. One remarkable case is that of the semipalmated sandpiper, which migrates from the arctic, stopping at the Bay of Fundy in Canada to restore body reserves before a non-stop flight to South America. During this 2-week stopover, the birds double in body weight. Interestingly, the birds feed on a particular mud shrimp, which has unusually high levels of n-3 polyunsaturated fatty acids and it has been hypothesised that this singular diet contains ‘performance-enhancing’ substances [30]. Most fat is deposited in adipose depots and to a lesser extent in muscle and the fat composition of the birds is modified during refuelling, with an increase in the level of unsaturated fats, so as to maximise efficiency of conversion. This massive intake of particular fatty acids modifies the muscle membranes of the birds and increases efficiency for flight. Whereas this study showed that there was very little deposition of fat in the liver, another recent study of migratory dunlins showed that the liver synthesises leptin [31]; as to whether the latter represents an important signalling system in the bird, remains to be determined.

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Understanding the particular dietary preferences and the factors that drive appetite and appetite preference during migration would be most instructive. The study of hibernating animals could also prove most instructive with respect to the mechanisms controlling food intake and energy expenditure. Whereas much is known about the phenomenon and the switch from carbohydrate to fatty acid metabolism, very little is known about the central control mechanisms [32]. Understanding the profound circannual cycles of appetite drive, fat deposition and altered metabolism during torpor could provide key indicators of points at which fat accumulation and/or utilisation could be manipulated.

Early-Life Events and Epigenetics

Large animals with relatively long gestation periods are ideal for the study of fetal development because the fetus can be accessed surgically for the purpose of sampling or manipulation. Studies on effects of maternal overfeeding, underfeeding, disease, environmental stress (such as heat) and toxins have been carried out in ruminants, pigs and horses. Intra-uterine growth retardation of the fetus in the sheep has been widely used as a model of low birth weight. This can be achieved by surgical removal of some of the placental caruncles, restriction of the uterine arterial flow, prenatal testosterone treatment (female fetuses only) or altered nutrition (over-nutrient or under-nutrition) of the mother. The result is reduced birth weight, followed by catchup growth with significant sequelae in adult life, such as disturbed metabolic ‘set point’, insulin resistance, hypertension and relative obesity amongst other things [reviewed in 33] and with focus on large animals [34]. In general, intra-uterine growth retardation results in reduced muscle fibre and increased adipose mass in the offspring. Some work on possible epigenetic effects of environmental factors has been carried out in sheep. For example, treatment of ewes with bisphenol between days 30–90 of gestation led to reduced birth weights and minor reproductive disruptions, but metabolic effects were not reported [35]. It would be of considerable interest to ascertain whether domesticated and/or wild animals show metabolic perturbations and/or develop obesity following endocrine disruption, since exposure to environmental disruptors such as pesticides is likely in particular habitats.

Conclusion

This brief review has sought to highlight how work on species other than laboratory rodents has contributed to our understanding of mechanisms that control adiposity. The examples that have been discussed exemplify the utility of work in these species, in an attempt to promulgate the notion that examination of a range of animal models will enhance our knowledge of the fundamental mechanisms of homeostasis.

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References 1 Allen MS, Bradford BJ, Harvatine KJ: The cow as a model to study food intake regulation. Annu Rev Nutr 2005;25:523–547. 2 Henry BA, Goding JW, Tilbrook AJ, Dunshea FR, Blache D, Clarke IJ: Leptin-mediated effects of undernutrition or fasting on luteinizing hormone and growth hormone secretion in ovariectomized ewes depend on the duration of metabolic perturbation. J Neuroendocrinol 2004;16:244–255. 3 Adam CL, Findlay PA, Miller DW: Blood-brain leptin transport and appetite and reproductive neuroendocrine responses to intracerebroventricular leptin injection in sheep: influence of photoperiod. Endocrinology 2006;147:4589–4598. 4 Henry BA, Goding JW, Alexander WS, Tilbrook AJ, Canny BJ, Dunshea F, Rao A, Mansell A, Clarke IJ: Central administration of leptin to ovariectomized ewes inhibits food intake without affecting the secretion of hormones from the pituitary gland: evidence for a dissociation of effects on appetite and neuroendocrine function. Endocrinology 1999;140: 1175–1182. 5 Pal R, Sahu A: Leptin signaling in the hypothalamus during chronic central leptin infusion. Endocrinology 2003;144:3789–3798. 6 Pogoda P, Egermann M, Schnell JC, Priemel M, Schilling AF, Alini M, Schinke T, Rueger JM, Schneider E, Clarke I, Amling M: Leptin inhibits bone formation not only in rodents, but also in sheep. J Bone Miner Res 2006;21:1591–1599. 7 Lohmus M, Sundstrom LF, Silverin B: Chronic administration of leptin in Asian Blue Quail. J Exp Zoolog A Comp Exp Biol 2006;305:13–22. 8 Otto B, Spranger J, Benoit SC, Clegg DJ, Tschop MH: The many faces of ghrelin: new perspectives for nutrition research? Br J Nutr 2005;93:765–771. 9 Sun Y, Ahmed S, Smith RG: Deletion of ghrelin impairs neither growth nor appetite. Mol Cell Biol 2003;23:7973–7981. 10 Sugino T, Hasegawa Y, Kikkawa Y, Yamaura J, Yamagishi M, Kurose Y, Kojima M, Kangawa K, Terashima Y: A transient ghrelin surge occurs just before feeding in a scheduled meal-fed sheep. Biochem Biophys Res Commun 2002;295:255–260. 11 Iqbal J, Kurose Y, Canny B, Clarke IJ: Effects of central infusion of ghrelin on food intake and plasma levels of growth hormone, luteinizing hormone, prolactin, and cortisol secretion in sheep. Endocrinology 2006;147: 510–519. 12 Grouselle D, Bluet-Pajor MT, Caraty A, Tillet Y, Epelbaum J: Circulating but not CSF, ghrelin is involved in food intake in sheep. In Proceedings 6th International Congress Neuroendocrinology. Pittsburgh, USA, 2006:35.

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13 Kurose Y, Iqbal J, Rao A, Murata Y, Hasegawa Y, Terashima Y, Kojima M, Kangawa K, Clarke IJ: Changes in expression of the genes for the leptin receptor and the growth hormone-releasing peptide/ ghrelin receptor in the hypothalamic arcuate nucleus with long-term manipulation of adiposity by dietary means. J Neuroendocrinol 2005;17: 331–340. 14 Geelissen SM, Swennen Q, Geyten SV, Kuhn ER, Kaiya H, Kangawa K, Decuypere E, Buyse J, Darras VM: Peripheral ghrelin reduces food intake and respiratory quotient in chicken. Domest Anim Endocrinol 2006;30:108–116. 15 Miner JL, Della-Fera MA, Paterson JA, Baile CA: Lateral cerebroventricular injection of neuropeptide Y stimulates feeding in sheep. Am J Physiol 1989;257: R383–R387. 16 Richardson RD, Boswell T, Raffety BD, Seeley RJ, Wingfield JC, Woods SC: NPY increases food intake in white-crowned sparrows: effect in short and long photoperiods. Am J Physiol 1995;268: R1418–R1422. 17 Barker-Gibb ML, Clarke IJ: Increased galanin and neuropeptide-Y immunoreactivity within the hypothalamus of ovariectomised ewes following a prolonged period of reduced body weight is associated with changes in plasma growth hormone but not gonadotropin levels. Neuroendocrinology 1996;64: 194–207. 18 Clarke IJ, Backholer K, Tilbrook AJ: Y2 receptorselective agonist delays the estrogen-induced luteinizing hormone surge in ovariectomized ewes, but y1-receptor-selective agonist stimulates voluntary food intake. Endocrinology 2005;146:769–775. 19 Batterham RL, Cowley MA, Small CJ, Herzog H, Cohen MA, Dakin CL, Wren AM, Brynes AE, Low MJ, Ghatei MA, Cone RD, Bloom SR: Gut hormone PYY(3–36) physiologically inhibits food intake. Nature 2002;418:650–654. 20 Schwartz MW, Woods SC, Porte D Jr, Seeley RJ, Baskin DG: Central nervous system control of food intake. Nature 2000;404:661–671. 21 Henry BA, Tilbrook AJ, Dunshea FR, Rao A, Blache D, Martin GB, Clarke IJ: Long-term alterations in adiposity affect the expression of melaninconcentrating hormone and enkephalin but not proopiomelanocortin in the hypothalamus of ovariectomized ewes. Endocrinology 2000;141: 1506–1514. 22 Wagner CG, McMahon CD, Marks DL, Daniel JA, Steele B, Sartin JL: A role for agouti-related protein in appetite regulation in a species with continuous nutrient delivery. Neuroendocrinology 2004;80: 210–218.

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23 Chan JL, Matarese G, Shetty GK, Raciti P, Kelesidis I, Aufiero D, De Rosa V, Perna F, Fontana S, Mantzoros CS: Differential regulation of metabolic, neuroendocrine, and immune function by leptin in humans. Proc Natl Acad Sci USA 2006;103:8481–8486. 24 Francis SM, Venters SJ, Duxson MJ, Suttie JM: Differences in pituitary cell number but not cell type between genetically lean and fat Coopworth sheep. Domest Anim Endocrinol 2000;18:229–239. 25 Liefers SC, Veerkamp RF, Te Pas MF, Chilliard Y, van der Lende T: Genetics and physiology of leptin in periparturient dairy cows. Domest Anim Endocrinol 2005;29:227–238. 26 Buchanan FC, Fitzsimmons CJ, Van Kessel AG, Thue TD, Winkelman-Sim DC, Schmutz SM: Association of a missense mutation in the bovine leptin gene with carcass fat content and leptin mRNA levels. Genet Sel Evol 2002;34:105–116. 27 Mesteig K, Tyler NJ, Blix AS: Seasonal changes in heart rate and food intake in reindeer (Rangifer tarandus tarandus). Acta Physiol Scand 2000;170: 145–151. 28 Anukulkitch C, Rao A, Dunshea FR, Blache D, Lincoln GA, Clarke IJ: Influence of photoperiod and gonadal status on food intake, adiposity and gene expression of hypothalamic appetite regulators in a seasonal mammal. Am J Physiol Regul Integr Comp Physiol 2006;292:R242–R252.

29 Clarke IJ, Rao A, Chilliard Y, Delavaud C, Lincoln GA: Photoperiod effects on gene expression for hypothalamic appetite-regulating peptides and food intake in the ram. Am J Physiol Regul Integr Comp Physiol 2003;284:R101–R115. 30 Maillet D, Weber JM: Performance-enhancing role of dietary fatty acids in a long-distance migrant shorebird: the semipalmated sandpiper. J Exp Biol 2006; 209:2686–2695. 31 Kochan Z, Karbowska J, Meissner W: Leptin is synthesized in the liver and adipose tissue of the dunlin (Calidris alpina). Gen Comp Endocrinol 2006;148: 336–339. 32 Carey HV, Andrews MT, Martin SL: Mammalian hibernation: cellular and molecular responses to depressed metabolism and low temperature. Physiol Rev 2003;83:1153–1181. 33 Gluckman PD, Hanson MA, Pinal C: The developmental origins of adult disease. Matern Child Nutr 2005;1:130–141. 34 Wu G, Bazer FW, Wallace JM, Spencer TE: Boardinvited review: intrauterine growth retardation: implications for the animal sciences. J Anim Sci 2006;84:2316–2337. 35 Savabieasfahani M, Kannan K, Astapova O, Evans NP, Padmanabhan V: Developmental Programming: Differential Effects Of Prenatal Exposure To Bisphenol-A Or Methoxychlor On Reproductive Function. Endocrinology 2006.

Prof. Iain J. Clarke Department of Physiology, Monash University Building 13F, Clayton, Melbourne, Victoria 3800 (Australia) Tel. ⫹61 3 9905 2554, Fax ⫹61 3 9905 2547, E-Mail [email protected]

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The ␤-Cell in Type 2 Diabetes and in Obesity Guy A. Ruttera
Laura E. Partonb a

Department of Cell Biology, Division of Medicine, Imperial College London, London, UK; bBeth Israel Deaconess Medical Center, Harvard Medical School, Boston, Mass., USA

Abstract The current worldwide epidemic of obesity and metabolic diseases has energised the search for new approaches to treat these conditions. Type 2 diabetes appears to involve an interplay between susceptible genetic backgrounds and environmental factors including highly calorific westernised diets. The latter may generate ‘glucolipotoxic’ conditions which affect both the pancreatic β -cell and insulin-sensitive tissues. Here we focus on efforts to better understand the basic signalling mechanisms through which the β -cell senses changes in glucose concentration and how this process may become defective in type 2 diabetes. The recent demonstrations, through whole genome association studies, of important roles for genes involved in the control of cell cycle, as well as intracellular ion homeostasis, further highlight the central role of the β -cell in both the pathogenesis of the disease and as a therapeutic target. Copyright © 2008 S. Karger AG, Basel

Diabetes mellitus is emerging as an ‘epidemic’ of the 21st century, currently afflicting 150 million individuals and projected to affect 200 million by 2025 [1]. Likewise, obesity is a growing health care problem with ⬃20% of UK. (http://www.statistics.gov.uk/StatBase/) and 30% of the U.S. (http://www.cdc.gov/nccdphp/dnpa/obesity/) adult population now clinically obese. In this review we will discuss potential mechanisms whereby -cell function and/or mass are altered both in each condition.

Stimulus-Secretion Coupling in the Healthy ␤-Cell

Insulin secretion is usually tightly regulated and ensures near-constant blood glucose levels in the face of changes in supply and utilisation. At the level of the individual -cell, glucose is generally believed to prompt the first phase of insulin secretion via metabolic coupling mechanisms [2–6]. Uptake of

glucose involves a facilitative glucose transporter (Glut2 in rodents; Glut2 or Glut1 in man) [7], followed by glucokinase-mediated glucose phosphorylation [8–10] and the glycolytic generation of pyruvate. Oxidation of pyruvate by mitochondria is strongly favoured over its conversion to lactate by remarkably low levels of lactate dehydrogenase [11–14] and plasma membrane lactate/monocarboxylate (MCT) transport activity [15, 16] in -cells: disposal of glycolytic NADH is facilitated by correspondingly elevated levels of mitochondrial glycerolphosphate dehydrogenase activity [11, 17] and ensures that at least 85% of glucose carbon is fully oxidised to CO2 and H2O [11, 13]. Enhanced mitochondrial oxidative metabolism [18] then leads to increases in intracellular ATP/(ADPAMP) ratio [19, 20] and the closure of ATP-sensitive K (KATP) channels [21–23] followed by cell depolarisation. The consequent opening of L-type voltage-gated Ca2 channels and Ca2 influx [24] prompt the exocytosis of preformed large dense core insulin-containing vesicles at the plasma membrane [25] via still incompletely defined vesicle-associated Ca2 sensors such as synaptotagmin V [26], Ca2-dependent activator protein for secretion [27] or cysteine string protein [28, 29]. Increases in intracellular citrate, malonyl-CoA [30] or glutamate [31] have each been proposed to play a role in amplification, although both the ‘malonyl-CoA’ [32] and ‘glutamate’ [33] hypotheses have been challenged. A further important player in the control of insulin secretion are nutrient-sensitive protein kinases including AMP-activated protein kinase (AMPK) [34–38] and perarnt-sim kinase [39]. Whereas the former seems likely to be regulated through changes in the concentrations of adenine nucleotides [34] and to affect recruitment of dense core vesicles to the cell surface [40], the means by which per-arnt-sim kinase is regulated remain obscure. These mechanisms are illustrated in figure 1.

The ␤-Cell in Diabetes and Obesity

The ability of the -cell to respond to changes in secretory demand is illustrated by the fact that while most type 2 diabetics are insulin resistant, the majority of insulin resistant obese patients are not diabetic. At present, however, the cues through which insulin resistance leads to compensatory increases in -cell function and mass remain for the most part obscure. Whereas the autoimmuno-mediated destruction of -cells underlies the pathology of type 1 diabetes [41], the role of any loss of -cell mass in type 2 diabetes (T2D), although observed to be as great as 60% [42], is hotly debated [21]. Importantly, large changes in -cell number were only observed in cases of the very advanced disease associated with islet amyloidosis, and it is unclear whether these are either the ‘primary’ cause of the secretory inadequacy or even sufficient to lead to frank diabetes. By contrast, there seems little doubt that deterioration of glucose-stimulated insulin secretion (GSIS) from the extant -cell complement in humans [43] as well as in other species [44–47] is an early and critical component in the pathophysiology of this disease [48].

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ATP-sensitive K channel closes

X

Glucose



ATP

Lactate

Glut2 Glucose

Ca2 channel opens

GK

Pyruvate

Ca2

AMPK

G-6-P

PKC IP3 PLC

DAG

Docking

Priming Ca

Kiss and run

2

Ca2

Insulin release Fig. 1. Molecular mechanisms of GSIS in the healthy -cell. Uptake and phosphorylation (via glucokinase, GK) of glucose ultimately leads to increases in intracellular ATP concentrations, closure of KATP, plasma membrane depolarisation and Ca2 influx. Fusion of docked vesicles at the plasma membrane via a transient ‘kiss and run’ or ‘cavity recapture’ mechanisms leads to release of cargo insulin. Increases in ATP/AMP ratio also cause inhibition of AMPK, leading to the mobilisation of vesicles from a reserve to a readily releasable pool and the ‘second phase’ of insulin release. Note that non-nutrient secretagogues such as acetyl-choline may potentiate secretion by activating phospholipase C, generating Ca2 increase and the activation of protein kinase C (PKC) as well as other proteins. See Rutter [6] for other details and the text for discussion of changes in the T2D -cell.

Molecular Basis of Defective Insulin Secretion in T2D

It seems likely that a combination of susceptibility genes and of a ‘pro-diabetic’ environment underlie the development of T2D. We consider these contributions below.

Genetic Contribution As discussed above, both insulin resistance [49] and changes in -cell function [50, 51] appear to underlie the progression of T2D (fig. 2). Strongly suggestive of an inherited component, concordance rates for T2D in monozygotic twins have been reported to be as high as 90% [52], and the dynamics of insulin release (i.e. the size of the first and second phases) are remarkably similar between monozygotic twins [53]. Clear linkage has been demonstrated to individual genes in monogenic forms of diabetes (‘maturity-onset diabetes of the young’, MODY) [54], and has been shown to involve

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Glucotoxicity Lipotoxicity Oxidative stress/ROS Alterations in AMPK activity Elevated SREBP1c/PPAR Apoptosis

NGT

IGT

Rising insulin resistance

Loss of -cell mass

Loss of glucosesensing

-Cell failure

Type 2 diabetes

Fig. 2. Contribution of -cell failure to the development of T2D. Increases in blood glucose concentration during the development of diabetes are indicated by the pyramid on the left, showing the change from normal (NGT) to impaired (IGT) glucose tolerance, before the onset of frank diabetes.

mutations in the transcription factors hepatocyte nuclear factor (HNF) 4 and HNF1 (MODY 1 and MODY3), as well as PDX1/IPF (MODY4) glucokinase (MODY2), all of which affect -cell development or function. Recent studies have also revealed mutations in subunits of the ATP-sensitive potassium channel, kir6.2 (encoded by KCNJ11) [55] and SUR1 (ABCC8) [56] as contributing to certain cases of permanent neonatal diabetes. On the other hand, genetic associations with more common forms of T2D have been more elusive [57]. Genome scanning in several different ethnic groups has identified chromosome regions harbouring susceptibility to more common forms of T2D such as calpain 10 (CAPN10) [58], E23K in KCNJ11 [59] and P12A in PPARG [60], where the ‘odds ratio’ (i.e. the increased likelihood of affected individuals having the disease) is small (⬃1.2). Most recently, and dramatically, a highly significant association with T2D has been demonstrated for polymorphisms in an intronic region of the transcription factor TCF7L2 (formerly known as TCF-4) gene [61–75] involved in wnt/-catenin signalling. The odds ratio for inheritance of the at-risk allele (i.e. the increased risk of diabetes compared to the common allele) is ~1.5. These findings have been confirmed in recent ‘whole genome scans’ [76–78], and it has been estimated that as much as 20% of disease cases may be attributable to variants in this gene [74]. The functional significance of the effects of these intronic changes remains unclear,

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though it is interesting that increases in TCF7L2 are observed in a rodent model of T2D [47]. Likewise, changes in the activity of the zinc transporter ZnT8 appear also to be important, raising the possibility of therapeutics which target zinc accumulation across the plasma membrane as well as into dense core vesicles [79].

Environmental Factors Glucolipotoxicity and b-Cell Dysfunction Both glucose and free fatty acids (FFAs) are essential -cell fuels in the normal state and act to stimulate and potentiate insulin secretion, respectively. However, chronic hyperglycaemia is known to induce multiple -cell defects including early reductions in GSIS [80, 81] and irreversible changes in insulin gene transcription [81–84] and cell mass [85]. Similarly, chronic exposure to FFAs ( 24 h) is known to inhibit insulin gene expression [86–88] and secretion [89–93]. -Cell ‘damage’, and loss of the first phase of insulin secretion, occur in the development of T2D even before fasting blood glucose levels are more than 7 mM, considerably below those considered to be ‘glucotoxic’. Thus, in vivo, other ‘potentiating’ factors, which may include elevated triglyceride or FFA levels [94], as well as proinflammatory cytokines such as IL-1 which may be released directly from -cells [95] [but see 96 for an alternative view] are required to observe deleterious effects on -cell function and/or survival. The toxic effects of chronic glucose may thus result, at least in part, from the effects of glucose on lipid partitioning and the resulting inhibition of FFA oxidation and potentiation of de novo FFA synthesis [97–99]. This observation, along with reports that elevated glucose concentrations are required for the deleterious effects of FFAs [87, 100], have lead to the perhaps more appropriate term ‘glucolipotoxicity’.

FA-Induced b-Cell Apoptosis: Evidence from Animal Models Chronic exposure of pancreatic islets to FFA has been shown to induce -cell death by apoptosis [101, 102] and reductions in -cell proliferation [101, 102] in vitro. Further evidence for a role of FFA in the induction of -cell apoptosis comes from obese animal models of T2D. In rodents, mutations in the ob gene [103], or in genes that encode the leptin receptor (Ob-R), cause leptin deficiency and leptin unresponsiveness, respectively. The ob/ob mouse is an example of absence of functional leptin [103], whereas the db/db mouse [104] and the fa/fa rat [105, 106] lack functional ObR. These animals are hyperphagic due to lack of leptin action on the appetite centres of the hypothalamus [107, 108], which rapidly leads to obesity. This imbalance between calorific intake and energy expenditure is associated not only with an increase in adipose tissue mass, but also with a progressive increase in lipid deposition in non-adipose tissues, including the pancreatic islets [109].

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In the Zucker diabetic fatty (ZDF) rat, onset of obesity occurs at about 4 weeks of age. Initially, as islet lipid content rises to about ten times that of normal levels, there is increased -cell proliferation and -cell mass, along with associated increases in insulin secretion [110, 111]. Initially this ensures sufficient insulin production to compensate for rising insulin resistance. However, as this pre-diabetic stage of obesity progresses, islet lipid content continues to rise and severe mitochondrial abnormalities develop together with marked increases in -cell apoptosis [112]. At this stage, the rate of apoptosis exceeds the rate of -cell replication and a net loss of -cells occurs. Insulin secretion no longer compensates for the increased insulin resistance and so overt diabetes develops.

Role of SREBP1c in b-Cell Failure Sterol regulatory element binding proteins are a family of transcription factors involved in the regulation of genes associated with cholesterol and FFA metabolism. SREBP1c is expressed in pancreatic islet -cells [113] where its expression [114, 115] and activity [116] are induced by elevated [glucose]. Increased expression of SREBP1c has been described in the islets of several animal models of T2D and mounting evidence supports a role for SREBP1c in the development of -cell dysfunction. Increased SREBP1c expression has been described in the islets of ZDF and diet-induced obese rats, where it is correlated with increased lipogenic gene expression and triglyceride content [47, 117]. Furthermore, suppression of SREBP1c expression by treatment with the anti-lipogenic drug, troglitazone, reduces islet triglyceride levels and partially prevents the development of diabetes in ZDF rats [117]. Hence, SREBP1c may play a key role in inducing triglyceride accumulation and as a consequence -cell dysfunction. This induction of lipogenic gene expression by SREBP1c in the -cell was supported by initial studies in -cell lines that demonstrated increased lipogenic gene expression and lipid accumulation in response to overexpression of the active N-terminal portion of SREBP1c [114, 118, 119]. Similar results have since been described in rodent islets, where overexpression of SREBP1c results in increased lipid content which is associated with reduced GSIS [120]. Islets from transgenic mice that overexpress SREBP1c specifically in -cells have increased FAS mRNA expression and elevated lipid content. Importantly, these mice display impaired glucose tolerance as a result of defective insulin secretion [121]. The deleterious effects of SREBP1c on islet function are thought to involve an impairment in glucose-induced ATP production, mediated by an increase in the mitochondrial uncoupling protein, UCP2 [119]. Overexpression of SREBP-1 may also lead to an endoplasmic reticulum stress response, leading ultimately to cell death [120]. We [47] recently showed that inhibition of endogenous SREBP1c activity in ZDF rat islets, whilst restoring elevated basal insulin secretion, had little effect on GSIS, suggesting that SREBP1c alone is not sufficient to induce defective insulin secretion.

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PPARg and Defective Insulin Secretion The thiazolidinedione (TZD) class of antidiabetic drugs act as high-affinity peroxisome proliferator-activated receptors (PPAR) agonists [122]. Mice that lack PPAR specifically in -cells ( knockout mice) have increased islet size, largely due to cell hyperplasia [123]. When fed a high-fat diet to induce insulin resistance and obesity,  knockout islets are protected from obesity-induced hyperplasia, suggesting that PPAR is required for the normal expansion of islet mass observed in obese rodents. The effects of TZD treatment appear to be specific for PPAR in the islet in vitro, as rosiglitazone treatment increased basal and GSIS in normal islets but had no effect on  knockout islets. In contrast however, no differences in serum insulin levels in wild-type or  knockout mice were detected in response to rosiglitazone treatment in vivo, suggesting that TZD action within -cells does not significantly contribute to the effect of these drugs. Whether deletion of PPAR in islet -cells affects the efficacy of TZDs in the context of other models of T2D remains to be established.

Other Molecular Players in Defective Insulin Secretion in T2D As discussed above, the precise molecular basis of the changes in glucose sensing in the T2D -cell remains as yet unclear. There is nonetheless good evidence for decreased glucose-induced ATP rises, and oxygen consumption appears to be decreased in diseased islets from T2D subjects [124] and in rodents, e.g. GK rat [44]. Changes in the expression of multiple genes were identified in islets from ZDF rats [46], including in the pre-diabetic stage, where glucose-induced insulin secretion is abnormal [47]. Of note, the latter study revealed decreases in the expression of the glucose transporter, Glut2. Very recent studies have shown that Glut2 is subject to post-translational modification (N-glycosylation), a process which may become defective in T2D models [125]. A recent microarray-based study of human T2D islets [126] revealed changes in the expression of a wide range of mRNAs, including the glycolytic genes glucose 6phosphate isomerise and phosphofructokinase (note that GK and Glut2 mRNAs displayed a non-significant tendency towards lower levels in T2D islets), and most strikingly in the expression of a PAS domain containing transcription factor termed ARNT/HIF1 (hydrocarbon nuclear receptor translocator/hypoxia-inducible factor 1, decreased 90%). Moreover, knock-out of ARNT selectively in the -cells of mice or silencing in MIN6 cells led to defective glucose-induced insulin secretion. Both in rodent and human studies, decreases in the expression of glycolytic genes were observed, and seem likely to underlie the defective glucose-induced increases in metabolic signalling. Interestingly, increases in the levels of lactate dehydrogenase, which are vanishingly low in -cells [11] (see above and fig. 1) are also observed at least in some models of -cell dysfunction including partial resection [127] and in

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the ZDF rat [47] and suggest that a loss of pyruvate ‘funnelling’ towards mitochondrial oxidation may contribute to defective stimulation of insulin release. Likewise, mutations shown to lead to increases in the level of the plasma membrane lactate/pyruvate (monocarboxylate) transporter, MCT-1, normally absent from mature -cells, have recently been reported [128] in patients suffering from exerciseinduced hyperinsulinism [129]. Whether such individuals display abnormal glucoseinduced insulin secretion, or a greater risk of T2D in later life, is presently unclear. If defective glucose-induced increases in electrical activity and calcium influx are strongly implicated in the pathology of defective insulin secretion in T2D islets, there seems little doubt that ‘downstream’ processes, including the fusion of secretory vesicles at the plasma membrane, may also be affected. Thus, Ostensen et al. [130] found decreases in the expression (at the protein and mRNA levels) of several proteins implicated in exocytosis including SNAP-25, nSec1/Munc18, synpatophysin and synaptotagmin V in human islets obtained post-mortem from 4 T2D subjects versus normoglycaemic controls. Likewise, Abderrahmani et al. [131] reported decreases in the levels of the key exocytotic proteins, including granuphilin/slp4, Noc2, Rab3a and Rab27a, which are proposed to result from an up-regulation of the CREB family member, CREM or ICER (inducible cAMP early repressor) as a result of hyperglycaemia-induced increases in intracellular cAMP levels. By contrast, other studies in animal models [132] have proposed that increases in granuphilin levels are associated with defective insulin secretion in the islets of mice overexpressing the lipogenic transcription factor, SREBP-1c. Finally, inactivation of the Rab protein Rab27, implicated in the regulation of vesicle trafficking to the plasma membrane [131], led to defective GSIS in ashen mice [133]. Whether the expression of downstream effectors of Rab27, including myosin Va [134] might also be altered in T2D is unclear, though we have reported that knockdown of this molecule leads to defective secretion and vesicle motility in MIN6 -cells [135].

Role of Changes in Mitochondrial Oxidative Metabolism in T2D Conditions such as hyperglycaemia and hyperlipidaemia are known to increase superoxide production in pancreatic islets through increased mitochondrial oxidation of glucose and FFAs [136]. In turn, accelerated superoxide production results in increased exposure of the -cell to reactive oxygen species (ROS) which are known to have deleterious effects on islet cell function and survival [137, 138]. A potentially interesting insight into a mechanism of defective GSIS which seems unlikely to involve changes in triglyceride accumulation are substrains of C57BL6 mice, which carry a mutation in the nicotinamide nucleotide transhydrogenase gene [139]. This may lead to a lowering in intramitochondrial levels of reduced glutathione and enhanced damage by ROS. The latter are of particular interest, being implicated in the -cell damage elicited by ‘oxidative stress’ ensuing from chronic elevation of glucose

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concentrations (see above) [140]. In this context, the role of AMPK, whose activation appears to block GSIS [36, 141] (fig. 1), is interesting since activation of the latter enzyme, e.g. with the antidiabetic agent metformin [37, 142], has been suggested to inhibit insulin release from ‘healthy’ -cells and islets, consistent with findings in which activated forms of AMPK are expressed [36, 40, 141] whereas metformin appeared to improve secretion from diabetic islets. One possible explanation for this dichotomy is that the activation of AMPK leads to decreases in islet triglyceride (thanks to the inhibition of acetyl-CoA carboxylase and the consequent lowering of malonyl-CoA and release of inhibition of fatty acid oxidation on carnitine transferase) in islets from (obese) type 2 diabetics. Alternatively, the metformin-elicited inhibition of the respiratory chain at complex I [143], whilst decreasing ATP levels in the healthy -cell and thus compromising glucose-induced ATP increases and stimulated insulin secretion, may chiefly lead to decreased ROS production in the diabetic -cell, thus removing or reducing damage to the cell by these compounds. Whether in vivo exposure to metformin (and, indeed rosiglitazone, which also activates AMPK) at physiologically relevant doses (⬃15 M in the case of metformin) also leads to improved -cell function in diabetes remains to be determined, but is an important question with respect to the development of future therapies based on AMPK as a target. Whether, for example, it may be possible selectively to activate AMPK in extrapancreatic tissues (liver, muscle and adipose tissue), thus exerting positive effects on blood glucose and lipid levels, whilst leaving the -cell enzyme unaffected, is presently unclear. It should be emphasised that this is likely to require drugs, possibly derived from modifications of 5-amino-imidazole-4-carboxamide riboside, which act directly on AMPK complexes of bearing different combinations of the subunits, rather than on agents such as metformin and the TZDs which act via the mitochondrial respiratory chain [143]. Alternatively, agents which act on leptin signalling pathways may also be promising given the fact that leptin stimulates AMPK activity in muscle, whilst exerting no apparent effect on AMPK in -cells [37]. These questions are all the more pressing, given the reported pro-apoptotic effects of AMPK activation in the -cell [142].

Changes in the Mode of Dense Core Vesicle Exocytosis in T2D In an attempt to explore the impact of the above changes in gene expression on insulin-containing vesicle behaviour in living -cells, we have used vesicle targeted probes, a method previously deployed in healthy -cells [144, 145] to monitor dense core vesicle dynamics in rat -cells exposed to elevated glucose concentrations [146]. This treatment, which aimed to mimic the hyperglycaemia observed in vivo in diabetes, led to a shift in the type of exocytosis observed. Thus, studied using total internal refection of fluorescence microscopy, a significant (fivefold) but reversible decrease in the proportion of secretory events which led to the complete release of

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labelled insulin from individual vesicles from ⬃25 to just 5% was found (fig. 1). Whilst it was not possible to observe release events from -cells obtained from 6- to 8-week-old ZDF rats [47], we noted fewer vesicles at the cell surface. On the other hand, treatment of MIN6 -cells with an adenovirus overexpressing SREBP-1c, a manoeuvre previously shown to decrease insulin release at the population level [114] reduced the number and type of event. The latter studies, which involved the use of animals or of model systems in which the -cell triglyceride content was massively increased, may or may not adequately mimic the situation in the ‘typical’ T2D -cell; future studies are required to compare the alterations in vesicle behaviour and the nature of the fusion event in -cells from lean and T2D -cells. Likewise, it will be important to dissect the nature of the changes in gene expression in each scenario.

MicroRNAs

Another exciting area is that of microRNAs. Recent studies by Poy et al. [147] and by Plaisance et al. [148] have revealed the important role played by this new class of short RNA species, termed microRNAs [147], in regulating -cell function. Thus, miR-375 regulates late events in vesicle movement and fusion [147] whereas miR-9 appears to be involved in the control of multiple genes including granuphilin/slp4 [148] which in turn regulate the secretory process. Similarly, our own data [149] indicate that miR-124, an miR whose expression is massively induced during pancreatic development, controls the expression of both key proteins involved in determining cell excitability and Ca2 influx (including KATP channel subunits) but also regulating the sensitivity of the secretory machinery to these Ca2 changes.

Acknowledgements G.A.R. is supported by grants from the Wellcome Trust and the Medical Research Council UK. L.E.P. thanks the American Heart Association for a Fellowship.

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116 Sandberg MB, Fridriksson J, Madsen L, Rishi V, Vinson C, Holmsen H, Berge RK, Mandrup S: Glucose-induced lipogenesis in pancreatic betacells is dependent on SREBP-1. Mol Cell Endocrinol 2005;240:94–106. 117 Kakuma T, Lee Y, Higa M, Wang Z, Pan W, Shimomura I, Unger RH: Leptin, troglitazone, and the expression of sterol regulatory element binding proteins in liver and pancreatic islets [In Process Citation]. Proc Natl Acad Sci USA 2000;97: 8536–8541. 118 Wang H, Maechler P, Antinozzi PA, Herrero L, Hagenfeldt-Johansson KA, Bjorklund A, Wollheim CB: The transcription factor SREBP-1c is instrumental in the development of beta-cell dysfunction. J Biol Chem 2003;278:16622–16629. 119 Yamashita T, Eto K, Okazaki Y, Yamashita S, Yamauchi T, Sekine N, Nagai R, Noda M, Kadowaki T: Role of uncoupling protein-2 up-regulation and triglyceride accumulation in impaired glucose-stimulated insulin secretion in a beta-cell lipotoxicity model overexpressing sterol regulatory element-binding protein-1c. Endocrinology 2004;145:3566–3577. 120 Wang H, Kouri G, Wollheim CB: ER stress and SREBP-1 activation are implicated in beta-cell glucolipotoxicity. J Cell Sci 2005;118:3905–3915. 121 Takahashi A, Motomura K, Kato T, Yoshikawa T, Nakagawa Y, Yahagi N, Sone H, Suzuki H, Toyoshima H, Yamada N, Shimano H: Transgenic mice overexpressing nuclear SREBP-1c in pancreatic beta-cells. Diabetes 2005;54:492–499. 122 Lehmann JM, Moore LB, Smith-Oliver TA, Wilkison WO, Willson TM, Kliewer SA: An antidiabetic thiazolidinedione is a high affinity ligand for peroxisome proliferator-activated receptor gamma (PPAR gamma). J Biol Chem 1995;270:12953–12956. 123 El Assaad W, Buteau J, Peyot ML, Nolan C, Roduit R, Hardy S, Joly E, Dbaibo G, Rosenberg L, Prentki M: Saturated fatty acids synergize with elevated glucose to cause pancreatic beta-cell death. Endocrinology 2003;144:4154–4163. 124 Del Prato S, Marchetti P: Beta- and alpha-cell dysfunction in type 2 diabetes. Horm Metab Res 2004; 36:775–781. 125 Ohtsubo K, Takamatsu S, Minowa MT, Yoshida A, Takeuchi M, Marth JD: Dietary and genetic control of glucose transporter 2 glycosylation promotes insulin secretion in suppressing diabetes. Cell 2005; 123:1307–1321. 126 Gunton JE, Kulkarni RN, Yim S, Okada T, Hawthorne WJ, Tseng YH, Roberson RS, Ricordi C, O’Connell PJ, Gonzalez FJ, Kahn CR: Loss of ARNT/HIF1beta mediates altered gene expression and pancreatic-islet dysfunction in human type 2 diabetes. Cell 2005;122:337–349.

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127 Jonas JC, Sharma A, Hasenkamp W, Ilkova H, Patane G, Laybutt R, Bonner-Weir S, Weir GC: Chronic hyperglycemia triggers loss of pancreatic beta cell differentiation in an animal model of diabetes. J Biol Chem 1999;274:14112–14121. 128 Otonkoski T, Jiao H, Kaminen-Ahola N, Tapia-Piaz I, Ullah MS, Parton LE, Schuit FC, Quintens R, Sipila I, Mayatepek E, Meissner T, Halestrap AP, Rutter GA, Kere J: Physical exercise-induced hyperinsulinemic hypoglycemia caused by failure of monocarboxylate transporter 1 silencing in pancreatic beta cells. Am J Hum Genet 2007, in press (Abstract). 129 Otonkoski T, Kaminen N, Meissner T, Mayatepek E, Kere J, Sipila I: Abnormal responsiveness of insulin release to exogenous pyruvate in patients with physical exercise-induced hyperinsulinemic hypoglycemia. Diabetologia 2001;44:A19 (Abstract). 130 Ostenson CG, Gaisano H, Sheu L, Tibell A, Bartfai T: Impaired gene and protein expression of exocytotic soluble N-ethylmaleimide attachment protein receptor complex proteins in pancreatic islets of type 2 diabetic patients. Diabetes 2006;55:435–440. 131 Abderrahmani A, Cheviet S, Ferdaoussi M, Coppola T, Waeber G, Regazzi R: ICER induced by hyperglycemia represses the expression of genes essential for insulin exocytosis. EMBO J 2006. 132 Kato T, Shimano H, Yamamoto T, Yokoo T, Endo Y, Ishikawa M, Matsuzaka T, Nakagawa Y, Kumadaki S, Yahagi N, Takahashi A, Sone H, Suzuki H, Toyoshima H, Hasty AH, Takahashi S, Gomi H, Izumi T, Yamada N: Granuphilin is activated by SREBP-1c and involved in impaired insulin secretion in diabetic mice. Cell Metab 2006;4:143–154. 133 Kasai K, Ohara-Imaizumi M, Takahashi N, Mizutani S, Zhao S, Kikuta T, Kasai H, Nagamatsu S, Gomi H, Izumi T: Rab27a mediates the tight docking of insulin granules onto the plasma membrane during glucose stimulation. J Clin Invest 2005;115:388–396. 134 Vale RD: Myosin V motor proteins: marching stepwise towards a mechanism. J Cell Biol 2003;163:445–450. 135 Varadi A, Tsuboi T, Rutter GA: Myosin va transports dense core secretory vesicles in pancreatic MIN6 {beta}-cells. Mol Biol Cell 2005;16:2670–2680. 136 Brownlee M: A radical explanation for glucoseinduced beta cell dysfunction. J Clin Invest 2003;112: 1788–1790. 137 Turrens JF: Mitochondrial formation of reactive oxygen species. J Physiol 2003;552:335–344. 138 Robertson RP: Defective insulin secretion in NIDDM: integral part of a multiplier hypothesis. J Cell Biochem 1992;48:227–233. 139 Freeman H, Shimomura K, Horner E, Cox RD, Ashcroft FM: Nicotinamide nucleotide transhydrogenase: a key role in insulin secretion. Cell Metab 2006;3:35–45.

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140 Robertson RP, Harmon J, Tran PO, Poitout V: Beta-cell glucose toxicity, lipotoxicity, and chronic oxidative stress in type 2 diabetes. Diabetes 2004;53:S119–S124. 141 Richards SK, Parton LE, Leclerc I, Rutter GA, Smith RM: Over-expression of AMP-activated protein kinase impairs pancreatic {beta}-cell function in vivo. J Endocrinol 2005;187:225–235. 142 Kefas BA, Heimberg H, Vaulont S, Meisse D, Hue L, Pipeleers D, van de CM: AICA-riboside induces apoptosis of pancreatic beta cells through stimulation of AMP-activated protein kinase. Diabetologia 2003;46:250–254. 143 Owen MR, Doran E, Halestrap AP: Evidence that metformin exerts its anti-diabetic effects through inhibition of complex 1 of the mitochondrial respiratory chain. Biochem J 2000;348:607–614. 144 Pouli AE, Emmanouilidou E, Zhao C, Wasmeier C, Hutton JC, Rutter GA: Secretory granule dynamics visualised in vivo with a phogrin-green fluorescent protein chimaera. Biochem J 1998;333:193–199. 145 Tsuboi T, Rutter GA: Multiple forms of kiss and run exocytosis revealed by evanescent wave microscopy. Curr Biol 2003;13:563–567.

146 Tsuboi T, Ravier MA, Parton LE, Rutter GA: Sustained exposure to elevated glucose concentrations modifies glucose signalling and the mechanics of secretory vesicle fusion in primary rat pancreatic -cells. Diabetes 2006;55:1057–1065. 147 Poy MN, Eliasson L, Krutzfeldt J, Kuwajima S, Ma X, MacDonald PE, Pfeffer S, Tuschl T, Rajewsky N, Rorsman P, Stoffel M: A pancreatic islet-specific microRNA regulates insulin secretion. Nature 2004; 432:226–230. 148 Plaisance V, Abderrahmani A, Perret-Menoud V, Jacquemin P, Lemaigre F, Regazzi R: MicroRNA-9 controls the expression of granuphilin/SLp4 and the secretory response of insulin-producing cells. J Biol Chem 2006. 149 Baroukh N, Ravier MA, Loder MK, Hill EV, Bounacer A, Scharfmann R, Rutter GA, vanObberghen E: MicroRNA-124a2 regulates Foxa2 expression and intracellular signaling in pancreatic -cell lines. J Biol Chem 2007, in press.

Prof. Guy A. Rutter Department of Cell Biology, Division of Medicine, Imperial College London Sir Alexander Fleming Building, Exhibition Road London SW7 2AZ (UK) Tel. 44 20 759 43340, Fax 44 20 759 43351, E-Mail [email protected]

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Role of the Endocannabinoid System in Energy Balance Regulation and Obesity Daniela Cota Department of Psychiatry, University of Cincinnati, Cincinnati, Ohio, USA

Abstract The endogenous cannabinoid system (ECS) is a neuromodulatory system recently recognized to have a role in the regulation of various aspects of eating behavior and energy balance through central and peripheral mechanisms. In the central nervous system, cannabinoid type 1 receptors and their endogenous ligands, the endocannabinoids, are involved in modulating food intake and motivation to consume palatable food. Moreover, the ECS is present in peripheral organs, such as liver, white adipose tissue, muscle, and pancreas, where it seems to be involved in the regulation of lipid and glucose homeostasis. Dysregulation of the ECS has been associated with the development of obesity and its sequelae, such as dyslipidemia and diabetes. Conversely, recent clinical trials have shown that cannabinoid type 1 receptor blockade may ameliorate these metabolic abnormalities. Although further investigation is needed to better define the actual mechanisms of action, pharmacologic approaches targeting the ECS may provide a novel, effective option for the management of obesity, type 2 diabetes and cardiovascular disease. Copyright © 2008 S. Karger AG, Basel

The appetite-stimulating and antiemetic properties of Cannabis sativa have been known for centuries. However, although synthetic and plant-derived cannabinoids (CBs) such as ⌬9-tetrahydrocannabinol (⌬9-THC) have long been recognized to influence food intake, only recently CB receptors, endogenous CBs (endocannabinoids), and their biosynthetic and degradative pathways have been identified. Collectively, these discoveries have led to the characterization of an endogenous signaling system now known as the endocannabinoid system (ECS). In mammals, the ECS has been recently recognized to have a role in the modulation of several physiological processes, including neuroprotection, regulation of hormone secretion, locomotion, and energy homeostasis, among others. The purpose of this chapter is to outline the recent advances in our understanding of the role of the ECS in energy balance regulation and to discuss the clinical relevance of this work with respect to obesity and associated metabolic risk factors.

Cannabinoid agonists Ca2⫹ Extracellular CB1 G K⫹

Intracellular

AC MAPK

Fig. 1. Cellular effects of CB1 receptor activation in the brain. Activation of CB1 through the action of CB receptor agonists, such as ECs, induces stimulation of Gi/o heterotrimetic proteins (G). CB1 stimulation of Gi/o proteins is directly coupled to stimulation of inwardly rectifying K⫹ channels and inhibition of voltage-activated Ca2⫹ channels, effects that in neurons lead to inhibition of neurotransmitter release. Activation of CB1 leads also to inhibition of adenylate cyclase (AC) or to stimulation of mitogen-activated protein kinase (MAPK). Modulation of these intracellular pathways may affect gene expression. See also references [1–3, 7].

Components of the ECS

CB Receptors The first CB receptor was characterized using reverse pharmacology approach in 1988. This receptor was named CB1 after the second CB receptor, named CB2, was cloned in 1993. CB1 is found in the central nervous system as well as in peripheral organs [1]. CB2 is mainly localized in the immune system, and shares a 48% homology with CB1 [2, 3]. Both are 7-transmembrane-spanning Gi/o receptors that inhibit adenylyl cyclase and activate mitogen-activated protein kinase [1, 2]. In addition, the CB1 receptor is reported to affect both potassium and calcium channels (fig. 1). CB1 receptors are among the most abundant G-protein-coupled receptors in the brain, having similar densities as receptors for ␥-aminobutyric acid (GABA) and glutamate-gated ion channels [2]. In the central nervous system, the distribution of CB1 receptors is heterogeneous, with higher densities in the basal ganglia, hippocampus and cerebellum [2]. CB1 expression in the hypothalamus, a key integrative area in the regulation of energy homeostasis, is relatively low; however, activation of hypothalamic CB1 is highly efficient [2]. Activation of CB1 receptors,

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usually located presynaptically, modulates the release of several neurotransmitters, such as GABA, dopamine (DA), noradrenaline, glutamate and serotonin. CB1 receptors are expressed by astrocytes as well as by neurons [4]. Interestingly, activation of CB1 on astrocytes increases available energy to local neuronal circuits, and the administration of CB agonists increases overall energy metabolism in the brain [4]. CB1 are also located on nerve terminals innervating the gastrointestinal tract as well as found in other organs involved in energy balance regulation, such as white adipose tissue, liver, pancreas and skeletal muscle [reviewed in 5]. Although CB2 are located mainly in tissues of the immune system, they have recently also been identified in neurons in several regions of the brain [6]. For an in-depth description of the characteristics and functions of CB receptors, the reader should refer to recent reviews [1–3, 7].

Endocannabinoids All endocannabinoids (ECs) identified so far are amides, esters or ethers of arachidonic acid, a long-chain polyunsaturated fatty acid that is a constituent of cell membranes [1]. Anandamide (the ethanolamide of arachidonic acid) was the first described EC, and 2-AG was later identified. These two ECs are the best characterized and their properties have been largely studied in recent years. Their biosynthetic and degradative pathways have been recently reviewed [1, 2]. Other putative ECs have been discovered [1]; however, neither the mechanisms of synthesis and deactivation, nor the specific functions have been defined for these other compounds. The concentrations of anandamide and 2-AG within the brain vary considerably by area. In most brain areas, 2-AG is around two orders of magnitude more abundant than anandamide, with the highest levels of both occurring in the brainstem, hippocampus and striatum [1, 2, 7]. Both anandamide and 2-AG are produced in several peripheral tissues, including skin, gut, liver, adipose tissue and testis [1, 2, 7]. Moreover, although its physiological function is unknown, anandamide is present in the bloodstream where it is bound to albumin [reviewed in 5]. Anandamide and 2-AG each bind to both CB1 and CB2 receptors. However, 2-AG is a full agonist at CB1, whereas anandamide is a partial CB1 agonist [1]. Unlike the case for classical neurotransmitters which are synthesized and stored in vesicles until needed, ECs are made de novo during neuronal hyperactivity and immediately released into the extracellular space of synapses [7]. As they bind to CB1, a sequence of intracellular events is induced, and the ECs are then rapidly eliminated via reuptake and degradation by neurons and possibly glia cells [1, 2]. This ‘on demand’ process is mainly utilized by neurons during periods of high-frequency membrane stimulation [7]. For instance, many CNS CB1 receptors are localized presynaptically on GABAergic interneurons. ECs released from postsynaptic membranes therefore must diffuse or be transported retrogradely in order to interact with CB1 and thereby decrease the release of presynaptic neurotransmitter. The generally accepted model is that when electrical activity in the form

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of repeated action potentials in the presynaptic neuron (i.e. a GABAergic neuron) becomes particularly high, the continuous neurotransmitter-induced modulation of the postsynaptic membrane results in the accumulation of intracellular Ca2⫹ in the postsynaptic neuron. One important consequence is that elevated Ca2⫹ activates enzymes that synthesize ECs in the postsynaptic neuron’s cell membrane, thus generating anandamide and/or 2-AG. The ECs then retrogradely diffuse back across the synapse, where they bind to presynaptic CB1 receptors [1–3, 7]. Therefore, activation of CB1 results in reduced neurotransmitter (i.e. GABA) release from the presynaptic neuron in response to further action potentials. In fact CB1 are located on many GABAergic neurons, where they are involved with what has been called depolarization-induced suppression of inhibition; however, they are also found on neurons which have excitatory influences on postsynaptic cells [1, 2]. For instance, some glutamatergic neurons also express CB1 which are therefore involved with depolarization-induced suppression of excitation. Recent evidence has demonstrated that there might be constitutive release of ECs, in the absence of external stimulation, in both the hippocampus [8] and the arcuate nucleus (ARC) in the hypothalamus [9].

Synthetic CB Receptor Agonists and Antagonists Among the synthetic CB receptor agonists, CP 55,940, WIN 55,212-2 and HU 210 are worth mentioning since they are largely used to investigate the physiological functions of the ECS and, specifically, its role in energy balance. Among the synthetic antagonists, SR 141716, AM 251 and AM 281 are specific for CB1 receptors. Several inhibitors of anandamide cellular reuptake or transport and of anandamide hydrolysis have also been identified, presenting multiple pharmacologic opportunities to modulate the ECS [1].

Role of the ECS in the Regulation of Energy Balance

The ECS influences energy balance at many sites in the brain and throughout the body, with the net and highly coordinated effect being anabolic, i.e. increased EC activity enhances food intake and supports fat deposition. Within the brain, CB1 and ECs are present in both hypothalamic and extrahypothalamic circuits. Both networks contribute to excessive eating when ECS activity is high. Within peripheral organs including adipose tissue, liver and skeletal muscle, locally produced ECs are also anabolic, contributing to lipogenesis and fat storage and reduced energy expenditure. In the gastrointestinal system, ECs are thought to increase fuel absorption and decrease satiety. Thus, the ECS biases behavioral and metabolic processes to promote obesity. Consistent with this, genetic polymorphisms of components of the ECS have been associated with overweight and obesity in humans [10].

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The ECS and the Hypothalamus The hypothalamus is an important integrative center for the control of energy homeostasis. Neurons in the ARC are sensitive to the circulating hormones leptin and insulin, and the ARC in turn projects to many other sites in the hypothalamus and elsewhere in the brain [11]. Two categories of ARC neurons are known to participate in energy balance regulation. ARC pro-opiomelanocortin (POMC) neurons synthesize the neurotransmitter ␣-melanocyte-stimulating hormone (␣MSH), which acts at melanocortin receptors to induce reduction in food intake and body weight [11]. Increased plasma levels of leptin and/or insulin, both signals of increased body fat, stimulate POMC neurons, initiating the catabolic response. POMC cells in the ARC release ECs [9], and leptin reduces hypothalamic endocannabinoid levels, inhibits ECs’ action in several hypothalamic areas, thus facilitating its anorexigenic effect [12]. Other ARC neurons synthesize the anabolic neurotransmitters neuropeptide Y (NPY) and agouti-related protein [11]. NPY acts on Y receptors throughout the hypothalamus to stimulate food intake, whereas agouti-related protein antagonizes ␣MSH, thus inhibiting the catabolic action of POMC cells [11]. Recent findings have demonstrated that CB agonists increase the secretion of NPY in the hypothalamus, consistent with ECs increasing food intake. Moreover, hypothalamic 2-AG levels increase during fasting, decline as animals are re-fed and return to normal values when animals eat to satiation [13]. CB1 mRNA has been colocalized with many hypothalamic neuropeptides involved in energy balance regulation, including corticotropin-releasing hormone, cocaine-amphetamine-regulated transcript, pre-pro-orexin and melanin-concentrating hormone [14]. Moreover, as discussed above, CB1 are also expressed by GABAergic neurons entering the ARC [9]. Intrahypothalamic administration of ⌬9-THC increases food intake in laboratory rats. Anandamide not only increases food intake when administered systemically, but does so also when injected into the hypothalamus; and pretreatment with the selective CB1 antagonist SR 141716 (rimonabant) attenuates anandamide-induced hyperphagia [reviewed in 5]. Interestingly, animals lacking CB1 (CB1⫺/⫺ mice) have reduced food intake, decreased body weight and a lean phenotype [14]. CB1⫺/⫺ mice have increased levels of hypothalamic anorexigenic neuropeptides [14], and they do not increase their food intake when treated with orexigenic compounds, such as NPY. Moreover, the hyperphagia elicited by ghrelin and by orexin A can be attenuated by administration of SR 141716; and CB1 are coexpressed with orexin 1 receptors [reviewed in 5]. Thus, the actions of several orexigenic peptides within the hypothalamus, including NPY, orexin and ghrelin, seem to be facilitated by ECs. Furthermore, administration of SR 141716 attenuates the orexigenic effect of melanocortin antagonists, but the administration of melanocortin agonists does not interfere with the action of CB agonists, suggesting that endocannabinoids may act downstream of melanocortins [reviewed in 5].

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The ECS and the Brain Reward System The system controlling the rewarding aspects of food is comprised of a series of synaptically interconnected circuits linking the prefrontal cortex, the amygdala, the ventral tegmental area, the nucleus accumbens and the ventral pallidum. This integrated network connects forebrain, hindbrain and midbrain areas with hypothalamic areas and is thus able to modulate food intake [15]. In the 1970s, anecdotal observations of humans smoking marijuana pointed out that they have increased appetite and often have cravings for palatable foods [reviewed in 16]. Studies carried out in rats following administration of ⌬9-THC found increased preference for palatable foods, such as sucrose. Thus, ECs might help mediate palatability or other positive aspects of food [reviewed in 17]. Accordingly, systemic SR 141716 not only decreases intake of bland food, but specifically reduces intake of alcohol, sucrose and other sweet foods in animals [reviewed in 16]. While only 2-AG is increased in the hypothalamus, both anandamide and 2-AG levels are increased in limbic areas of rats that have been fasted, thus indicating that brain ECs are responsive to fasting and that there are differences in the response between the brain areas controlling energy homeostasis and reward, respectively [13]. Interestingly, rats maintained on a palatable diet for 10 weeks, not only become obese, but have less CB1 mRNA in several limbic areas. Furthermore, when 2AG is administered directly into the shell of the nucleus accumbens (a limbic area with high levels of CB1 that is strongly associated with reward processes) a rapid and profound hyperphagia is reported [13]. The neurotransmitter DA is recognized to be an important mediator of the rewarding effects of food and drugs of abuse. The ECS has been recently reported to directly and indirectly affect DA release [16]. Moreover, serotonin, another neurotransmitter involved with both reward and food intake, also interacts with the ECS. Finally, there is considerable anatomical overlap between CB1, opioid receptors and their respective endogenous ligands in brain areas involved with food intake and reward mechanisms [reviewed in 16]. Interactions between the ECS and opioid system have been hypothesized to be a critical component for the rewarding aspects of food intake and are thought to possibly provide a molecular basis for drug or food dependence [reviewed in 16].

The ECS and Peripheral Metabolism In agreement with the hypothesis that peripheral metabolic mechanisms might be directly modulated by the ECS independent of and in addition to modulation by central nervous pathways, CB1 and ECs are present in several peripheral tissues related to energy homeostasis. White adipocytes express CB1 receptors [14, 18, 19], and in vitro experiments demonstrate that the CB1 agonist WIN-55,212 stimulates adipocyte differentiation and increases the activity of lipoprotein lipase; both actions are blocked by pretreatment with SR 141716 [14]. These findings imply that EC activity in white fat might facilitate growth of new fat cells as well as enhance the ability to remove fat from the bloodstream and deposit it into fat cells. Furthermore, mature adipocytes express a

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greater number of CB1, and CB1 expression is higher in adipocytes of obese as compared to lean animals [18]. Studies in humans also indicate that mature adipocytes have increased CB1 expression as compared to preadypocytes [19]. ECs are also produced in the liver [20] where they help regulate hepatic blood flow. Interestingly, activation of CB1 in hepatic tissue induces lipogenic enzymes, thus stimulating de novo fatty acid synthesis [20]. Diet-induced obese mice have increased activity of this hepatic lipogenic pathway as well as elevated expression of CB1 and increased anandamide levels in the liver [20]. Interestingly, recent evidence has shown that the activity of AMPK (an enzyme that may mediate the action of antidiabetic drugs, such as metformin) is inhibited by CB agonists in both liver and adipose tissue [21]. Most meal-generated satiety signals from the gastrointestinal tract reach the hindbrain via vagal afferent nerves, and the signals are then conveyed to the hypothalamus and elsewhere in brain areas controlling food intake. CB1 have been localized in vagal afferent nerves, in neurons in the dorsal vagal complex and in vagal efferent nerves. The observation that cholecystokinin (CCK) inhibits the expression of CB1 on vagal afferents [22] is consistent with CCK’s role as a satiety signal that reduces food intake. Moreover, anandamide levels are greatly increased in the small intestine during fasting, and decreased during refeeding, suggesting a role of ECs in influencing food intake via modulation of gastrointestinal signals [reviewed in 5]. In fact, ECs modulate many aspects of gastrointestinal function including gastric emptying and intestinal peristalsis. Although its function is less defined, the ECS is also present in the muscle (with increased levels of CB1 expression during exposure to high fat diet) and in the pancreas. Furthermore, few studies suggest that ECs might affect energy expenditure. The administration of the CB1 antagonist SR 141716 reportedly increases glucose uptake and basal oxygen consumption of skeletal muscle from genetically obese mice, and chronic administration of SR 141716 elicits increased expression of genes for thermogenesis in brown adipose tissue [reviewed in 5].

Role of the ECS in Obesity

There is mounting evidence that the ECS, normally transiently activated, might become tonically overactivated in both animal models of genetic and diet-induced obesity and in human obesity. For instance, the levels of hypothalamic ECs are increased in genetically obese rodents with defective leptin signaling, and treatment of these genetically obese mice with a CB1 antagonist attenuates their hyperphagia and reduces their weight gain, implying that overactivation of the hypothalamic ECS system may be a contributing factor in the development of obesity [12]. Interestingly, CB1⫺/⫺ mice do not become obese on a high-fat diet and have increased sensitivity to the catabolic action of leptin [reviewed in 5]. In animal models of diet-induced obesity, there is evidence of increased CB1 receptor expression and ECs levels in both adipose tissue and liver. In obese women, plasma anandamide and 2-AG levels are significantly elevated compared with

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lean women [19]. However, differently from the data reported in animals, obese humans have decreased CB1 mRNA levels in the adipose tissue. Moreover, they also show decreased levels of FAAH mRNA, the primary enzyme responsible for the degradation of anandamide [19]. Dysregulation of the ECS is also associated with eating disorders. Patients diagnosed with restricting anorexia nervosa or binge eating disorder have elevated plasma levels of anandamide. Moreover, specific gene polymorphisms affecting the regulation of the ECS activity seem to contribute to the development of obesity. For instance, a specific gene missense polymorphism, FAAH 385 A/A, has been found to occur at a higher frequency among individuals with a higher body mass index (BMI) [10]. A number of environmental factors may contribute to dysregulation of the ECS, including stress and diet. Stress may impact the ECS as suggested by evidence linking glucocorticoids and the ECS in the hypothalamus. Preliminary findings also indicate that diets high in long-chain polyunsaturated fatty acids, known biological precursors of N-acylethanolamines (anandamide and 2-AG), are associated with increased levels of N-acylethanolamines in the brain. Finally, alteration in the function or in the circulating levels of hormones involved in the regulation of energy balance (i.e. leptin, CCK, ghrelin) might be linked to dysregulation of the ECS [7].

Use of CB1 Antagonists to Treat Obesity

When rodents are fed a high-fat diet, they overeat and become obese. In one series of experiments, mice maintained on high-fat diet and chronically administered the CB1 antagonist SR 141716 ate less food [reviewed in 5]. Significantly, the anorectic action of the antagonists lessened and then disappeared altogether over a week or two in spite of continued dosing, indicating the development of tolerance. Nevertheless, there was a sustained reduction of body weight and body fat mass over the next several weeks, suggesting that possibly other actions of the CB1 antagonists were continuing to exert metabolic effects. At the end of the experiments, mice receiving the CB1 antagonists chronically had reduced body weight, body fat, plasma leptin and plasma insulin, and an improved lipid profile, perhaps due to the ability of the drug to increase adiponectin expression and secretion from adipose tissue [reviewed in 5]. Adiponectin, differently from other adipokines, improves insulin sensitivity and induces fatty acid oxidation in muscle and liver. Somehow related to these findings, experiments using CB1⫺/⫺ mice revealed that the lean phenotype is predominantly caused by decreased caloric intake when the mice are young, whereas peripheral metabolic factors are the major cause of maintaining the lean phenotype in the adults [14]. As discussed above, CB1⫺/⫺ mice are resistant to diet-induced obesity. They maintain their lean phenotype and they do not develop hyperglycemia or insulin resistance. Accordingly, chronic treatment with the CB1 antagonist in mice exposed to high-fat diet altered gene expression profiles in both white and brown adipose tissue to support fat oxidation and increased thermogenesis [reviewed in 5]. Therefore,

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chronic treatment with a selective CB1 antagonist reduces body fat as well as improves many alterations commonly associated with the metabolic syndrome. Hence, it seems that the early transient reduction of food intake when animals are started upon the antagonist cannot account for the sustained improvement in metabolic parameters. Recent data from clinical studies seem also to support this hypothesis. Clinical trials conducted on overweight and obese patients with BMI ⱖ27 who were on a hypocaloric diet, have shown that oral CB1 receptor antagonist (rimonabant 20 mg/day) administration for 1 year resulted in weight loss of up to 8.6 kg compared with 2.3 kg in placebo-treated individuals [23, 24]. The reduction in body weight occurred during the first 36–40 weeks of the study period. Long-term treatment (2 years) with CB1 receptor antagonist was able to stabilize body weight and prevent weight regain [25]. CB1 receptor antagonist (rimonabant 20 mg/day) treatment in overweight and obese individuals also improved lipid profiles by increasing high-density lipoprotein cholesterol (HDL) and decreasing triglycerides [23, 24]. Levels of HDL increased continuously throughout 2-year treatment with the CB1 receptor antagonist, whereas body weight stabilized [25]. Significantly, only a portion (approximately 50%) of the effect associated with CB1 receptor antagonist treatment on HDL and triglycerides was attributed to weight loss. Hence, the additional improvement in lipid profile appears to involve a CB1 receptor-mediated effect that is independent of weight loss [25]. Moreover, fasting glucose and fasting insulin in overweight and obese individuals are decreased following CB1 receptor antagonist therapy [24]. The amelioration of metabolic indices related to insulin sensitivity after treatment with CB1 receptor antagonists are, at least in part, a consequence of the decrease in body weight, and specifically in adipose tissue. However, a direct effect of CB1 receptor antagonists on peripheral metabolism, specifically glucose homeostasis, cannot be currently excluded. A possible explanation that has been given for the improved glucose metabolism resides in the increased mRNA and plasma levels of adiponectin observed after treatment with CB1 receptor antagonists.

Conclusions

To summarize, the ECS is a varied intercellular communication system whose activity leads to a net anabolic tone. This is manifest in the brain as an increased tendency to consume more food, and especially palatable, rewarding food. In several peripheral organs including adipose tissue, liver, skeletal muscle and gut, increased levels of ECs facilitate the intake of food and the formation and storage of fat, while possibly simultaneously decreasing energy expenditure (table 1). The administration of CB1 antagonists to normal and especially obese animals reverses many of these actions, thus implying that CB1 antagonists may be useful for treating obesity and related metabolic complications. Data from clinical trials recently disclosed support the efficacy of CB1 blockade in reducing body weight and in ameliorating peripheral metabolic parameters.

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Table 1. Effects of the ECS on energy balance and peripheral metabolism Site of action

Effects

Hypothalamus

increased food intake; interaction with neuropeptides/hormones regulating food intake; stimulation of AMPK activity increased consumption of palatable food; interaction with DA, serotonin and opioid pathways increased lipogenesis; decreased adiponectin; inhibition of AMPK activity induction of de novo fatty acid synthesis; inhibition of AMPK activity modulation of peristalsis and gastric emptying; interaction with satiety hormones role in glucose uptake? effect on insulin secretion?

Brain reward areas (nucleus accumbens) White adipose tissue Liver Gastrointestinal tract Muscle Pancreas

References 1 Piomelli D: The molecular logic of endocannabinoid signalling. Nat Rev Neurosci 2003;4:873–884. 2 Freund TF, Katona I, Piomelli D: Role of endogenous cannabinoids in synaptic signaling. Physiol Rev 2003;83:1017–1066. 3 Howlett AC, Barth F, Bonner TI, Cabral G, Casellas P, Devane WA, Felder CC, Herkenham M, Mackie K, Martin BR, Mechoulam R, Pertwee RG: International Union of Pharmacology. XXVII. Classification of cannabinoid receptors. Pharmacol Rev 2002;54: 161–202. 4 Stella N: Cannabinoid signaling in glial cells. Glia 2004;48:267–277. 5 Cota D, Woods SC: The role of the endocannabinoid system in the regulation of energy homeostasis. 6 van Sickle MD, Duncan M, Kingsley PJ, Mouihate A, Urbani P, Mackie K, Stella N, Makriyannis A, Piomelli D, Davison JS, Marnett LJ, Di Marzo V, Pittman QJ, Patel KD, Sharkey KA: Identification and functional characterization of brainstem cannabinoid CB2 receptors. Science 2005;310:329–332. 7 Di Marzo V, Matias I: Endocannabinoid control of food intake and energy balance. Nat Neurosci 2005;8:585–589. 8 Losonczy A, Biro AA, Nusser Z: Persistently active cannabinoid receptors mute a subpopulation of hippocampal interneurons. Proc Natl Acad Sci USA 2004;101:1362–1367. 9 Hentges ST, Low MJ, Williams JT: Differential regulation of synaptic inputs by constitutively released endocannabinoids and exogenous cannabinoids. J Neurosci 2005;25:9746–9751.

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10 Sipe JC, Waalen J, Gerber A, Beutler E: Overweight and obesity associated with a missense polymorphism in fatty acid amide hydrolase (FAAH). Int J Obes (Lond) 2005;29:755–759. 11 Schwartz MW, Woods SC, Porte D Jr, Seeley RJ, Baskin DG: Central nervous system control of food intake. Nature 2000;404:661–671. 12 Di Marzo V, Goparaju SK, Wang L, Liu J, Batkai S, Jarai Z, Fezza F, Miura GI, Palmiter RD, Sugiura T, Kunos G: Leptin-regulated endocannabinoids are involved in maintaining food intake. Nature 2001; 410:822–825. 13 Kirkham TC, Williams CM, Fezza F, Di Marzo V: Endocannabinoid levels in rat limbic forebrain and hypothalamus in relation to fasting, feeding and satiation: stimulation of eating by 2-arachidonoyl glycerol. Br J Pharmacol 2002;136:550–557. 14 Cota D, Marsicano G, Tschop M, Grubler Y, Flachskamm C, Schubert M, Auer D, Yassouridis A, Thone-Reineke C, Ortmann S, Tomassoni F, Cervino C, Nisoli E, Linthorst AC, Pasquali R, Lutz B, Stalla GK, Pagotto U: The endogenous cannabinoid system affects energy balance via central orexigenic drive and peripheral lipogenesis. J Clin Invest 2003;112: 423–431. 15 Saper CB, Chou TC, Elmquist JK: The need to feed: homeostatic and hedonic control of eating. Neuron 2002;36:199–211. 16 Cota D, Tschop MH, Horvath TL, Levine AS: Cannabinoids, opioids and eating behavior: the molecular face of hedonism? Brain Res Brain Res Rev 2006;51:85–107.

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17 Kirkham TC, Williams CM: Endogenous cannabinoids and appetite. Nutr Res Rev 2001;14:65–86. 18 Bensaid M, Gary-Bobo M, Esclangon A, Maffrand JP, Le Fur G, Oury-Donat F, Soubrie P: The cannabinoid CB1 receptor antagonist SR141716 increases Acrp30 mRNA expression in adipose tissue of obese fa/fa rats and in cultured adipocyte cells. Mol Pharmacol 2003;63:908–914. 19 Engeli S, Bohnke J, Feldpausch M, Gorzelniak K, Janke J, Batkai S, Pacher P, Harvey-White J, Luft FC, Sharma AM, Jordan J: Activation of the peripheral endocannabinoid system in human obesity. Diabetes 2005;54:2838–2843. 20 Osei-Hyiaman D, DePetrillo M, Pacher P, Liu J, Radaeva S, Batkai S, Harvey-White J, Mackie K, Offertaler L, Wang L, Kunos G: Endocannabinoid activation at hepatic CB1 receptors stimulates fatty acid synthesis and contributes to diet-induced obesity. J Clin Invest 2005;115:1298–1305. 21 Kola B, Hubina E, Tucci SA, Kirkham TC, Garcia EA, Mitchell SE, Williams LM, Hawley SA, Hardie DG, Grossman AB, Korbonits M: Cannabinoids and ghrelin have both central and peripheral metabolic and cardiac effects via AMP-activated protein kinase. J Biol Chem 2005;280:25196–25201.

22 Burdyga G, Lal S, Varro A, Dimaline R, Thompson DG, Dockray GJ: Expression of cannabinoid CB1 receptors by vagal afferent neurons is inhibited by cholecystokinin. J Neurosci 2004;24:2708–2715. 23 Despres JP, Golay A, Sjostrom L; Rimonabant in Obesity-Lipids Study Group: Effects of rimonabant on metabolic risk factors in overweight patients with dyslipidemia. N Engl J Med 2005;353: 2121–2134. 24 van Gaal LF, Rissanen AM, Scheen AJ, Ziegler O, Rossner S; RIO-Europe Study Group: Effects of the cannabinoid-1 receptor blocker rimonabant on weight reduction and cardiovascular risk factors in overweight patients: 1-year experience from the RIO-Europe study. Lancet 2005;365:1389–1397. 25 Pi-Sunyer FX, Aronne LJ, Heshmati HM, Devin J, Rosenstock J; RIO-North America Study Group: Effect of rimonabant, a cannabinoid-1 receptor blocker, on weight and cardiometabolic risk factors in overweight or obese patients: RIO-North America: a randomized controlled trial. JAMA 2006;295: 761–775.

Daniela Cota, MD Department of Psychiatry, University of Cincinnati 2170 East Galbraith Road Cincinnati, OH 45237 (USA) Tel. ⫹1 513 558 5866, Fax ⫹1 513 558 8990, E-Mail [email protected]

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11␤-Hydroxysteroid Dehydrogenase Type 1 and Obesity Nicholas M. Morton ⭈ Jonathan R. Seckl Endocrinology Unit, Centre for Cardiovascular Sciences, Queens Medical Research Institute, Edinburgh University, Edinburgh, UK

Abstract The metabolic syndrome consists of a constellation of co-associated metabolic abnormalities such as insulin resistance, type 2 diabetes, dyslipidaemia, hypertension and visceral obesity. For many years endocrinologists have noted the striking resemblance between this disease state and that associated with Cushing’s syndrome. However, in the metabolic syndrome plasma cortisol levels tend to be normal or lower than in normal individuals. Nevertheless there is strong evidence that glucocorticoid action underlies metabolic disease, largely from rodent obesity models where removing glucocorticoids reverses obesity and its metabolic abnormalities. The apparent paradox of similar metabolic defects – despite the opposing plasma glucocorticoid profiles of Cushing’s and idiopathic metabolic syndrome – remained intriguing until the discovery that intracellular glucocorticoid reactivation was elevated in adipose tissue of obese rodents and humans. The enzyme that mediates this activation, conversion of cortisone (11-dehydrocorticosterone in rodents) to cortisol (corticosterone in rodents), locally within tissues is 11 β -hydroxysteroid dehydrogenase type 1 (11 β -HSD1). In order to determine whether elevated tissue 11 β -HSD1 contributed to obesity and metabolic disease, transgenic mice overexpressing 11 β -HSD1 in adipose tissue or liver were made. Adipose-selective 11 β -HSD1 transgenic mice exhibited elevated intra-adipose and portal, but not systemic corticosterone levels, abdominal obesity, hyperglycaemia, insulin resistance, dyslipidaemia and hypertension. In contrast, transgenic overexpression of 11 β -HSD1 in liver yielded an attenuated metabolic syndrome with mild insulin resistance, dyslipidaemia, hypertension and fatty liver, but not obesity or glucose intolerance. Together with early data using non-selective 11 β -HSD1 inhibitors to insulin sensitise humans, this corroborated the notion that the enzyme may be a good therapeutic target in the treatment of the metabolic syndrome. Further, a transgenic model of therapeutic 11 β -HSD1 inhibition, 11 β -HSD1 gene knock-out (11 β -HSD1⫺/⫺) mice, exhibited improved glucose tolerance, a ‘cardioprotective’ lipid profile, reduced weight gain and visceral fat accumulation with chronic high-fat feeding. Recent evidence further suggests that high fat-mediated downregulation of adipose 11 β -HSD1 may be an endogenous pathway that underpins adaptive disease resistance in genetically predisposed mouse strains. This mechanism could feasibly make up a genetic component of innate obesity resistance in humans. The efficacy of 11 β -HSD1 inhibitors has recently been extended to include increased energy expenditure and reduction of arteriosclerosis, and therefore may be of significant therapeutic value in the metabolic syndrome, with complementary effects upon liver adipose tisCopyright © 2008 S. Karger AG, Basel sue, muscle, pancreas and plaque-prone vessels.

The incidence of obesity and its associated metabolic syndrome has grown to ‘epidemic’ proportions globally. At least 300 million are estimated to be clinically obese (World Health Organisation 2003) and more than three times this number are overweight. Within the next 5 years a quarter of adults in the UK are expected to be clinically obese and obesity of childhood, a predictor of morbidity and mortality in adulthood, is accelerating [1]. Whilst obesity associates with a substantially increased risk of serious adverse effects ranging from type 2 diabetes, hypertension and cardiovascular disease to cancers and arthritis, and thus to increased mortality, not every obese subject will get these complications. The distribution of fat is important in determining the risks of such disorders; excess upper body, centripetal or visceral adipose excess has a dire reputation, whereas peripheral lower body or peripheral fat has little adverse effect. There are strong morphological and metabolic resemblances between rare Cushing’s syndrome of glucocorticoid excess and the extremely common insulin resistance or metabolic syndrome [2]. Excess cortisol production and release in Cushing’s syndrome causes predominantly visceral (abdominal) fat deposition, while peripheral fat is reduced. This may result from opposing effects of glucocorticoids on these distinct adipose depots and their differential sensitivity both to catecholamine-mediated fat mobilisation and insulin-mediated lipogenic effects. Whilst the association between visceral adiposity and metabolic and cardiovascular disease risk is clear [3], the molecular mechanisms linking the two are unknown. Current theories include evidence that visceral fat is relatively more sensitive to lipolytic stimuli from the adrenergic receptor system, though subcutaneous adipocytes are bigger and make a larger overall contribution to whole body lipolysis [4]. On the other hand other factors such as adipokines and cortisol may be released from the visceral fat and have a pronounced and more immediate detrimental impact on hepatic function because these fat depots drain directly into the liver. This is the ‘portal hypothesis’ of insulin resistance [5]. In terms of glucocorticoid action, glucocorticoid receptors are more highly expressed in visceral fat [6, 7] and therefore glucocorticoids are expected to have a greater impact on both metabolic responses and adipokine expression and release into the portal blood (fig. 1). Thus, glucocorticoids may increase lipolysis and downregulate lipoprotein lipase, liberating free fatty acids from peripheral fat, but also stimulate pre-adipocyte differentiation and lipoprotein lipase expression, enhance glyceroneogenesis and triglyceride synthesis in visceral adipose tissue [6–9]. However, despite the phenotypic similarities with Cushing’s syndrome, in the metabolic syndrome plasma cortisol levels are modestly if at all elevated and are usually reduced in ‘simple’ obesity [2, 9–10].

The Hypothalamic-Pituitary-Adrenal Axis and Obesity

Plasma glucocorticoid levels are determined by the activity of the hypothalamicpituitary-adrenal axis (HPA) (fig. 1). Forward HPA ‘drive’ at the diurnal maximum (morning in humans, evening in rodents) or during stress is rapidly and negatively controlled

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Cortisone 11-DHC

KEY

11␤-HSD2

11␤-HSD1

Cortisol corticosterone

HPA Negative feedback ACTH

Subcutaneous fat

Skeletal muscle All tissues

Pancreas Ffa adipokines

Insulin glucagon Visceral fat

Adrenal Cortisol Liver

Kidney Excretion clearance

Ffa adipokines cortisol All tissues

?

Portal blood

Cortisone

Fig. 1. 11␤-HSD1 is a local tissue amplifier of glucocorticoid action. Glucocorticoid action is modulated by 11␤-HSD1 (re-activation of inert cortisone in humans, 11-dehydrocorticosterone in rodents) and is represented by the rightward-pointing green arrow boxes. The number of boxes represents an approximation of the relative expression levels of the enzyme within the tissues. Substrate for 11␤-HSD1 is created principally by the inactivation of adrenal gland-derived circulating cortisol (corticosterone in rodents) by the enzyme 11␤-HSD2 (leftward-pointing red arrow boxes) in the kidney. Circulating cortisol (corticosterone) is generated largely by the adrenal gland through the action of the HPA axis of the brain by release of ACTH from the anterior pituitary and by clearance through the kidney and liver. Tissue-specific changes in the expression level of 11␤-HSD1 may contribute to altered glucocorticoid action in obesity and metabolic syndrome with increased adipose 11␤-HSD1 and reduced hepatic 11␤-HSD1.

by ‘feedback’ of glucocorticoids upon (1) adrenocorticotrophin (ACTH)-producing pituitary corticotrophs, (2) corticotrophin-releasing hormone producing hypothalamic paraventricular nucleus neurons and (3) a number of suprahypothalamic sites [11]. Although plasma cortisol levels appear normal in the metabolic syndrome or obesity, the response of cortisol to stressors or food intake is enhanced [12, 13]. Moreover, obesity, particularly abdominal obesity, associates with increased urinary free cortisol excretion [12, 14] and increased total cortisol production rates [15, 16]. Thus, there are exaggerated cortisol responses in obesity, suggesting that there is either increased forward drive upon the HPA axis [10] or reduced sensitivity to feedback inhibition [9, 17, 18]. Whilst obesity has been associated with reduced sensitivity to glucocorticoid feedback [9, 18], this appears somewhat counter-intuitively given their more favourable fat distribution, more profound in women than in men [19]. Similarly, whilst several case-control and cross-sectional studies of metabolic

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syndrome patients have suggested associations with elevated cortisol concentrations [e.g. 20, 21], these are independent of obesity. Obesity per se appears associated with lower plasma cortisol. Overall, such studies do not strongly support a causative role for enhanced cortisol secretion in the metabolic syndrome in humans in contrast with certain rodent obesity models [22]. Increased total cortisol secretion in the presence of lower plasma cortisol levels during peak secretion in obesity may also reflect increased peripheral cortisol clearance (fig. 1). Increased glucocorticoid clearance could both reduce plasma levels and invoke a compensatory HPA axis activation. Metabolic clearance rates for cortisol are elevated in obesity [15, 23, 24], a phenomenon which is probably explained by increased hepatic inactivation of cortisol by ‘A-ring reductases’ such as 5␣-reductase type 1 and 5␤-reductase [15, 25]. Whether the increased (largely hepatic) clearance of glucocorticoids via this route is a primary driver of the altered HPA response in obesity is unknown. On the other hand, peripheral metabolism of cortisol by the 11␤-hydroxysteroid dehydrogenase (11␤-HSD) enzymes is strongly implicated in determining local corticosteroid receptor activation, and perhaps HPA regulation.

Tissue Metabolism of Glucocorticoids

11␤-HSD, discovered over 50 years ago, catalyses the interconversion of active glucocorticoids (cortisol, corticosterone) and their inert 11-keto forms (cortisone, 11-dehydrocorticosterone) that have very low affinity for nuclear receptors. 11␤-HSD activity was subsequently described in a broad range of cells and tissues [26]. In the 1980s Monder and White [27] purified an 11␤-HSD activity from rat liver. Homogenates, microsomal preparations and purified enzyme catalysed both 11␤-dehydrogenation of cortisol to inert cortisone and the 11␤-reduction of cortisone to active cortisol. As with A-ring reductases, 11␤-HSD was originally thought to represent one of several routes for clearing glucocorticoids. Subsequently, two 11␤-HSD isozymes, the products of distinct genes, were characterised and their cDNAs cloned [28, 29]. 11␤-HSD type 2 is a high affinity, NAD-dependent 11␤-dehydrogenase which catalyses the rapid conversion of active cortisol to inert cortisone [28, 30]. In adults 11␤-HSD2 is expressed principally in tissues where aldosterone induces its effects on sodium excretion, including distal nephron, sweat glands, salivary glands and colonic mucosa as well as the endothelium [30]. The 11␤-HSD2 enzyme excludes active glucocorticoids from intrinsically non-selective MR in vivo [31]. Kidney 11␤-HSD2 serves also as a high throughput clearance route for circulating cortisol and indeed as the principal generator of substrate for the 11␤-HSD1 enzyme and therefore regulation of its activity may have profound effects on circulating and tissue glucocorticoid levels where 11␤-HSD1 is expressed [32]. 11␤-HSD1 has a much lower affinity for cortisol and corticosterone and is an NADP(H)-dependent enzyme. 11␤-HSD1 is widely expressed in many tissues [33],

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most highly in lung and liver [34, 35] and at lower levels in adipose tissue [36, 67], skeletal muscle, cardiac and vascular smooth muscle [38, 39], anterior pituitary gland, hippocampus [40], hypothalamic feeding centres [41] and adrenal cortex [42]. These are organs with high glucocorticoid receptor expression [43], with the exception of hippocampus and heart where MR act as high-affinity sites for glucocorticoids rather than aldosterone in vivo. In intact cells, when there is adequate provision of co-factor by the co-localised enzyme hexose-6-phosphate dehydrogenase [44, 45], 11␤-HSD1 acts as an 11-ketoreductase, reactivating inert cortisone into cortisol [2]. Intriguingly, 11␤-HSD1 also metabolises 7-ketocholesterol, a pro-atherogenic cholesterol metabolite that is present in high concentrations in atherosclerotic plaques and in cholesterol-rich food [46]. These data suggest that glucocorticoid and cholesterol metabolism may be closely linked in a number of tissues, particularly the liver, adipose and plaque macrophages. Oral administration of cortisone (the first pharmacological glucocorticoid used in man) is rapidly activated to cortisol in humans, and very little oral cortisone reaches the systemic circulation [47] and hepatic vein cortisol/cortisone ratios are high [48]. Recent studies have employed hepatic vein sampling in humans using radioactive tracers that reflect the fate of substrate metabolism and the rate of product appearance (deuterated cortisol method [49]) to determine the magnitude of splanchnic cortisol production by 11␤-HSD1 [50, 51]. These studies indicate that splanchnic (liver and visceral tissue blood supply) cortisol production is comparable to rates of adrenal cortisol secretion. In one study [51], oral cortisone was administered to distinguish the contribution of hepatic generation of cortisol from generation in visceral adipose tissue; the resulting model estimated that visceral adipose contributes somewhat more cortisol production than does the liver, in part because portal vein concentrations of cortisone are predicted to be lower than arterial concentrations. However, this view was challenged by similar studies in dogs infused with tracer cortisol that suggest, at least in this species, that most if not all splanchnic cortisol production occurs in the liver [52].

Substrate Levels for 11␤-HSD1

In vivo, the main source of 11-ketosteroid is 11␤-HSD2 dehydrogenation of cortisol and corticosterone, occurring predominantly in the kidney [53, 54]. In humans, cortisone circulates at 50–100 nM [48], similar to cortisol levels at night, the diurnal nadir. Whilst this level is lower than morning peak cortisol ⬃400–600 nM, around 95% of cortisol is sequestered by binding to plasma proteins such as CBG (cortisolbinding globulin) [55]. Estimates of ‘free’ cortisol levels are rather imprecise, but approximate 0.5–1 nM at the diurnal nadir. Cortisone in contrast shows no pronounced diurnal rhythm and is unbound to CBG and so is available for conversion to

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active glucocorticoid [48]. In the rat, plasma concentrations of 11-dehydrocorticosterone are also approximately 50 nM, and around 3–5 nM in mice [56]. Thus, for at least the quiescent part of the diurnal cycle, circulating cortisone levels equal or exceed free cortisol levels and similar ratios pertain in rodents. However, recent findings in mice homozygous for disruptive mutations of the gene encoding CBG suggest a role for CBG in active delivery of steroid to target tissues, so perhaps such calculations are erroneous [57]. Nevertheless, studies in 11␤-HSD1 null mice suggest that the enzyme contributes significantly to intracellular glucocorticoid load in vivo.

Functional Studies of 11␤-HSD1

Liver Studies using relatively non-selective liquorice-based inhibitors [58, 59] and, more recently with selective 11␤-HSD1 inhibitors [60, 61] show that reduced activity of 11␤-HSD1 in liver is associated with features of reduced glucocorticoid action, reduced glucocorticoid levels and increased insulin sensitivity in hepatocytes. 11␤HSD1⫺/⫺ mice develop normally and are viable, fertile and grossly normotensive [56]. This model demonstrated that 11␤-HSD1 is the sole major 11␤-reductase, at least in mice, since adrenalectomised 11␤-HSD1 knockout mice cannot convert exogenous 11-dehydrocorticosterone to corticosterone. Plasma corticosterone levels are modestly elevated at the diurnal nadir, presumably due to somewhat deficient feedback upon the HPA axis (11␤-HSD1 is expressed in hippocampus, paraventricular nucleus and anterior pituitary) [62]. This is in contrast to the H6PDH null mice, which have low circulating corticosterone because the 11␤-HSD1 lacks co-factor to drive reduction of 11-dehydrocorticosterone and thus acts as a dehydrogenase [45]. Despite slightly elevated basal plasma corticosterone levels, 11␤-HSD1⫺/⫺ mice have a phenotype compatible with impaired intracellular glucocorticoid regeneration and increased insulin sensitivity. They show impaired induction of the key glucocorticoid-inducible hepatic enzymes PEPCK and glucose-6-phosphatase with fasting and attenuated hyperglycaemic responses to novel environment stress or chronic high-fat feeding [56, 63]. Importantly, the mice have more than adequate stress-induced HPA axis responses [62] and do not exhibit hypoglycaemia with prolonged fasting [56]. 11␤-HSD1⫺/⫺ mice also have lower plasma triglyceride and elevated ‘cardioprotective’ HDL cholesterol levels [63], including higher circulating levels of the HDL apolipoprotein AI, whereas serum apolipoprotein CIII, which interferes with efficient transfer of triglycerides into the liver, is reduced. There is evidence for increased hepatic insulin sensitivity when the mice are re-fed after fasting, and hepatic expression of the co-ordinate transcription factor that drives fatty acid ␤-oxidation, PPAR␣, is elevated in 11␤-HSD1⫺/⫺ mice. Additionally, preliminary data suggest favourable effects upon haemostasis as in liver the glucocorticoid-inducible A␣-fibrinogen transcript levels are reduced [63].

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Adipose Tissue Glucocorticoids play a key role both in the regulation of adipose tissue metabolism and in the differentiation of pre-adipocytes into adipocytes [64]. 11␤-HSD1, but not 11␤HSD2, mRNA is expressed in rat [37] and in human white adipose tissue [36, 65–75]. 11␤-HSD1 is also expressed in differentiated murine fibroblast-adipocyte 3T3-F442A and 3T3-L1 cell lines and the reaction direction is 11-ketoreduction [37]. By contrast, intact human adipose stromal cells have varying developmental expression of hexose-6phosphate dehydrogenase and appear to inactivate the substrate [76]. Intriguingly, whilst polymorphisms in the gene encoding 11␤-HSD1 (HSD11B1) correlate rather poorly if at all [77, 78] with adipose distribution in humans, it may be changes in the hexose-6-phosphate dehydrogenase gene that are key. Thus, in 3 rare patients with cortisone reductase deficiency [79] more subtle mutations in both HSD11B1 and in H6PDH have been found although their significance is debatable since such digenic triallelic polymorphisms have also been found commonly in normal subjects [80, 81].

Regulation of 11b-HSD1 in Fat and Liver in Obesity A host of hormones including insulin pro- and anti-inflammatory cytokines, adrenergic agonists corticotrophin-releasing hormone and ACTH reduce 11␤-HSD1, and metabolic factors have been proposed to regulate 11␤-HSD1 [reviewed in 82]. These controls are tissue-specific. Although glucocorticoids reduce adipose 11␤-HSD1 expression in some systems, in others they stimulate its activity with effects reflecting the assay conditions and perhaps species. Whilst PPAR␥ ligands such as the anti-diabetic thiazolidinediones downregulate 11␤-HSD1 in 3T3 cells in vitro and in epididymal fat in mice in vivo [83], the same was not observed in lean or obese Zucker rats [25]. LXR agonists also partially downregulate 11␤-HSD1 in vitro and in vivo [84]. In terms of more direct effects, the transcription factor C/EBP␣, which is a master regulator of metabolic pathways in many tissues, binds to several sites on the 11␤-HSD1 promoter. C/EBP␣ increases 11␤-HSD1 promoter activity in transfected hepatoma cells [85]. In contrast, C/EBP␤ acts as a dominant negative repressor of (11␤-HSD1) transcription when added to C/EBP␣, though alone it is a weak inducer. These data suggest the possibility that 11␤-HSD1 regulation in the adipocyte by insulin and glucocorticoids is indirectly mediated through changes in C/EBP-related proteins. Since under many circumstances the regulation of 11␤-HSD1 in liver and adipose tissue is discordant, indeed often reciprocal [25, 86] as for other well-characterised C/EBP-regulated genes such as PEPCK [87], the fine details of control are expected to be tissue-specific and modulated by tissue-specific changes in insulin (and other hormones) sensitivity.

11b-HSD1 in Obesity Studies in leptin-resistant obese Zucker rats and leptin deficient ob/ob mice revealed that obesity was associated with decreased 11␤-HSD1 expression and activity in liver,

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but increased 11␤-HSD1 in omental adipose tissue [7, 25, 88]. However, this is not a universal finding and is likely strain-dependent in rodents [86, 89]. For example, db/db mice with monogenic obesity resulting from leptin receptor deficiency have higher hepatic 11␤-HSD1 activity [89] and mice with obesity of polygenic origin have low adipose but elevated liver 11␤-HSD1 [86] again reflecting a tissue reciprocity in regulation of the enzyme that hints at a conserved tissue-specific regulatory mechanism. In humans, conversion of cortisone after oral administration to cortisol in peripheral plasma, reflecting first pass metabolism by hepatic 11␤-HSD1, is reduced in obesity [16, 66–67]. In contrast, in subcutaneous abdominal adipose tissue 11␤-HSD1 activity is increased both in vivo and in vitro [66–69, 71–73, 75]. Further studies have confirmed that the increased 11␤-HSD1 activity in adipose biopsies is accompanied by increased 11␤-HSD1 mRNA, and microdialysis in subcutaneous adipose tissue directly demonstrated an increased 11␤-HSD1 activity in obesity [90]. Recent studies suggest that, in contrast to earlier reports [73, 74], 11␤-HSD1 mRNA levels are also increased in intact omental adipose tissue of obese women and are a strong predictor of fat cell size in this visceral depot [75]. Further studies on visceral adipose tissue are needed to clarify this important potential aspect of 11␤-HSD1 dysregulation given the unproven ‘Cushing’s disease of the omentum’ hypothesis [36] and the impact that elevated visceral fat glucocorticoid action and production would have on the portal hypothesis of insulin resistance described below. The mechanisms underlying increased adipose 11␤-HSD1 in simple obesity are uncertain. Attempts to link the 11␤-HSD1 genotype with obesity have not been successful [77, 91], though specific polymorphisms of 11␤-HSD1 gene do associate with hypertension [78] insulin sensitivity/diabetes [92] and with apolipoprotein levels [93]. Studies in identical twins also support environmental causes for the association between increased adipose 11␤-HSD1 expression and obesity [94].

Adipose 11␤-HSD1 Overexpressing Mice: A Model of the Metabolic Syndrome

The key question is whether increased adipose 11␤-HSD1 is a cause or a consequence of obesity and the associated metabolic syndrome. To dissect this, mice overexpressing 11␤-HSD1 selectively in adipose tissue have been generated exploiting the adipocyte fatty acid-binding protein (aP2) promoter [7]. These aP2-11␤-HSD1 mice express 2- to 3-fold more 11␤-HSD1 in all adipose depots, but not in other tissues. The transgene causes approximately doubled corticosterone levels within adipose tissue and increased release of corticosterone into the portal circulation, but systemic glucocorticoid levels are unaltered. As a consequence of local intra-adipose glucocorticoid excess, aP2-11␤-HSD1 mice are modestly obese, presenting with predominantly intra-abdominal obesity. The visceral adipose expansion in these animals may relate to the higher levels of glucocorticoid receptor in visceral than peripheral

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fat depots in the mice [7]. In association with this aP2-11␤-HSD1 mice develop all major features of the metabolic syndrome. The animals are markedly glucose intolerant and insulin resistant, features exacerbated by high-fat feeding. They do not show obvious fasting hyperglycaemia, but this becomes markedly manifest after a glucose load suggesting the deficit is in peripheral glucose uptake, rather than hepatic glucose production. They show elevated free fatty acid and triglyceride levels [7]. Glucocorticoids stimulate leptin secretion from adipose tissue and serum leptin levels are elevated in the transgenic animals disproportionately to the obesity, suggesting leptin resistance. Within adipocytes, aP2-11␤-HSD1 mice show changes concordant with decreased sensitivity to insulin and/or increased corticosterone levels with decreased expression of the insulin sensitising adipokine adiponectin [95] and increased expression of TNF␣ which causes insulin resistance [96]. Serum TNF␣ levels are also elevated. Curiously, adipose mRNA encoding resistin, which promotes insulin resistance in mice [97], is reduced in aP2-HSD1 transgenic adipose tissue, perhaps also as a consequence of the glucocorticoid excess [98]. Intriguingly, angiotensinogen mRNA, which is normally expressed at low levels in adipose tissue, is strikingly elevated in aP2-11␤-HSD1 transgenic mouse fat. This glucocorticoidregulated transcript may underpin the marked hypertension seen in these animals [99]. Finally the animals exhibit hyperphagia, which may be partly related to their leptin resistance or other novel mechanisms of adipose-satiety centre cross-talk. Thus the aP2-11␤-HSD1 transgenic mouse faithfully models the major features of the metabolic syndrome. The effects of adipose 11␤-HSD1 overexpression and deficiency on food intake deserve special note. Whilst adipose-specific 11␤-HSD1 overexpression causes hyperphagia [7] and adipose expression of the glucocorticoid inactivating enzyme 11␤-HSD2 (see below) causes hypophagia [100], global 11␤HSD1 deficiency leads to transient hyperphagia [41, 101]. It is likely that this divergence in otherwise complementary phenotypes is due to the effects of 11␤-HSD1 deficiency within the hypothalamic feeding centres and indeed recent data suggest that 11␤-HSD1 can constrain high-fat diet-induced hyperphagia perhaps by modulating opiate reward centres linked to the hypothalamic agouti-related peptide expressing cells within the arcuate nucleus of the hypothalamus [68]. These data have implications for therapeutic inhibitor strategies where hyperphagia would be an undesirable side effect. However, recent studies on the effects of specific 11␤HSD1 inhibitors on food intake in mice show in fact that they reduce food intake and increase energy expenditure [102], perhaps because of predominant peripheral actions of the drugs involved.

Liver Glucocorticoid Excess: ApoE-11␤-HSD1 Transgenic Mice

Blood from visceral adipose tissue drains via the hepatic portal vein to the liver. Unsurprisingly therefore, aP2-11␤-HSD1 transgenic mice have elevated levels of

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corticosterone and free fatty acids in the portal plasma, implying that the liver is exposed to excess glucocorticoids. The ‘portal theory’ contends that release of factors such as glucocorticoids, free fatty acids and adipocytokines directly leads to pronounced hepatic insulin resistance [5]. To address this and to examine whether excess 11␤-HSD1 in the liver per se, as can be found in certain rodent models of diabetes and obesity [86, 89], can cause the metabolic syndrome, transgenic mice overexpressing 11␤-HSD1 in the liver have been generated under the ApoE promoter [103]. These mice are also viable and appear normal. As adults, the mice show modest insulin resistance and hypertriglyceridaemia along with slightly elevated fat accumulation in the liver. ApoE-11␤-HSD1 transgenic mice have normal weight and adipose depot mass and show normal glucose tolerance. However, the animals are hypertensive, with activation of the renin-angiotensin-aldosterone system, in part due to overexpression of angiotensinogen in the liver. Thus liver overexpression of 11␤-HSD1 produces an attenuated metabolic syndrome phenotype without visceral obesity. This might be of pathogenic relevance in patients with the insulinresistance of myotonic dystrophy [104] and in some metabolic syndrome models [86, 89] in which liver 11␤-HSD1 activity is raised. One notable recent finding is that liver 11␤-HSD1 overexpression can correct the basal hypercorticosteronaemia found in the 11␤-HSD1⫺/⫺ mice when the two models are inter-crossed [105]. This suggests that hepatic cortisol regeneration by 11␤-HSD1 may have an important regulatory influence on the HPA axis and, by inference, the downregulation of hepatic 11␤-HSD1 found in human obesity may contribute to the altered HPA responsiveness described above, with implications for therapeutic 11␤-HSD1 inhibition in the context of chronic stress.

Deficiency of 11␤-HSD1

Adiposity in 11␤-HSD1 knock-out animals has been examined on both intrinsically obesity-resistant and obesity-prone genetic backgrounds. Intra-adipose corticosterone levels are substantially lowered in the 11␤-HSD1⫺/⫺ animals in the face of modestly elevated plasma corticosterone concentrations [106]. On the obesity-prone C57Bl/6J genetic background, high-fat diet fed 11␤-HSD1⫺/⫺ mice gain significantly less weight than controls despite a relative hyperphagia. This appears due to an enhanced metabolic rate as inferred by increased core body temperature [106]. With high-fat diet on either genetic background, 11␤-HSD1 knock-out mice preferentially gain adipose tissue in the ‘metabolically safer’ peripheral depots rather than in the ‘metabolically disadvantageous’ visceral sites. Whilst the explanation of this redistribution of fat in 11␤-HSD1⫺/⫺ animals is uncertain, these mice show higher expression of the thiazolidinedione target adipogenic transcription factor PPAR␥ receptor in all adipose tissue beds. Further, 11␤-HSD1⫺/⫺ mice show a greater increase in adipose PPAR␥ receptors with high-fat feeding than wild type. PPAR␥ ligands cause fat redistribution

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to the periphery and this may underpin the favourable morphology [107, 108] seen if increased circulating free fatty acids are acting as endogenous ligands [109] for the PPAR␥ receptors. Additionally, the 11␤-HSD1 null animals also show greater induction of uncoupling protein-2 in mesenteric adipose tissue than wild type [106] which may allow local calorie wastage rather than storage as fat. Uncoupling protein-2 is downregulated by glucocorticoids [110] and upregulated by PPAR␥ activation [111], so its induction in the 11␤-HSD1 null mouse adipose tissue is not unexpected. In terms of adipose endocrine changes, adipose leptin mRNA and plasma leptin levels are reduced in 11␤-HSD⫺/⫺ mice, particularly in peripheral adipose [106]. On the obesity-prone C57Bl/6J genetic background, the 11␤-HSD1 null animals are clearly insulin-sensitised and resist hyperglycaemia that occurs with high-fat feeding in wild-type mice. This occurs at least partly at the level of the adipocyte since isolated primary adipocytes show increased basal and insulin-stimulated glucose uptake [106]. Adipocyte resistin and TNF␣ mRNAs are reduced whereas adiponectin is increased, again compatible with an adipose-mediated insulin-sensitised phenotype. Thus, overall the mouse shows improved glucose tolerance, increased insulin sensitivity and reduced intra-tissue glucocorticoid levels in the face of modest hypercorticosteronaemia. These beneficial effects of deletion in 11␤-HSD1 in adipose tissue are accompanied by changes in hepatic gene expression consistent with increased ␤-oxidation of lipids in the liver, as described above [63]. A further recent development within the obesity field is the growing recognition that not only are the pro-inflammatory cytokines described above increased in obesity, in fact there is an adiposespecific infiltration of macrophages that correlates with obesity in rodents and humans [112]. 11␤-HSD1 is induced upon macrophage activation [113] and modulates macrophage phagocytosis and potentially resolution of inflammatory processes [114]. This suggests that regulation of 11␤-HSD1 within these cells and their interaction with adipocytes, where 11␤-HSD1⫺/⫺ adipocytes have lower levels of proinflammatory cytokines, will be a key determinant of metabolic disease progression. Intriguing new data also demonstrate that 11␤-HSD1 deficiency, through reducing the angiostatic effects of glucocorticoids, increases angiogenesis in vitro and in 11␤HSD1⫺/⫺ mice after myocardial infarction in vivo [115]. Therefore, 11␤-HSD1 inhibition potentially promotes improved healing in ischaemia in myocardial infarction and tissue injury.

Adipose 11␤-HSD2 Overexpressing Mice: A Model of Adipose-Specific Glucocorticoid Deficiency

To confirm the key importance of reduced adipose tissue glucocorticoid levels in the beneficial phenotype of 11␤-HSD1 null mice, a new strain ectopically expressing the glucocorticoid inactivating isozyme, 11␤-HSD2, selectively in adipose tissue were generated, again exploiting the aP2 promoter [100]. Expression levels were chosen to

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mimic levels seen in kidney, where the isozyme completely excludes corticosterone from intrinsically high-affinity MR. In aP2-11␤-HSD2 mice, as expected, plasma corticosterone and markers of systemic glucocorticoid exposure, such as thymic and adrenal weight and bone mineral density, are unaltered. However, the mice resist weight gain on high-fat diet due to reduced fat mass accumulation. The improved energy balance appears due to decreased food intake, increased energy expenditure and improved glucose tolerance and insulin sensitivity. Again, as anticipated by the phenotype of 11␤-HSD1⫺/⫺ mice, aP2-11␤-HSD2 transgenic mice show decreased expression of leptin and resistin and increased expression of adiponectin, PPAR␥ and uncoupling protein 2 in fat tissues. This model reinforces the concept that reducing levels of active corticosterone exclusively in adipose tissue engenders a favourable, metabolic disease-resistant phenotype. Intriguingly, aP2-11␤-HSD2 mice show highest transgene expression in subcutaneous adipose tissue. Whilst the independent risk associated with visceral adiposity and cardiometabolic disease has long been clear [3, 116], these data also demonstrate that the peripheral fat stores and glucocorticoid metabolism within them – 11␤-HSD1 is expressed at highest levels in this depot in mice [88] – have a large impact on metabolism and systemic insulin sensitivity. Thus, the human observations of correlations between 11␤-HSD1 in subcutaneous rather than visceral adipose tissue [74] and adverse metabolic outcomes may also pertain in mice. The precise roles of glucocorticoids, other hormones (such as insulin) and transcription factors (such as PPAR␥) and their downstream adipokines in each adipose depot require further dissection.

Downregulation of Endogenous Adipose 11b-HSD1 as an Adaptation to Chronic High-Fat Feeding Intriguing recent data have shown that wild-type mice markedly downregulate 11␤HSD1 mRNA and activity in fat in response to a high-fat diet [88]. Similar findings have been reported in rats [117]. Whether this downregulation results in lower intraadipose glucocorticoid levels is still under investigation since one study showed that adipose 11␤-HSD1 levels were indeed higher in obese and diabetic KKY mice but that intra-adipose corticosterone levels were not affected by high-fat diet [118]. Differences in experimental design and methodology require careful consideration in future studies. Strikingly, the metabolic disease-resistant A/J mouse strain has lower basal levels of 11␤-HSD1 mRNA and activity in visceral and peripheral fat depots, and downregulates adipose 11␤-HSD1 more markedly upon high-fat feeding than metabolic disease-prone C57Bl/6 mice. The AJ strain almost completely shuts off adipose 11␤-HSD1 upon high-fat diet becoming, in effect, null in this tissue [88]. This suggests that downregulation of adipose 11␤-HSD1 represents an adaptive mechanism, more pronounced in metabolic disease-resistance, that tends to counteract the adverse metabolic consequences of chronic high-fat diet [88]. In rats, high-fat diet downregulation of adipose 11␤-HSD1 is transient and reverses after several months

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as weight is gained and insulin resistance develops, suggesting that this metabolic adaptation is not sustained with worsening obesity in rats [117].

11␤-HSD1 as a Drug Target for Therapy in Obesity and the Metabolic Syndrome

11␤-HSD1 inhibition is an attractive target for treatment of the metabolic syndrome. Carbenoxolone has been a ‘prototype’, though non-selective 11␤-HSD1 inhibitor. Carbenoxolone enhances insulin sensitivity in the euglycaemic hyperinsulinaemic clamp technique in humans [58]. Indeed, in lean patients with type 2 diabetes carbenoxolone inhibits hepatic glucose production [59]. However, by contrast with enhanced adipose insulin sensitivity and glucose disposal in 11␤-HSD1⫺/⫺ and aP2-11␤-HSD2 mice, carbenoxolone has no effect on glucose disposal, perhaps because carbenoxolone inhibits adipose 11␤-HSD1 poorly [119]. Moreover, liver 11␤-HSD1 appears to be downregulated in human and several rodent models of obesity so that the incremental effect of inhibition may be smaller in the obese [25, 66, 67]. Whether inhibition in liver alone is sufficient to confer clinically useful metabolic benefits thus remains uncertain. Future studies using tissue-specific knock down with transgenic cre-lox technology will be important to answer the roles of respective tissue-specific inhibition. With a view to therapeutic development, several new classes of selective 11␤HSD1 inhibitors have been described, including the arylsulfonamidothiazoles [120], and more recently perhydroquinolylbenzamides [121]. These compounds inhibit 11␤-HSD1 and enhance insulin action in liver, lowering blood glucose concentrations in diabetic and obese mouse models [60, 61]. However, euglycaemic hyperinsulinaemic clamps in mice suggest that arylsulfonamidothiazoles do not increase peripheral glucose uptake [61]. Despite this, selective 11␤-HSD1 inhibitors improve metabolic syndrome, dyslipidaemia and, in a key recent observation, prevent the development of atherosclerosis in mice that are prone to developing plaque lesions [102]. Overall, the efficacy of 11␤-HSD1 inhibition in attenuating obesity and, particularly, in disconnecting obesity from its metabolic consequences remains to be established in the crucial setting of human therapy. The rodent data highlight that care should be taken when extrapolating to potential clinical outcomes, and emphasise the crucial influence of species, strain background and environmental influences such as dietary fat, stress and inflammatory status on glucocorticoid-mediated processes within adipose, other key metabolic tissues and on the HPA axis.

Acknowledgments Work described from the authors’ laboratory was generously funded by a Wellcome Trust Programme Grant (J.R.S.) and a Wellcome Trust Intermediate Research Fellowship (N.M.M.). N.M.M. is a Wellcome Trust Career Development Fellow.

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59 Andrews RC, Rooyackers O, Walker BR: Effects of the 11 beta-hydroxysteroid dehydrogrenase inhibitor carbenoxolone on insulin sensitivity in men with type 2 diabetes. J Clin Endocrinol Metab 2003;88: 285–291. 60 Alberts P, Engblom L, Edling N, Forsgren M, Klingstrom G, Larsson C, Ronquist-Nii Y, Ohman B, Abrahmsen L: Selective inhibition of 11 beta-hydroxysteroid dehydrogenase type 1 decreases blood glucose concentrations in hyperglycaemic mice. Diabetologia 2002;45:1528–1532. 61 Alberts P, Nilsson C, Selen G, Engblom L, Edling N, Norling S, et al: Selective inhibition of 11␤-hydroxysteroid dehydrogenase type 1 improves hepatic insulin sensitivity in hyperglycaemic mice strain. Endocrinol 2003;144:4755–4762. 62 Harris HJ, Kotelevtsev Y, Mullins JJ, Seckl JR, Holmes MC: 11␤-hydroxysteroid dehydrogenase type 1 null mice have altered hypothalamic-pituitary-adrenal axis activity: a novel control of glucocorticoid feedback. Endocrinol 2001;142: 114–120. 63 Morton NM, Holmes MC, Fievet C, Staels B, Mullins JJ, Seckl JR: Improved lipid and lipoprotein profile, hepatic insulin sensitivity and glucose tolerance in 11beta-hydroxysteroid dehydrogenase type 1 null mice. J Biol Chem 2001;276: 41293–41300. 64 Gaillard D, Wabitsch M, Pipy B, Negrel R: Control of terminal differentiation of adipose precursor cells by glucocorticoids. J Lipid Res 1991;32:569–579. 65 Paulmyer-Lacroix O, Boullu S, Oliver C, Alessi MC, Grino M: Expression of the mRNA coding for 11 beta-hydroxysteroid dehydrogenase type 1 in adipose tissue from obese patients: an in situ hybridization study. J Clin Endocrinol Metab 2002;87: 2701–2705. 66 Rask E, Olsson T, Soderberg S, Andrew R, Livingstone DEW, Johnson O, Walker BR: Tissuespecific dysregulation of cortisol metabolism in human obesity. J Clin Endocrinol Metab 2001;86: 1418–1421. 67 Rask E, Walker BR, Soderberg S, Livingstone DE, Eliasson M, Johnson O, Andrew R, Olsson T: Tissuespecific changes in peripheral cortisol metabolism in obese women: increased adipose 11beta-hydroxysteroid dehydrogenase type 1 activity. J Clin Endocrinol Metab 2002;87: 3330–3336. 68 Lindsay RS, Wake DJ, Nair S, Bunt J, Livingstone DEW, Permana PA, Tataranni PA, Walker BR: Subcutaneous adipose 11 beta-hydroxysteroid dehydrogenase type 1 activity and messenger ribonucleic acid levels are associated with adiposity and insulinemia in Pima Indians and Caucasians. J Clin Endocrinol Metab 2003;88:2738–2744.

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69 Wake DJ, Rask E, Livingstone DEW, Soderberg S, Olsson T, Walker BR: Local and systemic impact of transcriptional up-regulation of 11 beta-hydroxysteroid dehydrogenase type 1 in adipose tissue in human obesity. J Clin Endocrinol Metab 2003;88: 3983–3988. 70 Westerbacka J, Yki-Järvinen H, Vehkavaara S, Häkkinen A, Andrew R, Wake D, Seckl JR, Walker BR: Body fat distribution and cortisol metabolism in healthy men: enhanced 5-reductase and lower cortisol/cortisone metabolite ratios in men with fatty liver. J Clin Endocrinol Metab 2003;88: 4924–4931. 71 Engeli S, Bohnke J, Feldpausch M, Gorzelniak K, Heintze U, Janke J, Luft FC, Sharma AM: Regulation of 11 beta-HSD genes in human adipose tissue: Influence of central obesity and weight loss. Obes Res 2004;12:9–17. 72 Kannisto K, Pietilainen KH, Ehrenborg E, Rissanen A, Kaprio J, Hamsten A, Yki-Yarvinen H: Overexpression of 11 beta-hydroxysteroid dehydrogenase-1 in adipose tissue is associated with acquired obesity and features of insulin resistance: Studies in young adult monozygotic twins. J Clin Endocrinol Metab 2004;89:4414–4421. 73 Goedecke JH, Wake DJ, Levitt NS, Lambert EV, Collins MR, Morton NM, Andrew R, Seckl JR, Walker BR: Glucocorticoid metabolism within superficial subcutaneous rather than visceral adipose tisuue is a ssociated with features of the metabolic syndrome in South African women. Clin Endocrinol 2006;65:81–87. 74 Tomlinson JW, Sinha B, Bujalska I, Hewison M, Stewart PM: Expression of 11 beta-hydroxysteroid dehydrogenase type 1 in adipose tissue is not increased in human obesity. J Clin Endocrinol Metab 2002;87:5630–5635. 75 Michailidou Z, Jensen M, Dumesic D, Seckl JR, Walker BR, Morton NM: Omental fat 11beta-HSD1, but not GR, is correlated with fat cell size independently of obesity’ in its current form for publication in Obesity. Obesity 2007;15: 1155–1163. 76 Bujalska IJ, Walker EA, Hewison M, Stewart PM: A switch in dehydrogenase to reductase activity of 11, 6-hydroxysteroid dehydrogenase type 1 upon differentiation of human omental adipose stromal cells. J Clin Endocrinol Metab 2002;87:1205–1210. 77 Draper N, Echwald SM, Lavery GG, Walker EA, Fraser R, Davies E, Astrup A, Adamski J, Hewison M, Connell JM, Pedersen O, Stewart PM: Association studies between microsatellite markers within the gene encoding human 11 beta-hydroxysteroid dehydrogenase type 1 and body mass index, waist to hip ratio, and glucocorticoid metabolism. J Clin Endocrinol Metab 2002;87:4984–4990.

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78 Franks PW, Knowler WC, Nair S, Koska J, Lee YH, Lindsay RS, Walker BR, Looker HC, Permana PA, Tataranni PA, Hanson RL: Interaction between an 11 beta HSD1 gene variant and birth era modifies the risk of hypertension in Pima Indians. Hypertens 2004;44:681–688. 79 Draper N, Walker EA, Bujalska IJ, Tomlinson JW, Chalder SM, Arlt W, Lavery GG, Bedendo O, Ray DW, Laing I, Malunowicz E, White PC, Hewison M, Mason PJ, Connell JM, Shackleton CH, Stewart PM: Mutations in the genes encoding 11 beta-hydroxysteroid dehydrogenase type 1 and hexose-6-phosphate dehydrogenase interact to cause cortisone reductase deficiency. Nat Genetics 2003;34: 434–439. 80 San Millan JL, Botella-Carretero JI, Alvarez-Blasco F, Luque-Ramirez M, Sancho J, Moghetti P, EscobarMorreale HF: A study of the hexose-6-phosphate dehydrogenase gene R453Q and 11beta-hydroxysteroid dehydrogenase type 1 gene 83557insA polymorphisms in the polycystic ovary syndrome. J Clin Endocrinol Metab 2005;90: 4157–4162. 81 White PC: Genotypes at 11beta-hydroxysteroid dehydrogenase type 11B1 and hexose-6-phosphate dehydrogenase loci are not risk factors for apparent cortisone reductase deficiency in a large populationbased sample. J Clin Endocrinol Metab 2005;90: 5880–5883. 82 Seckl J, Chapman K, Morton N, Walker B: Glucocorticoids and 11beta-hydroxysteroid dehydrogenase in adipose tissue. Rec Prog Horm Res 2004;59: 359–393. 83 Berger J, Tanen M, Elbrecht A, HermanowskiVosatka A, Moller DE, Wright SD, Thieringer R: Peroxisoime proliferator-activated receptor-␥ ligands inhibit adipocyte 11␤-hydroxysteroid dehydrogenase type 1 expression and activity. J Biol Chem 2001;276:12629–12635. 84 Stulnig TM, Oppermann U, Steffensen KR, Schuster GU, Gustafsson JA: Liver X receptors downregulate 11 beta-hydroxysteroid dehydrogenase type 1 expression and activity. Diabetes 2002;51:2426–2433. 85 Williams LJS, Lyons V, MacLeod I, Rajan V, Darlington GJ, Poli V, Seckl JR, Chapman KE: C/EBP␣ regulates hepatic transcription of 11␤-hydroxysteroid dehydrogenase type 1; a novel mechanisms for crosstalk between the C/EBP and glucocorticoid signalling pathways. J Biol Chem 2000;275:30232–30239. 86 Morton NM, Densmore V, Wamil M, Ramage L, Nichol K, Bunger L, Seckl JR, Kenyon CJ: A polygenic model of the metabolic syndrome with reduced circulating and intra-adipose glucocorticoid action. Diabetes 2005;54:3371–3378.

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87 Olswang Y, Blum B, Cassuto H, Cohen H, Biberman Y, Hanson RW, Reshef L: Glucocorticoids repress transcription of phosphoenolpyruvate carboxykinase (GTP) gene in adipocytes by inhibiting its C/EBP-mediated activation. J Biol Chem 2003;278: 12929–12936. 88 Morton N, Ramage L, Seckl J: Down-regulation of adipose 11␤-hydroxysteroid dehydrogenase type 1 by high fat feeding in mice: a potential adaptive mechanism counteracting metabolic disease. Endocrinol 2004;145:2707–2712. 89 Liu YJ, Nakagawa Y, Wang Y, Sakurai R, Tripathi PV, Lutfy K, Friedman TC: Increased glucocorticoid receptor and 11 beta-hydroxysteroid dehydrogenase type 1 expression in hepatocytes may contribute to the phenotype of type 2 diabetes in db/db mice. Diabetes 2005;54:32–40. 90 Sandeep TC, Andrew R, Homer NZM, Andrews RC, Smith K, Walker BR: Increased in vivo regeneration of cortisol in adipose tissue in human obesity and effects of the 11 beta-hydroxysteroid dehydrogenase type 1 inhibitor carbenoxolone. Diabetes 2005;54: 872–879. 91 Caramelli E, Strippoli P, Di Giacomi T, Tietz C, Carinci P, Pasquali R: Lack of mutations of type 1 11 beta-hydroxysteroid dehydrogenase gene in patients with abdominal obesity. Endocr Res 2001;27:47–61. 92 Nair S, Lee Y, Lindsay R, Walker B, Tataranni P, Bogardus C, Baier LJ, Permana PA: The 11beta hydroxysteroid dehydrogenase type 1 gene: genetic polymorphisms are associated with type 2 diabetes in Pima indians independently of obesity and expression in adipocyte and muscle. Diabetologia 2004;47:1088–1095. 93 Robitaille J, Brouillette C, Houde A, Despres JP, Tchernof A, Vohl MC: Molecular screening of the 11 beta-HSD1 gene in men characterized by the metabolic syndrome. Obes Res 2004;12:1570–1575. 94 Kannisto K, Pietilainen KH, Ehrenborg E, Rissanen A, Kaprio J, Hamsten A, Yki-Yarvinen H: Overexpression of 11 beta-hydroxysteroid dehydrogenase-1 in adipose tissue is associated with acquired obesity and features of insulin resistance: Studies in young adult monozygotic twins. J Clin Endocrinol Metab 2004;89:4414–4421. 95 Yamauchi T, Kamon J, Minokoshi Y, Ito Y, Waki H, Uchida S, Yamashita S, Noda M, Kita S, Ueki K, Eto K, Akanuma Y, Froguel P, Foufelle F, Ferre P, Carling D, Kimura S, Nagai R, Kahn BB, Kadowaki T: Adiponectin stimulates glucose utilization and fattyacid oxidation by activating AMP-activated protein kinase. Nat Med 2002;8:1288–1295. 96 Hotamisligil G, Shargill N, Spiegelman B: Adipose expression of tumor necrosis factor-alpha: direct role in obesity-linked insulin resistance. Science 1993;259:87–91.

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97 Steppan C, Bailey S, Bhat S, Brown E, Banerjee R, Wright C, Patel HR, Ahima RS, Lazar M: The hormone resistin links obesity to diabetes. Nature 2001;409:307–312. 98 Viengchareun S, Zennaro MC, Pascual-Le Tallec L, Lombes M: Brown adipocytes are novel sites of expression and regulation of adiponectin and resistin. FEBS Letters 2002;532:345–350. 99 Masuzaki H, Yamamoto H, Kenyon CJ, Elmquist JK, Morton NM, Paterson JM, Shinyama H, Sharp MG, Fleming S, Mullins JJ, Seckl JR, Flier JS: Transgenic amplification of glucocorticoid action in adipose tissue causes high blood pressure in mice. J Clin Invest 2003;112:83–90. 100 Kershaw E, Morton N, Dhillon H, Ramage L, Seckl J, Flier J: Adipocyte-specific glucocorticoid inactivation protects against diet-induced obesity. Diabetes 2005;54:1023–1031. 101 Wang SJ, Birtles S, de Schoolmeester J, Swales J, Moody G, Hislop D, O’Dowd J, Smith DM, Turnbull AV, Arch JR: Inhibition of 11beta-hydroxysteroid dehydrogenase type 1 reduces food intake and weight gain but maintains energy expenditure in diet-induced obese mice. Diabetologia 2006;49: 1333–1337. 102 Hermanowski-Vosatka A, Balkovec JM, Cheng K, Chen HY, Hernandez M, Koo GC, Le Grand CB, Li Z, Metzger JM, Mundt SS, Noonan H, Nunes CN, Olson SH, Pikounis B, Ren N, Robertson N, Schaeffer JM, Shah K, Springer MS, Strack AM, Strowski M, Wu K, Wu T, Xiao J, Zhang BB, Wright SD, Thieringer R: 11␤-HSD1 inhibition ameliorates metabolic syndrome and prevents progression of atherosclerosis in mice. J Exp Med 2005;202:517–527. 103 Paterson J, Morton N, Fievet C, Kenyon C, Holmes M, Staels B, Seckl JR, Mullins JJ: Metabolic syndrome without obesity: hepatic over-expression of 11␤-hydroxysteroid dehydrogenase type 1 in transgenic mice. Proc Natl Acad Sci USA 2004;101: 7088–7093. 104 Johansson A, Andrew R, Forsberg H, Cederquist K, Walker BR, Olsson T: Glucocorticoid metabolism and adrenocortical reactivity to ACTH in myotonic dystrophy. J Clin Endocrinol Metab 2001;86: 4276–4283. 105 Paterson JM, Holmes MC, Kenyon CJ, Carter R, Mullins JJ, Seckl JR: Liver-selective transgene rescue of hypothalamic-pituitary-adrenal axis dysfunction in 11beta-hydroxysteroid dehydrogenase type 1deficient mice. Endocrinology 2007;148:961–966. 106 Morton N, Paterson J, Masuzaki H, Holmes MC, Staels B, Fievet C, Walker BR, Flier JS, Mullins JJ, Seckl JR: Novel adipose tissue-mediated resistance to diet induced visceral obesity in 11␤-hydroxysteroid dehydrogenase type 1 deficient mice. Diabetes 2004;53: 931–938.

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107 Sewter CP, Blows F, Vidal-Puig A, O’Rahilly S: Regional differences in the response of human preadipocytes to PPAR gamma and RXR alpha agonists. Diabetes 2002;51:718–723. 108 Kelly IE, Han TS, Walsh K, Lean MEJ: Effects of a thiazolidinedione compound on body fat and fat distribution of patients with type 2 diabetes. Diabetes Care 1999;22:288–293. 109 Xu HE, Lambert MH, Montana VG, Parks DJ, Blanchard SG, Brown PJ, Sternbach DD, Lehmann JM, Wisely GB, Willson TM, Kliewer SA, Milburn MV: Molecular recognition of fatty acids by peroxisome proliferator-activated receptors. Mol Cell 1999;3:397–403. 110 Udden J, Folkesson R, Hoffstedt J: Downregulation of uncoupling protein 2 mRNA in women treated with glucocorticoids. Int J Obes 2001;25:1615–1618. 111 Digby JE, Crowley VEF, Sewter CP, Whitehead JP, Prins JB, O’Rahilly S: Depot-related and thiazolidinedione-responsive expression of uncoupling protein 2 (UCP2) in human adipocytes. Int J Obes 2000;24: 585–592. 112 Weisberg SP, McCann D, Desai M, Rosenbaum M, Leibel RL, Ferrante AW Jr: Obesity is associated with macrophage accumulation in adipose tissue. J Clin Invest 2003;112:1796–1808. 113 Theiringer R, LeGrand LB, Carbin L, Cai TQ, Wong B, Wright SD, Hermanowski-Vosatka A: 11␤ hydroxysteroid dehydrogenase is induced in human monocytes upon differentiation to macrophages. J Immunol 2001;167:30–35. 114 Gilmour JS, Coutinho AE, Cailhier JF, Man TY, Clay M, Thomas G, Harris HJ, Mullins JJ, Seckl JR, Savill JS, Chapman KE: Local amplification of glucocorticoids by 11 beta-hydroxysteroid dehydrogenase type 1 promotes macrophage phagocytosis of apoptotic leukocytes. J Immunol 2006;176:7605–7611. 115 Small GR, Hadoke PW, Sharif I, Dover AR, Armour D, Kenyon CJ, Gray GA, Walker BR: Preventing local regeneration of glucocorticoids by 11betahydroxysteroid dehydrogenase type 1 enhances angiogenesis. Proc Natl Acad Sci USA 2005;102: 12165–12170.

116 Kissebah AH, Vydelingum N, Murray R, Evans DJ, Hartz AJ, Kalkhoff RK, Adams PW: Relation of body fat distribution to metabolic complications of obesity. J Clin Endocrinol Metab 1982;54:254–260. 117 Drake AJ, Livingstone DEW, Andrew R, Seckl JR, Morton NM, Walker BR: Reduced adipose glucocorticoid reactivation and increased hepatic glucocorticoid clearance as an early adaptation to high-fat feeding in Wistar rats. Endocrinol 2005;146: 913–919. 118 Alberts P, Ronquist-Nii Y, Larsson C, Klingstrom G, Engblom L, Edling N, Lidell V, Berg I, Edlund PO, Ashkzari M, Sahaf N, Norling S, Berggren V, Bergdahl K, Forsgren M, Abrahmsen L: Effect of high-fat diet on KKAy and ob/ob mouse liver and adipose tissue corticosterone and 11-dehydrocorticosterone concentrations. Horm Metab Res 2005; 37:402–407. 119 Livingstone DEW, Walker BR: Is 11 beta-hydroxysteroid dehydrogenase type 1 a therapeutic target? Effects of carbenoxolone in lean and obese Zucker rats. J Pharma Exp Therap 2003;305:167–172. 120 Barf T, Vallgarda J, Emond R, Haggstrom C, Kurz G, Nygren A, Larwood V, Mosialou E, Axelsson K, Olsson R, Engblom L, Edling N, Ronquist-Nii Y, Ohman B, Alberts P, Abrahmsen L: Arylsulfonamidothiazoles as a new class of potential antidiabetic drugs. Discovery of potent and selective inhibitors of the 11 beta-hydroxysteroid dehydrogenase type 1. J Med Chem 2002;45:3813–3815. 121 Coppola GM, Kukkola PJ, Stanton JL, Neubert AD, Marcopulos N, Bilci NA, Wang H, Tomaselli HC, Tan J, Aicher TD, Knorr DC, Jeng AY, Dardik B, Chatelain RE: Perhydroquinolylbenzamides as novel inhibitors of 11beta-hydroxysteroid dehydrogenase type 1. J Med Chem 2005;48:6696–6712.

Prof. Jonathan R. Seckl Endocrinology Unit, Centre for Cardiovascular Sciences Queens Medical Research Institute, Edinburgh University Edinburgh EH16 4TJ (UK) Tel. ⫹44 131 242 6077, Fax ⫹44 131 242 6079, E-Mail [email protected]

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Korbonits M (ed): Obesity and Metabolism. Front Horm Res. Basel, Karger, 2008, vol 36, pp 165–181

Gut and Hormones and Obesity Alison M. Wren Department of Endocrinology, Chelsea and Westminster Hospital NHS Foundation Trust, London, UK

Abstract Following the discovery of secretin in 1902, a host of further peptide hormones that are synthesised and released from the gastrointestinal tract have been identified. While their roles in the regulation of gastrointestinal function have been known for some time, it is now evident that many of these hormones also physiologically regulate energy balance. Our understanding of how gut hormones signal to the brain has advanced significantly in recent years. Several hormones, including peptide YY, pancreatic polypeptide, oxyntomodulin, glucagon-like peptide 1 and cholecystokinin function as satiety signals. In contrast, only ghrelin, produced by the stomach, has emerged as a putative hunger signal, appearing to act both as a meal initiator and a long-term body weight regulator. Recent research suggests that gut hormones can be manipulated to regulate energy balance in man and that obese subjects retain sensitivity to the actions of gut hormones. The worldwide obesity pandemic continues unabated, despite public health initiatives and current best therapy. Future gut hormone-based therapies may provide an effective and well-tolerated treatment for obesity. Copyright © 2008 S. Karger AG, Basel

Energy Homeostasis and Obesity

The concept of the gut as an endocrine organ is hardly a new. The gut peptide secretin was the first substance to be termed a hormone whilst the appetite inhibitory actions of cholecystokinin (CCK) were first reported over 30 years ago [1, 2]. However, in recent years, further scientific endeavour in this field has been motivated by the need to develop new strategies to tackle the global pandemic of obesity. The prevalence of obesity in adults has increased by over 75% worldwide since 1980. Given that obesity is causally associated with cardiovascular disease, type 2 diabetes, hypertension, stroke, obstructive sleep apnoea and certain cancers, this has translated into healthcare costs of over half a billion pounds every year in the UK alone. Obesity is not only a problem in the developed world, but is set to overtake infectious diseases as the most significant contributor to ill-health worldwide and has been classified as an epidemic by the World Health Organization [3].

Public health initiatives have failed to reverse the rising incidence of obesity. Medical and behavioural interventions, with the exception of bariatric surgery, have limited success, as discussed in the treatment section of this volume. This chapter will focus on the peptide hormone signals from the gut that communicate the status of body energy stores to the brain and the brain centres on which they act. These regulatory systems are not only of academic interest, but are likely to underpin any future strategy to tackle obesity, by providing drug targets for the holy grail of safe sustainable weight loss.

Long-Term and Short-Term Energy Balance Signals

Peripheral signals that regulate energy balance are often categorised as long and short acting. Long-acting signals, typified by the adipocyte hormone leptin, characteristically reflect the levels of energy stores and regulate body weight and the amount of energy stored as fat [see the chapter by Ahima and Osei, this vol., pp. 182–197]. Short-acting gastrointestinal signals are typified by gut hormones such as CCK and mechanical factors, such as gastric distension, which characteristically relay a sense of ‘fullness’ resulting in post-prandial satiation and meal termination. Other gut hormones, including peptide YY (PYY) and ghrelin appear to blur the boundaries between long- and short-term signals, having features of both. Current evidence suggests that they not only regulate appetite on a meal-by-meal basis but also participate in longer term energy balance.

Central Integration of Peripheral Signals

Neuro-hormonal signals from the gut and adipose tissue converge on the hypothalamus where they are integrated and in turn regulate energy intake and energy expenditure. The hypothalamic neurocircuits regulating energy balance have been reviewed extensively [4, 5]. In brief, the hypothalamic arcuate nucleus (Arc), a circumventricular organ which is accessible to circulating factors via a relatively deficient blood brain barrier, may be viewed as a conduit for peripheral signals. Two key neuronal populations have been identified within the Arc with opposing effects on energy balance. A group of neurones in the medial Arc co-express neuropeptide Y (NPY) and agouti-related peptide (AgRP) and act to stimulate food intake and weight gain. In contrast pro-opiomelanocortin (POMC) and cocaine- and amphetamineregulated transcript co-expressing neurones in the lateral Arc inhibit feeding and promote weight loss. The balance of activity between these two groups of neurones is critical to body weight regulation. The Arc neurones have multiple connections with other central nervous system (CNS) sites of appetite regulation, including the brainstem and other hypothalamic nuclei.

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Adiponectin Resistin Visfatin Leptin Adipose tissue Insulin PP

Pancreas Vagal afferents OXM PYY 3-36 CCK + GLP-1

Ghrelin Intestine

Stretch Nutrients

Stomach Fig. 1. Overview of peripheral factors regulating energy balance and their routes of signalling to the brain; from Wren and Bloom [29].

The nucleus of the solitary tract (NTS) and the area postrema (AP), components of the dorsal vagal complex are also key central integrators of peripheral signals. They receive inputs from vagal afferents and circulating factors and are reciprocally connected with hypothalamic nuclei controlling energy balance. These brainstem centres can also respond independently to peripheral signals when communication with higher brain centres are surgically interrupted [6]. In addition, inputs from the cortex (emotional, social and behaviour cues) and the mesolimbic dopaminergic reward circuits influence energy balance and communicate with the hypothalamus. Peripheral feedback to the hypothalamus is complex, as illustrated in figure 1. Many circulating signals, including gut hormones, have access to the Arc and leptin, for example, is thought to act primarily via a direct action here [5]. In contrast, other peripheral

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signals influence the hypothalamus indirectly via afferent neuronal pathways and brainstem circuits. The most extensively characterised of these is CCK, which binds to receptors on the vagus nerve, thus activating the NTS, which in turn relays information to the hypothalamus. In the cases of ghrelin and PYY, there is evidence for both a direct action on the Arc and an action via the vagus nerve and brainstem.

Ghrelin, the Hunger Hormone

Ghrelin is the only known circulating orexigen. In contrast, all the other peripheral factors that regulate energy balance act to restrain eating and weight gain. Ghrelin was discovered as an endogenous ligand for the growth hormone (GH) secretagogue receptor (GHS-R1a) [7]. However, early work on this peptide demonstrated a GHindependent action to powerfully increase food intake and body weight. The predominant focus of subsequent research has shifted onto the role of ghrelin in energy balance [8–11]. Ghrelin is a 28-amino acid peptide, cleaved from a precursor, preproghrelin [7]. It is principally synthesised in endocrine cells of the stomach, termed X/A-like or ghrelin cells, and particularly found in the gastric fundus. About two thirds to three quarters of circulating ghrelin is of gastric origin. Lesser concentrations of ghrelin are found throughout the small intestine, with the duodenum producing approximately ten times less than the stomach and progressively lower concentrations found more distally [12]. Ghrelin undergoes post-translational modification with attachment of a medium-chain fatty acid, typically octanoic acid, to the serine-3 residue. This acylation is entirely unique among biologically active peptides and is required for ghrelin to bind to and activate its classical receptor, the GHS-R1a [7]. The GHS-R1a is widely expressed. In the CNS, it is found in areas involved in regulation of appetite and energy balance including hypothalamic nuclei, the dorsal vagal complex and the mesolimbic dopaminergic system. Peripherally, it is expressed in the pituitary, and pharmacologically ghrelin acts at both pituitary and hypothalamic levels to powerfully stimulate GH secretion [7, 9, 12, 13]. The physiological relevance of ghrelin in GH regulation is debated. Ghrelin is not essential for GH secretion as ghrelin and GHS-R1a null mice are not growth restricted, but it may play a role in augmentation of GHRH-stimulated GH pulses. GHS-R1a receptor expression has also been described in diverse peripheral sites including the myocardium, stomach, small intestine, pancreas, colon, adipose tissue, liver, kidney, placenta, and T cells. An equally diverse series of biological actions of exogenous ghrelin have been documented, including effects on glucose homeostasis, gut motility, pancreatic exocrine secretion, cardiovascular function, immunity and inflammation. The physiological relevance of these actions remains unclear. There is also evidence for a number of pharmacological actions of des-acyl (or unacylated) ghrelin, which must be mediated via receptors other than the GHS-R1a. The physiological significance of these actions is contentious,

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as reviewed elsewhere. However, experiments in GHS-R1a knockout mice have definitively established that this receptor is required for the orexigenic and GH-stimulating effects of acylated ghrelin [14–16]. When administered into the CNS, ghrelin stimulates food intake as potently as NPY, previously the most powerful known orexigen, and more powerfully than any other substance examined [8–10]. Ghrelin also stimulates appetite and food intake when administered systemically in rodents [8, 11] and humans [17]. This property is unique to ghrelin and not shared by any known neuropeptide or circulating hormone. The duration of feeding stimulation in response to central or peripheral ghrelin administration is short, similar to that observed for central NPY. Indeed several lines of evidence suggest that ghrelin acts via arcuate NPY/AgRP neurones, almost all of which express the GHS-R1a. Ghrelin stimulates feeding most potently when injected directly into the Arc and also stimulates release of NPY from hypothalamic explants in vitro [11, 13]. Arcuate NPY/AgRP neurones are activated by ghrelin, as demonstrated by enhanced cfos, NPY and AgRP expression following ghrelin administration and by electrophysiological studies. Further, the orexigenic actions of ghrelin are abolished in NPY/AgRP dual knockout mice and in mice with post-embryonic ablation of NPY/AgRP neurones. Whilst this neuronal population is the most well-characterised ghrelin target, there is also evidence for an indirect action on these neurons via the vagus nerve. Some authors have found that surgical vagotomy or vagal deafferentation using capsaicin abolished the feeding and Arc c-fos response to peripheral ghrelin. However, more recently it has been reported that the orexigenic response to peripheral ghrelin is intact in rats following subdiaphragmatic vagal deafferentation, suggesting that the acute orexigenic actions of ghrelin do not require vagal afferent signalling. Other ghrelin targets include several other hypothalamic nuclei, the dorsal vagal complex of the brainstem and components of the mesolimbic dopaminergic system [14–16].

Does Ghrelin Contribute to Pre-Prandial Hunger?

Circumstantially, the distribution of ghrelin, predominantly in the stomach and upper small intestine, is ideal to monitor meal to meal nutrient intake. In keeping with a role as a putative meal initiator, systemic administration of ghrelin stimulates food intake, at doses which result in plasma concentrations similar to those found in the fasted (hungry) state [11]. The onset of action is rapid, duration is short and ghrelin appears to delay latency to feed and promote food seeking behaviour in rodents. Ghrelin stimulates food intake across a broad range of species, including humans [17]. In further support of ghrelin’s role as an endogenous appetite stimulator, blocking CNS ghrelin action, by infusion of anti-ghrelin antibodies into the rat brain, inhibits fasting-induced feeding [10]. Plasma ghrelin levels were first noted to increase on fasting and fall on re-feeding in rodents, as would befit a hunger signal [8, 11]. Subsequently more detailed studies

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have demonstrated pre-prandial plasma ghrelin elevation in humans and animals fed at scheduled times. More importantly, plasma ghrelin also peaks pre-prandially in human subjects, who have been deprived of time cues, initiating meals voluntarily. These plasma ghrelin peaks correlated well with hunger scores. Post-prandially, plasma ghrelin is suppressed in proportion to calories ingested, when macronutrient content and volume are kept constant. Interestingly, fat appears to suppress ghrelin less potently per calorie than carbohydrate or protein. This may in part explain the reduced satiety and enhanced weight gain associated with high fat diets. Thus, most data suggest that ghrelin is a physiological meal initiator. By extension, inhibition of ghrelin may be able to reduce meal size or frequency [15].

Ghrelin and Long-Term Energy Homeostasis

In addition ghrelin appears to promote long-term weight gain. Chronic administration of ghrelin in rodents results in prolonged hyperphagia and weight gain [8, 9]. The weight gain observed is greater than that expected for the degree of hyperphagia. This may reflect several reported actions of ghrelin which could combine to promote weight gain. These include stimulation of adipogenesis, inhibition of adipocyte apoptosis, transfer from fatty acid oxidation to glycolysis for energy expenditure and inhibition of sympathetic nervous system activity. Thus, prolonged elevation of plasma ghrelin promotes weight, in contrast to the classical short-term appetite regulator CCK, where prolonged administration does not reduce body weight. However, this does not prove that endogenous ghrelin physiologically regulates body weight [15]. In humans, ghrelin levels are inversely correlated with adiposity, being low in the obese, higher in lean subjects and markedly elevated in subjects who are cachectic due to a diverse range of conditions including anorexia nervosa, cancer and chronic cardiac failure [15, 18]. These findings are the converse of those for plasma leptin and similarly, this has been interpreted as an adaptive response to restrain further overeating in the obese or to stimulate it in the underweight, to maintain a stable body weight. This hypothesis is supported by longitudinal studies. Ghrelin is increased in response to weight loss achieved either by diet alone or diet and exercise and is suppressed by overfeeding or successful treatment of anorexia nervosa. However, longer total fasting in healthy volunteers, (several days or longer) results in low circulating ghrelin levels. An exception to the inverse correlation of ghrelin with body weight is observed in subjects with Prader-Willi syndrome (PWS), who have very high fasting and post-prandial ghrelin levels, which may contribute to their obesity [12, 15] [as discussed in the chapter by Goldstone and Beales, this vol., pp. 37–60]. If ghrelin is critical for body weight maintenance, reduction in ghrelin should promote weight loss and restoration of ghrelin should promote weight re-gain. This has been demonstrated in mice undergoing total gastrectomy that have an 80% reduction in circulating ghrelin associated with weight loss. Replacement of ghrelin

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to physiological levels results in weight regain. However, mice with global deletions of ghrelin or the GHS-R1a were initially reported to have minimal disruption of body weight homeostasis. As always with such mouse models, one must consider confounding by developmental adaptation, and indeed the phenotype of conditional knockout models is more robust. Further studies on mice lacking ghrelin or the GHSR1a have demonstrated resistance to diet-induced obesity in mature mice. The phenotype of ghrelin null mice might be further complicated by the observation that the gene that codes for ghrelin has been found to code for another peptide, named obestatin. Obestatin was originally reported to reduce food intake when administered peripherally or ICV, and to reduce body weight gain when administered peripherally, via the orphan G protein-coupled receptor, GPR39. However, subsequent reports have not supported the initial findings and suggest that obestatin may not signal via GPR39 or play a role in the regulation of food intake. Mice devoid of ghrelin signalling certainly lack the extreme phenotypes associated with mice lacking leptin signalling. However, taken together, data from knockout models are compatible with a role for ghrelin in long-term energy homeostasis [15].

Ghrelin as a Drug Target

Given that circulating ghrelin is already low in obese subjects, one might question how much therapeutic benefit could be obtained from further ghrelin suppression. However, it has been shown that obese subjects may be more sensitive to appetite stimulation by exogenous ghrelin [19]. Thus inhibition of ghrelin may have therapeutic potential, particularly in enhancing further weight loss and preventing weight regain following diet-induced weight loss, when ghrelin levels become elevated. Several major pharmaceutical companies have pursued programmes investigating ghrelin inhibition. Interestingly, the GHS-R1a exhibits constitutive activity, suggesting that an inverse agonist may be more therapeutically useful than an antagonist. Another strategy is to design compounds which bind to ghrelin itself and prevent interaction with its receptor. A novel group of molecules called RNA spiegelmers, oligonucleotides containing L-ribose, have been designed which block interaction of ghrelin with the GHS-R1a, inhibit ghrelin-induced GH secretion and reduce food intake and adiposity in mice fed a high-fat diet. As expected, these molecules have no effect in ghrelin knockout mice, attesting to the specificity of their actions. To date, no ghrelin-blocking products have progressed as far as phase I trials [14–16]. Theoretically, there may be safety concerns about ghrelin blocking agents in view of the possible role of ghrelin in regulation of the growth axis, as well as reported beneficial cardiovascular and anti-inflammatory effects of ghrelin. Regulation of octanoylation of ghrelin may provide an alternative drug target which is, as yet, relatively unexplored. A more direct therapeutic application of ghrelin is in the treatment of anorexia and cachexia. To this end, proof of principle studies have demonstrated

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that ghrelin stimulates appetite in patients with anorexia and weight loss due to cancer and chronic kidney disease, and may also improve meal enjoyment, without any adverse effects [20, 21]. Ghrelin administration by intravenous infusion over 3 weeks results in weight gain in patients with cardiac cachexia and chronic obstructive pulmonary disease [22, 23]. However, weight gain due to improved cardiac function and to placebo effect are significant possible confounders in these latter open-label studies. Whilst intravenous infusion is not a practical route for chronic administration in most therapeutic settings, ghrelin is also effective when given by subcutaneous injection in healthy lean individuals and in malnourished patients on peritoneal dialysis [21, 24]. Further placebo-controlled trials of long-term subcutaneous ghrelin administration in anorectic/cachectic patients are required to establish whether this may be a useful therapy. In addition, a wide variety of orally active agonists for the GHS-R1a were developed throughout the 1980s and 1990s which may have therapeutic potential in this context [25].

Satiety Signals

After a meal, nutrients pass into the stomach and intestine, and a number of gastrointestinal signals are released. These peptides and other signals act to optimise the digestive process. Some also function as short-term satiety signals and possibly long-term regulators of body weight. CCK is the prototypical satiety hormone. It is now over 30 years since CCK was first shown to inhibit food intake and it remains one of the most intensively studied of the gut hormones. CCK will not be discussed in depth and the reader is directed to an excellent recent review [26]. However, the use of CCK as a potential novel obesity therapy is in some doubt as, in animals, repeated pre-prandial administration of CCK reduces food intake but also increases meal frequency, with no net effect on body weight. Furthermore, continuous CCK administration is ineffective after the first 24 h.

PYY and Pancreatic Polypeptide

PYY, pancreatic polypeptide (PP) and NPY are members of the PP-fold peptide family, i.e. they share a common tertiary structure, called the PP-fold structural motif. In addition there is significant homology between peptide sequences within the family. They all have 36 amino acids, contain several tyrosine residues and require C-terminal amidation for biological activity. PYY and PP are putative satiety signals. The PP-fold family exert their effects via the Y family of G protein-coupled receptors. Four receptor subtypes have been identified, Y1, Y2, Y4 and Y5, all of which are expressed in the hypothalamus. Y1 and Y5 have both been put forward as the putative receptors via which NPY exerts its orexigenic action. The Y2 receptor is thought to function as an autoinhibitory pre-synaptic

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receptor, expressed on NPY neurones, and to mediate the anorectic actions of PYY, whilst the Y4 receptor appears to mediate the anorectic actions of PP [27–29]

Peptide YY

PYY occurs in two forms, PYY1–36 and PYY3–36. PYY3–36, the major circulating form, is a truncated 34-amino acid form created by cleavage of the N terminal Tyr-Pro residues by dipeptidyl peptidase IV (DPPIV). Whilst full length PYY binds with similar affinity to all Y receptors, PYY3–36 shows selectivity for the Y2 receptor, for which it has high affinity. PYY is secreted from entero-endocrine L-cells. These PYY immunoreactive cells are found throughout the entire gastrointestinal tract, but particularly in the distal portion. PYY immunoreactivity is almost absent in the stomach, sparse in the duodenum and jejunum, common in the ileum and colon, and at very high levels in the rectum (the opposite distribution pattern to that observed for ghrelin). The pattern of secretion is also a mirror image of ghrelin, i.e. PYY is released into the circulation following meals and suppressed by fasting [27–29]. PYY has long been known to exert numerous effects on the gastrointestinal tract. Administration of PYY increases the absorption of fluids and electrolytes from the ileum after a meal and inhibits pancreatic and gastric secretions, gallbladder contraction and gastric emptying. Peripheral administration of PYY, like ghrelin, also exerts effects on numerous other body systems. For example, it reduces cardiac output, causes vasoconstriction and reduction in glomerular filtration rate, plasma renin and aldosterone activity. The physiological significance of these numerous actions has not been established [27–29].

Does PYY Contribute to Post-Prandial Satiety?

PYY levels rise to a plateau at 1–2 h post-prandially, with these peak levels influenced by both the number of calories and the composition of the food consumed. The onset of PYY release occurs before nutrients have reached the predominant sites of PYY production in the distal gastrointestinal tract. This implies that peptide release may occur via a neural reflex, possibly through the vagus nerve. Systemic administration of PYY3–36 inhibits food intake in rodents and man. Initially these findings were contentious, with several authors unable to reproduce feeding inhibition in rodents. A probable explanation is that the effects of anorectic agents in rodents are easily masked by stress, causing a reduction of food intake in the control group. Thus significant inhibition of feeding by PYY3–36 cannot be detected in rodents that are not fully acclimatised to experimental procedures or following transfer to a novel environment [27–29]. In man intravenous infusion of PYY3–36 reduces appetite and food intake at a subsequent meal by about one third. Whilst initial reports detected inhibition of food intake at plasma PYY concentrations in the physiological range, others

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have only detected an effect at pharmacological doses. Thus whilst there remains some debate over whether PYY3–36 is a physiological meal terminator in man, appetite inhibition in response to exogenous PYY, in both lean and obese individuals is a robust and reproducible finding [27–29].

PYY and Long-Term Energy Homeostasis

Chronic systemic administration of PYY3–36 results in weight loss in some but not all rodent models. Data from knockout mice also provide evidence for a long-term role for PYY in regulation of energy balance. Three separate knockout models have been generated, two of which develop obesity. One model did not become obese. This model also had disruption of expression of the PP gene. One would not predict that loss of a second putative anorectic signal would attenuate obesity. However, subtle differences in technique and background strain have frequently been reported to result in differing phenotypes in other knockout mouse models. In one of the obese PYY null models administration of PYY corrected the phenotype, suggesting that obesity was being driven by PYY deficiency [28, 29]. In contrast to leptin, circulating levels of PYY are not elevated in obese individuals who also retain full sensitivity to the anorectic actions of PYY3–36. It has been reported that obese subjects have lower fasting and post-prandial circulating PYY than lean subjects. In order to produce an equivalent stimulation of PYY and equivalent satiety, obese individuals needed to consume a much greater caloric load than their lean counterparts. However, not all studies have detected a difference in fasting PYY concentrations between lean and obese groups. Current data suggest that impaired post-prandial PYY release may, at least, impair satiety and help to maintain obesity, if not act as a primary driver of initial development of obesity. Whether or not reduced PYY signalling is a primary cause of obesity, it is certainly true that retained PYY sensitivity in the obese make it an attractive therapeutic target [27–29].

Mechanism of Action of PYY

The exact mechanism whereby PYY3–36 inhibits appetite and food intake is unclear. Interestingly, in contrast to peripheral administration, intracerebroventricular administration of PYY stimulates food intake. This is thought to be via an action on Y1 and Y5 receptors in the paraventricular nucleus (PVN), the second order neurones targeted by orexigenic Arc NPY neurones. Several lines of investigation suggest a direct anorectic action of circulating PYY3–36 on the Arc. C-fos is observed in the Arc in response to peripheral administration of PYY3–36 and direct micro-injection into the Arc inhibits feeding. This action is thought to be via auto-inhibitory Y2 receptors on the orexigenic NPY neurones. In support of this hypothesis, a highly specific Y2

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agonist inhibits feeding following intra-arcuate injection, whilst the anorectic actions of PYY3–36 are absent in the Y2 knockout mouse and blocked by a Y2 receptor antagonist. Furthermore, PYY3–36 reduces expression of NPY in the Arc and release of NPY from hypothalamic explants. Electrophysiological studies suggest that PYY3–36 directly inhibits activity of arcuate NPY neurones, secondarily disinhibiting anorectic POMC neurones. However, POMC does not appear to be essential for the anorectic action as PYY3–36 is effective in mice with lacking melanocortin signalling. However, the picture appears to be more complex than a simple action on the Arc. There is also evidence that PYY3–36 acts at the level of the vagus and the dorsal vagal complex. The relative contribution of these various putative sites of action to physiological appetite regulation remains unclear. The ascending vagal-brainstem-hypothalamic pathways have, however, been implicated in mediating sensations of nausea. In keeping with this PYY3–36 has, at high doses, been reported to cause conditioned taste avoidance in rodents and nausea in humans. However, lower doses inhibit appetite and food intake in rodents and humans without aversive effects or nausea. Coupled with the observation that PYY null mice become obese, this suggests a role for PYY in appetite regulation independent of aversive effects [27–29]. Drug companies developing analogues of PYY for treatment of obesity will need to be mindful of the potentially narrow therapeutic window in order to design successful agents.

Pancreatic Polypeptide

PP is produced largely in the endocrine pancreas, but also in the exocrine pancreas, colon, and rectum. Like PYY, PP is released in response to a meal and inhibits appetite. PP binds with greatest affinity to Y4 receptors (with greater affinity than PYY) and Y5 receptors. The role of PP in appetite regulation has been investigated for over 30 years. It was initially noted that ob/ob mice lacked pancreatic PP cells, and peripheral administration of PP reduced their food intake and body weight. Peripheral administration of PP resulting in physiological plasma levels has been shown to reduce food intake in normal mice, with associated reduction in gastric emptying and gastric ghrelin expression and increased vagal tone. PP also increased oxygen consumption and stimulated sympathetic activity, leading to the suggestion that PP may also increase energy expenditure. In normal-weight human volunteers infusion of PP reduces food intake without altering gastric emptying. Subjects with PWS are reported to have suppressed basal and post-prandial PP levels, whilst PP administration to PWS subjects reduces food intake. It is, therefore, possible that PP deficiency contributes to the hyperphagia in this obesity syndrome [28, 29]. Apart from its acute effects on appetite and food intake, PP may also modulate long-term energy balance. Transgenic mice that overexpress PP have a lean phenotype with reduced food intake [30]. Repeated administration of PP to ob/ob mice

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decreases body weight gain and ameliorates insulin resistance and dyslipidaemia. However, rodents with diet-induced obesity are less sensitive to the anorectic actions of PP. Plasma PP is increased in individuals with anorexia nervosa, and there have been reports of suppressed plasma PP in obese subjects. However, the effects of obesity on circulating concentrations of PP are conflicting; others have found no difference between lean and obese subjects or between obese subjects before and after weight loss. The effects of PP on appetite and body weight in obese humans are unknown. Further investigation in obese subjects may indicate whether PP has the potential to be a novel treatment for obesity. PP, like PYY, has opposing effects on appetite, depending on the route of administration. Injection of PP into the third ventricle stimulates daytime food intake in satiated rats. Similarly, central injection of PP has the opposite effect to peripheral administration on gastric motility, stimulating rather than inhibiting gastric emptying. These contrasting effects of central and peripheral administration of PP probably reflect differing sites of receptor activation. PP is unable to cross the blood brain barrier. Circulating PP acts on circumventricular CNS sites, such as the AP. The anorectic effect of peripheral PP probably occurs via the Y4, which is highly expressed in this region [28, 29].

Oxyntomodulin and Glucagon-Like Peptide-1

Oxyntomodulin and glucagon-like peptide-1 (GLP-1) are products of the preproglucagon gene. Preproglucagon is expressed in the pancreas, L-cells of the intestine and in the NTS of the brainstem and undergoes differential processing by prohormone convertase 1 and 2 depending on the site of synthesis [31, 32] as illustrated in figure 2. In the pancreas, classical preproglucagon processing yields glucagon and the apparently inactive N-terminal fragment glicentin-related PP (GRPP) whilst the GLP sequences remain within a larger peptide, major proglucagon fragment (MPGF). The post-translational processing in the gut and brain are very similar. In these tissues, the glucagon sequence remains in a larger peptide, glicentin, which is thought to be inactive. Glicentin is further cleaved to yield oxyntomodulin and GRPP. Oxyntomodulin is a 37-amino acid peptide comprising the 29 amino acids of pancreatic glucagon with an eight amino acid c-terminal extension, sometimes called spacer peptide 1. The other major products of preproglucagon processing in gut and brain are the two GLPs, GLP-1 and GLP-2. GLP-1 and oxyntomodulin, along with PYY, are released from intestinal L-cells in response to food intake and appear to act in part as satiety signals as well as possibly participating in long-term body weight regulation. GLP-1 is the most powerful known incretin in humans and manipulation of the GLP-1 system forms the basis of several major new treatments for type 2 diabetes. These include a subcutaneously administered DPPIV-resistant GLP-1R agonist, exendin-4, a peptide component of

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Glucagon

GRPP

SP1

Oxyntomodulin

Gut

GRPP

Glucagon

SP1

Brain GLP-1

GLP-2

Glicentin

GRPP

Glucagon

GLP-1

SP2

Signal peptide

SP1

Preproglucagon GLP-2

Pancreas

GRPP

Glucagon

GLP-1

SP2

MPGF GLP-2

Fig. 2. Overview of differential preproglucagon processing in pancreas versus brain and gut. SP ⫽ Spacer peptide.

Gila monster saliva marketed as exenatide (Byetta), as well as orally active DPPIV inhibitors. Central administration of GLP-1, both intracerebroventricularly and into the PVN, reduces food intake in rodents, whilst the GLP-1 receptor antagonist exendin 9–39 increases food intake. Chronic administration of GLP-1 into the CNS attenuates weight gain and peripheral GLP-1 injection inhibits food intake in rodents and man. Evidence suggests GLP-1 secretion is reduced in obese subjects, and weight loss normalises the levels. Reduced GLP-1 secretion could, therefore, contribute to obesity, and replacement may restore satiety. Obese subjects receiving subcutaneous GLP-1 for 5 days, just before each meal, reduced their calorie intake by 15% and lost 0.5 kg in weight. However, in view of the powerful incretin action of GLP-1 there is a risk of hypoglycaemia in non-diabetic subjects [28, 29].

Oxyntomodulin

Oxyntomodulin inhibits caloric intake in rodents when given either centrally or peripherally and results in decreased weight gain when administered peripherally. Oxyntomodulin is also effective in humans. An infusion of oxyntomodulin to normal-weight volunteers reduced immediate caloric intake by 19.3% and suppressed

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plasma ghrelin. It is possible that postprandial oxyntomodulin release may contribute to the normal physiological inhibition of plasma ghrelin after meals. In contrast to GLP-1, oxyntomodulin is a much less potent incretin but may have more potent effects on weight loss. Oxyntomodulin only causes a small increase in plasma insulin without affecting plasma glucose. When administered three times daily in overweight volunteers for 4 weeks, subcutaneous oxyntomodulin resulted in 2.3 kg weight loss compared with 0.5 kg in the control group. Enhanced weight loss in response to oxyntomodulin may be due to increased energy expenditure. Acute administration of oxyntomodulin has been shown to increase voluntary activity in human subjects and to increase heart rate in rodents. The circulating levels of oxyntomodulin in obesity remain to be established [28, 29]. Both GLP-1 and oxyntomodulin are thought to exert their effects via the GLP-1 receptor (GLP-1R). Antagonists of the GLP-1R, such as exendin (9–39), antagonise the effect of both GLP-1 and oxyntomodulin, and both peptides are ineffective in the GLP-1 receptor knockout mouse. However, the affinity of oxyntomodulin for GLP1R is approximately 2 orders of magnitude less than that of GLP-1, yet both peptides appear to be similarly effective at reducing food intake. The mechanisms of action of GLP-1 and oxyntomodulin appear to be similar but not identical. Peripheral and central GLP-1 administration have been reported to activate neurones in the hypothalamic Arc, PVN, NTS and AP. In addition ablation of vagal-brainstem-hypothalamic projections attenuates feeding inhibition by GLP-1 [33]. Whilst systemic oxyntomodulin administration results in a similar pattern of neuronal activation to GLP-1, intra-arcuate administration of exendin 9–39 blocks the anorectic effects on oxyntomodulin but not GLP-1, suggesting a direct action of oxyntomodulin on the Arc [28, 29]. The duration of inhibition of food intake in response to peripheral oxyntomodulin administration is short, necessitating three times daily subcutaneous injection in weight loss studies in humans. This may be due to rapid cleavage of the two N-terminal amino acid residues by DPPIV, as observed for GLP-1 and PYY. DDPIV-resistant analogues of oxyntomodulin may, thus, have greater therapeutic potential than the native peptide [28, 29].

Dietary Manipulation of Gut Hormones

It has been suggested that a cause of the current obesity epidemic may be that modern processed foods bypass our natural satiety mechanisms. Low fat diets are the most well-established means of dietary weight loss. It has been reported that weight loss in response to a low-fat diet does not produce the expected elevation in plasma ghrelin. This may be due to an increase in the proportion of calories consumed as carbohydrate that more potently suppresses ghrelin per calorie consumed than does fat. High-protein diets have also become popular in recent years as a means to

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promote satiety and weight loss. Diets high in protein have recently been reported to elevate circulating PYY and enhance satiety more effectively that other macronutrients; however, previous data suggested that, at a single meal, higher plasma concentrations of PYY were stimulated by high fat isocaloric meals, compared with protein or carbohydrate. It is an intriguing possibility that designer diets may help promote the most favourable gut hormone profile to allow sustained weight loss [15, 28, 29].

An Obesity Poly-Pill?

The only treatment to date associated with dramatic and sustained weight loss in the morbidly obese is gastric bypass surgery. However, its cost and associated morbidity and mortality make it an impractical treatment for the majority of obese patients and it is generally reserved for the morbidly obese. Gastric bypass results in significant increases in plasma PYY, GLP-1 and oxyntomodulin whilst ghrelin either falls or fails to rise, despite significant weight loss [15, 28, 29]. Interestingly, bypass patients report dramatically reduced hunger long before substantial weight loss occurs. Furthermore, in rodent models many of the beneficial effects of bypass can be mimicked by gut hormone administration [34]. It is notable that the changes in the four gut hormones above all favour weight loss following gastric bypass. This coordinated action, mimicking natural satiety, may be a key to effective anti-obesity therapy. As noted above, individual gut hormones administered at high concentrations, have been associated with aversive behaviours in rodents and nausea in humans. We have reported that low doses of PYY3–36 and GLP-1 inhibit food intake additively [35]. Analogous to current treatment for hypertension where several agents are commonly used, a smart cocktail of gut hormone-based drugs may prove a more effective anti-obesity treatment than targeting a single hormone. This approach could potentially provide the sustained weight loss offered by gastric bypass surgery, without the associated morbidity and mortality. The major therapeutic disadvantage of gut hormones is their short duration of action and the requirement for subcutaneous or intravenous administration. In the GLP-1 system, degradation-resistant analogues and drugs that inhibit enzymes that degrade the endogenous hormone have already been brought to market for the treatment of type 2 diabetes. Similar approaches may be successful for oxyntomodulin, PYY and PP, whilst intra-nasal delivery systems or development of orally active small molecule mimetics could avoid the need for administration by injection. The obesity epidemic is advancing relentlessly and current treatments, bar bariatric surgery, are insufficiently effective. Recent data suggest that gut hormones regulate when and how much we eat for every meal and offer a logical drug target. Mimicking natural satiety mechanisms by delivering combinations of gut hormones may replace bariatric surgery as the only truly effective anti-obesity treatment.

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34 Le Roux CW, Aylwin SJ, Batterham RL, Borg CM, Coyle F, Prasad V, Shurey S, Ghatei MA, Patel AG, Bloom SR: Gut hormone profiles following bariatric surgery favor an anorectic state, facilitate weight loss, and improve metabolic parameters. Ann Surg 2006; 243:108–114. 35 Neary NM, Small CJ, Druce MR, Park AJ, Ellis SM, Semjonous NM, Dakin CL, Filipsson K, Wang F, Kent AS, Frost GS, Ghatei MA, Bloom SR: Peptide YY3–36 and glucagon-like peptide-17–36 inhibit food intake additively. Endocrinol 2005;146: 5120–5127.

Dr. Alison M. Wren Department of Endocrinology Chelsea and Westminster Hospital NHS Foundation Trust Fulham Road London SW10 9NH (UK) Tel. ⫹44 20 8237 2730, Fax ⫹44 20 8237 2732, E-Mail [email protected]

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Adipokines in Obesity Rexford S. Ahima ⭈ Suzette Y. Osei Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, University of Pennsylvania School of Medicine, Philadelphia, Pa., USA

Abstract Adipose tissue is the source of soluble mediators (adipokines), secreted mainly by adipocytes. Leptin acts on the brain and peripheral organs to regulate energy homeostasis and the neuroendocrine axis. Adiponectin regulates glucose and lipid metabolism by targeting the liver and skeletal muscle. Adiposederived proinflammatory cytokines, vasoactive peptides, coagulation and complement factors, visfatin, vaspin and retinol-binding protein signal through paracrine and hormonal mechanisms. Understanding the biology of adipose tissue and the rapidly growing list of adipokines provides new insights into normal physiological regulation, as well as the pathogenesis and treatment of obesity, diabetes and disorCopyright © 2008 S. Karger AG, Basel ders of lipid metabolism and cardiovascular system.

The obesity epidemic has focused attention on the biology of adipose tissue. Adipocytes provide a large energy storage capacity mainly in the form of triglyceride [1]. The levels of insulin, glucose and nutrients increase during feeding and stimulate energy storage in the liver and adipocytes [1]. Conversely, fasting activates the sympathetic nervous system and increases the levels of glucagon, epinephrine and glucocorticoids, leading to enhancement of glycogenolysis and gluconeogenesis, and maintenance of glucose supply to the brain and vital organs [1]. Prolonged fasting also stimulates lipolysis, generating fatty acids to be used by muscle, liver and peripheral organs, in addition to providing ketones for the brain [1]. Interactions between adipose tissue and the vascular and immune systems are now increasingly recognized [2, 3]. As the number and size of adipocytes expand in obesity, the vascular supply increases under stimulation of angiogenic factors secreted by adipocytes [2]. Conversely, antiangiogenic treatment reduces adipose vascularity and prevents obesity in rodents [4, 5]. Obesity is associated with histological and biochemical changes characteristic of inflammation [3]. Adipose tissue from obese individuals accumulates a large number of activated macrophages which form giant cells

and secrete cytokines, e.g. tumor necrosis factor-␣ (TNF-␣) and interleukin-6 (IL-6) [3] (table 1). C-reactive protein is also increased in obesity, and the levels of intracellular cell-adhesion molecule-1 and platelet/endothelial cell adhesion molecule-1 increase in adipose endothelial cells, inducing the migration and adhesion of monocytes [3]. Monocyte chemoattractant protein-1 and various chemokines recruit monocytes to adipose tissue [6, 7]. Collectively, these changes induce insulin resistance in adipose and liver, and interact with adipose-derived coagulation factors and vasoactive peptides, leading to cardiovascular disease in obesity [3]. This chapter will discuss the current understanding of leptin as a prototypic adipokine related to energy balance and neuroendocrine regulation. Next, the role of adiponectin in glucose and lipid metabolism and vascular biology will be reviewed. Finally, putative roles of retinol-binding protein 4 (RBP4), resistin and visfatin and various adipokines will be reviewed (table 1).

Leptin

The discovery of leptin more than a decade ago was a major turning point in our understanding of adipokines [8]. Mice and humans homozygous for the leptin gene mutation develop hyperphagia, severe early-onset obesity, insulin resistance preceding obesity, excess lipid accumulation outside adipose tissue (steatosis), and neuroendocrine abnormalities, notably, hypothalamic hypogonadism and tertiary hypothyroidism [9–11]. Moreover, there is evidence for immunosuppression in congenital leptin deficiency [10, 11]. Leptin is expressed and secreted mainly by adipocytes, but low levels are present in the gastric fundus, mammary gland, placenta, pituitary and skeletal muscle [9]. Leptin has a relative molecular mass of 16 kDa and circulates in free or bound forms. The latter represents leptin bound mainly to its soluble receptor and is thought to be inactive. The concentration of leptin is higher in obese than lean individuals [9]. Leptin falls rapidly during fasting and increases gradually during feeding. Studies in rodents and human indicate a link between these changes in leptin and insulin [9]. Higher leptin level in women is explained partly by increased production in subcutaneous adipose tissue and stimulation by estrogens. On the other hand, leptin is suppressed by androgens in males [9]. Chronic glucocorticoid exposure, TNF-␣ and IL-6 increase leptin, while adrenergic stimulation decreases leptin [9]. Leptin exhibits a diurnal rhythm, peaking at night in humans and in the morning in rodents [12, 13]. A pulsatile leptin rhythm has also been recognized in humans, although the underlying mechanisms and functional significance are unknown [12, 14]. Five leptin receptor isoforms, LRa–LRe, are derived from alternate splicing of lepr mRNA [15, 16]. LRa is the predominant ‘short leptin receptor’ which lacks the key cytoplasmic domain required for signaling through the JAK/STAT (signal transduction and activators of transcription) pathway. LRa is abundantly present in brain capillary endothelium and peripheral tissues, and is thought to mediate leptin transport.

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Table 1. Actions of adipokines Adipokine: source and signaling

Energy, lipid and glucose metabolism

Immune system

Other actions

Leptin – produced by adipocytes; subcutaneous expression is higher than visceral adipose; low levels expressed by muscle, gastric fundus, intestine and placenta; plasma levels increase in obesity; circulates as free or LRe bound leptin; signals via LRb and JAK2-STAT3

Inhibits feeding Increases energy expenditure (in rodents) Reduces body fat Lowers glucose, insulin and lipids Stimulates fatty acid oxidation

Proinflammatory Induces TNF-␣, IL-6, IL-12, neutrophil activation and chemotaxis, reactive oxygen species Stimulates lymphopoeisis, T-cell proliferation and TH1 response, and reduces TH2 response

Trophic action on hypothalamic feeding circuits Stimulates reproductive and thyroid hormones Promotes brain growth in leptindeficient animals Inhibits bone formation in rodents

Adiponectin – produced by adipocytes;level in visceral adipose is higher than subcutaneous; low expression has been reported in liver and skeletal muscle; plasma levels decrease in obesity; LMW and HMW complexes in plasma; trimer and LMW in cerebrospinal fluid; TZD treatment increases total and HMW adiponectin; signals via AdipoR1 and AdipoR2 and AMPK

Chronic peripheral or central treatment reduces body weight and fat in rodents Stimulates thermogenesis (in rodents) Increases insulin sensitivity; suppresses glucose production Stimulates fatty acid oxidation

Anti-inflammatory and anti-atherogenic Reduces endothelial adhesion, NF-␬B, TNF-␣, IL-6, IL-10, IFN␥ Reduces B- and T-cell responses

Resistin – produced by adipocytes in rodents and monocytes/ macrophages in humans; plasma levels increase in obese rodents; trimer and hexamer form in mouse serum; receptor is unknown; inhibits AMPK phosphorylation and induces SOCS3

Inhibits adipogenesis in rodents Stimulates thermogenesis in rodents Induces insulin resistance and gluconeogenesis in rodents

Proinflammatory Increases endothelial adhesion, TNF-␣, IL-1␤, IL-6, IL-12, NF-␬B

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Table 1. (continued) Adipokine: source and signaling

Energy, lipid and glucose metabolism

Immune system

TNF-␣ – produced by adipocytes and stromal cells; precursor 17-kDa active protein; 26-kDa transmembrane protein is cleaved into expression in subcutaneous adipose is greater than visceral adipose; signals via type I and II TNF receptors

Inhibits feeding Induces cachexia Induces insulin resistance, hyperglycemia and dyslipidemia in rodents, but role in glucose and lipid metabolism in humans is controversial

Proinflammatory

IL-6 – produced by adipocytes and stromovascular cells; expression in visceral adipose is greater than subcutaneous adipose; circulates in multiple glycosylated forms; IL-6 receptor exists in membrane bound and secreted forms

Inhibits feeding Increases energy expenditure Decreases weight Induces insulin resistance, hyperglycemia and dyslipidemia

Proinflammatory

Plasminogen activator inhibitor (PAI)-1– produced by adipocytes and stromal cells; levels are higher in visceral adipose; plasma levels increase in obesity; TZD treatment suppresses PAI-1

Increases adiposity in rodents Induces insulin resistance in rodents

Stimulated by TNF-␣

Adipsin (complement factor D/ASP) – produced by adipocytes; adipsin is reduced in obese rodents but increased in humans; ASP is increased in obesity

ASP promotes fatty acid uptake, decreases fatty acid release from adipocytes, increases glucose uptake by adipocytes, and enhances insulin secretion in rodents

RBP4 – produced by adipocytes; plasma levels increase in obesity; reduced by TZD treatment

Increases insulin sensitivity in rodents Reduces glucose and lipids in rodents

Adipocyte Hormones

Other actions

Promotes thrombosis and atherogenesis

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Table 1. (continued) Adipokine: source and signaling

Energy, lipid and glucose metabolism

Renin-angiotensin system – peptides, enzymes and receptors of RAS are expressed in adipocytes and stromal cells; adipose levels of angiotensin II increase in obesity

Angiotensin II promotes adipogenesis and lipogenesis, reduces insulin sensitivity, and enhances gluconeogenesis and glycogenolysis

Visfatin – produced by adipocytes and mostly by the liver and B cells; levels reportedly increased in obese rodents but uncertain in humans

Visfatin acts as insulin-mimetic in cells and mice Role in glucose and lipid metabolism in humans is uncertain

Immune system

Other actions ? influences vascular reactivity

LRe is shed into the circulation as a ‘soluble receptor’ and binds leptin. The ‘long leptin receptor’ LRb mediates effects of leptin in the brain [15, 16]. Leptin crosses the blood-brain-barrier via a saturable transport system, and acts directly on neurons in the arcuate nucleus that express neuropeptide Y (NPY), agouti-related peptide (AGRP), pro-opiomelanocortin (POMC) and cocaine-and amphetamine-regulated transcript [9]. Binding of leptin to LRb leads to association with JAK2, autophosphorylation of JAK2, phosphorylation of Tyr985 and Tyr1138 on LRb and phosphorylation and activation of signal transducer of transcription-3 (STAT3), which acts as a transcription factor to regulate neuropeptides and various leptin target genes [16]. LRb-phosphorylated Tyr985 recruits the tyrosine phosphatase SHP-2 (Src-homology protein tyrosine phosphatase-2), and terminates leptin signaling via induction of suppressor of cytokine signaling-3 (SOCS3) [16]. Rising leptin level suppresses NPY and AGRP and increases ␣-melanocyte-stimulating hormone (␣-MSH), derived from POMC. ␣-MSH inhibits feeding and induces thermogenesis through melanocortin 4 receptors in the paraventricular nucleus and other areas of the hypothalamus [14]. Normally, AGRP serves as an antagonist of ␣-MSH and stimulates feeding in concert with NPY. Thus, by suppressing expression of AGRP and NPY, leptin has a net effect to reduce appetite and decrease weight [9]. As predicted, deletion of LRb or STAT3 in the hypothalamus, or specifically in POMC neurons, resulted in obesity [17–20]. In contrast, SOCS3 deficiency prevented obesity by improving sensitivity to leptin [21–23]. Studies have also revealed a cross-talk between

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leptin and insulin signaling in the hypothalamus mediated through JAK2, phosphoinositide 3-kinase and insulin receptor substrate 1 and 2 [9, 16]. Leptin plays a dual role in energy homeostasis. The fall in leptin during fasting signals to the brain, leading to hyperphagia, reduced energy expenditure, and suppression of thyroid, reproduction and growth hormones, and immune system [10, 13]. These changes are similar, albeit less profound, to congenital deficiency of leptin [10, 11, 24, 25]. Furthermore, fasting-induced hypoleptinemia and congenital leptin deficiency both increase NPY and AGRP and reduce POMC in the hypothalamus [9]. We have proposed that the dominant action of leptin is that of a ‘starvation signal’ [9, 13]. In agreement, leptin deficiency in lipodystrophic rodents and humans induces hyperphagia and changes in neuroendocrine and immune function akin to fasting [26–31]. In contrast with the robust responses to low leptin level, increasing leptin from the fasted to fed levels elicits a minimal response [9]. Obesity is typically associated with elevated leptin levels and reduced sensitivity to leptin treatment, indicative of ‘leptin resistance’ [32]. Studies have demonstrated a reduction in brain leptin transport in obese rodents, but whether this occurs in humans is uncertain [33]. Leptin resistance has been attributed to induction of SOCS3 and protein tyrosine phosphatase 1B which normally inhibit leptin signal transduction [21, 22, 34, 35]. Fatty acids and amino acids have also been implicated in the disruption of leptin signaling in the hypothalamus [36, 37]. Leptin receptors are present in extrahypothalamic sites including the nucleus solitarius, lateral parabrachial nucleus and ventral tegmental area [9]. Leptin treatment activates neurons in these areas via STAT3 phosphorylation [38]. Recent studies have shown a crucial role of leptin in the feeding-reward circuitry, through induction of STAT3 phosphorylation in dopamine and ␥-amino butyric acid neurons of the ventral tegmental area and mesoaccumbens [39, 40]. AMP-activated protein kinase (AMPK) is another leptin target of interest [41]. AMPK is phosphorylated and activated in response to energy deficits during cellular stress or fasting, leading to increased fatty acid oxidation and inhibition of anabolic pathways. In the hypothalamus, AMPK is colocalized with STAT3, NPY and other peptides implicated in energy balance. Leptin inhibits the phosphorylation and activation of AMPK in the hypothalamus, leading to appetite suppression [42]. Studies in rodents suggest that melanocortin 4 receptor is a critical mediator of the leptinAMPK interaction in the hypothalamus [42]. Leptin exerts rapid effects on neurotransmission that cannot be explained by JAKSTAT signaling. For example, leptin depolarizes hypothalamic POMC neurons and decreases the inhibitory tone of ␥-amino butyric acid on POMC neurons [43]. Conversely, leptin hyperpolarizes and inactivates NPY neurons in the arcuate nucleus [43]. The fall in leptin during fasting increases the action potential frequency in NPY/AGRP neurons in the arcuate nucleus, and the latter correlates with hyperphagia [44]. Leptin hyperpolarizes glucose-responsive neurons in the hypothalamus by

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opening KATP channels, an effect that has been linked to appetite suppression and weight loss [45]. Short- and long-term effects of leptin on neuronal plasticity have been described [46, 47]. Leptin normalizes synaptic density in NPY and POMC neurons in the hypothalamus within 6 h after systemic treatment, and this change has been linked to inhibition of food intake [46]. Congenital leptin deficiency results in reduced brain size due to impaired myelination and neuronal loss [47, 48]. Leptin treatment restores brain structure in leptin-deficient rodents and humans [47, 48]. Furthermore, leptin enhances the maturation of neuronal projections from the arcuate nucleus to the paraventricular nucleus, and this trophic action may provide insights into neuronal defects underlying eating disorders and obesity [49]. Leptin has profound effects on the neuroendocrine axis [9]. Leptin stimulates gonadotropin-releasing hormone secretion, and is positively related to gonadotropinreleasing hormone and estradiol pulsatility [12, 30]. Leptin-deficient mice and humans fail to undergo normal pubertal maturation and are sterile [24, 25]. Leptin treatment, but not weight loss per se, restores fertility in congenital leptin deficiency [50]. Furthermore, leptin has a permissive effect to restore menstrual cycles in patients with functional or exercise-induced amenorrhea [30, 31]. These effects may involve all levels of the reproductive axis, i.e. hypophysiotropic neurons in the paraventricular hypothalamus, anterior pituitary and gonads [9]. Leptin prevents the fasting-induced suppression of prothyrotropin-releasing hormone mRNA in paraventricular nucleus neurons, reverses the inhibitory effect of fasting on pulsatile thyrotropin secretion and blunts the decline in thyroid hormone levels during fasting [29, 51]. Leptin inhibits somatostatin and prevents the suppression of IGF-I during fasting [29]. Interestingly, leptin-deficient rodents exhibit reduced linear growth, consistent with lack of permissive effect on growth hormone [9]. The ability of insulin-induced hypoglycemia to stimulate growth hormone is blunted in patients with leptin receptor mutation [52]. These patients have low IGF-I and IGF-binding protein-3 levels and delayed growth [52]. LRb is also expressed in peripheral tissues and involved in metabolism. Deletion of LRb from pancreatic ␤-cells results in increased islet mass, hyperinsulinemia, impaired glucose-stimulated insulin release and glucose intolerance [53]. However, unlike the complete loss of LRb, attenuation of leptin signaling in islets does not alter food intake [53]. LRb is expressed by CD34⫹ hematopoietic bone marrow precursors, monocytes and macrophages and T and B cells. Leptin promotes innate immunity through activation of monocytes/macrophages, neutrophils and natural killer cells [10, 11]. Leptin also influences adaptive immunity by increasing the expression of adhesion molecules by CD4⫹ T cells, proliferation and secretion of IL-2 by naive CD4⫹ T cells, and promoting T helper 1 cell responses on memory CD4⫹ T cells. Leptin deficiency is associated with reduced numbers of circulating CD4⫹ T cells, impaired T cell proliferation and cytokine release and thymic atrophy, all reversible by leptin

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treatment [10, 11]. A leptin surge has been linked to the development of pathogenic T cell responses and autoimmune encephalomyelitis in mice, while leptin antagonism prevents this condition [54, 55].

Adiponectin

Adiponectin was discovered independently by several laboratories, hence its various names: Acrp30 (adipocyte complement-related protein of 30 kDa), apM1 (adipose most abundant gene transcript 1), adipoQ and GBP28 (gelatin-binding protein of 28 kDa) [56]. Adiponectin is composed of an N terminal sequence, hypervariable domain, 15 collagenous repeats and a C terminal domain [56]. A trimeric form of adiponectin is secreted by adipocytes and gives rise to higher order complexes, i.e. dimers of trimers (hexamers, low molecular weight, LMW) and six trimers (18-mers, high molecular weight, HMW) through noncovalent bonding. HMW adiponectin is thought to be the bioactive form in plasma [56]. In contrast, trimeric and hexameric adiponectin is predominant in the cerebrospinal fluid [57]. Adiponectin also undergoes posttranslational modifications including glycosylation [56]. Although there is structural similarity between the globular (head) of adiponectin and TNF-␣, these adipokines do not appear to be functionally related [58]. In contrast to other polypeptide hormones, adiponectin circulates at very high concentrations (␮g/ml), raising the possibility that a smaller cleaved product mediates its action on various tissues [56]. Total and HMW adiponectin are more abundant in females, partly due to suppression of adiponectin by androgens in males [56]. Adiponectin is inversely related to adiposity, in contrast to leptin and most adipokines. Thus, adiponectin is markedly reduced in obesity and rises with prolonged fasting and severe weight reduction. Adiponectin, particularly HMW, is increased by thiazolidinediones (TZDs) and mediates the insulin sensitizing effect of this class of antidiabetic drugs [59, 60]. A role for adiponectin in glucose homeostasis is further exemplified by hepatic insulin resistance in rodents and humans lacking adiponectin [59–61]. In contrast, adiponectin treatment enhances insulin sensitivity, primarily by suppressing glucose production [59, 60, 62]. Adiponectin produced in bacteria has been shown to decrease glucose, stimulate fatty acid oxidation and reduce body weight and fat; however, these are likely to be pharmacological effects since bacterially derived adiponectin is incapable of forming high order complexes [63–66]. Administration of full length or globular adiponectin via systemic or intracerebroventricular injection induces thermogenesis, fatty acid oxidation and weight loss in mice [67]. These actions are abrogated in agouti mice (Ay/a), indicating a crucial role for melanocortin signaling in the central action of adiponectin [67]. Hypoadiponectinemia is related to insulin resistance, inflammation, dyslipidemia and cardiovascular risk among various populations [56]. Lack of adiponectin promotes atherosclerosis in rodents [56]. Adiponectin reverses this by inhibiting monocyte

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adhesion, macrophage transformation, proliferation and migration of smooth muscle cells in blood vessels. Studies have implicated activation of AMPK and inhibition of nuclear factor ␬B (NF-␬B) and vascular adhesion molecules as putative mechanisms underlying the effects of adiponectin on the vascular system [56]. Adiponectin also exerts a protective action in myocardial remodeling in response to acute ischemiareperfusion [68]. Adiponectin-deficient mice had increased myocardial apoptosis and infarct size than wild-type [68]. Importantly, adiponectin treatment diminished infarct size, apoptosis and TNF-␣ production in both knockout and wild-type mice. These actions appear to be mediated through activation of AMPK, induction of cyclooxygenase-2-dependent synthesis of prostaglandin E2 [68]. Adiponectin receptors (AdipoR1 and AdipoR2) contain seven transmembrane domains, but are structurally and functionally distinct from G-protein-coupled receptors [69]. AdipoR1 is abundant in muscle and binds with high affinity to globular adiponectin and low affinity to the full-length protein, whereas AdipoR2 is enriched in liver and has intermediate affinity for globular and full-length adiponectin. Both receptors mediate the phosphorylation and activation of AMPK [69]. Although studies have failed to demonstrate a blood-brain transport of adiponectin, both AdipoR1 and AdipoR2 are distributed widely in the brain [70–72]. Injection of adiponectin into the 4th ventricle depolarized AdipoR1 and AdipoR2-positive neurons in the area postrema, suggesting a potential mechanism for its central adiponectin action [72]. In a recent study, adenovirus-mediated expression of AdipoR1 and AdipoR2 activated AMPK and peroxisome proliferator-activated receptor (PPAR)-␣ in the liver of lepr null mice, reduced gluconeogenesis and increased fatty acid oxidation [73]. Targeted disruption of AdipoR1 prevented adiponectin-induced AMPK activation, whereas disruption of AdipoR2 decreased PPAR-␣ activity [73]. Disruption of both AdipoR1 and AdipoR2 abolished adiponectin binding and induced steatosis, inflammation, oxidative stress, insulin resistance and glucose intolerance [73]. Together, these results support a role of AdipoR1 and AdipoR2 as major mediators of adiponectin action on glucose and lipid metabolism.

Resistin

Resistin has a relative mass of 12 kDa and belongs to a family of cysteine-rich C-terminal domain proteins called resistin-like molecules [74, 75]. Initial studies demonstrated that resistin was suppressed by TZDs and induced insulin resistance when administered in rodents [74]. Multimeric complexes of resistin have been identified [76]. Each resistin protomer consists of a C-terminal disulphide-rich ␤-sandwich head and an Nterminal ␣-helical tail. The latter associates with itself forming three-stranded coils. Interchain disulphide linkages form tail-to-tail hexamers. Thus, resistin exists as hexamers and trimers in mouse serum [76]. As predicted, the lack of resistin decreased glucose and enhanced insulin sensitivity in mice [77]. Conversely, transgenic overexpression

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of resistin or infusion of recombinant resistin, particularly a mutant resistin protein lacking the intertrimer disulphide bonds, induced insulin resistance [78, 79]. The resistin receptor is not known; however, studies in rodents suggest that resistin inhibits the phosphorylation and activation of AMPK, and induces SOCS3 [77–79]. Unlike rodents where resistin is produced exclusively by adipocytes, human resistin is secreted by mononuclear cells and activated macrophages [80]. So far, the role of resistin in glucose homeostasis in humans remains controversial [81]. Resistin has been associated with insulin resistance and obesity in some studies, but others have failed to establish such a relationship [82–86]. A connection between resistin and inflammation and atherogenesis has been reported, but whether this is clinically relevant is unknown [87–89].

Additional Adipokines

Adipose tissue produces TNF␣, IL-6, plasminogen activator inhibitor-1 as well as complement factors B, C3 and adipsin (factor D). These latter factors interact, leading to cleavage of acylation-stimulating protein (ASP) from C3. ASP mediates effects of adipsin on glucose and lipids. Visfatin was discovered as a secretory protein highly enriched in rodent and human visceral adipocytes; however, this adipokine is also expressed by liver, muscle, bone marrow and lymphocytes, where it was first identified as pre-B-cell colonystimulating factor [90]. Initial studies described an increase in adipose and circulating visfatin in obesity, and this was related to preservation of insulin sensitivity [90]. Visfatin appeared to exert an insulin-mimetic effect in adipocytes, hepatocytes and myotubes, and following systemic administration in mice [90]. However, the link between visfatin and adiposity and glucose in humans is uncertain [91]. Studies have not consistently found an association between visfatin and adiposity, insulin sensitivity and glucose, and the functional relevance in humans remains to be ascertained [92, 93]. RBP4 is important in the metabolism of vitamin A. RBP4 was discovered in mice lacking glucose transport-4 and shown to be elevated in insulin-resistant mice and obese and diabetic patients [94, 95]. Conversely, antidiabetic treatment and exercise reduced RBP4 in parallel with improvement in insulin sensitivity [94, 95]. Transgenic overexpression of human RBP4 or injection of recombinant RBP4 induced insulin resistance in mice, whereas deletion of Rbp4 enhanced insulin sensitivity [94]. Some studies have confirmed that serum RBP4 levels are increased in insulin-resistant human subjects even before overt diabetes develops [96, 97]. RBP4 is related to adiposity, lipids and cardiovascular risk [96, 97]. On the other hand, other studies have not consistently observed a relation between RBP4 and glucose and lipid metabolism [98, 99]. This discrepancy may be attributed to the current RBP4 assays [100]. The Kahn laboratory, which first described the link between RBP4 and glucose, has

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shown that a quantitative Western blotting standardized to full-length RBP4 protein is more sensitive and reliable than commercial immunoassays [100]. Recent reports have provided compelling data regarding the molecular identity of an RBP receptor [101, 102]. Kawaguchi et al. [101] showed that STRA6, a high-affinity cell-surface receptor for RBP, is highly expressed in the basolateral membrane of retinal pigment cells, blood vessels of the retina, hippocampus and spleen. Pasutto et al. [102] showed that homozygous mutations in the human STRA6 gene causes multisystem malformation and lethality in the perinatal period. The malformations are commonly associated with maternal retinoid deficiency and disruption of transcription factors involved in retinol metabolism, RAR and RXR. Whether STRA6 binds to RBP4 and affects adiposity, lipids or glucose remains to be determined. Vaspin (visceral adipose tissue-derived serpin) is a member of serine protease inhibitor family isolated from visceral white adipose tissue of Otsuka Long-Evans Tokushima fatty rat, a model of abdominal obesity and type 2 diabetes [103]. The tissue expression of vaspin and its serum levels decreased in diabetes and increased in response to insulin or TZD treatment [103]. Administration of vaspin to dietinduced obese mice improved glucose tolerance and insulin sensitivity [103].

Conclusions

This review highlights the switch from the notion of adipose tissue as a dormant tissue to one where adipose tissue actively regulates energy homeostasis and diverse systems via adipokines. Key areas under investigation include the depot-specific functions of adipose tissue, and how these relate to normal physiology and disease. Apart from their classic roles as hormones, leptin and various adipokines have paracrine and autocrine actions which may serve to modulate adipogenesis, nutrient fluxes and metabolic changes locally and in adjacent organs. Knowledge of specific signaling pathways will benefit the treatment of obesity and associated metabolic diseases. There is no doubt that genetic technology in rodents has contributed immensely to the current understanding of adipokines and their targets in the brain and peripheral organs. In some cases, e.g. leptin and adiponectin, the biology of adipokines is similar between mice and humans (table 1). However, there are numerous examples where putative adipokines in rodents are derived from different sources and act differently in humans (table 1). Thus, future research on adipokines demands a combination of cellular and molecular approaches, rodent physiology and importantly human studies.

Acknowledgements This work was supported by grant RO1-DK62348 and PO1-DK49210 from the National Institutes of Health.

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Rexford S. Ahima, MD, PhD Division of Endocrinology, Diabetes and Metabolism University of Pennsylvania School of Medicine 415 Curie Boulevard, 764 Clinical Research Building Philadelphia, PA 19104 (USA) Tel. ⫹1 215 573 1872, Fax 215 573 1874, E-Mail [email protected]

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Section Title Korbonits M (ed): Obesity and Metabolism. Front Horm Res. Basel, Karger, 2008, vol 36, pp 198–211

The Role of AMP-Activated Protein Kinase in Obesity Blerina Kola ⭈ Ashley B. Grossman ⭈ Márta Korbonits Department of Endocrinology, Barts and the London, Queen Mary’s School of Medicine and Dentistry, University of London, London, UK

Abstract AMP-activated protein kinase (AMPK) is a major regulator of energy metabolism at both the cell and at the whole body level. Numerous genetic and obesity models as well as human studies have suggested a role for AMPK in the physiological regulation of fatty acid and glucose metabolism, and in the regulation of appetite. Changes in AMPK activity have been reported in obesity, type 2 diabetes, the metabolic syndrome and cardiovascular disease, which jointly represent a major health and economical problem worldwide. Whether AMPK changes are one of the causes or the consequence of these pathological conditions remains a matter of debate, but AMPK clearly represents a major potential pharmacological Copyright © 2008 S. Karger AG, Basel target in the treatment of these conditions.

Obesity is a major health and economic problem in both Western and developing societies. Its continuing rise in prevalence, 20% in England and 30% in USA [1, 2] seems to be unstoppable despite multiple efforts to attempt to halt this trend. Obesity is characterised by multiple metabolic changes such as insulin resistance, dyslipidaemia and hypertension. The diseases arising as a consequence of obesity such as type 2 diabetes (T2D), cardiovascular disease and certain cancers, are increasingly important causes of morbidity and mortality. In the last decades, a huge amount of research has been dedicated to the study of the complex pathophysiology of obesity and to the research for new medical therapies. AMP-activated protein kinase (AMPK) has emerged in the last years as a major regulator of cell and whole body metabolism. Numerous papers have reported evidence for its role in the regulation of appetite, of body weight and of metabolism [3–5]. Therefore, it is natural to consider AMPK as a major player in the development of obesity. The AMPK complex is an evolutionally conserved serine/threonine heterotrimer kinase complex consisting of ␣-, ␤- and ␥-subunits [for detailed reviews see 5, 6]. AMPK is

activated by cellular stress, which depletes cellular ATP leading to a concomitant rise in AMP. AMP activates AMPK by three distinct mechanisms: (a) allosteric activation, (b) stimulation of phosphorylation of the ␣-subunit on Thr172 by upstream kinase(s) [LKB1 and calmodulin kinase kinase-␣ or -␤ and recently a new possible AMPK kinase candidate, the transforming growth factor-␤-activated kinase (TAK1), which phosphorylates AMPK on Thr-172 in HeLa cells [7], has been reported], and (c) inhibition of dephosphorylation by protein phosphatases [5, 8–10]. Cellular stresses that cause a rise in the AMP/ATP ratio include metabolic poisons (arsenite, oligomycin), oxidative stresses, hypoxia, low glucose, muscle contraction and nutrient deprivation. Osmotic stress also activates AMPK even without a change in the AMP/ATP ratio. Once activated, AMPK switches off anabolic pathways such as gluconeogenesis, glycogen, fatty acid, triglyceride, cholesterol and protein synthesis (mTOR-p70SK-E2 pathway), and switches on catabolic pathways such as glycolysis, glucose uptake, and fatty acid oxidation. It also leads to mitochondrial biogenesis, which improves the ATP synthesis capacity of the cell [11]. Metabolic changes induced by AMPK are both acute changes due to phosphorylation of key enzymes and longer-term effects on the expression of genes involved in metabolic regulation. AMPK, through several mediators, plays a role in various physiological and pathological processes in different tissues (fig. 1). Therefore, it was logical to hypothesise that abnormal AMPK activity would be present in conditions of deregulated energy balance, such as obesity and T2D.

Role of AMPK in Normal Physiology

Role of AMPK in Skeletal Muscle Metabolism Skeletal muscle is the major site of glucose uptake [12], a process that is mainly stimulated by insulin but also by other alternative pathways. Exercise stimulates glucose uptake in the skeletal muscle independently of the insulin pathway and AMPK appears to be the mediator of this effect, primarily in the glycolytic white muscle. These conclusions derived from studies in which in vivo AMP-mimetic 5-aminoimidazole-4-carboxamide ribonucleoside (AICAR) treatment stimulated glucose uptake [13]. The effect was not inhibited by the inhibition of the insulin-dependent PI3K pathway and was additive to insulin-stimulated glucose uptake [14]. AICAR also stimulates glucose transporter GLUT4 expression [15, 16] and its translocation to the cell membrane in rat skeletal muscles [17]. Chronic AMPK activation also increases the expression of hexokinase II, the first enzyme of the glycolysis pathway [18] and inactivates glycogen synthase [19]. The effect of AMPK is fibre dependent and is different in resistance (weight lifting) or endurance (distance running) exercise. AMPK stimulates glucose uptake and GLUT4 expression/transport in fast-twitch (glycolytic, white) muscle but not in slow-twitch (oxidative, red) muscle [20]. AMPK in muscle is activated during exercise, probably as a result of the exercise-induced IL-6 release, a cytokine which activates AMPK in isolated rat muscles [21]. Moreover, it seems that

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GLUT4 (MEF-2, GEF, AS160) hexokinaseII, glycogen synthase ACC Skeletal muscle PEPCK, G6Pase, GPAT, L-PK, ChREBP, TORC2, HNF␣,

Liver

ACC, FAS, SREBP-1c, HMGR, Spot 14 GLUT4 HSL, PEPCK, FAS, SREBP-1c, ACC

Adipose tissue Fig. 1. Metabolic targets of AMPK in muscle, liver and adipose tissues. AMPK regulates the expression and phosphorylation of enzymes and genes involved in glucose and lipid metabolism. GLUT4 ⫽ Glucose transporter 4; MEF-2 ⫽ myocyte enhancer factor-2; GEF ⫽ GLUT4 enhancer factor; AS-160 ⫽ Akt-substrate-of-160 kDa; ACC ⫽ acetyl-coenzyme A carboxylase; PEPCK ⫽ phosphoenolpyruvate carboxykinase; G6Pase ⫽ glucose-6-phosphatase; GPAT ⫽ glycerol-3-phosphate acyltransferase; L-PK ⫽ L-pyruvate kinase; ChREBP ⫽ carbohydrate response element-binding protein; TORC2 ⫽ transducer of regulated CREB activity 2; HNF␣ ⫽ hepatic nuclear factor ␣; FAS ⫽ fatty acid synthase; SREBP-1c ⫽ sterol regulatory element binding protein-1; HMGR ⫽ 3-hydroxy-3methylglutaryl-coenzyme A reductase; HSL ⫽ hormone-sensitive lipase.

only endurance exercise and not resistance exercise can induce AMPK activation [20, 22]. AMPK activation in endurance exercise could also explain the lack of muscle hypertrophy in distance running in contrast to weight lifting. This is possibly due to the effect of AMPK on the mTOR pathway [20]. The mTOR pathway stimulates protein synthesis and hence cell growth and hypertrophy in response to growth factors and amino acids. Therefore, AMPK inhibition of this pathway would result in inhibition of protein synthesis and lack of muscle hypertrophy. AMPK also stimulates fatty acid oxidation in muscle. This results in lower lipid deposition and increases the ability of the muscle to meet energy needs by increasing glucose uptake and fatty acid oxidation as well. Studies with transgenic animals (AMPK ␣1 and ␣2 knockout mice, muscle-specific over-expression of dominant negative AMPK ␣2, AMPK ␥3 knockout, muscle-specific over-expression of AMPK ␥3 and muscle-specific over-expression of AMPK ␥3 R225Q overactive mutant and skeletal muscle-specific LKB1 knockout [for detailed descriptions, see 20, 23]), have provided further evidence for AMPK being the main mediator, although not the only one, of the adaptations (i.e. increased

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glucose uptake, fatty acid oxidation, inhibition of glycogen synthesis) of skeletal muscle in response to exercise.

Role of AMPK in Liver Metabolism The liver is the major site for storage and release of carbohydrates and for fatty acid synthesis. It responds to fasting with increased glucose output and increased fatty acid oxidation, while in post-prandial conditions liver glucose uptake increases with consequent glycogen and triglyceride synthesis [24]. AMPK regulates liver lipid and glucose homeostasis via phosphorylation of multiple enzymes (e.g. ACC1 – ↓ lipid synthesis, ACC2 – ↑ lipid oxidation, 3-hydroxy-3-methylglutaryl-coenzyme A reductase – ↓ cholesterol synthesis, glycerol-3-phosphate acyltransferase – ↓ glycerolipid synthesis), and influences the expression of genes involved in gluconeogenic, glycolytic and lipogenic processes and their upstream regulators [for a comprehensive review on the topic, see 25]. Therefore, overall AMPK activation in the liver results in inhibition of gluconeogenesis, fatty acid, triglyceride and cholesterol synthesis, and stimulation of fatty acid oxidation. Changes in hepatic metabolism are certainly present in obesity and T2D. Elevated glucose production by the liver is the major cause of fasting hyperglycaemia, and it is possible that AMPK activation by decreasing gluconeogenesis and cholesterol synthesis could be beneficial in these patients. Nevertheless, one needs to be cautious as AMPK activation, by increasing fatty acid oxidation and ketogenesis, might lead to ketoacidosis, and by inhibiting protein synthesis might lead to a negative nitrogen balance together with enhanced urea synthesis [25].

Role of AMPK in Adipose Tissue Metabolism Adipose tissue has been considered for decades simply as an energy storage organ, while in the last years it has emerged as an active endocrine organ, which by secreting several proteins, known as adipokines, contributes to the regulation of appetite and metabolism. AMPK ␣1 subunit is the prevalent AMPK subunit expressed in the adipose tissue [26 and our own unpublished data]. AMPK regulates lipogenesis and lipolysis in adipose tissue. Activation of AMPK in rodent adipocytes leads to a decreased fatty acid uptake, decreased triglyceride synthesis and increased fatty acid oxidation via inhibition of ACC1 and ACC2 and, as in the liver, inhibition of the expression of lipogenic genes [27, 28]. During fasting, lipolysis is activated in adipose tissue in order to provide fatty acids and glycerol as fuels for peripheral tissues, but reports on the effect of AMPK activation on lipolysis are contradictory. There is evidence that AMPK activation, either by AICAR or by over-expression of a constitutively active AMPK isoform or by biguanide treatment, has an inhibitory effect on lipolysis [26, 29]. In conditions where lipolysis is activated, such as fasting and exercise, AMPK is also activated but as a feedback mechanism this activation leads to inhibition of lipolysis, which is an energy-consuming process for the adipocytes [27]. Furthermore, in the AMPK ␣1 knockout mice, the size of the

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adipocytes is reduced and basal and isoprotenerol-induced lipolysis is higher than that of control adipocytes [26]. On the contrary, the study of Yin et al. [30] suggested a lipolytic action for AMPK and the study by Koh et al. [31] suggested that the adrenalineinduced lipolysis is due to AMPK activation. There are also contradictory findings related to the effect of AMPK on glucose transport in adipose tissue [32–34]. In conclusion, AMPK activation in adipose tissue, under conditions such as exercise, fasting or after stimulation with leptin, adiponectin or biguanides, decreases lipogenesis, triglyceride synthesis and lipolysis and increases fatty acid oxidation, contributing therefore to improved insulin sensitivity.

Role of AMPK in Endocrine Pancreas The effects of AMPK activation in ␤-cells are complex: further data for the role of AMPK in endocrine pancreas are available in the chapter by Rutter and Parton [this vol., pp. 118–134]. AMPK might be involved in the expression of insulin receptor family members, such as the IGF-I receptor, insulin receptor and insulin receptor-related receptor, which are mandatory for several steps in insulin secretion [35], while AICAR increases the phosphorylation of insulin receptor substrate-1 (IRS-1) on Ser789 leading to increased IRS-1 activity [35]. On the other hand, AICAR and metformin inhibit rapid insulin release [35] and the activation of AMPK also enhances ␤-cell apoptosis; it remains to be determined if this is the cause or the consequence of the altered glucose metabolism [36–38]. AMPK appears to be a key regulator of hepatocyte nuclear factor-4␣, which is linked to type 1 maturity-onset diabetes of the young [for further details, see 36]. The overall effect of AMPK on glucose homeostasis [6, 36] is determined by the joint effect on insulin secretion in addition to the prominent effects of AMPK activation on glucose transport, gluconeogenesis and glycogenolysis, in muscle and liver.

Role of AMPK in Hypothalamus The role of AMPK in the regulation of body weight and energy homeostasis is not limited to its actions in the peripheral tissues. AMPK is a central regulator of food intake. AMPK mediates the effects of multiple orexigenic and anorexigenic signals in the hypothalamus [35]. Fasting increases and refeeding decreases the AMPK activity in the hypothalamus [39]. The downstream pathways of AMPK in the hypothalamus could involve the ACC-malonyl-CoA-CPT1 pathway [3] and the mTOR pathway [40, 41] (fig. 2). Leptin and changes in glucose concentration affect the activity of glucose-inhibited cells (40% of which are NPY-expressing neurons) in the hypothalamus via AMPK [42]. Actually, AMPK activity in the hypothalamus is probably responsible for some of the peripheral effects of leptin, of hypoglycaemia and of the FAS inhibitor C75 [3, 35], emphasising the complexity of the regulation of whole body metabolism and the role of AMPK, being not only a peripheral or a central mediator but also a key enzyme in coordinating the interaction between peripheral and central energy regulation.

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Appetite stimulation Ghrelin Cannabinoids AgRP 2-deoxyglucose (hypoglycemic-like state) Fasting

AMPK

PI 3-kinase/PDK1 PKB1 TSC1/TSC2

Appetite inhibition Leptin Insulin ␣-MSH C75 (fatty acid synthase inhibitor) ␣-lipoicacid High glucose Refeeding Leucine

ACC Acetyl CoA

Malonyl CoA

CPT1

mTOR p70S6K

4E-BP1

Long-chain Acyl CoA

β-oxidation

Fig. 2. Regulation of hypothalamic AMPK and possible downstream pathways. Black lines show pathways established in the hypothalamus, grey lines show pathways that have been described in rat muscle, rat liver, myotubes, hepatocytes, fibroblasts and lung carcinoma cells [97, 98] but not directly in the hypothalamus.

AMPK as a Mediator of Action of Metabolically Active Hormones

AMPK mediates the effects of many hormones/peptides/substances/drugs in numerous physiological and pathological processes. Insulin, leptin, adiponectin, cannabinoids and ghrelin influence peripheral metabolism at least partially via activation or inhibition of AMPK activity in the skeletal muscle, liver, adipose tissue and the hypothalamus (table 1) [35]. AMPK has been found to be the mediator of many hormones and its role in the interplay between these compounds and their metabolic effects is being actively investigated [for a detailed review on the topic, see 35].

AMPK in Animal Models of Obesity

Animal models of obesity and diabetes have provided evidence for implication of AMPK in the pathogenesis of these conditions and also provided evidence for a possible role of AMPK modulators in their treatment (table 2).

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Table 1. Effect of hormones and drugs on AMPK activity in different tissues [modified and updated from 35] Hormone/ substance

Hypothalamus Skeletal Muscle

Liver

Adipose tissue

Pancreas: ␤-cells

High glucose Insulin Leptin Ghrelin Adiponectin Resistin Glucagon Cannabinoids Metformin Rosiglitazone

⇓[39, 60, 61] ⇓[39] ⇓[39, 42, 66] ⇑[66, 74]

⇓[36] ⇒[36] ⇑[67, 68] ⇒[74] ⇑[76–78] ⇓[82]

⇓[36, 63, 64] ⇒[36] ⇓[30] ⇒[36] ⇑[71] ⇒[63] ⇓[74] ⇑[33] ⇑[79]

⇓[65] ⇒[72, 73] ⇑[74] ⇑[80, 81]

⇑[74] ⇓[60]

⇒[74] ⇑[86] ⇑[54, 89, 90]

⇓[62] ⇒[50] ⇑[46, 50, 69, 70] ⇓[74, 75] ⇑[50, 76, 77] ⇓[83, 84] ⇑[85] ⇓[74] ⇑[62, 86]

⇓[74] ⇑[87]

⇑[74] ⇑[88]

⇑[37, 63]

Cardiac muscle

References are listed in brackets. ⇑ ⫽ Stimulation; ⇓ ⫽ inhibition; ⇒ ⫽ no change.

Martin et al. [43] showed that diet-induced obesity (DIO) in mice alters the effect of leptin on AMPK activity both in skeletal muscle and in the hypothalamus. Leptin increases AMPK activity in the skeletal muscle of chow-fed mice and decreases it in the hypothalamus of the same animals but does not have an effect in the DIO mice. While, most interestingly, a ciliary neurotrophic factor analogue (CNTFAx15) given intracerebroventricularly not only reduces food intake in high-fat diet (HFD) mice but also suppresses hypothalamic AMPK activity, bypassing therefore diet-induced leptin resistance [44]. Rats on an HFD for 5 months exhibited decreased AMPK phosphorylation and expression in skeletal muscle associated with decreased levels of ACC and GLUT4 as well. Metformin treatment restored insulin sensitivity and increased AMPK activity [45]. In Zucker rats who do not respond to leptin treatment because of defects in the leptin receptor, administration of the AMPK activator AICAR results in leptinomimetic effects, leading to the prevention of ectopic lipid deposition and diabetes [46]. Transgenic mice over-expressing leptin in liver are lean on a chow diet but despite the high pre-existing leptin levels become obese and insulin resistant on an HFD [47]. HFD for 15 weeks abolishes the increase in muscle AMPK activity observed in the same animals on a chow diet. Short hepatic over-expression of a constitutively active form of AMPK decreased blood glucose levels in normal mouse, abolished hyperglycaemia in streptozotocininduced and in ob/ob mice and also reduced gluconeogenic enzyme expression. The resulting low glucose levels led to a switch from glucose utilisation to fatty acid utilisation, associated with a decrease in white adipose tissue mass and development of fatty liver [48].

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Table 2. AMPK changes in animal models of obesity Animal model

AMPK-related changes

Reference

Obese Zucker fa/fa rat

AICAR increased muscle glucose transport and suppresses endogenous glucose production and lipolysis Reduced AMPK and ACC phosphorylation LKB1 activity and PGC-1 content Rosiglitazone restores AMPK ␣2 activity in skeletal muscle Chronic AICAR/exercise training prevented hyperglycaemia and increased whole-body insulin sensitivity

[91, 92] [90] [46, 93]

ob/ob and db/db mice

AICAR and short hepatic over-expression of a constitutively active form of AMPK decreased blood glucose levels

[48, 94]

HFD in rats

Reduction of AMPK activity, ACC and GLUT4 levels in skeletal muscle. Metformin increases AMPK activity Rosiglitazone enhanced AICAR-stimulated glucose uptake in muscle and adipose tissue. Total AMPK and AMPK ␣2 activity increased in muscle

[45]

DIO mouse

AICAR administration blocked weight gain, reduced total content epididymal fat and lipid accumulation in adipocytes, restored adiponectin levels, improved glucose tolerance and insulin sensitivity DIO mice compared to chow-fed mice ate less, had lower respiratory exchange rate and lower ACC activity in muscle. Leptin did not improve either of these parameters or the AMPK ␣2 activity in muscle and hypothalamus of the DIO Ciliary neurotrophic factor analogue reduced food intake and AMPK hypothalamic activity, bypassing therefore diet-induced leptin resistance

[95]

[96]

[43]

[44]

Adiponectin inhibits glucose production in wild-type mouse and also in T2D mouse (ob/ob, non-obese diabetic or streptozotocin-treated mice) [49] and the effect of adiponectin is completely dependent on the presence of hepatic AMPK ␣2 subunit [50]. Studies on ob/ob and adiponectin double knockout mice or knockout only for adiponectin showed an impaired ability to improve glucose tolerance with rosiglitazone treatment and this was, at least partly, due to reduced activation of AMPK [51]. These results not only showed the role of adiponectin as a TZD mediator but also confirm the importance of AMPK activation in the mechanism of action of TZD type anti-diabetic drugs.

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AMPK in Human Obesity

The majority of the research studies published have been performed on animals, and it is important to establish that their conclusions can be extrapolated to human physiology and pathology as the number of studies about AMPK activity in human diseases is much more limited. Skeletal muscle AMPK activity has been analysed in a limited number of obese vs. lean subjects, in obese diabetic versus obese nondiabetic patients and in healthy subjects before and after exercise. Obesity in humans is associated with leptin and insulin resistance and lipid accumulation. Adiponectin or AICAR activate muscle AMPK in obese rodents, which stimulates fatty acid oxidation, and it is reasonable therefore to hypothesise that pharmacological activation of AMPK might be of therapeutic benefit in human obesity. However, AMPK is not down-regulated in human skeletal muscle of obese females [52] and AMPK activity and specific isoform expression are similar in muscle of obese subjects with and without T2D [53]. These data suggest that impaired insulin action on glycogen synthesis and lipid oxidation in skeletal muscle of these patients is unlikely to involve changes in AMPK expression and activity. However, AICAR treatment of muscle biopsies stimulated AMPK ␣2 activity and fatty acid oxidation, suggesting that AMPK activation above basal levels may still be a valid therapeutic approach [52]. In contrast to the previous studies, Bandyopadhyay et al. [54] showed that there is a decrease in AMPK activity and an increase in ACC activity in insulinresistant muscle from obese and from T2D patients that results in elevated intracellular levels of malonyl-CoA. Because, for the most part, the defects appear to be expressed equally in the obese subjects and in T2D subjects (who were also obese), the authors conclude that these differences from lean control subjects are caused by insulin resistance/obesity rather than hyperglycaemia/diabetes. Finally, when the T2D subjects were treated for 3 months with rosiglitazone, the various defects in fatty acid and mitochondrial metabolism reverted towards normal. The beneficial effect of AMPK activation in muscle was demonstrated in a study which showed that acute intensive exercise (3 h) increased AMPK and ACC phosphorylation altogether with an increase in expression of adiponectin receptor in the skeletal muscle of 5 healthy females [55]. Interestingly, Roepstorff et al. [56] showed that AMPK activation in muscle is sex-dependent: 90 min of exercise activated AMPK in skeletal muscle of healthy male volunteers but in contrast to the former study, not in females. Further data are needed to study the role of oestradiol on skeletal muscle AMPK activity. The effect of exercise on AMPK is probably due, at least in part, to IL-6, which is synthesised and released from skeletal muscle in large amounts during exercise [57], and in rodents, the resultant increase in IL-6 concentration correlates with increases in AMPK activity in multiple tissues. There are no direct data of the effect of IL-6 on AMPK activity in humans but IL-6 treatment was recently shown to enhance insulin-stimulated glucose disposal in humans in vivo [58].

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Role of AMPK in Cardiovascular Disease Cardiovascular disease is a common consequence of obesity and obese patients are often treated for hypertension, atherosclerosis, and heart failure. AMPK is a key regulator of energy metabolism in the heart, too [for an extensive review on the topic, see 59]. The obvious beneficial effects of AMPK activation in ischaemia could be counterbalanced by the excessive fatty acid oxidation and reduced glucose oxidation leading to accumulation of pyruvate and protons [59]. In view of this and in view of the fact that AMPK is proposed as a possible target for obesity and diabetes treatments, it is important to know the role of AMPK in cardiac physiology and pathology in order to avoid possible side effects of future AMPK activators/inhibitors.

AMPK as an Overall Metabolic Regulator

In conclusion, AMPK has emerged as a key regulatory enzyme of cell and whole body metabolism. It influences cell metabolism in a way that favours insulin sensitivity and maintains a favourable body energy homeostasis. It is the mediator of the metabolic effects of many of the known hormones, nutrients and drugs. Thus, not only are changes in AMPK implicated in the pathogenesis of insulin-resistant states, but AMPK might also constitute a target for new treatments of these conditions. However, a note of caution is required as generalised AMP activation might result in unwanted effects (i.e. an appetite-stimulating effect and ␤-cell inhibition), and thus there is a need for tissue-specific modulators.

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69 Brabant G, Muller G, Horn R, Anderwald C, Roden M, Nave H: Hepatic leptin signaling in obesity. FASEB J 2005;19:1048–1050. 70 Uotani S, Abe T, Yamaguchi Y: Leptin activates AMPactivated protein kinase in hepatic cells via a JAK2dependent pathway. Biochem Biophys Res Commun 2006;351:171–175. 71 Wang MY, Orci L, Ravazzola M, Unger RH: Fat storage in adipocytes requires inactivation of leptin’s paracrine activity: implications for treatment of human obesity. Proc Natl Acad Sci USA 2005;102: 18011–18016. 72 Atkinson LL, Fischer MA, Lopaschuk GD: Leptin activates cardiac fatty acid oxidation independent of changes in the AMP-activated protein kinase-acetylCoA carboxylase-malonyl-CoA axis. J Biol Chem 2002;277:29424–29430. 73 Russell RR, III, Li J, Coven DL, Pypaert M, Zechner C, Palmeri M, Giordano FJ, Mu J, Birnbaum MJ, Young LH: AMP-activated protein kinase mediates ischemic glucose uptake and prevents postischemic cardiac dysfunction, apoptosis, and injury. J Clin Invest 2004;114:495–503. 74 Kola B, Hubina E, Tucci SA, Kirkham TC, Garcia EA, Mitchell SE, Williams LM, Hawley SA, Hardie DG, Grossman AB, Korbonits M: Cannabinoids and ghrelin have both central and peripheral metabolic and cardiac effects via AMP-activated protein kinase. J Biol Chem 2005;280:25196–25201. 75 Barazzoni R, Bosutti A, Stebel M, Cattin MR, Roder E, Visintin L, Cattin L, Biolo G, Zanetti M, Guarnieri G: Ghrelin regulates mitochondrial-lipid metabolism gene expression and tissue fat distribution in liver and skeletal muscle. Am J Physiol Endocrinol Metab 2005;288:E228–E235. 76 Yamauchi T, Kamon J, Minokoshi Y, Ito Y, Waki H, Uchida S, Yamashita S, Noda M, Kita S, Ueki K, Eto K, Akanuma Y, Froguel P, Foufelle F, Ferre P, Carling D, Kimura S, Nagai R, Kahn BB, Kadowaki T: Adiponectin stimulates glucose utilization and fatty-acid oxidation by activating AMP-activated protein kinase. Nat Med 2002;8:1288–1295. 77 Tomas E, Tsao TS, Saha AK, Murrey HE, Zhang CC, Itani SI, Lodish HF, Ruderman NB: Enhanced muscle fat oxidation and glucose transport by ACRP30 globular domain: acetyl-CoA carboxylase inhibition and AMP-activated protein kinase activation. Proc Natl Acad Sci USA 2002;99:16309–16313. 78 Yoon MJ, Lee GY, Chung JJ, Ahn YH, Hong SH, Kim JB: Adiponectin increases fatty acid oxidation in skeletal muscle cells by sequential activation of AMPactivated protein kinase, p38 mitogen-activated protein kinase, and peroxisome proliferator-activated receptor alpha. Diabetes 2006;55:2562–2570.

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79 Huypens P, Moens K, Heimberg H, Ling Z, Pipeleers D, van de CM: Adiponectin-mediated stimulation of AMP-activated protein kinase (AMPK) in pancreatic beta cells. Life Sci 2005;77:1273–1282. 80 Shibata R, Sato K, Pimentel DR, Takemura Y, Kihara S, Ohashi K, Funahashi T, Ouchi N, Walsh K: Adiponectin protects against myocardial ischemia-reperfusion injury through. Nat Med 2005;11: 1096–1103. 81 Liao Y, Takashima S, Maeda N, Ouchi N, Komamura K, Shimomura I, Hori M, Matsuzawa Y, Funahashi T, Kitakaze M: Exacerbation of heart failure in adiponectin-deficient mice due to impaired regulation of AMPK and glucose metabolism. Cardiovasc Res 2005;67:705–713. 82 Palanivel R, Sweeney G: Regulation of fatty acid uptake and metabolism in L6 skeletal muscle cells by resistin. FEBS Lett 2005;579:5049–5054. 83 Muse ED, Obici S, Bhanot S, Monia BP, McKay RA, Rajala MW, Scherer PE, Rossetti L: Role of resistin in diet-induced hepatic insulin resistance. J Clin Invest 2004;114:232–239. 84 Banerjee RR, Rangwala SM, Shapiro JS, Rich AS, Rhoades B, Qi Y, Wang J, Rajala MW, Pocai A, Scherer PE, Steppan CM, Ahima RS, Obici S, Rossetti L, Lazar MA: Regulation of fasted blood glucose by resistin. Science 2004;303:1195–1198. 85 Kimball SR, Siegfried BA, Jefferson LS: Glucagon represses signaling through the mammalian target of rapamycin in rat liver by activating AMP-activated protein kinase. J Biol Chem 2004;279:54103–54109. 86 Zhou G, Myers R, Li Y, Chen Y, Shen X, FenykMelody J, Wu M, Ventre J, Doebber T, Fujii N, Musi N, Hirshman MF, Goodyear LJ, Moller DE: Role of AMP-activated protein kinase in mechanism of metformin action. J Clin Invest 2001;108:1167–1174. 87 Huypens P, Quartier E, Pipeleers D, van de CM: Metformin reduces adiponectin protein expression and release in 3T3-L1 adipocytes involving activation of AMP activated protein kinase. Eur J Pharmacol 2005;518:90–95. 88 An D, Kewalramani G, Chan JK, Qi D, Ghosh S, Pulinilkunnil T, Abrahani A, Innis SM, Rodrigues B: Metformin influences cardiomyocyte cell death by pathways that are dependent and independent of caspase-3. Diabetologia 2006;49:2174–2184.

89 Fryer LG, Parbu-Patel A, Carling D: The anti-diabetic drugs rosiglitazone and metformin stimulate AMPactivated protein kinase through distinct signaling pathways. J Biol Chem 2002;277:25226–25232. 90 Lessard SJ, Chen ZP, Watt MJ, Hashem M, Reid JJ, Febbraio MA, Kemp BE, Hawley JA: Chronic rosiglitazone treatment restores AMPK{alpha}2 activity in insulin-resistant rat skeletal muscle. Am J Physiol Endocrinol Metab 2006;290:E251–E257. 91 Bergeron R, Previs SF, Cline GW, Perret P, Russell RR III, Young LH, Shulman GI: Effect of 5-aminoimidazole-4-carboxamide-1-beta-D-ribofuranoside infusion on in vivo glucose and lipid metabolism in lean and obese Zucker rats. Diabetes 2001;50:1076–1082. 92 Sriwijitkamol A, Ivy JL, Christ-Roberts C, Defronzo RA, Mandarino LJ, Musi N: LKB1-AMPK signaling in muscle from obese insulin-resistant Zucker rats and effects of training. Am J Physiol Endocrinol Metab 2006;290:E925–E932. 93 Pold R, Jensen LS, Jessen N, Buhl ES, Schmitz O, Flyvbjerg A, Fujii N, Goodyear LJ, Gotfredsen CF, Brand CL, Lund S: Long-term AICAR administration and exercise prevents diabetes in ZDF rats. Diabetes 2005;54:928–934. 94 Halseth AE, Ensor NJ, White TA, Ross SA, Gulve EA: Acute and chronic treatment of ob/ob and db/db mice with AICAR decreases blood glucose concentrations. Biochem Biophys Res Commun 2002;294: 798–805. 95 Ye JM, Dzamko N, Hoy AJ, Iglesias MA, Kemp B, Kraegen E: Rosiglitazone treatment enhances acute AMP-activated protein kinase-mediated muscle and adipose tissue glucose uptake in high-fat-fed rats. Diabetes 2006;55:2797–2804. 96 Giri S, Rattan R, Haq E, Khan M, Yasmin R, Won JS, Key L, Singh AK, Singh I: AICAR inhibits adipocyte differentiation in 3T3L1 and restores metabolic alterations in diet-induced obesity mice model. Nutr Metab (Lond) 2006;3:31. 97 Motoshima H, Goldstein BJ, Igata M, Araki E: AMPK and cell proliferation–AMPK as a therapeutic target for atherosclerosis and cancer. J Physiol (Lond) 2006;574:63–71. 98 Du M, Shen QW, Zhu MJ, Ford SP: Leucine stimulates mTOR signalling in C2C12 myoblasts in part through inhibition of AMP-activated protein kinase. J Anim Sci 2006;919–927.

Márta Korbonits, MD, PhD Department of Endocrinology Barts and the London, Queen Mary’s School of Medicine and Dentistry University of London Charterhouse Square London EC1M 6BQ (UK) Tel. ⫹44 20 7882 6238, Fax ⫹44 20 7882 6197, E-Mail [email protected]

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Section Title Korbonits M (ed): Obesity and Metabolism. Front Horm Res. Basel, Karger, 2008, vol 36, pp 212–228

Classical Endocrine Diseases Causing Obesity Jolanta U. Weaver School of Clinical Medical Sciences, University of Newcastle, Newcastle upon Tyne, UK

Abstract Obesity is associated with several endocrine diseases, including common ones such as hypothyroidism and polycystic ovarian syndrome to rare ones such as Cushing’s syndrome, central hypothyroidism and hypothalamic disorders. The mechanisms for the development of obesity vary in according to the endocrine condition. Hypothyroidism is associated with accumulation of hyaluronic acid within various tissues, additional fluid retention due to reduced cardiac output and reduced thermogenesis. The pathophysiology of obesity associated with polycystic ovarian syndrome remains complex as obesity itself may simultaneously be the cause and the effect of the syndrome. Net excess of androgen appears to be pivotal in the development of central obesity. In Cushing’s syndrome, an interaction with thyroid and growth hormones plays an important role in addition to an increased adipocyte differentiation and adipogenesis. This review also describes remaining rare cases: hypothalamic obesity due to central hypothyCopyright © 2008 S. Karger AG, Basel roidism and combined hormone deficiencies.

The heterogeneity of physical appearance in obesity and the association with various endocrine disorders lead some clinicians and almost every patient with obesity to believe that there is an underlying hormonal imbalance to account for their problem. This is the reason that endocrinologists are frequently consulted to advise on the possible underlying diagnosis for secondary obesity. The term ‘secondary’ means that obesity accompanies another illness that is considered to be the primary disease state. Secondary morbid obesity (body mass index, BMI, ⱖ40) due to endocrine causes is quite rare and is usually associated with hypothalamic disorders. However, lesser degrees of a weight problem (BMI 26–39) are associated with thyroid disorders, polycystic ovarian syndrome (PCOS) or Cushing’s syndrome. An appropriate evaluation is the first step in developing a treatment plan for overweight patients. A medical history should evaluate the natural history of the development of obesity and aetiological factors involved. The physical examination and laboratory testing should extend this evaluation to exclude likely endocrine causes. The result of those then provide a guide to selecting an appropriate treatment plan.

The conditions associated with overweight to morbid obesity are discussed below, in order of prevalence.

Hypothyroidism

Primary hypothyroidism is the commonest cause of hypothyroidism (99% of cases) and occurs in 2% of women and up to 0.2% of adult men: 54% of patients with overt hypothyroidism report weight gain as opposed to13.8% in controls [1]. Undiagnosed hypothyroidism may go unnoticed for sometime due to insidious onset of symptoms including weight gain. There is a slow progression of thyroid underactivity and development of clinical symptoms. Most patients with hypothyroidism gain a moderate amount of weight. As early symptoms are variable and non-specific, there should be a low threshold for screening patients for primary hypothyroidism with a serum thyrotropin-stimulating hormone (TSH) determination.

Symptoms of Overt Hypothyroidism Hypothyroidism often remains undetected because of the difficulty of ascribing symptoms to the disease. The pathophysiological changes generally require months or years to manifest as clinical signs and symptoms. Furthermore, the onset of hypothyroidism is so insidious that even classic symptomatology may go unnoticed or undiagnosed [2]. Although most hypothyroid patients do have some signs and symptoms indicative of disease [3], it may be difficult to identify a ‘classic’ clinical picture because symptoms may be non-specific and thus confused with other health problems [4]. The relation between symptoms and physiologic disease is so complex that clinicians turn to biochemical measures of thyroid dysfunction for diagnosis. However, it is unclear who should be tested. Many investigators and several organisations, such as the American Thyroid Association and the American Association of Clinical Endocrinologists, recommend testing persons who have a greater likelihood of being hypothyroid [5]. The U.S. Preventive Services Task Force does not recommend for or against screening in high-risk patients, such as older women. However, it does alert clinicians to maintain a low threshold for diagnostic evaluation of thyroid function when subtle or non-specific symptoms of thyroid dysfunction occur in such patients [6]. In the UK, the Working Group of the Royal College of Physicians and Society for Endocrinology, in a consensus statement for good practice in thyroid diseases, did not focus on presenting symptoms or signs since they were detailed in the report by the American Thyroid Association [7]. Evidence is lacking as to which symptom or symptoms increase the likelihood of confirming biochemical hypothyroidism. However, patients who report more symptoms, and more recently developed symptoms, are more likely to have hypothyroidism. Therefore patients who report more symptoms,

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Table 1. Laboratory investigations of patients suspecting hypothyroidism TSH

Free T4 index

Thyroid peroxidase antibodies

Diagnosis

⬍0.4 mU/l

low/low-normal

⫺/⫹

post-hyperthyroid hypothyroidism

0.4–4.0mU/l

low

2

central hypothyroidism

⬎4 and ⬍10 mU/l

normal/low

⫺/⫹

subclinical hypothyroidism; early primary autoimmune hypothyroidism; central hypothyroidism

⬎10 mU/l

low

⫹

Primary hypothyroidism due to autoimmune thyroid disease

⬎10 mU/l

low/low-normal

⫺

inter-current illness, drug-induced external radiation, iodine deficiency

⬎10 nM

normal/elevated

⫺/⫹

thyroid hormone resistance, drugs; assay artefact

particularly recent symptoms, should be tested with serum thyroid function tests [8]. The same group showed that change in symptoms is more powerful in predicting the disease than current symptoms and the higher number of symptoms reported (current as well as changed), the greater the likelihood of predicting hypothyroidism. It is interesting that weight problem has not been even listed among the common symptoms by Canaris et al. [8] but featured highly among symptoms and signs analysed by Zulewski et al. [1]. Thus, overt hypothyroidism is a common condition that may be difficult to diagnose on clinical grounds alone and requires a high degree of clinical suspicion as well as confirmation with biochemical testing for serum TSH and thyroid hormone levels. Reliance on blood testing to diagnose an easily treatable condition has led to a spectrum of results listed below (table 1). Untreated hypothyroidism is well known to lead to hypercholesterolaemia, which may improve or completely normalise on statin treatment. In cardiovascular patients who do not reduce their cholesterol levels adequately on statin treatment, testing for hypothyroidism is essential. Cardiovascular disease in hypothyroidism may have the potential to be fatal, although this has never been tested in a controlled fashion.

The Mechanism of Thyroid Hormones Action Thyroid hormones are essential for the regulation of a number of important processes in the body including growth and development, neuromuscular activity, thermogenesis, energy consumption and many metabolic reactions [9]. The thyroxine (T4) pro-hormone

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is synthesised in the thyroid gland together with a small amount of the active hormone 3,5,3⬘-L-triiodothyronine (T3). However, the majority of circulating T3 is generated by pre-receptor ligand metabolism resulting from activity of the iodothyronine deiodinase enzymes D1 and D2 which convert T4 to T3 by 5⬘ monodeiodination. In contrast, the D3 enzyme inactivates T4 and T3 by inner ring or 5-deiodination. The intracellular level of T3 is dependent on the relative activities of these three deiodinases [10].

The Classical Genomic and Non-Genomic Actions of T3 T3 regulates nuclear gene expression by binding the thyroid hormone receptors TR␣ and TR␤ (genomic action). Thyroid hormone receptors recognise specific thyroid response elements in the promoters of T3-target genes and activate or repress transcription in response to hormone. The effect of genomic action of T3 can vary as from hours to days [9]. Some of the T3 effects occur rapidly and are unaffected by inhibitors of transcription and protein synthesis. Those actions are defined as non-genomic [11].

The Pathophysiology of Weight Gain in Hypothyroidism The weight gain in hypothyroidism is related to several factors: 1 Hypothyroidism causes accumulation of hyaluronic acid within the dermis and other tissues. As this material is hygroscopic, it produces mucinous oedema responsible for thickened structures and puffy appearance [12]. 2 Due to reduced bowel peristalsis and deposition of glycoproteins in the bowel wall, patients invariably complain of constipation which may lead to faecal impaction, myxoedema, megacolon and myxoedema ileus. 3 The effects of thyroid hormone have also been reported in the myocardium and vasculature. T3 enhances cardiac output and reduced systemic vascular resistance in normal adult males within 3 min [13], and cell culture studies suggest that thyroid hormones rapidly, and non-genomically, regulate the Ca2⫹ATPase enzyme, the Na⫹ channel via PKC, the K⫹ channel via PI3-kinase, the Na⫹/H⫹ anti-porter via PKC and MAPK and the inward rectifying potassium channel [14]. T3 also increases sarcoplasmic reticulum Ca2⫹, cell shortening, contractility and calciummediated arrhythmic activity, suggesting that T3 has a non-genomic, positive ionotropic and arrhythmogenic effect [15]. Thus, lack of thyroid hormones leads to a reduced inotropic and chronotropic effect on the cardiac output. In severe hypothyroidism, patients may develop pericardial and pleural effusion but rarely ascites. The exudates are rich in protein and glycosaminoglycans. Renal blood flow, glomerular filtration rate, tubular reabsorption and secretion are also reduced. This leads to reduced water clearance due to reduced urine flow. There is an overall

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increase in total body water content due to retention of water by the hydrophylic deposits in the tissues. There is an increase in the extravascular space also due to increased permeability of the capillaries to proteins.

Interaction with Other Hormones Growth Hormone Lack of thyroid hormones leads to reduced growth hormone secretion and thus generation of IGF-I [16].

Leptin Leptin and thyroid hormones possess the ability to increase energy expenditure. Hypothyroidism has been shown to be associated with raised leptin levels [17]. Furthermore, thyroxine treatment reduced leptin secretion independently of adiposity and noradrenaline levels. It appears that thyroxine and leptin are closely regulated by a negative feedback mechanism. Leptin has been shown to stimulate the hypothalamopituitary-thyroid axis and also to modulate 5⬘-deiodinases in different tissues, depending on the energy status of animals. Leptin promoted a rise in serum TSH, suggesting that leptin acts at the hypothalamus in order to stimulate the thyrotropin-releasing hormone (TRH)-TSH axis. Leptin may alter thus hypothalamic, pituitary and brown adipose tissue functions by regulation of 5⬘-deiodinases and directing the local T3 production [18]. In animals, hypothyroidism is characterised by decreased insulin responsiveness, partly mediated by an exaggerated glucose-fatty acid cycle that is partly alleviated by intracerebroventricular leptin administration [19].

Catecholamines The hypothyroid state is characterised by decreased adrenergic activity due to reduced responsiveness of cAMP to adrenaline. This may be related to the effect of thyroid hormones on cAMP generation [20]. In vivo, thyroxine regulates thermogenesis and the lipolytic activities of catecholamines within 30 min [21]. However, there is also evidence for a direct effect of thyroid hormone on mitochondrial gene expression and oxidative phosphorylation [22]. Thyroid hormones accumulate in mitochondria [23] and are major regulators of mitochondrial biogenesis playing a role in proliferation, differentiation and maturation [24]. The synergistic actions of catecholamines and thyroid hormone together result in a threefold increase in mitochondrial UCP1 levels in brown adipose tissue [25]. Catecholamines also rapidly increase D2 expression in brown adipose tissue, leading to tissue-specific hyperthyroidism and a twofold increase in plasma T3. Uniquely in brown adipose tissue, D2 induction is potentiated by T3, whereas in other tissues T3 suppresses D2 [10]. In addition,

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thyroid hormones have been shown to increase expression of the non-BAT UCP2, UCP3 and the ADP/ATP carrier suggesting a regulatory role in mitochondria from other tissues [26]. Thyroid hormones also regulate mitochondrial gene expression by increasing steady state mitochondrial mRNA levels, respiration, enzyme activity and protein synthesis [27], and by increasing mitochondrial metabolic activity [28, 29].

Direct Actions of Thyroid Hormone on the Mitochondria In isolated rat liver mitochondria, T3 can increase oxidative phosphorylation within minutes [30] but it has been suggested that these very rapid effects are in fact mediated by 3,3⬘-diiodothyronine (T2) [31]. In vitro, T3 also rapidly stimulates the mitochondrial adenine nuclear translocase [32] and a 28-kDa truncated isoform of TR␣ (p28) co-localises with the adenine nucleotide translocase and the mitochondrial uncoupling proteins on the inner mitochondrial membrane, therefore possibly mediating a rapid thermogenic response to T3 [22, 33]. Thus, both T3 and T2 facilitate very rapid mitochondrial responses. Thyroid hormone has been shown to directly increase transcription of genes coded either by the nucleus or the mitochondrial genome (mitochondrial transcription factor A [34], ATPase subunit six [35], NADH dehydrogenase subunit three [36] and subunits of cytocrome-c-oxidase [37]). It has recently been shown that T3 stimulates the tub gene, which when mutated in tubby mice causes obesity and insulin resistance [38]. Hypothyroidism in rats has been shown to be associated with altered tub mRNA and protein in discrete brain areas and T3/T4 treatment restored normal tub expression.

Polycystic Ovarian Syndrome

PCOS is a very common endocrine problem occurring in up to 10% of premenopausal women [39]. This is by far the most complex endocrine disorder associated with obesity. The diagnosis of PCOS is made by excluding non-classical adrenal hyperplasia, androgen-secreting tumours, Cushing’s syndrome and hyperprolactinaemia. PCOS is viewed as a heterogeneous disorder of multifactorial aetiology. In 1990, the National Institutes of Health criteria for PCOS were published and agreed the following diagnostic features of the syndrome which should be present: hyperandrogenism and ovarian dysfunction, while the presence of PCO morphology was not required [40]. In contrast, 13 years later at the 2003 Rotterdam consensus it was agreed that PCOS should be considered a syndrome of ovarian dysfunction, with features of hyperandrogenism and PCO morphology [41]. Taking the heterogeneity of the syndrome into consideration, none of the criteria was considered absolutely required for the diagnosis. The new criteria truly reflect the meaning of syndrome. It broadens rather than replaces the previous National Institutes of Health criteria for PCOS diagnosis. Under the new criteria, the

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prevalence of PCOS among the general female population could well rise. According to the Rotterdam consensus criteria, additional PCOS phenotypes would include PCO and hyperandrogenism in women with normal menstrual cycles and especially women presenting with PCO and anovulation without androgen excess. The most prominent feature of PCOS is the history of ovulatory dysfunction: amenorrhoea or oligomenorrhoea of pubertal onset. The weight gain in later life may also lead to acquired insulin resistance and clinical picture of PCOS: 50% of women with PCOS are obese [42] or even higher (between 38 to 88%) as shown by others [43, 44]. Increased body weight has been one of the features for the diagnosis of PCOS in the classical description by Stein and Leventhal. The adiposity appears to play a crucial role in maintaining and presumably precipitating PCOS. Obesity per se probably also contributes to features of hyperandrogenism even in women with normal ovaries [45]. The support for a pivotal role of obesity in PCOS comes from often dramatic improvement in menstrual regularity in response to weight reduction in women with PCOS [46]. There is no doubt, therefore, that adiposity plays a crucial role in the development and maintenance of PCOS and strongly influences the severity of both its clinical and endocrine features in many women with the condition. However, the cause of obesity in PCOS is not fully clarified. Evidence from family-based and association studies suggest that PCOS has a significant genetic basis, although the genes predisposing to PCOS have yet to be clearly defined. Likely candidate genes for PCOS include those involved in the regulation of ovarian steroidogenesis but also those genes that influence BMI and adiposity. A likely explanation for the mechanisms underlying the development of obesity in women with PCOS is the combined effect of a genetic predisposition to obesity in the context of a Western environment (high caloric diet and reduced exercise). The development of obesity in women with PCOS in turn amplifies and may even unmask the biochemical and clinical abnormalities characteristic of this condition. However, most women with PCOS have insulin resistance to a significantly greater extent than in age- and BMI-matched control women, this disparity being more marked for higher BMIs [47].

Body Fat Distribution in PCOS The majority of currently presenting cases of PCOS are characterised by obesity (BMI ⬎27) with a central fat distribution. As this type of overweight is particularly associated with increased risk of cardiovascular disease and type 2 diabetes, obesity is often defined as a waist circumference of more than 80 cm for women in classifications that serve the diagnosis of the metabolic syndrome. In a large case series studies from Pittsburg, the waist/hip ratio was associated with PCOS independently of BMI [48]. The insulin resistance seen in PCOS is partly determined by the presence of central obesity, as when PCOS women are compared with control women matched for abdominal adiposity, the difference in insulin resistance between the two groups is much less marked than if the two groups are matched for BMI [49].

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The possible causal relation for central fat deposition may exist between excessive testosterone concentrations in early development. This relation exists in women during adulthood as seen in non-obese female to male transsexuals [50] and obese postmenopausal women [51]. In addition, hyperinsulinaemia, through its direct effect on the adipocyte, has also been suggested to be a possible determinant of android fat deposition in women with PCOS [52]. In women with PCOS, it is possible that android fat deposition per se contributes to hyperandrogenaemia through its adverse effects on insulin sensitivity and consequent gonadotrophic effects of hyperinsulinaemia on the ovaries. Thus, in women with PCOS, android fat may be both a cause and an effect of hyperandrogenaemia [53]. The vicious circle in which android fat begets android fat, and further exacerbates the predisposition towards weight gain in women with PCOS has been suggested. The cycle can be interrupted by dietary intervention and/or use of insulin-sensitising drugs. Simple obesity and obesity associated with PCOS is associated with increasing androgenicity due to generation of: (1) testosterone from androstendione mediated by 17␤-hydroxysteroid dehydrogenase [54]; (2) dihydrotestosterone from testosterone mediated by 5␣ reductase [55]; (3) reduced generation of oestradiol from testosterone, oestrone from androstenedione and oestriol from 16␣-hydroxylated dehydroepiandrosterone due to reduced aromatase activity [56].

Role of Glucocorticoid Steroids in PCOS Both simple obesity and PCOS-associated obesity are characterised by the mechanisms of limiting exposure to cortisol by reducing cortisol generation from cortisone. Two key enzymes involved in the metabolism of cortisol include 11␤-hydroxysteroid dehydrogenase type 1 (11␤-HSD1), involved in the conversion of cortisone to cortisol, and 5␣-reductase, involved in the catabolism of cortisol in addition to the conversion of testosterone to dihydrotestosterone. It has been demonstrated that in normal men and women or hypopituitary patients on fixed hydrocortisone replacement therapy, obesity is associated with impairment of 11␤-HSD1 reductase and enhanced 5␣-reductase activities [57, 58]. It has also been shown that 11␤-HSD1 activity is strongly related to fat distribution [57, 58]. The changes in 11␤-HSD and 5␣-reductase activity are even more accentuated when studied in PCOS as compared to BMI-matched controls [59].

Hormones Involved in Appetite Regulation in PCOS Leptin plays an important role in the regulation of appetite, body weight and metabolism and reproductive capacity in women, buts its role in PCOS is controversial. It has been proposed that abnormally high serum concentrations of serum leptin may

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provide a link between PCOS and ovarian dysfunction (leading to anovulation and infertility) in some women with this condition [60]. Hyperleptinaemia has been shown to contribute towards the lowered sensitivity of dominant ovarian follicles to IGF-1, resulting in anovulation and impaired regulation of human ovarian follicle development [61]. Serum leptin concentrations correlated with percentage body fat in both normal controls and PCOS [62]. Serum leptin concentrations do not correlate with PCOS but rather with adiposity [63]. However, there are no significant differences in serum leptin concentrations between ovulatory and anovulatory women with PCOS, suggesting that serum leptin probably does not play an important role in regulating ovulation in these women [63]. Ghrelin is known to enhance appetite, reduce fat utilisation and cause adiposity following central or peripheral administration to both rodents and humans [64]. Circulating ghrelin typically increases on fasting and decreases following food intake. In PCOS, ghrelin is negatively correlated to insulin resistance [65]. The concentrations of ghrelin are particularly low in insulin-resistant PCOS similar to gastrectomised subjects [66], unlike women with PCOS and normal insulin sensitivity in whom levels are similar to controls [65, 66]. A further study suggests that the primary association is primarily between ghrelin and androgens rather than ghrelin and insulin [67]. More importantly, dysregulation of hunger, food consumption and subsequent weight gain in PCOS may be related to lack of reduction of ghrelin postprandially [68]. Achievement of weight loss and its maintenance in PCOS proves to be continuing clinical challenge for both patients and their clinicians.

Cushing’s Syndrome

Obese patients may present an opportunity for an early diagnosis of Cushing’s syndrome. This requires a high level of clinical suspicion as patient may not display all the typical symptoms or signs. Harvey Cushing first described in 1932 a constellation of symptoms of obesity, hirsutism and amenorrhoea attributed to primary pituitary abnormality associated with adrenal hyperplasia. Adrenal tumours causing the same syndrome were later described, but Cushing’s syndrome due to ectopic adrenocorticotrophin hormone (ACTH) was only recognised in 1962 [69]. The term Cushing’s syndrome describes all causes of cortisol excess, whereas Cushing’s disease one type due to pituitary-dependent disease. In Cushing’s syndrome, patients usually present with insidious onset of weight gain with central fat deposition. In addition, patients develop a ‘buffalo hump’ due to fat accumulation over the thoracocervical spine. Typical ‘moon shape facies’ are produced by fat depots over cheeks and temporal regions. The most discriminating symptoms and signs of Cushing’s syndrome are bruising, muscle weakness, skin thinning, plethora and truncal obesity. The associated conditions include diabetes mellitus, hypertension, hirsutism, osteoporosis and impaired glucose tolerance test [70].

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Table 2. Causes of Cushing’s syndrome ACTH dependent Cushing’s disease (pituitary dependent) Ectopic ACTH syndrome Ectopic CRH syndrome Macronodular adrenal hyperplasia Iatrogenic treatment with ACTH 1-24 ACTH independent Adrenal adenoma and carcinoma Primary pigmented adrenal nodular hyperplasia and Carney’s syndrome McCune-Albright syndrome Aberrant receptor expression (gastric inhibitory polypeptide, interleukin-1␤) Iatrogenic Cushing (pharmacological doses of steroids) Pseudo-Cushing’s syndrome Alcoholism Depression

Symptoms and signs of Cushing’s syndrome arise as a result of prolonged exposure to excessive free cortisol levels, either endogenous cortisol or exogenous (iatrogenic Cushing’s syndrome) due to prednisolone, dexamethasone or various forms of topical and inhaled steroids. The causes of Cushing’s syndrome are listed in table 2. Cushing’s disease is quite rare; its incidence is 5–10 cases per million population per year, whereas iatrogenic Cushing’s is very common due to the widespread use of exogenous steroids. Ectopic ACTH secretion is associated with 0.5% of bronchogenic carcinomas being the most common cause of ectopic Cushing’s followed by carcinoid tumours (pancreas, lung) and other carcinomas. Patients with iatrogenic Cushing’s syndrome develop varying symptoms and signs depending on the individual sensitivity, dose and length of exposure to glucocorticoids. They are less likely to have symptoms or signs associated with androgen excess such as oligomenorrhoea, amenorrhoea or hirsutism.

Pseudo-Cushing’s Syndrome This is a condition characterised by some if not all clinical features of Cushing’s syndrome. There may be evidence of an intermittent rise of cortisol. Chronic alcoholism is the commonest cause of pseudo-Cushing’s syndrome; however, the aetiology is not very clear. Studies showed that in chronic liver disease there is an impairment of cortisol metabolism. Furthermore, alcohol and/or vasopressin raised in patients with alcohol excess were found to be a stimulant for cortisol secretion [71]. Many patients with Cushing’s syndrome are depressed [72]. On the other hand, depression may display some of the features of Cushing’s syndrome which resolve on

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the treatment of depression. How do we then differentiate those two conditions? Patients with depression have greater suppressibility after dexamethasone and a lesser ACTH response to the corticotrophin-releasing hormone (CRH) test. In order to distinguish true from pseudo-Cushing’s it has been recommended to perform a CRH test after low-dose dexamethasone suppression test or use loperamide which lowers cortisol in pseudo-Cushing’s [73, 74]. Simple obesity is occasionally listed as pseudo-Cushing’s syndrome. But simple obesity is not associated with cortisol excess, and morning cortisol values can actually be lower than in normal-weight controls. If urinary free cortisol is raised in exceptional circumstances, this reflects the reduced specificity of this investigation. Obesity is characterised by reduced cortisone to cortisol conversion due to suppressed 11␤HSD1 activity by increasing fatness and insulin resistance. It appears that increasing energy intake may downregulate cortisol production in vivo [58]. Another form of the syndrome difficult to diagnose is cyclical Cushing’s syndrome. It is characterised by intermittent periods of normal cortisol activity. It is relatively rare but can cause serious problems during diagnosis. Sometimes it occurs in patients previously operated on for Cushing’s disease [75], but it can occur with all aetiologies.

The Diagnosis of Cushing’s Syndrome The diagnosis of glucocorticoid excess and differential diagnosis is complex and requires a number of investigations, as listed in table 3. As very few investigations of Cushing’s syndrome have 100% sensitivity and specificity, endocrine expertise combined with clinical experience is essential in the diagnosis of Cushing’s syndrome. The specificity of the investigations will increase with adjustment of the threshold levels of measured hormones. The ambulatory investigation may need to be repeated as in-patient if there is uncertainty about the investigations.

The Mechanism of Obesity in Cushing’s Syndrome Cortisol acts via intracellular receptor glucocorticoid and mineralocorticoid receptors, members of thyroid/steroid hormone receptor superfamily of transcription factors. Exposure to glucocorticoids stimulates adipocyte differentiation and adipogenesis by transcriptional activation of key differentiation genes including lipoprotein lipase, glycerol-3-phosphate dehydrogenase and leptin [76]. The chronic effect of glucocorticoids on adipose tissue leads to the characteristic increased deposition of central fat ‘lemon-on-stick’ appearance. This may be related to greater expression of 11␤-HSD1 in visceral as opposed to subcutaneous tissues, responsible for the conversion of cortisone to cortisol [77].

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Table 3. Diagnostic tests used in investigations of Cushing’s syndrome Application

Sensitivity/specificity %

Screening for glucocorticoid excess Spot urine cortisol/creatinine ratio 24-hours urinary free cortisol (2–3⫻) Overnight 1 mg dex suppression test (F ⬍ 50 nM) Low-dose dex suppression test Midnight cortisol ⬎200 nM/l

ambulatory ambulatory ambulatory ambulatory in-patient

85–92/95 85–92/95 100/82 95/99 90/100

Differential diagnosis 9 am plasma ACTH (Cushing’s disease) ACTH ⬎5 PM at midnight (ACTH dependent) Plasma potassium, bicarbonate (ectopic ACTH) High-dose dex suppression test (Cushing’s disease) High-dose dex suppression test (ectopic ACTH) CRH test (Cushing’s disease) CRH test (exclude ectopic ACTH) Imaging pituitary and adrenal (MRI) Imaging pituitary and adrenal (CT) Inferior petrosal sinus sampling

ambulatory in-patient ambulatory ambulatory ambulatory ambulatory ambulatory ambulatory ambulatory in-patient

50/70 100/99 95/90 90/1001 50/95 90/90 100/1002 80/78 20–60/78 97/100

Sensitivity and specificity values calculated from data provided in Stewart [87]. F ⫽ Cortisol; CT ⫽ computed tomography; MRI ⫽ magnetic resonance imagining; dex ⫽ dexamethasone. 1 Ninety percent suppression of urinary basal cortisol. 2 Hundred percent rise in ACTH and 50% rise in cortisol excludes ectopic ACTH.

Indirect Mechanisms Effect on Thyroid Status. Glucocorticoids suppress thyroid axis through suppression of TSH secretion and additionally inhibiting 5⬘deiodination mediating the conversion of thyroxine to triiodothyronine. Effect on Growth Hormone Status. Chronic hypercortisolism leads to a blunting response of GH secretion to all stimuli [78]. Successful treatment of Cushing’s syndrome and a reduction of glucocorticoid levels lead to remarkable weight loss, although often not back to the pre-morbid state.

Hypothalamic Obesity

Acquired obesity not present from infancy, coupled with headache, a growth disorder and other growth dysfunction, requires investigations for hypothalamic/pituitary disease. Selective pituitary failure can arise from the deficiency of one or more hypothalamic hormones, including TRH and growth hormone-releasing hormone. The reason for obesity associated with TRH deficiency has been discussed above under the section ‘Hypothyroidism’.

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One of the most severe types of endocrine obesity results from hypothalamic damage. Commonly, this is due to craniopharyngiomas and/or their surgical solutions damaging the hypothalamus. Craniopharyngiomas are benign suprasellar tumours that arise from epithelial remnants of the Rathke’s pouch. Morbid obesity develops in 52% of patients [79]. Historically, the mechanism had been thought to be largely due to hypothalamic dysregulation of feeding, leading to hyperphagia, obesity and insulin resistance [80]. The medial hypothalamus (‘satiety centre’) synthesises anorectic hormones such POMC and CART in the arcuate nucleus and CRH and TRH in the paraventricular nucleus and damage to these neurons in animal models leads to obesity. Harz et al. [81] suggested that craniopharyngioma patients develop hypothalamic obesity because their hypothalamic structures are insensitive to endogenous leptin. The elevated serum leptin concentrations found only in patients with a suprasellar tumour may be explained by a disturbed feedback mechanism from the hypothalamic leptin receptors to the adipose tissue and reduced physical activity, and abnormal thermoregulation in addition to the effect of reduced pituitary hormones is a combination which presents a powerful drive towards obesity. Patients with growth hormone deficiency have increased fat mass and reduced fatfree mass compared to BMI-matched healthy subjects of around 6–8 kg [82]. The fat mass in males is related to GH deficiency as judged from the insulin-like growth factor-1 levels [82]. The increased adiposity in growth hormone deficiency is centrally distributed [83] and is reversed by GH replacement therapy [84]. As the treatment of childhood brain tumours has improved, long-term survival has become more common. Cognitive, physical and psychological complications of the tumour and its treatment have been recognised more frequently in long-term survivors. Endocrine complications are uncommon when the tumour has been treated with surgery alone. The risk of developing endocrine dysfunction is increased by radiotherapy, and some studies suggest that chemotherapy has an additional deleterious effect. Primary hypothyroidism may be caused by scattered irradiation from spinal and cranial radiotherapy. Direct involvement of the hypothalamus by the tumour, and hypothalamic damage secondary to surgery or radiotherapy, may cause obesity [85]. An attempt to understand the factors that contribute to the long-term morbidity of childhood brain tumours can lead to changes in treatment that improve the quality of life in survivors. Prevention, early recognition and treatment of these complications are attainable goals. Central hypothyroidism is a rare cause of hypothyroidism, generally due to either pituitary or hypothalamic defects. On the basis of its aetiology, it is possible to distinguish acquired and hereditary forms. Hereditary central hypothyroidism can be isolated or associated with combined pituitary hormone deficiency. In the former case, alterations of only two genes, TSH-␤ and the TRH receptor, have so far been described as responsible for the disorder. In hereditary central hypothyroidism associated with combined pituitary hormone deficiency, inactivating mutations of different pituitary transcription factors (HESX1, PROP-1, PIT1) have been found to be

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involved in the pathogenesis of the disease. Finally, an association between central hypothyroidism and severe obesity has been described in patients with leptin receptor mutations. The clinical consequences of central hypothyroidism in adult life vary greatly depending on the aetiology, the severity of the thyroid impairment, the extent of the associated hormone deficiencies, and the age of the patient at the time of the onset of the disease. In general, acquired central hypothyroidism is less severe than the congenital form because of the constitutive activity of the wild-type TSH receptor. Symptoms and signs of thyroid insufficiency are usually milder than those of primary hypothyroidism, and goitre is always absent [86]. One can conclude that obesity due to endocrine causes is not as common as perceived by patients and some physicians. However, since the effective treatment of less common underlying endocrine causes is available, the clinical management of patients with obesity should include appropriate screening for endocrine conditions which may be amenable to treatment.

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Dr. Jolanta U. Weaver, PhD, FRCP Department of Diabetes, School of Clinical Medical Sciences University of Newcastle, Framlington Place Newcastle upon Tyne NE2 4HH (UK) Tel. ⫹44 191 445 2181, Fax ⫹44 191 445 6186, E-Mail [email protected]

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Emerging Concepts in the Medical and Surgical Treatment of Obesity Simon Aylwin ⭈ Yayha Al-Zaman Department of Endocrinology, King’s College Hospital NHS Foundation Trust, London, UK

Abstract The relentless rise in the prevalence of obesity predicts an exponential increase in the incidence of obesity-related complications. Medical and surgical treatments are necessary to prevent and treat obese co-morbidities, thereby avoiding disability and premature death. Interventions for obesity should be evaluated not by weight loss alone but against the new incidence in obesity-related co-morbidities, their remission or improvement. In combination with lifestyle measures, currently available pharmacological therapies – rimonabant, orlistat and sibutramine – achieve 5–10% weight loss, although a return to baseline is the norm after cessation of medication. All these agents demonstrate approximately 0.5% reduction in HbA1c in diabetic subjects; orlistat also reduces the new incidence of type 2 diabetes. Modest improvement in lipid profiles and reduced calculated cardiovascular risk is observed, but data on improvement of other co-morbidities are sparse. In contrast, surgical procedures that restrict food ingestion and/or curtail the absorptive surface area of the gut consistently achieve substantial weight loss, typically 20–35%, effect resolution of co-morbid conditions and improve quality of life. Although mortality is low, complications and hospitalisation are not uncommon after bariatric surgery. Intriguingly, surgical patients experience a reduction in appetite and report changes in food preference. Accentuation of the normal gastrointestinal hormonal response to food intake and possible changes in vagal afferent signalling are proposed to induce satiety. Increased understanding of body weight homeostasis and appetite regulation has provided an impressive list of potential targets for drug development, with the promise that single or combination therapy may ultimately challenge the supremacy of bariatric surgery. Copyright © 2008 S. Karger AG, Basel

The rising prevalence of obesity has attracted the attention of the medical and scientific communities, editors of highbrow and popular lay press, broadcasters, intergovernmental agencies and political bodies. Whilst headlines, opinions and initiatives abound, solutions at a population level remain elusive. Far from reversing the trend, the number of WHO-defined obese subjects with a body mass index (BMI) ⬎ 30 grows at an everincreasing rate. In 2001 20.9% of the US population were obese (BMI ⬎ 30) and 2.3% had a BMI ⬎ 40. Concerning as these statistics may be, the enormity of the problem

may be underestimated since the most pronounced trends in body weight are seen in children, adolescents and young adults [1]. Although the degree to which prepubertal obesity tracks to adult disease remains controversial, it is likely that by the age of 11 obesity is established [1]. Moreover, recent data from a large prospective study demonstrate that weight gain during early adulthood confers a particularly high risk of developing type 2 diabetes (T2D) [2]. The more costly obesity-related complications evolve slowly, becoming more evident and demanding more resource with increasing age and therefore the health burden is set to rise even if the prevalence of obesity were to stabilise [3].

Management of Obesity

Management of obesity in its broadest sense can be summarised in four steps: Step 1: Prevention; Step 2: Weight management; Step 3: Prevention of complications; Step 4: Management of complications.

Step 1: Prevention Prevention of obesity is the public health holy grail. It is self-evident that energy and resource need to be dedicated to what is rapidly becoming the most pressing public health issue worldwide. In a BMJ review, Anjali Jain articulated the problem facing public health policy makers: ‘The high rates of obesity do not allow us to wait for treatments to be proved effective by the standards of evidence-based medicine. In other words, something must be done soon, but we don’t know what.’ [4]. Public health measures are both aimed to reduce the new incidence of obesity, and to limit the pace of weight gain amongst overweight and obese individuals. Strictly speaking, there are therefore both preventative and weight management (step 2) aspects to a program devised for the general population. Furthermore, the health benefits of both weight loss through lifestyle intervention, and altered diet without weight loss are disproportionately beneficial, and therefore public health measures may also be considered as a component of step 3 – limiting the development of complications [5]. Success might be defined as by achieving a fitter, if not thinner population, and it remains to be seen whether the governmental rhetoric will be followed by a proliferation in public facilities for exercise, cycle lanes and so forth. However, the evidence base for effective large-scale interventions is limited [6, 7], and this area is largely outside the scope of this review.

Step 2: Weight Management Behind the statistics, however, a growing number of individuals are experiencing the medical complications, the reduced function and quality of life, and also the social and

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workplace disadvantage, all of which conspire to magnify the burden of morbid obesity [8]. The next approach is therefore to focus on the individual, recognise either the presence or risk of related complications and intervene by aiming to reduce weight with a form of weight management. The best combination lifestyle interventions in a primary care setting only achieve a weight loss of up to 5% in fewer than half of individuals (43% of study-compliant patients in the Counterweight program) [9]. It is here that the overwhelming effort of the pharmaceutical industry is primarily targeted, attempting to develop new agents that will effectively reduce individual weight. Presently, surgical strategies offer the best chances of clinically relevant weight loss, but the capacity, let alone desirability, of treating large numbers of young individuals with surgical procedures is doubtful, except for the minority with severe obesity.

Step 3: Prevention of Complications Recognizing the limitations of existing non-surgical treatment, we might aim for damage limitation, and since the principal cause of mortality, particularly in visceral obesity, is ischaemic heart disease, focus on the management of other cardiovascular risk factors. In addition, an emerging paradigm is to limit the natural history of the condition, aiming to reduce the risk of complications notably T2D. At present, there are no large-scale studies that have demonstrated that a pharmaceutical strategy has any effect on mortality, although the SCOUT study using sibutramine is designed to address this issue. In contrast to the management of hypertension, diabetes and hyperlipidaemia, we have few data that address endpoints of obesity other than the surrogate endpoint of weight, and these are long overdue.

Step 4: Treatment of Complications Most of the medical cost of obesity is attributable to the management of the complications, and in this aspect the evidence base for specific complications is broad. It should be emphasised that the ‘complications’ of obesity include the loss of individual income, increase in social security benefits and also the secondary consequences if a severely obese individual ceases to provide a carer role through their own disability. Paradoxically, the management of the complications of obesity – particularly in relation to prevention of the complications of T2D – may in fact involve sacrificing the primary goal of weight loss to address the greater evil of hyperglycaemia. Analysis of preventative measures against obesity, behavioural and dietary interventions and the management of established complications are outside the scope of this chapter [for review, see 10]. We will focus on the treatment of the obese adult individual. A central theme will also be to evaluate the effectiveness of interventions not merely in terms of weight but with equal emphasis on the improvement and

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remission of obesity-related co-morbidities. This article will examine existing therapies, their efficacy in terms of weight and weight-related complications; it will address the recent research in both surgical and medical approaches, and will briefly provide an obesity assessment tool for clinical practice (table 1).

The Role of Medical and Surgical Intervention in the Management of Obese States

Obesity matters because it represents a predisposition to the development of obesityrelated medical and psychosocial complications. Broadly speaking, the complications of obesity can be divided into three overlapping categories: Category A, related to total fat mass: conditions arising as a consequence of total fat mass, including psychosocial and functional disturbance. Category B, related to visceral adiposity: conditions associated with visceral adiposity, largely the constellation of cardiovascular risk factors that make up the metabolic syndrome (MetS). Category C, complicating unrelated chronic diseases: where obesity is a significant exacerbating factor for morbidity and disability. These conditions are graphically illustrated in figure 1.

Complications Related to Total Fat Mass The medical complications attributable to total fat mass (category A) are principally those that place medical strain on the musculoskeletal system and cause morbidity by pain or disability. In addition, the psychosocial factors including body image dysphoria, relationship difficulties, poor workplace achievement and economic handicap through the requirement for assistance in activities of daily living appear to be related to the degree of total obesity, although in one study, abdominal circumference also contributed to disability [11]. Certain other conditions occur too infrequently (e.g. idiopathic intracranial hypertension, IIH, obesity-related cardiomyopathy, glomerulopathy) to clearly establish their aetiology in relation to total or visceral fat. The procoagulant tendency that remains a risk factor for venous thromboembolism and arterial occlusion may be related both to visceral and total adiposity. In general, the use of BMI and WHO definitions stratifies the risk of these conditions effectively and they tend to occur at higher degrees of obesity. BMI is therefore a reasonable surrogate marker of risk.

Complications Relating to Visceral Fat Mass BMI in isolation is less convincing as the best marker for risks associated with visceral obesity, and other co-morbidities in category B. Although the gold standard for the

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measurement of visceral adiposity requires axial CT or MRI scan, a useful approximation can be obtained by the simple measurement of waist circumference obtained at the level of the iliac crest [12]. Although BMI remains tightly related to T2D in a population basis, at lower levels of obesity, consideration of anthropomorphic indices relating to fat mass distribution becomes relevant [13]. Insulin resistance and the attendant risks of T2D are more closely related to visceral adiposity than overall obesity, and increasingly evidence indicates that subcutaneous fat is metabolically neutral. Indeed, in the rare syndromes of partial lipodystrophy with absent subcutaneous fat caused by monogenic defects in the lamin A/C and PPAR␥ genes, extreme MetS derangements occur despite, or actually because of a lack of subcutaneous fat. The modest metabolic disadvantage of subcutaneous fat is further emphasised by the lack of effect of large-volume abdominal liposuction, which did not improve metabolic parameters of insulin resistance, levels of inflammatory mediators or other risk factors for coronary artery disease [14]. Decreasing subcutaneous adipose tissue alone does not achieve the metabolic benefits of visceral weight loss. Although in severe obesity (BMI ⬎ 40), visceral fat is normally present in excess, measurement of BMI alone risks misattributing individuals at both high risk and low risk of T2D and MetS in lower categories (BMI 30–40), and in these circumstances, assessment of visceral obesity is appropriate [12]. The association of visceral adiposity with features of MetS is now formalised in both the International Diabetes Federation and National Cholesterol Education Program definitions [15]. The global epidemiological Interheart study has also highlighted that worldwide, visceral adiposity measured by waist-hip circumference, is a powerful predictor of cardiovascular disease and probably better than BMI [16]. Individuals with visceral fat have a greater risk of commonly measured cardiovascular risk factors, and waist circumference represents an additional independent risk factor. Similarly, MetS has a high prevalence amongst patients attending cardiac rehabilitation [17]. In older individuals (over 65 years) in the prospective Cardiovascular Health Study, increasing BMI in fact had a significant protective effect, whereas visceral adiposity was associated with increasing mortality [18]. A recent systematic review failed to demonstrate an increased risk for coronary heart disease progression in relation to BMI, indeed, reported improved outcome in patients in the overweight and mildly obese groups [19].

Conditions Exacerbated by Obesity Finally, in category C there are a number of conditions where obesity compounds the morbidity of medical diseases, notably reducing the functional capacity of patients with angina, congestive cardiac failure and asthma where modest weight loss may be beneficial [20]. Intriguingly, there are circumstances where obesity confers a statistical advantage: obese patients on dialysis programmes appear to have reduced mortality.

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Table 1. King’s obesity staging criteria

Criterion

Stage 0

A

Airway /apnoea

Normal, and Epworth score ⬍10

B

BMI and w.c

⬍35 (WHO I) and w.c. ⬍102 cm (M) ⬍88 cm (F)

C

CVD risk

⬍10% over 10 years

D

Diabetes

Normal

E

Economic complications

No effects

F

Functional status and musculoskeleal

No limitation

G

Gonadal and reproductive axis

Normal sexual and reproductive function

H

Health status (perceived) and stages of change

Follows behavioural recommendations ‘Maintenance phase’

I

Body image/eating behaviour

No concern Normal eating pattern Good quality diet

O

Other medical complications

No other complication

© 2007 King’s College Hospital NHS Foundation Trust, London SE5 9RS, UK. All rights reserved; permission CCF ⫽ Congestive cardiac failure; IFG ⫽ impaired fasting glucose; requests should be addressed to CVD ⫽ cardiovascular disease. Dr. Simon Aylwin.

Following cardiac artery bypass grafting, but not percutaneous revascularisation, obese subjects also have an improved prognosis [21]. Defining obesity in terms of BMI provides a common terminology that enables demographic comparison between populations and over time, and is a reasonable means of describing the participants in a clinical trial. However, defining overweight

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Stage 1

Stage 2

Stage 3

Epworth score ⱖ10 Apnoea-hypopnoea index 5–20

Uses CPAP Apnoea-hypopnoea index ⬎20

Cor pulmonale Obesity hypoventilation syndrome

35–40 (WHO II) or w.c. ⱖ102 cm (M) ⱖ88 cm (F)

40–50 (WHO III)

⬎50 (superobese)

ⱖ10% over 10 years

Stable established ischaemic heart disease, or CCF NYHA I–II

Severe angina, or CCF NYHA III–IV

IFG/IGT, or previous GDM, or HOMA-IR ⬎3.0

T2DM

Uncontrolled T2DM, or insulin-requiring

Financial impact of obesity (travel, clothes)

Workplace disadvantage

Unemployed, or financial effect on 3rd party, or receiving benefits

Limitation on work or recreation

3rd party assistance for ADL or for dependents

Housebound or wheel chair dependent

PCOS Low testosterone (men)

Infertility Impaired sexual function

Marital/relationship breakdown due to obesity

Just started or preparing for lifestyle change ‘Preparation/action’

Adequately informed and concerned about future ‘Contemplative’

Indifferent No willingness for change ‘Precontemplative’

Comfort eating Inappropriate eating cues Mild body image dysphoria

Severe body image dysphoria Night eating disorder Mild-moderate depression

Binge eating disorder Bulimia

Complications with low degree of morbidity

Significant morbidity from additional complications

Life or independence threatened (e.g. IIH)

Severe depression

IGT ⫽ impaired glucose tolerance; GDM ⫽ gestational diabetes mellitus; w.c. ⫽ waist circumference;

and obesity merely in terms of BMI, and evaluating intervention on the basis of change in weight alone is inadequate in categorizing individual risk and treatment response. In brief, although we may have an increased repertoire of agents to deploy, it will become ever more important to define those where greatest impact can be achieved.

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Complications of obesity A Related to total fat

Osteoarthritis Disability Poor economic status Body image dysphoria Obstetric complications

B Related to visceral fat

OSA thrombosis

T2D Dyslipidaemia Hypertension PCOS NASH

C Obesity exacerbating unrelated disease Congestive cardiac failure Asthma Fig. 1. Complications of obesity. Complications of obesity can be segregated into those that relate to total fat mass (A), visceral fat (B), and those that represent an exacerbating effect of obesity on other co-existing medical conditions (C).

Pharmacological Therapy for Obesity

Is There a Magic Bullet? Proof beyond doubt that severe obesity can arise from organic disease is evident in those with structural hypothalamic disease, in whom hyperphagia and severe weight gain can occur. The recognition of a series of monogenic disorders involving appetite regulatory pathways serves to further emphasise that excess body weight is not merely founded in a lack of conscious restraint [22]. The astonishing reduction in obesity demonstrated with leptin treatment for the handful of individuals with monogenic leptin deficiency provides powerful proof of principle that biological processes underpinning obesity can be manipulated [23]. For the vast majority of obese individuals, however, the pathogenesis of obesity reflects a multi-factorial individual predisposition coming into collision with an increasingly unsuitable environment, where genetic and epigenetic factors, intrauterine adaptation, early life feeding, learned attitudes to eating and social customs define the at-risk individual [8]. Unlike the correction of congenital leptin deficiency, and like a number of other disease states, monotherapy is insufficient in a polygenic disorder, and the nihilism that surrounds the medical management of obesity is in part due to the fact that with only a few agents, effective combination therapy has not yet been achieved. The first prerequisite for any drug is that it should not cause harm, the second is that it should have some effect, and the third that it should confer some meaningful

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benefit on the patients that take it. In the ‘hall of fame’ of therapies for obesity there are many good ideas that fell foul of the first proposition. Thus the use of thyroid hormone, amphetamine derivatives, the centrally acting appetite suppressants dexphenfluramine, and the combination of fenfluramine with phentermine (fen-phen) achieved a degree of weight loss but with unacceptable side effects and were never demonstrated to impact usefully on hard endpoints. Phentermine can still be prescribed in the USA but its potency is limited as a single agent and can only prescribed for short-term use. Examination of extinct pharmacological approaches is not within the scope of this article, and the reader is referred to the excellent review by Weigle [24]. We will concentrate in this section on the currently available treatments for mediumto long-term treatment and their impact on weight.

Orlistat Arguably the only widely used obesity therapy to have been specifically designed for its purpose, orlistat is an intestinal lipase inhibitor that limits the amount of dietary fat that can be converted into an absorbable form as a mono- or di-glyceride. Orlistat has been shown to have a significant benefit over placebo and after 12 months, patients treated with orlistat in addition to lifestyle intervention achieved a weight reduction of 8.4% compared to 2.6% with placebo [25]. Later studies confirmed an effect of orlistat superior to placebo, but only a third of patients achieved a 5% weight loss at 12 months [26]. Once the benefits of intensive dietary support are removed in a primary care setting, the use of orlistat with brief counselling effected a 4.8-kg weight loss at 6 months, compared to 3.8 kg with drug alone and 1.7 kg with brief counselling alone; although no significant differences were present at 12 months [27]. However, if we can ignore the statistical violation of continuation on the basis of early response, results are more interesting, and patients randomised to either 500- or 1,000-kcal deficit diets, after initial success of 5% weight loss at 3 and 6 months, achieved weight loss of over 10 kg at 1 year [28]. In a similar study, 3-month weight loss of ⬎5% also predicted 11.9% loss at 2 years [29]. In the absence of any a priori determinant of response, weight loss at 3 months appears to be of benefit in identifying those patients who might achieve meaningful change in the longer term.

Sibutramine Sibutramine was developed as a potential anti-depressant and combines serotonin and noradrenaline re-uptake inhibition, but was recognised as having a weight loss side effect. The STORM study subsequently demonstrated an effective role for sibutramine in an experimental paradigm where patients were administered active compound for 6 months in conjunction with intensive dietary intervention and then

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Body weight (kg)

104 102 100 98 96 94 92 90 88

Placebo

Sibutramine 0

a

2

4

6

8

Weight loss

10

12 14 Month

16

18

20

22

24

Weight maintenance Year 2

Year 1

Placebo/placebo 20 mg of rimonabant/placebo 20 mg of rimonabant/20 mg of rimonabant Change from baseline (kg)

Change from baseline (kg)

Placebo 5 mg of rimonabant 20 mg of rimonabant 0 ⫺2 ⫺4 ⫺6 ⫺8 ⫺10 0

b

12

24

36

413 833 863

347 711 750

⫺4 ⫺6 ⫺8 ⫺10

52

52

309 619 672

Patients Placebo/placebo 292 284 254 247 233 20 mg of rimonabant/ 323 315 301 277 261 placebo 20 mg of rimonabant/ 328 319 297 286 272 20 mg of rimonabant

Weeks

Patients Placebo 590 496 5 mg of rimonabant 1191 1004 20 mg of rimonabant 1189 1017

0 ⫺2

60

68

76 84 Weeks

92

104

222 240

216 225

263

256

Fig. 2. Recidivism after pharmacological weight loss. Following weight loss achieved through pharmacological intervention, cessation of therapy is followed by a return to baseline weight. a Sibutramine: following induction of weight loss with hypocaloric diet and sibutramine, subjects were randomised to sibutramine or placebo [30]. b Rimonabant: weight loss induced by rimonabant (left panel) is superior to placebo. After 12 months, re-randomisation to active compound or placebo (right panel) demonstrates the effect of rimonabant on weight loss maintenance [99].

randomised to sibutramine or placebo. Forty-three percent of the patients responding to the initial period, and randomised to sibutramine, maintained ⬎80% of weight loss [30]. This paper reaffirmed that obesity treatment can be effective in those demonstrating early response, but also graphically illustrated that even amongst responders, cessation of therapy would lead inexorably back to baseline (fig. 2a). Combining sibutramine as a follow-up to weight loss with a very low calorie diet is also to be considered, but only 30% of subjects maintained weight loss at 18 months

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compared to 20% with placebo [31]. In contrast, intensive adjuvant support (30 group sessions) with sibutramine is far more effective than drug treatment alone [32]. Similar to orlistat, initial response of 4-kg weight loss at 3 months amongst participants in the STORM study was a good predictor for later results [33].

Rimonabant Rimonabant, a selective cannabinoid receptor type 1 antagonist, was developed as a treatment for smoking cessation, but was noted to induce weight loss, and is the most recent agent to be licensed in Europe for weight loss in obese patients. In four similarly designed trials in the RIO program, with a total study group of over 6,000 patients, subjects were randomised to either 5- or 20-mg doses of rimonabant or placebo [see review in 34]. Study populations were selected on the basis of obesity or overweight alone (RIO-North America and RIO-Europe), or in the presence of coexisting T2D or dyslipidaemia. In the Obesity-Lipids (RIO-Lipids) study, rimonabant treatment at a dose of 20 mg was associated with increased HDL-C, and reduced triglycerides, as well as significant weight loss compared to placebo with a mean weight loss of 6.7 kg. Similar results were found in RIO-North America, where patients in the active treatment group were re-randomised to receive either placebo or rimonabant, and patients who were shifted from rimonabant 20 mg to placebo experienced weight regain (fig. 2b). The return to baseline after cessation of therapy appears to be a feature of all the major pharmacological interventions for obesity. It remains to be determined how effective and well-tolerated rimonabant will be outside the context of pharmaceutical studies, since concerns have arisen with regard to the development of depression in patients with a previous history.

Metformin Although never proposed as a ‘diet pill’, metformin has long been recognised as having at least a neutral effect on weight in comparison to sulphonylureas in the management of T2D. Amongst obese diabetic subjects in the United Kingdom Prospective Diabetes Study, metformin was the only treatment that was not associated with weight gain. Although a neutral effect on weight is hardly headline-grabbing, this is undoubtedly beneficial when seen in comparison to sulphonylurea and insulin therapy. Metformin was associated with a significant reduction in any diabetic-related event, diabetic-related death and all cause mortality and 39% reduction in myocardial infarction [35]. Metformin has also been proven of value in the prevention of T2D: in the Diabetes Prevention Trial metformin reduced the new incidence of diabetes and was associated with greater weight loss (mean 2.1 kg) compared to placebo. Although, first and foremost a treatment for T2D, conceptually at least, metformin has a broad

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and intriguing role in the management of overweight and obesity. With reference to the principles set out in section 1, metformin can be recognised as having beneficial effects in relation to weight management (step 2), complication prevention (step 3) and complication management (step 4).

Combination of Pharmacotherapy for Obesity Intentional weight loss is followed by a counter-regulatory response to increase appetite, conserve energy and restore homeostasis. In this circumstance, manipulation of the pathway at two or more points offers the theoretical possibility of undermining the feedback loop. The combination of fenfluramine and phentermine in a previous era was undoubtedly an effective strategy, achieving weight loss of 10–15% [36], and arguably represented the only medical therapy to date that allowed sufficient weight loss for a clinically apparent effect. Unfortunately, reports of valvular heart disease and pulmonary hypertension [37] in patients taking the combination phen-fen led to its withdrawal, and were followed by evidence that these side effects were not uncommon [38]. Nevertheless, the theoretical advantage of combing agents was demonstrated. Disappointingly, both clinical practice and trial data do not support the combination of orlistat and sibutramine. In a short (12 weeks) randomised open-labelled trial involving 86 obese subjects, sibutramine combined with orlistat was found to be equally effective as either drug alone [39] and this lack of effect has been replicated. At present, there are no data that have evaluated the effect of rimonabant in combination with either sibutramine or orlistat.

Surgical Treatment

Defining Success The surgical literature has a lexicon that is different from that used by the rest of the medical community. Obesity is typically defined as excess weight in relation to the actuarial concept of ideal body weight at a BMI of 22. Hence, an individual with a BMI of 33 has 50% ‘excess weight’, and one of 44, 100%. Whereas pharmacological treatments might consider the proportion of patients losing 5 or 10% total weight as a useful outcome measure, surgical reviews consider a weight loss of less than 10% as being failure and 20% as being poor. Surgical procedures (fig. 3) for obesity aim either to restrict the amount of foods that can be ingested, or to reduce the effective surface area of the gut available for absorption, and are referred to as ‘restrictive’ and ‘malabsorptive’, respectively. A further category of procedures combines both of these elements, and is sometimes referred to as ‘hybrid’. Although the first recognised surgical procedure for obesity was the jejuno-ileal bypass

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Malabsorptive procedures

a

b

c

e

f

Restrictive procedures

d

Fig. 3. Bariatric surgical procedures. a–c Malabsorptive bariatric procedures. a JIB. b BPD. c DS. d–f Restrictive bariatric procedures. d VBG. e LAGB. f RYGB. Reproduced with permission from the Endocrine Society [100].

(JIB), this procedure resulted in an unacceptable rate of complications, and restrictive operative approaches developed that avoided the potential for protein-calorie malabsorption. More recently, there has been an increase in the popularity of procedures that have a milder degree of malabsorption, such as the Roux-en-Y gastric bypass (RYGB).

Restrictive Bariatric Surgery After the disappointment and bad press that followed the JIB (see the section below), focus shifted to restriction of the gastric pouch rather than risking the long-term effects of iatrogenic malabsorption. A variety of techniques emerged, most notably the vertical banded gastroplasty (VBG), or ‘stomach stapling’ in the vernacular. This

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procedure – in which a smaller stomach pouch is created with a staple line and a silastic band forms an artificial distal stomach sphincter – has largely been superseded by laparoscopic adjustable gastric banding (LAGB or Lap Band). In the LAGB, a small silastic ring encircles the proximal stomach leaving an effective gastric pouch of 15 ml, the band being connected to a subcutaneous port in the abdominal subcutaneous fat that can be used to subsequently tighten the band by the introduction of saline. Correct positioning of the band is crucial to achieving optimal results, and although the remainder of the stomach remains in continuity with the gastric pouch, the upper portion of the stomach is richly innervated with vagal afferent fibres that respond to stretch and transduce the sensation of fullness [40]. The LAGB procedure is relatively straightforward, does not risk malabsorption and the degree of restriction can be readily adjusted. Increasingly, LAGB is performed as a day-case procedure increasing the capacity of bariatric units. The principal disadvantage is the need for continued follow-up and adjustment of the band tension, and variability in results amongst units probably reflects accurate positioning and dedication to follow-up. Particularly in Europe, however, the LAGB has become the most commonly performed procedure.

Malabsorptive and ‘Hybrid’ Procedures The original prototype malabsorptive procedure, the JIB was first pioneered in the 1950s. In this operation, the jejunum is divided and the proximal limb anastomosed directly to the last portion of the ileum (fig. 3). Pancreatic and biliary secretions remain in the newly fashioned tract, but a long segment of ileum remained as a blind loop. The JIB was undoubtedly effective, but whilst many patients benefited, the side effects eventually led to its becoming discredited and abandoned. In addition to protein-calorie malnutrition and fat malabsorption with its attendant vitamin deficiencies, increased oxalate levels led to a polyarthritis, crystal nephropathy and poorly understood cirrhosis. Newer techniques combine an element of malabsorption and restriction. The Scopinaro procedure, or bilio-pancreatic diversion (BPD) and duodenal switch (DS) are the modern descendants of purely malabsorptive operations, although they may be additionally combined with a reduction in gastric volume. In both procedures, the alimentary limb remains short, but the pancreatic and biliary secretions drain into the redundant ileal limb. Diversion of the secretions has two effects: firstly to reduce the mixing of food with digestive juices, and secondly to avoid the consequences of a blind loop of intestine. Whilst treading a fine line between adequate weight loss and clinical malnutrition – the surgical equivalent of a narrow therapeutic window – performed by experienced practitioners these procedures are remarkably effective, achieving a 40% weight loss in a typical bariatric patient. Currently, the most widely used hybrid procedure is the RYGB. In this technique, the jejunum is divided and the distal limb anastomosed to the stomach remnant of

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20–30 ml. The remaining stomach, duodenum and proximal jejunum form the ‘Roux’ limb that is re-connected to the alimentary tract. Uniquely, this provides a relatively long common limb; malabsorption is held to a minimum and is not usually clinically evident. Despite minimal metabolic disruption, the RYGB achieves a degree of excess weight loss that is consistently greater than purely restrictive procedure, and is the leading bariatric operation worldwide [41].

Effect of Bariatric Procedures on Weight Loss In the most comprehensive meta-analysis (22,094 patients) exploring weight loss after bariatric surgery, it was shown that the mean percentage of excess weight loss was 47.5% for LAGB, 68.2% for RYGB and 70.1% for BPD/DS [42], although there is a lack of randomised studies and after longer-term follow-up the differences may be less marked [43]. A recent study has reported the effect of bariatric surgery in patients with lesser degrees of obesity than the typical bariatric patient, randomizing patients to LAGB or combined behavioural and medical therapy [44]. At 2 years, subjects with a pre-operative BMI of 30–35 had lost 21.6% of total weight compared to 5.5% in the medical group [44]. The Swedish obese subjects trial (SOS) study, at least, provides long-term outcome data with a carefully matched control group that had elected not to undergo surgery, and demonstrated a gradual return of lost weight, but at 10 years surgical subjects remained with 16.1% total weight loss (equivalent to 34.7% excess weight loss), compared to a weight gain of 1.6% in the medically treated group [45]. This study, however, included a large number of patients undergoing restrictive procedures including the fading VBG, and is at the lower end of the true treatment effect of modern bariatric surgery. At present, the main determinants of surgical referral are patient preference and surgical availability, since the capacity to treat patients remains limited, particularly outside North America [41].

Complications of Bariatric Surgery As with any surgical procedure, the risks of the operation include those of any general anaesthetic, and early and late consequences of the specific operation in question. Evidently, obese subjects pose certain generic anaesthetic problems with airway management and a higher risk of thrombotic complications. LAGB involves the introduction of a foreign body into the peritoneum, and as well as the potential for infection (rare) the band may on occasion lead to erosion through the upper stomach. The most feared early complication of other surgical procedures is anastomotic leakage, which may be difficult to diagnose promptly due to the difficulty in clinical examination of the obese abdomen. Later complications include excessive capacity for ingestion,

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or the consequences of malnutrition, including vitamin and trace metal deficiency. Rarely, weight loss may be excessive, with reversal leading to a syndrome of re-feeding oedema [unpubl. obs.]. The loss of fat mass may be so substantial, that despite body image dysphoria being a major driver for surgical intervention, redundant skin folds may require extensive secondary correction. In a review of 64 studies of LAGB and 57 VBG or RYGB, short-term mortality for LAGB was 0.05%, for VBG 0.31% and for RYGB 0.50% [43]; morbidity rates were higher at 11.3, 25.7 and 23.6% respectively. Risk-adjusted data evaluating 30-day morbidity estimates from a multi-centre, prospective study, showed that LAGB (7.0%) led to fewer complications than open gastric bypass (14.5%) [46]. The above data may underestimate the mortality of surgical procedures, and in the 10-year follow-up of the SOS study, there was no survival advantage for those undergoing bariatric surgery compared to matched medically treated controls [45]. Recent data from the SOS study have illustrated an advantage in favour of surgery amongst all subjects, including those with pre-existing cardiovascular disease, representing a milestone – demonstrating that the excess mortality from obesity can be modulated by a specific therapy [101].

Treatment for Obesity and the Effect on Co-Morbidities

Overwhelmingly, investigations into the efficacy of intervention in the management of obesity have been initially evaluated in relation to their effects on weight. Although BMI and visceral adiposity (waist circumference) are useful markers for obesityrelated complications, the MetS and T2D, they remain surrogate markers. Once proof-of-principle is established in terms of weight loss, however, it is also relevant to evaluate therapies on the basis of their effect on co-morbidities. In this regard, surgical treatments have a far greater evidence base, although ‘placebo’-controlled trials are essentially impossible with surgical intervention. Studies using established medical therapies have also begun to address their efficacy in relation to more pertinent endpoints that reflect the morbidity of severe obesity.

Type 2 Diabetes In addition to their effects on weight loss, currently available weight loss treatments are effective in improving glycaemic control amongst patients with T2D. The Xendos trial demonstrated a reduction in the new incidence of T2D amongst orlistat-treated obese patients treated with impaired or normal fasting glucose. The RIO-Diabetes study demonstrated approximately 0.5% reduction in HbA1c in patients treated with rimonabant. Similar improvements in glycaemic control have been observed with sibutramine [47].

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In an idiosyncratically titled paper – ‘Who would have thought it? An operation proves to be the most effective therapy for adult-onset diabetes mellitus’ –, Walter Pories rattled the cages of the medical diabetes community with his series of 608 patients – with a remarkable ⬎97% follow-up – undergoing RYGB in whom diabetes remission was seen amongst 82.9% of 146 patients with diabetes at long-term followup [48]. Other single centre studies and meta-analyses have demonstrated a high proportion of patients with T2D going into effective remission after RYGB and BPD. A pooled meta-analysis of 22,094 patients undergoing variety of bariatric procedures demonstrated diabetes resolution in 76.8% [42]. Similarly, in the case-controlled SOS study, where the majority of patients underwent restrictive bariatric procedures, rates of remission were 72% at 2 years. However, at 10-year follow-up this had dropped to 36%. Whereas improvement in insulin resistance alone might be anticipated after weight loss, restored islet cell function can also be demonstrated after bariatric surgery. Following BPD, Polyzogopoulou et al. [49] documented restoration of firstphase insulin response to an intravenous glucose tolerance test in patients with T2D, formerly considered a hallmark of the beta cell defect of the diabetic state. Although it remains an attractive hypothesis and the suspicion of many observers, a specific effect – over and above weight loss – of RYBG and/or BPD in resolving T2D and preserving euglycaemia is still unproven.

Metabolic Syndrome Dyslipidaemia improves with both sibutramine and orlistat, whereas blood pressure falls amongst patients treated with orlistat. Rimonabant has beneficial effects on components of the MetS. Consistent with the total weight loss, visceral adipose fat mass has been shown to be reduced after bariatric surgery [50]. Improvements in components of the MetS have also been observed consistently following bariatric surgery, although they are less spectacular than the effects on diabetes. In the SOS study, patients with hypertriglyceridaemia improved after surgical treatment compared to controls, although there were only trends towards improvement in hypercholesterolaemia at 10 years and no significant improvement in hypertension. In a randomised controlled clinical trial comparing the effect of treating mild to moderate obesity with LAGB to intensive medical program for 24 months there was a significant improvement of the MetS [44]. Although potentially beneficial, these differences fall short of the effects of specific lipid-lowering medications.

Hepato-Biliary Disease Fatty liver, non-alcoholic steatohepatitis or non-alcoholic fatty liver disease (NAFLD) are interchangeable terms that describe the excess fat deposition that may accompany

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obesity and specifically insulin resistance and visceral adiposity, progressing to fibrosis and portal hypertension. In a recent double-blind randomised placebo-controlled trial on 52 patients with NAFLD, orlistat was shown to improve serum transaminase levels and steatosis on ultrasound in NAFLD patients [51]. In a prospective, histological study examining subjects undergoing gastric bypass, reduced fat content and the expression of factors associated with inflammation was demonstrated but there was no effect on fibrosis itself [52]. At present, no hard outcome measures are available in relation to retarding the natural history of NAFLD.

Obstructive Sleep Apnoea Obstructive sleep apnoea (OSA) is a common complication of obesity, and its incidence may be underestimated. In a recent open-labelled, uncontrolled cohort study, 10% weight loss with sibutramine was associated with a reduction in the severity of OSA, with improvement in the Respiratory Disturbance Index and Epworth score (a measure of sleepiness) over a 6-month period [53]. Most of the available data show a reduction in the severity of OSA after bariatric surgery; in a prospective study, Dixon et al. [54] showed improvement in the apnoea/hypopnoea index, Epworth score and the need for assisted ventilation after LAGB.

Idiopathic Intracranial Hypertension IIH is a well-recognised but relatively uncommon complication of obesity, although data regarding the influence of dietary weight loss on IIH are scanty. Gastric bypass has been shown to improve cerebrospinal fluid pressure and headache [55]. The authors suggested that bariatric surgery should be the long-term procedure of choice for severely obese patients with a higher rate of success than cerebrospinal fluidperitoneal shunting reported in the literature, although no comparative studies between bariatric surgery and specific medical or surgical treatments have been undertaken.

Infertility and Polycystic Ovarian Syndrome Polycystic ovarian syndrome (PCOS) is a complex metabolic disorder that is defined by irregular or anovulatory cycles, excess circulating androgens and morphological changes that are recognised as structural polycystic change within the ovary. A large body of evidence has arisen that demonstrates that PCOS is accompanied by insulin resistance and subjects with PCOS are at higher risk of T2D and premature coronary artery disease. In addition to the effects on fertility in relation to PCOS, obese subjects have additional independent risk for infertility and pregnancy-associated complications.

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Demonstration of the effects of metformin on hyperinsulinaemia, ovarian steroidogenesis and as an adjunct to clomiphene in the management of subfertility established a role for metformin in PCOS [56]. Metformin appears to be safe to the fetus when taken for the purpose of inducing fertility [57]. Improving insulin resistance either through behavioural means or metformin appears to have similar beneficial effects on the reproductive axis. There are few data regarding the effect of other modalities of weight loss through pharmacological or surgical treatment. Essentially, weight loss through medication as a means of treating PCOS is a largely unexplored area. According to self-reporting, some previously subfertile women appear to have achieved successful pregnancy after surgical weight loss [reviewed in 58], and several series have reported reasonable perinatal outcomes after bariatric surgery in a population known to be at risk of obstetric complications.

Psychiatric Co-Morbidity and Quality of Life Psychiatric co-morbidity is common amongst obese individuals seeking specialist management for obesity, with approximately 50% reporting symptoms of depression and anxiety. In unselected population studies, increasing weight is associated with reduced HRQL particularly in relation to physical functioning [59]. Successful weight loss through behavioural interventions has proved valuable in short-term studies with very low calorie diets [60]. Predictably, the degree of benefit after longer-term follow-up is related to the maintenance of weight loss [61]. Medical treatment with sibutramine amongst patients with T2D did not independently lead to improved HRQL when compared to placebo, but patients that lost weight in either active or placebo groups did benefit [62]. A crude estimate suggests that a 10–15% weight loss is required to reap relevant improvement, and since relatively few patients succeed in maintaining this degree of weight loss through either behavioural or medical strategies, it is difficult to advocate such approaches on the basis of improving psychological functioning or HRQL. In contrast, numerous studies have demonstrated a positive effect for patients undergoing bariatric surgery [42], and bariatric surgery is regarded as cost-effective when considering outcome in terms of quality of life adjusted years. The SOS study demonstrated an improved quality of life following bariatric surgery when compared to matched obese subjects undergoing non-surgical management [63]. In a recent randomised controlled trial specifically designed to evaluate comparing adjustable gastric banding to intensive medical program for the treatment of mild to moderate obesity, it was shown that the surgery group was associated with significant improvement in the quality of life after 24 months of the trial [44]. We explored the relationship between the change in eating behaviour and quality of life and proposed that the specific effects of bariatric surgery brought about a shift in the perception of control over eating contributing to an overall improvement in health status [64].

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Advances in Bariatric Surgery

Arguably, the success of bariatric surgery owes more to serendipity than to science. Drug discovery proceeds with an orderly and regulated progression through phase I to phase V clinical trials. In contrast, surgical procedures evolve, building on the success and adverse events of earlier procedures, and the mechanisms of weight loss are somewhat obscure. Two observations have defined recent advances in this area: (1) a reduction in appetite accompanies weight loss and (2) resolution of T2D appears to occur prior to significant weight loss.

Effects of Bariatric Surgery on Appetite Reduction The reduction in appetite after bariatric surgery has been long recognised but not extensively studied. In a prospective study design, our group demonstrated that following RYGB, the sensation of fullness and a reduction in hunger was evident immediately after a test meal and was maintained for 180 min. This effect was observed at 1 month and persisted at least to 6 months, despite a reduction in circulating leptin and insulin and an increase in ghrelin that would be expected to increase hunger [65]. We further demonstrated that the reduction in appetite was accompanied by an increase in the negative experience of eating, which can be interpreted as either a conscious adaptation to a smaller stomach or evidence for a restoration of negative feedback [64]. As early as 1982, researchers began to examine a possible humoral effect of the then popular JIB. In a rarely cited paper, Atkinson and Brent demonstrated in a rodent model of the JIB that serum derived from rats undergoing JIB had anorectic properties when injected into control animals [66]. With the advent of radioimmunoassay for gut peptides, patients who had undergone JIB were found to have elevated levels of peptide YY (PYY) and enteroglucagons – a composite of products of the pre-pro-glucagon gene. These observations became more significant once interest was reignited with the growing body of evidence that gut peptides might contribute to appetite regulation. In brief, a number of peptide hormones: peptide YY, glucagon-like peptide 1 (GLP-1), pancreatic polypeptide and oxyntomodulin (OXM) have been shown to have inhibitory effects on food intake in human and rodent studies, whereas the acylated peptide hormone ghrelin is unique in stimulating food intake and appetite [67]. Shortly after the isolation of ghrelin from the stomach and the demonstration of its orexigenic effects on meal size in man and adiposity in rodents, Cummings et al. [68] showed that patients that had undergone RYBG had completely suppressed ghrelin levels. This study provided the provocative notion that alterations in the levels of a gut hormone might mediate the effects of malabsorptive bariatric surgery. A flotilla of similar studies followed that had conflicting results, with ghrelin levels being unchanged, decreased or even increased after RYGB. Variability of assays, surgical techniques and experimental protocols may all contribute to the variability in results,

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50 After bypass Lean controls

40 PYY (pM)

Obese controls 30

After banding

20 10 0 ⫺30

0

30

60 90 120 Time point (min)

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Fig. 4. PYY increase after bariatric surgery. PYY response to a test meal amongst obese and lean controls, and patients undergoing RYGB or LAGB, demonstrating the exaggerated PYY response after RYGB [74].

but overall it is unlikely that changes in circulating ghrelin represent a major contributor to the appetite-reducing effects of bariatric surgery [69]. Greater impetus to the effect of gut hormones on appetite regulation arose when Batterham et al. [70] showed that peptide YY3–36 (PYY) limited meal size in lean human volunteers and obese subjects. PYY null mice develop central adiposity that may be reversed with exogenous treatment. Of particular interest, obese subjects are not resistant to the effects of PYY, but exhibit a relative deficit that requires a larger meal size to achieve an equivalent PYY response. Our group demonstrated that following RYBG, formerly obese patients had a greatly amplified PYY response to a small test meal (fig. 4) [71]. This effect was not observed in patients who had lost similar weight after LAGB, demonstrating that the effect was not simply a consequence of weight loss. We further demonstrated in a prospectively designed study, a progressive rise in PYY after RYGB over 6 months [65], maintained at least until 24 months [unpubl. data]. Other groups have confirmed these results in both cross-sectional and prospective designs with remarkable consistency [72]. The gut hormone response to a meal is not restricted to PYY and ghrelin. GLP-1, better known for its incretin effects (see below), also has an effect on satiety. GLP-1 and other members of the enteroglucagon family are released in excess after RYBG, and we have shown that enteroglucagon and GLP-1 are released in increased amounts after RYGB [65]. See Aylwin [69] for a comprehensive review of the effects of bariatric surgery on gut hormones. Further work from Le Roux and colleagues [unpubl. data] has shown that suppression of the post-prandial PYY response with a somatostatin analogue increases meal size and reduces satiety. Whilst these data do not specifically implicate PYY in mediating the

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enhanced satiety after RYBG since somatostatin suppresses the release of other hormones, they provide compelling evidence that gut hormones have a role in mediating the reduction in appetite and food intake observed post-operatively in RYBG patients. Taken together, the current data indicate a pleiotropic endocrine response to RYGB that probably accounts for a significant component of its effectiveness. In contrast, purely restrictive procedures have little effect on the enteric endocrine system. Dixon et al. [40] recorded post-prandial satiety in obese subjects who had undergone LAGB, and randomly assigned them in a cross-over design to have different degrees of band tightening by varying the amount of saline injected into the device. Although the subjects may not have been entirely blind to the band tension, they reported increased satiety with narrower band aperture in a dose-dependent manner. The proximal portion of the stomach is richly innervated by vagal fibres that send signals to the nucleus tractus solitarus that in turn relays information to the hypothalamus. Although evidence is lacking, it is a promising hypothesis that interruption, a change in pulse frequency or hyperstimulation of vagal afferents might account for the anorexigenic effects of restrictive bariatric procedures.

Effects of Bariatric Surgery on Diabetes Debate continues as to whether the improvement or even cure of T2D after bariatric surgery arises as a consequence of weight loss alone or due to some ‘magic’ from the procedure. In an animal model it was shown that the bypass of the duodenum and jejunum could improve post-prandial glucose excursion in rats prone to diabetes, independent of an effect on weight loss [73]. In human subjects undergoing BPD, recovery of first-phase insulin release in patients with T2D became progressively apparent over a 12-month period: impressive evidence that the pathological hallmark of T2D could be reversed. Potential mechanisms for these effects have been proposed to involve an increase in the islet cell trophic incretin hormones, GLP-1 and gastric insulinotrophic peptide, thought to be respectively either reduced or less effective in T2D. Both RYGB and BPD increase GLP-1 secretion both during fasting and after meal, a consequence of L-cell stimulation by early arrival of nutrients in the distal ileum [65, 74]. The secretion of GLP-1 promotes insulin release, influences glucose metabolism by inhibiting glucagon secretion, delaying gastric emptying and stimulating glycogenesis and GLP-1 has also been shown to inhibit pancreatic islet cell apoptosis. The potential for stimulation of insulin release after RYGB has been highlighted by the development of post-prandial hypoglycaemia amongst formerly obese subjects who had previously undergone RYGB and the development of nesidioblastosis has been observed [75]. The unwanted hypoglycaemia may be an unusual side effect of RYGB but it is strong circumstantial evidence that RYGB somehow induces an exaggerated insulin response that might be mediated by increased release of GLP-1 [74].

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Potential New Pharmacological Strategies

Centrally Acting Agents In many cases, agents have been developed for other indications, and an effect on body weight observed as a side effect during phase II/III development. It is worth noting that both sibutramine and rimonabant owe their current indications to a similar provenance. The following are examples of each class and the available data on their proposed effect in the management of obesity. For an exhaustive account of agents under evaluation, see Foster-Schubert et al. [76].

Antidepressants: Fluoxetine/Sertraline and Bupropion Fluoxetine and sertraline are selective serotonin reuptake inhibitors and have effects beyond their antidepressant actions, and have been used to treat both binge eating disorder and night eating syndrome which are associated with severe psychological morbidity and depression. Night eating syndrome was shown to respond to sertraline in a small double-blind trial that demonstrated improved symptoms and modest weight loss [77]. The use of fluoxetine may also improve psychopathology and depressive symptoms in subjects with binge eating disorder, although weight loss was not enhanced and cognitive behavioural therapy is more potent in reducing bingeing [78]. The data available on the effect of fluoxetine on achieving body weight reduction remain conflicting. In a meta-analysis including 13 studies, fluoxetine was shown to induce weight loss ranging from 14.5 kg to a weight gain of 0.4 kg in studies lasting at least 12 months [79]. Bupropion is an atypical antidepressant that inhibits the neuronal uptake of noradrenaline, serotonin and dopamine and is currently indicated for the treatment of depression and smoking cessation. In one randomised double-blinded clinical trial, which involved 400 obese individuals with depressive symptoms, sustained release bupropion was shown to induce more weight loss (average of 4.2 kg) than placebo (1.7 kg); interestingly, resolution of depressive symptoms was more pronounced in those who lost ⬎5% weight regardless of treatment category [80]. Similarly, Anderson’s group reported a randomised double-blind control trial on 327 obese subjects in which bupropion induced a net 5.1% weight loss over placebo.

Anti-Seizure Agents: Topiramate, Zonisamide The anti-epileptic agent topiramate has been investigated as an anti-obesity agent following the recognition that patients using the drug for epilepsy had unintentional weight loss. In a randomised double blind study of 385 patients, topiramate reduced weight by 6.3% compared to 2.6% in the placebo arm [81]. Adverse effects of topiramate include somnolence, cognitive dysfunction and transient amnesia leading to withdrawal in 21%, although arguably better tolerated than other currently marketed obesity agents. A

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recently reported multi-centre double-blind study amongst patients with T2D demonstrated 6.5% weight loss compared to placebo (1.7%), with 0.6% reduction in HbA1c [82]. Zonisamide is another agent used in the treatment of partial epilepsy with dopaminergic and serotonergic effects that has been investigated for obesity treatment with promising early data.

Agents Affecting the Leptin-Melanocortin Pathway The discovery of leptin initiated an avalanche of research into appetite regulation that proceeded to define the interface of peripheral hormones and the hypothalamus, outlined in the chapter by Wren [this vol., pp. 165–181]. In brief, the system includes an orexigenic (EAT!) pathway initiated in a low-insulin/low-leptin state, by activation of NPY and AgRP expressing hypothalamic neurones, and an opposing anorexigenic (DON’T EAT!) pathway via leptin’s stimulation of POMC expression acting at the melanocortin receptor MC4R. Experimental mouse models demonstrate that both sides of the system are required for normal functioning, and human subjects with congenital mutations [22] testify to the importance of this system in extreme monogenic obesity. To what extent appetite regulatory mechanisms are relevant to more common forms of human obesity remains to be established.

Leptin To the far-from satiated pharmaceutical industry, the discovery of leptin – a hormone produced by adipocytes that circulated in proportion to fat mass, crossed the blood brain barrier and influenced satiety [83] – seemed almost too good to be true. Although a handful of individuals have been found to have congenital leptin deficiency, in the vast majority of obese subjects leptin levels are increased. The higher level of leptin suggests either (1) a state of relative leptin resistance, where the anorexigenic effect of rising leptin concentration eventually overcomes the refractory central sensor and reaches a steady state set at a high body weight; or (2) that leptin is merely an anti-starvation hormone, relevant only in deficiency where low levels promote an overwhelming drive to eat, but higher concentrations have no effect beyond a threshold level. In the first randomised double-blind dose-ranging controlled trial with recombinant leptin, obese subjects lost up to 7.1 kg after 24 weeks at the highest dose compared to 0.7 kg for placebo [84]. The effect, however, was non-specific, being dependent on doses that achieved levels 20- to 40-fold higher than at baseline. In a further study using a pegylated form of leptin in which levels of total serum leptin were increased twofold, in 30 obese men over a 12 week period, there were no differences on weight loss, percent body fat or respiratory quotient between pegylated leptin and placebo. Leptin treatment has, however, been shown to undermine the homeostatic

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response to weight loss in the reduced-obese state and may yet have a role in weight maintenance after behavioural weight loss.

Recombinant Variant Ciliary Neurotrophic Factor Overcoming leptin resistance or augmenting the effect of circulating leptin represents an appealing strategy for drug development. Ciliary neurotrophic factor (CNTF) is an endogenous neuroprotective protein present in Schwann cells and astrocytes that was found unexpectedly to induce marked weight loss in a study of patients with motor neurone disease. CNTF activates the STAT3 2nd messenger system utilised by leptin, and animal studies showed that CNTF could induce weight loss in both diet-induced obese and leptin-deficient animal models [85], with initial blinded studies showing a short-term effect. More intriguing are the apparently durable effects of CNTF beyond the treatment period, recently demonstrated to be due to a trophic effect on POMC neurones that participate in mediating an anorexigenic effect [86]. Alone amongst medical strategies for the treatment of obesity, these early data suggest that CNTF might offer a treatment that is not required on an open-ended basis to maintain its effect.

NPY/AgRP The roles of NPY in the regulation of food intake and energy expenditure are highly complex, due to the number of receptors (Y1–Y6) and their widespread expression. A Y5R antagonist was demonstrated to suppress weight gain in rodents but in a large double-blind study of 1,661 subjects, the Y5R antagonist MK-0557 had no clinically relevant effect [87].

Gastrointestinal Hormone Agonists and Antagonists Whilst leptin, and to an extent insulin, provide information about the state of available energy stores, a parallel system exists to inform the organism about the nutritional value of recently ingested meals, and a growing number of peptides have been identified that are released from the gastrointestinal tract in response to food intake. Hormones released in the periphery that have an influence on central appetite regulation make obvious and attractive targets for the treatment of obesity.

Amylin (Pramlintide) Amylin is a peptide secreted by the pancreas in response to nutrients and other insulinogenic stimuli; pramlintide is a subcutaneously administered synthetic analogue of amylin. Amongst severely obese subjects (BMI ⬎ 40), pramlintide resulted in significant reduction in weight (⫺3.2 kg placebo corrected) at 26 weeks with

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favourable effect on glycaemic control [88]. Whilst this degree of weight loss in severely obese individuals is unlikely to reap any additional benefit other than the improvement in glycaemic control, this should be contrasted against the likely weight gain were those subjects to have been treated with increasing insulin doses.

Cholecystokinin Cholecystokinin (CCK) was the first gut peptide recognised to have anorexigenic effects. CCK is released from the proximal small bowel into the circulation and its effects on gall bladder contraction are well established. CCK also acts at as an agonist at the CCKA receptors (alternately named CCK1R) present on peripheral vagal afferents. Peripheral CCK administration has been shown to increase satiety and reduce food intake acutely in man [89], although the effects of CCK are short-term and chronic administration has not been shown to have a sustained effect [90].

Ghrelin In contrast to other gastrointestinal hormones, ghrelin remains the only known circulating orexigenic peptide. Ghrelin was initially identified as the ligand for the formerly orphan G-protein-coupled growth hormone secretagogue GHSR1a receptor, but its principal effects are to co-ordinate a protective response to energy depletion both through peripheral effects on intermediary metabolism and a central effect on feeding, augmenting calorie intake in man [91]. Ghrelin antagonism therefore represents an obvious potential strategy in the management of obesity. Proof-of-principle for the validity of ghrelin antagonism has been demonstrated with the use of polyethylene glycol stabilised RNA oligonucleotides (Spiegelmers) that have been demonstrated to inhibit the orexigenic properties of ghrelin [92]. The oligonucleotide NOX-B11-2 had caused reduced intake and weight loss in diet-induced obese mice with chronic treatment [92]. Whether such agents can be deployed in human studies is far from clear, but it is likely that other antagonists of ghrelin will be actively pursued.

Oxyntomodulin OXM is a member of the enteroglucagon family of polypeptides, which includes glucagon and glucagon-like peptides GLP-1 and GLP-2, products of the preproglucagon gene. OXM is part of a co-ordinated ileal satiety response and its actions are similar to PYY and GLP-1, and it is also released from the same cells [93]. OXM, given pre-prandially by subcutaneous injection three times a day in a parallel blinded study design, resulted in a mean weight loss of 2.3 kg over 4 weeks compared to 0.5 kg in the placebo group [94]. The precise mechanism of action of OXM is not known, but its effects can be inhibited by a central GLP-1 antagonist and abolished in a GLP-1 receptor knockout model, suggesting that it acts via GLP1 receptors. The development

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and use of GLP-1 agonists should allow experimental approaches to determine whether there is an additive effect of OXM in conjunction with GLP-1.

GLP-1 Agonists The development of the long-acting GLP-1 analogue exendin-4 (exenatide) represents the first incursion of a gut hormone into the mainstream pharmacopoeia. Exenatide is resistant to degradation by dipeptidyl peptidase IV, has a longer half-life than the native peptide and is now established as a therapy for T2D. In addition to its effects on glycaemic control, exenatide has the additional advantage of weight loss, mean 4.0 kg at 1.5 years [95]. Future studies are awaited in normoglycaemic obese human subjects.

Peptide YY PYY was originally identified as modulator of gastrointestinal motility, released from the L-cells of the small bowel and may have a role in delaying gut transit in malabsorptive conditions. The principal active form of PYY, PYY3–36 acts at the inhibitory Y2 (NPY) receptor expressed by NPY neurons in the arcuate nucleus and has been shown to act as an inhibitor of food intake. Recent data with an intranasal formulation, however, have failed to demonstrate weight loss over a 12-week period [96].

Perspective

Public health initiatives, research into the aetiology of obesity, drug development and establishing an increased capacity for bariatric surgery all have a role to play in the future handling of obesity, but probably not in that order. It is becoming obvious that increasing energy supply is closely associated with the increase of overweight and obesity in western countries [97], and simply praying for a plateau rather than expecting a reversal of the underlying trend may be more realistic. The explosive pace of research into the adipose-hypothalamic and enterohypothalamic axes that have emerged as the principal homeostatic co-ordinators of body weight will continue to offer opportunities for drug discovery. In this arena, combination treatments have been slow to emerge, although it should be noted that the combination of fenfluramine with fluoxetine has been shown recently to be almost as potent as the now discredited fenfluramine-phentermine combination [98]. Medical therapy for obesity is very much in its infancy in comparison to other chronic disorders, but it would be a fair bet to predict that treating individuals with severe obesity will more resemble multi-drug treatment of hypertension rather than the one-size-fits-all approach to hyperlipidaemia. Management of the obese individual must extend beyond the boundaries of weight management, and interventions need to be judged by their effects on co-morbidities.

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We have developed a shorthand holistic assessment tool (King’s obesity staging criteria) to categorise obese patients in a 10-dimensional matrix that enables us to assess interventions against a range of metrics in a given individual (table 1). We have chosen to combine both medical and psychosocial co-morbidities, since this provides a more complete guide to assessing and advising an individual. Just as treatment for a specific cancer is tailored to suit the stage and grade of disease, so the management of obesity can range from watchful waiting to palliative. In its broadest sense, both medical and surgical strategies are essentially a means to cause a ‘down-staging’ of disease. One fundamental premise to our approach is that individuals up to obesity WHO grade I may have no evidence of any complications of obesity (i.e. all criteria in stage 0), and under these circumstances it is highly questionable whether either short- or long-term medical intervention might be considered appropriate. Individuals with co-morbidities in stage 1 are candidates for intervention, whether it might be a statin to reduce cardiovascular risk, or behavioural support to move their ‘stage of change’ from preparation through to action and maintenance. Most of the co-morbidities that fall under the functional and psychosocial categories, and being relatively weight-loss resistant, may be good indications for surgical intervention. It should be emphasised that our staging system approach only represents a strategy for individual assessment, and does not represent a means of either stratifying overall risk or specifying a treatment. It is hoped that over time, more obesity interventions will be evaluated against a broad range of criteria, later facilitating the development of the model, and providing the data to recommend interventions likely to be of benefit to patients with specific sets of complications. At the time of writing, surgical treatment remains the gold standard for the management of severe obesity, and the progressive analysis of the mechanisms responsible for the success of these procedures will continue to offer not only scope for more refined procedures but also insight into the pathophysiology of morbid obesity in an unusual interpretation of reverse pharmacology – understanding the disease through examination of its treatment. It remains to be seen when or indeed whether medical strategies will compete for supremacy. References 1 Wardle J, et al: Development of adiposity in adolescence: five year longitudinal study of an ethnically and socioeconomically diverse sample of young people in Britain. BMJ 2006;332:1130–1135. 2 Schienkiewitz A, et al: Body mass index history and risk of type 2 diabetes: results from the European Prospective Investigation into Cancer and Nutrition (EPIC)Potsdam Study. Am J Clin Nutr 2006;84: 427–433. 3 Gunderson EP, et al: Excess gains in weight and waist circumference associated with childbearing: The Coronary Artery Risk Development in Young Adults Study (CARDIA). Int J Obes Relat Metab Disord 2004;28:525–535.

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56 Ehrmann DA: Polycystic ovary syndrome. N Engl J Med 2005;352:1223–1236. 57 Gilbert C, Valois M, Koren G: Pregnancy outcome after first-trimester exposure to metformin: a metaanalysis. Fertil Steril 2006;86:658–663. 58 Merhi ZO: Weight loss by bariatric surgery and subsequent fertility. Fertil Steril 2006. 59 Ford ES, et al: Self-reported body mass index and health-related quality of life: findings from the Behavioral Risk Factor Surveillance System. Obes Res 2001;9:21–31. 60 Kaukua J, et al: Health-related quality of life in WHO class II-III obese men losing weight with very-lowenergy diet and behaviour modification: a randomised clinical trial. Int J Obes Relat Metab Disord 2002;26:487–495. 61 Kaukua J, et al: Health-related quality of life in obese outpatients losing weight with very-low-energy diet and behaviour modification: a 2-y follow-up study. Int J Obes Relat Metab Disord 2003;27:1072–1080. 62 Kaukua JK, Pekkarinen TA, Rissanen AM: Healthrelated quality of life in a randomised placebocontrolled trial of sibutramine in obese patients with type II diabetes. Int J Obes Relat Metab Disord 2004;28:600–605. 63 Karlsson J, Sjostrom L, Sullivan M: Swedish obese subjects (SOS)–an intervention study of obesity. Two-year follow-up of health-related quality of life (HRQL) and eating behavior after gastric surgery for severe obesity. Int J Obes Relat Metab Disord 1998;22: 113–126. 64 Ogden J, et al: Exploring the impact of obesity surgery on patients’ health status: a quantitative and qualitative study. Obes Surg 2005;15:266–272. 65 Borg CM, et al: Progressive rise in gut hormone levels after Roux-en-Y gastric bypass suggests gut adaptation and explains altered satiety. Br J Surg 2006;93: 210–215. 66 Atkinson RL, Brent EL: Appetite suppressant activity in plasma of rats after intestinal bypass surgery. Am J Physiol 1982;243:R60–R64. 67 Wynne K, Stanley S, Bloom S: The gut and regulation of body weight. J Clin Endocrino Metab 2004;89: 2576–2582. 68 Cummings DE, et al: Plasma ghrelin levels after dietinduced weight loss or gastric bypass surgery. N Eng J Med 2002;346:1623–1630. 69 Aylwin S: Gastrointestinal surgery and gut hormones. Cur Opin Endocrinol Diabetes 2005;12: 89–98. 70 Batterham RL, et al: Critical role for peptide YY in protein-mediated satiation and body-weight regulation. Cell Metab 2006;4:223–233. 71 le Roux CW, et al: Attenuated peptide YY release in obese subjects is associated with reduced satiety. Endocrinology 2006;147:3–8.

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72 Korner J, et al: Effects of Roux-en-Y gastric bypass surgery on fasting and postprandial concentrations of plasma ghrelin, peptide YY, and insulin.[see comment]. J Clin Endocrin Metab 2005;90: 359–365. 73 Rubino F, Marescaux J: Effect of duodenal-jejunal exclusion in a non-obese animal model of type 2 diabetes: a new perspective for an old disease. Ann Surg 2004;239:1–11. 74 le Roux CW, et al: Gut hormone profiles following bariatric surgery favor an anorectic state, facilitate weight loss, and improve metabolic parameters. Ann Surg 2006;243:108–114. 75 Service GJ, et al: Hyperinsulinemic hypoglycemia with nesidioblastosis after gastric-bypass surgery. N Engl J Med 2005;353:249–254. 76 Foster-Schubert KE, Cummings DE: Emerging therapeutic strategies for obesity. Endocr Rev 2006;27: 779–793. 77 O’Reardon JP, et al: A randomized, placebo-controlled trial of sertraline in the treatment of night eating syndrome. Am J Psychiatry 2006;163: 893–898. 78 Devlin MJ, et al: Cognitive behavioral therapy and fluoxetine as adjuncts to group behavioral therapy for binge eating disorder. Obes Res 2005;13: 1077–1088. 79 Li Z, et al: Meta-analysis: pharmacologic treatment of obesity. Ann Intern Med 2005;142:532–546. 80 Jain AK, et al: Bupropion SR vs. placebo for weight loss in obese patients with depressive symptoms. Obes Res 2002;10:1049–1056. 81 Bray GA, et al: A 6-month randomized, placebo-controlled, dose-ranging trial of topiramate for weight loss in obesity. Obes Res 2003;11:722–733. 82 Toplak H, et al: Efficacy and safety of topiramate in combination with metformin in the treatment of obese subjects with type 2 diabetes: a randomized, double-blind, placebo-controlled study. Int J Obes (Lond) 2007;31:138–146. 83 Ahima RS, Flier JS: Leptin. Ann Rev Physiol 2000;62: 413–437. 84 Heymsfield SB, et al: Recombinant leptin for weight loss in obese and lean adults: a randomized, controlled, dose-escalation trial. JAMA 1999;282: 1568–1575. 85 Lambert PD, et al: Ciliary neurotrophic factor activates leptin-like pathways and reduces body fat, without cachexia or rebound weight gain, even in leptin-resistant obesity. Proc Natl Acad Sci USA 2001;98:4652–4657. 86 Kokoeva MV, Yin H, Flier JS: Neurogenesis in the hypothalamus of adult mice: potential role in energy balance. Science 2005;310:679–683.

87 Erondu N, et al: Neuropeptide Y5 receptor antagonism does not induce clinically meaningful weight loss in overweight and obese adults. Cell Metab 2006;4:275–282. 88 Hollander P, et al: Effect of pramlintide on weight in overweight and obese insulin-treated type 2 diabetes patients. Obes Res 2004;12:661–668. 89 Gutzwiller JP, et al: Interaction between GLP-1 and CCK-33 in inhibiting food intake and appetite in men. Am J Physiol – Regulatory Integrative Comp Physiol 2004;287: R562–R567. 90 Little TJ, Horowitz M, Feinle-Bisset C: Role of cholecystokinin in appetite control and body weight regulation. Obes Rev 2005;6:297–306. 91 Korbonits M, et al: Ghrelin–a hormone with multiple functions. Front Neuroendocrinol 2004;25: 27–68. 92 Shearman LP, et al: Ghrelin neutralization by a ribonucleic acid-SPM ameliorates obesity in diet-induced obese mice. Endocrinology 2006;147: 1517–1526. 93 Cohen MA, et al: Oxyntomodulin suppresses appetite and reduces food intake in humans. J Clin Endocrin Metab 2003;88:4696–4701. 94 Wynne K, et al: Subcutaneous oxyntomodulin reduces body weight in overweight and obese subjects: a double-blind, randomized, controlled trial. Diabetes 2005;54:2390–2395. 95 Riddle MC, et al: Exenatide elicits sustained glycaemic control and progressive reduction of body weight in patients with type 2 diabetes inadequately controlled by sulphonylureas with or without metformin. Diabetes Metab Res Rev 2006;22:483–491. 96 Gantz I, et al: Efficacy and safety of intranasal peptide YY3–36 for weight reduction in obese adults. J Clin Endocrinol Metab 2007;92:1754–1757. 97 Silventoinen K, et al: Trends in obesity and energy supply in the WHO MONICA Project. Int J Obes Relat Metab Disord 2004;28:710–718. 98 Whigham LD, et al: Comparison of combinations of drugs for treatment of obesity: body weight and echocardiographic status. Int J Obes (Lond) 2006. 99 Pi-Sunyer FX, et al: Effect of rimonabant, a cannabinoid-1 receptor blocker, on weight and cardiometabolic risk factors in overweight or obese patients: RIO-North America: a randomized controlled trial. JAMA 2006;295:761–775. 100 Cummings DE, Overduin J, Foster-Schubert KE: Gastric bypass for obesity: mechanisms of weight loss and diabetes resolution. J Clin Endocrinol Metab 2004;89: 2608–2615. 101 Sjöström L, et al: Effects of bariatric surgery on mortality in Swedish obese subjects. N Engl J Med 2007; 357:741–752.

Dr. Simon Aylwin Department of Endocrinology, King’s College Hospital NHS Foundation Trust Denmark Hill, London SE5 9RS (UK) Tel. ⫹44 20 3299 2996, Fax ⫹44 20 3299 3790, E-Mail [email protected]

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The Sociology of Obesity Annika Rosengrena ⭈ Lauren Lissnerb a

Department of Medicine, Sahlgrenska Universital Hospital/Ostra, and bDepartment of Public Health and Community Medicine, Sahlgrenska Academy at Göteborg University, Göteborg, Sweden

Abstract The current obesity epidemic is largely driven by environmental factors, including nutritional transition towards refined and fatty foods with the growing production of energy-dense food at relatively low cost, increased access to motor vehicles, mechanisation of work and sedentary lifestyles. These influences in modern society are modified by individual characteristics. Ultimately, energy intake in excess of caloric expenditure causes obesity, but why this occurs in some but not all individuals is not known. Obesity is more prevalent in the lower socioeconomic classes but even so, there is a varying relation of socioeconomic status with obesity between countries at different stages of development and, even in the Western world, socioeconomic gradients with respect to obesity are both heterogeneous and in transition. Potential mechanisms for an effect of obesity on subsequent social status have been proposed, the most obvious being related to the stigmatisation experienced by the obese. Obesity seems to be causally related to mood disturbances, whereas there is no conclusive evidence that the reverse is true. When considering psychological aspects of obesity, depressive symptoms are more likely to be conseCopyright © 2008 S. Karger AG, Basel quences, rather than causes of obesity.

While there is, by now, little doubt that the current obesity epidemic is largely driven by environmental factors, it is equally clear that these influences in modern society are modified by individual characteristics. There is a growing literature on sociological and psychosocial factors in relation to obesity. In this chapter, we describe psychosocial correlates of obesity from a societal, as well as from an individual, perspective.

Societal Factors

Obesity is ‘man-made’ and the current obesity epidemic is a sociodemographic phenomenon. The remarkable recent changes in the prevalence in obesity worldwide, coupled with the large geographical variation, bear witness to this. The obesity

Table 1. Causes of decreased energy expenditure in urban society

Separation of work, housing and shopping necessitating mechanised transportation Increased automation at work Increasing availability of home entertainment (computer, television)

pandemic was first apparent in the US, subsequently spreading to the rest of the westernised world. With the urban transition in the developing countries in the world, obesity is now rapidly increasing worldwide. Many developing countries are currently affected by high rates of overweight that often surpass underweight as a public health nutrition problem. In the case of urban Africa, recent analyses of national data on body mass index (BMI) from women showed that prevalence of BMI ⱖ25 exceeded that of BMI ⬍18.5 in 17 of 19 countries [1]. By 2020, two thirds of the global burden of disease is estimated to be attributable to chronic non-communicable diseases, most of them strongly associated with overnutrition [2]. The causes of the obesity epidemic are multifactorial, including nutritional transition towards refined and fatty foods with increased production of energy-dense food at relatively low cost, access to motorised transport, mechanisation of work, and sedentary lifestyles. This increasingly ‘obesogenic’ environment, reinforced by the cultural changes associated with globalisation and urbanisation, makes the adoption of healthy lifestyles difficult. At the same time undernutrition, while an uncommon problem in high- and middleincome countries, still prevails in large segments of the population in low-income countries. On a societal level, decreasing daily physical activity is one of the important driving forces behind the obesity epidemic (table 1). Separation of work, housing and shopping, often over long distances, necessitates the use of motorised transportation. Contemporary city structures strive to accommodate cars, buses and trams, but not walking or cycling. Within the work environment, increased automation has led to a lower workload with less physical exhaustion and with lower caloric expenditure. During leisure, substantial portions of time are taken up by watching television and using the computer. All these separate influences add up in expending less energy, with obvious consequences for the net energy balance. It has been estimated that a positive daily energy imbalance of 100 kcal/day results in slightly less than 1 kg of weight gain per year, which is close to the actual weight gain observed in some US populations [3]. The steady decline in physical activity is probably a major contributor to the rising prevalence of obesity. Small changes in behaviour, such as 30 min per day of brisk walking or eating less at each meal, might contribute to arresting the obesity epidemic [4]. Using terminology from a classic epidemiological triad (host, vector, environment), it has been proposed that the contributions of physical inactivity to the obesity epidemic may be attributed to combined effects of machines that enable us to reduce the energy cost of everyday activities (‘disease vectors’) and other

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aspects of the physical environment that promote sedentary living patterns in the ‘host’ [5]. At the same time, dietary patterns represent the other side of the positive energy balance equation, and may also be considered as potential vectors in the obesity epidemic. According to Swinburn and Egger [5], the main candidates for food-based vectors of passive overconsumption of total energy are energy-dense foods (usually high in fat and low in water and fibre), energy-dense drinks (usually high in sugar) and large portion sizes. As the price of food relative to income has decreased, inadequate calorie intake is now rare in most Western societies. Overconsumption of energy is induced by abundant availability of inexpensive and energy-dense food. Food production has moved away from the home to wholesale and marketing by large corporations. In restaurants, servings have become increasingly larger. The social and commercial environments are conducive to eating more, with increasing energy intake. The types of foods consumed have also changed, for instance, with a rise in refined carbohydrates such as soft drinks, which may balance out decreases in fat intake. Commercial policies and other environments influencing food prices and availability affect the choices people make in selecting healthy versus unhealthy food. Calorie-dense food is often less expensive than low calorie dense bulky foods. Again, using the epidemiologic triad metaphor, food-based vectors interact with obesity environments and cultural practices to produce obesity in the host. The combined influences of increasingly obesity promoting agents and environments produce a situation in which positive energy balance is accelerated whereas forces that inhibit this development are overwhelmed [6]. Whereas obesity has primarily been considered to result from individual choices against a background of genetics, it is becoming increasingly clear that the current obesity epidemic is driven by an environment that promotes obesity, by affecting individual lifestyle in the context of society. The rapid increase in obesity over the past few decades, during which the environment has changed markedly, will obviously not be explained by major changes in genetic make-up. While studies of genetic factors may improve our knowledge of the pathogenesis of obesity, we also need to know more about societal changes that drive the changes in the prevalence of obesity. A recent study demonstrated that local area is an important predictor of adult BMI in women but not in men, supporting the need to focus on improving local environments to reduce socioeconomic inequalities in overweight and obesity [7]. This illustrates that individual lifestyle choices are constrained by societal factors, hampering to a varying degree the individual’s ability to change lifestyle. Nevertheless, while societal changes are what probably drives the obesity epidemic overall, there is still scope to examine which individual determinants cause obesity in a given individual. The health hazards of being obese have been abundantly documented, but, in contrast, individual determinants for obesity are not well understood. Ultimately, energy intake in excess of caloric expenditure causes obesity, but why this occurs in an increasing number of men and women is not known, nor do we know why this occurs in some, but not all, individuals.

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Socioeconomic Status and Obesity

Is it relevant, when discussing individual determinants of obesity, to consider the common assumption that specific psychological and/or risk factor profiles are causally related to obesity? The reverse question must also be asked: what is the evidence that psychological and socioeconomic characteristics are not the consequences, but rather the causes, of the obese state? There has been a strong interest in studying the relation between socioeconomic status (SES) and obesity. Previous studies have shown that the association between SES and obesity may vary by population, sex and age [8, 9]. However, a person’s body weight status may also affect his/her education and occupational opportunities, which subsequently affect his/her SES. A good understanding of the association between obesity and SES has many important public health and policy implications, particularly for the prevention and management of obesity. It is well known that obesity is more prevalent in the lower socioeconomic classes and that this pattern is more common among women than men. Even so, there is a varying relation of SES with obesity between countries at different stages of development [10]. A landmark review of studies published prior to 1989 on SES and obesity proposed that obesity in the developing world would be essentially a disease of the socioeconomic elite [11]. However, in a recent review [12] on studies conducted in adult populations from developing countries, the authors concluded from the studies they had reviewed that obesity in the developing world is no longer a disease of groups with higher SES. Additionally, the burden of obesity in each developing country tends to shift towards the groups with lower SES as the country’s gross national product increases. They also concluded that the shift of obesity in women with low SES apparently occurs at an earlier stage of economic development than it does for men. Socioeconomic gradients with respect to obesity, even in the Western world, are both heterogeneous and in transition. For example, it has long been accepted that in the US population, groups with low SES are at greater risk to be obese than people of high SES. This perception, however, was challenged in a recent study [13]. Based on nationally representative data collected in the National Health and Nutrition Examination Surveys from American adults since the 1970s, the findings indicated an overall trend of a weakening association between SES and obesity, with differing patterns across ethnic groups. In a study of children and young people grouped by race, sex, and age, different results were observed in the association between overweight and SES. Between 1988–1994 and 1999–2002, the ratio in the prevalence of overweight in adolescent boys with a low or high SES decreased from 2.5 to 1.1 and from 3.1 to 1.6 in girls (fig. 1) [14]. Consistently across almost all SES groups, the prevalence of overweight was much higher in blacks than in whites, indicating highly complex patterns in the association of SES and overweight [14]. The authors speculated that television viewing might have been the primary type of inactivity in poor adolescents during the early part of the period they were studying, whereas computers and computer games became more widely accessible and

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20

Low SES Medium SES High SES

Prevalence of overweight (%)

Boys

Girls

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2 00

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Fig. 1. Prevalence trends in the disparity of overweight in American adolescents 10–18 years (1971–2002) in the low, medium, and high SES groups. Overweight was defined as a BMI ⱖ95th percentile. Adapted from [14].

affordable during the more recent part of the observation period, especially in high SES groups. Accordingly, the energy intake and expenditure patterns of all adolescents regardless of SES, particularly for white adolescent boys, became more similar, resulting in smaller economic disparities of the proportion of overweight subjects. Similarly, data from the US show that in the 70s there was as much as a 50% relative difference in obesity prevalence among those with less than high school education, compared to people with college education, but by 1999–2000, the difference had decreased to 14% [13]. Similar observations of a decreasing socioeconomic gradient in obesity were reported in one Swedish study of young adults [15]. These findings underline that individual characteristics are probably not the main cause of the current obesity epidemic. In addition, changing patterns of consumption and of physical activity directly affect socioeconomic differences in a way that is not always predictable. The other side of the obesity-SES association is whether obesity can be shown to be a risk factor for subsequent changes in SES. Among the first studies indicating that obesity might affect social mobility was based on a Swedish population-based sample of women examined in the late 1960s showing that the shift toward higher socioeconomic level since childhood was more common in normal-weight than in overweight women [16]. However, this study did not establish which subjects were already overweight as children. One US study, which classified adolescents and young adults as being overweight or normal at baseline, found that the overweight group, 7 years later, were less often married, had lower income and had completed fewer years of education.

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These prospective findings were independent of initial SES, suggesting that obesity created a situation of downward social mobility, and, in addition, more often occurring in women than in men. Thus, in addition to the physical health consequences of obesity, obese people, particularly obese women, suffer from social stigmatization, prejudice, and discrimination [17]. In a Swedish longitudinal study, no social difference in overweight was observed at age 16 years but at 30 years educational level was associated with overweight, reflecting the cumulative influence of multiple adverse circumstances experienced from adolescence to young adulthood [18]. Potential mechanisms for an effect of obesity on subsequent social status have been proposed, the most obvious being related to the stigmatisation experienced by the obese. Leanness is often equated with beauty, success, fitness and self-control. Obesity, on the other hand, is considered as undesirable as leanness is desirable, for reasons that are more often related to cosmetic concerns than to actual or potential medical complications. Specific examples for discrimination may be seen in the areas of marital, employment and educational opportunities. If obesity has both social causes and effects, a self-perpetuating cycle may be created that reinforces the relationship between low SES and obesity.

Stigmatisation in Obesity

When considering psychological aspects of obesity, it is widely believed that most psychological disturbances are more likely to be consequences, rather than causes, of obesity. One of the most compelling illustrations was reported in the early 1990s, based on 47 patients who were, on average, 66 kg overweight before surgery for morbid obesity, who lost 45 kg or more subsequently and who successfully maintained weight loss for at least 3 years [19]. As a group, they perceived their previous morbid obesity as having been extremely distressful. Most patients said that they would prefer to be normal weight with a major handicap (deaf, dyslexic, diabetic, legally blind, very bad acne, heart disease, one leg amputated) than to be morbidly obese. All patients said they would rather be normal weight than a morbidly obese multi-millionaire. Thus obesity, as perceived by obese individuals themselves, is an extremely serious handicap, although not always perceived as such by others. A recent review summarised that extreme obesity is associated with significant psychiatric morbidity and impaired health-related quality of life that in many cases imposes a greater burden of suffering than the physical complications of obesity [20]. A second issue is how the obese individuals are treated by others. Negative attitudes are prevalent, and exacerbated by idealisation of thinness in many Western cultures. There are numerous examples of obesity-related discrimination, including how children perceive overweight and obese peers, among employers, students’ ideas about suitable spouses [21]. In a classic study on childhood stereotypes, young children associated overweight in children with being lazy, dirty, stupid, cheats and liars

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[22]. The perception of obesity as a self-inflicted condition creates little sympathy for the obese. Studies of morbidly obese patients show that in many instances they feel that they are treated disrespectfully by the medical profession because of their weight, that people look critically at them and their shopping cart when they go shopping, and that their spouses and children do not like them to accompany them to social functions because of their weight [23]. At the workplace, they often feel that they are placed out of sight of the public and they may be passed over for promotion. When the 47 patients studied by Rand and Macgregor [19] were asked the same questions after gastric bypass surgery which they had answered before the surgery, their responses were dramatically different showing a much more positive view of their own position. Therefore, these perceptions are reversible, following efficient treatment of obesity. This demonstrates that the obese suffer not only from negative attitudes but, in addition, also from frank discriminatory behaviour [21]. Unsuccessful dieting may have negative psychological consequences, due to a sense of distress, failure and self-blame assumed to accompany the visible consequences of weight gain. The data supporting this are, however, mixed. In severely obese subjects, the number of previous dieting attempts was associated with mood disturbance and anxiety, and was a strong predictor of obesity-related psychosocial problems in women. In contrast, an evaluation of young women before and after treatment at a weight clinic did not detect any significant effect of one cycle of weight loss and regain on mood [24].

Obesity, Mood and Well-Being

In studies of the general population, early studies showed few consistent patterns with respect to psychosocial distress and obesity, partly due to small samples and varying assessment tools [21]. The relationship between BMI, smoking status, and depressive symptoms was studied in a large US national sample, using validated instruments. The investigators found that the relationship between obesity and depression varied by sex. Among women, but not men, greater BMI was weakly associated with elevated reports of depressive symptoms. This relationship remained significant after controlling for age, years of education, and smoking status, indicating that relative body weight is weakly related to psychological distress among women but not men [25]. Another US study sought to test the relationships between relative body weight and clinical depression, suicidal thoughts and suicide attempts in an adult US general population sample comprising over 40,000 people. Outcome measures were past year major depression, suicidal thoughts and suicide attempts. Among women, increased BMI was associated with both major depression and suicide ideation. Among men, lower BMI was associated with major depression, suicide attempts and suicidal thoughts. There were no racial differences [26]. Studies of clinical populations have used psychometric instruments for assessment of mental health and psychological functioning in obese individuals and compared

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them with healthy reference populations. In a much-cited Swedish study [27], severely obese men and women reported distinctly poorer current health and less positive mood states than the reference subjects, a situation that was worse in women than in men. Anxiety and/or depression on a level indicating psychiatric morbidity were more often seen in the obese, again more often in women. The obese subjects rated their mental well-being worse than chronically ill or injured patients, for example patients with rheumatoid arthritis, cancer survivors with no recurrence and spinal-cord injured persons several years after injury. These symptoms improved with subsequent weight loss from bariatric surgical treatment, providing further support for the idea that obesity was driving the psychological impairment. In contrast to the widely accepted view that much of the observed psychopathology associated with obesity is secondary to the obesity itself, one line of research suggests that psychosocial stress induces central obesity and the metabolic syndrome [28]. Originally suggested by Björntorp and colleagues, subsequent research has been hampered by largely cross-sectional designs and lack of prospective data. However, a recent study found that the effect of job strain on subsequent weight change was dependent on baseline BMI in men but not in women [29]. In the leanest quintile (BMI ⬍ 22) at baseline, high job strain and low job control were associated with weight loss, whereas among those in the highest BMI quintile (⬎27), these stress indicators were associated with subsequent weight gain. No corresponding interaction between baseline BMI and weight change was seen among women. Furthermore, the metabolic syndrome, with abdominal obesity as an important determinant, was recently demonstrated to be closely related to cumulative exposure to work stressors over 14 years, independent of other relevant risk factors [30]. Employees with chronic work stress (three or more exposures) were more than twice as likely to have the syndrome compared to those without work stress. Altered adrenocortical function induced by stress might influence hepatic lipoprotein metabolism and insulin sensitivity at target organs, providing a partial explanation for the social inequalities in obesity and obesity-related disorders. However, given the recent development with decreasing socioeconomic differences in obesity seen in the US this is obviously a complex issue. Other than the conventional view of obesity as a condition carrying both medical and psychosocial disabilities to the individual, obesity may also been viewed as a sociological problem deriving from current cultural norms of beauty, normality and socially acceptable behaviour. In other cultural contexts, where food was less plentiful, obesity was often considered beautiful. On the other hand, a negative attitude towards obesity, with stigmatisation of obese individuals, is not entirely a recent phenomenon, with ascetism and self-denial idealised in many Western societies throughout the centuries. One of the views driving the stigma of obesity is the notion that it is self-inflicted, with the cardinal sins of sloth and gluttony emanating from low morals and poor character. Despite increasing knowledge about the importance of heritability in obesity, and the societal changes behind the obesity epidemic, these attitudes still prevail.

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The growing stigma attached to all degrees of overweight reflects a society with a contemporary ideal of extreme leanness. There is a belief that this attitude is setting the stage for an epidemic of dieting and eating disorders. One of the repeating themes emerging from research on psychosocial aspects of obesity is the necessity for a gender perspective. Medically, due to the greater likelihood of central adiposity in men, obesity may be said to confer a greater risk among males. However, there is much evidence suggesting that the psychological and social consequences of obesity are far worse for women. Even so, it should be noted that increased prevalence of body dissatisfaction is occurring in both men and women. Is there an association between obesity and depression? In Western society, being overweight has been associated with increased risk for low self-esteem and depression. It has, however, not been quite clear whether obesity increases the risk of depression, or if depression increases the risk of obesity, or if there is a reciprocal relation such that the obese are at increased risk of depression and the depressed are at increased risk of obesity. Roberts et al. [31] summarised 11 studies studying this association using cross-sectional or prevalence study designs, with seven of these finding some evidence of greater risk of depression among the obese. But while seven of these studies found support for the proposition that the obese are at a greater risk for depression, evidence was not uniformly robust, and the temporal relation between obesity and depression was unclear. In their own study of 2,123 adults age 50 and older, participants reported their height, weight and depressive symptoms during interviews in 1994 and 1999. Subjects who were obese in 1994 had twice the risk of becoming depressed in 1999 than subjects who were not obese in 1994. They did not find any support for depression predicting subsequent obesity, after adjusting for baseline obesity, or limiting the analyses to the non-obese at baseline. Accordingly, to date, there is little conclusive evidence that obesity is caused by depression, whereas obese people do seem more prone to develop future depression.

Conclusions and Implications

The obesity epidemic is driven by societal changes, as detailed above, but on an individual level there is little evidence that psychosocial factors are causally related to obesity. The socioeconomic influences are complex and variable; even in some of the developing countries obesity is now becoming increasingly more associated with low, not high, SES, whereas in the US, socioeconomic gradients may be decreasing. Obesity seems to be causally related to mood disturbances, whereas there is no conclusive evidence that the reverse is true. Although this has not been systematically investigated, it seems plausible that negative and stigmatising attitudes towards obesity play a role in the obesity-depression link. The low self-esteem in which many overweight and obese people hold themselves secondary to the prevalent attitudes in society is probably also important. In this respect, there are key areas in which these negative consequences

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should be mitigated. Current public attitudes towards ideal body weight need to be examined, although there is a conflict in that a healthy body weight with respect to the development of cardiovascular disease probably is in the low-normal range. In addition, longevity has been linked to reduced calorie intake in several species [32]. However, on balance, there is no clear health benefit from the extremely lean aesthetic ideal propagated by advertisements, magazines and films. Discrimination and prejudice against the obese are inexcusable, particularly by health professionals, and should be put on an equal level as discrimination based on race, sex or religion.

References 1 Mendez MA, Monteiro CA, Popkin BM: Overweight exceeds underweight among women in most developing countries. Am J Clin Nutr 2005;81:714–721. 2 Chopra M, Galbraith S, Darnton-Hill I: A global response to a global problem: the epidemic of overnutrition. Bull World Health Organ 2002;80:952–958. 3 Hill JO, Wyatt HR, Reed GW, Peters JC: Obesity and the environment: where do we go from here? Science 2003;299:853–855. 4 Morabia A, Costanza MC: Does walking 15 minutes per day keep the obesity epidemic away? Simulation of the efficacy of a population wide campaign. Am J Public Health 2004;94:437–440. 5 Swinburn B, Egger G: Preventive strategies against weight gain and obesity. Obesity Reviews 2002:3: 289–301. 6 Swinburn B, Egger G: The runaway weight gain train: too many accelerators, not enough brakes. BMJ 2004; 329:736–739. 7 King T, Kavanagh AM, Jolley D, Turrell G, Crawford D: Weight and place: a multilevel cross-sectional survey of area-level social disadvantage and overweight/obesity in Australia. Int J Obes 2006;30:281–287. 8 Stunkard AJ, Sorensen TIA: Obesity and socioeconomic status: a complex relation. N Engl J Med 1993; 329:1036–1037. 9 Sundquist J, Johansson SE: The influence of socioeconomic status, ethnicity and lifestyle on body mass index in a longitudinal study. Int J Epidemiol 1998; 27:57–63. 10 Song YM: Commentary: varying relation of socioeconomic status with obesity between countries at different stages of development. Int J Epidemiol 2006; 35:112–113. 11 Sobal J, Stunkard AJ: Socioeconomic status and obesity: a review of the literature. Psychol Bull 1989;105: 260–275. 12 Monteiro CA, Moura EC, Conde WL, Popkin BM: Socioeconomic status and obesity in adult populations of developing countries: a review. Bull World Health Organ 2004;82:940–946.

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13 Zhang Q, Wang Y: Trends in the association between obesity and socioeconomic status in U.S. adults: 1971 to 2000. Obes Res 2004;12:1622–1632. 14 Wang Y, Zhang Q: Are American children and adolescents of low socioeconomic status at increased risk of obesity? Changes in the association between overweight and family income between 1971 and 2002. Am J Clin Nutr 2006;84:707–716. 15 Lissner L, Johansson SE, Qvist J, Rossner S, Wolk A: Social mapping of the obesity epidemic in Sweden. Int J Obes Relat Metab Disord 2000;24:801–805. 16 Hallstrom T, Noppa H: Obesity in women in relation to mental illness, social factors and personality traits. J Psychosom Res 1981;25:75–82. 17 Gortmaker SL, Must A, Perrin JM, Sobol AM, Dietz WH: Social and economic consequences of overweight in adolescence and young adulthood. N Engl J Med 1993;329:1008–1012. 18 Novak M, Ahlgren C, Hammarström A: A life-course approach in explaining social inequity in obesity among young adult men and women. Int J Obes 2006;30:191–200. 19 Rand CS, Macgregor AM: Successful weight loss following obesity surgery and the perceived liability of morbid obesity. Int J Obes 1991;15:577–579. 20 Wadden TA: Adverse psychosocial consequences of extreme obesity and the effects of surgically induced weight loss. Surg Obes Relat Diseases 2005;1: 56–58. 21 Fabricatore AN, Wadden TA: Psychological aspects of obesity. Clin Dermatol 2004;22:332–337. 22 Staffieri JR: A study of social stereotype of body image in children. J Pers Soc Psychol 1967;7:101–104. 23 Rand CS, Macgregor AM: Morbidly obese patients’ perceptions of social discrimination before and after surgery for obesity. South Med J 1990;83:1390–1395. 24 Foster GD, Wadden TA, Kendall PC, Stunkard AJ, Vogt RA: Psychological effects of weight loss and regain: a prospective evaluation. J Consult Clin Psychol 1996;64:752–757.

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25 Istvan J, Zavela K, Weidner G: Body weight and psychological distress in NHANES I. Int J Obes Relat Metab Disord 1992;16:999–1003. 26 Carpenter KM, Hasin DS, Allison DB, Faith MS: Relationships between obesity and DSM-IV major depressive disorder, suicide ideation, and suicide attempts: results from a general population study. Am J Public Health 2000;90:251–257. 27 Sullivan M, Karlsson J, Sjostrom L, Backman L, Bengtsson C, Bouchard C, Dahlgren S, Jonsson E, Larsson B, Lindstedt S, et al: Swedish obese subjects (SOS)–an intervention study of obesity. Baseline evaluation of health and psychosocial functioning in the first 1743 subjects examined. Int J Obes Relat Metab Disord 1993;17:503–512. 28 Epel ES, McEwen B, Seeman T, Matthews K, Castellazzo G, Brownell KD, et al: Stress and body shape: stress-induced cortisol secretion is consistently greater among women with central fat. Psychosom Med 2000;62:623–632.

29 Kivimaki M, Head J, Ferrie JE, Shipley MJ, Brunner E, Vahtera J, Marmot MG: Work stress, weight gain and weight loss: evidence for bidirectional effects of job strain on body mass index in the Whitehall II study. Int J Obes 2006;30:982–987. 30 Chandola T, Brunner E, Marmot M: Chronic stress at work and the metabolic syndrome: prospective study. BMJ 2006;332:521–525. 31 Roberts RE, Deleger S, Strawbridge WJ, Kaplan GA: Prospective association between obesity and depression: evidence from the Alameda County Study. Int J Obes Relat Metab Disord 2003;27:514–521. 32 Ingram DK, Zhu M, Mamczarz J, Zou S, Lane MA, Roth GS, deCabo R: Calorie restriction mimetics: an emerging research field. Aging Cell 2006;5:97–108.

Prof. Annika Rosengren, MD Department of Medicine, Sahlgrenska Universital Hospital/Ostra SE–416 85 Göteborg (Sweden) Tel. ⫹46 31 343 4000, Fax ⫹46 31 25 89 33, E-Mail [email protected]

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Korbonits M (ed): Obesity and Metabolism. Front Horm Res. Basel, Karger, 2008, vol 36, pp 271–286

Obesity in Art – A Brief Overview Rosalind Woodhouse Department of Endocrinology, Barts and the London, Queen Mary’s School of Medicine and Dentistry, University of London, London, UK

Abstract This brief overview of obesity in art will look at how fatness has been depicted in Western art and its antecedents from classical times to the present day; what, if anything, this can tell us about how prevalent obesity was in previous centuries, and how the meanings attached to being fat may have altered Copyright © 2008 S. Karger AG, Basel over the years.

The earliest sculptural representations of the body all show it as female, large-buttocked, obese even, although the smooth contours of the Venuses of Willendorf (c. 30,000–22,000 BC; fig. 1), and Lespugue (c. 34,000–29,000 BC; fig. 2) contrast with the lumpy obesity of the Venus of Laussel (c. 25,000–20,000 BC; fig. 3). Nigel Spivey, writing about the emphasis on ‘fatness and fertility’ in primitive art, offers neuroscientist Vilanyur. S. Ramachandran’s theory that ‘in technical terms these [excessively fleshy] features amount to hypernormal stimuli that activate neuron responses in our brain . . . something that comes naturally to us because our brains are hard-wired to concentrate perceptive focus upon objects with pleasing associations, or those parts of objects that matter most. For palaeolithic people, the female parts that mattered most were those required for successful reproduction: the breasts and pelvic girdle. The circuit of the palaeolithic brain, therefore, isolated these parts and amplified them’ [1].

Spivey argues that the tendency to distort images of the body recurs across many cultures and periods of history. In other words: ‘The drift of all popular art is towards the lowest common denominator, and there are more women who look like a potato than the Cnidian Venus. The shape to which the female body tends to return is one which emphasises its biological functions . . .’ [2].

Other theorists have denied the element of exaggeration in prehistoric art, pointing instead to the ‘relative linearity of warm-dwelling peoples, and the relative globularity of cold-dwelling ones’ at least as far back as the Palaeolithic era, so that, even allowing for some artistic licence, the figurines probably bear some credible relation to the models

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Fig. 1. Venus of Willendorf (c. 30,000–22,000 BC). Naturhistorisches Museum, Vienna. © Naturhistorisches Museum, Vienna. Photo: Alice Schumacher. With kind permission. Fig. 2. Venus of Lespugue (c. 34,000–29,000 BC). Musée de l’Homme, Paris. Reproduced with kind permission from the Musée de l’Homme, Paris. Fig. 3. Venus of Laussel (c. 25,000–20,000 BC). Musée d’Aquitaine, Bordeaux. © City of Bordeaux. Photo J.M. Arnaud. With kind permission.

who posed for them, and the archaeological evidence can therefore be assumed to establish ‘an empirical record as well as a merely aesthetic one’. This is particularly likely in view of the fact that the most globular of the figures were found at sites which must have been the coldest at the time they were sculpted, while the more linear figures have tended to be uncovered at more southerly or warmer sites [3]. Ancient Greek and Roman sculpture and pottery art tended to portray the human body in an idealised though still naturalistic way, a corollary of the ancient concept of anthropomorphism – the belief that deities took human shape – but also perhaps as further evidence of the influence of climate on body shape. However, in the interest of aesthetics it was quite usual to attenuate the limbs, particularly the legs, and to downplay or omit any deformity or sign of ageing or disease, even in portrait sculpture. True obesity, as opposed to bulky muscularity, is notably absent in surviving examples: excessive fat tended to produce a too solid ‘line’, rather than the more desirable qualities of fluidity and movement.

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Because Greek civilisation laid down that pleasure was to be moderated for the civic good, overindulgence, whether in food or other sensual pleasures, was seen as ugly and improper. Hippocrates, father of Greek medicine, viewed fat as a disease [4], and Plato, believing that excessive eating led to illness, recommended what in modern times has become known as the ‘Mediterranean diet’ of mainly cereals, vegetables, fruits and fish, with strictly limited meat, alcohol and sweet things [5]. Fat people, perceived as flawed, were marginalised by Greek society; the obesus, or stock fat character in Greek comedies by Aristophanes and others, was a sponger, drunkard, glutton or idler, far from the contemporary ideal; a figure of mockery with the additional function of flattering the spectator’s sense of superiority. If the Greeks sought to moderate pleasure in eating, Christians sought to extinguish it altogether. Food was seen by some theologians as a distraction from religious duties, as ‘external’ and polluting; preoccupation with food was viewed as the gateway to worse sins, sloth and lust [6] and so over centuries, the Church evolved a complex set of rules controlling when certain foods and drink could be consumed; Wednesdays, Fridays and the period of Lent became by custom meatless, fasting days, and by the later mediaeval period, it is thought there were between 140 and 160 designated fast days per year [7]. The early mediaeval painters shared the same overall idea of linkage between body shape and moral character that the Greeks did, but with a completely different concept of the value of the body: whereas to the ancients its form had been shared with the gods and was essentially admirable, to Christians the flesh had been seen as a cause of shame and humiliation since the Fall of Man in the Garden of Eden – and therefore as implicitly sinful and not to be flaunted. Individual portraiture as we know it today was unknown; one legacy of the Jewish foundation of Christianity had been the belief that images of the human form could be construed as the breach of the 2nd Commandment (to make no graven images). Rulers were often depicted on a larger scale than ordinary people; saints and lay people all tended to be shown as slim or even emaciated, perhaps reflecting the great emphasis in the Early Church on asceticism, fasting and the overall denial of the flesh or maybe just mirroring a world where consumption was restricted, for the mass of people, and hedged around by religious prescription even for the prosperous few. By contrast, clerical exemplars of gluttony seem to have been confined to fiction (Friar Tuck in the legend of Robin Hood, and Chaucer’s monk, who ‘liked a fat swan best, and roasted whole’) [8]. As with Classical painters and sculptors, though, it was generally only those at the margins of the painter’s vision – working people, such as the wine taster depicted here, the old, the sick and wrong-doers, who were depicted as obese (fig. 4). This resonates with contemporary experience that in the West, it is the lower socio-economic groups, with least disposable income, who are most prone to obesity due to the cheapness of high-calorie foods. Artistic realism, or at least the beginnings of it, is thought to originate with the work of Jan van Eyck, claimed as the first real portrait painter [9], although the later

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Fig. 4. Giotto, The Marriage at Cana (c. 1305–1313). Arena Chapel, Padua. Assessorato ai Musei. Politiche Culturali e Spettacolo del Comune di Padova. With kind permission. Fig. 5. Robert Campin, Portrait of a Stout Man, Robert de Masmines (c. 1425). Museo ThyssenBornemisza, Madrid. © Museo Thyssen-Bornemisza, Madrid. With kind permission.

mediaeval convention of enacting great events of religious and secular history only by stylised or idealised physical specimens went on for a long time – perhaps into the mid-19th century [10]. Van Eyck’s contemporary, Robert Campin (c. 1375–1444), could paint a realistic head and shoulders portrait of a stout aristocrat, Robert de Masmines (fig. 5), but such realism was not extended to larger-scale works such as the apocalyptic scenes of Hieronymous Bosch (c. 1450–1510) or the tableaux from peasant life of his artistic heir Pieter Bruegel the Elder (1525–1569), which were intended to portray character types rather than individual personalities. But in one Bruegel painting, The Land of Cockaigne (1567; fig. 6), three plump characters are depicted in a fairyland in which there is food in abundance (although the winter of 1564 had been the coldest of the century, followed by harvest failure in 1565) [11]. Cockaigne or Luilekkerland, Dutch for ‘lazy-glutton land’, was a popular fantasy in an age when food supplies were an obsessive concern. In addition to the stock character of the greedy peasant, a soldier and a clerk have bedded down to sleep after a feast. Beyond, a goose lies ready-cooked on a plate, a pig brings its own carving knife with it, and the fence is made of sausages [12]. Cockaigne apart, there was a real dearth of images of body types at the extremes of normal; shorter life expectancy may have contributed, because gross obesity, or conversely, emaciation, possibly suggesting serious disease states, would have been less

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Fig. 6. Peter Brueghel the Elder, The Land of Cockaigne (detail), 1576. Alte Pinakothek, Munich. Reproduced with kind permission from the Alte Pinakothek, Munich.

sustainable in earlier times. On the other hand, there may have been less natural variation in body type within localities – perhaps a corollary of restricted population mobility, and greater consanguinity. Obesity in the context of intermittent but unrelenting famines and plagues might seem unlikely, but perhaps something to do with the repeated switching between restricted and abundant fare, led to rebound weight gain like that observed in ‘yo-yo’ dieting today, especially as Western populations would have been largely of the ‘thrifty genotype’ variety. Also, the ever-present threat of food shortages, coupled with the Church’s alternating seasons of feast and fast, may well have shaped people’s eating habits in ways difficult to imagine in affluent societies today; for instance, socially sanctioned binge eating in times of plenty may have been the general rule, rather than the exception [13]. There would have been an annual cycle of plenty in winter, when the animals were slaughtered, and scarcity in spring and summer in the lead up to the harvest, and people’s eating habits would have mirrored this, with excess at harvest time and Christmas, and frugality during Lent. Unpredictable food supplies may not have been the only stressor; long, cold winters, and, for most people, extended periods of hard physical work with little time for rest at harvest time, may have edged the population towards excess weight gain related to relative sleep deprivation, which has recently been linked to up-regulation of orexigenic ghrelin and down-regulation of anorexigenic leptin [14, 15].

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Fig. 7. Antonio Moro, Catherine of Austria, Queen of Portugal (c. 1552). Museo Nacional del Prado, Madrid. All rights reserved. © Museo Nacional del Prado, Madrid. With kind permission. Fig. 8. Frans Hals, The Banquet of the Officers of the St George Militia of Haarlem, 1616. Frans Hals Museum, Haarlem. (Nicolaes van der Meer is shown in the centre foreground.) Reproduced with kind permission from the Frans Hals Museum, Haarlem. Fig. 9. Frans Hals, Nicolaes van der Meer, 1631. Frans Hals Museum, Haarlem. Reproduced with kind permission from the Frans Hals Museum, Haarlem.

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Women’s body shape might have been more vulnerable to the prevailing conditions than men’s, especially as women generally entered adulthood with a higher percentage of body fat than men. Menarche could be delayed if food supplies were sparse, resulting, potentially, in fewer births before the menopause occurred. Again, in theory, multiparous women would tend to lay down more body fat over the course of their fertile lives (pregnancy itself being an obesogenic process) than the childless, presumably leading to a greater difference in BMI, all other factors being equal, between young, unmarried women, and older mothers, than between groups of men of comparable ages. Social historian Peter Laslett has estimated the average number of children per pre-1700 marriage in England as 5, or up to 8 provided that maternal death did not supervene [16]. If the rich and powerful did became obese, though, they could choose to disguise it with flattering clothing or flaunt it as a privilege of rank, as in the many ‘swagger’ portraits of Henry VIII [17]. Antonio Moro’s painting of Catherine of Austria, Queen of Portugal (fig. 7), though, is an example of the Renaissance tradition of pregnancy portraiture, rather than a depiction of an obese woman. Pregnancy portraits like these served both as a commemoration of the important occasion of pregnancy and a visual ‘insurance policy’ in case the mother did not survive: at the time of painting, Catherine of Austria was 45 years old, and as a very elderly mother-to-be was probably at far greater risk from childbirth than a younger woman.

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During the later mediaeval period, the introduction of calorie-dense food crops from the New World, most importantly rice, maize and the potato [18], innovations in the preservation of meat and fish by salting, and the weakening of religious embargos on consumption, led to an enrichment of the common diet. At the same time, the upheaval of the Reformation, urbanisation and the beginnings of capitalism, led to big changes in who commissioned works of art and why. Church patronage of art continued, but increasing numbers of lay people with the funds to engage artists often chose from a wider range of secular and religious themes: the body ceased to be a focus of shame, to be depicted modestly, in a stylised way, and started to be a marketable commodity, to be promoted by the sitter or the artist, depending on who held the real power in the transaction. By the early 17th century, Amsterdam had overtaken Antwerp as the great international port of the north and the chief banking centre of Europe, and it was here, in the Dutch Republic, that there was a great boom in portraiture, and evidence in these pictures of the results of a more stable food supply and better diet. Art historian Kenneth Clark wrote that we know more about what the 17th-century Dutch looked like than we do about any other society, except perhaps the 1st-century Romans, and he singled out Frans Hals’ work as the exemplification of this [19]. Interestingly, there is at least one occasion where Hals (1582–1666) painted the same individual more than once, enabling us to see his figure bulking up over the years: Nicolaes van der Meer, a

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This picture is available at http://www.nationalgallery.org.uk. Fig. 10. Emmanuel de Witte, Adriana van Heusden and Her Daughter at the New Fishmarket in Amsterdam, 1661–1663. The National Gallery, London. Photo © The National Gallery, London. With kind permission.

Fig. 11. Peter Paul Rubens, Drunken Silenus and Satyrs, 1616–1617. Alte Pinakothek, Munich. Reproduced with kind permission from the Alte Pinakothek, Munich.

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wealthy Haarlem brewer and burgomaster, depicted first, aged about 42 in 1616, in a large group portrait, Banquet of the Officers of the St George Militia (fig. 8), and subsequently in a portrait of 1631 when he would have been around 57 years of age (fig. 9). The well-fleshed appearance of people from all social strata in Holland reflected the absence of the severe famines experienced elsewhere in Europe during this period [20] and gives an air of bustling prosperity to genre paintings such as the market scene by Emmanuel de Witte (c. 1616–1691/1692) (fig. 10). Allegedly it is with 17thcentury Dutch portraiture that breasts are seen as attractive for the first time: previously, unobtrusive, small breasts were to be preferred; large breasts being linked with moral laxity and even witchcraft [21]: this novel voluptuousness gives another clue to Holland’s being a well-nourished society. A handful of prominent Flemish and Dutch artists stand out as advocates of the larger body. Among the best known, Rubens (1577–1640) and Rembrandt (1606–1669) both dwelt on the texture of flesh, but whereas Rubens concentrated on allegorical portraits or tableaux featuring the legendary or the lusciously nubile (fig. 11, 12), Rembrandt, chronicler of life’s misfortunes, depicted all conditions of men and women, and was accused in his day of seeking out the gratuitously unappealing (fig. 13). Later, Degas (1834–1917) used a similarly wide range of (mainly female) subjects as Rembrandt for his mainstream work, but it is in his less well-known brothel monotypes that he explores a very different kind of nude: squat, short-necked (fig. 14) and very far from his pastel studies of ballet dancers (fig. 15). Renoir’s (1841–1919) women ‘massive, ruddy … with the weight and unity of great sculpture’ (fig. 16) [22] continue in the tradition of Rubens, rather than emulating Degas’ realism. However, in both instances there was an emphasis on fleshiness, either as a mark of sensuality or as a reflection of moral turpitude. Heaviness and obesity had now been synonymous with wealth, success and elevated social status, for centuries, but the link was soon to be put into reverse by a complex blend of economic and social developments: by the later 19th century, advances in agricultural methods, food processing and transportation, had brought a varied, calorie-rich diet within the reach of all but the poorest, across much of Europe and North America, opening up the possibility of fatness as a life ‘choice’ for the many, for the first time ever. However, for the elites, ‘Slimness, together with speed, productivity and efficiency, were beginning to be advocated as a new aesthetic and cultural model. A new Puritanism, which shared obvious traits with traditional Christian penitence, re-launched the image of a lean, slender and productive body; the bourgeois body which ‘sacrifices itself ’ to the production of goods and wealth’ [23].

For the privileged minority, eating to excess as a way of displaying wealth and privilege was gradually being replaced by other forms of conspicuous consumption: the concept of overweight being a hindrance to living a newer, more fluid kind of ‘good life’ dawned first with those at the top of society, and diffused downwards [24]. In the early stages of this transformation, slimness would have been seen as a novelty (just as obesity had once been) for the majority of people. Fleshiness was still linked to prosperity in the collective mind, and thinness was often associated with tuberculosis

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Fig. 12. Peter Paul Rubens, The Three Graces, 1636–1638. Museo Nacional del Prado, Madrid. All rights reserved. © Museo Nacional del Prado, Madrid. With kind permission.

Fig. 13. Rembrandt van Rijn, Seated Female Nude (c. 1631). British Museum, London. © The Trustees of the British Museum, London. With kind permission.

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and chronic ill health [25]. It was only at the very end of the 19th century that slimness, rather than obesity, began to be seen (first in the United States) as a desirable standard for all, heralded by a dramatic increase in the numbers of advertisements for diets, and editorials in the popular and medical press on the dangers of excess weight [26]. Suggested reasons for why disapproval of obesity gained ground so quickly have included deliberate manipulation by an emergent diet industry, an increased middle-class interest in athleticism (manifested by the bicycle craze of the 1880s and 1890s), both perhaps springing from accelerating industrialisation and urbanisation, and an overall speeding up of the pace of life for everyone. Now, to aspire to slimness meant to stand out from the crowd, just at a time when most people had managed to achieve the requisite standard of living to be plump. In addition, photographic portraiture was becoming increasingly accessible and revealing to its subjects their true, three-dimensional shape for the first time. More reliable methods of contraception were becoming generally available; women could more easily limit family size, and so reduce the amount of weight they put on during their fertile years. In this new ‘machine age’, carrying excess weight might become a hindrance to ‘staying ahead’ [27]. Once the connection between slimness and social advantage had been formed, it was just a short step to obesity becoming socially undesirable, stigmatising even: by the early 20th century, this had been reinforced by insurance companies’ promoting ideal body weight to height tables [28], showing that the medical establishment was now ‘on board’ also. During the century since then, the desirability of slimness over heaviness has been accepted more or less unchallenged by Western societies: its cachet has increased as the average Western citizen has become fatter and the population profile has aged – slimness now being associated with youth. ‘Super-sizing’ of the human body in art has continued, but has been reserved for monumental sculpture such as the socialist realist statuary of countries of the former Soviet bloc, for surrealist interpreters of the human form or for artists with agendas involving humour, social comment, or a voyeuristic take on the outsized. Lucien Freud (b. 1922), speaking about his model for Benefits Supervisor Resting, admitted that he ‘had perhaps a predilection towards people of unusual or strange proportions’ and had become aware of ‘all kinds of spectacular things to do with her size, like amazing craters and things one’s never seen before’ [29]. (Indeed, one art historian found her body proportions to be identical to those of the Willendorf Venus [30]). Jenny Saville (b. 1970) was well-known for her massive, uncompromising canvases of naked, obese women even before the controversial 1997 ‘Sensation’ exhibition at the Royal Academy, where she shared billing with others similarly engaged with mutilated and abnormal forms. She started painting in the 1980s when ‘everyone was obsessed with the body – it was all about dieting, the gym, the body beautiful. Pornography and AIDS were the big debates’ [31]. Her huge images were always female, ‘massive as the Eiger’, ‘daunting’ and ‘confrontational’ [32]. Sponsored by Charles Saatchi, she spent part of 1994 in New York observing and photographing

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plastic surgery procedures, subsequently expanding her repertoire with studies of liposuction, bariatric surgery, and transgendered bodies (fig. 17). James Gillray (1756–1815) and Thomas Rowlandson (1756–1827; fig. 18), popular and successful satirists, underlined the foibles of British society by means of exaggerated body types for stock characters such as the obese country squire or rector, the skinny doctor and the plump young woman ‘on the make’, and it was usually the fat characters who were the most comical and lampooned. This was especially true for Gillray, whose political cartoons made much of ‘Prinny’s’ (the future King George IV’s) corpulence, and linked this with his supposed moral and intellectual torpor. Napoleon was similarly dismissed in his cartoons as a quarrelsome, squat dwarf. Beryl Cook (b. 1926) has continued the humorous tradition, depicting benevolent endomorphs in a style blending surreal and naive. Comparisons have been made with Gillray, Hogarth and Pieter Bruegel. Cook admits to going out to clubs, bars and other likely settings for her paintings and covertly making sketches to work from – she has described herself as being an introvert – not as at all as her characters appear – and so her pictures involve a sometimes voyeuristic, but never sleazy, take on everyday pleasures. Her figures are invariably rotund and so she may be the artist who best represents the recent upward trend in BMI. In fact, this could be one factor in her popularity. Fernando Botero’s characters share the same pneumatic body type as Beryl Cook’s, but his subject matter is wider ranging, encompassing religious and political themes and still life as well as genre scenes. Botero (b. 1932) emigrated from South America to Europe, studying the old masters before arriving at his now familiar style by the mid-1960s: figurative, but not realistic; inflated, balloon-like figures against backdrops of similarly voluminous objects. Perhaps influenced by growing up in Colombia during a time of civil unrest, some of his pictures show a critical awareness of political realities. In Botero’s Official Portrait of the Military Junta the implicit menace and violence of the military subjects depicted are here at variance with the style of their depiction, as plump, sometimes childlike figures. Unfortunately, the image can not be reproduced here, on the grounds that ‘Mr Botero objects to any usage of his work in connection with health or weight issues. This is not the message that his work is designed to send’ [33]. Mariana Hanstein, writing about Botero’s style explained it thus: ‘Whatever theme he takes up, his eccentrically expansive style robs it of harshness, viciousness, extremism . . . his exaggerated volumes are precisely the magic wand with which he transforms life and the world and transports them into a floating unreality [34].

Others have seen his subject matter as ‘tamed to death’ representing a created world ‘enormously fat and complacent’ [35], although Botero himself has explained his use of obese forms as ‘ the expression of abundance’ and claimed that ‘in art, as long as you have ideas and think, you are bound to deform nature. Art is deformation’. Returning to the question of whether obesity was more or less prevalent compared with our own times, it is of course, impossible to be sure. Moderate and extreme

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Fig. 14. Edgar Degas, The Customer, 1876–1877. Musée d’Orsay, Paris. Reproduced with kind permission from the Musée d’Orsay, Paris.

Fig. 15. Edgar Degas, Dance Lesson, 1872. Musée d’Orsay, Paris. Reproduced with kind permission from the Musée d’Orsay, Paris.

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16

17 Fig. 16. Pierre-Auguste Renoir, The Great Bathers (The Nymphs), 1918–1919. Musée d’Orsay, Paris. Reproduced with kind permission from the Musée d’Orsay, Paris. Fig. 17. Jenny Saville, Plan, 1993. Gagosian Gallery, New York, N.Y. © Jenny Saville. Courtesy of Gagosian Gallery, New York, N.Y. Photograph by Robert McKeever.

obesity as we understand it today in industrialised societies, whether at first hand, or via the media, seems much less prevalent in the artworks of previous centuries, whatever type of person is being represented, across all social strata. But it is always difficult to determine how far paintings and sculpture revealed the painter’s aspirations for his sitters (and the sitters’ for themselves) rather than how things actually were in reality. (Kenneth Clark has commented that in art, the instinctive desire is ‘not to imitate but to perfect’ [36]). Imagery is culturally determined and these images should be read, not as straightforward documents, but within a framework of contemporary artistic practices. Certainly, gender differences in how obesity was depicted run through the whole history of Western art: women were depicted nude more often then men, and hence it is much easier to assess the amount of fat they carry. Anne Hollander, costume historian, has written that the naked body is rendered in art as if it retains the imprint of its dress – that though clothing has been removed, the nude body has been cast in its mould [37]. Fashion has probably always influenced how the body was represented: what was currently unfashionable at any time may have been simply ‘edited out’. It does seem likely, though, that once being fat had ceased to be a life-saving tactic for the mass of people, there came to be an innate sense of what was acceptable for the body in terms of size – a ‘happy medium’ where the body had enough padding for warmth and protection, but not sufficient to get in the way of everyday

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Fig. 18. Thomas Rowlandson, A Little Tighter, 1791. British Museum, London. © The Trustees of the British Museum, London. With kind permission.

activities [38]. In other words, once the daily business of survival ceased to be a struggle, conspicuous markers of success other than extreme obesity would have been sought. Even so, from the evidence, the ideal weights for both men and women would probably have been heavier than present day ones for most of the period under consideration. The trim, athletic body proportions of classical art were feasible in the context of stable, prosperous, mercantile societies with good and varied food supplies and a warm climate, but when, subsequently, contentious northern European civilisations operating in colder climates became pre-eminent, the depicted ideal was replaced by a stockier, less elegant model. From early modern times until just over a century ago, excess weight had positive associations with wealth, success, physical strength and health, and none of its current negative associations with sudden death, chronic disease, shorter life expectancy and ‘loser’ social status. Intimations that excess weight might have drawbacks coincided with a ‘democratisation’ of obesity, as a high-calorie diet came within the reach of the majority, and led to slimness soon replacing heaviness as a mark of social distinction, initially just for women, but subsequently for men as well. Fat in art could no longer be so ‘mainstream’, once the body ideal had shifted, but still remained as an important theme in painting, particularly in naive art. The paradox now is that while thinness has become ever more valued, in real life and in the media, the prevalence of obesity in society has soared, and even Saville and Freud nudes no longer outscale everyone around them.

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References 1 Spivey N: How Art Made the World. London, BBC Books, 2005, pp 57–60. 2 Clark K: The Nude. London, Penguin Books, 1964, p 87. 3 Spivey N: How Art made the World. London, BBC Books, 2005, p 63. 4 Klein R: Eat Fat. New York, Vintage Books, 1996, p 123. 5 Skiadas PK, Lascaratos JG: Dietetics in ancient Greek philosophy: Plato’s concepts of healthy diet. Eur J Clin Nutr 2001;55:532–537. 6 Coveney J: Food, morals and meaning: the pleasures and anxiety of eating. London, Routledge, 2000, pp 32–44. 7 Pleij H: Dreaming of Cockaigne: medieval fantasies of the perfect life. New York, Columbia University Press, 1997, p 132. 8 Chaucer G: The Canterbury Tales: the Prologue. Translated by Nevill Coghill. Penguin Books, 1975, p 24. 9 Clark K: Civilisation. London, BBC & John Murray Books, 1969, p 104. 10 Clark K: Civilisation. London, BBC & John Murray Books, 1969, p 133. 11 Koenigsberger HG, Mosse GL, Bowler GQ: Europe in the sixteenth century, ed 2, New York, Longman, 1989, Appendix. 12 Pleij H: Dreaming of Cockaigne: medieval fantasies of the perfect life. New York, Columbia University Press, 1997, Introduction. 13 Pleij H: Dreaming of Cockaigne: medieval fantasies of the perfect life. New York, Columbia University Press, 1997, p 130. 14 Spiegel K, Knutson K, Leproult R, Tasali E, van Cauter E: Sleep loss: a novel risk factor for insulin resistance and type 2 diabetes. J Appl Physiol 2005;99: 2008–2019. 15 Spiegel K, Tasali E, Penev P, van Cauter E: Brief communication: sleep curtailment in healthy young men is associated with decreased leptin levels, elevated ghrelin levels, and increased hunger and appetitie. Ann Intern Med 2004;141:846–850. 16 Laslett P: The World We Have Lost. London, Methuen Books, 1979, p 129. 17 Klein R: Eat Fat. New York, Vintage Books, 1996, pp 234–236. 18 Montanari G: The Culture of Food. Oxford, Blackwell, 1994, pp 101–104.

19 Clark K: Civilisation. London, BBC & John Murray Books, 1969, p 195. 20 Schama S: The Embarrassment of Riches: An Interpretation of Dutch Culture in the Golden Age. London, Fontana, 1987, pp 167–172. 21 Korda H: Extreme beauty: the body transformed. New York, The Metropolitan Museum of Art, 2001, p 52. 22 Clark K: The Nude. London, Penguin Books, 1964, p 159. 23 Montanari G: The Culture of Food. Oxford, Blackwell, 1994, p 167. 24 Featherstone M, Hepworth M, Turner BS: The Body: Social Process and Cultural Theory. London, Sage Publications, 1991, p 147. 25 Klein R: Eat Fat. New York, Vintage Books, 1996, p 39. 26 Kersh R, Morone J: How the personal becomes the political: prohibitions, public health, and obesity. Studies in American Political Development 2002;16: 162–175. 27 Klein R: Eat Fat. New York, Vintage Books, 1996, p 110. 28 Beller AS: Fat and Thin: A Natural History of Obesity. New York, Farrar, Straus and Giroux, 1977, p 5. 29 Feaver W: Lucien Freud. London, Tate Gallery Publishing, 2002, p 45. 30 Hersey GL: The Evolution of Allure: Sexual Selection from the Medici Venus to the Incredible Hulk. Cambridge, Massachusetts, Ltd. The MIT Press, 1996, pp 42–43. 31 Mackenzie S: Under the Skin. London, The Guardian, 2005. 32 Graham-Dixon A: She ain’t heavy, she’s my sister. London, The Independent, Feb 8, 1994. 33 E-mail communication from Karen Kadlecsik, Marlborough Gallery, New York, 9th February 2007. 34 Hanstein M: Botero. Cologne, Taschen, 2003, p 54. 35 Moravia A quoted in Hanstein M: Botero. Cologne, Taschen, 2003, p 58. 36 Clark K: The Nude. London, Penguin Books, 1964, p 90. 37 Hollander A quoted in Korda H: Extreme Beauty: the body transformed. New York, The Metropolitan Museum of Art, 2001, Introduction. 38 Featherstone M, Hepworth M, Turner BS: The Body: Social Process and Cultural Theory. London, Sage Publications, 1991, p 147.

Rosalind Woodhouse, BA Hons Department of Endocrinology Barts and the London, Queen Mary’s School of Medicine and Dentistry, University of London Charterhouse Square, London EC1M 6BQ (UK) Tel. ⫹44 20 7882 6238, Fax ⫹44 20 7882 6197, E-Mail [email protected] Copyright. The author and the publisher have made every effort to obtain permission for all copyright-protected material. Any omissions are entirely unintentional. The publisher would be pleased to hear from anyone whose rights unwittingly have been infringed.

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Author Index

Ahima, R.S. 182 Al-Zaman, Y. 229 Armitage, J.A. 73 Aylwin, S. 229

Lissner, L. 260

Beales, P.L. 37 Beedle, A.S. 61

Osei, S.Y. 182

Clarke, I.J. 107 Cota, D. 135 Farooqi, I.S. 1 Gluckman, P.D. 61 Goldstone, A.P. 37 Grossman, A.B. VII, 198 Hanson, M.A. 61 Kiess, W. 12 Kola, B. 198 Korbonits, M. IX, 198 Körner, A. 12 Kovacs, P. 12

McPhee Chapman, I. 97 Morton, N.M. 146

Parton, L.E. 118 Poston, L. 73 Raubenheimer, D. 61 Rosengren, A. 260 Rutter, G.A. 118 Sabin, M.A. 85 Seckl, J.R. 146 Shield, J.P.H. 85 Stumvoll, M. 12 Taylor, P.D. 73 Weaver, J.U. 212 Woodhouse, R. 271 Wren, A.M. 165

287

Subject Index

Adipokines, see also specific adipokines functional overview 182, 183, 189, 190 types and functions 184, 185 Adiponectin AMP-activated protein kinase interactions 190 functional overview 184 levels in obesity 189 receptors 190 structure 189 Adipsin, functional overview 185 Aging, see Elderly Agouti-related peptide (AgRP) antagonist therapy 253 appetite regulation 109, 139, 169 Albright’s hereditary osteodystrophy (AHO), features 42, 43 Alström syndrome (ALS), features 40, 41 AMP-activated protein kinase adiponectin interactions 190 adipose tissue metabolism role 201, 202 AMP activation mechanisms 199 endocrine pancreas effects 202 functional overview 198, 199 hypothalamus function 202 insulin secretion control 119, 126 leptin targeting 187 liver metabolism role 201 metabolic hormone mediation 203 obesity activity animal models 203–205

288

humans 206, 207 skeletal muscle metabolism role 199–201 whole body metabolism regulation 207 Amylin, therapeutic potential 253, 254 Anandamide, endocannabinoid system 137, 138 Angiotensin II, adipokine activity 186 Animal models, obesity AMP-activated protein kinase activity 203–205 appetite regulation in large animals and birds 108–110 epigenetic effects 115 genetic models in livestock 112 insulin dynamics in ruminants 111 leptin effects 111, 112 livestock advantages over rodents 107, 108 natural models of adiposity changes hibernation 115 migratory birds 114, 115 reindeer 114 predisposition studies 113 Apoptosis, fatty acid induction in ␤-cells 122, 123 Appetite aging effects 98, 99 agouti-related peptide regulation 109, 139, 169 bariatric surgery effects 248–250 cholecystokinin satiety signaling 172 ghrelin regulation 109, 110, 168, 169

leptin regulation 109 melanocortin system regulation 110 neuroendocrine control 68 neuropeptide Y regulation 109, 110, 139, 169 regulation in large animals and birds 108–110 2-Arachidonoylglycerol (2-AG), endocannabinoid system 137, 138 Area postrema (AP), peripheral signals of energy balance 167 ARNT, expression in diabetic ␤-cells 124 Art, obesity representations ancient Greece and Rome 271–273 18th century 279 famine and plague 275 medieval period 273–277 19th century 279, 281, 283 prospects 285 17th century 277–280 20th century 281, 282, 284 women’s body shape 276, 284 Association analysis, genetic dissection of complex diseases 16 Bardet-Biedl syndrome (BBS) association studies 40 clinical features 37–40 obesity 40 Bariatric surgery appetite reduction effects 248–250 childhood obesity management 94 complications 243, 244 diabetes type 2 response 245, 250, 251 elderly patients 104, 105 gut hormone response 179, 248–250 malabsorptive and hybrid procedures 242, 243 metabolic syndrome response 245 non-alcoholic fatty liver disease response 246 obstructive sleep apnea response 246 restrictive bariatric surgery 241, 242 success criteria 240, 241 weight loss outcomes 243 Beta-cell AMP-activated protein kinase activation effects 202 diabetes type 2 defective insulin secretion mechanisms dense core vesicle exocytosis 126, 127

Subject Index

DNA microarray analysis 124, 125 fatty acid-induced apoptosis 122, 123 genetics 120–122 glucolipotoxicity 122 mitochondrial oxidative metabolism 125, 126 peroxisome proliferator-activated receptor-␥ 124 signaling processes 125 SREBP1c 123 loss in diabetes 119 microRNA regulation of function 127 stimulus-secretion coupling 118, 119 Body mass index (BMI) children 86–88 classification 73, 229 elderly 97, 98 Börjeson-Forssman-Lehmann syndrome (BFLS), features 52 Bupropion, obesity management 251 Candidate gene approach for genetic dissection of complex diseases 14 diabetes type 2 risk genes CDKAL1 26 CDKN2A 26 genome-wide scans 25, 26 IGFBP2 26 TCF7L2 24, 25 gene-gene interactions 26, 27 obesity risk genes ENPP1 21, 22 FTO 22, 23 INSIG2 20, 21 peroxisome proliferator-activated receptor-␥ 18–20 Cannabinoids, see Endocannabinoid system Cardiovascular disease, AMP-activated protein kinase role 207 Carpenter syndrome, features 42 Catecholamines, thyroid hormone interactions 216 CDKAL1, diabetes type 2 candidate gene 26 CDKN2A, diabetes type 2 candidate gene 26 Children genetic dissection of complex diseases 17 growth importance on long-term health 85, 86

289

obesity causes 88, 89 consequences cardiovascular complications 90 metabolic syndrome 90 non-alcoholic fatty liver disease 91 respiratory complications 90, 91 type 2 diabetes 89, 90 definition 86–88 management 92–94 prevalence 88 prevention 91, 92 Cholecystokinin (CCK) energy balance signaling 166 satiety signaling 141, 172 therapeutic potential 254 Ciliary neurotrophic factor (CNTF), obesity management 253 Cohen syndrome (CS), features 41, 42 Cortisol cortisone therapy 150 levels in obesity 148, 149 Cushing’s syndrome causes 220, 221 clinical features 147, 220 diagnosis 222, 223 epidemiology 221 11␤-hydroxysteroid dehydrogenase defects, see 11␤-Hydroxysteroid dehydrogenase obesity mechanisms 222 pseudo-Cushing’s syndrome 221, 222 Depression, obesity comorbidity 266–268 Developmental origins of health and disease, see Fetal environment; Maternal obesity Diabetes type 2 beta-cell defective insulin secretion mechanisms dense core vesicle exocytosis 126, 127 DNA microarray analysis 124, 125 fatty acid-induced apoptosis 122, 123 genetics 120–122 glucolipotoxicity 122 mitochondrial oxidative metabolism 125, 126 peroxisome proliferator-activated receptor-␥ 124 signaling processes 125

290

SREBP1c 123 loss 119 candidate genes CDKAL1 26 CDKN2A 26 genome-wide scans 25, 26 IGFBP2 26 TCF7L2 24, 25 childhood obesity effects 89, 90, 230 maternal effects on offspring 76, 77 obesity treatment response 244, 245, 250 Pima Indian studies 17, 18 Diet-induced obesity mouse, AMP-activated protein kinase activity 204 Elderly aging effects body composition fat stores 99 sarcopenia 99, 100 body weight 98, 99 food intake and appetite changes 98 obesity causes 100 consequences beneficial effects 102 morbidity 102 mortality 101 management lifestyle modification 103, 104 medications and surgery 104, 105 rationale 102, 103 prevalence 97 Endocannabinoid system (ECS) cannabinoid receptors agonists and antagonists 138 antagonist therapy for obesity 142, 143 overview 136, 137 endocannabinoid types 137, 138 energy balance regulation hypothalamus 139 peripheral metabolism 140, 141, 144 reward system 140 obesity role 141, 142 ENPP1, obesity candidate gene 21, 22 Epidemiology, obesity 74, 147, 165, 198 Epigenetics complex diseases 27, 28 environmental factor studies in livestock 115

Subject Index

mechanisms of nutritional programming 66, 67 Exercise AMP-activated protein kinase response 206 see also Lifestyle modification Exocytosis, defects in diabetic ␤-cells 125–127 Fat deposition aging effects 99 patterns 62, 63 polycystic ovarian syndrome patterns 218, 219 Fat intake, maternal role in developmental origins of health and disease 80, 81 Fatty acid synthetase (FAS), Pima Indian polymorphisms 18 Fen-phen 237, 240 Fetal environment, see also Maternal obesity developmental origins of disease 63, 75–77 fetal size constraints 69 mechanisms of nutritional programming epigenetics 66, 67 glucocorticoids 67 metabolic partitioning 67 neuroendocrine control of appetite 68 overnutrition effects 66 thrifty phenotype model 64 Fluoxetine, obesity management 251 Food intake, see Appetite Fragile X syndrome, features 52 FTO, obesity candidate gene 22, 23 Gastric surgery, see Bariatric surgery Genetics of Obesity Study (GOOS) 1, 2 Gestational diabetes, offspring outcomes 78 Ghrelin appetite regulation 109, 110, 168, 169 bariatric surgery response 248, 249 growth hormone regulation 168 long-term energy homeostasis role 170, 171 polycystic ovarian syndrome levels 220 pre-prandial hunger contributions 169, 170 processing 168 receptor 168, 171 therapeutic targeting 171, 172, 254 Glucagon-like peptide-1 (GLP-1) bariatric surgery response 249 processing 176 receptor 178

Subject Index

satiety signaling 176 therapeutic potential 177, 179, 255 Glucocorticoids levels in obesity 148, 149 mechanisms of nutritional programming 67 metabolism, see 11␤-Hydroxysteroid dehydrogenase polycystic ovarian syndrome role 219 Glucolipotoxicity, ␤-cell dysfunction 122 GLUT2, expression in diabetic ␤-cells 124 GLUT4, AMP-activated protein kinase regulation of expression 199 Growth hormone Cushing’s syndrome levels 223 ghrelin regulation 168 thyroid hormone interactions 216 Heritability, obesity 1 Hexokinase II, AMP-activated protein kinase regulation of expression 199 11␤-Hydroxysteroid dehydrogenase glucocorticoid metabolism 149, 150 isoforms 149, 150 polycystic ovarian syndrome levels 219 type 1 enzyme ApoE combination transgenic mouse phenotype 154, 155 circulating substrate levels 150, 151 functional studies adipose tissue 152 liver 151 regulation in obesity 152, 153 knockout mouse phenotype 155, 156 therapeutic targeting 158 transgenic mouse model of metabolic syndrome 153, 154 type 2 enzyme transgenic mouse 156–158 Hypothalamic-pituitary-adrenal axis, glucocorticoid levels 147, 148 Hypothalamus AMP-activated protein kinase function 202 central hypothyroidism 223–225 energy balance regulation 139 peripheral signals of energy balance 166–168 Hypothyroidism central hypothyroidism 223–225 clinical features 213, 214 epidemiology 213 thyroid hormone, see Thyroid hormone weight gain mechanisms 215, 216

291

Idiopathic intracranial hypertension (IIH), obesity treatment response 246 IGFBP2, diabetes type 2 candidate gene 26 Infant nutrition effects in later life 66 mechanisms of nutritional programming epigenetics 66, 67 glucocorticoids 67 metabolic partitioning 67 neuroendocrine control of appetite 68 INSIG2, obesity candidate gene 20, 21 Insulin resistance, see Diabetes type 2; Metabolic syndrome ruminant dynamics 111 secretion, see Beta-cell Interleukin-6 (IL-6), adipokine activity 185 Intraflagellar transport (IFT), Bardet-Biedl syndrome defects 38–40 Isolated populations, genetic dissection of complex diseases 16, 17 Leptin AMP-activated protein kinase activity in knockout mice 205 targeting 187 appetite regulation 109 deficiency clinical phenotypes 3, 4 therapy responses 3–5 endocrine and reproductive effects in livestock 111, 112 functional overview 184, 187 gene polymorphisms in livestock 112 immune function 188, 189 levels in obesity 183 mechanisms of action 187, 188 mutations 2 polycystic ovarian syndrome levels 219, 220 receptor isoforms 183, 185 mutations 2 signaling 184, 186 tissue distribution 187, 188 therapeutic potential 252, 253 thyroid hormone interactions 216 Lifestyle modification childhood obesity management 92, 93 elderly 103, 104 societal factors in obesity 260–262

292

Linkage analysis, genetic dissection of complex diseases 14, 15 Livestock, see Animal models, obesity Macrosomia, obesity, macrocephaly, and ocular abnormalities (MOMO), features 53 Maternal obesity developmental origins of health and disease maternal obesity and fat intake role 80, 81 programming vectors 78–80 gestational diabetes outcomes 78 offspring effects animal studies 75, 76 complications 82 observational studies 75 type 2 diabetes 76, 77 Maturity-onset diabetes of the young (MODY), gene mutations 120, 121 MCT-1, expression in diabetic ␤-cells 125 Melanocortin receptor MC4R Albright’s hereditary osteodystrophy mutations 43 deficiency in obesity 7–9 Melanocortin system, appetite regulation 110 ␤-Melanocyte-stimulating hormone (␤-MSH), mutations in obesity 6 Metabolic syndrome childhood obesity sequelae 90 11␤-hydroxysteroid dehydrogenase type 1 therapeutic targeting 158 transgenic mouse model 153, 154 obesity treatment response 245 Metformin clinical trials in obesity 239, 240 polycystic ovarian syndrome response 247 MicroRNA, ␤-cell function regulation 127 Mismatch pathway evolutionary perspective 68, 69 fetal overnutrition effects 66 mechanisms of nutritional programming epigenetics 66, 67 glucocorticoids 67 metabolic partitioning 67 neuroendocrine control of appetite 68 overview 62 mTOR pathway, inhibition by AMP-activated protein kinase 200

Subject Index

Neuropeptide Y (NPY) antagonist therapy 253 appetite regulation 109, 110, 139, 169 receptors 172, 173 Non-alcoholic fatty liver disease (NALFD) childhood obesity 91 obesity treatment response 245, 246 Nucleus of the solitary tract (NTS), peripheral signals of energy balance 167 Obesity complications, see also specific complications classification 232–235 prevention 231 treatment 231, 232 Obesity staging, King’s College criteria 234, 235 Obstructive sleep apnea (OSA), obesity treatment response 246 Orlistat childhood obesity management 93 clinical trials in obesity 237 diabetes type 2 response 244 elderly patients 104 non-alcoholic fatty liver disease response 246 sibutramine combination therapy 240 Oxyntomodulin processing 176 receptor 178 satiety signaling 176, 177, 254 therapeutic potential 177, 178, 254, 255 Pancreatic polypeptide (PP) appetite effects 176 long-term energy homeostasis role 175 post-prandial satiety role 175 receptors 172, 173 secretion 175 Paraventricular nucleus (PVN), Prader-Willi syndrome defects 40 Peptide YY (PYY) bariatric surgery response 248, 249 dietary manipulation 179 energy balance signaling 166 forms 173 long-term energy homeostasis role 174 mechanism of action 174, 175 post-prandial satiety role 173, 174 receptors 172, 173 secretion 173

Subject Index

therapeutic potential 255 Peroxisome proliferator-activated receptor-␥ (PPAR-␥) ␤-cell dysfunction role in diabetes 124 obesity candidate gene 18–20 Phentermine 237 PHF6, Börjeson-Forssman-Lehmann syndrome mutations 52 Pima Indians, type 2 diabetes studies 17, 18 Plasminogen activator inhibitor-1 (PAI-1), adipokine activity 185 Polycystic ovarian syndrome (PCOS) appetite-regulating hormone levels 219, 220 clinical features 217, 218 diagnostic criteria 217 epidemiology 217, 218 fat distribution patterns 218, 219 glucocorticoid roles 219 metformin response 247 obesity treatment response 246, 247 Population attributable fraction (PAF), genetic dissection of complex diseases 15, 16 Positional cloning, genetic dissection of complex diseases 14, 15 Prader-Willi syndrome (PWS) association studies 50 chromosome deletions 1p36 50, 51 2q37 51 6q16 50 9q34.3 51 chromosome 14 maternal uniparental disomy 51 clinical features 43–45 functional neuroimaging studies 50 genetics 45, 46 hypothalamic abnormalities 49 obesity 46, 47 peripheral appetite signals 47–49 Pramlintide, see Amylin Prevention obesity 230 obesity complications 231 Prohormone convertase 1 (PC1), mutations in obesity 6, 7 Proopiomelanocortin (POMC) complete deficiency 5 haploinsufficiency 5, 6 mutations in obesity 6

293

Psychiatric comorbidity, obesity obesity treatment response 247 overview 266–268 Quality of life, obesity treatment response 247 RAB23, Carpenter syndrome mutations 42 RBP4 functional overview 185, 191, 192 receptor 192 Resistin functional overview 184 secretion 191 structure 190 Rimonobant childhood obesity management 93 clinical trials in obesity 143, 239 Rubinstein-Taybi syndrome, features 43 Sarcopenia, aging 99, 100 Sertraline, obesity management 251 Sibutramine childhood obesity management 93 clinical trials in obesity 237–239 elderly patients 104 obstructive sleep apnea response 246 orlistat combination therapy 240 Single nucleotide polymorphism (SNP), genetic dissection of complex diseases 15, 16 Sleep apnea, see Obstructive sleep apnea Societal factors, obesity causes 260–262 Socioeconomic status (SES), obesity association studies 263–265 SREBP1c, ␤-cell failure role in diabetes 123

294

Stigmatization, obesity 265, 266 Surgery, see Bariatric surgery TCF7L2, diabetes type 2 candidate gene 24, 25, 121, 122 ⌬9-Tetrahydrocannabinol (THC), food intake effects 135 Thrifty genotype hypothesis 12, 13, 63 Thyroid hormone hypothyroidism, see Hypothyroidism interactions catecholamines 216, 217 growth hormone 216 leptin 216 mitochondrial actions 217 receptors 215 types 214, 215 Topiramate, obesity management 251, 252 Trends, obesity 74 Tropomyosin-related kinase B (TrkB), mutations in obesity 9 Tumor necrosis factor-␣ (TNF-␣), adipokine activity 185 Vaspin, adipokine activity 192 Visfatin functional overview 186, 191 secretion 191 Weight management, compliance 231 Zonisamide, obesity management 251, 252 Zucker rat, AMP-activated protein kinase activity 204

Subject Index