Dietary intake of fiber, whole grains and risk of colorectal cancer: An updated analysis according to food sources, tumor location and molecular subtypes in two large US cohorts
Bibliographic record
Abstract
Epidemiologic evidence relating fiber intake to colorectal cancer (CRC) remains inconclusive and data are limited on different food sources of fiber and heterogeneity by tumor subsite and molecular profile. We prospectively followed for CRC incidence 90,869 women from the Nurses’ Health Study (1980–2012) and 47,924 men from the Health Professionals Follow‐up Study (1986–2012), who completed a validated food frequency questionnaire every 4 years. Cox proportional hazards regression was used to examine the associations with CRC risk for total, cereal, fruit and vegetable fiber and whole grains. We also assessed the associations according to tumor subsites (proximal colon, distal colon and rectum) and molecular markers (microsatellite instability, BRAF mutation, CpG island methylator phenotype and KRAS mutation). We documented 3,178 CRC cases during 3,685,903 person‐years of follow‐up in the NHS and HPFS. Intake of total dietary fiber was not associated with CRC risk after multivariable adjustment in either women (hazard ratio [HR] comparing extreme deciles, 1.17; 95% CI, 0.92–1.48, ptrend = 0.55) or men (HR, 0.90; 95% CI, 0.67–1.21, ptrend = 0.47). Higher intake of cereal fiber and whole grains was associated with lower CRC risk in men with an HR of 0.75 (95% CI, 0.57–1.00) and 0.72 (95% CI, 0.54–0.96), respectively. No heterogeneity was detected by tumor subsite or molecular markers (pheterogeneity > 0.05). Higher intake of total dietary fiber within the range of a typical American diet is unlikely to substantially reduce CRC risk. The potential benefit of cereal fiber and whole grains in men warrants further confirmation.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".