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Updated: Dec 25, 2025
Author Spotlight: Advancing the Analysis of Plasma Extracellular Vesicle Proteome for Cardiovascular Biomarker Studies
Published on: January 31, 2025
Multiple Plasma Biomarkers for Risk Stratification in Patients With Heart Failure and Preserved Ejection Fraction
Julio A Chirinos1, Alena Orlenko2, Lei Zhao3
1Division of Cardiovascular Medicine, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania; University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania.
Insights
A novel multi-marker blood test accurately predicts adverse outcomes in heart failure with preserved ejection fraction (HFpEF). This machine learning approach improves risk stratification for better patient care and clinical trial design.
Area of Science:
- Cardiology
- Biomarker Discovery
- Translational Medicine
Background:
- Heart failure with preserved ejection fraction (HFpEF) lacks effective risk stratification strategies.
- Improved methods are crucial for clinical care and designing clinical trials in HFpEF.
Purpose of the Study:
- To evaluate a targeted plasma multi-marker approach for enhanced phenotypic characterization and risk prediction in HFpEF.
- To assess the predictive value of novel circulating biomarkers for adverse outcomes in HFpEF patients.
Main Methods:
- Measured 49 plasma biomarkers in 379 HFpEF participants from the TOPCAT trial using a Multiplex assay.
- Utilized a tree-based pipeline optimizer to develop a multimarker predictive model for death or heart failure-related hospital admission (DHFA).
- Validated the model in an independent cohort of 156 HFpEF patients from the PHFS study.
Main Results:
- Identified dominant biomarker clusters related to fibrosis, inflammation, renal/liver injury, and metabolism.
- Multiple biomarkers, including fibroblast growth factor-23, osteoprotegerin, and inflammatory markers, predicted DHFA.
- A machine-learning model combining biomarkers strongly predicted DHFA risk (HR 2.85) and improved prediction beyond the MAGGIC score, validated in the PHFS cohort (HR 2.74).
Conclusions:
- Novel circulating biomarkers across pathophysiological domains predict outcomes in HFpEF.
- A multimarker approach using machine learning shows promise for enhancing risk stratification in HFpEF.
- This strategy can improve clinical decision-making and trial design for HFpEF patients.
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.
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