# Targeting Residual ASCVD Risk by Integrating Genetics and Clinical Data

> **NIH NIH R01** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2024 · $874,792

## Abstract

Patients regularly have myocardial infarctions or strokes, despite being on treatment to lower low-density
lipoprotein cholesterol (LDL-C) and blood pressure (BP). This residual risk of atherosclerotic cardiovascular
disease (ASCVD) remains a substantial medical burden globally. To understand and control residual ASCVD
risk, we need to answer three questions: (1) what are the underlying mechanisms? (2) who is at higher risk? (3)
are there new treatment options? Building on our experience, we propose to fill these knowledge gaps:
(1) The genetics of residual ASCVD risk remain largely unexplored. Existing genetic studies focus primarily on
baseline risk (e.g., risk of CHD) or response to CHD-relevant drugs (e.g., LDL-C change on statins), not the
residual ASCVD risk. Our group has previously identified and replicated LPA variants associated with residual
CHD events in patients on statin treatment independent of LDL-C change. In Aim 1, we will identify new users
of drugs to lower LDL-C or BP in the biobank at Vanderbilt (BioVU) and conduct a GWAS comparing those who
have an ASCVD event while on treatment to those without an event. We will conduct replication in eMERGE and
All of US (AoU) and construct polygenic risk scores. (2) There is a dearth of generalizable and accurate models
to predict residual ASCVD risk. Electronic health records (EHRs) contain granular, longitudinal, real-world data
that are inherently medically relevant. Additionally, large programs, such as AoU, collect information on lifestyle
(e.g., smoking) and social determinants of health (e.g., insurance and employment status). Recently, we applied
machine learning (ML) algorithms for CHD risk prediction on longitudinal EHR data and found that ML
outperformed the ACC/AHA risk equation for prediction of ASCVD. The performance was further improved
by including additional factors. In Aim 2, we will leverage longitudinal EHRs at Vanderbilt and apply advanced
ML method to construct prediction models for residual ASCVD risk. We will then validate the model in AoU
and eMERGE, and improve the performance by including genetic, environmental, and social information. (3)
There are few drug therapies to reduce ASCVD risk other than ones to lower LDL-C and BP. Recent advances
in genetics, informatics, and large real-world clinical datasets provide opportunities for systematic drug
repurposing. Our group recently pioneered a new drug-repurposing approach. We first leveraged existing GWAS
to impute genetically determined transcriptome signatures (the virtual transcriptome) associated with elevated
LDL-C or raised BP. We then screened the transcriptome signature in a drug perturbation database to identify
candidate drugs, followed by validating them in real-world clinical data. In Aim 3, we will conduct drug
repurposing for residual ASCVD risk. We will use two approaches: (a) impute the genetically determined
transcriptome and (b) large-scale Mendelian randomization. We will validate the identifie...

## Key facts

- **NIH application ID:** 10780351
- **Project number:** 1R01HL171809-01
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** Qiping Feng
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $874,792
- **Award type:** 1
- **Project period:** 2024-07-15 → 2028-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10780351

## Citation

> US National Institutes of Health, RePORTER application 10780351, Targeting Residual ASCVD Risk by Integrating Genetics and Clinical Data (1R01HL171809-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10780351. Licensed CC0.

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