Principal Investigator: Dr William Cawthorn
Institution: University of EdinburghTags: 48697, bone marrow adipose tissue, featured, GWAS, Machine Learning, Mendelian randomisation, MRI, PheWAS
Collaborator: Professor Jimmy Bell, University of Westminster, UK; Professor Ewan Pearson, University of Dundee, UK
Stop and think about your bones: what images come to mind? Perhaps a skull with grinning jaws, or the strong white limbs stretching out towards your fingers and toes. You might even think of the bone marrow within them, producing the blood that courses through your veins. But this is not the whole picture, for your skeleton hides a secret: it is full of fat, and no one knows why.
This unsolved mystery is surprising. Scientists first noticed that our bone marrow contains fat-storing cells, called adipocytes, over a century ago. This bone marrow adipose tissue (BMAT) comprises over 10% of our total fat stores and further increases with ageing and in diverse diseases. This suggests roles for BMAT not only in the normal functioning of our bodies, but also in disease development and progression.
Unfortunately, study of BMAT has been relatively limited, and BMAT has never been measured across large populations. Consequently, the roles of BMAT in normal physiology and disease remain poorly understood. So, what is the function of BMAT, and how might it impact human health?
Our research seeks to answer these key questions by using information being collected by the UK Biobank, a major study that is following the health and wellbeing of 500,000 volunteers from across the UK. The UK Biobank is now doing magnetic resonance imaging (MRI) scans of 100,000 participants. Using these scans, we will measure BMAT in each participant. This will include the application of artificial intelligence-based methods to automate the MRI analysis, thereby allowing us to establish how the amount of BMAT varies across this very large population.
The power of the UK Biobank is that it has also collected DNA samples and health data on each participant. Therefore, once we have measured participants’ BMAT we will be able to discover how this relates to other aspects of health and disease.
Together, our research will help to unravel the mystery of BMAT whilst also establishing new methods for automated MRI analysis. The latter will be of huge help to the NHS, which currently faces a major backlog of unanalysed MRI scans. More broadly, understanding the impact of BMAT on human health has great potential to improve diagnoses and treatment of numerous diseases, including osteoporosis, diabetes, cardiovascular disease and several types of cancer. This will be vital if we are to reduce the public health impact of these worldwide health problems.
Last updated Sep 30, 2019