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Published on: January 28, 2020
Endothelial Cell-Related Proteins in Plasma Predict Major Adverse Cardiovascular Events and Worsening Heart Failure
Shogo Tamura1, Keitaro Akita2, Michael A Fifer3
1Department of Biological Sciences Columbia University New York NY USA.
Insights
Plasma levels of endothelial cell (EC)-related proteins can predict major adverse cardiovascular events (MACE) and heart failure (HF) in hypertrophic cardiomyopathy (HCM) patients. This finding may improve risk stratification for better patient outcomes.
Area of Science:
- Cardiovascular Medicine
- Biomarker Discovery
- Proteomics
Background:
- Hypertrophic cardiomyopathy (HCM) is associated with major adverse cardiovascular events (MACE) and heart failure (HF).
- Endothelial cell (EC) dysfunction plays a role in HCM pathogenesis.
- The prognostic significance of EC-related proteins in HCM remains unclear.
Purpose of the Study:
- To investigate the prognostic value of plasma EC-related proteins for MACE and worsening HF in patients with HCM.
- To develop and validate machine learning models using EC-related proteins for risk prediction in HCM.
Main Methods:
- Prospective cohort study involving 722 patients with HCM.
- Measurement of 90 plasma EC-related proteins at enrollment.
- Development of machine learning models for MACE and HF prediction, validated in independent datasets.
Main Results:
- The EC-related protein-based model achieved an AUC of 0.71 for predicting MACE and 0.71 for worsening HF.
- High-risk groups identified by the model showed significantly increased risks of MACE and HF (P<0.001).
Conclusions:
- Plasma EC-related proteins serve as significant predictors of MACE and worsening HF in HCM.
- These proteins show potential as novel biomarkers for improved risk stratification in HCM.
Background:
Hypertrophic cardiomyopathy (HCM) often causes major adverse cardiovascular events (MACE) and worsening heart failure (HF). Endothelial cell (EC) dysfunction is known to be involved in the pathogenesis of HCM. However, the prognostic value of proteins related to EC function (EC-related proteins) in HCM is unknown.
Methods:
In this prospective cohort study of patients with HCM, we measured plasma levels of 90 EC-related proteins upon enrollment. The primary outcome measure was MACE. The secondary outcome measure was worsening HF. We developed machine learning models based on EC-related proteins to predict MACE or worsening HF 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 analyses.
Results:
The study included 722 patients (n=458 in the training set and n=264 in the test set). Using our EC-related protein-based model, the area under the receiver-operating-characteristic curve to predict MACE was 0.71 (95% CI, 0.64-0.77) and that for worsening HF was 0.71 (95% CI, 0.63-0.79). When we divided the test set into low- and high-risk groups according to the predicted probabilities derived from the training set, the high-risk groups had significantly higher risks of developing MACE and worsening HF compared with the low-risk groups (both Plogrank<0.001).
Conclusions:
The present prospective study demonstrated that EC-related proteins in plasma predict MACE and worsening HF in patients with HCM. These EC-related proteins have a potential to become novel biomarkers for risk stratification in HCM to improve current prediction models.
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