89f952bd54f1774037964f0cae9689cc ijms-26-09897-v2.pdf b2e2e191a9d2fa3f95991ed06bd9d537269d24f1 ijms-26-09897-v2.pdf 356e638cbcde73c504fe322a43a3edca182244d52c06980fa479be6d544b1ba2 ijms-26-09897-v2.pdf Title: Predicting Metabolic Syndrome Using Supervised Machine Learning: A Multivariate Parameter Approach "2279 Subject: 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. Keywords: metabolic syndrome; machine learning; body roundness index; sdLDL-C; high-molecular-weight adiponectin Author: Rodolfo Iván Valdez Vega, Jacqueline Alejandra Noboa-Velástegui, Ana Lilia Fletes-Rayas, Iñaki Álvarez, Martha Eloisa Ramos-Marquez, Sandra Luz Ruíz-Quezada, Nora Magdalena Torres-Carrillo and Rosa Elena Navarro-Hernández Creator: LaTeX with hyperref Producer: pdfTeX-1.40.25; modified using OpenPDF 1.4.2 CreationDate: Tue Oct 21 04:14:16 2025 CEST ModDate: Tue Oct 21 06:50:21 2025 CEST Custom Metadata: yes Metadata Stream: no Tagged: no UserProperties: no Suspects: no Form: none JavaScript: no Pages: 19 Encrypted: no Page size: 595.276 x 841.89 pts (A4) Page rot: 0 File size: 2400866 bytes Optimized: no PDF version: 1.7 name type encoding emb sub uni object ID ------------------------------------ ----------------- ---------------- --- --- --- --------- GDARJP+URWPalladioL-Bold Type 1 Custom yes yes yes 156 0 PCXKPF+URWPalladioL-Roma Type 1 Custom yes yes yes 157 0 DFMHUB+URWPalladioL-Ital Type 1 Custom yes yes yes 158 0 PTVOAX+EURM10 Type 1 Builtin yes yes yes 196 0 YLGYQQ+CMSY10 Type 1 Builtin yes yes yes 197 0 WBVKDJ+URWPalladioL-BoldItal Type 1 Custom yes yes yes 198 0 ODKKNK+PalatinoLinotype TrueType WinAnsi yes yes no 213 0 ODKKNI+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 214 0 ODKKNK+PalatinoLinotype TrueType WinAnsi yes yes no 230 0 ODKKNI+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 231 0 ODKKNK+PalatinoLinotype TrueType WinAnsi yes yes no 250 0 ODKKNI+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 251 0 ODKKNK+PalatinoLinotype TrueType WinAnsi yes yes no 263 0 ODKLLL+PalatinoLinotype,BoldItalic TrueType WinAnsi yes yes no 269 0 ODKKNK+PalatinoLinotype TrueType WinAnsi yes yes no 270 0 ODKKNI+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 271 0 ODKKNK+PalatinoLinotype TrueType WinAnsi yes yes no 293 0 ODKKNI+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 294 0 NROZPH+VnURWPalladioL Type 1 Custom yes yes yes 443 0 ODKKNM+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 453 0 ODKKNK+PalatinoLinotype TrueType WinAnsi yes yes no 454 0 ODKKNI+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 455 0 ODLBIL+Arial TrueType WinAnsi yes yes no 456 0 ODKKNM+PalatinoLinotype,Italic TrueType WinAnsi yes yes no 461 0 ODKKNK+PalatinoLinotype TrueType WinAnsi yes yes no 462 0 ODKKNI+PalatinoLinotype,Bold TrueType WinAnsi yes yes no 463 0 ODLBIL+Arial TrueType WinAnsi yes yes no 464 0 Jhove (Rel. 1.28.0, 2023-05-18) Date: 2025-11-12 03:09:26 CET RepresentationInformation: ijms-26-09897-v2.pdf ReportingModule: PDF-hul, Rel. 1.12.4 (2023-03-16) LastModified: 2025-11-11 14:13:29 CET Size: 2400866 Format: PDF Version: 1.7 Status: Well-Formed and valid SignatureMatches: PDF-hul MIMEtype: application/pdf PDFMetadata: Objects: 631 FreeObjects: 1 IncrementalUpdates: 0 DocumentCatalog: ViewerPreferences: HideToolbar: false HideMenubar: false HideWindowUI: false FitWindow: true CenterWindow: false DisplayDocTitle: false NonFullScreenPageMode: UseNone Direction: L2R ViewArea: CropBox ViewClip: CropBox PrintArea: CropBox PageClip: CropBox PageLayout: SinglePage PageMode: UseNone Outlines: Item: Title: Introduction Item: Title: Results Children: Item: Title: Demographic, Clinical, and Anthropometric Variables of the Study Subjects Item: Title: Data Pre-Processing Children: Item: Title: Recursive Feature Elimination with Cross-Validation Item: Title: Cross-Validation: Evaluating Model Performance Item: Title: Test Data Performance Item: Title: Random Forest and Logistic Regression as the Optimal Models: Interpretability Analysis Item: Title: Discussion Item: Title: Materials and Methods Children: Item: Title: Study Population Item: Title: Blood Sample Collection Item: Title: Biochemical Parameters Item: Title: Blood Pressure Measurements Item: Title: Metabolic and Anthropometric Parameters Item: Title: Definition of Metabolic Syndrome Item: Title: Development of Predictive Models Using Supervised Machine Learning Children: Item: Title: Data Selection and Preprocessing Item: Title: Cross-Validation and Feature Selection Item: Title: Model Performance Evaluation Item: Title: Statistics Analysis Item: Title: Conclusions Item: Title: Appendix A Item: Title: Appendix B Item: Title: Appendix C Item: Title: References Info: Title: Predicting Metabolic Syndrome Using Supervised Machine Learning: A Multivariate Parameter Approach "2279 Author: Rodolfo Iván Valdez Vega, Jacqueline Alejandra Noboa-Velástegui, Ana Lilia Fletes-Rayas, Iñaki Álvarez, Martha Eloisa Ramos-Marquez, Sandra Luz Ruíz-Quezada, Nora Magdalena Torres-Carrillo and Rosa Elena Navarro-Hernández Subject: 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. 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