Obesity Epidemiology

74 STUDY DESIGNS AND MEASUREMENTShave combined skinfold thicknesses with circumferences and other anthropometric measuresto predict percent body fat. 140 However, all prediction equations are populationspecific.In epidemiologic studies, a variety of skinfold-related variables have been used todescribe peripheral fat distribution (such as skinfold thickness in individual anatomicalsites, mean values of skinfold thicknesses across several sites, and the ratio of thesubscapular to triceps skinfolds). However, it is not yet fully established whether thesemeasures independently predict disease risk. In the Northwick Park Heart Study, Kimet al. 141 showed that subscapular, forearm, and triceps skinfolds were predictive of fatalCHD and that subscapular skinfold was predictive of all-cause mortality in women.There was a significant association between BMI and CHD in both men and women, butnone of the skinfold measures predicted risk of CHD or mortality in men. Tanne et al. 142demonstrated that the ratio of subscapular to triceps skinfold thickness (as an indicator oftrunk versus peripheral distribution of body fat) was more predictive of stroke mortalitythan was subscapular skinfold alone (as an indicator of trunk and overall adiposity). Inseveral other studies, skinfold thicknesses did not appear to be independent predictorsof CHD or mortality risk. 143,144 Several factors are probably responsible for the lack ofconsistent associations between skinfold thicknesses and morbidity and mortality. First,the measurement error is greater for skinfold thicknesses than for other anthropometricvariables. Second, skinfold thicknesses are unreliable measurements of intra-abdominalfat or central adiposity. Finally, various skinfold sites are markers of different fat distributionsdespite high correlations among these sites.Statistical Models of Anthropometric Variables and Disease RiskMultivariate regression models are commonly used to evaluate the relationships betweenanthropometric variables and morbidity and mortality. These models should be carefullyinterpreted because of strong intercorrelations among the anthropometric variables andchanges in the meaning of one variable after adjustments for another. In the simplestmodel, which includes height and weight as independent variables (model 1 in Table 5.4),the coefficient for weight can be interpreted as the effect of weight among individualsof identical height, which largely reflects overall adiposity across different individuals. 57However, the interpretation of height adjusted for weight is uncertain. Conceptually, itis difficult to interpret variations in height among individuals of identical weight, whichmay largely reflect differences in lean body mass (body structure and muscle mass).In model 2, interpretation of both variables is straightforward because there is little orno correlation between height and BMI. In this case, BMI represents the effects of overalladiposity, while height can be interpreted as an overall measure of body size or a surrogatemeasure of childhood and adolescent nutrition and energy balance (discussed earlier).An alternative method of obtaining weight—adjusted—for height is to calculate theresiduals of weight by using a simple regression, with height as the independent variableand weight as the dependent variable. This procedure is analogous to that used for adjustingnutrient intake for total energy (see Chapter 6). The residuals of weight, by definition,are not correlated with height, and thus the same model (model 3) can be used to fullyinterpret weight adjusted for height (which represents overall adiposity) and height.Researchers commonly include BMI and WC or WHR in the same model to comparethe effects of overall adiposity and central or abdominal obesity (model 4).Although the meaning of WC or WHR is conceptually clear in such a model, themeaning of BMI is altered: instead of reflecting overall fatness, the model tends toreflect lean body mass to a greater degree because abdominal fatness is accounted