Polymorphisms in the methylenetetrahydrofolate reductase (<i>MTHFR</i>) gene, intakes of folate and related B vitamins and colorectal cancer: a case–control study in a population with relatively low folate intake
Bibliographic record
Abstract
Folate is key in one-carbon metabolism, disruption of which can interfere with DNA synthesis, repair, and methylation. Efficient one-carbon metabolism requires other B vitamins and the optimal activity of enzymes including 5,10-methylenetetrahydrofolate reductase (MTHFR). We report a population-based case-control study of folate intake, related dietary factors and MTHFR polymorphisms (C677T, A1298C) and colorectal cancer in a population with relatively high colorectal cancer incidence and relatively low folate intake. A total of 264 cases with histologically confirmed incident colorectal cancer and 408 controls participated. There was no clear trend in risk with reported intakes of total, or dietary, folate, riboflavin, vitamin B12 or vitamin B6, nor were there interactions between folate intake and the other B vitamins or alcohol. For C677T, risk decreased with increasing variant alleles (multivariate OR for CT v. CC = 0.77 (95 % CI 0.52, 1.16); OR for TT v. CC = 0.62 (95 % CI 0.31, 1.24)), which, although not statistically significant, was consistent with previous studies. For A1298C, compared with AA subjects, CC subjects had modest, non-significant, reduced risk (multivariate OR = 0.81 (95 % CI 0.45, 1.49)). There were significant interactions between total folate and C677T (P = 0.029) and A1298C (P = 0.025), and total vitamin B6 and both polymorphisms (C677T, P = 0.016; A1298C, P = 0.033), although the patterns observed differed from previous studies. Seen against the setting of low folate intake, the results suggest that the role of folate metabolism in colorectal cancer aetiology may be more complex than previously thought. Investigation of particular folate vitamers (for example, tetrahydrofolate, 5,10-methylenetetrahydrofolate) may help clarify carcinogenesis pathways.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".