Susan Stienen1, João Pedro Ferreira1,2, Masatake Kobayashi1, Gregoire Preud'homme1, Daniela Dobre1,3, Jean-Loup Machu1, Kevin Duarte1, Emmanuel Bresso4, Marie-Dominique Devignes4, Natalia López5, Nicolas Girerd1, Svend Aakhus6,7, Giuseppe Ambrosio8, Hans-Peter Brunner-La Rocca9, Ricardo Fontes-Carvalho10, Alan G Fraser11, Loek van Heerebeek12, Stephane Heymans13,14,15, Gilles de Keulenaer16, Paolo Marino17, Kenneth McDonald18, Alexandre Mebazaa19, Zoltàn Papp20, Riccardo Raddino21, Carsten Tschöpe22, Walter J Paulus23, Faiez Zannad1, Patrick Rossignol1. 1. CHRU de Nancy, F-CRIN INI-CRCT (Cardiovascular and Renal Clinical Trialists), INSERM U1116, Centre d'Investigation Clinique et Plurithématique 1433, INSERM, Université de Lorraine, Nancy, France. 2. Department of Physiology and Cardiothoracic Surgery, Cardiovascular Research and Development Unit, Faculty of Medicine, University of Porto, Porto, Portugal. 3. Clinical research and Investigation Unit, Psychotherapeutic Center of Nancy, Laxou, France. 4. Equipe CAPSID, LORIA (CNRS, Inria NGE, Université de Lorraine), Vandoeuvre-lès-Nancy, France. 5. Navarrabiomed, Complejo Hospitalario de Navarra (CHN), Universidad Pública de Navarra (UPNA), IdiSNA, Pamplona, Spain. 6. Department of Cardiology and Institute for Surgical Research, Oslo University Hospital, Oslo, Norway. 7. ISB, Norwegian University of Science and Technology, Trondheim, Norway. 8. Division of Cardiology, University of Perugia School of Medicine, Perugia, Italy. 9. Department of Cardiology, Maastricht University Medical Center, Maastricht, the Netherlands. 10. Department of Surgery and Physiology, Cardiovascular Research Unit (UnIC), Faculty of Medicine, University of Porto, Porto, Portugal. 11. Wales Heart Research Institute, Cardiff University, Cardiff, UK. 12. Department of Cardiology, Onze Lieve Vrouwe Gasthuis, Amsterdam, the Netherlands. 13. Department of Cardiology, CARIM School for Cardiovascular Diseases Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands. 14. Department of Cardiovascular Sciences, Centre for Molecular and Vascular Biology, Leuven, Belgium. 15. William Harvey Research Institute, Barts Heart Centre, Queen Mary University of London, London, UK. 16. Laboratory of Physiopharmacology, Antwerp University, and ZNA Hartcentrum, Antwerp, Belgium. 17. Clinical Cardiology, Università del Piemonte Orientale, Department of Translational Medicine, Azienda Ospedaliero Universitaria "Maggiore della Carità", Novara, Italy. 18. School of Medicine and Medical Sciences, St Michael's Hospital Dun Laoghaire Co. Dublin, Dublin, Ireland. 19. Department of Anaesthesiology and Critical Care Medicine, Saint Louis and Lariboisière University Hospitals and INSERM UMR-S 942, Paris, France. 20. Division of Clinical Physiology, Department of Cardiology, Faculty of Medicine, University of Debrecen, Debrecen, Hungary. 21. Department of Cardiology, Spedali Civili di Brescia, Brescia, Italy. 22. Department of Cardiology, Campus Virchow-Klinikum, C, Harite Universitaetsmedizin Berlin, Berlin Institute of Health - Center for Regenerative Therapies (BIH-BCRT), and the German Center for Cardiovascular Research (DZHK; Berlin partner site), Berlin, Germany. 23. Amsterdam Cardiovascular Sciences, Amsterdam University Medical Centers, Amsterdam, the Netherlands.
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.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.
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 HFpEFpatients 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 significanceMore insight is obtained in the mechanisms related to poor outcome in HFpEFpatients 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 HFpEFpatients 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.
Authors: Olivier Huttin; Alan G Fraser; Lars H Lund; Erwan Donal; Cecilia Linde; Masatake Kobayashi; Tamas Erdei; Jean-Loup Machu; Kevin Duarte; Patrick Rossignol; Walter Paulus; Faiez Zannad; Nicolas Girerd Journal: ESC Heart Fail Date: 2021-03-03
Authors: Paolo Morfino; Alberto Aimo; Vincenzo Castiglione; Giuseppe Vergaro; Michele Emdin; Aldo Clerico Journal: J Cardiovasc Dev Dis Date: 2022-08-10