Artificial Intelligence-Enhanced Electrocardiography for the Prediction of Future Type 2 Diabetes Mellitus: a model-development and multicentre validation study
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ID: 321859
2026
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Abstract
Abstract Background A significant proportion of type 2 diabetes cases remain undiagnosed despite screening advances, carrying substantial cardiometabolic risk. Artificial intelligence-enhanced electrocardiography (AI-ECG) detects subtle ECG changes in subclinical disease, potentially enabling opportunistic screening. Methods We developed AIRE-DM, a convolutional neural network with discrete-time survival loss, for diagnosis of prevalent and prediction of incident type 2 diabetes. It was trained on 1,163,401 ECGs from 189,537 individuals from Beth Israel Deaconess Medical Center (BIDMC) and externally validated in UK Biobank (N = 65,606) and ELSA-Brasil (N = 13,739). Results AIRE-DM demonstrated moderate discrimination for prevalent type 2 diabetes (AUC: BIDMC 0.724, UK Biobank 0.733, ELSA-Brasil 0.706) and incident type 2 diabetes (C-index: BIDMC 0.667, UKB 0.688, ELSA-Brasil 0.625). The highest AIRE-DM risk quartile had elevated incident diabetes risk versus the lowest (HR: BIDMC 4.75, UKB 7.52, ELSA-Brasil 3.96). AIRE-DM was non-inferior to the ADA Diabetes Risk Test in BIDMC, with improved predictive accuracy when combined. In normoglycaemic patients, AIRE-DM was superior to HbA1c for predicting incident diabetes in BIDMC and non-inferior in ELSA-Brasil. The highest risk quartile reached 5% cumulative T2DM incidence 5.4 years (BIDMC) and 4.8 years (ELSA-Brasil) earlier than the lowest risk quartile, after adjusting for HbA1c, age and sex. Phenome- and genome-wide association studies revealed biologically plausible associations with glucose regulation, cardiac morphology, diastolic dysfunction, arterial stiffness, and lipid metabolism. Conclusion AIRE-DM detects prevalent type 2 diabetes and predicts incident disease, uniquely identifying high-risk individuals within the normoglycaemic range. Combined with clinical scores or biomarkers, it enhances risk stratification, enabling earlier intervention.
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| Authors | Libor Pastika, Konstantinos Patlatzoglou, Ewa Sieliwończyk, Joseph Barker, Boroumand Zeidaabadi, Kathryn A McGurk, Sandhi Maria Barreto, Lidyane Camelo, Sadia Khan, William R Scott, Declan P O’Regan, Bruce Bartholow Duncan, María Inês Schmidt, James S Ware, Shivani Misra, Daniel B. Kramer, Jonathan W. Waks, Nicholas S. Peters, Antônio Luiz Pinho Ribeiro, Arunashis Sau, Fu Siong Ng |
| Journal | European Heart Journal - Digital Health |
| Year | 2026 |
| DOI |
10.1093/ehjdh/ztag118
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| URL | |
| Keywords | Keywords not found |
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