Lauren Beussink-Nelson

Effects of ranolazine on exercise capacity, right ventricular indices, and hemodynamic characteristics in pulmonary arterial hypertension: a pilot study.

Ranolazine, a late inward sodium current and fatty acid oxidation inhibitor, may improve right ventricular (RV) function in pulmonary arterial hypertension (PAH); however, the safety and efficacy of ranolazine in humans with PAH is unknown. Therefore, we sought to (1) determine whether ranolazine is safe and well tolerated in PAH and (2) explore ranolazines effect on symptoms, exercise capacity, RV structure and function, and hemodynamic characteristics. We therefore conducted a 3-month, prospective, open-label pilot study involving patients with symptomatic PAH (n = 11) and echocardiographic evidence of RV dysfunction. We evaluated the safety and tolerability of ranolazine and compared symptoms, exercise capacity, exercise bicycle echocardiographic parameters, and invasive hemodynamic parameters between baseline and 3 months of ranolazine therapy using paired t tests. Of the 11 patients enrolled, one discontinued ranolazine therapy due to a drug-drug interaction after 3 days of therapy. All 10 of the remaining patients continued therapy for 3 months, and 8 (80%) of 10 completed all study tests. After 3 months, ranolazine administration was safe and associated with improvement in functional class (P = 0.0013), reduction in RV size (P = 0.015), improved RV function (improvement in RV strain during exercise at 3 months; P = 0.037), and a trend toward improved exercise time and exercise watts on bicycle echocardiography (P = 0.06 and 0.01, respectively). Ranolazine was not associated with improvement in invasive hemodynamic parameters. In conclusion, in a pilot study involving PAH, ranolazine therapy was safe and well tolerated, and it resulted in improvement in symptoms and echocardiographic parameters of RV structure and function but did not alter invasive hemodynamic parameters.

Reduced haemodynamic coupling and exercise are associated with vascular stiffening in pulmonary arterial hypertension

Objective Inadequate right ventricular (RV) and pulmonary arterial (PA) functional responses to exercise are important yet poorly understood features of pulmonary arterial hypertension (PAH). This study combined invasive catheterisation with echocardiography to assess RV afterload, RV function and ventricular–vascular coupling in subjects with PAH. Methods Twenty-six subjects with PAH were prospectively recruited to undergo right heart catheterisation and Doppler echocardiography at rest and during incremental exercise, and cardiac MRI at rest. Measurements at rest included basic haemodynamics, RV function and coupling efficiency (η). Measurements during incremental exercise included pulmonary vascular resistance (Z0), characteristic impedance (ZC, a measure of proximal PA stiffness) and proximal and distal PA compliance (CPA). Results In patients with PAH, the proximal PAs were significantly stiffer at maximum exercise (ZC =2.31±0.38 vs 1.33±0.15 WU×m2 at rest; p=0.003) and PA compliance was decreased (CPA=0.88±0.10 vs 1.32±0.17 mL/mm Hg/m2 at rest; p=0.0002). Z0 did not change with exercise. As a result, the resistance–compliance (RC) time decreased with exercise (0.67±0.05 vs 1.00±0.07 s at rest; p<10−6). When patients were grouped according to resting coupling efficiency, those with poorer η exhibited stiffer proximal PAs at rest, a lower maximum exercise level, and more limited CPA reduction at maximum exercise. Conclusions In PAH, exercise causes proximal and distal PA stiffening, which combined with preserved Z0 results in decreased RC time with exercise. Stiff PAs at rest may also contribute to poor haemodynamic coupling, reflecting reduced pulmonary vascular reserve that contributes to limit the maximum exercise level tolerated.

Phenomapping for the Identification of Hypertensive Patients with the Myocardial Substrate for Heart Failure with Preserved Ejection Fraction

We sought to evaluate whether unbiased machine learning of dense phenotypic data (“phenomapping”) could identify distinct hypertension subgroups that are associated with the myocardial substrate (i.e., abnormal cardiac mechanics) for heart failure with preserved ejection fraction (HFpEF). In the HyperGEN study, a population- and family-based study of hypertension, we studied 1273 hypertensive patients utilizing clinical, laboratory, and conventional echocardiographic phenotyping of the study participants. We used machine learning analysis of 47 continuous phenotypic variables to identify mutually exclusive groups constituting a novel classification of hypertension. The phenomapping analysis classified study participants into 2 distinct groups that differed markedly in clinical characteristics, cardiac structure/function, and indices of cardiac mechanics (e.g., phenogroup #2 had a decreased absolute longitudinal strain [12.8 ± 4.1 vs. 14.6 ± 3.5%] even after adjustment for traditional comorbidities [p < 0.001]). The 2 hypertension phenogroups may represent distinct subtypes that may benefit from targeted therapies for the prevention of HFpEF.

Prevalence and correlates of coronary microvascular dysfunction in heart failure with preserved ejection fraction: PROMIS-HFpEF

Aims To date, clinical evidence of microvascular dysfunction in patients with heart failure (HF) with preserved ejection fraction (HFpEF) has been limited. We aimed to investigate the prevalence of coronary microvascular dysfunction (CMD) and its association with systemic endothelial dysfunction, HF severity, and myocardial dysfunction in a well defined, multi-centre HFpEF population. Methods and results This prospective multinational multi-centre observational study enrolled patients fulfilling strict criteria for HFpEF according to current guidelines. Those with known unrevascularized macrovascular coronary artery disease (CAD) were excluded. Coronary flow reserve (CFR) was measured with adenosine stress transthoracic Doppler echocardiography. Systemic endothelial function [reactive hyperaemia index (RHI)] was measured by peripheral arterial tonometry. Among 202 patients with HFpEF, 151 [75% (95% confidence interval 69-81%)] had CMD (defined as CFR <2.5). Patients with CMD had a higher prevalence of current or prior smoking (70% vs. 43%; P = 0.0006) and atrial fibrillation (58% vs. 25%; P = 0.004) compared with those without CMD. Worse CFR was associated with higher urinary albumin-to-creatinine ratio (UACR) and NTproBNP, and lower RHI, tricuspid annular plane systolic excursion, and right ventricular (RV) free wall strain after adjustment for age, sex, body mass index, atrial fibrillation, diabetes, revascularized CAD, smoking, left ventricular mass, and study site (P < 0.05 for all associations). Conclusions PROMIS-HFpEF is the first prospective multi-centre, multinational study to demonstrate a high prevalence of CMD in HFpEF in the absence of unrevascularized macrovascular CAD, and to show its association with systemic endothelial dysfunction (RHI, UACR) as well as markers of HF severity (NTproBNP and RV dysfunction). Microvascular dysfunction may be a promising therapeutic target in HFpEF.

Machine Learning of the Cardiac Phenome and Skin Transcriptome to Categorize Heart Disease in Systemic Sclerosis

Background Cardiac involvement is a leading cause of death in systemic sclerosis (SSc/scleroderma). The complexity of SSc cardiac manifestations is not fully captured by the current clinical SSc classification, which is based on extent of skin involvement and specific autoantibodies. Therefore, we sought to develop a clinically relevant SSc cardiac disease classification to improve clinical care and increase understanding of SSc cardiac disease pathobiology. We hypothesized that machine learning could identify novel SSc cardiac disease subgroups, and that gene expression assessment of skin could provide insights into molecular pathogenesis of these SSc pheno-groups. Methods We used unsupervised model-based clustering (phenomapping) of SSc patient echocardiographic and clinical data to identify clinically relevant SSc pheno-groups in a discovery cohort (n=316), and validated these findings in an external SSc validation cohort (n=67). Cox regression was used to evaluate survival differences among groups. Gene expression profiles from skin biopsies from a subset of SSc patients (n=68) and controls (n=18) were analyzed with weighted gene co-expression network analyses to identify gene modules that were associated with cardiac pheno-groups and echocardiographic parameters. Results Four SSc cardiac pheno-groups were identified with distinct profiles. Pheno-group #1 displayed a predominant cutaneous phenotype without cardiac involvement; pheno-group #2 had long-standing SSc with limited skin and cardiac involvement; pheno-group #3 had diffuse skin involvement, a high frequency of interstitial lung disease (88%), and significant right heart remodeling/dysfunction; and pheno-group #4 had prolonged SSc disease duration, limited skin involvement, and marked biventricular cardiac involvement. After multivariable adjustment, pheno-group #3 (hazard ratio [HR] 7.8, 95% confidence interval [CI] 1.5–33.0) and pheno-group #4 (HR 10.5, 95% CI 2.1–52.7) remained associated with mortality (P<0.05). The addition of pheno-group classification was additive to conventional survival models (P<0.05 by likelihood ratio test for all models), a finding that was replicated in the validation cohort. Skin gene expression analysis identified 2 gene modules (representing fibrosis and skin integrity, respectively) that differed among the cardiac pheno-groups and were associated with specific echocardiographic parameters. Conclusions Machine learning of echocardiographic and skin gene expression data in SSc identifies clinically relevant subgroups with distinct cardiac phenotypes, survival, and associated molecular pathways in skin.