Vascular ageing biomarkers have been found to be predictive of cardiovascular risk independently of classical risk factors, yet are not widely used in clinical practice. In this review we present two basic approaches for using machine learning (ML) to assess vascular age: parameter estimation and risk classification. We then summarize their role in developing new techniques to assess vascular ageing quickly and accurately. We discuss the methods used to validate ML-based markers, the evidence for their clinical utility, and key directions for future research. The review is complemented by case studies of the use of ML in vascular age assessment which can be replicated using freely available data and code.

The YVC is a retrospective, multicentre study and will collate data on vascular ageing in children (5-12 years), adolescents (13-18 years) and young adults (19-40 years), as well as routine clinical biochemistry, lifestyle, sociodemographic factors and parental health. ConclusionTo date, 31 research groups from 19 countries have joined the YVC. To our knowledge, this will be the largest study of its kind to investigate vascular ageing in youth.