Using Four Machine Learning Methods to Analyze the Association Between Polycyclic Aromatic Hydrocarbons and Visual Impairment in American Adults: Evidence from NHANES.

The causes of visual impairment are complex and may be influenced by exposure to environmental pollutants. Using data from the 2003-2004 National Health and Nutrition Examination Survey (NHANES), we examined the association between exposure to ten polycyclic aromatic hydrocarbons (PAHs) and vision problems in 1149 U.S. adults. We employed various supervised learning methods, including variable selection techniques such as Lasso and elastic net, weighted quantile sum regression (WQS), and Bayesian kernel machine regression (BKMR), to assess the association between PAHs and the occurrence of visual impairments. The mediation effects between urinary 2-fluorene and inflammation were evaluated using mediation analysis. Both the lasso and elastic net models consistently identified two specific PAH congeners, 2-fluorene and 1-phenanthrene, as significant predictors. The WQS regression revealed a positive relationship between the PAH mixture and visual impairment, with notable contributions from urinary 2-fluorene (weight = 0.39) and 9-fluorene (weight = 0.21). BKMR analysis indicated that the likelihood of visual impairment increases with higher PAH exposure, showing a general upward trend. This trend also revealed a positive association between visual impairment and exposure to four specific PAH metabolites, including 2-fluorene. A significant mediation effect was observed for alkaline phosphatase (p = 0.03), with a proportion mediated of 10.48%. Our findings suggest a significant association between PAHs and visual impairment, with multiple statistical models consistently emphasizing the crucial role of 2-fluorene exposure. This study highlights the importance of considering environmental pollutants as significant contributors to visual health outcomes, providing insights for preventing visual impairment.