Structural equation modeling of the inflammatory response to traffic air pollution

Abstract

Several epidemiological studies have reported conflicting results on the effect of traffic-related pollutants on markers of inflammation. In a Bayesian framework, we examined the effect of traffic pollution on inflammation using structural equation models (SEMs). We studied measurements of C-reactive protein (CRP), soluble vascular cell adhesion molecule-1 (sVCAM-1), and soluble intracellular adhesion molecule-1 (sICAM-1) for 749 elderly men from the Normative Aging Study. Using repeated measures SEMs, we fit a latent variable for traffic pollution that is reflected by levels of black carbon, carbon monoxide, nitrogen monoxide and nitrogen dioxide to estimate its effect on a latent variable for inflammation that included sICAM-1, sVCAM-1 and CRP. Exposure periods were assessed using 1-, 2-, 3-, 7-, 14- and 30-day moving averages previsit. We compared our findings using SEMs with those obtained using linear mixed models. Traffic pollution was related to increased inflammation for 3-, 7-, 14- and 30-day exposure periods. An inter-quartile range increase in traffic pollution was associated with a 2.3% (95% posterior interval (PI): 0.0-4.7%) increase in inflammation for the 3-day moving average, with the most significant association observed for the 30-day moving average (23.9%; 95% PI: 13.9-36.7%). Traffic pollution adversely impacts inflammation in the elderly. SEMs in a Bayesian framework can comprehensively incorporate multiple pollutants and health outcomes simultaneously in air pollution-cardiovascular epidemiological studies.

Figure 1

Path diagram for the effect of traffic pollution on inflammation. Ellipses are used to denote latent constructs, rectangles are used to denote the observed variables measuring and affecting these constructs, and single-headed arrows are used to denote directional relationships, from predictor to outcome.

Figure 2

Relation of latent variables of Inflammation (for a 1-day moving average exposure) (a) and Traffic (for a 30-day moving average exposure) (b) to marker variables as factor loading values.

Figure 3

Density plots of posterior estimated percentage change in inflammation or sVCAM-1 associated with an IQR increase in BC (reference pollutant of latent traffic pollution) exposure at different daily moving averages (MA). (a) SEM in Bayesian Framework: effect of traffic pollution on inflammation and (b) Bayesian LMM: effect of BC on sVCAM-1.