Predictive Model Development and Validation for Low Appendicular Skeletal Muscle Mass in Chinese Older Adults

Low skeletal muscle mass is associated with adverse clinical outcomes. Due to constraints related to cost and equipment availability, direct measurement of skeletal muscle mass is often not feasible in routine practice. This study aimed to develop and validate a practical predictive model for identifying low appendicular skeletal muscle mass (ASMM) in older adults.

Body composition was assessed using DXA in the development group and BIA in the validation group. Standard questionnaires and anthropometric measurements were completed by trained testers. A predictive model was developed through multivariable binary logistic regression analysis and subsequently underwent external validation using an independent cohort to evaluate its generalizability.

A total of 393 elderly adults, aged 60-86 years (204 males, 189 females) participated in the study. The prevalence of Low ASMI was 74.0% in males and 58.2% in females. A predictive model for low ASMI was developed as Y = 75-BMI-CC (+10 if male). ROC analysis showed an AUC of 0.808 in the model development group, with sensitivity and specificity of 88.7% and 43.4% in males, and 83.6% and 44.3% in females, respectively. The model also demonstrated high accuracy in predicting low ASMI in the validation group. Using the prediction model, participants were categorized into three risk groups: high risk, low risk, and intermediate risk. The low-risk category, for whom skeletal muscle mass measurement is probably unnecessary, represented 28.9% of males and 9.2% of females.

This simple predictive model provides an easily calculable tool for estimating low ASMM in daily clinical practice and can facilitate the screening for reduced skeletal muscle mass in older adults.