Machado Dalmo, Silva Analiza, Gobbo Luis, Elias Paula, de Paula Francisco J A, Ramos Nilo
School of Physical Education and Sport of Ribeirao Preto, University of Sao Paulo, Bandeirantes Ave. 3900, Monte Alegre, Ribeirão Preto, SP 14040-900 Brazil.
Exercise and Health Laboratory, CIPER, Faculdade de Motricidade Humana, Universidade de Lisboa, Lisboa, Portugal.
BMC Sports Sci Med Rehabil. 2017 Dec 21;9:23. doi: 10.1186/s13102-017-0088-7. eCollection 2017.
Anthropometric models remain appropriate alternatives to estimate body composition of peripubertal populations. However, these traditional models do not consider other body components that undergo major changes during peripubertal growth spurt, with restrictions to a multicompartimental approach as a quantitative growth. DXA has great potential to determine pediatric body composition in more than one component (3-C), but has limited use in field settings. Thus, the aim of this study was to propose and validate an anthropometric model for simultaneous estimation of lean soft tissue (LST), bone mineral content (BMC) and fat mass (FM) in healthy girls, from a multivariate approach of densitometric technique, as the criterion method.
A sample of 84 Brazilian girls (7-17 years) was defined by chronological age and maturity offset. Whole total and regional DXA body scan were performed and, the components were defined (LST, BMC and FM) and considered as dependent variables. Twenty-one anthropometric measures were recorded as independent variables. From a multivariate regression, an anthropometric multicompartmental model was obtained.
It was possible to predict DXA body components with only four predictive measurements: body weight (BW); supra-iliac skinfold (SiSk); horizontal abdominal skinfold (HaSk) and contracted arm circumference (CaCi) with high coefficients of determination and low estimation errors ( = 0.6662657 BW - 0. 2157279 SiSk - 0.2069373 HaSk + 0.3411678 CaCi - 1.8504187; = 0.0222185 BW - 0.1001097 SiSk - 0.0064539 HaSk - 0.0084785 CaCi + 0.3733974 and = 0.3645630 BW + 0.1000325 SiSk - 0.2888978 HaSk - 0.4752146 CaCi + 2.8461916). The cross-validation was confirmed through the sum of squares of residuals (PRESS) method, presenting accurate coefficients (Q from 0.81 to 0.93) and reduced error reliability (S from 0.01 to 0.30).
When sophisticated instruments are not available, this model provides valid estimates of multicompartmental body composition of girls in healthy Brazilian pediatric populations.
人体测量模型仍然是估计青春期前人群身体成分的合适替代方法。然而，这些传统模型没有考虑到在青春期前生长突增期间经历重大变化的其他身体成分，限制了多成分方法作为一种定量生长方法的应用。双能X线吸收法（DXA）在确定多个身体成分（3-C）方面具有很大潜力，但在现场环境中的应用有限。因此，本研究的目的是从密度测量技术的多变量方法（作为标准方法）出发，提出并验证一种用于同时估计健康女孩瘦软组织（LST）、骨矿物质含量（BMC）和脂肪量（FM）的人体测量模型。
根据实际年龄和成熟度偏移确定了84名巴西女孩（7 - 17岁）的样本。进行了全身和局部的DXA身体扫描，并确定了各成分（LST、BMC和FM），将其视为因变量。记录了21项人体测量指标作为自变量。通过多元回归获得了一个人体测量多成分模型。
仅通过四项预测测量就可以预测DXA身体成分：体重（BW）；髂上皮肤褶厚度（SiSk）；腹部水平皮肤褶厚度（HaSk）和上臂围（CaCi），其决定系数高且估计误差低（ = 0.6662657 BW - 0. 2157279 SiSk - 0.2069373 HaSk + 0.3411678 CaCi - 1.8504187； = 0.0222185 BW - 0.1001097 SiSk - 0.0064539 HaSk - 0.0084785 CaCi + 0.3733974以及 = 0.3645630 BW + 0.1000325 SiSk - 0.2888978 HaSk - 0.4752146 CaCi + 2.8461916）。通过残差平方和（PRESS）方法进行交叉验证，呈现出准确的系数（Q从0.81到0.93）和降低的误差可靠性（S从0.01到0.30）。
当没有精密仪器时，该模型可为健康巴西儿科人群中女孩的多成分身体成分提供有效的估计。
