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Construct prediction models for low muscle mass with metabolic syndrome using machine learning.

Yanxuan Wu1,2, Fu Li1,2, Hao Chen1,2

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This study developed a machine learning model to identify low muscle mass in young adults with metabolic syndrome. Key predictors include height, gender, waist circumference, thigh length, and alkaline phosphatase, aiding early sarcopenia screening.

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Area of Science:

  • Gerontology
  • Metabolic Health
  • Biostatistics

Background:

  • Metabolic syndrome (MetS) and sarcopenia are significant global health issues, particularly concerning in younger populations.
  • Co-occurrence of MetS and sarcopenia elevates mortality risk, necessitating effective identification strategies.
  • Current methods for detecting low muscle mass in MetS patients are underdeveloped.

Purpose of the Study:

  • To develop and validate a machine learning model for identifying low muscle mass in young MetS patients.
  • To identify key predictors associated with low muscle mass in this demographic.
  • To create an accessible tool for sarcopenia screening in at-risk individuals.

Main Methods:

  • Utilized data from 2,467 MetS patients (aged 18-59) from the 2011-2018 NHANES.
  • Employed Least Absolute Shrinkage and Selection Operator (LASSO) regression for feature selection.
  • Constructed and evaluated nine machine learning models, including Logistic Regression, using metrics like AUC and F1-score, and interpreted predictors with SHAP values.

Main Results:

  • The Logistic Regression model demonstrated superior performance with an AUC of 0.925, an F1-score of 0.87, and specificity of 0.89.
  • Identified height, gender, waist circumference, thigh length, and alkaline phosphatase (ALP) as significant predictors of low muscle mass.
  • SHAP values were used to interpret the contribution of each predictor.

Conclusions:

  • An interpretable machine learning model was successfully developed to pinpoint risk factors for low muscle mass in young MetS patients.
  • The findings provide a foundation for improved sarcopenia screening and management in this population.
  • A web-based tool was created to enhance the practical application of these findings for early detection.