Network-Based Approach for Analyzing Intra- and Interfluid Metabolite Associations in Human Blood, Urine, and Saliva

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.