Abstract 18382: Clinical and Genetic Associations of Deep-Learning Estimated Peak Oxygen Consumption From the Resting 12-Lead Electrocardiogram

Abstract

Introduction: Oxygen consumption at peak exercise (VO2 peak ) is the gold standard for cardiorespiratory fitness, but prior genome-wide association studies (GWAS) have been limited by the availability of cardiopulmonary exercise testing (CPET). Recent deep learning methods to estimate VO2 peak from the resting 12-lead electrocardiogram (ECG) may enhance genetic studies of cardiorespiratory fitness. Methods: We applied a validated deep learning model (Deep ECG-VO2) to estimate VO2 peak among UK Biobank participants with a 12-lead ECG. We assessed for associations between estimated VO2 peak and incident hypertension, diabetes, and atrial fibrillation (AF) using Cox proportional hazards models adjusted for age and sex, and plotted cumulative risk of each outcome stratified by tertile of estimated VO2 peak . We then performed a multi-ancestry GWAS of estimated VO2 peak using BOLT-LMM, adjusted for age, sex, array, and the first five principal components of ancestry. Candidate genes were prioritized based on proximity to the lead variant. Results: We applied Deep ECG-VO2 to estimate VO2 peak using the resting 12-lead ECG of 40,801 UK Biobank participants (age 65±8, 52% women). Greater estimated VO2 peak was associated with lower risks of hypertension (hazard ratio per 1-standard deviation 0.76, 95% CI 0.70-0.83), diabetes (0.60, 95% CI 0.53-0.67) and AF (0.82, 95% CI 0.74-0.90). Cumulative risk of each outcome was higher with decreasing estimated VO2 peak ( Figure ). In a GWAS of estimated VO2 peak within 39,716 participants with genetic data (age 65±8, 52% women, 90% European), we identified 10 novel genome-wide significant loci, including variants near genes involved in cardiac structure ( CCDC141/TTN, BAG3) , cardiac conduction ( SCN5A ), and adiposity ( FTO ). Conclusions: Leveraging artificial intelligence-enabled estimation of VO2 peak from the resting 12-lead ECG, we identify 10 novel common genetic variants associated with cardiorespiratory fitness.