Abstract 485: Platelet Aggregation Is Increased In Psoriasis And Associated With Biomarkers Of Vascular Health
Bibliographic record
Abstract
Background: Psoriasis is associated with vascular inflammation and increased cardiovascular (CV) risk. Platelet hyperactivity is implicated in impaired vascular health and CV disease (CVD). The association between platelet activity, psoriasis, and vascular health has not been fully explored. Objective: To measure platelet aggregation in patients with psoriasis and controls and explore the association with biomarkers of inflammation and vascular stiffness. Methods: Participants with psoriasis (n=33, age 51 ± 16 years, 61% male), affecting 7% ± 14 of their body surface area (BSA), were compared to healthy controls (n=15) (Table 1A). Platelet aggregation in response to adenosine diphosphate (ADP) agonists, epinephrine (Epi), and arachidonic acid (AA) was measured via light transmission aggregometry (LTA). Vascular stiffness was assessed by pulse wave velocity (PWV), a metrics of arterial stiffness and biomarker of CV risk. High-sensitivity C-reactive protein (hs-CRP) was measured in a clinical laboratory. Results: Enrolled psoriasis participants were older, were more frequently white, and had higher body mass index and elevated hs-CRP and PWV compared to controls (Table 1A). Platelet aggregation was higher in psoriasis patients after stimulation with ADP (p=0.01), Epi (p=0.05), and AA with ex vivo aspirin co-incubation (p=0.04) when compared with controls in multivariable models. Platelet aggregation to ADP trended to positive association with PWV (r=0.25, p=0.08), and hs-CRP (r=0.29, p=0.06) after adjustment for age and sex (Figure 1C). Conclusion: Platelet aggregation was increased in participants with psoriasis and trended towards positive association with biomarkers of vascular stiffness and inflammation. These findings have important implications for future clinical trials of targeting platelet activity to reduce CV risk in psoriasis.
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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 | 0.001 | 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 | 0.001 | 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".