The complex pro-atherosclerotic role of lipoprotein(a): a multiplicity of cellular targets
Bibliographic record
Abstract
PURPOSE OF REVIEW: Elevated plasma lipoprotein(a) (Lp(a)) is a causal and independent risk factor for atherosclerotic cardiovascular disease; therefore, understanding the fundamental mechanisms underlying Lp(a)-mediated pathogenesis is of significant clinical importance. This review summarizes recent advances in understanding the precise cellular targets of Lp(a) in atherogenesis, uncovering potential therapeutic avenues worth exploring. RECENT FINDINGS: Genetic evidence reveals that Lp(a) is six-fold more atherogenic per particle than LDL, and clinical imaging studies show increased atherosclerotic plaque burden and severity in patients with elevated Lp(a). A novel study using human monocytes uncovered diacylglycerols and lysophosphatidic acid as lipid species that contribute to the pro-inflammatory impacts of Lp(a), independent of the known pro-inflammatory oxidized phospholipids. The identification of a novel cell-surface receptor on endothelial cells involved in Lp(a) uptake offers another exploratory direction in vascular cells involved in atherosclerosis. Several studies have also pointed to accelerated coagulation as a potential target of Lp(a), involving Lp(a)-mediated impacts on platelet aggregation and monocyte tissue factor expression. SUMMARY: An understanding of these cell-specific targets of Lp(a) in atherogenesis will aid the Lp(a) field in identifying novel therapeutic targets for patients with elevated Lp(a), for whom few available therapeutic strategies currently exist.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".