Evidence map›Paper›PMID 41328392›Full record

ArticleDiabetes, metabolic syndrome and obesity : targets and therapy2025

Optimising GLP-1RA Efficacy: A Meta-Analysis of Baseline Age and HbA1c as Predictors of MACE Reduction in T2DM.

Samit Ghosal, Anuradha Ghosal

Abstract read
In one paragraph

Article in Diabetes, metabolic syndrome and obesity : targets and therapy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

2 authors.

Samit GhosalNightingale Hospital, Kolkata, India.ORCID 0000-0002-4141-5187
Anuradha GhosalAston Medical School, Birmingham, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Type 2 diabetes mellitus (T2DM) increases major adverse cardiovascular event (MACE) risk, requiring effective interventions. Glucagon-like peptide-1 receptor agonists (GLP-1RAs) reduce MACE, but the impact of baseline characteristics on their efficacy is unclear from previous analyses. Methods: This PRISMA-guided systematic review and meta-analysis included randomised controlled trials (RCTs) comparing GLP-1RAs with placebo in patients with T2DM, sourced from PubMed and Google Scholar. We extracted MACE hazard ratios (HR), 95% confidence intervals (CI), and baseline characteristics (age, BMI, SBP, HbA1c, eGFR, male proportion, diabetes duration, CVD prevalence). A random-effects model estimated pooled HR, with heterogeneity assessed via prediction intervals. Meta-regression identified moderators. Sensitivity analyses and the RoB 2 Tool assessed bias; GRADE evaluated the certainty of evidence. Results: Across 11 RCTs (83,536 participants), the pooled HR for MACE was 0.87 (95% CI: 0.81-0.93), indicating a 13% risk reduction with moderate heterogeneity (prediction interval: 0.79-0.96). After excluding T2DM duration due to multicollinearity, multivariate meta-regression identified age (p = 0.02) and HbA1c (p = 0.03) as significant moderators, which persisted after excluding FREEDOM-CVO (age, p = 0.03; HbA1c, p = 0.04). Baseline CVD prevalence did not moderate outcomes (p = 0.892). Bias was low; evidence certainty was moderate. Conclusion: GLP-1RAs reduce MACE in T2DM, particularly in older patients with lower baseline HbA1c. This fills a critical gap in prior meta-analyses by identifying actionable pre-treatment predictors that support personalised therapy. Protocol Registration: Ghosal et al INPLASY protocol 202580045. doi:10.37766/inplasy2025.8.0045 INPLASY202580045.

Indexed as

ageGLP-1 receptor agonistsHbA1cmajor adverse cardiovascular eventsmeta-analysistype 2 diabetes mellitus

Identifiers

PMID41328392
PMCPMC12665223

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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.