An Apolipoprotein(a) Peptide Delays Chylomicron Remnant Clearance and Increases Plasma Remnant Lipoproteins and Atherosclerosis In Vivo
Bibliographic record
Abstract
OBJECTIVE: Humans with high expression of apolipoprotein(a) [apo(a)] and high plasma levels of lipoprotein(a) [Lp(a)] are at increased risk for atherosclerosis, but the mechanism is not known. We have previously shown that the KIV(5-8) domain of apo(a) has unique cell-surface binding properties, and naturally occurring fragments of apo(a) encompassing this domain are thought to be atherogenic in humans. To investigate the effect of KIV(5-8) on lipoprotein metabolism and atherosclerosis in vivo, we created several independent lines of liver-targeted KIV(5-8) transgenic mice. METHODS AND RESULTS: The transgenic mice have plasma apo(a) peptide concentrations that are similar to Lp(a) concentrations in humans at risk for coronary artery disease. Remarkably, the transgenic mice had a 2- to 4-fold increase in cholesterol-rich remnant lipoproteins (RLPs) when fed a cholesterol-rich diet, and a 5- to 20-fold increase in atherosclerosis lesion area in the aortic root. Using an in vivo clearance study, we found only slight differences in the triglyceride and apolipoprotein B secretion rates between the 2 groups of mice, suggesting an RLP clearance defect. Using an isolated perfused mouse liver system, we showed that transgenic livers had a slower rate of RLP removal, which was retarded further when KIV(5-8), full-length apo(a), or Lp(a) were added to the perfusate. An apo(a) peptide that does not interact with cells, K(IV2)3, did not retard RLP removal, and low-density lipoprotein (LDL) had a much smaller effect than Lp(a). CONCLUSIONS: We propose that high levels of apo(a)/Lp(a), perhaps acting via a specific cell-surface binding domain, inhibit hepatic clearance of remnants, leading to high plasma levels of RLPs and markedly enhanced atherosclerosis. We speculate that the KIV(5-8) region of apo(a) competes with one or more receptors for remnant clearance in the liver and that this process may represent one mechanism accounting for increased atherosclerosis in humans with high secretion levels of apo(a).
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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 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".