Original Investigation
Multiple Plasma Biomarkers for Risk Stratification in Patients With Heart Failure and Preserved Ejection Fraction

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Abstract

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.

Key Words

HFpEF
biomarkers
fibrosis
inflammation
kidney
liver
Penn Heart Failure Study
TOPCAT trial

Abbreviations and Acronyms

CI
confidence interval
DHFA
death or heart failure-related hospital admission
FABP
fatty acid binding protein
FGF
fibroblast growth factor
GDF
growth differentiation factor
HF
heart failure
hFABP
heart-type fatty acid binding protein
HFpEF
heart failure with preserved ejection fraction
IL
interleukin
ML
machine learning
MMP
matrix metalloproteinase
MPO
myeloperoxidase
NGAL
neutrophil gelatinase-associated lipocalin
OPG
osteoprotegerin
OPN
osteopontin
PFI
permutation feature importance
sFLT
soluble fms-like tyrosine kinase
sTNF
soluble tumor necrosis factor-receptor
TIMP
tissue inhibitor of metalloproteinases
TNF
tumor necrosis factor
TPOT
tree-based pipeline optimizer
VEGF
vascular endothelial growth factor

Cited by (0)

This work was founded by an Investigator-Initiated research grant from Bristol-Myers Squibb (to Dr. Chirinos) and National Institutes of Health (NIH) grant R01HL088577 (to Dr. Cappola). Dr. Chirinos is supported by NIH grants R01-HL 121510-01A1, R61-HL-146390, R01-AG058969, 1R01-HL104106, P01-HL094307, R03-HL146874-01, and R56-HL136730; has received consulting honoraria from Sanifit, Microsoft, Fukuda-Denshi, Bristol-Myers Squibb, OPKO Healthcare, Ironwood Pharmaceuticals, Pfizer, Akros Pharma, Merck and Bayer; has received research grants from the National Institutes of Health, American College of Radiology Network, Fukuda-Denshi, Bristol-Myers Squibb, and Microsoft; and he is named as inventor in a University of Pennsylvania patent for the use of inorganic nitrates/nitrites for the treatment of HFpEF and a patent application for the use of novel neoepitope biomarkers of tissue fibrosis in HFpEF. Drs. Zhao, Basso, Cvijic, Spires, Wang, and Seiffert are employees of and own stock in Bristol-Myers Squibb. Drs. Li, Yarde, and Gordon are employees of Bristol-Myers Squibb. Dr. Zamani is supported by grant K23-HL-130551; and has been a consultant for Vyaire. Dr. Margulies has received research grants from Merck, Sanofi, GlaxoSmithKline, AstraZeneca, and Luitpold; and has received consulting honoraria from Merck, GlaxoSmithKline, and Luitpold. Dr. Car is an employee and stockholder of Bristol-Myers Squibb; and is a future employee and stock holder of Agios Pharmaceuticals. Dr. Moore is supported by NIH grant LM010098. Dr. Cappola has received research funding from Bristol-Myers Squibb. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose.

Listen to this manuscript's audio summary by Editor-in-Chief Dr. Valentin Fuster on JACC.org.

Drs. Chirinos and Olenko have contributed equally to this work.