{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,27]],"date-time":"2026-07-27T17:22:32Z","timestamp":1785172952299,"version":"3.55.0"},"reference-count":21,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T00:00:00Z","timestamp":1783555200000},"content-version":"vor","delay-in-days":8,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Diabetes Metabolism Res"],"published-print":{"date-parts":[[2026,7]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Chronic hepatitis B virus (HBV) infection is associated with an increased risk of diabetes; however, early detection of diabetes remains challenging due to silent progression in early stages. Given that 30%\u201350% of diabetic patients develop severe complications such as cardiovascular disease, renal failure, and neuropathy, timely risk assessment is critical for prevention.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We developed a machine learning model using data from 14,287 HBV\u2010positive adults across eight NHANES cycles (2003\u20132018) to identify diabetes. Eleven inflammation\u2010immune composite indicators were integrated with demographic and biochemical parameters. Following LASSO variable selection and a 7:3 train\u2010test split, seven algorithms were compared. Model performance was evaluated using the area under the ROC curve (AUC), calibration curve, and decision curve analysis (DCA).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The Artificial Neural Network (ANN) emerged as the optimal model for diabetes risk assessment. It achieved an AUC of 0.83 (95% CI: 0.80\u20130.85) and an accuracy of 82% (95% CI: 0.79\u20130.85). SHAP analysis identified age and UHR as the most influential predictors, while HRR showed significant protective effects.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>This study presents a robust, interpretable model for diabetes risk assessment in HBV patients that integrates novel inflammation\u2010immune composite indicators. These findings highlight the model's utility as a potential clinical tool for screening individuals with undiagnosed diabetes.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/dmrr.70193","type":"journal-article","created":{"date-parts":[[2026,7,9]],"date-time":"2026-07-09T10:51:20Z","timestamp":1783594280000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Assessment of Diabetes Risk in Patients With Hepatitis B: A Machine Learning Approach Integrating 11 Inflammatory and Immune Indicators"],"prefix":"10.1002","volume":"42","author":[{"given":"Zhisheng","family":"Cai","sequence":"first","affiliation":[{"name":"Jinjiang Municipal Hospital  Quanzhou Fujian China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jingtao","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Basic Medical Sciences China Three Gorges University  Yichang 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