Diabetes in relation to Barrett’s esophagus and adenocarcinomas of the esophagus: A pooled study from the International Barrett’s and Esophageal Adenocarcinoma Consortium
Bibliographic record
Abstract
Background Diabetes is positively associated with various cancers, but its relationship with tumors of the esophagus/esophagogastric junction remains unclear. Methods Data were harmonized across 13 studies in the International Barrett’s and Esophageal Adenocarcinoma Consortium, comprising 2309 esophageal adenocarcinoma (EA) cases, 1938 esophagogastric junction adenocarcinoma (EGJA) cases, 1728 Barrett's esophagus (BE) cases, and 16,354 controls. Logistic regression was used to estimate study‐specific odds ratios (ORs) and 95% CIs for self‐reported diabetes in association with EA, EGJA, and BE. Adjusted ORs were then combined using random‐effects meta‐analysis. Results Diabetes was associated with a 34% increased risk of EA (OR, 1.34; 95% CI, 1.00‐1.80; I2 = 48.8% [where 0% indicates no heterogeneity, and larger values indicate increasing heterogeneity between studies]), 27% for EGJA (OR, 1.27; 95% CI, 1.05‐1.55; I2 = 0.0%), and 30% for EA/EGJA combined (OR, 1.30; 95% CI, 1.06‐1.58; I2 = 34.9%). Regurgitation symptoms modified the diabetes‐EA/EGJA association (P for interaction = .04) with a 63% increased risk among participants with regurgitation (OR, 1.63; 95% CI, 1.19‐2.22), but not among those without regurgitation (OR, 1.03; 95% CI, 0.74‐1.43). No consistent association was found between diabetes and BE. Conclusions Diabetes was associated with increased EA and EGJA risk, which was confined to individuals with regurgitation symptoms. Lack of an association between diabetes and BE suggests that diabetes may influence progression of BE to cancer.
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 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.024 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".