PCSK9 inhibition-mediated reduction in Lp(a) with evolocumab: an analysis of 10 clinical trials and the LDL receptor's role
Bibliographic record
Abstract
Lipoprotein (a) [Lp(a)] is independently associated with CVD risk. Evolocumab, a monoclonal antibody (mAb) to proprotein convertase subtilisin/kexin type 9 (PCSK9), decreases Lp(a). The potential mechanisms were assessed. A pooled analysis of Lp(a) and LDL cholesterol (LDL-C) in 3,278 patients from 10 clinical trials (eight phase 2/3; two extensions) was conducted. Within each parent study, biweekly and monthly doses of evolocumab statistically significantly reduced Lp(a) at week 12 versus control (P < 0.001 within each study); pooled median (quartile 1, quartile 3) percent reductions were 24.7% (40.0, 3.6) and 21.7% (39.9, 4.2), respectively. Reductions were maintained through week 52 of the open-label extension, and correlated with LDL-C reductions [with and without correction for Lp(a)-cholesterol] at both time points (P < 0.0001). The effect of LDL and LDL receptor (LDLR) availability on Lp(a) cell-association was measured in HepG2 cells: cell-associated LDL fluorescence was reversed by unlabeled LDL and Lp(a). Lp(a) cell-association was reduced by coincubation with LDL and PCSK9 and reversed by adding PCSK9 mAb. These studies support that reductions in Lp(a) with PCSK9 inhibition are partly due to increased LDLR-mediated uptake. In most situations, Lp(a) appears to compete poorly with LDL for LDLR binding and internalization, but when LDLR expression is increased with evolocumab, particularly in the setting of low circulating LDL, Lp(a) is reduced.
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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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".