Macdonald Jamie Hugo, Marcora Samuele Maria, Kumwenda Mick John, Jibani Mahdi, Roberts Gareth, Glover Ruth, Barron Jeffrey, Lemmey Andrew Bruce
Health and Exercise Sciences, University of Wales, Bangor, George Building, Bangor, Gwynedd LL57 2PZ, UK.
Nephrol Dial Transplant. 2006 Dec;21(12):3488-94. doi: 10.1093/ndt/gfl430. Epub 2006 Aug 25.
In this study (the first of two related papers), we report whether the relationship between the demographic and anthropometric variables (DA, i.e. age, gender, height and weight) employed in current creatinin (Cr)-based glomerular filtration rate (GFR) estimation equations and actual GFR is mediated by muscle mass.
We studied 77 patients (mean age +/- SD, 65.1 +/- 11.9 years) with chronic kidney disease (mean GFR 45.7 +/- 28.6 ml/min/1.73 m2). Actual GFR was measured by the renal clearance of inulin (GFR(inu)). Appendicular lean mass (ALM) and its index (ALMI) by dual energy X-ray absorptiometry provided markers of muscle mass. Multiple regression analyses identified variables explaining variance in (i) GFR, (ii) ALM and (iii) Cr.
(i) The DA variables used in the abbreviated modification of diet in renal disease (MDRD) equation accounted for only 59.6% (P < 0.001) of the variance in GFR(inu), whilst adding ALMI explained an additional 10.4% variance (P < 0.001). If ALMI was entered first, the relationship between DA variables and GFR(inu) was reduced (for weight) or completely abolished (for age, gender and height). (ii) After inputting all the commonly used DA variables, 17.2% of the variance in ALM was unexplained. (iii) All the DA variables explained only 60.6% (P < 0.001) of the variance in Cr, whilst adding ALM explained an additional 4.2% variance (P < 0.005).
Muscle mass explained more variance in GFR(inu) than MDRD DA variables and mediated the relationship between GFR(inu) and DA variables. Furthermore, DA variables failed to account for individual differences in muscle mass or Cr. Consequently, there is a need to validate simpler, clinically obtainable measures of muscle mass and determine whether these measures will improve GFR estimation.
在本研究（两篇相关论文中的第一篇）中，我们报告了目前基于肌酐（Cr）的肾小球滤过率（GFR）估算方程中所采用的人口统计学和人体测量学变量（DA，即年龄、性别、身高和体重）与实际GFR之间的关系是否由肌肉量介导。
我们研究了77例慢性肾脏病患者（平均年龄±标准差，65.1±11.9岁）（平均GFR 45.7±28.6 ml/min/1.73 m²）。通过菊粉肾清除率测量实际GFR（GFR(inu)）。采用双能X线吸收法测量的上肢去脂体重（ALM）及其指数（ALMI）作为肌肉量的标志物。多元回归分析确定了解释（i）GFR、（ii）ALM和（iii）Cr变异的变量。
（i）简化的肾脏病饮食改良（MDRD）方程中使用的DA变量仅占GFR(inu)变异的59.6%（P<0.001），而加入ALMI后又解释了10.4%的变异（P<0.001）。如果首先纳入ALMI，则DA变量与GFR(inu)之间的关系减弱（对于体重）或完全消失（对于年龄、性别和身高）。（ii）输入所有常用的DA变量后，ALM的变异仍有17.2%无法解释。（iii）所有DA变量仅解释了Cr变异的60.6%（P<0.001），而加入ALM后又解释了4.2%的变异（P<0.005）。
肌肉量比MDRD的DA变量能解释更多GFR(inu)的变异，并介导了GFR(inu)与DA变量之间的关系。此外，DA变量无法解释肌肉量或Cr的个体差异。因此，有必要验证更简单、临床上可获得的肌肉量测量方法，并确定这些方法是否能改善GFR估算。
