Abstract

Metabolomics is an analysis technique used to characterise the set of small-molecule metabolites found in a biofluid, and has commonly been used to investigate biochemical differences between diseased and non-diseased groups. Limited research has looked at how metabolomics profiles differ in occupationally exposed and unexposed groups, and what the challenges and opportunities of using metabolomics in occupational exposure sciences are.

Metabolomics has been proposed as a valuable tool in helping to characterise the exposome—though further study of its utility as an exposure assessment tool, particularly in occupational settings, is warranted. We explored metabolomics profiles related to manganese (Mn) exposure in urine, plasma, and whole blood in two geographically diverse metalworking cohorts. In the first cohort, a full-shift personal inhalable air sample was taken on all participants, and a spot urine sample was collected at the end of the day. In the second cohort, cumulative and recent exposures were modelled from an exposure questionnaire, and a whole blood sample, plasma sample, and spot urine sample were collected. All biofluids underwent global metabolomics analyses on a QTOF-LC/MS, and both exposed and unexposed individuals in each cohort were divided into a training set and demographically-matched internal validation set. For each biofluid, ions found to be significantly associated with exposure (adjusted p-value <0.1) in the training groups were independently verified in the internal validation group, and the abundances of these validated ions were compared with measured and modelled exposure to examine the association. Urine metabolites verified in the validation set of one cohort were subject to external validation in the other cohort.

Results from this work could lead not only to a greater understanding of the biochemical pathways Mn perturbs after inhalation and risk interpretation for Mn exposure, but also inform future studies looking to use metabolomics to characterise exposure in occupational cohorts.