Heart failure subphenotypes based on repeated biomarker measurements are associated with clinical characteristics and adverse events (Bio-SHiFT study)
This study aimed to identify heart failure (HF) subphenotypes using 92 repeatedly measured circulating proteins in 250 patients with heart failure with reduced ejection fraction, and to investigate their clinical characteristics and prognosis. Clinical data and blood samples were collected tri-month...
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| Published in: | International journal of cardiology Vol. 364; pp. 77 - 84 |
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| Main Authors: | , , , , , , , , , , , , , |
| Format: | Journal Article |
| Language: | English |
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Elsevier B.V
01-10-2022
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| Abstract | This study aimed to identify heart failure (HF) subphenotypes using 92 repeatedly measured circulating proteins in 250 patients with heart failure with reduced ejection fraction, and to investigate their clinical characteristics and prognosis.
Clinical data and blood samples were collected tri-monthly until the primary endpoint (PEP) or censoring occurred, with a maximum of 11 visits. The Olink Cardiovascular III panel was measured in baseline samples and the last two samples before the PEP (in 66 PEP cases), or the last sample before censoring (in 184 PEP-free patients). The PEP comprised cardiovascular death, heart transplantation, Left Ventricular Assist Device implantation, and hospitalization for HF. Cluster analysis was performed on individual biomarker trajectories to identify subphenotypes. Then biomarker profiles and clinical characteristics were investigated, and survival analysis was conducted.
Clustering revealed three clinically diverse subphenotypes. Cluster 3 was older, with a longer duration of, and more advanced HF, and most comorbidities. Cluster 2 showed increasing levels over time of most biomarkers. In cluster 3, there were elevated baseline levels and increasing levels over time of 16 remaining biomarkers. Median follow-up was 2.2 (1.4–2.5) years. Cluster 3 had a significantly poorer prognosis compared to cluster 1 (adjusted event-free survival time ratio 0.25 (95%CI:0.12–0.50), p < 0.001). Repeated measurements clusters showed incremental prognostic value compared to clusters using single measurements, or clinical characteristics only.
Clustering based on repeated biomarker measurements revealed three clinically diverse subphenotypes, of which one has a significantly worse prognosis, therefore contributing to improved (individualized) prognostication.
•We repeatedly measured 92 circulating proteins in 250 ambulant patients with HFrEF.•Clustering of individual biomarker trajectories identified 3 HFrEF subphenotypes.•Subphenotypes were clinically diverse and associated with adverse events.•Thus temporal biomarker pattern subphenotypes may aid in personalized prognostication. |
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| AbstractList | This study aimed to identify heart failure (HF) subphenotypes using 92 repeatedly measured circulating proteins in 250 patients with heart failure with reduced ejection fraction, and to investigate their clinical characteristics and prognosis.
Clinical data and blood samples were collected tri-monthly until the primary endpoint (PEP) or censoring occurred, with a maximum of 11 visits. The Olink Cardiovascular III panel was measured in baseline samples and the last two samples before the PEP (in 66 PEP cases), or the last sample before censoring (in 184 PEP-free patients). The PEP comprised cardiovascular death, heart transplantation, Left Ventricular Assist Device implantation, and hospitalization for HF. Cluster analysis was performed on individual biomarker trajectories to identify subphenotypes. Then biomarker profiles and clinical characteristics were investigated, and survival analysis was conducted.
Clustering revealed three clinically diverse subphenotypes. Cluster 3 was older, with a longer duration of, and more advanced HF, and most comorbidities. Cluster 2 showed increasing levels over time of most biomarkers. In cluster 3, there were elevated baseline levels and increasing levels over time of 16 remaining biomarkers. Median follow-up was 2.2 (1.4–2.5) years. Cluster 3 had a significantly poorer prognosis compared to cluster 1 (adjusted event-free survival time ratio 0.25 (95%CI:0.12–0.50), p < 0.001). Repeated measurements clusters showed incremental prognostic value compared to clusters using single measurements, or clinical characteristics only.
Clustering based on repeated biomarker measurements revealed three clinically diverse subphenotypes, of which one has a significantly worse prognosis, therefore contributing to improved (individualized) prognostication.
•We repeatedly measured 92 circulating proteins in 250 ambulant patients with HFrEF.•Clustering of individual biomarker trajectories identified 3 HFrEF subphenotypes.•Subphenotypes were clinically diverse and associated with adverse events.•Thus temporal biomarker pattern subphenotypes may aid in personalized prognostication. BACKGROUNDThis study aimed to identify heart failure (HF) subphenotypes using 92 repeatedly measured circulating proteins in 250 patients with heart failure with reduced ejection fraction, and to investigate their clinical characteristics and prognosis. METHODSClinical data and blood samples were collected tri-monthly until the primary endpoint (PEP) or censoring occurred, with a maximum of 11 visits. The Olink Cardiovascular III panel was measured in baseline samples and the last two samples before the PEP (in 66 PEP cases), or the last sample before censoring (in 184 PEP-free patients). The PEP comprised cardiovascular death, heart transplantation, Left Ventricular Assist Device implantation, and hospitalization for HF. Cluster analysis was performed on individual biomarker trajectories to identify subphenotypes. Then biomarker profiles and clinical characteristics were investigated, and survival analysis was conducted. RESULTSClustering revealed three clinically diverse subphenotypes. Cluster 3 was older, with a longer duration of, and more advanced HF, and most comorbidities. Cluster 2 showed increasing levels over time of most biomarkers. In cluster 3, there were elevated baseline levels and increasing levels over time of 16 remaining biomarkers. Median follow-up was 2.2 (1.4-2.5) years. Cluster 3 had a significantly poorer prognosis compared to cluster 1 (adjusted event-free survival time ratio 0.25 (95%CI:0.12-0.50), p < 0.001). Repeated measurements clusters showed incremental prognostic value compared to clusters using single measurements, or clinical characteristics only. CONCLUSIONSClustering based on repeated biomarker measurements revealed three clinically diverse subphenotypes, of which one has a significantly worse prognosis, therefore contributing to improved (individualized) prognostication. |
| Author | van Ramshorst, Jan Rizopoulos, Dimitris Akkerhuis, K. Martijn Germans, Tjeerd de Lange, Iris Constantinescu, Alina A. Umans, Victor A.W.M. Manintveld, Olivier C. Petersen, Teun B. Caliskan, Kadir Kardys, Isabella de Bakker, Marie Boersma, Eric Brugts, Jasper J. |
| Author_xml | – sequence: 1 givenname: Iris surname: de Lange fullname: de Lange, Iris organization: Department of Cardiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands – sequence: 2 givenname: Teun B. surname: Petersen fullname: Petersen, Teun B. organization: Department of Cardiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands – sequence: 3 givenname: Marie surname: de Bakker fullname: de Bakker, Marie organization: Department of Cardiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands – sequence: 4 givenname: K. Martijn surname: Akkerhuis fullname: Akkerhuis, K. Martijn organization: Department of Cardiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands – sequence: 5 givenname: Jasper J. surname: Brugts fullname: Brugts, Jasper J. organization: Department of Cardiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands – sequence: 6 givenname: Kadir surname: Caliskan fullname: Caliskan, Kadir organization: Department of Cardiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands – sequence: 7 givenname: Olivier C. surname: Manintveld fullname: Manintveld, Olivier C. organization: Department of Cardiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands – sequence: 8 givenname: Alina A. surname: Constantinescu fullname: Constantinescu, Alina A. organization: Department of Cardiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands – sequence: 9 givenname: Tjeerd surname: Germans fullname: Germans, Tjeerd organization: Department of Cardiology, Northwest Clinics, Alkmaar, the Netherlands – sequence: 10 givenname: Jan surname: van Ramshorst fullname: van Ramshorst, Jan organization: Department of Cardiology, Northwest Clinics, Alkmaar, the Netherlands – sequence: 11 givenname: Victor A.W.M. surname: Umans fullname: Umans, Victor A.W.M. organization: Department of Cardiology, Northwest Clinics, Alkmaar, the Netherlands – sequence: 12 givenname: Eric surname: Boersma fullname: Boersma, Eric organization: Department of Cardiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands – sequence: 13 givenname: Dimitris surname: Rizopoulos fullname: Rizopoulos, Dimitris organization: Department of Biostatistics, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands – sequence: 14 givenname: Isabella surname: Kardys fullname: Kardys, Isabella email: i.kardys@erasmusmc.nl organization: Department of Cardiology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, the Netherlands |
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| Keywords | Heart failure Cluster analysis Biomarkers Phenotype Prognosis Repeated measurements |
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| Title | Heart failure subphenotypes based on repeated biomarker measurements are associated with clinical characteristics and adverse events (Bio-SHiFT study) |
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