Abstract TH972: Pulmonary Artery Pulsatility Measured by Cardiac Magnetic Resonance Imaging in Over 42,000 Participants Reveals Novel Genetic Risk Loci

Abstract

Background: Pulmonary artery (PA) hemodynamics are predictors of mortality, yet 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. Our investigation leveraged cardiac magnetic resonance (CMR) imaging to non-invasively determine and characterize PA pulsatility, an established measure of pulmonary vascular compliance. Methods: A deep learning model measured CMR-derived PA pulsatility in 42,774 UK Biobank participants. Associations of PA pulsatility with demographics and comorbidities were assessed using multivariate logistic regression models, and its association with all-cause mortality was evaluated using a multivariate Cox proportional hazards model. A genome-wide association study (GWAS) was performed, followed by genomic and in vitro functional characterization of candidate effector genes. Results: The mean (SD) PA pulsatility was 0.36 (0.11), and pulsatility decreased by 0.035 for every decade increase in age. We observed an inverse association between PA pulsatility and odds of congestive heart failure (odds ratio [OR] 1.55; 95% CI 1.37-1.76), chronic obstructive pulmonary disease (OR 1.41; 95% CI 1.29-1.53), diabetes (OR 1.18; 95% CI 1.13-1.23), and age- and sex-adjusted mortality (hazard ratio [HR] 1.13; 95% CI 1.03-1.23). A genome-wide association study of 40,496 participants identified loci on chromosome 2 (top SNP: rs6738973) and chromosome 10 (rs2077218) associated with PA pulsatility at genome-wide significance (p < 5x10 -8 ). Multiple lines of evidence from integrative fine-mapping analyses identified PKDCC and PLCE1 among likely effector genes. Using mass spectrometry, differential binding on electrophoretic mobility shift assays of two factors, NONO and SFPQ, at the lead loci were identified and shown to regulate downstream expression of PKDCC and PLCE1. Conclusions: This is the first large-scale, population-based epidemiologic and genomics study of PA pulsatility. Lower PA pulsatility is associated with older age, higher rates of comorbidities, and higher mortality odds. The genes PKDCC and PLCE1 are among likely effectors of PA pulsatility and warrant further functional characterization.