Proprotein convertase subtilisin/kexin type 9 inhibitors and lipoprotein(a)-mediated risk of atherosclerotic cardiovascular disease: more than meets the eye?
Bibliographic record
Abstract
PURPOSE OF REVIEW: Evidence continues to mount for elevated lipoprotein(a) [Lp(a)] as a prevalent, independent, and causal risk factor for atherosclerotic cardiovascular disease. However, the effects of existing lipid-lowering therapies on Lp(a) are comparatively modest and are not specific to Lp(a). Consequently, evidence that Lp(a)-lowering confers a cardiovascular benefit is lacking. Large-scale cardiovascular outcome trials (CVOTs) of inhibitory mAbs targeting proprotein convertase subtilisin/kexin type 9 inhibitors (PCSK9i) may address this issue. RECENT FINDINGS: Although the ability of PCSK9i to lower Lp(a) by 15-30% is now clear, the mechanisms involved continue to be debated, with in-vitro and in-vivo studies showing effects on Lp(a) clearance (through the LDL receptor or other receptors) and Lp(a)/apolipoprotein(a) biosynthesis in hepatocytes. The FOURIER CVOT showed that patients with higher baseline levels of Lp(a) derived greater benefit from evolocumab and those with the lowest combined achieved Lp(a) and LDL-cholesterol (LDL-C) had the lowest event rate. Meta-analysis of ten phase 3 trials of alirocumab came to qualitatively similar conclusions concerning achieved Lp(a) levels, although an effect independent of LDL-C lowering could not be demonstrated. SUMMARY: Although it is not possible to conclude that PCSK9i specifically lower Lp(a)-attributable risk, patients with elevated Lp(a) could derive incremental benefit from PCSK9i therapy.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".