Abstract

Manganese (Mn) is a known neurotoxicant, and given its health effects and ubiquitous nature in metal-working settings, identification of a valid and reproducible biomarker of exposure is of interest. Global metabolomics were previously utilised to determine metabolites that differ between occupational groups defined by Mn exposure status, in hopes of informing a biomarker of exposure. Nine metabolites were found to differ between exposure groups in urine samples collected October 2014. To test the reproducibility of these metabolites, these nine metabolites were investigated in a second set of urine samples collected January 2015 from the same workers.

Levels of the nine metabolites found in October 2014 were compared to the January 2015 data using principal components analysis and descriptive measures. Also, an elastic net regression was fit using the nine metabolites from the October 2014 data; this model was tested in the January 2015 data.

Four of the nine ions remained significantly different between exposed and unexposed workers in the January data, though levels of most ions also exhibited regression to the mean. The elastic net model was able to correctly classify exposure status in 66% of the January samples; slightly better than classification by chance alone.

Metabolomics is a novel technique for exposure assessment, but few studies have looked at the reproducibility of metabolomics data by collecting repeat samples from the same workers. This analysis found several ions that do seem to remain stable over time, and identification of these ions should be pursued as potential biomarkers of Mn exposure.