For our next talk, in Digital Phenotyping, we will hear from Dr Stefan van Duijvenboden, Researcher in Health Data Science, Nuffield Department of Population Health, University of Oxford. We are delighted to host Stefan in what promises to be a great talk!
Title: Prognostic significance & genetic basis of premature ventricular contractions during exercise
Date: 8th May 2024
Time: 2:00 pm – 3:00 pm
Venue: Big Data Institute, Seminar Room 0; followed by refreshments in the atrium
Speaker(s): Dr Stefan van Duijvenboden, Researcher in Health Data Science, Nuffield Department of Population Health, University of Oxford
Abstract: The clinical consequences and underlying biology of exercise-induced premature ventricular contractions (PVCs) in asymptomatic individuals remain unclear. In the first part of this work, we trained a neural network to count PVCs from electrocardiogram recordings taken during exercise and recovery in 48 315 participants from UK Biobank without known cardiovascular disease. We show that different PVC burden during and after exercise are associated with increased risk for major adverse cardiovascular events independently of established clinical risk factors. To further investigate the underlying mechanisms, we conducted a genome-wide association study (GWAS) and identified 4 loci for PVC burden during exercise, and 1 locus for PVC burden during recovery. Candidate genes included CRIM1, FLNC, and BAG3 for which mutations have been linked in the pathogenesis of (arrhythmogenic) cardiomyopathy.
Bio: I was trained as a technical physician (University of Twente) and performed my PhD studies in biomedical engineering (UCL) where I studied the neural mechanisms of cardiac stability. I then joined the Electrogenomics Group (QMUL/UCL) where I studied the genetic architecture of exercise ECG traits under supervision of Profs Munroe, Tinker, and Lambiase. Currently, I am working as a researcher in health data science within the Wearables group (prof. Doherty) at the Big Data Institute and Nuffield Department of Population Health. I develop methods to analyse complex time-series datasets to investigate if wearable ECG sensors can improve the prediction of, and discovery of novel mechanisms for, cardiovascular disease.
Hybrid Option:
Please note that these meetings are closed meetings and only open to members of the University of Oxford. Please respect our speakers and do not share the link with anyone outside of the University. The purpose of these seminars is to foster more communication among employees throughout the University, so we strongly advise in-person attendance whenever feasible.
Microsoft Teams meeting
Meeting ID: 372 228 697 690
Passcode: LD3WgU
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