Evaluating effects of prenatal exposure to phthalate mixtures on birth weight: A comparison of three statistical approaches. (April 2018)

• Record Type: Journal Article
• Authors: Chiu, Yu-Han; Bellavia, Andrea; James-Todd, Tamarra; Correia, Katharine F.; Valeri, Linda; Messerlian, Carmen; Ford, Jennifer B.; Mínguez-Alarcón, Lidia; Calafat, Antonia M.; Hauser, Russ; Williams, Paige L.
• Is Part Of: Environment international. Volume 113(2018)
• Page Start: 231
• Page End: 239
• Publication Date: 2018-04
• Subjects: BKMR Bayesian Kernel Machine Regression -- PCA principal component analysis -- SEM structural equation modeling; Chemical mixtures -- Principal component analysis -- Structural equation models -- Bayesian Kernel Machine Regression; Environmental protection -- Periodicals; Environmental health -- Periodicals; Environmental monitoring -- Periodicals; Environmental Monitoring -- Periodicals; Environnement -- Protection -- Périodiques; Hygiène du milieu -- Périodiques; Environnement -- Surveillance -- Périodiques; Environmental health; Environmental monitoring; Environmental protection; Periodicals; 333.705
• DOI: 10.1016/j.envint.2018.02.005
• Languages: English
• ISSNs: 0160-4120

Abstract

Abstract: Objectives: We applied three statistical approaches for evaluating associations between prenatal urinary concentrations of a mixture of phthalate metabolites and birth weight. Methods: We included 300 women who provided 732 urine samples during pregnancy and delivered a singleton infant. We measured urinary concentrations of metabolites of di(2-ethylhexyl)-phthalate, di-isobutyl-, di- n -butyl-, butylbenzyl-, and diethyl phthalates. We applied 1) linear regressions; 2) classification methods [principal component analysis (PCA) and structural equation models (SEM)]; and 3) Bayesian kernel machine regression (BKMR), to evaluate associations between phthalate metabolite mixtures and birth weight adjusting for potential confounders. Data were presented as mean differences (95% CI) in birth weight (grams) as each phthalate increased from the 10th to the 90th percentile. Results: When analyzing individual phthalate metabolites using linear regressions, each metabolite demonstrated a modest inverse association with birth weight [from −93 (−206, 21) to −49 (−164, 65)]. When simultaneously including all metabolites in a multivariable model, inflation of the estimates and standard errors were noted. PCA identified two principal components, both inversely associated with birth weight [−23 (−68, 22), −27 (−71, 17), respectively]. These inverse associations were confirmed when applying SEM. BKMR further identified that monoethyl and mono(2-ethylhexyl) phthalate and phthalate concentrations were linearly related to lower birth weight [−51(−164, 63) and −122 (−311, 67), respectively], and suggested no evidence of interaction between metabolites. Conclusions: While none of the methods produced significant results, we demonstrated the potential issues arising using linear regression models in the context of correlated exposures. Among the other selected approaches, classification techniques identified common sources of exposures with implications for interventions, while BKMR further identified specific contributions of individual metabolites. Highlights: We applied 3 statistical methods to evaluate the associations between a mixture of phthalate metabolites and birth weight. We demonstrated potential issues arising using linear regression models in the context of correlated exposures. Classification techniques identified common sources of exposures with implications for intervention. Bayesian Kernel Machine Regression identified specific contributions of individual metabolites to reduced birth weight.