Multiple Plasma Biomarkers for Risk Stratification in Patients With Heart Failure and Preserved Ejection Fraction.
HFpEF
Penn Heart Failure Study
TOPCAT trial
biomarkers
fibrosis
inflammation
kidney
liver
Journal
Journal of the American College of Cardiology
ISSN: 1558-3597
Titre abrégé: J Am Coll Cardiol
Pays: United States
ID NLM: 8301365
Informations de publication
Date de publication:
24 03 2020
24 03 2020
Historique:
received:
18
07
2019
revised:
22
12
2019
accepted:
23
12
2019
entrez:
21
3
2020
pubmed:
21
3
2020
medline:
15
12
2020
Statut:
ppublish
Résumé
Better risk stratification strategies are needed to enhance clinical care and trial design in heart failure with preserved ejection fraction (HFpEF). 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. 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). 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. 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.
Sections du résumé
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.
Identifiants
pubmed: 32192654
pii: S0735-1097(20)30263-1
doi: 10.1016/j.jacc.2019.12.069
pmc: PMC7147356
mid: NIHMS1568695
pii:
doi:
Substances chimiques
Biomarkers
0
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
1281-1295Subventions
Organisme : NHLBI NIH HHS
ID : R03 HL146874
Pays : United States
Organisme : NHLBI NIH HHS
ID : P01 HL094307
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL104106
Pays : United States
Organisme : NHLBI NIH HHS
ID : R61 HL146390
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL121510
Pays : United States
Organisme : NHLBI NIH HHS
ID : R56 HL136730
Pays : United States
Organisme : NHLBI NIH HHS
ID : K23 HL130551
Pays : United States
Organisme : NHLBI NIH HHS
ID : R01 HL088577
Pays : United States
Organisme : NLM NIH HHS
ID : R01 LM010098
Pays : United States
Organisme : NIA NIH HHS
ID : R01 AG058969
Pays : United States
Commentaires et corrections
Type : CommentIn
Informations de copyright
Copyright © 2020 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved.
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