biostudies-literatureWieczorek PWroclaw Medical UniversityWrocław Research Centre EIT+8624https://www.ebi.ac.uk/biostudies/studies/S-EPMC12733403biostudies-literatureUnknown14(24)<b>Background/Objectives</b>: Metabolic syndrome (MetS) increases the risk of type 2 diabetes and cardiovascular disease. We aimed to identify the key metabolic predictors of MetS in a Central European cohort and to compare classical statistics with modern machine learning (ML) models. <b>Methods</b>: We analysed 956 adults from the Lower Silesia Healthy Donors cohort. Clinical, anthropometric, biochemical, and lifestyle variables were collected using standardised procedures. Group differences were tested with Mann-Whitney U tests and effect sizes. A multivariable logistic regression (outcome: binary MetS defined as ≥3 harmonised components, MetS_bin) estimated adjusted odds ratios. In parallel, ML models (logistic regression, Random Forest, XGBoost, LightGBM, CatBoost) were trained with sJournal of clinical medicineRisk Analysis in the Lower Silesia Healthy Donors Cohort: Statistical Insights and Machine Learning Classification.PMC12733403SUBK.D250.25.047POIG.01.01.02-02-00308-00POIG.01.01.02.02.003/09Matera-Witkiewicz AGazinska PKrupinska MWieczorek PfalseRisk Analysis in the Lower Silesia Healthy Donors Cohort: Statistical Insights and Machine Learning Classification.<b>Background/Objectives</b>: Metabolic syndrome (MetS) increases the risk of type 2 diabetes and cardiovascular disease. We aimed to identify the key metabolic predictors of MetS in a Central European cohort and to compare classical statistics with modern machine learning (ML) models. <b>Methods</b>: We analysed 956 adults from the Lower Silesia Healthy Donors cohort. Clinical, anthropometric, biochemical, and lifestyle variables were collected using standardised procedures. Group differences were tested with Mann-Whitney U tests and effect sizes. A multivariable logistic regression (outcome: binary MetS defined as ≥3 harmonised components, MetS_bin) estimated adjusted odds ratios. In parallel, ML models (logistic regression, Random Forest, XGBoost, LightGBM, CatBoost) were trained with s2025-01-01T00:00:00Z2025 Dec2026-05-27T03:14:39.685Z2026-05-27T03:11:19.433ZS-EPMC127334034146452710.3390/jcm14248624