New study in Obesity reveals metabolic phenotypes described by skewed body fat distributions
Linköping, Sweden [May 22, 2018]: Specific patterns of fat distribution are linked to the presence of Coronary Heart Disease (CHD) and Type 2 Diabetes (T2D), according to a new study published in Obesity.1 AMRA, the international leader in body composition analysis, today announced the results of a body composition study of over 6,000 subjects, stressing the need to measure and investigate several fat compartments in order to understand and develop treatments for multiple metabolic diseases. The new findings go far beyond what can be described by sex, age, lifestyle, BMI, or a single fat compartment, and have the potential to strongly impact how metabolic conditions will be prevented and managed in the future.
The Obesity study was co-authored in collaboration between AMRA, Pfizer, Westminster University and Linköping University. The 6,000 subjects analyzed are part of the UK Biobank Imaging Study, a major national and international health resource. In 2015, UK Biobank announced that AMRA would perform the automated analysis of MRI images for precise fat and muscle measurements. AMRA has now developed the technique of body composition profiling, which allows for precise analysis of multiple variables to describe the complex associations and interactions between fat distribution, muscle volumes, and metabolic status.
Regardless of normal, overweight, or obese BMI class, AMRA’s body composition profiling of the subjects revealed a number of skewed fat distribution patterns, or phenotypes, that cannot be described when looking at a single fat or muscle measurement. These phenotypes are associated with different metabolic disease profiles: some exhibit no metabolic disease, while others exhibit CHD, T2D, or the co-morbidity of the two. Specifically, higher visceral fat and muscle fat was associated with CHD and T2D (p<0.001) while higher liver fat was associated with T2D (p<0.001) and lower liver fat with CHD (p<0.05). Lower visceral fat and muscle fat was also associated with metabolic health (p<0.001), whereas liver fat was non-significant. Associations remained significant when adjusting for sex, age, BMI, alcohol, smoking, and physical activity.
Dr Olof Dahlqvist Leinhard, senior author of the study, commented, “It has been known for some time that there are fat distributions that are disadvantageous from a health perspective. Today, new techniques provide high accuracy and precision, enabling in-depth analyses of the clinical importance of body composition at a large scale. What’s exciting is that, by using a multivariable approach and an intuitive visualization of body composition, we’ve been able to identify a wide range of body composition profiles that could provide the link to increased risk of metabolic diseases.”
Tommy Johansson, Chief Executive Officer of AMRA, added, “These ground-breaking results allow a glimpse into the future where precision diagnostics will provide the backbone to personalized medicine. By better understanding of muscle and fat volumes, and where fat is located in the body, we hope to help redefine disease risk and suitability for treatment. Our vision is that, in the future, our research and technology will be used to assist in the improved prevention, diagnosis, and treatment of a wide range of diseases.”
Today a quarter of the world’s adults have Metabolic Syndrome – a cluster of factors that increase the risk of several chronic diseases, such as heart disease, cancer, stroke, liver disease and diabetes.2 Obesity, CVD and T2D are growing pandemics and leading causes of early death globally, presenting some of the greatest challenges to patients and healthcare systems worldwide.3,4,5,6
The first 6,021 participants from the UK Biobank imaging sub-study – mean age 62.3±7.5 years, and BMI 26.7±4.4 kg/m2 – were scanned using a Rapid MRI protocol providing a water and fat separated volumetric dataset covering neck to knees, and a single slice for proton density fat fraction (PDFF) assessment in the liver. For body composition, acquired image data were analyzed for visceral adipose tissue, abdominal subcutaneous adipose tissue, thigh muscle volume, muscle fat infiltration in the anterior thighs, and liver PDFF. Briefly, the image analysis comprised of (1) image calibration, (2) fusion of image stacks, (3) image segmentation, (4) quantification of fat and muscle volumes7,8,9,10,11 and manual quality control by an analysis engineer.
Diagnosis information was gathered through in-patient electronic healthcare records and via touchscreen questionnaires followed by interviews performed by trained nurses. For CHD and T2D, two matched control groups were stratified matched on (1) sex and age, and (2) sex, age, and BMI. Participants were considered metabolic disease free if they did not report any conditions considered to be serious enough to represent metabolically-focused health concerns (e.g. cardiovascular and metabolic diseases, severe chronic conditions, neurological diseases, and cancers).12
The study was carried out as a collaboration between AMRA Medical, Pfizer, Westminster University and Linköping University. Funding support was provided by Pfizer.
AMRA is the first in the world to transform images from a rapid, Rapid whole body MRI scan into precise, 3D-volumetric fat and muscle measurements. AMRA’s cloud-based analysis service offers precise, automated insights that have far-reaching implications for the pharmaceutical industry, academic R&D and, soon, clinical practice. AMRA was founded in 2010 as a spin-off of the Center for Medical Image Science and Visualization (CMIV), the Department of Biomedical Engineering (IMT) and the Department of Medicine and Health (IMH) at Linköping University, Sweden. For more information, visit www.amramedical.com.
SVP Global Business Development & Marketing
About UK Biobank
UK Biobank is a major national and international health resource with the aim of improving the prevention, diagnosis and treatment of a wide range of serious and life-threatening illnesses – including cancer, heart diseases, stroke, diabetes, arthritis, eye disorders, depression and forms of dementia. UK Biobank recruited 500,000 people aged between 40–69 years in 2006–2010 from across the country, when they provided lots of information about their health and well-being and donated samples of blood and urine for long-term storage and analysis, including genetic information. The project has permission to follow participants’ health through medical records. UK Biobank has also embarked on a major project to MRI scan the vital internal organs and body composition of 100,000 participants. Over many years, these detailed data will build a powerful resource to help scientists discover why some people develop particular diseases and others do not, and to suggest new ways of preventing and treating them.
Head of Communications
2 Worldwide Definition of the Metabolic Syndrome, International Diabetes Federation (IDF)
3 Chang S-H, et al. JAMA Surg 2014;149:275–87
4 IDF diabetes atlas. 2015. Available at: http://www.diabetesatlas.org [last accessed 06.10.16]
5 Branca F, et al. The Challenge of Obesity in the WHO European Region and the Strategies for Response: Summary. 2007
6 Klein S, et al. Obesity (Silver Spring) 2011;19:581–7
7 West J, Dahlqvist Leinhard O, Romu T, et al. Feasibility of MR-based Body Composition Analysis in Large Scale Population Studies. PLoS ONE 2016;11(9)
8 Borga M, Thomas EL, Romu T, et al. Validation of a Fast Method for Quantification of Intra-abdominal and Subcutaneous Adipose Tissue for Large Scale Human Studies. NMR Biomed 2015;28(12):1747–53
9 OD Leinhard, A Johansson, J Rydell, et al. Quantitative Abdominal Fat Estimation Using MRI Pattern Recognition. In: Proceedings of the 19th International Conference on Pattern Recognition (ICPR) 08–11 Dec, 2008; Tampa, FL
10 Karlsson A, Rosander J, Romu T, et al. Automatic and Quantitative Assessment of Regional Muscle Volume by Multi-Atlas Segmentation Using Whole-Body Water-Fat MRI. JMRI 2015;41(6):1558–69
11 West J, Romu T, Thorell S, et al.. Precision of MRI-based body composition measurements of postmenopausal women. PLoS One 2018;13(2):e0192495
12 UK Biobank Data Showcase. Available at: http://biobank.ctsu.ox.ac.uk/crystal/ [last accessed 16.11.17]