Effects of <scp>GLP</scp> ‐1 receptor agonists on cognitive function in patients with type 2 diabetes: A systematic review and meta‐analysis based on randomized controlled trials
Post-publication record
OpenAlex flags this work as retracted, but it carries no matching Retraction Watch record in this frame.
Bibliographic record
Abstract
Abstract Aims Patients with type 2 diabetes mellitus (T2DM) have a significantly increased risk of cognitive impairment, and the protective effects of traditional hypoglycaemic drugs on cognitive function remain unclear. This study systematically evaluated the neuroprotective effects of GLP‐1 receptor agonists (GLP‐1RAs) based on randomized controlled trial (RCT) evidence, aiming to provide key evidence‐based insights for optimizing diabetes management strategies. Materials and Methods A comprehensive literature search was conducted across PubMed, Embase, Cochrane Library, Web of Science, CNKI, Wanfang, and ClinicalTrials.gov databases to identify studies investigating the impact of GLP‐1RAs on cognitive function in T2DM patients, with the search timeframe extending up to July 2025. The analysis focused on RCTs comparing the effects of GLP‐1RAs versus placebo (or conventional therapy) on neurocognitive function in T2DM patients. Results Based on the inclusion criteria, 18 RCTs involving 11 114 participants were included in the primary meta‐analysis. Pooled results demonstrated that, compared to the placebo group, GLP‐1RA treatment significantly increased Mini‐Mental State Examination (MMSE) scores by a weighted mean difference (WMD) of 1.33 (95% CI: 0.67–1.99; I 2 = 82%) and Montreal Cognitive Assessment (MoCA) scores by a WMD of 1.70 (95% CI: 0.83–2.56; I 2 = 96%). The effect size on MMSE was significantly greater with long‐term GLP‐1RA use (≥24 weeks) compared to short‐term use (<24 weeks) (mean difference = 3.74; t = 6.52, df = 269, p < 0.0001; Cohen's d = 0.79). Sensitivity analyses yielded results closely aligned with the primary analysis, indicating robust stability. Jadad scale assessment confirmed that all included studies achieved a score ≥3. Conclusions Current evidence indicates that GLP‐1 RA‐based therapy may improve cognitive function in patients with type 2 diabetes mellitus compared with placebo. Furthermore, long‐term administration or early initiation of GLP‐1 RA treatment may offer greater cognitive benefits.
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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 | 0.013 | 0.026 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.022 | 0.034 |
| Bibliometrics | 0.007 | 0.007 |
| 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".