The DOI matches its Crossref record; 67 of the 67 references it cites were found in a registry or an index.
Gender-specific pathway differences in the human serum metabolome
Open accessAbstract
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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USING THE CORRECT STATISTICAL TEST FOR THE EQUALITY OF REGRESSION COEFFICIENTS
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Cited by
Aguiar-Pulido, V., Huang, W., Suarez-Ulloa, V., Cickovski, T., Mathee, K., & Narasimhan, G. (2016) · Evolutionary Bioinformatics · doi:10.4137/ebo.s36436 · Cited by 499
Multi-omics integration in biomedical research – A metabolomics-centric review
Wörheide, M. A., Krumsiek, J., Kastenmüller, G., & Arnold, M. (2021) · Analytica Chimica Acta · doi:10.1016/j.aca.2020.10.038 · Cited by 334
Self-citationThe Mouse Microbiome Is Required for Sex-Specific Diurnal Rhythms of Gene Expression and Metabolism
Weger, B. D., Gobet, C., Yeung, J., Martin, E., Jimenez, S., Betrisey, B., Foata, F., Berger, B., Balvay, A., Foussier, A., et al. (2019) · Cell Metabolism · doi:10.1016/j.cmet.2018.09.023 · Cited by 269
Fluge, Ø., Mella, O., Bruland, O., Risa, K., Dyrstad, S. E., Alme, K., Rekeland, I. G., Sapkota, D., Røsland, G. V., Fosså, A., et al. (2016) · JCI Insight · doi:10.1172/jci.insight.89376 · Cited by 209
Rist, M. J., Roth, A., Frommherz, L., Weinert, C. H., Krüger, R., Merz, B., Bunzel, D., Mack, C., Egert, B., Bub, A., et al. (2017) · PLOS ONE · doi:10.1371/journal.pone.0183228 · Cited by 202
Metadata
- Authors
- Krumsiek, J., Mittelstrass, K., Do, K. T., Stückler, F., Ried, J., Adamski, J., Peters, A., Illig, T., Kronenberg, F., Friedrich, N., Nauck, M., Pietzner, M., Mook-Kanamori, D. O., Suhre, K., Gieger, C., Grallert, H., Theis, F. J., & Kastenmüller, G.
- Venue
- Metabolomics
- Published
- 2015-08-04
- Volume
- 11
- Issue
- 6
- Pages
- 1815-1833
- ISSN
- 1573-3882 (ISSN-L)
- 1573-3890
- OpenAlex
- W1961515348
- Cited by
- 291
- References
- 67
- FWCI
- 4.76
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Krumsiek, J., Mittelstrass, K., Do, K. T., Stückler, F., Ried, J., Adamski, J., Peters, A., Illig, T., Kronenberg, F., Friedrich, N., Nauck, M., Pietzner, M., Mook-Kanamori, D. O., Suhre, K., Gieger, C., Grallert, H., Theis, F. J., & Kastenmüller, G. (2015). Gender-specific pathway differences in the human serum metabolome. Metabolomics. https://doi.org/10.1007/s11306-015-0829-0
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
