Publication Name	World Journal of Cardiology
Manuscript ID	119396
Country	United States

Category	Medical Informatics
Manuscript Type	Editorial
Article Title	Hearing diabetes in a one-minute electrocardiogram: Why phenotype-stratified machine learning may outperform one-size-fits-all screening
Manuscript Source	Invited Manuscript
All Author List	Mehrnaz Azarian
Funding Agency and Grant Number
Corresponding Author	Mehrnaz Azarian, MD, Post Doctoral Researcher, Postdoc, Center for Innovations in Quality, Michael E DeBakey VA Medical Center, Effectiveness and Safety, 2450 Holcombe Blvd, Suite 01Y, Houston, TX 77021, United States. mehrnaz.azarian@bcm.edu
Key Words	Diabetes mellitus; Machine learning; Electrocardiography; Digital biomarkers; Single-lead electrocardiogram
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.
Citation	Azarian M. Hearing diabetes in a one-minute electrocardiogram: Why phenotype-stratified machine learning may outperform one-size-fits-all screening. World J Cardiol 2026; In press

Received	2026-01-26 00:39
Peer-Review Started	2026-01-26 00:42
First Decision by Editorial Office Director	2026-02-04 09:42
Return for Revision	2026-02-04 09:42
Revised	2026-02-18 19:26
Publication Fee Transferred
Second Decision by Editor	2026-04-16 02:35
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2026-04-16 07:17
Articles in Press	2026-04-16 07:17
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1949-8462 (online)
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