Abstract 1274: Alcohol and lung cancer risk: a pooled analysis using International Lung Cancer Consortium studies
Bibliographic record
Abstract
Abstract Background: Alcohol consumption is known to be associated with risk of developing several cancers. It is unclear, however, whether alcohol consumption is a risk factor for lung cancer. The relationship between lung cancer and alcohol consumption is likely to be confounded by smoking. To minimize potential confounding by tobacco consumption, we conducted a pooled analysis to examine the association of alcohol consumption with lung cancer risk in a large sample of never-smokers. Methods: We pooled data from 22 case-control and cohort studies from North America, Europe and Asia within the International Lung Cancer Consortium (ILCCO) and SYNERGY Consortium. We examined the association of average lifetime alcohol consumption (expressed as average grams per day intake) with lung cancer risk in never smokers using logistic regression to model categories of alcohol consumption (0<5g per day, 5<10g per day, 10<20g per day, 20<30g per day, 30<45g per day, 45+ g per day). To investigate the shape of the dose response relationship, we applied restricted cubic spline models to examine the association for lung cancer risk overall and by histological subtype. Additional analyses examined wine, beer and liquor consumption in relation to risk, with mutual adjustment for each alcoholic beverage. All analyses were adjusted for age, sex, education, ethnicity and study. Results: A total of 2548 never-smoking cases and 9362 never-smoking controls were included in the analysis. The results showed lower risk among consumers of alcohol with strongest evidence found for moderate drinkers relative to non-drinkers with ORs of 0.80 (95% CI 0.70-0.90) and 0.82 (95% CI 0.69-0.99) for <5grams and 5-10 grams of alcohol per day respectively. Non-linear restricted cubic splines showed reduced lung cancer risk among moderate drinkers relative to non-drinkers with risk increasing towards the null as consumption increased. Similar results were seen for adenocarcinoma and squamous cell carcinoma. Associations with lung cancer differed for wine and beer consumption. Reduced risk was observed for wine drinking particularly at low levels of drinking, OR of 0.80 (95% CI=0.69-0.94) for <5g per day. Risk for beer consumption increased from close to null among occasional drinkers to 1.54 (95% CI 0.90-2.65) among consumers of 20-30g of alcohol per day (test for trend P=0.09). Conclusions: These results indicate an inverse association between moderate drinking and lung cancer risk relative to never drinkers. However, the inverse association was restricted to wine consumption, not consumption of beer. Lifestyle differences between consumers of beer and wine may play a role in differing patterns of risk found by alcohol type. Citation Format: Gordon Fehringer, Darren Brenner, Zuo-Feng Zhang, Yuan-Chin Amy Lee, Keitaro Matsuo, Isabelle Stucker, Paolo Vineis, Paolo Boffetta, Paul Brennan, Maria T. Landi, Hal Morgenstern, Curtis C. Harris, Qing Lan, Yun-Chul Hong, Jack Siemiatycki, John R. McLaughlin, Philip Lazarus, Joshua Muscat, Ann G. Schwartz, Juan M. Barros Dios, Alberto R. Raviña, Gad Rennert, David C. Christiani, Adonina Tardon, Loic Le Marchand, Irene Orlow, Eric J. Duell, Angeline S. Andrew, Hermann Brenner, Dario Consonni, Ann Olsson, Kurt Straif, Rayjean J. Hung. Alcohol and lung cancer risk: a pooled analysis using International Lung Cancer Consortium studies. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1274. doi:10.1158/1538-7445.AM2014-1274
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| grok | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Meta-analysis | high |
| opus | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | medium |
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 | 0.028 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.030 |
| Bibliometrics | 0.012 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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, unvalidatedLabeled directly by 3 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".