摘要目的:分析吉林和山西省高水砷暴露地区人群尿砷代谢产物，探讨不同人群砷代谢模式和可能的影响因素。方法:2018年10月至2019年8月，采用整群抽样方法，以山西省吕梁市和吉林省白城市部分乡（镇）的饮水砷含量超标村（水砷≥0.05 mg/L）为调查采样点，选择饮用当地集中式供水和小井水的35岁以上常住居民作为砷暴露组，以临近饮食居住习惯相同、经济条件相近的低砷水源地区人群为对照组，开展流行病学调查和一般健康状况检查。采集两组人群尿样，用液相色谱-原子荧光光谱仪（LC-AFS）技术分离和检测4种形态砷化合物，包括三价无机砷（iAs Ⅲ）、五价无机砷（iAs Ⅴ）、一甲基胂酸（MMA）、二甲基胂酸（DMA）。计算总砷（tAs）、无机砷百分比（iAs%）、MMA百分比（MMA%）、DMA百分比（DMA%）、甲基化率（PMI）和二甲基化率（SMI）。采用多元线性回归分析砷代谢的影响因素。 结果:共调查1 415名村民，其中砷暴露组1 256人，对照组159人；对照组与砷暴露组年龄、性别比例、职业分布情况比较，差异均无统计学意义（ P均> 0.05），而吸烟、饮酒、体质指数（BMI）和受教育程度分布情况比较，差异均有统计学意义（ P均< 0.05）。对照组和砷暴露组尿tAs、iAs%、MMA%、DMA%、PMI和SMI中位数（ M）分别为12.86 μg/L、15.03、5.23、76.35、84.97、93.68和69.68 μg/L、10.24、8.37、79.31、89.76、90.65，砷暴露组尿tAs、DMA%和PMI水平均高于对照组，而iAs%和SMI均低于对照组（ U=- 13.87、- 4.30、- 6.64、- 6.64、- 1.99， P均< 0.05）。对人群尿砷代谢影响因素分析发现，年龄和BMI是iAs%的影响因素（ β=- 0.08、- 0.08， P均< 0.05）；性别、饮酒、BMI和受教育程度是MMA%的影响因素（ β=- 0.11、- 0.09、- 0.07、0.08， P均< 0.05）；年龄、性别、BMI和受教育程度是DMA%的影响因素（ β= 0.06、0.09、0.10、- 0.09， P均< 0.05）；年龄和BMI是PMI的影响因素（ β=0.08、0.08， P均< 0.05）；性别、饮酒、BMI和受教育程度是SMI的影响因素（ β=0.09、0.08、0.08、- 0.09， P均< 0.05）。 结论:不同砷暴露人群尿砷代谢模式存在差异，年龄、性别、吸烟、饮酒、BMI以及受教育程度可能是不同砷代谢模式的影响因素。

abstractsObjective:Through determination of urinary arsenic metabolites in high water arsenic exposed areas of Jilin and Shanxi provinces, to explore the mode and possible influencing factors of arsenic metabolism in different populations.Methods:From October 2018 to August 2019, a cluster sampling was carried out in villages (arsenic in drinking water ≥0.05 mg/L) of some townships (towns) in Lyuliang City, Shanxi Province and Baicheng City, Jilin Province for epidemiological investigation and general health examination. The residents over 35 years old drinking water from local centralized water supply and small well water sources were selected as arsenic exposure group, and people (nearby low-arsenic water source areas) with the same diet and living habits and similar economic conditions were selected as control group. Urine samples were collected. Liquid chromatography-atomic fluorescence spectrometry(LC-AFS) technology was used to separate and detect 4 species of arsenic compounds, including trivalent inorganic arsenic (iAs Ⅲ), pentavalent inorganic arsenic (iAs Ⅴ), methylated arsine (MMA), and dimethylated arsine (DMA). Total arsenic (tAs), inorganic arsenic percentage (iAs%), MMA percentage (MMA%), DMA percentage (DMA%), primary methylation index (PMI) and the secondary methylation index (SMI) were calculated. The influencing factors of arsenic metabolism were analyzed by multiple linear regression. Results:A total of 1 415 villagers were investigated, including 1 256 in arsenic exposure group and 159 in control group. Compared with the control group, there were no significant differences in age, gender ratio and occupation distribution between arsenic exposure group and control group ( P > 0.05), but there were significant differences in smoking, drinking, body mass index (BMI) and education level distribution ( P < 0.05). The median of urinary tAs, iAs%, MMA%, DMA%, PMI and SMI in control group and arsenic exposure group were 12.86 μg/L, 15.03, 5.23, 76.35, 84.97, 93.68 and 69.68 μg/L, 10.24, 8.37, 79.31, 89.76, 90.65, respectively, the levels of urinary tAs, DMA% and PMI in arsenic exposed group were higher than those in control group, while iAs% and SMI were lower than those in control group, the differences were statistically significant ( U=- 13.87, - 4.30, - 6.64, - 6.64, - 1.99, P < 0.05). After analysis of the factors influencing urinary arsenic metabolism in the population, we found that age and BMI had an impact on iAs% ( β=- 0.08, - 0.08, P < 0.05); gender, drinking, BMI and education level were influencing factors of MMA% ( β =- 0.11, - 0.09, - 0.07, 0.08, P < 0.05); DMA% was mainly affected by age, gender, BMI and education level ( β = 0.06, 0.09, 0.10, - 0.09, P < 0.05); PMI was mainly affected by age and BMI ( β = 0.08, 0.08, P < 0.05); while SMI was affected by gender, drinking, BMI and education level ( β=0.09, 0.08, 0.08, - 0.09, P < 0.05). Conclusions:The urinary arsenic metabolism models of different arsenic exposed groups are different. Age, gender, smoking, drinking, BMI and education level may be influencing factors of different arsenic metabolism models.