Lipoprotein(a) as a therapeutic target in cardiovascular disease
Bibliographic record
Abstract
INTRODUCTION: Recent advances in genetics and epidemiology have once again thrust lipoprotein(a) (Lp(a)) into the clinical spotlight. Elevated plasma concentrations of Lp(a) are an independent, causal risk factor for coronary heart disease. The mechanisms underlying the pathogenicity of Lp(a) remain obscure, and uncertainty continues to surround the appropriate use of Lp(a) in the clinic. AREAS COVERED: We summarize the most recent findings on the biology and epidemiology of Lp(a), and use this as a platform to discuss strategies to lower plasma Lp(a) concentrations. The majority of the existing approaches are not Lp(a) specific since they also improve other aspects of the lipid profile. It is possible, however, that the unique characteristics of Lp(a) can be exploited to design therapeutics to specifically lower Lp(a). EXPERT OPINION: Lp(a) should be measured in selected patients, including those with a family history of cardiovascular disease (CVD), those with several risk factors for CVD and those who exhibit resistance to statins. Lp(a) lowering should not be the primary driver of choice of therapy, as it has not yet been established through randomized controlled trials that Lp(a) lowering per se has clinical benefit. The development of agents that specifically lower Lp(a) will allow interrogation of this question.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".