# Molecular predictors of cardiovascular events and resilience in chronic coronary artery disease

> **NIH NIH R01** · NEW YORK UNIVERSITY SCHOOL OF MEDICINE · 2024 · $727,693

## Abstract

PROJECT ABSTRACT
State-of-the-art risk assessments in chronic coronary artery disease (CAD) only partially capture risk for
cardiovascular events (CVEs), leaving substantial ‘residual risk’ unaddressed. Current risk assessments also
incompletely capture resilience to CAD, defined as those at high risk by contemporary algorithms—but without
disease. This ‘residual protection’ highlights novel resiliency factors protective against the development of
CAD. In this context, it is crucial to understand factors related to both residual risk and resiliency to personalize
risk prediction and help clinicians and patients make better treatment decisions. Our overarching hypothesis
is that a multi ‘omics’ approach can identify molecular features of residual risk and resilience in CAD.
Historically, omics studies of CAD were limited by 1) phenotypic heterogeneity—reliance on variable definitions
of CAD and CVEs, biasing results and limiting prediction; and 2) risk homogeneity—constraining identification
of novel pathways and limiting generalizability. We overcome these limitations by leveraging unique access to
landmark NHLBI CAD strategy trials and a cohort study with aligned core-lab confirmed testing, molecular
data, and adjudicated CVEs. Collectively, these studies span the CAD risk continuum—a feature critical to
assessing performance of biomarkers and molecular features and overcoming prior limitations. Preliminary
data supporting our hypothesis show: 1) substantial, unexplained residual risk (>30%) for death/myocardial
infarction with a clinical model of risk factors and CAD severity, 2) biomarkers of inflammation, myocyte injury
and distension improve model performance, and 3) novel transcriptome modules of inflammation and
interferon signaling further improve prediction. New preliminary data from the imputed transcriptome of
‘resilient’ patients without CAD demonstrates dysregulated pathways and genes of fatty acid metabolism. Our
overall goal is to leverage well-phenotyped participants from these landmark studies to improve CVE
prediction and better understand resilience to CAD. We propose the following specific aims. Aim 1: Improve
prediction of CVEs in patients with established CAD. We will test and validate (1a) candidate biomarkers,
polygenic risk scores for CAD and (1b) transcriptomics to improve CVE prediction beyond a clinical model of
risk factors and state-of-the-art testing (core-lab confirmed severity of CAD and ischemia). Aim 2: Identify
biomarkers and molecular features of resilience to CAD. We will test the association of (2a) candidate
biomarkers and (2b) transcriptomics among resilient patients without CAD despite a high probability of disease
by clinical and polygenic risk scores for CAD. In the applicant’s opinion, this proposal is innovative and departs
from the status quo by using meticulously adjudicated CVEs and phenotype from patients across the CAD risk
spectrum and is significant because it will accelerate personalized risk ...

## Key facts

- **NIH application ID:** 10898900
- **Project number:** 5R01HL165208-02
- **Recipient organization:** NEW YORK UNIVERSITY SCHOOL OF MEDICINE
- **Principal Investigator:** JONATHAN D NEWMAN
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $727,693
- **Award type:** 5
- **Project period:** 2023-08-15 → 2028-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10898900

## Citation

> US National Institutes of Health, RePORTER application 10898900, Molecular predictors of cardiovascular events and resilience in chronic coronary artery disease (5R01HL165208-02). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10898900. Licensed CC0.

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