Anthropometric predictive equations for estimating body composition

Abstract

BACKGROUND: Precise and accurate measurements of body composition are useful in achieving a greater understanding of human energy metabolism in physiology and in different clinical conditions, such as, cardiovascular disease and overall mortality. Dual-energy x-ray absorptiometry (DXA) can be used to measure body composition, but the easiest method to assess body composition is the use of anthropometric indices. This study has been designed to evaluate the accuracy and precision of body composition prediction equations by various anthropometric measures instead of a whole body DXA scan. MATERIALS AND METHODS: We identified 143 adult patients underwent DXA evaluation of the whole body. The anthropometric indices were also measured. Datasets were split randomly into two parts. Multiple regression analysis with a backward stepwise elimination procedure was used as the derivation set and then the estimates were compared with the actual measurements from the whole-body scans for a validation set. The SPSS version 20 for Windows software was used in multiple regression and data analysis. RESULTS: Using multiple linear regression analyses, the best equation for predicting the whole-body fat mass (R(2) = 0.808) included the body mass index (BMI) and gender; the best equation for predicting whole-body lean mass (R(2) = 0.780) included BMI, WC, gender, and age; and the best equation for predicting trunk fat mass (R(2) = 0.759) included BMI, WC, and gender. CONCLUSIONS: Combinations of anthropometric measurements predict whole-body lean mass and trunk fat mass better than any of these single anthropometric indices. Therefore, the findings of the present study may be used to verify the results in patients with various diseases or diets.