Cardiac comorbidity role in the diagnosis of diabetes mellitus using single lead electrocardiogram utilizing machine learning model

doi:10.4330/wjc.126754

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World Journal of Cardiology

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1949-8462

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Baishideng Publishing Group Inc, 7041 Koll Center Parkway, Suite 160, Pleasanton, CA 94566, USA

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Copyright: ©Author(s) 2026. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial (CC BY-NC 4.0) license. No commercial re-use. See permissions. Published by Baishideng Publishing Group Inc.

World J Cardiol. Nov 26, 2026; 18(11): 126754
Published online Nov 26, 2026. doi: 10.4330/wjc.126754

Cardiac comorbidity role in the diagnosis of diabetes mellitus using single lead electrocardiogram utilizing machine learning model

Anna Dmitrievna Karbovskaya, Basheer Abdullah Marzoog, Snezhana Nikolaevna Demchuk, Anastasia Stroeva, Daniil Andreevich Zelenov, Peter Chomakhidze, Natalia Kuznetsova, Syrkin Abromavich, Valentin V Fadeev, Irina V Poluboyarinova, Sevindzh M Ismailova, Philipp Kopylov

Anna Dmitrievna Karbovskaya, State Budgetary Healthcare Institution of the Tver Region, Konakovskaya Central District Hospital, Moscow 11953, Russia

Basheer Abdullah Marzoog, Anastasia Stroeva, Daniil Andreevich Zelenov, Peter Chomakhidze, Natalia Kuznetsova, Philipp Kopylov, Institute of Personalized Cardiology of The Center “Digital Biodesign and Personalized Healthcare” of Biomedical Science and Technology Park, Sechenov First Moscow State Medical University, Moscow 119991, Moskva, Russia

Snezhana Nikolaevna Demchuk, Medical Institute, Federal State Autonomous Educational Institution of Higher Education ‘Belgorod State National Research University’, Belgorod 308015, Belgorodskaya Oblast’, Russia

Syrkin Abromavich, Department of Cardiology, Functional and Ultrasound Diagnostics, Sechenov First Moscow State Medical University, Moscow 119991, Moskva, Russia

Valentin V Fadeev, Irina V Poluboyarinova, Sevindzh M Ismailova, Sechenov First Moscow State Medical University, Moscow 119991, Moskva, Russia

Co-first authors: Anna Dmitrievna Karbovskaya and Basheer Abdullah Marzoog.

Author contributions: Karbovskaya AD and Marzoog BA contributed to writing - original draft, review and editing; Zelenov DA and Marzoog BA contributed to biostatistical analysis; Demchuk SN, Stroeva A, Kuznetsova N, Poluboyarinova IV, Ismailova SM, Chomakhidze P, Fadeev VV, and Abromavich S contributed to data collection; Chomakhidze P, Fadeev VV, Kopylov P, and Abromavich S contributed to concept development; Kopylov P contributed to project supervision. All authors have read and approved the final version of the manuscript.

AI contribution statement: No AI tools were used in the preparation of this manuscript.

Supported by the 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; and No. 03.000. B. 166.

Institutional review board statement: The study was approved by the Ethics Committee of Sechenov University, Russia (approval No. 19-23, dated: October 26, 2023).

Informed consent statement: All study participants, or their legal guardian, provided informed written consent prior to study enrollment.

Conflict-of-interest statement: All the authors report no relevant conflicts of interest for this article.

STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.

Data sharing statement: Applicable on reasonable request from the corresponding author.

Corresponding author: Basheer Abdullah Marzoog, PhD, Senior Researcher, Senior Scientist, 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, Moskva, Russia. marzug@mail.ru

Received: August 12, 2026
Revised: September 6, 2026
Accepted: November 5, 2026
Published online: November 26, 2026
Processing time: 92 Days and 16.2 Hours

Core Tip

Core Tip: This study demonstrates that single-lead electrocardiography combined with anthropometric variables provides moderate internal discrimination of type 1 and type 2 diabetes mellitus, with median areas under the receiver operating characteristic curve of 0.693 and 0.661, respectively. Although model performance varied across cardiac-comorbidity subgroups, none of these differences remained statistically significant after adjustment for multiple comparisons. A reduced seven-predictor model demonstrated broadly comparable discrimination, suggesting that fewer variables may be sufficient. However, substantial probability miscalibration and threshold-dependent clinical net benefit limit immediate clinical applicability. These findings highlight the potential of electrocardiography-based machine learning while emphasizing the need for patient-level and external validation before clinical implementation.