使用3d全身扫描仪预测印度健康男性的脂肪量并验证

L. R. Varte, S. Rawat, I. Singh, Shilpa Choudhary, S. R. Singh
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Intra-and inter-individual error margins in traditional anthropometric measurements frequently arises for large sample studies, besides, manually taking measurements are time-consuming and challenging to perform within acceptable limits. Six hundred and eight (608) healthy adults were scanned using a 3D whole body scanner and their body composition was measured using the Tanita Bipodal bioelectrical impedance analysis (BIA). 486 formed the development group (for the prediction equation) and 20% of the total (i.e. 122 formed the validation group) based on alphabetical order of participant’s name. Linear regressions were performed to predict an equation wherein the body circumferential measurements like Waist Girth, Hip Girth and Chest Girth were predictors and Fat Mass was the dependent variable. Results: Predictive body composition equation based on volumetric body circumference (girth) proposed for healthy Indian males is FM = 0.420 (Waist Girth) + 0.241 (Chest Girth) + 0.051 (Hip Girth) 51.817. The predicted fat mass value was used for the validated population and we did not see much of a difference between the predicted and measured fat mass. The mean age among the 608 volunteers was 32.54 ± 6.3 years, weight was 71.26 kg ±7.8 kg and BMI was 24.1 ± 2.57 kg/mt. Average difference between body fat measured by BIA predicted body fat mass was 0.13 kg, median 0.50 kg, IQR: 0.80 to 0.20 kg, adjusted R 0.74. Conclusions: 3D whole body scanner technology offer defined and accurate automated anthropometric dimensions and measurements of body shape. 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引用次数: 0

摘要

背景:BMI通常高估了肥胖(即身体脂肪组织),而低估了瘦体重较少的人体内多余的脂肪。目的:我们想评估通过生物阻抗分析(BIA)测量的脂肪质量(FM)与使用3D扫描人体测量尺寸预测的体脂质量(BFM)相当的假设。方法:研究三维全身扫描仪人体测量是否能提供临床可靠的肥胖预测方程。3D全身扫描仪提供了一种快速和精确的替代方案,扫描仪在几秒钟内提供多达140次测量。传统的人体测量在大样本研究中经常出现个体内和个体间的误差范围,此外,手动测量既耗时又具有挑战性,难以在可接受的范围内完成。采用3D全身扫描仪对668名(608名)健康成人进行扫描,并使用Tanita双足生物电阻抗分析(BIA)测量他们的身体成分。486人组成了开发组(用于预测方程),总人数的20%(即122人组成了验证组)基于参与者姓名的字母顺序。进行线性回归来预测一个方程,其中腰围、臀围和胸围等身体周长测量是预测因子,脂肪量是因变量。结果:印度健康男性基于体围(围)的预测体构成方程为FM = 0.420(腰围)+ 0.241(胸围)+ 0.051(臀围)51.817。预测的脂肪质量值用于验证人群,我们没有看到预测和测量脂肪质量之间的太大差异。608名志愿者平均年龄为32.54±6.3岁,体重为71.26 kg±7.8 kg, BMI为24.1±2.57 kg/mt。BIA测得体脂预测体脂质量的平均差异为0.13 kg,中位数为0.50 kg, IQR为0.80 ~ 0.20 kg,调整后R为0.74。结论:三维全身扫描仪技术提供了定义和准确的自动化人体测量尺寸和体型测量。有必要进行进一步的研究,以揭示体型、身体成分和代谢健康之间的重要关系,这些关系跨越性别、年龄、体重指数和种族群体。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Prediction of Fat Mass and Validation Using 3D-Whole Body Scanner in Healthy Indian Males
Background: BMI generally overestimates adiposity (i.e. body fat tissue) and underestimates excess body fat on those with less lean body mass. Aim: We wanted to assess the hypothesis that Fat Mass (FM) measured by bio impedance analysis (BIA) is comparable to the predicted Body Fat Mass (BFM) using 3D scanned anthropometric dimensions. Methods: The present paper investigates whether anthropometric measurements using 3D whole body scanner can provide clinically reliable prediction equation to assess adiposity. 3D whole body scanner provides a fast and precise alternative where the scanner gives upto 140 measurements within a few seconds. Intra-and inter-individual error margins in traditional anthropometric measurements frequently arises for large sample studies, besides, manually taking measurements are time-consuming and challenging to perform within acceptable limits. Six hundred and eight (608) healthy adults were scanned using a 3D whole body scanner and their body composition was measured using the Tanita Bipodal bioelectrical impedance analysis (BIA). 486 formed the development group (for the prediction equation) and 20% of the total (i.e. 122 formed the validation group) based on alphabetical order of participant’s name. Linear regressions were performed to predict an equation wherein the body circumferential measurements like Waist Girth, Hip Girth and Chest Girth were predictors and Fat Mass was the dependent variable. Results: Predictive body composition equation based on volumetric body circumference (girth) proposed for healthy Indian males is FM = 0.420 (Waist Girth) + 0.241 (Chest Girth) + 0.051 (Hip Girth) 51.817. The predicted fat mass value was used for the validated population and we did not see much of a difference between the predicted and measured fat mass. The mean age among the 608 volunteers was 32.54 ± 6.3 years, weight was 71.26 kg ±7.8 kg and BMI was 24.1 ± 2.57 kg/mt. Average difference between body fat measured by BIA predicted body fat mass was 0.13 kg, median 0.50 kg, IQR: 0.80 to 0.20 kg, adjusted R 0.74. Conclusions: 3D whole body scanner technology offer defined and accurate automated anthropometric dimensions and measurements of body shape. Further studies are warranted to reveal important relationships between body shape, body composition and metabolic health across sex, age, BMI and ethnicity groups.
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