Identification of patient subtypes based on protein expression for prediction of heart failure after myocardial infarction
Wilfried Heyse,
Vincent Vandewalle,
Guillemette Marot,
Philippe Amouyel,
Christophe Bauters,
Florence Pinet
Affiliations
Wilfried Heyse
UniversityLille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167 - RID-AGE - Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement, 59000 Lille, France; Inria, Modal, 59000 Lille, France
Vincent Vandewalle
UniversityLille, CHU Lille, URL2694-METRICS - Evaluation des technologies de santé et des pratiques médicales, 59000 Lille, France; Inria, Modal, 59000 Lille, France
Guillemette Marot
UniversityLille, CHU Lille, URL2694-METRICS - Evaluation des technologies de santé et des pratiques médicales, 59000 Lille, France; Inria, Modal, 59000 Lille, France
Philippe Amouyel
UniversityLille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167 - RID-AGE - Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement, 59000 Lille, France
Christophe Bauters
UniversityLille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167 - RID-AGE - Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement, 59000 Lille, France
Florence Pinet
UniversityLille, Inserm, CHU Lille, Institut Pasteur de Lille, U1167 - RID-AGE - Facteurs de risque et déterminants moléculaires des maladies liées au vieillissement, 59000 Lille, France; Corresponding author
Summary: 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.
