Cross Calibration of the GE Prodigy and iDXA for the Measurement of Total and Regional Body Composition in Adults
Cross Calibration of the GE Prodigy and iDXA for the Measurement of Total and Regional Body Composition in Adults
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DOI：
10.1016/j.jocd.2017.05.009
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发表时间：
2018-07-01
影响因子：
2.5
通讯作者：
Hind, Karen
中科院分区：
文献类型：
--
作者：
Oldroy, Brian;Treadgold, Laura;Hind, Karen
双能X射线吸收测量仪(DXA)在临床和研究环境中广泛进行身体成分测量，能够快速和非侵入性地估计整个和区域脂肪和瘦肉组织。DXA升级可能发生在纵向监测或研究期间；因此，需要对新旧吸光度计进行交叉校准。我们比较了GE Prodigy(GE Healthcare，麦迪逊，威斯康星州)和较新的iDXA(GE Healthcare)的软组织估计，并开发了转换方程，使Prodigy值能够转换为iDXA值。研究对象为83名男性和女性，年龄20.1~63.3岁，体重指数17.0~34.4 kg/m(2)。59名参与者(41名女性和18名男性)组成交叉校准组，24名(14名女性和10名男性)组成验证组。在24小时内对每个受试者进行全身Prodigy和iDXA扫描。通过对数据的线性回归得出总体和局部软组织参数的预测方程。瘦组织和脂肪组织的测量高度相关(R2=0.95-0.99)，但机器之间存在显著差异和变异性。Bland-Altman分析显示，大多数指标都存在显著的偏差，特别是对手臂、机器人和女性脂肪质量(12.3%-22.7%)。推导出的转换方程减少了大多数参数的偏差和差异，尽管一致性限度超过了IDXA最小的显著变化。总之，在神童和iDXA之间检测到软组织估计的可变性，支持在纵向监测中使用转换方程的必要性。所导出的方程适用于群体分析，但不适用于个体分析。
Dual-energy X-ray absorptiometry (DXA) body composition measurements are widely performed in both clinical and research settings, and enable the rapid and noninvasive estimation of total and regional fat and lean mass tissues. DXA upgrading can occur during longitudinal monitoring or study; therefore, cross calibration of old and new absorptiometers is required. We compared soft tissue estimations from the GE Prodigy (GE Healthcare, Madison, WI) with the more recent iDXA (GE Healthcare) and developed translational equations to enable Prodigy values to be converted to iDXA values. Eighty-three males and females aged 20.1-63.3 yr and with a body mass index range of 17.0-34.4 kg/m(2) were recruited for the study. Fifty-nine participants (41 females and 18 males) comprised the cross-calibration group and 24 (14 females and 10 males) comprised the validation group. Total body Prodigy and iDXA scans were performed on each subject within 24 h. Predictive equations for total and regional soft tissue parameters were derived from linear regression of the data. Measures of lean and fat tissues were highly correlated (R-2 = 0.95-0.99), but significant differences and variability between machines were identified. Bland-Altman analysis revealed significant biases for most measures, particularly for arm, android, and gynoid fat mass (12.3%-22.7%). The derived translational equations reduced biases and differences for most parameters, although limits of agreement exceeded iDXA least significant change. In conclusion, variability in soft tissue estimates between the Prodigy and iDXA were detected, supporting the need for translational equations in longitudinal monitoring. The derived equations are suitable for group analysis but not individual analysis.