Wine Consumption and Lung Cancer Risk: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Background/Objectives: Lung cancer is one of the leading causes of cancer-related mortality, with tobacco smoking being the primary risk factor. However, a significant percentage of lung cancer patients are non-smokers, suggesting the involvement of other risk factors, including alcohol consumption. The IARC classifies ethanol as a Group 1 carcinogen, but unlike other alcoholic beverages, wine contains polyphenols with potential health benefits. Some meta-analyses even suggest a protective effect, which led us to conduct our own meta-analysis to further investigate this possible correlation. Methods: We conducted a systematic review and stratified the risk across population subgroups based on smoking status and gender. We then performed a categorical “highest vs. lowest” meta-analysis, comparing heavy consumers with very occasional drinkers, using a random-effects model. Only studies examining the risk of developing lung cancer in wine drinkers were included, excluding those with different outcomes, non-primary, ineligible populations, or involving pregnant women. The literature search was conducted in three databases: PubMed, Scopus, and Web of Science. The risk of bias was assessed with the Newcastle–Ottawa quality rating scale for both case–control and cohort studies (NOS), while statistical analyses were performed using the ProMeta 3.0 software. Results: The overall analysis showed a non-statistically significant 11% reduction in lung cancer risk (OR = 0.89; 95% CI: 0.77–1.03). The analysis among smokers revealed a significant 22% reduction in lung cancer risk associated with wine consumption (OR = 0.78; 95% CI: 0.62–0.97). However, this effect was lost when the analysis was conducted separately based on the study design. Conclusions: No correlation emerged between wine consumption and lung cancer incidence, either in a protective sense or in terms of increased risk. However, further studies are needed to investigate this correlation more accurately, particularly among non-smokers.
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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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.034 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".