Prenatal phthalate, paraben, and phenol exposure and childhood allergic and respiratory outcomes: Evaluating exposure to chemical mixtures

Abstract

Background: Chemicals found in personal care products and plastics have been associated with asthma, allergies, and lung function, but methods to address real life exposure to mixtures of these chemicals have not been applied to these associations.

Methods: We quantified urinary concentrations of eleven phthalate metabolites, four parabens, and five other phenols in mothers twice during pregnancy and assessed probable asthma, aeroallergies, and lung function in their age seven children. We implemented Bayesian Profile Regression (BPR) to cluster women by their exposures to these chemicals and tested the clusters for differences in outcome measurements. We used Bayesian Kernel Machine Regression (BKMR) to fit biomarkers into one model as joint independent variables.

Results: BPR clustered women into seven groups characterized by patterns of personal care product and plastic use, though there were no significant differences in outcomes across clusters. BKMR showed that monocarboxyisooctyl phthalate and 2,4-dichlorophenol were associated with probable asthma (predicted probability of probable asthma per IQR of biomarker z-score (standard deviation) = 0.08 (0.09) and 0.11 (0.12), respectively) and poorer lung function (predicted probability per IQR = -0.07 (0.05) and -0.07 (0.06), respectively), and that mono(3-carboxypropyl) phthalate and bisphenol A were associated with aeroallergies (predicted probability per IQR = 0.13 (0.09) and 0.11 (0.08), respectively). Several biomarkers demonstrated positive additive effects on other associations.

Conclusions: BPR and BKMR are useful tools to evaluate associations of biomarker concentrations within a mixture of exposure and should supplement single-chemical regression models when data allow.

Keywords: Bayesian Kernel Machine Regression; Bayesian Profile Regression.