Genetic variants associated with vein graft stenosis after coronary artery bypass grafting.

BACKGROUND: Vein graft stenosis after coronary artery bypass grafting (CABG) is common. Identifying genes associated with vein graft stenosis after CABG could reveal novel mechanisms of disease and discriminate patients at risk for graft failure. We hypothesized that genome-wide association would identify these genes. METHODS: We performed a genome-wide association study on a subset of patients presenting for cardiac catheterization for concern of ischemic heart disease, who also underwent CABG and subsequent coronary angiography after CABG for clinical indications (n = 521). Cases were defined as individuals with ≥50% stenosis in any vein graft on any cardiac catheterization, and controls were defined as those who did not have vein graft stenosis on any subsequent cardiac catheterization. Multivariable logistic regression was used to assess the association between single nucleotide polymorphisms (SNPs) and vein graft stenosis. RESULTS: Sixty-nine percent of patients had vein graft failure after CABG. Seven SNPs were significantly associated with vein graft stenosis, including intronic SNPs in the genes PALLD (Rs6854137, P = 3.77 × 10(-6)), ARID1B (Rs184074, P = 5.97 × 10(-6)), and TMEM123 (Rs11225247, P = 8.25 × 10(-6)); and intergenic SNPs near the genes ABCA13 (Rs10232860, P = 4.54 × 10(-6)), RMI2 (Rs9921338, P = 6.15 × 10(-6)), PRM2 (Rs7198849, P = 7.27 × 10(-6)), and TNFSF4 (Rs17346536, P = 9.33 × 10(-6)). CONCLUSIONS: We have identified novel genetic variants that may predispose to risk of vein graft failure after CABG, many within biologically plausible pathways. These polymorphisms merit further investigation, as they could assist in stratifying patients with multi-vessel coronary artery disease, which could lead to alterations in management and revascularization strategy.

Fine mapping of a linkage peak with integration of lipid traits identifies novel coronary artery disease genes on chromosome 5.

BACKGROUND: Coronary artery disease (CAD), and one of its intermediate risk factors, dyslipidemia, possess a demonstrable genetic component, although the genetic architecture is incompletely defined. We previously reported a linkage peak on chromosome 5q31-33 for early-onset CAD where the strength of evidence for linkage was increased in families with higher mean low density lipoprotein-cholesterol (LDL-C). Therefore, we sought to fine-map the peak using association mapping of LDL-C as an intermediate disease-related trait to further define the etiology of this linkage peak. The study populations consisted of 1908 individuals from the CATHGEN biorepository of patients undergoing cardiac catheterization; 254 families (N = 827 individuals) from the GENECARD familial study of early-onset CAD; and 162 aorta samples harvested from deceased donors. Linkage disequilibrium-tagged SNPs were selected with an average of one SNP per 20 kb for 126.6-160.2 MB (region of highest linkage) and less dense spacing (one SNP per 50 kb) for the flanking regions (117.7-126.6 and 160.2-167.5 MB) and genotyped on all samples using a custom Illumina array. Association analysis of each SNP with LDL-C was performed using multivariable linear regression (CATHGEN) and the quantitative trait transmission disequilibrium test (QTDT; GENECARD). SNPs associated with the intermediate quantitative trait, LDL-C, were then assessed for association with CAD (i.e., a qualitative phenotype) using linkage and association in the presence of linkage (APL; GENECARD) and logistic regression (CATHGEN and aortas). RESULTS: We identified four genes with SNPs that showed the strongest and most consistent associations with LDL-C and CAD: EBF1, PPP2R2B, SPOCK1, and PRELID2. The most significant results for association of SNPs with LDL-C were: EBF1, rs6865969, p = 0.01; PPP2R2B, rs2125443, p = 0.005; SPOCK1, rs17600115, p = 0.003; and PRELID2, rs10074645, p = 0.0002). The most significant results for CAD were EBF1, rs6865969, p = 0.007; PPP2R2B, rs7736604, p = 0.0003; SPOCK1, rs17170899, p = 0.004; and PRELID2, rs7713855, p = 0.003. CONCLUSION: Using an intermediate disease-related quantitative trait of LDL-C we have identified four novel CAD genes, EBF1, PRELID2, SPOCK1, and PPP2R2B. These four genes should be further examined in future functional studies as candidate susceptibility loci for cardiovascular disease mediated through LDL-cholesterol pathways.

Metabolic profiles predict adverse events after coronary artery bypass grafting.

OBJECTIVE: Clinical models incompletely predict the outcomes after coronary artery bypass grafting. Novel molecular technologies can identify biomarkers to improve risk stratification. We examined whether metabolic profiles can predict adverse events in patients undergoing coronary artery bypass grafting. METHODS: The study population comprised 478 subjects from the CATHGEN biorepository of patients referred for cardiac catheterization who underwent coronary artery bypass grafting after enrollment. Targeted mass spectrometry-based profiling of 69 metabolites was performed in frozen, fasting plasma samples collected before surgery. Principal components analysis and Cox proportional hazards regression modeling were used to assess the relation between the metabolite factor levels and a composite outcome of postcoronary artery bypass grafting myocardial infarction, the need for percutaneous coronary intervention, repeat coronary artery bypass grafting, and death. RESULTS: During a mean follow-up period of 4.3 ± 2.4 years, 126 subjects (26.4%) experienced an adverse event. Three principal components analysis-derived factors were significantly associated with an adverse outcome on univariate analysis: short-chain dicarboxylacylcarnitines (factor 2, P = .001); ketone-related metabolites (factor 5, P = .02); and short-chain acylcarnitines (factor 6, P = .004). These 3 factors remained independently predictive of an adverse outcome after multivariate adjustment: factor 2 (adjusted hazard ratio, 1.23; 95% confidence interval, 1.10-1.38; P < .001), factor 5 (odds ratio, 1.17; 95% confidence interval, 1.01-1.37; P = .04), and factor 6 (odds ratio, 1.14; 95% confidence interval, 1.02-1.27; P = .03). CONCLUSIONS: Metabolic profiles are independently associated with adverse outcomes after coronary artery bypass grafting. These profiles might represent novel biomarkers of risk that can augment existing tools for risk stratification of coronary artery bypass grafting patients and might elucidate novel biochemical pathways that mediate risk.