Vitamin D Intake Is Negatively Associated with Promoter Methylation of the Wnt Antagonist Gene<i>DKK1</i>in a Large Group of Colorectal Cancer Patients
Bibliographic record
Abstract
Diet and lifestyle influence colorectal cancer (CRC) risk but the molecular events that mediate these effects are poorly characterized. Several dietary and lifestyle factors can modulate DNA methylation suggesting that they may influence CRC risk through epigenetic regulation of cancer-related genes. The Wnt regulatory genes DKK1 and Wnt5a are important contributors to colonic carcinogenesis and are often silenced by promoter hypermethylation in CRC; however, the dietary contributions to these events have not been explored. To investigate the link between dietary/lifestyle factors and epigenetic regulation of these Wnt signaling genes, we assessed promoter methylation of these genes in a large cohort of Canadian CRC patients from Ontario (n = 549) and Newfoundland (n = 443) and examined associations to dietary/lifestyle factors implicated in CRC risk and/or DNA methylation including intake of vitamins, fats, cholesterol, fiber, and alcohol as well as body mass index (BMI), and smoking status. Several factors were associated with methylation status including alcohol intake, BMI, and cigarette smoking. Most significantly, however, dietary vitamin D intake was strongly negatively associated with DKK1 methylation in Newfoundland (P = 0.001) and a similar trend was observed in Ontario. These results suggest that vitamin D and other dietary/lifestyle factors may alter CRC risk by mediating extracellular Wnt inhibition.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".