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 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) | 0.000 | 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 | 0.000 | 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".