Does Eating More Variety of Fruits and Vegetables Reduce Risk of Cancer? Findings from a Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Specific food groups, particularly fruits and vegetables, have been shown to reduce the risk of some cancers. However, cancer reduction from greater variety within these food groups is under-studied. This study aimed to evaluate the evidence on the relationship between greater variety, especially within fruits and vegetables, and incident cancer. A systematic search of recent prospective studies was conducted in PubMed, Scopus, Web of Science, supplemented by hand-searching, and appraised for quality. Summary risk ratios (RRs) and 95% confidence intervals were estimated using fixed- and random-effects models for high vs. low intake categories and for linear associations; Cochrane Q test detected sources of heterogeneity among included studies. Total sample size was 2285,720 with 9420 cases of cancers in 5 included studies. All were assessed as high quality and 4 provided 7 risk estimates for meta-analysis. The risk of all cancers was not associated with high vs. low variety of both fruit and vegetable items, fruits, vegetables, and subtypes of vegetable items. Studies adjusting for BMI showed an inverse association for high variety of vegetable subgroups (RR 0.80, 95% CI: 0.68, 0.95). Risk of stomach cancer or rectal cancer increased with high variety of fruit and vegetable, or fruit variety. However, lung cancer risk reduced with high vegetable subgroup variety and, in men only, colon cancer risk reduced with high variety of all foods across 5 food groups. Although no dose-response was found between fruits and/or vegetables variety and risk of all cancers, the risk of esophageal squamous cancer significantly decreased with two new fruit, or fruit and vegetable, items by 24% and 12%, respectively. We found that only higher variety of vegetables subgroups was linked to lower risk of cancer, particularly lung. Total diet variety lowered men's risk of colon cancer. None.
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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 | 0.015 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.041 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".