Incretin-Based Drugs and the Risk of Acute Liver Injury Among Patients With Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: To determine whether the use of dipeptidyl peptidase 4 (DPP-4) inhibitors and glucagon-like peptide 1 receptor agonists (GLP-1 RAs), separately, is associated with an increased risk of acute liver injury compared with the use of sodium-glucose cotransporter 2 (SGLT-2) inhibitors. RESEARCH DESIGN AND METHODS: We used the U.K. Clinical Practice Research Datalink linked with the Hospital Episode Statistics Admitted Patient Care and the Office for National Statistics databases to assemble two new-user, active-comparator cohorts. The first included 106,310 initiators of DPP-4 inhibitors and 27,277 initiators of SGLT-2 inhibitors, while the second included 9,470 initiators of GLP-1 RAs and 26,936 initiators of SGLT-2 inhibitors. Cox proportional hazards models with propensity score fine stratification weighting were used to estimate hazard ratios (HRs) and 95% CIs of acute liver injury. RESULTS: Compared with SGLT-2 inhibitors, DPP-4 inhibitors were associated with a 53% increased risk of acute liver injury (HR 1.53, 95% CI 1.02-2.30). In contrast, GLP-1 RAs were not associated with an overall increased risk of acute liver injury (HR 1.11, 95% CI 0.57-2.16). However, an increased risk was observed among female users of both DPP-4 inhibitors (HR 3.22, 95% CI 1.67-6.21) and GLP-1 RAs (HR 3.23, 95% CI 1.44-7.25). CONCLUSIONS: In this population-based study, DPP-4 inhibitors were associated with an increased risk of acute liver injury compared with SGLT-2 inhibitors in patients with type 2 diabetes. In contrast, an increased risk of acute liver injury was observed only among female GLP-1 RA users.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".