The latest epidemiological data suggest that cardiovascular diseases (CVD) are still the leading cause of morbidity and mortality worldwide. In order to improve CVD outcomes, we need new strategies that incorporate the complex interplay of different driving forces behind atherosclerosis pathophysiology in addition to the traditional risk factors. AtheroNET aims to consolidate and connect experts from different fields into European and international network that will focus on the use of multiple omics technologies and data integration through machine learning/artificial intelligence ML/AI approach to bring novel paradigms in prevention, diagnosis, and treatment of atherosclerotic cardiovascular disease (ASCVD). Current CVD-related initiatives and networks are focused on specific aspects of CVD and/or specific methodologies. AtheroNET offers a comprehensive environment in which different stakeholders (basic scientists, clinicians, bioinformaticians, industry representatives, patients’ representatives) will address current challenges.
EHN’s role is to help bridge the gap between the scientific and patient communities by bringing in the patient’s perspective, facilitating patients’ engagement and raising awareness of new ASCVD therapeutic strategies in the CVD field.
Project Key Facts
Action Chair: Prof Paolo Magni at the Universita’ degli Studi di Milano
Start Date: 19 October 2022
Funding: COST European Cooperation in Science and Technology
To learn more about this project visit this website.