Using three statistical methods to analyze the association between aldehyde exposure and markers of inflammation and oxidative stress

Abstract

Background: Exposure to aldehydes has been linked to adverse health outcomes such as inflammation and oxidative stress, but research on the effects of these compounds is limited. This study is aimed at assessing the association between aldehyde exposure and markers of inflammation and oxidative stress.

Methods: The study used data from the NHANES 2013-2014 survey (n = 766) and employed multivariate linear models to investigate the relationship between aldehyde compounds and various markers of inflammation (alkaline phosphatase (ALP) level, absolute neutrophil count (ANC), and lymphocyte count) and oxidative stress (bilirubin, albumin, and iron levels) while controlling for other relevant factors. In addition to generalized linear regression, weighted quantile sum (WQS) and Bayesian kernel machine regression (BKMR) analyses were applied to examine the single or overall effect of aldehyde compounds on the outcomes.

Results: In the multivariate linear regression model, each 1 standard deviation (SD) change in propanaldehyde and butyraldehyde was significantly associated with increases in serum iron levels (beta and 95% confidence interval, 3.25 (0.24, 6.27) and 8.40 (0.97, 15.83), respectively) and the lymphocyte count (0.10 (0.04, 0.16) and 0.18 (0.03, 0.34), respectively). In the WQS regression model, a significant association was discovered between the WQS index and both the albumin and iron levels. Furthermore, the results of the BKMR analysis showed that the overall impact of aldehyde compounds was significantly and positively correlated with the lymphocyte count, as well as the levels of albumin and iron, suggesting that these compounds may contribute to increased oxidative stress.

Conclusions: This study reveals the close association between single or overall aldehyde compounds and markers of chronic inflammation and oxidative stress, which has essential guiding value for exploring the impact of environmental pollutants on population health.

Keywords: Aldehyde; Bayesian kernel machine regression (BKMR); Inflammation and oxidative stress; Multipollutant; Weighted quantile sum (WQS) regression.