👤 Jason C Kovacic

Coronary artery disease (CAD) is a complex, heterogeneous disease with distinct etiological mechanisms. These different etiologies may give rise to multiple subtypes of CAD that could benefit from alternative preventions and treatments. However, so far, there have been no systematic efforts to predict CAD subtypes using clinical and genetic factors. Here, we trained and applied statistical models incorporating clinical and genetic factors to predict CAD subtypes in 26 036 patients with CAD in the UK Biobank. We performed external validation of the UK Biobank models in the US-based All of Us cohort (8598 patients with CAD). Subtypes were defined as high versus normal LDL (low-density lipoprotein) levels, high versus normal Lpa (lipoprotein A) levels, ST-segment-elevation myocardial infarction versus non-ST-segment-elevation myocardial infarction, occlusive versus nonocclusive CAD, and stable versus unstable CAD. Clinical predictors included levels of ApoA, ApoB, HDL (high-density lipoprotein), triglycerides, and CRP (C-reactive protein). Genetic predictors were genome-wide and pathway-based polygenic risk scores (PRSs). Results showed that both clinical-only and genetic-only models can predict CAD subtypes, while combining clinical and genetic factors leads to greater predictive accuracy. Pathway-based PRSs had higher discriminatory power than genome-wide PRSs for the Lpa and LDL subtypes and provided insights into their etiologies. The 10-pathway PRS most predictive of the LDL subtype involved cholesterol metabolism. Pathway PRS models had poor generalizability to the All of Us cohort. In summary, we present the first systematic demonstration that CAD subtypes can be distinguished by clinical and genomic risk factors, which could have important implications for stratified cardiovascular medicine. Show less

Genetic loss-of-function variants (LoFs) associated with disease traits are increasingly recognized as critical evidence for the selection of therapeutic targets. We integrated the analysis of genetic and clinical data from 10,511 individuals in the Mount Sinai BioMe Biobank to identify genes with loss-of-function variants (LoFs) significantly associated with cardiovascular disease (CVD) traits, and used RNA-sequence data of seven metabolic and vascular tissues isolated from 600 CVD patients in the Stockholm-Tartu Atherosclerosis Reverse Network Engineering Task (STARNET) study for validation. We also carried out in vitro functional studies of several candidate genes, and in vivo studies of one gene. We identified LoFs in 433 genes significantly associated with at least one of 10 major CVD traits. Next, we used RNA-sequence data from the STARNET study to validate 115 of the 433 LoF harboring-genes in that their expression levels were concordantly associated with corresponding CVD traits. Together with the documented hepatic lipid-lowering gene, APOC3, the expression levels of six additional liver LoF-genes were positively associated with levels of plasma lipids in STARNET. Candidate LoF-genes were subjected to gene silencing in HepG2 cells with marked overall effects on cellular LDLR, levels of triglycerides and on secreted APOB100 and PCSK9. In addition, we identified novel LoFs in DGAT2 associated with lower plasma cholesterol and glucose levels in BioMe that were also confirmed in STARNET, and showed a selective DGAT2-inhibitor in C57BL/6 mice not only significantly lowered fasting glucose levels but also affected body weight. In sum, by integrating genetic and electronic medical record data, and leveraging one of the world's largest human RNA-sequence datasets (STARNET), we identified known and novel CVD-trait related genes that may serve as targets for CVD therapeutics and as such merit further investigation. Show less

Despite extensive evidence demonstrating the beneficial effects of statins on clinical outcomes, the mechanisms underlying these effects remain elusive. This study assessed changes in plaque morphology using intravascular imaging, with a comprehensive evaluation of cholesterol efflux capacity (CEC) and peripheral blood mononuclear cell (PBMC) transcriptomics in patients receiving high-dose statin therapy. In a prospective study, 85 patients with stable coronary artery disease underwent percutaneous coronary intervention for a culprit lesion, followed by intracoronary multimodality imaging, including optical coherence tomography (OCT) of an obstructive nonculprit lesion. All subjects received 40 mg of rosuvastatin daily for 8 to 12 weeks, when the nonculprit lesion was reimaged and intervention performed. Blood samples were drawn at both times to assess CEC and transcriptomic profile in PBMC. Baseline OCT minimal fibrous cap thickness (FCT) was 100.9 ± 41.7 μm, which increased to 108.6 ± 39.6 μm at follow-up, and baseline CEC was 0.81 ± 0.14, which increased at follow-up to 0.84 ± 0.14 (p = 0.003). Thin-cap fibroatheroma prevalence decreased from 20.0% to 7.1% (p = 0.003). Changes in FCT were independently associated with CEC increase by multivariate analysis (β: 0.30; p = 0.01). PBMC microarray analysis detected 117 genes that were differentially expressed at follow-up compared to baseline, including genes playing key roles in cholesterol synthesis (SQLE), regulation of fatty acids unsaturation (FADS1), cellular cholesterol uptake (LDLR), efflux (ABCA1 and ABCG1), and inflammation (DHCR24). Weighted coexpression network analysis revealed unique clusters of genes associated with favorable FCT and CEC changes. The study demonstrated an independent association between fibrous cap thickening and improved CEC that may contribute to morphological changes suggesting plaque stabilization among patients taking intensive statin therapy. Furthermore, the significant perturbations in PBMC transcriptome may help determine the beneficial effects of statin on plaque stabilization. (Reduction in Coronary Yellow Plaque, Lipids and Vascular Inflammation by Aggressive Lipid Lowering [YELLOW II]; NCT01837823). Show less