The purpose of this study is to establish a powerful diagnostic and prognostic model based on metabolites in CAD patients.A total of 821 hospitalized patients with CAD and 200 healthy volunteers are enrolled.Specifically, 200 CAD patients from single-centre are regarded as discovery set, 300 CAD patients from multicentre are regarded as validation set. A third independent set including 321patitents is regarded as application set. Untargeted and targeted metabolomics analysis about serum and urine samples in all subjects will be performed using high-performance liquid chromatography coupled with mass spectrometry technology. Univariate and multivariate analysis methods are used to extract and analyze the differential mebabolites. By exploring the relationship between the differential mebabolites and clinical manifestions, a set of diagnostic biomarkers for CAD will be identified. By elucidating the correlation between the differential mebabolites and MACEs, a effective prognostic model will be established. Adverse events are defined as the combined endpoints of death or major adverse cardiovascular events(MACEs) in patients with CAD for at least 1 year follow-up after discharge. In brief, we aim to establish valuabe diagnostic and predictive models based on novel baseline metabolites in patients with CAD.
This study sought to assess the diagnostic and prognostic values of metabolomics in coronary artery disease(CAD).