• Yunxiao Yang, Zhiyong Du, Meng Fang, Ying Ma, Yuhua Liu, Tianguang Wang, Zhongyi Han, Zhan Peng, Yilin Pan, Haokai Qin, Yanwen Qin, Yong Jiang, Pengfei Tu, Xiaoyu Guo, Yingyuan Lu, Xiubin Yang, and Kun Hua.
• Beijing Institute of Heart, Lung and Blood Vessel Diseases, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
• Transl Res. 2023 Jun 1; 256: 304030-40.

AbstractPostoperative atrial fibrillation (POAF) is a common complication of coronary artery bypass grafting (CABG) procedures. However, the molecular mechanism of POAF remains poorly understood, hence the absence of effective prevention strategies. Here we used targeted metabolomics on pericardial fluid and serum samples from CABG patients to investigate POAF-associated metabolic alterations and related risk prediction of new-onset AF. Nine differential metabolites in various metabolic pathways were found in both pericardial fluid and serum samples from patients with POAF and without POAF. By using machine learning algorithms and regression models, a 4-metabolite (aceglutamide, ornithine, methionine, and arginine) risk prediction model was constructed and showed accurate performance in predicting POAF in both discovery and validation sets. This work extends the metabolic insights of the cardiac microenvironment and blood in patients with POAF and paves the way for the use of targeted metabolomics for predicting POAF in patients with CABG surgery.Copyright © 2023 Elsevier Inc. All rights reserved.

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