Abstract
Background and Aim: Prenatal and early life exposure to air pollution has been shown to be associated with autism spectrum disorder (ASD) risk, but the results have been mixed and to our knowledge, no study has reported on effects of combined exposures to multiple air pollutants using a mixtures approach. We applied a multidomain, multipollutant approach to assess the association between ASD and air pollution. Methods: The study consisted of 484 TD children and 707 children with ASD from the CHARGE case-control study. Air pollution exposures for NO2, and Ozone, fine (PM2.5) and ultrafine (PM0.1) particles were predicted using a chemical transport model with statistical bias adjustment based on ground-based monitors. Averages were calculated for each pregnancy period (pre-pregnancy, each trimester of pregnancy, and first and second year of life) for all births between the year 2000 and 2016. The air pollution (AP) variables were natural log transformed and then standardized. We estimated individual and joint effects of AP exposure with ASD and evaluated potential interactions among AP variables for each pregnancy period, using component-wise and hierarchal Bayesian Kernel Machine Regression (BKMR) models. Results: In component-wise BKMR models that included PM0.1, NO2, and Ozone, we found a strong increasing risk of ASD in year 2 of life with increasing PM0.1 (Posterior inclusion probability, PIP, = 0.99). This held true in hierarchal models when grouped by time or by pollutant. In component-wise BKMR models that included PM2.5, NO2, and Ozone, we found that NO2 and Ozone in years 1 and 2 were associated with ASD in an inverted U shape. No robust associations were observed in the prenatal or pregnancy periods. Conclusions: PM0.1 appears to be associated with an increased risk of ASD in year 2 of life. Future research should examine ultrafine particulate matter in relation to ASD.
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