B Vitamins, Methionine and Alcohol Intake and Risk of Colon Cancer in Relation to BRAF Mutation and CpG Island Methylator Phenotype (CIMP)
Bibliographic record
Abstract
BACKGROUND: One-carbon metabolism appears to play an important role in DNA methylation reaction. Evidence suggests that a low intake of B vitamins or high alcohol consumption increases colorectal cancer risk. How one-carbon nutrients affect the CpG island methylator phenotype (CIMP) or BRAF mutation status in colon cancer remains uncertain. METHODS: Utilizing incident colon cancers in a large prospective cohort of women (the Nurses' Health Study), we determined BRAF status (N = 386) and CIMP status (N = 375) by 8 CIMP-specific markers [CACNA1G, CDKN2A (p16), CRABP1, IGF2, MLH1, NEUROG1, RUNX3, and SOCS1], and 8 other CpG islands (CHFR, HIC1, IGFBP3, MGMT, MINT-1, MINT-31, p14, and WRN). We examined the relationship between intake of one-carbon nutrients and alcohol and colon cancer risk, by BRAF mutation or CIMP status. RESULTS: Higher folate intake was associated with a trend towards low risk of CIMP-low/0 tumors [total folate intake ≥400 µg/day vs. <200 µg/day; the multivariate relative risk = 0.73; 95% CI = 0.53-1.02], whereas total folate intake had no influence on CIMP-high tumor risks (P(heterogeneity) = 0.73). Neither vitamin B(6), methionine or alcohol intake appeared to differentially influence risks for CIMP-high and CIMP-low/0 tumors. Using the 16-marker CIMP panel did not substantially alter our results. B vitamins, methionine or alcohol intake did not affect colon cancer risk differentially by BRAF status. CONCLUSIONS: This molecular pathological epidemiology study suggests that low level intake of folate may be associated with an increased risk of CIMP-low/0 colon tumors, but not that of CIMP-high tumors. However, the difference between CIMP-high and CIMP-low/0 cancer risks was not statistically significant, and additional studies are necessary to confirm these observations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".