Division of Hematology, Oncology & Bone Marrow Transplant, Department of Pediatrics, British Columbia Children's Hospital and Research Institute, University of British Columbia, Vancouver, BC, Canada.
Division of iHOPE, Department of Pediatrics, Stollery Children's Hospital, University of Alberta, Edmonton, AB, Canada.
Eur J Clin Nutr. 2023 Jun;77(6):684-691. doi: 10.1038/s41430-023-01272-0. Epub 2023 Feb 17.
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) (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.
Regression models can predict whole-body skeletal muscle and fat in pediatric patients utilizing cross-sectional abdominal images.
尽管身体成分是儿童健康结果的重要决定因素，但我们缺乏在临床实践中常规评估它的工具。我们分别为儿科肿瘤学和健康儿科队列中的患者定义了模型，以预测通过双能 X 线吸收法（DXA）或全身磁共振成像（MRI）测量的全身骨骼肌和脂肪成分。
正在接受腹部 CT 的儿科肿瘤学患者（≥5 至≤18 岁）前瞻性地招募进行同期 DXA 扫描的研究。在每个腰椎水平（L1-L5）量化骨骼肌和总脂肪组织的横截面积，并定义最佳线性回归模型。分别分析来自先前招募的健康儿童队列（≥5 至≤18 岁）的全身和横截面 MRI 数据。
共纳入 80 名儿科肿瘤学患者（57%为男性；年龄范围 5.1-18.4 岁）。腰椎水平（L1-L5）的骨骼肌和总脂肪组织横截面积与全身瘦软组织量（LSTM）（R=0.896-0.940）和脂肪量（FM）（R=0.874-0.936）（p<0.001）相关。通过添加身高可提高预测 LSTM 的线性回归模型（调整 R=0.946-0971；p<0.001），通过添加身高和性别（调整 R=0.930-0.953）（p<0.001）可提高预测全身 FM 的线性回归模型。在另一组 73 名健康儿童中，通过全身 MRI 测量的骨骼肌和脂肪的全身体积与腰椎横截面组织面积之间的高度相关性得到了证实。
利用腹部横断面图像，回归模型可以预测儿科患者的全身骨骼肌和脂肪。
