The protective effect of dietary folate intake on gastric cancer is modified by alcohol consumption: A pooled analysis of the StoP Consortium
Bibliographic record
Abstract
Abstract Dietary folate intake has been identified as a potentially modifiable factor of gastric cancer (GC) risk, although the evidence is still inconsistent. We evaluate the association between dietary folate intake and the risk of GC as well as the potential modification effect of alcohol consumption. We pooled data for 2829 histologically confirmed GC cases and 8141 controls from 11 case–control studies from the international Stomach Cancer Pooling Consortium. Dietary folate intake was estimated using food frequency questionnaires. We used linear mixed models with random intercepts for each study to calculate adjusted odds ratios (OR) and 95% confidence interval (CI). Higher folate intake was associated with a lower risk of GC, although this association was not observed among participants who consumed >2.0 alcoholic drinks/day. The OR for the highest quartile of folate intake, compared with the lowest quartile, was 0.78 (95% CI, 0.67–0.90, P ‐trend = 0.0002). The OR per each quartile increment was 0.92 (95% CI, 0.87–0.96) and, per every 100 μg/day of folate intake, was 0.89 (95% CI, 0.84–0.95). There was a significant interaction between folate intake and alcohol consumption ( P ‐interaction = 0.02). The lower risk of GC associated with higher folate intake was not observed in participants who consumed >2.0 drinks per day, OR Q4v Q1 = 1.15 (95% CI, 0.85–1.56), and the OR 100 μg/day = 1.02 (95% CI, 0.92–1.15). Our study supports a beneficial effect of folate intake on GC risk, although the consumption of >2.0 alcoholic drinks/day counteracts this beneficial effect.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.019 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".