Alikhani Radin, Horbal Steven, Rothberg Amy E, Pai Manjunath P
Department of Clinical Pharmacy, College of Pharmacy, University of Michigan, 428 Church St, Ann Arbor, MI, 48108, USA.
Michigan Clinical Outcomes Research and Reporting Program, University of Michigan, Ann Arbor, MI, USA.
Clin Pharmacokinet. 2025 Jun 1. doi: 10.1007/s40262-025-01530-3.
While dual-energy X-ray absorptiometry (DEXA) is the gold standard for measuring lean body weight (LBW), computed tomography (CT) provides muscle composition and distribution metrics that can refine LBW for better weight-based dosing. We explored how existing computed tomography (CT) images could be utilized to better estimate LBW.
Sixty-three adult patients (71.4% female) with a median age of 53.4 years and mean BMI of 36.84 having both DEXA and CT scans were retrospectively analyzed to assess the relationship between CT-based skeletal muscle variables and DEXA-derived LBW.
Linear regression results revealed significant correlations. CT-derived skeletal muscle area (SMA) strongly predicted DEXA-derived LBW (p value < 0.05 and R between 0.67 and 0.80) at four different vertebra levels. DEXA-derived LBW showed a strong correlation with a height, weight, and sex-based estimate of LBW using an equation developed in 2005 (LBW). A final model incorporating SMA with the LBW equation improved the coefficient of determination at all four vertebra levels (R 0.82-0.86).
DISCUSSIONS/CONCLUSION: This study demonstrates opportunistic CT scan data may improve an existing equation for LBW that has been predictive of select drug pharmacokinetic parameters. Improving LBW estimation may enable improved personalized drug dosing strategies in patients with obesity and other populations that benefit from using LBW over total body weight.
虽然双能X线吸收法（DEXA）是测量瘦体重（LBW）的金标准，但计算机断层扫描（CT）可提供肌肉组成和分布指标，从而优化LBW以实现更精准的基于体重的给药剂量。我们探讨了如何利用现有的计算机断层扫描（CT）图像来更好地估计LBW。
回顾性分析了63例成年患者（71.4%为女性），他们的年龄中位数为53.4岁，平均体重指数为36.84，同时进行了DEXA和CT扫描，以评估基于CT的骨骼肌变量与DEXA得出的LBW之间的关系。
线性回归结果显示出显著相关性。在四个不同椎体水平，CT得出的骨骼肌面积（SMA）强烈预测了DEXA得出的LBW（p值<0.05，R值介于0.67和0.80之间）。DEXA得出的LBW与使用2005年开发的一个公式（LBW）得出的基于身高、体重和性别的LBW估计值高度相关。将SMA与LBW公式相结合的最终模型在所有四个椎体水平提高了决定系数（R值为0.82 - 0.86）。
讨论/结论：本研究表明，利用机会性CT扫描数据可能改进现有的LBW公式，该公式已被用于预测某些药物的药代动力学参数。改善LBW估计可能有助于在肥胖患者和其他受益于使用LBW而非总体重的人群中制定更好的个性化给药策略。
