Department of Radiology, University of Mississippi Medical Center, Jackson, Mississippi, USA.
Pennington Biomedical Research Center, Louisiana State University System, Baton Rouge, Louisiana, USA.
Clin Obes. 2024 Aug;14(4):e12660. doi: 10.1111/cob.12660. Epub 2024 Apr 11.
To harmonise computed tomography (CT) and dual-energy x-ray absorptiometry (DXA) body composition measurements allowing easy conversion in longitudinal assessments and across cohorts to assess cardiometabolic risk and disease. Retrospective cross-sectional observational study from 1996 to 2008 included participants in the Pennington Center Longitudinal Study (PCLS) (N = 1967; 571 African American/1396 White). Anthropometrics, whole-body DXA and abdominal CT images were obtained. Multi-layer segmentation techniques (Analyze; Rochester, MN) quantified visceral adipose tissue (VAT). Clinical biomarkers were obtained from routine blood samples. Linear models were used to predict CT-VAT from DXA-VAT and examine the effects of traditional biomarkers on cross-sectional-VAT. Predicted CT-VAT was highly associated with measured CT-VAT using ordinary least square linear regression analysis and random forest models (R = 0.84; 0.94, respectively, p < .0001). Model stratification effects showed low variability between races and sexes. Overall, associations between measured CT-VAT and DXA-predicted CT-VAT were good (R > 0.7) or excellent (R > 0.8) and improved for all stratification groups except African American men using random forest models. The clinical effects on measured CT-VAT and DXA-VAT showed no significant clinical difference in the measured adipose tissue areas (mean difference = 0.22 cm). Random forest modelling seamlessly predicts CT-VAT from measured DXA-VAT to a degree of accuracy that falls within the bounds of universally accepted standard error.
为了协调计算机断层扫描（CT）和双能 X 射线吸收法（DXA）的身体成分测量，以便在纵向评估和跨队列中进行方便的转换，以评估心血管代谢风险和疾病。本研究为 1996 年至 2008 年的回顾性横断面观察性研究，纳入了彭宁顿中心纵向研究（PCLS）的参与者（N=1967；571 名非裔美国人/1396 名白人）。测量了人体测量学、全身 DXA 和腹部 CT 图像。采用多层分割技术（Analyze；Rochester，MN）对内脏脂肪组织（VAT）进行定量分析。从常规血液样本中获得临床生物标志物。线性模型用于预测 DXA-VAT 中的 CT-VAT，并检查传统生物标志物对横断面-VAT 的影响。使用普通最小二乘线性回归分析和随机森林模型（R=0.84；0.94，分别，p<0.0001），预测 CT-VAT 与实测 CT-VAT 高度相关。模型分层效果表明，种族和性别之间的变异性较低。总体而言，实测 CT-VAT 与 DXA 预测 CT-VAT 之间的相关性良好（R>0.7）或极好（R>0.8），除了使用随机森林模型的非裔美国男性外，所有分层组的相关性都有所提高。实测 CT-VAT 和 DXA-VAT 的临床影响表明，实测脂肪组织面积的临床差异不显著（平均差异=0.22cm）。随机森林模型可以无缝地根据实测 DXA-VAT 预测 CT-VAT，其准确性在普遍接受的标准误差范围内。
