{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T19:19:09Z","timestamp":1778786349096,"version":"3.51.4"},"reference-count":65,"publisher":"Wiley","issue":"5","license":[{"start":{"date-parts":[[2023,4,13]],"date-time":"2023-04-13T00:00:00Z","timestamp":1681344000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82103939"],"award-info":[{"award-number":["82103939"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Diabetes"],"published-print":{"date-parts":[[2023,5]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Background<\/jats:title><jats:p>Type 2 diabetes mellitus (T2DM) is highly prevalent worldwide and may lead to a higher rate of cognitive dysfunction. This study aimed to develop and validate a nomogram\u2010based model to detect mild cognitive impairment (MCI) in T2DM patients.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Inpatients with T2DM in the endocrinology department of Xiangya Hospital were consecutively enrolled between March and December 2021. Well\u2010qualified investigators conducted face\u2010to\u2010face interviews with participants to retrospectively collect sociodemographic characteristics, lifestyle factors, T2DM\u2010related information, and history of depression and anxiety. Cognitive function was assessed using the Mini\u2010Mental State Examination scale. A nomogram was developed to detect MCI based on the results of the multivariable logistic regression analysis. Calibration, discrimination, and clinical utility of the nomogram were subsequently evaluated by calibration plot, receiver operating characteristic curve, and decision curve analysis, respectively.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 496 patients were included in this study. The prevalence of MCI in T2DM patients was 34.1% (95% confidence interval [CI]: 29.9%\u201338.3%). Age, marital status, household income, diabetes duration, diabetic retinopathy, anxiety, and depression were independently associated with MCI. Nomogram based on these factors had an area under the curve of 0.849 (95% CI: 0.815\u20130.883), and the threshold probability ranged from 35.0% to 85.0%.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Almost one in three T2DM patients suffered from MCI. The nomogram, based on age, marital status, household income, duration of diabetes, diabetic retinopathy, anxiety, and depression, achieved an optimal diagnosis of MCI. Therefore, it could provide a clinical basis for detecting MCI in T2DM patients.<\/jats:p><\/jats:sec>","DOI":"10.1111\/1753-0407.13384","type":"journal-article","created":{"date-parts":[[2023,4,14]],"date-time":"2023-04-14T06:41:01Z","timestamp":1681454461000},"page":"448-458","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["The use of nomogram for detecting mild cognitive impairment in patients with type 2 diabetes mellitus"],"prefix":"10.1111","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3388-3861","authenticated-orcid":false,"given":"Rehanguli","family":"Maimaitituerxun","sequence":"first","affiliation":[{"name":"Department of Epidemiology and Health Statistics, Xiangya School of Public Health Central South University  Changsha China"},{"name":"Hunan Provincial Key Laboratory of Clinical Epidemiology  Changsha 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