Author + information
- Received July 18, 2019
- Revision received December 22, 2019
- Accepted December 23, 2019
- Published online March 16, 2020.
- Julio A. Chirinos, MD, PhDa,b,∗∗ (, )@JulioChirinosMd,
- Alena Orlenko, PhDb,∗,
- Lei Zhao, MD, PhDc,
- Michael D. Basso, MSc,
- Mary Ellen Cvijic, PhDc,
- Zhuyin Li, PhDc,
- Thomas E. Spires, MSc,
- Melissa Yarde, MSc,
- Zhaoqing Wang, MSc,
- Dietmar A. Seiffert, MDc,
- Stuart Prenner, MDa,b,
- Payman Zamani, MD, MTRa,b,
- Priyanka Bhattacharya, MDa,b,
- Anupam Kumar, MDd,
- Kenneth B. Margulies, MDa,b,
- Bruce D. Car, PhDc,
- David A. Gordon, PhDc,
- Jason H. Moore, PhDa,b and
- Thomas P. Cappola, MD, ScMa,b
- aDivision of Cardiovascular Medicine, Hospital of the University of Pennsylvania, Philadelphia, Pennsylvania
- bUniversity of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania
- cBristol-Myers Squibb Company, Lawrenceville, New Jersey
- dVanderbilt University Medical Center, Nashville, Tennessee
- ↵∗Address for correspondence:
Dr. Julio A. Chirinos, South Tower, Room 11-138, Perelman Center for Advanced Medicine, 3400 Civic Center Boulevard, Philadelphia, Pennsylvania 19104.
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.
↵∗ Drs. Chirinos and Olenko have contributed equally to this work.
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.
- Received July 18, 2019.
- Revision received December 22, 2019.
- Accepted December 23, 2019.
- 2020 American College of Cardiology Foundation
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