Carrot intake is consistently negatively associated with cancer incidence: A systematic review and meta-analysis of prospective observational studies
Bibliographic record
Abstract
Carrots are main dietary sources of several potential anti-cancer compounds, including polyacetylenes, while β-carotene has shown no benefits in controlled cancer trials. Accordingly, associations between carrot intake and cancer incidence were quantified, where necessary using α-carotene as a non-causal biomarker of carrot consumption, by searching for studies published before June 2022 reporting risk estimates for relationships of cancer incidence with carrot intake or α-carotene intake or α-carotene plasma concentration, supplemented with hand searches of included studies and reviews. Meta-analyses comparing highest and lowest reported intakes in prospective studies using a random-effects model estimated summary relative risks (RRs) with 95% confidence intervals (CIs), separately for carrot intake or α-carotene plasma concentration, and the corresponding dose-responses. Of 198 observational studies, in 50 prospective studies with 52000 cases recording carrot intake, the cancer-risk was substantially reduced (RR 0.90, 95% CI 0.87–0.94, p ˂ 0·00004). In 30 prospective studies with 9331 cases reporting plasma α-carotene levels, summary RR was 0.80 (0.72–0.89, p ˂ 0·00006). For both exposure types, inter-study heterogeneity was moderate, interaction with cancer types insignificant, and the dose-response significant (p ˂ 0·01). In conclusion, carrot consumption is robustly associated with decreased cancer-risk; carrot consumption should be encouraged, and the causal mechanisms further investigated.
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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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.037 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".