Title :

Identification of patient subtypes based on protein expression for prediction of heart failure after myocardial infarction

Author(s) :

Heyse, Wilfried [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Facteurs de Risque et Déterminants Moléculaires des Maladies liées au Vieillissement - U 1167 [RID-AGE]
Vandewalle, Vincent [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
MOdel for Data Analysis and Learning [MODAL]
Briend, Guillemette [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
MOdel for Data Analysis and Learning [MODAL]
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

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 >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

ANR Project :

STOP-AS

Collections :

• Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL