先进的三维光学成像技术与双能x射线吸收仪比较,监测身体成分变化进行干预研究

M. Wong, J. Bennett, L. Leong, I. Tian, Y. E. Liu, N. Kelly, C. McCarthy, Julia M. W. Wong, C. Ebbeling, D. S. Ludwig, B. Irving, Matthew C. Scott, James E. Stampley, B. Davis, Neil M. Johannsen, Rachel Matthews, Cullen M Vincellette, A. Garber, G. Maskarinec, E. Weiss, J. Rood, Alyssa N. Varanoske, S. Pasiakos, S. Heymsfield, J. Shepherd
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引用次数: 4

摘要

最近的3D光学(3DO)成像技术的进步为评估人体成分提供了更方便、更经济、更自主的机会。3DO对于双能x线吸收仪(DXA)的临床测量是准确和精确的。然而,监测身体成分随时间变化与3DO体型的敏感性尚不清楚。因此,本研究旨在评估3DO在多重干预研究中监测身体成分变化的能力。对健康成人的干预研究进行了回顾性分析,这些研究与横断面研究“Shape Up!”成年人。每位参与者在基线和随访时接受DXA (Hologic Discovery/ a系统)和3DO (Fit3D ProScanner)扫描。使用Meshcapade对3DO网格进行数字配准和重构,实现顶点和姿态的标准化。利用建立的统计形状模型将每个3DO网格转换成主成分(PCs)。pc被用来预测全身和区域的身体成分值,使用已发表的方程。体成分变化(随访减去基线)与DXA进行线性回归比较。该分析包括6项研究中的128名参与者(43名女性)。平均(SD)随访时间为13(5)周,范围为3-23周;脂肪百分比的变化为2.8%(2.1%),范围为2.4 - 8.3%。3DO和DXA对总脂肪质量(FM)、总无脂质量(FFM)和阑尾瘦质量变化的一致性(R2)分别为0.89、0.77和0.69,雌性的rmse分别为1.78、1.42和0.37 kg;雄性的rse分别为0.77、0.76和0.46,rmse分别为2.28、1.67和0.51 kg。在大多数样本中,对于总FM(70%)和FFM (81%), DXA和3DO都同意个体变化的统计显著性。人口统计描述符的进一步调整改善了3DO变化与DXA观察到的变化的一致性。与DXA相比,3DO在检测体型随时间变化方面非常敏感。3DO方法足够灵敏,可以在干预研究期间检测到身体成分的微小变化。3DO的安全性和可及性允许用户在整个干预过程中经常进行自我监测。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Monitoring Body Composition Change for Intervention Studies with Advancing 3D Optical Imaging Technology in Comparison to Dual-Energy X-Ray Absorptiometry
Recent 3D optical (3DO) imaging advancements have provided more accessible, affordable, and self-operating opportunities for assessing body composition. 3DO is accurate and precise with respect to clinical measures made by dual-energy X-ray absorptiometry (DXA). However, the sensitivity for monitoring body composition change over time with 3DO body shape is unknown. Therefore, this study aimed to evaluate 3DO ability to monitor body composition changes across multiple intervention studies. A retrospective analysis was performed using intervention studies on healthy adults that were complimentary to the cross-sectional study, Shape Up! Adults. Each participant received a DXA (Hologic Discovery/A system) and 3DO (Fit3D ProScanner) scan at baseline and follow-up. 3DO meshes were digitally registered and reposed using Meshcapade to standardize the vertices and pose. Each 3DO mesh was transformed into principal components (PCs) using an established statistical shape model. The PCs were used to predict whole-body and regional body composition values using published equations. Body composition changes (follow-up minus baseline) were compared to DXA with linear regression. The analysis included 128 participants (43 females) in six studies. The mean (SD) length of follow-up was 13 (5) weeks, range of 3-23 weeks; change in percent fat was 2.8% (2.1%), with a range of 2.4 - 8.3%. Agreement between 3DO and DXA (R2) for changes in total fat mass (FM), total fat-free mass (FFM), and appendicular lean mass, respectively, were 0.89, 0.77, and 0.69 with RMSEs of 1.78 kg, 1.42 kg, and 0.37 kg in females, and 0.77, 0.76, and 0.46 with RMSEs of 2.28 kg, 1.67 kg, and 0.51 kg in males. Statistical significance of individual changes agreed for both DXA and 3DO in the majority of the sample for total FM (70%) and FFM (81%). Further adjustment with demographic descriptors improved the 3DO change agreement to changes observed with DXA. Compared to DXA, 3DO was highly sensitive in detecting body shape changes over time. The 3DO method was sensitive enough to detect even small changes in body composition during intervention studies. The safety and accessibility of 3DO allow users to self-monitor frequently throughout interventions.
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