Category: Bone Densitometry or Body Composition

32 - Validation Of Body Composition Measures From Forearm and Lateral Distal Femur Scans of Children

Background: Measuring body composition by dual-energy x-ray absorptiometry (DXA) at regional locations is feasible and may be useful in very young children when whole body scans are impractical.

The purpose of this study was to derive methods to quantify percent (%) body fat measurements from forearm and distal femur DXA scans for children.

We retrospectively analyzed forearm, lateral distal femur and whole body scans of children, ages 3-4 years. Scans were acquired on Hologic DXA system and analyzed using Hologic Apex (version 5.5). Forearm and lateral distal femur scans were converted to infant whole body scan type and analyzed. ROI box on regional scans were made based on forearm and femur length. Regions of interest comparable to forearm and lateral distal femur scans were selected on the whole body scan of the same child. Associations of body composition measures from the regional (forearm and lateral distal femur) and whole body scans were evaluated by correlation and multiple regression. All statistical analysis was done using Stata software (version 12).

We evaluated scans from 59 participants with mean age, height and weight of 41.5 ± 3.5 months, 98.1 ± 4.2 cm and 15.0 ± 1.6 kg, respectively. Average sub-region forearm bone mineral density (BMD) was 0.26 ± 0.029 and average sub-region distal femur BMD was 0.501 ± 0.084. The association of regional distal femur %Fat with whole body distal femur %Fat was significant in a univariate linear regression analysis (beta = 0.79, p = 0.0001, R2 = 0.63, RMSE = 4.28) and the association of whole body distal femur %Fat with whole body %Fat (beta = 0.42, p = 0.0001, R2 = 0.79, RMSE = 1.54) performed similarly to the association of distal femur %Fat (from regional scans) with whole body %Fat (beta = 0.35, p = 0.0001, R2 = 0.6, RMSE = 2.24). Adding of body mass index (BMI) and age in the models didn’t change R2 value significantly and R2 values were higher in these models than when BMI and age were used as predictors alone. Like-wise the association of regional forearm %Fat with whole body forearm %Fat remained significant in univariate regression analysis (beta = 0.50, p = 0.0001, R2 = 0.25, RMSE = 5.31) and the association of whole body forearm %Fat with whole body %Fat remained significant with beta = 0.35, p = 0.0001, R2 = 0.4 and RMSE = 2.63 as performed by the association of forearm %Fat (from regional scans) with whole body %Fat (beta = 0.40, p = 0.0001, R2 = 0.5, RMSE = 2.37) in regression analysis.

A regional DXA scan predicts body composition in children better than demographic data like BMI and age alone. This may be useful in those children where whole body scans are impractical.

Bo Fan

San Francisco, California

Heidi Kalkwarf

UC Department of Pediatrics
Cincinnati, Ohio

Leila Kazemi

Program Manager
San Francisco, California

John Shepherd

Adjunct Professor
San Francisco, California

Professor John Shepherd is a Professor in the Department of Radiology and the Director of the Body Composition, Exercise Physiology, and Energy Metabolism Lab at the University of California, San Francisco. He is a Fulbright fellow to the Karolinska Institute in Stockholm, and the current President of the International Society for Clinical Densitometry. He is an expert in quantitative breast imaging as well as musculoskeletal imaging using X-ray absorptiometry techniques. Dr. Shepherd received his BS in Engineering Physics from Texas Tech University and his PhD in Engineering Physics from the University of Virginia followed by a postdoctoral fellowship in Biophysics at Princeton University. He is also a Certified Clinical Densitometrist.

Dr. Shepherd’s research interests involve quantitative imaging methods for tissue composition using X-rays. He is the PI for the Shape Up! Study to examine 3D optical whole body scans in 1500 indivuduals from 5 to 85 years, and the PI of the 3CB study to extend mammography to measure the composition of invasive lesions. He has been the DXA CORE director for NHANES study since 1999. His current research interests include shape and appearance modeling and deep learning methods to big imaging datasets including DXA bone denisty scans. He has published over 150 peer reviewed articles in these fields.

Babette Zemel

Division of GI, Hepatology and Nutrition The Children's Hospital of Philadelphia
Philadelphia, Pennsylvania

Natasha Din

Clinical Research Coordinator, Radiology, UCSF
University Of California, San Francisco (UCSF)
Ssf, California