摘要：Background Recent evidence has shown that humans metabolize benzene more efficiently at environmental air concentrations than at concentrations > 1 ppm. This led us to speculate that an unidentified metabolic pathway was mainly responsible for benzene metabolism at ambient levels. Objective We statistically tested whether human metabolism of benzene is better fitted by a kinetic model having two pathways rather than one. Methods We fit Michaelis-Menten-like models to levels of urinary benzene metabolites and the corresponding air concentrations for 263 nonsmoking Chinese females. Estimated benzene concentrations ranged from less than 0.001 ppm to 299 ppm, with 10th and 90th percentile values of 0.002 ppm and 8.97 ppm, respectively. Results Using values of Akaike’s information criterion obtained under the two models, we found strong statistical evidence favoring two metabolic pathways, with respective affinities (benzene air concentrations analogous to K m values) of 301 ppm for the low-affinity pathway (probably dominated by cytochrome P450 enzyme 2E1) and 0.594 ppm for the high-affinity pathway (unknown). The exposure-specific metabolite level predicted by our two-pathway model at nonsaturating concentrations was 184 μM/ppm of benzene, a value close to an independent estimate of 194 μM/ppm for a typical nonsmoking Chinese female. Our results indicate that a nonsmoking woman would metabolize about three times more benzene from the ambient environment under the two-pathway model (184 μM/ppm) than under the one-pathway model (68.6 μM/ppm). In fact, 73% of the ambient benzene dose would be metabolized via the unidentified high-affinity pathway. Conclusion Because regulatory risk assessments have assumed nonsaturating metabolism of benzene in persons exposed to air concentrations well above 10 ppm, our findings suggest that the true leukemia risks could be substantially greater than currently thought at ambient levels of exposure—about 3-fold higher among nonsmoking females in the general population.
关键词：benzene; biomonitoring; cancer risk; cytochrome P450; metabolism
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