Sodium-glucose Cotransporter-2 Inhibitor Initiation and Incident Dementia in Heart Failure With Diabetes: A Population-based Cohort Study
Bibliographic record
Abstract
BACKGROUND: Heart failure often coexists with important dementia-risk factors, such as diabetes, atrial fibrillation and hypertension. Sodium-glucose cotransporter-2 (SGLT2) inhibitors have been associated with a lower dementia risk in general diabetes populations, but evidence is limited, specifically in heart failure with comorbid diabetes. OBJECTIVE: To investigate the association of SGLT2 inhibitors with incident dementia in people with heart failure and diabetes. METHODS: This target trial emulation cohort study used linkable administrative databases from Ontario, Canada. New users of SGLT2 inhibitors or dipeptidyl peptidase-4 (DPP4) inhibitors aged ≥ 66 years with diabetes and heart failure (July 2016-December 2020) entered this cohort. A 180-day lag time was implemented to mitigate reverse causality. The primary analysis used an intention-to-treat exposure definition. Cause-specific hazard ratios (HRs), with death as a competing risk, were estimated by using Cox models with propensity-score fine stratification weights. Weighted incidence-rate differences (IRDs) per 1000 person-years were also estimated. RESULTS: Among 4402 SGLT2 inhibitor and 6319 DPP4 inhibitor new users, over a median follow-up of 3.95 years from treatment initiation, SGLT2 inhibitor vs DPP4 inhibitor initiation was associated with lower dementia risk (HR 0.73, 95% confidence interval [CI] 0.60-0.87; IRD -8.1, 95% CI -12.7 to -3.5). The secondary as-treated analysis showed greater risk reduction (HR 0.53, 95% CI 0.39-0.70; IRD -14.2, 95% CI -20.1 to -8.4) than the primary intention-to-treat analysis. CONCLUSIONS: SGLT2 inhibitor initiation was associated with dementia risk reduction in heart failure and diabetes, a population at a high risk of developing dementia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".