Erik B van den Akker1,2,3, Stella Trompet1,4, Jurriaan J H Barkey Wolf1, Marian Beekman1, H Eka D Suchiman1, Joris Deelen1,5, Folkert W Asselbergs6,7,8,9, Eric Boersma10, Davy Cats1, Petra M Elders11,12, J Marianne Geleijnse13, M Arfan Ikram14,15,16, Margreet Kloppenburg17,18, Haillang Mei1,19, Ingrid Meulenbelt1, Simon P Mooijaart2, Rob G H H Nelissen20, Mihai G Netea21, Brenda W J H Penninx12,22, Mariska Slofstra23, Coen D A Stehouwer24,25, Morris A Swertz23, Charlotte E Teunissen26, Gisela M Terwindt27, Leen M 't Hart1,28,12,29,30, Arn M J M van den Maagdenberg31, Pim van der Harst32, Iwan C C van der Horst33, Carla J H van der Kallen24,25, Marleen M J van Greevenbroek24,25, W Erwin van Spil34, Cisca Wijmenga23, Alexandra Zhernakova23, Aeilko H Zwinderman35, Naveed Sattar36, J Wouter Jukema37, Cornelia M van Duijn14, Dorret I Boomsma37,38, Marcel J T Reinders2,3, P Eline Slagboom1,5. 1. Department of Molecular Epidemiology (E.B.v.d.A., J.J.H.B.W., M.B., H.E.D.S., J.D., D.C., H.M., I.M., L.M.'t.H., P.E.S.). 2. Department of Biomedical Data Sciences, Leiden Computational Biology Center (E.B.v.d.A., M.J.T.R.), Leiden University Medical Center, the Netherlands. 3. Department of Pattern Recognition and Bioinformatics, Delft University of Technology, the Netherlands (E.B.v.d.A., M.J.T.R.). 4. Department of Internal Medicine, Division of Gerontology and Geriatrics (S.T., S.P.M.), Leiden University Medical Center, the Netherlands. 5. Max Planck Institute for Biology of Ageing, Cologne, Germany (J.D., P.E.S.). 6. Department of Cardiology, Division of Heart and Lungs (F.W.A.), University Medical Center Utrecht, the Netherlands. 7. Durrer Center for Cardiovascular Research, Netherlands Heart Institute, Utrecht (F.W.A.). 8. Faculty of Population Health Sciences, Institute of Cardiovascular Science (F.W.A.), Institute of Health Informatics, UCL, London, United Kingdom. 9. Farr Institute of Health Informatics Research (F.W.A.), Institute of Health Informatics, UCL, London, United Kingdom. 10. Thorax Center (E.B.), Erasmus Medical Center, Rotterdam, the Netherlands. 11. Department of General Practice and Elderly Care Medicine (P.M.E.), VU University Medical Center, the Netherlands. 12. Amsterdam Public Health Research Institute (P.M.E., B.W.J.H.P., L.M.'t.H., D.I.B.), VU University Medical Center, the Netherlands. 13. Division of Human Nutrition and Health, Wageningen University, the Netherlands (J.M.G.). 14. Department of Epidemiology (M.A.I., C.M.v.D.), Erasmus Medical Center, Rotterdam, the Netherlands. 15. Department of Radiology (M.A.I.), Erasmus Medical Center, Rotterdam, the Netherlands. 16. Department of Neurology (M.A.I.), Erasmus Medical Center, Rotterdam, the Netherlands. 17. Department of Rheumatology (M.K.), Leiden University Medical Center, the Netherlands. 18. Department of Clinical Epidemiology (M.K.), Leiden University Medical Center, the Netherlands. 19. Department of Biomedical Data Sciences, Sequencing Analysis Support Core (H.M.), Leiden University Medical Center, the Netherlands. 20. Department of Orthopaedics (R.G.H.H.N.), Leiden University Medical Center, the Netherlands. 21. Department for Genomics and Immunoregulation, Life and Medical Sciences Institute, University of Bonn, Germany (M.G.N.). 22. Department of Psychiatry (B.W.J.H.P.), VU University Medical Center, the Netherlands. 23. Department of Genetics, University of Groningen, the Netherlands (M.S., M.A.S., C.W., A.Z.). 24. Department of Internal Medicine, Maastricht University Medical Center, the Netherlands (C.D.A.S., C.J.H.v.d.K., M.M.J.v.G.). 25. School for Cardiovascular Diseases (Cardiovascular Research Institute Maastricht [CARIM]), Maastricht University, Maastricht, the Netherlands (C.D.A.S., C.J.H.v.d.K., M.M.J.v.G.). 26. Neurochemistry Laboratory, Clinical Chemistry Department (C.E.T.), Amsterdam University Medical Center, the Netherlands. 27. Department of Neurology (G.M.T.), Leiden University Medical Center, the Netherlands. 28. Department of Cell and Chemical Biology (L.M.'t.H.), Leiden University Medical Center, the Netherlands. 29. Department of Epidemiology and Biostatistics (L.M.'t.H.), Amsterdam University Medical Center, the Netherlands. 30. Department of General Practice (L.M.'t.H.), Amsterdam University Medical Center, the Netherlands. 31. Department of Human Genetics (A.M.J.M.v.d.M.), Leiden University Medical Center, the Netherlands. 32. Department of Cardiology (P.v.d.H.), University Medical Center Groningen, the Netherlands. 33. Department of Critical Care (I.C.C.v.d.H.), University Medical Center Groningen, the Netherlands. 34. Department of Rheumatology and Clinical Immunology (W.E.v.S.), University Medical Center Utrecht, the Netherlands. 35. Department of Clinical Epidemiology, Biostatistics and Bioinformatics, Academic Medical Center, University of Amsterdam, the Netherlands (A.H.Z.). 36. Institute of Cardiovascular and Medical Sciences, Cardiovascular Research Center, University of Glasgow, United Kingdom (N.S.). 37. Department of Cardiology (S.T., J.W.J.), Leiden University Medical Center, the Netherlands. 38. Netherlands Twin Register, Department of Biological Psychology, Vrije University, Amsterdam (D.I.B.).
Abstract
BACKGROUND: The blood metabolome incorporates cues from the environment and the host's genetic background, potentially offering a holistic view of an individual's health status. METHODS: We have compiled a vast resource of proton nuclear magnetic resonance metabolomics and phenotypic data encompassing over 25 000 samples derived from 26 community and hospital-based cohorts. RESULTS: Using this resource, we constructed a metabolomics-based age predictor (metaboAge) to calculate an individual's biological age. Exploration in independent cohorts demonstrates that being judged older by one's metabolome, as compared with one's chronological age, confers an increased risk on future cardiovascular disease, mortality, and functionality in older individuals. A web-based tool for calculating metaboAge (metaboage.researchlumc.nl) allows easy incorporation in other epidemiological studies. Access to data can be requested at bbmri.nl/samples-images-data. CONCLUSIONS: In summary, we present a vast resource of metabolomics data and illustrate its merit by constructing a metabolomics-based score for biological age that captures aspects of current and future cardiometabolic health.
BACKGROUND: The blood metabolome incorporates cues from the environment and the host's genetic background, potentially offering a holistic view of an individual's health status. METHODS: We have compiled a vast resource of proton nuclear magnetic resonance metabolomics and phenotypic data encompassing over 25 000 samples derived from 26 community and hospital-based cohorts. RESULTS: Using this resource, we constructed a metabolomics-based age predictor (metaboAge) to calculate an individual's biological age. Exploration in independent cohorts demonstrates that being judged older by one's metabolome, as compared with one's chronological age, confers an increased risk on future cardiovascular disease, mortality, and functionality in older individuals. A web-based tool for calculating metaboAge (metaboage.researchlumc.nl) allows easy incorporation in other epidemiological studies. Access to data can be requested at bbmri.nl/samples-images-data. CONCLUSIONS: In summary, we present a vast resource of metabolomics data and illustrate its merit by constructing a metabolomics-based score for biological age that captures aspects of current and future cardiometabolic health.
Entities:
Keywords:
aging; cardiovascular disease; data science; metabolomics
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