Meiduo Zhao1, Jing Xu2, Ang Li3, Yayuan Mei4, Xiaoyu Ge5, Xiaolin Liu6, Lanping Wei7, Qun Xu8. 1. Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College. Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China. Electronic address: meiduo_zhao@126.com. 2. Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College. Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China. Electronic address: xujing_inbj@163.com. 3. Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College. Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China. Electronic address: 691250197@qq.com. 4. Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College. Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China. Electronic address: mei809000@163.com. 5. Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College. Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China. Electronic address: gxyuzzu@163.com. 6. Department of Epidemiology and Biostatistics, Jinzhou Medical University, Jinzhou 121001, Liaoning, China. Electronic address: 1186409306@qq.com. 7. Jinzhou Central Hospital, Jinzhou 121001, Liaoning, China. Electronic address: 13904168063@163.com. 8. Department of Epidemiology and Biostatistics, Institute of Basic Medical Sciences Chinese Academy of Medical Sciences, School of Basic Medicine Peking Union Medical College. Center of Environmental and Health Sciences, Chinese Academy of Medical Sciences, Peking Union Medical College, Beijing 100005, China. Electronic address: xuqun@ibms.cams.cn.
Abstract
BACKGROUND: Environmental hexavalent chromium contamination in northeast China has been ongoing for over 60 years and health outcomes related with chromium (Cr) pollution were observed in polluted arears, but exposure pathways remains unclear. This study aims to evaluate the association between Cr exposure dose through multiple exposure pathways and Cr concentration in urine, and identify the most contributed pathway. METHODS: We used risk assessment tools with individual exposure parameters to estimate eight individual Cr exposure doses (CD) for three exposure routes (inhalation, ingestion, and dermal contact) with four environmental media (underground water, soil, household dust, and PM10 in ambient air) in 134 residents living in three chromium polluted villages. We used the covariate-adjusted standardized urinary Cr concentration (casUCr) as the internal Cr exposure biomarker. Ridge Regression, Weighted Quantile Sum Regression (WQS) and Bayesian Kernel Machine Regression (BKMR) models were used to assess the effect of overall eight CDs on urine Cr concentration and compare the contribution of each CD. RESULTS: In the ridge regression analysis, Cr exposure through ingestion of dust (βstd = 0.418, p-value = 0.009), inhalation of dust (βstd = 0.384, p = 0.031) and dermal contact with soil (βstd = 0.264, p = 0.192) had the highest impact on casUCr. In the WQS model, the overall CDs demonstrated a non-significant positive association with casUCr. CDs of dust ingestion, air inhalation and dust inhalation had the largest contribution on casUCr when fitted in the WQS model. In the BKMR model, the hierarchical variable selection showed that casUCr was mainly affected by CDs of household dust and dermal contact with soil. CD of dermal contact with soil exhibited a negative association with casUCr, while CDs of dust showed positive or non-linear trend. CONCLUSIONS: This research proposed a new method to calculate individual Cr exposure dose of multi-pathway and applied different statistical methods to identify predominant pathway. For this study, Cr exposure through dust has the strongest effect on Cr concentration in urine. The results could help conduct target interventions to reduce Cr intake, such as blocking dust exposure to reduce Cr uptake for villagers living in these contaminated areas.
BACKGROUND: Environmental hexavalent chromium contamination in northeast China has been ongoing for over 60 years and health outcomes related with chromium (Cr) pollution were observed in polluted arears, but exposure pathways remains unclear. This study aims to evaluate the association between Cr exposure dose through multiple exposure pathways and Cr concentration in urine, and identify the most contributed pathway. METHODS: We used risk assessment tools with individual exposure parameters to estimate eight individual Cr exposure doses (CD) for three exposure routes (inhalation, ingestion, and dermal contact) with four environmental media (underground water, soil, household dust, and PM10 in ambient air) in 134 residents living in three chromium polluted villages. We used the covariate-adjusted standardized urinary Cr concentration (casUCr) as the internal Cr exposure biomarker. Ridge Regression, Weighted Quantile Sum Regression (WQS) and Bayesian Kernel Machine Regression (BKMR) models were used to assess the effect of overall eight CDs on urine Cr concentration and compare the contribution of each CD. RESULTS: In the ridge regression analysis, Cr exposure through ingestion of dust (βstd = 0.418, p-value = 0.009), inhalation of dust (βstd = 0.384, p = 0.031) and dermal contact with soil (βstd = 0.264, p = 0.192) had the highest impact on casUCr. In the WQS model, the overall CDs demonstrated a non-significant positive association with casUCr. CDs of dust ingestion, air inhalation and dust inhalation had the largest contribution on casUCr when fitted in the WQS model. In the BKMR model, the hierarchical variable selection showed that casUCr was mainly affected by CDs of household dust and dermal contact with soil. CD of dermal contact with soil exhibited a negative association with casUCr, while CDs of dust showed positive or non-linear trend. CONCLUSIONS: This research proposed a new method to calculate individual Cr exposure dose of multi-pathway and applied different statistical methods to identify predominant pathway. For this study, Cr exposure through dust has the strongest effect on Cr concentration in urine. The results could help conduct target interventions to reduce Cr intake, such as blocking dust exposure to reduce Cr uptake for villagers living in these contaminated areas.