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). (17th February 2020)

• Record Type: Journal Article
• Authors: Stienen, Susan; Ferreira, João Pedro; Kobayashi, Masatake; Preud'homme, Gregoire; Dobre, Daniela; Machu, Jean-Loup; Duarte, Kevin; Bresso, Emmanuel; Devignes, Marie-Dominique; López, Natalia; Girerd, Nicolas; Aakhus, Svend; Ambrosio, Giuseppe; Brunner-La Rocca, Hans-Peter; Fontes-Carvalho, Ricardo; Fraser, Alan G.; van Heerebeek, Loek; Heymans, Stephane; de Keulenaer, Gilles; Marino, Paolo; McDonald, Kenneth; Mebazaa, Alexandre; Papp, Zoltàn; Raddino, Riccardo; Tschöpe, Carsten; Paulus, Walter J.; Zannad, Faiez; Rossignol, Patrick
• Is Part Of: Biomarkers. Volume 25:Number 2(2020)
• Page Start: 201
• Page End: 211
• Publication Date: 2020-02-17
• Subjects: HFPEF -- machine learning -- biomarkers -- cluster analysis -- phenotype -- complex-network analysis; Biochemical markers -- Periodicals; 610.28
• DOI: 10.1080/1354750X.2020.1727015
• Languages: English
• ISSNs: 1354-750X

Abstract

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 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.