Evidence mapPaperPMID 42260171Full record

SynthesisSurgical endoscopy2026

Differential impact of glucagon-like peptide-1 (GLP-1) receptor agonists for weight loss in the type 2 diabetic and non-diabetic populations.

Iwanger-I-Ter T Jia, Grace C Bloomfield, Mike Y Chen, Marcus H Cunningham, Annie Wang, Shaun C Daly, Marcelo W Hinojosa, Brian R Smith, Ninh T Nguyen, Dan E Azagury and 1 more

Abstract readMeta-Analysis
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In one paragraph

Synthesis in Surgical endoscopy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Iwanger-I-Ter T JiaSchool of Medicine, Georgetown University, Washington, DC, USA.
Grace C BloomfieldSchool of Medicine, Georgetown University, Washington, DC, USA.
Mike Y ChenSchool of Medicine, Georgetown University, Washington, DC, USA.
Marcus H CunninghamSchool of Medicine, Georgetown University, Washington, DC, USA.
Annie WangDivision of Gastrointestinal Surgery, Department of Surgery, Irvine Medical Center, University of California, Irvine, 101 The City Drive South, Orange, CA, 92868, USA.
Shaun C DalyDivision of Gastrointestinal Surgery, Department of Surgery, Irvine Medical Center, University of California, Irvine, 101 The City Drive South, Orange, CA, 92868, USA.
Marcelo W HinojosaDivision of Gastrointestinal Surgery, Department of Surgery, Irvine Medical Center, University of California, Irvine, 101 The City Drive South, Orange, CA, 92868, USA.
Brian R SmithDivision of Gastrointestinal Surgery, Department of Surgery, Irvine Medical Center, University of California, Irvine, 101 The City Drive South, Orange, CA, 92868, USA.
Ninh T NguyenDivision of Gastrointestinal Surgery, Department of Surgery, Irvine Medical Center, University of California, Irvine, 101 The City Drive South, Orange, CA, 92868, USA.
Dan E AzaguryDepartment of Surgery, Stanford University School of Medicine, Palo Alto, CA, USA.
Nicholas J PrindezeDivision of Gastrointestinal Surgery, Department of Surgery, Irvine Medical Center, University of California, Irvine, 101 The City Drive South, Orange, CA, 92868, USA. nprindez@hs.uci.edu.ORCID http://orcid.org/0000-0002-0928-1799

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGlucagon-like peptide-1 (GLP-1) receptor agonists have emerged as a cornerstone therapy for obesity management, yet long-term comparative effectiveness across diabetes status, adherence, and specific agents remains unclear. This study evaluates multi-year weight-loss outcomes associated with GLP-1 therapy and characterizes medication- and phenotype-specific differences.

methodsThis study represents a subgroup analysis of a large-scale meta-analysis including clinical trials, observational and case-control studies published from 2010 to 2025 reporting long-term weight outcomes for GLP-1 agents. Outcomes were evaluated across mixed, intention-to-treat (ITT), and treatment-adherent populations and stratified by diabetes status. Mixed-effects meta-regression was performed to evaluate independent predictors of weight loss.

resultsA total of 56,580 patients from 45 studies were included. GLP-1 therapy consistently produced greater weight loss than placebo across all populations. Non-diabetic participants achieved the greatest reductions, losing 15.7% of baseline weight at 12 months versus 5.1% in diabetic users in the mixed cohort. ITT results were similar but modestly attenuated, whereas adherent patients demonstrated the largest reductions. Diabetes status was a strong effect modifier: non-diabetic individuals achieved 2.5-4 × greater reductions than diabetics receiving the same agents. Meta-regression confirmed diabetes status as an independent negative predictor of weight loss (β =  - 1.77, p < 0.001). Semaglutide produced significantly greater reductions than liraglutide after adjustment (β =  + 1.67, p < 0.001), while baseline BMI was inversely associated with percent weight change (β =  + 0.46, p < 0.001).

conclusionGLP-1 therapies produce durable weight loss with outcomes strongly influenced by diabetes status, adherence, and agent selection. These findings support a personalized, phenotype-based approach to GLP-1 prescribing.

Indexed as

Diabetes Mellitus, Type 2Glucagon-Like Peptide-1 Receptor AgonistsHypoglycemic AgentsObesityWeight LossHumansLiraglutideSemaglutideGlucagon-Like Peptide-1 Receptor AgonistsHypoglycemic AgentsLiraglutideSemaglutideDiabetesGLP-1 outcomesMeta-analysisWeight loss

Identifiers

PMID42260171

What Socratic holds

Texttitle and abstract
Read underepoch 390

Registered trials

None linked

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 Socratic graph.