Platelet/Endothelial Biomarkers in Depressed Patients Treated With the Selective Serotonin Reuptake Inhibitor Sertraline After Acute Coronary Events
Bibliographic record
Abstract
BACKGROUND: Depression after acute coronary syndromes (ACSs) has been identified as an independent risk factor for subsequent cardiac death. Enhanced platelet activation has been hypothesized to represent 1 of the mechanisms underlying this association. Selective serotonin reuptake inhibitors (SSRIs) are known to inhibit platelet activity. Whether treatment of depressed post-ACS patients with SSRIs alters platelet function was not known. Accordingly, we serially assessed the release of established platelet/endothelial biomarkers in patients treated with sertraline vs placebo in the Sertraline AntiDepressant Heart Attack Randomized Trial (SADHART). METHODS AND RESULTS: Plasma samples (baseline, week 6, and week 16) were collected from patients randomized to sertraline (n=28) or placebo (n=36). Anticoagulants, aspirin, and ADP-receptor inhibitors were permitted in this study. Platelet factor 4, beta-thromboglobulin (betaTG), platelet/endothelial cell adhesion molecule-1, P-selectin, thromboxane B2, 6-ketoprostaglandin F1a, vascular cell adhesion molecule-1, and E-selectin were measured by ELISA. Treatment with sertraline was associated with substantially less release of platelet/endothelial biomarkers than was treatment with placebo. These differences attained statistical significance for betaTG (P=0.03) at weeks 6 and 16 and for P-selectin (P=0.04) at week 16. Repeated-measures ANOVA revealed a significant advantage for sertraline vs placebo for diminishing E-selectin and betaTG concentrations across the entire treatment period. CONCLUSIONS: Treatment with sertraline in depressed post-ACS patients is associated with reductions in platelet/endothelial activation despite coadministration of widespread antiplatelet regimens including aspirin and clopidogrel. The antiplatelet and endothelium-protective properties of SSRIs might represent an attractive additional advantage in patients with depression and comorbid coronary artery and/or cerebrovascular disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.001 | 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".