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Identifying risk factors for sarcopenia using machine learning: insights from multimodal data.

Felicita Urzi^1,2, Domen Šoberl³, Ornella Caputo⁴

¹Faculty of Mathematics, Natural Sciences and Information Technologies, University of Primorska, Koper, Slovenia. felicita.urzi@upr.si.

European Geriatric Medicine

|June 5, 2025

Summary

Machine learning models identified key risk factors for sarcopenia, including functional, nutritional, and clinical data. These models improve early detection and personalized interventions for sarcopenia risk.

Keywords:

Genetics Nutrition Risk factors Sarcopenia

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Area of Science:

Gerontology and Geriatric Medicine
Biomedical Data Science
Computational Biology

Background:

Sarcopenia poses a significant health challenge in aging populations, necessitating improved early detection and intervention strategies.
Current screening methods for sarcopenia may not fully capture the multifactorial nature of the condition.
Integrating diverse data types is crucial for developing more accurate predictive models.

Purpose of the Study:

To identify critical risk factors for sarcopenia using machine learning (ML) models.
To develop and validate predictive models for early sarcopenia detection and risk stratification.
To enhance diagnostic accuracy and facilitate personalized interventions for individuals at risk of sarcopenia.

Main Methods:

Analysis of multimodal data from 484 older adults, including anthropometric, biochemical, functional, nutritional, and genetic data.
Application of a three-stage ML modeling process with feature reduction and optimization.
Evaluation of model performance using AUC, accuracy, sensitivity, specificity, and SHAP values for predictor ranking.

Main Results:

Key predictors of sarcopenia included functional measures (chair stand, gait speed), nutritional indicators (protein, folate), clinical factors (diabetes, LDL), and anthropometric markers (BMI, calf circumference).
Genetic features also contributed to risk stratification.
The best ML model, incorporating the SARC-F screening test, achieved an AUC of 0.951 and 93.62% accuracy.

Conclusions:

Machine learning models integrating functional, nutritional, clinical, and genetic data offer a more accurate tool for sarcopenia screening and risk stratification.
These advanced models can enhance early detection and personalized interventions for sarcopenia.
Further validation in diverse, longitudinal cohorts is recommended to confirm the predictive utility of these models.