{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T05:54:29Z","timestamp":1781589269471,"version":"3.54.5"},"reference-count":28,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T00:00:00Z","timestamp":1770854400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,2,12]],"date-time":"2026-02-12T00:00:00Z","timestamp":1770854400000},"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":["Ibrain"],"published-print":{"date-parts":[[2026,3]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Visual impairment has been recognized as a potential risk factor for depressive symptoms (DS) in diabetes patients, yet the role of visual function in predicting DS remains unexplored. This study aims to develop and validate a predictive model for DS risk in type 2 diabetes mellitus (T2DM) patients in community health settings, incorporating a visual function index (VF14). We conducted a cross\u2010sectional study involving 542 T2DM patients from four community health centers in Guiyang. Univariate and multivariate logistic regressions identified significant predictors, while 10 machine learning algorithms were employed to construct the predictive model. Model performance was assessed using such metrics as receiver operating characteristic curves, accuracy, sensitivity, specificity, F1 score, Brier score, C\u2010index, calibration curves, and decision curve analysis. A restricted cubic spline (RCS) analysis evaluated the score\u2010dependent risk profiles between the VF14 and DS. Key predictors included body mass index (BMI), self\u2010reported glycemic status, age\u2010related macular degeneration, glycated hemoglobin (HbA1c), and VF14. Among the models, the gradient boosting machine exhibited the robust predictive performance, with an area under the curve of 0.73 and sensitivity of 0.72. The Shapley additive explanations analysis identified VF14, BMI, and HbA1c as the top risk factors. RCS analysis revealed a score\u2010dependent risk profile between VF14 and DS risk. This study introduces a clinically interpretable tool for early DS risk stratification in T2DM patients, offering potential for improved risk assessment and timely intervention in community health settings.<\/jats:p>","DOI":"10.1002\/ibra.70014","type":"journal-article","created":{"date-parts":[[2026,2,13]],"date-time":"2026-02-13T02:19:23Z","timestamp":1770949163000},"page":"123-136","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Development of a predictive model for depressive symptoms in type 2 diabetes mellitus patients under community management: Based on visual function index"],"prefix":"10.1002","volume":"12","author":[{"given":"Rong\u2010song","family":"Sun","sequence":"first","affiliation":[{"name":"School of Public Health, the Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education Guizhou Medical University, Guian New Area  China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sheng\u2010xu","family":"Huo","sequence":"additional","affiliation":[{"name":"College of Computer Science and Electronic Engineering Hunan University Changsha China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tian\u2010lin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Health Management Department Guizhou Nursing Vocational College Guiyang China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hao\u2010jiang","family":"Ying","sequence":"additional","affiliation":[{"name":"Department of Psychology Soochow University Suzhou China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun\u2010hua","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Public Health, the Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education Guizhou Medical University, Guian New Area  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