Personal Genomics for Preventive Cardiology

• change in weight [ Time Frame: 6 mo ] [ Designated as safety issue: No ]
• change in exercise [ Time Frame: 6 mo ] [ Designated as safety issue: No ]
• medication compliance [ Time Frame: 6 mo ] [ Designated as safety issue: No ]
• non-HDL cholesterol [ Time Frame: 6 mo ] [ Designated as safety issue: No ]
• blood pressure [ Time Frame: 6 mo ] [ Designated as safety issue: No ]

The purpose of this study is to see if providing information to a person on their inherited (genetic) risk of cardiovascular disease (CVD) helps to motivate that person to change their diet, lifestyle or medication regimen to alter their risk.

Genome wide association studies (GWAS) have identified over 1000 disease associated SNPs, including many related to cardiovascular disease (CVD). Associations have been found for most traditional risk factors including lipids, blood pressure /hypertension, weight/body mass index, smoking behavior, and diabetes. Importantly, GWAS have also identified susceptibility variants for coronary heart disease/ myocardial infarction (CHD/MI), many of which are independent of traditional risk factors and thus cannot currently be assessed by surrogate measures. The first, and so far the strongest, of these signals was found in the 9p21.3 locus and are associated with a 20-40% increase in the relative risk of coronary heart disease among Caucasian and East Asian populations. Like most of the associations identified to date, the function of the non-coding 9p21.3 chromosomal region remains unclear. These markers predict disease and can modesty improve reclassification indices. For instance, in a very recent example, 13 SNPs previously identified in GWAS as associated with CHD/MI were incorporated into a multilocus model to estimate the association of a genetic risk score with incident CHD/MI in several large prospective studies. Even after adjusting for family history and traditional risk factors, individuals in the top quintile were at 1.66 times increased risk compared with those at the bottom quintile 36. There was a significant improvement in reclassification of intermediate risk patients. The use of these markers has not yet been shown to outperform models including traditional risk factors and family history. This shortcoming is probably because the vast majority of heritable risk remains undiscovered. The basis for this heritability gap remains unclear but is the focus of intense investigation. Despite the heritability gap, it is still possible that the use of known genetic risk factors may improve patient outcomes. For instance, genetic testing can improve patient adherence and risk factor reduction for Mendelian forms of coronary disease like familial hypercholesterolemia (FH). However, for "garden variety" coronary disease, there has never been a clinical trial that indicates that using genetic markers improves outcomes. There are strong signals from the NIH, the US Preventive Services Task Force and other independent prevention centers that genetic screening will be highly scrutinized until such trials exist. Currently, both the Evaluation of Genomic Applications in Practice and Prevention (EGAPP) Working Group and the ACC/AHA Taskforce on Practice Guidelines recommend against genetic testing for coronary disease 39,40 because there is no clinical trial data supporting their use. Despite these recommendations, and lack of efficacy data, there are huge financial pressures to increase genetic testing by "direct-to-consumer" companies. In this context, there is a perfect opportunity to develop well-designed clinical trials to test these variants.

• standard of care plus genetic information
  Intervention: Behavioral: genetic risk score for coronary risk factors
• No Intervention: usual standard of care without genetic information

Inclusion Criteria:

• Adults age > 18
• Patient seeking cardiovascular risk evaluation
• At intermediate (6-20%) or high risk (> 20%) over 10 years of CAD as defined by Framingham 10 year risk score AND/OR at > 20% risk of CAD over 30 years using the Framingham 30 year risk calculator
• The genetic risk factors have been evaluated predominantly in white/European subjects. However, there is considerable overlap in the genetic architecture of South Asians and Hispanic/Latino populations. Therefore, we will limit our initial studies to these three race/ethnicity groups.

Exclusion Criteria:

• History of myocardial infarction, angina, stroke, peripheral arterial disease, PCI, or CABG
• Already on lipid lowering therapy
• Anticipated survival <1 year (e.g. metastatic cancer)
• Serious conditions that would limit ability to adhere to recommendations (inability to take statins, exercise)
• Already had genetic testing
• Concurrent enrollment in another clinical trial
• Pregnant or breastfeeding