Cerebrovascular Safety of Sulfonylureas: The Role of KATP Channels in Neuroprotection and the Risk of Stroke in Patients With Type 2 Diabetes
Bibliographic record
Abstract
Sulfonylureas are ATP-sensitive potassium (KATP) channel blockers commonly used in the treatment of type 2 diabetes mellitus (T2DM). Activation of KATP channels plays a neuroprotective role in ischemia; thus, whether sulfonylureas affect the outcomes of stroke in patients with T2DM needs to be further studied. In our study, streptozotocin (STZ)-induced diabetic mice subjected to transient middle cerebral artery occlusion (MCAO) showed larger areas of brain damage and poorer behavioral outcomes. Blocking the KATP channel by tolbutamide increased neuronal injury induced by oxygen-glucose deprivation (OGD) in vitro and permanent MCAO (pMCAO) in vivo. Activating the KATP channel by diazoxide reduced the effects of both the OGD and pMCAO. Western blot analysis in STZ mouse brains indicated an early increase in protein levels of N-methyl-d-aspartate receptor 2B and postsynaptic density protein-95, followed by a decrease in phosphorylation of glycogen synthase kinase 3β. Our systematic meta-analysis indicated that patients with T2DM treated with sulfonylureas had a higher odds ratio for stroke morbidity than those who received comparator drugs. Taken together, these results suggest that sulfonylurea treatment in patients with T2DM may inhibit the neuroprotective effects of KATP channels and increase the risk of stroke.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".