Improved Prediction of Body Fat by Measuring Skinfold Thickness, Circumferences, and Bone Breadths

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Abstract
To develop improved predictive regression equations for body fat content derived from common anthropometric measurements. 117 healthy German subjects, 46 men and 71 women, 26 to 67 years of age, from two different studies were assigned to a validation and a cross-validation group. Common anthropometric measurements and body composition by DXA were obtained. Equations using anthropometric measurements predicting body fat mass (BFM) with DXA as a reference method were developed using regression models. The final best predictive sex-specific equations combining skinfold thicknesses (SF), circumferences, and bone breadth measurements were as follows: BFM(New) (kg) for men = -40.750 + {(0.397 x waist circumference) + [6.568 x (log triceps SF + log subscapular SF + log abdominal SF)]} and BFM(New) (kg) for women = -75.231 + {(0.512 x hip circumference) + [8.889 x (log chin SF + log triceps SF + log subscapular SF)] + (1.905 x knee breadth)}. The estimates of BFM from both validation and cross-validation had an excellent correlation, showed excellent correspondence to the DXA estimates, and showed a negligible tendency to underestimate percent body fat in subjects with higher BFM compared with equations using a two-compartment (Durnin and Womersley) or a four-compartment (Peterson) model as the reference method. Combining skinfold thicknesses with circumference and/or bone breadth measures provide a more precise prediction of percent body fat in comparison with established SF equations. Our equations are recommended for use in clinical or epidemiological settings in populations with similar ethnic background.