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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 1, 2, 3, 4 Joao Pedro Ferreira 1, 2, 3, 4, 5 Masatake Kobayashi 1, 2, 3, 4 Gregoire Preud'Homme 1, 2, 3, 4 Daniela Dobre 1, 2, 3, 4, 6 Jean-Loup Machu 1, 2, 3, 4 Kévin Duarte 1, 2, 3, 4 Emmanuel Bresso 7 Marie-Dominique Devignes 7 Natalia López Andrés 8, 9 Nicolas Girerd 1, 2, 3, 4 Svend Aakhus 10, 11 Giuseppe Ambrosio 12 Hans-Peter Brunner-La Rocca 13 Ricardo Fontes-Carvalho 14 Alan Fraser 15 Loek van Heerebeek 16 Stephane Heymans 17, 18, 19 Gilles de Keulenaer 20 Paolo Marino 21 Kenneth Mcdonald 22 Alexandre Mebazaa 23, 24 Zoltàn Papp 25 Riccardo Raddino 26 Carsten Tschöpe 27, 28 Walter Paulus 29 Faiez Zannad 1, 2, 3, 4 Patrick Rossignol 1, 2, 3, 4
7 CAPSID - Computational Algorithms for Protein Structures and Interactions
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
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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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, Taylor & Francis, 2020, 25 (2), pp.201-211. ⟨10.1080/1354750X.2020.1727015⟩. ⟨hal-02732968⟩

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