HFrEF subphenotypes based on 4210 repeatedly measured circulating proteins are driven by different biological mechanismsResearch in context
Summary: 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...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-07-01
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| Series: | EBioMedicine |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352396423002207 |
| _version_ | 1827924576163594240 |
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| author | 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 |
| author_facet | 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 |
| author_sort | Teun B. Petersen |
| collection | DOAJ |
| description | Summary: 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. |
| first_indexed | 2024-03-13T05:12:34Z |
| format | Article |
| id | doaj.art-f7d15c4f779b430a93c911178ae4dfdd |
| institution | Directory Open Access Journal |
| issn | 2352-3964 |
| language | English |
| last_indexed | 2024-03-13T05:12:34Z |
| publishDate | 2023-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | EBioMedicine |
| spelling | doaj.art-f7d15c4f779b430a93c911178ae4dfdd2023-06-16T05:10:05ZengElsevierEBioMedicine2352-39642023-07-0193104655HFrEF subphenotypes based on 4210 repeatedly measured circulating proteins are driven by different biological mechanismsResearch in contextTeun B. Petersen0Marie de Bakker1Folkert W. Asselbergs2Magdalena Harakalova3K. Martijn Akkerhuis4Jasper J. Brugts5Jan van Ramshorst6R. Thomas Lumbers7Rachel M. Ostroff8Peter D. Katsikis9Peter J. van der Spek10Victor A. Umans11Eric Boersma12Dimitris Rizopoulos13Isabella Kardys14Department of Cardiology, Erasmus MC, University Medical Center Rotterdam, Doctor Molewaterplein 40, Rotterdam, the Netherlands; Department of Biostatistics, Erasmus MC, University Medical Center Rotterdam, Doctor Molewaterplein 40, Rotterdam, the NetherlandsDepartment of Cardiology, Erasmus MC, University Medical Center Rotterdam, Doctor Molewaterplein 40, Rotterdam, the NetherlandsAmsterdam University Medical Centers, Department of Cardiology, University of Amsterdam, Meibergdreef 9, Amsterdam, the Netherlands; Health Data Research UK and Institute of Health Informatics, University College London, Gower St, London, United KingdomDepartment of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, University of Utrecht, Heidelberglaan 100, Utrecht, the Netherlands; Regenerative Medicine Center Utrecht, University Medical Center Utrecht, University of Utrecht, Heidelberglaan 100, Utrecht, the NetherlandsDepartment of Cardiology, Erasmus MC, University Medical Center Rotterdam, Doctor Molewaterplein 40, Rotterdam, the NetherlandsDepartment of Cardiology, Erasmus MC, University Medical Center Rotterdam, Doctor Molewaterplein 40, Rotterdam, the NetherlandsDepartment of Cardiology, Northwest Clinics, Wilhelminalaan 12, Alkmaar, the NetherlandsBritish Heart Foundation Research Accelerator, University College London, Gower St, London, UK; Institute of Health Informatics, University College London, Gower St, London, UK; Health Data Research UK London, University College London, Gower St, London, UKSomaLogic, Inc., 2945 Wilderness Pl, Boulder, United StatesDepartment of Immunology, Erasmus MC, University Medical Center Rotterdam, Doctor Molewaterplein 40, Rotterdam, the NetherlandsDepartment of Pathology, Erasmus MC, University Medical Center Rotterdam, Doctor Molewaterplein 40, Rotterdam, the NetherlandsDepartment of Cardiology, Northwest Clinics, Wilhelminalaan 12, Alkmaar, the NetherlandsDepartment of Cardiology, Erasmus MC, University Medical Center Rotterdam, Doctor Molewaterplein 40, Rotterdam, the NetherlandsDepartment of Biostatistics, Erasmus MC, University Medical Center Rotterdam, Doctor Molewaterplein 40, Rotterdam, the NetherlandsDepartment of Cardiology, Erasmus MC, University Medical Center Rotterdam, Doctor Molewaterplein 40, Rotterdam, the Netherlands; Corresponding author. Department of Cardiology, Erasmus MC, University Medical Center Rotterdam, Room Na-316, P.O. Box 2040, 3000 CA Rotterdam, the Netherlands.Summary: 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.http://www.sciencedirect.com/science/article/pii/S2352396423002207ProteomicsBiomarkersPhenotypesUnsupervised machine learningHeart Failure |
| spellingShingle | 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 HFrEF subphenotypes based on 4210 repeatedly measured circulating proteins are driven by different biological mechanismsResearch in context EBioMedicine Proteomics Biomarkers Phenotypes Unsupervised machine learning Heart Failure |
| title | HFrEF subphenotypes based on 4210 repeatedly measured circulating proteins are driven by different biological mechanismsResearch in context |
| title_full | HFrEF subphenotypes based on 4210 repeatedly measured circulating proteins are driven by different biological mechanismsResearch in context |
| title_fullStr | HFrEF subphenotypes based on 4210 repeatedly measured circulating proteins are driven by different biological mechanismsResearch in context |
| title_full_unstemmed | HFrEF subphenotypes based on 4210 repeatedly measured circulating proteins are driven by different biological mechanismsResearch in context |
| title_short | HFrEF subphenotypes based on 4210 repeatedly measured circulating proteins are driven by different biological mechanismsResearch in context |
| title_sort | hfref subphenotypes based on 4210 repeatedly measured circulating proteins are driven by different biological mechanismsresearch in context |
| topic | Proteomics Biomarkers Phenotypes Unsupervised machine learning Heart Failure |
| url | http://www.sciencedirect.com/science/article/pii/S2352396423002207 |
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