Data from Association between Folate Levels and CpG Island Hypermethylation in Normal Colorectal Mucosa
Bibliographic record
Abstract
Abstract Gene-specific promoter methylation of several genes occurs in aging normal tissues and may predispose to tumorigenesis. In the present study, we investigate the association of blood folate levels and dietary and lifestyle factors with CpG island (CGI) methylation in normal colorectal mucosa. Subjects were enrolled in a multicenter chemoprevention trial of aspirin or folic acid for the prevention of large bowel adenomas. We collected 1,000 biopsy specimens from 389 patients, 501 samples from the right colon and 499 from the rectum at the follow-up colonoscopy. We measured DNA methylation of estrogen receptor alpha (ERα) and secreted frizzled related protein-1 (SFRP1), using bisulfite pyrosequencing. We used generalized estimating equations regression analysis to examine the association between methylation and selected variables. For both ERα and SFRP1, percentage methylation was significantly higher in the rectum than in the right colon (P = 0.001). For each 10 years of age, we observed a 1.7% increase in methylation level for ERα and a 2.9% increase for SFRP1 (P < 0.0001). African Americans had a significantly lower level of ERα and SFRP1 methylation than Caucasians and Hispanics. Higher RBC folate levels were associated with higher levels of both ERα (P = 0.03) and SFRP1 methylation (P = 0.01). Our results suggest that CGI methylation in normal colorectal mucosa is related to advancing age, race, rectal location, and RBC folate levels. These data have important implications regarding the safety of supplementary folate administration in healthy adults, given the hypothesis that methylation in normal mucosa may predispose to colorectal neoplasia. Cancer Prev Res; 3(12); 1552–64. ©2010 AACR.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".