From Biomarkers to Therapies: A Multi-Omics Framework for Thoracic Aortic Dissection Prediction and Prevention

Project Overview

Type A aortic dissection often strikes without warning, affecting individuals who may appear otherwise healthy. Current imaging-based surveillance fails to identify many at-risk individuals, with nearly half of dissections occurring in patients whose aortas do not meet the size thresholds typically used for intervention. This underscores a critical need for better predictive tools to identify risk before symptoms develop.

Reliance on aortic diameter as a predictor is inadequate, as approximately 50% of dissections occur in individuals with smaller diameters.

This project aims to transform aortic dissection from an unpredictable emergency into a preventable condition. Dr Nikhil Paliwal and his team will harness the extensive resources of the UK Biobank, which includes health data from over 500,000 participants such as blood samples, heart and body scans, and genetic information. The research will search for molecular warning signs in the blood, including proteins and metabolites that may indicate inflammation or weakening of the aortic wall. Simultaneously, artificial intelligence will be applied to cardiac imaging to detect subtle, early changes in the aorta that are invisible to the human eye. Genetic analysis will further reveal inherited risk factors, helping to explain individual susceptibility.

By integrating these diverse data sources, including biochemical, imaging and genetic information, the team aims to develop a multi-omics predictive tool that clinicians could use to assess a patient’s risk of dissection years before symptoms occur. Ultimately, this tool could guide personalised monitoring strategies, inform early treatment decisions and identify potential therapies to strengthen the aorta and prevent dissection altogether.