Principal Investigator: Elizabeth Thomas
Klarismo Limited, Research and Development, Scrutton Street, London
Lead Collaborators: 1) Dr Elizabeth Thomas
Collaborating Institutions and Addresses: 1) University of Westminster, Department of Life Sciences, LondonTags: 23889, biomarkers, body composition, Fat, featured, Imaging, Muscle
1a: Direct measurements of body composition through magnetic resonance imaging (MRI) can provide a much better description of the population than traditional indirect measurements, such as body mass index (BMI) or waist circumference. Anatomically-specific measurements of fat and muscle have the potential to reduce the duration of clinical studies and speed up the approval of new medicines for obesity, type 2 diabetes or other metabolic disorders. Klarismo has developed a fully-automated image analysis pipeline for body composition analysis to transform MRI data into a rich set of imaging biomarkers.
1b: Each MRI scan will be transformed into a rich set of volume-based measurements that are biologically relevant and anatomically specific. These results will be utilized by other researchers to investigate how a variety of symptoms and diseases correlate with both imaging and non-imaging data. The combination of distinct biomarkers from the abdominal imaging component has the potential to define new sub-phenotypes of the population and opens up the potential for personalized medicine. This will be undertaken by creating data-driven categories based on non-imaging variables and exploring the distribution of body composition measurements within each category.
1c: The MRI data will be analyzed using Klarismo’s proven image analysis pipeline to produce precise volumetric measurements of fat (subcutaneous and visceral), major muscle groups and internal organs. Associations between image-based measurements and non-imaging data related to obesity and obesity-related diseases will be investigated. Information in the non-imaging data will be used to construct a set of relevant categories based on health, lifestyle, etc. The variability of body composition measurements from the imaging data within each of these data-driven categories will be quantified.
1d: The full cohort of subjects in the imaging enhancement study (100,000 participants) will be analyzed.
We aim to:
1- Derive different measures of fat distribution, muscle mass and abdominal organ volumes from MRI scans
2- Understand the genetic background of each measurements
3- Understand the association between each measurements and different biomarkers and disease risk. Diseases of most interest include those where alterations in body fat distribution and ectopic fat in organs are known to play a causal role in affecting metabolic health such as non-alcoholic fatty liver disease and related liver morbidities, type-2 diabetes, and cardiovascular disease.
Last updated Jun 10, 2019