Lipoprotein(a)
Bibliographic record
Abstract
PURPOSE OF REVIEW: Lipoprotein(a) [Lp(a)] is a risk factor for cardiovascular disease; we highlight the most recent research initiatives that have sought to define Lp(a)-dependent pathogenicity as well as pharmacologic approaches to lowering Lp(a). RECENT FINDINGS: Recent large-scale meta-analyses have confirmed elevated Lp(a) concentrations to be a moderate but consistent prospective coronary heart disease (CHD) risk factor. The Mendelian randomization approach has also associated LPA variants with Lp(a) concentration and CHD risk. Discoveries linking Lp(a) to oxidized phospholipid burden have implicated a proinflammatory role for Lp(a) hinting at a new mechanism underlying the association with CHD risk, which adds to previous atherogenic and thrombogenic mechanisms. Most existing Lp(a)-lowering drug treatments almost always show simultaneous effects on other lipoproteins, making it difficult to assign any clinical outcome specifically to the effects of Lp(a) lowering. Early experiments with antisense oligonucleotides targeting apolipoprotein(a) mRNA seem to indicate the pleiotropic effects of Lp(a) reduction on LDL and HDL in mice. The mechanism linking Lp(a) concentration with concentrations of other blood lipids remains unknown but may provide an insight into Lp(a) metabolism. SUMMARY: Despite the wealth of epidemiologic evidence supporting Lp(a) concentration as a CHD risk factor, the lack of a definitive functional mechanism involving an Lp(a)-dependent pathway in CHD pathogenesis has limited the potential clinical connotation of Lp(a). However, the application of novel technologies to the long-standing mysteries of Lp(a) biology seems to provide the opportunity for expanding our understanding of Lp(a) and its complex role in cardiovascular health.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.031 | 0.021 |
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".