Dietary Choline and Betaine Intake and Risk of Colorectal Cancer in an Iranian Population
Bibliographic record
Abstract
BACKGROUND: Colorectal cancer (CRC) is increasing in low- and middle-income countries, likely due to changing lifestyle habits, including diet. We aimed to investigate the relationship between dietary betaine, choline, and choline-containing compounds and CRC risk. METHODS: We analyzed data from a case-control study, including 865 CRC cases and 3206 controls from Iran. Detailed information was collected by trained interviewers using validated questionnaires. The intake of free choline, phosphocholine (Pcho), glycerophosphocholine (GPC), phosphatidylcholine (PtdCho), and sphingomyelin (SM), as well as of betaine was estimated from food frequency questionnaires and categorized into quartiles. The odds ratios (OR) and 95% confidence intervals (CI) of CRC for choline and betaine quartiles were calculated using multivariate logistic regression by adjusting for potential confounders. RESULTS: We observed excess risk of CRC in the highest versus lowest intake of total choline (OR = 1.23, 95% CI 1.13, 1.33), GPC (OR = 1.13, 95% CI 1.00, 1.27), and SM (OR = 1.14, 95% CI 1.01, 1.28). The intake of betaine exerted an inverse association with CRC risk (OR = 0.91, 95% CI 0.83, 0.99). There was no association between free choline, Pcho, PtdCho, and CRC. Analyses stratified by gender showed an elevated OR of CRC in men for SM intake OR = 1.20, 95% CI 1.03, 1.40) and a significantly decreased CRC risk in women for betaine intake (OR = 0.84, 95% CI 0.73, 0.97). CONCLUSION: Dietary modifications leading to an increase in betaine sources and managing the use of animal products as references for SM or other choline types might contribute to decreasing the risk of CRC.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".