Prediction of new-onset atrial fibrillation in patients with hypertrophic cardiomyopathy using plasma proteomics profiling

Abstract

Background and Aims Atrial fibrillation (AF) is the most common sustained arrhythmia among patients with hypertrophic cardiomyopathy (HCM), increasing symptom burden and stroke risk. We aimed to construct a plasma proteomics-based model to predict new-onset AF in patients with HCM and determine dysregulated signaling pathways. Methods In this prospective, multi-center cohort study, we conducted plasma proteomics profiling of 4,986 proteins at enrollment. We developed a proteomics-based machine learning (ML) model to predict new-onset AF using samples from one institution (training set) and tested its predictive ability using independent samples from another institution (test set). We performed a survival analysis to compare the risk of new-onset AF among high- and low-risk groups in the test set. We performed pathway analysis of proteins significantly (univariable p<0.05) associated with new-onset AF using a false discovery rate (FDR) threshold of 0.001. Results The study included 284 patients with HCM (training set: 193, test set: 91). Thirty-seven (13%) patients developed AF during median follow-up of 3.2 years [25–75 percentile: 1.8-5.2]. Using the proteomics-based prediction model developed in the training set, the area under the receiver-operating-characteristic curve (AUC) was 0.89 (95% confidence interval 0.78-0.99) in the test set. In the test set, patients categorized as high-risk had a higher rate of developing new-onset AF (log-rank p=0.002). The Ras-MAPK pathway was dysregulated in patients who developed incident AF during follow-up (FDR<1.0×10-6). Conclusion This is the first study to demonstrate the ability of plasma proteomics to predict new-onset AF in HCM and identify dysregulated signaling pathways.