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NDT Advance Access originally published online on August 22, 2005
Nephrology Dialysis Transplantation 2006 21(1):84-87; doi:10.1093/ndt/gfi102
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© The Author [2005]. Published by Oxford University Press on behalf of ERA-EDTA. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Original Articles: Clinical Nephrology

Glomerular filtration rate can be accurately predicted using lean mass measured by dual-energy X-ray absorptiometry

Timothy P. Taylor1, Wei Wang2, M. Zakarea Shrayyef3, DeAnna Cheek4, Florence N. Hutchison5 and Crystal A. Gadegbeku6

1 Department of Medicine, Medical University of South Carolina, Charleston, SC, 2 Department of Biostatistics, Epidemiology and Bioinformatics, Medical University of South Carolina, Charleston, SC, 3 Unity Health System, Affiliate University of Rochester, Rochester, NY, 4 Department of Pediatrics, Medical University of South Carolina, Charleston, SC, 5 Ralph H. Johnson, VAMC, Charleston, SC and 6 Department of Internal Medicine, University of Michigan, Ann Arbor, MI

Correspondence and offprint requests to: Crystal A. Gadegbeku, University of Michigan, 102 Observatory Road, Simpson Building, Ann Arbor, MI 48109, USA. Email: cgadegbe{at}med.umich.edu



   Abstract
Top
 Abstract
Introduction
 Subjects and methods
 Results
 Discussion
 References
 
Background. Accurate assessment of renal function is important in the management of patients with kidney disease yet is often difficult to obtain. Formulae, designed for clinical use, have been developed to predict glomerular filtration rate (GFR) utilizing serum creatinine (Scr). Additional parameters are included in these formulae to account for variations in Scr due to differences in total body lean mass in kg (LM). Therefore, the purpose of this study was to derive a simple formula to predict GFR based on Scr and direct quantification of LM.

Methods. Ten subjects with a wide range of renal function had GFRs determined by [125I]iothalamate clearance and LM determined by dual-energy X-ray absorptiometry as well as fasting measurements of Scr, serum and 24 h urine urea nitrogen, and albumin.

Results. The following formula was derived using LM (kg) and Scr (mg/dl): predicted GFR = (2.4 x LM) – (0.75 x LM x Scr). The correlation coefficient for this formula was 0.97, when compared with [125I]iothalamate clearances, and similar to the MDRD formulae (R = 0.87–0.95).

Conclusion. Although further validation is necessary, these findings suggest that total body non-invasive measurement of LM along with Scr can be used to accurately predict GFR.

Keywords: body composition; glomerular filtration rate; kidney disease; lean mass



   Introduction
Top
 Abstract
 Introduction
Subjects and methods
 Results
 Discussion
 References
 
Assessment of renal function is important in the management of renal disease and for early detection of renal impairment. Techniques to measure glomerular filtration rate (GFR) using exogenous markers, such as inulin and radionuclide substances, are accurate yet costly, labour intensive, inconvenient and, therefore, impractical for widespread clinical use [1]. In contrast, 24 h creatinine clearance, which uses serum creatinine (Scr) as an endogenous biomarker, is commonly used but much less accurate for various reasons. First, this test tends to overestimate GFR due to creatinine tubular secretion particularly as GFR declines [2]. In addition, variations in results occur with changes in meat ingestion [3] and day-to-day analytical error [4]. Most importantly, the accuracy of this test depends heavily on patient compliance with urine collection procedures in the clinical setting [2,4].

Numerous attempts have been made to establish formulae that can accurately predict GFR utilizing Scr as well as other parameters such as bodyweight, age, sex and race [1,5]. Traditionally, practitioners used the formula established by Cockcroft and Gault [6]. However, this formula tends to be less accurate because it was derived from creatinine clearance data and not from more sensitive and accurate techniques that use exogenous biomarkers to determine GFR.

More recently, the authors of the Modification of Diet in Renal Disease (MDRD) Study have published elaborate predictive formulae based on [125I]iothalamate clearances from a large study population with renal disease [7,8]. Although very accurate, the MDRD study formulae require up to six parameters and are mathematically cumbersome.

One of the primary reasons for including additional parameters in these formulae is to account for individual variations in total body lean muscle mass (LM), which has a direct influence on Scr levels. For example, LM declines with age, is greater in men than women and is greater in African-Americans than whites [6,7]. Our hypothesis is that Scr and direct measurements of LM can be used to simply and accurately predict GFR. Therefore, in the current study, a formula was derived to predict GFR, based on [125I]iothalamate clearances, using Scr and radiographic quantification of LM in a diverse group of subjects with varying degrees of renal dysfunction.



   Subjects and methods
Top
 Abstract
 Introduction
 Subjects and methods
Results
 Discussion
 References
 
Study population
Ten subjects with a previous history of renal disease were recruited by advertisement and were compensated for participation in the study. Subjects provided written informed consent approved by the Institutional Review Board. A history and physical examination was performed on all volunteers along with screening laboratory assessment. Etiologies of renal disease in the subject population included glomerulonephritis, hypertensive nephropathy, solitary kidney and polycystic kidney disease.

Study protocol
Initially, a fasting metabolic panel was obtained from each subject including the measurement of serum creatinine, serum urea nitrogen and albumin. In addition, subjects collected 24 h urine samples for the measurement of urea nitrogen. These laboratory measurements were analysed in the clinical laboratory of the Medical University of South Carolina. Scr was measured using the Jaffe rate method with picrate, serum urea and urine urea nitrogen were measured using the enzymatic conductivity rate method with urease and serum albumin was measured using the bichromatic digital endpoint method with bromcresol purple reagent (Beckman Coulter Synchron LX20 Chemistry Information Manual, May 2000). Subsequently, LM was determined by dual X-ray absorptiometry (DEXA) with a Hologic QDR 4500 W densitometer (Hologic, Waltham, Massachusetts) [9,10]. GFR was determined by [125I]iothalamate clearance over a 4 h period after a single injection calculated using the plasma to urine ratio and expressed per 1.73 m2 of body surface area [11]. This method has been validated [12] and used in recent large clinical trials [13,7]. Subjects were off all medications including anti-hypertensive agents on the day of the test. The mean intra-individual coefficient of variation for the GFRs in our subject population was 4.6%.

Data analysis
A multiple linear regression analysis was conducted to develop a model to predict GFR with the known variables, LM and Scr. Type I error rate was held at 0.05 for each analysis. The predicted GFRs from this model and those of four other previously published formulae were compared with the [125I]iothalamate GFRs [6–8].



   Results
Top
 Abstract
 Introduction
 Subjects and methods
 Results
Discussion
 References
 
As shown in Table 1, the diverse study population consisted of a wide range of LM and GFR values. As expected, GFR was significantly inversely correlated with Scr (R = –0.64, P = 0.04). In this population, age, gender and race had no significant effect on GFR (P = 0.67, 0.13 and 0.14, respectively). The following formula that predicts GFR was derived using the variables, LM (kg) and Scr (mg/dl):

For our model, the F test indicated that the overall model was statistically significant [F(2, 8) = 506.34, P<0.0001]. Also, the variables LM [F(1, 8) = 195.93, P<0.0001] and LM x Scr [F(1, 8) = 58.90, P<0.0001] played significant roles in predicting GFR.


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  		Table 1. Subject characteristics

 
In addition to our equation, four previously published equations by Cockroft and Gault [6] and Levey et al. [7,8] that predict GFR from multiple variables (Table 2) were compared with [125I]iothalamate GFR data in the 10 subjects (Table 3). The predicted GFR by our equation is at least as accurate as the more complex MDRD equations for our group. Interestingly, among the previously published formulae, the MDRD-simplified was more accurate in our study population than other formulae that contained more variables. The individual data points and regression lines generated from comparison of [125I]iothalamate GFR to predicted GFR using our formula as well as the Cockroft–Gault and the MDRD-simplified formulae are illustrated in Figure 1.


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  		Table 2. GFR prediction equationsa

 

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  		Table 3. Summary of predicted GFRs

 


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  		Fig. 1. Relationship of predicted GFR to measured GFR. Correlation of measured [125I]iothalamate GFR and GFR predicted by (A) LM-based formula, (B) Cockroft and Gault, (C) MDRD-simplified.

 


   Discussion
Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
References
 
In the current study, we generated a model using LM determined by DEXA scan and Scr to accurately predict GFR. The addition of a simple non-invasive test to quantify LM allowed derivation of an uncomplicated formula using the common laboratory test, Scr. This formula, which is derived from [I125]iothalamate GFR data, adds further support to the idea that the limitations of Scr in predicting renal function are primarily related to muscle mass.

Few studies have directly examined the relationship between kidney function, Scr and LM. In support of our data, Donadio et al. [14] were able to predict GFR with a high degree of accuracy using a formula incorporating urinary creatinine, Scr, and muscle mass measured by bioelectrical impedance analysis. However, these authors did not provide a formula to predict GFR that could be applied universally. Furthermore, in our study, we used DEXA scanning which is thought to be a precise technique for quantification of LM [15–17]. In contrast, the accuracy of bioelectrical impedance for determination of LM is controversial [18].

With regard to the established formulae, our data support that the Cockroft–Gault [6] is inaccurate and should no longer be recommended to predict GFR. This formula has the weakest correlation and largest percent error (Table 3) [1,2,7]. The limited accuracy of this formula is likely due to the fact that it was derived from creatinine clearance data. It is well established that tubular creatinine secretion increases as GFR declines. In addition, tubular creatinine secretion can be highly variable from individual to individual [19]. Clearly, exogenous markers are superior for the determination of GFR. The use of Scr as an endogenous biomarker in any predictive GFR equation is an intrinsic flaw [2,3]. The GFR formulae reported by the MDRD study use measured parameters such as bodyweight, age, sex and race as well as biochemical measurements of urine and serum urea nitrogen and albumin. The addition of these parameters does not add predictive power to our formula. This observation supports the fact that the additional variables are simply predictors of LM. Therefore, until a more suitable endogenous biomarker is established, using Scr and LM measured by DEXA may be a simple and effective alternative for GFR estimation for clinical and research use.

One limitation of the current study is that the subject population is small and limited in age range (Table 1). Results by Donadio et al. [14], who studied subjects up to age 81, suggest that this approach to predicting GFR would be equally accurate in the elderly population. Although small, our subject population did include a broad range of LM and renal function, had an equal distribution of men and women and a significant representation of African Americans. Our formula did not need adjustment for these demographic variables. Clearly, this novel formula needs validation in a larger population to determine whether it is as predictive as the MDRD equations.

Despite these limitations, we have derived a simple formula to estimate GFR using readily available techniques in the clinical setting. The results are obtained quickly using non-invasive procedures that are not labour-intensive, time consuming or dependent on patient compliance. With the growing recognition of the prevalence of renal disease and focus on therapy aimed at slowing the progression of the disease, this novel formula has the potential to be highly useful in the assessment of renal function in the clinical setting.



   Acknowledgments
 
This work was supported by National Institutes of Health RO1-HL58794 which included a minority supplement, K23 RR15542, K24 HL04290, General Clinical Research Center grants M01 RR01070 (Medical University of South Carolina) from the Division of Research Resources, research funds from Dialysis Clinics, Inc. and by the Research and Development Service of the Department of Veterans Affairs.

Conflict of interest statement. None declared.



   References
Top
 Abstract
 Introduction
 Subjects and methods
 Results
 Discussion
 References
 

  1. Bostom AG, Kronenberg F, Ritz E. Predictive performance of renal function equations for patients with chronic kidney disease and normal serum creatinine levels. J Am Soc Nephrol 2002; 13: 2140–2144[Abstract/Free Full Text]
  2. Perrone RD, Madias NE, Levey AS. Serum creatinine as an index of renal function: new insights into old concepts. Clin Chem 1992; 38: 1933–1953[Abstract]
  3. Payne RB. Creatinine clearance: a redundant clinical investigation. Ann Clin Biochem 1986; 23: 243–250[Web of Science][Medline]
  4. Gabriel R. Time to scrap creatinine clearance? Br Med J 1986; 293: 1119–1120[Free Full Text]
  5. Manjunath G, Sarnak MJ, Levey AS. Prediction equations to estimate glomerular filtration rate: an update. Curr Opin Nephrol Hypertens 2001; 10: 785–792[CrossRef][Web of Science][Medline]
  6. Cockroft DW, Gault MH. Prediction of creatinine clearance from serum creatinine. Nephron 1976; 16: 31–41[Web of Science][Medline]
  7. Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Ann Intern Med 1999; 130: 461–470[Abstract/Free Full Text]
  8. National Kidney Foundation. K/DOQI Clinical practice guidelines for chronic kidney disease: evaluation, classification and stratification. Am J Kidney Dis 2002; 39 [Suppl 1]: S76–S92[CrossRef]
  9. Jebb SA. Measurement of soft tissue composition by dual energy x-ray absorptiometry. Br J Nutr 1997; 77: 151–163[CrossRef][Web of Science][Medline]
  10. Slosman DO, Casez J-P, Pichard C et al. Assessment of whole-body composition with dual-energy x-ray absorptiometry. Radiology 1992; 185: 593–598[Abstract/Free Full Text]
  11. Israelit AH, Long DL, White MG, Hull AR. Measurement of glomerular filtration rate utilizing a single subcutaneous injection of 125I-iothalamate. Kidney Int 1973; 4: 346–349[Web of Science][Medline]
  12. Perrone RD, Steinman TI, Beck GJ et al. Utility of radioisotopic filtration markers in chronic renal insufficiency: simultaneous comparison of 125I-iothalamate, 169Yb-DTPA, 99mTc-DTPA, and inulin. Am J Kidney Dis 1990; 16: 224–235[Web of Science][Medline]
  13. Lewis J, Agodoa L, Cheek D et al. Comparison of cross-sectional renal function measurements in African Americans with hypertension nephrosclerosis and of primary formulas to estimate glomerular filtration rate. Am J Kidney Dis 2001; 38: 744–753[Web of Science][Medline]
  14. Donadio C, Annalisa L, Tramonti G, Bianchi C. Creatinine clearance predicted from body cell mass is a good indicator of renal function. Kidney Int 1997; [Suppl 63]: S166–S168
  15. Figueroa-Colon R, Mayo MS, Treuth MS, Aldridge RA, Weinsier RL. Reproducibility of dual-energy x-ray absorptiometry measurements in prepubertal girls. Obes Res 1998; 6: 262–267[Web of Science][Medline]
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Received for publication: 30. 6.04
Accepted in revised form: 2. 8.05


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