Circulating membrane aminophospholipids contribute to thrombotic risk in rheumatoid arthritis
Bibliographic record
Abstract
Patients with rheumatoid arthritis (RA) are at elevated risk of thrombotic events, yet the underlying mechanisms remain unknown. The contribution of the procoagulant membrane surface provided by aminophospholipids (aPLs) in driving thrombotic risk in RA has not been investigated. Specifically, neither the type of aPL exposed on circulating blood cell membranes in patients is characterized nor is their ability to support thrombin generation is known. Here, lipidomics was used to characterize the external-facing and total levels of aPL molecular species in RA, specifically phosphatidylserine and phosphatidylethanolamine on extracellular vesicles (EVs), platelets, and white blood cells (WBCs). The ability of the cells and EVs to support thrombin generation from patients and healthy controls was compared using an in vitro prothrombinase assay. RA patient plasma had significantly higher levels of thrombin-antithrombin and d-dimers, indicating increased thrombotic activity in vivo. Higher EV and platelet counts were seen in RA, but WBC counts were not elevated. EVs from RA patients supported higher levels of thrombin generation compared with healthy controls, whereas for platelets and WBC, thrombin generation was similar for both groups. EVs from RA patients also showed elevated external-facing phosphatidylserine molecular species, with total aPL also increased. For platelets and WBC, total and external-facing aPL levels were similar. Thrombin-antithrombin (TAT) complexes significantly correlated with EV particle counts, indicating that their circulating numbers are directly related to coagulation in vivo. Overall, our data suggest that elevated plasma EV levels in RA are a major source of procoagulant membranes, contributing to thrombotic risk in RA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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".