ArticleCardiovascular diabetology2025
Artificial intelligence identifies individuals with prediabetes using single-lead electrocardiograms.
Article in Cardiovascular diabetology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- From Beat to Risk: How Heart Rate Variability Predicts Arrhythmias in Type 2 Diabetes.Life (Basel, Switzerland) · 2026Review
- A clinically interpretable prediction model for acute mortality in patients with pneumonia requiring mechanical ventilation.Respiratory research · 2026Article
- A multi-model fusion approach incorporating conventional radiological and machine learning features across age spectrum for periorbital fat status prediction.Frontiers in medicine · 2026Article
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Authors and funding
16 authors.
Funding
Abstract
backgroundEarly detection of prediabetes is crucial for diabetes prevention, yet it remains challenging due to its asymptomatic nature and low screening rates. This study aimed to develop and rigorously validate artificial intelligence (AI) models to identify individuals with prediabetes solely using electrocardiograms (ECGs).
methodsWe defined prediabetes/diabetes based on fasting plasma glucose ≥ 110 mg/dL, hemoglobin A
resultsThe best-performing model, a LightGBM-based algorithm we termed DiaCardia, achieved an area under the receiver operating characteristic curve (AUROC) of 0.851 in the internal test dataset (sensitivity: 85.7%, specificity: 70.0%). The model demonstrated robust generalizability, achieving an AUROC of 0.785 in the external validation cohort. Furthermore, DiaCardia maintained substantial predictive ability (AUROC: 0.789) after adjustment for six major confounders using propensity score matching. Higher R-wave amplitude in leads aVL and I, and smaller peak interval dispersion were prominent predictors. Notably, a version of DiaCardia using only single-lead (lead I) ECG data achieved a comparable AUROC of 0.844 (sensitivity: 82.3%; specificity: 70.2%).
conclusionsThis study establishes that an AI model, DiaCardia, can accurately identify individuals with prediabetes from an ECG alone, with performance that is robust across different patient cohorts and independent of major clinical confounders. Our highly generalizable, single-lead DiaCardia model offers a promising solution for scalable prediabetes screening via wearable devices, potentially enabling early, home-based detection and transforming diabetes prevention strategies.
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