Air pollution exposure is associated with gene expression in children
Bibliographic record
Abstract
Abstract Environmental exposures, including air pollutants and lack of natural spaces, are associated with suboptimal health outcomes in children. We aimed to study the associations between environmental exposures and gene expression in children. Associations of exposure to particulate matter (PM) with diameter <2.5 (PM2.5) and < 10 (PM10) micrometers, nitrogen dioxide, green spaces, and blue space, with whole-blood gene expression were explored in children from the Dutch Generation R Study (n = 172). Analyses were adjusted for age, sex, batch, maternal education, and area socioeconomic status. Follow-up analysis was carried out using lymphoblastoid cell line gene expression in children from the ALSPAC Study (n = 946). Gene set enrichment analysis (GSEA) using hallmark and immune gene sets from the molecular signature database was carried out to identify significantly over-represented gene sets for insights into biological mechanisms Exposure to PM2.5 was associated with expression of 86 genes in discovery analyses in the Generation R Study [false discovery rate (FDR)-adjusted P-value < .25]. Of these, PM2.5 was also associated with GNG11 expression in the same direction in follow-up analysis (FDR-adjusted P-value < .05). The remaining exposures showed much fewer associations in the discovery analyses. GSEA using PM2.5 association results for both cohorts indicated suppression of gene sets related to interferon response and response to bacterial and viral exposure. In conclusion, gene expression analysis performed in two independent cohorts suggests that PM2.5 exposure in children may be involved in interferon and microbial infection responses.
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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 | 0.001 |
| 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) | 0.002 | 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".