Glutathione S-Transferase Variants and Their Interaction with Smoking on Lung Function
Bibliographic record
Abstract
We studied glutathione S-transferase (GST) polymorphisms in 1,098 whites with the lowest (n = 544, FEV(1) % predicted mean +/- SEM = 62.6 +/- 0.1) and the highest (n = 554, FEV(1) % predicted mean +/- SEM = 91.8 +/- 0.1) lung function at the beginning of the Lung Health Study. Homozygosity for GSTP1 105Val was significantly more frequent in the low- than in the high-function group (13.2 vs. 9.3%) (odds ratio = 1.69, 95% confidence interval [CI] = 1.11-2.61, p = 0.016), after adjustment for confounding variables. Subjects with 105Val homozygotes had higher rates of lung function decline in the high-function group (p = 0.017). The frequencies of GSTM1, GSTT1 null genotypes were similar between the high- and low-function groups, but subjects with the GSTT1 null genotype had a faster decline of lung function in the low-function group (p = 0.032). In addition, there was a significant interaction of GSTT1 genotype and pack-years on lung function. When comparing individuals with GSTT1 null genotype with wild type, the adjusted odds ratio was 3.49 (95% CI, 1.48-8.39, p = 0.005) in mild smokers (< or = 25 pack years). We conclude that GST genotypes are risk factors for rapid decline or low lung function in smokers with mild to moderate airflow obstruction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".