{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["13(8)"],"submitter":["Zhou Z"],"pubmed_abstract":["Oxidative stress plays a central role in the development of peripheral artery disease (PAD), yet composite indices quantifying its impact remain underutilized. The oxidative balance score (OBS), integrating dietary and lifestyle exposures, may offer a comprehensive approach to vascular risk stratification. We analyzed data from 7249 U.S. adults in NHANES 1999-2004 to examine the association between OBS and PAD, defined by an ankle-brachial index ≤ 0.9. Multivariable logistic regression and restricted cubic spline models were used to assess both linear and nonlinear associations. Twelve machine learning models were constructed to predict PAD status; the top-performing model (GLMNet) was interpreted using SHapley Additive exPlanations (SHAP) to identify key predictors. Higher OBS levels were"],"journal":["Food science & nutrition"],"pagination":["e70798"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12358680"],"repository":["biostudies-literature"],"pubmed_title":["Evaluating the Oxidative Balance Score for Peripheral Artery Disease Risk: Integrating Epidemiologic Modeling and SHAP-Interpretable Machine Learning in NHANES."],"pmcid":["PMC12358680"],"pubmed_authors":["Jiang Y","Qian J","Lee H","Zhu X","Li K","Zhou Z"],"additional_accession":[]},"is_claimable":false,"name":"Evaluating the Oxidative Balance Score for Peripheral Artery Disease Risk: Integrating Epidemiologic Modeling and SHAP-Interpretable Machine Learning in NHANES.","description":"Oxidative stress plays a central role in the development of peripheral artery disease (PAD), yet composite indices quantifying its impact remain underutilized. The oxidative balance score (OBS), integrating dietary and lifestyle exposures, may offer a comprehensive approach to vascular risk stratification. We analyzed data from 7249 U.S. adults in NHANES 1999-2004 to examine the association between OBS and PAD, defined by an ankle-brachial index ≤ 0.9. Multivariable logistic regression and restricted cubic spline models were used to assess both linear and nonlinear associations. Twelve machine learning models were constructed to predict PAD status; the top-performing model (GLMNet) was interpreted using SHapley Additive exPlanations (SHAP) to identify key predictors. Higher OBS levels were","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Aug","modification":"2026-05-02T03:50:14.961Z","creation":"2026-04-07T17:36:18.075Z"},"accession":"S-EPMC12358680","cross_references":{"pubmed":["40831942"],"doi":["10.1002/fsn3.70798"]}}