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Updated: May 26, 2026
Estimating Bilateral Atrial Function by Cardiovascular Magnetic Resonance Feature Tracking in Patients with Paroxysmal Atrial Fibrillation
Published on: July 20, 2022
A risk model based on miR-483-5p and miR-150 for atrial fibrillation recurrence
Wenwen Lai1, Hong Chen1, Mingwei Huang1
1Department of Cardiovascular Medicine, Second Affiliated Hospital of Fujian Medical University, Quanzhou, 362000 China.
None:
Atrial Fibrillation (AF) is a common arrhythmia with high recurrence after Radiofrequency Ablation, and the absence of universal predictive tools impedes clinical prognosis evaluation and treatment optimization. To address this gap, we developed a retrospective microRNA (miRNA)-based recurrence risk stratification model for post-RFA AF patients using an integrated bioinformatics and clinical validation approach. First, we performed differential expression analysis of two Gene Expression Omnibus (GEO) datasets (GSE144384, GSE71963) via GEO2R and R software (version 4.2.1, Limma package) with strict thresholds (|logFC| > 1.5, P < 0.05) identified upregulated miR-483-5p and downregulated miR-150 as core biomarkers. This finding was cross-validated across both datasets (P < 0.001). Subsequently, we enrolled 122 AF patients after RFA and 60 patients with sinus rhythm as controls, and we measured plasma miRNA levels by Quantitative real-time Polymerase Chain Reaction and left atrial functional/structural parameters via color Doppler ultrasound. Receiver Operating Characteristic curve analysis determined optimal cut-off values (miR-483-5p: 1.025; miR-150: 0.805) for constructing a three-tier recurrence risk stratification model. Validation during 12-month follow-up confirmed the model, showing gradient recurrence rates of 69.05%, 24.14% and 4.55% across high/medium/low-risk groups (P < 0.001). The combined Area Under the Curve for predictive performance was 0.888 (superior to that of single biomarkers). We propose a reproducible miRNA-driven predictive framework integrating public omics mining and clinical validation, providing an engineering-based tool for early identification of high-risk post-RFA patients and guidance for individualized treatment. The framework is extendable to other arrhythmias, laying a foundation for translational research of miRNA-based predictive models.
Supplementary Information:
The online version contains supplementary material available at 10.1007/s13205-026-04842-8.
