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Discriminating diabetes mellitus from single-lead electrocardiography using machine learning and multinomial regression
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, Philipp Kopylov
Anna Dmitrievna Karbovskaya, State Budgetary Healthcare Institution of the Tver Region “Konakovskaya Central District Hospital”, Moscow 11953, Moskva, Russia
Basheer Abdullah Marzoog, Anastasia Stroeva, Peter Chomakhidze, Daria Gognieva, Natalia Kuznetsova, Alexander Suvorov, Philipp Kopylov, 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), Moscow 119991, Moskva, Russia
Abromavich Syrkin, Department of Cardiology, 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), Moscow 119991, Moskva, Russia
Valentin V Fadeev, Irina V Poluboyarinova, 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), Moscow 119991, Moskva, Russia
Sevindzh M Ismailova, Sechenov University, Moscow 11936, Moskva, Russia
Author contributions: Karbovskaya AD and Marzoog BA writing the original draft and reviewing; Stroeva A, Gognieva D, Kuznetsova N, Syrkin A, Poluboyarinova IV, and Ismailova SM were responsible for data collection; Chomakhidze P and Fadeev VV were responsible for data collection and concept development; Suvorov A was responsible for biostatistical analysis of the sample; Kopylov P concept development and project supervision; all authors have read and approved the final version of the manuscript.
Supported by the Government Assignment Application of Mass Spectrometry and Exhaled Air Emission Spectrometry for Cardiovascular Risk Stratification, No. 1023022600020-6; and the Priority 2030 Program of the Ministry of Science and Higher Education of Russia, No. 03.000.B.163 and No. 03.000.B.166.
Institutional review board statement: The study protocol was approved by the local Ethical Committee of Sechenov University (No. 14-19).
Informed consent statement: All participants provided informed consent.
Conflict-of-interest statement: All authors declare no conflict of interest in publishing the manuscript.
CONSORT 2010 statement: The authors have read the CONSORT 2010 Statement, and the manuscript was prepared and revised according to the CONSORT 2010 Statement.
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
Received: November 3, 2025
Revised: December 4, 2025
Accepted: January 19, 2026
Published online: March 26, 2026
Processing time: 140 Days and 14.6 Hours
BACKGROUND
Current advances in diagnostic and therapeutic strategies remain insufficient to reduce the prevalence and incidence rate of diabetes mellitus (DM).
AIM
To investigate any association between single-lead electrocardiography (ECG) parameters and the diagnosis of DM.
METHODS
A single center study involved participants of Caucasian origin for the period between May 2, 2022 and August 23, 2025 with or without DM and aged ≥ 18 years. All participants participating in the study passed the cardiologist’s, random glucose measurement using a glucometer, single lead-ECG registration (using Cardio-Qvark®) and transthoracic echocardiography. Statistical analysis conducted using the R programming language (version 4.5).
RESULTS
The built logistic regression machine learning model demonstrated diagnostic performance in discriminating (area under the curve) type 1 DM 0.84 (95%CI: 0.76-0.91), type 2 DM 0.69 (95%CI: 0.61-0,76), and healthy control 0.82 (95%CI: 0.76-0.87).
CONCLUSION
The developed model demonstrates an association between single-lead ECG parameters and diabetes status that can support the clinical identification of individuals who would benefit from confirmatory testing. This is probably attributable to relatively stable and long-term physiological alterations associated with the state of the disease.
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 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.
