# Virtual metabolomics as a discovery tool for novel cardiometabolic disease biology

> **NIH NIH R01** · VANDERBILT UNIVERSITY MEDICAL CENTER · 2022 · $542,938

## Abstract

PROJECT SUMMARY / ABSTRACT
Dysregulated metabolism underlies many of the leading causes of mortality and morbidity in the US including
cardiometabolic diseases. Metabolomics studies can identify novel disease biomarkers, novel therapeutic
targets, and biological pathways with pathological relevance. Emerging technologies in metabolomics allow
the interrogation of large numbers of metabolites from diverse pathways. However, these approaches remain
expensive and time-consuming. Applying metabolomics to very large cohorts of individuals to conduct
epidemiological studies is not feasible, due to the practical challenges and costs of implementing these assays
at scale. These challenges have limited discovery of novel biomarker-disease associations. We propose to
address these limitations with a genetics-based “virtual” metabolite study design that will allow us to define
genetic predictors of metabolite concentrations in a small population in whom the metabolite was measured,
and then use these genetic predictors to impute metabolite concentrations in a large population in whom the
metabolite was not measured. This approach vastly amplifies the sample size for discovery, and can rapidly
identify novel biomarkers for downstream validation. The primary aims of this proposal are to: 1) construct
single nucleotide polymorphism (SNP)-based predictors of circulating metabolites, and identify associations
with cardiometabolic phenotypes, including type 2 diabetes and coronary artery disease; 2) validate the
associations with direct metabolite measurements; 3) identify pleiotropic associations between metabolite
genetic predictors and the clinical phenome. These analyses are enabled by genetic approaches that allow us
to integrate data from large scale genome-wide association studies (GWAS) of cardiometabolic diseases and a
collection of electronic health record linked-DNA biobanks comprising over 700,000 subjects. Innovative
features of this approach include the efficiency and scale of the analysis, inclusion of under-represented and
vulnerable populations and implementation of a re-usable and scalable analytical framework that will
accelerate biomarker discovery and implementation. Upon completion of this project, we will construct a
publicly accessible online resource of metabolite-disease associations that will be available to researchers as a
source for both hypothesis testing and generation. Ultimately, these studies will advance the field of
metabolomics by rapidly advancing the process of linking metabolites to clinically-relevant diseases.

## Key facts

- **NIH application ID:** 10414765
- **Project number:** 5R01HL142856-04
- **Recipient organization:** VANDERBILT UNIVERSITY MEDICAL CENTER
- **Principal Investigator:** Jane F Ferguson
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $542,938
- **Award type:** 5
- **Project period:** 2019-04-01 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10414765

## Citation

> US National Institutes of Health, RePORTER application 10414765, Virtual metabolomics as a discovery tool for novel cardiometabolic disease biology (5R01HL142856-04). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10414765. Licensed CC0.

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