TY - JOUR
T1 - 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)
T2 - insights from the MEDIA-DHF study (The Metabolic Road to Diastolic Heart Failure)
AU - Stienen, Susan
AU - Ferreira, João Pedro
AU - Kobayashi, Masatake
AU - Preud’homme, Gregoire
AU - Dobre, Daniela
AU - Machu, Jean-Loup
AU - Duarte, Kevin
AU - Bresso, Emmanuel
AU - Devignes, Marie-Dominique
AU - López, Natalia
AU - Girerd, Nicolas
AU - Aakhus, Svend
AU - Ambrosio, Giuseppe
AU - Brunner-la Rocca, Hans-Peter
AU - Fontes-Carvalho, Ricardo
AU - Fraser, Alan G.
AU - van Heerebeek, Loek
AU - Heymans, Stephane
AU - de Keulenaer, Gilles
AU - Marino, Paolo
AU - McDonald, Kenneth
AU - Mebazaa, Alexandre
AU - Papp, Zoltàn
AU - Raddino, Riccardo
AU - Tschöpe, Carsten
AU - Paulus, Walter J.
AU - Zannad, Faiez
AU - Rossignol, Patrick
AU - Preud'homme, Gregoire
PY - 2020/2/17
Y1 - 2020/2/17
N2 - 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.
AB - 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.
KW - HFPEF
KW - biomarkers
KW - cluster analysis
KW - complex-network analysis
KW - machine learning
KW - phenotype
UR - http://www.scopus.com/inward/record.url?scp=85079733476&partnerID=8YFLogxK
U2 - https://doi.org/10.1080/1354750X.2020.1727015
DO - https://doi.org/10.1080/1354750X.2020.1727015
M3 - Article
C2 - 32063068
SN - 1354-750X
VL - 25
SP - 201
EP - 211
JO - Biomarkers
JF - Biomarkers
IS - 2
ER -