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Prediction of worsening heart failure in hypertrophic cardiomyopathy using plasma proteomics
Heidi S Lumish1, Lusha W Liang1, Kohei Hasegawa2
1Division of Cardiology, Department of Medicine, Columbia University Irving Medical Center, New York, New York, USA.
Insights
A new plasma proteomics model accurately predicts worsening heart failure (HF) in hypertrophic cardiomyopathy (HCM) patients. This study also identified dysregulated Ras-MAPK signaling pathways as potential mechanisms driving HF progression in HCM.
Area of Science:
- Cardiovascular proteomics
- Biomarker discovery for heart failure
Background:
- Heart failure (HF) is a major complication of hypertrophic cardiomyopathy (HCM), significantly impacting patient lifestyle.
- Current clinical measures have limitations in predicting HF worsening in HCM patients.
- The underlying mechanisms of HF development in HCM remain unclear.
Purpose of the Study:
- To develop a plasma proteomics-based model for predicting HF worsening in HCM.
- To identify specific signaling pathways dysregulated in HCM patients who develop worsening HF.
Main Methods:
- A multi-center, prospective cohort study involving 389 HCM patients.
- Plasma proteomics profiling of 4986 proteins at enrollment.
- Development and external validation of a random forest proteomics model to predict HF worsening.
- Pathway analysis of dysregulated proteins using a false discovery rate (FDR) threshold of <0.001.
Main Results:
- An 11-protein model demonstrated high predictive accuracy for HF worsening (AUC 0.87 in the test set).
- Pathway analysis identified the Ras-MAPK pathway and related pathways as significantly dysregulated in patients who developed worsening HF (FDR < 0.00001).
Conclusions:
- Plasma proteomics profiling offers a highly accurate method for predicting HF worsening in HCM.
- The Ras-MAPK signaling pathway is implicated as a potential mechanism in the progression of HF in HCM.
Objective:
Heart failure (HF) is one of the most common and lifestyle-limiting complications of hypertrophic cardiomyopathy (HCM). Prediction of worsening HF using clinical measures alone remains limited. Moreover, the mechanisms by which patients with HCM develop worsening HF have not been elucidated. Therefore, the aim of this study was to develop a plasma proteomics-based model to predict worsening HF among patients with HCM and to identify signalling pathways that are differentially regulated in those who subsequently develop worsening HF.
Methods:
In this multi-centre, prospective cohort study of 389 patients with HCM, plasma proteomics profiling of 4986 proteins was performed at enrolment. A proteomics-based random forest model was developed to predict worsening HF using data from one institution (training set, n=268). This model was externally validated in patients from a different institution (test set, n=121). Pathway analysis of proteins significantly dysregulated in patients who subsequently developed worsening HF compared with those who did not was executed, using a false discovery rate (FDR) threshold of <0.001.
Results:
Using the 11-protein proteomics-based model derived from the training set, the area under the receiver-operating characteristic curve to predict worsening HF was 0.87 (95% CI: 0.76 to 0.98) in the test set. Pathway analysis revealed that the Ras-MAPK pathway (FDR<0.00001) and related pathways were dysregulated in patients who subsequently developed worsening HF.
Conclusions:
The present study with comprehensive plasma proteomics profiling demonstrated a high accuracy to predict worsening HF in patients with HCM and identified the Ras-MAPK and related signalling pathways as potential underlying mechanisms.
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