Atopic dermatitis: Interaction between genetic variants of <i><scp>GSTP</scp>1</i>,<i><scp>TNF</scp></i>,<i><scp>TLR</scp>2</i>, and <i><scp>TLR</scp>4</i> and air pollution in early life
Bibliographic record
Abstract
Abstract Background Associations between traffic‐related air pollution (TRAP) and childhood atopic dermatitis (AD) remain inconsistent, possibly due to unexplored gene‐environment interactions. The aim of this study was to examine whether a potential effect of TRAP on AD prevalence in children is modified by selected single nucleotide polymorphisms (SNPs) related to oxidative stress and inflammation. Methods Doctor‐diagnosed AD up to age 2 years and at 7‐8 years, as well as AD symptoms up to age 2 years, was assessed using parental‐reported questionnaires in six birth cohorts (N = 5685). Associations of nitrogen dioxide (NO2) estimated at the home address of each child at birth and nine SNPs within the GSTP1, TNF, TLR2, or TLR4 genes with AD were examined. Weighted genetic risk scores (GRS) were calculated from the above SNPs and used to estimate combined marginal genetic effects of oxidative stress and inflammation on AD and its interaction with TRAP. Results GRS was associated with childhood AD and modified the association between NO2 and doctor‐diagnosed AD up to the age of 2 years (P(interaction) = .029). This interaction was mainly driven by a higher susceptibility to air pollution in TNF rs1800629 minor allele (A) carriers. TRAP was not associated with the prevalence of AD in the general population. Conclusions The marginal genetic association of a weighted GRS from GSTP1, TNF, TLR2, and TLR4SNPs and its interaction with air pollution supports the role of oxidative stress and inflammation in AD.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".