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Metabolomics. 2015;11(6):1815-1833. Epub 2015 Aug 4.

Gender-specific pathway differences in the human serum metabolome.

Author information

1
Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany ; German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany.
2
Research Unit of Molecular Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany ; Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Germany.
3
Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.
4
Institute of Genetic Epidemiology, Helmholtz Zentrum München, Neuherberg, Germany.
5
German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany ; Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, Neuherberg, Germany ; Lehrstuhl für Experimentelle Genetik, Technische Universität München, Freising-Weihenstephan, Germany.
6
German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany ; Institute of Epidemiology II, Helmholtz Zentrum München, Neuherberg, Germany ; German Center for Cardiovascular Disease Research (DZHK e.V.), Munich, Germany.
7
Hannover Unified Biobank, Hannover Medical School, Hannover, Germany.
8
Division of Genetic Epidemiology, Department of Medical Genetics, Molecular and Clinical Pharmacology, Innsbruck Medical University, Innsbruck, Austria.
9
Institute of Clinical Chemistry and Laboratory Medicine, University Medicine Greifswald, Greifswald, Germany ; DZHK (German Center for Cardiovascular Research), Greifswald, Greifswald, Germany.
10
Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands ; Department of Endocrinology, Leiden University Medical Center, Leiden, The Netherlands ; Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Qatar Foundation, Doha, Qatar.
11
Department of Physiology and Biophysics, Weill Cornell Medical College in Qatar, Qatar Foundation, Doha, Qatar ; Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.
12
Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany ; Department of Mathematics, Technische Universität München, Garching, Germany.
13
German Center for Diabetes Research (DZD e.V.), Neuherberg, Germany ; Institute of Bioinformatics and Systems Biology, Helmholtz Zentrum München, Neuherberg, Germany.

Abstract

The susceptibility for various diseases as well as the response to treatments differ considerably between men and women. As a basis for a gender-specific personalized healthcare, an extensive characterization of the molecular differences between the two genders is required. In the present study, we conducted a large-scale metabolomics analysis of 507 metabolic markers measured in serum of 1756 participants from the German KORA F4 study (903 females and 853 males). One-third of the metabolites show significant differences between males and females. A pathway analysis revealed strong differences in steroid metabolism, fatty acids and further lipids, a large fraction of amino acids, oxidative phosphorylation, purine metabolism and gamma-glutamyl dipeptides. We then extended this analysis by a network-based clustering approach. Metabolite interactions were estimated using Gaussian graphical models to get an unbiased, fully data-driven metabolic network representation. This approach is not limited to possibly arbitrary pathway boundaries and can even include poorly or uncharacterized metabolites. The network analysis revealed several strongly gender-regulated submodules across different pathways. Finally, a gender-stratified genome-wide association study was performed to determine whether the observed gender differences are caused by dimorphisms in the effects of genetic polymorphisms on the metabolome. With only a single genome-wide significant hit, our results suggest that this scenario is not the case. In summary, we report an extensive characterization and interpretation of gender-specific differences of the human serum metabolome, providing a broad basis for future analyses.

KEYWORDS:

Epidemiology; Gender differences; Metabolic networks; Metabolomics; Systems biology

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