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) - Université de Lorraine Access content directly
Journal Articles Biomarkers Year : 2020

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)

1 CIC-P - Centre d'investigation clinique plurithématique Pierre Drouin [Nancy]
2 DCAC - Défaillance Cardiovasculaire Aiguë et Chronique
3 INI-CRCT - Cardiovascular and Renal Clinical Trialists [Vandoeuvre-les-Nancy]
4 Cardiovascular & Renal Clinical Trialists - CRCT - French-Clinical Research Infrastructure Network - F-CRIN [Paris]
5 Department of Physiology and Cardiothoracic Surgery Cardiovascular Research and Development Unit Faculty of Medicine University of Porto
6 CPN - Centre Psychothérapique de Nancy [Laxou]
7 CAPSID - Computational Algorithms for Protein Structures and Interactions
8 UPNA - Universidad Pública de Navarra [Espagne] = Public University of Navarra
9 IdiSNA - Instituto de Investigacion Sanitaria de Navarra
10 Department of Cardiology [Oslo]
11 ISB, Norwegian University of Science and Technology, Trondheim
12 UNIPG - Università degli Studi di Perugia = University of Perugia
13 Department of Cardiology, Maastricht University Medical Center
14 Department of Surgery and Physiology, Cardiovascular Research Unit (UnIC), Faculty of Medicine, University of Porto
15 WHRI - Wales Heart Research Institute [Cardiff]
16 Department of Cardiology, Onze Lieve Vrouwe Gasthuis, Amsterdam
17 Department of Cardiology, CARIM School for Cardiovascular Diseases Faculty of Health, Medicine and Life Sciences, Maastricht University
18 KU Leuven - Catholic University of Leuven = Katholieke Universiteit Leuven
19 William Harvey Research Institute, Barts Heart Centre, Queen Mary University of London
20 UA - University of Antwerp
21 Clinical Cardiology, Università del Piemonte Orientale, Department of Translational Medicine, Azienda Ospedaliero Universitaria "Maggiore della Carità"
22 St Michael's Hospital Dun Laoghaire Co. Dublin
23 Service d'Anesthésie-Réanimation [AP-HP Hôpitaux Saint-Louis Lariboisière]
24 MASCOT (UMR_S_942 / U942) - Marqueurs cardiovasculaires en situation de stress
25 Division of Clinical Physiology, Department of Cardiology, Faculty of Medicine, University of Debrecen
26 Department of Cardiology, Spedali Civili di Brescia
27 Charité Campus Virchow-Klinikum (CVK)
28 Department of Cardiology, Campus Virchow-Klinikum, Charite Universitaetsmedizin Berlin, Berlin Institute of Health – Center for Regenerative Therapies (BIH-BCRT), and the German Center for Cardiovascular Research (DZHK ; Berlin partner site)
29 Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers

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.
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Dates and versions

hal-02732968 , version 1 (02-06-2020)

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Susan Stienen, Joao Pedro Ferreira, Masatake Kobayashi, Gregoire Preud'Homme, Daniela Dobre, et al.. 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). Biomarkers, 2020, 25 (2), pp.201-211. ⟨10.1080/1354750X.2020.1727015⟩. ⟨hal-02732968⟩
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