MRI-based Body Composition Analysis

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Metabolic risk related to body-fat accumulation is strongly dependent on fat distribution. Central obesity and, in particular, ectopic fat accumulation, are important metabolic risk factors. The only way to directly assess body-fat distribution is to use tomographic imaging techniques. This project has developed a method for quantitative assessment of body composition that measures both fat distribution and muscle volume. 

Today, it is well known that the metabolic risk related to body-fat accumulation is strongly dependent on fat distribution. Central obesity and in particular ectopic fat accumulation, are important metabolic risk factors. Large amounts of visceral adipose tissue is associated with increased risk of cardiovascular disease, type-2 diabetes, liver disease, and cancer. But more importantly, it has been shown that disease risks tend to be related to specific patterns of fat accumulation.

The only way to directly assess body-fat distribution is to use tomographic imaging techniques. Magnetic resonance imaging (MRI) can also measure muscle volumes, muscle fat infiltration and other ectopic fat accumulation, which makes it a powerful tool for advanced body composition assessment. 

A Quantitative Method

To be able to measure fat accumulation in different parts of the abdomen an image analysis method was combined with knowledge in MR physics.The measurement technique has been refined and can now measure in more detail and larger parts of the body, fat infiltration in the muscles as well as muscle volume.

MRI is not in itself a quantitative method. In the project a postprocessing technique that calibrated the images against the fat signal to produce a quantitative result has been developed. This technique was patented and placed in the spin-off company AMRA.

AMRA allows the use of an industrial production process that would otherwise not be possible in a research environment. The research group is now in the process of analyzing 100 000 whole body scans.

The large study population allows the research group to use big data components to find correlations between body composition and other health aspects as heart disease prevalence. With follow up data it might be possible to predict disease outcome by looking at the body composition.

The identification of specific fat distributions associated with different diseases enables the development of more targeted and effective treatments. One example of how this research can be used is as a tool in clinical trials. As MRI-based body composition analysis greatly individualizes the description of the patient, it provides information that can identify and define the populations in clinical trials, bringing them one step closer to precision medicine.  

Copyright AMRA AB

Key Publications
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Magnus Borga, Janne West, Jimmy Bell, Nicholas Harvey, Thobias Romu, Steven Heymsfield, Olof Dahlqvist Leinhard (2018)

Journal of Investigative Medicine , Vol.66 , s.887-895 Continue to DOI

Jennifer Linge, Magnus Borga, Janne West, Theresa Tuthill, Melissa R. Miller, Alexandra Dumitriu, E. Louise Thomas, Thobias Romu, Patrik Tunon, Jimmy D. Bell, Olof Dahlqvist Leinhard (2018)

Obesity , Vol.26 , s.1785-1795 Continue to DOI

Michael Middleton, William Haufe, Jonathan Hooker, Magnus Borga, Olof Dahlqvist Leinhard, Thobias Romu, Patrik Tunón, Gavin Hamilton, Tanya Wolfson, Anthony Gamst, Rohit Loomba, Claude Sirlin (2017)

Radiology , Vol.283 , s.438-449 Continue to DOI

Contacts
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