Associations of BMI, waist circumference, body fat, and skeletal muscle with type 2 diabetes in adults

The present study applied published, validated, equations based on data routinely collected in most large national health surveys to estimate BF% and SM%. We confirmed the known associations of BMI and WC with HbA1c and T2D. We observed a stronger relationship between BF% and T2D, and also a significant inverse relationship between SM% and T2D. While causal relationships cannot be proved in cross-sectional studies, the enormously powerful, well-established relationship of adiposity with T2D is already accepted as its main causal factor. Weight gain which exceeds the variable ‘personal fat threshold’ reveals a predisposition, most likely epigenetic, for metabolic syndrome and T2D with ectopic (liver and pancreas) fat accumulation [13]. The inverse relationship with SM% may provide further insights into the aetiology of T2D, supporting the notion that muscle is generally protective against metabolic syndrome and T2D. This would potentially explain the protective association of high hip circumference (from gluteal muscle) [11, 12].

There was also evidence of significant associations of T2D and HbA1C > 6.5% with both low SM% and high BF%, but this combination does not always co-exist within individuals. This suggests that these two measures detect different individuals with T2D, which could point towards T2D being promoted by different body-composition phenotypes, potentially demanding different approaches to treatment and prevention.

High BF%, particularly intra-abdominal adipose tissue, may have a key intermediary role in the development of insulin resistance and subsequent T2D. It is now recognised that intra-abdominal fat is an active endocrine organ producing a number of adipocytokines such as adiponectin, leptin, resistin, and interleukins, which play a crucial role in appetite and energy regulation [14]. An imbalanced production of these factors by an expanded intra-abdominal fat mass probably contributes to weight-related metabolic disorders [17].

We have previously shown that the prevalence of metabolic syndrome was increased in those with either low BMI and high WC, or high BMI and low WC, compared to individuals who had low BMI and low WC [18]. However, a high BMI may be misinterpreted as overweight or even obesity among people in training of power sports such as American football players or heavyweight boxers, where a high BMI reflects a high SM mass [19]. This is the reason for the use of the diametrically opposed measurements of FM% and SM% herein to improve discrimination between fat and muscle components.

The significant association of HbA1c and T2D with low SM% demonstrated here has not been well documented in the literature. In a cross-sectional study of Koreans, SM was 2–4-fold lower in individuals with T2D than those without [20] and an NHANES study found inverse associations of insulin resistance index (HOMA-IR) and pre-diabetes (based on HbA1c) with a ‘skeletal muscle index’ derived from bioelectrical impedance, adjusted for age, sex, ethnicity, and adiposity [21]. It is not possible to define the mechanisms, or even direction of causality, between SM% and HbA1c or T2D, likely involving multiple factors. SM commonly falls during the clinical presentation of T2D as a direct result of diminished insulin activity [22], but it is also the main oxidative organ and site of glucose disposal. Low SM% by mass is likely to be a very crude correlate of the whole-body metabolic functions of different muscle components. T2D has been associated with a relative paucity of type 1 (oxidative) muscle fibres, and excess of type 2 muscle fibres [23]. Muscle mass may be low or reduced for genetic reasons, and by inflammatory diseases as well as malnutrition. An association between impaired glucose tolerance and T2D with low SM has been shown previously in conditions with primary muscle loss or atrophy, such as immobility [24], muscular dystrophies, myopathies [25], cancer [26], or stroke [27], while low SM has also been shown to associate with hypertension [28].

There are certain limitations to the present study, including the cross-sectional design, but that should not diminish the validity of the new analysis for BF% and SM%, particularly as the analyses for BMI and WC are in line with published data. The previous studies have shown slightly variable associations, e.g., stronger associations with WC and incidence of T2D in long term follow-up studies [29], than in cross-sectional studies where WHR tends to be more powerful. The present study adds support to the view that small hip circumference (thus higher waist:hip ratio) is predictive, because it may reflect reduced gluteal muscle mass. The equations for calculating BF% and SM% were derived in studies of predominantly Caucasian adults, but with few elderly individuals [2, 16]. The HSE and SHS databases used in the present study also comprise mostly Caucasians (86.2%), so the anthropometric equations are likely to be appropriate for their body compositions. Estimates of BF% or SM% in older people, or with different ethnic backgrounds, may be less accurate and different equations may be needed. Equations used to estimate SM include HC as one of the variables may be less accurate in older adults and in females due to their higher proportion of adipose tissue for a given HC. Our study, therefore, excluded subjects over 85 years old. However, the inclusion of this oldest age group did not substantially change our findings. The survey base was large, and surveys are typical representative national surveys, but study power was reduced, because not all the measures were made for every subject in every year.

The use of existing survey data is economical, but has drawbacks, because the number of collected variables is fixed and may limit analyses. For example, we did not have information on the duration of diabetes, but we did attempt to reduce associated errors by excluding insulin-treated patients (likely to have longer duration of diabetes). Measurement errors are always possible. Accuracy is important at individual level to avoid misclassification of subjects in categories of interest such as BMI, but the sizes of errors in measuring height, weight, or waist circumference are not great enough to cause frequent misclassification, which is more severely affected in self-reported than in measured data [30]. When analysing large national surveys to establish associations, as in the present study, minor measurement errors are unlikely to be problematic, since the large study number allows confidence in the findings. The lack of information on oral antihyperglycaemic agents is another limitation, but the patterns of associations between body composition with T2D and body composition with HbA1c are similar, suggesting a valid observation. Another common drawback with surveys is bias in participation, which may explain the relatively low prevalence of T2D in this present study, e.g., high proportion of younger adults (with low rates of T2D) and recruiting a low proportion of those with T2D because of health problems and hospital appointments, etc. Excluding those treated with insulin for the present analysis would also have lowered the apparent rates of T2D.

In conclusion, estimated BF% associates with T2D. Low SM% is also a significant factor. These two simple measures, available from data collected in most health surveys, may be more useful than using BMI or WC for assessing T2D. These results suggest two distinct body-composition phenotypes driving type-2 diabetes, with implications for prevention and treatment strategies. Further exploration would be valuable, especially confirmation in longitudinal follow-up studies.