P267 The association between obesity and malignant progression of barrett’s oesophagus: a systematic review and dose-response meta-analysis
Bibliographic record
Abstract
Introduction Barrett’s oeosphagus (BO) is the precursor to oesophageal adenocarcinoma (OAC), a malignancy with a poor overall prognosis. Endoscopic surveillance of patients with BO aims to detect dysplasia and OAC to improve long-term outcomes. However, the majority of patients with BO do not progress to cancer. It is therefore important to establish the relevant clinical risk factors for malignant progression in this cohort to enable risk stratification and facilitate a personalised approach to management. While obesity is an independent risk factor for both BO and OAC, the role of obesity in the malignant progression of BO is unclear. We undertook a systematic review and dose-response meta-analysis to estimate the association between obesity and malignant progression in patients with BO. Methods We searched MEDLINE and EMBASE databases via the OVID interface to December 2023. Studies which reported the effect of body mass index (BMI) on the progression of BO (no dysplasia or low-grade dysplasia[LGD] at baseline) to HGD or OAC were included. Two-stage dose-response meta-analysis was performed to estimate the association between BMI and malignant progression. Study quality was appraised using a modified Newcastle-Ottawa scale. The review was registered on PROSPERO (CRD42017051046). Results 20 studies reported the association between obesity and malignant progression of BO were included, comprising 39156 patients with baseline non-dysplastic BO or LGD, of whom 1689 progressed to HGD/OAC. Each 5 kg/m2 increase in BMI was associated with a 4% increase in malignant progression to HGD or OAC (unadjusted OR 1.04; 95% CI 1.00 to 1.07; p<0.001; I2 57.2%). The association remained consistent after adjusting for potential confounders (adjusted OR 1.06; 95% CI 1.02 to 1.10; p<0.001; I2 0%). Nineteen studies were considered moderate to high quality. Conclusions Our meta-analysis suggests that obesity as measured by BMI is associated with malignant progression of BO with a dose-response relationship. The findings from our study are consistent with mechanistic evidence for the role of obesity in the pathogenesis of OAC. Future risk prediction models could incorporate measures of obesity to potentially improve risk stratification in patients with BO. Weight loss might reduce the risk of malignant progression in patients with BO.
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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 | 0.013 | 0.033 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.031 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".