Hyperglycemia enhances coagulation and reduces neutrophil degranulation, whereas hyperinsulinemia inhibits fibrinolysis during human endotoxemia
Bibliographic record
Abstract
Type 2 diabetes is associated with altered immune and hemostatic responses. We investigated the selective effects of hyperglycemia and hyperinsulinemia on innate immune, coagulation, and fibrinolytic responses during systemic inflammation. Twenty-four healthy humans were studied for 8 hours during clamp experiments in which either plasma glucose, insulin, both, or none was increased, depending on randomization. Target plasma concentrations were 5 versus 12 mM for glucose, and 100 versus 400 pmol/L for insulin. After 3 hours, 4 ng/kg Escherichia coli endotoxin was injected intravenously to induce a systemic inflammatory and procoagulant response. Endotoxin administration induced cytokine release, activation of neutrophils, endothelium and coagulation, and inhibition of fibrinolysis. Hyperglycemia reduced neutrophil degranulation (plasma elastase levels, P < .001) and exaggerated coagulation (plasma concentrations of thrombin-antithrombin complexes and soluble tissue factor, both P < .001). Hyperinsulinemia attenuated fibrinolytic activity due to elevated plasminogen activator-inhibitor-1 levels (P < .001). Endothelial cell activation markers and cytokine concentrations did not differ between clamps. We conclude that in humans with systemic inflammation induced by intravenous endotoxin administration hyperglycemia impairs neutrophil degranulation and potentiates coagulation, whereas hyperinsulinemia inhibits fibrinolysis. These data suggest that type 2 diabetes patients may be especially vulnerable to prothrombotic events during inflammatory states.
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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 | 0.000 | 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 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".