Integrative Metabolomics Differentiate Coronary Artery Disease, Peripheral Artery Disease, and Venous Thromboembolism Risks

Author:

Lee Jiwoo1ORCID,Gilliland Thomas C.1ORCID,Dron Jacqueline12ORCID,Koyama Satoshi1ORCID,Nakao Tetsushi2ORCID,Lannery Kim2,Wong Megan2ORCID,Peloso Gina M.3ORCID,Hornsby Whitney E.1ORCID,Natarajan Pradeep14ORCID

Affiliation:

1. Program in Medical and Population Genetics and the Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (J.L., T.C.G., J.D., S.K., T.N., W.E.H., P.N.).

2. Cardiovascular Research Center and Center for Genomic Medicine, Massachusetts General Hospital, Boston (J.L., T.C.G., J.D., T.N., K.L., M.W., W.E.H., P.N.).

3. Department of Biostatistics, Boston University School of Public Health, MA (G.M.P.).

4. Department of Medicine, Harvard Medical School, Boston, MA (P.N.).

Abstract

BACKGROUND: Arterial and venous cardiovascular conditions, such as coronary artery disease (CAD), peripheral artery disease (PAD), and venous thromboembolism (VTE), are genetically correlated. Interrogating underlying mechanisms may shed light on disease mechanisms. In this study, we aimed to identify (1) epidemiological and (2) causal, genetic relationships between metabolites and CAD, PAD, and VTE. METHODS: We used metabolomic data from 95 402 individuals in the UK Biobank, excluding individuals with prevalent cardiovascular disease. Cox proportional-hazards models estimated the associations of 249 metabolites with incident disease. Bidirectional 2-sample Mendelian randomization (MR) estimated the causal effects between metabolites and outcomes using genome-wide association summary statistics for metabolites (n=118 466 from the UK Biobank), CAD (n=184 305 from CARDIoGRAMplusC4D 2015), PAD (n=243 060 from the Million Veterans Project), and VTE (n=650 119 from the Million Veterans Project). Multivariable MR was performed in subsequent analyses. RESULTS: We found that 196, 115, and 74 metabolites were associated ( P <0.001) with CAD, PAD, and VTE, respectively. Further interrogation of these metabolites with MR revealed 94, 34, and 9 metabolites with potentially causal effects on CAD, PAD, and VTE, respectively. There were 21 metabolites common to CAD and PAD and 4 common to PAD and VTE. Many putatively causal metabolites included lipoprotein traits with heterogeneity across different sizes and lipid subfractions. Small VLDL (very-low-density lipoprotein) particles increased the risk for CAD while large VLDL particles decreased the risk for VTE. We identified opposing directions of CAD and PAD effects for cholesterol and triglyceride concentrations within HDLs (high-density lipoproteins). Subsequent sensitivity analyses including multivariable MR revealed several metabolites with robust, potentially causal effects of VLDL particles on CAD. CONCLUSIONS: While common vascular conditions are associated with overlapping metabolomic profiles, MR prioritized the role of specific lipoprotein species for potential pharmacological targets to maximize benefits in both arterial and venous beds.

Publisher

Ovid Technologies (Wolters Kluwer Health)

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