A pilot randomised controlled trial to reduce colorectal cancer risk markers associated with B-vitamin deficiency, insulin resistance and colonic inflammation
Bibliographic record
Abstract
Colorectal cancer risk is associated with biochemical markers for B-vitamin deficiency, insulin resistance and colonic inflammation, suggesting that these three conditions are each involved in colon carcinogenesis. We expected that dietary supplements of folic acid, n-3 fatty acids and calcium would reduce the markers and thus possibly cancer risk. We therefore randomised 98 participants, with previous colonic polyps or intramucosal carcinomas, to a combined treatment of supplementary folic acid, fish oil and calcium carbonate, or placebos for 28 days. Blood and faecal samples were obtained prior to and at the conclusion of the intervention and analysed for plasma folate, homocysteine, insulin, free fatty acids, triglycerides and faecal calprotectin. In addition, plasma vitamin B12, thiamin, glucose and C-reactive protein were assessed. Our supplemental strategy modestly affected some of the biomarkers associated with folate metabolism and insulin resistance, but had no effect on those associated with colonic inflammation. This pilot study demonstrates the feasibility and practicality of clinical trials aimed at reducing diet-related biochemical risk markers for colon cancer. We suggest that long-term intervention studies with tumour-related end points should be undertaken when the intervention agents used are found effective in short-term biochemical risk marker trials.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".