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Background: Better risk stratification strategies are needed to enhance clinical care and trial design in heart failure with preserved ejection fraction (HFpEF)., Objectives: The purpose of this study was to assess the value of a targeted plasma multi-marker approach to enhance our phenotypic characterization and risk prediction in HFpEF., Methods: In this study, the authors measured 49 plasma biomarkers from TOPCAT (Treatment of Preserved Cardiac Function Heart Failure With an Aldosterone Antagonist) trial participants (n = 379) using a Multiplex assay. The relationship between biomarkers and the risk of all-cause death or heart failure-related hospital admission (DHFA) was assessed. A tree-based pipeline optimizer platform was used to generate a multimarker predictive model for DHFA. We validated the model in an independent cohort of HFpEF patients enrolled in the PHFS (Penn Heart Failure Study) (n = 156)., Results: Two large, tightly related dominant biomarker clusters were found, which included biomarkers of fibrosis/tissue remodeling, inflammation, renal injury/dysfunction, and liver fibrosis. Other clusters were composed of neurohormonal regulators of mineral metabolism, intermediary metabolism, and biomarkers of myocardial injury. Multiple biomarkers predicted incident DHFA, including 2 biomarkers related to mineral metabolism/calcification (fibroblast growth factor-23 and OPG [osteoprotegerin]), 3 inflammatory biomarkers (tumor necrosis factor-alpha, sTNFRI [soluble tumor necrosis factor-receptor I], and interleukin-6), YKL-40 (related to liver injury and inflammation), 2 biomarkers related to intermediary metabolism and adipocyte biology (fatty acid binding protein-4 and growth differentiation factor-15), angiopoietin-2 (related to angiogenesis), matrix metalloproteinase-7 (related to extracellular matrix turnover), ST-2, and N-terminal pro-B-type natriuretic peptide. A machine-learning-derived model using a combination of biomarkers was strongly predictive of the risk of DHFA (standardized hazard ratio: 2.85; 95% confidence interval: 2.03 to 4.02; p < 0.0001) and markedly improved the risk prediction when added to the MAGGIC (Meta-Analysis Global Group in Chronic Heart Failure Risk Score) risk score. In an independent cohort (PHFS), the model strongly predicted the risk of DHFA (standardized hazard ratio: 2.74; 95% confidence interval: 1.93 to 3.90; p < 0.0001), which was also independent of the MAGGIC risk score., Conclusions: Various novel circulating biomarkers in key pathophysiological domains are predictive of outcomes in HFpEF, and a multimarker approach coupled with machine-learning represents a promising strategy for enhancing risk stratification in HFpEF., (Copyright © 2020 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Objectives: This study sought to assess if clinical phenogroups differ in comprehensive biomarker profiles, cardiac and arterial structure/function, and responses to spironolactone therapy., Background: Previous studies identified distinct subgroups (phenogroups) of patients with heart failure with preserved ejection fraction (HFpEF)., Methods: Among TOPCAT (Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist Trial) participants, we performed latent-class analysis to identify HFpEF phenogroups based on standard clinical features and assessed differences in multiple biomarkers measured from frozen plasma; cardiac and arterial structure/function measured with echocardiography and arterial tonometry; prognosis; and response to spironolactone., Results: Three HFpEF phenogroups were identified. Phenogroup 1 (n = 1,214) exhibited younger age, higher prevalence of smoking, preserved functional class, and the least evidence of left ventricular (LV) hypertrophy and arterial stiffness. Phenogroup 2 (n = 1,329) was older, with normotrophic concentric LV remodeling, atrial fibrillation, left atrial enlargement, large-artery stiffening, and biomarkers of innate immunity and vascular calcification. Phenogroup 3 (n = 899) demonstrated more functional impairment, obesity, diabetes, chronic kidney disease, concentric LV hypertrophy, high renin, and biomarkers of tumor necrosis factor-alpha-mediated inflammation, liver fibrosis, and tissue remodeling. Compared with phenogroup 1, phenogroup 3 exhibited the highest risk of the primary endpoint of cardiovascular death, heart failure hospitalization, or aborted cardiac arrest (hazard ratio [HR]: 3.44; 95% confidence interval [CI]: 2.79 to 4.24); phenogroups 2 and 3 demonstrated similar all-cause mortality (phenotype 2 HR: 2.36; 95% CI: 1.89 to 2.95; phenotype 3 HR: 2.26, 95% CI: 1.77 to 2.87). Spironolactone randomized therapy was associated with a more pronounced reduction in the risk of the primary endpoint in phenogroup 3 (HR: 0.75; 95% CI: 0.59 to 0.95; p for interaction = 0.016). Results were similar after excluding participants from Eastern Europe., Conclusions: We identified important differences in circulating biomarkers, cardiac/arterial characteristics, prognosis, and response to spironolactone across clinical HFpEF phenogroups. These findings suggest distinct underlying mechanisms across clinically identifiable phenogroups of HFpEF that may benefit from different targeted interventions., (Copyright © 2020 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.)

Background Heart failure ( HF ) with "recovered" ejection fraction ( HF rec EF ) is an emerging phenotype, but no tools exist to predict ejection fraction ( EF ) recovery in acute HF . We hypothesized that indices of baseline cardiac structure and function predict HF rec EF in nonischemic cardiomyopathy and reduced EF . Methods and Results We identified a nonischemic cardiomyopathy cohort with EF <40% during the first HF hospitalization (n=166). We performed speckle-tracking echocardiography to measure longitudinal, circumferential, and radial strain, and the average of these measures (myocardial systolic performance). HF rec EF was defined as follow-up EF ≥40% and ≥10% improvement from baseline EF . Fifty-nine patients (36%) achieved HF rec EF (baseline EF 26±7%; follow-up EF 51±7%) within a median of 135 (interquartile range 58-239) days after the first HF hospitalization. Baseline demographics, biomarker profiles, and comorbid conditions (except lower chronic kidney disease in HF rec EF ) were similar between HF rec EF and persistent reduced- EF groups. HF rec EF patients had smaller baseline left ventricular end-systolic dimension (3.6 versus 4.8 cm; P<0.01), higher baseline myocardial systolic performance (9.2% versus 8.1%; P=0.02), and improved survival (adjusted hazard ratio 0.27, 95% confidence interval 0.11, 0.62). We found a significant interaction between baseline left ventricular end-systolic dimension and absolute longitudinal strain. Among patients with left ventricular end-systolic dimension >4.35 cm, higher absolute longitudinal strain (≥8%) was associated with HF rec EF (unadjusted odds ratio=3.9, 95% CI )confidence interval 1.2, 12.8). Incorporation of baseline indices of cardiac mechanics with clinical variables resulted in a predictive model for HF rec EF with c-statistic=0.85. Conclusions Factors associated with achieving HF rec EF were specific to cardiac structure and indices of cardiac mechanics. Higher baseline absolute longitudinal strain is associated with HF rec EF among nonischemic cardiomyopathy patients with reduced EF and larger left ventricular dimensions.