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

TLDR

Urine metabolomics is widely used for biomarker research in medicine and toxicology, yet physiological variation under basal conditions is poorly characterized, making it critical to avoid confounding in cohort studies. The study examined how age, BMI, and gender influence urinary metabolite concentrations in 183 adults using LC‑HRMS. A comprehensive statistical workflow combining univariate hypothesis testing and OPLS modeling was implemented and made available via Workflow4Metabolomics.org. We identified 108 metabolites whose concentrations varied with age, BMI, or gender, and clustering analysis enabled cohort stratification, underscoring the need to account for these factors in metabolomics study design.

Abstract

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.