Systematic review of microRNA biomarkers in acute coronary syndrome and stable coronary artery disease
Bibliographic record
Abstract
The aim of this systematic review was to assess dysregulated miRNA biomarkers in coronary artery disease (CAD). Dysregulated microRNA (miRNAs) have been shown to be linked to cardiovascular pathologies including CAD and may have utility as diagnostic and prognostic biomarkers. We compared miRNAs identified in acute coronary syndrome (ACS) compared with stable CAD and control populations. We conducted a systematic search of controlled vocabulary and free text terms related to ACS, stable CAD and miRNA in Biosis Previews (OvidSP), The Cochrane Library (Wiley), Embase (OvidSP), Global Health (OvidSP), Medline (PubMed and OvidSP), Web of Science (Clarivate Analytics), and ClinicalTrials.gov which yielded 7370 articles. Of these, 140 original articles were appropriate for data extraction. The most frequently reported miRNAs in any CAD (miR-1, miR-133a, miR-208a/b, and miR-499) are expressed abundantly in the heart and play crucial roles in cardiac physiology. In studies comparing ACS cases with stable CAD patients, miR-21, miR-208a/b, miR-133a/b, miR-30 family, miR-19, and miR-20 were most frequently reported to be dysregulated in ACS. While a number of miRNAs feature consistently across studies in their expression in both ACS and stable CAD, when compared with controls, certain miRNAs were reported as biomarkers specifically in ACS (miR-499, miR-1, miR-133a/b, and miR-208a/b) and stable CAD (miR-215, miR-487a, and miR-502). Thus, miR-21, miR-133, and miR-499 appear to have the most potential as biomarkers to differentiate the diagnosis of ACS from stable CAD, especially miR-499 which showed a correlation between the level of their concentration gradient and myocardial damage. Although these miRNAs are potential diagnostic biomarkers, these findings should be interpreted with caution as the majority of studies conducted predefined candidate-driven assessments of a limited number of miRNAs (PROSPERO registration: CRD42017079744).
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.006 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".