Estimation of Appendicular Skeletal Muscle Mass for Women Aged 60-70Years Using a Machine Learning Approach.

Abstract

This article aimed to develop and validate an anthropometric equation based on the least absolute shrinkage and selection operator (LASSO) regression, a machine learning approach, to predict appendicular skeletal muscle mass (ASM) in 60-70-year-old women. A cross-sectional study. Community-dwelling women aged 60-70years. A total of 1296 community-dwelling women aged 60-70years were randomly divided into the development or the validation group (1:1 ratio). ASM was evaluated by bioelectrical impedance analysis (BIA) as the reference. Variables including weight, height, body mass index (BMI), sitting height, waist-to-hip ratio (WHR), calf circumference (CC), and 5 summary measures of limb length were incorporated as candidate predictors. LASSO regression was used to select predictors with 10-fold cross-validation, and multiple linear regression was applied to develop the BIA-measured ASM prediction equation. Paired t test and Bland-Altman analysis were used to validate agreement. Weight, WHR, CC, and sitting height were selected by LASSO regression as independent variables and the equation is ASM= 0.2308× weight (kg) - 27.5652× WHR+ 8.0179× CC (m)+ 2.3772× Sitting height (m)+ 22.2405 (adjusted R2= 0.848, standard error of the estimate= 0.661kg, P < .001). Bland-Altman analysis showed a high agreement between BIA-measured ASM and predicted ASM that the mean difference between the 2 methods was-0.041kg, with the 95% limits of agreement of-1.441 to 1.359kg. The equation for 60-70-year-old women could provide an available measurement of ASM for communities that cannot equip with BIA, which promotes the early screening of sarcopenia at the community level. Additionally, sitting height could predict ASM effectively, suggesting that maybe it can be used in further studies of muscle mass.