Can Certain Genotypes Predispose to Poor Asthma Control in Children? A Pharmacogenetic Study of 9 Candidate Genes in Children with Difficult Asthma
Bibliographic record
Abstract
OBJECTIVE: We tested the hypothesis that patients with difficult asthma have an increased frequency of certain genotypes that predispose them to asthma exacerbations and poor asthma control. METHODS: A total of 180 Caucasian children with confirmed asthma diagnosis were selected from two phenotypic groups; difficult (n = 112) versus mild/moderate asthma (n = 68) groups. All patients were screened for 19 polymorphisms in 9 candidate genes to evaluate their association with difficult asthma. KEY RESULTS: The results indicated that LTA4H A-9188>G, TNFα G-308>A and IL-4Rα A1727>G polymorphisms were significantly associated with the development of difficult asthma in paediatric patients (p<0.001, p = 0.019 and p = 0.037, respectively). Haplotype analysis also revealed two haplotypes (ATA haplotype of IL-4Rα A1199>C, IL-4Rα T1570>C and IL-4Rα A1727>G and CA haplotype of TNFα C-863>A and TNFα G-308>A polymorphisms) which were significantly associated with difficult asthma in children (p = 0.04 and p = 0.018, respectively). CONCLUSIONS AND CLINICAL RELEVANCE: The study revealed multiple SNPs and haplotypes in LTA4H, TNFα and IL4-Rα genes which constitute risk factors for the development of difficult asthma in children. Of particular interest is the LTA4H A-9188>G polymorphism which has been reported, for the first time, to have strong association with severe asthma in children. Our results suggest that screening for patients with this genetic marker could help characterise the heterogeneity of responses to leukotriene-modifying medications and, hence, facilitate targeting these therapies to the subset of patients who are most likely to gain benefit.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".