Abstract
Dual-energy x-ray absorptiometry (DXA) scanning is used for objective determination of body composition, but instrumentation is expensive and not generally available in customary clinical practice. Anthropometric surrogates are often substituted as anticipated correlates of absolute and relative body fat content in the clinical management of obesity and its associated medical risks. DXA and anthropometric data from a cohort of 9230 randomly selected American subjects, available through the ongoing National Health and Nutrition Examination Survey, was used to evaluate combinations of surrogates (age, height, total weight, waist circumference) as predictors of DXA-determined absolute and relative body fat content. Multiple regression analysis yielded linear combinations of the 4 surrogates that were closely predictive of DXA-determined absolute fat content (R(2) = 0.93 and 0.96 for male and female subjects). Accuracy of the new algorithm was improved over customary surrogate-based predictors such as body mass index. However prediction of relative body fat was less robust (R(2) less than 0.75), probably due to the nonlinear relation between degree of obesity (based on body mass index) and relative body fat. The paradigm was validated using an independent cohort from the National Health and Nutrition Examination Survey, as well as two independent external subject groups. The described regression-based algorithm is likely to be a sufficiently accurate predictor of absolute body fat (but not relative body fat) to substitute for DXA scanning in many clinical situations. Further work is needed to assess algorithm validity for subgroups of individuals with "atypical" body construction.