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)

• Tipo: Artigo em Revista Científica Internacional
• Data: 2020

Autores

Stienen, S (Autor) Outra; Ferreira, JP (Autor) FMUP; Kobayashi, M (Autor) Outra; Preud'homme, G (Autor) Outra; Dobre, D (Autor) Outra; Machu, JL (Autor) Outra; Duarte, K (Autor) Outra; Bresso, E (Autor) Outra; Devignes, MD (Autor) Outra; Lopez, N (Autor) Outra; Girerd, N (Autor) Outra; Aakhus, S (Autor) Outra; Ambrosio, G (Autor) Outra; Brunner La Rocca, HP (Autor) Outra; Fontes-Carvalho R (Autor) FMUP; Fraser, AG (Autor) Outra; van Heerebeek, L (Autor) Outra; Heymans, S (Autor) Outra; de Keulenaer, G (Autor) Outra; Marino, P (Autor) Outra; McDonald, K (Autor) Outra; Mebazaa, A (Autor) Outra; Papp, Z (Autor) Outra; Raddino, R (Autor) Outra; Tschope, C (Autor) Outra; Paulus, WJ (Autor) Outra; Zannad, F (Autor) Outra; Rossignol, P (Autor) Outra

Revista

Título: Biomarkers

Vol. 25

Páginas: 201-211

ISSN: 1354-750X

Editora: Taylor & Francis

Indexação

ISI Web of Knowledge - 25 Citações

Scopus - 25 Citações

Outras Informações

ID Authenticus: P-00R-S3P

DOI: 10.1080/1354750x.2020.1727015

Abstract (EN): 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.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 11