Metabolic syndrome is diagnosed clinically whenat least 3 of these 5 factors come together: abdominal obesity, high blood pressure, high triglycerides, low HDL cholesterol and high fasting blood sugar.
Metabolic syndrome can lead to heart attack, stroke, and other serious health problems, such as diabetes, of course, but also dementia and liver disease. The condition today affects about a third of the population in wealthy countries and a quarter of the world’s population. There is a great need for better screening strategies.
Lead author Dr. Betsy Medina Inojosa, a Mayo Clinic researcher, said: “Body mass index measurements and bioimpedance scales that measure body fat and muscle are inaccurate for many patients. Our research shows that this AI model can be a reliable tool to guide clinicians and patients towards the treatment decision best suited to their metabolic health.”
The study which resulted in the development of the tool validates the AI model using data from 1,280 participants who took 3D volumetric body scans, answered standard clinical questionnaires, performed blood tests and traditional body shape measurements . The first model was refined with data from a second group of 133 volunteers.
Analysis reveals that digitally measuring a patient’s body volume index with 3D imaging provides very precise measurement of shapes and volumes in critical regions where unhealthy visceral fat is deposited, such as the abdomen and chest. The scanners also record the volume of the hips, buttocks and legs, a measurement linked to muscle mass and “healthy” fat.
3D information on body volume in these key regions thus accurately signals the presence and severity of metabolic syndrome,
using imaging instead of invasive testing.
The model will now be optimized using a larger sample of participants suffering from different forms of this syndrome.
