Epigenetics, Built Environment and Atopy
Bibliographic record
Abstract
RationaleAccurate estimation of gestational age (GA) using DNA methylation (DNAm) of umbilical cord blood, a novel development, provides relevant information on developmental stage. Studies of environmental exposures on GA are plentiful, but not using DNAm GA. We evaluated associations between DNAm GA, environmental correlates of the built environment and atopy.MethodsCord blood samples from 145 selected participants in the Canadian Healthy Infant Longitudinal Development study were examined, together with allergy skin prick tests at age 1 year.Exposure to greenness using satellite imagery, and individual exposure estimates to air pollution using land use regression models were assessed at home addresses throughout pregnancy.Associations of air pollution and greenness with DNAm GA residuals were tested while adjusting for maternal and neonatal characteristics. We further examined whether sensitization related to these environmental factors and DNAm GA.ResultsThe mean GA was 276 (SD: 7.9) days, and mean DNAm GA was 279(5.8) days. There was a significantly positive correlation between these measues (r=0.66; p<0.001).Prenatal exposure to greenness showed borderline association with DNAm GA acceleration (i.e., older DNAm GA than chronological GA) (0.6 days, 95% CI: [-.6; 1.8] for one IQR exposure increase). Conversely, DNAm GA deceleration (i.e., younger DNAm GA) was associated with air pollution exposure during the first trimester (-2.1 days [-4.8; 0.7] for a 10 μg/m3 exposure increase).29% of this atopy-enriched sub-cohort were sensitized to at least one of 10 allergens. DNAm GA was a strong predictor of decreased risk of sensitization (OR: 0.93, 95%CI: [0.88; 0.99]), while air pollution exposure modestly increased the risk of sensitization by 4% (p=0.1).ConclusionOur findings warrant in depth investigation of potential mediating role of DNA methylation on the association between allergic disorders and the built environment.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 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".