{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Wieczorek P"],"funding":["Wroclaw Medical University","Wrocław Research Centre EIT+"],"pagination":["8624"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12733403"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["14(24)"],"pubmed_abstract":["<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 s"],"journal":["Journal of clinical medicine"],"pubmed_title":["Risk Analysis in the Lower Silesia Healthy Donors Cohort: Statistical Insights and Machine Learning Classification."],"pmcid":["PMC12733403"],"funding_grant_id":["SUBK.D250.25.047","POIG.01.01.02-02-00308-00","POIG.01.01.02.02.003/09"],"pubmed_authors":["Matera-Witkiewicz A","Gazinska P","Krupinska M","Wieczorek P"],"additional_accession":[]},"is_claimable":false,"name":"Risk Analysis in the Lower Silesia Healthy Donors Cohort: Statistical Insights and Machine Learning Classification.","description":"<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 s","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Dec","modification":"2026-05-27T03:14:39.685Z","creation":"2026-05-27T03:11:19.433Z"},"accession":"S-EPMC12733403","cross_references":{"pubmed":["41464527"],"doi":["10.3390/jcm14248624"]}}