Association of sodium-glucose cotransporter-2 inhibitors with time to dementia: a population-based cohort study
Bibliographic record
Abstract
Objective Type 2 diabetes increases dementia risk, but clear evidence to recommend interventions that can mitigate that risk remains lacking. This population-based retrospective cohort study aimed to determine whether new use of sodium-glucose cotransporter-2 (SGLT2) inhibitors compared to dipeptidyl peptidase-4 (DPP4) inhibitors was associated with lower dementia risk. Research Design and Methods Ontario residents aged ≥66 years who were new users of an SGLT2 inhibitor or a DPP4 inhibitor from July 1st, 2016 to March 31st, 2021 entered the cohort. Incident dementia was identified using a validated algorithm for Alzheimer’s disease and related dementias. Propensity-score weighted Cox proportional hazards models were used to obtain adjusted hazard ratios (aHR) and confidence intervals (CI) for time to incident dementia. To address reverse causality and disease latency, the observation window started at 1-year lag-time from cohort entry. The primary analysis followed intention-to-treat exposure definition, and a secondary as-treated analysis was performed. Results Among 106,903 individuals, SGLT2 inhibitors compared with DPP4 inhibitors were associated with lower risk of dementia (14.2/1000 person-years; aHR [95% CI]=0.80 [0.71-0.89]) over a mean follow-up of 2.80 years from cohort entry. When stratified by different SGLT2 inhibitors, dapagliflozin exhibited the lowest risk (aHR [95% CI]=0.67 [0.53-0.84]), followed by empagliflozin (aHR [95% CI]=0.78 [0.69-0.89]), whereas canagliflozin showed no association (aHR [95% CI]=0.96 [0.80-1.16]). The as-treated analysis observed a larger association (aHR [95% CI]=0.66 [0.57-0.76]) than the intention-to-treat analysis. Conclusions SGLT2 inhibitors showed an association with lower dementia risk in older people with type 2 diabetes. Randomized controlled trials are warranted.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".