PeerJ. 2026 Aug 6;14:e21628. doi: 10.7717/peerj.21628. eCollection 2026.
ABSTRACT
BACKGROUND: Atrial fibrillation (AF), one of the most common cardiac arrhythmias worldwide, carries a high risk of severe complications. Patients diagnosed with paroxysmal atrial fibrillation (PAF) may progress to persistent atrial fibrillation (PerAF) following a period of time. This study aimed to identify metabolites associated with PerAF using machine learning (ML) and predict the probability of PAF progressing to PerAF, enabling the adjustment of subsequent treatments in clinical practice.
METHODS: AF patients without any anticoagulant therapy within the last 7 days were enrolled between July 2020 and July 2022 at two hospitals in China. Targeted metabolomic profiling was performed on the participating patients' plasma. Differential metabolites and clinical features were identified through univariate and multivariate analyses. Patients were divided into discovery and validation cohorts (70%:30%). Four ML models (logistic regression, random forest, XGBoost, and LightGBM) were developed for PerAF prediction. Model predictive performance was measured mainly using the area under the curve (AUC).
RESULTS: One hundred patients (65 PerAF, 35 PAF) were enrolled. Eight metabolites (kynurenine, N-A cetylaspartic acid, glyceric acid, adipic acid, citramalic acid, malic acid, isocitric acid, and oxoglutaric acid) and two clinical features (NT-proBNP and uric acid) were significantly associated with PerAF. Pathway enrichment analysis highlighted alterations in the citrate cycle and glyoxylate/dicarboxylate metabolism. XGBoost was chosen for establishing the final model since its predictive performance outperformed that of the other algorithms. The model based on clinical parameters, metabolites, and demographics achieved the highest AUC in both the discovery cohort (0.751 (95% CI [0.631~0.867])) and validation cohort (0.985 (95% CI [0.940~1.000])). A simplified model with three features (NT-proBNP, citramalic acid, and uric acid) retained robust performance.
CONCLUSIONS: This study identified eight PerAF-related metabolites via targeted metabolomics and ML, and developed accurate predictive models (including a simplified, clinically feasible model) with favorable predictive performance for PerAF risk stratification. Future directions should include large-scale multi-center external validation, comprehensive adjustment for potential confounding factors, and the application of multiple metabolic platforms to deeply explore AF-related metabolic alterations.
PMID:42572653 | PMC:PMC13453139 | DOI:10.7717/peerj.21628