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Predictive Model Development and Validation for Low Appendicular Skeletal Muscle Mass in Chinese Older Adults
Minmin Chen1, Xiaoqing Wu1, Yanping Du1
1Research Section of Geriatric Metabolic Bone Disease, Shanghai Geriatric Institute, Department of Osteoporosis and Bone Disease Huadong Hospital, Fudan University Shanghai China.
Aging Medicine (Milton (N.S.W))
|May 14, 2026
Summary
A new predictive model helps identify low skeletal muscle mass in older adults without costly equipment. This tool aids clinical screening for reduced muscle mass, improving health outcomes in the elderly.
Area of Science:
- Gerontology
- Clinical Nutrition
- Biostatistics
Background:
- Low skeletal muscle mass is linked to poor health outcomes in older adults.
- Direct measurement of skeletal muscle mass is often impractical in clinical settings due to cost and equipment limitations.
Purpose of the Study:
- To develop and validate a practical predictive model for identifying low appendicular skeletal muscle mass (ASMM) in older adults.
Main Methods:
- A predictive model was developed using multivariable binary logistic regression.
- Body composition was assessed using DXA and BIA.
- The model underwent external validation in an independent cohort.
Main Results:
- The study included 393 adults aged 60-86 years.
- A predictive model (Y = 75 - BMI - CC + 10 if male) was developed with an AUC of 0.808.
- The model accurately predicted low ASMM and identified a low-risk group (28.9% males, 9.2% females) not requiring further measurement.
Conclusions:
- A simple, calculable predictive model for low ASMM is available for clinical practice.
- This tool facilitates screening for reduced skeletal muscle mass in the elderly population.
