Risk Factors and Predictive Models for Sarcopenia in Older Adults

Sarcopenia as an age-related syndrome is marked by a progressive loss of muscle strength and mass or reduced physical function. It is insidious in onset and presents a high prevalence. This study aimed to explore risk factors for sarcopenia in the elderly population and construct predictive models.

Patients (n = 335) aged 60-93 years and received an examination by a dual-energy X-ray absorptiometry (DXA) or a body composition analyzer (InBody) between January 2020 and May 2024 were included. Clinical data and laboratory test results were collected from these subjects. LASSO and logistic regression models were constructed to identify and evaluate significant risk factors for sarcopenia. A nomogram and a decision tree model were established for the prediction of sarcopenia probability in the elderly. Random forest was employed to rank the importance of variables in predicting sarcopenia.

The potential risk factors for sarcopenia in this study were body mass index, prealbumin, albumin/globulin ratio, serum creatinine, and phosphorus. A nomogram and a decision tree model were constructed based on the factors, showing a high discriminative ability and a high classification accuracy, respectively. Both models were effective in predicting sarcopenia in the elderly, and the nomogram showed a notably reliable predictive performance.

This study identified risk factors and developed predictive models for sarcopenia in older adults, contributing to timely intervention and treatment of the disease. The nomogram provided an intuitive way to measure the probability of sarcopenia in the elderly population, and the decision tree model made the assessment of sarcopenia simple and rapid. Both models are helpful for clinical staff in early screening and identifying sarcopenia.