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The attached spreadsheet contains measurements derived from 10,016 neck to knee body scans using Klarismo Ltd s proprietary image analysis platform. The platform employs a combination of computer vision and machine learning procedures. All results were reviewed manually. For some datasets some results were not obtainable due to data quality issues. These were omitted partially. Description of results: UKBB ID This is the ID assigned by the UKBB to each dataset for Klarismo. Abdominal Subcutaneous Adipose Tissue Volume (ml) Adipose subcutaneous tissue between the top of the femoral head and the top of the thoracic vertebrae T9 in milliliters. Visceral Adipose Tissue Volume (ml) Fat tissue inside the abdominal cavity between the top of the femoral head and the top of the thoracic vertebrae T9 in milliliters. Total Thigh Muscle Volume (ml) Combined volume of posterior thigh muscles (gluteus, iliacus, adductor and hamstring) and anterior thigh muscles (quadriceps femoris and sartorius) in milliliters. Left Iliopsoas Muscle Volume (ml) Total volume of left iliopsoas (iliacus and psoas major) in milliliters. Right Iliopsoas Muscle Volume (ml) Total volume of right iliopsoas (iliacus and psoas major) in milliliters. Archive contains 6 imaging derived measurements on abdominal composition, category 149.
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 project aims to create data-driven categories based on non-imaging variables and to explore the distribution of body composition measurements within each category. The MRI data was analyzed using Klarismos 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 are investigated. The variability of body composition measurements from the imaging data within each of these data-driven categories is being quantified.