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Systematic quantification of health parameters from UK Biobank abdominal MRI

Cardiometabolic diseases are an increasing health burden. While well established socioeconomic, environmental, behavioural, and genetic risk factors have been identified, our understanding of their drivers and mechanisms remain incomplete. Thus, a better understanding of these factors is required to develop more effective interventions. Magnetic resonance imaging (MRI) has been used to assess organ health in a number of studies, but large-scale population-based studies are still in their infancy. Using deep learning to segment individual organs (liver, pancreas, spleen, kidneys, lungs and adipose depots) from up to 38,683 abdominal MRI scans in the UK Biobank, we demonstrate that image derived phenotypes such as volume, fat and iron content reflect overall organ health. We further show that these traits have a substantial heritable component, and identify 93 independent genome-wide significant associations. Overall our work demonstrates the feasibility and power of high-throughput MRI scanning to generate new insight into health.