Association of early-life dust allergens and endotoxin with childhood asthma and lung function: An analysis of the CHILD study
Bibliographic record
Abstract
Rationale: Childhood asthma is characterized by altered lung function and airway inflammation, thought to result from complex gene-environment interactions, especially with allergens. Previous studies show inconsistent associations between allergen exposure and asthma. Objectives: We aimed to examine the longitudinal relationship between indoor allergen exposure during infancy with subsequent asthma and spirometry, and potential effect modification by genetic factors. Methods: Data from a subcohort of the CHILD study with analyzed dust samples (including Can f1 (dog), Fel d1 (cat), and endotoxin) and physician diagnosed asthma or spirometry were used to examine relationships between dust analytes at 3 months and asthma and, separately, FEV1 at 5 years, including potential effect modification by genetic factors using polygenic scores (PGSs). Results: Among 1050 children with dust samples (mean age 3.94 months), 6.6% developed asthma by age 5 years. In an adjusted multivariable model, higher Can f1 significantly decreased the risk of asthma (OR 0.52, 95% CI 0.25, 0.98). Compared to children exposed to low Can f1, those exposed to high Can f1 had significantly higher FEV1 z-scores (β=0.23, 95% CI 0.06, 0.40) regardless of asthma status. There was a significant gene-environment interaction between Can f1 and PGSs on FEV1 (β=-0.14, 95%CI -0.32, 0.03). Conclusions: In a general population birth cohort, early-life exposure to high levels of Can f1 was associated with improved lung function and was protective against asthma at age 5. There were significant gene-environment interactions in the relationship between Can f1 and PGSs on lung function.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".