Prediction of worsening heart failure in patients with hypertrophic cardiomyopathy using plasma proteomics profiling

Abstract Background While hypertrophic cardiomyopathy (HCM) is the most common genetic cardiovascular disease affecting 1 in 200–500 people, it is a heterogeneous disease. Only a subset of patients with HCM develop heart failure (HF; prevalence 35–50%). Prediction of worsening HF using clinical measures alone (e.g., cardiac imaging, genetic testing) remains limited. Furthermore, the underlying mechanism by which patients with HCM develop worsening HF has not been fully investigated. Proteomics profiling measures concentrations of thousands of proteins simultaneously and has been used to predict worsening of HF and to highlight which signaling pathways mediate worsening HF in non-HCM populations. Purpose In patients with HCM, we aimed to develop a plasma proteomics-based model to predict which patients with HCM would develop worsening HF and to identify signaling pathways that are differentially regulated in those who subsequently develop worsening HF. Methods We conducted a prospective cohort study in our multi-center biorepository of patients with HCM. We performed plasma proteomics profiling of 5032 proteins. We then developed a random forest model to predict worsening HF using proteomics profiling data from patients enrolled through one institution (training set). The outcome of worsening HF was defined as an increase in New York Heart Association functional class by at least 1 class. We externally validated this model in independent samples from patients enrolled through a different institution (test set). Further, we executed pathway analysis of proteins significantly dysregulated (i.e., univariable p<0.05) in patients who developed worsening HF compared to those who did not. Pathways with false discovery rate [FDR]<0.05 were considered to be dysregulated. Results There were 398 patients included in the study, with 278 in the training set and 120 in the test set. During a median follow-up of 1.8 years [interquartile range, 1.2–2.6], 60 (15%) patients developed worsening HF symptoms (45 patients in the training set and 15 patients in the test set). Using the proteomics-based model derived from the training set, the area under the receiver-operating-characteristic curve to predict worsening HF was 0.85 (95% confidence interval: 0.75–0.95) in the test set (Figure 1). Pathway analysis revealed that the Ras-MAPK pathway (FDR<0.00001) and its upstream PI3K-Akt pathway (FDR<0.00001) were dysregulated in patients who subsequently developed worsening HF (Figure 2). Conclusions Our study is the first to apply proteomics profiling to the prediction of worsening HF symptoms in patients with HCM, identifying patients who are at high risk of worsening HF and elucidating that the Ras-MAPK and related signaling pathways as potential underlying mechanisms. These findings support the potential application of proteomics profiling to clinical risk stratification and the investigation of signaling pathways underlying disease progression in HCM. Funding Acknowledgement Type of funding sources: Public grant(s) – National budget only. Main funding source(s): NIH/NHLBI R01 grant