RESUMEN
Maternal exposure to air pollution has been associated with a higher birth defect (BD) risk. Previous studies suffer from inaccurate exposure assessment methods, confounding individual-level variations, and classical analytical modelling. This study aimed to examine the association between maternal exposure to criteria air pollutants and BD risk. A total of 553 cases and 10,214 controls were identified from private and public databases. Two subgroups were then formed: one for a matched case-control design, and another for Feature Selection (FS) analysis. Exposure assessment was based on the mean air pollutant-specific levels in the mother's residential area during the specific BD gestational time window of risk (GTWR) and other time intervals. Multivariate regression models outcomes consistently showed a significant protective effect for folic acid intake and highlighted parental consanguinity as a strong BD risk factor. After adjusting for these putative risk factors and other covariates, results show that maternal exposure to PM2.5 during the first trimester is significantly associated with a higher overall BD risk (OR:1.05, 95%CI:1.01-1.09), and with a higher risk of genitourinary defects (GUD) (OR:1.06, 95%CI:1.01-1.11) and neural tube defects (NTD) (OR:1.10, 95%CI:1.03-1.17) during specific GTWRs. Maternal exposure to NO2 during GTWR exhibited a significant protective effect for NTD (OR:0.94, 95%CI:0.90-0.99), while all other examined associations were not statistically significant. Additionally, maternal exposure to SO2 during GTWR showed a significant association with a higher GUD risk (OR:1.17, 95%CI:1.08-1.26). When limiting selection to designated monitor coverage radiuses, PM2.5 maintained significance with BD risk and showed a significant gene-environment interaction for GUD (p = 0.018), while NO2 protective effect expanded to other subtypes. On the other hand, FS analysis confirmed maternal exposure to PM2.5 and NO2 as important features for GUD, CHD, and NTD. Our findings, set the basis for building a novel BD risk prediction model.