Gender-specific pathway differences in the human serum metabolome

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...

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Published in:Metabolomics Vol. 11; no. 6; pp. 1815 - 1833
Main Authors: Krumsiek, Jan, Mittelstrass, Kirstin, Do, Kieu Trinh, Stückler, Ferdinand, Ried, Janina, Adamski, Jerzy, Peters, Annette, Illig, Thomas, Kronenberg, Florian, Friedrich, Nele, Nauck, Matthias, Pietzner, Maik, Mook-Kanamori, Dennis O., Suhre, Karsten, Gieger, Christian, Grallert, Harald, Theis, Fabian J., Kastenmüller, Gabi
Format: Journal Article
Language:English
Published: New York Springer US 01.12.2015
Springer Nature B.V
Subjects:
Biochemistry
Biomedical and Life Sciences
Biomedicine
Cell Biology
Developmental Biology
Life Sciences
Molecular Medicine
Original
Original Article
Metabolomics
Gender differences
Systems biology
Epidemiology
Metabolic networks
ISSN:1573-3882, 1573-3890
Online Access:Get full text
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Summary: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.
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ISSN:1573-3882
1573-3890
DOI:10.1007/s11306-015-0829-0