Kim Sungkyoon, Vermeulen Roel, Waidyanatha Suramya, Johnson Brent A, Lan Qing, Smith Martyn T, Zhang Luoping, Li Guilan, Shen Min, Yin Songnian, Rothman Nathaniel, Rappaport Stephen M
School of Public Health, University of North Carolina, CB 7431, Chapel Hill, NC 27599.
Cancer Epidemiol Biomarkers Prev. 2006 Nov;15(11):2246-52. doi: 10.1158/1055-9965.EPI-06-0262.
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.
我们使用自然样条（NS）模型，在中国天津的386名暴露组和对照组工人中，研究苯代谢物（E，E - 粘康酸、S - 苯巯基尿酸、苯酚、对苯二酚和儿茶酚）水平与苯暴露之间的非线性关系。在调整背景水平（根据60名苯暴露最低的对照组受试者估算）后，所有代谢物水平的预期平均趋势随苯空气浓度从0.03 ppm增加到88.9 ppm而升高。苯酚、对苯二酚和E，E - 粘康酸的摩尔分数随空气浓度增加而持续变化，这表明竞争性细胞色素P450介导的代谢途径在20 ppm以下有利于E，E - 粘康酸和对苯二酚，而在20 ppm以上有利于苯酚。E，E - 粘康酸、苯酚、对苯二酚和儿茶酚的剂量特异性水平（微摩尔/升/ppm苯）的平均趋势均随苯暴露增加而降低，总代谢物总体降低了9倍。令人惊讶的是，每种主要代谢物剂量特异性水平约90%的降低发生在约3 ppm以下。使用带有NS平滑函数的广义线性模型（GLM + NS模型），我们检测到性别、年龄和吸烟状况对代谢物水平有显著影响。女性的代谢物水平约高20%，且每年下降1%至2%。此外，吸烟受试者的对苯二酚和儿茶酚水平更高。总体而言，我们的结果表明，苯暴露超过0.03 ppm时，苯代谢具有高度非线性，并且当前的人体毒物动力学模型不能准确预测3 ppm以下的苯代谢。我们的结果还表明，GLM + NS模型是评估环境暴露与人体生物标志物水平之间非线性关系的理想模型。
