Cognitive protection of incretin‐based therapies in patients with type 2 diabetes mellitus: A systematic review and meta‐analysis based on clinical studies
Bibliographic record
Abstract
AIMS/INTRODUCTION: Cognitive dysfunction, including mild cognitive impairment and dementia, is increasingly recognized as an important complication of type 2 diabetes mellitus. The aims of the preset study was to investigate the cognitive protection of incretin-based therapies, including glucagon-like peptide-1 receptor agonists and dipeptidyl peptidase-4 inhibitors, in patients with type 2 diabetes mellitus. MATERIALS AND METHODS: PubMed, EMBASE, Cochrane library, Web of Science and PsycINFO were searched from the inception through 17 January 2023 for randomized controlled trials and cohort studies on the association between incretin-based therapies and cognitive function. A total of 15 studies were finally included in our systematic review, and eight of which were incorporated into our meta-analysis. RESULTS: Pooled results showed that the Mini-Mental State Examination score in incretin-based therapy groups was increased by 1.20 compared with the control group (weighted mean difference 1.20, 95% confidence interval 0.39-2.01). The results of eight studies assessed by the Newcastle Ottawa Quality Assessment Scale and the Cochrane Collaboration's tool, and the quality of the eight studies were at a relatively high level. Egger's regression did not show significant publication bias. CONCLUSIONS: Current evidence shows that incretin-based therapies might be more effective, when compared with the other hypoglycemic drugs, for cognitive improvement in patients with type 2 diabetes mellitus.
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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 | 0.015 | 0.030 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.037 |
| Bibliometrics | 0.006 | 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) | 0.002 | 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".