SGLT2 inhibitors versus DPP-4 inhibitors and dementia risk in type 2 diabetes: A meta-analysis of cohort studies
Bibliographic record
Abstract
BACKGROUND: Sodium-glucose cotransporter 2 (SGLT2) inhibitors have shown neuroprotective potential. This meta-analysis aimed to compare the effects of SGLT2 inhibitors and dipeptidyl peptidase-4 (DPP-4) inhibitors on dementia risk in patients with type 2 diabetes mellitus (T2DM). METHODS: A systematic search of PubMed, EMBASE, Cochrane Library, and Web of Science was conducted from inception through May 2025. Cohort studies comparing dementia incidence in T2DM patients treated with SGLT2 inhibitors versus DPP-4 inhibitors were included. Study quality was assessed using the Newcastle-Ottawa Scale (NOS). Data were pooled using a random-effects model in STATA 12.0, with incidence rate ratios (IRRs) and 95 % confidence intervals (CIs) calculated for dementia outcomes. RESULTS: Eight cohort studies (1275,257 participants) were analyzed. SGLT2 inhibitor use was associated with a 33 % lower risk of all-cause dementia compared to DPP-4 inhibitors (I² = 0.0 %, P = 0.816; IRR = 0.666; 95 % CI: 0.484-0.918; P < 0.05). Subgroup analyses indicated non-significant risk reductions for Alzheimer's disease (I² = 0.0 %, P = 0.994; IRR = 0.654; 95 % CI: 0.352-1.212; P = 0.177) and vascular dementia (I² = 0.0 %, P = 0.971; IRR = 0.573; 95 % CI: 0.204-1.605; P = 0.289). CONCLUSIONS: SGLT2 inhibitors are associated with a significantly reduced risk of all-cause dementia in T2DM patients compared to DPP-4 inhibitors. While trends favoring SGLT2 inhibitors were observed for dementia subtypes, further long-term studies are needed to confirm these associations.
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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 | 0.020 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.062 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| 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".