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Prediction of Major Adverse Cardiovascular Events in Patients With Hypertrophic Cardiomyopathy Using Proteomics Profiling

Originally published11 Oct 2022https://doi.org/10.1161/CIRCGEN.121.003546Circulation: Genomic and Precision Medicine. 2022;15

Abstract

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.

Hypertrophic cardiomyopathy (HCM) is the most common genetic cardiac disease, affecting 1 in 200 to 500 people in the United States.1 Whereas many patients with HCM enjoy normal life expectancy without developing any symptoms, others experience major adverse cardiovascular events (MACE)—eg, arrhythmias (ie, atrial fibrillation [AF], sustained ventricular tachycardia, and ventricular fibrillation), stroke, heart failure, and sudden cardiac death (SCD).2 Indeed, HCM is the leading cause of SCD in the young in the United States, especially among competitive athletes.3

The identification of high-risk patients with HCM who are more likely to experience MACE is critically important because surveillance and invasive therapies are available for the early detection and prevention of MACE—eg, Holter monitor for AF (AF in HCM is a class I indication for anticoagulation),4,5 implantable cardioverter-defibrillators to abort SCD.6 Further, a comprehensive prediction system incorporating both HCM-related morbidities (eg, AF and resultant stroke, HF) and mortality (eg, SCD) is necessary to inform the patient how substantially this disease would affect their quality of life along with longevity. Nevertheless, no risk stratification systems to predict MACE are available to date, and the current models only predict SCD with modest accuracy.5,7,8

Proteomics profiling is a recently developed technology that simultaneously measures thousands of protein concentrations in the tissue or fluid. Proteomics profiling has been applied to a variety of cardiovascular diseases (eg, HF,9 coronary artery disease,10 and hypertension11) to investigate the underlying signaling pathways and to discover circulating biomarkers that are non-invasively available and can predict future cardiovascular events. However, proteomics profiling has never been applied to a prospective cohort of patients with HCM. We, therefore, designed the present multicenter prospective cohort study to examine, in patients with HCM, whether plasma proteomics profiling predicts MACE. We also aimed to determine signaling pathways that are differentially regulated between patients with HCM who subsequently develop MACE and those who do not.

Methods

The Partners Institutional Review Board and that of Columbia University Irving Medical Center approved the study protocol, and all participants provided written informed consent. Additional methods are available in Supplemental Methods. The data that support the findings of this study are available from the corresponding author upon reasonable request.

Results

A total of 257 patients with HCM underwent proteomics profiling and MACE data were unavailable in 12 patients. Therefore, the analytic cohort included 245 patients with HCM. The training set consisted of 174 patients with HCM followed at Massachusetts General Hospital. The test set was comprised of 71 patients with HCM who were followed at Columbia University Irving Medical Center. Baseline patient characteristics are shown in Table 1. Patients who subsequently experienced MACE had a higher degree of HF symptoms and mitral regurgitation as well as thicker left ventricular walls and lower exercise tolerance at baseline.

Table 1. Baseline Clinical Characteristics of the Study Sample in the Training Set
Characteristics* No major adverse cardiac event Major adverse cardiac event P value
n=112 n=62
Demographics
 Age, y 60±15 64±13 0.10
 Male 76 (68) 30 (48) 0.01
 NYHA class ≥2 37 (34) 41 (66) <0.001
 Race and ethnicity 0.49
  White† 102 (91) 53 (85)
  Black 2 (2) 3 (5)
  Asian 3 (3) 1 (2)
  Other or unidentified 5 (5) 5 (8)
Medical and family history
 Prior AF 32 (29) 16 (26) 0.70
 Prior VT/VF 3 (3) 3 (5) 0.67
 Prior nonsustained VT 23 (21) 12 (19) 0.81
 Prior syncope 24 (21) 12 (20) 0.79
 Prior septal myectomy 15 (13) 14 (23) 0.11
 Prior alcohol septal ablation 11 (10) 9 (15) 0.33
 Prior ICD implantation 20 (18) 17 (27) 0.14
 Family history of sudden cardiac death 9 (8) 8 (13) 0.27
 Family history of HCM 26 (23) 20 (33) 0.15
Medications
 β-blocker 72 (64) 48 (77) 0.07
 Calcium channel blocker 18 (16) 19 (31) 0.02
 ACE inhibitor 8 (7) 7 (11) 0.35
 ARB 11 (10) 13 (21) 0.04
 Diuretic
  Loop diuretic 10 (9) 15 (24) 0.006
  Thiazide 9 (8) 6 (10) 0.71
  Potassium sparing diuretic 6 (5) 3 (5) >0.99
 Disopyramide 9 (8) 6 (10) 0.71
 Amiodarone 2 (2) 3 (5) 0.35
Echocardiographic measurements
 Left atrial size, mm 42±7 43±7 0.12
 Systolic blood pressure, mm Hg 123±14 125±13 0.46
 Diastolic blood pressure, mm Hg 74±9 74±9 0.58
 Interventricular septum thickness, mm 16±4 18±5 0.001
 Posterior wall thickness, mm 12±3 13±2 0.03
 Maximum wall thickness, mm 19±4 21±5 0.02
 Left ventricular outflow tract gradient at rest, mm Hg 0 [0–20] 7 [0–41] 0.07
 Left ventricular outflow tract gradient with Valsalva maneuver, mm Hg 0 [0–51] 0 [0–75] 0.37
 Left ventricular ejection fraction, % 72±6 71±9 0.37
 Left ventricular end-diastolic diameter, mm 43±6 41±6 0.03
 Left ventricular end-systolic diameter, mm 26±4 26±6 0.35
 Systolic anterior motion of mitral valve leaflet 52 (46) 27 (44) 0.79
 Degree of mitral regurgitation‡ 2 [1–2] 2 [1–2.5] 0.03
Exercise stress test characteristics (n=134)
 METs achieved with stress test 9.7±3.7 7.6±3.1 0.009
 Peak heart rate during stress 141±29 129±27 0.03
 Peak systolic blood pressure during stress, mm Hg 172±29 162±26 0.03
 Peak diastolic blood pressure during stress, mm Hg 82±15 78±11 0.11
 Left ventricular outflow tract gradient with stress, mm Hg 22 [7–62] 50 [32–74] 0.08
5-year sudden cardiac death risk, % 2.1 [1.3–3.0] 2.1 [1.5–3.3] 0.52
Cardiac magnetic resonance imaging (n=104)
 Late gadolinium enhancement 42 (59) 20 (62) 0.75
Genetic testing (n=74)
 Pathogenic or likely pathogenic 15 (31) 9 (36) 0.64

ACE indicates angiotensin-converting enzyme; AF, atrial fibrillation; ARB, angiotensin II receptor blocker; HCM, hypertrophic cardiomyopathy; ICD, implantable cardioverter-defibrillator; MET, metabolic equivalent; NYHA, New York Heart Association; and VT/VF, ventricular tachycardia or ventricular fibrillation.

* Data are expressed as number (percentage), mean±SD, or median [interquartile range].

† Among these patients, 2 were self-identified as having Hispanic ethnicity.

‡ Degree of mitral regurgitation was converted to numerical values according to the following rule: none=0, trace=1, trace to mild=1.5, mild=2, mild to moderate=2.5, moderate=3, moderate to severe=3.5, severe=4.

The median follow-up duration was 4.3 years (interquartile range, 3.4-4.9 years). A total of 69 outcome events were observed in 62 patients during the prospective follow-up period. Namely, each year 9.0% of patients had MACE. Details of MACE experienced by the participants are summarized in Table 2. The event rate was 10.0 events per 100 patient-years. This event rate is comparable to what has been documented in the literature.2 The most common MACE was an increase in New York Heart Association functional class (5.2 events per 100 patient-years). Reasons for emergency department visit or nonelective hospitalization for HCM-related morbidities included acute decompensated HF (5 patients), AF with rapid ventricular response (4 patients), and symptoms attributable to HCM (eg, chest pain, palpitations, and syncope; 6 patients).

Table 2. Primary Outcome Events Observed During the Study Period
Events* n=174
New-onset atrial fibrillation 9 (5)
Sustained VT/VF 4 (2)
Cerebrovascular accident 5 (3)
Increase in NYHA functional class by ≥1 class 36 (21)
Heart transplant or left ventricular assist device implantation 0 (0)
Death due to heart failure 1 (0.6)
SCD with successful resuscitation† 1 (0.6)
SCD without successful resuscitation† 0
ED visit or nonelective hospitalization for HCM-related morbidities 13 (7)
Total number of patients with at least one event 62 (36)
Follow-up time (years) 4.3 [3.4-4.9]
Event rate per patient-year (%) 10.0
SCD event rate per patient-year (%) 0.1

ED indicates emergency department; HCM, hypertrophic cardiomyopathy; NYHA, New York Heart Association; SCD, sudden cardiac death; and VT/VF, ventricular tachycardia or ventricular fibrillation.

* Data are expressed as number (percentage) or median [interquartile range] unless specified otherwise.

† Successful resuscitation refers to return of spontaneous circulation with external shocks or internal shocks from implantable cardioverter-defibrillator.

As shown in Figure 1, the proteomics profiles were well separated between patients who subsequently developed MACE and those who did not. The predictive model derived from the training set had an area under the curve of 0.81 (95% CI, 0.68–0.93) in the test set (Figure 2). The area under the curve of the model adjusted for the clinical parameters was 0.81 (95% CI, 0.54–1.00), which was unchanged compared to that of the unadjusted model (Figure S1). In contrast to the proteomics-based prediction model, the 3 prediction models using known biomarkers to predict prognosis showed modest predictive accuracy. The area under the curve of the prediction model based on NT-proBNP (N-terminal pro-brain natriuretic peptide) was 0.66 (95% CI, 0.50–0.82; P=0.039 compared with the proteomics-based model; Figure S2), that with only cardiac troponin I was 0.51 (95% CI, 0.30–0.71; P=0.003; Figure S3), and that using both NT-proBNP and cardiac troponin I was 0.63 (95% CI, 0.46–0.81; P=0.046; Figure S4). When patients in the test set were classified into high-risk (31 patients) and low-risk (40 patients) groups according to the proteomics-based predictive model derived from the training set, the high-risk group had significantly higher rate of developing MACE compared to the low-risk group (hazard ratio, 13.6 [95% CI, 1.7–107]; P=0.01; Figure 3).

Figure 1.

Figure 1. Two-dimensional score plot of proteomics profiling in patients with hypertrophic cardiomyopathy who subsequently developed major adverse cardiovascular event and those who did not. Each green dot represents the baseline proteomic profile of a patient who subsequently experienced major adverse cardiovascular event. Each orange dot corresponds to that of a patient who did not.
Figure 2.

Figure 2. Area under the receiver-operating-characteristic curve to predict major adverse cardiovascular event in the external test set for validation, using the proteomics-based prediction model derived from the training set. AUC indicates area under the curve
Figure 3.

Figure 3. Kaplan-Meier curve for the risk of major adverse cardiovascular event (MACE) in the external test set for validation, using the proteomics-based prediction model derived from the training set.

The 20 most important proteins to predict MACE are displayed in Figure 4. The list included NT-proBNP and cardiac troponin I, biomarkers known to predict prognosis in HCM.12,13 There were 654 proteins that were differentially regulated between patients with and without MACE with false discovery rate (FDR) <5% (Table S1). When pathway analysis was performed using these 654 proteins, the Ras (FDR=1.4×10-8) and MAPK (mitogen-activated protein kinase; FDR=6.0×10-11) pathways were among the top-ranked pathways (Table 3) that were upregulated in patients who subsequently developed MACE. Pathways that are upstream and downstream to the Ras and MAPK pathways were also upregulated—eg, ErbB, PI3K (phosphoinositide 3-kinase)-Akt, HIF (hypoxia-inducible factor)-1, FcεRI, Rap1, EGFR (epidermal growth factor receptor), and FoxO (forkhead box protein O) pathways. Pathways that are known to be associated with disease progression in HCM—eg, the TGF (transforming growth factor)-β pathway14—were upregulated as well in patients who experienced MACE along with pathways related to inflammation. Network analysis of these predictive proteins demonstrated that member proteins of the Ras and MAPK pathways were at the hub of the protein interaction network (Figure S5).

Table 3. Pathways That Were Differentially Regulated Between Patients With Hypertrophic Cardiomyopathy Who Subsequently Developed Major Adverse Cardiovascular Event and Those Who Did Not
Pathway description Number of matching proteins Number of proteins in the pathway False discovery rate
Endocytosis 40 242 3.8×10-13
Focal adhesion 35 197 1.8×10-12
VEGF signaling pathway 21 59 3.0×10-12
FcεRI signaling pathway 22 67 3.0×10-12
Chemokine signaling pathway 32 181 1.1×10-11
FcγR-mediated phagocytosis 23 89 2.9×10-11
MAPK signaling pathway 39 293 6.0×10-11
B cell receptor signaling pathway 20 71 1.9×10-10
Sphingolipid signaling pathway 22 116 8.5×10-9
FoxO signaling pathway 23 130 9.7×10-9
Ras signaling pathway 30 228 1.4×10-8
T cell receptor signaling pathway 20 99 1.5×10-8
ErbB signaling pathway 18 83 3.5×10-8
Insulin signaling pathway 22 134 5.5×10-8
EGFR tyrosine kinase inhibitor resistance 17 78 7.6×10-8
Adherens junction 16 71 1.2×10-7
Rap 1 signaling pathway 26 203 1.7×10-7
Regulation of actin cytoskeleton 26 205 1.9×10-7
Cellular senescence 22 156 4.3×10-7
Phospholipase D signaling pathway 21 145 5.4×10-7
Natural killer cell–mediated cytotoxicity 19 124 9.5×10-7
Toll-like receptor signaling pathway 17 102 1.4×10-6
Apelin signaling pathway 19 133 2.3×10-6
Endocrine resistance 16 95 2.7×10-6
PI3K-Akt signaling pathway 32 348 3.3×10-6
Platelet activation 18 123 3.3×10-6
HIF-1 signaling pathway 16 98 3.7×10-6
mTOR signaling pathway 19 148 8.0×10-6
Adipocytokine signaling pathway 13 69 9.0×10-6
Leukocyte transendothelial migration 16 112 1.4×10-5
GnRH signaling pathway 14 88 1.9×10-5
AMPK signaling pathway 16 120 2.8×10-5
TNF signaling pathway 15 108 3.4×10-5
Purine metabolism 19 173 4.7×10-5
Wnt signaling pathway 17 143 0.0001
Th1 and Th2 cell differentiation 13 88 0.0001
Th17 cell differentiation 14 102 0.0001
Pentose phosphate pathway 8 30 0.0001
NF-κB signaling pathway 13 93 0.0001
Signaling pathways regulating pluripotency of stem cells 16 138 0.0001
Vasopressin-regulated water reabsorption 9 44 0.0001
cGMP-PKG signaling pathway 17 160 0.0002
Protein processing in endoplasmic reticulum 17 161 0.0002
Biosynthesis of amino acids 11 72 0.0002
Carbon metabolism 14 116 0.0002
Longevity regulating pathway 12 88 0.0002
Fluid shear stress and atherosclerosis 15 133 0.0002
Tight junction 17 167 0.0002
Apoptosis 15 135 0.0003
Cytokine-cytokine receptor interaction 22 263 0.0003
TGF-β signaling pathway 11 83 0.0005
NOD-like receptor signaling pathway 16 166 0.0006
Vascular smooth muscle contraction 13 119 0.0008

AMPK indicates AMP-activated protein kinase; EGFR, epidermal growth factor receptor; FcγR, Fc gamma receptor; Fox, forkhead box protein; GnRH, gonadotropin-releasing hormone; HIF, hypoxia-inducible factor; MAPK, mitogen-activated protein kinase; mTOR, mammalian target of rapamycin; NF-κB, nuclear factor-κB; NOD, nucleotide-binding domain; PI3K, phosphoinositide 3-kinase; PKG, protein kinase G; TGF, transforming growth factor; TNF, tumor necrosis factor; Th, T helper; TOR, target of rapamycin; TRP, transient receptor potential; and VEGF, vascular endothelial growth factor.
Figure 4.

Figure 4. The 20 most discriminant proteins to predict major adverse cardiovascular event (MACE) in patients with hypertrophic cardiomyopathy. An orange box indicates that the protein concentration was increased in patients who subsequently developed major adverse cardiovascular event, while a green box means that the concentration was decreased in these patients. P values were computed using the Mann-Whitney-Wilcoxon test. Bmp1‚ bone morphogenetic protein 1; BNP, brain natriuretic peptide; CUB, complement C1r/C1s; and HTRA, high temperature requirement A.

Discussion

Summary of Findings

In the present multicenter prospective cohort study of 245 patients with HCM, our new model based on comprehensive proteomics profiling of 4979 proteins exhibited a strong ability to predict MACE. Furthermore, proteomics profiling revealed both novel (eg, Ras-MAPK) and previously recognized (eg, TGF-β) signaling pathways that were differentially regulated in patients who subsequently developed MACE. The current study represents the first investigation to apply proteomics profiling to the prediction of MACE in HCM, demonstrating high predictive ability and specifying signaling pathways associated with the development of MACE.

Results in the Context

The cumulative prevalence and incidence of MACE are both high in patients with HCM. A recent study showed that among patients with HCM diagnosed <40 years of age (about one-third of the HCM population), 90% experience MACE during their lifetime, 70% develop AF, 65% have HF symptoms limiting their daily life, and 30% develop ventricular tachycardia/ventricular fibrillation or SCD.2 During a median follow-up of 2.9 years, the incidence was 41% for MACE, 20% for new-onset AF, 22% for new-onset HF, 6% for ventricular tachycardia/ventricular fibrillation or SCD, and 4% for stroke.2 A composite outcome of MACE was used in the present study because HCM affects not only the quantity (eg, SCD) but also the quality of life (eg, arrhythmias, HF, and stroke) and both HCM-related mortality and morbidities matter to the patients.

Interventions to prevent MACE are available but carry risk of complications. For example, implantable cardioverter-defibrillators can effectively and reliably abort potentially lethal ventricular tachycardia/ventricular fibrillation, thereby reducing the incidence of SCD and extending longevity in the HCM population.6 Early detection of AF (which increases the risk of stroke by ≈18-fold in HCM15) and initiation of anticoagulation prevent stroke.4,5 However, some therapies are invasive and can cause complications. For instance, implantable cardioverter-defibrillators can result in inappropriate shocks, infection, and bleeding related to the initial implantation, as well as lead fractures and repetitive battery changes in the long run.6 Therefore, it is critically important to precisely identify high-risk patients with HCM who are more likely to develop MACE to avoid both overtreatment (eg, performing unnecessary invasive procedures) and undertreatment (eg, failure to prevent MACE).8 Despite the importance of predicting both mortality and morbidities associated with HCM, risk stratification systems are currently available only for SCD and offer modest predictive ability. For example, in a meta-analysis of 13 studies, the area under the curve to predict SCD was 0.56 (95% CI, 0.50–0.63) in North America.16 Identification of biomarkers that can be non-invasively obtained with low cost (eg, blood) and predict MACE in HCM are urgently needed.

Prior studies have specified plasma proteins that predict prognosis based on proteomics data in non-HCM populations such as patients with HF,9 coronary artery disease,10 and hypertension.11 In patients with HCM, a few plasma biomarkers have been reported to have prognostic value—eg, NT-proBNP, cardiac troponins.12,13 Nonetheless, no prior studies have performed proteomics profiling to reveal protein biomarkers to predict MACE in HCM. The current investigation corroborates the prior studies showing the usefulness of plasma proteomics profiling in the prognostication of non-HCM populations. Furthermore, the present study adds to the body of knowledge by demonstrating, for the first time, that comprehensive proteomics profiling predicts MACE in the HCM population and outperforms the known predictors of worse prognosis in HCM—that is, NT-proBNP and cardiac troponins.

Ras-MAPK Pathway and Its Potential Role in HCM Pathogenesis and Progression

There are several diseases that can cause cardiac morphological changes that mimic those of HCM even without sarcomeric mutations—that is, HCM phenocopies. Examples include RASopathies.17 RASopathies are caused by upregulation of the Ras-MAPK pathway and often lead to HCM-like cardiac changes despite the absence of sarcomeric mutations.17 For instance, 95% of individuals with a gain-of-function mutation in the Raf1 gene—a key member of the Ras-APK pathway—display cardiac morphology resembling that of HCM.18,19 The prevalence of HCM-like cardiac changes in patients with RASopathies is high: 70% in LEOPARD syndrome, 33% in cardio-facio-cutaneous syndrome, and 20% in Noonan syndrome.17 These are much higher than the prevalence of HCM in the general population (0.2%–0.5%). The Ras-MAPK pathway is particularly relevant from the standpoint of novel targeted pharmacotherapy since the pathway is known to be modifiable. Indeed, in patients with RASopathies, the HCM-like cardiac changes can be attenuated by Ras-MAPK inhibition.20–22

In HCM, Ras gene mutations have been suggested to contribute to disease pathogenesis and progression.23,24 Furthermore, our prior case-control studies (HCM cases vs. controls with and without left ventricular hypertrophy)25,26 showed that the Ras-MAPK pathway was upregulated in HCM and correlated with early clinical markers of MACE—eg, New York Heart Association class, left atrial diameter. Another study using right ventricular myocardial specimens also reported upregulation of the Ras-MAPK pathway in HCM.27 The contribution of the Ras-MAPK pathway to the pathogenesis of HCM has been implicated in animal models as well.28–32 Statins (inhibitors of the Ras-MAPK pathway) led to the reversal of cardiac abnormalities in a rabbit model of HCM.33,34 Findings in the present study are in agreement with these prior studies and collectively suggest that the Ras-MAPK pathway plays a role not only in disease pathogenesis but also in progression to MACE in patients with HCM.

Inferences From Plasma Proteomics Profiling

The literature has recently documented that changes in the myocardium at the molecular level can be reflected in plasma.35 For instance, troponins are detected in plasma of patients with HF, along with proteins related to fibrotic changes and inflammation in the heart.25,26,35,36 These findings suggest that intracellular molecules can be released from the myocardium.37 There are several potential mechanisms to explain the release of intracardiac components into circulation. First, some exosomes in plasma stem from the cardiomyocytes.38 Second, it has been known that there is a low-level constant turnover of the myocardium with the release of intracellular materials.37 Third, in patients with pressure or volume overload of the ventricles, myocardial destruction occurs constantly without myocardial infarction.37 These mechanisms explain the detection of intracellular components and proteins at the cell surface of cardiomyocytes in plasma.

Strengths of the Present Study

A variety of methods were used to address the issue of false positive and false negative discovery and to enhance the internal and external validity of the present study. First, to augment the external validity and to avoid model overfitting, the predictive ability of the proteomics-based model derived from the training set was validated in the external test set. Second, only proteins with FDR of <0.05 were included in the pathway analysis, and only pathways with FDR of <0.001 were declared positive. These measures reduce false positive declarations as the FDR threshold of 0.001 limits the study-wide rate of false positive findings to <1 out of 1000 pathways declared positive. Furthermore, the biological plausibility of the findings is buttressed by our application of pathway analysis as the member proteins are biologically interrelated as opposed to isolated findings in the univariable analysis.39 Third, regarding false negative findings, the list of dysregulated proteins and signaling pathways in the present study included those known to be associated with MACE in HCM (eg, NT-proBNP, cardiac troponin I, and the TGF-β pathway).12–14 These serve as positive controls and further support the notion that proteomics profiling in plasma provides information on differentially regulated signaling pathways that are associated with disease progression to MACE. Last, the present study consists of the largest number of patients with HCM undergoing proteomics profiling (n=245) with the most comprehensive proteomics profiling to date (4979 proteins).25,26,36 The large size strengthened the internal validity of the study and allowed for external validation. Moreover, the comprehensiveness of our proteomics profiling lowered the possibility of missing important proteins and signaling pathways—that is, false negatives.

Potential Limitations

Inferences from the present study should be interpreted with several limitations in mind. First, misclassifications were possible in both exposure (ie, proteomics profiling) and outcome (i.e., MACE). To minimize the possibility, we used the SomaScan assay with high accuracy and adjudicated the outcome events by 2 independent cardiologists. Additionally, these would have been independent nondifferential misclassification, thereby biasing the inferences toward the null. Second, both institutions included in the present study are tertiary care centers with high-volume HCM programs. Therefore, the findings may not be generalizable to other HCM populations (eg, patients with less severe manifestations of HCM). Third, our study did not have sufficient statistical power to detect differences in the risk of SCD. Fourth, the possibility of informative censoring could not be excluded. Last, proteomics profiling was performed in 1 institution in the present study. Therefore, inter-institutional variability of proteomics profiling data was not addressed.

Conclusions

The present study serves as the first prospective investigation to exhibit the predictive value of proteomics profiling in HCM, identifying novel plasma protein biomarkers that predict the development of MACE. These findings can provide useful information for the clinical management of HCM. For example, if a patient is deemed to be at high risk of developing AF, the treating physicians would increase the frequency of surveillance Holter monitoring, as AF in HCM is a class I indication for anticoagulation.4,5 Our work should also prompt further investigation into the role of proteomics profiling to refine the prediction of outcomes for which specific preventive interventions are already available (eg, implantable cardioverter-defibrillator for SCD). The current work also demonstrated that multiple signaling pathways—both novel (eg, Ras-MAPK) and previously recognized (eg, TGF-β)—were differentially regulated in patients who subsequently experienced MACE. Inferences from the present analysis would facilitate further research on the underlying signaling pathways that lead to the progression to MACE, which in turn would aid in the development and testing of targeted therapeutic strategies. The relevance of such investigations is further underscored by the availability of commercially-available inhibitors of the Ras-MAPK pathway,33,34 and our proteomics-based approach would contribute to the identification of high-risk patients with Ras-MAPK upregulation who would benefit most from such pharmacological interventions.

Article Information

Sources of Funding

Dr Shimada is supported by National Institutes of Health (NIH) R01 HL157216 and R01 HL168382, the American Heart Association National Clinical and Population Research Awards, the American Heart Association Career Development Award, Korea Institute of Oriental Medicine, Feldstein Medical Foundation, Columbia University Irving Medical Center Irving Institute for Clinical & Translational Research Precision Medicine Pilot Award, and Columbia University Irving Medical Center Marjorie and Lewis Katz Cardiovascular Research Prize. Dr Reilly is supported by NIH UL1 TR001873 and K24 HL107643. Dr Maurer is supported by NIH K24 AG036778. The funding organizations did not have any role in the study design, collection, analysis, or interpretation of data, in writing of the manuscript, or in the decision to submit the article for publication. The researchers were independent from the funding organizations.

Supplemental Material

Supplemental Methods

Table S1

Figure S1–S5

References40–47

Nonstandard Abbreviations and Acronyms

AF

atrial fibrillation
FDR

false discovery rate
HCM

hypertrophic cardiomyopathy
MACE

major adverse cardiovascular events
NT-proBNP

N-terminal pro-brain natriuretic peptide
SCD

sudden cardiac death

Disclosures None.

Footnotes

Supplemental Material is available at https://www.ahajournals.org/doi/suppl/10.1161/CIRCGEN.121.003546.

For Sources of Funding and Disclosures, see page 568.
Correspondence to: Yuichi J. Shimada, MD, MPH, Division of Cardiology, Department of Medicine‚ Columbia University Irving Medical Center, 622 West 168th St, PH 3-342 New York, NY, 10032. Email .

References

  • 1. Maron BJ . Clinical course and management of hypertrophic cardiomyopathy.N Engl J Med. 2018; 379:655–668. doi: 10.1056/NEJMra1710575CrossrefMedlineGoogle Scholar
  • 2. Ho CY, Day SM, Ashley EA, Michels M, Pereira AC, Jacoby D, Cirino AL, Fox JC, Lakdawala NK, Ware JS., et al . Genotype and lifetime burden of disease in hypertrophic cardiomyopathy: insights from the sarcomeric human cardiomyopathy registry (SHaRe).Circulation. 2018; 138:1387–1398. doi: 10.1161/CIRCULATIONAHA.117.033200LinkGoogle Scholar
  • 3. Maron BJ, Doerer JJ, Haas TS, Tierney DM, Mueller FO . Sudden deaths in young competitive athletes: analysis of 1866 deaths in the United States, 1980-2006.Circulation. 2009; 119:1085–1092. doi: 10.1161/CIRCULATIONAHA.108.804617LinkGoogle Scholar
  • 4. Zegkos T, Efthimiadis GK, Parcharidou DG, Gossios TD, Giannakoulas G, Ntelios D, Ziakas A, Paraskevaidis S, Karvounis HI . Atrial fibrillation in hypertrophic cardiomyopathy: a turning point towards increased morbidity and mortality.Hellenic J Cardiol. 2017; 58:331–339. doi: 10.1016/j.hjc.2017.01.027CrossrefMedlineGoogle Scholar
  • 5. Ommen SR, Mital S, Burke MA, Day SM, Deswal A, Elliott P, Evanovich LL, Hung J, Joglar JA, Kantor P., et al . 2020 ACCF/AHA guideline for the diagnosis and treatment of hypertrophic cardiomyopathy.Circulation. 2020; 142:e558–e631. doi: 10.1161/CIR.0000000000000937LinkGoogle Scholar
  • 6. Schinkel AF, Vriesendorp PA, Sijbrands EJ, Jordaens LJ, ten Cate FJ, Michels M . Outcome and complications after implantable cardioverter defibrillator therapy in hypertrophic cardiomyopathy: systematic review and meta-analysis.Circ Heart Fail. 2012; 5:552–559. doi: 10.1161/CIRCHEARTFAILURE.112.969626LinkGoogle Scholar
  • 7. O’Mahony C, Jichi F, Pavlou M, Monserrat L, Anastasakis A, Rapezzi C, Biagini E, Gimeno JR, Limongelli G, McKenna WJ., et al . A novel clinical risk prediction model for sudden cardiac death in hypertrophic cardiomyopathy (HCM risk-SCD).Eur Heart J. 2014; 35:2010–2020. doi: 10.1093/eurheartj/eht439CrossrefMedlineGoogle Scholar
  • 8. Desai MY, Smedira NG, Dhillon A, Masri A, Wazni O, Kanj M, Sato K, Thamilarasan M, Popovic ZB, Lever HM . Prediction of sudden death risk in obstructive hypertrophic cardiomyopathy: Potential for refinement of current criteria.J Thorac Cardiovasc Surg. 2018; 156:750–759.e3. doi: 10.1016/j.jtcvs.2018.03.150CrossrefMedlineGoogle Scholar
  • 9. Lemesle G, Maury F, Beseme O, Ovart L, Amouyel P, Lamblin N, de Groote P, Bauters C, Pinet F . Multimarker proteomic profiling for the prediction of cardiovascular mortality in patients with chronic heart failure.PLoS One. 2015; 10:e0119265. doi: 10.1371/journal.pone.0119265CrossrefMedlineGoogle Scholar
  • 10. Ngo D, Sinha S, Shen D, Kuhn EW, Keyes MJ, Shi X, Benson MD, O’Sullivan JF, Keshishian H, Farrell LA., et al . Aptamer-based proteomic profiling reveals novel candidate biomarkers and pathways in cardiovascular disease.Circulation. 2016; 134:270–285. doi: 10.1161/CIRCULATIONAHA.116.021803LinkGoogle Scholar
  • 11. Zhang ZY, Thijs L, Petit T, Gu YM, Jacobs L, Yang WY, Liu YP, Koeck T, Zurbig P, Jin Y., et al . Urinary proteome and systolic blood pressure as predictors of 5-year cardiovascular and cardiac outcomes in a general population.Hypertension. 2015; 66:52–60. doi: 10.1161/HYPERTENSIONAHA.115.05296LinkGoogle Scholar
  • 12. Coats CJ, Gallagher MJ, Foley M, O’Mahony C, Critoph C, Gimeno J, Dawnay A, McKenna WJ, Elliott PM . Relation between serum N-terminal pro-brain natriuretic peptide and prognosis in patients with hypertrophic cardiomyopathy.Eur Heart J. 2013; 34:2529–2537. doi: 10.1093/eurheartj/eht070CrossrefMedlineGoogle Scholar
  • 13. Nagarajan V, Hernandez AV, Tang WH . Prognostic value of cardiac troponin in chronic stable heart failure: a systematic review.Heart. 2012; 98:1778–1786. doi: 10.1136/heartjnl-2012-301779CrossrefMedlineGoogle Scholar
  • 14. Shimada YJ, Passeri JJ, Baggish AL, O’Callaghan C, Lowry PA, Yannekis G, Abbara S, Ghoshhajra BB, Rothman RD, Ho CY., et al . Effects of losartan on left ventricular hypertrophy and fibrosis in patients with nonobstructive hypertrophic cardiomyopathy.JACC: Heart Failure. 2013; 1:480–487. doi: 10.1016/j.jchf.2013.09.001CrossrefMedlineGoogle Scholar
  • 15. Olivotto I, Cecchi F, Casey SA, Dolara A, Traverse JH, Maron BJ . Impact of atrial fibrillation on the clinical course of hypertrophic cardiomyopathy.Circulation. 2001; 104:2517–2524. doi: 10.1161/hc4601.097997LinkGoogle Scholar
  • 16. Wang J, Zhang Z, Li Y, Xu Y, Wan K, Chen Y . Variable and limited predictive value of the European Society of Cardiology hypertrophic cardiomyopathy sudden-death risk model: a meta-analysis.Can J Cardiol. 2019; 35:1791–1799. doi: 10.1016/j.cjca.2019.05.004CrossrefMedlineGoogle Scholar
  • 17. Rauen KA . The RASopathies.Annu Rev Genomics Hum Genet. 2013; 14:355–369. doi: 10.1146/annurev-genom-091212-153523CrossrefMedlineGoogle Scholar
  • 18. Pandit B, Sarkozy A, Pennacchio LA, Carta C, Oishi K, Martinelli S, Pogna EA, Schackwitz W, Ustaszewska A, Landstrom A., et al . Gain-of-function RAF1 mutations cause Noonan and LEOPARD syndromes with hypertrophic cardiomyopathy.Nat Genet. 2007; 39:1007–1012. doi: 10.1038/ng2073CrossrefMedlineGoogle Scholar
  • 19. Razzaque MA, Nishizawa T, Komoike Y, Yagi H, Furutani M, Amo R, Kamisago M, Momma K, Katayama H, Nakagawa M., et al . Germline gain-of-function mutations in RAF1 cause Noonan syndrome.Nat Genet. 2007; 39:1013–1017. doi: 10.1038/ng2078CrossrefMedlineGoogle Scholar
  • 20. Dhandapany PS, Fabris F, Tonk R, Illaste A, Karakikes I, Sorourian M, Sheng J, Hajjar RJ, Tartaglia M, Sobie EA., et al . Cyclosporine attenuates cardiomyocyte hypertrophy induced by RAF1 mutants in Noonan and LEOPARD syndromes.J Mol Cell Cardiol. 2011; 51:4–15. doi: 10.1016/j.yjmcc.2011.03.001CrossrefMedlineGoogle Scholar
  • 21. Marin TM, Keith K, Davies B, Conner DA, Guha P, Kalaitzidis D, Wu X, Lauriol J, Wang B, Bauer M., et al . Rapamycin reverses hypertrophic cardiomyopathy in a mouse model of LEOPARD syndrome-associated PTPN11 mutation.J Clin Invest. 2011; 121:1026–1043. doi: 10.1172/JCI44972CrossrefMedlineGoogle Scholar
  • 22. Wu X, Simpson J, Hong JH, Kim KH, Thavarajah NK, Backx PH, Neel BG, Araki T . MEK-ERK pathway modulation ameliorates disease phenotypes in a mouse model of Noonan syndrome associated with the Raf1(L613V) mutation.J Clin Invest. 2011; 121:1009–1025. doi: 10.1172/JCI44929CrossrefMedlineGoogle Scholar
  • 23. Sana ME, Quilliam LA, Spitaleri A, Pezzoli L, Marchetti D, Lodrini C, Candiago E, Lincesso AR, Ferrazzi P, Iascone M . A novel HRAS mutation independently contributes to left ventricular hypertrophy in a family with a known MYH7 mutation.PLoS One. 2016; 11:e0168501. doi: 10.1371/journal.pone.0168501CrossrefMedlineGoogle Scholar
  • 24. Kaski JP, Syrris P, Shaw A, Alapi KZ, Cordeddu V, Esteban MT, Jenkins S, Ashworth M, Hammond P, Tartaglia M., et al . Prevalence of sequence variants in the RAS-mitogen activated protein kinase signaling pathway in pre-adolescent children with hypertrophic cardiomyopathy.Circ Cardiovasc Genet. 2012; 5:317–326. doi: 10.1161/CIRCGENETICS.111.960468LinkGoogle Scholar
  • 25. Shimada YJ, Hasegawa K, Kochav SM, Mohajer P, Jung J, Maurer MS, Reilly MP, Fifer MA . Application of proteomics profiling for biomarker discovery in hypertrophic cardiomyopathy.J Cardiovasc Transl Res. 2019; 12:569–579. doi: 10.1007/s12265-019-09896-zCrossrefMedlineGoogle Scholar
  • 26. Shimada YJ, Raita Y, Liang LW, Maurer MS, Hasegawa K, Fifer MA, Reilly MP . Comprehensive proteomics profiling reveals circulating biomarkers of hypertrophic cardiomyopathy.Circ Heart Fail. 2021; 14:e007849. doi: 10.1161/CIRCHEARTFAILURE.120.007849LinkGoogle Scholar
  • 27. Kai H, Muraishi A, Sugiu Y, Nishi H, Seki Y, Kuwahara F, Kimura A, Kato H, Imaizumi T . Expression of proto-oncogenes and gene mutation of sarcomeric proteins in patients with hypertrophic cardiomyopathy.Circ Res. 1998; 83:594–601. doi: 10.1161/01.res.83.6.594LinkGoogle Scholar
  • 28. Hunter JJ, Tanaka N, Rockman HA, Ross J, Chien KR . Ventricular expression of a MLC-2v-ras fusion gene induces cardiac hypertrophy and selective diastolic dysfunction in transgenic mice.J Biol Chem. 1995; 270:23173–23178. doi: 10.1074/jbc.270.39.23173CrossrefMedlineGoogle Scholar
  • 29. Gottshall KR, Hunter JJ, Tanaka N, Dalton N, Becker KD, Ross J, Chien KR . Ras-dependent pathways induce obstructive hypertrophy in echo-selected transgenic mice.Proc Natl Acad Sci USA. 1997; 94:4710–4715. doi: 10.1073/pnas.94.9.4710CrossrefMedlineGoogle Scholar
  • 30. Bueno OF, De Windt LJ, Tymitz KM, Witt SA, Kimball TR, Klevitsky R, Hewett TE, Jones SP, Lefer DJ, Peng CF., et al . The MEK1-ERK1/2 signaling pathway promotes compensated cardiac hypertrophy in transgenic mice.EMBO J. 2000; 19:6341–6350. doi: 10.1093/emboj/19.23.6341CrossrefMedlineGoogle Scholar
  • 31. Streicher JM, Ren S, Herschman H, Wang Y . MAPK-activated protein kinase-2 in cardiac hypertrophy and cyclooxygenase-2 regulation in heart.Circ Res. 2010; 106:1434–1443. doi: 10.1161/CIRCRESAHA.109.213199LinkGoogle Scholar
  • 32. Tanaka K, Honda M, Takabatake T . Redox regulation of MAPK pathways and cardiac hypertrophy in adult rat cardiac myocyte.J Am Coll Cardiol. 2001; 37:676–685. doi: 10.1016/s0735-1097(00)01123-2CrossrefMedlineGoogle Scholar
  • 33. Senthil V, Chen SN, Tsybouleva N, Halder T, Nagueh SF, Willerson JT, Roberts R, Marian AJ . Prevention of cardiac hypertrophy by atorvastatin in a transgenic rabbit model of human hypertrophic cardiomyopathy.Circ Res. 2005; 97:285–292. doi: 10.1161/01.RES.0000177090.07296.acLinkGoogle Scholar
  • 34. Patel R, Nagueh SF, Tsybouleva N, Abdellatif M, Lutucuta S, Kopelen HA, Quinones MA, Zoghbi WA, Entman ML, Roberts R., et al . Simvastatin induces regression of cardiac hypertrophy and fibrosis and improves cardiac function in a transgenic rabbit model of human hypertrophic cardiomyopathy.Circulation. 2001; 104:317–324. doi: 10.1161/hc2801.094031LinkGoogle Scholar
  • 35. Ho CY, Lopez B, Coelho-Filho OR, Lakdawala NK, Cirino AL, Jarolim P, Kwong R, Gonzalez A, Colan SD, Seidman JG., et al . Myocardial fibrosis as an early manifestation of hypertrophic cardiomyopathy.N Engl J Med. 2010; 363:552–563. doi: 10.1056/NEJMoa1002659CrossrefMedlineGoogle Scholar
  • 36. Captur G, Heywood WE, Coats C, Rosmini S, Patel V, Lopes LR, Collis R, Patel N, Syrris P, Bassett P., et al . Identification of a multiplex biomarker panel for hypertrophic cardiomyopathy using quantitative proteomics and machine learning.Mol Cell Proteomics. 2020; 19:114–127. doi: 10.1074/mcp.RA119.001586CrossrefMedlineGoogle Scholar
  • 37. Park KC, Gaze DC, Collinson PO, Marber MS . Cardiac troponins: from myocardial infarction to chronic disease.Cardiovasc Res. 2017; 113:1708–1718. doi: 10.1093/cvr/cvx183CrossrefMedlineGoogle Scholar
  • 38. Ribeiro-Rodrigues TM, Laundos TL, Pereira-Carvalho R, Batista-Almeida D, Pereira R, Coelho-Santos V, Silva AP, Fernandes R, Zuzarte M, Enguita FJ., et al . Exosomes secreted by cardiomyocytes subjected to ischaemia promote cardiac angiogenesis.Cardiovasc Res. 2017; 113:1338–1350. doi: 10.1093/cvr/cvx118CrossrefMedlineGoogle Scholar
  • 39. Pawitan Y, Michiels S, Koscielny S, Gusnanto A, Ploner A . False discovery rate, sensitivity and sample size for microarray studies.Bioinformatics. 2005; 21:3017–3024. doi: 10.1093/bioinformatics/bti448CrossrefMedlineGoogle Scholar
  • 40. Hensley P . SOMAmers and SOMAscan – A protein biomarker discovery platform for rapid analysis of sample collections from bench top to the clinic.J Biomol Tech. 2013( 24( Suppl.)):S5. Accessed November 7, 2022. Available at https://europepmc.org/article/PMC/PMC3635296Google Scholar
  • 41. Gold L, Ayers D, Bertino J, Bock C, Bock A, Brody EN, Carter J, Dalby AB, Eaton BE, Fitzwater T., et al . Aptamer-based multiplexed proteomic technology for biomarker discovery.PLoS One. 2010; 5:e15004. doi: 10.1371/journal.pone.0015004CrossrefMedlineGoogle Scholar
  • 42. Kraemer S, Vaught JD, Bock C, Gold L, Katilius E, Keeney TR, Kim N, Saccomano NA, Wilcox SK, Zichi D., et al . From SOMAmer-based biomarker discovery to diagnostic and clinical applications: a SOMAmer-based, streamlined multiplex proteomic assay.PLoS One. 2011; 6:e26332. doi: 10.1371/journal.pone.0026332CrossrefMedlineGoogle Scholar
  • 43. Gramolini A, Lau E, Liu PP . Identifying low-abundance biomarkers: aptamer-based proteomics potentially enables more sensitive detection in cardiovascular diseases.Circulation. 2016; 134:286–289. doi: 10.1161/CIRCULATIONAHA.116.022940LinkGoogle Scholar
  • 44. Smith JG, Gerszten RE . Emerging affinity-based proteomic technologies for large-scale plasma profiling in cardiovascular disease.Circulation. 2017; 135:1651–1664. doi: 10.1161/CIRCULATIONAHA.116.025446LinkGoogle Scholar
  • 45. Le Cao KA, Boitard S, Besse P . Sparse PLS discriminant analysis: biologically relevant feature selection and graphical displays for multiclass problems.BMC Bioinf. 2011; 12:253. doi: 10.1186/1471-2105-12-253CrossrefMedlineGoogle Scholar
  • 46. Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P., et al . The STRING database in 2017: quality-controlled protein-protein association networks, made broadly accessible.Nucleic Acids Res. 2017; 45:D362–D368. doi: 10.1093/nar/gkw937CrossrefMedlineGoogle Scholar
  • 47. Chong J, Soufan O, Li C, Caraus I, Li S, Bourque G, Wishart DS, Xia J . MetaboAnalyst 4.0: towards more transparent and integrative metabolomics analysis.Nucleic Acids Res. 2018; 46:W486–W494. doi: 10.1093/nar/gky310CrossrefMedlineGoogle Scholar

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