Effects of GLP-1 receptor agonist therapy on resolution of steatohepatitis in non-alcoholic fatty liver disease: a systematic review and meta-analysis
Bibliographic record
Abstract
Abstract Background Non-alcoholic fatty liver disease (NAFLD) is common, can progress to cirrhosis and hepatic decompensation, and has no approved medical therapy in Canada. Objective We conducted a systematic review on whether glucagon-like peptide-1 receptor agonists (GLP-1RA) improve non-alcoholic steatohepatitis (NASH) compared to standard care in NAFLD. Methods We searched Medline Ovid, EMBASE(Elsevier), Cochrane CENTRAL, Clinical Trials.gov, and the World Health Organization International Clinical Trials Registry Platform in November 2023 for randomized controlled trials. Inclusion criteria included patients ≥13 years with NAFLD receiving GLP-1RA for ≥6 months compared to standard care/placebo. Cochrane risk-of-bias 2.0 tool was used for each outcome. After screening results in duplicate, we performed meta-analysis and reported odds ratios (OR) for dichotomous and mean difference of change score for continuous outcomes. Results Six studies with 478 patients met inclusion criteria; 3 studies reported on the primary endpoint resolution of NASH. GLP-1RA likely leads to resolution of NASH (OR 4.45 (95% CI 1.92, 10.3)) and reduction in liver steatosis on imaging (–5.09% (95% CI −7.49, −2.69), but little to no reduction in liver stiffness on imaging (mean difference –0.17 kPa (95% CI −0.34, 0)). Interpretation Treatment with GLP-1RA in NAFLD patients for ≥6 months can probably lead to improvement in NASH on liver biopsy and reduce liver steatosis on imaging. Whether improvements in steatosis on biopsy or imaging results in clinically significant outcomes need to be elucidated as the effects of GLP-1RA on liver fibrosis are unclear; larger ongoing trials may provide more definitive answers. Protocol Registration: PROSPERO–CRD42023472186.
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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 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.025 | 0.038 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".