Glucagon-Like Peptide 1 Receptor Agonists and Sodium–Glucose Cotransporter 2 Inhibitors and the Prevention of Cirrhosis Among Patients With Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: To determine whether glucagon-like peptide 1 receptor agonists (GLP-1RAs) and sodium-glucose cotransporter 2 (SGLT-2) inhibitors, separately, compared with dipeptidyl peptidase 4 (DPP-4) inhibitors are associated with a reduced risk of cirrhosis and other adverse liver outcomes among patients with type 2 diabetes. RESEARCH DESIGN AND METHODS: With an active comparator, new-user approach, we conducted a cohort study using the U.K. Clinical Practice Research Datalink linked with hospital and national statistics databases. Cox proportional hazards models using propensity score fine stratification weighting were used to calculate hazard ratios (HRs) and 95% CIs for cirrhosis (primary outcome) and decompensated cirrhosis, hepatocellular carcinoma, and liver-related mortality (secondary outcomes). RESULTS: In the first cohort comparing 25,516 patients starting GLP-1RAs and 186,752 starting DPP-4 inhibitors, GLP-1RAs were not associated with the incidence of cirrhosis (HR 0.90, 95% CI 0.68-1.19) or the secondary outcomes. In a separate cohort comparing 33,161 patients starting SGLT-2 inhibitors and 124,431 starting DPP-4 inhibitors, SGLT-2 inhibitors were associated with a reduced incidence of cirrhosis (HR 0.64, 95% CI 0.46-0.90), as also decompensated cirrhosis (HR 0.74, 95% CI 0.54-1.00), but not with a lower risk of hepatocellular carcinoma or liver-related mortality. CONCLUSIONS: In patients with type 2 diabetes in the U.K., GLP-1RAs were not associated with a lower risk of cirrhosis compared with DPP-4 inhibitors in patients with type 2 diabetes. However, SGLT-2 inhibitors were associated with a lower risk of cirrhosis compared with DPP-4 inhibitors.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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".