Lipoprotein(a) prolongs ex vivo plasma clot lysis times through effects on clot formation rate and fibrin structure
Bibliographic record
Abstract
BACKGROUND: Elevated levels of lipoprotein(a) (Lp[a]) are a causal risk factor for atherosclerotic cardiovascular disease. Similarities between the apolipoprotein(a) (apo[a]) component of Lp(a) and plasminogen suggest that antifibrinolytic properties may account for the pathological effects of Lp(a). However, the antifibrinolytic effects of apo(a) do not appear to be retained by the complete Lp(a) particle. OBJECTIVES: We evaluated the effects of Lp(a), apo(a), and various apo(a) variants on clot formation and lysis times, thrombin generation, plasminogen activation, and fibrin architectures in ex vivo plasma clots. We also constructed predictive protein models to gain insight into the apo(a)-plasminogen interaction. RESULTS: Apo(a) strongly inhibited fibrinolysis, an effect dependent on the presence of the apo(a) protease domain and mediated by Lys216 in this domain. Modeling of apo(a) suggests that Lys216 is blocked from binding to plasminogen in the Lp(a) particle by the presence of the apoB-containing lipoprotein. Lp(a) and apo(a) shortened plasma clot formation times, and accounting for this revealed a small but significant prolongation of fibrinolysis by Lp(a). The procoagulant effects involved the development of lysis-resistant clot architectures and were mediated through the strong lysine-binding site in apo(a) kringle IV type 10. In addition, Lp(a) (but not apo[a]) accelerated thrombin generation. CONCLUSIONS: The strong antifibrinolytic effects of apo(a) do not appear to be retained in the complete Lp(a) particle. However, Lp(a) and apo(a) displayed procoagulant effects, in part dependent on the kringle 4-like lysine-binding site. Further analysis is required to determine whether these reported procoagulant effects of Lp(a) impact thrombosis in vivo.
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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) | 0.001 | 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".