Identification of patient subtypes based ...
Document type :
Article dans une revue scientifique: Article original
PMID :
Title :
Identification of patient subtypes based on protein expression for prediction of heart failure after myocardial infarction
Author(s) :
Heyse, Wilfried [Auteur]
Facteurs de Risque et Déterminants Moléculaires des Maladies liées au Vieillissement - U 1167 [RID-AGE]
MOdel for Data Analysis and Learning [MODAL]
Vandewalle, Vincent [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Briend, Guillemette [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Amouyel, Philippe [Auteur]
Facteurs de Risque et Déterminants Moléculaires des Maladies liées au Vieillissement - U 1167 [RID-AGE]
Bauters, Christophe [Auteur]
Facteurs de Risque et Déterminants Moléculaires des Maladies liées au Vieillissement - U 1167 [RID-AGE]
PINET, FLORENCE [Auteur correspondant]
Facteurs de Risque et Déterminants Moléculaires des Maladies liées au Vieillissement - U 1167 [RID-AGE]
Facteurs de Risque et Déterminants Moléculaires des Maladies liées au Vieillissement - U 1167 [RID-AGE]
MOdel for Data Analysis and Learning [MODAL]
Vandewalle, Vincent [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Briend, Guillemette [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Amouyel, Philippe [Auteur]
Facteurs de Risque et Déterminants Moléculaires des Maladies liées au Vieillissement - U 1167 [RID-AGE]
Bauters, Christophe [Auteur]
Facteurs de Risque et Déterminants Moléculaires des Maladies liées au Vieillissement - U 1167 [RID-AGE]
PINET, FLORENCE [Auteur correspondant]

Facteurs de Risque et Déterminants Moléculaires des Maladies liées au Vieillissement - U 1167 [RID-AGE]
Journal title :
Iscience
Pages :
106171
Publisher :
Elsevier
Publication date :
2023-03
HAL domain(s) :
Sciences du Vivant [q-bio]
Sciences du Vivant [q-bio]/Médecine humaine et pathologie/Cardiologie et système cardiovasculaire
Sciences du Vivant [q-bio]/Médecine humaine et pathologie/Cardiologie et système cardiovasculaire
English abstract : [en]
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 ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
ANR Project :
Collections :
Source :
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