The Use of High-Resolution Metabolomics in Occupational Exposure and Health Research

<span class="paragraphSection">In this issue of the <span style="font-style:italic;">Annals of Work Exposures and Health</span><a href="#CIT0001" class="reflinks">Baker <span style="font-style:italic;">et al.</span> (2017)</a> report on the use of high-resolution metabolomics (HRM) to identify biological signatures of manganese (Mn) exposure. Metabolomics concerns the study of metabolomes, the complement of naturally occurring (endogenous) and exogenuous (e.g. environmental, occupational pollutants), low-molecular-weight metabolites present within biological systems (<a href="#CIT0013" class="reflinks">Viant <span style="font-style:italic;">et al.</span>, 2017</a>). The authors were able to identify and validate several mass spectrometry (MS) features that differentiated between exposed and unexposed workers of which several displayed an exposure-response relation with measured air concentrations of Mn. Unfortunately, the authors were not able to annotate (name or empirical structure) these mass spectrometry features limiting biological interpretability and hampering the assessment if these features are truly exposure related or driven by some unmeasured confounders. As such this study may not sound as a big step forward in occupational exposure and health research but this notion would be incorrect. As <a href="#CIT0001" class="reflinks">Baker <span style="font-style:italic;">et al.</span> (2017)</a> note for many occupational exposures we currently lack good biomarkers of exposure. Moreover, knowledge about how occupational exposures exert their health effects is for most occupational exposures still very limited. In this regard, Mn is a perfect example where good biomarkers of exposure and effect are lacking hampering both exposure and risk assessment. The study by <a href="#CIT0001" class="reflinks">Baker <span style="font-style:italic;">et al.</span> (2017)</a> demonstrates how HRM allows for the agnostic screening of such markers by measuring thousands of metabolic features.</span>

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