Ran Shanshan, Zhang Jingyi, Tian Fei, Qian Zhengmin Min, Wei Shengtao, Wang Yuhua, Chen Ge, Zhang Junguo, Arnold Lauren D, McMillin Stephen Edward, Lin Hualiang
Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China.
Department of Epidemiology and Biostatistics College for Public Health & Social Justice, Saint Louis University, Saint Louis, MO, USA.
J Hepatol. 2025 Apr;82(4):560-570. doi: 10.1016/j.jhep.2024.09.033. Epub 2024 Sep 28.
BACKGROUND & AIMS: Air pollution is a significant public health issue and an important risk factor for metabolic dysfunction-associated steatotic liver disease (MASLD), though the underlying mechanisms of this association are unknown. Herein, we aimed to identify metabolic signatures associated with exposure to ambient air pollution and to explore their associations with the risk of MASLD.
We utilized data from the UK Biobank cohort. Annual mean concentrations of PM, PM, NO and NO were assessed for each participant using bilinear interpolation. The elastic net regression model was used to identify metabolites associated with four air pollutants and to construct metabolic signatures. Associations between air pollutants, metabolic signatures and MASLD were analyzed using Cox models. Mendelian randomization (MR) analysis was used to examine potential causality. Mediation analysis was employed to examine the role of metabolic signatures in the association between air pollutants and MASLD.
A total of 244,842 participants from the UK Biobank were included in this analysis. We identified 87, 65, 76, and 71 metabolites as metabolic signatures of PM, PM, NO, and NO, respectively. Metabolic signatures were associated with risk of MASLD, with hazard ratios (HRs) and 95% CIs of 1.10 (1.06-1.14), 1.06 (1.02-1.10), 1.24 (1.20-1.29) and 1.14 (1.10-1.19), respectively. The four pollutants were associated with increased risk of MASLD, with HRs (95% CIs) of 1.03 (1.01-1.05), 1.02 (1.01-1.04), 1.01 (1.01-1.02) and 1.01 (1.00-1.01), respectively. MR analysis indicated an association between PM, NO and NO-related metabolic signatures and MASLD. Metabolic signatures mediated the association of PM, PM, NO and NO with MASLD.
PM, PM, NO and NO-related metabolic signatures appear to be associated with MASLD. These signatures mediated the increased risk of MASLD associated with PM, PM, NO and NO.
Air pollution is a significant public health issue and an important risk factor for metabolic dysfunction-associated steatotic liver disease (MASLD), however, the mechanism by which air pollution affects MASLD remains unclear. Our study used integrated serological metabolic data of 251 metabolites from a large-scale cohort study to demonstrate that metabolic signatures play a crucial role in the elevated risk of MASLD caused by air pollution. These results are relevant to patients and policymakers because they suggest that air pollution-related metabolic signatures are not only potentially associated with MASLD but also involved in mediating the process by which PM, PM, NO, and NO increase the risk of MASLD. Focusing on changes in air pollution-related metabolic signatures may offer a new perspective for preventing air pollution-induced MASLD and serve as protective measures to address this emerging public health challenge.
空气污染是一个重大的公共卫生问题,也是代谢功能障碍相关脂肪性肝病(MASLD)的重要风险因素,尽管这种关联的潜在机制尚不清楚。在此,我们旨在确定与暴露于环境空气污染相关的代谢特征,并探讨它们与MASLD风险的关联。
我们利用了英国生物银行队列的数据。使用双线性插值法评估了每位参与者的PM、PM、NO和NO的年均浓度。弹性网络回归模型用于识别与四种空气污染物相关的代谢物,并构建代谢特征。使用Cox模型分析空气污染物、代谢特征与MASLD之间的关联。采用孟德尔随机化(MR)分析来检验潜在的因果关系。采用中介分析来检验代谢特征在空气污染物与MASLD关联中的作用。
本分析纳入了来自英国生物银行的244,842名参与者。我们分别确定了87、65、76和71种代谢物作为PM、PM、NO和NO的代谢特征。代谢特征与MASLD风险相关,风险比(HRs)及95%置信区间分别为1.10(1.06 - 1.14)、1.06(1.02 - 1.10)、1.24(1.20 - 1.29)和1.14(1.10 - 1.19)。这四种污染物与MASLD风险增加相关,HRs(95%置信区间)分别为1.03(1.01 - 1.05)、1.02(1.01 - 1.04)、1.01(1.01 - 约1.02)和1.01(1.00 - 1.01)。MR分析表明PM、NO和与NO相关的代谢特征与MASLD之间存在关联。代谢特征介导了PM、PM、NO和NO与MASLD的关联。
与PM、PM、NO和NO相关的代谢特征似乎与MASLD相关。这些特征介导了与PM、PM、NO和NO相关的MASLD风险增加。
空气污染是一个重大的公共卫生问题,也是代谢功能障碍相关脂肪性肝病(MASLD)的重要风险因素,然而,空气污染影响MASLD的机制仍不清楚。我们的研究使用了来自大规模队列研究的251种代谢物的综合血清代谢数据,以证明代谢特征在空气污染导致的MASLD风险升高中起关键作用。这些结果与患者和政策制定者相关,因为它们表明与空气污染相关的代谢特征不仅可能与MASLD相关,而且还参与介导PM、PM、NO和NO增加MASLD风险的过程。关注与空气污染相关的代谢特征变化可能为预防空气污染引起的MASLD提供新的视角,并作为应对这一新兴公共卫生挑战的保护措施。