Design of a randomized controlled trial of disclosing genomic risk of coronary heart disease: the Myocardial Infarction Genes (MI-GENES) study

BMC Medical Genomics

Kent R. Bailey¹  Victor M. Montori¹  Janet E. Olson¹  Hayan Jouni¹  Iftikhar J. Kullo¹ 

[1] From the Division of Cardiovascular Diseases, Department of Medicine (IJK, HJ), Department of Health Sciences Research (JEO, KRB), Knowledge and Evaluation Research Unit (VMM), Mayo Clinic, 200 First Street SW, Rochester 55905, MN, USA

关键词: Personalized medicine;    Genetic testing;    Genetic risk;    Genetics;    Disclosure;    Coronary heart disease;    Design;    Clinical trial;   

Others  :  1222988
DOI  :  10.1186/s12920-015-0122-0

 received in 2015-06-21, accepted in 2015-07-15,  发布年份 2015

【 摘 要 】

Background

Whether disclosure of a genetic risk score (GRS) for a common disease influences relevant clinical outcomes is unknown. We describe design of the Myocardial Infarction Genes (MI-GENES) Study, a randomized clinical trial to assess whether disclosing a GRS for coronary heart disease (CHD) leads to lowering of low-density lipoprotein cholesterol (LDL-C) levels.

Methods and design

We performed an initial screening genotyping of 28 CHD susceptibility single-nucleotide polymorphisms (SNPs) that are not associated with blood pressure or lipid levels, in 1000 individuals from Olmsted County, Minnesota who were participants in the Mayo Clinic BioBank and met eligibility criteria. We calculated GRS based on 28 SNPs and will enroll 110 patients each in two CHD genomic risk categories: high (GRS ≥1.1), and average/low (GRS <1.1). The study coordinator will obtain informed consent for the study that includes placing genetic testing results in the electronic health record. Participants will undergo a blood draw and return 6-10 weeks later (Visit 2) once genotyping is completed and a GRS calculated. At this visit, patients will be randomized (1:1) to receive CHD risk estimates from a genetic counselor based on a conventional risk score (CRS) vs. GRS, followed by shared decision making with a physician regarding statin use. Three and six months following the disclosure of CHD risk, participants will return for measurement of fasting lipid levels and assessment of changes in dietary fat intake and physical activity levels. Psychosocial measures will be assessed at baseline and after disclosure of CHD risk.

Discussion

The proposed trial will provide insights into the clinical utility of genetic testing for CHD risk assessment.

Clinical trial registration

ClinicalTrials.gov registration number: NCT01936675.

【 授权许可】

   
2015 Kullo et al.

【 图 表 】

Fig. 1.

【 参考文献 】

• [1]Manolio TA: Bringing genome-wide association findings into clinical use. Nat Rev Genet 2013, 14:549-58.
• [2]The Wellcome Trust Case Control Consortium: Genome-wide association study of 14,000 cases of seven common diseases and 3000 shared controls Nature 2007, 447:661-78.
• [3]Deloukas P, Kanoni S, Willenborg C, Farrall M, Assimes TL, Thompson JR, et al.: Large-scale association analysis identifies new risk loci for coronary artery disease. Nat Genet 2013, 45:25-33.
• [4]The Coronary Artery Disease (C4D) Genetics Consortium: A genome-wide association study in Europeans and south Asians identifies five new loci for coronary artery disease Nat Genet 2011, 43:339-344.
• [5]Morrison AC, Bare LA, Chambless LE, Ellis SG, Malloy M, Kane JP, et al.: Prediction of coronary heart disease risk using a genetic risk score: The atherosclerosis risk in communities study. Am J Epidemiol 2007, 166:28-35.
• [6]Ripatti S, Tikkanen E, Orho-Melander M, Havulinna AS, Silander K, Sharma A, et al.: A multilocus genetic risk score for coronary heart disease: Case-control and prospective cohort analyses. Lancet 2010, 376:1393-400.
• [7]Wilson PW, D'Agostino RB, Levy D, Belanger AM, Silbershatz H, Kannel WB: Prediction of coronary heart disease using risk factor categories. Circulation 1998, 97:1837-47.
• [8]Kullo IJ, Jarvik GP, Manolio TA, Williams MS, Roden DM: Leveraging the electronic health record to implement genomic medicine. Genet Med 2013, 15:270-1.
• [9]Goff DC Jr, Lloyd-Jones DM, Bennett G, Coady S, D'Agostino RB, Gibbons R, et al.: 2013 ACC/AHA guideline on the assessment of cardiovascular risk: A report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. Circulation 2014, 129:S49-73.
• [10]Weymiller AJ, Montori VM, Jones LA, Gafni A, Guyatt GH, Bryant SC, et al.: Helping patients with type 2 diabetes mellitus make treatment decisions: Statin choice randomized trial. Arch Intern Med 2007, 167:1076-82.
• [11]Ding K, Bailey KR, Kullo IJ: Genotype-informed estimation of risk of coronary heart disease based on genome-wide association data linked to the electronic medical record. BMC Cardiovasc Disord 2011, 11:66. BioMed Central Full Text
• [12]Kaphingst KA, McBride CM, Wade C, Alford SH, Reid R, Larson E, et al.: Patients’ understanding of and responses to multiplex genetic susceptibility test results. Genet Med 2012, 14:681-7.
• [13]Downs JR, Clearfield M, Weis S, Whitney E, Shapiro DR, Beere PA, et al.: Primary prevention of acute coronary events with lovastatin in men and women with average cholesterol levels: Results of AFCAPS/TEXCAPS. Air force/Texas Coronary Atherosclerosis Prevention Study. JAMA 1998, 279:1615-22.
• [14]Shepherd J, Cobbe SM, Ford I, Isles CG, Lorimer AR, MacFarlane PW, et al.: Prevention of coronary heart disease with pravastatin in men with hypercholesterolemia. West of scotland coronary prevention study group. N Engl J Med 1995, 333:1301-7.
• [15]Pocock SJ, Simon R: Sequential treatment assignment with balancing for prognostic factors in the controlled clinical trial. Biometrics 1975, 31:103-15.
• [16]Wyatt KD, Branda ME, Anderson RT, Pencille LJ, Montori VM, Hess EP, et al.: Peering into the black box: a meta-analysis of how clinicians use decision aids during clinical encounters. Implement Sci 2014, 9:26. BioMed Central Full Text
• [17]Elwyn G, Hutchings H, Edwards A, Rapport F, Wensing M, Cheung WY, et al.: The OPTION scale: Measuring the extent that clinicians involve patients in decision-making tasks. Health Expect 2005, 8:34-42.
• [18]Harris PA, Taylor R, Thielke R, Payne J, Gonzalez N, Conde JG: Research electronic data capture (REDCap)--a metadata-driven methodology and workflow process for providing translational research informatics support. J Biomed Inform 2009, 42:377-81.
• [19]Ding K, Kullo IJ: Genome-wide association studies for atherosclerotic vascular disease and its risk factors. Circ Cardiovasc Genet 2009, 2:63-72.
• [20]Kullo IJ, Cooper LT: Early identification of cardiovascular risk using genomics and proteomics. Nat Rev Cardiol 2010, 7:309-17.
• [21]Forrester JS: Redefining normal low-density lipoprotein cholesterol: a strategy to unseat coronary disease as the nation’s leading killer. J Am Coll Cardiol 2010, 56:630-6.
• [22]Lloyd-Jones DM: Cardiovascular risk prediction: basic concepts, current status, and future directions. Circulation 2010, 121:1768-77.
• [23]Knowles JW, Assimes TL, Kiernan M, Pavlovic A, Goldstein BA, Yank V, et al.: Randomized trial of personal genomics for preventive cardiology: Design and challenges. Circ Cardiovasc Genet 2012, 5:368-76.
• [24]Paulos JA: Innumeracy: Mathematical illiteracy and its consequences. Hill and Wang, New York; 1988.