Key result
A two-stage genome-wide association study identified 8 common genetic variants in 3 genes or intergenic regions associated with perioperative myocardial infarction (p<10(-5)).
Why the study?
Are specific genetic variants associated with perioperative myocardial infarction in patients undergoing coronary artery bypass grafting?
Case-Control (n=2,055)
Yes
Are specific genetic variants associated with perioperative myocardial infarction in patients undergoing coronary artery bypass grafting?
p-value: p=<10(-5)
A two-stage GWAS identified several potential susceptibility loci for perioperative MI after CABG, highlighting pathways related to extracellular matrix remodeling and inflammation.
These variants should not yet inform CABG risk models or management; hypothesis-generating pending replication and functional validation.
OBJECTIVES: Identification of patient subpopulations susceptible to develop myocardial infarction (MI) or, conversely, those displaying either intrinsic cardioprotective phenotypes or highly responsive to protective interventions remain high-priority knowledge gaps. We sought to identify novel common genetic variants associated with perioperative MI in patients undergoing coronary artery bypass grafting using genome-wide association methodology. SETTING: 107 secondary and tertiary cardiac surgery centres across the USA. PARTICIPANTS: We conducted a stage I genome-wide association study (GWAS) in 1433 ethnically diverse patients of both genders (112 cases/1321 controls) from the Genetics of Myocardial Adverse Outcomes and Graft Failure (GeneMAGIC) study, and a stage II analysis in an expanded population of 2055 patients (225 cases/1830 controls) combined from the GeneMAGIC and Duke Perioperative Genetics and Safety Outcomes (PEGASUS) studies. Patients undergoing primary non-emergent coronary bypass grafting were included. PRIMARY AND SECONDARY OUTCOME MEASURES: The primary outcome variable was perioperative MI, defined as creatine kinase MB isoenzyme (CK-MB) values ≥10× upper limit of normal during the first postoperative day, and not attributable to preoperative MI. Secondary outcomes included postoperative CK-MB as a quantitative trait, or a dichotomised phenotype based on extreme quartiles of the CK-MB distribution. RESULTS: Following quality control and adjustment for clinical covariates, we identified 521 single nucleotide polymorphisms in the stage I GWAS analysis. Among these, 8 common variants in 3 genes or intergenic regions met p<10(-5) in stage II. A secondary analysis using CK-MB as a quantitative trait (minimum p=1.26×10(-3) for rs609418), or a dichotomised phenotype based on extreme CK-MB values (minimum p=7.72×10(-6) for rs4834703) supported these findings. Pathway analysis revealed that genes harbouring top-scoring variants cluster in pathways of biological relevance to extracellular matrix remodelling, endoplasmic reticulum-to-Golgi transport and inflammation. CONCLUSIONS: Using a two-stage GWAS and pathway analysis, we identified and prioritised several potential susceptibility loci for perioperative MI.
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Kertai et al. (2015) conducted a case-control in Perioperative myocardial infarction (n=2,055). Common genetic variants was evaluated on Perioperative MI, defined as creatine kinase MB isoenzyme (CK-MB) values ≥10× upper limit of normal during the first postoperative day, and not attributable to preoperative MI (p=<10(-5)). A two-stage genome-wide association study identified 8 common genetic variants in 3 genes or intergenic regions associated with perioperative myocardial infarction (p<10(-5)).