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)

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.

Keywords:

• HFPEF
• machine learning
• biomarkers
• cluster analysis
• phenotype
• complex-network analysis

Acknowledgments

We thank other colleagues for their important contributions to the MEDIA study in the participating centres, including the CRB Lorrain for the biobanking, Gabor Kunszt (Oslo University Hospital), Tamas Erdei and Julie Edwards (Cardiff University).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study was supported by a grant from the European Union [FP7-HEALTH-2010-MEDIA], by the French Programme Hospitalier de Recherche Clinique (PHRC) and by the RHU Fight-HF, a public grant overseen by the French National Research Agency (ANR) as part of the second ‘Investissements d’Avenir’ program [reference: ANR-15-RHU-0004], the GEENAGE [ANR-15-IDEX-04-LUE] program, by the Contrat de Plan Etat Région Lorraine and FEDER IT2MP.) N-LA was supported by a Miguel Servet contract CP13/00221 from the ‘Instituto de Salud Carlos III-FEDER’. WJP and LvH were supported by CVON, Dutch Heart Foundation, The Hague, The Netherlands (RECONNECT and EARLY-HFpEF projects). AM received speaker’s honoraria from Orion, Otsuka, Philips, Roche and Servier. AM received fees as member of advisory board and/or Steering Committee and/or research grant from Adrenomed, Epygon, Neurotronik, Roche, Sanofi and Sphyngotec. AM owns shares in S-Form Pharma. SS acknowledges funding received from the European Society of Cardiology in form of an ESC Research Grant [R-2018-18686]. SH was supported by IMI2-CARDIATEAM [N° 821508], research grants from the Netherlands Organization for Scientific Research [NOW; Vidi 91796338], the Netherlands Cardiovascular Research Initiative, an initiative with support of the Dutch Heart Foundation, CVON2016-Early HFPEF, 2015-10, CVON She-PREDICTS, grant 2017-21, CVON Arena-PRIME, 2017-18 and the ERA-Net-CVD project MacroERA, 01KL1706, and FWO G091018N.