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Record W2176484374 · doi:10.1093/eurpub/ckt126.227

Traffic-related air pollution as a risk factor for the development of childhood allergic diseases: the “Traffic, Asthma and Genetics” project

2013· article· en· W2176484374 on OpenAlexaffabout
Elaine Fuertes, Elaina MacIntyre, Erik Melén, Joachim Heinrich, Marjan Kerkhof, Göran Pershagen, Ulrike Gehring, Anita L. Kozyrskyj, Moira Chan‐Yeung, Michael Bräuer, Chris Carlsten

Bibliographic record

VenueEuropean Journal of Public Health · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicAir Quality and Health Impacts
Canadian institutionsWomen and Children’s Health Research InstituteUniversity of AlbertaUniversity of British Columbia
Fundersnot available
KeywordsAsthmaEnvironmental healthAir pollutionMedicineDemographyImmunologyBiologyEcology

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.836
Threshold uncertainty score0.330

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.048
GPT teacher head0.292
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2013
Admission routes2
Has abstractyes

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