Key result
A proteomics-based predictive model identified a high-risk group of patients with hypertrophic cardiomyopathy who had a significantly higher rate of developing MACE (HR 13.6; 95% CI 1.7-107; P=0.01).
Why the study?
No models currently exist to predict MACE in hypertrophic cardiomyopathy, and the signaling pathways mediating MACE remain unclear.
Can a proteomics-based model predict major adverse cardiovascular events in patients with hypertrophic cardiomyopathy?
Cohort (n=245)
Yes
Can a proteomics-based model predict major adverse cardiovascular events in patients with hypertrophic cardiomyopathy?
Hazard Ratio: 13.6 (95% CI 1.7–107)
p-value: p=0.01
Plasma proteomics profiling can predict major adverse cardiovascular events in patients with hypertrophic cardiomyopathy and identifies upregulation of Ras-MAPK and TGF-β pathways.
May support proteomics-based MACE stratification in hypertrophic cardiomyopathy; hypothesis-generating for Ras-MAPK targeting and requires validation.
Background: Hypertrophic 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. Methods: In 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. Results: The 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]; P =0.01). The Ras -MAPK (mitogen-activated protein kinase) pathway was upregulated in patients who subsequently developed MACE (false discovery rate<1.0×10 -7 ). Pathways involved in inflammation and fibrosis—for example, the TGF (transforming growth factor)-β pathway—were also upregulated. Conclusions: This study serves as the first to demonstrate the ability of proteomics profiling to predict MACE in hypertrophic cardiomyopathy, exhibiting both novel (eg, Ras -MAPK) and known (eg, TGF-β) pathways differentially regulated in patients who subsequently experience MACE.
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Shimada et al. (2022) conducted a cohort in Hypertrophic cardiomyopathy (n=245). High-risk classification by proteomics-based predictive model vs. Low-risk classification was evaluated on Major adverse cardiovascular events (MACE) (HR 13.6, 95% CI 1.7-107, p=0.01). A proteomics-based predictive model identified a high-risk group of patients with hypertrophic cardiomyopathy who had a significantly higher rate of developing MACE (HR 13.6; 95% CI 1.7-107; P=0.01).
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