Gestational exposure to organochlorine compounds and metals and infant birth weight: effect modification by maternal hardships.

BACKGROUND: Gestational exposure to toxic environmental chemicals and maternal social hardships are individually associated with impaired fetal growth, but it is unclear whether the effects of environmental chemical exposure on infant birth weight are modified by maternal hardships. METHODS: We used data from the Maternal-Infant Research on Environmental Chemicals (MIREC) Study, a pan-Canadian cohort of 1982 pregnant females enrolled between 2008 and 2011. We quantified eleven environmental chemical concentrations from two chemical classes - six organochlorine compounds (OCs) and five metals - that were detected in ≥ 70% of blood samples collected during the first trimester. We examined fetal growth using birth weight adjusted for gestational age and assessed nine maternal hardships by questionnaire. Each maternal hardship variable was dichotomized to indicate whether the females experienced the hardship. In our analysis, we used elastic net to select the environmental chemicals, maternal hardships, and 2-way interactions between maternal hardships and environmental chemicals that were most predictive of birth weight. Next, we obtained effect estimates using multiple linear regression, and plotted the relationships by hardship status for visual interpretation. RESULTS: Elastic net selected trans-nonachlor, lead, low educational status, racially minoritized background, and low supplemental folic acid intake. All were inversely associated with birth weight. Elastic net also selected interaction terms. Among those with increasing environmental chemical exposures and reported hardships, we observed stronger negative associations and a few positive associations. For example, every two-fold increase in lead concentrations was more strongly associated with reduced infant birth weight among participants with low educational status (ß = -100 g (g); 95% confidence interval (CI): -215, 16), than those with higher educational status (ß = -34 g; 95% CI: -63, -3). In contrast, every two-fold increase in mercury concentrations was associated with slightly higher birth weight among participants with low educational status (ß = 23 g; 95% CI: -25, 71) compared to those with higher educational status (ß = -9 g; 95% CI: -24, 6). CONCLUSIONS: Our findings suggest that maternal hardships can modify the associations of gestational exposure to some OCs and metals with infant birth weight.

Using Latent Profile Analysis to Identify Associations Between Gestational Chemical Mixtures and Child Neurodevelopment.

BACKGROUND: Unsupervised machine learning techniques have become increasingly popular for studying associations between gestational exposure mixtures and human health. Latent profile analysis is one method that has not been fully explored. METHODS: We estimated associations between gestational chemical mixtures and child neurodevelopment using latent profile analysis. Using data from the Maternal-Infant Research on Environmental Chemicals (MIREC) research platform, a longitudinal cohort of pregnant Canadian women and their children, we generated latent profiles from 27 gestational exposure biomarkers. We then examined the associations between these profiles and child Verbal IQ, Performance IQ, and Full-Scale IQ, measured with the Wechsler Preschool and Primary Scale of Intelligence, Third Edition (WPPSI-III). We validated our findings using k-means clustering. RESULTS: Latent profile analysis detected five latent profiles of exposure: a reference profile containing 61% of the study participants, a high monoethyl phthalate (MEP) profile with moderately low persistent organic pollutants (POPs) containing 26%, a high POP profile containing 6%, a low POP profile containing 4%, and a smoking chemicals profile containing 3%. We observed negative associations between both the smoking chemicals and high MEP profiles and all IQ scores and between the high POP profile and Full-Scale and Verbal IQ scores. We also found a positive association between the low POP profile and Full-Scale and Performance IQ scores. All associations had wide 95% confidence intervals. CONCLUSIONS: Latent profile analysis is a promising technique for identifying patterns of chemical exposure and is worthy of further study for its use in examining complicated exposure mixtures.

Association Between Gestational Exposure to Toxicants and Autistic Behaviors Using Bayesian Quantile Regression.

Autism spectrum disorder, which is characterized by impaired social communication and stereotypic behaviors, affects 1%-2% of children. Although prenatal exposure to toxicants has been associated with autistic behaviors, most studies have been focused on shifts in mean behavior scores. We used Bayesian quantile regression to assess the associations between log2-transformed toxicant concentrations and autistic behaviors across the distribution of behaviors. We used data from the Maternal-Infant Research on Environmental Chemicals study, a pan-Canadian cohort (2008-2011). We measured metal, pesticide, polychlorinated biphenyl, phthalate, bisphenol-A, and triclosan concentrations in blood or urine samples collected during the first trimester of pregnancy. Using the Social Responsiveness Scale (SRS), in which higher scores denote more autistic-like behaviors, autistic behaviors were assessed in 478 children aged 3-4 years old. Lead, cadmium, and most phthalate metabolites were associated with mild increases in SRS scores at the 90th percentile of the SRS distribution. Manganese and some pesticides were associated with mild decreases in SRS scores at the 90th percentile of the SRS distribution. We identified several monotonic trends in which associations increased in magnitude from the bottom to the top of the SRS distribution. These results suggest that quantile regression can reveal nuanced relationships and, thus, should be more widely used by epidemiologists.