# Exploring Statin Pleiotropic Effects within a Very Large EHR Cohort

> **NIH NIH R01** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2020 · $395,483

## Abstract

Statins among the most widely prescribed agents worldwide for prevention of
cardiovascular diseases, produce substantial pleiotropic effects. Pleiotropic effects are
unanticipated outcomes other than those for which the drug was originally developed,
either therapeutic (beneficial) or detrimental (adverse drug reactions). Statin pleiotropic
effects are unanticipatedly broad, including increasing the risk of developing type 2
diabetes mellitus and cataract, decreasing cancer-related mortality, and reducing
dementia. Many effects are still not determined. In addition, individual responses to
statins are highly variable. Genetics studies have identified loci that are significantly
associated with statin response. However, it is unclear if either of the genetic variants
within these regions is also associated with statin pleiotropic effects.
We propose to investigate statin pleiotropic effects using whole de-identified electronic
health records (EHRs) of >2.5 million individuals at Vanderbilt, including >110,000 statin
exposure individuals. By linking this cohort to BioVU, the Vanderbilt de-identified DNA
biobank, >10,000 of these statin exposure individuals have extant genome-wide
genotyping. We argue that 1) previous inconclusive results are largely caused by
inconsistent phenotype definitions, and 2) using the EHR to develop a novel, drug-based
phenome-wide association studies (PheWAS) provides an ideal approach to discover
unknown statin effects. The still-growing Vanderbilt de-identified EHRs allow large
amounts of individuals' clinical data shared to support validation of known pleiotropic
effects and to enable novel discoveries. Our previous work demonstrated our ability to
develop consistent EHR-based phenotype definitions that can be deployed across
multiple EHRs and institutions. We have expertise leveraging state-of-the-art informatics
techniques, including natural language processing and ontologies, for pharmacogenetic
studies, including for statins. We first described the PheWAS approach to not only
replicate genetic associations but also discover novel, pleiotropic associations. Our
informatics expertise combined with an ideal EHR/DNA population, will enable us to
validate and discover statin pleiotropic effects. Accordingly, we propose the following
three aims: 1. develop and test EHR-based phenotype algorithms for four controversial
statin pleiotropic effects, 2. conduct a PheWAS to discover unknown statin pleiotropic
effects, and 3. evaluate and discover genetic predictors of statin pleiotropic effects.

## Key facts

- **NIH application ID:** 9881338
- **Project number:** 5R01HL133786-04
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** Wei-Qi Wei
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $395,483
- **Award type:** 5
- **Project period:** 2017-04-01 → 2023-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9881338

## Citation

> US National Institutes of Health, RePORTER application 9881338, Exploring Statin Pleiotropic Effects within a Very Large EHR Cohort (5R01HL133786-04). Retrieved via AI Analytics 2026-06-23 from https://api.ai-analytics.org/grant/nih/9881338. Licensed CC0.

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