Genetic Basis and Prognostic Value of Exercise QT Dynamics

Abstract

Background: Abnormal QT interval responses to heart rate (QT dynamics) is an independent risk predictor for cardiovascular disease in patients, but its genetic basis and prognostic value in a population-based cohort have not been investigated. Methods: QT dynamics during exercise and recovery were derived in 56 643 individuals from UK Biobank without a history of cardiovascular events. Genome-wide association studies were conducted to identify genetic variants and bioinformatics analyses were performed to prioritize candidate genes. The prognostic value of QT dynamics was evaluated for cardiovascular events (death or hospitalization) and all-cause mortality. Results: Heritability of QT dynamics during exercise and recovery were 10.7% and 5.4%, respectively. Genome-wide association studies identified 20 loci, of which 4 loci included genes implicated in mendelian long-QT syndrome. Five loci did not overlap with previously reported resting QT interval loci; candidate genes included KCNQ4 and KIAA1755 . Genetic risk scores were not associated with cardiovascular events in 357 882 unrelated individuals from UK Biobank. We also did not observe associations of QT dynamics during exercise and recovery with cardiovascular events. Increased QT dynamics during recovery was significantly associated with all-cause mortality in the univariate Cox regression analysis (hazard ratio, 1.09 [95% CI, 1.05–1.13], P =2.28×10 -5 ), but the association was not significant after adjusting for clinical risk factors. Conclusions: QT interval dynamics during exercise and recovery are heritable markers but do not carry independent prognostic information for clinical outcomes in the UK Biobank, a population-based cohort. Their prognostic importance may relate to cardiovascular disease cohorts where structural heart disease or ischemia may influence repolarization dynamics. The strong overlap between QT dynamics and resting QT interval loci suggests common biological pathways; however, nonoverlapping loci suggests alternative mechanisms may exist that underlie QT interval dynamics.

Analysing electrocardiographic traits and predicting cardiac risk in UK biobank

“…Using a different method for creation of a GRS with only genome-wide significant variants for QT response to exercise, gave no association with cardiovascular events. 7 This analysis had a similar sample size as the TMR response to recovery GRS, indicating that a GRS for TMR response to recovery may be useful to explore further for cardiovascular risk prediction.…”

“…Raw digital ECG recordings enabled detailed analysis of exercise related biomarkers. We focused on subjects without a history of known cardiovascular disease as this affects heart rate., Automatic quantification of the ECG indices was performed as follows: 4 – 7 Computation of signal-averaged ECG waveforms : Signal averaging is a standard and effective technique to reduce noise and artefacts from ECG recordings. ECG waveforms of successive heartbeats within short intervals of interest (e.g.…”

“…Using the UK Biobank exercise test cohort, we recently published a study of this marker. 7 Figure 2 illustrates how the measurements were made. QT dynamics was not a significant independent predictor of cardiovascular risk, suggesting its prognostic importance may relate to individuals with an underlying arrhythmogenic substrate, such as those with existing ischaemic heart disease.…”

Interaction Between ECG and Genetic Markers of Coronary Artery Disease

“…The integration of ECG and genetic markers into a combined score could also be potentially used to study the evolution and the mechanisms of CAD risk (genotypephenotype interactions) [12]. In addition, recent publications on the genetics of ECG markers of cardiovascular risk have reported GRSs for these ECG risk markers [5,9,13,14]. However, the role that these GRSs play when integrated into combined risk scores with independent clinical and non-invasive cardiac markers remains to be elucidated.…”

Bivariate Entropy Analysis of Electrocardiographic RR–QT Time Series

“…Representing ventricular repolarization lability, the beat-to-beat QT interval variability (QTV) in the electrocardiogram (ECG) is an accepted biomarker for the risk of ventricular arrhythmias and cardiac mortality [ 1 , 2 , 3 , 4 ]. On one hand, the autonomic nervous system is believed to exert direct influence on the depolarization and repolarization of the ventricle [ 5 , 6 ], making the QTV not simply a substitute of heart rate variability (HRV), a well-established indicator of cardiovascular health [ 7 , 8 , 9 , 10 ].…”