Diabetes, antidiabetic medications and risk of dementia: A systematic umbrella review and meta‐analysis
Bibliographic record
Abstract
AIMS: The objective of this umbrella review and meta-analysis was to evaluate the effect of diabetes on risk of dementia, as well as the mitigating effect of antidiabetic treatments. MATERIALS AND METHODS: We conducted a systematic umbrella review on diabetes and its treatment, and a meta-analysis focusing on treatment. We searched MEDLINE/PubMed, Embase, PsycINFO, CINAHL and the Cochrane Library for systematic reviews and meta-analyses assessing the risk of cognitive decline/dementia in individuals with diabetes until 2 July 2023. We conducted random-effects meta-analyses to obtain risk ratios and 95% confidence intervals estimating the association of metformin, thiazolidinediones, pioglitazone, dipeptidyl peptidase-4 inhibitors, α-glucosidase inhibitors, meglitinides, insulin, sulphonylureas, glucagon-like peptide-1 receptor agonists (GLP1RAs) and sodium-glucose cotransporter-2 inhibitors (SGLT2is) with risk of dementia from cohort/case-control studies. The subgroups analysed included country and world region. Risk of bias was assessed with the AMSTAR tool and Newcastle-Ottawa Scale. RESULTS: We included 100 reviews and 27 cohort/case-control studies (N = 3 046 661). Metformin, thiazolidinediones, pioglitazone, GLP1RAs and SGLT2is were associated with significant reduction in risk of dementia. When studies examining metformin were divided by country, the only significant effect was for the United States. Moreover, the effect of metformin was significant in Western but not Eastern populations. No significant effect was observed for dipeptidyl peptidase-4 inhibitors, α-glucosidase inhibitors, or insulin, while meglitinides and sulphonylureas were associated with increased risk. CONCLUSIONS: Metformin, thiazolidinediones, pioglitazone, GLP1RAs and SGLT2is were associated with reduced risk of dementia. More longitudinal studies aimed at determining their relative benefit in different populations should be conducted.
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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 | 0.023 | 0.052 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.039 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 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".