Frontiers in endocrinology · 2026
A novel QTc-RR differential biomarker for the early assessment of autonomic dysfunction in type 2 diabetes.
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Liao Xuwei, Yang Shanglin, Wu Hsientsai, Lin Yuyang, Zhou Hongbin, Liou Juin J
Аннотация
PURPOSE: Prolonged QT intervals are clinically relevant in patients with type 2 diabetes mellitus (T2DM). However, accurately detecting the Q and T points in electrocardiogram (ECG) signals remains challenging owing to the variability in T-wave morphology, and few engineering solutions have effectively addressed this issue.
METHODS: We analysed 30-min Lead II ECG recordings from 88 patients with T2DM and 93 healthy controls. A novel automated algorithm was developed to accurately detect the Q and T points, accommodating five classified T-wave morphological types. Two electrophysiologists verified the accuracy of the algorithm. Following validation, the QTc intervals derived from the algorithm were compared between the groups. Patients with T2DM exhibited significantly prolonged QTc values and a higher incidence of long QT patterns. A new parameter, RQTdiff, the difference between the QTc and RR interval (RRI), was introduced and compared with the conventional RRI in assessing heart rate variability (HRV).
RESULTS: The proposed method accurately detected the Q and T points across diverse ECG morphologies and classified the T-wave patterns. QTc values derived from this method significantly distinguished T2DM patients from controls. The novel parameter, RQTdiff, was strongly correlated with RRI and outperformed it in HRV analysis. Receiver operating characteristic (ROC) analysis revealed that RQTdiff improved the area under the curve (AUCs) for the SSR and LHR indices by 27% and 13%, respectively. Additionally, logistic regression using the Baroreflex Entropy Index demonstrated better predictive performance for RQTdiff over RRI, with enhanced odds ratios (Exp[B]: 0.150 vs. 0.270) and slightly improved classification accuracy (64.6% vs. 63.5%).
CONCLUSION: This interdisciplinary study presents an engineering-based solution for robust Q- and T-point detections. The novel RQTdiff metric offers physiologically meaningful insights beyond the traditional RRI and enhances HRV analysis, particularly in populations with T2DM and long QT patterns.
METHODS: We analysed 30-min Lead II ECG recordings from 88 patients with T2DM and 93 healthy controls. A novel automated algorithm was developed to accurately detect the Q and T points, accommodating five classified T-wave morphological types. Two electrophysiologists verified the accuracy of the algorithm. Following validation, the QTc intervals derived from the algorithm were compared between the groups. Patients with T2DM exhibited significantly prolonged QTc values and a higher incidence of long QT patterns. A new parameter, RQTdiff, the difference between the QTc and RR interval (RRI), was introduced and compared with the conventional RRI in assessing heart rate variability (HRV).
RESULTS: The proposed method accurately detected the Q and T points across diverse ECG morphologies and classified the T-wave patterns. QTc values derived from this method significantly distinguished T2DM patients from controls. The novel parameter, RQTdiff, was strongly correlated with RRI and outperformed it in HRV analysis. Receiver operating characteristic (ROC) analysis revealed that RQTdiff improved the area under the curve (AUCs) for the SSR and LHR indices by 27% and 13%, respectively. Additionally, logistic regression using the Baroreflex Entropy Index demonstrated better predictive performance for RQTdiff over RRI, with enhanced odds ratios (Exp[B]: 0.150 vs. 0.270) and slightly improved classification accuracy (64.6% vs. 63.5%).
CONCLUSION: This interdisciplinary study presents an engineering-based solution for robust Q- and T-point detections. The novel RQTdiff metric offers physiologically meaningful insights beyond the traditional RRI and enhances HRV analysis, particularly in populations with T2DM and long QT patterns.