Clinical and prognostic value of biomarkers in patients with non-obstructive coronary artery disease: a systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Cardiovascular disease (CVD) remains the leading cause of premature mortality worldwide. Among its manifestations, ischemia with non-obstructive coronary arteries (INOCA) and myocardial infarction with non-obstructive coronary arteries (MINOCA) are increasingly recognized and associated with major adverse cardiovascular events. While biomarkers are established tools for diagnosing and predicting outcomes in CVD, their role in INOCA and MINOCA remains unclear. This review summarizes current evidence on cardiovascular biomarkers and their clinical relevance in the context of INOCA and MINOCA. METHODS: A systematic review of original studies was conducted using Ovid-MedLine and Embase databases. Eligible studies included adult patients diagnosed with INOCA or MINOCA, with measurements of specific serum biomarkers. RESULTS: Of 1,493 records identified, 53 were included in the quantitative analysis, encompassing 10 biomarkers. Among inflammatory markers, only C-reactive protein was significantly higher in INOCA patients compared to healthy controls. Metabolic, coagulation and endothelial biomarkers showed no differences. Limited data in the MINOCA population precluded comprehensive biomarker analysis. CONCLUSION: Elevated biomarkers of inflammation in INOCA suggest underlying mechanisms such as oxidative stress, cytokine activation, and immune-mediated microvascular dysfunction. Their diagnostic and prognostic potential in INOCA remains promising but requires further validation in clinical studies.
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 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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.023 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".