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Identification of patient subtypes based on protein expression for prediction of heart failure after myocardial infarction
This study investigates the ability of high-throughput aptamer-based platform to identify circulating biomarkers able to predict occurrence of heart failure (HF), in blood samples collected during hospitalization of patients suffering from a first myocardial infarction (MI). REVE-1 (derivation) and...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10006628/ https://www.ncbi.nlm.nih.gov/pubmed/36915695 http://dx.doi.org/10.1016/j.isci.2023.106171 |
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author | Heyse, Wilfried Vandewalle, Vincent Marot, Guillemette Amouyel, Philippe Bauters, Christophe Pinet, Florence |
author_facet | Heyse, Wilfried Vandewalle, Vincent Marot, Guillemette Amouyel, Philippe Bauters, Christophe Pinet, Florence |
author_sort | Heyse, Wilfried |
collection | PubMed |
description | This study investigates the ability of high-throughput aptamer-based platform to identify circulating biomarkers able to predict occurrence of heart failure (HF), in blood samples collected during hospitalization of patients suffering from a first myocardial infarction (MI). REVE-1 (derivation) and REVE-2 (validation) cohorts included respectively 254 and 238 patients, followed up respectively 9 · 2 ± 4 · 8 and 7 · 6 ± 3 · 0 years. A blood sample collected during hospitalization was used for quantifying 4,668 proteins. Fifty proteins were significantly associated with long-term occurrence of HF with all-cause death as the competing event. k-means, an unsupervised clustering method, identified two groups of patients based on expression levels of the 50 proteins. Group 2 was significantly associated with a higher risk of HF in both cohorts. These results showed that a subset of 50 selected proteins quantified during hospitalization of MI patients is able to stratify and predict the long-term occurrence of HF. |
format | Online Article Text |
id | pubmed-10006628 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-100066282023-03-12 Identification of patient subtypes based on protein expression for prediction of heart failure after myocardial infarction Heyse, Wilfried Vandewalle, Vincent Marot, Guillemette Amouyel, Philippe Bauters, Christophe Pinet, Florence iScience Article This study investigates the ability of high-throughput aptamer-based platform to identify circulating biomarkers able to predict occurrence of heart failure (HF), in blood samples collected during hospitalization of patients suffering from a first myocardial infarction (MI). REVE-1 (derivation) and REVE-2 (validation) cohorts included respectively 254 and 238 patients, followed up respectively 9 · 2 ± 4 · 8 and 7 · 6 ± 3 · 0 years. A blood sample collected during hospitalization was used for quantifying 4,668 proteins. Fifty proteins were significantly associated with long-term occurrence of HF with all-cause death as the competing event. k-means, an unsupervised clustering method, identified two groups of patients based on expression levels of the 50 proteins. Group 2 was significantly associated with a higher risk of HF in both cohorts. These results showed that a subset of 50 selected proteins quantified during hospitalization of MI patients is able to stratify and predict the long-term occurrence of HF. Elsevier 2023-02-11 /pmc/articles/PMC10006628/ /pubmed/36915695 http://dx.doi.org/10.1016/j.isci.2023.106171 Text en © 2023 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Heyse, Wilfried Vandewalle, Vincent Marot, Guillemette Amouyel, Philippe Bauters, Christophe Pinet, Florence Identification of patient subtypes based on protein expression for prediction of heart failure after myocardial infarction |
title | Identification of patient subtypes based on protein expression for prediction of heart failure after myocardial infarction |
title_full | Identification of patient subtypes based on protein expression for prediction of heart failure after myocardial infarction |
title_fullStr | Identification of patient subtypes based on protein expression for prediction of heart failure after myocardial infarction |
title_full_unstemmed | Identification of patient subtypes based on protein expression for prediction of heart failure after myocardial infarction |
title_short | Identification of patient subtypes based on protein expression for prediction of heart failure after myocardial infarction |
title_sort | identification of patient subtypes based on protein expression for prediction of heart failure after myocardial infarction |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10006628/ https://www.ncbi.nlm.nih.gov/pubmed/36915695 http://dx.doi.org/10.1016/j.isci.2023.106171 |
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