Total-body skeletal muscle mass: development and cross-validation of anthropometric prediction models

Abstract

Skeletal muscle (SM) is a large body compartment of biological importance, but it remains difficult to quantify SM with affordable and practical methods that can be applied in clinical and field settings. The objective of this study was to develop and cross-validate anthropometric SM mass prediction models in healthy adults. SM mass, measured by using whole-body multislice magnetic resonance imaging, was set as the dependent variable in prediction models. Independent variables were organized into 2 separate formulas. One formula included mainly limb circumferences and skinfold thicknesses [model 1: height (in m) and skinfold-corrected upperarm, thigh, and calf girths (CAG, CTG, and CCG, respectively; in cm)]. The other formula included mainly body weight (in kg) and height (model 2). The models were developed and cross-validated in nonobese adults [body mass index (in kg/m(2)) < 30]. Two SM (in kg) models for nonobese subjects (n = 244) were developed as follows: SM = Ht x (0.00744 x CAG(2) + 0.00088 x CTG(2) + 0.00441 x CCG(2)) + 2.4 x sex - 0.048 x age + race + 7.8, where R:(2) = 0.91, P: < 0.0001, and SEE = 2.2 kg; sex = 0 for female and 1 for male, race = -2.0 for Asian, 1.1 for African American, and 0 for white and Hispanic, and SM = 0.244 x BW + 7.80 x Ht + 6.6 x sex - 0.098 x age + race - 3.3, where R:(2) = 0.86, P: < 0.0001, and SEE = 2.8 kg; sex = 0 for female and 1 for male, race = -1.2 for Asian, 1.4 for African American, and 0 for white and Hispanic. These 2 anthropometric prediction models, the first developed in vivo by using state-of-the-art body-composition methods, are likely to prove useful in clinical evaluations and field studies of SM mass in nonobese adults.