Recovery from myocardial infarction (MI) is associated with a host of symptoms, including angina, depression, and worse quality of life (QoL) but little attention has been paid to these patient-centered health status outcomes. More than 10 million people in the US suffer from angina and approximately 500,000 new cases occur each year at an estimated cost of $20 billion dollars annually. We propose to identify genomic variants that contribute to inter-individual variation in post-MI angina and health status outcomes by using the TRIUMPH population, an NIH-funded cohort with exquisite disease-specific health status assessments at admission, and 1-month, 6-months and 1-year post-MI, along with adjudicated 1-year major adverse cardiovascular events and 5-year mortality. The study group is particularly well-qualified to perform this research, having expertise in genomics, pharmacogenomics, patient screening and risk profiling, outcomes research, and statistical genomics. We will also take advantage of the strengths of Washington University's CTSA, including its Cores and programs. Collectively, we will address the following Aims:
AIM 1. To define the genetic contribution to the observed inter-individual variation in post-MI angina. The primary outcome will be post-MI angina over the first year, as measured by the well-validated, disease-specific Seattle Angina Questionnaire (SAQ) Angina Frequency score. Secondary outcomes are SAQ QoL score and depressive symptoms, as measured by PHQ-9. An unbiased GWAS approach will identify common genetic variants associated with these outcomes using two novel statistical genomic methods (Growth Curve Estimation and Pleiotropy). We will then use a novel, cost-efficient (multi-plexed, 'bar-coded') exomic sequencing method to finely map all exons in the genes under the association peaks and identify rare variants that are associated with these outcomes.
AIM 2. To identify non-genomic factors that may potentially moderate the effects of the genetic variants identified in AIM 1. We will construct multivariable models that include genetic, clinical and treatment characteristics, with a specific focus upon interactions. These models, especially if important interactions with treatment are discovered, can serve as the foundation for estimating symptom outcomes as a function of treatment and, using these models, we can generate personalized treatment strategies.
AIM 3. To test the feasibility of translating - into 'real world practice'- a prognostic modeling tool (PRISMTM) that includes genetic variants identified by AIM 1, to predict health status response to post-MI treatment. Our team has developed information technology with which to implement multivariable prediction models, executed with patient-specific data, in the process of clinical care. We will use these models to create a personalized risk profile and therapeutic strategy for post-MI treatment. In summary, we will use cutting edge experimental, statistical, and diagnostic methods to identify variants associated with post-MI angina and other health status outcomes and lay the foundation to personalize post-MI care and reduce symptom burden.

Public Health Relevance

Sequence differences in genes may influence an individual's response to chest pain (angina) after a heart attack. This study proposes identifying specific gene sequence differences that predict more or less chest pain after heart attack and to determine how to best translate the findings into medical practice. The ultimate goal of this proposal is to advance personalized care for adults with coronary artery disease who have had a heart attack.

National Institute of Health (NIH)
National Institute of Nursing Research (NINR)
Research Project (R01)
Project #
Application #
Study Section
Special Emphasis Panel (ZNR1-REV-M (09))
Program Officer
Tully, Lois
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Washington University
Internal Medicine/Medicine
Schools of Medicine
Saint Louis
United States
Zip Code
Doll, Jacob A; Tang, Fengming; Cresci, Sharon et al. (2016) Change in Angina Symptom Status After Acute Myocardial Infarction and Its Association With Readmission Risk: An Analysis of the Translational Research Investigating Underlying Disparities in Acute Myocardial Infarction Patients' Health Status (TRIUMPH) Re J Am Heart Assoc 5:
Labos, Christopher; Martinez, Sara C; Leo Wang, Rui Hao et al. (2015) Utility of a genetic risk score to predict recurrent cardiovascular events 1 year after an acute coronary syndrome: A pooled analysis of the RISCA, PRAXY, and TRIUMPH cohorts. Atherosclerosis 242:261-7
Do, Ron; Stitziel, Nathan O; Won, Hong-Hee et al. (2015) Exome sequencing identifies rare LDLR and APOA5 alleles conferring risk for myocardial infarction. Nature 518:102-6
Depta, J P; Lenzini, P A; Lanfear, D E et al. (2015) Clinical outcomes associated with proton pump inhibitor use among clopidogrel-treated patients within CYP2C19 genotype groups following acute myocardial infarction. Pharmacogenomics J 15:20-5
Buchanan, Donna M; Arnold, Suzanne V; Gosch, Kensey L et al. (2015) Association of Smoking Status With Angina and Health-Related Quality of Life After Acute Myocardial Infarction. Circ Cardiovasc Qual Outcomes 8:493-500
Arnold, Suzanne V; Spertus, John A; Lipska, Kasia J et al. (2015) Association between diabetes mellitus and angina after acute myocardial infarction: analysis of the TRIUMPH prospective cohort study. Eur J Prev Cardiol 22:779-87
Chen, Li-Shiun; Bach, Richard G; Lenzini, Petra A et al. (2014) CHRNA5 variant predicts smoking cessation in patients with acute myocardial infarction. Nicotine Tob Res 16:1224-31
Myocardial Infarction Genetics Consortium Investigators; Stitziel, Nathan O; Won, Hong-Hee et al. (2014) Inactivating mutations in NPC1L1 and protection from coronary heart disease. N Engl J Med 371:2072-82
Cresci, Sharon; Depta, Jeremiah P; Lenzini, Petra A et al. (2014) Cytochrome p450 gene variants, race, and mortality among clopidogrel-treated patients after acute myocardial infarction. Circ Cardiovasc Genet 7:277-86
Karrowni, Wassef; Li, Yan; Jones, Philip G et al. (2013) Insulin resistance is associated with significant clinical atherosclerosis in nondiabetic patients with acute myocardial infarction. Arterioscler Thromb Vasc Biol 33:2245-51

Showing the most recent 10 out of 12 publications