Evaluation of Plasma Adenosine as a Marker of Cardiovascular Risk: Analytical and Biological Considerations
Bibliographic record
Abstract
Background Adenosine is a ubiquitous regulatory molecule known to modulate signaling in many cells and processes vital to vascular homeostasis. While studies of adenosine receptors have dominated research in the field, quantification of adenosine systemically and locally remains limited owing largely to technical restrictions. Given the potential clinical implications of adenosine biology, there is a need for adequately powered studies examining the role of plasma adenosine in vascular health. We sought to describe the analytical and biological factors that affect quantification of adenosine in humans in a large, real‐world cohort of patients undergoing evaluation for coronary artery disease. Methods and Results Between November 2016 and April 2018, we assessed 1141 patients undergoing angiography for evaluation of coronary artery disease. High‐performance liquid chromatography was used for quantification of plasma adenosine concentration, yielding an analytical coefficient of variance (CV a ) of 3.2%, intra‐subject variance (CV i ) 35.8% and inter‐subject variance (CV g ) 56.7%. Traditional cardiovascular risk factors, medications, and clinical presentation had no significant impact on adenosine levels. Conversely, increasing age ( P =0.027) and the presence of obstructive coronary artery disease ( P =0.026) were associated with lower adenosine levels. Adjusted multivariable analysis supported only age being inversely associated with adenosine levels ( P =0.039). Conclusions Plasma adenosine is not significantly impacted by traditional cardiovascular risk factors; however, advancing age and presence of obstructive coronary artery disease may be associated with lower adenosine levels. The degree of intra‐ and inter‐subject variance of adenosine has important implications for biomarker use as a prognosticator of cardiovascular outcomes and as an end point in clinical studies.
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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 | 0.004 | 0.013 |
| 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) | 0.000 | 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".