Generalized Equations for Predicting Percent Body Fat from Anthropometric Measures Using a Criterion Five-Compartment Model

Abstract

Introduction: Anthropometric-based equations are used to estimate percent body fat (%BF) when laboratory methods are impractical or not available. However, because these equations are often derived from two-compartment models, they are prone to error because of the assumptions regarding fat-freemass composition. The purpose of this study was to develop a new anthropometric-based equation for the prediction of%BF, using a five-compartment (5C) model as the criterion measure. Methods: A sample of healthy adults (52.2% female; age, 18 to 69 yr; body mass index, 15.7 to 49.5 kg.m(-2)) completed hydrostatic weighing, dual-energy x-ray absorptiometry, and bioimpedance spectroscopy measurements for calculation of 5C%BF (%BF5C), as well as skinfolds and circumferences.%BF5C was regressed on anthropometric measures using hierarchical variable selection in a random sample of subjects (n = 279). The resulting equation was cross-validated in the remaining participants (n = 78). New model performance was also comparedwith several common anthropometric-based equations. Results: The new equation [%BFNew = 6.083 + (0.143 x SSnew) - (12.058 x sex) - (0.150 x age) - (0.233 x body mass index) + (0.256 x waist) + (0.162 x sex x age)] explained a significant proportion of variance in %BF5C (R-2 = 0.775, SEE = 4.0%). Predictors included sum of skinfolds (SSnew, midaxillary, triceps, and thigh) and waist circumference. The new equation cross-validated well against %BF5C when compared with other existing equations, producing a large intraclass correlation coefficient (0.90), small mean bias and limits of agreement (0.4% +/- 8.6%), and small measures of error (SEE = 2.5%). Conclusions: %BFNew improved on previous anthropometric-based equations, providing better overall agreement and less error in %BF estimation. The equation described in this study may provide an accurate estimate of %BF5C in healthy adults when measurement is not practical.