Prediction of new-onset atrial fibrillation in patients with hypertrophic cardiomyopathy using proteomics profiling

Background

Hypertrophic cardiomyopathy (HCM) is one of the most common genetic cardiac disorders and affects 1 in 200–500 individuals. HCM is known to be heterogeneous. Approximately 20–30% of patients with HCM develop atrial fibrillation (AF), which can lead to stroke and worsening of heart failure symptoms. As AF increases the risk of stroke by 8-fold, AF in HCM is a Class 1 indication for anticoagulation. Despite its high prevalence and clinical importance of early AF detection, there are no risk stratification tools available to predict new-onset AF in patients with HCM. Furthermore, it is still unknown which signaling pathways mediate AF in HCM. Proteomics profiling can determine concentrations of thousands of proteins and potentially reveal underlying molecular mechanisms of disease progression.

Purpose

To develop plasma proteomics-based model to predict new-onset AF in patients with HCM and to determine signaling pathways dysregulated in those who subsequently develop AF.

Methods

In this prospective, multi-center cohort study, we conducted plasma proteomics profiling of 5,032 proteins on 397 patients with HCM. We developed a proteomics-based random forest machine learning model to predict new-onset AF using samples from one institution (training set, n=278). We tested the predictive ability of the model using independent samples from the other institution (test set, n=119). We estimated the hazard ratio for new-onset AF using a Cox proportional hazards model comparing high- and low-risk groups as determined by the proteomics-based model. We also performed pathway analysis of proteins significantly (i.e., univariable P<0.05) associated with new-onset AF using a false discovery rate (FDR) threshold of 0.05.

Results

A total of 15 patients in the training set (5.4%) and 7 in the test set (5.9%) developed new-onset AF. Using the proteomics-based model developed in the training set, the area under the receiver-operating characteristic (ROC) curve to predict new-onset AF was 0.87 (95% confidence interval [CI] 0.77–0.98; Figure) in the test set. The sensitivity was 0.86 (95% CI 0.42–0.99) and the specificity was 0.77 (95% CI 0.68–0.84). In the test set, patients categorized as high-risk based on the proteomics model had a significantly higher rate of developing new-onset AF (hazard ratio 8.18; 95% CI 1.55–43.20; P=0.01). Pathway analysis revealed that the Ras-MAPK pathway was dysregulated in patients who subsequently developed AF (FDR=0.01; Table). Pathways involved in inflammation were also dysregulated.

Conclusions

This study serves as the first to demonstrate the ability of proteomics profiling to predict new-onset AF in patients with HCM, exhibiting dysregulation of both novel (e.g., Ras-MAPK) and known pathways in patients who subsequently experience AF. These results not only exhibit the utility of proteomics profiling for clinical risk stratification but also suggest mechanisms underlying the development of AF in HCM.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): NIH/NHLBI

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

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

Proteomics profiling reveals signaling pathways associated with major adverse cardiovascular events in patients with hypertrophic cardiomyopathy

Background

Hypertrophic cardiomyopathy (HCM) is the most common genetic cardiomyopathy affecting 1 in 200–500 people in the US. It is characterized by a diverse clinical course, and only a subset of patients with HCM experience major adverse cardiovascular events (MACE) such as arrhythmias (e.g., ventricular tachycardia/fibrillation [VT/VF], atrial fibrillation [AF]), stroke, and heart failure. However, the molecular mechanisms underlying the presence of MACE in HCM are still not well understood.

Purpose

Our aim was to reveal signaling pathways associated with prior MACE in patients with HCM by applying plasma proteomics profiling.

Methods

We conducted a multicenter case-control study of patients with HCM comparing those with and without a prior history of MACE. We performed plasma proteomics profiling of 5032 proteins. We defined prior MACE as a composite outcome of sustained VT/VF, AF, stroke/transient ischemic attack, left ventricular ejection fraction ≤50%, New York Heart Association functional class ≥2 symptoms, resuscitated cardiac arrest, or appropriate implantable cardioverter defibrillator therapy. We applied the random forest method to derive a proteomics-based discrimination model developed in patients enrolled at one institution (training set) and externally validated the model on patients enrolled at another institution (test set). We then performed pathway analysis of proteins differentially regulated in patients with prior MACE. Pathways with a false discovery rate (FDR) <0.05 with at least 5 associated proteins were declared positive.

Results

A total of 396 patients were included, with 278 in the training set and 118 in the test set. In this cohort, 251 (63%) patients had prior MACE (171 in the training set and 80 in the test set). Using the proteomics-based model derived from the training set, the area under the receiver operating characteristic curve was 0.81 (95% CI 0.73–0.88) in the test set (Figure 1). There were 632 differentially expressed proteins (univariable p<0.05). Pathway analysis identified significantly dysregulated pathways in patients with prior MACE (Figure 2). This included both pathways known to be associated with MACE (e.g., TGF-β [FDR=0.03]) and novel pathways (e.g., Ras-MAPK [FDR=0.01] and its upstream PI3K-Akt [FDR=7.7x10–7] pathways). Pathways involved in cellular metabolism/proliferation (e.g., HIF 1 [FDR=0.01] and Wnt [FDR=0.04] pathways) and inflammation (e.g., complement and coagulation cascades [FDR=2.7x10–21], cytokine-cytokine receptor interaction [FDR=8.1x10–16]) were also significantly dysregulated.

Conclusions

Our study in patients with HCM reveals that those with a prior history of MACE have a distinctive plasma proteomics profile. We further identified both previously known and novel pathways dysregulated in this subset with a more severe form of HCM. Our findings may aid in development of targeted therapies for the prevention of MACE in HCM.

Funding Acknowledgement

Type of funding sources: Public grant(s) – National budget only. Main funding source(s): R01 HL157216