Protein Biomarkers of Adverse Clinical Features and Events in Sarcomeric Hypertrophic Cardiomyopathy

Abstract

Background: Hypertrophic cardiomyopathy (HCM) is a heterogeneous condition that can lead to atrial fibrillation, heart failure, and sudden cardiac death in many individuals but mild clinical impact in others. The mechanisms underlying this phenotypic heterogeneity are not well defined. The aim of this study was to use plasma proteomic profiling to help illuminate biomarkers that reflect or inform the heterogeneity observed in HCM.

Methods: The Olink antibody-based proteomic platform was used to measure plasma proteins in patients with genotype positive (sarcomeric) HCM participating in the HCM Registry. We assessed associations between plasma protein levels with clinical features, cardiac magnetic resonance imaging metrics, and the development of atrial fibrillation.

Results: We measured 275 proteins in 701 patients with sarcomeric HCM. There were associations between late gadolinium enhancement with proteins reflecting neurohormonal activation (NT-proBNP [N-terminal pro-B-type natriuretic peptide] and ACE2 [angiotensin-converting enzyme 2]). Metrics of left ventricular remodeling had novel associations with proteins involved in vascular development and homeostasis (vascular endothelial growth factor-D and TM [thrombomodulin]). Assessing clinical features, the European Society of Cardiology sudden cardiac death risk score was inversely associated with SCF (stem cell factor). Incident atrial fibrillation was associated with mediators of inflammation and fibrosis (MMP2 [matrix metalloproteinase 2] and SPON1 [spondin 1]).

Conclusions: Proteomic profiling of sarcomeric HCM identified biomarkers associated with adverse imaging and clinical phenotypes. These circulating proteins are part of both established pathways, including neurohormonal activation and fibrosis, and less familiar pathways, including endothelial function and inflammatory proteins less well characterized in HCM. These findings highlight the value of plasma profiling to identify biomarkers of risk and to gain further insights into the pathophysiology of HCM.

Keywords: biomarkers; cardiomyopathy, hypertrophic; coronary artery disease; heart failure; proteomics.

Figure 1:. Proteomic associations with cardiac structure, function and myocardial fibrosis.

A) Proteins associated with maximum left ventricular wall thickness, LVMi, LVEF, and LVOT gradient. B) Proteins associated with LGE and ECV. All analyses adjusted for age, sex, BMI, HTN and genotype. FDR was considered significant if <5%. C. Proteins associated with imaging variables after adjustment of BNP. *P<0.05 after adjustment for clinical variables and BNP †No proteins remained associated with LVOT obstruction and maximum LV wall thickness after adjustment for BNP LVMi: left ventricular mass index; LVEF: left ventricular ejection fraction; LVOT: left ventricular outflow tract; LGE: late gadolinium enhancement; ECV: extracellular volume; BMI: body mass index; HTN: hypertension; FDR: false discovery rate

Figure 2:. Overlapping proteomic associations with cardiac structure, function and myocardial fibrosis.

Upset plot showing shared protein findings between cardiac imaging variables. LVOT: left ventricular outflow gradient; LGE: late gadolinium enhancement; ECV: extracellular volume; LVMi: left ventricular mass index.

Figure 3:. Proteomic associations with ESC Sudden Cardiac Death Risk Score.

A. Proteins associated with ESC risk score. Effect size (beta) represents change in ESC risk score per log2 increase in protein value. B. Correlations of proteins with individual components of ESC risk score (Pearson coefficient for continuous variables and point-biserial coefficient for dichotomous variables). *FDR < 5% was used to account for multiple hypothesis testing for all comparisons. ESC: European Society Congress; NSVT: Non-sustained ventricular tachycardia; LVOT: left ventricular outflow tract

Figure 4:. Overlapping proteomic associations between left atrial volume index and prevalent atrial fibrillation.

Twenty-six proteins were associated with prevalent AF and 4 proteins were associated with LAVI only. Fifteen proteins are significantly associated with both LAVi and prevalent atrial fibrillation at baseline (FDR<5% for both). Twelve of these proteins are directly associated with LAVi and AF and 3 are inversely associated with LAVi and AF. Models adjusted for age, sex, BMI, HTN and genotype status. LAVi: left atrial volume index; AF: atrial fibrillation; BMI: body mass index

Figure 5:. Proteomic associations with incident atrial fibrillation.

A. Principal component analyses of the 15 proteins overlapping LAVi and prevalent AF. PC1 accounted for 29.8% of the variation of the 15 protein levels. B. Proteins and PC1 loadings with the top weighted proteins: MMP2, SPON1, NOTCH3. C. Time to event analyses for PC1 and incident AF in: 1) unadjusted and 2) adjusted model (age, sex, BMI, HTN and genotype). Cox regression models were used to assess risk of incident AF per standard deviation increase in PC1. PC: Principal component; AF: atrial fibrillation; BMI: body mass index; HTN: hypertension

Figure 6:. Main proteins and pathways associated with clinical and imaging features in sarcomeric HCM

ESC: European society of cardiology; SCD: sudden cardiac death