Pleiotropic actions of factor Xa inhibition in cardiovascular prevention: mechanistic insights and implications for anti-thrombotic treatment
Bibliographic record
Abstract
Atherosclerosis is a chronic inflammatory disease in which atherothrombotic complications lead to cardiovascular morbidity and mortality. At advanced stages, myocardial infarction, ischaemic stroke, and peripheral artery disease, including major adverse limb events, are caused either by acute occlusive atherothrombosis or by thromboembolism. Endothelial dysfunction, vascular smooth muscle cell activation, and vascular inflammation are essential in the development of acute cardiovascular events. Effects of the coagulation system on vascular biology extend beyond thrombosis. Under physiological conditions, coagulation proteases in blood are pivotal in maintaining haemostasis and vascular integrity. Under pathological conditions, including atherosclerosis, the same coagulation proteases (including factor Xa, factor VIIa, and thrombin) become drivers of atherothrombosis, working in concert with platelets and vessel wall components. While initially atherothrombosis was attributed primarily to platelets, recent advances indicate the critical role of fibrin clot and plasma coagulation factors. Mechanisms of atherothrombosis and hypercoagulability vary depending on plaque erosion or plaque rupture. In addition to contributing to thrombus formation, factor Xa and thrombin can affect endothelial dysfunction, oxidative stress, vascular smooth muscle cell function as well as immune cell activation and vascular inflammation. By these mechanisms, they promote atherosclerosis and contribute to plaque instability. In this review, we first discuss the postulated vasoprotective mechanisms of protease-activated receptor signalling induced by coagulation enzymes under physiological conditions. Next, we discuss preclinical studies linking coagulation with endothelial cell dysfunction, thromboinflammation, and atherogenesis. Understanding these mechanisms is pivotal for the introduction of novel strategies in cardiovascular prevention and therapy. We therefore translate these findings to clinical studies of direct oral anticoagulant drugs and discuss the potential relevance of dual pathway inhibition for atherothrombosis prevention and vascular protection.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".