Development and validation of a new anthropometric equation to predict skeletal muscle mass in a heterogeneous Caucasian population

Abstract Assessment of skeletal muscle mass (SMM) is essential to monitor physical performance and health status. The most widely used anthropometric equations have repeatedly demonstrated to overestimate or underestimate SMM in different populations. Herein, we developed and cross-validated a new anthropometric regression equation for estimating SMM, using DXA as the reference method. A group of 206 healthy Caucasian participants aged 18–65 years were included in the final analysis. Participants underwent a dual-energy X-ray absorptiometry (DXA) scan, and body mass, stature, four skinfolds (biceps, triceps, subscapular, and supracrestal) and four breadths (femoral, humeral, ankle, and wrist) were assessed by an accredited anthropometrist. Accuracy was assessed by mean differences, coefficient of determination, standard error of the estimate (SEE), concordance correlation coefficient (CCC), and Bland–Altman plots. The proposed equation explained 91.3% of the variance in the DXA-derived SMM percentage, with a low random error (SEE = 1.95%), and a very strong agreement (CCC = 0.94). In addition, it demonstrated no fixed or proportional bias and a relatively low individual variability (3.84%). The new anthropometric equation can accurately predict SMM percentage in a Caucasian population with a wide age range (18–65 years).