Glucagon-Like Peptide-1 Receptor Agonists and Risk for Gastroesophageal Reflux Disease in Patients With Type 2 Diabetes
Bibliographic record
Abstract
BACKGROUND: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs), medications used to treat type 2 diabetes and obesity, are associated with delayed gastric emptying, which is a risk factor for gastroesophageal reflux disease (GERD). However, evidence linking these drugs to GERD is limited. OBJECTIVE: To estimate the effect of GLP-1 RAs compared with sodium-glucose cotransporter-2 (SGLT-2) inhibitors on the risk for GERD and its complications among patients with type 2 diabetes. DESIGN: Active-comparator new-user cohort study emulating a target trial. SETTING: U.K. Clinical Practice Research Datalink. PARTICIPANTS: Adults aged 18 years or older with type 2 diabetes initiating GLP-1 RAs or SGLT-2 inhibitors between 1 January 2013 and 31 December 2021, with follow-up until 31 March 2022. MEASUREMENTS: The primary outcome was incident GERD, and the secondary outcome was its complications. Three-year risk differences (RDs) and risk ratios (RRs) were estimated and weighted using propensity score fine stratification. RESULTS: The study included 24 708 new users of GLP-1 RAs and 89 096 new users of SGLT-2 inhibitors. Over a median follow-up of 3.0 years, the RRs were 1.27 (95% CI, 1.14 to 1.42) for GERD, with an RD of 0.7 per 100 patients, and 1.55 (95% CI, 1.12 to 2.29) for its complications, with an RD of 0.8 per 1000 patients, among GLP-1 RA users compared with SGLT-2 inhibitor users. LIMITATION: Residual confounding due to lack of information on dietary or lifestyle factors. CONCLUSION: The estimated effect of GLP-1 RAs compared with SGLT-2 inhibitors suggested a higher risk for GERD and its complications in patients with type 2 diabetes. Clinicians should be aware of this potential adverse effect to provide timely prevention and treatment strategies. PRIMARY FUNDING SOURCE: Canadian Institutes of Health Research.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".