ArticleCirculation. Genomic and precision medicine2022
Prediction of Major Adverse Cardiovascular Events in Patients With Hypertrophic Cardiomyopathy Using Proteomics Profiling.
Article in Circulation. Genomic and precision medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 13 citations in OpenAlex.
- Deep Survival Modeling With Echocardiographic Myocardial Texture Radiomics for Prediction of Major Adverse Cardiovascular Events in Hypertrophic Cardiomyopathy.Journal of cardiovascular translational research · 2026Article
- Effect of the geometry and severity of left ventricular hypertrophy on cardiovascular mortality in dialysis patients.Kidney research and clinical practice · 2026Article
- Endothelial Cell-Related Proteins in Plasma Predict Major Adverse Cardiovascular Events and Worsening Heart Failure in Patients With Hypertrophic Cardiomyopathy.Journal of the American Heart Association · 2025Article
- Comprehensive Plasma Proteomic Profiling Reveals Differentially Regulated Signaling Pathways Underlying Left Ventricular Hypertrophy Between Hypertrophic Cardiomyopathy and Aortic Stenosis.Journal of cardiovascular translational research · 2025Article
- Machine learning and multi-omics integration: advancing cardiovascular translational research and clinical practice.Journal of translational medicine · 2025Review
- Cardiac Fibrosis in the Multi-Omics Era: Implications for Heart Failure.Circulation research · 2025Review
- The Role of Signalling Pathways in Myocardial Fibrosis in Hypertrophic Cardiomyopathy.Reviews in cardiovascular medicine · 2025Review
- The design and rationale of the cardiac REHABilitation to improve metabolic health in Hypertrophic CardioMyopathy (REHAB-HCM) Study.American heart journal plus : cardiology research and practice · 2025Article
- Plasma SVEP1 Levels Predict Cardiovascular Events in Hypertrophic Cardiomyopathy Beyond Conventional Clinical Risk Models Including NT-proBNP.Circulation. Heart failure · 2025Article
- Impact of glucose metabolism on myocardial fibrosis and inflammation in hypertrophic cardiomyopathy: a cardiac MR study.Frontiers in endocrinology · 2025Article
- Comprehensive Proteomics Profiling Identifies Circulating Biomarkers to Distinguish Hypertrophic Cardiomyopathy From Other Cardiomyopathies With Left Ventricular Hypertrophy.Circulation. Heart failure · 2025Article
- Comprehensive Proteomic Profiling of Human Myocardium Reveals Signaling Pathways Dysregulated in Hypertrophic Cardiomyopathy.Journal of the American College of Cardiology · 2024Article
- Clinical Implications of a Proteomics-Based Approach for Cardiomyopathy and Heart Failure.Korean circulation journal · 2024Article
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
Abstract
backgroundHypertrophic cardiomyopathy often causes major adverse cardiovascular events (MACE), for example, arrhythmias, stroke, heart failure, and sudden cardiac death. Currently, there are no models available to predict MACE. Furthermore, it remains unclear which signaling pathways mediate MACE. Therefore, we aimed to prospectively determine protein biomarkers that predict MACE in hypertrophic cardiomyopathy and to identify signaling pathways differentially regulated in patients who subsequently develop MACE.
methodsIn this multi-centre prospective cohort study of patients with hypertrophic cardiomyopathy, we conducted plasma proteomics profiling of 4979 proteins upon enrollment. We developed a proteomics-based model to predict MACE using data from one institution (training set). We tested the predictive ability in independent samples from the other institution (test set) and performed time-to-event analysis. Additionally, we executed pathway analysis of predictive proteins using a false discovery rate threshold of <0.001.
resultsThe study included 245 patients (n=174 in the training set and n=71 in the test set). Using the proteomics-based model to predict MACE derived from the training set, the area under the receiver-operating-characteristic curve was 0.81 (95% CI, 0.68-0.93) in the test set. In the test set, the high-risk group determined by the proteomics-based predictive model had a significantly higher rate of developing MACE (hazard ratio, 13.6 [95% CI, 1.7-107];
conclusionsThis study serves as the first to demonstrate the ability of proteomics profiling to predict MACE in hypertrophic cardiomyopathy, exhibiting both novel (eg,
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