Partitioning the Genetic Architecture of Plasma Lipoprotein(a) and Kringle IV Type 2 Repeats: Implications for Therapeutic Lowering
Bibliographic record
Abstract
Circulating low-density lipoprotein cholesterol (LDL-c)3 remains the most widely accepted clinical risk factor and target of pharmacotherapy used in the management of lipid and lipoprotein-related risk of atherosclerotic cardiovascular disease (ASCVD). However, in the past decade, it has become increasingly clear that other lipid and lipoprotein risk pathways exist outside of the scope of what is captured by LDL-c (1). Like LDL-c, many of these lipids and lipoproteins can be up-taken into atherosclerotic plaque, where they incite an inflammatory cascade leading to the genesis and propagation of atherosclerosis. As LDL-c continues to decline in the population with the broadening use of pharmacotherapy and lifestyle interventions, it is anticipated that the relative importance of many of these non-LDL-c lipid and lipoprotein risk factors will become increasingly important. Therefore, understanding and characterizing such pathways has taken on greater relevance in the primary and secondary prevention of ASCVD. Lipoprotein(a) [Lp(a)] is likely one such risk factor (2), although the cholesterol content of Lp(a) is included in Friedewald-calculated LDL-c and in most direct measurements of LDL-c. Lp(a) is a complex LDL-like lipoprotein consisting of an apo(a) molecule covalently bound to another apolipoprotein, apoB. Plasma concentrations of Lp(a) are predominantly under the influence of genetic variations in the LPA gene coding for apo(a). Apo(a) is a surface protein that varies in size between individuals due to polymorphisms causing a variable number of kringle IV type 2 repeats (KIVRs), which are translated into proteins of varying size [apo(a) isoforms]. An inverse correlation exists between the size of the apo(a) isoform and the Lp(a) plasma concentration, such that large apo(a) isoforms are associated with low Lp(a) plasma concentrations and vice versa, with up to a 1000-fold difference in Lp(a) concentration noted between individuals. Approximately half of the genetically based variations in Lp(a) concentration are related to …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".