{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T22:58:10Z","timestamp":1774479490253,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T00:00:00Z","timestamp":1754524800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Geriatrics"],"abstract":"<jats:p>Background: Type 2 diabetes mellitus (T2DM) increases sepsis risk due to immune dysfunction and chronic inflammation. Antidiabetic medications, while primarily used for glycemic control, may modulate sepsis susceptibility through immune and inflammatory pathways. This study investigates the association between antidiabetic medication use and sepsis risk in T2DM patients. Methods: A longitudinal cohort study was conducted using clinical registry data from 5009 T2DM patients at the University Hospital, Debrecen, Hungary (2016\u20132020). Sepsis cases were identified via ICD-10 code A41, and antidiabetic medication use was categorized using ATC codes. Baseline comorbidities and laboratory parameters were extracted. Chi-square and Wilcoxon rank\u2013sum tests assessed associations between sepsis and categorical\/numerical variables, respectively. Time-adjusted multivariate logistic regression evaluated predictors of sepsis risk, with odds ratios (ORs) and 95% confidence intervals (CIs) reported. Results: Age, hypertension, ischemic heart disease, nephropathy, elevated blood glucose, C-reactive protein, and creatinine also independently increased sepsis risk. Insulin use was associated with a 2.6-fold increased sepsis risk (OR = 2.6, 95% CI: 2.09\u20133.34, p &lt; 0.001), while SGLT2 inhibitors (OR = 0.56, 95% CI: 0.34\u20130.91, p = 0.02) and GLP-1 receptor agonists (OR = 0.39, 95% CI: 0.19\u20130.79, p = 0.009) were protective. Conclusions: Insulin-treated patients may require closer infection monitoring, while SGLT2 inhibitors and GLP-1 RAs could be prioritized in high-risk individuals. These findings highlight the potential to inform risk stratification and guide personalized antidiabetic therapy to reduce sepsis risk in T2DM.<\/jats:p>","DOI":"10.3390\/geriatrics10040108","type":"journal-article","created":{"date-parts":[[2025,8,7]],"date-time":"2025-08-07T10:58:26Z","timestamp":1754564306000},"page":"108","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Effect of Anti-Diabetic Medication Use on Sepsis Risk in Type 2 Diabetes Mellitus: A Multivariate Analysis"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7022-5932","authenticated-orcid":false,"given":"Battamir","family":"Ulambayar","sequence":"first","affiliation":[{"name":"Department of Health Informatics, Faculty of Health Sciences, University of Debrecen, 4032 Debrecen, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-0593-0188","authenticated-orcid":false,"given":"Amr Sayed","family":"Ghanem","sequence":"additional","affiliation":[{"name":"Department of Health Informatics, Faculty of Health Sciences, University of Debrecen, 4032 Debrecen, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0554-7350","authenticated-orcid":false,"given":"Attila Csaba","family":"Nagy","sequence":"additional","affiliation":[{"name":"Department of Health Informatics, Faculty of Health Sciences, University of Debrecen, 4032 Debrecen, Hungary"},{"name":"Coordinating Centre for Epidemiology, University of Debrecen Clinical Centre, 4032 Debrecen, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2077","DOI":"10.1016\/S0140-6736(24)02317-1","article-title":"Worldwide trends in diabetes prevalence and treatment from 1990 to 2022: A pooled analysis of 1108 population-representative studies with 141 million participants","volume":"404","author":"Zhou","year":"2024","journal-title":"Lancet"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e70003","DOI":"10.1002\/cph4.70003","article-title":"Type 2 Diabetes Mellitus: A Comprehensive Review of Pathophysiology, Comorbidities, and Emerging Therapies","volume":"15","author":"Singh","year":"2025","journal-title":"Compr. 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