COLLABORATIVE STUDY OF 1,3 BUTADIENE BIOMARKERS

1，3-丁二烯(BD)是一种常见的汽油排放的空气污染物， 香烟烟雾和汽车尾气。在人类中，BD被氧化 由P450酶(CYP2E1、2E1)转化为环氧丁烯、环氧丁二醇和 二环氧丁烷(？)，它们被多态的环氧化物水解酶失活 (呃)。BD被归类为可疑的人类致癌物质。BD是 酶激活和失活，所以基于基因 易感性方面的差异是可以预料的。三种生物标志物 有希望估计环氧丁烯的内剂量和 环氧丁二醇；血红蛋白N-末端缬氨酸加合物，淋巴细胞7- 烷基鸟嘌呤DNA加合物和尿硫尿酸 丁烯二醇。多中心协作被提出到数量 每个剂量生物标志物的暴露-剂量关系。这 关系由生物标记物的背景水平、斜率、 精确度、偏差和干扰。这种关系受到以下因素的影响 个体的2E1表型、EH基因型和生活方式因素， 将会被评估。八十(80)名受试者将从男性中抽取 生产丁二烯胶乳及相关产品的台湾石化工人 在10家大型工厂生产产品(总共250名工人)。主题将是 被选为20名受试者的4个激活/去激活亚组 2E1激活率快或慢，以及正常或变异EH 基因分型。80(80)名对照组，年龄和代谢与 暴露的，将从当地未暴露的工人中抽取。初步 采样显示了两种类型的曝光：高强度(大于100 2-3小时的平均ppm。)在油罐清洗过程中，长期处于低水位 (中位数0.5-2 ppm全班次)在正常运行期间， 同时接触到苯乙烯或丙烯腈。二十台油箱清洗机 将提供有关高暴露反应的数据。长期风险敞口(5 WKS)将对80家运营商进行监控。血液和尿液样本 将在之前、3周后和5周后收集以确定 生物标志物水平及其在测量暴露期间的变化。 暴露-生物标记物的关系将通过回归分析 观察新陈代谢和个人因素影响的技巧。 模拟显示，该策略估计的斜率为Q20%(95% %CI)用于20的氧化去活化亚组，并且具有80% 检测Q10%差异的百分比功率(p小于0.05) 子组之间的坡度。生物标志物将与背景进行比较 水平、偏差和敏感度，以及样本收集和 分析。

1,3-butadiene (BD) is a common air pollutant released by gasoline, cigarette smoke, and automobile emissions. In humans, BD is oxidized by P450 enzymes (CYP2E1, 2E1) to epoxybutene, epoxybutanediol and diepoxybutane(?), which are deactivated by polymorphic epoxide hydrolase (EH). BD is classified as a suspect human carcinogen. BD is enzymatically activated and deactivated, so genetically based differences in susceptibility are expected. Three types of biomarkers show promise for estimating internal dose of epoxybutene and epoxybutanediol; hemoglobin N-terminal valine adducts, lymphocyte 7- alkylguanine DNA adducts, and the urinary mercapturic acid of butenediol. A multicenter collaboration is proposed to quantity exposure-dose relationships for each of these biomarkers of dose. This relationship is defined by a biomarker's background levels, slope, precision, biases and interferences. This relationship is affected by an individual's 2E1 phenotype, EH genotype, and lifestyle factors, which will be assessed. Eighty (80) subjects will be drawn from male Taiwanese petrochemical workers producing butadiene latex and related products at 10 large plants (250 workers total). Subjects will be selected as 4 activation/deactivation subgroups of 20 subjects with either fast or slow 2E1 activation rates, and normal or variant EH genotypes. Eighty (80) controls, age and metabolism matched to the exposed, will be drawn from local unexposed workers. Preliminary sampling showed two types of exposures: high intensity (greater than 100 ppm average for 2-3 hr.) during tank cleaning, and long-term low level (medians 0.5-2 ppm full shift) during normal operations, and little concurrent exposure to styrene or acrylonitrite. Twenty tank cleaners will provide data on high exposure responses. Long-term exposures (5 wks) will be monitored for the 80 operators. Blood and urine samples will be collected before, after 3 wks, and after 5 wks to determine biomarker levels and their changes during the measured exposure. Exposure-biomarkers relationships will be analyzed by regression techniques to see effects of metabolism and personal factors. Simulations showed the strategy estimated slopes to q20 percent (95 percent CI) for an oxidation-deactivation subgroup of 20, and had 80 percent power (p less than 0.05) to detect a q10 percent difference in slopes among subgroups. Biomarkers will be compared for background levels, biases and sensitivity, and ease of sample collection and analysis.