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

  • Am Heart J. 2014 Jul;168(1):45-52.e7. doi: 10.1016/j.ahj.2014.01.019.
Anika A M Vaarhorst  1 ,  Aswin Verhoeven  2 ,  Claudia M Weller  3 ,  Stefan Böhringer  4 ,  Sibel Göraler  2 ,  Axel Meissner  2 ,  André M Deelder  2 ,  Peter Henneman  3 ,  Anton P M Gorgels  5 ,  Piet A van den Brandt  6 ,  Leo J Schouten  7 ,  Marleen M van Greevenbroek  8 ,  Audrey H H Merry  9 ,  W M Monique Verschuren  10 ,  Arn M J M van den Maagdenberg  11 ,  Ko Willems van Dijk  12 ,  Aaron Isaacs  13 ,  Dorret Boomsma  14 ,  Ben A Oostra  13 ,  Cornelia M van Duijn  13 ,  J Wouter Jukema  15 ,  Jolanda M A Boer  10 ,  Edith Feskens  16 ,  Bastiaan T Heijmans  17 ,  P Eline Slagboom  18
Affiliations
  • 1. Department of Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands. Electronic address: [email protected].
  • 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: [email protected].
PMID: 24952859 DOI: 10.1016/j.ahj.2014.01.019
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.

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