Study Status: Completed February 2020
There is general agreement that statin-treatment of patients with high cholesterol can increase the incidence of type 2 diabetes (T2DM) in some individuals. This research proposal will study what metabolic characteristics and variables (for example high cholesterol or high triglycerides or both) will identify those people at highest risk of statin-induced T2DM. The investigators will evaluate how the medication atorvastatin (trade name Lipitor) works in regards to its effect on insulin action and insulin sensitivity to help further understand the possible cause of the increased occurrences of T2DM in people who are at risk for T2DM.
Under Dr. Snyder, a Co-director of the study, samples will be collected for integrated Personal Omics Profiling (iPOP), a monitoring approach developed by Dr. Snyder and his research colleagues. The investigators propose to analyze iPOP of individuals who participate in this study during and after taking the statin. In this pilot study, analyses will be done on previously-known drug effectiveness and on untargeted drug effectiveness, looking in to any unknown benefits that this medication may have. Analyses will also look at drug effects, such as those seen in some participants when given a statin. The hope is to obtain a better understanding of how to perform a personal omics profile when taking drugs, which would lead to the developing better use of drugs.
Study Status: Completed August 2017
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 is 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 modestly improve reclassification indices. For instance, in a very recent example, 13 SNPs previously identified in a GWAS as being 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. 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 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.