Application of three statistical models in association between polycyclic aromatic hydrocarbons exposure and cognitive level in workers

Abstract

BackgroundAs a complex organic pollutant, polycyclic aromatic hydrocarbons (PAHs) exposure shares the common exposure characteristics of multiple hydroxyl metabolites. Most studies have analyzed independent effect of each PAHs metabolite and have adjusted for the potential confounding effects induced by other metabolites concomitantly, without considering possible interactions among them. Proper statistical methods are needed to study their toxic effects.ObjectiveTo explore the applicability of logistic regression, weighted quantile sum (WQS) regression, and Bayesian kernel machine regression (BKMR) in evaluating the correlation between mixed exposures to exogenous chemicals and health outcomes, compare the advantages and limitations of the three models, and propose analytical strategies for evaluating the health effects of mixed chemical exposure for application in the analysis of the association between PAHs exposure and cognition.MethodsUrine samples were collected of workers from a coke oven plant and a water treatment plant in Shanxi Province, who participated in their routine employee healthexamination. Mono-hydroxylated PAHs were detected by high-performance liquid chromatography with tandem mass spectrometry (HPLC-MS/MS), cognitive function was assessed using the Montreal Cognitive Assessment (MoCA). A cut-off value of MoCA less than 26 was considered mild cognitive impairment (MCI). According to a predetermined inclusion and exclusion criteria, 1 051 cases were included in the final data analysis. Logistic regression, WQS regression, and BKMR were used to analyze the relationship between PAHs metabolites and MCI.ResultsThe prevalence rate of reporting MCI among the 1 051 workers was 21.7% (228/1 051). The concentration of 2-hydroxynathalene (2-OHNAP) was the highest among the 11 PAHs metabolites with a median concentration of 0.30 μg·L−1, followed by 9-hydroxyphenanthrene (9-OHPHE) (0.26 μg·L−1). There were significant differences between the two groups in 2-OHNAP, 1-hydroxynaphthalene (1-OHNAP), 2-hydroxyfluorene (2-OHFLU), 9-OHPHE, 1-hydroxyphenanthrene (1-OHPHE), and 1-hydroxypyrene (1-OHPYR) (all Ps<0.05). In the logistic regression, 2-OHNAP and 2-OHPHE were associated with MCI, and the OR (95%CI) for reporting MCI was 1.28 (1.01-1.67) and 1.27 (1.00-1.72) for each 10-fold increase in 2-OHNAP and 2-OHPHE concentrations, respectively. In the WQS regression analysis, the WQS index was positively correlated with the prevalence rate of reporting MCI (OR=1.37, 95%CI: 1.10-1.72). In the BKMR analysis, compared with the median exposure levels of all chemicals, the overall effect was statistically significant when all PAHs metabolites concentrations were at or above their 30th percentile; when all exposures were at the 75th percentile, the risk of reporting MCI increased by 6%.ConclusionBased on the results of these three models, 2-OHNAP and 2-OHPHE are the most important factors related to cognitive. It is recommended to use a combination of traditional logistic regression and either WQS or BKMR to study the association between PAHs and MCI.

Keywords

• polycyclic aromatic hydrocarbons
• logistic regression
• weighted quantile sum regression
• bayesian kernel machine regression
• cognition