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Title: Modeling human metabolism of benzene following occupational and environmental exposures.
Authors: Kim S,  Vermeulen R,  Waidyanatha S,  Johnson BA,  Lan Q,  Smith MT,  Zhang L,  Li G,  Shen M,  Yin S,  Rothman N,  Rappaport SM
Journal: Cancer Epidemiol Biomarkers Prev
Date: 2006 Nov
Branches: OEEB
PubMed ID: 17119053
PMC ID: not available
Abstract: We used natural spline (NS) models to investigate nonlinear relationships between levels of benzene metabolites (E,E-muconic acid, S-phenylmercapturic acid, phenol, hydroquinone, and catechol) and benzene exposure among 386 exposed and control workers in Tianjin, China. After adjusting for background levels (estimated from the 60 control subjects with the lowest benzene exposures), expected mean trends of all metabolite levels increased with benzene air concentrations from 0.03 to 88.9 ppm. Molar fractions for phenol, hydroquinone, and E,E-muconic acid changed continuously with increasing air concentrations, suggesting that competing CYP-mediated metabolic pathways favored E,E-muconic acid and hydroquinone below 20 ppm and favored phenol above 20 ppm. Mean trends of dose-specific levels (micromol/L/ppm benzene) of E,E-muconic acid, phenol, hydroquinone, and catechol all decreased with increasing benzene exposure, with an overall 9-fold reduction of total metabolites. Surprisingly, about 90% of the reductions in dose-specific levels occurred below about 3 ppm for each major metabolite. Using generalized linear models with NS-smoothing functions (GLM + NS models), we detected significant effects upon metabolite levels of gender, age, and smoking status. Metabolite levels were about 20% higher in females and decreased between 1% and 2% per year of life. In addition, levels of hydroquinone and catechol were greater in smoking subjects. Overall, our results indicate that benzene metabolism is highly nonlinear with increasing benzene exposure above 0.03 ppm, and that current human toxicokinetic models do not accurately predict benzene metabolism below 3 ppm. Our results also suggest that GLM + NS models are ideal for evaluating nonlinear relationships between environmental exposures and levels of human biomarkers.