Aging involes the progressive decline in physiological integrity, and it is correlated with higher mortality risk. As we age, our organ systems, including our cardiovascular system, gradually deteriorate. Notably, chronological age is generally different from biological age. Becasue of the fast work pace and severe stress, the organ systems of urbanites may age faster than their chronological age, predisposing them to a sub-health state and even various age-associated diseases, particularly cardiovascular diseases. However, one cannot easily predict biological age and thus premature ageing.

We therefore aim to develop an artificial intelligence (AI) and machine learning-based strategy that can predict the biological age of blood vessels based on the genetic profile of blood samples collected from customers. We will correlate the estimated age with the risks of various diseases to provide a pre-diagnostic service.

We will apply AI to analyse the vast amounts of genetic data currently available online, and we will collect first-hand data from volunteers and patients. The identified novel gene signatures of ageing will be validated by biochemical assays in human cell lines and experimental animals. The findings will lead to the identification of novel ageing markers and the development of a prediction system for vascular biological age. The predicted biological age of blood vessels will serve as a parameter to provide a pre-diagnostic risk assessment for various diseases, particularly cardiovascular diseases.