Liraglutide and semaglutide: Pooled post hoc analysis to evaluate risk of dementia in patients with type 2 diabetes
Bibliographic record
Abstract
Abstract Background Glucagon‐like peptide 1 receptor agonists (GLP‐1 RA) have previously shown improved measures of memory and reduced phospho‐tau burden in preclinical animal models relevant to progressive cognitive impairment (Hansen. J Alzheimers Dis.2015;46:877‐88; Hansen. Brain Res.2016;1634:158‐70). Liraglutide and semaglutide are structurally similar GLP‐1 RAs associated with potent glucose‐lowering effect, body weight loss and cardiovascular benefits shown in large cardiovascular outcomes trials (CVOTs) (Marso. N Engl J Med.2016;375:311‐22; Marso. N Engl J Med.2016;375:1834‐44; Husain. N Engl J Med.2019;381:841‐51). In order to investigate the potential effects of GLP‐1 RA on dementia in a clinical setting, a post‐hoc analysis was conducted based on pooled data from three CVOTs. Method LEADER, SUSTAIN 6 and PIONEER 6 were randomised, double‐blind, multicentre, placebo‐controlled CVOTs evaluating the cardiovascular effect of liraglutide or semaglutide vs. placebo, added to standard of care. The trials included patients with type 2 diabetes and established or high risk of cardiovascular disease. A post‐hoc analysis on pooled data from the three CVOTs was considered appropriate due to the similarities in trial design, patient population and treatment effects. Across all trials 15,820 patients with median follow‐up of 3.6 years were included in this analysis. Dementia‐related adverse events (AEs) were identified using Standardised MedDRA (version 21.1) Query for “dementia” narrow search terms. AE data collection across the trials differed in line with the regulatory requirements at the time of trial conduct. In LEADER and PIONEER 6 only serious AEs were systematically collected, while all AEs were collected in SUSTAIN 6. A time‐to‐event analysis based on the Cox proportional‐hazards model using treatment as covariate was used to estimate the hazard ratio for developing dementia. Result Across the three CVOTs, 15 GLP‐1 RA‐treated patients and 32 placebo‐treated patients were identified with development of dementia (Figure). Post‐hoc analysis on the pooled data showed a significant estimated hazard ratio of 0.47 [0.25; 0.86]95%CI in favour of the GLP‐1 RA treatment versus placebo. Conclusion Post‐hoc analysis based on pooled data from three double‐blinded CVOTs suggests, albeit with a low number of events, a reduced risk of dementia with liraglutide or semaglutide treatment in patients with type 2 diabetes.
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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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.021 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".