Metabolic profiling of glucuronides in human urine by LC-MS/MS and partial least-squares discriminant analysis for classification and prediction of gender.

Mass spectrometry (MS) is increasingly being used for metabolic profiling, but detection modes such as constant neutral loss or multiple reaction monitoring have not often been reported. These modes allow focusing on structurally related compounds, which could be advantageous for situations in which the trait under investigation is associated with a particular class of metabolites. In this study, we analyzed endogenous glucuronides excreted in human urine by monitoring characteristic transitions of putative steroid glucuronides by LC-MS/MS for discrimination of females from males. Two methods for data extraction were used: (i) a manual procedure based on visual inspection of the chromatograms and selection of 23 peaks and (ii) a software-supported method (MarkerView) set to extract 100 peaks. Data from 10 female and 10 male students were analyzed by principal component analysis (PCA) and partial least-squares discriminant analysis (PLS-DA) using software SIMCA. With PCA, only the manual peak selection resulted in clustering males and females. With PLS-DA, the manual method provided full separation on the basis of one single discriminant; the software-supported approach required a two-component model for complete separation. Loading plots were analyzed for their ability to reveal peaks with high discriminating power, that is, potential biomarkers. The PLS-DA models were validated with urine samples collected from five new females and five new males. Gender was correctly assigned for all. Our results indicate that inclusion of biological criteria for variable selection coupled to class-specific MS analysis and data extraction by appropriate software may constitute a valuable addition to the methods available for metabolomics.