Approved research

Integrative Analysis of Genetic, Functional Genomics, Clinical, and Imaging Data to Develop a Model for Stratifying Risk of Unruptured Intracranial Aneurysms

Lay summary

Our research aims to understand the factors that increase the risk of intracranial aneurysm rupture, which occurs when a weak spot in a blood vessel in the brain bursts, causing potentially life-threatening bleeding. We want to build a model that can predict which aneurysms are more likely to rupture by combining information from genetics, functional genomics, clinical data, and brain imaging.

Scientific rationale: The scientific basis for our study is that current methods for assessing the risk of aneurysm rupture are limited, and there's a lack of non-invasive ways to predict it. Previous research has shown a link between how fast a person's body ages and their risk of aneurysm rupture. Building on this, we want to use information about biological age acceleration, which is calculated based on metabolism and immune-related parameters, along with detailed analysis of brain imaging data to identify specific features of aneurysms that may indicate a higher risk of rupture.

Project duration: We'll be using data from the UK Biobank, which includes detailed imaging and genetic information from thousands of individuals, to build and validate our risk prediction model for unruptured intracranial aneurysms. The project is expected to last for 3 years.

Public health impact: By providing better ways to visualize and quantify intracranial aneurysms, our study could improve upon current imaging-based classifications and help doctors more accurately assess patients' risk factors and prognosis. Additionally, by establishing personalized risk profiles and quantifiable parameters for aneurysms, we could contribute to more tailored treatment strategies, ultimately improving patient outcomes and reducing the burden on healthcare systems.