SynthesisDiabetes, obesity & metabolism2026
GLP-1 receptor agonists for treating obesity without diabetes: A systematic review and meta-analysis of economic evaluations.
Synthesis in Diabetes, obesity & metabolism, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- GLP-1 receptor agonists for treating obesity without diabetes: A systematic review and meta-analysis of economic evaluations.Diabetes, obesity & metabolism · 2026Pooled it
- Patient and healthcare professional perceptions of weight reduction in the treatment of knee osteoarthritis: A systematic literature review.Osteoarthritis and cartilage open · 2026Review
- Landscape Analysis of Literature Using Quantitative Approaches to Synthesize Economic Evaluation Studies: A Scoping Review.PharmacoEconomics · 2026Review
- How users perceive meta-analysis of economic evaluations.International journal of technology assessment in health care · 2026Article
- Modern Management of CKM Syndrome: Use of GLP-1 Receptor Agonists in a Multidisciplinary Setting-Expert Group Recommendations from Kuwait.Diabetes therapy : research, treatment and education of diabetes and related disorders · 2026Review
- Tirzepatide for obesity without diabetes: mechanistic insights, clinical evidence, and future directions.Frontiers in pharmacology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
aimTo pool the incremental net benefits (INBs) of using glucagon-like peptide-1 receptor agonists (GLP-1RAs) for treating obesity without diabetes. MATERIALS AND
methodsPubMed, Embase, EconLit, CEA Registry, ProQuest Dissertation and Theses Global were searched from inception to April 2024. Cost-effectiveness studies were included if they reported economic outcomes of any GLP-1RAs in the treatment of obesity without diabetes for a minimum time horizon of 5 years. Details of the study characteristics, economic model inputs, costs, and outcomes were extracted. Monetary units were converted to 2023 US dollars. INBs with 95% confidence interval (CI) were pooled using a random-effects model. Statistical heterogeneity between studies was assessed using the I
resultsOf 634 studies identified, 9 from high-income countries (HICs) with 23 comparisons were included. The pooled INB demonstrated that semaglutide and liraglutide were not cost-effective compared to no intervention (-$3659 [95% CI, -$74 379 to $67 062] and -$32 032 [95% CI, -$101 534 to $37 488], respectively) and lifestyle interventions (-$84 060 [95% CI, -$152 645 to -$15 475] and -$70 563 [95% CI, -$106 520 to -$34 605], respectively).
conclusionsGLP-1RAs are generally not cost-effective for obesity treatment in patients without diabetes in HICs from a healthcare/payer perspective. However, they may be cost-effective in subgroups evaluated over longer time horizons. Most included studies focused on weight-related outcomes, potentially underestimating the broader economic value of GLP-1RAs. This pooled economic evidence may inform the decision-making process.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.