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.

Public Health Relevance

PROJECT 1: NARRATIVE Statins are the most commonly prescribed drug class for the treatment and prevention of coronary heart disease; however, the majority of cardiovascular events are not prevented by statins, and in some individuals, statin use may cause muscle toxicity or the development of type 2 diabetes. The goal of this project is to identify and validate novel biomarkers and/or determinants of statin efficacy for prevention of coronary heart disease, and adverse effects of statin treatment, by comparing results of molecular measurements in blood cell lines obtained from patients who, after being placed on statin treatment, developed either: 1) coronary heart disease, 2) statin-induced myopathy, 3) diabetes or 4) none of these outcomes. Identification of markers for benefit vs. risk of statin treatment will assist in providing guidance for effective and safe use of statins in clinical practice. In addition, the findings can lead to the development of new therapeutic approaches for improving statin efficacy and/or mitigating its adverse effects.

Agency
National Institute of Health (NIH)
Institute
National Institute of General Medical Sciences (NIGMS)
Type
Specialized Center (P50)
Project #
5P50GM115318-04
Application #
9560846
Study Section
Special Emphasis Panel (ZGM1)
Project Start
Project End
Budget Start
2018-09-01
Budget End
2019-08-31
Support Year
4
Fiscal Year
2018
Total Cost
Indirect Cost
Name
Children's Hospital & Res Ctr at Oakland
Department
Type
DUNS #
076536184
City
Oakland
State
CA
Country
United States
Zip Code
94609
El-Manzalawy, Yasser; Hsieh, Tsung-Yu; Shivakumar, Manu et al. (2018) Min-redundancy and max-relevance multi-view feature selection for predicting ovarian cancer survival using multi-omics data. BMC Med Genomics 11:71
Lee, Seung-Been; Wheeler, Marsha M; Patterson, Karynne et al. (2018) Stargazer: a software tool for calling star alleles from next-generation sequencing data using CYP2D6 as a model. Genet Med :
Kim, Dongwook; Shivakumar, Manu; Han, Seonggyun et al. (2018) Population-dependent Intron Retention and DNA Methylation in Breast Cancer. Mol Cancer Res 16:461-469
Miller, Jason E; Shivakumar, Manu K; Risacher, Shannon L et al. (2018) Codon bias among synonymous rare variants is associated with Alzheimer's disease imaging biomarker. Pac Symp Biocomput 23:365-376
Oni-Orisan, Akinyemi; Hoffmann, Thomas J; Ranatunga, Dilrini et al. (2018) Characterization of Statin Low-Density Lipoprotein Cholesterol Dose-Response Using Electronic Health Records in a Large Population-Based Cohort. Circ Genom Precis Med 11:e002043
Orozco, Luz D; Farrell, Colin; Hale, Christopher et al. (2018) Epigenome-wide association in adipose tissue from the METSIM cohort. Hum Mol Genet 27:1830-1846
Kim, Kyungpil; Theusch, Elizabeth; Kuang, Yu-Lin et al. (2018) ZNF542P is a pseudogene associated with LDL response to simvastatin treatment. Sci Rep 8:12443
Veturi, Yogasudha; Ritchie, Marylyn D (2018) How powerful are summary-based methods for identifying expression-trait associations under different genetic architectures? Pac Symp Biocomput 23:228-239
Lee, Younghee; Han, Seonggyun; Kim, Dongwook et al. (2018) Genetic variation affecting exon skipping contributes to brain structural atrophy in Alzheimer's disease. AMIA Jt Summits Transl Sci Proc 2017:124-131
Hoffmann, Thomas J; Theusch, Elizabeth; Haldar, Tanushree et al. (2018) A large electronic-health-record-based genome-wide study of serum lipids. Nat Genet 50:401-413

Showing the most recent 10 out of 21 publications