Hostname: page-component-586b7cd67f-tf8b9 Total loading time: 0 Render date: 2024-11-27T18:16:10.836Z Has data issue: false hasContentIssue false

Body composition and the monitoring of non-communicable chronic disease risk

Published online by Cambridge University Press:  21 October 2016

J. C. K. Wells*
Affiliation:
Childhood Nutrition Research Centre, UCL Institute of Child Health, 30 Guilford Street, London WC1N 1EH, UK
M. K. Shirley
Affiliation:
Childhood Nutrition Research Centre, UCL Institute of Child Health, 30 Guilford Street, London WC1N 1EH, UK
*
*Address for correspondence: J. C. K. Wells, Childhood Nutrition Research Centre, UCL Institute of Child Health, 39 Guilford Street, London WC1N 1EH, UK. (Email: [email protected])
Rights & Permissions [Opens in a new window]

Abstract

There is a need for simple proxies of health status, in order to improve monitoring of chronic disease risk within and between populations, and to assess the efficacy of public health interventions as well as clinical management. This review discusses how, building on recent research findings, body composition outcomes may contribute to this effort. Traditionally, body mass index has been widely used as the primary index of nutritional status in children and adults, but it has several limitations. We propose that combining information on two generic traits, indexing both the ‘metabolic load’ that increases chronic non-communicable disease risk, and the homeostatic ‘metabolic capacity’ that protects against these diseases, offers a new opportunity to improve assessment of disease risk. Importantly, this approach may improve the ability to take into account ethnic variability in chronic disease risk. This approach could be applied using simple measurements readily carried out in the home or community, making it ideal for M-health and E-health monitoring strategies.

Type
Review Article
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution, and reproduction in any medium, provided the original work is properly cited.
Copyright
Copyright © The Author(s) 2016

Introduction

For most of human history, the primary cause of morbidity and mortality was infectious disease. Life expectancy at birth averaged little more than three decades, and a large proportion of all those born died before reaching adulthood. Over the last two centuries, an increasing number of populations have undergone an epidemiological transition, characterized by demographic change associated with a decreased burden of infectious disease [Reference Omran1]. Consequently, the limiting factor for health and survival is increasingly the constitution of the body.

Globally, the leading cause of morbidity and mortality is now chronic non-communicable diseases, closely associated with the obesity epidemic, and the widespread adoption of unhealthy diets and behaviours such as smoking and physical inactivity [Reference Lopez and Mathers24]. In 2010, for example, ischaemic heart disease and stroke collectively killed one in four people worldwide, compared with one in five in 1990. Ischaemic heart disease is among the top four causes of death in every global region except Oceania and sub-Saharan Africa, and stroke is also one of the commonest causes of death in many regions. Already, 80% of the deaths from chronic diseases occur in low and middle-income countries, and a quarter occur in those below 60 years [Reference Lopez and Mathers2, 4].

This paper focuses on several chronic non-communicable diseases, namely hypertension, stroke, type II diabetes, and cardiovascular disease. Though these diseases affect different parts of the body, they have in common a generic life-course aetiology, as discussed below.

What kind of data can we use in order to (a) identify risk factors for these diseases, and (b) assess response to clinical management or public health interventions? We can search for such markers at many levels of biology: at the level of the gene, blood biochemistry, physiology, morphology, and behaviour. The challenge is that by seeking so many individual sources of information, we struggle to make sense of the complexity. What we need are simple proxies, suitable for widespread application, that provide reliable indications of relative disease risk.

The most obvious risk factor, demonstrated in large-scale epidemiological surveys, is excess body weight, most commonly expressed in the form of body mass index (weight divided by height squared, BMI). BMI can be relatively easily monitored within individuals through the life-course, although height may need to be re-measured from middle age onwards as it decreases slightly due to shrinkage. Given the close link between the epidemics of obesity and chronic diseases, BMI might appear a ‘panacea’ – the ideal trait for routine monitoring, and the best outcome for assessing the efficacy of public health interventions.

However, there is increasing dissatisfaction with BMI as a marker of chronic disease risk, for a number of reasons. First, within any population, there is substantial variability in the ratio of fat mass to lean mass at any given level of BMI, hence this outcome fails to reliably index any specific component of body composition [Reference Wells5, Reference Wells6]. Second, again within populations, not all individuals develop health risks at the same BMI threshold. Some who are ‘overweight’ demonstrate metabolic perturbations, whereas others are ‘fat but fit’ [Reference McAuley7], so that a high BMI may inadvertently flag metabolic ill-health in some who are actually healthy. Conversely, others may have metabolic risk despite their BMI lying in the normal range. Finally, between populations, there are systematic differences in the average level of body fat present at a given level of BMI [Reference Deurenberg, Yap and van Staveren8Reference Nightingale10].

More detailed measurements of body composition may offer a resolution to this scenario. Body composition reflects a wide variety of ‘levels’ of biology [Reference Wells11]. It is well established, for example, that body composition reflects the influences of genotype and gene expression [Reference Sorensen12Reference Relton15]. However, the same traits also reflect patterns of development [Reference Ong16Reference Wells20] as well as more immediate components of physiology such as glycemic control [Reference Lustig21, Reference Lustig22]. Finally, body composition also relates to behaviour and parental care in early life [Reference Wells23Reference Li25] and current diet and activity level [Reference Hallal26Reference Johnson30].

The aim of this paper is to briefly outline a conceptual model, demonstrating the potential utility of body composition data for indexing the risk of non-communicable diseases. Particular effort will be made to highlight how this approach may help address ethnic variability in chronic disease risk.

The capacity-load model of disease risk

In the 1980s, chronic disease risk was widely attributed to two principal factors: current lifestyle, encapsulating factors such as unhealthy diet, obesity, smoking and physical activity, and genotype [Reference Hales and Barker31]. The importance of genetic factors was initially highlighted through family studies, showing the tendency for chronic diseases to cluster within families [Reference Dawber32Reference Rissanen34].

From the late 1980s a new perspective emerged, as studies repeatedly demonstrated that patterns of growth in early life also shaped chronic disease risk in adulthood. The pioneering work of David Barker and colleagues demonstrated consistent associations between low birth weight and chronic disease risk [Reference Barker35Reference Frankel39], with subsequent studies identifying independent contributions of rapid weight gain during childhood [Reference Eriksson40Reference Barker42].

The first conceptual approach was developed by Hales and Barker [Reference Hales and Barker31], and was termed the ‘thrifty phenotype’ hypothesis. This model of disease assumed that the ability to resist the adverse metabolic consequences of unhealthy lifestyles in adulthood was undermined in those who had undergone poor growth in foetal life. It was suggested that low birth weight babies, experiencing foetal energy insufficiency, had sacrificed organs such as the pancreas in order to protect the brain [Reference Hales and Barker31, Reference Latini43]. The result would be impaired glucose tolerance later in life, exacerbated on exposure to dietary richness. This approach initially led to the assumption that the long-term risks pertaining to low birth weight derived from some form of overt ‘under-nutrition’ during foetal life.

While this conceptual approach catalyzed the field, it gave undue emphasis to those with low birth weight. In fact, relevant data repeatedly showed that an inverse dose response association between birth weight and adult chronic disease risk was evident across the majority of the range of birth weight [Reference Hales37, Reference Rich-Edwards44Reference Li46], though for some outcomes disease risk increased again in those with the highest birth weights [Reference Whincup47]. In other words, most chronic diseases in adulthood actually occur in those whose birth weight was within the normal range, and yet birth weight is still predictive of adult disease risk.

We therefore built on the thrifty phenotype hypothesis to develop an approach known as the ‘capacity-load’ model [Reference Wells48, Reference Wells49]. This approach assumes that many components of adult lifestyle contribute to chronic disease risk. These include diet, physical inactivity, stress, smoking and air pollution, alcohol intake, as well as some effects of chronic infectious diseases. Collectively, all of these factors impose a ‘metabolic load’ that challenges the body's ability to maintain homoeostasis at the levels of cells, organs or tissues. The concept of metabolic load has much in common with that of allostatic load [Reference McEwen50, Reference McEwen and Stellar51], but instead of emphasizing the stress response, it highlights components of homoeostasis addressing fuel/lipoprotein metabolism and cardiovascular function.

The ability to tolerate this metabolic load is then considered to depend on traits, collectively termed ‘metabolic capacity’, that enable homeostasis to be maintained. Crucially, these traits develop during early ‘critical windows’ of development, meaning that they are strongly shaped by growth patterns in foetal life and infancy [Reference Wells48, Reference Wells49]. Many specific physiological traits have been shown to scale relatively linearly with birth weight. Examples include nephron number in the kidney, neonatal lean mass, blood vessel caliber, airway size and metabolic functions such as insulin secretion. Broadly, the larger the size at birth, the greater the homeostatic capacity, though those with the highest birth weights may deviate from this pattern since much of their high weight is adipose tissue (metabolic load) rather than metabolic capacity. Consistent with the thrifty phenotype hypothesis, metabolic capacity is assumed to track from infancy into adulthood but may eventually deteriorate as part of the process of aging.

The risk of chronic degenerative diseases can then be modelled as a function of metabolic load relative to metabolic capacity (Fig. 1). The highest risk of disease is anticipated in those with high metabolic load but low capacity [Reference Wells49], a scenario which has been demonstrated for numerous disease outcomes (Table 1) and is illustrated for diabetes risk in Fig. 2.

Fig. 1. Schematic diagram of the ‘capacity-load’ model of chronic disease risk. Metabolic capacity promotes the maintenance of homoeostasis, and thereby reduces chronic disease risk. Metabolic load challenges homeostasis, and thereby elevates chronic disease risk. The highest risk of chronic disease is therefore found in those with high load and low capacity. Adapted and redrawn from ref 49.

Fig. 2. Empirical evidence supporting the capacity-load model of chronic disease risk for diabetes. The penalty for low birth weight steadily increases as the degree of unhealthy lifestyle increases. Based on data of Li et al. from 3 US cohorts [Reference Li45].

Table 1. Interactive associations between size at birth and subsequent weight in relation to chronic disease risk

For each outcome, lower birth weight (indexing reduced metabolic capacity) and higher BMI or adiposity (indexing metabolic load) independently increases disease risk. Reproduced with permission from ref 11.

Using this perspective, we can re-examine the utility of BMI as a marker of disease risk. BMI has been consistently associated with health and longevity in large populations, typically demonstrating a J shaped relationship [Reference Calle52, Reference Romero-Corral53]. The thinnest groups have an elevated risk of mortality relative to those within the normal range, after which there is a dose response association with increasing morbidity and mortality. Recently, data have suggested that the ‘optimum’ BMI may be higher than previously assumed, such that the overweight may have the greatest longevity, but they may still have elevated chronic disease risk relative to the normal range [Reference Romero-Corral53, Reference Flegal54].

BMI is a very simple proxy for body composition, and its limitations as an index of adiposity are well established [Reference Wells5], so why should it be able to index broader patterns of health status and disease risk? We have previously suggested that the utility of BMI derives from it indexing both current weight (metabolic load), and completed growth (height, associated with birth weight and hence metabolic capacity) [Reference Wells55]. For example, numerous studies have linked short stature with an increased risk of chronic diseases [Reference Gertler, Garn and White56Reference Nuesch60]. A high BMI value therefore provides a very simple index of capacity-load status.

However, the utility of BMI is much less impressive when we focus on individuals, and particularly when they belong to different ethnic groups. It is now clear that the association between BMI and chronic disease risk is confounded by ethnic differences in size, physique and adiposity. For example, Indians develop diabetes following relatively modest increments in BMI through young adulthood [Reference Bhargava41]. More detailed indices of body composition could therefore help resolve this scenario, by providing independent proxies for each of metabolic capacity and metabolic load.

Body composition and metabolic load

In terms of body composition, the most obvious component of metabolic load may be total fat mass. However, there is increasing recognition that the regional anatomical distribution of body fat also affects metabolic profile. Studies have repeatedly demonstrated that central abdominal fat, in particular visceral fat, is metabolically more harmful than peripheral fat in the gluteo-femoral region [Reference Snijder61Reference Snijder64]. For this reason, indices of adiposity that take into account its regional distribution may be more successful in predicting chronic disease risk.

A large study demonstrated that waist-hip ratio was more successful than BMI at predicting cardiovascular mortality across 52 countries [Reference Yusuf65]. Mortality was much greater in those with high waist girth but low BMI, compared with those with high BMI but low waist girth. Abdominal fat correlates with many components of the metabolic syndrome, including elevated fasting glucose and insulin levels, cholesterol levels, blood pressure, and inflammatory markers. Indeed, obesity manifests as a chronic inflammatory state [Reference Ramos66, Reference Xu67]. It is also widely recognized that low levels of physical activity increase the risk of obesity, while unhealthy diets (high in processed sugar) are also correlated. Both of these factors are independent components of unhealthy metabolism and predict mortality [Reference Liu68Reference Lee70].

We should not therefore be surprised that indices of adiposity are very valuable markers of chronic disease risk, by indexing metabolic load. This has been confirmed by extensive data indicating that the global obesity epidemic is a strong environmental factor driving the chronic disease epidemic [Reference Lopez and Mathers24].

However, measurements of adiposity may still require ethnic differences to be taken into account. It is already recognized that populations differ in their body fat content for a given BMI value. For example, Asian populations tend to have elevated body fat, and African or Caribbean populations lower levels of body fat, for a given BMI value compared with European populations [Reference Deurenberg, Yap and van Staveren8Reference Nightingale10, Reference Lee71]. This means that the threshold at which body weight becomes unhealthy is expected to differ across populations. An effort to resolve this has resulted in ethnic specific BMI cut-offs for defining overweight and obesity [72].

Direct measurements of body fat and body shape could overcome some of these limitations, especially as the regional distribution of body fat also differs between ethnic groups. Furthermore, some studies suggest that the metabolic toxicity of body fat varies between ethnic groups. For example, the association between body fat and insulin resistance was stronger in South Asian compared with European and African and Caribbean children in the UK [Reference Nightingale73].

This indicates that body composition can provide a very valuable index of metabolic load, though it may still be difficult to compare different ethnic groups on a common basis. There are now a number of techniques available for collecting body composition data, including DXA, air-displacement plethysmography, and magnetic resonance imaging [Reference Wells and Fewtrell74]. For widespread routine monitoring, waist girth remains the simplest option, though there is uncertainty as to whether it should be indexed to height or to hip girth [Reference McCarthy and Ashwell75], or simply expressed in absolute units. One potentially exciting opportunity is the development of 3-D photonic scanning of body shape [Reference Wells, Ruto and Treleaven76]. This non-invasive method provides a rapid but detailed assessment of physique, though not of internal tissues. It is ideal for monitoring body shape changes, and has already been used in large ‘sizing surveys’ for the clothing industry [Reference Wells77Reference Wells, Treleaven and Cole79]. With the instrumentation suitable for use in health clubs, shopping malls and clinics, it may prove to be a valuable means of monitoring metabolic load.

Another component of metabolic load relatively easily measured is physical inactivity, through the use of pedometers, or accelerometers worn on the waist or wrist. These could be readily adapted to download data to central digital data collection points.

Body composition and metabolic capacity

Where birth weight data are available, it is now clear that they provide valuable information on chronic disease risk. For example, in a study of Swedish adults, Leon et al. [Reference Leon80] showed that the metabolic penalties for tall height and obesity occurred primarily in those of low birth weight. In other words, the extent to which metabolic load increases disease risk is strongly shaped by metabolic capacity.

Recent studies have linked birth weight with more detailed structural and functional components of the cardiovascular system. These associations are evident across a wide age-span, indicating that they emerge early in life and then track subsequently. Relevant outcomes include endothelial function, aortic size and wall thickness, aortic root diameter and vascular mechanical properties of other integral arteries.

In infants, children and adolescents, for example, birth weight has been inversely associated with several measures of cardiac competence (Table 2). Skilton et al. [Reference Skilton81] reported a significant, negative relationship between birth weight and thickening of the aortic wall in 25 growth-retarded neonates when compared with those of normal birth size. Another study reported smaller vessel diameters in the abdominal aorta, popliteal artery, and common carotid artery in adolescents born small for gestational age [Reference Brodszki82]. These and other vascular properties affect cardiac load and the regulation of blood pressure, and imply an increased risk of cardiovascular complications.

Table 2. Birth weight associated with cardiac outcomes in children/adolescents

Investigations in adults show similar findings (Table 3). Low birth weight was associated with narrower retinal arteriolar caliber (a marker of hypertension and cardiovascular disease risk) among 3800 individuals aged 51–72 years [Reference Liew83]. In a Dutch cohort, birth weight was inversely associated with carotid intima media thickness (CIMT), indicative of subclinical atherosclerosis, in the lowest tertile of birth length [Reference Oren84]. Additionally, birth weight was inversely associated with CIMT in subjects demonstrating ‘catch-up’ growth in infancy, another risk factor for adult chronic disease and mortality [Reference Eriksson40]. However, it should be noted that links between early-life and cardiovascular outcomes in adults may be confounded by other conditions such as diabetes and hypertension that may reflect both developmental and current lifestyle influences [Reference Mitchell85].

Table 3. Birth weight associated with cardiac outcomes in adults

The main limitation of birth weight as a marker of metabolic capacity is that the information may not be available for many individuals, especially from low- and middle-income populations. However, other proxies can be used in its place.

Some aspects of metabolic capacity may be indexed by childhood growth patterns. It is now clear that poor childhood growth impacts the lower leg in particular, resulting in shorter legs relative to total height [Reference Gunnell86, Reference Gunnell87]. A number of studies have demonstrated elevated chronic disease risk in those with shorter leg length in adult life [Reference Lawlor58, Reference Lawlor88Reference Montagnese90].

Of particular interest, relative leg length (i.e. leg length/height) appears minimally correlated with birth weight, meaning that measurement of this trait in adult life provides an assessment of postnatal as opposed to foetal growth [Reference Gunnell86, Reference Bogin and Baker91]. Some aspects of metabolic capacity, such as the pancreas, appear to continue to develop during postnatal life [Reference Bouwens and Rooman92], potentially explaining why short leg length is an independent risk factor for diabetes. Relative leg length is therefore subtly different from leg length per se, by being independent of birth weight, and the two traits may potentially be used in combination to index metabolic capacity when data on birth weight are unavailable.

However, a cautionary note is necessary. A Swedish study showed that tall stature may also elevate disease risk in those born small [Reference Leon80]. This suggests that compensatory catch-up growth occurred after birth in this group, reducing the utility of height as a marker of metabolic capacity. As yet, it is unclear if this issue could be resolved by focusing in more detail on leg length or relative leg length.

Until recently, very few other simple options were available for assessing metabolic capacity in adult life. One approach is the prediction of lean mass using bio-electrical impedance analysis. However, even ignoring the relatively poor precision of this approach at the level of the individual, the association of total body lean mass with health also appears complex. On the one hand, lean mass incorporates muscle mass, which is widely considered to protect against diabetes. On the other hand, some studies have linked high levels of lean mass with higher blood pressure [Reference Montagnese90, Reference Grijalva-Eternod, Lawlor and Wells93]. Total lean mass may therefore be too generalized to act as a reliable proxy for metabolic capacity in adult life.

Recently, much attention has been paid to a more specific component of body composition, measured at the level of function rather than mass. Grip strength, often considered a marker of muscle strength, has attracted interest because it is positively associated with cardio-metabolic function in children (e.g. [Reference Cohen94]), and negatively related to morbidity and mortality in adults [Reference Rantanen95Reference Leong100]. Like BMI, grip strength may provide a valuable proxy for several different traits, each of which is associated with chronic disease risk. We therefore review this new opportunity for indexing metabolic capacity in more detail.

Grip strength as a potential marker of metabolic capacity

What is particularly valuable about grip strength is that it may simultaneously index both the early-life development of metabolic capacity, as well as reflecting current physical fitness, which is also important for health. Birth weight has been repeatedly associated both with lean mass [Reference Wells, Chomtho and Fewtrell101] and with grip strength, as discussed below. Looking in the reverse direction, grip strength therefore reflects foetal growth experience, and may act as a marker of metabolic capacity. Beyond this, grip strength also reflects current lifestyle, with those currently physically active likely to have greater physical fitness. This conceptual approach is summarized in Fig. 3.

Fig. 3. Schematic diagram illustrating how grip strength may act as a valuable marker of chronic disease risk through its ability to index two crucial components of metabolic capacity: foetal growth (its development) and adult physical fitness (its maintenance).

A number of authors have reported significant associations between birth weight and adult grip strength (Table 4). A recent meta-analysis found a 0.86 kg (95% CI 0.58–1.15) increase in grip strength per kg increase in birth weight in gender-pooled data from 13 studies [Reference Newsome113]. A number of the studies included in this meta-analysis are also included in Table 4. Variation in the reported B-coefficients is potentially due to several factors, including variation in subject age, methods of hand grip measurement, gender, and variable adjustment for potential confounders, but all studies show a significant positive association with the exception of Patel et al. [Reference Patel103], whose trend did not reach significance.

Table 4. Birth weight associated with adult grip strength

The utility of grip strength for predicting chronic disease outcomes was recently demonstrated by Leong et al. [Reference Leong100]. In a large, multi-ethnic and socioeconomically variable sample followed over 4 years, these authors showed inverse associations between grip strength and all-cause mortality, cardiovascular mortality, non-cardiovascular mortality, myocardial infarction, and stroke. Indeed, grip strength was a stronger predictor of all-cause and cardiovascular mortality than systolic blood pressure. This study thus highlights the potential for grip strength to assess chronic disease risk in individuals for whom overt symptoms are not yet evident, and for whom information on birth weight is not available.

A composite capacity-load model

We therefore propose an enhanced version of the ‘capacity-load’ model, for application in the assessment of chronic disease risk in populations where data on birth weight are lacking. Metabolic load can be categorised by a combination of BMI, waist girth, and physical inactivity. For example, a clustered z-score (the average of several raw z-scores, as already used for the assessment of metabolic risk in children [Reference Andersen104]) may be calculated based on raw data for these variables. Metabolic capacity may be categorised through grip strength, leg length and relative leg length, again using a clustered z-score approach. We then assume that chronic disease risk is greatest in those with a high ratio of metabolic load to metabolic capacity.

This approach is consistent with recent work intended to improve the assessment of sarcopenic obesity, where high levels of body fatness coexist with unhealthily low levels of lean tissue mass [Reference Prado105]. This condition is increasingly prevalent, and is considered a key pathway linking body composition with metabolic ill health. We have recently published capacity-load centile charts for sarcopenic obesity based on adult body composition, namely the ratio of fat mass to fat-free mass, and the ratio of trunk fat to appendicular skeletal muscle mass [Reference Siervo106]. This approach could therefore be extended as described above, to incorporate clustered scores of metabolic capacity and load.

Strengths and limitations

There is of course no panacea for assessing chronic disease risk in public health research and practice. No single trait can reliably index health risk in all individuals, or accurately summarize the beneficial responses to public health interventions. A limitation of our approach is that while data on early life growth and current body composition may surpass BMI at indexing chronic disease risk, they still may lack the sensitivity of physiological outcomes such as blood pressure or blood biochemistry. Moreover, sophisticated body composition measurements do not inevitably outperform BMI. In 2369 adults from Hyderabad in India, for example, waist-hip ratio was only slightly better than whole-body adiposity at predicting diabetes risk, and BMI performed as well as adiposity in predicting other markers of cardiovascular risk [Reference Kuper107].

Nevertheless, findings such as those illustrated in Fig. 2 suggest that integrating data on experience in early life and current phenotype should improve chronic disease risk assessment over measures of adult phenotype alone. Our hypothesis is that accurate measurements of load will categorize risk best when combined with accurate measurements of capacity.

A potential strength of our proposed approach is that it may prove adequate for monitoring changes over time, without the need for expensive or intrusive tests. Indeed, individuals may monitor most outcomes themselves in the community, offering the potential to link with M-health and E-health monitoring. Baseline measurements of leg length could be collected, while subjects could then monitor their weight, activity level using pedometry, waist girth, and grip strength.

This approach merits testing in large cohorts to establish its sensitivity for estimating chronic disease risk and mortality risk. Changes in weight, waist girth, and physical activity may be relatively sensitive to dietary shifts, which are relatively hard to quantify directly with accuracy. The recent demonstration that grip strength proved capable of indexing ethnic differences in chronic disease risk was particularly informative and encouraging.

Declaration of Interest

Both authors declare no conflict of interest.

References

1. Omran, AR. The epidemiological transition: a theory of the epidemiology of population change. Millbank MemorFund Q 1971; 49: 509538.Google Scholar
2. Lopez, AD, Mathers, CD. Measuring the global burden of disease and epidemiological transitions: 2002–2030. Annals of Tropical Medicine and Parasitology 2006; 100: 481499.Google Scholar
3. Lozano, R, et al. Global and regional mortality from 235 causes of death for 20 age groups in 1990 and 2010: a systematic analysis for the global burden of disease study 2010. Lancet 2012; 380: 20952128.Google Scholar
4. World Health Organization. Global Status Report on Noncommunicable Diseases 2010. Geneva: World Health Organization, 2011.Google Scholar
5. Wells, JC. A Hattori chart analysis of body mass index in infants and children. International Journal of Obesity and Related Metabolic Disorders 2000; 24: 325329.Google Scholar
6. Wells, JC, et al. A simplified approach to analysing bio-electrical impedance data in epidemiological surveys. International Journal of Obesity (Lond) 2007; 31: 507514.CrossRefGoogle ScholarPubMed
7. McAuley, PA, et al. Obesity paradox and cardiorespiratory fitness in 12 417 male veterans aged 40 to 70 years. Mayo Clinic Proceedings Mayo Clinic 2010; 85: 115121.Google Scholar
8. Deurenberg, P, Yap, M, van Staveren, WA. Body mass index and percent body fat: a meta analysis among different ethnic groups. International Journal of Obesity and Related Metabolic Disorders 1998; 22: 11641171.Google Scholar
9. Haroun, D, et al. Validation of bioelectrical impedance analysis in adolescents across different ethnic groups. Obesity (Silver Spring) 2010; 18: 12521259.Google Scholar
10. Nightingale, CM, et al. Patterns of body size and adiposity among UK children of South Asian, black African-Caribbean and white European origin: Child Heart And health Study in England (CHASE Study). International Journal of Epidemiology 2011; 40: 3344.Google Scholar
11. Wells, JC. Ethnic variability in adiposity, thrifty phenotypes and cardiometabolic risk: addressing the full range of ethnicity, including those of mixed ethnicity. Obesity Reviews: an Official Journal of the International Association for the Study of Obesity 2012; 13 (Suppl. 2): 1429.Google Scholar
12. Sorensen, TI, et al. Genetics of obesity in adult adoptees and their biological siblings. BMJ 1989; 298: 8790.Google Scholar
13. Bulik, CM, Allison, DB. The genetic epidemiology of thinness. Obesity Reviews 2001; 2: 107115.CrossRefGoogle ScholarPubMed
14. Schousboe, K, et al. Twin study of genetic and environmental influences on adult body size, shape, and composition. International Journal of Obesity and Related Metabolic Disorder 2004; 28: 3948.CrossRefGoogle ScholarPubMed
15. Relton, CL, et al. DNA methylation patterns in cord blood DNA and body size in childhood. PLoS ONE 2012; 7: e31821.Google Scholar
16. Ong, KK, et al. Association between postnatal catch-up growth and obesity in childhood: prospective cohort study. BMJ 2000; 320: 967971.Google Scholar
17. Wells, JC, et al. Fetal, infant and childhood growth: relationships with body composition in Brazilian boys aged 9 years. International Journal of Obesity (London) 2005; 29: 11921198.CrossRefGoogle ScholarPubMed
18. Sachdev, HS, et al. Anthropometric indicators of body composition in young adults: relation to size at birth and serial measurements of body mass index in childhood in the New Delhi birth cohort. American Journal of Clinical Nutrition 2005; 82: 456466.CrossRefGoogle ScholarPubMed
19. Ekelund, U, et al. Upward weight percentile crossing in infancy and early childhood independently predicts fat mass in young adults: the Stockholm Weight Development Study (SWEDES). American Journal of Clinical Nutrition 2006; 83: 324330.CrossRefGoogle ScholarPubMed
20. Wells, JC, et al. Associations of birth order with early growth and adolescent height, body composition, and blood pressure: prospective birth cohort from Brazil. American Journal of Epidemiology 2011; 174: 10281035.Google Scholar
21. Lustig, RH. The ‘skinny’ on childhood obesity: how our western environment starves kids’ brains. Pediatric Annals 2006; 35: 898–897.Google Scholar
22. Lustig, RH. Which comes first? The obesity or the insulin? The behavior or the biochemistry? Journal of Pediatrics 2008; 152: 601602.CrossRefGoogle ScholarPubMed
23. Wells, JC, et al. Investigation of the relationship between infant temperament and later body composition. International Journal of Obesity and Related Metabolic Disorders 1997; 21: 400406.Google Scholar
24. Benjamin Neelon, SE, et al. Age of achievement of gross motor milestones in infancy and adiposity at age 3 years. Maternal and Child Health Journal 2012; 16: 10151020.CrossRefGoogle ScholarPubMed
25. Li, R, et al. Relation of activity levels to body fat in infants 6 to 12 months of age. Journal of Pediatrics 1995; 126: 353357.CrossRefGoogle ScholarPubMed
26. Hallal, PC, et al. Physical inactivity: prevalence and associated variables in Brazilian adults. Medicine and Science in Sports & Exercise 2003; 35: 18941900.Google Scholar
27. Ness, AR, et al. Objectively measured physical activity and fat mass in a large cohort of children. PLoS Medicine 2007; 4: e97.CrossRefGoogle Scholar
28. Ekelund, U, et al. TV viewing and physical activity are independently associated with metabolic risk in children: the European Youth Heart Study. PLoS Medicine 2006; 3: e488.Google Scholar
29. Farnsworth, E, et al. Effect of a high-protein, energy-restricted diet on body composition, glycemic control, and lipid concentrations in overweight and obese hyperinsulinemic men and women. American Journal of Clinical Nutrition 2003; 78: 3139.Google Scholar
30. Johnson, L, et al. A prospective analysis of dietary energy density at age 5 and 7 years and fatness at 9 years among UK children. International Journal of Obesity (London) 2008; 32: 586593.Google Scholar
31. Hales, CN, Barker, DJ. Type 2 (non-insulin-dependent) diabetes mellitus: the thrifty phenotype hypothesis. Diabetologia 1992; 35: 595601.Google Scholar
32. Dawber, TR, et al. Some factors associated with the development of coronary heart disease: six years’ follow-up experience in the Framingham study. American Journal of Public Health and the Nation's Health 1959; 49: 13491356.Google Scholar
33. Slack, J, Evans, KA. The increased risk of death from ischaemic heart disease in first degree relatives of 121 men and 96 women with ischaemic heart disease. Journal of Medical Genetics 1966; 3: 239257.CrossRefGoogle ScholarPubMed
34. Rissanen, AM. Familial aggregation of coronary heart disease in a high incidence area (North Karelia, Finland). British Heart Journal 1979; 42: 294303.Google Scholar
35. Barker, DJ, et al. Weight in infancy and death from ischaemic heart disease. Lancet 1989; 2: 577580.Google Scholar
36. Barker, DJ, et al. Growth in utero and serum cholesterol concentrations in adult life. BMJ 1993; 307: 15241527.Google Scholar
37. Hales, CN, et al. Fetal and infant growth and impaired glucose tolerance at age 64. BMJ 1991; 303: 10191022.Google Scholar
38. Phillips, DI, et al. Thinness at birth and insulin resistance in adult life. Diabetologia 1994; 37: 150154.Google Scholar
39. Frankel, S, et al. Birthweight, body-mass index in middle age, and incident coronary heart disease. Lancet 1996; 348: 14781480.Google Scholar
40. Eriksson, JG, et al. Catch-up growth in childhood and death from coronary heart disease: longitudinal study. BMJ 1999; 318: 427431.CrossRefGoogle ScholarPubMed
41. Bhargava, SK, et al. Relation of serial changes in childhood body-mass index to impaired glucose tolerance in young adulthood. New England Journal of Medicine 2004; 350: 865875.CrossRefGoogle ScholarPubMed
42. Barker, DJ, et al. Trajectories of growth among children who have coronary events as adults. New England Journal of Medicine 2005; 353: 18021809.Google Scholar
43. Latini, G, et al. Foetal growth of kidneys, liver and spleen in intrauterine growth restriction: “programming” causing “metabolic syndrome” in adult age. Acta Paediatrica 2004; 93: 16351639.CrossRefGoogle Scholar
44. Rich-Edwards, JW, et al. Birth weight and risk of cardiovascular disease in a cohort of women followed up since 1976. BMJ 1997; 315: 396400.Google Scholar
45. Li, Y, et al. Birth weight and later life adherence to unhealthy lifestyles in predicting type 2 diabetes: prospective cohort study. BMJ 2015; 351: h3672.Google Scholar
46. Li, Y, et al. Joint association between birth weight at term and later life adherence to a healthy lifestyle with risk of hypertension: a prospective cohort study. BMC Medicine 2015; 13: 175.Google Scholar
47. Whincup, PH, et al. Birth weight and risk of type 2 diabetes: a systematic review. JAMA 2008; 300: 28862897.Google ScholarPubMed
48. Wells, JC. Historical cohort studies and the early origins of disease hypothesis: making sense of the evidence. Proceedings of the Nutrition Society 2009; 68: 179188.Google Scholar
49. Wells, JC. The thrifty phenotype: an adaptation in growth or metabolism? American Journal of Human Biology 2011; 23: 6575.Google Scholar
50. McEwen, BS. Protective and damaging effects of stress mediators. New England Journal of Medicine 1998; 338: 171179.CrossRefGoogle ScholarPubMed
51. McEwen, BS, Stellar, E. Stress and the individual. Mechanisms leading to disease. Archives of Internal Medicine 1993; 153: 20932101.Google Scholar
52. Calle, EE, et al. Body-mass index and mortality in a prospective cohort of U.S. adults. New England Journal of Medicine 1999; 341: 10971105.Google Scholar
53. Romero-Corral, A, et al. Association of bodyweight with total mortality and with cardiovascular events in coronary artery disease: a systematic review of cohort studies. Lancet 2006; 368: 666678.Google Scholar
54. Flegal, KM, et al. Excess deaths associated with underweight, overweight, and obesity. JAMA 2005; 293: 18611867.Google Scholar
55. Wells, JC. Commentary: the paradox of body mass index in obesity assessment: not a good index of adiposity, but not a bad index of cardio-metabolic risk. International Journal of Epidemiology 2014; 43: 672674.Google Scholar
56. Gertler, MM, Garn, SM, White, PD. Young candidates for coronary heart disease. Journal of the American Medical Association 1951; 147: 621625.Google Scholar
57. Paajanen, TA, et al. Short stature is associated with coronary heart disease: a systematic review of the literature and a meta-analysis. European Heart Journal 2010; 31: 18021809.Google Scholar
58. Lawlor, DA, et al. The association between components of adult height and Type II diabetes and insulin resistance: British Women's Heart and Health Study. Diabetologia 2002; 45: 10971106.Google ScholarPubMed
59. Moses, RG, Mackay, MT. Gestational diabetes: is there a relationship between leg length and glucose tolerance? Diabetes Care 2004; 27: 10331035.Google Scholar
60. Nuesch, E, et al. Adult height, coronary heart disease and stroke: a multi-locus Mendelian randomization meta-analysis. International Journal of Epidemiology 2015 Google Scholar
61. Snijder, MB, et al. Associations of hip and thigh circumferences independent of waist circumference with the incidence of type 2 diabetes: the Hoorn Study. American Journal of Clinical Nutrition 2003; 77: 11921197.Google Scholar
62. Snijder, MB, et al. Trunk fat and leg fat have independent and opposite associations with fasting and postload glucose levels: the Hoorn study. Diabetes Care 2004; 27: 372377.Google Scholar
63. Snijder, MB, et al. Low subcutaneous thigh fat is a risk factor for unfavourable glucose and lipid levels, independently of high abdominal fat. Health ABC Study. Diabetologia 2005; 48: 301308.Google ScholarPubMed
64. Snijder, MB, et al. Independent and opposite associations of waist and hip circumferences with diabetes, hypertension and dyslipidemia: the AusDiab Study. International Journal of Obesity and Related Metabolic Disorders 2004; 28: 402409.CrossRefGoogle ScholarPubMed
65. Yusuf, S, et al. Obesity and the risk of myocardial infarction in 27 000 participants from 52 countries: a case-control study. Lancet 2005; 366: 16401649.Google Scholar
66. Ramos, EJ, et al. Is obesity an inflammatory disease? Surgery 2003; 134: 329335.CrossRefGoogle ScholarPubMed
67. Xu, H, et al. Chronic inflammation in fat plays a crucial role in the development of obesity-related insulin resistance. Journal of Clinical Investigation 2003; 112: 18211830.Google Scholar
68. Liu, S, et al. A prospective study of dietary glycemic load, carbohydrate intake, and risk of coronary heart disease in US women. American Journal of Clinical Nutrition 2000; 71: 14551461.Google Scholar
69. Oba, S, et al. Dietary glycemic index, glycemic load, and intake of carbohydrate and rice in relation to risk of mortality from stroke and its subtypes in Japanese men and women. Metabolism 2010; 59: 15741582.Google Scholar
70. Lee, IM, et al. Effect of physical inactivity on major non-communicable diseases worldwide: an analysis of burden of disease and life expectancy. Lancet 2012; 380: 219229.CrossRefGoogle ScholarPubMed
71. Lee, S, et al. Ethnic variability in body size, proportions and composition in children aged 5 to 11 years: is ethnic-specific calibration of bioelectrical impedance required? PLoS ONE 2014; 9: e113883.Google Scholar
72. WHO Expert Consultation. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004; 36: 157163.Google Scholar
73. Nightingale, CM, et al. Influence of adiposity on insulin resistance and glycemia markers among United Kingdom Children of South Asian, Black African-Caribbean, and White European Origin: child heart and health study in England. Diabetes Care 2013.CrossRefGoogle ScholarPubMed
74. Wells, JC, Fewtrell, MS. Measuring body composition. Archives of Disease in Childhood 2006; 91: 612617.CrossRefGoogle ScholarPubMed
75. McCarthy, HD, Ashwell, M. A study of central fatness using waist-to-height ratios in UK children and adolescents over two decades supports the simple message--'keep your waist circumference to less than half your height’. International Journal of Obesity (London) 2006; 30: 988992.Google Scholar
76. Wells, JC, Ruto, A, Treleaven, P. Whole-body three-dimensional photonic scanning: a new technique for obesity research and clinical practice. International Journal of Obesity (London) 2008; 32: 232238.Google Scholar
77. Wells, JC, et al. Body shape in American and British adults: between-country and inter-ethnic comparisons. International Journal of Obesity (London) 2008; 32: 152159.Google Scholar
78. Wells, JC, Treleaven, P, Charoensiriwath, S. Body shape by 3-D photonic scanning in Thai and UK adults: comparison of national sizing surveys. International Journal of Obesity (London) 2012; 36: 148154.Google Scholar
79. Wells, JC, Treleaven, P, Cole, TJ. BMI compared with 3-dimensional body shape: the UK National Sizing Survey. American Journal of Clinical Nutrition 2007; 85: 419425.Google Scholar
80. Leon, DA, et al. Failure to realise growth potential in utero and adult obesity in relation to blood pressure in 50 year old Swedish men. BMJ 1996; 312: 401406.CrossRefGoogle ScholarPubMed
81. Skilton, MR, et al. Aortic wall thickness in newborns with intrauterine growth restriction. Lancet 2005; 365: 14841486.Google Scholar
82. Brodszki, J, et al. Impaired vascular growth in late adolescence after intrauterine growth restriction. Circulation 2005; 111: 26232628.Google Scholar
83. Liew, G, et al. Low birthweight is associated with narrower arterioles in adults. Hypertension 2008; 51(4): 933938.Google Scholar
84. Oren, A, et al. Birth weight and carotid intima-media thickness: new perspectives from the atherosclerosis risk in young adults (ARYA) study. Annals of Epidemiology 2004; 14: 816.Google Scholar
85. Mitchell, P, et al. Evidence of arteriolar narrowing in low-birth-weight children. Circulation 2008; 118: 518524.Google Scholar
86. Gunnell, D, et al. Separating in-utero and postnatal influences on later disease. Lancet 1999; 354: 15261527.CrossRefGoogle ScholarPubMed
87. Gunnell, DJ, et al. Socio-economic and dietary influences on leg length and trunk length in childhood: a reanalysis of the Carnegie (Boyd Orr) survey of diet and health in prewar Britain (1937–39). Paediatric and Perinatal Epidemiology 1998; 12 (Suppl. 1): 96113.Google Scholar
88. Lawlor, DA, et al. Associations of components of adult height with coronary heart disease in postmenopausal women: the British women's heart and health study. Heart 2004; 90: 745749.Google Scholar
89. Langenberg, C, et al. Influence of height, leg and trunk length on pulse pressure, systolic and diastolic blood pressure. Journal of Hypertension 2003; 21: 537543.Google Scholar
90. Montagnese, C, et al. Body composition, leg length and blood pressure in a rural Italian population: a test of the capacity-load model. Nutrition Metabolism and Cardiovascular Diseases 2014; 24: 12041212.Google Scholar
91. Bogin, B, Baker, J. Low birth weight does not predict the ontogeny of relative leg length of infants and children: an allometric analysis of the NHANES III sample. American Journal of Physical Anthropology 2012; 148: 487494.Google Scholar
92. Bouwens, L, Rooman, I. Regulation of pancreatic beta-cell mass. Physiological Reviews 2005; 85: 12551270.Google Scholar
93. Grijalva-Eternod, CS, Lawlor, DA, Wells, JC. Testing a capacity-load model for hypertension: disentangling early and late growth effects on childhood blood pressure in a prospective birth cohort. PLoS ONE 2013; 8: e56078.Google Scholar
94. Cohen, DD, et al. Low muscle strength is associated with metabolic risk factors in Colombian children: the ACFIES study. PLoS ONE 2014; 9: e93150.Google Scholar
95. Rantanen, T, et al. Muscle strength and body mass index as long-term predictors of mortality in initially healthy men. Journals of Gerontology A Biological Sciences and Medical Sciences 2000; 55: M168M173.Google Scholar
96. Newman, AB, et al. Strength, but not muscle mass, is associated with mortality in the health, aging and body composition study cohort. Journals of Gerontology A Biological Sciences and Medical Sciences 2006; 61: 7277.Google Scholar
97. Gale, CR, et al. Grip strength, body composition, and mortality. International Journal of Epidemiology 2007; 36: 228235.Google Scholar
98. Sasaki, H, et al. Grip strength predicts cause-specific mortality in middle-aged and elderly persons. American Journal of medicine 2007; 120: 337342.Google Scholar
99. Norman, K, et al. Hand grip strength: outcome predictor and marker of nutritional status. Clinical Nutrition 2011; 30: 135142.Google Scholar
100. Leong, DP, et al. Prognostic value of grip strength: findings from the Prospective Urban Rural Epidemiology (PURE) study. Lancet 2015; 386: 266273.Google Scholar
101. Wells, JC, Chomtho, S, Fewtrell, MS. Programming of body composition by early growth and nutrition. Proceedings of the Nutrition Society 2007; 66: 423434.Google Scholar
102. Dodds, PS, Rothman, DH, Weitz, JS. Re-examination of the “3/4-law” of metabolism. Journal of Theoretical Biology 2001; 209: 927.Google Scholar
103. Patel, HP, et al. Developmental influences, muscle morphology, and sarcopenia in community-dwelling older men. Journals of Gerontology A Biological Sciences and Medical Sciences 2012; 67: 8287.Google Scholar
104. Andersen, LB, et al. Physical activity and clustered cardiovascular risk in children: a cross-sectional study (The European Youth Heart Study). Lancet 2006; 368: 299304.Google Scholar
105. Prado, CM, et al. Sarcopenic obesity: a critical appraisal of the current evidence. Clinical Nutrition 2012; 31: 583601.Google Scholar
106. Siervo, M, et al. Body composition indices of a load-capacity model: gender- and BMI-specific reference curves. Public Health Nutrition 2015; 18: 12451254.Google Scholar
107. Kuper, H, et al. Is vulnerability to cardiometabolic disease in Indians mediated by abdominal adiposity or higher body adiposity. BMC Public Health 2014; 14: 1239.Google Scholar
108. Adair, LS, Cole, TJ. Rapid child growth raises blood pressure in adolescent boys who were thin at birth. Hypertension 2003; 41: 451456.CrossRefGoogle ScholarPubMed
109. Lurbe, E, et al. Influence of concurrent obesity and low birth weight on blood pressure phenotype in youth. Hypertension 2009; 53: 912917.Google Scholar
110. Huxley, RR, Shiell, AW, Law, CM. The role of size at birth and postnatal catch-up growth in determining systolic blood pressure: a systematic review of the literature. Journal of Hypertension 2000; 18: 815831.Google Scholar
111. Fagerberg, B, Bondjers, L, Nilsson, P. Low birth weight in combination with catch-up growth predicts the occurrence of the metabolic syndrome in men at late middle age: the Atherosclerosis and Insulin Resistance study. Journal of Internal Medicine 2004; 256: 254259.Google Scholar
112. Bavdekar, A, et al. Insulin resistance syndrome in 8-year-old Indian children – Small at birth, big at 8 years, or both? Diabetes 1999; 48: 24222429.Google Scholar
113. Newsome, CA, et al. Is birth weight related to later glucose and insulin metabolism?–A systematic review. Diabetic Medicine 2003; 20: 339348.Google Scholar
114. Forsen, T, et al. The fetal and childhood growth of persons who develop type 2 diabetes. Annals of Internal Medicine 2000; 133: 176182.Google Scholar
115. Roseboom, TJ, et al. Plasma lipid profiles in adults after prenatal exposure to the Dutch famine. American Journal of Clinical Nutrition 2000; 72: 11011106.Google Scholar
116. Cooper, R, Power, C. Sex differences in the associations between birthweight and lipid levels in middle-age: findings from the 1958 British birth cohort. Atherosclerosis 2008; 200: 141149.Google Scholar
117. Amigo, H, et al. Socioeconomic status and age at menarche in indigenous and non-indigenous Chilean adolescents. Cadernos de saude publica 2012; 28: 977983.Google Scholar
118. Tzoulaki, I, et al. Size at birth, weight gain over the life course, and low-grade inflammation in young adulthood: northern Finland 1966 Birth Cohort study. European Heart Journal 2008; 29: 10491056.Google Scholar
119. Bhuiyan, AR, et al. Influence of low birth weight on C-reactive protein in asymptomatic younger adults: the Bogalusa heart study. BMC Research Notes 2011; 4: 71.Google Scholar
120. Lakshmy, R, et al. Childhood body mass index and adult pro-inflammatory and pro-thrombotic risk factors: data from the New Delhi birth cohort. International Journal of Epidemiology 2011; 40: 102111.Google Scholar
121. Leon, DA, et al. Reduced fetal growth rate and increased risk of death from ischaemic heart disease: cohort study of 15 000 Swedish men and women born 1915–29. BMJ 1998; 317: 241245.Google Scholar
122. Stein, CE, et al. Fetal growth and coronary heart disease in south India. Lancet 1996; 348: 12691273.Google Scholar
123. Jiang, B, et al. Birth weight and cardiac structure in children. Pediatrics 2006; 117: e257ee61.CrossRefGoogle ScholarPubMed
124. Walther, FJ, et al. Normal values of aortic root measurements in neonates. Pediatric Cardiology 1985; 6: 6163.Google Scholar
125. Bonamy, AK, et al. Preterm birth and carotid diameter and stiffness in childhood. Acta Paediatrica 2008; 97: 434437.Google Scholar
126. Goodfellow, J, et al. Endothelial function is impaired in fit young adults of low birth weight. Cardiovascular Research 1998; 40: 600606.Google Scholar
127. Leeson, CP, et al. Flow-mediated dilation in 9- to 11-year-old children: the influence of intrauterine and childhood factors. Circulation 1997; 96: 22332238.Google Scholar
128. Franco, MC, et al. Effects of low birth weight in 8- to 13-year-old children: implications in endothelial function and uric acid levels. Hypertension 2006; 48: 4550.CrossRefGoogle ScholarPubMed
129. Martin, H, et al. Impaired endothelial function and increased carotid stiffness in 9-year-old children with low birthweight. Circulation 2000; 102: 27392744.CrossRefGoogle ScholarPubMed
130. Martyn, CN, et al. Growth in utero, adult blood pressure, and arterial compliance. British Heart Journal 1995; 73: 116121.Google Scholar
131. Martyn, CN, et al. Impaired fetal growth and atherosclerosis of carotid and peripheral arteries. Lancet 1998; 352: 173178.Google Scholar
132. Leeson, CP, et al. Impact of low birth weight and cardiovascular risk factors on endothelial function in early adult life. Circulation 2001; 103: 12641268.Google Scholar
133. Dodds, R, et al. Birth weight and muscle strength: a systematic review and meta-analysis. Journal of Nutrition Health and Aging 2012; 16: 609615.Google Scholar
134. Inskip, HM, et al. Size at birth and its relation to muscle strength in young adult women. Journal of Internal Medicine 2007; 262: 368374.Google Scholar
135. Kuh, D, et al. Birth weight, childhood size, and muscle strength in adult life: evidence from a birth cohort study. American Journal of Epidemiology 2002; 156: 627633.Google Scholar
136. Ridgway, CL, et al. Birth size, infant weight gain, and motor development influence adult physical performance. Medicine and Science in Sports & Exercise 2009; 41: 12121221.Google Scholar
137. Robinson, SM, et al. Diet and its relationship with grip strength in community-dwelling older men and women: the Hertfordshire cohort study. Journal of American Geriatrics Society 2008; 56: 8490.Google Scholar
138. Sayer, AA, et al. Are rates of ageing determined in utero? Age and Ageing 1998; 27: 579583.Google Scholar
139. Yliharsila, H, et al. Birth size, adult body composition and muscle strength in later life. International Journal of Obesity (London) 2007; 31: 13921399.Google Scholar
Figure 0

Fig. 1. Schematic diagram of the ‘capacity-load’ model of chronic disease risk. Metabolic capacity promotes the maintenance of homoeostasis, and thereby reduces chronic disease risk. Metabolic load challenges homeostasis, and thereby elevates chronic disease risk. The highest risk of chronic disease is therefore found in those with high load and low capacity. Adapted and redrawn from ref 49.

Figure 1

Fig. 2. Empirical evidence supporting the capacity-load model of chronic disease risk for diabetes. The penalty for low birth weight steadily increases as the degree of unhealthy lifestyle increases. Based on data of Li et al. from 3 US cohorts [45].

Figure 2

Table 1. Interactive associations between size at birth and subsequent weight in relation to chronic disease risk

Figure 3

Table 2. Birth weight associated with cardiac outcomes in children/adolescents

Figure 4

Table 3. Birth weight associated with cardiac outcomes in adults

Figure 5

Fig. 3. Schematic diagram illustrating how grip strength may act as a valuable marker of chronic disease risk through its ability to index two crucial components of metabolic capacity: foetal growth (its development) and adult physical fitness (its maintenance).

Figure 6

Table 4. Birth weight associated with adult grip strength