Specific patterns of fat distribution linked to metabolic disease, shows study

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. 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, Linköping University, and UK Biobank. 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. 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.

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