UMR7194/HNHP, Université de Perpignan Via Domitia, Centre Européen de Recherches Préhistoriques de Tautavel (EPCC-CERP), Tautavel, France.
Radiology Department, UZ Brussels, Vrije Universiteit Brussel, Ixelles, Brussels, Belgium.
Am J Phys Anthropol. 2018 May;166(1):26-42. doi: 10.1002/ajpa.23396. Epub 2018 Jan 18.
The frequently used prediction equations of body mass do not seem appropriate for elderly individuals. Here, we establish the relationship between femoral dimensions and known body mass in elderly individuals in order to develop prediction formulas and identify the factors affecting their accuracy.
The body mass linear least-squares regression is based on 17 femoral dimensions, including femoral volume, and 66 individuals. Body proportion and composition effects on accuracy are analyzed by means of the body mass index (BMI) and on a subset sample (n = 25), by means of the masses of adipose, bone and muscle tissues.
Most variables significantly reflect body mass. Among them, six dimensions (e.g., biepicondylar breadth, femoral volume, and head femoral diameter) present percent standard errors of estimate ranging from 9.5 to 11% (r = 0.72-0.81) in normal BMI samples. Correlations are clearly lower in samples with normal and abnormal BMI [r = 0.38-0.58; % of standard error of estimate (SEE) = 17.3-19.6%] and not significantly correlated in females (femoral volume) who present high proportions of abnormal BMI and adipose tissue. In the subset, femoral volume is well correlated with bone mass (r = 0.88; %SEE = 7.9%) and lean body mass (r = 0.67; %SEE = 17.2%).
Our body mass estimation equations for elderly individuals are relevant since relatively low correlations are recurrent in studies using younger individuals of known body mass. However, age, sex, lifestyle, and skeleton considerations of studied populations can provide information about the relevance of the body mass estimation, which is dependent on the BMI classification and the proportion of adipose tissue. Our general considerations can be used for studies of younger individuals.
常用的体重预测方程似乎并不适用于老年人。在这里，我们建立了老年人股骨尺寸与已知体重之间的关系，以便开发预测公式并确定影响其准确性的因素。
采用线性最小二乘法回归分析了 17 项股骨尺寸（包括股骨体积）与 66 名个体之间的关系。通过身体质量指数（BMI）和一个亚样本（n=25）分析了身体比例和成分对准确性的影响，该亚样本测量了脂肪、骨骼和肌肉组织的质量。
大多数变量与体重显著相关。其中，6 项尺寸（如双髁间宽度、股骨体积和股骨头直径）在正常 BMI 样本中，估计值的百分标准误差（%SEE）范围为 9.5%至 11%（r=0.72-0.81）。在正常和异常 BMI 样本中，相关性明显降低（r=0.38-0.58；%SEE=17.3%-19.6%），而在 BMI 和脂肪组织比例较高的女性（股骨体积）中，相关性不显著。在亚样本中，股骨体积与骨量（r=0.88；%SEE=7.9%）和瘦体重（r=0.67；%SEE=17.2%）高度相关。
我们的老年人体重估计方程是相关的，因为在使用已知体重的年轻个体进行的研究中，相关性相对较低。然而，研究人群的年龄、性别、生活方式和骨骼考虑因素可以提供有关体重估计相关性的信息，这取决于 BMI 分类和脂肪组织的比例。我们的一般考虑因素可用于年轻个体的研究。
