Glucagon-Like Peptide 1 Receptor Agonists and Sodium–Glucose Cotransporter 2 Inhibitors and Risk of Nonalcoholic Fatty Liver Disease Among Patients With Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: To determine whether glucagon-like peptide 1 receptor agonists (GLP-1 RA) and sodium-glucose cotransporter 2 (SGLT-2) inhibitors, separately, are associated with a decreased risk of nonalcoholic fatty liver disease (NAFLD) compared with dipeptidyl peptidase 4 (DPP-4) inhibitors among patients with type 2 diabetes. RESEARCH DESIGN AND METHODS: We assembled two new-user, active comparator cohorts using the U.K. Clinical Practice Research Datalink. The first included 30,291 and 225,320 new users of GLP-1 RA and DPP-4 inhibitors, respectively. The second included 41,184 and 148,421 new users of SGLT-2 inhibitors and DPP-4 inhibitors, respectively. Cox proportional hazards models weighted using propensity score fine stratification were fit to estimate hazard ratios (HRs) with 95% CIs of NAFLD. We also determined whether the study drugs were associated with a decreased risk of hepatic transaminase elevation within restricted subcohorts. RESULTS: GLP-1 RA were associated with a lower incidence of NAFLD with a wide CI compared with DPP-4 inhibitors (3.9 vs. 4.6 per 1,000 person-years, respectively; HR 0.86, 95% CI 0.73-1.01). SGLT-2 inhibitors were associated with a decreased risk of NAFLD (5.4 vs. 7.0 per 1,000 person-years, respectively; HR 0.78, 95% CI 0.68-0.89). In the restricted subcohorts, both GLP-1 RA and SGLT-2 inhibitors were associated with a decreased risk of hepatic transaminase elevation (HR 0.89, 95% CI 0.83-0.95, and HR 0.66, 95% CI 0.61-0.71). CONCLUSIONS: SGLT-2 inhibitors, and possibly GLP-1 RA, may be associated with a decreased incidence of NAFLD and hepatic transaminase elevation among patients with type 2 diabetes.
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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 | 0.001 |
| 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) | 0.002 | 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".