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Int J Epidemiol. 2018 Dec 1;47(6):2070-2081. doi: 10.1093/ije/dyy119.

Comparison of metabolite networks from four German population-based studies.

Author information

1
Department of Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.
2
Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.
3
German Center for Diabetes Research (DZD), München-Neuherberg, Germany.
4
Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.
5
German Cancer Research Center (DKFZ), Division of Cancer Epidemiology, Heidelberg, Germany.
6
Institute of Medical Epidemiology, Biostatistics and Informatics, Martin Luther University Halle-Wittenberg, Halle, Germany.
7
Institute of Experimental Genetics, Genome Analysis Center, Helmholtz Zentrum München, German Research Center for Environmental Health, Neuherberg, Germany.
8
Lehrstuhl für Experimentelle Genetik, Technische Universität München, Freising, Germany.
9
Helmholtz Centre for Environmental Research-UFZ, Leipzig, Germany.
10
Leibniz Institute for Prevention Research and Epidemiology-BIPS, Bremen, Germany.

Abstract

Background:

Metabolite networks are suggested to reflect biological pathways in health and disease. However, it is unknown whether such metabolite networks are reproducible across different populations. Therefore, the current study aimed to investigate similarity of metabolite networks in four German population-based studies.

Methods:

One hundred serum metabolites were quantified in European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam (n = 2458), EPIC-Heidelberg (n = 812), KORA (Cooperative Health Research in the Augsburg Region) (n = 3029) and CARLA (Cardiovascular Disease, Living and Ageing in Halle) (n = 1427) with targeted metabolomics. In a cross-sectional analysis, Gaussian graphical models were used to construct similar networks of 100 edges each, based on partial correlations of these metabolites. The four metabolite networks of the top 100 edges were compared based on (i) common features, i.e. number of common edges, Pearson correlation (r) and hamming distance (h); and (ii) meta-analysis of the four networks.

Results:

Among the four networks, 57 common edges and 66 common nodes (metabolites) were identified. Pairwise network comparisons showed moderate to high similarity (r = 63-0.96, h = 7-72), among the networks. Meta-analysis of the networks showed that, among the 100 edges and 89 nodes of the meta-analytic network, 57 edges and 66 metabolites were present in all the four networks, 58-76 edges and 75-89 nodes were present in at least three networks, and 63-84 edges and 76-87 edges were present in at least two networks. The meta-analytic network showed clear grouping of 10 sphingolipids, 8 lyso-phosphatidylcholines, 31 acyl-alkyl-phosphatidylcholines, 30 diacyl-phosphatidylcholines, 8 amino acids and 2 acylcarnitines.

Conclusions:

We found structural similarity in metabolite networks from four large studies. Using a meta-analytic network, as a new approach for combining metabolite data from different studies, closely related metabolites could be identified, for some of which the biological relationships in metabolic pathways have been previously described. They are candidates for further investigation to explore their potential role in biological processes.

PMID:
29982629
PMCID:
PMC6280930
DOI:
10.1093/ije/dyy119
[Indexed for MEDLINE]
Free PMC Article

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