A new study by researchers at Queen Mary University of London and University College London (UCL) has discovered 30 new gene locations that determine how the heart responds to and recovers from exercise
The study, published in the journal Nature Communications, was conducted using the genetic and electrocardiogram data of 67,000 people from UK Biobank.
The findings could be used to improve the identification of people with impaired heart rate during recovery and those at higher risk of heart disease mortality.
Increased resting heart rate (HR) has been demonstrated to be an independent risk factor for cardiovascular mortality even in healthy individuals. The heritability of resting HR is estimated to be 26–32% from family studies, and 55–63% in twin studies.
Consequently, genetic association studies have been undertaken to detect genetic determinants of resting HR. Seventy-three loci have been identified to date and a recent study including 64 loci that were robustly validated accounted for 2.5% of the trait variance.
The difference in heart rate response to exercise was as much as 3.15 beats per minute, depending on the genetic risk score of an individual, while the difference in heart rate response to recovery differed by as much as 10.4 beats per minute.
Lead researcher Patricia Munroe, Professor of Molecular Medicine at Queen Mary's William Harvey Research Institute said: "Our findings advance our knowledge on key pathways controlling heart rate response to exercise and recovery, information which may be valuable in the future for cardiovascular risk prediction."
Co-lead researcher Pier Lambiase, Professor of Cardiology at UCL said: "This first study by our "Electrodynamics" group is a wonderful example of the power of the collaboration between UCL Electrophysiology & QMUL Genomics, opening new avenues to dissect the mechanistic links between heart control and cardiovascular outcomes."
This study systematically investigates the genetic basis of HR response to exercise and recovery using a robust framework including independent discovery and validation samples. Dense HRC imputation yielded a high-quality data set including ~7.8 million variants at minor allele frequencies (MAF)>1% for testing in ~67,000 individuals.
The reliability of the phenotypes was examined by analysing raw-ECG recordings to identify and exclude any unreliable automated HR phenotypes before applying genetic analysis.
In total, 30 loci are discovered, eight being common across traits. Processes of neural development and modulation of adrenergic activity by the autonomic nervous system are enriched in these results. Our findings reinforce current understanding of HR response to exercise and recovery and could guide future studies evaluating its contribution to cardiovascular risk prediction.
The results have implications to target new therapies to treat abnormal heart rhythms and potentially increase heart health.