Objective: Aim: The current study introduces a novel diagnostic algorithm employing bioimpedance analysis to comprehensively evaluate body composition in children, assessing fat content, skeletal muscle content, and fat distribution.
Patients and methods: Materials and Methods: Bioelectrical impedance measurements were obtained using the TANITA MC-780 MA analyzer. Indicators such as body weight, BMI, total fat content, absolute limb muscle mass, skeletal muscle strength, and waist-to-hip ratio (WHR) were assessed. A sample of 101 children aged 9 to 14 were studied using the proposed algorithm, refining BMI-based classifications.
Results: Results: The algorithm comprises three steps, categorizing children based on fat content, presence of sarcopenia, and central fat distribution. It identified diverse somatotypes within the groups classified by BMI. Notably, it revealed prognostically unfavorable somatotypes, such as sarcopenic obesity with central fat distribution, highlighting potential health risks. Current BMI-centric diagnoses may misclassify cardiometabolic risks, making early detection challenging. The algorithm enables a detailed evaluation, unmasking metabolically unfavorable conditions like sarcopenic obesity. The incorporation of functional tests, such as a standardized hand-grip test, enhances diagnostic accuracy. The proposed WHR indicator for characterizing fat distribution provides a practical method for determining somatotypes in children.
Conclusion: Conclusions: This comprehensive algorithm offers an alternative to BMI-based classifications, enabling early detection of obesity and associated risks. Further validation through large-scale epidemiological studies is essential to establish correlations between somatotypes and cardiometabolic risks, fostering a more nuanced and individualized approach to pediatric obesity management.
Keywords: cardiometabolic risk; childhood obesity; body composition analysis; sarcopenic obesity; somatotypes.