Background: As the largest conduit vessel, the aorta is responsible for the conversion of phasic systolic inflow from ventricular ejection into more continuous peripheral blood delivery. Systolic distention and diastolic recoil conserve energy and are enabled by the specialized composition of the aortic extracellular matrix. Aortic distensibility decreases with age and vascular disease.
Objectives: In this study, we sought to discover epidemiologic correlates and genetic determinants of aortic distensibility and strain.
Methods: We trained a deep learning model to quantify thoracic aortic area throughout the cardiac cycle from cardiac magnetic resonance images and calculated aortic distensibility and strain in 42,342 UK Biobank participants.
Results: Descending aortic distensibility was inversely associated with future incidence of cardiovascular diseases, such as stroke (HR: 0.59 per SD; P = 0.00031). The heritabilities of aortic distensibility and strain were 22% to 25% and 30% to 33%, respectively. Common variant analyses identified 12 and 26 loci for ascending and 11 and 21 loci for descending aortic distensibility and strain, respectively. Of the newly identified loci, 22 were not significantly associated with thoracic aortic diameter. Nearby genes were involved in elastogenesis and atherosclerosis. Aortic strain and distensibility polygenic scores had modest effect sizes for predicting cardiovascular outcomes (delaying or accelerating disease onset by 2%-18% per SD change in scores) and remained statistically significant predictors after accounting for aortic diameter polygenic scores.
Conclusions: Genetic determinants of aortic function influence risk for stroke and coronary artery disease and may lead to novel targets for medical intervention.
Keywords: aorta; cardiovascular disease; deep learning; distensibility; genetics; strain.
Copyright © 2023 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.