Predicting Metabolic Syndrome Using Supervised Machine Learning : A Multivariate Parameter Approach
Valdez Vega, Rodolfo (Universidad de Guadalajara (Mèxic))
Noboa-Velástegui, Jacqueline Alejandra (Universitat Autònoma de Barcelona. Departament de Biologia Cel·lular, de Fisiologia i d'Immunologia)
Fletes Rayas, Ana Lilia (Universidad de Guadalajara (Mèxic))
Álvarez Pérez, Iñaki (Universitat Autònoma de Barcelona. Institut de Biotecnologia i de Biomedicina "Vicent Villar Palasí")
Ramos Marquez, Martha Eloisa (Universidad de Guadalajara (Mèxic))
Ruíz Quezada, Sandra Luz (Universidad de Guadalajara (Mèxic))
Torres Carrillo, Nora Magdalena (Universidad de Guadalajara (Mèxic))
Navarro Hernández, Rosa Elena (Universidad de Guadalajara (Mèxic))

Data:	2025
Resum:	Metabolic syndrome (MetS) is a complex condition characterized by a group of interconnected metabolic abnormalities. Due to its increasing prevalence, better predictive markers are needed. Therefore, this study aims to develop predictive models for MetS by integrating adipokines, metabolic and cardiovascular risk factors, and anthropometric indices. Data were collected from 381 subjects aged 20 to 59 years (242 women and 139 men) from Guadalajara, Jalisco, Mexico, who were classified as having MetS or non-MetS based on the ATP-III criteria. Four supervised machine learning models were developed-Logistic Regression (LR), Support Vector Machine (SVM), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost)-and their performance was evaluated using the Area under the Curve (AUC), calibration curves, Decision Curve Analysis (DCA), and local interpretability analysis. The RF and XGBoost models achieved the highest AUCs (0. 940 and 0. 954). The RF and LR models were the best calibrated and showed the highest net benefit in DCA. Key variables included age, anthropometric indices (BRI and DAI), insulin resistance measures (HOMA-IR), lipid profiles (sdLDL-C and LDL-C), and high-molecular-weight adiponectin, used to classify the presence of MetS. The results highlight the usefulness of specific models and the importance of anthropometric variables, cardiovascular risk factors, metabolic profiles, and adiponectin as indicators of MetS.
Drets:	Aquest document està subjecte a una llicència d'ús Creative Commons. Es permet la reproducció total o parcial, la distribució, la comunicació pública de l'obra i la creació d'obres derivades, fins i tot amb finalitats comercials, sempre i quan es reconegui l'autoria de l'obra original.
Llengua:	Anglès
Document:	Article ; recerca ; Versió publicada
Matèria:	Metabolic syndrome ; Machine learning ; Body roundness index ; sdLDL-C ; High-molecular-weight adiponectin
Publicat a:	International journal of molecular sciences, Vol. 26, Num. 20 (October 2025) , art. 9897, ISSN 1422-0067

DOI: 10.3390/ijms26209897
PMID: 41155195

19 p, 2.3 MB

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