Incretin‐Based Drugs and Risk of Intestinal Obstruction Among Patients With Type 2 Diabetes
Bibliographic record
Abstract
Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) and dipeptidyl peptidase-4 (DPP-4) inhibitors might increase the risk of intestinal obstruction, but real-world evidence for this severe adverse event is lacking. Thus, the objective of this study was to determine whether GLP-1 RAs and DPP-4 inhibitors are associated with an increased risk of intestinal obstruction compared with sodium-glucose cotransporter-2 (SGLT-2) inhibitors. We used the United Kingdom Clinical Practice Research Datalink and linked databases to assemble two new-user, active comparator cohorts (2013-2019). The first included 25,617 and 67,261 GLP-1 RA and SGLT-2 inhibitor users, respectively. The second included 131,927 and 40,615 DPP-4 inhibitor and SGLT-2 inhibitor users, respectively. Propensity score fine stratification weighted Cox proportional hazards models were fit to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) of intestinal obstruction requiring hospitalization. GLP-1 RAs were associated with an increased risk of intestinal obstruction compared with SGLT-2 inhibitors (1.9 vs. 1.1 per 1,000 person-years, respectively; HR: 1.69, 95% CI: 1.04-2.74). The highest HR was observed after 1.6 years of use (HR: 3.48, 95% CI: 1.79-6.79). DPP-4 inhibitors were also associated with an increased risk (2.7 vs. 1.0 per 1,000 person-years; HR: 2.59, 95% CI: 1.52-4.42), with the highest HR observed after 1.8 years of use (HR: 9.53, 95% CI: 4.47-20.30). The number needed to harm after 1 year of use was 1,223 and 603 for GLP-1 RAs and DPP-4 inhibitors, respectively. In this large real-world study, GLP-1 RAs and DPP-4 inhibitors were associated with an increased risk of intestinal obstruction.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| 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".