Effect of Alirocumab on Lipoprotein(a) Over ≥1.5 Years (from the Phase 3 ODYSSEY Program)
Bibliographic record
Abstract
Elevated lipoprotein(a) [Lp(a)] is independently associated with increased cardiovascular risk. However, treatment options for elevated Lp(a) are limited. Alirocumab, a monoclonal antibody to proprotein convertase subtilisin/kexin type 9, reduced low-density lipoprotein cholesterol (LDL-C) by up to 62% from baseline in phase 3 studies, with adverse event rates similar between alirocumab and controls. We evaluated the effect of alirocumab on serum Lp(a) using pooled data from the phase 3 ODYSSEY program: 4,915 patients with hypercholesterolemia from 10 phase 3 studies were included. Eight studies evaluated alirocumab 75 mg every 2 weeks (Q2W), with possible increase to 150 mg Q2W at week 12 depending on LDL-C at week 8 (75/150 mg Q2W); the other 2 studies evaluated alirocumab 150-mg Q2W from the outset. Comparators were placebo or ezetimibe. Eight studies were conducted on a background of statins, and 2 studies were carried out with no statins. Alirocumab was associated with significant reductions in Lp(a), regardless of starting dose and use of concomitant statins. At week 24, reductions from baseline were 23% to 27% with alirocumab 75/150-mg Q2W and 29% with alirocumab 150-mg Q2W (all comparisons p <0.0001 vs controls). Reductions were sustained over 78 to 104 weeks. Lp(a) reductions with alirocumab were independent of race, gender, presence of familial hypercholesterolemia, baseline Lp(a), and LDL-C concentrations, or use of statins. In conclusion, in addition to marked reduction in LDL-C, alirocumab leads to a significant and sustained lowering of Lp(a).
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.007 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".