Why, how and in whom should we measure levels of lipoprotein(a): A review of the latest evidence and clinical implications
Bibliographic record
Abstract
Lipoprotein(a) [Lp(a)] is a genetically determined, causal risk factor for atherosclerotic cardiovascular disease (ASCVD) and calcific aortic valve disease (CAVD). Despite robust evidence from epidemiological and genetic studies, Lp(a) remains underrecognised in clinical practice due to challenges in measurement, lack of guideline familiarity and limited therapeutic options. In this narrative review, we summarise the pathophysiological mechanisms linking Lp(a) to atherogenesis, thrombosis and inflammation, emphasising its unique structural features and causal role in cardiovascular disease. We discuss assay methodologies and make the case for a single lifetime measurement given the genetic stability of Lp(a). We review guideline-based indications for testing, highlighting high-risk populations such as those with premature ASCVD, a family history of cardiovascular disease and individuals of African or South Asian ancestry. We additionally outline clinical strategies to reduce ASCVD risk in individuals with elevated Lp(a), including lifestyle optimisation, statin therapy, PCSK9 inhibitors, and aspirin in select populations. Emerging targeted therapies, including antisense oligonucleotides and siRNA-based agents, demonstrate up to 90% Lp(a) reduction and are currently being evaluated in large-scale cardiovascular outcomes trials. As precision medicine advances, Lp(a) represents both a critical risk factor and a promising therapeutic target. Broader implementation of Lp(a) testing, particularly in high-risk individuals, will help improve ASCVD prevention efforts.
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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 | 0.008 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".