A metabolomic profile is associated with the risk of incident coronary heart disease

Metabolomics, defined as the comprehensive identification and quantification of low-molecular-weight metabolites to be found in a biological sample, has been put forward as a potential tool for classifying individuals according to their risk of coronary heart disease (CHD). Here, we investigated whe...

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Veröffentlicht in:	The American heart journal Jg. 168; H. 1; S. 45 - 52.e7
Hauptverfasser:	Vaarhorst, Anika A.M., Verhoeven, Aswin, Weller, Claudia M., Böhringer, Stefan, Göraler, Sibel, Meissner, Axel, Deelder, André M., Henneman, Peter, Gorgels, Anton P.M., van den Brandt, Piet A., Schouten, Leo J., van Greevenbroek, Marleen M., Merry, Audrey H.H., Verschuren, W.M. Monique, van den Maagdenberg, Arn M.J.M., van Dijk, Ko Willems, Isaacs, Aaron, Boomsma, Dorret, Oostra, Ben A., van Duijn, Cornelia M., Jukema, J. Wouter, Boer, Jolanda M.A., Feskens, Edith, Heijmans, Bastiaan T., Slagboom, P. Eline
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Mosby, Inc 01.07.2014 Elsevier Limited
Schlagworte:	Adult Angina pectoris Body mass index Cardiovascular Cardiovascular disease Cholesterol Coronary Disease - blood Coronary Disease - epidemiology Drug therapy Family medical history Female Follow-Up Studies Heart attacks Humans Incidence Lipids - blood Magnetic Resonance Spectroscopy - methods Male Metabolomics - methods Middle Aged Netherlands - epidemiology Odds Ratio Predictive Value of Tests Prognosis Prospective Studies Risk Factors Spectrum analysis Time Factors Young Adult Netherlands
ISSN:	0002-8703, 1097-6744, 1097-6744
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Abstract	Metabolomics, defined as the comprehensive identification and quantification of low-molecular-weight metabolites to be found in a biological sample, has been put forward as a potential tool for classifying individuals according to their risk of coronary heart disease (CHD). Here, we investigated whether a single-point blood measurement of the metabolome is associated with and predictive for the risk of CHD. We obtained proton nuclear magnetic resonance spectra in 79 cases who developed CHD during follow-up (median 8.1 years) and in 565 randomly selected individuals. In these spectra, 100 signals representing 36 metabolites were identified. Applying least absolute shrinkage and selection operator regression, we defined a weighted metabolite score consisting of 13 proton nuclear magnetic resonance signals that optimally predicted CHD. This metabolite score, including signals representing a lipid fraction, glucose, valine, ornithine, glutamate, creatinine, glycoproteins, citrate, and 1.5-anhydrosorbitol, was associated with the incidence of CHD independent of traditional risk factors (TRFs) (hazard ratio 1.50, 95% CI 1.12-2.01). Predictive performance of this metabolite score on its own was moderate (C-index 0.75, 95% CI 0.70-0.80), but after adding age and sex, the C-index was only modestly lower than that of TRFs (C-index 0.81, 95% CI 0.77-0.85 and C-index 0.82, 95% CI 0.78-0.87, respectively). The metabolite score was also associated with prevalent CHD independent of TRFs (odds ratio 1.59, 95% CI 1.19-2.13). A metabolite score derived from a single-point metabolome measurement is associated with CHD, and metabolomics may be a promising tool for refining and improving the prediction of CHD.
AbstractList	Metabolomics, defined as the comprehensive identification and quantification of low-molecular-weight metabolites to be found in a biological sample, has been put forward as a potential tool for classifying individuals according to their risk of coronary heart disease (CHD). Here, we investigated whether a single-point blood measurement of the metabolome is associated with and predictive for the risk of CHD. We obtained proton nuclear magnetic resonance spectra in 79 cases who developed CHD during follow-up (median 8.1 years) and in 565 randomly selected individuals. In these spectra, 100 signals representing 36 metabolites were identified. Applying least absolute shrinkage and selection operator regression, we defined a weighted metabolite score consisting of 13 proton nuclear magnetic resonance signals that optimally predicted CHD. This metabolite score, including signals representing a lipid fraction, glucose, valine, ornithine, glutamate, creatinine, glycoproteins, citrate, and 1.5-anhydrosorbitol, was associated with the incidence of CHD independent of traditional risk factors (TRFs) (hazard ratio 1.50, 95% CI 1.12-2.01). Predictive performance of this metabolite score on its own was moderate (C-index 0.75, 95% CI 0.70-0.80), but after adding age and sex, the C-index was only modestly lower than that of TRFs (C-index 0.81, 95% CI 0.77-0.85 and C-index 0.82, 95% CI 0.78-0.87, respectively). The metabolite score was also associated with prevalent CHD independent of TRFs (odds ratio 1.59, 95% CI 1.19-2.13). A metabolite score derived from a single-point metabolome measurement is associated with CHD, and metabolomics may be a promising tool for refining and improving the prediction of CHD. Background Metabolomics, defined as the comprehensive identification and quantification of low-molecular-weight metabolites to be found in a biological sample, has been put forward as a potential tool for classifying individuals according to their risk of coronary heart disease (CHD). Here, we investigated whether a single-point blood measurement of the metabolome is associated with and predictive for the risk of CHD. Methods and results We obtained proton nuclear magnetic resonance spectra in 79 cases who developed CHD during follow-up (median 8.1 years) and in 565 randomly selected individuals. In these spectra, 100 signals representing 36 metabolites were identified. Applying least absolute shrinkage and selection operator regression, we defined a weighted metabolite score consisting of 13 proton nuclear magnetic resonance signals that optimally predicted CHD. This metabolite score, including signals representing a lipid fraction, glucose, valine, ornithine, glutamate, creatinine, glycoproteins, citrate, and 1.5-anhydrosorbitol, was associated with the incidence of CHD independent of traditional risk factors (TRFs) (hazard ratio 1.50, 95% CI 1.12-2.01). Predictive performance of this metabolite score on its own was moderate (C-index 0.75, 95% CI 0.70-0.80), but after adding age and sex, theC-index was only modestly lower than that of TRFs (C-index 0.81, 95% CI 0.77-0.85 andC-index 0.82, 95% CI 0.78-0.87, respectively). The metabolite score was also associated with prevalent CHD independent of TRFs (odds ratio 1.59, 95% CI 1.19-2.13). Conclusion A metabolite score derived from a single-point metabolome measurement is associated with CHD, and metabolomics may be a promising tool for refining and improving the prediction of CHD. Metabolomics, defined as the comprehensive identification and quantification of low-molecular-weight metabolites to be found in a biological sample, has been put forward as a potential tool for classifying individuals according to their risk of coronary heart disease (CHD). Here, we investigated whether a single-point blood measurement of the metabolome is associated with and predictive for the risk of CHD.BACKGROUNDMetabolomics, defined as the comprehensive identification and quantification of low-molecular-weight metabolites to be found in a biological sample, has been put forward as a potential tool for classifying individuals according to their risk of coronary heart disease (CHD). Here, we investigated whether a single-point blood measurement of the metabolome is associated with and predictive for the risk of CHD.We obtained proton nuclear magnetic resonance spectra in 79 cases who developed CHD during follow-up (median 8.1 years) and in 565 randomly selected individuals. In these spectra, 100 signals representing 36 metabolites were identified. Applying least absolute shrinkage and selection operator regression, we defined a weighted metabolite score consisting of 13 proton nuclear magnetic resonance signals that optimally predicted CHD. This metabolite score, including signals representing a lipid fraction, glucose, valine, ornithine, glutamate, creatinine, glycoproteins, citrate, and 1.5-anhydrosorbitol, was associated with the incidence of CHD independent of traditional risk factors (TRFs) (hazard ratio 1.50, 95% CI 1.12-2.01). Predictive performance of this metabolite score on its own was moderate (C-index 0.75, 95% CI 0.70-0.80), but after adding age and sex, the C-index was only modestly lower than that of TRFs (C-index 0.81, 95% CI 0.77-0.85 and C-index 0.82, 95% CI 0.78-0.87, respectively). The metabolite score was also associated with prevalent CHD independent of TRFs (odds ratio 1.59, 95% CI 1.19-2.13).METHODS AND RESULTSWe obtained proton nuclear magnetic resonance spectra in 79 cases who developed CHD during follow-up (median 8.1 years) and in 565 randomly selected individuals. In these spectra, 100 signals representing 36 metabolites were identified. Applying least absolute shrinkage and selection operator regression, we defined a weighted metabolite score consisting of 13 proton nuclear magnetic resonance signals that optimally predicted CHD. This metabolite score, including signals representing a lipid fraction, glucose, valine, ornithine, glutamate, creatinine, glycoproteins, citrate, and 1.5-anhydrosorbitol, was associated with the incidence of CHD independent of traditional risk factors (TRFs) (hazard ratio 1.50, 95% CI 1.12-2.01). Predictive performance of this metabolite score on its own was moderate (C-index 0.75, 95% CI 0.70-0.80), but after adding age and sex, the C-index was only modestly lower than that of TRFs (C-index 0.81, 95% CI 0.77-0.85 and C-index 0.82, 95% CI 0.78-0.87, respectively). The metabolite score was also associated with prevalent CHD independent of TRFs (odds ratio 1.59, 95% CI 1.19-2.13).A metabolite score derived from a single-point metabolome measurement is associated with CHD, and metabolomics may be a promising tool for refining and improving the prediction of CHD.CONCLUSIONA metabolite score derived from a single-point metabolome measurement is associated with CHD, and metabolomics may be a promising tool for refining and improving the prediction of CHD. Background Metabolomics, defined as the comprehensive identification and quantification of low-molecular-weight metabolites to be found in a biological sample, has been put forward as a potential tool for classifying individuals according to their risk of coronary heart disease (CHD). Here, we investigated whether a single-point blood measurement of the metabolome is associated with and predictive for the risk of CHD. Methods and results We obtained proton nuclear magnetic resonance spectra in 79 cases who developed CHD during follow-up (median 8.1 years) and in 565 randomly selected individuals. In these spectra, 100 signals representing 36 metabolites were identified. Applying least absolute shrinkage and selection operator regression, we defined a weighted metabolite score consisting of 13 proton nuclear magnetic resonance signals that optimally predicted CHD. This metabolite score, including signals representing a lipid fraction, glucose, valine, ornithine, glutamate, creatinine, glycoproteins, citrate, and 1.5-anhydrosorbitol, was associated with the incidence of CHD independent of traditional risk factors (TRFs) (hazard ratio 1.50, 95% CI 1.12-2.01). Predictive performance of this metabolite score on its own was moderate ( C -index 0.75, 95% CI 0.70-0.80), but after adding age and sex, the C -index was only modestly lower than that of TRFs ( C -index 0.81, 95% CI 0.77-0.85 and C -index 0.82, 95% CI 0.78-0.87, respectively). The metabolite score was also associated with prevalent CHD independent of TRFs (odds ratio 1.59, 95% CI 1.19-2.13). Conclusion A metabolite score derived from a single-point metabolome measurement is associated with CHD, and metabolomics may be a promising tool for refining and improving the prediction of CHD.
Author	Böhringer, Stefan Göraler, Sibel Boer, Jolanda M.A. Oostra, Ben A. Jukema, J. Wouter Henneman, Peter van Duijn, Cornelia M. Verhoeven, Aswin Slagboom, P. Eline Weller, Claudia M. van Dijk, Ko Willems Vaarhorst, Anika A.M. Heijmans, Bastiaan T. Verschuren, W.M. Monique Boomsma, Dorret van den Brandt, Piet A. van Greevenbroek, Marleen M. Meissner, Axel Gorgels, Anton P.M. Schouten, Leo J. Isaacs, Aaron Merry, Audrey H.H. Deelder, André M. Feskens, Edith van den Maagdenberg, Arn M.J.M.
Author_xml	– sequence: 1 givenname: Anika A.M. surname: Vaarhorst fullname: Vaarhorst, Anika A.M. email: a.a.m.vaarhorst@lumc.nl organization: Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands – sequence: 2 givenname: Aswin surname: Verhoeven fullname: Verhoeven, Aswin organization: Department of Parasitology, Leiden University Medical Center, Leiden, The Netherlands – sequence: 3 givenname: Claudia M. surname: Weller fullname: Weller, Claudia M. organization: Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands – sequence: 4 givenname: Stefan surname: Böhringer fullname: Böhringer, Stefan organization: Department of Medical Statistics, Leiden University Medical Center, Leiden, The Netherlands – sequence: 5 givenname: Sibel surname: Göraler fullname: Göraler, Sibel organization: Department of Parasitology, Leiden University Medical Center, Leiden, The Netherlands – sequence: 6 givenname: Axel surname: Meissner fullname: Meissner, Axel organization: Department of Parasitology, Leiden University Medical Center, Leiden, The Netherlands – sequence: 7 givenname: André M. surname: Deelder fullname: Deelder, André M. organization: Department of Parasitology, Leiden University Medical Center, Leiden, The Netherlands – sequence: 8 givenname: Peter surname: Henneman fullname: Henneman, Peter organization: Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands – sequence: 9 givenname: Anton P.M. surname: Gorgels fullname: Gorgels, Anton P.M. organization: Department of Cardiology, Maastricht University Medical Centre, Maastricht, The Netherlands – sequence: 10 givenname: Piet A. surname: van den Brandt fullname: van den Brandt, Piet A. organization: Department of Epidemiology (CAPHRI School for Public Health and Primary Care), Maastricht University, Maastricht, The Netherlands – sequence: 11 givenname: Leo J. surname: Schouten fullname: Schouten, Leo J. organization: Department of Epidemiology (GROW School of Oncology and Developmental Biology), Maastricht University, Maastricht, The Netherlands – sequence: 12 givenname: Marleen M. surname: van Greevenbroek fullname: van Greevenbroek, Marleen M. organization: Department of Internal Medicine (CARIM School for Cardiovascular diseases), Maastricht University Medical Centre, Maastricht, The Netherlands – sequence: 13 givenname: Audrey H.H. surname: Merry fullname: Merry, Audrey H.H. organization: Department of Epidemiology (CAPHRI School for Public Health and Primary Care), Maastricht University, Maastricht, The Netherlands – sequence: 14 givenname: W.M. Monique surname: Verschuren fullname: Verschuren, W.M. Monique organization: National Institute for Public Health and the Environment, Bilthoven, The Netherlands – sequence: 15 givenname: Arn M.J.M. surname: van den Maagdenberg fullname: van den Maagdenberg, Arn M.J.M. organization: Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands – sequence: 16 givenname: Ko Willems surname: van Dijk fullname: van Dijk, Ko Willems organization: Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands – sequence: 17 givenname: Aaron surname: Isaacs fullname: Isaacs, Aaron organization: Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands – sequence: 18 givenname: Dorret surname: Boomsma fullname: Boomsma, Dorret organization: Biological Psychology, VU University, Amsterdam, The Netherlands – sequence: 19 givenname: Ben A. surname: Oostra fullname: Oostra, Ben A. organization: Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands – sequence: 20 givenname: Cornelia M. surname: van Duijn fullname: van Duijn, Cornelia M. organization: Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands – sequence: 21 givenname: J. Wouter surname: Jukema fullname: Jukema, J. Wouter organization: Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands – sequence: 22 givenname: Jolanda M.A. surname: Boer fullname: Boer, Jolanda M.A. organization: National Institute for Public Health and the Environment, Bilthoven, The Netherlands – sequence: 23 givenname: Edith surname: Feskens fullname: Feskens, Edith organization: Division of Human Nutrition, Wageningen University and Research Center, Wageningen, The Netherlands – sequence: 24 givenname: Bastiaan T. surname: Heijmans fullname: Heijmans, Bastiaan T. organization: Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands – sequence: 25 givenname: P. Eline surname: Slagboom fullname: Slagboom, P. Eline email: P.Slagboom@lumc.nl organization: Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands
BackLink	https://www.ncbi.nlm.nih.gov/pubmed/24952859$$D View this record in MEDLINE/PubMed
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Copyright	2014 Mosby, Inc. Mosby, Inc. Copyright © 2014 Mosby, Inc. All rights reserved. Copyright Elsevier Limited Jul 2014
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Snippet	Metabolomics, defined as the comprehensive identification and quantification of low-molecular-weight metabolites to be found in a biological sample, has been... Background Metabolomics, defined as the comprehensive identification and quantification of low-molecular-weight metabolites to be found in a biological sample,...
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SubjectTerms	Adult Angina pectoris Body mass index Cardiovascular Cardiovascular disease Cholesterol Coronary Disease - blood Coronary Disease - epidemiology Drug therapy Family medical history Female Follow-Up Studies Heart attacks Humans Incidence Lipids - blood Magnetic Resonance Spectroscopy - methods Male Metabolomics - methods Middle Aged Netherlands - epidemiology Odds Ratio Predictive Value of Tests Prognosis Prospective Studies Risk Factors Spectrum analysis Time Factors Young Adult
Title	A metabolomic profile is associated with the risk of incident coronary heart disease
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