Circulating Levels of Calprotectin as a Biomarker in Patients With Coronary Artery Disease: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
BACKGROUND: Calprotectin, also known as MRP8/14, is generated by immune cells and is altered in several inflammatory diseases. Studies have assessed their levels in patients with coronary artery disease (CAD) and its subtypes (stable CAD and acute coronary syndrome [ACS]). Herein, we aimed to systematically investigate these associations through a systematic review and meta-analysis. METHODS: A systematic search was conducted in four online databases, including PubMed, Scopus, Embase, and the Web of Science. Relevant studies were retrieved, screened, and extracted. Random-effect meta-analysis was performed for the calculation of standardized mean difference (SMD) and 95% confidence interval (CI). Blood calprotectin levels were compared between CAD patients and controls, as well as CAD subtypes. RESULTS: A total of 20 studies were included in the systematic review and meta-analysis, comprising 3300 CAD patients and 1230 controls. Patients with CAD had significantly higher calprotectin levels (SMD 0.81, 95% CI 0.32-1.30, p < 0.01). Similarly, patients with ACS were reported to have higher levels compared to those with stable CAD. However, there was no significant difference in terms of blood calprotectin levels between stable CAD cases and healthy controls. Finally, studies have shown that calprotectin could be used as a diagnostic biomarker of CAD while also predicting major adverse events and mortality in these patients. CONCLUSION: Based on our findings, calprotectin, as an inflammatory marker, could be used as a possible biomarker for patients with CAD and ACS. These suggest the possibility of pathophysiological pathways for this involvement and warrant further research on these associations as well as their clinical utility.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.029 |
| Bibliometrics | 0.006 | 0.008 |
| 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".