Anika A M Vaarhorst1, Aswin Verhoeven2, Claudia M Weller3, Stefan Böhringer4, Sibel Göraler2, Axel Meissner2, André M Deelder2, Peter Henneman3, Anton P M Gorgels5, Piet A van den Brandt6, Leo J Schouten7, Marleen M van Greevenbroek8, Audrey H H Merry9, W M Monique Verschuren10, Arn M J M van den Maagdenberg11, Ko Willems van Dijk12, Aaron Isaacs13, Dorret Boomsma14, Ben A Oostra13, Cornelia M van Duijn13, J Wouter Jukema15, Jolanda M A Boer10, Edith Feskens16, Bastiaan T Heijmans17, P Eline Slagboom18. 1. Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands. Electronic address: a.a.m.vaarhorst@lumc.nl. 2. Department of Parasitology, Leiden University Medical Center, Leiden, The Netherlands. 3. Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands. 4. Department of Medical Statistics, Leiden University Medical Center, Leiden, The Netherlands. 5. Department of Cardiology, Maastricht University Medical Centre, Maastricht, The Netherlands. 6. Department of Epidemiology (CAPHRI School for Public Health and Primary Care), Maastricht University, Maastricht, The Netherlands; Department of Epidemiology (GROW School of Oncology and Developmental Biology), Maastricht University, Maastricht, The Netherlands. 7. Department of Epidemiology (GROW School of Oncology and Developmental Biology), Maastricht University, Maastricht, The Netherlands. 8. Department of Internal Medicine (CARIM School for Cardiovascular diseases), Maastricht University Medical Centre, Maastricht, The Netherlands. 9. Department of Epidemiology (CAPHRI School for Public Health and Primary Care), Maastricht University, Maastricht, The Netherlands. 10. National Institute for Public Health and the Environment, Bilthoven, The Netherlands. 11. Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands; Department of Neurology, Leiden University Medical Center, Leiden, The Netherlands. 12. Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands; Department of Endocrinology, Leiden University Medical Center, Leiden, The Netherlands. 13. Department of Epidemiology, Erasmus Medical Center, Rotterdam, The Netherlands. 14. Biological Psychology, VU University, Amsterdam, The Netherlands. 15. Department of Cardiology, Leiden University Medical Center, Leiden, The Netherlands; The Durrer Center for Cardiogenetic Research, Amsterdam, The Netherlands; Interuniversity Cardiology Institute of the Netherlands (ICIN), Utrecht, The Netherlands. 16. Division of Human Nutrition, Wageningen University and Research Center, Wageningen, The Netherlands. 17. Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands. 18. Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands; Netherlands Consortium for Healthy Ageing, Leiden, The Netherlands. Electronic address: P.Slagboom@lumc.nl.
Abstract
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.
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.
Authors: Gesiane Tavares; Gabriela Venturini; Kallyandra Padilha; Roberto Zatz; Alexandre C Pereira; Ravi I Thadhani; Eugene P Rhee; Silvia M O Titan Journal: Metabolomics Date: 2018-02-27 Impact factor: 4.290
Authors: Cavin K Ward-Caviness; Tao Xu; Thor Aspelund; Barbara Thorand; Corinna Montrone; Christa Meisinger; Irmtraud Dunger-Kaltenbach; Astrid Zierer; Zhonghao Yu; Inga R Helgadottir; Tamara B Harris; Lenore J Launer; Andrea Ganna; Lars Lind; Gudny Eiriksdottir; Melanie Waldenberger; Cornelia Prehn; Karsten Suhre; Thomas Illig; Jerzy Adamski; Andreas Ruepp; Wolfgang Koenig; Vilmundur Gudnason; Valur Emilsson; Rui Wang-Sattler; Annette Peters Journal: Heart Date: 2017-03-02 Impact factor: 5.994
Authors: Nina P Paynter; Raji Balasubramanian; Franco Giulianini; Dong D Wang; Lesley F Tinker; Shuba Gopal; Amy A Deik; Kevin Bullock; Kerry A Pierce; Justin Scott; Miguel A Martínez-González; Ramon Estruch; JoAnn E Manson; Nancy R Cook; Christine M Albert; Clary B Clish; Kathryn M Rexrode Journal: Circulation Date: 2018-02-20 Impact factor: 29.690
Authors: Tõnu Esko; Joel N Hirschhorn; Henry A Feldman; Yu-Han H Hsu; Amy A Deik; Clary B Clish; Cara B Ebbeling; David S Ludwig Journal: Am J Clin Nutr Date: 2017-01-11 Impact factor: 7.045