Association of electrical bioimpedance vectors with the nutritional classification of vulnerable multiethnic children.

BACKGROUND & AIMS: Monitoring growth and body composition in childhood is vital for early detection of health and nutrition problems during child development. The bioelectrical impedance vector analysis (BIVA) revealed its effectiveness as an indicator of nutritional status. This study aimed to measure the bioimpedance vectors of a sample of multiethnic children and evaluate the utility of BIVA for the classification of the nutritional status in a group of children with greater vulnerability, including in the presence of obesity.

METHODS

This is a cross-sectional study that analyzed the bioelectrical impedance of a sample of 321 children from public daycare centers, aged 1-4 years, using the RXc graph method (tetrapolar analysis at 50 kHz frequency). The 95%, 75%, and 50% tolerance ellipses were plotted by age group. The Z-scores of bioimpedance parameters (Xc/H-Z and R/H-Z) were calculated regarding age, which were used to build reference tolerance ellipses for a single group, regardless of age. Such ellipses were evaluated for their ability to recognize the pattern and classify the deviations, using individual vectors of obese children.

RESULTS

The mean impedance vectors showed progressively higher reactances and lower resistances with age. In the group of obese children, the individual vectors located outside the 95% baseline tolerance ellipse were 12.5%, 18.75%, and 11.1% of the cases for the WHZ, WAZ, and BMIZ criteria, respectively, all above the expected 5%, but much below expectations. This may be a problem with the obesity criteria used, as WHZ, WAZ, and BMIZ do not differentiate fat from lean mass.

CONCLUSION

Bioimpedance vector analysis reflects differences in the bioelectric patterns of children classified as obese. Provided the obesity criteria are constructed based on fat measurements, rather than lean mass, the use of tolerance ellipses in the BIVA chart is promising as a tool for monitoring nutritional status where measuring body composition is difficult, because constant tissue hydration cannot be assumed.