von Willebrand Factor Is a Critical Mediator of Deep Vein Thrombosis in a Mouse Model of Diet-Induced Obesity
Bibliographic record
Abstract
OBJECTIVE: Obesity is characterized by chronic low-grade inflammation and consequentially a hypercoagulable state, associating with an increased incidence of venous thromboembolism. Increased VWF (von Willebrand factor) plasma concentration and procoagulant function are independent risk factors for venous thromboembolism and are elevated in obese patients. Here, we explore the pathobiological role of VWF in obesity-associated venous thrombosis using murine models. Approach and Results: We first showed that diet-induced obese mice have increased VWF plasma levels and FVIII (factor VIII) activity compared with littermate controls. Elevated VWF levels appeared to be because of both increased synthesis and impaired clearance. Diet-induced obesity-associated venous thrombosis was assessed using the inferior vena cava-stenosis model of deep vein thrombosis. Diet-induced obese mice developed larger venous thrombi that were rich in VWF, erythrocytes, and leukocytes. Administering a polyclonal anti-VWF antibody or an anti-VWF A1 domain nanobody was protective against obesity-mediated thrombogenicity. Delayed administration (3 hours post-inferior vena cava stenosis) similarly reduced thrombus weight in diet-induced obese mice. CONCLUSIONS: This study demonstrates the critical role of VWF in the complex, thrombo-inflammatory state of obesity. It adds to the growing rationale for targeting VWF-specific interactions in thrombotic disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".