The Metabolic Syndrome

The Metabolic Syndrome is a valuable reference text, covering all aspects of the metabolic syndrome and its constituent diseases including inflammation, oxidation and adipocytokines. This book explains the aetiology, pathogenesis and clinical treatment of all risk factors as well as the relationship with diabetes, non alcoholic fatty liver disease, polycystic ovary syndrome and coronary heart disease.

The Metabolic Syndrome  has been further improved from the 1st edition that was highly commended in 2006 Annual British Medical Association medical books competition.  All chapters from the first edition are fully updated and this new edition contains an increase in international contributions and five new chapters on:

• Childhood obesity and metabolic syndrome
• Bariatric surgery for obesity
• Fitness
• Brain insulin resistance and appetite
• The nature of the insulin resistance seen in metabolic syndrome.

This brand new edition of The Metabolic Syndrome will be an indispensable resource for all clinical researchers, physicians and scientists requiring detailed up-to-date information on the metabolic syndrome to further their own research or to treat and manage the syndrome and its complications. Specifically, the text will be of particular relevance to those involved and working in the fields of diabetes, endocrinology, obesity, cardiology, vascular disease and hepatology.

• Language: English
• Publisher: Wiley
• Release date: Aug 8, 2011
• ISBN: 9781444347302

Book preview

Preface

In 2005, Wiley published the first edition of this book just after the International Diabetes Federation (IDF) had published new criteria for identification of metabolic syndrome. In defining new criteria, the IDF placed the focus on central obesity as an essential requirement, combined with two other features (from raised blood pressure, glucose, and triglyceride and decreased high density lipoprotein cholesterol concentrations). In the last five years the value of defining and identifying the metabolic syndrome continues to generate a huge amount of interest and controversy amongst the scientific community. A search using the term metabolic syndrome of PubMed in October 2010, showed that there have been an astonishing 17,364 publications that mentioned metabolic syndrome since 2005. Despite the considerable amount of scientific interest in the metabolic syndrome, there remains a lack of agreement relating to its definition and pathogenesis.

In 2009, controversy surfaced again when the American Diabetes Association and the European Association for the Study of Diabetes failed to endorse the consensus statement on the metabolic syndrome that was endorsed by the IDF, National Heart, Lung, and Blood Institute, World Heart Federation, International Atherosclerosis Society, and the American Heart Association. It is ironic that two major diabetes organizations failed to sign up to the consensus statement when the justified concerns about the effect of categorizing continuous variables and the failure to identify a single causal pathway also apply to glucose levels and the definition and etiology of type 2 diabetes. In our opinion, these concerns should not detract from the clinical utility of identifying people with evidence of ectopic fat accumulation at risk of cardiometabolic diseases and offering them effective interventions.

In 2006 the first edition of this book was awarded a highly commended prize certificate in the Annual British Medical Association Medical text book competition. Five years after publication of the first edition, we have attempted to make the second edition even more comprehensive. This edition includes updated versions of 13 chapters and six completely new chapters. The majority of the original structure of the first edition has been retained and there are new chapters on metabolic syndrome in children, brain insulin resistance, bariatric surgery, psychological and psychiatric illness, fitness and measurement of insulin sensitivity. As for the first edition, this book has been written to appeal to both clinicians and scientists and we have included chapters that range from etiology and pathogenesis to complications, treatment and management. There is a focus on exciting new areas of research that are likely to have considerable impact on both scientific understanding of the metabolic syndrome and its clinical outcomes.

Since Gerald Reaven’s description of the insulin resistance syndrome (IRS) in 1988, a consensus has emerged that visceral and liver fat accumulation is key to the IRS and to the metabolic phenotype. In 2010, with improvements in our understanding of the metabolic syndrome, it could be referred to as a condition of ectopic fat accumulation associated with increased risk of cardiometabolic diseases. Perhaps such an ungainly term would generate less controversy!

This book is the culmination of 12 months of enjoyable collaboration with colleagues and friends who have written the chapters, and to whom we owe a debt of thanks for their hard work. We have valued their enthusiasm for their topics and the contributions they have made to the book. Just as both of us have developed a passion for research in the metabolic consequences of modern lifestyles over the last 20 years, we hope that this book will stimulate some members of future generations of researchers to work in this important area of science, policy and medicine.

Christopher D. Byrne & Sarah H. Wild

Southampton & Edinburgh

CHAPTER 1

The Epidemiology of the Metabolic Syndrome and its Association with Diabetes, Cardiovascular Disease and Other Conditions

Sarah H. Wild¹ and Christopher D. Byrne²

¹ Centre for Population Health Sciences, University of Edinburgh, Edinburgh, Scotland, UK

² Institute for Developmental Sciences, Southampton General Hospital, Southampton, UK

Introduction

The recognition of the existence of the metabolic syndrome has developed over the last two decades following the description of an insulin resistance syndrome or syndrome X in 1988 (Reaven 1988). Obesity is a common factor related to insulin resistance and all components of the metabolic syndrome and results from unhealthy lifestyles including inadequate levels of physical activity, poor diet and smoking. Depending on the definition used, the metabolic syndrome may include measures of general obesity (as reflected by body mass index [BMI] defined as weight in kilograms divided by height in meters squared), central obesity (as reflected by waist circumference [WC] or waist: hip ratio [WHR]), dyslipidemia (as reflected by low high-density lipoprotein [HDL] cholesterol and/or high triglyceride (TG) levels), hyperglycemia, high blood pressure and resistance to the action of insulin. Sex-specific cut-points are applied for HDL cholesterol and ethnic-specific cut-points for waist circumference are used in some definitions. Further detail about the different definitions is provided below.

Controversy exists regarding the usefulness of the concept of the metabolic syndrome and several papers discuss the issues involved (Gale 2005; Kahn et al. 2005; Gale 2008; Alberti & Zimmet 2008; Borch-Johnsen & Wareham 2010). There is no doubt that the individual components of the metabolic syndrome cluster together and that this clustering is associated with increased risk of both diabetes and cardiovascular disease (Grundy 2006; Vaidya et al. 2007). The main issue under discussion is whether the concept of the syndrome is at all useful for individuals, clinicians, researchers or policymakers and, if so, how the syndrome should be defined. However, the syndrome places a helpful focus on the assessment of central obesity and associated cardiometabolic risk factors. Given the failings of measures such as body mass index in the assessment of fat mass, fat location or even the assessment of an individual’s degree of total adiposity, the metabolic syndrome has considerable utility in placing the emphasis on the importance of ectopic fat accumulation. Many risk factors for cardiovascular disease including components of the metabolic syndrome are continuous variables that are categorized in order to define populations at particularly high risk. This provides a pragmatic approach to a complex issue and allows appropriate lifestyle changes to be encouraged and other components of metabolic syndrome to be measured, and, if appropriate, treated when abnormal levels of one component are identified (Reaven 1988; Kaplan 1989; Byrne & Wild 2000).

A World Health Organization expert consultation concluded that the metabolic syndrome may be useful as an educational concept but it lacks utility as diagnostic or management tool. The different definitions of the metabolic syndrome hamper its epidemiological utility. It should not be applied as a clinical diagnosis. It is, however, good practice to control the other factors when one of the signs of the metabolic syndrome is seen (Simmons et al. 2010). This statement dismisses the notion that identification of the metabolic syndrome allows the identification of individuals with central obesity and cardiometabolic risk factors. Such individuals are at increased risk of conditions such as type 2 diabetes, cardiovascular disease, non-alcoholic fatty liver disease (NAFLD) and sleep apnea syndrome (as discussed subsequently), but often the severity of the central fat accumulation, or other individual cardiometabolic risk factors is not perceived to warrant pharmacological treatment because the individual factor(s) fall below thresholds for initiating drug therapy. Decisions to initiate pharmacological treatment to decrease risk of cardiovascular events should be based on estimates of absolute risk, particularly when single risk factors are not markedly abnormal. The presence of metabolic syndrome should highlight the need for estimating an individual’s absolute cardiovascular risk with appropriate treatment (such as a statin) initiated if cardiovascular risk is greater than the threshold set in the local health care system for treatment. Identification of the presence of metabolic syndrome should heighten awareness for both the individual and clinicians of increased risk of associated diseases. The presence of the syndrome should also trigger an active strategy focused on lifestyle behavioral change, to attenuate the future impact of risk factors and associated diseases.

An alternative approach to dichotomous classification is the derivation of a continuous metabolic score. This approach has the advantage of increasing the power of studies in which the prevalence of metabolic syndrome defined using categorical variables is low. There are multiple ways in which continuous data can be combined including principal component analysis, z scores and centile rankings (see table 1 of (Eisenmann 2008) for a summary of approaches used for data in children). The z score approach results in the components being weighted equally (as in the dichotomous classification) but principal component analysis allows differential weightings of the different components. Continuous risk scores are specific to the populations from which they are derived and may not be feasible in a clinical setting because knowledge is required of the distribution of each component in the relevant population. The importance of further research in this area was identified in the joint statement by the American Diabetes Association and the European Association for the Study of Diabetes recommending critical appraisal of the metabolic syndrome. At present the use of continuous metabolic risk scores is likely to be mainly limited to research studies and the current approach to definition of the metabolic syndrome using cut-points of continuous variables to define categories is most practical for use in clinical settings. Not all overweight or even moderately obese individuals are at particularly high risk of vascular disease, type 2 diabetes and NAFLD. The presence of the metabolic syndrome identifies a subgroup of the overweight and obese population that is at particularly high risk of the consequences of ectopic fat accumulation and insulin resistance. Focusing the use of effective interventions on this subgroup of the ever increasing numbers of overweight and obese individuals would help make the best use of limited resources.

The increasing prevalence of obesity across the world is associated with increasing prevalence of the metabolic syndrome. This has important implications for future patterns of prevalence of diabetes and cardiovascular disease and their complications in both developed and less developed countries. Although cardiovascular disease mortality is declining it is uncertain whether increasing diabetes prevalence will reverse this trend as people with diabetes are at higher absolute risk of cardiovascular disease than people without diabetes (Fox et al. 2004). The increasing prevalence of the metabolic syndrome and diabetes might be expected to slow or even reverse the decline in cardiovascular disease mortality in general populations of developed countries and is expected to contribute to increasing cardiovascular disease mortality in less developed countries.

Describing the global burden of the metabolic syndrome is challenging for a variety of reasons, including the following:

1 There are several definitions of the insulin resistance/metabolic syndrome.

2 Data are limited, particularly from certain populations and for the young and the elderly.

3 Cut-points for each feature of the syndrome are likely to differ between populations in terms of their effect on risk of diabetes or cardiovascular disease.

These factors are discussed below.

Definitions of the metabolic syndrome from 1988 to 2010

The clustering of insulin resistance, dysglycemia, dyslipidemia and hypertension was originally defined as syndrome X (Reaven 1988) and central obesity was added to definitions developed after 1999. Early definitions of the metabolic syndrome by the World Health Organization (WHO) (World Health Organization Consultation 1999) and the European Group for the Study of Insulin Resistance (EGIR) (Balkau & Charles 1999) also included a measure of insulin resistance which is not practical for use in large populations. The National Cholesterol Education Program Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol in Adults (abbreviated to NCEP) criteria also recognized the association between the above factors of the metabolic syndrome and both pro-inflammatory and pro-thrombotic states as reflected by increased C-reactive protein (CRP) and plasma plasminogen activator inhibitor (PAI)-I levels respectively but these are not required for definition of the syndrome(National Cholesterol Education Program (NCEP) Expert Panel on Detection 2001). The International Diabetes Federation (IDF) added its definition to the literature in 2005 and included ethnic-specific waist cut-points (Alberti, Zimmet, & Shaw 2005). The American Heart Association/National Heart, Lung and Blood Institute revised the glucose criterion in the NCEP criteria in 2005 (Grundy et al. 2005). A further revision of the criteria in 2009 was presented in a consensus statement from International Diabetes Federation (IDF), the National Heart, Lung, and Blood Institute (NHLBI), the World Heart Federation, the International Atherosclerosis Society, and the American Heart Association (AHA) (Alberti et al. 2009). The European Association for the Study of Diabetes and the American Diabetes Association have not contributed to the consensus statement partly due to controversy over the value of identifying the metabolic syndrome and polarization of views between different professional groups. The criteria for the NCEP, IDF and Consensus statement 2009 definitions are summarized in Table 1.1.

Available data on prevalence of the metabolic syndrome

In the first edition of this book we attempted to estimate the global prevalence of the metabolic syndrome from studies published before the end of 2004. Most of those studies were based in developed countries or urban areas of developing countries, used the WHO or NCEP criteria and suggested a prevalence of metabolic syndrome in general adult populations of 15–30%. Several reviews have summarized more recent data on prevalence of the metabolic syndrome and suggest that prevalence varies between <10% and 84% in studies of different age groups and populations using different definitions of the syndrome (Procopiou & Philippe 2005; Desroches & Lamarche 2007; Kolovou et al. 2007).

Factors that influence prevalence of the metabolic syndrome and variation between populations

Prevalence of the metabolic syndrome is affected by a myriad of both non-modifiable and modifiable interlinked factors. Many of these factors are inter-related but an attempt to consider them individually is made below:

Table 1.1 Features of the NCEP 2001 & 2005, IDF 2005 and IDF, National Heart, Lung, and Blood Institute, World Heart Federation, International Atherosclerosis Society, and American Heart Association 2009 consensus criteria definitions of the metabolic syndrome

Age

The prevalence of most individual factors within the metabolic syndrome increase with age, at least to late middle-age (after which survival bias and cohort effects may cause prevalence of individual factors to level off or decline with increasing age) and prevalence of the metabolic syndrome is associated with age in the same way. For example, in the third National Health and Nutrition Examination Survey (NHANES III) performed in the United States (US) the prevalence of the metabolic syndrome (defined using NCEP criteria) increased from 6.7% among participants of 20–29 years of age to 43.5% for 60–69 year olds and was 42.0% for participants of 70 years or older (Ford, Giles, & Dietz 2002). The Norwegian HUNT 2 study reported that prevalence of IDF-defined metabolic syndrome increased in men from 11.0% in 20–29 year olds to 47.2% in the 80–89 year olds and in women from 9.2% to 64.4% for the same age groups (Hildrum et al. 2007). Given the importance of age as a risk factor for the metabolic syndrome meaningful comparisons of prevalence between populations can only be made if data are adjusted for differences in age distribution. Data for children are more limited than for adults but a systematic review published in 2009 reported that general population based estimates of prevalence of the metabolic syndrome ranged from 1.2% to 22.6% with prevalence of up to 60% in overweight and obese children (Tailor et al. 2010). Further discussion about metabolic syndrome in children is included in Chapter 5 by Weiss and Caprio.

Sex

As central obesity is one of the factors included in definition of the metabolic syndrome and, for a given body mass index, central obesity is more common in men, it might be expected that prevalence of the metabolic syndrome would be higher in men than women. Among non-diabetic European men and women from 8 populations the prevalence of the metabolic syndrome (defined using modified WHO criteria) was generally higher in men than women (Hu et al. 2004) but data for 2003–2006 from the US showed similar prevalence of the metabolic syndrome in men and women (Ervin 2009). The effect of generalized obesity is also extremely important (see below) such that, in populations in which obesity is more common in women than men, the prevalence of the metabolic syndrome will be higher in women than men. This pattern can be observed in Indian, Iranian and Turkish populations (Onat et al. 2002; Gupta et al. 2003; Ramachandran et al. 2003; Azizi et al. 2003; Ozsahin et al. 2004).

A cardiovascular risk factor survey in France identified that elevated body weight , waist girth and low HDL cholesterol were significantly larger contributors to the metabolic syndrome in women than in men whereas systolic and diastolic blood pressure contributed significantly less in women than in men and insulin, glucose and TGs made similar contributions in both sexes (Dallongeville et al. 2004). In contrast, in a Chinese population, hypertension was only related to other features of the metabolic syndrome in women (Chen et al. 2000). A study in Finland reported that the metabolic syndrome (defined using criteria similar to those of the WHO) was more common in men than in women among subjects with normal glucose tolerance (15 vs. 10%) and impaired fasting glucose and/or impaired glucose tolerance (64 vs. 42%), but not in patients with type 2 diabetes (84 vs. 78%) (Isomaa et al. 2001). In the Canary Islands hypertriglyceridemia, hypertension and hyperglycemia predominated in men whereas in women, abdominal obesity and low HDL-cholesterol were more common components of the metabolic syndrome (Alvarez Leon, Ribas, & Serra 2003). In data for the United States for 2003–2006 men had higher age adjusted prevalence of the TG, blood pressure and glucose components of the metabolic syndrome but lower age-adjusted prevalence of the central obesity and low HDL components of the metabolic syndrome compared to women (Ervin 2009).

Ethnicity

The relationship between ethnicity and the metabolic syndrome is discussed in further detail in Chapter 2 by Tillin & Forouhi. Some ethnic groups have a higher predisposition to central obesity than others: for example, prevalence of central obesity is higher among South Asians than Europeans and is higher among Europeans than Afro-Caribbeans. Other features of the metabolic syndrome show a differing pattern by ethnicity, for example, prevalence of hypertension is higher among Afro-Caribbeans than other ethnic groups. The association between anthropometric cut-points and other components of the metabolic syndrome and risk of diabetes and cardiovascular disease varies by ethnicity and ethnic-specific anthropometric cut-points have been proposed (e.g. for waist circumference as described above).

Among Asian populations the prevalence of the metabolic syndrome defined using standard criteria is generally lower than among European populations. When waist circumference criteria are modified to a lower cut-point as deemed appropriate for Asian populations, prevalence of the metabolic syndrome increases and becomes more similar to (for south-east Asian populations, e.g. from Korea) or higher than (for south Asian populations, e.g. urban Indians) than for European populations.

Limited data are available for African populations in Africa but data on African origin populations based in the US or UK suggest that prevalence of the metabolic syndrome is similar to that of white populations in these countries (Ford, Giles, & Dietz 2002; Barbato et al. 2004). Prevalence of the metabolic syndrome in Hispanic populations in the US appears to be higher than both the white population in the US and the Mexican populations (Ford, Giles, & Dietz 2002). Within middle-Eastern populations prevalence of the metabolic syndrome was similar in Oman to that observed in most European populations but was higher, particularly among women, in a study in Tehran (Azizi, Salehi, Etemadi, & Zahedi-Asl 2003; Al Lawati et al. 2003).

Obesity and fat distribution

As mentioned above, prevalence of obesity is an important factor in influencing prevalence of the metabolic syndrome. The association of central or general obesity and the metabolic syndrome varies with sex (Ho et al. 2001). Distribution of fat influences prevalence of the metabolic syndrome for a given BMI. The NHANES III study showed that prevalence of the metabolic syndrome (defined using the ATP III criteria) increased from 0.9% to 3.0% for people with a BMI in the range 18.5–20.9 kg/m² to 9.6–22.5% for people with a BMI in the range of 25.0–26.9 kg/m² depending on sex and ethnicity (St Onge, Janssen, & Heymsfield 2004). The influence of obesity on prevalence of the metabolic syndrome has also been observed in children. A detailed study of 439 obese, 31 overweight, and 20 non-obese children and adolescents in the United States found that prevalence of the metabolic syndrome increased with the severity of obesity and around half of the severely obese participants had the metabolic syndrome (Weiss et al. 2004). Data from 12–19 year old participants in NHANES III showed that prevalence of the metabolic syndrome (using ATP III criteria modified for age) varied between 0.1% for those whose BMI was below the 85th percentile to 29% among those whose BMI was above the 95th percentile (Cook et al. 2003).

The cut-points of BMI used to replace waist circumference as a proxy for obesity in different populations vary with age, sex and ethnicity and have not been clearly established. Lower cut-points of BMI to define obesity have been suggested for Asian populations (WHO Expert Consultation 2004).

Diet and physical activity

The major effects of levels of physical activity and fitness (discussed in more detail in Chapter 10 by McAuley and Blair) and diet (discussed in more detail in Chapter 17 by Te Morenga and Mann) on prevalence of the metabolic syndrome are at least partly mediated through their effects on fat distribution and obesity. A study of health, nutrition and physical activity (with the latter assessed by questionnaire) in Greece reported that the odds ratios for metabolic syndrome adjusted for age, sex, smoking habits, educational status and measurements of inflammation and coagulation factors (but not BMI) were 0.81 (95% CI, 0.68–0.98) among people who consumed a Mediterranean diet compared with those who did not eat a Mediterranean diet and 0.75 (95% CI, 0.65–0.86) among people who reported little to moderate physical activity compared to people with a sedentary lifestyle (Panagiotakos et al. 2004). In a study of 7104 women the age- and smoking-adjusted prevalence of the metabolic syndrome (defined using NCEP criteria) decreased across quintiles of cardiorespiratory fitness (19.0%, 6.7%, 6.0%, 3.6%, and 2.3% for quintiles 1 to 5 respectively (Farrell, Cheng, & Blair 2004).

There is some evidence to suggest that diet and physical activity may also have an effect on insulin resistance and the metabolic syndrome that is independent of obesity.

In the Framingham Offspring Study whole-grain intake (mainly from cereal fiber) was found to be associated with a lower prevalence of the metabolic syndrome whereas dietary glycemic index was positively associated with prevalence of the metabolic syndrome after adjustment for confounding lifestyle and dietary factors other than body mass index (sex, age, cigarette dose, total energy intake, alcohol intake, percentage saturated and polyunsaturated fat, multivitamin use, and physical activity) (McKeown et al. 2004). In the same report from the Framingham Offspring Study the authors also reported that insulin resistance [assessed using the homeostatic model assessment of insulin resistance (fasting insulin × fasting glucose/22.5)] was inversely associated with whole-grain foods, dietary fiber, cereal, and fruit fiber and positively associated with glycemic index and glycemic load after adjusting for BMI, WHR, and treatment for blood pressure in addition to the confounders listed above. It is not clear why BMI was not included in the model for the metabolic syndrome outcome particularly as the authors pointed out that only 3% of those participants with a BMI <25 kg/m² had the metabolic syndrome compared with 32% of the participants with a BMI ≥25 kg/m².

The Whitehall II study of 5153 white European civil servants in Britain found that moderate and vigorous physical leisure-time activity were associated with lower prevalence of the metabolic syndrome (defined using 2-hour glucose, systolic blood pressure, fasting TGs, waist-hip ratio, and HDL cholesterol) independently of age, smoking, and high alcohol intake (Rennie et al. 2003). This effect was at least partially mediated by lower BMI and increased cardiovascular fitness among active people. Moderate and vigorous leisure time physical activity was associated with decreased risk of the metabolic syndrome (defined by a modified WHO definition) in a prospective population-based cohort of 612 middle-aged Finnish men, even after extensive adjustment for potential confounding factors (Laaksonen et al. 2002).

Birthweight

Several studies have suggested that low birthweight is associated with higher prevalence of the metabolic syndrome in adult life (for reviews see Hales & Ozanne 2003; Nobili et al. 2008). The effect of low birthweight on increased risk of the metabolic syndrome appears to be particularly marked when it is associated with obesity in adulthood (Yarbrough et al. 1998). This issue is considered in greater detail in Chapter 3 by Torrens, Clough and Hanson and Chapter 4 by Ozanne and Siddle.

Genetic factors

Each component of the metabolic syndrome is determined by complex gene–environment interactions. The available data describing the role of genetic factors in determining prevalence of metabolic syndrome are limited and early findings were not replicated in other populations. Certain components of the metabolic syndrome may be more strongly influenced by the environment and others by genetic inheritance. For example, a study of twins in Denmark suggested that environmental factors were more important for waist-to-hip ratio, fasting insulin and TGs whereas genetic influences were most marked on glucose intolerance, overall obesity and low HDL-cholesterol (Poulsen et al. 2001). The role and the relevance of genetic factors to the prevalence of the metabolic syndrome are considered further in Chapter 7 by Xie, Frayling and Weedon.

Endocrine factors

Endocrine disturbances can influence prevalence of the metabolic syndrome and hyperandrogenemia/polycystic ovary syndrome (PCOS) is discussed in more detail in Chapter 16 by Sattar. The menopause may also influence development of the metabolic syndrome and a summary of the relationships between the metabolic syndrome and menopause/hormone replacement therapy (HRT) is discussed below. Low total testosterone and sex hormone binding globulin (SHBG) levels both independently predict development of the metabolic syndrome and diabetes in middle-aged Finnish men (Laaksonen et al. 2004). The growth hormone-insulin-like related growth factor 1 and glucocorticoid axes may also play a role in the development of the metabolic syndrome and more detail is provided about the associations between metabolic syndrome and endocrine axes in Chapter 8 by Holt and Sonksen.

Menopause/hormone replacement therapy

Limited data are available describing the effects of menopause and HRT on prevalence of the metabolic syndrome. More data are available on the effects of menopause and HRT on various measures of central obesity, but there are conflicting results. Menopause is associated with increased amounts of abdominal visceral fat and there appears to be an effect that is independent of ageing (Poehlman & Tchernof 1998; Tchernof et al. 1998).

A meta-analysis of 107 trials concluded that HRT reduces abdominal obesity, insulin resistance, new-onset diabetes, lipids, blood pressure, adhesion molecules and procoagulant factors in women without diabetes and reduced insulin resistance and fasting glucose in women with diabetes and it seems to reasonable to assume that HRT use would be associated with lower prevalence of the metabolic syndrome (Salpeter et al. 2006).

Inflammation

There is convincing evidence to suggest that chronic inflammation is associated with obesity, insulin resistance and the metabolic syndrome (Hotamisligil 2006). The majority of the components of the metabolic syndrome are positively associated with inflammatory parameters and this relationship appears to be independent of age, sex, physical activity, smoking and BMI (Temelkova-Kurktschiev et al. 2002). Further consideration of the relationship between inflammation and the metabolic syndrome is given in Chapter 13 by Devaraj, Siegel and Jialal.

Alcohol

Alcohol consumption is associated with increasing HDL-cholesterol levels, increasing TG levels and increasing blood pressure and therefore has different effects on different aspects of the metabolic syndrome (Yoon et al. 2004; Vernay et al. 2004). A meta-analysis of seven observational studies of the association between alcohol consumption and the metabolic syndrome suggested that alcohol consumption of less than 20 g/day among women, and of less than 40 g/day among men was associated with lower prevalence of the metabolic syndrome than among people classified as non-alcohol drinkers (Alkerwi et al. 2009). The association between type of alcohol and various health-related outcomes may be confounded by other lifestyle factors. A cross-sectional population based study of 4232 60-year-old men and women in Sweden reported that moderate wine drinkers generally had healthier lifestyles than either non-drinkers or spirit drinkers (Rosell, De Faire, & Hellenius 2003). In women, the metabolic syndrome was significantly more common in non-drinkers (20%), P < 0.05, and less common among wine drinkers (8%), P < 0.01, compared with a group with low alcohol intake. This effect persisted after adjustment for the measured lifestyle factors with a statistically significant low odds ratio for the metabolic syndrome for women wine drinkers (OR = 0.60, P < 0.05) but the finding could be due to residual confounding caused by the healthier lifestyles and relative affluence of wine drinkers.

Co-morbidity

Among people with diabetes, hypertension or coronary heart disease the prevalence of the metabolic syndrome is considerably higher than among the general population. For example, the prevalence of the metabolic syndrome was between 76 and 92% in various populations of people with diabetes (Relimpio et al. 2004; Ilanne-Parikka et al. 2004; Bruno et al. 2004; Ogbera 2010). Comparisons of prevalence of metabolic syndrome between the general population and people infected with HIV have given conflicting results (Gazzaruso et al. 2002; Jerico et al. 2005; Samaras et al. 2007). Use of anti-retroviral therapy appears to be associated with increased risk of features of the metabolic syndrome (Jerico, Knobel, Montero, Ordonez-Llanos, Guelar, Gimeno, Saballs, Lopez-Colomes, & Pedro-Botet 2005; Samaras 2008). Among people with mental illness, notably schizophrenia, the prevalence of the metabolic syndrome was higher than among general populations. There is increased prevalence of diabetes among people with schizophrenia which may be partially explained by the association between use of newer anti-psychotic drugs and weight gain (Holt, Peveler, & Byrne 2004). Further discussion of the association between metabolic syndrome and mental health is provided in Chapter 11 by Holt and Peveler.

It is not always clear whether population-based studies have included people with these various co-morbidities and differences in selection criteria and in prevalence of these conditions is certain to contribute to differences in prevalence of the metabolic syndrome between populations. Furthermore not all studies have indicated how data for people on treatment for diabetes or hypertension have been used. For example, it is not always clear whether people on anti-hypertensive treatment classified as being above the cut-point for blood pressure regardless of their actual blood pressure.

Trends in prevalence of the metabolic syndrome

Data on trends in prevalence of the metabolic syndrome suggest increasing prevalence over time, with differences by sex in different populations. A comparison of data from the US for 6436 men and women aged > or = 20 years who participated in NHANES III (undertaken between 1988 and 1994) and 1677 participants from NHANES 1999–2000. Age-adjusted prevalence of the metabolic syndrome as defined by NCEP criteria was 24.1 and 27.0% (P = 0.088), respectively. The age-adjusted prevalence increased by 23.5% among women (P = 0.021) and 2.2% among men (P = 0.831) between these two cross-sectional surveys (Ford, Giles, & Mokdad 2004). Similar patterns by sex over time were seen in two Finnish cross-sectional population surveys of 3495 45–64 year old participants with prevalence of the metabolic syndrome defined using IDF criteria increasing from 51.4 to 55.6% (P = 0.102) for men and from 38.0 to 45.3% (P = 0.002) for women between 1992 and 2002(Hu et al. 2008). In contrast a prospective study in Japan of 112,960 17–85 year olds that included annual examinations between 1989 and 2004 reported decreasing prevalence of metabolic syndrome over time by both NCEP and Japanese criteria among women but increasing prevalence among men (Kuzuya et al. 2007).

Interpretation of data on prevalence of the metabolic syndrome

The following issues should be considered in interpreting the available data on prevalence of the metabolic syndrome.

Chance

The small size of some studies means that some apparent differences reported between or within populations may be due to chance. Confidence intervals or results of significance testing for comparison of subgroups for the prevalence of the metabolic syndrome are not always provided and where they are not given apparent differences between subpopulations should be interpreted with caution.

Bias

Very few studies give information on response rates which makes it difficult to consider the potential effects of response bias. Response bias has the potential to either over-estimate prevalence of the metabolic syndrome (if people with the syndrome were more likely to participate in the study) or to underestimate prevalence (if the reverse were true). In older age groups survival bias may contribute to lower prevalence of the metabolic syndrome than among middle-aged groups. As discussed above, it is not always clear whether people with diabetes and other co-morbidities were included or excluded in studies of prevalence of the metabolic syndrome and this could result in selection bias. Studies that include people with relevant co-morbidities will give higher estimates of prevalence of the metabolic syndrome than those that exclude such people. Similarly populations with a higher prevalence of type 2 diabetes are likely to have higher prevalence of the metabolic syndrome in the whole population than those with lower prevalence of type 2 diabetes.

Table 1.2 Summary of reviews describing the relative risk of diabetes, cardiovascular disease and mortality associated with the metabolic syndrome

Confounding

Potential confounding factors between certain population characteristics such as age, sex, ethnicity, obesity and co-morbidity and the prevalence of the metabolic syndrome are considered above. Some prevalence studies have not considered potential confounding factors and, even among those that have considered these factors there remains the potential for residual confounding. A large proportion of apparent differences in prevalence of the metabolic syndrome between populations may be explicable by factors that are either confounders or on the causal pathway such as age or prevalence of obesity. A proportion of the higher prevalence of the metabolic syndrome among people with diabetes may be explained by the higher average age of people with diabetes than of general populations.

Consequences of the metabolic syndrome

The metabolic syndrome is associated with increased risk of a variety of disease outcomes including diabetes, cardiovascular disease (with the association with inflammation and cardiovascular disease discussed in Chapter 13 by Devaraj, Siegel and Jialal; atherothrombosis in Chapter 12 by Standeven and Grant); fatty liver and NAFLD (discussed in Chapter 15 by Ahmed & Byrne); polycystic ovary syndrome (discussed in Chapter 16 by Sattar) gallstones, asthma, sleep apnea and selected malignant diseases. Some patients with morbid obesity have features of metabolic syndrome and for the morbidly obese, there is increasing evidence of the benefit of bariatric surgery to facilitate loss of excess body fat. The use of bariatric surgery is discussed in Chapter 19 by Buchwald. The associations not covered in other chapters are discussed briefly below.

Metabolic syndrome, diabetes and cardiovascular disease

Several reviews and meta-analyses have summarised the findings of studies in a variety of populations, mainly based in developed countries, that have described the associations between the metabolic syndrome and diabetes (Ford, Li, & Sattar 2008), cardiovascular disease morbidity and mortality (Ford 2005; Galassi, Reynolds, & He 2006; Gami et al. 2007; Li et al. 2008; Mottillo et al. 2010) and all-cause mortality (Hui, Liu, & Ho 2010). The findings are summarized in Table 1.2. The estimates of relative risk (RR) vary between populations and different definitions of the metabolic syndrome and the outcomes. However, the relative risk of diabetes is over 3-fold higher among people with the metabolic syndrome than among those without the syndrome (Ford, Li, & Sattar 2008) and the pooled relative risk of mortality associated with all-cause mortality was approximately 50% higher among people with the metabolic syndrome (Hui, Liu, & Ho 2010; Mottillo, Filion, Genest, Joseph, Pilote, Poirier, Rinfret, Schiffrin, & Eisenberg 2010).

The most recent meta-analysis (published in September 2010) reported that relative risks of cardiovascular disease associated with metabolic syndrome were similar regardless of length of follow-up and of which NCEP definition was used, were higher among women than men and were lower among people without diabetes than reported in studies including all people with metabolic syndrome (Mottillo, Filion, Genest, Joseph, Pilote, Poirier, Rinfret, Schiffrin, & Eisenberg 2010). Funnel plots suggested that mild publication bias may influence the results of such meta-analyses. The association between metabolic syndrome with all-cause mortality was not statistically significant in people without diabetes although this estimate was based on the combined result of only two studies with 3622 participants so confidence intervals were wide (RR 1.32, 95%CI 0.65–2.47).

Based on data from the Framingham offspring cohort study of 3323 men and women (mean age, 52 years) with 8 years follow-up it has been estimated that the metabolic syndrome (as defined using NCEP criteria) contributed almost half of the population-attributable risk for diabetes and approximately a quarter of all incident cardiovascular disease (Grundy et al. 2004). The prevalence of the metabolic syndrome using standard definitions is likely to have different implications for relative risk of diabetes and cardiovascular disease in different populations and extrapolations of relative risks should not be made across populations. For example, metabolic syndrome and its components were not associated with risk of cardiovascular disease in two cohorts of elderly people, despite a strong association with risk of diabetes (Sattar et al. 2008).

Only a small number of studies have adjusted for one or more components of the metabolic syndrome when investigating the associations with diabetes or cardiovascular disease. The online appendix to the most recently published meta-analysis provides a useful summary of which studies have adjusted for metabolic syndrome components and other conventional cardiovascular disease risk factors in studies of cardiovascular disease and all-cause mortality (Mottillo, Filion, Genest, Joseph, Pilote, Poirier, Rinfret, Schiffrin, & Eisenberg 2010). In a Mauritian population the WHO 1999 definition (but not other definitions) of the metabolic syndrome provided a statistically significant addition to a model to predict incident diabetes that contained the components of the syndrome (Cameron et al. 2007). Metabolic syndrome provided no extra information on risk of diabetes among participants in the West of Scotland Coronary Prevention Study (WOSCOPS), a primary prevention trial of pravastatin in Scottish men recruited because of high cholesterol levels, or the AusDiab study, a population-based cohort study, beyond that provided by the individual components (Sattar et al. 2003; Cameron et al. 2008). Metabolic syndrome was a stronger predictor of coronary heart disease, cardiovascular disease and total mortality than its individual components in a US prospective cohort study of 6255 people of 30 to 75 years of age (Malik et al. 2004) but did not provide additional risk information beyond that provided by the individual components in a prospective study of Swedish men of 50 or 70 years of age at baseline (Sundstrom et al. 2006b).

Some studies suggest that metabolic syndrome confers additional risk of cardiovascular disease beyond that associated with the broader range of conventional risk factors beyond the components of the metabolic syndrome (Sattar, Gaw, Scherbakova, Ford, O’Reilly, Haffner, Isles, Macfarlane, Packard, Cobbe, & Shepherd 2003; Sundstrom et al. 2006a). It is clear that further research is needed in representative studies of different populations to assess whether metabolic syndrome is associated with incident diabetes, cardiovascular disease and other conditions associated with metabolic syndrome independently of its individual components and other conventional risk factors.

Other health outcomes associated with metabolic syndrome

Conditions associated with metabolic syndrome are also commonly associated with obesity and, for most of them, it is not clear whether metabolic syndrome confers additional risk over that associated with obesity or central obesity. The presence of metabolic syndrome may help to identify particularly insulin resistant individuals who are at higher risk of many of these outcomes than more insulin sensitive individuals. A Mexican study of 65 people with gallstones and 180 controls showed that the age and sex adjusted odds ratio (95% confidence interval of having gallstones increased from 2.36 (0.72–7.71) for people with one component of the metabolic syndrome to 5.54 (1.35–22.74) for people with four components of the metabolic syndrome compared to people who met none of the criteria. The age and sex adjusted odds ratio of having gallstones for people who met the waist criterion for the metabolic syndrome compared to those whose waist circumference was below the cut-point was 3.61 (1.95–6.71) (Mendez-Sanchez et al. 2005).

There may be a bi-directional association between obesity/metabolic syndrome and asthma in that asthma limits physical activity and thus promote weight gain and central obesity and there is some suggestion that inflammation associated with obesity and the metabolic syndrome may contribute to risk of asthma, although further research to establish causality is required (ten Hacken 2009). A similar vicious circle may exist in the association between sleep apnea and obesity and prevalence of the metabolic syndrome is higher in people with obstructive sleep apnea than obese people without sleep apnea [see (Levy, Bonsignore, & Eckel 2009) for review].

Obesity is known to be associated with incidence and mortality of cancers of the breast (in post-menopausal women), colon (with higher relative risks in men than women), endometrium, kidney, liver, esophagus, stomach and pancreas (Calle & Kaaks 2004). There is increasing evidence for particularly high risk for these cancers among people with central obesity and the metabolic syndrome and this may be mediated by hyperinsulinemia and its effect on cell growth and synthesis and binding of sex hormones [see (Calle & Kaaks 2004) for review]. There is limited information available about the association between metabolic syndrome and cancer although there appears to be an association between colon cancer and metabolic syndrome that extends beyond the risk associated with the individual components (Cowey & Hardy 2006). A study that is pooling data from cohorts in Norway, Austria and Sweden is in progress and follow-up data derived from cancer registries will be available for 578,700 participants (Stocks et al. 2010). This is an important emerging area of research and more data are needed not least to elucidate whether the presence of insulin resistance and the metabolic syndrome confers additional risk of cancer beyond that attributable to different measures of obesity. It is noteworthy that use of metformin, a drug that improves insulin sensitivity but may also have other actions that influence cancer cells, appears to be associated with lower risk of cancer among people with diabetes and that trials of metformin among non-diabetic women with early breast cancer are in progress.

Conclusions

The metabolic syndrome appears to affect between 10 and 25% of adult populations worldwide. The prevalence of the metabolic syndrome is likely to increase in association with increasing prevalence of obesity and diabetes (Wild et al. 2004). The increased risk of cardiovascular disease associated with the metabolic syndrome and diabetes may mean the secular declines in cardiovascular disease mortality in developed countries may slow or even be reversed and the burden of cardiovascular disease in less developed countries is likely to grow. Subsequent chapters consider other factors linked to metabolic syndrome such as the diagnosis of insulin resistance (Chapter 6 by Szendroedi, Phielix, and Roden); newer phenomena such as brain insulin resistance (Chapter 9 by Amiel); etiology and pathogenesis (chapters discussed above); and treatments both pharmacological (Chapter 18 by Hanefeld, Schatz, and Schaper) and nutritional (Chapter 17 by Te Morenga and Mann). Other consequences and manifestations of the syndrome are also discussed. A variety of individual and population-based approaches to prevention and management of obesity are required to reduce the impact of insulin resistance, the metabolic syndrome and their consequences but these approaches are extremely challenging to implement. An excellent overview of the complex area of tackling obesity is provided by the Foresight report (Butland et al. 2007).

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