A Prospective Study of Dietary Folate and Vitamin B and Colon Cancer According to Microsatellite Instability and <i>KRAS</i> Mutational Status
Bibliographic record
Abstract
Sporadic microsatellite instability (MSI)-high colon cancers are positively associated with MLH1 promoter methylation and inversely with KRAS mutation. One-carbon metabolism is critical for methylation reactions and nucleotide biosynthesis, but the influence of dietary one-carbon nutrients such as folate and B vitamins on molecular changes in colon cancer is not known. Using the database of two independent prospective cohort studies (88,691 women and 47,371 men), we examined the relation between dietary intake of one-carbon nutrients and the incidence of microsatellite instability and KRAS mutation in 669 incident colon cancers. The overall inverse association between folate and colon cancer did not differ significantly according to MSI status [relative ratio (RR), 0.79; 95% confidence interval (95% CI), 0.60-1.03 for microsatellite stable/MSI-low colon cancers; and RR, 0.61, 95% CI, 0.37-1.02 for MSI-high colon cancers; P(heterogeneity)=0.53] or KRAS status (RR, 0.66; 95% CI, 0.49-0.87 for KRAS wild-type colon cancers; and RR, 1.05; 95% CI, 0.68-1.61 for KRAS mutated colon cancers; P(heterogeneity)=0.12), although our analyses had limited power to preclude an effect of folate on KRAS wild-type colon cancers. Similarly, high vitamin B(6) or B(12) intake was inversely associated with colon cancers, regardless of MSI or KRAS status. No significant effect of methionine intake or alcohol consumption was observed for colon cancers with MSI high or KRAS mutation. In conclusion, the influence of dietary one-carbon nutrient intake on colon cancer risk does not seem to differ according to MSI or KRAS mutational status.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".