Risk of Stroke in Real-World US Individuals with Type 2 Diabetes Receiving Semaglutide or a Dipeptidyl Peptidase 4 Inhibitor
Bibliographic record
Abstract
INTRODUCTION: People with type 2 diabetes (T2D) have a higher risk of stroke and worse outcomes than those without T2D. Pooled data from randomized controlled trials indicate that the glucagon-like peptide 1 receptor agonist semaglutide is associated with stroke risk reduction in people with T2D at high cardiovascular risk. We compared real-world stroke risk in people with T2D or T2D plus atherosclerotic cardiovascular disease (ASCVD) initiating either semaglutide or a dipeptidyl peptidase 4 inhibitor (DPP4i). METHODS: Adults (≥ 18 years old) in a US claims database with a claim indicating initiation of either semaglutide or a DPP4i (index date) during the index period (1 January 2018-30 September 2020), a diagnosis code for T2D on or before the index date and at least 12 months' continuous enrolment in the database pre-index were included and propensity score matched 1:1 on baseline demographic and clinical characteristics. The primary outcome was time to first stroke event during follow-up. Healthcare resource utilization was also compared between groups. RESULTS: The analysis included 17,920 matched pairs with T2D and 4234 matched pairs with T2D and ASCVD. The groups were well matched on baseline characteristics. People initiating semaglutide had a lower risk of stroke over short-term follow-up than those initiating a DPP4i (T2D: hazard ratio 0.63 [95% confidence interval 0.41-0.95], p = 0.029; T2D plus ASCVD: 0.45 [0.24-0.86], p = 0.015). Semaglutide was also associated with a lower rate of inpatient, outpatient and emergency room visits compared with a DPP4i. CONCLUSION: This proof-of-concept analysis indicates that semaglutide has the potential to reduce the risk of stroke in people with T2D when prescribed in clinical practice.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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".