Variants in the Glutamate-Cysteine-Ligase Gene Are Associated with Cystic Fibrosis Lung Disease
Bibliographic record
Abstract
BACKGROUND: Chronic progressive lung disease is the most serious complication of cystic fibrosis (CF). Glutathione plays an important role in the protection of the CF lung against oxidant-induced lung injury. OBJECTIVES: We hypothesized that a polymorphism in a novel candidate gene that regulates glutathione synthesis might influence CF lung disease. METHODS: In a cross-sectional study, subjects were recruited from CF clinics in Seattle and multiple centers in Canada. We tested for an association between CF lung disease and a functional polymorphism in the glutamate-cysteine ligase catalytic subunit (GCLC) gene. Multiple linear regression was used to test for association between polymorphisms of GCLC and severity of CF lung disease while adjusting for age, Pseudomonas aeruginosa infection, and cystic fibrosis transmembrane conductance regulator (CFTR) genotype. Analysis was repeated for patients with CF stratified by CFTR genotype. MEASUREMENTS AND MAIN RESULTS: A total of 440 subjects with CF participated in the study (51% male; mean [+/- SD] age, 26 +/- 11 yr; mean FEV(1), 62 +/- 28% predicted). In the total population, there was a trend toward an association between GCLC genotypes and CF lung disease (linear regression coefficient [SEM], 1.68 [1.0]; p = 0.097). In the stratified analysis, there was a highly significant association between GCLC genotype and CF lung function in subjects with a milder CFTR genotype (linear regression coefficient [SEM], 5.5 (1.7); p = 0.001). CONCLUSIONS: In patients with CF with a milder CFTR genotype, there is a strong association between functional polymorphisms of the GCLC gene and CF lung disease severity.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".