Exposure-Associated Metabolic Changes in Electronic-Waste Recycling Workers: An Exploratory Untargeted Metabolomics...

Electronic-waste (e-waste) recycling is an important source of occupational exposure to complex mixtures of heavy metals, yet the broader metabolic consequences remain insufficiently characterized. Untargeted metabolomics provides a hypothesis-generating approach for identifying systemic biochemical alterations associated with such exposure. A cross-sectional serum metabolomics study was performed using liquid chromatography-quadrupole time-of-flight mass spectrometry (LC-QTOF-MS) in negative electrospray ionization mode, including 30 e-waste recycling workers and 46 non-exposed controls. After preprocessing, 2305 metabolic features were retained. Multivariate analysis indicated separation between exposed workers and controls. Univariate filtering identified 44 putatively annotated discriminatory features (p < 0.05, fold change > 1.5), of which 20 also showed VIP scores > 1.0. Steroid-related metabolites (pregnenolone, 17-hydroxyprogesterone, corticosterone, and tetrahydrocorticosterone) and eicosanoid/oxylipin-related features (thromboxane B2, prostaglandin H2, prostaglandin B2, 15-HETE, and 5,6-DHET) were among the main contributors to group discrimination. Pathway analysis indicated steroid-related metabolism as the strongest pathway-level signal, whereas arachidonic acid metabolism and several additional pathways showed exploratory trends. Overall, no statistically significant associations between individual blood metal levels and discriminatory metabolites remained after correction for multiple testing. These findings indicate that the observed metabolic differences are more appropriately characterized as exposure group associated rather than directly attributable to any single measured metal. Occupational e-waste exposure was associated with a distinct serum metabolic profile, with the strongest differences involving steroid-related metabolites and additional variation in eicosanoid/oxylipin-related features. These findings support the utility of untargeted metabolomics for characterizing exposure-associated metabolic signatures and provide a basis for future targeted and longitudinal studies.