Abstract
Objective: This study introduces a novel diagnostic algorithm utilizing bioimpedance analysis to comprehensively evaluate children's body composition, assessing body fat mass, muscle mass, and fat distribution.
Materials and Methods: Bioelectrical impedance measurements were conducted using a bioelectrical impedance analyzer (DongHWA DBA-510, China). Metrics including body weight, BMI, body fat mass, muscle mass, protein content, and waist-to-hip ratio were evaluated. The proposed algorithm was applied to a sample of 1826 children aged 6 to 10, enhancing the classification based on BMI. A comparison was made between BMI-based groupings and those based on BF% and MU.
Results: The algorithm consists of three steps, categorizing children according to BMI, body fat mass, and central fat distribution. Notably, it reveals prognostically unfavorable body types, such as sarcopenic obesity with central fat distribution, highlighting potential health risks. The current BMI-centered diagnostics might misclassify cardiometabolic risks, making early detection challenging. The introduced fat distribution characteristic, the WHR index, offers a practical method for determining children's body types.
Conclusion: This integrated algorithm provides an alternative to BMI-based classification, enabling early detection of obesity and associated risks. Further validation through large-scale epidemiological studies is crucial for establishing correlations between body types and cardiometabolic risks, thereby promoting a more nuanced and personalized approach to pediatric obesity management.
Impact Statement: BMI is still used as a diagnostic tool for childhood obesity and overweight. This study introduces a body composition analyzer and explores new calculation methods to make a new attempt at diagnosing childhood obesity.