Genetic polymorphism of catechol-O-methyltransferase modulates the association of green tea consumption and lung cancer
Bibliographic record
Abstract
Tea polyphenols are strong antioxidants, which can be rapidly O-methylated by catechol-O-methyltransferase (COMT). Thus, it is possible that the genetic polymorphism of COMT can modulate the association of green tea consumption and lung cancer. Here, we designed a case-control study to evaluate the combined effect of green tea consumption and COMT genotypes on the risk of lung cancer. A total of 237 lung cancer patients and 474 healthy controls were recruited. Questionnaires were administered to obtain demographic data, smoking status, green tea consumption, fruits and vegetables intake, exposure to cooking fumes, and family history of lung cancer. Genotypes for COMT were identified by PCR. Smoking, green tea consumption, exposure to cooking fumes, and family history of lung cancer were associated with the development of lung cancer. When green tea drinkers carrying COMT HL/LL genotypes were selected as the reference group, drinkers carrying the COMT HH genotype had a higher risk for the development of lung cancer (odds ratio: 1.97, 95% confidence interval: 0.99-3.91). Among the current and ever smokers, the elevated risk for lung cancer was more apparent in green tea drinkers carrying the COMT HH genotype compared with green tea drinkers carrying COMT HL/LL genotypes (odds ratio: 5.84, 95% confidence interval: 1.75-19.45). Green tea drinkers with greater activity of the COMT genotype, whereby polyphenols are effectively excluded, will gain fewer protective benefits against lung cancer development.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".