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Nutrition in acute and chronic diseases

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



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.

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Fig. 1: Representative CT and DXA images illustrating variations in fat mass in pediatric oncology patients.
Fig. 2: Representative univariable linear regression relationships in pediatric oncology cohort.
Fig. 3: Representative multivariable models in pediatric oncology cohort.

Data availability

De-identified data generated by this project are available to investigators upon request.


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We have deep appreciation for Dr. Boris Kuzeljevic and his statistical guidance and support, Dr. Jeff Bone for statistical review and Dr. David Eisenstat for initiating and supporting our collaboration. We thank Marlene Wardle, Elise Fairey and Tina Baker, registered dietitians, for completing all patient anthropometric measurements, to Drs. Claire Gowdy, Helen Nadel and Karin LePage from nuclear medicine and Dr. Rod Rassekh, pediatric oncologist, at British Columbia Children’s Hospital for their assistance and expertise. This study would not have been possible without our patients and their families and we are grateful for their participation.


This work was supported by the British Columbia Children’s Hospital Telethon grant (RJD), the Hair Massacure foundation in Alberta [43] and National Institutes of Health grants R01DK42618 (SH), R21DK73720 (WS) and P30DK026687.

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All authors contributed significantly to design this research (RJD, SD, MBS, and VB), research conduct (RJD, SD, AL, AG), data analysis (RJD, VB, AG, WS), primary paper writing (RJD, VB) and had primary responsibility for final content (RJD, VEB). Authors SH and WS provided data for the secondary analysis of a pediatric MRI cohort. All authors read and approved the final manuscript.

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Correspondence to Rebecca J. Deyell.

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The authors declare no competing interests.

Ethical approval

Research ethics boards of the two participating centers approved this study (University of British Columbia, Vancouver, Canada: Children’s and Women’s Research Ethics Board, H12-03232; University of Alberta, Edmonton, Canada: Health Research Board of Alberta-Cancer, HREBA.CC-16-0102). The healthy pediatric cohort data was obtained from a prior study which was approved by the Institutional Review Board of St. Luke’s-Roosevelt Hospital (IRB 00-069) and each subject gave written consent to participate [27].

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Deyell, R.J., Desai, S., Gallivan, A. et al. Prediction of whole body composition utilizing cross-sectional abdominal imaging in pediatrics. Eur J Clin Nutr 77, 684–691 (2023).

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