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

Abstract

Background: 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. Objective: The objective of this study was to develop and cross-validate anthropometric SM mass prediction models in healthy adults. Design: 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/m²) < 30]. Results: Two SM (in kg) models for nonobese subjects (n = 244) were developed as follows: SM = Ht × (0.00744 × CAG² + 0.00088 × CTG² + 0.00441 × CCG²) + 2.4 × sex − 0.048 × age + race + 7.8, where R² = 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 × BW + 7.80 × Ht + 6.6 × sex − 0.098 × age + race − 3.3, where R² = 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. Conclusion: 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.