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

@article{gaylord2026methodology,
  title = {{A methodology to identify a DNA methylation-based signature of prenatal air pollution exposure in the ECHO Cohort.}},
  author = {Abigail Gaylord and Julie D White and Tessa Bloomquist and Carrie Breton and Carlos A Camargo and Andres Cardenas and Elena Colicino and Dana Dabelea and Suman R Das and Rebecca C Fry and Jia Guo and Luhang Han and Christine W Hockett and Margaret R Karagas and Janine M LaSalle and Tengfei Ma and Amy Margolis and Grier Page and Frederica P Perera and Alicia K Peterson and Sima K Ramratnam and Annemarie Stroustrup and Rebecca J Schmidt and Jennifer K Straughen and Kristina W Whitworth and Shuang Wang and Julie B Herbstman},
  journal = {Environmental epigenetics},
  year = {2026},
  month = oct,
  doi = {10.1093/eep/dvag034},
  url = {https://europepmc.org/article/MED/42959478},
  note = {CC BY-NC},
}

TY  - JOUR
TI  - A methodology to identify a DNA methylation-based signature of prenatal air pollution exposure in the ECHO Cohort.
AU  - Abigail Gaylord
AU  - Julie D White
AU  - Tessa Bloomquist
AU  - Carrie Breton
AU  - Carlos A Camargo
AU  - Andres Cardenas
AU  - Elena Colicino
AU  - Dana Dabelea
AU  - Suman R Das
AU  - Rebecca C Fry
AU  - Jia Guo
AU  - Luhang Han
AU  - Christine W Hockett
AU  - Margaret R Karagas
AU  - Janine M LaSalle
AU  - Tengfei Ma
AU  - Amy Margolis
AU  - Grier Page
AU  - Frederica P Perera
AU  - Alicia K Peterson
AU  - Sima K Ramratnam
AU  - Annemarie Stroustrup
AU  - Rebecca J Schmidt
AU  - Jennifer K Straughen
AU  - Kristina W Whitworth
AU  - Shuang Wang
AU  - Julie B Herbstman
PY  - 2026
DA  - 2026/10/09
JO  - Environmental epigenetics
DO  - 10.1093/eep/dvag034
UR  - https://europepmc.org/article/MED/42959478
AB  - 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.
SN  - 2058-5888
ER  -