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

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.

• Authors: Yu-Han Chiu, Andrea Bellavia, Tamarra James-Todd, Katharine F Correia, Linda Valeri, Carmen Messerlian, Jennifer B Ford, Lidia Mínguez-Alarcón, Antonia M Calafat, Russ Hauser, Paige L Williams, EARTH Study Team
• Journal: Environment international (Environ Int) Vol. 113 Pg. 231-239 (04 2018) ISSN: 1873-6750 [Electronic] Netherlands
• PMID: 29453090 (Publication Type: Comparative Study, Journal Article, Research Support, N.I.H., Extramural)
• Copyright: Copyright © 2018 Elsevier Ltd. All rights reserved.

Chemical References

• Environmental Pollutants
• Phthalic Acids
• phthalic acid
• diethyl phthalate

Topics

• Adult
• Bayes Theorem
• Birth Weight (drug effects)
• Environmental Exposure (statistics & numerical data)
• Environmental Pollutants (toxicity)
• Female
• Humans
• Linear Models
• Male
• Phthalic Acids (toxicity, urine)
• Pregnancy
• Prenatal Exposure Delayed Effects
• Principal Component Analysis
• Prospective Studies