ArticleFrontiers in cardiovascular medicine2022
Comprehensive Proteomics Profiling Identifies Patients With Late Gadolinium Enhancement on Cardiac Magnetic Resonance Imaging in the Hypertrophic Cardiomyopathy Population.
Article in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed, 8 citations in OpenAlex.
- 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
- Detection of late gadolinium enhancement in patients with hypertrophic cardiomyopathy using machine learning.International journal of cardiology · 2025Article
- The Role of Signalling Pathways in Myocardial Fibrosis in Hypertrophic Cardiomyopathy.Reviews in cardiovascular medicine · 2025Review
- Comprehensive Proteomic Profiling of Human Myocardium Reveals Signaling Pathways Dysregulated in Hypertrophic Cardiomyopathy.Journal of the American College of Cardiology · 2024Article
- Prediction of worsening heart failure in hypertrophic cardiomyopathy using plasma proteomics.Heart (British Cardiac Society) · 2023Article
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Authors and funding
8 authors at 3 institutions in 1 country.
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Abstract
Introduction: In hypertrophic cardiomyopathy (HCM), late gadolinium enhancement (LGE) on cardiac magnetic resonance imaging (CMR) represents myocardial fibrosis and is associated with sudden cardiac death. However, CMR requires particular expertise and is expensive and time-consuming. Therefore, it is important to specify patients with a high pre-test probability of having LGE as the utility of CMR is higher in such cases. The objective was to determine whether plasma proteomics profiling can distinguish patients with and without LGE on CMR in the HCM population. Materials and Methods: We performed a multicenter case-control (LGE vs. no LGE) study of 147 patients with HCM. We performed plasma proteomics profiling of 4,979 proteins. Using the 17 most discriminant proteins, we performed logistic regression analysis with elastic net regularization to develop a discrimination model with data from one institution (the training set; Results: Overall, 82 of the 147 patients (56%) had LGE on CMR. The AUC of the 17-protein model was 0.83 (95% confidence interval [CI], 0.75-0.90) in the training set and 0.71 in the independent test set for validation (95% CI, 0.54-0.88). The sensitivity of the training model was 0.72 (95% CI, 0.61-0.83) and the specificity was 0.78 (95% CI, 0.66-0.90). The sensitivity was 0.71 (95% CI, 0.49-0.92) and the specificity was 0.74 (95% CI, 0.54-0.93) in the test set. Based on the discrimination model derived from the training set, patients in the test set who had high probability of having LGE had a significantly higher odds of having LGE compared to those who had low probability (odds ratio 29.6; 95% CI, 1.6-948.5; Conclusions: In this multi-center case-control study of patients with HCM, comprehensive proteomics profiling of 4,979 proteins demonstrated a high discriminative ability to distinguish patients with and without LGE. By identifying patients with a high pretest probability of having LGE, the present study serves as the first step to establishing a panel of circulating protein biomarkers to better inform clinical decisions regarding CMR utilization.
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