Maternal diet mitigates the effects of prenatal air pollution exposure on DNA methylation of immune-relevant genes in cord blood
Bibliographic record
Abstract
Prenatal air pollution exposure is associated with childhood asthma, but mechanisms underlying this association remain unclear. There is some evidence for biological embedding of prenatal air pollution exposure in fetal DNA methylation (DNAm), which may contribute to long-term changes in respiratory health. We used Illumina EPIC DNAm data from cord blood from the CANDLE study to determine whether DNAm links pregnancy-averaged NO2 PM2.5, or PM10 exposure with childhood persistent wheeze, transient wheeze, or diagnosed asthma, and whether maternal diet is associated with reductions in air pollution-associated DNAm changes. Using linear regression with significance thresholds of FDR<0.05 and effect size >5%, we found 19, seven, and six unique differentially methylated regions associated with prenatal NO2, PM2.5, or PM10, respectively, of which one, in HLA-DPA, mediated the association between both prenatal NO2 and PM2.5 and transient wheeze at age 4. Across the genome, maternal diet as measured by the Alternative Healthy Eating Index-Pregnancy moderated the effects of all air pollutants on DNAm in cord blood such that better quality diet reduced the impact of air pollution on DNAm. This same direction of moderation was observed with participation in an income supplementation program. Our results provide evidence of the biological embedding of prenatal air pollution and suggest specific pathways that may link air pollution exposure with childhood lung disease. In addition, our work suggests that programs to support high diet quality for low-income families may reduce the biological impact of air pollution on respiratory health in children.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".