UNIVERSAL CLASSIFICATION OF SOMATOTYPES BASED ON BIOIMPEDANCE ANALYSIS OF BODY COMPOSITION

Abstract

Introduction. In connection with the progressive spread of obesity in most countries of the world, early identification of overweight individuals and prevention of related metabolic disorders remains an urgent problem. Traditional body mass index (BMI) has limited specificity, making it difficult to identify risks. The goal of our work is to create a universal classification of somatotypes based on bioimpedance survey data, which will take into account the component composition of the body and its role in the pathogenesis of obesity. Methods: The study included 192 children aged 9 to 14 years. The "TANITA MC-780 MA" bioelectric impedance analyzer was used to measure indicators of body composition, with the help of which body weight, body mass index, total fat content, and absolute limb muscle mass were estimated. In addition, in order to determine the type of fat distribution in the body, the ratio of waist circumference to hip circumference was determined. Results: Analysis of body composition indicators and waist-to-hip ratio (WHR) in three groups of girls and boys, formed according to body mass classification by BMI, showed that most indicators of body composition and WHR did not differ statistically significantly between groups. The exception was the indicator of total fat content (TF), which distinguished the group of children with obesity from others. Despite this, other parameters, in particular MML%, did not show statistically significant differences between groups. In connection with the heterogeneity of the studied samples and the limitations of BMI as an indicator of obesity, we have proposed a new approach to the classification of somatotype, which is based on the ratio of the content of skeletal muscles, total fat and its distribution in different regions of the body. This classification, which is denoted by the abbreviation MFD (Muscles, Fat, Distribution), uses three key parameters and divides patients into 27 combinations according to the gradations of BMI%, TF% and WHR. This approach makes it possible to increase the accuracy of the classification of risk groups in relation to overweight and obesity.