Traffic-related air pollution as a risk factor for the development of childhood allergic diseases: the “Traffic, Asthma and Genetics” project
Bibliographic record
Abstract
Background Inter-individual variability in associations between traffic-related air pollution and childhood allergic diseases may be explained by genetics and unexplored gene-environment interactions. We combined previously collected data from two Canadian (CAPPS, SAGE) and four European (BAMSE, PIAMA, GINI, LISA) birth cohorts to examine whether exposure to traffic-related air pollution interacts with a child’s genetic profile to impact the risk of developing childhood asthma, allergic rhinitis and sensitization. Methods Asthma and allergic rhinitis were defined using a parent report of doctor diagnosis or reported symptoms (allergic rhinitis only) at age seven or eight years. Associations between nitrogen dioxide (NO2), particulate matter 2.5 mass, particulate matter 2.5 absorbance and ozone, individually assigned to each child's address at birth, and single nucleotide polymorphisms within the GSTP1 and TNF genes with asthma and the GSTPI, TNF, TLR2 and TLR4 genes with allergic rhinitis and sensitization were examined using logistic regression (total children = 15,299). Interaction terms tested for gene-environment associations. Results Children with at least one rs1138272 or rs1695 minor allele (both in GSTPI; OR: 2.59 [95%CI:1.43-4.68] and OR:1.43 [95%CI: 1.03-1.98] per 10 mg/m3 NO2, respectively) were at increased risk of asthma when exposed to air pollution versus homozygous major allele carries (OR:0.95 [95%CI: 0.68-1.32] and OR:0.82 [95%CI:0.52-1.32] per 10 mg/m3 NO2, respectively). Children carrying at least one minor rs1800629 (TNF; OR:1.19 [95%CI:1.00-1.41]) or rs1927911 (TLR4; OR:1.24 [95%CI:1.01-1.53]) allele were at an increased risk of allergic rhinitis, regardless of TRAP exposure. Conclusions Children with GSTP1 minor alleles constitute a susceptible population at increased risk of asthma associated with air pollution. Children with at least one minor rs1800629 allele (TNF) or one minor rs1927911 allele (TLR4) may be at a higher risk of developing allergic rhinitis by school-age. Key messages Our findings confirm the hypothesis that genetically susceptible subgroups are particularly vulnerable to long-term air pollution exposure. These results have important public health relevance given the large proportion of the population carrying minor alleles associated with increased risk of allergic diseases.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".