: Cardiovascular (CV) disease represents the most common cause of death in developed countries. Risk assessment is highly relevant to intervene at individual level and implement prevention strategies. Circulating extracellular vesicles (EVs) are involved in the development and progression of CV diseases and are considered promising biomarkers. We aimed at identifying an EV signature to improve the stratification of patients according to CV risk and likelihood to develop fatal CV events. EVs were characterized by nanoparticle tracking analysis and flow cytometry for a standardized panel of 37 surface antigens in a cross-sectional multicenter cohort (n=486). CV profile was defined by presence of different indicators (age, sex, body mass index, hypertension, hyperlipidemia, diabetes, coronary artery disease, cardiac heart failure, chronic kidney disease, smoking habit, organ damage) and according to the 10-year risk of fatal CV events estimated using SCORE charts of European Society of Cardiology. By combining expression levels of EV antigens using unsupervised learning, patients were classified into three clusters: Cluster-I (n=288), Cluster-II (n=83), Cluster-III (n=30). A separate analysis was conducted on patients displaying acute CV events (n=82). Prevalence of hypertension, diabetes, chronic heart failure, and organ damage (defined as left ventricular hypertrophy and/or microalbuminuria) increased progressively from Cluster-I to Cluster-III. Several EV antigens, including markers for platelets (CD41b-CD42a-CD62P), leukocytes (CD1c-CD2-CD3-CD4-CD8-CD14-CD19-CD20-CD25-CD40-CD45-CD69-CD86), and endothelium (CD31-CD105) were independently associated with CV risk indicators and correlated to age, blood pressure, glucometabolic profile, renal function, and SCORE risk. EV profiling, obtained from minimally invasive blood sampling, allows accurate patient stratification according to CV risk profile.

Supervised and unsupervised learning to define the cardiovascular risk of patients according to an extracellular vesicle molecular signature

Burrello, Jacopo;Amongero, Martina;Airale, Lorenzo;Mulatero, Paolo;Bussolati, Benedetta;Monticone, Silvia;
2022

Abstract

: Cardiovascular (CV) disease represents the most common cause of death in developed countries. Risk assessment is highly relevant to intervene at individual level and implement prevention strategies. Circulating extracellular vesicles (EVs) are involved in the development and progression of CV diseases and are considered promising biomarkers. We aimed at identifying an EV signature to improve the stratification of patients according to CV risk and likelihood to develop fatal CV events. EVs were characterized by nanoparticle tracking analysis and flow cytometry for a standardized panel of 37 surface antigens in a cross-sectional multicenter cohort (n=486). CV profile was defined by presence of different indicators (age, sex, body mass index, hypertension, hyperlipidemia, diabetes, coronary artery disease, cardiac heart failure, chronic kidney disease, smoking habit, organ damage) and according to the 10-year risk of fatal CV events estimated using SCORE charts of European Society of Cardiology. By combining expression levels of EV antigens using unsupervised learning, patients were classified into three clusters: Cluster-I (n=288), Cluster-II (n=83), Cluster-III (n=30). A separate analysis was conducted on patients displaying acute CV events (n=82). Prevalence of hypertension, diabetes, chronic heart failure, and organ damage (defined as left ventricular hypertrophy and/or microalbuminuria) increased progressively from Cluster-I to Cluster-III. Several EV antigens, including markers for platelets (CD41b-CD42a-CD62P), leukocytes (CD1c-CD2-CD3-CD4-CD8-CD14-CD19-CD20-CD25-CD40-CD45-CD69-CD86), and endothelium (CD31-CD105) were independently associated with CV risk indicators and correlated to age, blood pressure, glucometabolic profile, renal function, and SCORE risk. EV profiling, obtained from minimally invasive blood sampling, allows accurate patient stratification according to CV risk profile.
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Biomarker; Cardiovascular Risk; Extracellular Vesicles; Machine Learning
Burrello, Jacopo; Burrello, Alessio; Vacchi, Elena; Bianco, Giovanni; Caporali, Elena; Amongero, Martina; Airale, Lorenzo; Bolis, Sara; Vassalli, Giuseppe; Cereda, Carlo W; Mulatero, Paolo; Bussolati, Benedetta; Camici, Giovanni G; Melli, Giorgia; Monticone, Silvia; Barile, Lucio
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/1847186
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