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Human serum metabolic profiles are age dependent

  1. Zhonghao Yu1,†,
  2. Guangju Zhai2,3,†,
  3. Paula Singmann1,†,
  4. Ying He4,5,
  5. Tao Xu1,
  6. Cornelia Prehn6,
  7. Werner Römisch-Margl7,
  8. Eva Lattka1,
  9. Christian Gieger8,
  10. Nicole Soranzo2,9,
  11. Joachim Heinrich10,
  12. Marie Standl10,
  13. Elisabeth Thiering10,
  14. Kirstin Mittelstraß1,
  15. Heinz-Erich Wichmann10,11,12,
  16. Annette Peters1,13,14,
  17. Karsten Suhre7,15,16,
  18. Yixue Li4,5,
  19. Jerzy Adamski6,17,
  20. Tim D. Spector2,‡,
  21. Thomas Illig1,18,‡,
  22. Rui Wang-Sattler1,‡

Article first published online: 27 AUG 2012

DOI: 10.1111/j.1474-9726.2012.00865.x

Issue

Aging Cell

Aging Cell

Volume 11, Issue 6, pages 960–967, December 2012

Additional Information

How to Cite

Yu, Z., Zhai, G., Singmann, P., He, Y., Xu, T., Prehn, C., Römisch-Margl, W., Lattka, E., Gieger, C., Soranzo, N., Heinrich, J., Standl, M., Thiering, E., Mittelstraß, K., Wichmann, H.-E., Peters, A., Suhre, K., Li, Y., Adamski, J., Spector, T. D., Illig, T. and Wang-Sattler, R. (2012), Human serum metabolic profiles are age dependent. Aging Cell, 11: 960–967. doi: 10.1111/j.1474-9726.2012.00865.x

Author Information

  1. 1

    Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany

  2. 2

    Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK

  3. 3

    Discipline of Genetics, Faculty of Medicine, Memorial University of Newfoundland, St John’s, NL, Canada

  4. 4

    Shanghai Center for Bioinformation Technology, 200235 Shanghai, China

  5. 5

    Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 200031 Shanghai, China

  6. 6

    Genome Analysis Center, Institute of Experimental Genetics, Helmholtz Zentrum München, 85764 Neuherberg, Germany

  7. 7

    Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany

  8. 8

    Institute of Genetic Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany

  9. 9

    Wellcome Trust Sanger Institute Genome Campus, Hinxton, UK

  10. 10

    Institute of Epidemiology I, Helmholtz Zentrum München, 85764 Neuherberg, Germany

  11. 11

    Institute of Medical Informatics, Biometry and Epidemiology, Chair of Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany

  12. 12

    Klinikum Grosshadern, Munich, Germany

  13. 13

    Institute of Epidemiology II, Helmholtz Zentrum München, 85764 Neuherberg, Germany

  14. 14

    Department of Environmental Health, Harvard School of Public Health Adjunct Associate Professor of Environmental Epidemiology, Boston, MA, USA

  15. 15

    Faculty of Biology, Ludwig-Maximilians-Universität, 82152 Planegg-Martinsried, Germany

  16. 16

    Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, 24144 Education City–Qatar Foundation, Doha, Qatar

  17. 17

    Institute of Experimental Genetics, Life and Food Science Center Weihenstephan, Technische Universität München, 85354 Freising-Weihenstephan, Germany

  18. 18

    Hannover Unified Biobank, Hannover Medical School, 30625 Hannover, Germany

  1. †

    Zhonghao Yu, Guangju Zhai and Paula Singmann contributed equally.

  2. ‡

    Tim D. Spector, Thomas Illig and Rui Wang-Sattler contributed equally.

* Rui Wang-Sattler, Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany. Tel.: +49 89 3187 3978; fax: +49 89 3187 2428; e-mail: rui.wang-sattler@helmholtz-muenchen.de

  1. Carried out at the Research Unit of Molecular Epidemiology, Helmholtz ZentrumMünchen, 85764 Neuherberg, Germany, and the Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK.

  2. †

    Zhonghao Yu, Guangju Zhai and Paula Singmann contributed equally.

  3. ‡

    Tim D. Spector, Thomas Illig and Rui Wang-Sattler contributed equally.

  4. Co-corresponding authors. Tim D. Spector, Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK and Thomas Illig, Hannover Unified Biobank, Hannover Medical School, 30625 Hannover, Germany. Tel.: +49 89 3187 3978; fax: +49 89 3187 2428; e-mails: tim.spector@kcl.ac.uk; illig@helmholtz-muenchen.de

  5. Re-use of this article is permitted in accordance with the Terms and Conditions set out at http://wileyonlinelibrary.com/onlineopen#OnlineOpen_Terms

Publication History

  1. Issue published online: 15 NOV 2012
  2. Article first published online: 27 AUG 2012
  3. Accepted manuscript online: 26 JUL 2012 12:19PM EST
  4. Accepted for publication 16 July 2012

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Keywords:

  • age;
  • aging;
  • epidemiology;
  • metabolomics;
  • population-based study

Summary

Understanding the complexity of aging is of utmost importance. This can now be addressed by the novel and powerful approach of metabolomics. However, to date, only a few metabolic studies based on large samples are available. Here, we provide novel and specific information on age-related metabolite concentration changes in human homeostasis. We report results from two population-based studies: the KORA F4 study from Germany as a discovery cohort, with 1038 female and 1124 male participants (32–81 years), and the TwinsUK study as replication, with 724 female participants. Targeted metabolomics of fasting serum samples quantified 131 metabolites by FIA-MS/MS. Among these, 71/34 metabolites were significantly associated with age in women/men (BMI adjusted). We further identified a set of 13 independent metabolites in women (with P values ranging from 4.6 × 10−04 to 7.8 × 10−42, αcorr = 0.004). Eleven of these 13 metabolites were replicated in the TwinsUK study, including seven metabolite concentrations that increased with age (C0, C10:1, C12:1, C18:1, SM C16:1, SM C18:1, and PC aa C28:1), while histidine decreased. These results indicate that metabolic profiles are age dependent and might reflect different aging processes, such as incomplete mitochondrial fatty acid oxidation. The use of metabolomics will increase our understanding of aging networks and may lead to discoveries that help enhance healthy aging.

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