Abstract 505: Circulating Monocyte-Platelet Aggregates are Different Across Phenotypes of Vascular Disease
Bibliographic record
Abstract
Background: Platelets are a major culprit in the pathogenesis of cardiovascular disease (CVD). Although vascular disease in different arterial beds share common risk factors, the role of platelet activity may differ by vascular bed. Clinical trials suggest that more potent antiplatelet therapy is needed in lower extremity peripheral artery disease (PAD) than coronary artery disease (CAD). We investigated circulating monocyte platelet aggregates (MPA), a reproducible ex vivo measurement of platelet activity, in patients with CVD (PAD, CAD, carotid artery stenosis [CAS], and abdominal aortic aneurysm [AAA]) and disease controls. Methods: Subjects with CVD (n=351) and disease controls (n=73) had MPA measured using strict quality control, all of whom were on aspirin monotherapy. MPA were identified by CD14/CD61 positivity. Data is represented as median (IQR, interquartile range). Wilcoxon rank sum, Wilcoxon signed rank, and multivariable linear regression were used to analyze data. Results: Platelets aggregated with monocytes had a higher platelet surface expression of PAC-1, P-selectin, and CD40 versus platelets not aggregated with monocytes (P<0.05 for each comparison). Compared with controls, MPA was significantly higher in CVD (14.5 [10.3, 27.8] vs. 9.4 [8.2, 11.5], P<0.001) which remained significant after multivariable adjustment for demographics and risk factors (β=9.1 (SER=3.9), P=0.02). For each vascular disease phenotype, MPA was higher than controls ( Figure ). In a multivariable linear regression model including demographics, risk factors and each vascular bed, PAD was the only vascular disease associated with a higher value of MPA (β=10.2 (SER=2.4), P<0.001). Conclusions: Platelets aggregated with monocytes have increased platelet activity and are significantly elevated in subjects with PAD. Future studies understanding the platelet profile in PAD and its clinical relevance are needed.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".