The overall goal of this application is to apply genomics to risk assessment in acute myocardial infarction (MI). Acute MI is triggered by atherosclerotic plaque rupture and subsequent coronary thrombosis. Functional polymorphisms in genes encoding platelet adhesion molecules, coagulation factors, and fibrinolytic proteins have been identified. These polymorphisms have been associated with an increased risk of MI. However, the functional and clinical significance of these potentially prothrombotic polymorphisms in the setting of an acute MI remains unexplored. Our hypothesis is that in acute MI, prothrombotic polymorphisms will alter the balance between thrombosis and fibrinolysis, impair resolution of coronary thrombi, and thereby affect responses to pharmacologic reperfusion therapy and outcomes. We will test this hypothesis in CLARITY-TIMI 28, a phase Ill clinical trial evaluating clopidogrel in 2200 patients undergoing thrombolysis for acute Ml.
Our specific aims are to: (1) determine the associations between prespecified polymorphisms and thrombosis-related outcomes including platelet activation, hemostatic markers of coagulation and fibrinolysis, ST segment resolution, infarct-related artery patency, and clinical ischemic events; (2) determine whether significant gene-environment interactions exist between prothrombotic polymorphisms and clinical cardiovascular risk factors; and (3) determine whether significant pharmacogenomic interactions exist between prothrombotic polymorphisms and antithrombotic therapies. Baseline blood samples will be obtained in patients for DNA extraction. Known polymorphisms in genes involved in hemostasis will be genotyped by the Harvard-Partners Genotyping Facility. As part of the parent trial, platelet function studies, ECGs, and coronary angiography will be performed, and patients will be followed for 30 days for death and recurrent ischemic events. In addition, for this proposed ancillary study, hemostatic parameters will be measured at baseline and prior to angiography. Crude associations between genotypes and outcomes will be assessed using (2 tests and ANOVA. Relevant baseline clinical characteristics will be adjusted for in multivariable regression analyses using an additive genetic model. Genomic control will be employed to detect and control for population stratification. Interaction terms will be incorporated into regression models to explore gene-environment and pharmacogenomic interactions.
