{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,10]],"date-time":"2025-12-10T16:03:37Z","timestamp":1765382617357,"version":"build-2065373602"},"reference-count":48,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T00:00:00Z","timestamp":1757462400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Background\/Objectives: Differentiating Type 1 from Type 2 diabetes mellitus (T1DM vs. T2DM) remains clinically challenging, especially in early-onset cases with overlapping features. This study assessed the diagnostic utility of diabetes-related autoantibodies in an Omani cohort and evaluated their predictive performance using machine learning. Methods: Clinical and laboratory data from 448 patients (aged \u2265 2 years) in Al Batinah North, Oman, were retrospectively analyzed. We assessed autoantibody positivity (anti-GAD, anti-islet, anti-TPO, anti-tissue), age, sex, and HbA1c. Receiver operating characteristic (ROC) curves and a neural network model were used to evaluate diagnostic accuracy. Results: Anti-GAD and anti-islet antibodies were significantly more prevalent in T1DM (69.0% and 64.1%) than T2DM (7.4% and 3.8%; p &lt; 0.0001). HbA1c was elevated in both subtypes but lacked discriminatory specificity. Nearly half (48.5%) of T1DM patients showed multiple antibody positivity, especially in younger age groups. Anti-TPO and anti-tissue antibodies were more frequently detected in T1DM, suggesting broader autoimmunity. ROC analysis showed strong predictive value for anti-islet (AUC = 0.835) and anti-GAD (AUC = 0.827). Neural network modeling identified anti-GAD, anti-islet, and age as the most informative predictors, achieving over 92% classification accuracy. Importantly, antibody positivity in a subset of insulin-treated T2DM patients suggested potential latent autoimmune diabetes (LADA) misclassification. Conclusions: This is the first study in Oman to combine autoantibody screening with AI-based modeling to refine diabetes classification. Our findings highlight the value of immunological profiling in early diagnosis, uncover possible misclassification, and support AI integration to guide individualized management.<\/jats:p>","DOI":"10.3390\/diagnostics15182296","type":"journal-article","created":{"date-parts":[[2025,9,10]],"date-time":"2025-09-10T10:27:12Z","timestamp":1757500032000},"page":"2296","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Autoantibody Profiling for Accurate Differentiation of Type 1 and Type 2 Diabetes Mellitus in Omani Patients: A Retrospective Study"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4106-2737","authenticated-orcid":false,"given":"Souad","family":"Al-Okla","sequence":"first","affiliation":[{"name":"College of Medicine and Health Sciences, National University of Science and Technology, P.O. Box 391, Sohar 321, Oman"},{"name":"Department of Biology, Faculty of Sciences, Damascus University, Damascus P.O. Box 30621, Syria"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8741-9642","authenticated-orcid":false,"given":"Salima","family":"Al Maqbali","sequence":"additional","affiliation":[{"name":"Department of Pathology & Blood Bank, Suhar Hospital, Ministry of Health, Al Batinah, P.O. Box 49, Sohar 311, Oman"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7445-5687","authenticated-orcid":false,"given":"Hamdi","family":"Al Mutori","sequence":"additional","affiliation":[{"name":"College of Medicine and Health Sciences, National University of Science and Technology, P.O. Box 391, Sohar 321, Oman"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amna Mohammed","family":"Al-Hinai","sequence":"additional","affiliation":[{"name":"College of Medicine and Health Sciences, National University of Science and Technology, P.O. Box 391, Sohar 321, Oman"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rayyan Hassan","family":"Al Bloushi","sequence":"additional","affiliation":[{"name":"College of Medicine and Health Sciences, National University of Science and Technology, P.O. Box 391, Sohar 321, Oman"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mallak Ahmed","family":"Aljabri","sequence":"additional","affiliation":[{"name":"College of Medicine and Health Sciences, National University of Science and Technology, P.O. Box 391, Sohar 321, Oman"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haya Nasser","family":"Alsenani","sequence":"additional","affiliation":[{"name":"College of Medicine and Health Sciences, National University of Science and Technology, P.O. Box 391, Sohar 321, Oman"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Al Shafaee","sequence":"additional","affiliation":[{"name":"College of Medicine and Health Sciences, National University of Science and Technology, P.O. Box 391, Sohar 321, Oman"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,9,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"566","DOI":"10.1007\/s44197-023-00138-9","article-title":"The Description and Prediction of Incidence, Prevalence, Mortality, Disability-Adjusted Life Years Cases, and Corresponding Age-Standardized Rates for Global Diabetes","volume":"13","author":"Sun","year":"2023","journal-title":"J. Epidemiol. Glob. Health"},{"key":"ref_2","first-page":"A99","article-title":"Addressing the threat of chronic diseases in Oman","volume":"5","author":"Mabry","year":"2008","journal-title":"Prev. 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