Functional mechanisms and body-brain imaging
How do the genes that are associated with comorbid cardiovascular disease and mental ill-health change the body and/or brain?
Principal Investigator: Anders Dale
From genes to body
Several genes have already been identified that appear to increase people’s risk of developing cardiovascular disease and/or mental ill-health – and in work packages 2 and 3 we hope to identify more.
However, what effect do these genes actually have on the body or brain?
- Where are the genes active [expressed]? Are any expressed both in the heart and the brain?
- How do gene edits affect the cells? (e.g. cellular process slowed down).
- Would it be possible to detect any structural changes on an MRI scan?
- Very few genes work alone. What happens when you consider them as a network, interacting with and controlling one another?
From body to genes
Researchers can also look at things the other way around i.e. start with changes in the body or brain and use these to calculate which genes might be involved.
Through CoMorMent, our researchers will gain access to over 60,000 MRI scans.
By using cutting edge machine learning to compare the images of healthy people to those who have comorbid cardiovascular disease/mental ill-health, we will identify key areas of difference.
By considering which genes control these key areas/functions, we will be able to suggest genes for work packages 2 and 3 to investigate.
Our aim is to produce a comprehensive information map, which links together genetics, lifestyle, whole body imaging and disease mechanism data.
We will make use of the data and infrastructure put in place by Work Package 1 and the genes discovered by work packages 2 and 3, to understand more about the causes of comorbid cardiovascular disease and mental ill-health.
The brain and body can age at different rates. The reasons for this are partly genetic and partly due to lifestyle.
By studying MRI scans of the brain, researchers can allocate each person a brain ‘age’, which may be older or younger than their chronological age (number of birthdays).
Could these same ‘brain age’ markers help us predict who is at highest risk of developing co-morbid cardiovascular disease and mental ill-health, before their symptoms even appear?
Using advanced machine learning techniques, we will combine MRI brain imaging data with genetic, lifestyle and health data.
We will then use our results to establish a ‘risk profile’ which could help doctors identify those patients most at risk and who would benefit most from changing their lifestyle or taking a different medication etc.
Body Fatty Tissue Age
The distribution of fat in the body can be indicative of overall health.
For example, ‘belly fat’ has been associated with an increased risk of cardiovascular disease.
Using whole-body-imaging (MRI), we can examine both overall fat distribution, as well as consider each area in detail (e.g. fat within muscles, which may be associated with age-related muscle weakness).
To standardise such measurements, our industry partner AMRA has created a set of profiles (‘body maps’) which can used as a reference point for a healthy body.
In this project, we will explore how these body fat ‘maps’ are changed by mental ill-health and comorbid cardiovascular disease (or the genetic/lifestyle risk factors for them).
In the future, we hope to be able to allocate a ‘body fatty tissue age’ to people (similar to ‘brain age’ above). People with a ‘body fatty tissue age’ older than their chronological age (number of birthdays), may be at higher risk of developing comorbid CVD/mental ill-health.
Drugs, Interventions and prevention
Once we understand the mechanisms that link together our genes and health outcomes, it gives us the opportunity to intervene. This could be through the development of new drugs, or a new process for identifying patients who are most at risk from medication side effects.
We aim to adapt AMRA’s 6 minute MRI scan, so that it can be used to predict a patient’s risk of cardiovascular disease. This is of particular relevance to patients with mental ill-health, who are at increased risk of developing cardiovascular disease.
Our aim is to support doctors and psychiatrists to adapt their methods and treatment strategies in order to prevent cardiovascular and mental ill-health.
We will use a wide range of tools within this work package including:
- Single cell RNA sequencing
- Post-mortem brain expression
- Functional genomic data
We will make use of large international repositories (Allen Brain Atlas, http://portal.brain-map.org/, PsychENCODE http://www.psychencode.org/), as well as in-house samples of single-cell RNAseq, to explore shared mechanisms and cellular pathways.
As far as possible, we will make our tools and code openly available for others to use and adapt.
Cole, J., Ritchie, S., Bastin, M. et al. Brain age predicts mortality. Mol Psychiatry 23, 1385–1392 (2018). https://doi.org/10.1038/mp.2017.62