{"doi":"10.1093/europace/euae267","title":"Prediction of new-onset atrial fibrillation in patients with hypertrophic cardiomyopathy using plasma proteomics profiling","abstract":"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.","journal":"EP Europace","year":2024,"id":449452,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":5,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9471,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2024-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":554852,"name":"Nina Harano","orcid":null,"position":1,"is_corresponding":false},{"id":838151,"name":"Lusha W. Liang","orcid":"0000-0003-3931-4085","position":2,"is_corresponding":false},{"id":242824,"name":"Kohei Hasegawa","orcid":"0000-0002-5739-7999","position":3,"is_corresponding":false},{"id":104002,"name":"Mathew S. Maurer","orcid":"0000-0001-5400-5008","position":4,"is_corresponding":false},{"id":982698,"name":"Albree Tower‐Rader","orcid":null,"position":5,"is_corresponding":false},{"id":704957,"name":"Michael A. Fifer","orcid":"0000-0002-8363-445X","position":6,"is_corresponding":false},{"id":75509,"name":"Muredach P. Reilly","orcid":"0000-0002-3035-9386","position":7,"is_corresponding":false},{"id":594621,"name":"Yuichi J. Shimada","orcid":"0000-0002-3494-307X","position":8,"is_corresponding":false},{"id":1262107,"name":"Heidi Hartman","orcid":"0000-0002-5154-0221","position":0,"is_corresponding":true}],"reference_count":58,"raw_metadata":null,"created_at":"2026-07-19T02:02:16.291892Z","pmid":"39441047","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}