Analysis of the Human Adult Urinary Metabolome Variations with Age, Body Mass Index, and Gender by Implementing a Comprehensive Workflow for Univariate and OPLS Statistical Analyses

Urine
metabolomics is widely used for biomarker research in the
fields of medicine and toxicology. As a consequence, characterization
of the variations of the urine metabolome under basal conditions becomes
critical in order to avoid confounding effects in cohort studies.
Such physiological information is however very scarce in the literature
and in metabolomics databases so far. Here we studied the influence
of age, body mass index (BMI), and gender on metabolite concentrations
in a large cohort of 183 adults by using liquid chromatography coupled
with high-resolution mass spectrometry (LC-HRMS). We implemented a
comprehensive statistical workflow for univariate hypothesis testing
and modeling by orthogonal partial least-squares (OPLS), which we
made available to the metabolomics community within the online Workflow4Metabolomics.org resource. We found 108 urine metabolites
displaying concentration variations with either age, BMI, or gender,
by integrating the results from univariate p-values
and multivariate variable importance in projection (VIP). Several
metabolite clusters were further evidenced by correlation analysis,
and they allowed stratification of the cohort. In conclusion, our
study highlights the impact of gender and age on the urinary metabolome,
and thus it indicates that these factors should be taken into account
for the design of metabolomics studies.