Genome-wide insights into severity and progression of CCTA-defined coronary disease phenotypes in HURRICANE study
M Zanfardino, C C Carlo Cavaliere, M O Mariaelena Occhipinti, E P Emanuela Passaro, S R Silvia Rocchiccioli, A M Antonio Morlando, G S Ginevra Sarnacchiaro, R R Rosetta Ragusa, A C Alberto Clemente, F C Filippo Cademartiri, A G Alessia Gimelli, C C Chiara Caselli, M F Monica Franzese, D N Danilo Neglia, K P Katia PaneAbstract
Background
Atherosclerotic coronary artery disease (CAD) remains a leading cause of cardiovascular mortality worldwide. In chronic coronary syndromes, the presence and extent of CAD assessed by computed tomography angiography (CTA) stratify risk and guide clinical decision making. Genome-Wide association studies (GWAS) have identified thousands of CAD-associated loci, yet the genetic architecture underlying imaging-defined CAD risk phenotypes remains largely unexplored.
Purpose
We aimed to discover genetic features that can subtend different CAD phenotypes and risk profiles assessed by advanced CTA imaging in patients with CCS.
Methods
Among 561 patients enrolled in the HURRICANE study (Health Improvement by Understanding RR In CAD and NEw targets for treatment), we analyzed data from 480 patients (mean age 64.9±10.6 years, 69.4% male) with both high-quality CCTA imaging and genotyping array. CAD phenotypes were stratified in end-points (EP) for:1. obstruction (EP1) (CAD-RADS2.0 Score 0/absent, 1-2/non-obstructive, 3-4-5obstructive); 2 extension (EP2) (P score 1-2/less extensive; 3-4/more extensive); 3. integrated risk(EP3 (Leiden Score <5/low, 5-20/intermediate, >20/high). We performed downstream analysis, based on SNP and gene levels, pathway-based approaches and regression models adjusted for age and sex. Effect size correlations between endpoints were analyzed to distinguish susceptibility from severity genetic mechanisms.
Results
Our genotyping analysis identified 1095 SNPs associated with EP1, 963 with EP2, and 546 with EP3 (p<0.05). Venn diagram analysis revealed an overlap of 107 SNPs shared between EP2 andEP3), overlap of 36 SNPs shared between EP1 and EP3, while 411 SNPs were unique to EP3. Gene-level analysis identified distinct genes across endpoints with EP3 which shares 5 genes with EP1 and 7 genes with EP2, while 30 genes were unique to EP3. Among the EP1 and EP3 genes, we found the AKT serine/threonine kinase 3 (EP1∩EP3), involved in a wide variety of biological processes including angiogenesis and some members of the ATP-binding cassette subfamily 3 (EP3-only). Pearson correlation analysis between EP1 and EP3-associated genes were of r=0.3 (p<0.05), and of r=0.68 (p<0.05) between EP2 and EP3-associated genes. Notably, EP1-associated genes show a weaker association with the integrated risk compared to the EP2-associated genes, demonstrating that genetic variants linked to integrated CAD Risk and influencing obstruction operate through partially distinct biological pathways compared to those underlying extensive CAD.
Conclusions
Our findings demonstrate that the correlation of genetic effects between CAD extension and Leiden Score suggests common mechanisms subtending atherosclerotic disease and associated risk, while the correlation with stenosis severity might imply that the luminal obstruction involve partially independent genetic determinants.Figure