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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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Fig. S1 Correlation matrix for women in KORA F4. Values of Pearson correlation coefficients between every pair of metabolites were displayed in a heat map. Red indicates a positive correlation, while yellow corresponds to a negative-to-zero correlation. Grey boxes represent groups of metabolites clustered together. AA, amino acid; AC, acylcarnitines; PC aa, phosphatidylcholinediacyl; PC ae, phosphatidylcholine acyl-alkyl; and lyso PC a, lysophosphatidylcholine acyl.

Fig. S2 Heat map of the fold standard deviation changes between ages, and clustering of these changes over all ages, in 1124 males from KORA F4. The heat map shows changes of x-fold standard deviation from the overall mean concentration for each age year in a color-coded depiction. Green squares represent a decrease, and red squares an increase. Grey boxes represent groups of metabolites with similar changes, with number of metabolites in parentheses. Metabolite names in red indicate our set of 12 metabolites. AA, amino acid; AC, acylcarnitines; PC aa, phosphatidylcholinediacyl; PC ae, phosphatidylcholine acyl-alkyl; and lyso PC a, lysophosphatidylcholine acyl.

Fig. S3 Concentration trends of 13 potentialbiomarkers in women from KORA F4. Age is shown on thex-axis; metabolite concentration (μM) plotted, on they-axis; grey area, 95% prediction interval. Plots weretruncated from 1–99 percentiles of observations.

Fig. S4 Concentration trends of 12 selectedmetabolites in males from KORA F4. Age is shown on thex-axis; metabolite concentration (μM) plotted, on they-axis; grey area, 95% prediction interval. Plots weretruncated from 1–99 percentiles of observations.

Fig. S5 Concentration trends of the set of 13metabolites in women from the TwinsUK study. Age is shown on thex-axis; metabolite concentration (μM) plotted, on they-axis; grey area, 95% prediction interval. Plots weretruncated from 1–99 percentiles of observations. Note thatcurve progression at the margins should not be overrated, as n islow.

Table S1 Characteristics of 163 metabolites inKORA F4. Abbreviations and full biochemical names of 163metabolites are shown in the first and second columns,respectively. The next three columns display correlationcoefficients (r) between two duplicated measurements on 144re-measured samples, the percentage of 3080 individuals above thelimit of detection (LOD), and the mean value of the coefficient ofvariance (CV) of the three quality controls, respectively. The nextcolumn indicates the status (used/excluded) for each metabolite.The mean and standard deviation of the concentration of each usedmetabolites, along with their correlation with BMI, are listed inthe last two columns.

Table S2 Potential biomarkers for age in menfrom KORA F4. Mean concentrations and standard deviation, alongwith the beta and P values from the regression models, are displayed in the table for the 12 potential age biomarkers in males from KORA F4.

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