The distribution of body fat, and particularly fat accumulated around the abdomen, is a determining factor in the risk of developing metabolic and cardiovascular diseases. However, not all abdominal fat has the same impact on health: subcutaneous fat, located beneath the skin, does not carry the same risks as visceral fat, which accumulates deeper and can come into direct contact with vital organs. Identifying which type of fat predominates in a person is essential for assessing their metabolic risk and better guiding clinical interventions. In the light of this, a study led by researchers from the Department of Medicine and Surgery at the Universitat Rovira i Virgili (URV) has analysed the extent to which abdominal ultrasound can be a reliable and useful tool for measuring visceral fat, compared to magnetic resonance imaging, which is currently considered to be the best technique.
Currently, in routine clinical practice, the risk associated with abdominal fat is assessed using waist circumference. This measurement, however, is as simple as it is limited because it does not allow for the distinction between subcutaneous and visceral fat. Imaging techniques such as magnetic resonance imaging or computed tomography offer this information with great precision, but they are expensive, require specialised equipment and are not immediately accessible in primary care. Because of these problems, the research team decided to explore more agile and accessible alternatives; specifically, they set out to see if abdominal ultrasound could be used to characterise fat distribution. "Ultrasound devices are present in most primary care centres and hospitals and, when used by trained healthcare professionals, provide real-time images at a very low cost," explains Claudia Jiménez-ten Hoevel, a researcher in the Department of Medicine and Surgery at URV and co-author of the article.
The key question was, therefore, whether ultrasound could assess abdominal fat with the same level of accuracy as magnetic resonance imaging. To answer this, the research team worked with a sample of 113 abdominally obese adult volunteers from Reus and the surrounding area. All of them underwent an MRI and an abdominal ultrasound scan within the same very short period of time (three to four days) to ensure that the measurements were comparable.
A valid and precise tool
The study's results, published in the journal Diabetes, Obesity and Metabolism, reveal that the measurements of visceral fat made using ultrasound compared well with the data obtained via magnetic resonance imaging. However, they found that ultrasound had certain limitations when assessing subcutaneous fat, a fact that had already been noted in other studies. "The main conclusion is that ultrasound can be particularly useful when the aim is to identify visceral fat, which is the type of fat most clearly associated with metabolic risk," notes Anna Pedret, a researcher in the same department.
Visceral fat: an indicator of metabolic risk
Building on this finding, the researchers decided to go a step further to determine whether the amount of visceral fat measured with ultrasound can predict future metabolic alterations. Their results revealed a relationship between elevated levels of visceral fat and the presence of prediabetes (a condition in which blood sugar levels are elevated but still below the threshold for type 2 diabetes) and metabolic syndrome (a cluster of risk factors that increase the risk of cardiovascular disease.)
More specifically, the study identified a level of visceral fat above which the risk of associated diseases increased considerably. For example, a fat thickness of 7.35 centimetres or more has been linked to a high risk of prediabetes, while a thickness of 5.77 centimetres is associated with the likelihood of metabolic syndrome. These results demonstrate ultrasound's potential as an investigative tool. "Incorporating abdominal ultrasound into routine clinical practice, particularly in the fields of nutrition and primary care, could allow for better risk stratification and more accurate personalisation of interventions," the researchers argue.
