Comprehensive plasma metabolites profiling reveals phosphatidylcholine species as potential predictors for cardiac resynchronization therapy response. (19th November 2020) - Yang, Shengwen; Hu, Yiran; Zhao, Junhan; Jing, Ran; Wang, Jing; Gu, Min; Niu, Hongxia; Chen, Liang; Hua, Wei
Abstract: Aims: This study aimed to identify the plasma metabolite fingerprint in patients with heart failure and to develop a prediction tool based on differential metabolites for predicting the response to cardiac resynchronization therapy (CRT). Methods and results: We prospectively recruited 32 healthy individuals and 42 consecutive patients with HF who underwent CRT between January 2018 and January 2019. Peripheral venous blood samples, clinical data, and echocardiographic signatures were collected before CRT implantation. Liquid chromatography‐mass spectrometry was used to perform untargeted metabolites profiling for peripheral plasma under ESI+ and ESI− modes. After 6 month follow‐up, patients were categorized as CRT responders or non‐responders based on the alterations of echocardiographic characteristics. Compared with healthy individuals, patients with HF had distinct metabolomic profiles under both ESI+ and ESI− modes, featuring increased free fatty acids, carnitine, β‐hydroxybutyrate, and dysregulated lipids with heterogeneous alterations such as phosphatidylcholines (PCs) and sphingomyelins. Disparities of baseline metabolomics profile were observed between CRT responders and non‐responders under ESI+ mode but not under ESI− mode. Further metabolites analysis revealed that a group of 20 PCs metabolites under ESI+ mode were major contributors to the distinct profiles between the two groups. We utilized LASSO regression model and identified a panel of four PCs metabolites [including PC (20:0/18:4), PC (20:4/20:0), PC 40:4, and PC (20:4/18:0)] as major predictors for CRT response prediction. Among our whole population ( n  = 42), receive operating characteristics analysis revealed that the four PCs‐based model could nicely discriminate the CRT responders from non‐responders (area under the curve = 0.906) with a sensitivity of 83.3% and a specificity of 90.0%. Cross‐validation analysis also showed a satisfactory and robust performance of the model with the area under the curve of 0.910 in the training dataset and 0.880 in the testing dataset. Conclusions: Patients with HF held significantly altered plasma metabolomics profile compared with the healthy individuals. Within the HF group, the non‐responders had a distinct plasma metabolomics profile in contrast to the responders to CRT, which was characterized by increased PCs species. A novel predictive model incorporating four PCs metabolites performed well in identifying CRT non‐responders. These four PCs might severe as potential biomarkers for predicting CRT response. Further validations are needed in multi‐centre studies with larger external cohorts.