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

Abstract

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. 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. 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) (R2 = 0.896-0.940) and fat mass (FM) (R2 = 0.874-0.936) (p < 0.001). Linear regression models were improved by the addition of height for prediction of LSTM (adjusted R2 = 0.946-0.971; p < 0.001) and by the addition of height and sex (adjusted R2 = 0.930-0.953) (p < 0.001)) 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. Regression models can predict whole-body skeletal muscle and fat in pediatric patients utilizing cross-sectional abdominal images.