Maternal exposure to ambient PM10 during pregnancy increases the risk of congenital heart defects: Evidence from machine learning models

Highlights: • This study extends the application of machine learning model to birth outcomes and air pollution research. • Maternal exposure to PM₁₀ was identified as the primary risk factor for CHDs in two machine learning models. • Our models consistently suggested that maternal exposure to PM₁₀ can increase the risk of congenital heart defects. • Machine learning model has better predictive performance than traditional logistic regression models. Previous research suggested an association between maternal exposure to ambient air pollutants and risk of congenital heart defects (CHDs), though the effects of particulate matter ≤10 μm in aerodynamic diameter (PM₁₀) on CHDs are inconsistent. We used two machine learning models (i.e., random forest (RF) and gradient boosting (GB)) to investigate the non-linear effects of PM₁₀ exposure during the critical time window, weeks 3–8 in pregnancy, on risk of CHDs. From 2009 through 2012, we carried out a population-based birth cohort study on 39,053 live-born infants in Beijing. RF and GB models were used to calculate odds ratios for CHDs associated with increase in PM₁₀ exposure, adjusting for maternal and perinatal characteristics. Maternal exposure to PM₁₀ was identified as the primary risk factor for CHDs in all machine learning models. We observed a clear non-linear effect of maternal exposure to PM₁₀ on CHDs risk. Compared to 40 μg m⁻³, the following odds ratios resulted: 1) 92 μg m⁻³ [RF: 1.16 (95% CI: 1.06, 1.28); GB: 1.26 (95% CI: 1.17, 1.35)]; 2) 111 μg m⁻³ [RF: 1.04 (95% CI: 0.96, 1.14); GB: 1.04 (95% CI: 0.99, 1.08)]; 3) 124 μg m⁻³ [RF: 1.01 (95% CI: 0.94, 1.10); GB: 0.98 (95% CI: 0.93, 1.02)]; 4) 190 μg m⁻³ [RF: 1.29 (95% CI: 1.14, 1.44); GB: 1.71 (95% CI: 1.04, 2.17)]. Overall, both machine models showed an association between maternal exposure to ambient PM₁₀ and CHDs in Beijing, highlighting the need for non-linear methods to investigate dose-response relationships.