Health Policy Research Center, Institute of Health, Shiraz University of Medical Sciences, Shiraz, Iran.
Department of Animal Science, Yasouj University, Yasouj, Iran.
BMC Pediatr. 2022 Sep 3;22(1):524. doi: 10.1186/s12887-022-03580-z.
Adolescents' body composition is considered an important measure to evaluate health status. An examination of any of the segmental compartments by anthropometric indices is a more usable method than direct methods.
To propose a method based on the network approach for predicting segmental body composition components in adolescent boys and girls using anthropometric measurements.
A dual-energy X-ray absorptiometry (DXA) dataset in the south of Iran, including 476 adolescents (235 girls and 241 boys) with a range of 9-18 years, was obtained. Several anthropometric prediction models based on the network approach were fitted to the training dataset (TRD 80%) using bnlearn, an R add-in package. The best fitted models were applied to the validation dataset (VAD 20%) to assess the prediction accuracy.
Present equations consisting of age, weight, height, body mass index (BMI), and hip circumference accounted for 0.85 (P < 0.001) of the variability of DXA values in the corresponding age groups of boys. Similarly, reasonable estimates of DXA values could be obtained from age, weight, height, and BMI in girls over 13 years, and from age, weight, height, BMI, and waist circumference in girls under 13 years, respectively, of 0.77 and 0.83 (P < 0.001). Correlations between robust Gaussian Bayesian network (RGBN) predictions and DXA measurements were highly significant, averaging 0.87 for boys and 0.82 for girls (P < 0.001).
The results revealed that, based on the present study's predictive models, adolescents' body composition might be estimated by input anthropometric information. Given the flexibility and modeling of the present method to test different motivated hypotheses, its application to body compositional data is highly appealing.
青少年的身体成分被认为是评估健康状况的重要指标。使用人体测量学指标来检查任何节段隔室都比直接方法更可行。
提出一种基于网络方法的方法，使用人体测量学测量值预测青少年男孩和女孩的节段身体成分。
从伊朗南部获得了一个双能 X 射线吸收法 (DXA) 数据集，其中包括 476 名年龄在 9-18 岁之间的青少年（235 名女孩和 241 名男孩）。使用 R 插件 bnlearn 对训练数据集（TRD 80%）拟合了几个基于网络方法的人体预测模型。将最佳拟合模型应用于验证数据集（VAD 20%）以评估预测准确性。
由年龄、体重、身高、体重指数（BMI）和臀围组成的现有方程在男孩的相应年龄组中占 DXA 值变化的 0.85（P<0.001）。同样，对于 13 岁以上的女孩，可以从年龄、体重、身高和 BMI 中获得 DXA 值的合理估计，对于 13 岁以下的女孩，可以从年龄、体重、身高、BMI 和腰围中获得分别为 0.77 和 0.83（P<0.001）的估计值。稳健高斯贝叶斯网络 (RGBN) 预测值与 DXA 测量值之间的相关性非常显著，男孩的平均值为 0.87，女孩的平均值为 0.82（P<0.001）。
结果表明，基于本研究的预测模型，可以通过输入人体测量学信息来估计青少年的身体成分。鉴于本方法的灵活性和建模能力，可以检验不同的动机假设，因此非常适合应用于身体成分数据。
