ArticleDiabetology & metabolic syndrome2025
Atrial fibrillation identification in patients with non-alcoholic fatty liver disease: a machine learning model based on immune-inflammatory markers.
Article in Diabetology & metabolic syndrome, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- Exploring the interplay between systemic immune-inflammatory response, nutritional patterns, and metabolic health in MAFLD.Scientific reports · 2026Article
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7 authors.
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Abstract
backgroundThe global prevalence of non-alcoholic fatty liver disease (NAFLD) is continuously rising, making it a significant public health concern. Recent studies indicate that NAFLD is independently associated with an increased risk of atrial fibrillation (AF), potentially mediated by chronic inflammation and immune responses. However, there is currently a lack of AF risk identification tools specifically for the NAFLD population. This study aimed to identify AF and model its association with immune-inflammatory markers in NAFLD patients.
methodsThis study enrolled 723 patients with ultrasound-confirmed NAFLD (AF group: n = 203, non-AF group: n = 520). Clinical data were collected, and 10 immune-inflammatory markers (including Systemic Immune-inflammation Index (SII), Systemic Inflammation Response Index (SIRI), Neutrophil-to-Lymphocyte Ratio (NLR), Platelet-to-Lymphocyte Ratio (PLR), Monocyte-to-HDL Cholesterol Ratio (MHR), etc.) were calculated. Feature selection was performed using univariate logistic regression, LASSO, and the Boruta algorithm. Ten machine learning algorithms were employed to construct models, optimized via 10-fold cross-validation. SHapley Additive exPlanations (SHAP) were used to interpret the model, and the best-performing model was ultimately deployed as an online web calculator.
resultsMultivariate analysis identified SII, NLR, PLR, MHR, and SIRI as independent predictors of AF (all P < 0.05). In the test set, the Support Vector Machine (SVM) model demonstrated the best predictive performance, with an AUC of 0.848 (95% CI: 0.785-0.911). Accuracy, specificity, precision, and F1-score were 0.847, 0.910, 0.745, and 0.713, respectively. SHAP analysis revealed age, PLR, and creatinine as the most influential predictive variables. Based on the final model, we developed a user-friendly online risk calculator.
conclusionThis study constructed and validated a machine learning model integrating multiple immune inflammatory markers for effectively identifying the risk of atrial fibrillation in patients with NAFLD. The SVM model exhibited excellent discriminatory ability and clinical applicability. The developed web tool aids in the identification of high-risk individuals and offers new strategies for preventive interventions against AF.
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