Abstract: Mostcardiovascular (CV) risk scores used in clinical practice predict the probability of CV events using information on the seven traditional cardiovascular risk factors: age, gender, hypertension, dyslipidemia, obesity, smoking and diabetes. These scores, however, fail to identify young, healthy individuals potentially at risk based on their extension or progression of subclinical atherosclerosis, mainly characterized using imaging techniques. By means of deep phenotyping and omics data analyzed with machine learning methods we aim to develop new risk scores to refine the prediction of 10-year cardiovascular risk in young, asymptomatic individuals. Moreover, this data-driven approach to CVD is improving our understanding about how the molecular profile and a variety of psychosocial, lifestyle, dietary and demographic variables affects the genesis of the disease and its progression and, eventually, how and when SA will lead to cardiovascular events.

Venue: Sala d’actes de la FiB (Campus Nord)