# Resolving Causal Influences Among Correlated Risk Biomarkers for Coronary Artery Disease

> **NIH NIH R01** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2021 · $423,750

## Abstract

PROJECT SUMMARY / ABSTRACT
 Epidemiological studies have shown correlations among numerous biomarkers (defined as
measurable indicators of the severity or presence of a disease state) and risk for coronary artery disease
(CAD). However, it's unknown whether many of these biomarkers represent causal processes for CAD.
Inferring causality of a biomarker with CAD has the potential to identify risk factors that may lead to
pathophysiological processes for the development of CAD.
 Recently, we developed a method, called Multi-Phenotype Mendelian Randomization, that disentangles
causal influences for a disease among a set of correlated biomarkers. We applied our method to plasma
triglycerides and showed that the effect size of a SNP on triglycerides is linearly related to its effect size on
CAD, before and after accounting for the same SNP's potential effect on plasma low-density lipoprotein
cholesterol (LDL-C) and/or high-density lipoprotein cholesterol (HDL-C). This finding has since been validated
by other studies. Together, these results suggest that plasma triglycerides may capture causal processes that
may promote atherosclerosis and CAD.
 We propose to expand on our prior work by inferring causal relationships between a wide range of 32
cardiometabolic traits, 245 metabolites and >2,000 clinical phenotypes from electronic medical records with
subclinical CAD endophenotypes. In Aim 1, we will evaluate current Mendelian randomization methods and
refine the approach to allow for detection of pleiotropy (or detection of violation of a basic assumption of
Mendelian randomization), which can improve statistical properties of these methods. In Aim 2, we will infer
causality of a wide range of 32 cardiometabolic and 245 metabolite traits with subclinical atherosclerosis and
cardiac structure and function endophenotypes for CAD. In Aim 3, we will perform a novel framework called
Phenome-Wide Mendelian Randomization to infer causality of CAD traits with >2,000 clinical phenotypes from
electronic medical records (EMR).
 The proposal is innovative because we are utilizing novel approaches for causal inference, along with a
detailed repository of cardiometabolic traits, metabolites, EMR clinical phenotypes, and subclinical CAD
disease traits. We propose to use the following resources: 1) new causal inference approach that accounts for
pleiotropy; 2) extensive set of cardiometabolic traits (32 in total) and metabolites (245 in total); 3) subclinical
CAD outcomes (42 subclinical atherosclerosis and 54 cardiac structure and function traits); and 4) EMR
phenotypes from large-scale Mount Sinai's BioMe Biobank and UK Biobank (>2,000).
 This proposal has the potential to reveal new causal risk biomarkers for subclinical CAD disease outcomes.
It can provide new avenues for the development of new therapeutics for the prevention and treatment of CAD.

## Key facts

- **NIH application ID:** 10088462
- **Project number:** 5R01HL139865-04
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Ron Do
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $423,750
- **Award type:** 5
- **Project period:** 2018-02-01 → 2023-01-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10088462

## Citation

> US National Institutes of Health, RePORTER application 10088462, Resolving Causal Influences Among Correlated Risk Biomarkers for Coronary Artery Disease (5R01HL139865-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10088462. Licensed CC0.

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