Diet‐wide association study of 92 foods and nutrients and lung cancer risk in the European Prospective Investigation into Cancer and Nutrition study and the Netherlands Cohort Study
Bibliographic record
Abstract
It is unclear whether diet, and in particular certain foods or nutrients, are associated with lung cancer risk. We assessed associations of 92 dietary factors with lung cancer risk in 327 790 participants in the European Prospective Investigation into Cancer and Nutrition (EPIC). Cox regression yielded adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) per SD higher intake/day of each food/nutrient. Correction for multiple comparisons was performed using the false discovery rate and identified associations were evaluated in the Netherlands Cohort Study (NLCS). In EPIC, 2420 incident lung cancer cases were identified during a median of 15 years of follow-up. Higher intakes of fibre (HR per 1 SD higher intake/day = 0.91, 95% CI 0.87-0.96), fruit (HR = 0.91, 95% CI 0.86-0.96) and vitamin C (HR = 0.91, 95% CI 0.86-0.96) were associated with a lower risk of lung cancer, whereas offal (HR = 1.08, 95% CI 1.03-1.14), retinol (HR = 1.06, 95% CI 1.03-1.10) and beer/cider (HR = 1.04, 95% CI 1.02-1.07) intakes were positively associated with lung cancer risk. Associations did not differ by sex and there was less evidence for associations among never smokers. None of the six associations with overall lung cancer risk identified in EPIC were replicated in the NLCS (2861 cases), however in analyses of histological subtypes, inverse associations of fruit and vitamin C with squamous cell carcinoma were replicated in the NLCS. Overall, there is little evidence that intakes of specific foods and nutrients play a major role in primary lung cancer risk, but fruit and vitamin C intakes seem to be inversely associated with squamous cell lung cancer.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".