Lipoprotein(a) as a Causal Risk Factor for Cardiovascular Disease
Bibliographic record
Abstract
Purpose of Review: Lipoprotein(a) [Lp(a)], an atherogenic low-density lipoprotein cholesterol (LDL-C)-like molecule, has emerged as an important risk factor for the development of atherosclerotic cardiovascular disease (ASCVD). This review summarizes the evidence supporting Lp(a) as a causal risk factor for ASCVD and calcific aortic valve stenosis (CAVS). Recent Findings: Lp(a) is largely (~ 90%) genetically determined and approximately 20% of the global population has elevated Lp(a). The unique structure of Lp(a) leads to proatherogenic, proinflammatory, and antifibrinolytic properties. Data from epidemiological, genome-wide association, Mendelian randomization, and meta-analyses have shown a clear association between Lp(a) and ASCVD, as well as CAVS. There are emerging data on the association between Lp(a) and ischemic stroke, peripheral arterial disease, and heart failure; however, the associations are not as strong. Summary: Several lines of evidence support Lp(a) as a causal risk factor for ASCVD and CAVS. The 2024 National Lipid Association guidelines, 2022 European Atherosclerosis Society, and 2021 Canadian Cardiology Society guidelines recommend testing Lp(a) once in all adults to guide primary prevention efforts. Further studies on cardiovascular outcomes with Lp(a) targeted therapies will provide more insight on causal relationship between Lp(a) and cardiovascular disease.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".