Urinary metabolomics offers a non-invasive means of obtaining information about the system-wide biological health of a patient. Untargeted metabolomics approaches using one-dimensional gas chromatography (GC) are limited due to the chemical complexity of urine, which poorly detects co-eluting low-abundance analytes. Metabolite detection and identification can be improved by applying comprehensive two-dimensional GC, allowing for the discovery of additional viable biomarkers of disease. In this work, we applied comprehensive two-dimensional GC coupled with time-of-flight mass spectrometry (GC × GC-TOFMS) to the analysis of urine samples collected daily across 28-days from 10 healthy female subjects for a personalized approach to female reproductive health monitoring. Through this analysis, we identified 935 unique volatile metabolites. Two statistical methods, a modified T-statistic and Wilcoxon Rank Sum, were applied to analyze differences in metabolome abundance on ovulation days as compared to non-ovulation days. Four metabolites (2-pentanone, 3-penten-2-one, carbon disulfide, acetone) were identified as statistically significant by the modified T-statistic but not the Rank Sum, after a false-discovery rate of 0.1 was set using a Benjamini-Hochberg correction. Subsequent analyses by boxplot indicated that the putative volatile metabolic biomarkers for fertility are expressed in increased or decreased abundance in urine on the day of ovulation. Individual analysis of metabolome expression across 28-days revealed some subject-specific features, which suggest a potential for long-term, personalized fertility monitoring using metabolomics.
Keywords: Fertility; GC × GC-TOFMS; Ovulation; Personalized diagnostics; Urine.
Copyright © 2019. Published by Elsevier B.V.