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

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 signalling pathways. METHODS AND RESULTS: In this prospective, multi-centre cohort study, we conducted plasma proteomics profiling of 4986 proteins at enrolment. We developed a proteomics-based machine learning 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. 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 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 signalling pathways.