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The increasing use of electronic cigarettes (e-cigarettes) has raised concerns regarding potential health effects associated with both direct use and secondhand exposure to e-cigarette aerosols. However, human evidence describing systemic metabolic alterations related to these exposures remains limited. In this study, metabolomics was applied to compare urinary metabolic profiles among active e-cigarette users, individuals exposed to secondhand e-cigarette aerosols, and unexposed control participants. Multivariate analyses were performed to identify exposure-related metabolic signatures, followed by pathway enrichment and evaluation of candidate biomarkers. Distinct metabolic differences were observed across exposure groups. Direct e-cigarette use was associated with alterations in amino acid metabolism, mitochondrial energy pathways, and redox-related processes, with glutathione metabolism identified as a key associated pathway and oxidized glutathione highlighted as a candidate urinary biomarker of redox-related metabolic alterations. Secondhand exposure was primarily associated with alterations in alanine, aspartate, and glutamate metabolism, with L-glutamic acid demonstrating discriminatory performance as a candidate metabolite associated with metabolic differences related to secondhand e-cigarette aerosol exposure. Comparative pathway analyses suggested that direct e-cigarette use was associated with more extensive metabolic alterations than secondhand exposure, particularly in pathways linking glutamine-glutamate metabolism, the tricarboxylic acid cycle, and glutathione metabolism, suggesting a possible graded biological response to aerosol exposure. In addition, nicotine metabolism-related metabolites and e-liquid constituents, including propylene glycol, glycerol, and cotinine N -oxide, were identified as candidate urinary exposure biomarkers of e-cigarette exposure. Overall, these findings suggest that e-cigarette aerosol exposure may be associated with measurable metabolic responses, even at indirect exposure levels, and highlight the potential utility of metabolomics as an approach for identifying both exposure biomarkers and biomarkers of biological effect.
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Tajai et al. (2026) studied this question.
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