Abstract
PURPOSE: Manganese (Mn) is found in environmental and occupational settings, and can cause cognitive and motor impairment. Existing Mn exposure studies have not reached consensus on a valid and reproducible biomarker for Mn exposure. METHODS: Previously, global metabolomics data was generated from urine collected in October 2014 using mass spectrometry (MS). Nine ions were found to be different between persons exposed and unexposed to Mn occupationally, though their identity was not able to be determined. Here, we investigated these nine ions in a follow-up set of urine samples taken from the same cohort in January 2015, and in urine samples from a separate Mn-exposed cohort from Wisconsin. We fit an elastic net model fit using the nine ions found in the October 2014 data. RESULTS: The elastic net correctly predicted exposure status in 72% of the follow-up samples collected in January 2015, and the area under the curve of the receiver operating characteristic (ROC) curve was 0.8. In the Wisconsin samples, the elastic net performed no better than chance in predicting exposure, possibly due to differences in Mn exposure levels, or unmeasured occupational or environmental co-exposures. CONCLUSIONS: This work underscores the importance of taking repeat samples for replication studies when investigating the human urine metabolome, as both within- and between-person variances were observed. Validating and identifying promising results remains a challenge in harnessing global metabolomics for biomarker discovery in occupational cohorts.