Polymorphisms in the IL-4, IL-4 Receptor α Chain, TNF-α, and Lymphotoxin-α Genes and Risk of Asthma in Hong Kong Chinese Adults
Bibliographic record
Abstract
BACKGROUND: Susceptibility to the development of asthma and other atopic diseases is known to be associated with genetic components. However, association studies with interleukin-4 (IL-4), IL-4 receptor alpha subunit (IL-4R alpha), tumor necrosis factor-alpha (TNF-alpha) and lymphotoxin-alpha (LT-alpha) genes were inconclusive, as both positive and negative results were obtained in several populations studied. We aimed to investigate the association of the polymorphisms for IL-4 (C-589T), IL-4R alpha (Gln576Arg), TNF-alpha (G-308A) and LT-alpha (A252G) genes as candidates and asthma in adult Hong Kong Chinese population. METHODS: The association study was conducted in an age- and smoking status-matched case-control design in asthma patients (n = 292) and healthy controls (n = 292) using polymerase chain reaction and restriction fragment length polymorphism. RESULTS: No significant differences were found in allele and genotype frequencies of all four genes between patients and controls. After stratification by atopic status, the heterozygous AG genotype of LT-alpha (A252G) was found to increase risk of asthma in atopic population [odds ratio (OR) = 2.00, 95% CI 1.09-3.67, p = 0.024]. When stratified by smoking status, we found increased risk of asthma with subjects carrying the heterozygous AG and homozygous GG genotypes of LT-alpha in ever-smokers (OR = 2.73, 95% CI 1.11-6.69, p = 0.028 for heterozygotes; OR = 3.34, 95% CI 1.16-9.62, p = 0.026 for homozygotes). CONCLUSION: Our results suggest that the variability of LT-alpha genotypes may have potential implications for individual susceptibility to asthma in atopic or in ever-smoking Chinese adults in Hong Kong.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".