Evaluating Co‐occurrence as a Criterion for Identification of Undocumented Xenobiotic Exposures in Human Metabolomics.

R2142 --> 699.10 --> High resolution mass spectrometry (HRMS) detects tens of thousands of unknown features in human plasma. Many of these features could be indicative of xenobiotic exposure, but identifying candidates poses a challenge due to low abundance and lack of authentic standards. Here we propose using co‐occurrence of metabolites as a criterion to aid identification of undocumented xenobiotic exposures and co‐exposures in humans. Plasma from 49 volunteer Emory University/Georgia Tech employees in a longitudinal study were analyzed with HRMS, and aligned features were arranged as nodes in networks connected according to feature co‐occurrence and common biotransformation mass differences, marking possible xenobiotic pathways. These networks were compared against curated metabolic profiles of xenobiotic reference material generated on an enzyme‐based biotransformation system (liver S9 fractions). Profile matches, coupled with available MS/MS, suggest confident exposure identification of zolpidem, dextromethorphan, nicotine, citalopram, caffeine and tramadol within the cohort. Further investigation of co‐occurrence networks also facilitated suggestion of possible xenobiotic co‐exposures beyond biotransformation pathways. In this cohort, Zolpidem metabolites co‐occurred with high abundance features having the the masses of melatonin and its metabolites, suggesting co‐exposure due to polypharmacy for a single condition. [ABSTRACT FROM AUTHOR]