In utero pyrethroid pesticide exposure and child cognitive development from 6 to 36 months in the MARBLES longitudinal cohort

Abstract

Introduction: Pyrethroid (PYR) pesticides have neurotoxicological properties but little is known about the effects of in utero exposure on child development. We assessed the relation or in utero PYR exposure and child cognitive development from 6-36 months. Methods: Mother-child pairs (n=132) in the MARBLES (Markers of Autism Risk in Babies-Learning Early Signs) cohort were examined. MARBLES enrolls pregnant women who already had a child with autism spectrum disorder (ASD) and, therefore are at increased risk (up to 20%) for having another child who will develop ASD. Maternal urine samples comprising an individual spot urine and a pooled sample of three individual spot urine samples, from each of the latter two trimesters were analyzed for PYR metabolite 3-phenoxybenzoic acid (3PBA). For 9 mothers we analyzed all samples, without pooling, to study intra-pregnancy variability. The intraclass correlation coefficient (ICC) from these 9 moms and total number of urine samples for each pregnancy were used to compute weights for linear regression models of child cognition using the Mullen Scales of Early Learning (MSEL) a norm referenced cognitive measure. Results: Median 3PBA concentration in the variability study was 0.63 ng/ml with an ICC = 0.65 for specific gravity-adjusted 3PBA. In all 132 moms, median 3PBA concentration was 0.83 ng/ml. After applying weights to 3PBA concentration and adjusting for specific gravity, mom’s age at delivery, child’s sex, and season of conception, we found significantly lower MSEL scores at 6 months (β= -0.55, p-value 0.002). Lower scores were also observed for MSEL at 12, 24 and 36 months but these results were not significant. Conclusion: This may be the first study to show an association with in utero PYR pesticide exposure and decreased cognitive development at 6 months. These are preliminary findings and results will be updated with a larger sample and more rigorous measurement error modeling approaches.