Publication Name	World Journal of Cardiology
Manuscript ID	116217
Country	Russia

Category	Cardiac & Cardiovascular Systems
Manuscript Type	Observational Study
Article Title	Machine learning-based detection of diabetes mellitus from single-lead electrocardiography: A phenotype-stratified approach
Manuscript Source	Unsolicited Manuscript
All Author List	Anna Dmitrievna Karbovskaya, Basheer Abdullah Marzoog, Anastasia Stroeva, Alexander Suvorov, Peter Chomakhidze, Daria Gognieva, Natalia Kuznetsova, Abromavich Syrkin, Valentin V Fadeev, Sevindzh M Ismailova, Irina V Poluboyarinova and Philipp Kopylov
Funding Agency and Grant Number	Funding Agency Grant Number Priority 2030 Program of the Ministry of Science and Higher Education of Russia 03.000.B.163, 03.000. B. 166 Government Assignment Application of Mass Spectrometry and Exhaled Air Emission Spectrometry for Cardiovascular Risk Stratification 1023022600020-6
Corresponding Author	Basheer Abdullah Marzoog, MD, PhD, Researcher, Institute of Personalized Cardiology of The Center “Digital Biodesign and Personalized Healthcare” of Biomedical Science and Technology Park, Sechenov First Moscow State Medical University, 8-2 Trubetskaya Street, Moscow 119991, Russia. marzug@mail.ru
Key Words	Diabetes mellitus; Machine learning model; Diagnosis; Single lead electrocardiography; Hyperglycemia; Metabolic syndrome
Core Tip	This study introduces a crucial paradigm shift for non-invasive diabetes detection using electrocardiography. Instead of a one-size-fits-all model, we employed phenotypic clustering to disentangle the confounding effects of cardiovascular disease. We identified a specific patient profile (cluster 4: High diabetes prevalence with significant but non-severe comorbidities) where a machine learning model, analyzing single-lead electrocardiography features like T-wave morphology and atrial conduction, achieves optimal and clinically viable performance (area under the curve 0.88). This proves that diabetes-specific cardiac “whispers” are detectable, but only with a precision medicine approach that tailors diagnostics to distinct clinical phenotypes.
Citation	Karbovskaya AD, Marzoog BA, Stroeva A, Suvorov A, Chomakhidze P, Gognieva D, Kuznetsova N, Syrkin A, Fadeev VV, Ismailova SM, Poluboyarinova IV, Kopylov P. Machine learning-based detection of diabetes mellitus from single-lead electrocardiography: A phenotype-stratified approach. World J Cardiol 2026; In press

Received	2025-11-05 08:13
Peer-Review Started	2025-11-05 08:13
First Decision by Editorial Office Director	2025-11-19 10:09
Return for Revision	2025-11-19 10:09
Revised	2025-11-19 14:33
Publication Fee Transferred	2025-11-24 20:08
Second Decision by Editor	2026-01-12 02:38
Second Decision by Editor-in-Chief
Final Decision by Editorial Office Director	2026-01-12 09:34
Articles in Press	2026-01-12 09:34
Edit the Manuscript by Language Editor
Typeset the Manuscript

ISSN	1949-8462 (online)
Open Access	This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
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