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Can electrocardiographic repolarization predict diabetes incidence: the Atherosclerosis Risk in Communities Study.
Chunyu Zhang1, Peng Yu2, Ming Liu3
1Shanghai Institute of Cardiovascular Diseases, Shanghai Engineering Research Center of AI Technology for Cardiopulmonary Diseases, Zhongshan Hospital, Shanghai Medical College of Fudan University, Shanghai, China.
Journal of Electrocardiology
|February 1, 2025
Summary
Electrocardiogram (ECG) markers like T-wave amplitude and T/R ratio can predict type 2 diabetes risk. These findings highlight ECG
Area of Science:
- Cardiology
- Metabolic Disorders
- Preventive Medicine
Background:
- Diabetes mellitus is characterized by metabolic dysregulation and elevated cardiovascular risk.
- Early cardiac changes often precede diabetes onset, but prediction remains difficult.
Purpose of the Study:
- To investigate the predictive value of electrocardiogram (ECG) repolarization parameters for incident type 2 diabetes.
- To explore the association between ECG findings, metabolic parameters, and myocardial fat content.
Main Methods:
- Analysis of 12-lead ECGs from 11,297 participants (aged 45-64) without baseline diabetes or heart disease.
- Cox regression models assessed repolarization parameters (QTc intervals, J-point level, T-wave amplitude, T/R ratio) against diabetes risk.
- Correlation analyses examined links between ECG findings, metabolic factors, and myocardial fat.
Main Results:
- Over 23 years, 29.5% developed type 2 diabetes.
- A reduced T-wave to R-wave (T/R) ratio demonstrated a dose-response relationship with increased diabetes risk (e.g., Lead I HR 1.22 per SD decrease).
- Weaker associations were observed for J-point level and T-wave amplitude; repolarization markers correlated negatively with metabolic parameters.
Conclusions:
- ECG parameters, particularly T-wave amplitude and T/R ratio, are predictive of incident type 2 diabetes.
- These ECG markers may serve as early biomarkers for diabetes-related cardiac changes.
- Findings emphasize the clinical utility of ECG in identifying individuals at high risk for diabetes.
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