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)

• Type: Article in International Scientific Journal
• Date: 2020

Authors

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

Journal

Title: Biomarkers

Vol. 25

Pages: 201-211

ISSN: 1354-750X

Publisher: Taylor & Francis

Indexing

ISI Web of Knowledge - 25 Citations

Scopus - 25 Citations

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 11