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@ARTICLE{Iqbal:142437,
      author       = {K. Iqbal and S. Dietrich and C. Wittenbecher and J.
                      Krumsiek and T. Kühn$^*$ and M. E. Lacruz and A. Kluttig
                      and C. Prehn and J. Adamski and M. von Bergen and R.
                      Kaaks$^*$ and M. B. Schulze and H. Boeing and A. Floegel},
      title        = {{C}omparison of metabolite networks from four {G}erman
                      population-based studies.},
      journal      = {International journal of epidemiology},
      volume       = {47},
      number       = {6},
      issn         = {1464-3685},
      address      = {Oxford},
      publisher    = {Oxford Univ. Press},
      reportid     = {DKFZ-2019-00157},
      pages        = {2070 - 2081},
      year         = {2018},
      abstract     = {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.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.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.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.},
      cin          = {C020},
      ddc          = {610},
      cid          = {I:(DE-He78)C020-20160331},
      pnm          = {313 - Cancer risk factors and prevention (POF3-313)},
      pid          = {G:(DE-HGF)POF3-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:29982629},
      pmc          = {pmc:PMC6280930},
      doi          = {10.1093/ije/dyy119},
      url          = {https://inrepo02.dkfz.de/record/142437},
}