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

In this issue of the Annals of Work Exposures and Health Baker et al. (2017) 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 (Viant et al., 2017). 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 Baker et al. (2017) 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 Baker et al. (2017) demonstrates how HRM allows for the agnostic screening of such markers by measuring thousands of metabolic features. While metabolomics has been used successfully to identify biomarkers of physiological alterations related to disease status (Jones, 2016) the application of this technique to identify exposure related biomarkers and/ or their biological effects has been much more limited. One of the first applications in this regard were in the nutritional field. For example in the INTERMAP study, urinary metabolic measurements, using 1H NMR spectroscopy, showed clear differences in metabolic phenotypes between 17 populations across China, Japan, UK, and USA (Holmes et al. 2008). Among the discriminatory metabolites, urinary formate, alanine and hippurate excretion were associated with blood pressure supporting the association between dietary patterns and cardiovascular health. More recently, Cheung et al. (2017) reported on a metabolic study of urinary biomarkers of meat and fish intake, using HRM. A total of 249 mass spectrometry features related to meat and/ or fish intake were identified. Using 18 of these features highly predictive classifiers for meat and fish intake could be developed. Among these 18 features, 8 were successfully annotated resulting in specific biomarkers for chicken (anserine), fish (trimethylamine-N-oxide), and meat (carnosine) intake. The identification of these biomarkers is potentially a game changer in nutritional research as it allows now their application in epidemiological studies on meat and fish intake and health which previously depended on self-reported information. The applications in environmental and occupational research are more limited. In 2013, Saberi Hosnijeh et al. (2013) published on serum metabolomic perturbations among workers exposed to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) using HRM. More recently, more studies on occupational exposures and circumstances have appeared. For example on the metabolic patterns among shiftworkers (Campagna et al., 2016) and military personnel (Liu et al., 2016), DDT and HCB exposed individuals (Salihovic et al., 2016), and workers exposed to trichloroethylene (TCE) (Walker et al., 2016b) and benzo(a)pyrene (Walker et al., 2016a). Similar as to the study of Baker et al. (2017), these studies identified several metabolic features related to the occupational circumstance or exposure of interest but