Evaluation of lipoprotein(a) as a prothrombotic factor: progress from bench to bedside
Bibliographic record
Abstract
PURPOSE OF REVIEW: Since the homology between apolipoprotein(a) (apo(a)) and plasminogen was discovered in 1987, the role of lipoprotein(a) (Lp(a)) as an inhibitor of the normal fibrinolytic role of plasmin(ogen) has been a major research focus. In this review we summarize recent basic research aimed at identifying mechanisms by which Lp(a) can either inhibit fibrinolysis or promote coagulation, as well as recent clinical studies of Lp(a) as a risk factor for thrombosis either in the presence or absence of atherosclerosis. RECENT FINDINGS: It has recently been reported that the inhibition of plasminogen activation by apo(a) results from the interaction of apo(a) with the ternary complex of tissue-type plasminogen activator, plasminogen and fibrin, rather than competition of apo(a) and plasminogen for binding sites on fibrin. Lp(a) species containing smaller apo(a) isoforms bind more avidly to fibrin and are better inhibitors of plasminogen activation. Recent clinical studies have provided strong evidence that Lp(a), either alone or in synergy with other thrombotic risk factors, significantly increases the risk of venous thromboembolism and ischemic stroke. SUMMARY: Lp(a) both attenuates fibrinolysis, through inhibition of plasminogen activation, and promotes coagulation, through alleviation of extrinsic pathway inhibition. Further basic and clinical studies are required to more clearly define the role of Lp(a) in thrombotic disorders, and to determine the extent to which thrombotic risk is dependent on apo(a) isoform size.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".