Lipoprotein (a) in calcific aortic valve disease: from genomics to novel drug target for aortic stenosis
Bibliographic record
Abstract
Calcific aortic stenosis (AS) is the most common form of valve disease in the Western world and affects over 2.5 million individuals in North America. Despite the large burden of disease, there are no medical treatments to slow the development of AS, due at least in part to our incomplete understanding of its causes. The Cohorts for Heart and Aging Research in Genetic Epidemiology extra-coronary calcium consortium reported a genome-wide association study demonstrating that genetic variants in LPA are strongly associated with aortic valve (AV) calcium and clinical AS. Using a Mendelian randomization study design, it was demonstrated that the effect of this genetic variant is mediated by plasma lipoprotein (a) [Lp(a)], directly implicating elevations in Lp(a) as a cause of AV calcium and progression to AS. This discovery has sparked intense interest in Lp(a) as a modifiable cause for AV disease. Herein, we will review the mounting epidemiological and genetic findings in support of Lp(a)-mediated valve disease, discuss potential mechanisms underlying this observation, and outline the steps to translate this discovery to a much needed novel preventive and/or therapeutic strategy for AV disease.
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 | 0.002 | 0.001 |
| 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) | 0.003 | 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".