Association between Dietary and Supplemental Antioxidants Intake and Lung Cancer Risk: Evidence from a Cancer Screening Trial
Bibliographic record
Abstract
Previous studies provided inconsistent results on the effects of antioxidant nutrient intake on lung cancer prevention. We aimed to evaluate the association between antioxidant consumption from food and supplemental sources and lung cancer incidence. Data were obtained from the Prostate, Lung, Colorectal, and Ovarian (PLCO) cancer screening trial. A total of 98,451 participants were included in the data analysis. We used a multivariable Cox proportional hazards regression model to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for the association between antioxidant intake and lung cancer risk. Dose-response assessments for individual nutrients were conducted. We also selected the model for the best combination of antioxidants for reducing lung cancer risk using machine learning methods. After the median follow-up of 12.2 years, 1642 new cases were identified. Intake of the calculated HRs indicated a trend for a higher quartile of food-based Composite Dietary Antioxidant Index (fCDAI) associated with a lower lung cancer risk after adjusting for covariates (HRQ4vs.Q1 = 0.64, 95% CI: 0.52, 0.79; P for trend < 0.001). Protective effects of dietary antioxidant intake were observed across all individual antioxidant micronutrients except magnesium. Random forests model suggested the dietary intake group of α-carotene, magnesium, vitamin C, vitamin E, lycopene, selenium, lutein, and zeaxanthin, and β-carotene had the most favorable effects on lung cancer prevention. Higher consumption of antioxidants from food sources has a protective effect against lung cancer, while no effects were shown in the supplemental group. It is recommended to consume a combination of various antioxidants due to the potential benefits from the interaction, while more research should be performed to investigate the underlying mechanisms of antioxidant synergic effects on lung cancer risk reduction.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".