HFrEF subphenotypes based on 4210 repeatedly measured circulating proteins are driven by different biological mechanisms

Teun B. Petersen; Marie de Bakker; Folkert W. Asselbergs; Magdalena Harakalova; K. Martijn Akkerhuis; Jasper J. Brugts; Jan van Ramshorst; R. Thomas Lumbers; Rachel M. Ostroff; Peter D. Katsikis; Peter J. van der Spek; Victor A. Umans; Eric Boersma; Dimitris Rizopoulos; Isabella Kardys

doi:10.1016/j.ebiom.2023.104655

HFrEF subphenotypes based on 4210 repeatedly measured circulating proteins are driven by different biological mechanisms

Teun B. Petersen, Marie de Bakker, Folkert W. Asselbergs, Magdalena Harakalova, K. Martijn Akkerhuis, Jasper J. Brugts, Jan van Ramshorst, R. Thomas Lumbers, Rachel M. Ostroff, Peter D. Katsikis, Peter J. van der Spek, Victor A. Umans, Eric Boersma, Dimitris Rizopoulos, Isabella Kardys^*

^*Corresponding author for this work

Research output: Contribution to journal › Article › Academic › peer-review

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Abstract

Background: HFrEF is a heterogenous condition with high mortality. We used serial assessments of 4210 circulating proteins to identify distinct novel protein-based HFrEF subphenotypes and to investigate underlying dynamic biological mechanisms. Herewith we aimed to gain pathophysiological insights and fuel opportunities for personalised treatment. Methods: In 382 patients, we performed trimonthly blood sampling during a median follow-up of 2.1 [IQR:1.1–2.6] years. We selected all baseline samples and two samples closest to the primary endpoint (PEP; composite of cardiovascular mortality, HF hospitalization, LVAD implantation, and heart transplantation) or censoring, and applied an aptamer-based multiplex proteomic approach. Using unsupervised machine learning methods, we derived clusters from 4210 repeatedly measured proteomic biomarkers. Sets of proteins that drove cluster allocation were analysed via an enrichment analysis. Differences in clinical characteristics and PEP occurrence were evaluated. Findings: We identified four subphenotypes with different protein profiles, prognosis and clinical characteristics, including age (median [IQR] for subphenotypes 1–4, respectively:70 [64, 76], 68 [60, 79], 57 [47, 65], 59 [56, 66]years), EF (30 [26, 36], 26 [20, 38], 26 [22, 32], 33 [28, 37]%), and chronic renal failure (45%, 65%, 36%, 37%). Subphenotype allocation was driven by subsets of proteins associated with various biological functions, such as oxidative stress, inflammation and extracellular matrix organisation. Clinical characteristics of the subphenotypes were aligned with these associations. Subphenotypes 2 and 3 had the worst prognosis compared to subphenotype 1 (adjHR (95%CI):3.43 (1.76–6.69), and 2.88 (1.37–6.03), respectively). Interpretation: Four circulating-protein based subphenotypes are present in HFrEF, which are driven by varying combinations of protein subsets, and have different clinical characteristics and prognosis. Clinical Trial Registration: ClinicalTrials.gov Identifier: NCT01851538 https://clinicaltrials.gov/ct2/show/NCT01851538. Funding: EU/ EFPIA IMI2JU BigData@Heart grant n° 116074, Jaap Schouten Foundation and Noordwest Academie.

Original language	English
Article number	104655
Journal	EBioMedicine
Volume	93
DOIs	https://doi.org/10.1016/j.ebiom.2023.104655
Publication status	Published - Jul 2023

Bibliographical note

Funding Information:
This work was supported by the EU / EFPIA Innovative Medicines Initiative 2 Joint Undertaking BigData@Heart grant n° 116074 . It was also supported by the Jaap Schouten Foundation and Noordwest Academie , both non-profit. Folkert Asselbergs is supported by UCL Hospitals NIHR Biomedical Research Centre .

Funding Information:
EU/EFPIA IMI2JU BigData@Heart grant n°116074, Jaap Schouten Foundation and Noordwest Academie.This work was supported by the EU/EFPIA Innovative Medicines Initiative 2 Joint Undertaking BigData@Heart grant n° 116074. It was also supported by the Jaap Schouten Foundation and Noordwest Academie, both non-profit. Folkert Asselbergs is supported by UCL Hospitals NIHR Biomedical Research Centre.

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© 2023 The Author(s)

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HFrEF subphenotypes based on 4210 repeatedly measured circulating proteins are driven by different biological mechanismsFinal published version, 3.86 MBLicence: CC BY

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Petersen, T. B., de Bakker, M., Asselbergs, F. W., Harakalova, M., Akkerhuis, K. M., Brugts, J. J., van Ramshorst, J., Lumbers, R. T., Ostroff, R. M., Katsikis, P. D., van der Spek, P. J., Umans, V. A., Boersma, E., Rizopoulos, D., & Kardys, I. (2023). HFrEF subphenotypes based on 4210 repeatedly measured circulating proteins are driven by different biological mechanisms. EBioMedicine, 93, Article 104655. https://doi.org/10.1016/j.ebiom.2023.104655

@article{05dacd43cc7345f086ef002f5b02035d,

title = "HFrEF subphenotypes based on 4210 repeatedly measured circulating proteins are driven by different biological mechanisms",

abstract = "Background: HFrEF is a heterogenous condition with high mortality. We used serial assessments of 4210 circulating proteins to identify distinct novel protein-based HFrEF subphenotypes and to investigate underlying dynamic biological mechanisms. Herewith we aimed to gain pathophysiological insights and fuel opportunities for personalised treatment. Methods: In 382 patients, we performed trimonthly blood sampling during a median follow-up of 2.1 [IQR:1.1–2.6] years. We selected all baseline samples and two samples closest to the primary endpoint (PEP; composite of cardiovascular mortality, HF hospitalization, LVAD implantation, and heart transplantation) or censoring, and applied an aptamer-based multiplex proteomic approach. Using unsupervised machine learning methods, we derived clusters from 4210 repeatedly measured proteomic biomarkers. Sets of proteins that drove cluster allocation were analysed via an enrichment analysis. Differences in clinical characteristics and PEP occurrence were evaluated. Findings: We identified four subphenotypes with different protein profiles, prognosis and clinical characteristics, including age (median [IQR] for subphenotypes 1–4, respectively:70 [64, 76], 68 [60, 79], 57 [47, 65], 59 [56, 66]years), EF (30 [26, 36], 26 [20, 38], 26 [22, 32], 33 [28, 37]%), and chronic renal failure (45%, 65%, 36%, 37%). Subphenotype allocation was driven by subsets of proteins associated with various biological functions, such as oxidative stress, inflammation and extracellular matrix organisation. Clinical characteristics of the subphenotypes were aligned with these associations. Subphenotypes 2 and 3 had the worst prognosis compared to subphenotype 1 (adjHR (95%CI):3.43 (1.76–6.69), and 2.88 (1.37–6.03), respectively). Interpretation: Four circulating-protein based subphenotypes are present in HFrEF, which are driven by varying combinations of protein subsets, and have different clinical characteristics and prognosis. Clinical Trial Registration: ClinicalTrials.gov Identifier: NCT01851538 https://clinicaltrials.gov/ct2/show/NCT01851538. Funding: EU/ EFPIA IMI2JU BigData@Heart grant n° 116074, Jaap Schouten Foundation and Noordwest Academie.",

author = "Petersen, {Teun B.} and {de Bakker}, Marie and Asselbergs, {Folkert W.} and Magdalena Harakalova and Akkerhuis, {K. Martijn} and Brugts, {Jasper J.} and {van Ramshorst}, Jan and Lumbers, {R. Thomas} and Ostroff, {Rachel M.} and Katsikis, {Peter D.} and {van der Spek}, {Peter J.} and Umans, {Victor A.} and Eric Boersma and Dimitris Rizopoulos and Isabella Kardys",

note = "Funding Information: This work was supported by the EU / EFPIA Innovative Medicines Initiative 2 Joint Undertaking BigData@Heart grant n° 116074 . It was also supported by the Jaap Schouten Foundation and Noordwest Academie , both non-profit. Folkert Asselbergs is supported by UCL Hospitals NIHR Biomedical Research Centre . Funding Information: EU/EFPIA IMI2JU BigData@Heart grant n°116074, Jaap Schouten Foundation and Noordwest Academie.This work was supported by the EU/EFPIA Innovative Medicines Initiative 2 Joint Undertaking BigData@Heart grant n° 116074. It was also supported by the Jaap Schouten Foundation and Noordwest Academie, both non-profit. Folkert Asselbergs is supported by UCL Hospitals NIHR Biomedical Research Centre. Publisher Copyright: {\textcopyright} 2023 The Author(s)",

year = "2023",

month = jul,

doi = "10.1016/j.ebiom.2023.104655",

language = "English",

volume = "93",

journal = "EBioMedicine",

issn = "2352-3964",

publisher = "Elsevier",

}

Petersen, TB , de Bakker, M, Asselbergs, FW, Harakalova, M, Akkerhuis, KM , Brugts, JJ, van Ramshorst, J, Lumbers, RT, Ostroff, RM, Katsikis, PD , van der Spek, PJ, Umans, VA, Boersma, E , Rizopoulos, D & Kardys, I 2023, 'HFrEF subphenotypes based on 4210 repeatedly measured circulating proteins are driven by different biological mechanisms', EBioMedicine, vol. 93, 104655. https://doi.org/10.1016/j.ebiom.2023.104655

TY - JOUR

T1 - HFrEF subphenotypes based on 4210 repeatedly measured circulating proteins are driven by different biological mechanisms

AU - Petersen, Teun B.

AU - de Bakker, Marie

AU - Asselbergs, Folkert W.

AU - Harakalova, Magdalena

AU - Akkerhuis, K. Martijn

AU - Brugts, Jasper J.

AU - van Ramshorst, Jan

AU - Lumbers, R. Thomas

AU - Ostroff, Rachel M.

AU - Katsikis, Peter D.

AU - van der Spek, Peter J.

AU - Umans, Victor A.

AU - Boersma, Eric

AU - Rizopoulos, Dimitris

AU - Kardys, Isabella

N1 - Funding Information: This work was supported by the EU / EFPIA Innovative Medicines Initiative 2 Joint Undertaking BigData@Heart grant n° 116074 . It was also supported by the Jaap Schouten Foundation and Noordwest Academie , both non-profit. Folkert Asselbergs is supported by UCL Hospitals NIHR Biomedical Research Centre . Funding Information: EU/EFPIA IMI2JU BigData@Heart grant n°116074, Jaap Schouten Foundation and Noordwest Academie.This work was supported by the EU/EFPIA Innovative Medicines Initiative 2 Joint Undertaking BigData@Heart grant n° 116074. It was also supported by the Jaap Schouten Foundation and Noordwest Academie, both non-profit. Folkert Asselbergs is supported by UCL Hospitals NIHR Biomedical Research Centre. Publisher Copyright: © 2023 The Author(s)

PY - 2023/7

Y1 - 2023/7

N2 - Background: HFrEF is a heterogenous condition with high mortality. We used serial assessments of 4210 circulating proteins to identify distinct novel protein-based HFrEF subphenotypes and to investigate underlying dynamic biological mechanisms. Herewith we aimed to gain pathophysiological insights and fuel opportunities for personalised treatment. Methods: In 382 patients, we performed trimonthly blood sampling during a median follow-up of 2.1 [IQR:1.1–2.6] years. We selected all baseline samples and two samples closest to the primary endpoint (PEP; composite of cardiovascular mortality, HF hospitalization, LVAD implantation, and heart transplantation) or censoring, and applied an aptamer-based multiplex proteomic approach. Using unsupervised machine learning methods, we derived clusters from 4210 repeatedly measured proteomic biomarkers. Sets of proteins that drove cluster allocation were analysed via an enrichment analysis. Differences in clinical characteristics and PEP occurrence were evaluated. Findings: We identified four subphenotypes with different protein profiles, prognosis and clinical characteristics, including age (median [IQR] for subphenotypes 1–4, respectively:70 [64, 76], 68 [60, 79], 57 [47, 65], 59 [56, 66]years), EF (30 [26, 36], 26 [20, 38], 26 [22, 32], 33 [28, 37]%), and chronic renal failure (45%, 65%, 36%, 37%). Subphenotype allocation was driven by subsets of proteins associated with various biological functions, such as oxidative stress, inflammation and extracellular matrix organisation. Clinical characteristics of the subphenotypes were aligned with these associations. Subphenotypes 2 and 3 had the worst prognosis compared to subphenotype 1 (adjHR (95%CI):3.43 (1.76–6.69), and 2.88 (1.37–6.03), respectively). Interpretation: Four circulating-protein based subphenotypes are present in HFrEF, which are driven by varying combinations of protein subsets, and have different clinical characteristics and prognosis. Clinical Trial Registration: ClinicalTrials.gov Identifier: NCT01851538 https://clinicaltrials.gov/ct2/show/NCT01851538. Funding: EU/ EFPIA IMI2JU BigData@Heart grant n° 116074, Jaap Schouten Foundation and Noordwest Academie.

AB - Background: HFrEF is a heterogenous condition with high mortality. We used serial assessments of 4210 circulating proteins to identify distinct novel protein-based HFrEF subphenotypes and to investigate underlying dynamic biological mechanisms. Herewith we aimed to gain pathophysiological insights and fuel opportunities for personalised treatment. Methods: In 382 patients, we performed trimonthly blood sampling during a median follow-up of 2.1 [IQR:1.1–2.6] years. We selected all baseline samples and two samples closest to the primary endpoint (PEP; composite of cardiovascular mortality, HF hospitalization, LVAD implantation, and heart transplantation) or censoring, and applied an aptamer-based multiplex proteomic approach. Using unsupervised machine learning methods, we derived clusters from 4210 repeatedly measured proteomic biomarkers. Sets of proteins that drove cluster allocation were analysed via an enrichment analysis. Differences in clinical characteristics and PEP occurrence were evaluated. Findings: We identified four subphenotypes with different protein profiles, prognosis and clinical characteristics, including age (median [IQR] for subphenotypes 1–4, respectively:70 [64, 76], 68 [60, 79], 57 [47, 65], 59 [56, 66]years), EF (30 [26, 36], 26 [20, 38], 26 [22, 32], 33 [28, 37]%), and chronic renal failure (45%, 65%, 36%, 37%). Subphenotype allocation was driven by subsets of proteins associated with various biological functions, such as oxidative stress, inflammation and extracellular matrix organisation. Clinical characteristics of the subphenotypes were aligned with these associations. Subphenotypes 2 and 3 had the worst prognosis compared to subphenotype 1 (adjHR (95%CI):3.43 (1.76–6.69), and 2.88 (1.37–6.03), respectively). Interpretation: Four circulating-protein based subphenotypes are present in HFrEF, which are driven by varying combinations of protein subsets, and have different clinical characteristics and prognosis. Clinical Trial Registration: ClinicalTrials.gov Identifier: NCT01851538 https://clinicaltrials.gov/ct2/show/NCT01851538. Funding: EU/ EFPIA IMI2JU BigData@Heart grant n° 116074, Jaap Schouten Foundation and Noordwest Academie.

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U2 - 10.1016/j.ebiom.2023.104655

DO - 10.1016/j.ebiom.2023.104655

M3 - Article

C2 - 37327673

AN - SCOPUS:85161634050

SN - 2352-3964

VL - 93

JO - EBioMedicine

JF - EBioMedicine

M1 - 104655

ER -

HFrEF subphenotypes based on 4210 repeatedly measured circulating proteins are driven by different biological mechanisms

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