Exposure to prenatal phthalate mixtures and neurodevelopment in the Conditions Affecting Neurocognitive Development and Learning in Early childhood (CANDLE) study

Christine T. Loftus, Nicole R. Bush, Drew B. Day, Yu Ni, Frances A. Tylavsky, Catherine J. Karr, Kurunthachalam Kannan, Emily S. Barrett, Adam A. Szpiro, Sheela Sathyanarayana, Kaja Z. LeWinn

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Background: Findings from epidemiological studies of prenatal phthalate exposure and child cognitive development are inconsistent. Methods for evaluating mixtures of phthalates, such as weighted quantile sum (WQS) regression, have rarely been applied. We developed a new extension of the WQS method to improve specificity of full-sample analyses and applied it to estimate associations between prenatal phthalate mixtures and cognitive and language outcomes in a diverse pregnancy cohort. Methods: We measured 22 phthalate metabolites in third trimester urine from mother-child dyads who completed early childhood visits in the Conditions Affecting Neurodevelopment and Learning in Early childhood (CANDLE) study. Language and cognitive ability were assessed using the Bayley Scales of Infant Development (age 3) and the Stanford Binet-5 (age 4–6), respectively. We used multivariable WQS regression to identify phthalate mixtures that were negatively and positively associated with language score and full-scale IQ, in separate models, adjusted for maternal IQ, race, marital status, smoking, BMI, socioeconomic status (SES), child age, sex, and breastfeeding. We evaluated effect modification by sex and SES. If full sample 95% WQS confidence intervals (which are known to be anti-conservative) excluded the null, we calculated a p-value using a permutation test (ppermutation). The performance of this new approach to WQS regression was evaluated in simulated data. We compared the power and type I error rate of WQS regression conducted within datasets split into training and validation samples (WQSSplit) and in the full sample (WQSNosplit) to WQS regression with a permutation test (WQSpermutation). Individual metabolite associations were explored in secondary analyses. Results: The analytic sample (N = 1015) was 62.1% Black/31.5% White, and the majority of mothers had a high school education or less (56.7%) at enrollment. Associations between phthalate mixtures and primary outcomes (language score and full-scale IQ) in the full sample were null. Individual metabolites were not associated with IQ, and only one metabolite (mono-benzyl phthalate, MBzP) was associated with Bayley language score (β = −0.68, 95% CI: −1.37, 0.00). In analyses stratified by sex or SES, mixtures were positively and negatively associated with outcomes, but the precision of full-sample WQS regression results were not supported by permutation tests, with one exception. In the lowest SES category, a phthalate mixture dominated by mono-methyl phthalate (MMP) and mono-carboxy-isooctyl phthalate (MCOP) was associated with higher language scores (βlow SES = 2.41, full-sample 95%CI: 0.58, 4.24; ppermutation = 0.04). Performance testing in simulated data showed that WQSpermutation had improved power over WQSSplit (90% versus 56%) and a lower type I error rate than WQSNosplit (7% versus 47%). Conclusions: In the largest study of these relationships to date, we observed predominantly null associations between mixtures of prenatal phthalates and both language and IQ. Our novel extension of WQS regression improved sensitivity to detect true associations by obviating the need to split the data into training and test sets and should be considered for future analyses of exposure mixtures.

Original languageEnglish (US)
Article number106409
JournalEnvironment international
StatePublished - May 2021
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Environmental Science(all)


  • Exposure mixtures
  • Neurodevelopment
  • Phthalates
  • Prenatal exposures


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