Effect of Endothelial Adhesion Molecules on Atrial Fibrillation: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background: Endothelial adhesion molecules (EAMs), and more specifically vascular cell adhesion molecule-1 (VCAM-1) and intercellular adhesion molecule-1 (ICAM-1), belong to a family of immunoglobulin-like molecules and are found to have increased expression in inflamed microvessels. Due to the growing evidence regarding EAM effects on cardiovascular diseases, we aimed to investigate the link between EAMs and atrial fibrillation (AF) to discover the efficacy of EAMs assessment as predictive markers in high-risk patients. Methods: We searched for articles published from January 1990 to April 2022. Two independent researchers selected studies that examined the relationship between VCAM-1 and ICAM-1 levels and AF. Study design, patient characteristics, VCAM-1 and ICAM-1 levels, and measurement methods were extracted from the selected articles. Results: Of 181 records, 22 studies were finally included in the systematic review. Meta-analyses showed a significant difference in serum levels of EAMs in patients with AF compared with patients with sinus rhythms (VCAM-1: mean difference [MD] 86.782, 95% CI 22.805–150.758, p=0.008; ICAM-1: MD 28.439 ng/mL, 95% CI 12.540–44.338, p<0.001). In subgroup analysis of persistent AF, the differences were still significant (VCAM-1: MD 98.046, 95% CI 26.582–169.510, p=0.007; ICAM-1: MD 25.091, 95% CI 12.952–37.230, p<0.001). We also found the mean ranges of VCAM-1 (95% CI 661.394–927.984 ng/mL) and ICAM-1 (95% CI 190.101–318.169 ng/mL) in patients with AF. Conclusion: This study suggests a positive association between serum levels of VCAM-1 and ICAM-1 with AF, but there is a need for further large-scale studies.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.017 | 0.026 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".