Effect of Alirocumab, a Monoclonal Proprotein Convertase Subtilisin/Kexin 9 Antibody, on Lipoprotein(a) Concentrations (a Pooled Analysis of 150 mg Every Two Weeks Dosing from Phase 2 Trials)
Bibliographic record
Abstract
Lipoprotein(a) [Lp(a)] is an independent risk factor for cardiovascular disease, with limited treatment options. This analysis evaluated the effect of a monoclonal antibody to proprotein convertase subtilisin/kexin 9, alirocumab 150 mg every 2 weeks (Q2W), on Lp(a) levels in pooled data from 3 double-blind, randomized, placebo-controlled, phase 2 studies of 8 or 12 weeks' duration conducted in patients with hypercholesterolemia on background lipid-lowering therapy (NCT01266876, NCT01288469, and NCT01288443). Data were available for 102 of 108 patients who received alirocumab 150 mg Q2W and 74 of 77 patients who received placebo. Alirocumab resulted in a significant reduction in Lp(a) from baseline compared with placebo (-30.3% vs -0.3%, p <0.0001). Median percentage Lp(a) reductions in the alirocumab group were of a similar magnitude across a range of baseline Lp(a) levels, resulting in greater absolute reductions in Lp(a) in patients with higher baseline levels. Regression analysis indicated that <5% of the variance in the reduction of Lp(a) was explained by the effect of alirocumab on low-density lipoprotein cholesterol. In conclusion, pooled data from 3 phase 2 trials demonstrate substantive reduction in Lp(a) with alirocumab 150 mg Q2W, including patients with baseline Lp(a) >50 mg/dl. Reductions in Lp(a) only weakly correlated with the magnitude of low-density lipoprotein cholesterol lowering.
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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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.010 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".