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Machine learning based on biomarker profiles identifies distinct subgroups of heart failure with preserved ejection fraction. / Woolley, Rebecca J.; Ceelen, Daan; Ouwerkerk, Wouter; Tromp, Jasper; Figarska, Sylwia M.; Anker, Stefan D.; Dickstein, Kenneth; Filippatos, Gerasimos; Zannad, Faiez; Marco, Metra; Ng, Leong; Samani, Nilesh; van Veldhuisen, Dirk; Lang, Chim; Lam, Carolyn S.; Voors, Adriaan A.

In: European journal of heart failure, Vol. 23, No. 6, 06.2021, p. 983-991.

Research output: Contribution to journalArticleAcademicpeer-review

Harvard

Woolley, RJ, Ceelen, D, Ouwerkerk, W, Tromp, J, Figarska, SM, Anker, SD, Dickstein, K, Filippatos, G, Zannad, F, Marco, M, Ng, L, Samani, N, van Veldhuisen, D, Lang, C, Lam, CS & Voors, AA 2021, 'Machine learning based on biomarker profiles identifies distinct subgroups of heart failure with preserved ejection fraction', European journal of heart failure, vol. 23, no. 6, pp. 983-991. https://doi.org/10.1002/ejhf.2144

APA

Woolley, R. J., Ceelen, D., Ouwerkerk, W., Tromp, J., Figarska, S. M., Anker, S. D., Dickstein, K., Filippatos, G., Zannad, F., Marco, M., Ng, L., Samani, N., van Veldhuisen, D., Lang, C., Lam, C. S., & Voors, A. A. (2021). Machine learning based on biomarker profiles identifies distinct subgroups of heart failure with preserved ejection fraction. European journal of heart failure, 23(6), 983-991. https://doi.org/10.1002/ejhf.2144

Vancouver

Woolley RJ, Ceelen D, Ouwerkerk W, Tromp J, Figarska SM, Anker SD et al. Machine learning based on biomarker profiles identifies distinct subgroups of heart failure with preserved ejection fraction. European journal of heart failure. 2021 Jun;23(6):983-991. https://doi.org/10.1002/ejhf.2144

Author

Woolley, Rebecca J. ; Ceelen, Daan ; Ouwerkerk, Wouter ; Tromp, Jasper ; Figarska, Sylwia M. ; Anker, Stefan D. ; Dickstein, Kenneth ; Filippatos, Gerasimos ; Zannad, Faiez ; Marco, Metra ; Ng, Leong ; Samani, Nilesh ; van Veldhuisen, Dirk ; Lang, Chim ; Lam, Carolyn S. ; Voors, Adriaan A. / Machine learning based on biomarker profiles identifies distinct subgroups of heart failure with preserved ejection fraction. In: European journal of heart failure. 2021 ; Vol. 23, No. 6. pp. 983-991.

BibTeX

@article{431640603d374a2a99d6aab61ccd4b2b,
title = "Machine learning based on biomarker profiles identifies distinct subgroups of heart failure with preserved ejection fraction",
abstract = "Aims: The lack of effective therapies for patients with heart failure with preserved ejection fraction (HFpEF) is often ascribed to the heterogeneity of patients with HFpEF. We aimed to identify distinct pathophysiologic clusters of HFpEF based on circulating biomarkers. Methods and results: We performed an unsupervised cluster analysis using 363 biomarkers from 429 patients with HFpEF. Relative differences in expression profiles of the biomarkers between clusters were assessed and used for pathway over-representation analyses. We identified four distinct patient subgroups based on their biomarker profiles: cluster 1 with the highest prevalence of diabetes mellitus and renal disease; cluster 2 with oldest age and frequent age-related comorbidities; cluster 3 with youngest age, largest body size, least symptoms and lowest N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels; and cluster 4 with highest prevalence of ischaemic aetiology, smoking and chronic lung disease, most symptoms, as well as highest NT-proBNP and troponin levels. Over a median follow-up of 21 months, the occurrence of death or heart failure hospitalization was highest in clusters 1 and 4 (62.1% and 62.8%, respectively) and lowest in cluster 3 (25.6%). Pathway over-representation analyses revealed that the biomarker profile of patients in cluster 1 was associated with activation of inflammatory pathways while the biomarker profile of patients in cluster 4 was specifically associated with pathways implicated in cell proliferation regulation and cell survival. Conclusion: Unsupervised cluster analysis based on biomarker profiles identified mutually exclusive subgroups of patients with HFpEF with distinct biomarker profiles, clinical characteristics and outcomes, suggesting different underlying pathophysiological pathways.",
keywords = "Cluster analysis, Heart failure, Heart failure with preserved ejection fraction, Machine learning",
author = "Woolley, {Rebecca J.} and Daan Ceelen and Wouter Ouwerkerk and Jasper Tromp and Figarska, {Sylwia M.} and Anker, {Stefan D.} and Kenneth Dickstein and Gerasimos Filippatos and Faiez Zannad and Metra Marco and Leong Ng and Nilesh Samani and {van Veldhuisen}, Dirk and Chim Lang and Lam, {Carolyn S.} and Voors, {Adriaan A.}",
note = "Funding Information: This work was supported by the Netherlands Cardiovascular Research Initiative: an initiative with support of the Dutch Heart Foundation (CVON2014-11 RECONNECT). Conflict of interest: S.D.A. reports receiving grant support from Abbott and Vifor Pharma, and fees from Abbott, Bayer, Boehringer Ingelheim, Cardiac Dimension, Impulse Dynamics, Novartis, Servier, and Vifor Pharma. G.F. reports being a committee member in trials sponsored by Medtronic, Vifor, Servier, Novartis, and BI, outside the submitted work. M.M. received consulting honoraria from Abbott Vascular, Actelion, Amgen, AstraZeneca, Bayer, Edwards Therapeutics, Servier, Vifor Pharma, WindTree for participation in trials committees or speeches at sponsored meetings. C.S.L. is supported by a Clinician Scientist Award from the National Medical Research Council of Singapore; has received research support from Boston Scientific, Bayer, Roche Diagnostics, AstraZeneca, Medtronic, and Vifor Pharma; has served as consultant or on the Advisory Board/Steering Committee/Executive Committee for Abbott Diagnostics, Amgen, Applied Therapeutics, AstraZeneca, Bayer, Biofourmis, Boehringer Ingelheim, Boston Scientific, Corvia Medical, Cytokinetics, Darma Inc., Eko.ai Pte Ltd, JanaCare, Janssen Research & Development LLC, Medtronic, Menarini Group, Merck, MyoKardia, Novartis, Novo Nordisk, Radcliffe Group Ltd., Roche Diagnostics, Sanofi, Stealth BioTherapeutics, The Corpus, Vifor Pharma and WebMD Global LLC; and serves as co-founder and non-executive director of EKo.ai Pte Ltd. All other authors have nothing to disclose. Publisher Copyright: {\textcopyright} 2021 The Authors. European Journal of Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.",
year = "2021",
month = jun,
doi = "10.1002/ejhf.2144",
language = "English",
volume = "23",
pages = "983--991",
journal = "European journal of heart failure",
issn = "1388-9842",
publisher = "Wiley-Blackwell",
number = "6",

}

RIS

TY - JOUR

T1 - Machine learning based on biomarker profiles identifies distinct subgroups of heart failure with preserved ejection fraction

AU - Woolley, Rebecca J.

AU - Ceelen, Daan

AU - Ouwerkerk, Wouter

AU - Tromp, Jasper

AU - Figarska, Sylwia M.

AU - Anker, Stefan D.

AU - Dickstein, Kenneth

AU - Filippatos, Gerasimos

AU - Zannad, Faiez

AU - Marco, Metra

AU - Ng, Leong

AU - Samani, Nilesh

AU - van Veldhuisen, Dirk

AU - Lang, Chim

AU - Lam, Carolyn S.

AU - Voors, Adriaan A.

N1 - Funding Information: This work was supported by the Netherlands Cardiovascular Research Initiative: an initiative with support of the Dutch Heart Foundation (CVON2014-11 RECONNECT). Conflict of interest: S.D.A. reports receiving grant support from Abbott and Vifor Pharma, and fees from Abbott, Bayer, Boehringer Ingelheim, Cardiac Dimension, Impulse Dynamics, Novartis, Servier, and Vifor Pharma. G.F. reports being a committee member in trials sponsored by Medtronic, Vifor, Servier, Novartis, and BI, outside the submitted work. M.M. received consulting honoraria from Abbott Vascular, Actelion, Amgen, AstraZeneca, Bayer, Edwards Therapeutics, Servier, Vifor Pharma, WindTree for participation in trials committees or speeches at sponsored meetings. C.S.L. is supported by a Clinician Scientist Award from the National Medical Research Council of Singapore; has received research support from Boston Scientific, Bayer, Roche Diagnostics, AstraZeneca, Medtronic, and Vifor Pharma; has served as consultant or on the Advisory Board/Steering Committee/Executive Committee for Abbott Diagnostics, Amgen, Applied Therapeutics, AstraZeneca, Bayer, Biofourmis, Boehringer Ingelheim, Boston Scientific, Corvia Medical, Cytokinetics, Darma Inc., Eko.ai Pte Ltd, JanaCare, Janssen Research & Development LLC, Medtronic, Menarini Group, Merck, MyoKardia, Novartis, Novo Nordisk, Radcliffe Group Ltd., Roche Diagnostics, Sanofi, Stealth BioTherapeutics, The Corpus, Vifor Pharma and WebMD Global LLC; and serves as co-founder and non-executive director of EKo.ai Pte Ltd. All other authors have nothing to disclose. Publisher Copyright: © 2021 The Authors. European Journal of Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology. Copyright: Copyright 2021 Elsevier B.V., All rights reserved.

PY - 2021/6

Y1 - 2021/6

N2 - Aims: The lack of effective therapies for patients with heart failure with preserved ejection fraction (HFpEF) is often ascribed to the heterogeneity of patients with HFpEF. We aimed to identify distinct pathophysiologic clusters of HFpEF based on circulating biomarkers. Methods and results: We performed an unsupervised cluster analysis using 363 biomarkers from 429 patients with HFpEF. Relative differences in expression profiles of the biomarkers between clusters were assessed and used for pathway over-representation analyses. We identified four distinct patient subgroups based on their biomarker profiles: cluster 1 with the highest prevalence of diabetes mellitus and renal disease; cluster 2 with oldest age and frequent age-related comorbidities; cluster 3 with youngest age, largest body size, least symptoms and lowest N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels; and cluster 4 with highest prevalence of ischaemic aetiology, smoking and chronic lung disease, most symptoms, as well as highest NT-proBNP and troponin levels. Over a median follow-up of 21 months, the occurrence of death or heart failure hospitalization was highest in clusters 1 and 4 (62.1% and 62.8%, respectively) and lowest in cluster 3 (25.6%). Pathway over-representation analyses revealed that the biomarker profile of patients in cluster 1 was associated with activation of inflammatory pathways while the biomarker profile of patients in cluster 4 was specifically associated with pathways implicated in cell proliferation regulation and cell survival. Conclusion: Unsupervised cluster analysis based on biomarker profiles identified mutually exclusive subgroups of patients with HFpEF with distinct biomarker profiles, clinical characteristics and outcomes, suggesting different underlying pathophysiological pathways.

AB - Aims: The lack of effective therapies for patients with heart failure with preserved ejection fraction (HFpEF) is often ascribed to the heterogeneity of patients with HFpEF. We aimed to identify distinct pathophysiologic clusters of HFpEF based on circulating biomarkers. Methods and results: We performed an unsupervised cluster analysis using 363 biomarkers from 429 patients with HFpEF. Relative differences in expression profiles of the biomarkers between clusters were assessed and used for pathway over-representation analyses. We identified four distinct patient subgroups based on their biomarker profiles: cluster 1 with the highest prevalence of diabetes mellitus and renal disease; cluster 2 with oldest age and frequent age-related comorbidities; cluster 3 with youngest age, largest body size, least symptoms and lowest N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels; and cluster 4 with highest prevalence of ischaemic aetiology, smoking and chronic lung disease, most symptoms, as well as highest NT-proBNP and troponin levels. Over a median follow-up of 21 months, the occurrence of death or heart failure hospitalization was highest in clusters 1 and 4 (62.1% and 62.8%, respectively) and lowest in cluster 3 (25.6%). Pathway over-representation analyses revealed that the biomarker profile of patients in cluster 1 was associated with activation of inflammatory pathways while the biomarker profile of patients in cluster 4 was specifically associated with pathways implicated in cell proliferation regulation and cell survival. Conclusion: Unsupervised cluster analysis based on biomarker profiles identified mutually exclusive subgroups of patients with HFpEF with distinct biomarker profiles, clinical characteristics and outcomes, suggesting different underlying pathophysiological pathways.

KW - Cluster analysis

KW - Heart failure

KW - Heart failure with preserved ejection fraction

KW - Machine learning

UR - http://www.scopus.com/inward/record.url?scp=85102633082&partnerID=8YFLogxK

U2 - 10.1002/ejhf.2144

DO - 10.1002/ejhf.2144

M3 - Article

C2 - 33651430

VL - 23

SP - 983

EP - 991

JO - European journal of heart failure

JF - European journal of heart failure

SN - 1388-9842

IS - 6

ER -

ID: 17542837