Long-term exposure to ambient PM2.5 and its constituents is associated with MAFLD

Bing Guo; Shourui Huang; Sicheng Li; Xinyu Han; Hualiang Lin; Yajie Li; Zixiu Qin; Xiaoman Jiang; Zihao Wang; Yongyue Pan; Juying Zhang; Jianzhong Yin; Xing Zhao; China Multi-Ethnic Cohort (CMEC) collaborative group

doi:10.1016/j.jhepr.2023.100912

Long-term exposure to ambient PM2.5 and its constituents is associated with MAFLD

JHEP Rep. 2023 Sep 17;5(12):100912. doi: 10.1016/j.jhepr.2023.100912. eCollection 2023 Dec.

Authors

Affiliations

¹ West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, Sichuan, China.
² Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China.
³ Tibet Center for Disease Control and Prevention, Lhasa, China.
⁴ School of Public Health, the Key Laboratory of Environmental Pollution Monitoring and Disease Control, Ministry of Education, Guizhou Medical University, Guiyang 550025, China.
⁵ Chengdu Center for Disease Control and Prevention, Chengdu, China.
⁶ Chongqing Municipal Center for Disease Control and Prevention, China.
⁷ Tibet University, Lhasa, China.
⁸ School of Public Health, Kunming Medical University, Kunming, Yunnan, China.
⁹ Baoshan College of Traditional Chinese Medicine, Baoshan, Yunnan, China.

Abstract

Background & aims: Existing evidence suggests that long-term exposure to ambient fine particulate pollution (PM_2.5) may increase metabolic dysfunction-associated fatty liver disease (MAFLD) risk. However, there is still limited evidence on the association of PM_2.5 constituents with MAFLD. Therefore, this study explores the associations between the five main chemical constituents of PM_2.5 and MAFLD to provide more explicit information on the liver exposome.

Methods: A total of 76,727 participants derived from the China Multi-Ethnic Cohort, a large-scale epidemic survey in southwest China, were included in this study. Multiple linear regression models were used to estimate the pollutant-specific association with MAFLD. Weighted quantile sum regression was used to evaluate the joint effect of the pollutant-mixture on MAFLD and identify which constituents contribute most to it.

Results: Three-year exposure to PM_2.5 constituents was associated with a higher MAFLD risk and more severe liver fibrosis. Odds ratios for MAFLD were 1.480, 1.426, 1.294, 1.561, 1.618, and 1.368 per standard deviation increase in PM_2.5, black carbon, organic matter, ammonium, sulfate, and nitrate, respectively. Joint exposure to the five major chemical constituents was also positively associated with MAFLD (odds ratio 1.490, 95% CI 1.360-1.632). Nitrate contributed most to the joint effect of the pollutant-mixture. Further stratified analyses indicate that males, current smokers, and individuals with a high-fat diet might be more susceptible to ambient PM_2.5 exposure than others.

Conclusions: Long-term exposure to PM_2.5 and its five major chemical constituents may increase the risk of MAFLD. Nitrate might contribute most to MAFLD, which may provide new clues for liver health. Males, current smokers, and participants with high-fat diets were more susceptible to these associations.

Impact and implications: This large-scale epidemiologic study explored the associations between constituents of fine particulate pollution (PM_2.5) and metabolic dysfunction-associated fatty liver disease (MAFLD), and further revealed which constituents play a more important role in increasing the risk of MAFLD. In contrast to previous studies that examined the effects of PM_2.5 as a whole substance, this study carefully explored the health effects of the individual constituents of PM_2.5. These findings could (1) help researchers to identify the specific particles responsible for hepatotoxicity, and (2) indicate possible directions for policymakers to efficiently control ambient air pollution, such as targeting the sources of nitrate pollution.

Keywords: Metabolic dysfunction-related fatty liver disease; ambient fine particulate matter constituents; hepatotoxicity; joint exposure analysis.