Construction of an adverse outcome pathway framework based on integrated data to evaluate arsenic-induced non-alcoholic fatty liver disease

不良结局途径 脂肪肝 环境卫生 医学 结果(博弈论) 不利影响 酒精性脂肪肝 疾病 重症监护医学 内科学 化学 生物 数学 计算生物学 有机化学 数理经济学
作者
Bowen Fan,Cheng Cheng,Yi Yang,Peiwen Wang,Haibo Xia,Meng Wu,Han Li,Binafsha Manzoor Syed,Qizhan Liu
出处
期刊:Environment International [Elsevier]
卷期号:183: 108381-108381 被引量:5
标识
DOI:10.1016/j.envint.2023.108381
摘要

Arsenic is a recognized environmental pollutant naturally occurring in aquifers through geological processes. Toxicological studies have revealed that liver is the main target organ harmed by arsenic exposure. However, systematic studies of non-alcoholic fatty liver disease (NAFLD) are not comprehensive, and information regarding threats and risk assessment remains insufficient. This research aimed to examine the association between arsenic exposure and NAFLD and uncover the role of molecular initiating events and key events in disease development using the Adverse Outcome Pathway (AOP). Data from 8,104 adults in the National Health and Nutrition Examination Survey were used to explore the relationship between urinary arsenic and NAFLD. In a logistic regression model, urinary inorganic arsenic levels positively correlated with NAFLD (odds ratio = 1.12, 95% confidence interval = 1.07-1.16). Subsequently, to gain a deeper understanding of arsenic-induced NAFLD, an AOP framework was constructed, revealing that arsenic exposure led to elevate levels of TNF-α, which regulated the NF-κB pathway and led to hepatic lipid deposition, causing NAFLD. This AOP was assessed as "high" according to the Organization for Economic Co-operation and Development users' handbook, and in vitro and in vivo models validated the AOP framework. In summary, this study highlights the potential mechanisms of arsenic-induced NAFLD. We combined the AOP with classical toxicological approaches with a view of establishing, rapidly and accurately, the lowest level at which environmental arsenic exposure can have adverse effects on the body, thereby contributing to risk assessment strategies for arsenic exposure through iterative and animal modeling at the population level.
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