Plasma Metabolites of One-Carbon Metabolism Are Associated With Esophageal Adenocarcinoma in a Population-Based Study
Bibliographic record
Abstract
INTRODUCTION: Esophageal adenocarcinoma (EAC) develops through histopathological stages, including Barrett's esophagus (BE). We analyzed the associations between plasma levels of one-carbon metabolism factors and risks of long-segment BE or EAC. METHODS: Plasma levels were measured from an Irish population-based case-control study (Factors INfluencing the Barrett Adenocarcinoma Relationship study; 204 long-segment BE cases, 211 EAC cases, and 251 controls). A "methyl replete score" was derived by assigning a score of 0 ( median) to the levels of 3 dietary methyl donors (methionine, choline, and betaine) and summing across the metabolites. Multinomial logistic regression models were used to estimate odds ratios (ORs) and 95% confidence intervals (CIs) for the associations between EAC or BE and sex-specific quartiles or score using the lowest level as the reference category and adjusted for potential confounders. RESULTS: Highest methionine, betaine, vitamin B6, and choline levels were all associated with 62%-82% reduced risks of EAC ( Ptrends < 0.001). Conversely, S-adenosylmethionine, S-adenosylmethionine/S-adenosylhomocysteine ratio, total homocysteine, and cystathionine were associated with a greater than 2-fold increased EAC risk. A higher methyl replete score was associated with reduced EAC risk (OR 0.33; 95% CI 0.16-0.66). The highest vs lowest plasma methionine levels were borderline statistically significantly associated with long-segment BE (OR 0.55; 95% CI 0.28-1.07), but all other associations were null. DISCUSSION: Several biomarkers of one-carbon metabolism are associated with EAC risk, particularly markers of dietary methyl group donors. Future studies to replicate and prospectively evaluate these markers are warranted.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".