Hearing diabetes in a one-minute electrocardiogram: Why phenotype-stratified machine learning may outperform one-size-fits-all screening

Core Tip

Core Tip: A one-minute, single-lead electrocardiogram (ECG) may enable scalable screening for diabetes mellitus (DM) when paired with machine learning. Karbovskaya et al introduced a phenotype-clustering strategy that explicitly accounts for clinical heterogeneity and cardiovascular comorbidity, revealing that DM-related ECG signatures are most detectable in specific patient subgroups rather than uniformly across populations. By emphasizing interpretable electrophysiologic features and testing model transportability across phenotypes, this work advances a “precision screening” paradigm and provides a practical roadmap for translating ECG-based metabolic risk detection into real-world cardiometabolic workflows.