Abstract 14841: Whole-genome microRNA Sequencing Reveals Circulating microRNAs as High-Risk Markers in Non-ST-Elevation Acute Coronary Syndrome
Bibliographic record
Abstract
Introduction: MicroRNAs (miRs) have emerged as promising circulating biomarkers in cardiovascular disease (CVD). Given the complex relationship between clinical risk factors and non-ST-elevation acute coronary syndrome (NSTE-ACS) outcomes, understanding the relationship of miRs and high-risk factors may help dissect independent versus mediating effects of miRs on NSTE-ACS outcomes. Hypothesis: There are associations between specific circulating miRs and established clinical risk factors in patients with NSTE-ACS. Methods: Whole-genome miR sequencing was performed on total RNA extracted from whole blood of 199 patients with NSTE-ACS from the TRILOGY-ACS trial with similar baseline characteristics. Generalized linear models were used to test associations between 247 identified miRs and 13 high-risk factors CVD risk factors including atrial fibrillation (AF), Global Registry of Acute Coronary Events (GRACE) score on presentation and chronic heart failure (HF). A false discovery rate of 0.05 was used to correct for multiple comparisons. Results: Overall, 205 risk factor-miR associations were nominally significant (p Conclusions: We identified circulating miRs with expression patterns associated with high-risk factors in NSTE-ACS. MiRs 3135b, 126-5p, 142-5p, 144-5p and miR 28-3p are known mediators of CV development or disease, suggesting their potential role in modulating genomic risk in NSTE-ACS. These miRs may serve as prognostic biomarkers for risk stratification to better predict poor outcomes in patients with NSTE-ACS.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".