Daring to dream: Targeting lipoprotein(a) as a causal and risk-enhancing factor
Bibliographic record
Abstract
Lipoprotein(a) [Lp(a)], a distinct lipoprotein class, has become a major focus for cardiovascular research. This review is written in light of the recent guideline and consensus statements on Lp(a) and focuses on 1) the causal association between Lp(a) and cardiovascular outcomes, 2) the potential mechanisms by which elevated Lp(a) contributes to cardiovascular diseases, 3) the metabolic insights on the production and clearance of Lp(a) and 4) the current and future therapeutic approaches to lower Lp(a) concentrations. The concentrations of Lp(a) are under strict genetic control. There exists a continuous relationship between the Lp(a) concentrations and risk for various endpoints of atherosclerotic cardiovascular disease (ASCVD). One in five people in the Caucasian population is considered to have increased Lp(a) concentrations; the prevalence of elevated Lp(a) is even higher in black populations. This makes Lp(a) a cardiovascular risk factor of major public health relevance. Besides the association between Lp(a) and myocardial infarction, the relationship with aortic valve stenosis has become a major focus of research during the last decade. Genetic studies provided strong support for a causal association between Lp(a) and cardiovascular outcomes: carriers of genetic variants associated with lifelong increased Lp(a) concentration are significantly more frequent in patients with ASCVD. This has triggered the development of drugs that can specifically lower Lp(a) concentrations: mRNA-targeting therapies such as anti-sense oligonucleotide (ASO) therapies and short interfering RNA (siRNA) therapies have opened new avenues to lower Lp(a) concentrations more than 95%. Ongoing Phase II and III clinical trials of these compounds are discussed in this review.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".