Associations between single and combined exposures to environmental phenols and ulcerative colitis in American adults

Abstract

Objective: The etiology of ulcerative colitis (UC) is complex and involves multiple factors, with exposure to environmental toxins potentially contributing greatly to its pathogenesis. Therefore, this study was carried out with the purpose of delving into the associations between single and combined exposures to environmental phenols and UC among American adults.

Methods: Survey data from the 2009-2010 National Health and Nutrition Examination Survey were selected for our research. The associations between single and combined exposures to environmental phenols and the prevalence of UC were analyzed using weighted multivariate logistic regression models as well as Bayesian kernel machine regression (BKMR).

Results: A total of 1,422 adults aged 20 years old and above were included in this study, 17 of whom had UC. The correlation matrix showed strong associations between 2,4-dichlorophenol (2,4-DCP) and 2,5-dichlorophenol (2,5-DCP) (R = 0.81), as well as between 2,4,5-trichlorophenol (2,4,5-TCP) and 2,4,6-trichlorophenol (2,4,6-TCP) (R = 0.73). The logistic regression model revealed that, after adjusting for confounders, exposure to environmental phenols was positively associated with the prevalence of UC, with 2,4,6-TCP showing a significant association (OR = 2.37, 95 % CI = 1.10, 5.09, P = 0.037). The BKMR analysis indicated an upward trend in the overall effect of combined exposures to environmental phenols on UC. All five phenols contributed to this effect, with 2,4,6-TCP exhibiting the most pronounced effect. When other compounds were fixed at the 50th percentile, the impact of the five phenols on UC demonstrated a positive association, without any noteworthy interaction among the compounds.

Conclusion: Our findings suggested that exposure to environmental phenols may contribute to the occurrence of UC among American adults.

Keywords: Bayesian kernel machine regression; Environmental phenols; Machine learning methods; National health and nutrition examination survey; Ulcerative colitis.