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 a proven track record in omics discovery, aims to find new approaches to prevent and treat CVD in T2D.

Public Health Relevance

We aim to use blood omic biomarkers in the NHLBI TOPMed study to illuminate the pathobiology of cardiovascular disease (CVD) in type 2 diabetes (T2D). T2D is driving a major global epidemic of CVD, where CVD events occur over twice as frequently in people with T2D as without. The reasons for this excess risk are unknown but likely involve perturbations across multiple omic dimensions, including whole genome sequence variation and whole blood methylation, transcription, proteomics and metabolomics, where individually and together they contribute to novel pathways to CVD in T2D.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Research Project (R01)
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Cancer, Heart, and Sleep Epidemiology A Study Section (CHSA)
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Minear, Mollie A
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Massachusetts General Hospital
United States
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