Examining the association between gestational phenol exposure and infant non-nutritive suck in two Environmental influences on Child Health Outcomes cohorts

作者：Megan Woodbury, Andréa Aguiar, Sarah Dee Geiger, Max T. Aung, Shukhan Ng, Morgan Hines, Alaina Martens, Deborah J. Watkins, Gredia Huerta-Montañez, José F. Cordero, John D. Meeker, Akram N. Alshawabkeh, Susan L. Schantz, Emily Zimmerman, on behalf of program collaborators for Environmental influences on Child Health Outcomes · 发表于：Environmental Epidemiology · 年份：2025 · DOI：10.1097/ee9.0000000000000399 · 被引用次数：2 · 研究领域：Effects and risks of endocrine disrupting chemicals、Folate and B Vitamins Research、Birth, Development, and Health

Background: Non-nutritive suck (NNS) is a measure of neurofunction sensitive to environmental exposures in utero. This study aimed to evaluate the relationship between gestational phenol exposure and NNS patterning. Methods: Mother-infant pairs from two diverse prospective cohorts were enrolled in the Environmental influences on Child Health Outcomes Program. Phenols were measured in prenatal maternal urine samples and adjusted for specific gravity. NNS was sampled in 1-8-week-old infants using a custom pacifier for ~5 minutes. Associations of 11 phenols and triclocarban with 5 NNS outcomes were assessed individually and as a mixture using generalized linear models adjusted for cohort, child sex and assessment age, and maternal age and education. Results: Altogether, 215 mother-infant pairs were included. Bisphenol-F was related to a lower NNS frequency. Triclosan was associated with a higher NNS frequency. Propylparaben, 2,4-dichlorophenol, and 2,5-dichlorophenol were associated with lower NNS amplitude. Benzophenone-3, 2,4-dichlorophenol, and 2,5-dichlorophenol were related to more NNS bursts/minute. Propylparaben was associated with more NNS cycles/bursts. Seven phenols were included in mixture analyses: 2,4-dichlorophenol, 2,5-dichlorophenol, benzophenone-3, bisphenol-A, bisphenol-S, methylparaben, and propylparaben. Both Bayesian kernel machine regression and quantile g-computation showed that higher concentrations of the mixture were associated with lower amplitude but ...