The metabolic-inflammatory axis in chronic heart failure: integrating the NLR and TyG index for recent-onset atrial fibrillation prediction via machine learning and mediation analysis.
C, Y., X, L., R, L., D, S., K, Y., L, Y., H, Q., B, W., X, H., & J, Y. (2026). The metabolic-inflammatory axis in chronic heart failure: integrating the NLR and TyG index for recent-onset atrial fibrillation prediction via machine learning and mediation analysis.. Frontiers in endocrinology. https://doi.org/10.3389/fendo.2026.1831904
C Y, X L, R L, D S, K Y, L Y, et al. The metabolic-inflammatory axis in chronic heart failure: integrating the NLR and TyG index for recent-onset atrial fibrillation prediction via machine learning and mediation analysis.. Frontiers in endocrinology. 2026; doi: 10.3389/fendo.2026.1831904
C Y, X L, R L, et al. The metabolic-inflammatory axis in chronic heart failure: integrating the NLR and TyG index for recent-onset atrial fibrillation prediction via machine learning and mediation analysis.[J]. Frontiers in endocrinology. 2026. DOI: 10.3389/fendo.2026.1831904.
@article{c2026,
author = {Yao C and Liu X and Liu R and Su D and Yang K and Yao L and Qiu H and Wang B and Hou X and Yao J},
title = {The metabolic-inflammatory axis in chronic heart failure: integrating the NLR and TyG index for recent-onset atrial fibrillation prediction via machine learning and mediation analysis.},
journal = {Frontiers in endocrinology},
year = {2026},
doi = {10.3389/fendo.2026.1831904},
note = {PMID: 42657346},
}
TY - JOUR AU - Yao C AU - Liu X AU - Liu R AU - Su D AU - Yang K AU - Yao L AU - Qiu H AU - Wang B AU - Hou X AU - Yao J TI - The metabolic-inflammatory axis in chronic heart failure: integrating the NLR and TyG index for recent-onset atrial fibrillation prediction via machine learning and mediation analysis. T2 - Frontiers in endocrinology PY - 2026 DO - 10.3389/fendo.2026.1831904 AN - PMID:42657346 ER -
INTRODUCTION: Recent-onset atrial fibrillation (AF) is a common complication of chronic heart failure (CHF), potentially involving both inflammatory and metabolic dysregulation. This study aimed to develop and externally validate an interpretable machine learning (ML) model for predicting recent-onset AF in patients with CHF and to explore the relationships among inflammatory dysregulation, metabolic dysregulation, and recent-onset AF. METHODS: In this retrospective multicenter study, 4,872 hospitalized patients with CHF from Guang'anmen Hospital and Xiyuan Hospital were included, with external validation performed in 277 additional patients. Demographic, clinical, and laboratory variables, including the triglyceride-glucose (TyG) index and neutrophil-to-lymphocyte ratio (NLR), were analyzed. Nine ML algorithms were compared for AF prediction. Model interpretability was assessed using SHapley Additive exPlanations (SHAP). Segmented regression was used to examine nonlinear threshold effects, and mediation analysis was performed to explore the immunometabolic pathway linking TyG, NLR, and AF. RESULTS: The Extra Trees model performed best, achieving an area under the receiver operating characteristic curve of 0.853 in the training cohort and 0.766 in the external validation cohort. NLR showed a nonlinear association with AF risk, with a steeper increase below 4.24, whereas TyG showed a threshold-dependent J-shaped relationship, with the risk increasing significantly above 5.91. The combination of TyG and NLR further improved model discrimination. Mediation analysis suggested that the estimated indirect association through NLR accounted for a substantial proportion of the observed association between TyG and AF. CONCLUSIONS: An interpretable ML framework identified the immune-metabolic axis as an important predictive component of recent-onset AF in CHF. NLR and TyG are inexpensive, clinically accessible biomarkers that may improve early risk stratification and identify a high-risk phenotype characterized by concurrent metabolic stress and inflammation. Trial registration: ChiCTR (ITMCTR2025001576).