Enhanced clinical phenotyping by mechanistic bioprofiling in heart failure with preserved ejection fraction: insights from the MEDIA-DHF study (The Metabolic Road to Diastolic Heart Failure)

Susan Stienen, Joao Pedro Ferreira, Masatake Kobayashi, Gregoire Preud'homme, Daniela Dobre, Jean-Loup Machu, Kevin Duarte, Emmanuel Bresso, Marie-Dominique Devignes, Natalia Lopez, Nicolas Girerd, Svend Aakhus, Giuseppe Ambrosio, Hans-Peter Brunner-La Rocca, Ricardo Fontes-Carvalho, Alan G. Fraser, Loek Van Heerebeek, Stephane Heymans, Gilles De Keulenaer, Paolo MarinoKenneth McDonald, Alexandre Mebazaa, Zoltan Papp, Riccardo Raddino, Carsten Tschoepe, Walter J. Paulus, Faiez Zannad, Patrick Rossignol*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

15 Citations (Web of Science)

Abstract

Background: Heart failure with preserved ejection fraction (HFpEF) is a heterogeneous syndrome for which clear evidence of effective therapies is lacking. Understanding which factors determine this heterogeneity may be helped by better phenotyping. An unsupervised statistical approach applied to a large set of biomarkers may identify distinct HFpEF phenotypes. Methods: Relevant proteomic biomarkers were analyzed in 392 HFpEF patients included in Metabolic Road to Diastolic HF (MEDIA-DHF). We performed an unsupervised cluster analysis to define distinct phenotypes. Cluster characteristics were explored with logistic regression. The association between clusters and 1-year cardiovascular (CV) death and/or CV hospitalization was studied using Cox regression. Results: Based on 415 biomarkers, we identified 2 distinct clusters. Clinical variables associated with cluster 2 were diabetes, impaired renal function, loop diuretics and/or betablockers. In addition, 17 biomarkers were higher expressed in cluster 2 vs. 1. Patients in cluster 2 vs. those in 1 experienced higher rates of CV death/CV hospitalization (adj. HR 1.93, 95% CI 1.12-3.32, p = 0.017). Complex-network analyses linked these biomarkers to immune system activation, signal transduction cascades, cell interactions and metabolism. Conclusion: Unsupervised machine-learning algorithms applied to a wide range of biomarkers identified 2 HFpEF clusters with different CV phenotypes and outcomes. The identified pathways may provide a basis for future research.

Original languageEnglish
Pages (from-to)201-211
Number of pages11
JournalBiomarkers
Volume25
Issue number2
DOIs
Publication statusPublished - 17 Feb 2020

Keywords

  • HFPEF
  • machine learning
  • biomarkers
  • cluster analysis
  • phenotype
  • complex-network analysis
  • HEPARIN-BINDING PROTEIN
  • ANGIOPOIETIN RECEPTOR TIE-2
  • BRAIN NATRIURETIC PEPTIDE
  • GROWTH-FACTOR
  • PLASMA ANGIOPOIETIN-1
  • L-SELECTIN
  • GRANULYSIN
  • EXPRESSION
  • DISEASE
  • DATABASE

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