Association between visceral fat accumulation and sarcopenia: A cross-sectional study

Area of Science:

• Gerontology
• Metabolic Health
• Body Composition Analysis

Background:

• Sarcopenia incidence is rising globally, necessitating improved risk assessment tools.
• Visceral fat accumulation is a significant, yet often under-recognized, factor associated with sarcopenia.

Purpose of the Study:

• To investigate the association between six visceral fat indicators and sarcopenia risk.
• To develop and validate machine learning models for predicting sarcopenia risk using these indicators.

Main Methods:

• Analysis of 5200 participants from NHANES 2011-2018.
• Evaluation of relative fat mass (RFM), lipid accumulation product (LAP), weight-adjusted waist index (WWI), triglyceride glucose-waist-to-height ratio (TyG-WHtR), metabolic score for insulin resistance (METS-IR), and metabolic score for visceral fat (METS-VF).
• Utilized multivariable logistic regression, smoothed curve fitting, threshold effect analysis, and nine machine learning models with SHAP for interpretability.

Main Results:

• All evaluated visceral fat indicators showed a significant positive association with sarcopenia risk.
• Threshold effect analysis identified specific saturation points for each indicator concerning sarcopenia.
• The logistic regression model achieved an AUC-ROC of 0.878, with WWI identified as the most potent predictor via SHAP analysis.

Conclusions:

• Visceral fat accumulation indicators are crucial for assessing sarcopenia risk.
• The weight-adjusted waist index (WWI) is the most significant predictor among the evaluated indicators.
• Machine learning models demonstrate high accuracy, supporting their use in promoting healthy aging by managing visceral fat.