Using urinary biomarkers to elucidate dose-related patterns of human benzene metabolism

University of California, Berkeley, Berkeley, California, United States
Carcinogenesis (Impact Factor: 5.33). 05/2006; 27(4):772-81. DOI: 10.1093/carcin/bgi297
Source: PubMed

ABSTRACT Although the toxicity of benzene has been linked to its metabolism, the dose-related production of metabolites is not well understood in humans, particularly at low levels of exposure. We investigated unmetabolized benzene in urine (UBz) and all major urinary metabolites [phenol (PH), E,E-muconic acid (MA), hydroquinone (HQ) and catechol (CA)] as well as the minor metabolite, S-phenylmercapturic acid (SPMA), in 250 benzene-exposed workers and 139 control workers in Tianjin, China. Median levels of benzene exposure were approximately 1.2 p.p.m. for exposed workers (interquartile range: 0.53-3.34 p.p.m.) and 0.004 p.p.m. for control workers (interquartile range: 0.002-0.007 p.p.m.). (Exposures of control workers to benzene were predicted from levels of benzene in their urine.) Metabolite production was investigated among groups of 30 workers aggregated by their benzene exposures. We found that the urine concentration of each metabolite was consistently elevated when the group's median benzene exposure was at or above the following air concentrations: 0.2 p.p.m. for MA and SPMA, 0.5 p.p.m. for PH and HQ, and 2 p.p.m. for CA. Dose-related production of the four major metabolites and total metabolites (micromol/l/p.p.m. benzene) declined between 2.5 and 26-fold as group median benzene exposures increased between 0.027 and 15.4 p.p.m. Reductions in metabolite production were most pronounced for CA and PH<1 p.p.m., indicating that metabolism favored production of the toxic metabolites, HQ and MA, at low exposures.

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    • "ations of ∑BT and 8 - OHdG were observed in this study . The results agreed with several previous reports ( Fan et al . , 2012a ; Buthbumrung et al . , 2008 ) . Some of the studies showed strong and positive relation - ships between urinary 8 - OHdG concentration and PAH exposure ( Lee et al . , 2012 ; Kuang et al . , 2013 ) and benzene exposure ( Kim et al . , 2006a ) . For example , Kuang et al . ( 2013 ) recruited 1333 coke oven workers and found that the ∑OH - PAHs in the urine and the benzo [ a ] pyrene - r - 7 , t - 8 , t - 9 , c - 10 - tetrahydotetrol - albumin adducts in the plasma could cause significant dose - related increases in the oxidative damage to DNA and lipids . The high PAH expos"
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    ABSTRACT: Coke plant Biomass fuel Polycyclic aromatic hydrocarbons Benzene and toluene Biomarker 8-hydroxy-2 0 -deoxyguanosine a b s t r a c t Large amounts of carcinogenic polycyclic aromatic hydrocarbons (PAHs), benzene and toluene (BT) might be emitted from incomplete combustion reactions in both coal tar factories and biomass fuels in rural China. The health effects arising from exposure to PAHs and BT are a concern for residents of rural areas close to coal tar plants. To assess the environmental risk and major exposure sources, 100 coke plant workers and 25 farmers in Qujing, China were recruited. The levels of 10 mono-hydroxylated PAHs (OH-PAHs), four BT metabolites and 8-hydroxy-2 0 -deoxyguanosine (8-OHdG) in the urine collected from the subjects were measured. The 8-OHdG levels in the urine were determined to evaluate the oxidative DNA damage induced by the PAHs and BT. The results showed that the levels of the OH-PAHs, particularly those of 1-hydroxynathalene and 1-hydroxypyrene, in the farmers were 1–7 times higher than those in the workers. The concentrations of the BT metabolites were comparable between the workers and farmers. Although the exact work location within a coke oven plant might affect the levels of the OH-PAHs, one-way ANOVA revealed no significant differences for either the OH-PAHs levels or the BT concentrations among the three groups working at different work sites. The geometric mean concentration (9.17 mg/g creatinine) of 8-OHdG was significantly higher in the farmers than in the plant workers (6.27 mg/g creatinine). The levels of 8-OHdG did not correlate with the total concentrations of OH-PAHs and the total levels of BT metabolites. Incompletely combusted biomass fuels might be the major exposure source, contributing more PAHs and BT to the local residents of Qujing. The estimated daily intakes (EDIs) of naphthalene and fluorene for all of the workers and most of the farmers were below the reference doses (RfDs) recommended by the U.S. Environmental Protection Agency (EPA), except for the pyrene levels in two farmers. However, the EDIs of benzene in the workers and local farmers ranged from 590 to 7239 mg/day, and these levels were 2-to 30-fold higher than the RfDs recommended by the EPA. Biomass fuel combustion and industrial activities related to coal tar were the major sources of the PAH and BT exposure in the local residents. Using biomass fuels for household cooking and heating explains the higher exposure levels observed in the farmers relative to the workers at the nearby coal tar-related industrial facility.
    Environmental Research 08/2014; 135. DOI:10.1016/j.envres.2014.08.021 · 4.37 Impact Factor
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    • "Urinary benzene and SPMA. Similar trends are seen for urinary benzene (Ayi Fanou et al., 2006; Bergamaschi et al., 1999; Fustinoni et al., 2005a,b; Ghittori et al., 1995; Kim et al., 2006a; Kivisto et al., 1997; Kok & Ong, 1994; Lagorio et al., 1998; Maestri et al., 1993; Ong et al., 1996; Pezzagno et al., 1999; Waidyanatha et al., 2001) and SPMA (Aston et al., 2002; Ayi Fanou et al., 2006; Boogaard & van Sittert, 1996; Crebelli et al., 2001; Einig et al., 1996; Fustinoni et al., 2005a; Garte et al., 2005; Ghittori et al., 1995, 1999; Hotz et al., 1997; Kim et al., 2006a; Kivisto et al., 1997; Maestri et al., 1993, 2005; Melikian et al., 1999b, 2002; Navasumrit et al., 2008; Pople et al., 2002; Stommel et al., 1989; Waidyanatha et al., 2004) biomonitoring data from North America, Europe, Asia and Africa (Figures 4 and 5). As expected, the urinary benzene and SPMA concentrations for smokers were generally higher than for non-smokers. "
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    ABSTRACT: Abstract A framework of "Common Criteria" (i.e. a series of questions) has been developed to inform the use and evaluation of biomonitoring data in the context of human exposure and risk assessment. The data-rich chemical benzene was selected for use in a case study to assess whether refinement of the Common Criteria framework was necessary, and to gain additional perspective on approaches for integrating biomonitoring data into a risk-based context. The available data for benzene satisfied most of the Common Criteria and allowed for a risk-based evaluation of the benzene biomonitoring data. In general, biomarker (blood benzene, urinary benzene and urinary S-phenylmercapturic acid) central tendency (i.e. mean, median and geometric mean) concentrations for non-smokers are at or below the predicted blood or urine concentrations that would correspond to exposure at the US Environmental Protection Agency reference concentration (30 µg/m(3)), but greater than blood or urine concentrations relating to the air concentration at the 1 × 10(-5) excess cancer risk (2.9 µg/m(3)). Smokers clearly have higher levels of benzene exposure, and biomarker levels of benzene for non-smokers are generally consistent with ambient air monitoring results. While some biomarkers of benzene are specific indicators of exposure, the interpretation of benzene biomonitoring levels in a health-risk context are complicated by issues associated with short half-lives and gaps in knowledge regarding the relationship between the biomarkers and subsequent toxic effects.
    Critical Reviews in Toxicology 02/2013; 43(2):119-53. DOI:10.3109/10408444.2012.756455 · 5.10 Impact Factor
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    • "Furthermore, some evidence shows that benzene metabolism favors the production of the toxic metabolites hydroquinone and muconic acid at low exposures (Kim et al. 2006a). This is especially important because hydroquinone is the precursor of 1,4-benzoquinone, which is generally regarded as the most hematotoxic metabolite of benzene (Kim et al. 2006a). The nonlinear production of benzene's toxic metabolites would have important consequences for risk assessment because one would expect this to result in a nonlinear relationship between benzene exposure and health outcomes as well. "
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    ABSTRACT: Previous evaluations of the shape of the benzene-leukemia exposure-response curve (ERC) were based on a single set or on small sets of human occupational studies. Integrating evidence from all available studies that are of sufficient quality combined with flexible meta-regression models is likely to provide better insight into the functional relation between benzene exposure and risk of leukemia. We used natural splines in a flexible meta-regression method to assess the shape of the benzene-leukemia ERC. We fitted meta-regression models to 30 aggregated risk estimates extracted from nine human observational studies and performed sensitivity analyses to assess the impact of a priori assessed study characteristics on the predicted ERC. The natural spline showed a supralinear shape at cumulative exposures less than 100 ppm-years, although this model fitted the data only marginally better than a linear model (p = 0.06). Stratification based on study design and jackknifing indicated that the cohort studies had a considerable impact on the shape of the ERC at high exposure levels (> 100 ppm-years) but that predicted risks for the low exposure range (< 50 ppm-years) were robust. Although limited by the small number of studies and the large heterogeneity between studies, the inclusion of all studies of sufficient quality combined with a flexible meta-regression method provides the most comprehensive evaluation of the benzene-leukemia ERC to date. The natural spline based on all data indicates a significantly increased risk of leukemia [relative risk (RR) = 1.14; 95% confidence interval (CI), 1.04-1.26] at an exposure level as low as 10 ppm-years.
    Environmental Health Perspectives 04/2010; 118(4):526-32. DOI:10.1289/ehp.0901127 · 7.98 Impact Factor
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