ECG features improve multimodal deep learning prediction of incident T2DM in a Middle Eastern cohort.

Type 2 Diabetes Mellitus (T2DM) remains a significant global health challenge, underscoring the need for early and accurate risk prediction tools to enable timely interventions. This study introduces ECG-DiaNet, a multimodal deep learning model that integrates electrocardiogram (ECG) features with established clinical risk factors (CRFs) to improve the prediction of T2DM onset. Using data from the Qatar Biobank (QBB), we compared ECG-DiaNet against unimodal models based solely on ECG or CRFs. A development cohort (n = 2043) was utilized for model training and internal validation, while a separate longitudinal cohort (n = 395) with a median five-year follow-up served as the test set. ECG-DiaNet demonstrated superior predictive performance, achieving a higher area under the receiver operating characteristic curve (AUROC) compared to the CRF-only model (0.845vs.0.8217), which was statistically significant based on the DeLong test (p < 0.001), thus highlighting the added predictive value of incorporating ECG signals. Reclassification metrics reinforced these improvements, with a significant Net Reclassification Improvement (NRI = 0.0153,p < 0.001) and Integrated Discrimination Improvement (IDI = 0.0482,p = 0.0099), confirming the enhanced risk stratification. Furthermore, stratifying participants into Low-, Medium-, and High-risk categories revealed that ECG-DiaNet achieved a higher positive predictive value (PPV) in the high-risk group compared to CRF-only models. These findings, together with the non-invasive nature and wide accessibility of ECG technology, suggest the potential of ECG-DiaNet for clinical implementation. However, further validation using larger and more diverse datasets is needed to improve generalizability.