Metabolic Age Based on the BBMRI-NL (1)H-NMR Metabolomics Repository as 
Biomarker of Age-related Disease

van den Akker, E. B.; Trompet, S.; Barkey Wolf, J. J. H.; Beekman, M.; Suchiman, H. E. D.; Deelen, J.; Asselbergs, F. W.; Boersma, E.; Cats, D.; Elders, P. M.; Geleijnse, J. M.; Ikram, M. A.; Kloppenburg, M.; Mei, H.; Meulenbelt, I.; Mooijaart, S. P.; Nelissen, Rghh; Netea, M. G.; Penninx, Bwjh; Slofstra, M.; Stehouwer, C. D. A.; Swertz, M. A.; Teunissen, C. E.; Terwindt, G. M.; t Hart, L. M.; van den Maagdenberg, Amjm; van der Harst, P.; van der Horst, I. C. C.; van der Kallen, C. J. H.; van Greevenbroek, M. M. J.; van Spil, W. E.; Wijmenga, C.; Zhernakova, A.; Zwinderman, A. H.; Sattar, N.; Jukema, J. W.; van Duijn, C. M.; Boomsma, D. I.; Reinders, M. J. T.; Slagboom, P. E.

doi:10.1161/CIRCGEN.119.002610

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Metabolic Age Based on the BBMRI-NL (1)H-NMR Metabolomics Repository as Biomarker of Age-related Disease

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Deelen, J.
Deelen – Genetics and Biomarkers of Human Ageing, Research Groups, Max Planck Institute for Biology of Ageing, Max Planck Society;

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Slagboom, P. E.
Slagboom – Molecular Epidemiology, External and Associated Groups, Max Planck Institute for Biology of Ageing, Max Planck Society;

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https://www.ncbi.nlm.nih.gov/pubmed/33079603
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Citation

van den Akker, E. B., Trompet, S., Barkey Wolf, J. J. H., Beekman, M., Suchiman, H. E. D., Deelen, J., et al. (2020). Metabolic Age Based on the BBMRI-NL (1)H-NMR Metabolomics Repository as Biomarker of Age-related Disease. Circ Genom Precis Med, 13(5), 541-547. doi:10.1161/CIRCGEN.119.002610.

Cite as: https://hdl.handle.net/21.11116/0000-000B-2DC4-6

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.