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Muscle Mass Moderates Metabolic Syndrome Risk Associated with Adiposity: A SHAP-Based Machine Learning Study
Rodrigo Yáñez-Sepúlveda1,2, Boryi A Becerra-Patiño3, Santiago Ramos Bermúdez4
1Faculty Education and Humanities, Universidad Andres Bello, Viña del Mar 2520000, Chile.
Nutrients
|May 13, 2026
Summary
Muscle mass significantly reduces obesity risk by acting as a metabolic mediator. Machine learning models, particularly neural networks, effectively predict obesity risk from body composition data in adults.
Area of Science:
- Physiology
- Data Science
Background:
- Muscle mass and visceral fat impact metabolic health.
- Limited research exists on machine learning (ML) for muscle mass and adiposity risk.
Purpose of the Study:
- Identify obesity predictors using ML on adult body composition data.
- Analyze the relationship between muscle mass and obesity risk.
Main Methods:
- Cross-sectional analysis of 13,663 adults (6877 men, 6786 women).
- Body composition analysis using 8-point multifrequency BIA (Inbody® Model 770).
- Logistic models and probability heatmaps visualized ML algorithm performance.
Main Results:
- Multilayer perceptron (MLP) showed superior predictive performance (AUC-ROC 0.981 men, 0.993 women).
- High accuracy (>95%) observed, particularly in the female cohort.
- Muscle mass demonstrated a significant role in modulating visceral adiposity risk.
Conclusions:
- Muscle mass acts as a key metabolic mediator, reducing visceral adiposity risk.
- ML algorithms, especially neural networks, are effective for analyzing visceral fat-related risks.
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