Potent reduction of plasma lipoprotein (a) with an antisense oligonucleotide in human subjects does not affect ex vivo fibrinolysis
Bibliographic record
Abstract
It is postulated that lipoprotein (a) [Lp(a)] inhibits fibrinolysis, but this hypothesis has not been tested in humans due to the lack of specific Lp(a) lowering agents. Patients with elevated Lp(a) were randomized to antisense oligonucleotide [IONIS-APO(a) Rx ] directed to apo(a) ( n = 7) or placebo ( n = 10). Ex vivo plasma lysis times and antigen concentrations of plasminogen, factor XI, plasminogen activator inhibitor 1, thrombin activatable fibrinolysis inhibitor, and fibrinogen at baseline, day 85/92/99 (peak drug effect), and day 190 (3 months off drug) were measured. The mean ± SD baseline Lp(a) levels were 477.3 ± 55.9 nmol/l in IONIS-APO(a) Rx and 362.1 ± 89.9 nmol/l in placebo. The mean± SD percentage change in Lp(a) for IONIS-APO(a) Rx was −69.3 ± 12.2% versus −5.4 ± 6.9% placebo ( P < 0.0010) at day 85/92/99 and −15.6 ± 8.9% versus 3.2 ± 12.2% ( P = 0.003) at day 190. Clot lysis times and coagulation/fibrinolysis-related biomarkers showed no significant differences between IONIS-APO(a) Rx and placebo at all time points. Clot lysis times were not affected by exogenously added Lp(a) at concentrations up to 200 nmol/l to plasma with very low (12.5 nmol/l) Lp(a) levels, whereas recombinant apo(a) had a potent antifibrinolytic effect. In conclusion, potent reductions of Lp(a) in patients with highly elevated Lp(a) levels do not affect ex vivo measures of fibrinolysis; the relevance of any putative antifibrinolytic effects of Lp(a) in vivo needs further study.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".