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Diabetes-Related Alterations in Vectorcardiographic Repolarization Geometry: Insights from Physiological and
1Department of Electrocardiogram, The First Affiliated Hospital, and College of Clinical Medicine of Henan University of Science and Technology, Luoyang, Henan, 471003, People's Republic of China.
Summary
Diabetes alters cardiac electrophysiology, with vectorcardiography (VCG) revealing spatial repolarization changes. Spatial QRS-T angle is a key indicator of these metabolic influences on heart electrical function.
Area of Science:
- Cardiology and Electrophysiology
- Metabolic Disease Research
- Biomedical Signal Processing
Background:
- Diabetes mellitus is linked to cardiac electrophysiological changes.
- The precise impact of metabolic dysfunction on spatial ventricular repolarization geometry is not fully understood.
- Vectorcardiography (VCG) offers a spatial view of cardiac electrical activity, potentially revealing subtle metabolic disease-related alterations.
Purpose of the Study:
- To analyze VCG features in adults to define the relationship between diabetes and depolarization-repolarization dynamics.
- To investigate if VCG parameters can capture metabolic influences on cardiac electrical activity.
- To identify key VCG parameters associated with diabetes and metabolic dysregulation.
Main Methods:
- Reconstructed orthogonal leads from standard ECGs were used to extract spatial VCG parameters (e.g., QRS-T angle, T-wave magnitude, loop morphology).
- Pearson correlation and principal component analysis evaluated associations between clinical variables and VCG parameters.
- A Random Forest classifier, validated by permutation testing and SHAP analysis, assessed VCG features' ability to discriminate diabetic status.
Main Results:
- The Random Forest model showed moderate discrimination between diabetic and non-diabetic individuals (ROC AUC = 0.878).
- Spatial QRS-T angle was the most significant predictor, followed by T-wave magnitude, systolic blood pressure, T-loop area, and BMI.
- Repolarization-related VCG features were the most influential factors in the model's predictions.
Conclusions:
- Diabetes is associated with detectable changes in ventricular repolarization geometry.
- Spatial QRS-T angle and other VCG parameters serve as crucial electrophysiological signatures of metabolic influence.
- Vectorcardiographic phenotyping is a promising noninvasive method for studying cardiometabolic effects on myocardial electrical function.
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