Building a comprehensive aortic aneurysm and dissection prediction model incorporating genetic and non-genetic factors

There is an urgent need to identify individuals at risk of aortic aneurysms and dissections as the condition is usually silent until a catastrophic complication such as rupture or dissection occurs. Early identification of those at elevated risk may allow preventative measures, such as blood pressure control and prophylactic surgery, to be instituted.

Screening the entire population for thoracic aortic disease with cross-sectional imaging is not practical, safe or financially feasible. Screening may be justified, however, in high risk individuals (for example first degree relatives of patients). The current screening program for abdominal aortic aneurysm has a low-yield (~1% of all those scanned have a AAA) and approximately 10% of aortic ruptures occur below the age of screening currently advocated in the UK. AAA screening is also limited by the fact that females are not offered screening (despite representing 42% of AAA related deaths). In breast cancer it has been shown that addition of genetic and or clinical information to national screening programs has the potential to reduce the proportion of the population eligible for formal screening tests, identify younger individuals at highest risk (currently ineligible for screening) and reduce the harms of screening (such as overdiagnosis/treatment). These factors are relevant in AAD but as yet have not been evaluated.

Susceptibility to AAD has a strong genetic component and low-risk common genetic variants, intermediate risk rare variants and highly penetrant very rare genetic variants all play a role. Under a precision medicine model, the use of genetic and/or other clinical data may identify individuals at high risk of aortic diseases may allow development of personalised screening and prevention measures.

In this program of research we aim to quantify the absolute risk of AAD, stratified by age, gender, clinical and genetic risk factors using both the UKBiobank and primary incidence data derived from the OxVasc study. When this has been performed we will be able to model the potential feasibility of stratified screening algorithm.

As an extension to this, we aim to characterise the phenotypic consequence of rare, intermediate risk variants that are carried by asymptomatic individuals in the population. In order to do this we will evaluate imaging findings of such carriers in the UKBiobank and also perform a recall by genotype study for individuals recruited to the UKInterval study. Carriers of rare, intermediate risk variants will be recalled for detailed phenotypic evaluation including advanced aortic imaging studies.

Aortic aneurysm and dissection (AAD) is a potentially devastating vascular condition that is often silent until a significant complication occurs. Monogenic, polygenic and clinical risk factors all play a major role in AAD susceptibility and prognosis. Screening and preventative interventions can reduce risk of death and disability from AAD but early identification remains a problem. It is possible that comprehensive risk models, based upon age, electronic health records, genetic data and/or imaging could stratify the population to enable provision of efficient personalised screening. These data could also inform individualised surgical treatment and post-operative surveillance strategies. We aim to evaluate the impact of genetic and other clinical factors on the risk of aortic diseases in the UKBiobank and then model risk distribution using primary incidence rates from the OxVasc study to generate data that could be used to inform screening policies. As an extension to this we aim to develop an ambitious recall-based study to evaluate advanced aortic imaging features of individuals with high genetic risk to evaluate presence of subclinical phenotypes that could guide preventative treatment strategies.

The ultimate desired impact of this award is a long-term transition to a senior clinical academic role for Dr Harrison, to establish an independent world leading research program into aortic aneurysms and dissections using large scale genetic studies, electronic health records and advanced imaging. Insights from this may be used to inform public health policy and large scale aetiology studies. Whilst this may not be realistic within a 3 year time frame there are a number of intermediate impacts that can be achieved win the grant period toward this.

Who might benefit from this research?

1) Policy makers involved in design of preventative strategies and precision medicine

Policy makers may benefit directly from the results of modelling and consider the different policy scenarios for prevention of complications secondary to aortic aneurysms and dissections. The models will improve the the ability to predict likely effects of a precision medicine approach to aortic aneurysms and dissections.

2) Cambridge University Hospitals NHS Trust

The care quality commission (CQC) has acknowledged that NHS trusts with high research activity have excellent clinical outcomes (even for patients not enrolled in clinical studies) but overstretched clinicians struggle to find time to lead high quality research. This partnership will allow Dr Harrison to establish a long term research interest. Vascular surgery is an underrepresented speciality both locally and nationally so this award has the potential to improve care for vascular surgery patients.

3) Patients enrolled in large scale genetic studies and research policy makers

The American College of Medical Genetics supports return of genetic results for patients enrolled in large scale genetic studies. This is not the case in the UK because the impact of these variants in asymptomatic populations is not known. This research, evaluating the impact of rare genetic variants in high impact genes will give funders of major genetic studies data to plan policies regarding return of genetic information to patients.

4) Attracting Research and development funding

This research will provide novel insights into the aetiology of aortic diseases using genetic data. This may be used to catalyse further R&D investments and augment further industry collaboration, for example via AstraZeneca's centre for genetic research.