Performance of Plasma Adenosine as a Biomarker for Predicting Cardiovascular Risk
Bibliographic record
Abstract
Adenosine boasts promising preclinical and clinical data supporting a vital role in modulating vascular homeostasis. Its widespread use as a diagnostic and therapeutic agent have been limited by its short half-life and complex biology, though adenosine-modulators have shown promise in improving vascular healing. Moreover, circulating adenosine has shown promise in predicting cardiovascular (CV) events. We sought to delineate whether circulating plasma adenosine levels predict CV events in patients undergoing invasive assessment for coronary artery disease. Patients undergoing invasive angiography had clinical data prospectively recorded in the Cardiovascular and Percutaneous ClInical TriALs (CAPITAL) revascularization registry and blood samples collected in the CAPITAL Biobank from which adenosine levels were quantified. Tertile-based analysis was used to assess prediction of major adverse cardiovascular events (MACE; composite of death, myocardial infarction, unplanned revascularization, and cerebrovascular accident). Secondary analyses included MACE subgroups, clinical subgroups and adenosine levels. There were 1,815 patients undergoing angiography who had blood collected with adenosine quantified in 1,323. Of those quantified, 51.0% were revascularized and 7.3% experienced MACE in 12 months of follow-up. Tertile-based analysis failed to demonstrate any stratification of MACE rates (log rank, P = 0.83), when comparing low-to-middle (hazard ratio (HR) 1.10, 95% confidence interval (CI) 0.68-1.78, P = 0.70) or low-to-high adenosine tertiles (HR 0.95, 95% CI 0.56-1.57, P = 0.84). In adjusted analysis, adenosine similarly failed to predict MACE. Finally, adenosine did not predict outcomes in patients with acute coronary syndrome nor in those revascularized or treated medically. Plasma adenosine levels do not predict subsequent CV outcomes or aid in patient risk stratification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".