{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T05:58:33Z","timestamp":1790747913979,"version":"4.1.0"},"reference-count":23,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"name":"2024 Xiamen Municipal Health High-Quality Development Science and Technology Plan Projec","award":["3502Z20244ZD1045"],"award-info":[{"award-number":["3502Z20244ZD1045"]}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Science Progress"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Stroke remains the second leading global cause of death. Traditional risk factors and statistical models fail to fully clarify its pathogenesis or quantify the interactive effects of dietary antioxidants and heavy metals.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Based on the data of 35,171 subjects from the National Health and Nutrition Examination Survey (NHANES) conducted in the United States between 2005 and 2018, this Cross-Sectional study constructed a Composite Dietary Antioxidant Index (CDAI) calibrated for the study population. By incorporating blood lead, blood cadmium, manganese elements, antioxidant nutrients and traditional stroke risk factors, five tree-based machine learning models were trained and validated. Meanwhile, with the application of the SHapley Additive exPlanations (SHAP), Restricted Cubic Spline (RCS) and piecewise regression analysis, this research analyzed the mechanism of the models, verified the non-linear correlations, and defined the protective threshold of the Composite Dietary Antioxidant Index as well as the recommendations for dietary protection.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The HistGradientBoosting model achieved the best performance (AUC=0.7771, 95%CI: 0.765\u20130.791). CDAI showed a non-linear protective effect against stroke, with an optimal threshold of 1.67 (95%CI: 1.45\u20131.89), above which stroke risk decreased by 40.2% (OR=0.60, 95%CI: 0.48\u20130.75, P&lt;0.001). High blood lead (\u22652.2 \u03bcg\/dL) and cadmium (\u22650.5 \u03bcg\/L) significantly attenuated CDAI\u2019s protective effect by 146%\u2013167%, while high CDAI may account for mitigated heavy metal-related stroke risk. The model showed stable performance in gender subgroups and adults aged &lt;65 years.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This cross-sectional study constructed an interpretable machine learning framework for stroke risk using NHANES data. The HistGradientBoosting model performed best. We identified a nonlinear protective threshold of CDAI at 1.67, above which stroke risk decreased by 40.2%. High blood lead and cadmium significantly attenuated the protective effect of CDAI, while manganese showed synergistic antioxidant protection. SHAP and RCS analyses confirmed robust interactions between CDAI and heavy metals. These findings provide evidence for personalized dietary antioxidant interventions in stroke prevention, especially for individuals with heavy metal exposure.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/00368504261460667","type":"journal-article","created":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T09:01:14Z","timestamp":1783069274000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Stroke risk associated with the interaction between composite dietary antioxidant index and heavy metals: A cross-sectional explainable machine learning study using NHANES data"],"prefix":"10.1177","volume":"109","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-6179-2935","authenticated-orcid":false,"given":"Yixuan","family":"He","sequence":"first","affiliation":[{"name":"The First Affiliated Hospital of Xiamen University"},{"name":"School of Medicine"},{"name":"Xiamen University"},{"name":"Xiamen Key Laboratory of Brain Center, Fujian Province, China"},{"name":"The First Affiliated Hospital of Xiamen University"},{"name":"Fuzhou University Affiliated Provincial Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kai","family":"Gong","sequence":"additional","affiliation":[{"name":"The First Affiliated Hospital of Xiamen University"},{"name":"School of Medicine"},{"name":"Xiamen University"},{"name":"Fuzhou University Affiliated Provincial Hospital"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Quan","family":"Lan","sequence":"additional","affiliation":[{"name":"The First Affiliated Hospital of Xiamen University"},{"name":"School of Medicine"},{"name":"Xiamen University"},{"name":"Xiamen Key Laboratory of Brain Center, Fujian Province, China"},{"name":"Fujian Provincial Clinical Research Center for Brain Diseases, Fujian Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,7,3]]},"reference":[{"key":"e_1_3_5_2_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41572-019-0118-8"},{"key":"e_1_3_5_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jstrokecerebrovasdis.2016.12.002"},{"key":"e_1_3_5_4_2","doi-asserted-by":"publisher","DOI":"10.3389\/fimmu.2024.1306059"},{"issue":"3","key":"e_1_3_5_5_2","first-page":"14","article-title":"Research on Heavy Metal Exposure and Stroke Risk Prediction Based on Interpretable Machine Learning Models","volume":"26","author":"Xu D","year":"2024","unstructured":"Xu D, Liu CH, Su FH, et al. Research on Heavy Metal Exposure and Stroke Risk Prediction Based on Interpretable Machine Learning Models. Chin J Mod Med 2024; 26(3): 14\u201319.","journal-title":"Chin J Mod Med"},{"key":"e_1_3_5_6_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.arth.2023.08.045"},{"key":"e_1_3_5_7_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12911-024-02663-4"},{"key":"e_1_3_5_8_2","doi-asserted-by":"publisher","DOI":"10.7326\/0003-4819-147-8-200710160-00010"},{"key":"e_1_3_5_9_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12889-023-17373-1"},{"key":"e_1_3_5_10_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13098-023-01150-6"},{"key":"e_1_3_5_11_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12889-024-18047-2"},{"key":"e_1_3_5_12_2","first-page":"4768","article-title":"A unified approach to interpreting model predictions","volume":"30","author":"Lundberg SM","year":"2017","unstructured":"Lundberg SM, Lee SI. A unified approach to interpreting model predictions. Adv Neural Inf Process Syst 2017; 30: 4768-4777.","journal-title":"Adv Neural Inf Process Syst"},{"key":"e_1_3_5_13_2","volume-title":"iHHO-SMOTe: A cleansed approach for handling outliers and reducing noise to improve imbalanced data classification","author":"Raslan KSH","year":"2025","unstructured":"Raslan KSH, Alsharkawy AS, Raslan KR. iHHO-SMOTe: A cleansed approach for handling outliers and reducing noise to improve imbalanced data classification. arXiv preprint arXiv:2504.12850, 2025."},{"key":"e_1_3_5_14_2","doi-asserted-by":"publisher","DOI":"10.3390\/brainsci13121723"},{"key":"e_1_3_5_15_2","doi-asserted-by":"publisher","DOI":"10.3390\/antiox13080938"},{"key":"e_1_3_5_16_2","doi-asserted-by":"publisher","DOI":"10.1161\/JAHA.123.033474"},{"key":"e_1_3_5_17_2","doi-asserted-by":"publisher","DOI":"10.3390\/jox15020047"},{"key":"e_1_3_5_18_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijheh.2022.114050"},{"key":"e_1_3_5_19_2","doi-asserted-by":"publisher","DOI":"10.1289\/ehp.0800317"},{"key":"e_1_3_5_20_2","doi-asserted-by":"publisher","DOI":"10.3390\/toxics10100591"},{"key":"e_1_3_5_21_2","doi-asserted-by":"publisher","DOI":"10.1146\/annurev-nutr-071714-034419"},{"key":"e_1_3_5_22_2","volume-title":"Dietary Reference Intakes [Internet]","author":"United States Department of Agriculture","year":"2026","unstructured":"United States Department of Agriculture. Dietary Reference Intakes [Internet]: United States Department of Agriculture, 2026. Available from:. https:\/\/www.nal.usda.gov\/dietary-reference-intakes"},{"key":"e_1_3_5_23_2","doi-asserted-by":"publisher","DOI":"10.3390\/antiox13010076"},{"key":"e_1_3_5_24_2","doi-asserted-by":"publisher","DOI":"10.3390\/ijms161226183"}],"container-title":["Science Progress"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/00368504261460667","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.1177\/00368504261460667","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.1177\/00368504261460667","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,9,30]],"date-time":"2026-09-30T05:19:32Z","timestamp":1790745572000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.1177\/00368504261460667"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":23,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2026,7]]}},"alternative-id":["10.1177\/00368504261460667"],"URL":"https:\/\/doi.org\/10.1177\/00368504261460667","relation":{},"ISSN":["0036-8504","2047-7163"],"issn-type":[{"value":"0036-8504","type":"print"},{"value":"2047-7163","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,7]]},"article-number":"00368504261460667"}}