| 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/ |
| Copyright |
© The Author(s) 2026. Published by Baishideng Publishing Group Inc. All rights reserved. |
| Article Reprints |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/247
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| Permissions |
For details, please visit: http://www.wjgnet.com/bpg/gerinfo/207
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| Publisher |
Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA |
| Website |
http://www.wjgnet.com |
| Category |
Cardiac & Cardiovascular Systems |
| Manuscript Type |
Prospective Study |
| Article Title |
Discriminating diabetes mellitus from single-lead electrocardiography using machine learning and multinomial regression
|
| Manuscript Source |
Unsolicited Manuscript |
| All Author List |
Anna Dmitrievna Karbovskaya, Basheer Abdullah Marzoog, Anastasia Stroeva, Peter Chomakhidze, Daria Gognieva, Natalia Kuznetsova, Abromavich Syrkin, Valentin V Fadeev, Irina V Poluboyarinova, Sevindzh M Ismailova, Alexander Suvorov and Philipp Kopylov |
| ORCID |
|
| Funding Agency and Grant Number |
| Funding Agency |
Grant Number |
| the Government Assignment Application of Mass Spectrometry and Exhaled Air Emission Spectrometry for Cardiovascular Risk Stratification |
No. 1023022600020-6 |
| the Priority 2030 Program of the Ministry of Science and Higher Education of Russia |
No. 03.000.B.163 |
| the Priority 2030 Program of the Ministry of Science and Higher Education of Russia |
No. 03.000.B.166 |
|
| 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, Federal State Autonomous Educational Institution of Higher Education I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenovskiy University), 8-2 Trubetskaya Street, Moscow 119991, Moskva, Russia. marzug@mail.ru |
| Key Words |
Machine learning; Diabetes mellitus; Diagnosis; Hyperglycemia; Glycated hemoglobin; Glucose |
| Core Tip |
This study pioneers a novel, non-invasive screening strategy for diabetes mellitus by leveraging the ubiquity of single-lead electrocardiography. Using a machine learning model, we demonstrate that the diabetic state leaves a distinct electrophysiological signature on the heart, detectable from a simple, consumer-grade electrocardiography. The model excels at ruling out diabetes with high accuracy and can differentiate between type 1 and type 2 diabetes based on divergent cardiac electrical patterns, such as opposing prolonged QT interval interval behaviors. This approach transforms a common cardiac tool into a potential frontline, accessible screening method for one of the world’s most prevalent metabolic disorders. |
| Publish Date |
2026-03-23 08:54 |
| Citation |
Karbovskaya AD, Marzoog BA, Stroeva A, Chomakhidze P, Gognieva D, Kuznetsova N, Syrkin A, Fadeev VV, Poluboyarinova IV, Ismailova SM, Suvorov A, Kopylov P. Discriminating diabetes mellitus from single-lead electrocardiography using machine learning and multinomial regression. World J Cardiol 2026; 18(3): 116115 |
| URL |
https://www.wjgnet.com/1949-8462/full/v18/i3/116115.htm |
| DOI |
https://dx.doi.org/10.4330/wjc.v18.i3.116115 |
