• Open Access

Human serum metabolic profiles are age dependent

Authors

  • Zhonghao Yu,

    1. Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
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    • Zhonghao Yu, Guangju Zhai and Paula Singmann contributed equally.

  • Guangju Zhai,

    1. Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK
    2. Discipline of Genetics, Faculty of Medicine, Memorial University of Newfoundland, St John’s, NL, Canada
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    • Zhonghao Yu, Guangju Zhai and Paula Singmann contributed equally.

  • Paula Singmann,

    1. Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
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    • Zhonghao Yu, Guangju Zhai and Paula Singmann contributed equally.

  • Ying He,

    1. Shanghai Center for Bioinformation Technology, 200235 Shanghai, China
    2. Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 200031 Shanghai, China
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  • Tao Xu,

    1. Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
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  • Cornelia Prehn,

    1. Genome Analysis Center, Institute of Experimental Genetics, Helmholtz Zentrum München, 85764 Neuherberg, Germany
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  • Werner Römisch-Margl,

    1. Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
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  • Eva Lattka,

    1. Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
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  • Christian Gieger,

    1. Institute of Genetic Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
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  • Nicole Soranzo,

    1. Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK
    2. Wellcome Trust Sanger Institute Genome Campus, Hinxton, UK
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  • Joachim Heinrich,

    1. Institute of Epidemiology I, Helmholtz Zentrum München, 85764 Neuherberg, Germany
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  • Marie Standl,

    1. Institute of Epidemiology I, Helmholtz Zentrum München, 85764 Neuherberg, Germany
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  • Elisabeth Thiering,

    1. Institute of Epidemiology I, Helmholtz Zentrum München, 85764 Neuherberg, Germany
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  • Kirstin Mittelstraß,

    1. Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
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  • Heinz-Erich Wichmann,

    1. Institute of Epidemiology I, Helmholtz Zentrum München, 85764 Neuherberg, Germany
    2. Institute of Medical Informatics, Biometry and Epidemiology, Chair of Epidemiology, Ludwig-Maximilians-Universität, Munich, Germany
    3. Klinikum Grosshadern, Munich, Germany
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  • Annette Peters,

    1. Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
    2. Institute of Epidemiology II, Helmholtz Zentrum München, 85764 Neuherberg, Germany
    3. Department of Environmental Health, Harvard School of Public Health Adjunct Associate Professor of Environmental Epidemiology, Boston, MA, USA
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  • Karsten Suhre,

    1. Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
    2. Faculty of Biology, Ludwig-Maximilians-Universität, 82152 Planegg-Martinsried, Germany
    3. Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, 24144 Education City–Qatar Foundation, Doha, Qatar
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  • Yixue Li,

    1. Shanghai Center for Bioinformation Technology, 200235 Shanghai, China
    2. Key Lab of Systems Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, 200031 Shanghai, China
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  • Jerzy Adamski,

    1. Genome Analysis Center, Institute of Experimental Genetics, Helmholtz Zentrum München, 85764 Neuherberg, Germany
    2. Institute of Experimental Genetics, Life and Food Science Center Weihenstephan, Technische Universität München, 85354 Freising-Weihenstephan, Germany
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  • Tim D. Spector,

    1. Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK
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    • Tim D. Spector, Thomas Illig and Rui Wang-Sattler contributed equally.

  • Thomas Illig,

    1. Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
    2. Hannover Unified Biobank, Hannover Medical School, 30625 Hannover, Germany
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    • Tim D. Spector, Thomas Illig and Rui Wang-Sattler contributed equally.

  • Rui Wang-Sattler

    1. Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany
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    • Tim D. Spector, Thomas Illig and Rui Wang-Sattler contributed equally.


  • 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.

  • 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

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

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

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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