Modeling Human Metabolism of Benzene Following Occupational and Environmental Exposures Sungkyoon Kim, 1 Roel Vermeulen, 2 Suramya Waidyanatha, 1 Brent A. Johnson, 1 Qing Lan, 2 Martyn T. Smith, 3 Luoping Zhang, 3 Guilan Li, 4 Min Shen, 2 Songnian Yin, 4 Nathaniel Rothman, 2 and Stephen M. Rappaport 1 1 School of Public Health, University of North Carolina, Chapel Hill, North Carolina; 2 National Cancer Institute, NIH, Department of Health and Human Services, Bethesda, Maryland; 3 School of Public Health, University of California, Berkeley, California; and 4 Institute of Occupational Health and Poison Control, Chinese Center for Disease Control and Prevention, Beijing, China Abstract We used natural spline (NS) models to investigate nonlinear relationships between levels of benzene metabolites (E,E - muconic acid, S -phenylmercapturic acid, phenol, hydroqui- none, and catechol) and benzene exposure among 386 exposed and control workers in Tianjin, China. After adjust- ing 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 con- tinuously 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 (Mmol/L/ppm benzene) of E,E -muconic acid, phenol, hydro- quinone, and catechol all decreased with increasing benzene exposure, with an overall 9-fold reduction of total metabo- lites. 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, andsmokingstatus.Metabolitelevelswereabout20%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. (Cancer Epidemiol Biomarkers Prev 2006;15(11):2246–52) Introduction Benzene is an important industrial chemical that is also ubiquitous in the environment due to emissions from gasoline and combustion of hydrocarbons and tobacco (1, 2). Occupa- tional exposure to benzene can cause blood disorders, including aplastic anemia, myelodisplastic syndrome, and acute myelogenous leukemia (3, 4). Significant decreases in the numbers of WBC and platelets have recently been reported in workers exposed to <1 ppm benzene (5). These toxic effects are thought to arise from metabolism of benzene, which proceeds along several lines, as illustrated in Fig. 1. Of the various metabolites, 1,4-benzoquinone and the muconaldehydes are regarded as the most toxic species. However, the mechanism by which benzene causes toxicity and the shape of the exposure-response relationship are not well understood (6-8). We recently reported dose-specific urine concentrations of the major urinary metabolites of benzene (i.e., phenol, catechol, hydroquinone, and E,E -muconic acid) and a minor metabolite [S -phenylmercapturic acid (SPMA)] in 250 benzene-exposed and 139 control workers from Tianjin, China (9). After group- ing subjects according to their benzene exposures (30 subjects per group), median metabolite levels increased nonlinearly with increasing median benzene concentrations between 0.03 and 20 ppm, whereas median dose-specific levels of total metabolites (Amol/L/ppm benzene) decreased about 10-fold. We sought a parsimonious statistical model with which to elaborate on our previous grouped analyses (9) and to determine effects of significant covariates, such as gender, age, and smoking status, on the levels of benzene metabolites. Given the nonlinear relationships involved, we selected NS as basis functions for these models because they use standard (least-squares or maximum-likelihood) methods for estimating variables and for conducting formal tests; they can be used to represent predictors in final models; and they can easily be added to generalized linear models (GLM) for considering covariate effects (10-13). Although GLM + NS models have been used in time-series studies of health effects associated with community air pollution (14), we could find no reports of their applications to characterize exposure-biomarker relationships. Materials and Methods Subject Recruitment and Sample Collection. Exposed and control subjects, from two shoe-making factories and three clothes-manufacturing factories, respectively, in Tianjin, China, were recruited with informed consent as described previously (5, 9, 15). Exposed and control subjects were frequency matched by gender. After excluding three control subjects, who had missing values of at least one metabolite, the 2246 Cancer Epidemiol Biomarkers Prev 2006;15(11). November 2006 Received 4/4/06; revised 7/2/06; accepted 8/29/06. Grant support: National Institute for Environmental Health Sciences grants P42ES05948 (S.M. Rappaport), P30ES10126 (S.M. Rappaport), RO1ES06721 (M.T. Smith), P42ES04705 (M.T. Smith), and P30ES01896 (M.T. Smith) and National Cancer Institute intramural funds. The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact. Note: S.M. Rappaport has received consulting fees from a plaintiff’s attorney in a case involving exposure to benzene. M.T. Smith has received consulting and expert testimony fees from law firms representing both plaintiffs and defendants in cases involving exposure to benzene. G. Li has received funds from the American Petroleum Institute for consulting on benzene-related health research. Requests for reprints: Stephen M. Rappaport, School of Public Health, University of North Carolina, CB 7431, Chapel Hill, NC 27599. Phone: 919-966-5017; Fax: 919-966-0521. E-mail: smr@unc.edu Copyright D 2006 American Association for Cancer Research. doi:10.1158/1055-9965.EPI-06-0262 on May 22, 2020. © 2006 American Association for Cancer Research. cebp.aacrjournals.org Downloaded from