Association between use of sodium-glucose co-transporter-2 inhibitor and the risk of incident dementia: a population-based cohort study
Bibliographic record
Abstract
OBJECTIVES: To assess the association between sodium-glucose co-transporter-2 inhibitor (SGLT-2i) use and the risk of incident dementia compared with dipeptidyl peptidase-4 inhibitors (DPP-4i) use among individuals with type 2 diabetes. DESIGN: A population-based retrospective cohort study. SETTING: The Clinical Practice Research Datalink (CPRD) Aurum database from the UK. PARTICIPANTS: Individuals with type 2 diabetes, aged 40 years or older, newly prescribed SGLT-2i or DPP-4i on or after 2013-2021, registered in the CPRD Aurum database. MAIN OUTCOME MEASURE: The primary outcome was incident dementia, and the secondary outcome was incident mild cognitive impairment (MCI). Cox proportional hazard models were used to estimate the HR and corresponding 95% CI for the primary and secondary outcomes. Propensity score fine stratification weights were used to adjust for confounding. RESULTS: Among a cohort of 118 006 individuals, the incident rate (IR) of dementia was 0.56/1000 person-years over a median follow-up period of 1.54 years among SGLT-2i users compared with 2.67/1000 person-years in DPP-4i users, over a median follow-up period of 1.79 years. The adjusted HR for SGLT-2i use compared with DPP-4i use for dementia was 0.78 (95% CI 0.55 to 1.12), while for MCI was 0.86 (95% CI 0.80 to 0.92). The age-specific stratified analysis demonstrated the adjusted HR for SGLT-2i use compared with DPP-4i use for the risk of incident dementia among elderly, aged ≥65 years, was 0.50 (95% CI 0.31 to 0.80). CONCLUSION: Primary findings did not yield conclusive evidence to infer an association between SGLT-2i use and the risk of incident dementia.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".