# TOPMed Omics of Cardiovascular Disease in Diabetes

> **NIH NIH R01** · MASSACHUSETTS GENERAL HOSPITAL · 2020 · $920,724

## Abstract

Project Summary/Abstract
Blood omic biomarkers, including whole genome sequence (WGS), whole blood methylation (measured by
sequencing or microarrays), transcription (using RNA-seq), proteomics (SomaLogic aptamers) and
metabolomics (liquid chromatography/mass spectroscopy) have identified novel pathways to cardiovascular
disease (CVD: coronary heart disease (CHD) and stroke; subclinical atherosclerosis: elevated coronary
artery calcium (CAC), and carotid plaque and intimal-medial thickness (cIMT)). Individually, but especially
together in multidimensional network frameworks, omics data in the NHLBI Trans-Omics for Precision
Medicine (TOPMed) study are poised to address major problems in biomedicine. In this application we focus
on multi-omic investigation of CVD in type 2 diabetes (T2D). T2D is a growing scourge worldwide, driving a
major global epidemic of CVD. CVD events occur over twice as frequently in people with T2D. The reasons
for this persistent excess risk remain unknown but likely involve perturbations across multiple omic
dimensions, from genetic variation to networks that link insulin resistance, endothelial dysfunction and
atherogenesis. Elucidation of the biology underlying CVD in T2D offers a clear opportunity to stem the global
tide of CVD. We propose three Aims. In Aim 1, we will analyze genetic variation from WGS data from 23,903
people with T2D from 5 ancestry groups from 28 cohorts (11 longitudinal) of men and women of diverse
ages, including 15 with prevalent CVD and 11 with measures of subclinical atherosclerosis. We have linked
WGS data to diverse annotation resources (e.g. ENCODE). We have clinical covariates including age of T2D
onset and level of metabolic control, and longitudinal follow-up for incident CVD events. Analyses of common
and rare variation will elucidate known candidate CVD-T2D loci and lead to new discovery. Replication is
available in >220,000 T2D individuals of diverse ancestry from 5 biobanks. Validated variants will be used in
Mendelian Randomization tests of causality and polygenic scores for prediction. In Aim 2, we will analyze
blood omic biomarkers individually, guided by Aim 1 genomic associations with CVD in T2D. TOPMed has
omics data on a subset of 2,507 sequenced people with T2D from 4 ancestry groups, with more planned. A
clear understanding of the association of each omic dimension with CVD in T2D is useful to understand that
dimension as a unique exposure for CVD in T2D. Interpretation of Aim 2 omic associations will first be
informed by WGS association from Aim 1, then by combining WGS information with all four omic dimensions
(Aim 3). We will use both Aim 2-association-based and agnostic multidimensional-based approaches to build
multilevel network models of the pathobiology of CVD in T2D. “TOPMed Omics of Cardiovascular Disease in
Diabetes” is highly responsive to NOT-HL-19-676, leveraging TOPMed resources to elucidate the pathway
biology of heart, lung and blood diseases. Our team, with...

## Key facts

- **NIH application ID:** 9944034
- **Project number:** 1R01HL151855-01
- **Recipient organization:** MASSACHUSETTS GENERAL HOSPITAL
- **Principal Investigator:** JAMES B MEIGS
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $920,724
- **Award type:** 1
- **Project period:** 2020-07-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9944034

## Citation

> US National Institutes of Health, RePORTER application 9944034, TOPMed Omics of Cardiovascular Disease in Diabetes (1R01HL151855-01). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/9944034. Licensed CC0.

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