Influence of glucagon‐like peptide‐1 receptor agonists on hepatic events in type 2 diabetes: a systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND AND AIM: Type 2 diabetes mellitus (T2DM) is intrinsically linked to various etiologies of liver disease, with 69% of patients having concomitant metabolic dysfunction-associated steatotic liver disease (MASLD). Studies suggest glucagon-like peptide-1 receptor agonists (GLP-1RAs) can ameliorating liver disease. With this analysis, we address the gap in knowledge about the effectiveness of these agents in preventing different major adverse liver outcomes (MALOs). METHODS: PubMed, Embase, and The Cochrane Central of Trials were searched for articles reporting MALOs in T2DM patients. Publication bias-identifying methods, quality assessment and sensitivity analyses (subgroup analyses, leave-one-out meta-analyses, and meta-regression) were employed. Statistical analyses were performed in R using the "meta" and "metafor" packages. RESULTS: Nine cohort studies from 535 identified articles encompassing 579 256 T2DM patients were included in the main analyses. GLP-1RA use was associated with reduced risks of hepatocellular carcinoma (HR 0.74, 95% CI 0.56-0.96) and cirrhosis decompensation (HR 0.68, 95% CI 0.65-0.72). Within the latter, variceal bleeding and hepatic encephalopathy prevention were found to be significantly reduced. Egger's test, Begg's test, and funnel-plot analysis yielded no publication bias. No significant differences were observed in preventing cirrhosis or hepatic failure. Meta-regression analysis revealed a positive correlation between hepatocellular carcinoma incidence and both male sex and longer follow-up duration. CONCLUSIONS: This meta-analysis improves our understanding of the hepatoprotective effects of GLP-1RAs in T2DM patients and supports existing research, exhibiting superiority over other antidiabetic medications for hepatoprotection in this subgroup. Additional long-term follow-up studies are necessary to further validate these findings.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.043 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".