Abstract 18621: Platelet Aggregation is Heightened in Those With Metabolic Syndrome and Less Effectively Mediated by Aspirin Therapy
Bibliographic record
Abstract
Background: Patients with metabolic syndrome (MetS) are at a higher risk of developing cardiovascular disease (CVD); however, interventions to mitigate this risk are not fully known. Platelets play a significant role in atherothrombosis, and aspirin is an important, yet controversial therapy used for the primary prevention of CVD. In this study, we examined platelet phenotype in MetS and degree of inhibition from ex vivo aspirin administration. Methods: In a study examining the mechanisms of atherothrombosis in pro-inflammatory conditions, patients without clinical CVD were enrolled and underwent phenotyping. Patients with MetS (n = 34, age 53.3 ± 13 years, 59% male) were compared to patients with no MetS (n = 63, age 41.1 ± 16 years, 52% male). Platelet aggregation of freshly isolated platelets was measured via light transmission aggregometry (LTA) in resting and stimulated conditions, in response to agonists adenosine diphosphate (ADP), high and low dose of arachidonic acid (AA), and after ex vivo aspirin (ASA) administration. Results: MetS patients were older and similar in sex and race (Figure 1A). As expected, patients with MetS had higher individual components of MetS, yet similar LDL-C (Figure 1A). Platelet aggregation to low dose ADP (p=0.09) and AA (p=0.04) was significantly increased in MetS (Figure 1B). Platelet aggregation in response to AA and coincubation with ASA was significantly lower in the no MetS cohort (Figure 1C). The efficacy of aspirin therapy was significantly lower in MetS (Figure 1D). In response to high dose AA after ASA treatment, platelet aggregation increased as number of MetS parameters also increased (Figure 1E). However, no individual metabolic syndrome parameter appeared to be driving these findings (Figure 1E). Conclusion: Among MetS patients, platelet aggregation is heightened and less inhibited by aspirin than those with no MetS. Additional research is warranted to explore possible anti-platelet strategies in patients with MetS.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".