Analysis of urinary arsenic metabolism model and influencing factors of people chronic exposed to arsenic through drinking water
10.3760/cma.j.cn231583-20201126-00307
- VernacularTitle:慢性饮水型砷暴露人群尿砷代谢模式及影响因素分析
- Author:
Jian WANG
;
Chenlu FAN
;
Qun LOU
;
Meichen ZHANG
;
Fanshuo YIN
;
Zaihong ZHANG
;
Xin ZHANG
;
Yanmei YANG
;
Yanhui GAO
- From:
Chinese Journal of Endemiology
2021;40(4):268-272
- CountryChina
- Language:Chinese
-
Abstract:
Objective: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.
