Lipoprotein (a) integrates monocyte-mediated thrombosis and inflammation in atherosclerotic cardiovascular disease
Bibliographic record
Abstract
Elevated levels of lipoprotein (a) [Lp(a)], an apolipoprotein B particle, are causally linked to atherosclerotic cardiovascular disease (ASCVD). Lp(a) is thought to promote ASCVD through multiple mechanisms, including its effects on cholesterol transport, inflammation, and thrombosis. This study defines the mechanisms that integrate Lp(a)-mediated cholesterol accumulation, inflammation, and thrombosis. In this study, we employed systems biology approaches, including proteomics, transcriptomics, and mass cytometry, to define the immune cellular and molecular phenotypes in patients with ASCVD having high and low Lp(a) levels and the molecular mechanisms through which Lp(a) mediates monocyte-driven inflammation and thrombosis. In 64 stable patients with ASCVD (41 with high Lp(a) [median Lp(a) 228.7 nmol/L] and 23 with low Lp(a) [median Lp(a) 17.8 nmol/L]), we found that circulating markers of inflammation (CCL28, IL-17D) and vascular dysfunction (tissue factor [TF]; 6.4 vs. 5.7 normalized protein expression (NPX); P = 0.01) were elevated in patients with high Lp(a) levels compared with those with low Lp(a) levels. Although total monocyte and hsCRP levels were similar between the groups, CD14+ monocytes from patients with ASCVD having an elevated Lp(a) were primed and expressed more TF at baseline and in response to stress. Mechanistically, we found that Lp(a) itself can activate monocytes through Toll-like receptor 2 (TLR2) and nuclear factor kappa B (NFκB) signaling, driving both the induction of TF and TF activity. Overall, these studies are the first to link Lp(a) to monocyte-mediated inflammation and thrombosis. This study demonstrates a novel mechanism through TLR2, NFκB, and monocyte TF by which Lp(a) amplifies immunothrombotic risk.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".