Ioanna Tzoulaki1,2,3,4, Raphaële Castagné1,5, Claire L Boulangé6,7, Ibrahim Karaman1,2,4, Elena Chekmeneva7, Evangelos Evangelou1,2,3, Timothy M D Ebbels7, Manuja R Kaluarachchi6,7, Marc Chadeau-Hyam1,2, David Mosen1,2, Abbas Dehghan1,2,4, Alireza Moayyeri8, Diana L Santos Ferreira9, Xiuqing Guo10,11, Jerome I Rotter10,11, Kent D Taylor10,11, Maryam Kavousi12, Paul S de Vries12,13, Benjamin Lehne1, Marie Loh1, Albert Hofman12,14, Jeremy K Nicholson6,7, John Chambers1,15, Christian Gieger16, Elaine Holmes6,7, Russell Tracy17, Jaspal Kooner14,18, Philip Greenland19, Oscar H Franco11,20, David Herrington21, John C Lindon6,7, Paul Elliott1,2,4. 1. Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, Norfolk Place, London, UK. 2. MRC-PHE Centre for Environment and Health, School of Public Health, Imperial College London, Norfolk Place, London, UK. 3. Department of Hygiene and Epidemiology, University of Ioannina Medical School, University Campus Road 455 00, Ioannina, Greece. 4. Dementia Research Institute, Imperial College London, Norfolk Place, London, UK. 5. LEASP, UMR 1027, Inserm-Université Toulousse III Paul Sabatier, Toulousse, France. 6. Metabometrix Ltd, Imperial Incubator, Bessemer Building, Prince Consort Road, London, UK. 7. Division of Computational and Systems Medicine, Department of Surgery and Cancer, Imperial College London, South Kensington Campus, London, UK. 8. Farr Institute of Health Informatics Research, University College London Institute of Health Informatics, 222 Euston Road, London, UK. 9. MRC Integrative Epidemiology Unit, School of Social and Community Medicine, University of Bristol, Oakfield House, Oakfiled Grove, Bristol, UK. 10. Department of Pediatrics, Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, 1000 W Carson St, Torrance, CA, USA. 11. Department of Medicine, Institute for Translational Genomics and Population Sciences, Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center, 1000 W Carson St, Torrance, CA, USA. 12. Department of Epidemiology, Erasmus University Medical Center, University Medical Center Rotterdam, CA Rotterdam, the Netherlands. 13. Department of Epidemiology, Human Genetics, and Environmental Sciences, Human Genetics Center, School of Public Health, The University of Texas Health Science Center at Houston, 1200 Pressler Street, Houston, TX, USA. 14. Department of Epidemiology, Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA, USA. 15. London North West Healthcare NHS Trust, Northwick Park Hospital, Watford Rd, Harrow, UK. 16. German Research Centre for Environmental Health, Helmholtz Zentrum München, Ingolstädter Landstraße 1, D Neuherberg, Germany. 17. M.D. College of Medicine University of Vermont, The Robert Larner, Given Medical Bldg, E-126, 89 Beaumont Ave, Burlington, VT, USA. 18. National Heart & Lung Institute, Faculty of Medicine, Imperial College London, Guy Scadding Building, Dovehouse St, Chelsea, London, UK. 19. Department of Preventive Medicine, Northwestern University, Feinberg School of Medicine, 680 North Lake Shore Drive, Suite, 1400, Chicago, IL, USA. 20. Institute of Social and Preventive Medicine (ISPM), University of Bern, Mittelstrasse 43, Bern, Switzerland. 21. Section on Cardiovascular Medicine, Department of Internal Medicine, Wake Forest University School of Medicine, Medical Center Boulevard, Winston-Salem, NC, USA.
Abstract
AIMS: To characterize serum metabolic signatures associated with atherosclerosis in the coronary or carotid arteries and subsequently their association with incident cardiovascular disease (CVD). METHODS AND RESULTS: We used untargeted one-dimensional (1D) serum metabolic profiling by proton nuclear magnetic resonance spectroscopy (1H NMR) among 3867 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), with replication among 3569 participants from the Rotterdam and LOLIPOP studies. Atherosclerosis was assessed by coronary artery calcium (CAC) and carotid intima-media thickness (IMT). We used multivariable linear regression to evaluate associations between NMR features and atherosclerosis accounting for multiplicity of comparisons. We then examined associations between metabolites associated with atherosclerosis and incident CVD available in MESA and Rotterdam and explored molecular networks through bioinformatics analyses. Overall, 30 1H NMR measured metabolites were associated with CAC and/or IMT, P = 1.3 × 10-14 to 1.0 × 10-6 (discovery) and P = 5.6 × 10-10 to 1.1 × 10-2 (replication). These associations were substantially attenuated after adjustment for conventional cardiovascular risk factors. Metabolites associated with atherosclerosis revealed disturbances in lipid and carbohydrate metabolism, branched chain, and aromatic amino acid metabolism, as well as oxidative stress and inflammatory pathways. Analyses of incident CVD events showed inverse associations with creatine, creatinine, and phenylalanine, and direct associations with mannose, acetaminophen-glucuronide, and lactate as well as apolipoprotein B (P < 0.05). CONCLUSION: Metabolites associated with atherosclerosis were largely consistent between the two vascular beds (coronary and carotid arteries) and predominantly tag pathways that overlap with the known cardiovascular risk factors. We present an integrated systems network that highlights a series of inter-connected pathways underlying atherosclerosis.
AIMS: To characterize serum metabolic signatures associated with atherosclerosis in the coronary or carotid arteries and subsequently their association with incident cardiovascular disease (CVD). METHODS AND RESULTS: We used untargeted one-dimensional (1D) serum metabolic profiling by proton nuclear magnetic resonance spectroscopy (1H NMR) among 3867 participants from the Multi-Ethnic Study of Atherosclerosis (MESA), with replication among 3569 participants from the Rotterdam and LOLIPOP studies. Atherosclerosis was assessed by coronary artery calcium (CAC) and carotid intima-media thickness (IMT). We used multivariable linear regression to evaluate associations between NMR features and atherosclerosis accounting for multiplicity of comparisons. We then examined associations between metabolites associated with atherosclerosis and incident CVD available in MESA and Rotterdam and explored molecular networks through bioinformatics analyses. Overall, 30 1H NMR measured metabolites were associated with CAC and/or IMT, P = 1.3 × 10-14 to 1.0 × 10-6 (discovery) and P = 5.6 × 10-10 to 1.1 × 10-2 (replication). These associations were substantially attenuated after adjustment for conventional cardiovascular risk factors. Metabolites associated with atherosclerosis revealed disturbances in lipid and carbohydrate metabolism, branched chain, and aromatic amino acid metabolism, as well as oxidative stress and inflammatory pathways. Analyses of incident CVD events showed inverse associations with creatine, creatinine, and phenylalanine, and direct associations with mannose, acetaminophen-glucuronide, and lactate as well as apolipoprotein B (P < 0.05). CONCLUSION: Metabolites associated with atherosclerosis were largely consistent between the two vascular beds (coronary and carotid arteries) and predominantly tag pathways that overlap with the known cardiovascular risk factors. We present an integrated systems network that highlights a series of inter-connected pathways underlying atherosclerosis.
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