Childhood Nutrition Research Centre, Population, Policy and Practice Reseach and Teaching Department, UCL Great Ormond Street Institute of Child Health, 30 Guilford Street, London, WC1N 1EH, UK.
Childhood Nutrition Research Centre, Population, Policy and Practice Reseach and Teaching Department, UCL Great Ormond Street Institute of Child Health, 30 Guilford Street, London, WC1N 1EH, UK.
Clin Nutr. 2021 Mar;40(3):1147-1154. doi: 10.1016/j.clnu.2020.07.022. Epub 2020 Jul 31.
BACKGROUND & AIMS: Bioelectrical impedance analysis (BIA) is widely considered a body composition technique suitable for routine application. However, its utility in sick or malnourished children is complicated by variability in hydration. A BIA variant termed vector analysis (BIVA) aims to resolve this, by differentiating hydration from cell mass. However, the model was only partially supported by children's data. To improve accuracy, further adjustment for body shape variability has been proposed, known as specific BIVA (BIVA).
We re-analysed body composition data from 281 children and adolescents (46% male) aged 4-20 years of European ancestry. Measurements included anthropometry, conventional BIA, BIVA outcomes adjusted either for height (BIVA), or for height and body cross-sectional area (BIVA), and fat-free mass (FFM) and fat mass (FM) by the criterion 4-component model. Graphic analysis and regression analysis were used to evaluate different BIA models for predicting FFM and FM.
Age was strongly correlated with BIVA parameters, but weakly with BIVA parameters. FFM correlated more strongly with BIVA than with BIVA parameters, whereas the opposite pattern was found for FM. In multiple regression analyses, the best prediction models combined conventional BIA with BIVA parameters, explaining 97.0% and 89.8% of the variance in FFM and FM respectively. These models could be further improved by incorporating body weight.
The prediction of body composition can be improved by combining two different theoretical models, each of which appears to provide different information about the two components FFM and FM. Further work should test the utility of this approach in pediatric patients.
生物电阻抗分析（BIA）被广泛认为是一种适合常规应用的身体成分技术。然而，由于水合作用的变化，其在患病或营养不良儿童中的应用变得复杂。BIA 的一种变体，即向量分析（BIVA），旨在通过区分水合作用和细胞质量来解决这个问题。然而，该模型仅得到了部分儿童数据的支持。为了提高准确性，人们提出了进一步调整身体形状变异性的方法，称为特定 BIVA（BIVA）。
我们重新分析了 281 名欧洲裔儿童和青少年（46%为男性）的身体成分数据，年龄为 4-20 岁。测量包括人体测量学、常规 BIA、BIVA 结果，这些结果要么根据身高（BIVA）进行调整，要么根据身高和身体横截面积（BIVA）进行调整，以及通过 4 分量模型标准的去脂体重（FFM）和脂肪量（FM）。使用图形分析和回归分析评估了不同 BIA 模型对预测 FFM 和 FM 的效果。
年龄与 BIVA 参数强烈相关，但与 BIVA 参数弱相关。FFM 与 BIVA 相关性强于 BIVA 参数，而 FM 则相反。在多元回归分析中，最好的预测模型将常规 BIA 与 BIVA 参数相结合，分别解释了 FFM 和 FM 方差的 97.0%和 89.8%。通过纳入体重，这些模型可以进一步得到改善。
通过结合两种不同的理论模型，可以提高身体成分的预测，这两种模型似乎都为 FFM 和 FM 这两个成分提供了不同的信息。进一步的研究应该检验这种方法在儿科患者中的应用效果。
Zotero 插件
