{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,19]],"date-time":"2026-03-19T18:57:19Z","timestamp":1773946639375,"version":"3.50.1"},"reference-count":74,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T00:00:00Z","timestamp":1757289600000},"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. Med."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>The increasing prevalence of type 2 diabetes mellitus (T2DM) requires improved early detection strategies that integrate demographic, clinical, physiological, and pharmacological data. Electrocardiographic (ECG) biomarkers offer a non-invasive means to assess diabetes-related cardiac risk, particularly in individuals with hypertension (HT) and cardiovascular disease (CVD) comorbidities of diabetes.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>ECG data from 581 subjects were categorized by glycemic status (healthy, prediabetes, T2DM) and comorbidities. Demographic, clinical, and pharmaceutical data were merged with 10\u202fs and 5\u202fmin ECG recordings. SMOTE was used to correct class imbalance. Support Vector Machines (SVM) performed best among machine learning classifiers. Classification accuracy, sensitivity, specificity, and AUC were computed using 5-fold cross-validation. Feature importance was assessed through permutation analysis to identify the most discriminative ECG and medication-related predictors.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>T2DM patients, particularly those with HT and CVD, exhibited significant prolongation of QTc (10\u202fs), QTd (10\u202fs and 5\u202fmin), and PQ intervals, as well as changes in the QRS-Axis, indicating increased arrhythmic risk and electrical remodeling (<jats:italic>p<\/jats:italic>\u202f&amp;lt;\u202f0.001). Antihypertensive and lipid-lowering medications influenced QRS-Axis and PQ intervals, while antidepressant use was associated with QTd dispersion (<jats:italic>p<\/jats:italic>\u202f=\u202f0.010). Classification accuracy ranged from 0.64 to 0.88. Five-minute ECGs provided higher accuracy (~0.88) when medication data were included, while 10-s ECGs performed well in treated patients (~0.86\u20130.88).<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>This study shows that ECG-based, AI-driven screening captures the interaction between comorbidities, medication use, and cardiac electrophysiology. Integrating ECG biomarkers with medication data improved T2DM risk classification, enabling better treatment outcomes based on clinical use of non-invasive methods for risk classification.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fmed.2025.1646495","type":"journal-article","created":{"date-parts":[[2025,9,8]],"date-time":"2025-09-08T05:59:49Z","timestamp":1757311189000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["AI-based integration of ECG biomarkers for assessing cardiac risk in type 2 diabetes mellitus with comorbid conditions for patient stratification"],"prefix":"10.3389","volume":"12","author":[{"given":"Symeon","family":"Savvopoulos","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haralampos","family":"Hatzikirou","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Herbert 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