Identification of Biomarkers for Right Ventricular Dysfunction in Idiopathic Dilated Cardiomyopathy Via Urinary Proteomics and Machine Learning

作者：Anhu Wu, Yufei Wang, Zhengguang Guo, Jiaqi Yu, Keyi Mei, Jing Zhang, Xiaohan Qin, Yuhan Qin, Xiaoxiao Guo · 发表于：Journal of the American Heart Association · 年份：2025 · DOI：10.1161/jaha.125.044254 · 被引用次数：2 · 研究领域：Pulmonary Hypertension Research and Treatments、Cardiac Fibrosis and Remodeling、Cardiovascular Effects of Exercise

Background Right ventricular dysfunction (RVD) is a common complication of idiopathic dilated cardiomyopathy linked to poor outcomes. However, reliable noninvasive biomarkers for RVD remain lacking. This study aimed to identify urinary proteomic markers using mass spectrometry and machine learning. Methods In this prospective cohort, patients with idiopathic dilated cardiomyopathy were classified by cardiac magnetic resonance imaging into groups with RVD (RV ejection fraction <45%) and without RVD groups. Baseline urine samples were profiled by data‐independent acquisition mass spectrometry. Differentially expressed proteins were identified and selected by least absolute shrinkage and selection operator regression to build a diagnostic model, developed in a training set, and validated in a test set. The primary end point was a composite of cardiovascular death, heart failure rehospitalization, left ventricular assist device implantation, or heart transplantation. Results The study enrolled 147 patients with idiopathic dilated cardiomyopathy (64 with RVD, 83 without), with a median follow‐up of 19.3 months. Of 3579 quantified urinary proteins, 46 were differentially expressed between groups. A 3‐protein panel (RARRES1 [retinoic acid receptor responder protein 1], MVB12B [multivesicular body subunit 12B], GSK3A [glycogen synthase kinase 3 alpha]) was identified and showed excellent diagnostic accuracy (training area under the curve 0.946; validation area under the curve0.935...