Prediction of major adverse cardiovascular events in patients with hypertrophic cardiomyopathy using proteomics profiling
Authors: Shimada Y J , Raita Y , Maurer M S , Hasegawa K , Fifer M A , Reilly M P 
Published: October 2021

Abstract

Background
Hypertrophic cardiomyopathy (HCM) is one of the most common genetic cardiac diseases, affecting 1 in 200–500 people in the US. HCM often causes major adverse cardiovascular events (MACE) – e.g., arrhythmias (atrial fibrillation, ventricular tachycardia/fibrillation), cerebrovascular accident, heart failure (HF), and sudden cardiac death (SCD). Although these HCM-related morbidities and mortality substantially affect the patients' quality and quantity of life, no systems are available to predict MACE. Furthermore, it remains unclear which signaling pathways mediate MACE.

Purpose
To prospectively determine protein biomarkers that predict MACE in patients with HCM and to identify signaling pathways differentially regulated in patients with HCM who subsequently develop MACE.

Methods
In this multi-center prospective cohort study of patients with HCM, we carried out plasma proteomics profiling of 4,979 proteins upon enrollment. We defined MACE as a composite of new-onset arrhythmias, cerebrovascular accident, increase in New York Heart Association class, HF death, SCD, and emergency department visit or non-elective hospitalization for HCM-related morbidities. We performed a sparse partial least squares discriminant analysis to develop a proteomics-based model to predict MACE using data from one institution (i.e., the training set). We tested the predictive ability in independent samples from the other institution (i.e., the test set) and performed time-to-event analysis. Additionally, we executed pathway analysis of the predictive proteins. A pathway was declared positive (i.e., dysregulated) if the false discovery rate (FDR) of the pathway was &lt;0.01 and there were ≥5 proteins that map to the pathway.

Results
The study included 257 patients with HCM (n=186 in the training set; n=71 in the test set). The median follow-up duration was 2.5 years (interquartile range, 1.4–3.1 years). Each year, 11.7% of patients had MACE. Using the proteomics-based prediction model derived from the training set, the area under the receiver-operating-characteristic curve was 0.84 (95% confidence interval [CI] 0.72–0.96) in the test set (Figure 1). The sensitivity was 0.82 (95% CI, 0.72–0.91) and the specificity was 0.84 (95% CI, 0.79–0.89) in the test set. In the test set, high-risk group determined by the proteomics-based predictive model had significantly higher rate of developing MACE (hazard ratio 20.2; 95% CI 2.4–168; p=0.005; Figure 2). The Ras-MAPK and related pathways were upregulated in patients who subsequently developed MACE (FDR&lt;0.001). Pathways involved in inflammation and fibrosis – e.g., the TGF-β pathway – were also upregulated.

Conclusions
This multi-center prospective cohort study with validation serves as the first to demonstrate the ability of proteomics profiling to predict MACE in HCM, exhibiting both novel (e.g., Ras-MAPK) and known (e.g., TGF-β) pathways that are differentially regulated in patients who subsequently experience MACE.

Funding Acknowledgement
Type of funding sources: Public grant(s) – National budget only. Main funding source(s): National Institute of Health, American Heart Association Figure 1Figure 2