Human serum metabolic profiles are age dependent |
| |
| Authors: | Ying He Tao Xu Cornelia Prehn Werner Römisch‐Margl Eva Lattka Christian Gieger Nicole Soranzo Joachim Heinrich Marie Standl Elisabeth Thiering Kirstin Mittelstraß Heinz‐Erich Wichmann Annette Peters Karsten Suhre Yixue Li Jerzy Adamski Rui Wang‐Sattler |
| |
| Institution: | 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;3. Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany;4. Genome Analysis Center, Institute of Experimental Genetics, Helmholtz Zentrum München, 85764 Neuherberg, Germany;5. Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, 85764 Neuherberg, Germany;6. Institute of Genetic Epidemiology, Helmholtz Zentrum München, 85764 Neuherberg, Germany;7. Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK;8. Wellcome Trust Sanger Institute Genome Campus, Hinxton, UK;9. Institute of Epidemiology I, Helmholtz Zentrum München, 85764 Neuherberg, Germany;10. Institute of Medical Informatics, Biometry and Epidemiology, Chair of Epidemiology, Ludwig‐Maximilians‐Universit?t, Munich, Germany;11. Klinikum Grosshadern, Munich, Germany;12. Institute of Epidemiology II, Helmholtz Zentrum München, 85764 Neuherberg, Germany;13. Department of Environmental Health, Harvard School of Public Health Adjunct Associate Professor of Environmental Epidemiology, Boston, MA, USA;14. Faculty of Biology, Ludwig‐Maximilians‐Universit?t, 82152 Planegg‐Martinsried, Germany;15. Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, 24144 Education City–Qatar Foundation, Doha, Qatar;16. Institute of Experimental Genetics, Life and Food Science Center Weihenstephan, Technische Universit?t München, 85354 Freising‐Weihenstephan, Germany |
| |
| Abstract: | 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. |
| |
| Keywords: | age aging epidemiology metabolomics population‐based study |
|
|
