Non‐steroidal anti‐inflammatory drugs increase insulin release from beta cells by inhibiting ATP‐sensitive potassium channels
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Some non-steroidal anti-inflammatory drugs (NSAIDs) incidentally induce hypoglycemia, which is often seen in diabetic patients receiving sulphonylureas. NSAIDs influence various ion channel activities, thus they may cause hypoglycemia by affecting ion channel functions in insulin secreting beta cells. This study investigated the effects of the NSAID meclofenamic acid (MFA) on the electrical excitability and the secretion of insulin from pancreatic beta cells. EXPERIMENTAL APPROACH: Using patch clamp techniques and insulin secretion assays, the effects of MFA on the membrane potential and transmembrane current of INS-1 cells, and insulin secretion were studied. KEY RESULTS: Under perforated patch recordings, MFA induced a rapid depolarization in INS-1 cells bathed in low (2.8 mM), but not high (28 mM) glucose solutions. MFA, as well as acetylsalicylic acid (ASA) and flufenamic acid (FFA), excited the cells by inhibiting ATP-sensitive potassium channels (K(ATP)). In whole cell recordings, K(ATP) conductance consistently appeared when intracellular ATP was diluted. Intracellular glibenclamide prevented the development of K(ATP) activity, whereas intracellular MFA had no effect. At low glibenclamide concentrations, MFA induced additional inhibition of the K(ATP) current. Live cell Ca(2+) imaging displayed that MFA elevated intracellular Ca(2+) at low glucose concentrations. Furthermore, MFA dose-dependently increased insulin release under low, but not high, glucose conditions. CONCLUSIONS AND IMPLICATIONS: MFA blocked K(ATP) through an extracellular mechanism and thus increased insulin secretion. As some NSAIDs synergistically inhibit K(ATP) activity together with sulphonylureas, the risk of NSAID-induced hypoglycemia should be considered when glucose-lowering compounds are administered.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".