Associations of prenatal exposure to a mixture of persistent organic pollutants with social traits and cognitive and adaptive function in early childhood: Findings from the EARLI study

Abstract

Background: Literature suggests that maternal exposure to persistent organic pollutants (POPs) may influence child neurodevelopment. Evidence linking prenatal POPs and autism spectrum disorder has been inconclusive and few studies have examined the mixture effect of the POPs on autism-related traits.

Objective: To evaluate the associations between prenatal exposure to a mixture of POPs and autism-related traits in children from the Early Autism Risk Longitudinal Investigation study.

Methods: Maternal serum concentrations of 17 POPs (11 polychlorinated biphenyls [PCBs], 4 polybrominated diphenyls [PBDEs], and 2 persistent pesticides) in 154 samples collected during pregnancy were included in this analysis. We examined the independent associations of the natural log-transformed POPs with social, cognitive, and behavioral traits at 36 months of age, including Social Responsiveness Scale (SRS), Mullen Scales of Early Learning-Early Learning Composite (MSEL-ELC), and Vineland Adaptive Behavior Scales (VABS) scores, using linear regression models. We applied Bayesian kernel machine regression and quantile g-computation to examine the joint effect and interactions of the POPs.

Results: Higher ln-PBDE47 was associated with greater deficits in social reciprocity (higher SRS score) (β = 6.39, 95% CI: 1.12, 11.65) whereas higher ln-p,p'-DDE was associated with lower social deficits (β = -8.34, 95% CI: -15.32, -1.37). Positive associations were observed between PCB180 and PCB187 and cognitive (MSEL-ELC) scores (β = 5.68, 95% CI: 0.18, 11.17; β = 4.65, 95% CI: 0.14, 9.17, respectively). Adaptive functioning (VABS) scores were positively associated with PCB170, PCB180, PCB187, PCB196/203, and p,p'-DDE. In the mixture analyses, we did not observe an overall mixture effect of POPs on the quantitative traits. Potential interactions between PBDE99 and other PBDEs were identified in association with MSEL-ELC scores.

Conclusions: We observed independent effects of PCB180, PCB187, PBDE47, and p,p' DDE with ASD-related quantitative traits and potential interactions between PBDEs. Our findings highlight the importance of assessing the effect of POPs as a mixture.

Keywords: Autism-related traits; Bayesian kernel machine regression; Environmental mixture; Persistent organic pollutants; Quantile g-computation.

Figure 1.. Pearson correlation matrix for POPs.

Positive correlations are indicated in blue shades and negative correlations are indicated in red shades. POPs were natural log-transformed

Figure 2.

Association between single POP exposure and a) SRS score b) MSEL-ELC score c) VABS score. Estimated difference in outcome are shown for a 1-unit increase in exposures. All POP exposures were treated as continuous natural log-transformed variables. Models adjusted for study site, child’s sex, maternal education, maternal race, and maternal pre-pregnancy BMI

Figure 3.

The joint effect of the POP mixtures on A) SRS score, B) MSEL-ELC score, and C) VABS score estimated from the BKMR model. Estimates and 95% confidence intervals are shown in the figure when all exposures at particular percentiles were compared to all the congeners at their 50^th percentile. Models adjusted for study site, child’s sex, maternal education, maternal race, and maternal pre-pregnancy BMI

Figure 4.

Univariate exposure-response functions for SRS score from the BKMR model. Associations between each POP and SRS score (with corresponding 95% confidence intervals) are shown when setting all other POPs at their median. Models adjusted for study site, child’s sex, maternal education, maternal race, and maternal pre-pregnancy BMI.

Figure 5.

Univariate exposure-response functions for A) MSEL-ELC score and B) VABS score from the BKMR model. Associations between each POP and MSEL-ELC score (with corresponding 95% confidence intervals) are shown when setting all other POPs at their median. Models adjusted for study site, child’s sex, maternal education, maternal race, and maternal pre-pregnancy BMI.