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Cystatin C as a Multifaceted Biomarker in Kidney Disease and Its Role in Defining “Shrunken Pore Syndrome”
CHAPTER FIVE
Cystatin C as a Multifaceted Biomarker in Kidney Disease and Its Role in Defining “Shrunken Pore Syndrome” A. Grubb, MD, PhD
Department of Clinical Chemistry and Pharmacology, University Hospital, Lund University, Lund, Sweden
Contents Factors Influencing the Diagnostic Performance of Cystatin C- or Creatinine-Based GFR-Estimating Equations and Causing the Plethora of Equations: The Concepts of “Internal” or “External” Validation Optimizing the Use of Cystatin C- and Creatinine-Based GFR-Estimating Equations Cystatin C and Creatinine (eGFRcystatin C and eGFRcreatinine) as Markers of End-Stage Renal Disease (ESRD), Hospitalization, Cardiovascular Disease, and Death Identification of “Shrunken Pore Syndrome”: Its Influence on Mortality Cystatin C as an Indictor of the Circadian Rhythm of GFR Cystatin C as an Indicator of “Renal Reserve” References
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Cystatin C, a basic nonglycosylated 13.3 kDa protein comprising a single polypeptide chain of 120 amino acid residues, is produced at a stable rate from a housekeeping gene in all nucleated cells and eliminated from the circulation by free filtration through the glomerular membranes as described in the first edition of “Biomarkers of Kidney Disease” [1]. This discourse will report some of the progress in the use of cystatin C as a multifaceted marker of kidney disease, which has occurred since then, including its role in identifying the novel syndrome called “Shrunken Pore Syndrome.”
Biomarkers of Kidney Disease. http://dx.doi.org/10.1016/B978-0-12-803014-1.00005-4 Copyright © 2017 Elsevier Inc. All rights reserved.
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FACTORS INFLUENCING THE DIAGNOSTIC PERFORMANCE OF CYSTATIN C- OR CREATININEBASED GFR-ESTIMATING EQUATIONS AND CAUSING THE PLETHORA OF EQUATIONS: THE CONCEPTS OF “INTERNAL” OR “EXTERNAL” VALIDATION More than 100 different creatinine- and cystatin C-based estimating equations for glomerular filtration rate (GFR) have been described [2]. This has caused confusion and debate about which equation(s) is (are) to be preferred. In order to arrive at the best decision concerning selection of an estimating equation, the reasons for the generation of a multitude of cystatin C- and creatinine-based GFR-estimating equations must be known and they are as follows. A. Not using (or lack of) international reference materials (“calibrators”) for cystatin C or creatinine. B. Use of varying, noncommutable assays of cystatin C or creatinine. C. Use of different methods to measure GFR. D. Use of different statistical approaches to generate the equations. E. Use of different populations to generate the equations. The populations may vary in parameters, such as, disease panorama, proportion between healthy and sick persons, age distribution, GFR distribution, ethnicity, etc. Of these reasons, those in A have contributed heavily in producing differing GFR-estimating equations. Standard reference materials for creatinine were released in 2006 [3] and for cystatin C (ERM-DA471/IFCC) in 2010 [4–6]. Therefore, it was only recently possible for diagnostic companies to offer assay methods for cystatin C based on such reference materials [2]. This has resulted in decreased variation among the results for cystatin C obtained with different assays at different laboratories as reported by external quality assessment organizations, such as Equalis [2,7]. However, the problems mentioned in A will persist for a few years until the ongoing replacement of old reference materials with new international reference material will finish. The use of noncommutable assays for cystatin C and creatinine is now diminishing inter alia due to the availability of international reference materials for both cystatin C and creatinine. A further contributing factor is the availability of an internationally recognized reference method, isotope dilution mass spectrometry (IDMS) for the determination of plasma creatinine levels [8]. No such method is yet available for the concentration of any protein in any biologic fluid, although some efforts are being made concerning the cystatin C concentration in biologic fluids [9,10].
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As all GFR-estimating equations are generated using measured GFR values, it is obvious that if different methods to measure GFR produce different results, the corresponding generated GFR-estimating equations will also differ. Although renal clearance of inulin is generally accepted as the best way to measure GFR, it has rarely been used in efforts to generate GFR-estimating equations, as it requires expensive reagents and is based on a technically complicated and slow procedure [11,12]. Instead, several faster and less expensive methods to measure GFR have been used, for example, renal clearance of 51Cr–EDTA, 125I–iothalamate, 99Tc–DTPA or iohexol; or plasma clearance of iohexol, iothalamate, 51Cr–EDTA, or 99Tc–DTPA; and endogenous clearance of creatinine [11,12]. They produce different results and it is surprising that, only recently, a careful analysis has been done to evaluate which of these compounds produce results that agree with those obtained by the renal clearance of inulin [11–13]. The results of some of the methods studied differed very significantly from those produced by the renal clearance of inulin [11–13] and would therefore result in widely differing GFR-estimating equations, even if all other factors mentioned in A, B, D and E were identical in the development of the equations. In particular, endogenous creatinine clearance was found to strongly deviate from the results obtained by renal inulin clearance [11,12]. Although this has been known since 1935 [14,15], endogenous creatinine clearance continues to be used both to measure GFR and to produce GFR-estimating equations based on cystatin C or creatinine. Even those methods that were judged to be useful to replace renal inulin clearance in clinical practice [11–13] displayed small, but statistically significant, differences in measured GFR [11–13]. Also these small differences produce different GFR-estimating equations, although these differences probably would be clinically insignificant in most situations. Different statistical approaches to generate the GFR-estimating equations may be used, for example, to increase the diagnostic performance in certain GFR intervals at the expense of the diagnostic performance in other GFR intervals. One example is the use of the creatinine-based MDRD equation for a population dominated by persons with a decrease in GFR, whereas the creatinine-based CKD–EPI equation was developed to produce a better diagnostic performance in a population of persons with both normal- and abnormal GFR [16,17]. However, although the CKD–EPI equation displayed a significantly improved diagnostic performance in patients with normal GFR, it was at the expense of a poorer performance in patients with a decrease in GFR, at least in some populations [18].
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Different characteristics between populations concerning, for example, disease panorama, proportion between healthy and sick persons, age distribution, GFR distribution, ethnicity, etc., will result in the generation of different GFR-estimating equations even if all the other factors mentioned in A, B, C and D are equal.This cannot be considered problematic as it only reflects the varying and complex biomedical properties of such populations. As a matter of fact, if you know the characteristics of the population for which you are going to select the best GFR-estimating equation, it can be an advantage to be able to choose from a set of equations, which are specialized for different types of populations. To characterize a GFR-estimating equation, the concepts of internal or external validation are often used. “Internal” validation means that the diagnostic performance of the equation is tested in a population, which is a part of, or very similar, to the population used to generate the equation; whereas “external” validation means that the diagnostic performance has been tested in a population different from the one that was used to generate the equation. It is assumed, that the results of an “external” validation are more reliable than the results of an “internal” validation in assessing the diagnostic performance of an equation in other external populations.This is, however, an oversimplification, as all the factors in A, B, C, D and E must be considered, when you will try to anticipate the diagnostic performance of an estimating equation in a specific context. For example, if an estimating equation based on endogenous creatinine clearance to measure GFR of the individuals in a population, is “externally” validated in another population, also using endogenous creatinine clearance to measure GFR, the “external” validation may produce a good result. But the equation would, nevertheless, most probably display a bad diagnostic performance in another population for which renal clearance of inulin, rather than endogenous creatinine clearance, was used to measure GFR.
OPTIMIZING THE USE OF CYSTATIN C- AND CREATININE-BASED GFR-ESTIMATING EQUATIONS The first reports on cystatin C as a marker of GFR [19–22] used a slow assay, and it was not until a rapid and automated assay was developed [23] that its diagnostic performance as a marker of GFR could be carefully studied in large patient cohorts. Further development of several automated assays and the production of an international reference preparations have allowed a rapid growth of knowledge concerning the use of cystatin C as a marker of GFR and the search string “cystatin C AND
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(renal OR glomerular)” resulted in 2995 items on July 18, 2016 at www. ncbi.nlm.nih.gov/pubmed. A similar development has occurred for creatinine so that old, biased assays based on the Jaffe reaction, to a large part have been replaced by enzyme-based specific assays and international reference preparations have become available [3,24]. These advances have allowed the development of cystatin C- or creatinine-based GFR-estimating equations, which produce estimated GFR values, designated as eGFRcystatin C or eGFRcreatinine, 80–85% of which are between ±30% of GFR measured by invasive gold-standard methods [25–30]. Some of these equations use assays based on international reference preparations of cystatin C and creatinine and populations of many thousands individuals to generate and test the equations [2,31]. However, as shown in several investigations, the highest percentages of estimated GFR values between ±30% of measured GFR values are obtained using GFRestimating equations based on both cystatin C and creatinine [25–32]. Such equations might produce estimated GFR values, 90–91% of which are between ±30% of GFR measured by gold-standard methods [25,29].The imprecision of all gold-standard procedures to measure GFR means that even if a gold-standard procedure is repeated within a short interval on patients with stable kidney function, less than 100% of the second determination will be within ±30% of the first. It should also be noted that in evaluations of GFR values produced by GFR-estimating equations, it is axiomatically assumed that the imprecision of the gold-standard procedure used to measure GFR is 0%. This means that the calculated percentage of estimated GFR values between ±30% of the measured GFR values, obtained by any GFR-estimating equation, generally is lower than the true one, as the imprecision of the gold-standard procedure usually increases the number of estimated GFR values outside the ±30% interval. A GFR-estimating equation producing GFR values, 90–91% of which are within ±30% of GFR measured by gold-standard methods, is therefore close to what is theoretically possible. It has been demonstrated that the arithmetic mean of a cystatin C- and a creatinine-based equation, eGFRmean, displays a diagnostic performance at least as good as that displayed by more complex equations [29,33]. This observation has been used to further improve the diagnostic performance of cystatin C- and creatinine-based estimating equations [34,35]. Although GFR-estimating equations using both cystatin C and creatinine clearly seem to have a better diagnostic performance than equations based on only one of these GFR markers, such combined equations do not perform optimally in a number of clinical situations,
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for example, if the patient has an abnormally low muscle mass or is treated with a high dose of glucocorticoids [34]. A strategy for GFR estimation based on the automatic use of cystatain C- and creatinine-based equations will, in these cases, have a worse diagnostic performance than a strategy that only uses the cystatin C- or creatinine-based GFR-estimating equation not influenced by the specific patient characteristics [34]. Such a strategy requires that GFR is estimated by both a cystatin C- and creatinine-based equation, producing eGFRcystatin C or eGFRcreatinine, and that the results are compared. If the two equations produce similar estimates, their average is a very reliable estimate of GFR. If the estimates do not agree and a specific factor known to disturb either the cystatin C- or creatinine-based estimate is present, only the estimate produced by the equation not disturbed by this factor, is used [34]. This has been shown to further increase the estimates, which are within ±30% of GFR measured by gold-standard methods [35]. As a matter of fact, during the 22 years since 1994, when we introduced cystatin C-based estimations of GFR in parallel with creatinine-based estimations, we have had about 20 cases for which the GFR estimates based on cystatin C or creatinine agreed, but disagreed with GFR measured by our invasive gold-standard procedure (plasma clearance of iohexol). In all cases in which relevant information was available, it turned out that the error had to do with some technical problems in the execution of the gold-standard procedure. We therefore consider that, in practice, agreeing cystatin C- and creatinine-based estimates of GFR are at least as reliable as GFR measured by invasive gold-standard procedures. This strategy is described at the multilingual site www.egfr.se, which can also be implemented to calculate absolute GFR from relative GFR, which might be required in, for example, for dosing of medicines cleared by the kidneys.The site uses a creatinine-based GFR-estimating equation, the LMrev equation [18,36], which, in contrast to most or all other creatinine-based equations, works for both children and adults [37]. The cystatin C-based equation, the CAPA equation [2], was developed using a large international population of children and adults with known GFR.
CYSTATIN C AND CREATININE (eGFRCYSTATIN C AND eGFRCREATININE) AS MARKERS OF END-STAGE RENAL DISEASE (ESRD), HOSPITALIZATION, CARDIOVASCULAR DISEASE, AND DEATH A decrease in GFR signals increased risks for the development of ESRD, cardiovascular disease, hospitalization, and death and GFR estimations based on cystatin C (eGFRcystatin C) are consistently superior to GFR estimations
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based on creatinine (eGFRcreatinine) to predict these conditions [38–40]. The cause for the superiority of cystatin C as a risk marker is unknown, but observational studies have suggested that inflammation, old age, male gender, greater weight, and cigarette smoking increase the cystatin C level, thereby augmenting the potential of cystatin C as a risk marker [41]. However, statistical correlations in observational studies do not prove causal connections. As a matter of fact, a study of elective surgery of patients displayed a postoperative sharp rise in inflammation of the patients, with large increases in the levels of CRP and other inflammatory markers, but with no increase in the level of cystatin C, thus allowing rejection of the hypothesis that inflammation causes a raised cystatin C level [42]. The statistical correlations between inflammation, old age, male gender, greater weight, and cigarette smoking and cystatin C might be due to that all these factors promote the development of atherosclerosis, also in the renal arteries, thus producing a decrease in GFR and an increase in cystatin C [42]. Another hypothesis for the superiority of cystatin C as a risk marker is that an increase in cystatin C, with a molecular mass of 13.3 kDa, signals a shrinking of the pores in the glomerular membranes earlier than an increase in creatinine with a molecular mass of 113 Da [43,44]. The use of eGFRmean in clinical practice, as one of the best ways to estimate GFR, has allowed the recent identification of a new syndrome called “Shrunken Pore Syndrome,” which might be connected to the superiority of cystatin C to predict ESRD, cardiovascular disease, hospitalization, and death. This is described in the next section.
IDENTIFICATION OF “SHRUNKEN PORE SYNDROME”: ITS INFLUENCE ON MORTALITY The use of eGFRmean and the simultaneous comparison of eGFRcystatin C and eGFRcreatinine, as the best way to estimate GFR in clinical practice [34,35], identifies a significant number of patients with clear differences between eGFRcystatin C and eGFRcreatinine [33]. Some of these differences can be explained by factors, such as, muscle wasting or treatment with large doses of glucocorticoids, known to invalidate the GFR estimations based on creatinine or cystatin C [34]. However, most of the patients showing such differences between eGFRcystatin C and eGFRcreatinine, do not display such known factors and their eGFRmean is, despite the differences between eGFRcystatin C and eGFRcreatinine, still the best way to estimate GFR (Figure 5.1) [33]. A large part of the patients displaying these differences has a pattern of eGFRcystatin C and eGFRcreatinine in which eGFRcystatin C is less or equal to 60% of eGFRcreatinine
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Figure 5.1 Estimations of GFR (eGFR) by Cystatin C- (eGFRcystatin C) and Creatinine (eGFRcreatinine)-Based Estimation Equations and by the Mean of eGFRcystatin C and eGFRcreatinine (eGFRmean). The CAPA equation [2] and the LMrev equation [36] were used to estimate GFR in a patient population of 1112 individuals with GFR measured by plasma clearance of iohexol [33]. The bias between measured GFR (mGFR) and estimated GFR, when eGFR is estimated as eGFRmean, is small, even when there are big differences between eGFRcystatin C and eGFRcreatinine. When eGFRcystatin C ≤ 60% eGFRcreatinine the “Shrunken Pore Syndrome” [44] is diagnosed and these patients display a high increase in mortality [59].
(eGFRcystatin C ≤ 60% eGFRcreatinine) [44]. These patients display higher cystatin C–creatinine concentration ratios than patients with similar eGFRmean, but with agreeing values of eGFRcystatin C and eGFRcreatinine (eGFRcystatin ≈ eGFRcreatinine) [44].When the concentrations of other low-molecular mass C proteins, for example, β2-microglobulin, β-trace protein, and retinol-binding protein, are measured in patients with eGFRcystatin C ≤ 60% eGFRcreatinine, it can be observed that the concentration ratios of these proteins to creatinine are also higher than in patients in whom eGFRcystatin C ≈ eGFRcreatinine (Figure 5.2) [44]. The genes for these proteins are located at different chromosomes [45–48], have different regulation elements, and the synthesis of these proteins is not generally influenced by the factors that affect the production of cystatin C, in the same way [49–53]. This indicates that the production of these proteins is not coregulated and thus cannot explain the concordant increases of their plasma levels. But this concurrent increase can be explained if the proteins have a common clearance mechanism.
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Figure 5.2 The Ratios (mg/µmol) Between Cystatin C and Creatinine; β2-Microglobulin and Creatinine; and β-Trace Protein and Creatinine, When eGFRcystatin C ≈ eGFRcreatinine and When eGFRcystatin C ≤ 60% eGFRcreatinine. All differences between patients with eGFRcystatin C ≈ eGFRcreatinine and with eGFRcystatin C ≤ 60% eGFRcreatinine are statistically significant. The same pattern is present, whether (A) all patients, (B) patients with GFR < 60 mL/ min per 1.73 m2, or (C) patients with GFR > 60 mL/min per 1.73 m2 are studied.
As proteins below ≈20 kDa in molecular mass (<22 Å in Stokes–Einstein radius) are mainly excreted via glomerular transport [54], a reduction in their GFR would result in a simultaneous increase of their plasma levels. The simplest pathophysiologic way of interpreting this is that the normally high-sieving coefficients of these proteins drop significantly. According to the two-pore model of glomerular permeability, this can easily be explained by a reduction in the radii of the small pores of the glomerular filtration barrier. The explanation that creatinine and other small molecules do not simultaneously increase in concentration would then be, that their sieving coefficients are still close to unity (i.e., one) despite the shrunken pores.
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Therefore, it seems that the observation of eGFRcystatin C ≤ 60% eGFRcreatinine and a simultaneous increase of the level of low-molecular mass proteins in a patient, identify a new syndrome, tentatively called as “Shrunken Pore Syndrome” [44]. It is interesting that a similar mechanism previously has been suggested for the increase in plasma levels of low-molecular mass proteins in the third trimester of pregnancy [43,55,56] and for the development of still higher concentrations of low-molecular mass proteins in preeclampsia [57,58]. This might indicate that the (patho-)physiologic changes in pregnancy and preeclampsia are similar to those occurring in patients with “Shrunken Pore Syndrome.” As “Shrunken Pore Syndrome” was identified recently [44], few studies of its clinical consequences have been performed. However, one recent investigation shows, that the mortality in patients undergoing elective coronary artery bypass grafting and suffering from the syndrome preoperatively, is much higher than that in patients without the syndrome, whether the preoperative GFR is normal or decreased (Figs. 5.3 and 5.4) [59]. Another recent study also indicates that “Shrunken Pore Syndrome” in a population of healthy seniors predicts increased risks for mortality and overall morbidity [60]. More studies are required, and are underway, to elucidate the full clinical consequences of the syndrome and if cut-offs other than eGFRcystatin C ≤ 60% eGFRcreatinine can be used to identify patients with increased mortality more efficiently (Figs. 5.1, 5.3, and 5.4) [59]. It can be expected that the “Shrunken Pore Syndrome,” in addition to signaling a high mortality in different patient cohorts, also generally will indicate higher risks for the development of ESRD, cardiovascular disease, and for hospitalization.
CYSTATIN C AS AN INDICTOR OF THE CIRCADIAN RHYTHM OF GFR In healthy individuals and in most patients with renal disease, GFR displays a circadian rhythm such that GFR during the day is 20–40% higher than during the night [61,62]. This is mirrored by the diurnal variation of the cystatin C level, which is higher during the night in individuals with a normal circadian rhythm, thus mirroring the lower GFR [63]. In contrast, the creatinine level does not mirror the diurnal variation of GFR, as the tubular secretion of creatinine varies inversely with the GFR during a 24-h period [62,63].
Figure 5.3 Survival after Coronary Artery Bypass Surgery for Patients With and Without Shrunken Pore Syndrome (SPS). eGFRcystatin C was estimated using the CAPA equation and eGFRcreatinine using the LMrev equation. The cut-off level for SPS was eGFRcystatin C ≤ 70% of eGFRcreatinine. (A) Patients with GFR > 60 mL/min per 1.73 m2 with Shrunken Poor Syndrome [SPS, broken line (red broken line in web version)] and without [solid line (blue solid line in web version)]. (B) Patients with GFR < 60 mL/min per 1.73 m2 with Shrunken Poor Syndrome [SPS, broken line (red broken line in web version)] and without [solid line (blue solid line in web version)]. For both levels of GFR: p < 0.001 with log-rank test.
Figure 5.4 Survival After Coronary Artery Bypass Surgery for Patients With and Without Shrunken Pore Syndrome (SPS). eGFRcystatin C was estimated using the CKD–EPIcystatin C equation and eGFRcreatinine using the CKD–EPIcreatinine equation. The cut-off level for SPS was eGFRcystatin C ≤ 60% of eGFRcreatinine. (A) Patients with GFR > 60 mL/min per 1.73 m2 with Shrunken Poor Syndrome [SPS, broken line (red broken line in web version)] and without [solid line (blue solid line in web version)]. (B) Patients with GFR < 60 mL/min per 1.73 m2 with Shrunken Poor Syndrome [SPS, broken line (red broken line in web version)] and without [solid line (blue solid line in web version)]. For both levels of GFR: p < 0.001 with log-rank test.
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CYSTATIN C AS AN INDICATOR OF “RENAL RESERVE” The increase in GFR measured by, for example, renal inulin clearance, occurring after consumption of a large amount of boiled meat (1.5–2 g/kg) or intravenous infusion of amino acids is called “renal reserve” [64–66]. A loss of renal reserve is considered to predispose patients to chronic kidney disease but the technical difficulties in measuring “renal reserve” has made accumulation of reliable clinical data difficult. Estimations based on the level of plasma creatinine cannot be used, as the creatine/creatinine absorbed from meat will conceal the decrease in creatinine caused by an increase in GFR. However, a recent report by Fuhrman, Maier, and Schwartz [67] suggests that measuring cystatin C before and after a protein load demonstrates an increase in GFR, the “renal reserve,” as shown by a decrease in the cystatin C level. The small amount of cystatin C in meat is probably not absorbed and even if small amounts were absorbed, the antibodies used in the assays for cystatin C only recognize human cystatin C.
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[32] Rule AD, Bergstralh EJ, Slezak JM, et al. Glomerular filtration rate estimated by cystatin C among different clinical presentations. Kidney Int 2006;69:399–405. [33] Björk J, Grubb A, Larsson A, et al. Accuracy of GFR estimating equations combining standardized cystatin C and creatinine assays: A cross-sectional study in Sweden. Clin Chem Lab Med 2015;53:403–14. [34] Grubb A. Non-invasive estimation of glomerular filtration rate (GFR).The Lund model: simultaneous use of cystatin C- and creatinine-based GFR-prediction equations, clinical data and an internal quality check. Scand J Clin Lab Invest 2010;70:65–70. [35] Grubb A, Nyman U, Björk J. Improved estimation of glomerular filtration rate (GFR) by comparison of eGFRcystatin C and eGFRcreatinine. Scand J Clin Lab Invest 2012;72:73–7. [36] Björk J, Grubb A, Sterner G, Nyman U. Revised equations for estimating glomerular filtration rate based on the Lund-Malmö study cohort. Scand J Clin Lab Invest 2011;71:232–9. [37] Nyman U, Björk J, Lindström V, Grubb A.The Lund-Malmö creatinine-based glomerular filtration rate prediction equation for adults also performs well in children. Scand J Clin Lab Invest 2008;68:568–76. [38] Shlipak MG, Sarnak MJ, Katz R, et al. Cystatin C and the risk of death and cardiovascular events among elderly persons. N Engl J Med 2005;352:2049–60. [39] Peralta CA, Shlipak MG, Judd S, et al. Detection of chronic kidney disease with creatinine, cystatin C, and urine albumin-to-creatinine ratio and association with progression to end-stage renal disease and mortality. JAMA 2011;305:1545–52. [40] Shlipak MG, Matsushita K, Ärnlöv J, et al. Cystatin C versus creatinine in determining risk based on kidney function. N Engl J Med 2013;369:932–43. [41] Knight EL, Verhave JC, Spiegelman D, et al. Factors influencing cystatin C levels other than renal function and the impact on renal function measurement. Kidney Int 2004;65:1416–21. [42] Grubb A, Björk J, Nyman U, et al. Cystatin C, a marker for successful aging and glomerular filtration rate, is not influenced by inflammation. Scand J Clin Lab Invest 2011;71:145–9. [43] Grubb A, Lindström V, Kristensen K, et al. Filtration quality: a new measure of renal disease. Clin Chem Lab Med 2007;45(Suppl.):S273–4. [44] Grubb A, Lindström V, Jonsson M, et al. Reduction in glomerular pore size is not restricted to pregnant women. Evidence for a new syndrome: “Shrunken pore syndrome”. Scand J Clin Lab Invest 2015;75:333–40. [45] Abrahamson M, Islam MQ, Szpirer J, et al.The human cystatin C gene (CST3), mutated in hereditary cystatin C amyloid angiopathy, is located on chromosome 20. Hum Genet 1989;82:223–6. [46] Rocchi M, Covone A, Romeo G, et al. Regional mapping of RBP4 to 10q23-q24 and RBP1 to 3q21-q22 in man. Somat Cell Molec Genet 1989;15:185–90. [47] Goodfellow PN, Jones EA,Van Heyningen V, et al.The beta-2-microglobulin gene is on chromosome 15 and not in the HL-A region. Nature 1975;254:267–9. [48] White DM, Mikol DD, Espinosa R, et al. Structure and chromosomal localization of the human gene for a brain form of prostaglandin D-2 synthase. J Biol Chem 1992;267:23202–8. [49] Abbink FC, Laarman CA, Braam KI, et al. Beta-trace protein is not superior to cystatin C for the estimation of GFR in patients receiving corticosteroids. Clin Biochem 2008;41:299–305. [50] Gobin SJ, Biesta P,Van den Elsen PJ. Regulation of human beta 2-microglobulin transactivation in hematopoietic cells. Blood 2003;101:3058–64. [51] Hokland M, Larsen B, Heron I, Plesner T. Corticosteroids decrease the expression of beta 2-microglobulin and histocompatibility antigens on human peripheral blood lymphocytes in vitro. Clin Exp Immunol 1981;44:239–46.
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[52] Kotnik P, Fischer-Posovszky P, Wabitsch M. RBP4: a controversial adipokine. Eur J Endocrinol 2011;165:703–11. [53] Juraschek SP, Coresh J, Inker LA, et al. Comparison of serum concentrations of β-trace protein, β2-microglobulin, cystatin C, and creatinine in the US population. Clin J Am Soc Nephrol 2013;8:584–92. [54] Lund U, Rippe A,Venturoli D, et al. Glomerular filtration rate dependence of sieving of albumin and some neutral proteins in rat kidneys. Am J Physiol 2003;284:F1226–34. [55] Strevens H, Wide-Swensson D, Torffvit O, Grubb A. Serum cystatin C for assessment of glomerular filtration rate in pregnant and non-pregnant women. Indications of altered filtration process in pregnancy. Scand J Clin Lab Invest 2002;62:141–7. [56] Kristensen K, Lindström V, Schmidt C, et al. Temporal changes of the plasma levels of cystatin C, beta-trace protein, b2-microglobulin, urate and creatinine during pregnancy indicate continuous alterations in the renal filtration process. Scand J Clin Lab Invest 2007;67:612–8. [57] Strevens H, Wide-Swensson D, Grubb A. Serum cystatin C is a better marker for preeclampsia than serum creatinine or serum urate. Scand J Clin Lab Invest 2001;61:575–80. [58] Kristensen K, Wide-Swensson D, Schmidt C, et al. Cystatin C, beta-2-microglobulin and beta-trace protein in pre-eclampsia. Acta Obstet Gynecol Scand 2007;86:921–6. [59] Dardashti A, Nozohoor S, Grubb A, Bjursten H. Shrunken Pore Syndrome is associated with a sharp rise in mortality in patients undergoing elective coronary artery bypass grafting. Scand J Clin Lab Invest 2016;76:74–81. [60] Purde MT, Nock S, Risch L, et al.The cystatin C/creatinine ratio, a marker of glomerular filtration quality: associated factors, reference intervals, and prediction of morbidity and mortality in healthy seniors. Transl Res 2016;169:80–90. [61] Koopman MG, Koomen GC, Krediet RT, et al. Circadian rhythm of glomerular filtration rate in normal individuals. Clin Sci 1989;77:105–11. [62] van Acker BA, Koomen GC, Koopman MG, et al. Discrepancy between circadian rhythms of inulin and creatinine clearance. J Lab Clin Med 1992;120:400–10. [63] Larsson A, Akerstedt T, Hansson LO, Axelsson J. Circadian variability of cystatin C, creatinine, and glomerular filtration rate (GFR) in healthy men during normal sleep and after an acute shift of sleep. Chronobiol Int 2008;25:1047–61. [64] Bosch JP, Saccaggi A, Lauer A, et al. Renal functional reserve in humans. Effect of protein intake on glomerular filtration rate. Am J Med 1983;75:943–50. [65] Englund MS, Berg UB, Arfwidson K. Renal functional reserve in transplanted and native single kidneys of children and adults. Pediatr Nephrol 1997;11:312–7. [66] ter Wee PM, Geerlings W, Rosman JB, et al.Testing renal reserve filtration capacity with an amino acid solution. Nephron 1985;41:193–9. [67] Fuhrman DY, Maier PS, Schwartz GJ. Rapid assessment of renal reserve in young adults by cystatin C. Scand J Clin Lab Invest 2013;73:265–8.
Cystatin C as a Multifaceted Biomarker in Kidney Disease and Its Role in Defining “Shrunken Pore Syndrome” A. Grubb, MD, PhD
Department of Clinical Chemistry and Pharmacology, University Hospital, Lund University, Lund, Sweden
Contents Factors Influencing the Diagnostic Performance of Cystatin C- or Creatinine-Based GFR-Estimating Equations and Causing the Plethora of Equations: The Concepts of “Internal” or “External” Validation Optimizing the Use of Cystatin C- and Creatinine-Based GFR-Estimating Equations Cystatin C and Creatinine (eGFRcystatin C and eGFRcreatinine) as Markers of End-Stage Renal Disease (ESRD), Hospitalization, Cardiovascular Disease, and Death Identification of “Shrunken Pore Syndrome”: Its Influence on Mortality Cystatin C as an Indictor of the Circadian Rhythm of GFR Cystatin C as an Indicator of “Renal Reserve” References
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Cystatin C, a basic nonglycosylated 13.3 kDa protein comprising a single polypeptide chain of 120 amino acid residues, is produced at a stable rate from a housekeeping gene in all nucleated cells and eliminated from the circulation by free filtration through the glomerular membranes as described in the first edition of “Biomarkers of Kidney Disease” [1]. This discourse will report some of the progress in the use of cystatin C as a multifaceted marker of kidney disease, which has occurred since then, including its role in identifying the novel syndrome called “Shrunken Pore Syndrome.”
Biomarkers of Kidney Disease. http://dx.doi.org/10.1016/B978-0-12-803014-1.00005-4 Copyright © 2017 Elsevier Inc. All rights reserved.
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FACTORS INFLUENCING THE DIAGNOSTIC PERFORMANCE OF CYSTATIN C- OR CREATININEBASED GFR-ESTIMATING EQUATIONS AND CAUSING THE PLETHORA OF EQUATIONS: THE CONCEPTS OF “INTERNAL” OR “EXTERNAL” VALIDATION More than 100 different creatinine- and cystatin C-based estimating equations for glomerular filtration rate (GFR) have been described [2]. This has caused confusion and debate about which equation(s) is (are) to be preferred. In order to arrive at the best decision concerning selection of an estimating equation, the reasons for the generation of a multitude of cystatin C- and creatinine-based GFR-estimating equations must be known and they are as follows. A. Not using (or lack of) international reference materials (“calibrators”) for cystatin C or creatinine. B. Use of varying, noncommutable assays of cystatin C or creatinine. C. Use of different methods to measure GFR. D. Use of different statistical approaches to generate the equations. E. Use of different populations to generate the equations. The populations may vary in parameters, such as, disease panorama, proportion between healthy and sick persons, age distribution, GFR distribution, ethnicity, etc. Of these reasons, those in A have contributed heavily in producing differing GFR-estimating equations. Standard reference materials for creatinine were released in 2006 [3] and for cystatin C (ERM-DA471/IFCC) in 2010 [4–6]. Therefore, it was only recently possible for diagnostic companies to offer assay methods for cystatin C based on such reference materials [2]. This has resulted in decreased variation among the results for cystatin C obtained with different assays at different laboratories as reported by external quality assessment organizations, such as Equalis [2,7]. However, the problems mentioned in A will persist for a few years until the ongoing replacement of old reference materials with new international reference material will finish. The use of noncommutable assays for cystatin C and creatinine is now diminishing inter alia due to the availability of international reference materials for both cystatin C and creatinine. A further contributing factor is the availability of an internationally recognized reference method, isotope dilution mass spectrometry (IDMS) for the determination of plasma creatinine levels [8]. No such method is yet available for the concentration of any protein in any biologic fluid, although some efforts are being made concerning the cystatin C concentration in biologic fluids [9,10].
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As all GFR-estimating equations are generated using measured GFR values, it is obvious that if different methods to measure GFR produce different results, the corresponding generated GFR-estimating equations will also differ. Although renal clearance of inulin is generally accepted as the best way to measure GFR, it has rarely been used in efforts to generate GFR-estimating equations, as it requires expensive reagents and is based on a technically complicated and slow procedure [11,12]. Instead, several faster and less expensive methods to measure GFR have been used, for example, renal clearance of 51Cr–EDTA, 125I–iothalamate, 99Tc–DTPA or iohexol; or plasma clearance of iohexol, iothalamate, 51Cr–EDTA, or 99Tc–DTPA; and endogenous clearance of creatinine [11,12]. They produce different results and it is surprising that, only recently, a careful analysis has been done to evaluate which of these compounds produce results that agree with those obtained by the renal clearance of inulin [11–13]. The results of some of the methods studied differed very significantly from those produced by the renal clearance of inulin [11–13] and would therefore result in widely differing GFR-estimating equations, even if all other factors mentioned in A, B, D and E were identical in the development of the equations. In particular, endogenous creatinine clearance was found to strongly deviate from the results obtained by renal inulin clearance [11,12]. Although this has been known since 1935 [14,15], endogenous creatinine clearance continues to be used both to measure GFR and to produce GFR-estimating equations based on cystatin C or creatinine. Even those methods that were judged to be useful to replace renal inulin clearance in clinical practice [11–13] displayed small, but statistically significant, differences in measured GFR [11–13]. Also these small differences produce different GFR-estimating equations, although these differences probably would be clinically insignificant in most situations. Different statistical approaches to generate the GFR-estimating equations may be used, for example, to increase the diagnostic performance in certain GFR intervals at the expense of the diagnostic performance in other GFR intervals. One example is the use of the creatinine-based MDRD equation for a population dominated by persons with a decrease in GFR, whereas the creatinine-based CKD–EPI equation was developed to produce a better diagnostic performance in a population of persons with both normal- and abnormal GFR [16,17]. However, although the CKD–EPI equation displayed a significantly improved diagnostic performance in patients with normal GFR, it was at the expense of a poorer performance in patients with a decrease in GFR, at least in some populations [18].
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Different characteristics between populations concerning, for example, disease panorama, proportion between healthy and sick persons, age distribution, GFR distribution, ethnicity, etc., will result in the generation of different GFR-estimating equations even if all the other factors mentioned in A, B, C and D are equal.This cannot be considered problematic as it only reflects the varying and complex biomedical properties of such populations. As a matter of fact, if you know the characteristics of the population for which you are going to select the best GFR-estimating equation, it can be an advantage to be able to choose from a set of equations, which are specialized for different types of populations. To characterize a GFR-estimating equation, the concepts of internal or external validation are often used. “Internal” validation means that the diagnostic performance of the equation is tested in a population, which is a part of, or very similar, to the population used to generate the equation; whereas “external” validation means that the diagnostic performance has been tested in a population different from the one that was used to generate the equation. It is assumed, that the results of an “external” validation are more reliable than the results of an “internal” validation in assessing the diagnostic performance of an equation in other external populations.This is, however, an oversimplification, as all the factors in A, B, C, D and E must be considered, when you will try to anticipate the diagnostic performance of an estimating equation in a specific context. For example, if an estimating equation based on endogenous creatinine clearance to measure GFR of the individuals in a population, is “externally” validated in another population, also using endogenous creatinine clearance to measure GFR, the “external” validation may produce a good result. But the equation would, nevertheless, most probably display a bad diagnostic performance in another population for which renal clearance of inulin, rather than endogenous creatinine clearance, was used to measure GFR.
OPTIMIZING THE USE OF CYSTATIN C- AND CREATININE-BASED GFR-ESTIMATING EQUATIONS The first reports on cystatin C as a marker of GFR [19–22] used a slow assay, and it was not until a rapid and automated assay was developed [23] that its diagnostic performance as a marker of GFR could be carefully studied in large patient cohorts. Further development of several automated assays and the production of an international reference preparations have allowed a rapid growth of knowledge concerning the use of cystatin C as a marker of GFR and the search string “cystatin C AND
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(renal OR glomerular)” resulted in 2995 items on July 18, 2016 at www. ncbi.nlm.nih.gov/pubmed. A similar development has occurred for creatinine so that old, biased assays based on the Jaffe reaction, to a large part have been replaced by enzyme-based specific assays and international reference preparations have become available [3,24]. These advances have allowed the development of cystatin C- or creatinine-based GFR-estimating equations, which produce estimated GFR values, designated as eGFRcystatin C or eGFRcreatinine, 80–85% of which are between ±30% of GFR measured by invasive gold-standard methods [25–30]. Some of these equations use assays based on international reference preparations of cystatin C and creatinine and populations of many thousands individuals to generate and test the equations [2,31]. However, as shown in several investigations, the highest percentages of estimated GFR values between ±30% of measured GFR values are obtained using GFRestimating equations based on both cystatin C and creatinine [25–32]. Such equations might produce estimated GFR values, 90–91% of which are between ±30% of GFR measured by gold-standard methods [25,29].The imprecision of all gold-standard procedures to measure GFR means that even if a gold-standard procedure is repeated within a short interval on patients with stable kidney function, less than 100% of the second determination will be within ±30% of the first. It should also be noted that in evaluations of GFR values produced by GFR-estimating equations, it is axiomatically assumed that the imprecision of the gold-standard procedure used to measure GFR is 0%. This means that the calculated percentage of estimated GFR values between ±30% of the measured GFR values, obtained by any GFR-estimating equation, generally is lower than the true one, as the imprecision of the gold-standard procedure usually increases the number of estimated GFR values outside the ±30% interval. A GFR-estimating equation producing GFR values, 90–91% of which are within ±30% of GFR measured by gold-standard methods, is therefore close to what is theoretically possible. It has been demonstrated that the arithmetic mean of a cystatin C- and a creatinine-based equation, eGFRmean, displays a diagnostic performance at least as good as that displayed by more complex equations [29,33]. This observation has been used to further improve the diagnostic performance of cystatin C- and creatinine-based estimating equations [34,35]. Although GFR-estimating equations using both cystatin C and creatinine clearly seem to have a better diagnostic performance than equations based on only one of these GFR markers, such combined equations do not perform optimally in a number of clinical situations,
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for example, if the patient has an abnormally low muscle mass or is treated with a high dose of glucocorticoids [34]. A strategy for GFR estimation based on the automatic use of cystatain C- and creatinine-based equations will, in these cases, have a worse diagnostic performance than a strategy that only uses the cystatin C- or creatinine-based GFR-estimating equation not influenced by the specific patient characteristics [34]. Such a strategy requires that GFR is estimated by both a cystatin C- and creatinine-based equation, producing eGFRcystatin C or eGFRcreatinine, and that the results are compared. If the two equations produce similar estimates, their average is a very reliable estimate of GFR. If the estimates do not agree and a specific factor known to disturb either the cystatin C- or creatinine-based estimate is present, only the estimate produced by the equation not disturbed by this factor, is used [34]. This has been shown to further increase the estimates, which are within ±30% of GFR measured by gold-standard methods [35]. As a matter of fact, during the 22 years since 1994, when we introduced cystatin C-based estimations of GFR in parallel with creatinine-based estimations, we have had about 20 cases for which the GFR estimates based on cystatin C or creatinine agreed, but disagreed with GFR measured by our invasive gold-standard procedure (plasma clearance of iohexol). In all cases in which relevant information was available, it turned out that the error had to do with some technical problems in the execution of the gold-standard procedure. We therefore consider that, in practice, agreeing cystatin C- and creatinine-based estimates of GFR are at least as reliable as GFR measured by invasive gold-standard procedures. This strategy is described at the multilingual site www.egfr.se, which can also be implemented to calculate absolute GFR from relative GFR, which might be required in, for example, for dosing of medicines cleared by the kidneys.The site uses a creatinine-based GFR-estimating equation, the LMrev equation [18,36], which, in contrast to most or all other creatinine-based equations, works for both children and adults [37]. The cystatin C-based equation, the CAPA equation [2], was developed using a large international population of children and adults with known GFR.
CYSTATIN C AND CREATININE (eGFRCYSTATIN C AND eGFRCREATININE) AS MARKERS OF END-STAGE RENAL DISEASE (ESRD), HOSPITALIZATION, CARDIOVASCULAR DISEASE, AND DEATH A decrease in GFR signals increased risks for the development of ESRD, cardiovascular disease, hospitalization, and death and GFR estimations based on cystatin C (eGFRcystatin C) are consistently superior to GFR estimations
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based on creatinine (eGFRcreatinine) to predict these conditions [38–40]. The cause for the superiority of cystatin C as a risk marker is unknown, but observational studies have suggested that inflammation, old age, male gender, greater weight, and cigarette smoking increase the cystatin C level, thereby augmenting the potential of cystatin C as a risk marker [41]. However, statistical correlations in observational studies do not prove causal connections. As a matter of fact, a study of elective surgery of patients displayed a postoperative sharp rise in inflammation of the patients, with large increases in the levels of CRP and other inflammatory markers, but with no increase in the level of cystatin C, thus allowing rejection of the hypothesis that inflammation causes a raised cystatin C level [42]. The statistical correlations between inflammation, old age, male gender, greater weight, and cigarette smoking and cystatin C might be due to that all these factors promote the development of atherosclerosis, also in the renal arteries, thus producing a decrease in GFR and an increase in cystatin C [42]. Another hypothesis for the superiority of cystatin C as a risk marker is that an increase in cystatin C, with a molecular mass of 13.3 kDa, signals a shrinking of the pores in the glomerular membranes earlier than an increase in creatinine with a molecular mass of 113 Da [43,44]. The use of eGFRmean in clinical practice, as one of the best ways to estimate GFR, has allowed the recent identification of a new syndrome called “Shrunken Pore Syndrome,” which might be connected to the superiority of cystatin C to predict ESRD, cardiovascular disease, hospitalization, and death. This is described in the next section.
IDENTIFICATION OF “SHRUNKEN PORE SYNDROME”: ITS INFLUENCE ON MORTALITY The use of eGFRmean and the simultaneous comparison of eGFRcystatin C and eGFRcreatinine, as the best way to estimate GFR in clinical practice [34,35], identifies a significant number of patients with clear differences between eGFRcystatin C and eGFRcreatinine [33]. Some of these differences can be explained by factors, such as, muscle wasting or treatment with large doses of glucocorticoids, known to invalidate the GFR estimations based on creatinine or cystatin C [34]. However, most of the patients showing such differences between eGFRcystatin C and eGFRcreatinine, do not display such known factors and their eGFRmean is, despite the differences between eGFRcystatin C and eGFRcreatinine, still the best way to estimate GFR (Figure 5.1) [33]. A large part of the patients displaying these differences has a pattern of eGFRcystatin C and eGFRcreatinine in which eGFRcystatin C is less or equal to 60% of eGFRcreatinine
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Figure 5.1 Estimations of GFR (eGFR) by Cystatin C- (eGFRcystatin C) and Creatinine (eGFRcreatinine)-Based Estimation Equations and by the Mean of eGFRcystatin C and eGFRcreatinine (eGFRmean). The CAPA equation [2] and the LMrev equation [36] were used to estimate GFR in a patient population of 1112 individuals with GFR measured by plasma clearance of iohexol [33]. The bias between measured GFR (mGFR) and estimated GFR, when eGFR is estimated as eGFRmean, is small, even when there are big differences between eGFRcystatin C and eGFRcreatinine. When eGFRcystatin C ≤ 60% eGFRcreatinine the “Shrunken Pore Syndrome” [44] is diagnosed and these patients display a high increase in mortality [59].
(eGFRcystatin C ≤ 60% eGFRcreatinine) [44]. These patients display higher cystatin C–creatinine concentration ratios than patients with similar eGFRmean, but with agreeing values of eGFRcystatin C and eGFRcreatinine (eGFRcystatin ≈ eGFRcreatinine) [44].When the concentrations of other low-molecular mass C proteins, for example, β2-microglobulin, β-trace protein, and retinol-binding protein, are measured in patients with eGFRcystatin C ≤ 60% eGFRcreatinine, it can be observed that the concentration ratios of these proteins to creatinine are also higher than in patients in whom eGFRcystatin C ≈ eGFRcreatinine (Figure 5.2) [44]. The genes for these proteins are located at different chromosomes [45–48], have different regulation elements, and the synthesis of these proteins is not generally influenced by the factors that affect the production of cystatin C, in the same way [49–53]. This indicates that the production of these proteins is not coregulated and thus cannot explain the concordant increases of their plasma levels. But this concurrent increase can be explained if the proteins have a common clearance mechanism.
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Figure 5.2 The Ratios (mg/µmol) Between Cystatin C and Creatinine; β2-Microglobulin and Creatinine; and β-Trace Protein and Creatinine, When eGFRcystatin C ≈ eGFRcreatinine and When eGFRcystatin C ≤ 60% eGFRcreatinine. All differences between patients with eGFRcystatin C ≈ eGFRcreatinine and with eGFRcystatin C ≤ 60% eGFRcreatinine are statistically significant. The same pattern is present, whether (A) all patients, (B) patients with GFR < 60 mL/ min per 1.73 m2, or (C) patients with GFR > 60 mL/min per 1.73 m2 are studied.
As proteins below ≈20 kDa in molecular mass (<22 Å in Stokes–Einstein radius) are mainly excreted via glomerular transport [54], a reduction in their GFR would result in a simultaneous increase of their plasma levels. The simplest pathophysiologic way of interpreting this is that the normally high-sieving coefficients of these proteins drop significantly. According to the two-pore model of glomerular permeability, this can easily be explained by a reduction in the radii of the small pores of the glomerular filtration barrier. The explanation that creatinine and other small molecules do not simultaneously increase in concentration would then be, that their sieving coefficients are still close to unity (i.e., one) despite the shrunken pores.
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Therefore, it seems that the observation of eGFRcystatin C ≤ 60% eGFRcreatinine and a simultaneous increase of the level of low-molecular mass proteins in a patient, identify a new syndrome, tentatively called as “Shrunken Pore Syndrome” [44]. It is interesting that a similar mechanism previously has been suggested for the increase in plasma levels of low-molecular mass proteins in the third trimester of pregnancy [43,55,56] and for the development of still higher concentrations of low-molecular mass proteins in preeclampsia [57,58]. This might indicate that the (patho-)physiologic changes in pregnancy and preeclampsia are similar to those occurring in patients with “Shrunken Pore Syndrome.” As “Shrunken Pore Syndrome” was identified recently [44], few studies of its clinical consequences have been performed. However, one recent investigation shows, that the mortality in patients undergoing elective coronary artery bypass grafting and suffering from the syndrome preoperatively, is much higher than that in patients without the syndrome, whether the preoperative GFR is normal or decreased (Figs. 5.3 and 5.4) [59]. Another recent study also indicates that “Shrunken Pore Syndrome” in a population of healthy seniors predicts increased risks for mortality and overall morbidity [60]. More studies are required, and are underway, to elucidate the full clinical consequences of the syndrome and if cut-offs other than eGFRcystatin C ≤ 60% eGFRcreatinine can be used to identify patients with increased mortality more efficiently (Figs. 5.1, 5.3, and 5.4) [59]. It can be expected that the “Shrunken Pore Syndrome,” in addition to signaling a high mortality in different patient cohorts, also generally will indicate higher risks for the development of ESRD, cardiovascular disease, and for hospitalization.
CYSTATIN C AS AN INDICTOR OF THE CIRCADIAN RHYTHM OF GFR In healthy individuals and in most patients with renal disease, GFR displays a circadian rhythm such that GFR during the day is 20–40% higher than during the night [61,62]. This is mirrored by the diurnal variation of the cystatin C level, which is higher during the night in individuals with a normal circadian rhythm, thus mirroring the lower GFR [63]. In contrast, the creatinine level does not mirror the diurnal variation of GFR, as the tubular secretion of creatinine varies inversely with the GFR during a 24-h period [62,63].
Figure 5.3 Survival after Coronary Artery Bypass Surgery for Patients With and Without Shrunken Pore Syndrome (SPS). eGFRcystatin C was estimated using the CAPA equation and eGFRcreatinine using the LMrev equation. The cut-off level for SPS was eGFRcystatin C ≤ 70% of eGFRcreatinine. (A) Patients with GFR > 60 mL/min per 1.73 m2 with Shrunken Poor Syndrome [SPS, broken line (red broken line in web version)] and without [solid line (blue solid line in web version)]. (B) Patients with GFR < 60 mL/min per 1.73 m2 with Shrunken Poor Syndrome [SPS, broken line (red broken line in web version)] and without [solid line (blue solid line in web version)]. For both levels of GFR: p < 0.001 with log-rank test.
Figure 5.4 Survival After Coronary Artery Bypass Surgery for Patients With and Without Shrunken Pore Syndrome (SPS). eGFRcystatin C was estimated using the CKD–EPIcystatin C equation and eGFRcreatinine using the CKD–EPIcreatinine equation. The cut-off level for SPS was eGFRcystatin C ≤ 60% of eGFRcreatinine. (A) Patients with GFR > 60 mL/min per 1.73 m2 with Shrunken Poor Syndrome [SPS, broken line (red broken line in web version)] and without [solid line (blue solid line in web version)]. (B) Patients with GFR < 60 mL/min per 1.73 m2 with Shrunken Poor Syndrome [SPS, broken line (red broken line in web version)] and without [solid line (blue solid line in web version)]. For both levels of GFR: p < 0.001 with log-rank test.
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CYSTATIN C AS AN INDICATOR OF “RENAL RESERVE” The increase in GFR measured by, for example, renal inulin clearance, occurring after consumption of a large amount of boiled meat (1.5–2 g/kg) or intravenous infusion of amino acids is called “renal reserve” [64–66]. A loss of renal reserve is considered to predispose patients to chronic kidney disease but the technical difficulties in measuring “renal reserve” has made accumulation of reliable clinical data difficult. Estimations based on the level of plasma creatinine cannot be used, as the creatine/creatinine absorbed from meat will conceal the decrease in creatinine caused by an increase in GFR. However, a recent report by Fuhrman, Maier, and Schwartz [67] suggests that measuring cystatin C before and after a protein load demonstrates an increase in GFR, the “renal reserve,” as shown by a decrease in the cystatin C level. The small amount of cystatin C in meat is probably not absorbed and even if small amounts were absorbed, the antibodies used in the assays for cystatin C only recognize human cystatin C.
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