Volumetric measurements of body composition and their distribution across data-driven categories of health, lifestyle and well-being

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. 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. 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. The full cohort of subjects in the imaging enhancement study (100,000 participants) will be analyzed.