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Predicting Metabolic Syndrome Using Supervised Machine Learning: A Multivariate Parameter Approach.

Rodolfo Iván Valdez Vega1, Jacqueline Alejandra Noboa-Velástegui1,2, Ana Lilia Fletes-Rayas3

  • 1Programa de Doctorado en Ciencias Biomédicas, Centro Universitario de Ciencias de la Salud, Universidad de Guadalajara, Guadalajara C.P. 44340, Jalisco, Mexico.

International Journal of Molecular Sciences
|October 29, 2025
PubMed
Summary

Machine learning models effectively predict metabolic syndrome (MetS) using adipokines and risk factors. Key indicators include age, anthropometric indices, insulin resistance, lipid profiles, and adiponectin levels for early detection.

Keywords:
body roundness indexhigh-molecular-weight adiponectinmachine learningmetabolic syndromesdLDL-C

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Area of Science:

  • Biostatistics
  • Computational Biology
  • Endocrinology

Background:

  • Metabolic syndrome (MetS) presents a growing global health challenge.
  • Existing predictive markers for MetS require enhancement due to its increasing prevalence.

Purpose of the Study:

  • To develop and evaluate machine learning models for predicting MetS.
  • To integrate adipokines, metabolic, cardiovascular risk factors, and anthropometric indices for improved prediction.

Main Methods:

  • Utilized data from 381 subjects (20-59 years) in Guadalajara, Mexico.
  • Developed and compared four supervised machine learning models: Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost).
  • Evaluated model performance using AUC, calibration curves, and Decision Curve Analysis (DCA).

Main Results:

  • RF and XGBoost models demonstrated superior predictive performance with AUCs of 0.940 and 0.954, respectively.
  • RF and LR models exhibited the best calibration and highest net benefit in DCA.
  • Identified key predictive variables: age, anthropometric indices (BRI, DAI), HOMA-IR, sdLDL-C, LDL-C, and high-molecular-weight adiponectin.

Conclusions:

  • Machine learning models, particularly RF and XGBoost, show significant potential for MetS prediction.
  • Anthropometric variables, cardiovascular risk factors, metabolic profiles, and adiponectin are crucial indicators for MetS.
  • The study underscores the utility of integrated data and advanced modeling for identifying individuals at risk of MetS.