Associations of osteopontin and NT-proBNP with circulating miRNA levels in acute coronary syndrome
Bibliographic record
Abstract
The genomic regulatory networks underlying the pathogenesis of non-ST-segment elevation acute coronary syndrome (NSTE-ACS) are incompletely understood. As intermediate traits, protein biomarkers report on underlying disease severity and prognosis in NSTE-ACS. We hypothesized that integration of dense microRNA (miRNA) profiling with biomarker measurements would highlight potential regulatory pathways that underlie the relationships between prognostic biomarkers, miRNAs, and cardiovascular phenotypes. We performed miRNA sequencing using whole blood from 186 patients from the TRILOGY-ACS trial. Seven circulating prognostic biomarkers were measured: NH 2 -terminal pro-B-type natriuretic peptide (NT-proBNP), high-sensitivity C-reactive protein, osteopontin (OPN), myeloperoxidase, growth differentiation factor 15, monocyte chemoattractant protein, and neopterin. We tested miRNAs for association with each biomarker with generalized linear models and controlled the false discovery rate at 0.05. Ten miRNAs, including known cardiac-related miRNAs 25-3p and 423-3p, were associated with NT-proBNP levels (min. P = 7.5 × 10 −4 ) and 48 miRNAs, including cardiac-related miRNAs 378a-3p, 20b-5p and 320a, -b, and -d, were associated with OPN levels (min. P = 1.6 × 10 −6 ). NT-proBNP and OPN were also associated with time to cardiovascular death, myocardial infarction (MI), or stroke in the sample. By integrating large-scale miRNA profiling with circulating biomarkers as intermediate traits, we identified associations of known cardiac-related and novel miRNAs with two prognostic biomarkers and identified potential genomic networks regulating these biomarkers. These results, highlighting plausible biological pathways connecting miRNAs with biomarkers and outcomes, may inform future studies seeking to delineate genomic pathways underlying NSTE-ACS outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".