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

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.

Nuclear Magnetic Resonance metabolomics reveals an excretory metabolic signature of renal cell carcinoma

“…Generally, age-related changes were indeed small (effect sizes <1), healthy controls over 60 years excreting slightly higher levels of IS, and lower levels of citrate, cis- aconitate, creatine, creatinine, hypoxanthine and trigonellinamide and some unassigned resonances. Decreased cis- aconitate, creatine and cretinine confirmed previous reports48495051 but the remaining characteristics seem to be specific of the particular cohort considered here. In addition, healthy males were observed to excrete higher amounts of 1-methylhistidine, isoleucine, tartrate and unassigned resonances (Un) 3, 4 and 6 (at δ 0.92, 1.15, 6.19 respectively) and less glutamate, 1,6-anhydroglucose and Un 5 (δ 2.05) (Table 2).…”

Investigation of urine proteome of preterm newborns with respiratory pathologies

“…It finds the linear correlations between dependent variables and predictor variables. The ropls package was used for this calculation [30]. Statistical differences in urine protein constituents between three groups of infants were searched using the Kruskal-Wallis method and further pairwise analysis was performed using the Mann-Whitney test with Boferroni correction.…”

Baseline urine metabolic phenotype in patients with severe alcoholic hepatitis and its association with outcome

“…Although the preparation of urine samples for analysis is simple and the concentration of many metabolites is amplified by bladder storage, the biological interpretation of data is complicated by a variation in diuresis from subject to subject. Various normalization methods have been used and published to address this issue, including the traditional use of urinary creatinine concentration, osmolality,30, 31 total useful MS signal,30 and specific gravity19, 32 as well as a combination of creatinine concentration and normalization of the MS signal20 and the determination of the total concentration of chemically labeled metabolites by using liquid chromatography‐ultraviolet 33. However, many studies do not use normalization procedures, and there is still no consensus on this point 34.…”

“…However, many studies do not use normalization procedures, and there is still no consensus on this point 34. We employed a MSTUS normalization strategy,19, 31 which uses the total intensity of metabolites that are common to all samples and which is easy to implement and was found to perform better than creatinine normalization 20. For the selection of metabolites of interest, we chose to take into account metabolites that had concentration differences between Rs and NRs that were statistically significant with or without MSTUS normalization in order to improve result reliability.…”