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Published on: June 7, 2024
Development and Validation of a Skeletal Muscle Prediction Equation From Anthropometric and Demographic Data
Jianqin Sun1, Yanqiu Chen1, Jiajie Zang2
1Huadong Hospital Affiliated to Fudan University, Shanghai, China; Shanghai Elderly Nutrition and Health Quality Control Center, Shanghai, China.
Objectives:
Appendicular skeletal muscle mass (ASM), a core parameter for sarcopenia diagnosis, is difficult to measure in primary care facilities lacking specialized equipment. This study was conducted to develop and validate an ASM prediction equation based on simple anthropometric and demographic indices.
Design:
Cross-sectional study.
Setting And Participants:
The study included 5016 community-dwelling older adults (mean age, 71.0 ± 5.6 years; women, 55.9%).
Methods:
Anthropometric and demographic data were collected by uniformly trained medical staff. ASM was measured through bioelectrical impedance analysis (BIA). The participants were randomly divided (4:1) into a development group (n = 4013) and a validation group (n = 1003). Stepwise multivariate linear regression was performed to establish the ASM prediction equation.
Results:
The equation for predicting ASM was as follows: ASM (kg) = 0.232 × height (cm) + 0.128 × weight (kg) + 0.128 × calf circumference (cm) - 2.039 × sex (men: 1, women: 2) - 0.021 × age (years) - 27.129. It exhibited an adjusted R2 value of 0.90 and a standard error of estimate value of 1.34 kg. In the validation group, a strong correlation was observed between ASM measured using our equation and that measured through BIA (r = 0.952; P < .001). The Bland-Altman plot showed that the mean difference between the results for our equation and for BIA was -0.03 kg, with limits of agreement (mean 1.96 SD) of -2.4 to 2.3 kg. The intraclass correlation coefficient was 0.951 (95% CI, 0.945-0.957), indicating excellent between-method consistency.
Conclusions And Implications:
Our equation appears to have high predictive power. With rapid and simple measurement of anthropometric and demographic indices, the equation can be used to evaluate ASM in primary care facilities lacking specialized equipment.
