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


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.Clinical significance More insight is obtained in the mechanisms related to poor outcome in HFpEF patients since it was demonstrated that biomarkers associated with the high-risk cluster were related to the immune system, signal transduction cascades, cell interactions and metabolism Biomarkers (and pathways) identified in this study may help select high-risk HFpEF patients which could be helpful for the inclusion/exclusion of patients in future trials. Our findings may be the basis of investigating therapies specifically targeting these pathways and the potential use of corresponding markers potentially identifying patients with distinct mechanistic bioprofiles most likely to respond to the selected mechanistically targeted therapies.

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


  • biomarkers
  • cluster analysis
  • complex-network analysis
  • machine learning
  • phenotype


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