Prediction of whole body composition utilizing cross-sectional abdominal imaging in pediatrics.

Background: Although body composition is an important determinant of pediatric health outcomes, we lack tools to routinely assess it in clinical practice. We define models to predict whole-body skeletal muscle and fat composition, as measured by dual X-ray absorptiometry (DXA) or whole-body magnetic resonance imaging (MRI), in pediatric oncology and healthy pediatric cohorts, respectively.

Methods: Pediatric oncology patients (≥5 to ≤18 years) undergoing an abdominal CT were prospectively recruited for a concurrent study DXA scan. Cross-sectional areas of skeletal muscle and total adipose tissue at each lumbar vertebral level (L1-L5) were quantified and optimal linear regression models were defined. Whole body and cross-sectional MRI data from a previously recruited cohort of healthy children (≥5 to ≤18 years) was analyzed separately.

Results: Eighty pediatric oncology patients (57% male; age range 5.1-18.4 y) were included. Cross-sectional areas of skeletal muscle and total adipose tissue at lumbar vertebral levels (L1-L5) were correlated with whole-body lean soft tissue mass (LSTM) (R= 0.896-0.940) and fat mass (FM) (R= 0.874-0.936) (p < 0.001). Linear regression models were improved by the addition of height for prediction of LSTM (adjusted R = 0.946-0971; p < 0.001) and by the addition of height and sex (adjusted R = 0.930-0.953) (p < 0001)) for prediction of whole body FM. High correlation between lumbar cross-sectional tissue areas and whole-body volumes of skeletal muscle and fat, as measured by whole-body MRI, was confirmed in an independent cohort of 73 healthy children.

Conclusion: Regression models can predict whole-body skeletal muscle and fat in pediatric patients utilizing cross-sectional abdominal images.