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)
Creators: name: Stienen, Susan
name: Ferreira, João Pedro
name: Kobayashi, Masatake
name: Gregoire Preud’homme
name: Dobre, Daniela
name: Jean-Loup Machu
name: Duarte, Kevin
name: Bresso, Emmanuel
name: Marie-Dominique Devignes
name: López, Natalia
name: Girerd, Nicolas
name: Aakhus, Svend
name: Ambrosio, Giuseppe
name: Hans-Peter Brunner-La Rocca
name: Fontes-Carvalho, Ricardo
name: Fraser, Alan G.
name: Heerebeek, Loek Van
name: Stephane Heymans
name: Keulenaer, Gilles De
name: Marino, Paolo
name: McDonald, Kenneth
name: Mebazaa, Alexandre
name: Zoltàn Papp
name: Raddino, Riccardo
name: Tschöpe, Carsten
name: Paulus, Walter J.
name: Faiez Zannad
name: Rossignol, Patrick
Publisher: name: Taylor & Francis
Year: 2020
Subjects: subject: Medicine
subject: Cell Biology
subject: Genetics
subject: FOS: Biological sciences
subject: FOS: Biological sciences
subject: Pharmacology
subject: Immunology
subject: FOS: Clinical medicine
subject: FOS: Clinical medicine
subject: 69999 Biological Sciences not elsewhere classified
subject: 19999 Mathematical Sciences not elsewhere classified
subject: FOS: Mathematics
subject: FOS: Mathematics
subject: Cancer
subject: 60506 Virology

description: <b>Background:</b> 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. <b>Methods:</b> 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. <b>Results:</b> 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 <i>vs.</i> 1. Patients in cluster 2 <i>vs.</i> those in 1 experienced higher rates of CV death/CV hospitalization (adj. HR 1.93, 95% CI 1.12–3.32, <i>p</i> = 0.017). Complex-network analyses linked these biomarkers to immune system activation, signal transduction cascades, cell interactions and metabolism. <b>Conclusion:</b> 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 significanceMore 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 metabolismBiomarkers (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. 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.