Non-linear association between long-term air pollution exposure and risk of metabolic dysfunction-associated steatotic liver disease.

Abstract

Background: Metabolic Dysfunction-associated Steatotic Liver Disease (MASLD) has become a global epidemic, and air pollution has been identified as a potential risk factor. This study aims to investigate the non-linear relationship between ambient air pollution and MASLD prevalence.

Method: In this cross-sectional study, participants undergoing health checkups were assessed for three-year average air pollution exposure. MASLD diagnosis required hepatic steatosis with at least 1 out of 5 cardiometabolic criteria. A stepwise approach combining data visualization and regression modeling was used to determine the most appropriate link function between each of the six air pollutants and MASLD. A covariate-adjusted six-pollutant model was constructed accordingly.

Results: A total of 131,592 participants were included, with 40.6% met the criteria of MASLD. "Threshold link function," "interaction link function," and "restricted cubic spline (RCS) link functions" best-fitted associations between MASLD and PM_2.5, PM₁₀/CO, and O₃ /SO₂/NO₂, respectively. In the six-pollutant model, significant positive associations were observed when pollutant concentrations were over: 34.64 µg/m³ for PM_2.5, 57.93 µg/m³ for PM₁₀, 56 µg/m³ for O₃, below 643.6 µg/m³ for CO, and within 33 and 48 µg/m³ for NO₂. The six-pollutant model using these best-fitted link functions demonstrated superior model fitting compared to exposure-categorized model or linear link function model assuming proportionality of odds.

Conclusion: Non-linear associations were found between air pollutants and MASLD prevalence. PM_2.5, PM₁₀, O₃, CO, and NO₂ exhibited positive associations with MASLD in specific concentration ranges, highlighting the need to consider non-linear relationships in assessing the impact of air pollution on MASLD.