Abstract 11904: Lipoprotein(a) Integrates Monocyte-Mediated Thrombosis and Inflammation in Atherosclerotic Cardiovascular Disease
Bibliographic record
Abstract
Introduction: Lipoprotein (a) [Lp(a)] drives atherosclerotic cardiovascular disease (ASCVD) through multiple mechanisms including cholesterol accumulation, inflammation, and thrombosis. However, the effects of Lp(a) on monocyte activation and monocyte-mediated thrombosis are unknown. Hypothesis: Lp(a) itself can activate monocytes, driving inflammation and thrombosis in parallel. Methods: We employed systems biology approaches consisting of proteomics, transcriptomics, and mass cytometry to define the immune cellular and molecular phenotypes in ASCVD subjects with high and low Lp(a) levels and leveraged cell models to define the mechanisms through which Lp(a) drives monocyte inflammation and thrombosis. We included study participants with CHD who were aged 18 to 80 years with Lp(a) concentrations > 150 nmol/L (cases) and <75 nmol/L (controls). Results: Circulating markers of inflammation (CCL3, CD40) and vascular dysfunction (PAR1, tissue factor [TF]) were elevated in subjects with high Lp(a) levels compared with those with low Lp(a) levels. Additionally, although total monocytes levels and hs-CRP levels were similar between the groups, CD14+ monocytes from ASCVD subjects with 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 receptors (TLR) and nuclear factor kappa B (NFκB) signaling, driving both TF expression and TF activity (Figure). Conclusions: Overall, these studies are the first to link Lp(a) to monocyte-mediated inflammation and thrombosis in ASCVD subjects, demonstrating a novel mechanism through TLR2, NFκB, and monocyte TF through which Lp(a) amplifies immunothrombotic risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".