Effect of diabetes medications on the risk of developing dementia, mild cognitive impairment, or cognitive decline: a systematic review and meta‐analysis
Bibliographic record
Abstract
Abstract Background Diabetes is a risk factor for dementia, but we do not know whether specific diabetes medications ameliorate this risk. Objective: To systematically review and meta‐analyze such medication's effect on the risk of developing dementia, mild cognitive impairment (MCI), or cognitive decline. Method We searched three databases until 21.11.23. We included randomized controlled trials (RCT), cohort, and case‐control studies assessing association between antidiabetic medication and future dementia, MCI, or cognitive decline. We meta‐analyzed studies separately for individual drug classes and their comparators (no medication, placebo, or another drug). We appraised study quality using the Newcastle‐Ottawa Scale and Physiotherapy Evidence Database Scale. Result 42 studies fulfilled inclusion criteria. Glucagon‐like peptide‐1 receptor agonists (GLP‐1 RA) versus placebo reduced dementia risk by 53% in 3 RCTs ( n = 15,820, RR=0.47[0.25,0.86]) and 27% in 3 case‐control studies ( n = 312,856, RR=0.73[0.54,0.99], I 2 =96%). Repaglinide was superior to glibenclamide by 0.8 points on the Mini‐Mental State Examination scale in another RCT. Meta‐analysis of 7 longitudinal studies showed glitazones ( n = 1,081,519, RR=0.78[0.76,0.81], I 2 =0%) were associated with reduced dementia risk. Metformin ( n = 999,349, RR=0.94[0.79,1.13], I 2 =98.4%), sulfonylureas (RR=0.98[0.78,1.22], I 2 =83.3%), dipeptidyl peptidase‐IV inhibitors (DPP‐1V) ( n = 192,802, RR=0.86[0.65,1.15], I 2 =92.9%) and insulin ( n = 571,274, RR=1.09[0.95,1.25], I 2 =94.8%) were not. Most studies were observational and limited by confounding by indication. Conclusion In people with diabetes, RCTs consistently showed GLP‐RAs reduce future dementia risk. Glitazones consistently showed protective effects, without heterogeneity, suggesting potential generalizability of these results. Metformin, sulfonylureas, insulin, and DPP‐1V studies had inconsistent findings. If information is available future studies should consider dosage, severity, and duration.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.037 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".