Appendicular Skeletal Muscle Mass Prediction Equation for Older Adults: Anthropometric, Physical, and Biochemical Indicators.

OBJECTIVES

To develop and validate an equation for estimating Appendicular Skeletal Muscle Mass (ASM) using community-accessible indicators, thereby facilitating the rapid screening of sarcopenia among older adults in primary care settings.

METHODS

A total of 800 community-dwelling elders were included and randomly partitioned into a training set (80%) and a validation set (20%); an additional 200 elderly participants were independently recruited to serve as the external validation cohort. Demographic characteristics, anthropometric and biochemical parameters of the participants were collected and analyzed. Variables associated with ASM were screened via univariate analysis, and core predictors were identified using a Random Forest model. Subsequently, Least Absolute Shrinkage and Selection Operator (LASSO) regression was applied to optimize the model. Finally, Stepwise Multiple Linear Regression was utilized to develop the ASM prediction equation. Bland-Altman (B-A) plots and intraclass correlation coefficient (ICC) were used to assess the predictive validity and reliability of the model.

RESULTS

Height, weight, gender, waist-to-hip ratio, hemoglobin concentration, left calf circumference, and left hand grip strength were ultimately selected for model development. The B-A plots revealed no significant discrepancy, with over 95% of data points falling within the limits of agreement. The ICC values for the two validation sets were 0.958 and 0.954, indicating good consistency between the model-predicted values and the actual values.

CONCLUSIONS

The ASM prediction equation, developed by integrating anthropometric indicators, physical function metrics, and laboratory biochemical parameters, provides a reliable alternative for accurate assessment of skeletal muscle mass in older adults.