Although there is undoubtedly a causal link between benzene exposure and leukemia, the shape of the exposure–response relation- ship is controversial, particularly at low doses. Indeed, when considering regulatory actions, litigation, and potential cleanup costs in the billions of dollars, this uncertainty represents a major challenge for environmental toxicol- ogy and epidemiology. Recent action by the U.S. Environmental Protection Agency (EPA) to reduce cancer risks from mobile sources underscores this point (U.S. EPA 2007). In justifying its decision to lower the benzene content of gasoline, the U.S. EPA cited stud- ies pointing to supralinear (greater-than- proportional) production of benzene-related protein adducts at air concentrations < 1 ppm (Rappaport et al. 2002, 2005). Such behav- ior would likely result from saturation of the metabolism of benzene to benzene oxide/ oxepin. Because the U.S. EPA had previously assumed that human benzene metabolism proceeded according to nonsaturating (first- order) kinetics at exposure concentrations well above 10 ppm, saturation of metabo- lism below 1 ppm “could lead to substan- tial underestimation of leukemia risks” in the general population (U.S. EPA 2007). Because the dose-related metabolism of benzene had been poorly characterized in humans, we conducted a detailed investiga- tion of urinary metabolite levels among 386 workers in Tianjin, China, for whom indi- vidual levels of benzene in air had been docu- mented on the days of urine collection (Kim et al. 2006a). We observed that metabolite levels were significantly affected by benzene exposure as well as sex, age, smoking, sin- gle nucleotide polymorphisms of prominent metabolizing genes, and gene–environment and gene–smoking interactions (Kim et al. 2006b, 2007). Intriguingly, the exposure-spe- cific production of major metabolites (phenol, muconic acid, hydroquinone, and catechol, in micromolar per parts per million benzene) decreased continuously with estimated expo- sure levels over the range of 0.03–88.9 ppm, with the most pronounced decreases occur- ring at benzene concentrations < 1 ppm (Kim et al. 2006a). Given the unanticipated decline in expo- sure-specific metabolism at air concentrations < 1 ppm, we speculated that benzene metab- olism might be governed by two saturable pathways, one operating primarily > 1 ppm, and the other primarily at lower concentra- tions. To test this conjecture, we combined exposure and urinary metabolite data from two studies of Chinese workers, representing 428 subjects exposed to benzene at estimated levels ranging from < 0.001 ppm to > 300 ppm (Kim et al. 2006a; Waidyanatha et al. 2004). Because sex and smoking had previ- ously been shown to affect benzene metabolite Environmental Health Perspectives • volume 117 | number 6 | June 2009 Humans metabolize benzene via two pathways levels at a given exposure concentration in this population, we restricted our analyses to 263 nonsmoking females with valid data (esti- mated benzene exposure range, < 0.001–299 ppm; we removed one previously identified outlier). Because metabolic processes follow saturable (Michaelis-Menten) kinetics, we fit Michaelis-Menten-like models to the molar levels of urinary benzene metabolites (the sum of phenol, muconic acid, hydroquinone, cat- echol, and SPMA) in these subjects and used statistical fit criteria derived from information theory to weigh the evidence favoring the two models. Our results provide strong statistical evidence favoring two saturable pathways, one operating primarily at concentrations < 1 ppm and the other primarily at concentra- tions > 1 ppm. Materials and Methods Study population and air and biological monitoring. Study subjects derive from two cross-sectional molecular epidemiology stud- ies of Chinese benzene-exposed and control workers carried out in Shanghai in 1992 (Rothman et al. 1996, 1998; Waidyanatha et al. 2004) and in Tianjin in 2000–2001 (Kim et al. 2006a; Lan et al. 2004; Vermeulen et al. 2004). Subject enrollment and interview procedures, exposure assessment methods, and urinary metabolite measurements in these two studies were carried out by the same group of investigators using the same procedures. All subjects gave informed written consent to act as human subjects, and all applicable approv- als were obtained. Workers with occupational exposure to benzene were employed in facto- ries where benzene was present, and control workers were exposed to airborne benzene in the general environment, as determined by measurement of urinary benzene (Tianjin con- trols only) (Kim et al. 2006a). Among non- smoking females, 159 benzene-exposed and 84 control subjects had complete data. Table 1 shows summary statistics regarding benzene exposure, age, body mass index, and weight. The methods of sampling air and urine have been reported previously (Kim et al. 2006a; Lan et al. 2004; Vermeulen et al. 2004; Waidyanatha et al. 2001). Briefly, we matched personal full-shift air measurements with postshift urine samples from exposed and control workers. Statistical analyses used the sum of the molar concentrations of phe- nol, hydroquinone, catechol, muconic acid, and SPMA (hereafter “total metabolites”). Three subjects were missing measurements of SPMA. Because SPMA rarely exceeds 1% of total metabolites, we estimated levels of total metabolites for these three subjects as the sum of phenol, hydroquinone, catechol, and muconic acid. Of the 263 subjects in this O C S CH2 N CH CH3 COOH S-Phenylmercapturic acid via GST Nonezymatic rearrangement O NQO1 O MPO 1,4-Benzoquinone HO Hydroquinone Phenol CYP2E1 OH OH Benzene CYP2E1 O O CYP OCH HOOC Benzene oxide Oxepin CHO COOH E,E-Muconaldehyde E,E-Muconic acid Epoxide hydrolase OH CYP OH Benzene dihydrodiol O Benzene diol expoxide Dihyrodiol dehydrogenase OH NQO1 O Catechol OH MPO O 1,2-Benzoquinone OH OH Figure 1. Simplified metabolic scheme for benzene showing major pathways and metabolizing genes. GST, glutathione-S-transferase; NQO1, NAD(P)H:quinone oxidoreductase 1; MPO, myeloperoxidase; CYP2E1, cytochrome P450 2E1. Table 1. Summary statistics for the study population (nonsmoking female subjects, n = 263). Statistic Benzene exposure (ppm) Age (years) Body mass index (kg/m2) Weight (kg) Median value (range) 0.644 (1.5 × 10–4 to 299) 32 (18–52) 21.8 (15.4–38.2) 57 (39–96) 10th–90th percentiles 0.002–8.97 21–44 18.5–26.7 48–70 947