In Translational research : the journal of laboratory and clinical medicine
Post-operative 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 four-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.
Yang Yunxiao, Du Zhiyong, Fang Meng, Ma Ying, Liu Yuhua, Wang Tianguang, Han Zhongyi, Peng Zhan, Pan Yilin, Qin Haokai, Qin Yanwen, Jiang Yong, Tu Pengfei, Guo Xiaoyu, Lu Yingyuan, Yang Xiubin, Hua Kun
2023-Jan-10
coronary artery bypass grafting, pericardial fluid, post-operative atrial fibrillation, targeted metabolomics
