Impact of pharmacological treatment of diabetes mellitus on dementia risk: systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: The association between diabetes mellitus (DM) treatment and dementia is not well understood. OBJECTIVE: To investigate the association between treatment of diabetes, hypoglycemia, and dementia risk. RESEARCH DESIGN AND METHODS: We performed a systematic review and meta-analysis of pharmacological treatment of diabetes and incident or progressive cognitive impairment. We searched Ovid MEDLINE, Embase, Cochrane Central Registry of Controlled Trials, and PsychINFO from inception to 18 October 2017. We included cross-sectional, case-control, cohort, and randomized controlled studies. The study was registered with PROSPERO (ID CRD42017077953). RESULTS: We included 37 studies into our systematic review and 13 into our meta-analysis. Ten studies investigated any antidiabetic treatment compared with no treatment or as add-on therapy to prior care. Treatment with an antidiabetic agent, in general, was not associated with incident dementia (risk ratio (RR) 1.01; 95% CI 0.93 to 1.10). However, we found differential effects across drug classes, with a signal of harm associated with insulin therapy (RR 1.21; 95% CI 1.06 to 1.39), but potentially protective effects with thiazolidinedione exposure (RR 0.71; 95% CI 0.55 to 0.93). Severe hypoglycemic episodes were associated with a nearly twofold increased likelihood of incident dementia (RR 1.77; 95% CI 1.35 to 2.33). Most studies did not account for DM duration or severity. CONCLUSIONS AND LIMITATIONS: The association between treatment for diabetes and dementia is differential according to drug class, which is potentially mediated by hypoglycemic risk. Not accounting for DM duration and/or severity is a major limitation in the available evidence base.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".