Abstract
Serum creatinine does not share the properties of an ideal marker of glomerular filtration rate (GFR) like inulin, but continues to be the most widely used endogenous marker of GFR. In the search of a better biomarker of GFR, the small molecular weight protein cystatin C has been introduced with features more similar to that of inulin, such as constant production and no non-renal elimination. However, it has not enjoyed widespread use despite its significantly improved diagnostic performance in the detection of impaired GFR and its independence of body composition. A variety of formulae based on either cystatin C or creatinine or both have been developed to estimate GFR. We summarize the currently used methods of GFR measurement, their limitations and analytical errors. The review also summarizes the history, features and the feasibility of cystatin C measurements as well as the most widely used formulae for the estimation of GFR in children. The diagnostic performance of the cystatin C derived eGFR formulae at various levels of GFR is also discussed. An eGFR formula derived from pooled studies analyzing both creatinine and cystatin C, and using a biology-based mathematical approach may be advantageous.
Introduction
It is widely accepted that kidney function is best measured as glomerular filtration rate (GFR) (1). GFR cannot be measured directly. However, if a substance in the plasma is in a stable concentration, physiologically inert, and freely filtered at the glomerulus, but is not secreted, reabsorbed, synthesized, or metabolized by the kidney, the amount of that substance filtered at the glomerulus is equal to the amount excreted in the urine (2). The gold standard for the GFR measurement is inulin clearance (3). In 1934, Richards found that the polysaccharide inulin (a fructose polymer made from the Jerusalem artichoke) is freely filtered through collodion membranes but not absorbed, while studying water reabsorption in the renal tubule of amphibians (4). Inulin shares many features of an ideal marker of glomerular filtration, namely exclusive elimination by glomerular clearance with no tubular secretion and no non-renal excretion (5).
Inulin clearance
Inulin clearance is widely regarded as the gold standard for measuring GFR. The classic method of measuring inulin clearance requires an intravenous infusion with timed urine collections over a period of several hours. This is costly and cumbersome. In the traditional way developed by Homer Smith (6), an inulin bolus is given intravenously to increase the concentration in the extra-cellular water that needs to be equilibrated. This is then followed by an intravenous infusion for a period of several hours until a steady concentration in the plasma is obtained. In clinical practice, this is rarely achieved because the amount of inulin infused has to be estimated using surrogate markers of the renal function. This process can over- or under-estimate the amount of inulin that is required. Accurate urine collections are necessary over a period of a few hours. Ideally, the bladder should be catheterized and emptied, and saline washout and air displacement should be used at the end of each collection period. To minimize collection errors, several collection periods are required, typically three times per hour. In addition, there are considerable difficulties with the measurement of inulin. Reliable methods using reversed-phase high-performance liquid chromatography have only been published after the millennium (7). Because measuring inulin clearance is cumbersome and there are methodological problems with its accurate measurements and high inter- and intra-assay variability if methods other than mass spectrometry are used (5), alternate methods of measuring GFR have been developed.
Nuclear medicine GFR methods
Nuclear medicine techniques of GFR estimation have been in use as a replacement for inulin clearance since the 1970s (2). The main advantage of using a radiolabeled compound with characteristics that are similar to inulin is its immediate determination by counting the radioactivity. Today’s nuclear medicine methods form a new standard for GFR measurements because of its easily assayed, radiolabeled and stable compounds that meet the criteria of a good GFR marker, namely clearance only by glomerular filtration without tubular secretion and extra-renal elimination. The most widely used single bolus-injection technique utilizes the intravenous injection of a suitable compound at a precisely known quantity. It is important that no material is injected interstitially and all matter enters the intravascular space, which may be particularly challenging in the case of young children. Extravasation will cause significant overestimation of GFR (8). After the injection, the plasma is sampled from the opposite arm. The measured concentrations are plotted against time (Figure 1).
Logarithmic plot of plasma decay curve in single injection clearance techniques.
Ideally, a two-compartmental model should be used to accommodate the volume of distribution between the intravascular volume and the extracellular volume.
The different substances used for nuclear medicine GFR measurement are listed in Table 1.
Commonly used nuclear medicine radioisotopes or cold exogenous markers for GFR measurement.
| Substance | Tracer | Utilization |
|---|---|---|
| 51Cr-ethylenediamine tetra-acetic acid (EDTA) clearance (9) | 51Chrome | Europe, widely studied in children (10, 11) |
| 99Tc-diethylenetriamine penta-acetic acid (DTPA) (12) | 99Technetium | North America, widely studied in children (13, 14) |
| 125I-iothalamate (15) | 125Iodine or cold | North America and Europe, widely studied in children (16, 17), used as subcutaneous infusion in Montreal (18) |
| Iohexal (19, 20) | Cold | Scandinavia, North America (21) |
The accuracy of the subsequent calculations are affected by a number of factors, in particular the exact time and the frequency of the sampling. It is generally recognized that at least three sampling points are required (22). Unfortunately, there is poor standardization amongst most centers, and only two-point sampling time points are chosen to reduce the number of venipunctures in children.
Furthermore, most centers utilize only one-compartmental models. A single compartmental model results in some overestimation of GFR (23). Late sampling (4 and/or 5 hour concentrations after injection) could overcome this problem (24), but is rarely implemented. For a more accurate assessment of the extracellular water (especially important in children with altered fluid status, for instance if they are on long-term diuretics), an early sampling point is required (25). Also, if one-compartmental model is utilized, appropriate corrections for the overestimation should be employed (26).
What are the particular problems with GFR measurement in children?
The body of a child is in constant change. To accommodate for this change, GFR is normalized to body surface area, which is considered to be the best denominator, although the extracellular volume is also considered (27). While all nephrons are terminally differentiated at birth, only the juxtaglomerular glomeruli are used at birth and there is continuous recruitment of additional glomeruli until 18–24 months of age. Ideally, GFR should be reported as age-independent z-scores, rather than as absolute GFR or GFR/body surface area (28). In the first year of life, and during puberty, there are growth spurts and rapid increase of muscle mass, requiring special considerations for the calculation of GFR (29), especially in adolescent males (30). The most important determinant of GFR is height, which when taken into account led to the famous Schwartz formula (31), a GFR estimation model based on the height creatinine ratio. Gold-standard measurements of GFR are cumbersome and invasive, and those children at risk requiring frequent GFR monitoring, such as transplant recipients (32) are already subject to numerous other tests. As such, accurate non-invasive measurements of GFR are essential, and endogenous markers are required.
Endogenous surrogate markers of GFR
The earlier stated methods of inulin clearance and nuclear medicine methods are invasive and involve undesirable effects, such as radiation exposure or the need for multiple blood samples and urinary catheterization. Therefore, it has become a preferred practice to use an endogenous marker that is produced at a constant rate, shares the features of inulin, and thus eliminates the need for a compound injection. Popper and Mandel proposed the use of serum creatinine in 1937 (33), which remains to be the most widely used marker for GFR estimation in spite of its shortcomings. This marker remains the most widely used marker for estimation of GFR. Where serum endogenous markers are considered as suitable markers of GFR, the National Kidney Foundation guidelines recommend the use of serum marker based prediction equations for GFR estimation and rules against the use of serum markers alone in the assessment of renal function, as discussed in ref. (34).
Limitations of serum creatinine as endogenous marker of GFR
Serum creatinine is the most widely used endogenous marker to predict GFR. Creatinine is a metabolic product of creatine and phosphocreatine found in muscle and as such reflects muscle mass and varies little from day to day (35). Serum creatinine measurement is widely available at low cost. However, while creatinine may be a feasible marker of GFR in populations with near normal GFR, it has multiple limitations for individuals. There is substantial inter- and intra-patient variability due to differences in muscle mass (36). In childhood, there is age and muscle mass dependency of serum creatinine and accurate assessment of normal GFR even with the use of body length/creatinine ratios (see below) remains difficult. In certain pediatric patient clientele, such as patients with spina bifida (37), neuromuscular disease, anorexia nervosa, or liver cirrhosis, serum creatinine is completely unusable because of the abnormal muscle mass in these children who are often wheelchair bound (38, 39). In addition, the production of creatinine is not constant; the rate of turnover is also variable (40). It is also known that creatinine is also undergoing tubular secretion (41). Creatinine is secreted in the proximal convoluted tubule by active transport similar to that of organic cations. Tubular secretion of creatinine divided by inulin clearance increases progressively from 0.16 in adult patients with normal GFR to 0.92 in patients with GFR <40 mL/min (12). Furthermore, there used to be considerable variability in the reference range for serum creatinine based on the method used for its determination (42, 43). Proficiency testing surveys published periodically by the College of American Pathologists, evaluated the variability in creatinine measurement among various methods used by different institutions across the US and demonstrated substantial variability (44). This will make the validation of older methods to estimate GFR based on serum creatinine, measured by different techniques, subject to debate. Only more recently, the traceability of the creatinine measurements to higher-order reference methods [isotope dilution-mass spectrometry (IDMS) reference method] improved accuracy of creatinine measurements (45). This paper established improved pediatric reference intervals that may be adopted by any laboratory serving a similar population (predominantly Caucasian). Clinical implications of creatinine standardization have recently been reviewed (46).
Overall, the method used for serum creatinine is of high importance. Calculation of GFR based on serum creatinine measured by the alkaline picrate method is limited because of method non-specificity, low values in children, particularly in infants, and lack of appropriate GFR formulae. Therefore, enzymatic methods are preferred, as widely suggested in the literature (47).
However, the problem of tubular secretion of serum creatinine cannot be addressed with the methodology of measurement. Approximately a decade ago, blockade with H2 antagonists revealed promising results that may overcome the significant problem of tubular secretion of serum creatinine (48, 49). The use of cimetidine protocols in children remains scarce (50). Creatinine clearance determinations involving timed urine collections may provide greater accuracy but are difficult for pediatric patients to perform, time-consuming, and impractical for routine use. Only recently small molecular proteins have emerged as potentially superior endogenous markers of GFR (51).
Small molecular weight proteins as markers of GFR
Small molecular mass proteins have long been proposed as markers of GFR as they are normally almost freely filtered through the normal glomerular membrane (52, 53). In a normally functioning kidney these small molecular weight proteins should then be almost completely reabsorbed and degraded by the proximal tubular cells. Several proteins have been tested, such as β-2 microglobulin and β-trace protein [analyzed in children in ref. (13)], however, of all these markers, cystatin C appears to be the most promising.
What is cystatin C?
Cystatin C is a small molecular weight protein that was initially known as γ–trace protein. The amino acid sequence of the single polypeptide chain of human cystatin C was determined in 1981 (54). Two years later, cystatin C was identified as an inhibitor of cysteine proteases after discovering significant homology with the sequence of chicken cystatin – both proteins had a sequence identity of 44% (55).
Cystatin C is produced by all human nucleated cells, since immunochemical and Northern blot studies of human tissues and cell lines have shown that cystatin C and/or its mRNA is present in all investigated cell types (56). Likewise, investigations of the production of cystatin C by human cell lines in culture have displayed that all cell lines that were investigated all secrete cystatin C (57). Determination of the structure of the human cystatin C gene and its promoter has demonstrated that the gene is of the house-keeping type, which indicates a stable production rate of cystatin C by most nucleated cell types (56). Earlier studies of the serum level of cystatin C in large patient cohorts have failed to correlate the serum level to any pathophysiological state besides those affecting the glomerular filtration rate, which also is compatible with a stable secretion of cystatin C from most human tissues (58). However, very large doses of glucocorticoids have recently been described to increase the production of cystatin C (59, 60) whereas low and medium doses of glucocorticoids do not seem to alter the production of cystatin C (61). Also, thyroid dysfunction can affect cystatin C levels (62), but our group found no association with thyroid function markers in recent studies (63, 64).
The reference values for cystatin C obtained in a carefully selected population are 0.75±0.09 mg/L for children aged 4–19 years, 0.74±0.10 mg/L for males and 0.65±0.09 mg/L for females (aged 20–59 years), and 0.83±0.10 mg/L for older individuals (> or =60 years) (65). In the first year of life, renal function matures physiologically. Accordingly, much higher cystatin C values up to 2.8 mg/L were found at birth. These are subject to a rapid decline after birth reflecting maturation of kidney function (66). Age dependency also has to be considered in adults. New reference intervals with more detailed age distribution from central Europe have recently been published on 985 healthy subjects older than 25 years (67).
Studies of the handling of human cystatin C in rats have shown that the plasma renal clearance of cystatin C is 94% of that of the generally used GFR-marker 51Cr-EDTA and that cystatin C thus is practically freely filtered in the glomeruli (68). At least 99% of the filtered cystatin C is degraded in the tubular cells. When the GFR of a set of rats was variably lowered by constricting their aortas above the renal arteries, the renal plasma clearance of cystatin C correlated strongly with that of 51Cr-EDTA with a linear regression coefficient of 0.99 and with the y-intercept not being statistically different from 0 (69).
After these encouraging studies, additional studies suggested that the reciprocal of cystatin C correlates better with a gold standard GFR measurement than the reciprocal of serum creatinine (70–72). Cystatin C as a marker of GFR was found to be independent of body composition (36, 73, 74). Development of automated and rapid particle-enhanced immunoturbidimetric and immunonephelometric methods, also more precise than the original radioimmuno- or enzyme-linked immunosorbant assays (75, 76), has allowed large-scale use of serum cystatin C as a clinically useful GFR-marker (76).
The diagnostic performance of cystatin C in comparison with serum creatinine was first analyzed in 2002 with a meta-analysis of 46 studies, in both adults and children (77). The pooled data analysis compared correlation coefficients between GFR and the reciprocals of serum creatinine and cystatin C in 3703 individuals and found significantly better correlations (mean r=0.816 [95% confidence interval (CI) 0.804–0.826] vs. mean r=0.742 [95% CI 0.726–0.758]). ROC plots were available for a pooled sample size of 997 individuals, again showing a significantly better area under the [ROC] curve (mean=0.926 [95% CI 0.892–0.960] vs. mean r=0.837 [95% CI 0.796–0.878]). This meta-analysis suggests that cystatin C is superior to serum creatinine for the detection of impaired GFR in cross-sectional studies. More recently, another meta-analysis from 24 studies (n=2007) also confirmed that the diagnostic accuracy favored cystatin C (78), although in this more recent study, the diagnostic odds ratios started to overlap, most probably due to the IDMS testing of serum creatinine.
The need for standardization of cystatin C, similar to the IDMS traceability of serum creatinine
It should be highlighted that problems due to lack of standardization of cystatin C lead to similar problems as was highlighted above with regards to serum creatinine and the need for IDMS traceability (45). Results obtained with the two main assays that are commercially available, namely the DAKO kit (turbidimetric, PETIA) and the Siemens Healthcare assay (nephelometric, PENIA), can be quite different, as recently demonstrated with the publication of reference intervals for healthy term and preterm infants. The results with the PENIA method in our study (79) were significantly lower than those reported by Harmoinen et al. (80). Similar problems occurred when the results of the CKiD study were re-analyzed using PENIA, which lead to substantially better results (81). Recently, Anders Grubb reported the first certified standardized reference material for cystatin C (82). World-wide standardization, as just shown in the previous paragraph, is a powerful tool towards improving the diagnostic accuracy of GFR biomarkers, and the authors of this review are convinced that standardization of cystatin C similar to that of creatinine will further enhance the diagnostic performance of cystatin C.
Endogenous markers as basis for GFR estimation models
Studies have continuously reported the accuracy of serum endogenous marker based GFR estimation models in measuring GFR over the serum levels of such markers alone (34). As such, numerous GFR estimation models have been developed to estimate GFR using creatinine and cystatin C as base markers. Some of these formulae where either creatinine based, cystatin C based or formed by a combination of both markers.
Estimating GFR using creatinine
First and foremost, creatinine standardization is of paramount importance to account for the analytical variability of different creatinine methods. The various formulae cannot be compared if the creatinine measurements are not standardized (46). Fortunately, the National Kidney Disease Education Program (NKDEP), the College of American Pathologists (CAP), and the National Institute for Standard and Technology (NIST) have collaborated to prepare a serum-creatinine reference material (NIST 967) with demonstrated commutability with native clinical specimens in routine methods. These materials are value-assigned with the gas chromatography-isotope dilution mass spectrometry (GC-IDMS) and liquid chromatography (LC)-IDMS reference measurement procedures (83). Nearly all clinical laboratory methods are now expected to have calibration traceable to an IDMS reference measurement procedure, which may lead to better formulae for the estimation of GFR.
A wide range of creatinine based formulae were developed over the years to estimate GFR. The most commonly used creatinine based formulae are the Cockcroft-Gault (84), the Modification of Diet in Renal Disease (MDRD) formula (85), which later was replaced with a four-variable version and adjusted for standardized creatinine (86) and the chronic kidney disease-epidemiology collaboration CKD-EPI formula (87). The (CKD-EPI) equation was reported to perform with higher level of accuracy than the MDRD equation (87).
In the case of children, the most widely-used formula is the Schwartz-formula (31) together with modifications for adolescent boys (30) and infants (29). The Counahan-Barratt formula may be slightly more suitable for GFR estimation based on serum creatinine in children (88, 89).
It should be emphasized that adult formulae cannot be used in children. This was shown clearly for the Cockcroft-Gault formula (90) and the MDRD formula (91). The bias worsens with younger age. The literature on validation of the CKD-EPI formula in children remains scarce. One study of 850 children and adults aged 5–95 years comes to the conclusion that the formula should not be used for children and youth (92).
Recently, the Chronic Kidney Disease in Children (CKid) study recruited a cohort of approximately 600 children with chronic kidney disease in the USA and Canada that involved creatinine being referenced to IDMS (93). This has led to an improved Schwartz formula (21).
The commonly used creatinine based GFR estimation formulae are summarized in Table 2, some of which were also derived for children.
Equations to predict glomerular filtration rate using creatinine.
| Reference | Equation |
|---|---|
| a,bOriginal MDRD (85) | GFR=(170)×(SCr)–0.999×(age)–0.176×(urea)–0.170×(albumin)0.318×(0.762 if female)×(1.18 if black) |
| aAbbreviated MDRD (94) | GFR=(186)×(SCr)–1.154×(age)–0.203×(0.742 if female)×(1.21 if black) |
| cCockcroft-Gault (84) | GFR=[(140–age)×(weight)]/[SCr×72]×(0.85 if female) |
| c,dNankivell (95) | GFR=[6700/(SCr×88.4)]+(weight/4)–(urea/2)–[100/(height)2]+35 (if male) or 25 (if female) |
| aCKD-EPI (87) | GFR=141×min(SCr/κ, 1)α×max(SCr/κ, 1)–1.209×0.993Age×1.018 [if female]×1.159[if black], where κ is 0.7 for females and 0.9 for males, α is –0.329 for females and –0.411 for males, min indicates the minimum of Scr/κor 1, and max indicates the maximum of Scr/κ or 1 |
| a,eSchwartz latest CKiD formula (81) | GFR=(42.3)×(height/SCr)0.78 |
Estimating GFR using cystatin C
Recent research has reported cystatin C to be less dependent on muscle mass than creatinine and as a result simpler GFR prediction equations were derived from cystatin C compared to creatinine (13, 70, 96). These models proved to give a more accurate and precise measurement of GFR than creatinine based equations with less bias, irrespective of anthropometric data, especially in identifying patients with GFR below 60 mL/min/1.73 m2(97). Cystatin C based estimation formulae were also shown to correctly classify CKD patients in 61.5%–72.0% compared to 62.1%–64% of the creatinine based formula (97). Some authors have also suggested replacing the classic Schwartz formula with GFR calculated from serum cystatin C in children since serum cystatin C concentrations are only slightly overestimated while creatinine levels show considerable bias (98).
One important aspect is that pediatric formulae based on cystatin C work well in adults, as could be shown for both the Filler formula (98), which was prospectively validated in a cohort of adult renal transplant recipients (99), and Anders Grubb’s formula GFR=84.69×cystatin C (mg/L)–1.680×1.384 (if child <14 years) (91).
In contrast, a large number of different cystatin C based prediction models were produced using a plethora of different mathematical models, with the inclusion of different clinical variables, variable methods for the measurement of cystatin C, variations in cystatin C calibrators, and the different ethnical backgrounds (100). Perhaps the most striking differences of the accuracy of different formulae occurred when the same patients were compared using turbidimetric (21) vs. nephelometric methods (81). Based on that study it appears that the Siemens Healthcare cystatin C nephelometry assay may be preferable. There is a need for the development of a standardized cystatin C based GFR prediction model (97). Certainly, the recently developed standardized reference material for cystatin C forms a most important step towards this goal (82).
The commonly used cystatin C based GFR estimation formulae are summarized in Table 3 some of which were also derived for children.
Equations to estimate glomerular filtration rate (mL/min/1.73 m2) using serum creatinine and cystatin C.
| GFR estimating equation | Formulae | Glomerular filtration rate measurements | ||
|---|---|---|---|---|
| Cystatin C-based, mg/L | Filtration markers | Urine and serum samples Collection (min) | ||
| Bökenkamp (70) | eGFR | =137/CysC–20/4 | Inulin | SS: 60, 120, 180 |
| Bouvet (101) | eGFR | =[(SCr (microM)/96)(–0.35(±0.20))]. [(CysC (mg/l)/1.2)(–0.56 (±0.19))]. [(Body weight/45)(0.30 (±0.17))]. [Age/14)(0.40 (±0.16))]. | 51Cr-EDTA | SS: 120, 180, 240 |
| CKiDξ(21) | eGFR | =39.1 [height/Scr]–0.516 [1.8/CysC (mg/L)]0.294 [30/urea]0.169 [1.099]male [height/1.4]0.188 | Iohexol | Details in (102) |
| Filler (98) | Log eGFR | =1.962+[1.123×log(1/CysC)] | 51Cr-EDTA 99Tc-DTPA | SS: 120, 180, 240 |
| Grubba(91) | eGFR | =84.69×CysC–1.680×(1.384 if age <14 years) | Cold Iohexol | SS: >240, details not specified |
| Hoek (103) | eGFR | = –4.32+(80.35×1/CysC) | 125I-Iothalamate 131I-Hippuran | US: 120, 240 |
| Larssona(104) | eGFR | =77.239×(CysC–1.2623) | Cold Iohexol | SS: 240 |
| Le Bricon (105) | eGFR | =78/CysC+4 | 51Cr-EDTA | SS: 60, 90, 120, 150, 180 |
| Rule (106) | eGFR | =66.8×CysC–1.30 | Cold Iothalamate | US: 60 SS: 60, 105–120 |
| Zappitelli (CysCrEq) (107) | eGFR | =(507.76×e0.003×height)/(CysC0.635×SCr0.547) If renal transplant, ×1.165If spina bifida, ×(SCr0.925)/40.45 | Cold Iothalamate | SS: 60, 120 |
Combined creatinine and cystatin C based GFR estimation equations
Several other GFR estimation equations were developed using the combination of both serum markers, creatinine and cystatin C. Schwartz and co-workers were able to demonstrate that a cystatin C and creatinine-based eGFR formula (GFR=41.6 [height/creatinine]0.443 [1.8/cystatin C]0.479) was able to result in a correlation coefficient of R2=0.843 between the measured and estimated GFR with 90.1% of eGFR within 30% of measured GFR (81). Similarly, Zappitelli et al. also demonstrated the highest accuracy with a formula combining cystatin C and serum creatinine (107). In adults, Tidman et al. also reported a greater accuracy for GFR estimation using combined models than the cystatin C or the Creatinine based estimation equations (108). Also, the combined models have shown to correctly classify CKD more frequently and to have the best performance in those with near-normal renal function (97). Of these well-known formulae are the Bouvet et al. (101) and the Zappitelli et al. (107) in children.
The list of formulae for the estimation of GFR based on cystatin C±serum creatinine is constantly expanding. Several important themes emerge when comparing the various formulae:
Some formulae are based on cystatin C alone. These formulae are convenient and do not require any additional data in most cases. Some of them have been prospectively validated in adults and in children (99).
Some formulae use both creatinine and cystatin C (21, 107). These formulae require significant additional information, which may include height, gender and urea. Formulae that include creatinine require more data, such as gender, ethnicity etc. While formulae with cystatin C alone can be quite useful for both children and adults, there is not much point in using a formula based on both creatinine and cystatin C for adults and children, similar to creatinine-based eGFR formulae (90).
A wide variety of “gold-standard methods” have been employed to derive the eGFR formula. This in itself provides considerable bias, especially if only two sampling points are used (see Table 3). The proper use of two-compartmental model has been rather limited as well.
A very detailed analysis of these formulae was recently performed by our group (97). The diagnostic accuracy of various cystatin C equations varies with different levels of GFR. We compared various cystatin C equations across GFR strata <60, <90, ≥135, and ≥150 mL/min per 1.73 m2 for an accurate prediction and appropriate classification of the measured GFR. The CKiD (21), Zappitelli-CysEq (107), and Zappitelli-CysCrEq (107) equations had a higher accuracy, estimated by eGFR values within 10% and 30% of the respective (99m)Tc DTPA, in the GFR categories <60 and <90 mL/min per 1.73 m2, whereas the Bökenkamp (70), Bouvet (101), and Filler (98) equations had a greater accuracy in the GFR categories ≥135 and ≥150 mL/min per 1.73 m2. The Bouvet, CKiD, Filler, Zappitelli-CysEq, and Zappitelli-CysCrEq equations had a greater sensitivity to classify GFR <60 and <90 mL/min per 1.73 m2, whereas the Bökenkamp equation had a higher sensitivity for GFR ≥135 and ≥150 mL/min per 1.73 m2. In short, formulae always perform best in the range where most of the patients were recruited. As such, the CKiD formula (21) may be particularly good for patients with a GFR between 25 and 75 mL/min. The method of determination of cystatin C also plays an important role. As outlined above, the re-analysis of the CKiD study using the Siemens Healthcare assay rather than the DAKO assay gave convincingly better results (81). Given the variability in the performance of various equations with GFR, an ideal equation that can be applied to all remains a challenge. Short of an individualized approach based on GFR levels by center, further research on refining the equations should focus on data pooling, ensuring the quality of the gold-standard method, and choosing a mathematical model that best resembles the naturally occurring decline of isotope measurements in the time concentration curve. Ideally, non-linear mixed pharmacokinetic models that adjust for extracorporeal volume, gender, ethnicity and age as well as selecting an appropriate model with the number of compartments should be utilized. Perhaps the Baysian approach for the WinNonLin derived GFR calculations employed by Bouvet et al. can maximize the quality of the gold-standard method of measuring GFR (101). As outlined above, standardized calibration and uniformity in using cystatin C assays can improve the prediction by cystatin C equations (109).
Cystatin C as a potential marker for volume assessment and cardiovascular outcomes
In patients with chronic kidney diseases with or without dialysis, their volume status is an important but difficult clinical assessment. In our recent publication, we have found that in patients who are on dialysis with no residual kidney function, the cystatin C reduction ratio per single hemodialysis treatment is negatively influenced by ultrafiltration rate (64). As a result, cystatin C level, its volume of distribution and its kinetics relating to volume status, needs to be further explored.
Furthermore, the current evidence explaining the biological plausibility of the association between cystatin C levels and cardiovascular outcomes are scant, despite a significant number of studies that have found strong associations in adult population. Likely, the mechanisms are partly due to the fact that cystatin C is a marker of reduced residual renal function, which is associated with cardiovascular outcome. However, in patients with cardiovascular diseases, studies have shown similar associations independent of residual renal function (110). A systematic review of 15 studies on “cardiovascular disease and cystatin C” showed that cystatin C level >1.3 mg/L is a risk factor for the occurrence of fatal and non-fatal cardiovascular events (111). Furthermore, recently published studies have provided some possible explanations to the mechanisms of cystatin C leading to an increase in cardiovascular diseases. Cystatin C is a base proteinase inhibitor and is produced by all nucleated cells. Xie et al. demonstrated that oxidants induce cystatin C elevation. Cystatin C regulates Cathepsin B activity and its elevation can affect cardiac extracellular remodelling (112). In addition, there is a positive association between cystatin C and monocyte levels which can lead to atherosclerosis (113). One might argue that artherosclerosis is not prevalent among children with chronic kidney disease, however, there clearly is evidence for vascular disease with media calcifications in these patients, leading to the major cause of death (32). Finally, Funayama et al. have shown that cystatin C level is an independent predictor for vasospastic angina (114). Clearly, these studies have demonstrated some of the possible mechanisms by which this important marker can lead to increased cardiovascular diseases and alter left ventricular mass. More studies are therefore needed. Therefore, cystatin C can be an important marker for both assessing kidney function and predicting cardiovascular outcomes. The influence of these cardiovascular morbidities on the variability of both creatinine and cystatin C levels as well as the accuracy of eGFR estimations has been understudied.
Limitations of prediction equations
Although prediction equations have shown to be more accurate estimators of GFR than the serum levels of the markers alone, these prediction equations tend to have limitations to their use. Prediction equations have shown to represent only the relationship between the marker and its non-GFR determinants (74). They also vary across populations and over time which would yield inaccurate estimations when applied to different populations from the one used in developing these formulae (81). The use of prediction equations may also be limited by other laboratory factors, such as the different calibrators used in determining the cystatin C blood levels, non-accurate methods for cystatin C determination, and the higher cost associated with cystatin C in the case of the cystatin C based equations (115). If one is to control for these varying parameters, a universal model can be developed using the appropriate statistical approach.
Conclusions
In summary, gold standard GFR measurements are cumbersome and invasive. It is without doubt that cystatin C offers significant advantages over serum creatinine as endogenous markers of GFR. The Schwartz formula is a good estimate of GFR in children with normal body composition, but for more accurate assessment across the lower levels of GFR, the CKiD or Filler or Zappitelli formula offers significant advantages. In certain conditions, such as spina bifida patients, only cystatin C-derived eGFR correlates with measured GFR. Significant variability exists with regards to high quality of the “gold-standard GFR measurement” across the various studies. Very few centers use appropriate sampling frequency and appropriate two-compartmental models to determine the GFR. The pre-analytical error related to the body composition, anthropometry variability, body surface area calculation vs. extra-cellular volume and intra- and inter-patient variability of the GFR measurements has been understudied. This also applies to the effect of the patient cohort’s GFR range on the diagnostic performance of the various formulae. The best approach towards a better formula for worldwide use would be the pooling of data to generate more robust formulae with appropriate validation cohorts. The importance of standardization and calibration of the cystatin C assays also cannot be underestimated. Nonetheless, of all endogenous markers, cystatin C appears to be the best surrogate for GFR and it is hoped that serum creatinine be combined or replaced for the estimation of GFR in children.
Conflict of interest statement
Authors’ conflict of interest disclosure: The authors stated that there are no conflicts of interest regarding the publication of this article.
Research funding: None declared.
Employment or leadership: None declared.
Honorarium: None declared.
About the authors
Guido Filler, MD, PhD, FRCPC is professor and chairman, Department of Pediatrics, Medicine and Pathology, Western University, London, ON, Canada, and chief of the Department of Pediatrics, Children’s Hospital, London Health Sciences Center since 2006. German-born, after graduation from high school in Phoenix, AZ, USA, he obtained his medical degree from Hanover Medical School in Germany, completed his residency in pediatrics and pediatric nephrology at the same institution and also had two years of pediatric nephrology training with Professor Martin Barratt at Great Ormond Street Hospital, London, UK. After his first consultant position at the Charité Hospital at Berlin, Germany, he was appointed as Professor of Pediatrics at the University of Ottawa, ON, Canada, in 1999.
Dr. S. Huang completed her undergraduate medical degree at the University of Ottawa in 2005. This was followed by three years of Internal Medicine and two years of Nephrology training at the University of Western Ontario; in which she completed the clinical fellowship in 2010. She has been awarded: Canadian Institutes of Health Research Fellowship Health Professional Award, to further support her research training. She is completing her doctoral training in Medical Biophysics at the University of Western Ontario. Her doctoral research is on cystatin C clearances in patients with chronic kidney disease and dialysis. Her other research focus is on assessing and managing patients with chronic kidney disease, methods for preventing renal disease, and the managements of TTP/HUS.
Abeer Yasin, PhD is a researcher at the Department of Pediatrics, Western University, London, Ontario, Canada. After graduating from high school in London, Ontario, Canada, she attended and graduated from the Western University, London, Ontario, with a Bachelor of Science degree in Mathematics followed by a Masters of Science degree in Mathematics. Thereafter, she obtained her PhD in Engineering and Applied Sciences (Mathematics) from University of New Orleans. She also served as on ground professor at various institutions and continues as online facilitator as well as a doctoral dissertation mentor. The research focuses on mathematical application in the medical field.
References
1. Filler G, Browne R, Seikaly MG. Glomerular filtration rate as a putative’surrogate end-point’ for renal transplant clinical trials in children. Pediatr Transplant 2003;7:18–24.10.1034/j.1399-3046.2003.00015.xSearch in Google Scholar
2. Filler G, Sharma AP. How to monitor renal function in pediatric solid organ transplant recipients. Pediatr Transplant 2008;12:393–401.10.1111/j.1399-3046.2007.00885.xSearch in Google Scholar
3. Stickle D, Cole B, Hock K, Hruska KA, Scott MG. Correlation of plasma concentrations of cystatin C and creatinine to inulin clearance in a pediatric population. Clin Chem 1998;44:1334–8.10.1093/clinchem/44.6.1334Search in Google Scholar
4. Richards AN, Westfall BB, Pott PA. Renal excretion of inulin, creatinine and cylose in normal dogs. Proc Soc Exper Boil Med 1934;32:73–87.10.3181/00379727-32-7564PSearch in Google Scholar
5. Stevens LA, Levey AS. Measured GFR as a confirmatory test for estimated GFR. J Am Soc Nephrol 2009;20:2305–13.10.1681/ASN.2009020171Search in Google Scholar
6. Shannon JA, Smith HW. The excretion of inulin, xylose and urea by normal and phlorizinized man. J Clin Invest 1935;14: 393–401.10.1172/JCI100690Search in Google Scholar
7. Pastore A, Bernardini S, Dello Strologo L, Rizzoni G, Cortese C, Federici G. Simultaneous determination of inulin and p-aminohippuric acid in plasma and urine by reversed-phase high-performance liquid chromatography. J Chromatogr B Biomed Sci App 2001;751:187–91.10.1016/S0378-4347(00)00444-8Search in Google Scholar
8. Awdeh M, Kouris K, Hassan IM, Abdel-Dayem HM. Factors affecting the Gates’ measurement of glomerular filtration rate. Am J Physiol Imaging 1990;5:36–41.Search in Google Scholar
9. Chantler C, Garnett ES, Parsons V, Veall N. Glomerular filtration rate measurement in man by the single injection methods using 51Cr-EDTA. Clin Sci 1969;37:169–80.Search in Google Scholar
10. Chantler C, Barratt TM. Estimation of glomerular filtration rate from plasma clearance of 51-chromium edetic acid. Arch Dis Child 1972;47:613–7.10.1136/adc.47.254.613Search in Google Scholar PubMed PubMed Central
11. Filler G, Witt I, Priem F, Ehrich JH, Jung K. Are cystatin C and beta 2-microglobulin better markers than serum creatinine for prediction of a normal glomerular filtration rate in pediatric subjects? Clin Chem 1997;43:1077–8.10.1093/clinchem/43.6.1077Search in Google Scholar
12. Shemesh O, Golbetz H, Kriss JP, Myers BD. Limitations of creatinine as a filtration marker in glomerulopathic patients. Kidney Int 1985;28:830–8.10.1038/ki.1985.205Search in Google Scholar PubMed
13. Filler G, Priem F, Lepage N, Sinha P, Vollmer I, Clark H, et al. Beta-trace protein, cystatin C, beta (2)-microglobulin, and creatinine compared for detecting impaired glomerular filtration rates in children. Clin Chem 2002;48:729–36.10.1093/clinchem/48.5.729Search in Google Scholar
14. Shore RM, Koff SA, Mentser M, Hayes JR, Smith SP, Smith JP, et al. Glomerular filtration rate in children: determination from the Tc-99m-DTPA renogram. Radiology 1984;151:627–33.10.1148/radiology.151.3.6371888Search in Google Scholar
15. Barbour GL, Crumb CK, Boyd CM, Reeves RD, Rastogi SP, Patterson RM. Comparison of inulin, iothalamate, and 99mTc-DTPA for measurement of glomerular filtration rate. J Nucl Med 1976;17:317–20.Search in Google Scholar
16. Brouhard BH, Travis LB, Cunningham RJ, 3rd, Berger M, Carvajal HF. Simultaneous iothalamate, creatinine, and urea clearances in children with renal disease. Pediatrics 1977;59:219–23.10.1542/peds.59.2.219Search in Google Scholar
17. Bajaj G, Alexander SR, Browne R, Sakarcan A, Seikaly MG. 125Iodine-iothalamate clearance in children. A simple method to measure glomerular filtration. Pediatr Nephrol 1996;10:25–8.10.1007/BF00863432Search in Google Scholar
18. Sharma AK, Mills MS, Grey VL, Drummond KN. Infusion clearance of subcutaneous iothalamate versus standard renal clearance. Pediatr Nephrol 1997;11:711–3.10.1007/s004670050372Search in Google Scholar
19. Back SE, Krutzen E, Nilsson-Ehle P. Contrast media as markers for glomerular filtration: a pharmacokinetic comparison of four agents. Scand J Clin Lab Invest 1988;48:247–53.10.3109/00365518809167491Search in Google Scholar
20. Krutzen E, Back SE, Nilsson-Ehle P. Determination of glomerular filtration rate using iohexol clearance and capillary sampling. Scand J Clin Lab Invest 1990;50:279–83.10.3109/00365519009091579Search in Google Scholar
21. Schwartz GJ, Munoz A, Schneider MF, Mak RH, Kaskel F, Warady BA, et al. New equations to estimate GFR in children with CKD. J Am Soc Nephrol 2009;20:629–37.10.1681/ASN.2008030287Search in Google Scholar
22. Russell CD. Optimum sample times for single-injection, multisample renal clearance methods. J Nucl Med 1993;34:1761–5.Search in Google Scholar
23. Houlihan C, Jenkins M, Osicka T, Scott A, Parkin D, Jerums G. A comparison of the plasma disappearance of iohexol and 99mTc-DTPA for the measurement of glomerular filtration rate (GFR) in diabetes. Aust NZ J Med 1999;29:693–700.10.1111/j.1445-5994.1999.tb01617.xSearch in Google Scholar
24. Frennby B, Sterner G, Almen T, Chai CM, Jonsson BA, Mansson S. Clearance of iohexol, chromium-51-ethylenediaminetetraacetic acid, and creatinine for determining the glomerular filtration rate in pigs with normal renal function: comparison of different clearance techniques. Acad Radiol 1996;3:651–9.10.1016/S1076-6332(96)80191-7Search in Google Scholar
25. Itoh K, Tsushima S, Tsukamoto E, Tamaki N. Accuracy of plasma sample methods for determination of glomerular filtration rate with 99mTc-DTPA. Ann Nucl Med 2002;16:39–44.10.1007/BF02995290Search in Google Scholar
26. Peters AM, Henderson BL, Lui D, Blunkett M, Cosgriff PS, Myers MJ. Appropriate corrections to glomerular filtration rate and volume of distribution based on the bolus injection and single-compartment technique. Physiol Measure 1999;20:313–27.10.1088/0967-3334/20/3/308Search in Google Scholar
27. Peters AM. The kinetic basis of glomerular filtration rate measurement and new concepts of indexation to body size. Eur J Nucl Med Mol Imaging 2004;31:137–49.10.1007/s00259-003-1341-8Search in Google Scholar
28. Filler GM. The challenges of assessing acute kidney injury in infants. Kidney Int 2011;80:567–8.10.1038/ki.2011.172Search in Google Scholar
29. Schwartz GJ, Brion LP, Spitzer A. The use of plasma creatinine concentration for estimating glomerular filtration rate in infants, children, and adolescents. Pediatr Clin North Am 1987;34:571–90.10.1016/S0031-3955(16)36251-4Search in Google Scholar
30. Schwartz GJ, Gauthier B. A simple estimate of glomerular filtration rate in adolescent boys. J Pediatr 1985;106:522–6.10.1016/S0022-3476(85)80697-1Search in Google Scholar
31. Schwartz GJ, Haycock GB, Edelmann CM, Jr., Spitzer A. A simple estimate of glomerular filtration rate in children derived from body length and plasma creatinine. Pediatrics 1976;58:259–63.10.1542/peds.58.2.259Search in Google Scholar
32. Filler G. Challenges in pediatric transplantation: the impact of chronic kidney disease and cardiovascular risk factors on long-term outcomes and recommended management strategies. Pediatr Transplant 2011;15:25–31.10.1111/j.1399-3046.2010.01439.xSearch in Google Scholar PubMed
33. Popper H, Mandel E. Filtrations- und reabsorptionsleitung in der nierenpathologie. ergebnisse der inneren medizin und kinderheilkunde. 1937;53:685–94.Search in Google Scholar
34. Thomas L, Huber AR. Renal function – estimation of glomerular filtration rate. Clin Chem Lab Med 2006;44:1295–302.10.1515/CCLM.2006.239Search in Google Scholar PubMed
35. Heymsfield SB, Arteaga C, McManus C, Smith J, Moffitt S. Measurement of muscle mass in humans: validity of the 24-hour urinary creatinine method. Am J Clin Nutr 1983;37:478–94.10.1093/ajcn/37.3.478Search in Google Scholar PubMed
36. Vinge E, Lindergard B, Nilsson-Ehle P, Grubb A. Relationships among serum cystatin C, serum creatinine, lean tissue mass and glomerular filtration rate in healthy adults. Scand J Clin Lab Invest 1999;59:587–92.10.1080/00365519950185076Search in Google Scholar
37. Filler G, Gharib M, Casier S, Lodige P, Ehrich JH, Dave S. Prevention of chronic kidney disease in spina bifida. Int Urol Nephrol 2011 Jan 14 [Epub ahead of print].10.1007/s11255-010-9894-5Search in Google Scholar
38. Pham-Huy A, Leonard M, Lepage N, Halton J, Filler G. Measuring glomerular filtration rate with cystatin C and beta-trace protein in children with spina bifida. J Urol 2003;169:2312–5.10.1097/01.ju.0000060205.23406.13Search in Google Scholar
39. Brion LP, Boeck MA, Gauthier B, Nussbaum MP, Schwartz GJ. Estimation of glomerular filtration rate in anorectic adolescents. Pediatr Nephrol 1989;3:16–21.10.1007/BF00859618Search in Google Scholar
40. Fitch CD, Sinton DW. A study of creatine metabolism in diseases causing muscle wasting. J Clin Invest 1964;43:444–52.10.1172/JCI104929Search in Google Scholar
41. Shannon JA. The renal excretion of creatinine in man. J Clin Invest 1935;14:403–10.10.1172/JCI100691Search in Google Scholar
42. Horio M, Orita Y. Comparison of Jaffe rate assay and enzymatic method for the measurement of creatinine clearance. Nihon Jinzo Gakkai Shi 1996;38:296–9.Search in Google Scholar
43. Arant BS, Jr. Estimating glomerular filtration rate in infants. J Pediatr 1984;104:890–3.10.1016/S0022-3476(84)80488-6Search in Google Scholar
44. Proficiency testing survey, creatinine. Northfield Il: College of American Pathologists, 1995:29–30.Search in Google Scholar
45. Ceriotti F, Boyd JC, Klein G, Henny J, Queralto J, Kairisto V, et al. Reference intervals for serum creatinine concentrations: assessment of available data for global application. Clin Chem 2008;54:559–66.10.1373/clinchem.2007.099648Search in Google Scholar PubMed
46. Delanghe JR, Cobbaert C, Harmoinen A, Jansen R, Laitinen P, Panteghini M. Focusing on the clinical impact of standardization of creatinine measurements: a report by the EFCC Working Group on Creatinine Standardization. Clin Chem Lab Med 2011;49:977–82.10.1515/CCLM.2011.167Search in Google Scholar PubMed
47. Cobbaert CM, Baadenhuijsen H, Weykamp CW. Prime time for enzymatic creatinine methods in pediatrics. Clin Chem 2009;55:549–58.10.1373/clinchem.2008.116863Search in Google Scholar PubMed
48. Olsen NV, Ladefoged SD, Feldt-Rasmussen B, Fogh-Andersen N, Jordening H, Munck O. The effects of cimetidine on creatinine excretion, glomerular filtration rate and tubular function in renal transplant recipients. Scand J Clin Lab Invest 1989;49:155–9.10.3109/00365518909105415Search in Google Scholar
49. Roubenoff R, Drew H, Moyer M, Petri M, Whiting-O’Keefe Q, Hellmann DB. Oral cimetidine improves the accuracy and precision of creatinine clearance in lupus nephritis. Ann Intern Med 1990;113:501–6.10.7326/0003-4819-113-7-501Search in Google Scholar
50. Hellerstein S, Erwin P, Warady BA. The cimetidine protocol: a convenient, accurate, and inexpensive way to measure glomerular filtration rate. Pediatr Nephrol 2003;18:71–2.10.1007/s00467-002-1012-1Search in Google Scholar
51. Filler G. How to measure renal function in children – What is the role of cystatin C? Curr Pediatr Rev 2006;2:225–31.10.2174/157339606778019738Search in Google Scholar
52. Jung K, Schulze BD, Sydow K, Pergande M, Precht K, Schreiber G. Diagnostic value of low-molecular mass proteins in serum for the detection of reduced glomerular filtration rate. J Clin Chem Clin Biochem 1987;25:499–503.10.1515/cclm.1987.25.8.499Search in Google Scholar
53. Jung K. Low-molecular-mass proteins in serum and their relationship to the glomerular filtration rate. Nephron 1987;47:160.10.1159/000184483Search in Google Scholar
54. Grubb A, Lofberg H. Human gamma-trace, a basic microprotein: amino acid sequence and presence in the adenohypophysis. Proc Natl Acad Sci USA 1982;79:3024–7.10.1073/pnas.79.9.3024Search in Google Scholar
55. Barrett AJ, Davies ME, Grubb A. The place of human gamma-trace (cystatin C) amongst the cysteine proteinase inhibitors. Biochem Biophys Res Commun 1984;120:631–6.10.1016/0006-291X(84)91302-0Search in Google Scholar
56. Abrahamson M, Olafsson I, Palsdottir A, Ulvsback M, Lundwall A, Jensson O, et al. Structure and expression of the human cystatin C gene. Biochem J 1990;268:287–94.10.1042/bj2680287Search in Google Scholar PubMed PubMed Central
57. Ni J, Fernandez MA, Danielsson L, Chillakuru RA, Zhang J, Grubb A, et al. Cystatin F is a glycosylated human low molecular weight cysteine proteinase inhibitor. J Biol Chem 1998;273:24797–804.10.1074/jbc.273.38.24797Search in Google Scholar PubMed
58. Grubb AO. Cystatin C – properties and use as diagnostic marker. Adv Clin Chem 2000;35:63–99.10.1016/S0065-2423(01)35015-1Search in Google Scholar
59. Bjarnadottir M, Grubb A, Olafsson I. Promoter-mediated, dexamethasone-induced increase in cystatin C production by HeLa cells. Scand J Clin Lab Invest 1995;55:617–23.10.3109/00365519509110261Search in Google Scholar PubMed
60. Risch L, Herklotz R, Blumberg A, Huber AR. Effects of glucocorticoid immunosuppression on serum cystatin C concentrations in renal transplant patients. Clin Chem 2001;47:2055–9.10.1093/clinchem/47.11.2055Search in Google Scholar
61. Foster J, Reisman W, Lepage N, Filler G. Influence of commonly used drugs on the accuracy of cystatin C-derived glomerular filtration rate. Pediatr Nephrol 2006;21:235–8.10.1007/s00467-005-2075-6Search in Google Scholar PubMed
62. Stevens LA, Schmid CH, Greene T, Li L, Beck GJ, Joffe MM, et al. Factors other than glomerular filtration rate affect serum cystatin C levels. Kidney Int 2009;75:652–60.10.1038/ki.2008.638Search in Google Scholar PubMed PubMed Central
63. Al-Malki N, Heidenheim PA, Filler G, Yasin A, Lindsay RM. Cystatin C levels in functionally anephric patients undergoing dialysis: the effect of different methods and intensities. Clin J Am Soc Nephrol 2009;4:1606–10.10.2215/CJN.02910509Search in Google Scholar PubMed PubMed Central
64. Huang SH, Filler G, Yasin A, Lindsay RM. Cystatin C reduction ratio depends on normalized blood liters processed and fluid removal during hemodialysis. Clin J Am Soc Nephrol 2011;6:319–25.10.2215/CJN.05290610Search in Google Scholar PubMed PubMed Central
65. Galteau MM, Guyon M, Gueguen R, Siest G. Determination of serum cystatin C: biological variation and reference values. Clin Chem Lab Med 2001;39:850–7.10.1515/CCLM.2001.141Search in Google Scholar PubMed
66. Fischbach M, Graff V, Terzic J, Bergere V, Oudet M, Hamel G. Impact of age on reference values for serum concentration of cystatin C in children. Pediatr Nephrol 2002;17:104–6.10.1007/s00467-001-0777-ySearch in Google Scholar PubMed
67. Hannemann A, Friedrich N, Dittmann K, Spielhagen C, Wallaschofski H, V Lzke H, et al. Age- and sex-specific reference limits for creatinine, cystatin C and the estimated glomerular filtration rate. Clin Chem Lab Med 2011;50:919–26.Search in Google Scholar
68. Grubb A, Simonsen O, Sturfelt G, Truedsson L, Thysell H. Serum concentration of cystatin C, factor D and beta 2-microglobulin as a measure of glomerular filtration rate. Acta Med Scand 1985;218:499–503.10.1111/j.0954-6820.1985.tb08880.xSearch in Google Scholar PubMed
69. Tenstad O, Roald AB, Grubb A, Aukland K. Renal handling of radiolabelled human cystatin C in the rat. Scand J Clin Lab Invest 1996;56:409–14.10.3109/00365519609088795Search in Google Scholar PubMed
70. Bokenkamp A, Domanetzki M, Zinck R, Schumann G, Byrd D, Brodehl J. Cystatin C – a new marker of glomerular filtration rate in children independent of age and height. Pediatrics 1998;101:875–81.10.1542/peds.101.5.875Search in Google Scholar PubMed
71. Filler G, Priem F, Vollmer I, Gellermann J, Jung K. Diagnostic sensitivity of serum cystatin for impaired glomerular filtration rate. Pediatr Nephrol 1999;13:501–5.10.1007/s004670050646Search in Google Scholar PubMed
72. Ylinen EA, Ala-Houhala M, Harmoinen AP, Knip M. Cystatin C as a marker for glomerular filtration rate in pediatric patients. Pediatr Nephrol 1999;13:506–9.10.1007/s004670050647Search in Google Scholar PubMed
73. Bokenkamp A, Domanetzki M, Zinck R, Schumann G, Brodehl J. Reference values for cystatin C serum concentrations in children. Pediatr Nephrol 1998;12:125–9.10.1007/s004670050419Search in Google Scholar PubMed
74. Sharma AP, Kathiravelu A, Nadarajah R, Yasin A, Filler G. Body mass does not have a clinically relevant effect on cystatin C eGFR in children. Nephrol Dial Transplant 2009;24:470–4.10.1093/ndt/gfn505Search in Google Scholar PubMed
75. Kyhse-Andersen J, Schmidt C, Nordin G, Andersson B, Nilsson-Ehle P, Lindstrom V, et al. Serum cystatin C, determined by a rapid, automated particle-enhanced turbidimetric method, is a better marker than serum creatinine for glomerular filtration rate. Clin Chem 1994;40:1921–6.10.1093/clinchem/40.10.1921Search in Google Scholar
76. Li J, Dunn W, Breaud A, Elliott D, Sokoll LJ, Clarke W. Analytical performance of 4 automated assays for measurement of cystatin C. Clin Chem 2010;56:1336–9.10.1373/clinchem.2009.141531Search in Google Scholar PubMed
77. Dharnidharka VR, Kwon C, Stevens G. Serum cystatin C is superior to serum creatinine as a marker of kidney function: a meta-analysis. Am J Kidney Dis 2002;40:221–6.10.1053/ajkd.2002.34487Search in Google Scholar PubMed
78. Roos JF, Doust J, Tett SE, Kirkpatrick CM. Diagnostic accuracy of cystatin C compared to serum creatinine for the estimation of renal dysfunction in adults and children – a meta-analysis. Clin Biochem 2007;40:383–91.10.1016/j.clinbiochem.2006.10.026Search in Google Scholar PubMed
79. Bariciak E, Abeeryasin, Harrold J, Walker M, Lepage N, Filler G. Preliminary reference intervals for cystatin C and beta-trace protein in preterm and term neonates. Clin Biochem 2011;44:1156–9.10.1016/j.clinbiochem.2011.06.987Search in Google Scholar PubMed
80. Harmoinen A, Ylinen E, Ala-Houhala M, Janas M, Kaila M, Kouri T. Reference intervals for cystatin C in pre- and full-term infants and children. Pediatr Nephrol 2000;15:105–8.10.1007/s004670000421Search in Google Scholar PubMed
81. Schwartz G, Schneider M, Maier P, Moxey-Mims M, Dharnidharka V, Warady BA, et al. Improved GFR estimating equations in children with chronic kidney disease using immunonephelometric determination of cystatin C. Kidney Int 2012 May 23. doi:10.1038/ki.2012.169. [Epub ahead of print].10.1038/ki.2012.169.[EpubSearch in Google Scholar
82. Grubb A, Blirup-Jensen S, Lindstrom V, Schmidt C, Althaus H, Zegers I. First certified reference material for cystatin C in human serum ERM-DA471/IFCC. Clin Chem Lab Med 2010;48:1619–21.10.1515/CCLM.2010.318Search in Google Scholar
83. Hetu PO, Gingras ME, Vinet B. Development and validation of a rapid liquid chromatography isotope dilution tandem mass spectrometry (LC-IDMS/MS) method for serum creatinine. Clin Biochem 2010;43:1158–62.10.1016/j.clinbiochem.2010.05.018Search in Google Scholar
84. Cockcroft DW, Gault MH. Prediction of creatinine clearance from serum creatinine. Nephron 1976;16:31–41.10.1159/000180580Search in Google Scholar
85. 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. Modification of Diet in Renal Disease Study Group. Ann Intern Med 1999;130:461–70.10.7326/0003-4819-130-6-199903160-00002Search in Google Scholar
86. Levey AS, Coresh J, Greene T, Stevens LA, Zhang YL, Hendriksen S, et al. Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann Intern Med 2006;145:247–54.10.7326/0003-4819-145-4-200608150-00004Search in Google Scholar
87. Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, 3rd, Feldman HI, et al. A new equation to estimate glomerular filtration rate. Ann Intern Med 2009;150:604–12.10.7326/0003-4819-150-9-200905050-00006Search in Google Scholar
88. Counahan R, Chantler C, Ghazali S, Kirkwood B, Rose F, Barratt TM. Estimation of glomerular filtration rate from plasma creatinine concentration in children. Arch Dis Child 1976;51:875–8.10.1136/adc.51.11.875Search in Google Scholar
89. Barratt TM, Counahan R. Correlates of creatinine excretion. N Engl J Med 1977;296:176.10.1056/NEJM197701202960319Search in Google Scholar PubMed
90. Filler G, Foster J, Acker A, Lepage N, Akbari A, Ehrich JH. The Cockcroft-Gault formula should not be used in children. Kidney Int 2005;67:2321–4.10.1111/j.1523-1755.2005.00336.xSearch in Google Scholar PubMed
91. Grubb A, Nyman U, Bjork J, Lindstrom V, Rippe B, Sterner G, et al. Simple cystatin C-based prediction equations for glomerular filtration rate compared with the modification of diet in renal disease prediction equation for adults and the Schwartz and the Counahan-Barratt prediction equations for children. Clin Chem 2005;51:1420–31.10.1373/clinchem.2005.051557Search in Google Scholar PubMed
92. Nyman U, Grubb A, Sterner G, Bjork J. The CKD-EPI and MDRD equations to estimate GFR. Validation in the Swedish Lund-Malmo Study cohort. Scand J Clin Lab Invest 2011;71:129–38.10.3109/00365513.2010.543143Search in Google Scholar PubMed
93. Schwartz GJ, Kwong T, Erway B, Warady B, Sokoll L, Hellerstein S, et al. Validation of creatinine assays utilizing HPLC and IDMS traceable standards in sera of children. Pediatr Nephrol 2009;24:113–9.10.1007/s00467-008-0957-0Search in Google Scholar PubMed PubMed Central
94. Levey AS, Greene T, Kusek J, Beck GJ. A simplified equation to predict glomerular filtration rate from serum creatinine. (abstract). J Am Soc Nephrol 2000;11:A0828.Search in Google Scholar
95. Nankivell BJ, Gruenewald SM, Allen RD, Chapman JR. Predicting glomerular filtration rate after kidney transplantation. Transplantation 1995;59:1683–9.10.1097/00007890-199506270-00007Search in Google Scholar PubMed
96. Groesbeck D, Kottgen A, Parekh R, Selvin E, Schwartz GJ, Coresh J, et al. Age, gender, and race effects on cystatin C levels in US adolescents. Clin J Am Soc Nephrol 2008;3:1777–85.10.2215/CJN.00840208Search in Google Scholar PubMed PubMed Central
97. Sharma AP, Yasin A, Garg AX, Filler G. Diagnostic accuracy of cystatin C-based eGFR equations at different GFR levels in children. Clin J Am Soc Nephrol 2011;6:1599–608.10.2215/CJN.10161110Search in Google Scholar PubMed
98. Filler G, Lepage N. Should the Schwartz formula for estimation of GFR be replaced by cystatin C formula? Pediatr Nephrol 2003;18:981–5.10.1007/s00467-003-1271-5Search in Google Scholar PubMed
99. White C, Akbari A, Hussain N, Dinh L, Filler G, Lepage N, et al. Estimating glomerular filtration rate in kidney transplantation: a comparison between serum creatinine and cystatin C-based methods. J Am Soc Nephrol 2005;16:3763–70.10.1681/ASN.2005050512Search in Google Scholar PubMed
100. Weinert LS, Camargo EG, Soares AA, Silveiro SP. Glomerular filtration rate estimation: performance of serum cystatin C-based prediction equations. Clin Chem Lab Med 2011;49:1761–71.10.1515/cclm.2011.670Search in Google Scholar PubMed
101. Bouvet Y, Bouissou F, Coulais Y, Seronie-Vivien S, Tafani M, Decramer S, et al. GFR is better estimated by considering both serum cystatin C and creatinine levels. Pediatr Nephrol 2006;21:1299–306.10.1007/s00467-006-0145-zSearch in Google Scholar PubMed
102. Schwartz GJ, Furth S, Cole SR, Warady B, Munoz A. Glomerular filtration rate via plasma iohexol disappearance: pilot study for chronic kidney disease in children. Kidney Int 2006;69:2070–7.10.1038/sj.ki.5000385Search in Google Scholar PubMed
103. Hoek FJ, Kemperman FA, Krediet RT. A comparison between cystatin C, plasma creatinine and the Cockcroft and Gault formula for the estimation of glomerular filtration rate. Nephrol Dial Transplant 2003;18:2024–31.10.1093/ndt/gfg349Search in Google Scholar PubMed
104. Larsson A, Malm J, Grubb A, Hansson LO. Calculation of glomerular filtration rate expressed in mL/min from plasma cystatin C values in mg/L. Scand J Clin Lab Invest 2004;64:25–30.10.1080/00365510410003723Search in Google Scholar PubMed
105. Le Bricon T, Leblanc I, Benlakehal M, Gay-Bellile C, Erlich D, Boudaoud S. Evaluation of renal function in intensive care: plasma cystatin C vs. creatinine and derived glomerular filtration rate estimates. Clin Chem Lab Med 2005;43:953–7.10.1515/CCLM.2005.163Search in Google Scholar PubMed
106. Rule AD, Bergstralh EJ, Slezak JM, Bergert J, Larson TS. Glomerular filtration rate estimated by cystatin C among different clinical presentations. Kidney Int 2006;69:399–405.10.1038/sj.ki.5000073Search in Google Scholar PubMed
107. Zappitelli M, Parvex P, Joseph L, Paradis G, Grey V, Lau S, et al. Derivation and validation of cystatin C-based prediction equations for GFR in children. Am J Kidney Dis 2006;48:221–30.10.1053/j.ajkd.2006.04.085Search in Google Scholar PubMed
108. Tidman M, Sjostrom P, Jones I. A Comparison of GFR estimating formulae based upon s-cystatin C and s-creatinine and a combination of the two. Nephrol Dial Transplant 2008;23:154–60.10.1093/ndt/gfm661Search in Google Scholar PubMed
109. Miller WG. Estimating equations for glomerular filtration rate in children: laboratory considerations. Clin Chem 2009;55:402–3.10.1373/clinchem.2008.122218Search in Google Scholar PubMed
110. Ix JH, Shlipak MG, Chertow GM, Whooley MA. Association of cystatin C with mortality, cardiovascular events, and incident heart failure among persons with coronary heart disease: data from the Heart and Soul Study. Circulation 2007;115:173–9.10.1161/CIRCULATIONAHA.106.644286Search in Google Scholar PubMed PubMed Central
111. Ferraro S, Marano G, Biganzoli EM, Boracchi P, Bongo AS. Prognostic value of cystatin C in acute coronary syndromes: enhancer of atherosclerosis and promising therapeutic target. Clin Chem Lab Med 2011;49:1397–404.10.1515/CCLM.2011.607Search in Google Scholar PubMed
112. Xie L, Terrand J, Xu B, Tsaprailis G, Boyer J, Chen QM. Cystatin C increases in cardiac injury: a role in extracellular matrix protein modulation. Cardiovasc Res 2010;87: 628–35.10.1093/cvr/cvq138Search in Google Scholar PubMed PubMed Central
113. Evangelopoulos AA, Vallianou NG, Bountziouka V, Katsagoni C, Bathrellou E, Vogiatzakis ED, et al. Association between serum cystatin C, monocytes and other inflammatory markers. Intern Med J 2012;42:517–22.10.1111/j.1445-5994.2011.02500.xSearch in Google Scholar PubMed
114. Funayama A, Watanabe T, Tamabuchi T, Otaki Y, Netsu S, Hasegawa H, et al. Elevated cystatin C levels predict the incidence of vasospastic angina. Circ J 2011;75:2439–44.10.1253/circj.CJ-11-0008Search in Google Scholar
115. Filler G, Bokenkamp A, Hofmann W, Le Bricon T, Martinez-Bru C, Grubb A. Cystatin C as a marker of GFR – history, indications, and future research. Clin Biochem 2005;38: 1–8.10.1016/j.clinbiochem.2004.09.025Search in Google Scholar PubMed
©2012 by Walter de Gruyter Berlin Boston
