Lipoprotein(a): A Review of Risk Factors, Measurements, and Novel Treatment Modalities
Bibliographic record
Abstract
The study of lipoprotein(a) [Lp(a)] has long been a source of interest as a possible independent risk factor for atherosclerotic cardiovascular disease (ASCVD). The results of large sample observational studies, genome-wide association studies, and Mendelian randomization studies have been strong indicators supporting the link between ASCVD and Lp(a) despite early studies, with less sensitive assays, failing to show a connection. The recommendations for the indications and frequency of testing Lp(a) levels vary between US, Canadian, and European organizations due to the uncertain role of Lp(a) in ASCVD. The innovation of recent therapies, such as antisense oligonucleotides and small interfering RNA, designed to specifically target and reduce Lp(a) levels by targeting mRNA translation have once more thrust LP(a) into the spotlight of inquiry. These emerging modalities serve the dual purpose of definitively elucidating the connection between elevated Lp(a) levels and atherosclerotic cardiovascular risk, as well as the possibility of providing clinicians with the tools necessary to manage elevated Lp(a) levels in vulnerable populations. This review seeks to examine the mechanisms of atherogenicity of Lp(a) and explore the most current pharmacologic therapies currently in development.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".