Macdonald Jamie H, Marcora Samuele M, Jibani Mahdi, Roberts Gareth, Kumwenda Mick John, Glover Ruth, Barron Jeffrey, Lemmey Andrew Bruce
School of Sport, Health and Exercise Sciences, University of Wales-Bangor, George Building, Bangor, Gwynedd, LL57 2PZ.
Nephrol Dial Transplant. 2006 Dec;21(12):3481-7. doi: 10.1093/ndt/gfl432. Epub 2006 Sep 5.
In this article (the second of two companion studies), we report whether bioelectrical impedance analysis (BIA) can be used to predict muscle mass in patients with chronic kidney disease (CKD), and whether using this predicted muscle mass can improve the estimation of glomerular filtration rate (GFR).
Seventy five non-diabetic patients with CKD (mean age +/- SD, 65.1 +/- 12.0 years; mean GFR 45.9 +/- 28.8 ml/min/1.73 m2) underwent body composition analysis by dual energy X-ray absorptiometry to provide a criterion marker of skeletal muscle mass (appendicular lean mass, ALM). Validity of a published BIA equation to predict ALM was evaluated and a new BIA equation was generated (ALM(BIA)) and cross-validated by the leave-one-out procedure. Renal inulin clearance provided a criterion measure of GFR (GFR(inu)). The performance of the equation including ALM(BIA) to estimate GFR(inu) was compared with demographic variables as used in the modification of diet in renal disease (MDRD) equation, by determining bias, limits of agreement and accuracy.
The previously published BIA equation to predict ALM was not valid in these patients with CKD. In contrast, our new ALM(BIA) equation cross-validated successfully. Compared with the MDRD demographic variables, using ALM(BIA) to predict GFR(inu) improved estimation performance, showing reduced bias (4.3 vs 15.6 ml/min) and improved limits of agreement (41.1 vs 59.2 ml/min) and accuracy (69.7 vs 39.4% of patients' predicted GFR did not deviate by more than 30% of GFR(inu)).
ALM(BIA) provides a clinically obtainable and valid method to predict muscle mass in patients with CKD, and using ALM(BIA) improves the estimation of GFR(inu). Researchers developing future GFR estimation equations should consider including ALM(BIA).
在本文(两项配套研究中的第二项)中,我们报告生物电阻抗分析(BIA)是否可用于预测慢性肾脏病(CKD)患者的肌肉量,以及使用这种预测的肌肉量是否能改善肾小球滤过率(GFR)的估计。
75例非糖尿病CKD患者(平均年龄±标准差,65.1±12.0岁;平均GFR 45.9±28.8 ml/min/1.73 m²)接受双能X线吸收法进行身体成分分析,以提供骨骼肌量(四肢瘦体重,ALM)的标准标志物。评估已发表的用于预测ALM的BIA方程的有效性,并生成一个新的BIA方程(ALM(BIA)),并通过留一法进行交叉验证。肾菊粉清除率提供了GFR(GFR(inu))的标准测量值。通过确定偏差、一致性界限和准确性,将包含ALM(BIA)的方程用于估计GFR(inu)的性能与用于肾病饮食改良(MDRD)方程中的人口统计学变量进行比较。
先前发表的用于预测ALM的BIA方程在这些CKD患者中无效。相比之下,我们新的ALM(BIA)方程成功进行了交叉验证。与MDRD人口统计学变量相比,使用ALM(BIA)预测GFR(inu)可提高估计性能,偏差减小(4.3对15.6 ml/min),一致性界限改善(41.1对59.2 ml/min),准确性提高(69.7%对39.4%的患者预测GFR与GFR(inu)的偏差不超过30%)。
ALM(BIA)提供了一种临床上可获得的有效方法来预测CKD患者的肌肉量,并且使用ALM(BIA)可改善GFR(inu)的估计。开发未来GFR估计方程的研究人员应考虑纳入ALM(BIA)。