Molecular Determinants of Statin Efficacy and Adverse Effects in Cellular Models

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PROJECT 1: SUMMARY Nearly half of Americans between 40-75 yr are eligible for statin treatment for prevention of cardiovascular disease (CVD) according to new AHA/ACC guidelines for cholesterol management. However, a high proportion of statin-treated patients remain at risk for CVD, and there is significant potential for adverse effects of treatment, most notably myopathy and new onset diabetes. Currently, there is limited information regarding the basis for these varying outcomes and very few genetic or other biomarkers for their prediction. Thus, there is need for the development of precision medicine standards for statin therapy. Using transcriptomic analysis of in vitro simvastatin or sham exposed lymphoblastoid cell lines (LCLs) from participants in a statin clinical trial, we previously identified a number of novel genes implicated in modulating the lipid metabolic effects of statin treatment. Supporting evidence for the biologic and clinical roles of these genes was obtained by in vitro knock-down and overexpression studies as well as by identification of SNPs associated with both candidate gene expression (eQTLs) and in vivo statin lipid-lowering response. The overall objective of the present project is to utilize this general approach to identify novel biomarkers and/or determinants of statin clinical outcomes, namely efficacy for CVD prevention, and risk of statin-induced myopathy and type 2 diabetes. In Aim 1 we will utilize the POST Clinical Core at Kaiser Permanente Northern California (KPNC) to obtain LCLs from statin treated patients with: 1) a major adverse coronary event (MACE); 2) statin-induced myopathy; or 3) new onset type 2 diabetes; as well as matched controls for each outcome. The LCLs will be exposed to simvastatin vs. sham and transcriptomic and metabolomic measurements will be used to identify novel cellular biomarkers for statin efficacy and adverse effects. In Aim 2, in collaboration with Project 3 and the Informatics Core, DNA variants associated with these biomarkers will be identified and tested for association with MACE and statin adverse effects using genotype data from a very large KPNC cohort in which genome-wide genotype data are available. Finally, in Aim 3, novel genes associated with statin response identified within this Project or throughout the Center will be functionally validated using hypothesis-driven studies employing knock-down and overexpression in appropriate cell models. Overall, we anticipate that the completion of these aims will lead to identification and validation of new biomarkers (transcripts, metabolites, and/or SNPs) predictive of statin treatment outcomes that can add value in developing future therapeutic guidelines. Moreover, by expanding our knowledge of the underlying molecular determinants of variation in statin response, the "systems" approach employed in this project and throughout the POST Center could lead to development of new therapeutic approaches for augmenting statin's benefits and/or reducing its risks.