Dietary Carotenoids and Risk of Lung Cancer in a Pooled Analysis of Seven Cohort Studies
Bibliographic record
Abstract
Intervention trials with supplemental beta-carotene have observed either no effect or a harmful effect on lung cancer risk. Because food composition databases for specific carotenoids have only become available recently, epidemiological evidence relating usual dietary levels of these carotenoids with lung cancer risk is limited. We analyzed the association between lung cancer risk and intakes of specific carotenoids using the primary data from seven cohort studies in North America and Europe. Carotenoid intakes were estimated from dietary questionnaires administered at baseline in each study. We calculated study-specific multivariate relative risks (RRs) and combined these using a random-effects model. The multivariate models included smoking history and other potential risk factors. During follow-up of up to 7-16 years across studies, 3,155 incident lung cancer cases were diagnosed among 399,765 participants. beta-Carotene intake was not associated with lung cancer risk (pooled multivariate RR = 0.98; 95% confidence interval, 0.87-1.11; highest versus lowest quintile). The RRs for alpha-carotene, lutein/zeaxanthin, and lycopene were also close to unity. beta-Cryptoxanthin intake was inversely associated with lung cancer risk (RR = 0.76; 95% confidence interval, 0.67-0.86; highest versus lowest quintile). These results did not change after adjustment for intakes of vitamin C (with or without supplements), folate (with or without supplements), and other carotenoids and multivitamin use. The associations generally were similar among never, past, or current smokers and by histological type. Although smoking is the strongest risk factor for lung cancer, greater intake of foods high in beta-cryptoxanthin, such as citrus fruit, may modestly lower the risk.
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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 | 0.026 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.026 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".