{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,7]],"date-time":"2026-11-07T08:54:19Z","timestamp":1794041659801,"version":"4.8.0"},"reference-count":60,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T00:00:00Z","timestamp":1741132800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Endocrinol."],"abstract":"<jats:sec><jats:title>Objective<\/jats:title><jats:p>Depression is highly prevalent among individuals with type 2 diabetes mellitus (T2DM), often compounded by multiple chronic conditions. This study aimed to identify the key factors influencing depression in this population, with a particular focus on the relationship between the Cumulative Illness Rating Scale (CIRS) score and depression, and to evaluate the predictive value of a model incorporating sex, body mass index (BMI), low-density lipoprotein cholesterol (LDL-C), and CIRS score.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>A total of 308 hospitalized patients with type 2 diabetes from Quzhou Hospital, Wenzhou Medical University were enrolled. Their clinical and biochemical data were collected, alongside assessments of comorbidities and depressive symptoms using the CIRS and Self-Rating Depression Scale (SDS), respectively. LASSO regression with 10-fold cross-validation was used to identify the optimal variables for the predictive model. Multivariate analysis was performed to assess the independent associations between sex, BMI, LDL-C, and CIRS score with depression. The relationship between CIRS scores and depression was further explored across various subgroups. The predictive model\u2019s value was assessed through ROC curve analysis.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>Female sex (OR: 2.48, 95% CI: 1.50-4.10, p &amp;lt; 0.001), lower BMI (OR: 0.92, 95% CI: 0.86-0.98, p = 0.015), lower LDL-C (OR: 0.77, 95% CI: 0.61-0.98, p = 0.031), and higher CIRS scores (OR: 1.11, 95% CI: 1.05-1.18, p &amp;lt; 0.001) were independently linked to depression after adjusting for clinical variables. A strong association between CIRS score and depression was observed, particularly in males, patients under 60 years old, those with a disease duration of less than 5 years, and individuals with no history of smoking or alcohol consumption. Additionally, a predictive model incorporating sex, BMI, LDL-C, and CIRS score demonstrated high accuracy in identifying patients at risk for depression.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusions<\/jats:title><jats:p>Female, lower BMI, lower LDL-C and higher CIRS score were independently associated with depression in patients with type 2 diabetes. The CIRS score appeared to be more effective in predicting depression risk in people who were male, younger, shorter DM duration, no smoking or no drinking. A more comprehensive prediction model could help clinicians identify patients with type 2 diabetes who are at risk for depression.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fendo.2025.1555142","type":"journal-article","created":{"date-parts":[[2025,3,5]],"date-time":"2025-03-05T05:16:19Z","timestamp":1741151779000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":7,"title":["Predictive factors and risk model for depression in patients with type 2 diabetes mellitus: a comprehensive analysis of comorbidities and clinical indicators"],"prefix":"10.3389","volume":"16","author":[{"given":"Chengzheng","family":"Duan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Luo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weifeng","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hui","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yexing","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiyu","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaofang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaoli","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongjuan","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,3,5]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","DOI":"10.1016\/S0140-6736(23)01301-6","article-title":"Global, regional, and national burden of diabetes from 1990 to 2021, with projections of prevalence to 2050: a systematic analysis for the Global Burden of Disease Study 2021","volume":"402","author":"Ong","year":"2023","journal-title":"Lancet"},{"key":"B2","doi-asserted-by":"publisher","first-page":"2087","DOI":"10.1016\/S0140-6736(23)01296-5","article-title":"Diabetes: a defining disease of the 21st century","volume":"401","year":"2023","journal-title":"Lancet"},{"key":"B3","doi-asserted-by":"publisher","first-page":"2416","DOI":"10.1038\/s41598-020-59336-1","article-title":"MorbiNet: multimorbidity networks in adult general population. 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