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 ( SNP s) 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 ( NO 2 ) estimated at the home address of each child at birth and nine SNP s within the GSTP 1 , TNF , TLR 2 , or TLR 4 genes with AD were examined. Weighted genetic risk scores ( GRS ) were calculated from the above SNP s 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 NO 2 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 GSTP 1 , TNF , TLR 2 , and TLR 4 SNP s 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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".