A methodology to identify a DNA methylation-based signature of prenatal air pollution exposure in the ECHO Cohort

Abstract

Prenatal exposure to ambient air pollutants has been associated with adverse child health across different populations. In the absence of direct air pollution exposure measures, epigenetic biomarkers can serve as indicators of cumulative exposure. Previously, we developed a methodological pipeline to build a prediction model to distinguish infants highly exposed to air pollutants in utero based on DNA methylation in cord blood samples. Here, we test and refine this pipeline using data from multiple pregnancy cohort sites included in the Environmental influences on Child Health Outcomes (ECHO) Cohort. Levels of prenatal exposure to fine particulate matter (PM2.5), nitrogen dioxide (NO2), and 8-hour maximum ozone (O3) were modeled using geocoded address data and machine learning. DNA methylation was measured using 450K or EPIC arrays. Training and validation sets were created and matched based on exposure level, cohort site, and array type to test the performance of the pipeline prediction model using area under the curve metrics. Among the three exposures, epigenetic-based predictions of PM2.5 and NO2 produced higher area under the curve values (0.64-0.68 and 0.62-0.95, respectively) than those for O3 (0.50-0.75). We identified differences in the performance of the pipeline model across geographical regions, exposure windows, and thresholds used to distinguish "high" and "low" air pollutant exposure. Overall, this pipeline enabled the estimation of air pollution from DNA methylation data, though careful consideration of model inputs is required. Further studies will test the performance of the CpG sites identified by the pipeline to predict child health outcomes.