Measurements of renal function are common and important clinical analysis. Their routine use is growing rapidly with the increasing incidence of chronic kidney disease (CKD) worldwide, and increasing incidence of type 2 diabetes mellitus.1 According to the National Kidney Foundation and the American Diabetes Association, the glomerular filtration rate (GFR) can be estimated from serum creatinine (Scr) using the modification of diet in renal disease (MDRD) equation.2,3 The MDRD equation was reported to be highly accurate in transplant recipients4 and African-Americans with hypertensive nephrosclerosis,5 but less accurate in healthy subjects and normoalbuminuric diabetic patients.6 Moreover, the MDRD equation is known to underestimate high or normal GFR as described in the diabetes control and complications trial cohort study.7 Although diabetic nephropathy is quite a common cause of CKD, most diabetic subjects retain normal renal function. High GFR may also be present at the earliest stage of diabetic nephropathy. Owing to its underestimation of normal and high GFR,8,9 the MDRD equation is not adequate and new formulae are required. Limitations of Scr, the main GFR predictor in the MDRD equation also have prompted interest in using other biomarkers for measuring kidney function.
Cystatin C, freely filtered across the glomerular membrane and then completely reabsorbed and catabolised by tubular cells, has emerged as a marker of GFR. Serum cystatin C may provide a more accurate estimate of GFR than Scr,10 and is more sensitive in detecting early or mild diabetic nephropathy patients.11 Although cystatin C facilitates the recognition of incipient CKD without the need for correction for age and anthropometric data, several groups have recently developed equations to calculate estimated GFR (eGFR) from serum cystatin C using similar approaches that were described for creatinine.12,13 Whether estimates of GFR based on combined creatinine and cystatin C are better than those based solely on cystatin C has not been validated. Furthermore, from data collected in the prevention of renal and vascular end-stage disease (PREVEND) cohort study, there was no evidence that multivariate cystatin C based estimating equations were superior to multivariate creatinine-based estimates.14 Currently, there are few studies comparing cystatin C based equations with the MDRD equation and the available data are too preliminary to favor one equation over another. Whether their diagnostic accuracies in patients with CKD or diabetes are similar or different needs to be further evaluated. The present study was designed to compare the diagnostic performance of the MDRD equation with five cystatin C-based formulae (Stevens, Ma, Rule, Macisaac, Perkins8-10,12,13) for estimation of GFR. We used plasma clearance of99mTc-DTPA as a diagnostic gold standard for GFR in patients with CKD and diabetes.
A total of 166 CKD patients from patients admitted to Peking University First Hospital (97 males and 69 females) 16 to 86 years old (average age (54±16) years) were recruited based on their 99mTc-DTPA renal clearance. CKD was defined using the standard of American National Kidney Foundation (NKF)/KDOQI guidelines.15 Patients were assigned to five stages: CKD stage 1 with GFR ≥90 ml·min-1·1.73 m-2, stage 2 with GFR 60-89 ml·min-1·1.73 m-2, stage 3 with GFR 30-59 ml·min-1·1.73 m-2, stage 4 with GFR 15-29 ml·min-1·1.73 m-2, and stage 5 with GFR <15 ml·min-1·1.73 m-2. Patients with complications relating to acute kidney function deterioration, severe cardiac insufficiency, pleural or abdominal effusion, edema, malnutrition, skeletal muscle atrophy, ketoacidosis or patients who recently received dexamethasone therapy, who had disabled limbs or amputation were excluded. The most common etiologies of CKD were primary or secondary glomerular disease (18 cases), hypertension (24 cases), renovascular (15 cases), obstructive kidney disease (23 cases), kidney cystic disease (5 cases), diabetic nephropathy (13 cases), tubulointerstitial disease (6 cases), and other causes or causes unknown (62 cases). Clinical and laboratory characteristics of the CKD patients were shown in Table 1.
The study population consisted of 91 type 2 diabetic (53 males and 38 females) who had been admitted to the Department of Endocrinology, Peking University First Hospital during the period from December 2006 through January 2008. The mean duration of disease was (8.80±7.80) years. Type 2 diabetes was diagnosed according to the criteria published by WHO in 1999,16 and assigned to four groups according to the criteria published by ADA:3 GFR ≥90 ml·min-1·1.73 m-2, 60-89 ml·min-1·1.73 m-2, 30-59 ml·min-1·1.73 m-2, and 15-29 ml·min-1·1.73 m-2. We excluded the patients with GFR <15 ml·min-1·1.73 m-2. Patients with malignancies or who were receiving corticosteroid treatment (which can cause changes in serum cystatin C17) were excluded from this study. Written informed consent was obtained from the patients before the test. Scr and cystatin C were measured in a fasting blood sample collected on the morning of the99mTc-DTPA clearance test. Urinary albumin excretion rate (AER) was determined by the microalbumin concentration from overnight 8 hour timed urine (normal range: 0-20 μg/min). The mean value for AER was (13.52±19.76) μg/min. The mean value for hemoglobin A1c (HbA1c) was (8.95±2.31)% Hb, which reflected a non-successful glycaemia control. Other characteristics of these patients were shown in Table 1.
Scr was determined by Jaffe's kinetic assay using a Beckman LX20 autoanalyzer, calibrated with reagents and calibrators used in the original MDRD study.2 Cystatin C was measured by particle-enhanced immunonephelometric assay (N Latex Cystatin C, Dade Behring) using a nephelometer (BN-II, Dade Behring, Marburg, Germany). The coefficient of variation for creatinine determination was 3.1% at 115 μmol/L and 1.8% at 335 μmol/L, and for cystatin C it was 5.92% at 1.04 mg/L and 4.85% at 2.05 mg/L during the study period. HbA1c (normal value: 4%-6% Hb) was measured by HPLC (PRIMUS, Ultra2, USA). Urinary microalbumin concentration was determined using a Beckman IMMAGE analyzer with reagents from Beckman Diagnostics (Beckman, USA).
99mTc-DTPA clearance rate was assayed by the radionuclide imaging method. On the examination day, patients were allowed to eat and drink as normal but were asked to drink 300 ml of water before the procedure. Dynamic collection was performed followed by “pellet” injection of 185 MBq/ml of 99mTc-DTPA. The radioactivity of 99mTc-DTPA was assayed using the MillenniumTMMPR SPECT (GE, USA) counter with an energy window of 140 keV. The image was processed according to Gates' method to estimate GFR.18 Results obtained from the reference GFR method were corrected for body surface area (BSA, m2). The calculation was performed using the following formula: % total renal uptake of DTPA = (right kidney counts - background/ e-μχR)+(left kidney counts - background/ e-μχL)/(Preinjection counts - Postinjection counts) × 100, Where χR: right kidney depth = 13.3×(weight/height)+0.7; χL: left kidney depth = 13.2×(weight/height)+0.7; μ: 0.153/cm (liner attenuation coefficient of 99mTc in soft tissues); GFR (ml/min) = % total renal DTPA uptake×100×9.8127 - 6.82519; Measured GFR (mGFR) (ml·min-1·1.73 m-2) = GFR/standard BSA; BSA was calculated with equation of Du et al:19 BSA (m2)=0.007184×body weight (kg)0.425×body height (cm)0.725; Standard BSA = BSA/1.73 Estimated GFR was calculated using the abbreviated four-variable MDRD formula and five other cystatin C-based formulae, as shown in Table 2, where eGFR stands for estimated GFR.
Medcalc for Windows, version 10.4.3.0 (Medcalc Software, Mariekerke, Belgium) and SPSS 11.0 software (SPSS Inc, USA) were used for data analysis. Data were expressed as mean ± standard deviation (SD). P value <0.05 was considered statistically significant. Paired samples t-test was used to compare the different estimated GFR formulae with mGFR. Agreement between mGFR and eGFR formulae was tested with the Bland-Altman analysis. Mean difference between methods was used for estimating bias. Accuracy was measured as the percentage of eGFR results not deviating more than 15%, 30%, and 50% from the mGFR result. The percentages between the formulae were compared using the chi-square test.
Performance of GFR estimate equations compared with mGFR in CKD patients
Performance of the MDRD and cystatin C-based formulae for GFR are presented in Table 3. The results of estimated GFR had no significant difference in mean±SD (ml·min-1·1.73 m-2) as measured by the Ma equation 59.85±29.21 or the Macisaac equation 62.43±30.77 compared with the mGFR 66.12±33.11. The results of mean for MDRD, Stevens and Rule formulae were all significantly lower than mGFR (P <0.001).
The mean difference (bias) was the lowest between the estimating equations and mGFR, which was -3.7 ml·min-1·1.73 m-2 for the Macisaac formula (Table 3). To illustrate the magnitude of error in each of the six estimating equations (MDRD, Stevens, Ma, Perkins, Rule, and Macisaac) relative to direct measurement of GFR (mGFR), we plotted all of the available data in Figure 1. A 30% error margin for an estimate of GFR is considered tolerable agreement by current clinical practice guidelines15 (shaded regions in Figure 1). All estimating equations were biased below with more values above than upward in the shaded areas (except Perkins). In other words, all GFR estimating equations except Perkins significantly underestimated mGFR. The Perkins formula overestimated mGFR and showed higher bias than the other formulae (Figure 1 and Table 3). Our results further showed that the Perkins formula performed well with mGFR in CKD stage 1, whereas the other estimated equations significantly underestimated mGFR in the same stage (Figure 2A). In CKD stages 2-5, the mean results of eGFR derived from the Ma formula were closer to mGFR than other equations. The differences between Macisaac and mGFR in CKD stages 2-4 were significantly smaller than results in CKD stages 1 or 5. As shown in Figure 2A, the MDRD formula had a higher accuracy in CKD stages 3, 4 and 5 than the results in other stages.
The accuracy of the prediction equations was variable in CKD patients. The Ma and Macisaac equations provided more accurate results than MDRD equation for data within 15% (42.17%, 45.18% vs. 30.12%, P <0.05) of accuracy. On the other hand, the Perkins equation provided less accurate results than the MDRD equation (78.92% vs. 92.17%, P <0.01) within 50% accuracy. The Stevens and Rule equations showed similar accuracy compared with the MDRD formula (Table 3).
Performance of GFR estimate equations compared with mGFR in diabetic patients
There was no significant difference in mean (ml·min-1·1.73 m-2) as measured by the Stevens 83.29±22.62 and Rule 87.83±36.21 equations compared with the mGFR 86.71±27.52. The mean levels of Ma's, Macisaac's and Perkins' equations were all significantly higher than mGFR, whereas the mean value of the MDRD equation was lower than mGFR (Table 4).
The mean difference between the estimating equations and mGFR was the lowest, which was -3.6 ml·min-1·1.73 m-2 for Stevens, 1.0 ml·min-1·1.73 m-2 for Rule (Table 4). The performances of estimated GFR formulae in different GFR groups were analyzed and shown in Figure 2B. The Perkins formula showed a higher bias than that estimated by other formulae at all GFR levels. The mean results between Macisaac and mGFR were closer than those of other equations in the mGFR ≥90 ml·min-1·1.73 m-2 stage, and other equations underestimated GFR in cases of near normal mGFR. In the GFR 60-89 ml·min-1·1.73 m-2 stage, the MDRD formula showed the smallest difference compared with other equations. All equations underestimated GFR in cases with GFR <60 ml·min-1·1.73 m-2. To illustrate the magnitude of error in each of the six estimations relative to mGFR, we plotted all of the available data in Figure 3. Unlike patients with CKD, all estimates except MDRD were biased slightly upward with more values above than below in the shaded areas which indicate 30% agreement between estimating formulae and mGFR. In other words, all GFR estimating equations significantly overestimated mGFR in diabetic patients, except MDRD and Stevens (Figure 3 and Table 4).
All of the cystatin C based formulae showed similar accuracy within 15% and 30% of mGFR when compared with the MDRD formula, with the exception of the Perkins and Rule formulae. The prediction equations based on cystatin C, or combinations with creatinine estimates, provided less accurate results than the MDRD equation within 50% accuracy (Table 4).
GFR measurement is one of the critical methods for the diagnosis and evaluation of CKD. In recent years, some GFR estimated equations based on serum creatinine were widely employed to screen for CKD, especially in high-risk groups such as patients with diabetes.2,20 Though K/DOQI guidelines15 recommend using the abbreviated MDRD equation for GFR estimation in clinical laboratories, the MDRD has been recognized to have limitations.21,22 In response to these deficiencies, interest has focused on novel marker of kidney function-cystatin C. The vast majority of authors concluded that cystatin C performed better than creatinine and even better than equations for estimating GFR based on creatinine as a GFR marker.11,23 Several cystatin C-based formulae have been proposed for estimation of GFR.8-10 Few studies examined whether a single cystatin C formula is superior to the MDRD formula or if a combination of creatinine formulae are superior in different populations.
Three important aspects of the performance of an estimating formula were demonstrated in this study. First, our results showed that the MDRD formula had a greater accuracy within 50% of mGFR than the prediction equations based on cystatin C or cystatin C in combination with creatinine in diabetic patients. The MDRD formula, derived from 1628 Caucasian non-diabetic patients, may also provide accurate estimates of GFR when used to evaluate type 2 diabetics (Table 4). However, the MDRD formula underestimated normal and high GFR (GFR ≥90 ml·min-1·1.73 m-2) and was unacceptable for monitoring kidney function in type 2 diabetes patients with normal and hyperfiltration GFR. At this point, we would choose the Macisaac formula to estimate GFR; its results were closer to mGFR in GFR ≥90 ml·min-1·1.73 m-2. Two studies performed on type 1 and type 2 diabetes also showed that the MDRD formula underestimated normal and high GFR values.6,22 Our results were partially similar to the report of Chudleigh et al,24 in which the MDRD formula significantly underestimated isotopic GFR in type 2 diabetes. However, the author reported that all cystatin C based formulae were less biased than the MDRD formula, with a 95% confidence interval for the mean, while we found that the cystatin C based formulae had less or greater bias than the MDRD formula (Table 4). The Perkins equation demonstrated that the reciprocal of cystatin C levels correlated more closely with iothalamate clearance than the MDRD formula in subjects with type 2 diabetes.8 The fact that the relationship between cystatin C or creatinine and GFR is a non-liner curve means that the Perkins formula, based on a liner relationship, is short of accuracy compared to other equations that use a power curve. Although cystatin C was not influenced by renal tubular secretion, age, sex or muscle mass compared by creatinine, cystatin C-based equations were generated and validated in smaller samples in single center settings using different gold standard measurements for GFR: unlike the MDRD equation which was calculated from a data set of 1,628 patients in a multicenter study. This partially explained why the MDRD formula had slightly greater accuracy in diabetic patients than equations based on cystatin C.
Second, all estimated formulae, regardless of GFR stage, underestimated mGFR in CKD patients, with the exception of the Perkins formula (Table 3), and overestimated mGFR in diabetes, except for the MDRD and Stevens formulae (Table 4). The Perkins formula, with a large positive bias, was not suitable for CKD and diabetes patients, and the MDRD formula underestimated mGFR for the above two groups. We further evaluated the performance of estimated equations in different CKD stages and found that the MDRD formula had a higher accuracy in CKD stages 3-5 than in CKD stages 1 and 2. This was similar to Ma's study, who reported that both the original MDRD equations in Western CKD patients and the modified MDRD equations in Chinese CKD patients underestimated GFR in CKD stages 1 and 2.21 The underestimation might result in an unnecessary investigation and interventions in patients with near-normal kidney function. Interestingly, Macisaac formula derived from diabetics mainly free of CKD, performed better than Rule and Stevens formulae developed from CKD patients, when used to evaluate diagnostic bias and accuracy in CKD patients, especially in CKD stages 2-4 (Table 3 and Figure 2A). However, the Macisaac formula derived from diabetics, performed worse than the MDRD formula established from CKD patients, when used to evaluate GFR in diabetic. We observed that the MDRD formula based on creatinine had a negative bias compared to mGFR, both in patients with diabetes and CKD, whereas the bias from cystatin C based alone or from combination of cystatin C and creatinine formula did not have the same trend. Collectively, these results suggested that the relationship between cystatin C and GFR may be different in patients with CKD than in diabetes.
Third, the combination of cystatin C and creatinine markers in an equation (e.g. Stevens and Ma) with age, sex, and race was equivalent to the estimates from the equation that used cystatin C alone and the MDRD formula. However, the MDRD equation needs three parameters in addition to serum creatinine whereas most cystatin C based equations seem to perform at least equally well without additional covariates. According to our results, the Macisaac formula based on cystatin C were more suitable for CKD patients, especially in CKD stages 2-4, and the differences between Macisaac and mGFR were less than those of other equations in diabetic patients with mGFR ≥90 ml·min-1·1.73 m-2.
Several factors may affect our results. First, we did not use the isotope dilution mass spectrometry (IDMS)-traceable MDRD equation for Scr methods calibrated to IDMS.25 Variability in Scr measurement makes all estimation equations substantially less accurate in the normal and slightly increased range of Scr (<133 μmol/L), a range (GFR <60 ml·min-1·1.73 m-2) relevant to the detection of CKD at an early stage. Standardization of calibration does not correct analytical interferences (non-specificity bias) which exist in patient samples and affect the accuracy of eGFR.25 The cystatin C based formulae are dependent on the particular analytical method used for determining this component. The importance of using a correct formula when calculating eGFR is obvious since different equations will give different results. The differences were greatest at the high and low cystatin C values, depending upon the power regression used. Since there is no international standard for cystatin C, these GFR estimating equations vary with the analytical method. Our selected formulae were all based on the nephelometric determination of cystatin C using reagents from Dade-Behring to eliminate variability between methods. Second, different gold standard measurements for GFR may have considerable effects on the results obtained. The MDRD and Stevens formulae used 125I-iothalamate as the reference method. Perkins and Rule equations use a non-radiolabeled iothalamate clearance to measure GFR. Macisaac and Ma equations measure the plasma 99mTc-DTPA clearance to determine the mGFR. Our study used renal dynamic imaging determination clearance of 99mTc-DTPA. Zhang et al26 reported that GFR obtained by renal dynamic imaging was reliable for identifying the changes of renal function, and correlated well (r=0.8815, P <0.01) with GFR obtained by the two-sample method. However, others have stated that although the Gates method correlates well with the single or two plasma clearance method, it tends to overestimate the GFR.27 The single-injection and single-sample clearance of exogenous markers, radioactive and non-radioactive markers, such as iothalamate and 99mTc-DTPA, were less accurate than the two and multiple sample methods, which gave a substantial systematic error in GFR of up to 10-15 ml/min. Furthermore, some gold standard GFR measurements showed moderate precision and some bias with iothalamate, 99mTc-DTPA and 51Cr-EDTA clearances overestimating insulin clearance.28 These limitations, therefore, need to be taken into account when comparing eGFR values with the GFR gold standard method. Third, the discrepancies may be due to the different studied populations. Although the MDRD formula was derived from CKD population, it was also applied to diabetic patients. Similarly, the Macisaac equation was derived from diabetics and had greater accuracy than other formulae in estimating GFR for CKD patients. As shown in Figure 2, six estimated equations had different trends in patients with CKD and diabetes. Whether to establish specific estimated equation according to patient groups should be further evaluated.
We do hope that our findings can provide information resulting in a further validation and possible improvement of the performance of estimated GFR for subjects with CKD and diabetes. Future studies should further evaluate the GFR equations based on cystatin C solely or in combination with creatinine in large diverse populations and test whether estimated equations based on cystatin C leads to improved accuracy compared with MDRD.
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