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      Serum metabolites and risk of myocardial infarction and ischemic stroke: a targeted metabolomic approach in two German prospective cohorts

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          Abstract

          Metabolomic approaches in prospective cohorts may offer a unique snapshot into early metabolic perturbations that are associated with a higher risk of cardiovascular diseases (CVD) in healthy people. We investigated the association of 105 serum metabolites, including acylcarnitines, amino acids, phospholipids and hexose, with risk of myocardial infarction (MI) and ischemic stroke in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam (27,548 adults) and Heidelberg (25,540 adults) cohorts. Using case-cohort designs, we measured metabolites among individuals who were free of CVD and diabetes at blood draw but developed MI (n = 204 and n = 228) or stroke (n = 147 and n = 121) during follow-up (mean, 7.8 and 7.3 years) and among randomly drawn subcohorts (n = 2214 and n = 770). We used Cox regression analysis and combined results using meta-analysis. Independent of classical CVD risk factors, ten metabolites were associated with risk of MI in both cohorts, including sphingomyelins, diacyl-phosphatidylcholines and acyl-alkyl-phosphatidylcholines with pooled relative risks in the range of 1.21–1.40 per one standard deviation increase in metabolite concentrations. The metabolites showed positive correlations with total- and LDL-cholesterol (r ranged from 0.13 to 0.57). When additionally adjusting for total-, LDL- and HDL-cholesterol, triglycerides and C-reactive protein, acyl-alkyl-phosphatidylcholine C36:3 and diacyl-phosphatidylcholines C38:3 and C40:4 remained associated with risk of MI. When added to classical CVD risk models these metabolites further improved CVD prediction (c-statistics increased from 0.8365 to 0.8384 in EPIC-Potsdam and from 0.8344 to 0.8378 in EPIC-Heidelberg). None of the metabolites was consistently associated with stroke risk. Alterations in sphingomyelin and phosphatidylcholine metabolism, and particularly metabolites of the arachidonic acid pathway are independently associated with risk of MI in healthy adults.

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          The online version of this article (10.1007/s10654-017-0333-0) contains supplementary material, which is available to authorized users.

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              Metabolomics-based methods for early disease diagnostics.

              The emerging field of metabolomics, in which a large number of small-molecule metabolites from body fluids or tissues are detected quantitatively in a single step, promises immense potential for early diagnosis, therapy monitoring and for understanding the pathogenesis of many diseases. Metabolomics methods are mostly focused on the information-rich analytical techniques of NMR spectroscopy and mass spectrometry (MS). Analysis of the data from these high-resolution methods using advanced chemometric approaches provides a powerful platform for translational and clinical research and diagnostic applications. In this review, the current trends and recent advances in NMR- and MS-based metabolomics are described with a focus on the development of advanced NMR and MS methods, improved multivariate statistical data analysis and recent applications in the area of cancer, diabetes, inborn errors of metabolism and cardiovascular diseases.
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                Author and article information

                Contributors
                +49 (0)421 218-56826 , floegel@leibniz-bips.de
                Journal
                Eur J Epidemiol
                Eur. J. Epidemiol
                European Journal of Epidemiology
                Springer Netherlands (Dordrecht )
                0393-2990
                1573-7284
                27 November 2017
                27 November 2017
                2018
                : 33
                : 1
                : 55-66
                Affiliations
                [1 ]ISNI 0000 0004 0390 0098, GRID grid.418213.d, Department of Epidemiology, , German Institute of Human Nutrition Potsdam-Rehbruecke, ; Nuthetal, Germany
                [2 ]ISNI 0000 0000 9750 3253, GRID grid.418465.a, Leibniz Institute for Prevention Research and Epidemiology – BIPS, ; Achterstraße 30, 28359 Bremen, Germany
                [3 ]ISNI 0000 0004 0492 0584, GRID grid.7497.d, Division of Cancer Epidemiology, , German Cancer Research Center (DKFZ), ; Heidelberg, Germany
                [4 ]ISNI 0000 0004 0483 2525, GRID grid.4567.0, Institute of Experimental Genetics, Helmholtz Zentrum München, , German Research Center for Environmental Health, ; Neuherberg, Germany
                [5 ]ISNI 0000 0004 0492 3830, GRID grid.7492.8, Department of Molecular Systems Biology, , Helmholtz Centre for Environmental Research (UFZ), ; Leipzig, Germany
                [6 ]ISNI 0000 0000 8852 3623, GRID grid.417830.9, Department of Food Safety, , Federal Institute for Risk Assessment, ; Berlin, Germany
                [7 ]ISNI 0000 0001 2218 4662, GRID grid.6363.0, Institute for Social Medicine, Epidemiology and Health Economics, , Charité University Medical Center, ; Berlin, Germany
                [8 ]ISNI 0000 0000 9529 9877, GRID grid.10423.34, Hannover Unified Biobank, , Hannover Medical School, ; Hannover, Germany
                [9 ]ISNI 0000 0000 9529 9877, GRID grid.10423.34, Institute of Human Genetics, , Hannover Medical School, ; Hannover, Germany
                [10 ]ISNI 0000 0001 0742 471X, GRID grid.5117.2, University of Aalborg, ; Fredrik Bajers Vej 7H, 9220 Aalborg East, Denmark
                [11 ]ISNI 0000 0001 1014 0849, GRID grid.419491.0, Molecular Epidemiology Group, , Max Delbrück Center for Molecular Medicine (MDC), ; Berlin, Germany
                [12 ]ISNI 0000 0001 2218 4662, GRID grid.6363.0, Charité – Universitätsmedizin Berlin, ; Berlin, Germany
                [13 ]ISNI 0000 0004 5937 5237, GRID grid.452396.f, German Center for Cardiovascular Research (DZHK), Partner Site Berlin, ; Berlin, Germany
                Article
                333
                10.1007/s10654-017-0333-0
                5803284
                29181692
                99a1e7f3-26ab-435b-8192-c8ef878f73f0
                © The Author(s) 2017

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

                History
                : 29 July 2017
                : 20 November 2017
                Funding
                Funded by: FundRef http://dx.doi.org/10.13039/501100001656, Helmholtz-Gemeinschaft;
                Categories
                Cardiovascular Disease
                Custom metadata
                © Springer Science+Business Media B.V., part of Springer Nature 2018

                Public health
                metabolomics,myocardial infarction,stroke,biomarker,prospective cohort study
                Public health
                metabolomics, myocardial infarction, stroke, biomarker, prospective cohort study

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