title: Network-based approach for analyzing intra- and interfluid metabolite associations in human blood, urine, and saliva.
abstract: Most studies investigating human metabolomics measurements are limited to a single biofluid, most often blood or urine. An organism's biochemical pool, however, comprises complex transboundary relationships, which can only be understood by investigating metabolic interactions and physiological processes spanning multiple parts of the human body. Therefore, we here propose a data-driven network-based approach to generate an integrated picture of metabolomics associations over multiple fluids. We performed an analysis of 2251 metabolites measured in plasma, urine, and saliva, from 374 participants of the Qatar Metabolomics Study on Diabetes (QMDiab). Gaussian graphical models (GGMs) were used to estimate metabolite-metabolite interactions on different subsets of the data set. First, we compared similarities and differences of the metabolome and the association networks between the three fluids. Second, we investigated the cross-talk between the fluids by analyzing correlations occurring between them. Third, we propose a framework for the analysis of medically relevant phenotypes by integrating type 2 diabetes, sex, age, and body mass index into our networks. In conclusion, we present a generic, data-driven network-based approach for structuring and visualizing metabolite correlations within and between multiple body fluids, enabling unbiased interpretation of metabolomics multifluid data.
authors.affiliation: Institute of Computational Biology and ‡Institute of Bioinformatics and Systems Biology Helmholtz-Zentrum München , D-85764 Neuherberg, Germany.
authors.forename: Kieu Trinh
authors.initials: KT
authors.lastname: Do
authors.forename: Gabi
authors.initials: G
authors.lastname: Kastenmüller
authors.forename: Dennis O
authors.initials: DO
authors.lastname: Mook-Kanamori
authors.forename: Noha A
authors.initials: NA
authors.lastname: Yousri
authors.forename: Fabian J
authors.initials: FJ
authors.lastname: Theis
authors.forename: Karsten
authors.initials: K
authors.lastname: Suhre
authors.forename: Jan
authors.initials: J
authors.lastname: Krumsiek
mesh_terms: D001826:Body Fluids / Q000378:metabolism*; D006801:Humans; D055432:Metabolomics
keywords: Gaussian graphical models; metabolomics; multifluid; multiple body fluids; network inference; partial correlation; type 2 diabetes