Abstract

Introduction: Pulmonary artery (PA) hemodynamics are important predictors of mortality. However, genetic mechanisms underlying increased PA stiffness in diseases such as pulmonary hypertension are poorly understood, and targeted therapies are limited. Evaluation of PA hemodynamics frequently requires invasive right heart catheterization, limiting large-scale studies. This study leveraged cardiac magnetic resonance (CMR) imaging to non-invasively determine and characterize PA pulsatility, an established measure of pulmonary vascular compliance. Methods and Results: A deep-learning neural network was implemented to determine PA pulsatility, defined as the relative cross-sectional area change from diastole to systole, in 42,774 UK Biobank participants (intraclass correlation 0.94-0.96 compared with human readers). The mean (SD) PA pulsatility was 0.36 (0.11), and pulsatility decreased by 0.035 for every decade increase in age. Reduction in pulsatility by 0.1 was associated with the presence of congestive heart failure (OR 1.55 [95% CI 1.37-⁠1.76]), chronic obstructive pulmonary disease (OR 1.41 [1.29-⁠1.53]), or diabetes (OR 1.18 [1.13-⁠1.23]), and PA pulsatility was associated with increased all-cause mortality (HR 1.13 [1.03-1.23]) after adjusting for age and sex and excluding those with CHF or COPD (Fig A-B). A genome-wide association study of 40,496 participants identified loci on chromosomes 2 (top SNP: rs6738973) and 10 (rs2077218) associated with PA pulsatility at genome-wide significance ( p < 5 х 10 –8 ; Fig C). Integrative fine-mapping analyses identified PKDCC and PLCE1 among likely effector genes. Conclusions: This is the first population-based epidemiologic and genomics study of PA pulsatility. Decreased PA pulsatility is associated with advanced age, increased rates of comorbidities, and higher mortality. The genes PKDCC and PLCE1 are among likely effectors of PA pulsatility and warrant further functional investigation.

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