<?xml version="1.0" encoding="UTF-8"?><!DOCTYPE article  PUBLIC "-//NLM//DTD Journal Publishing DTD v3.0 20080202//EN" "http://dtd.nlm.nih.gov/publishing/3.0/journalpublishing3.dtd"><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" dtd-version="3.0" xml:lang="en" article-type="research article"><front><journal-meta><journal-id journal-id-type="publisher-id">OJNeph</journal-id><journal-title-group><journal-title>Open Journal of Nephrology</journal-title></journal-title-group><issn pub-type="epub">2164-2842</issn><publisher><publisher-name>Scientific Research Publishing</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.4236/ojneph.2022.124043</article-id><article-id pub-id-type="publisher-id">OJNeph-121695</article-id><article-categories><subj-group subj-group-type="heading"><subject>Articles</subject></subj-group><subj-group subj-group-type="Discipline-v2"><subject>Medicine&amp;Healthcare</subject></subj-group></article-categories><title-group><article-title>
 
 
  Assessment of Cystatin C-Based GFR Estimating Equations as a Novel Reliable Biomarker for Renal Pathology Diagnosis in Patients with Mild to Severe Tubular Affection
 
</article-title></title-group><contrib-group><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Mohamed</surname><given-names>Ali Ibrahim</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Norhan</surname><given-names>Nagdi</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref></contrib><contrib contrib-type="author" xlink:type="simple"><name name-style="western"><surname>Cherry</surname><given-names>Reda Kamel</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="corresp" rid="cor1"><sup>*</sup></xref></contrib></contrib-group><aff id="aff1"><addr-line>Nephrology Department, Faculty of Medicine, Ain Shams University, Cairo, Egypt</addr-line></aff><pub-date pub-type="epub"><day>26</day><month>10</month><year>2022</year></pub-date><volume>12</volume><issue>04</issue><fpage>426</fpage><lpage>441</lpage><history><date date-type="received"><day>23,</day>	<month>August</month>	<year>2022</year></date><date date-type="rev-recd"><day>5,</day>	<month>December</month>	<year>2022</year>	</date><date date-type="accepted"><day>8,</day>	<month>December</month>	<year>2022</year></date></history><permissions><copyright-statement>&#169; Copyright  2014 by authors and Scientific Research Publishing Inc. </copyright-statement><copyright-year>2014</copyright-year><license><license-p>This work is licensed under the Creative Commons Attribution International License (CC BY). http://creativecommons.org/licenses/by/4.0/</license-p></license></permissions><abstract><p>
 
 
  Background and Objective: Serum creatinine, a commonly used biomarker in determining glomerular filtration rate (GFR) and chronic kidney disease (CKD) stage, is highly variable biologically and does not rise until &gt; 50% of renal function (RF) impairment occurs. Also, its production is not constant &amp; is affected by many factors as muscle mass, age, inflammation. On the other hand, Cystatin C shows more stable production making it more suitable for assessment of kidney function. Also, It has been shown that the progression of CKD to renal failure, even in glomerular diseases, correlated better with the degree of tubular damage and interstitial fibrosis. So, our aim was to investigate the relation between kidney function assessed by different cystatin (Cys-C)-based estimated glomerular filtration rate (eGFR) in comparison to the gold standard Iohexol (Ioh) based measured (m)GFR in relation to the pathological degree of tubular damage in renal biopsy. To our knowledge, this is the first study that evaluates the relation of (Cys-C)-based eGFR to tubulointerstitial fibrosis in renal biopsy. 
  Methods: This cross-sectional study was performed on 20 CKD cases who attended the Nephrology Department at Ain Shams University, where a renal biopsy was obtained, and individuals were allocated into two groups: group A (GA) with mild tubular affection (TA) and group B (GB) with moderate to severe TA. All participants were referred for measure-ment of GFR using Iohexol (Ioh) together with serum Cys-C level and eGFR was calculated using different Cys-C-based GFR estimating equations, which were further compared using Multivariate Linear Regression and Bland-Altman analyses. 
  Results: Our results revealed a substantial statistical difference among the two studied groups regarding Hb, s creatinine, urea. GB had significantly lower levels for both eGFR and mGFR (82, 93, 115, or 115) ml/min/1.73m
  <sup>2</sup>, Vs. GA (200, 123, 162 or 124) ml/min/1.73m
  <sup>2</sup>, according to GFR_iohexol, Stevens, Grubb, and CKD_EPI_CYST equations, respectively, p &lt; 0.05. EGFR by CysC-based equations (Stevens, Grubb, and CKD_EPI_CYST) underestimated mGFR, when compared to Iohexol clearance with statistical significance in all patients (by Z = -3.280%, -2.878%, -3.280%, respectively) and cases with mild tubular affection (by Z = -3.11%, -2.657%, -2.972%, respectively) (p &lt; 0.05), but with non-statistical significance in moderate to severe tubular affection category (B), p &gt; 0.05. A significant correlation between CKD-EPI CYST and mGFR_Iohexol (Ioh) for GA was found (R = 0.601, p = 0.030), where there was a non-substantial relation between any of the used equations and the mGFR in category B (p &gt; 0.05). There was no independent association between the eGFR results and Iohexol clearance. Stevens eGFR had the highest-level bias 33.9 compared with CKD_EPI_CYST (28) and Grubb eGFR (22.85). 
  Conclusion: eGFR by CysC-based equations underestimate GFR in comparison to GFR-iohexol. There is significant correlation between eGFR by CysC-based equations and the gold standard GFR-iohexol only in mild degree of tubular affection and only with CKD-EPI-CYST equation. Stevens equation showed the highest bias while Grubb equation showed the least bias. Although cystatin-based equations have demonstrated a high level of correlation with measured GFR, they are still regarded as imprecise and cannot be established as equal to measured GFR or as a gold standard for GFR estimate.
 
</p></abstract><kwd-group><kwd>Cystatin C</kwd><kwd> Chronic Kidney Diseases</kwd><kwd> Glomerular Filtration Rate</kwd><kwd> Iohexol Clearance</kwd></kwd-group></article-meta></front><body><sec id="s1"><title>1. Introduction</title><p>CKD is described as the existence of renal impairment or an estimated eGFR &lt; 60 ml/min/1.73m<sup>2</sup> that lasts for 90 days or longer regardless of etiology and is graded into 6 phases depending on GFR (G1 to G5 with G3 split into 3a and 3b). It is a gradual decrease of kidney function that eventually necessitates the use of kidney dialysis or transplantation [<xref ref-type="bibr" rid="scirp.121695-ref1">1</xref>].</p><p>In glomerular diseases, although the disease course is usually prolonged, and in many cases there is a risk of chronic renal failure (CRF) development, its behaviour is difficult to predict. On the other hand, even advanced glomerular lesions seen in a biopsy do not necessarily have to be associated with a major impairment of renal function. Therefore, it is necessary to search for morphological and functional parameters that might facilitate the prediction for a further development of the disease [<xref ref-type="bibr" rid="scirp.121695-ref2">2</xref>].</p><p>In 1968, Risdon, Sloper and Wardener studied the associations between morphological parameters and renal function in patients with persistent glomerulonephritis and found a strong relation between the level of renal function, the degree of tubular loss and interstitial fibrosis and risk of renal failure progression [<xref ref-type="bibr" rid="scirp.121695-ref3">3</xref>]. In subsequent years, Bohle et al. published a series of reports where they emphasised the importance of tubulointerstitial lesions [<xref ref-type="bibr" rid="scirp.121695-ref4">4</xref>].</p><p>GFR and chronic renal disease grading are generally determined by monitoring the concentrations of endogenous blood indicators like serum creatinine. Creatinine (Cr), on the other hand, is prone to substantial biological variation, and Cr concentration doesn’t really increase till almost 50% of renal function is lost, resulting in erroneous CKD grading and false negatives [<xref ref-type="bibr" rid="scirp.121695-ref5">5</xref>]. In addition, in elderly people, serum Cr is not a useful indication of GFR. Moreover, to the significant influence of age on kidney structure and function, the same GFR level in various age groups may have varying pathophysiologic or non-pathophysiologic effects on renal function. Furthermore, the majority of the included studies demonstrated a gender difference in CKD prevalence. Females were more likely than males to have CKD. Females have less muscle mass than males, and muscle mass is a significant driver of blood creatinine levels [<xref ref-type="bibr" rid="scirp.121695-ref6">6</xref>].</p><p>To tackle these hurdles, Cystatin C has been demonstrated to be less susceptible to biological interference and more sensitive to early losses in renal function [<xref ref-type="bibr" rid="scirp.121695-ref5">5</xref>]. Cystatin C is a 13-kDa protein that is generated by all nucleated cells and belongs to the cysteine proteinase inhibitor class. Its production rate remains constant from 1 to 50 years of age. Cystatin C has gained widespread acceptance as an endogenous biomarker of GFR and is now routinely used in the assessment of CKD [<xref ref-type="bibr" rid="scirp.121695-ref7">7</xref>].</p><p>Reagents and clinical assays have varied significantly over time, resulting in a plethora of cystatin C-based estimated GFR equations (eGFR) with varying coefficients to account for the variation in concentrations measured [<xref ref-type="bibr" rid="scirp.121695-ref8">8</xref>].</p><p>The current work sought to evaluate the performance of Cystatin C-based eGFR equations evaluated by immunoturbidimetry in relation to the most constant renal pathological changes related to chronic kidney disease (CKD) which is tubular damage and tubulointerstitial fibrosis, in comparison to the gold standard mGFR by Iohexol clearance.</p></sec><sec id="s2"><title>2. Patients and Methods</title><p>This cross-sectional study was performed on 20 cases with CKD who attended the Nephrology Department at Ain Shams University hospital in Cairo, where a renal biopsy was obtained, and individuals were allocated into two categories: patients with mild tubular affection [group A, (score 1, 2)] and those with moderate to severe tubular affection [group B, (score 3, 4)].</p><p>Prior to the start of the study, the proposed procedures were announced to all individuals who agreed to participate and satisfied the inclusion criteria. A detailed history was taken, which included demographic information (age, weight, and body mass index kg/m<sup>2</sup>). The full general examination included pulse, blood pressure, respiratory, cardiovascular, and abdominal.</p><sec id="s2_1"><title>2.1. Exclusion Criteria</title><p>The following were the exclusion criteria: diabetes, advanced liver and cardiovascular disease, severe muscle wasting, severe malnutrition, and history of dye sensitivity.</p></sec><sec id="s2_2"><title>2.2. Methodology</title><p>After exclusion of patients with the above-mentioned exclusion criteria, informed signed consent of all study participants was taken. Ten (10 cc) of venous blood were withdrawn from every patient in each group under full aseptic condition after fasting overnight. Blood was transferred to an Eppendorf tube at 37˚C for 30 minutes to clot and centrifuged at 4000 rpm for a further ten min. The obtained serum was put in aliquots kept at −70˚C until the analysis time to determine marker serum level.</p><sec id="s2_2_1"><title>2.2.1. Measurement of Cystatin C</title><p>The CysC level in frozen-thawed serum was determined using a particle-enhanced turbidimetric immunoassay (PETIA) as reported early by [<xref ref-type="bibr" rid="scirp.121695-ref9">9</xref>]. EGFR calculated via the following 3 CysC-based equations:</p><p>Stevens: eGFR = 76.7 &#215; cys − 1.19 [<xref ref-type="bibr" rid="scirp.121695-ref10">10</xref>] (1)</p><p>Grubb: eGFR = 87 . 62 &#215; cys − 1 . 693 &#215; ( 0. 94iffemale ) [<xref ref-type="bibr" rid="scirp.121695-ref11">11</xref>] (2)</p><p>CKD-EPI CYST: [<xref ref-type="bibr" rid="scirp.121695-ref12">12</xref>] Equation (3)</p><p>• If serum cystatin is ≤0.8: →133 &#215; min (s.cys/0.8)<sup>−0.499</sup> &#215; 0.996<sup>age</sup> &#215; 0.932 if female</p><p>• If serum cystatin is &gt;0.8: →133 &#215; max (s.cys/0.8)<sup>−1.328</sup> &#215; 0.996<sup>age</sup> &#215; 0.932 if female</p></sec><sec id="s2_2_2"><title>2.2.2. Routine Investigations</title><p>All participants were referred for routine laboratory investigation tests, including complete blood picture (CBC), coagulation profile, renal function examination (serum urea, Cr, Na, and K), hepatic function test (ALT, AST, serum albumin, uric acid), complete urine analysis and protein/creatinine ratio.</p></sec><sec id="s2_2_3"><title>2.2.3. Measurement of GFR</title><p>The gold standard for measuring GER was serum IOHEXOL clearance. A 5 mL IV bolus of Ioh (Omnipaque 300) was administered. Blood samples were collected every 2, 3, 4, 5, and 24 hrs. The specimens had been centrifuged, and the values were obtained using High performance liquid chromatography (HPLC) and plotted into a curve to determine the area under the curve (AUC). Clearance was calculated according to the formula of one compartment model</p><p>Cl = Dose AUC (4)</p><p>where Dose is the full quantity of I<sub>2</sub> supplied during the bolus. The AUC is the area under the curve correlating to the body’s time spent in contact with Ioh. Plasma clearances (Cl<sub>p</sub>) were then computed using the formula of Brochner-Mortensen et al.,</p><p>Cl p = [ 0. 99 0 778 &#215; Cl ] − [ 0.00 1218 &#215; Cl 2 ] , [<xref ref-type="bibr" rid="scirp.121695-ref13">13</xref>] (5)</p><p>Although the blood specimen number was onerous, the 24-hour sample, when incorporated in the Cl<sub>p</sub> calculation, the GFR measurement became more reliable. Earlier blood specimens (T2 - T4 and T2 - T6) overestimated GFR, whereas for GFR &lt; 60 mL per min per 1.73 m<sup>2</sup> a late timespan (24 hr) is necessary to decrease bias testing, that causes a 10% overstatement of GFR [<xref ref-type="bibr" rid="scirp.121695-ref14">14</xref>].</p></sec><sec id="s2_2_4"><title>2.2.4. Renal Biopsy Examination</title><p>Renal biopsy was studied under a light and electron microscope, with a focus on tubular pathology. Tubular atrophy (TA), interstitial fibrosis (IF), interstitial edema (IE), interstitial inflammation, and acute tubular damage (ATD) all were evaluated semiquantitatively on a scale from 0 to 3 dependent on the proportion of cortex affected region (1, 1 to 25, 26 to 50, and more than 50%). Arteriosclerosis and arteriolosclerosis were graded from 0 to 3 (absent, mild, moderate, and severe) based on the degree of luminal constriction and artery wall thickening, respectively [<xref ref-type="bibr" rid="scirp.121695-ref15">15</xref>].</p></sec></sec><sec id="s2_3"><title>2.3. Ethical Consideration.</title><p>Approval of the study design was obtained from the Institutional Review Board (IRB) unit and the Research Ethical Committee in the faculty of Medicine; Ain shams University.</p></sec><sec id="s2_4"><title>2.4. Patient Consent</title><p>The proposed study methods were presented to all subjects, an oral and informed written permission consent document was signed by those who agreed to participate before sample collection.</p></sec><sec id="s2_5"><title>2.5. Statistical Analysis</title><p>On an IBM personal computer, data was evaluated utilizing the SPSS (Statistical Package for Special Science) software, Vr 25. The Spearman’s rank correlation coefficient analysis is utilized to ascertain the statistical dependency of two variables. The Mann-Whitney-U test is utilized to evaluate two sets of data whose distribution is unknown. Bias-Precision: the average difference between predicted and observed renal function was defined as bias, and the SD of this discrepancy was represented as precision. The Bland and Altman (BA) technique was utilized to show the discrepancies among calculated and measured GFR levels. Multivariate Linear Regression Analysis was utilized to look for an independent relationship between any of the estimated GFR outcomes and Iohexol clearance. The Wilcoxon test was used to compare Iohexol clearance to other eGFR techniques.</p></sec></sec><sec id="s3"><title>3. Results</title><p>Demographic characteristics of 20 CKD cases (40% were females), including 13 cases with mild tubular affection, and 7 cases with moderate to severe tubular affection, are presented in <xref ref-type="table" rid="table1">Table 1</xref>. The average age of all individuals involved in our current study was 35.9 &#177; 8.4 and 34.9 &#177; 16.2, respectively. <xref ref-type="table" rid="table1">Table 1</xref> demonstrated that there is no statistically significant difference regarding age (p = 0.847), gender (p = 0.052), and BMI (p = 0.863) among the 2 groups of the current research. Additionally, there was a non-significant difference with respect to the degree of tubular affection and virology among all studied categories (A and B), p &gt; 0.05.</p><p>The routine laboratory tests were presented in <xref ref-type="table" rid="table2">Table 2</xref>; the mean (hemoglobin) Hb value was 12.7 &#177; 2.9 and 8.6 &#177; 1.2 g/dl, for group A and B, respectively, with the same International Normalized Ratio (INR) ~ 1.0 &#177; 0.1 in both groups. Our results revealed that there was a substantial statistical difference among the two studied groups regarding Hb, kidney function test (s. creatinine, Urea and serum uric acid), and ALT, p &lt; 0.05, <xref ref-type="table" rid="table2">Table 2</xref>.</p><p>CysC-based eGFR was calculated using different equations (Stevens, Grubb, and CKD_EPI_CYST) in comparison to GFR_iohexol. As represented in <xref ref-type="table" rid="table3">Table 3</xref>, cases with moderate to severe tubular affection had significantly lower levels for both estimated and measured GFR (82, 93, 115, or 115) ml/min/1.73m<sup>2</sup>, Vs. cases with mild tubular affection (200, 123, 162 or 124) ml/min/1.73m<sup>2</sup>, according to GFR_iohexol, Stevens, Grubb, and CKD_EPI_CYST, respectively, p &lt; 0.05.</p><p>CysC-based eGFR using Stevens, Grubb, and CKD_EPI_CYST formulas and mGFR_Iohexol were calculated for multiple correlations. Our results demonstrated a significant correlation between CKD-EPI-CYST and mGFR_Iohexol at the mild degree of tubular affection (R = 0.601, p = 0.030), whereas there was a</p><table-wrap id="table1" ><label><xref ref-type="table" rid="table1">Table 1</xref></label><caption><title> Baseline characteristics of CKD patients among studied groups</title></caption><table><tbody><thead><tr><th align="center" valign="middle" ></th><th align="center" valign="middle" >Group A (N = 13)</th><th align="center" valign="middle" >Group B (N = 7)</th><th align="center" valign="middle" >X<sup>2</sup></th><th align="center" valign="middle" >p Value</th></tr></thead><tr><td align="center" valign="middle" >Age (Years)</td><td align="center" valign="middle" >35.9 &#177; 8.4</td><td align="center" valign="middle" >34.9 &#177; 16.2</td><td align="center" valign="middle" >0.196</td><td align="center" valign="middle" >0.847</td></tr><tr><td align="center" valign="middle" >Gender</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Male</td><td align="center" valign="middle" >10</td><td align="center" valign="middle" >2</td><td align="center" valign="middle" >4.43</td><td align="center" valign="middle" >0.052</td></tr><tr><td align="center" valign="middle" >Female</td><td align="center" valign="middle" >3</td><td align="center" valign="middle" >5</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >BMI (kg/m<sup>2</sup>)</td><td align="center" valign="middle" >26 &#177; 3.1</td><td align="center" valign="middle" >25.7 &#177; 3.4</td><td align="center" valign="middle" >0.175</td><td align="center" valign="middle" >0.863</td></tr><tr><td align="center" valign="middle" >HTN</td><td align="center" valign="middle" >5 (38.5%)</td><td align="center" valign="middle" >2 (28.6%)</td><td align="center" valign="middle" >0.196</td><td align="center" valign="middle" >0.526</td></tr><tr><td align="center" valign="middle" >% of patients with active urinary sediment (AUS)</td><td align="center" valign="middle" >3 (30%)</td><td align="center" valign="middle" >4 (60%)</td><td align="center" valign="middle" >2.32</td><td align="center" valign="middle" >0.151</td></tr><tr><td align="center" valign="middle" >Virology (HCV)</td><td align="center" valign="middle" >5 (38.5%)</td><td align="center" valign="middle" >0</td><td align="center" valign="middle" >3.59</td><td align="center" valign="middle" >0.083</td></tr></tbody></table></table-wrap><p>X<sup>2</sup> = Chi Square, HTN = hypertension.</p><table-wrap id="table2" ><label><xref ref-type="table" rid="table2">Table 2</xref></label><caption><title> Comparison of laboratory profile among studied groups</title></caption><table><tbody><thead><tr><th align="center" valign="middle" ></th><th align="center" valign="middle" >Group A (N = 13)</th><th align="center" valign="middle" >Group B (N = 7)</th><th align="center" valign="middle" >Z</th><th align="center" valign="middle" >p Value</th></tr></thead><tr><td align="center" valign="middle" >Hg (g/dl )</td><td align="center" valign="middle" >12.7 &#177; 2.9</td><td align="center" valign="middle" >8.6 &#177; 1.2</td><td align="center" valign="middle" >2.854</td><td align="center" valign="middle" >0.002*</td></tr><tr><td align="center" valign="middle" >INR</td><td align="center" valign="middle" >1.0 &#177; 0.1</td><td align="center" valign="middle" >1.0 &#177; 0.1</td><td align="center" valign="middle" >1.468</td><td align="center" valign="middle" >0.157</td></tr><tr><td align="center" valign="middle" >s. creatinine (mg/dl)</td><td align="center" valign="middle" >1.4 &#177; 1.4</td><td align="center" valign="middle" >5.0 &#177; 2.2</td><td align="center" valign="middle" >3.058</td><td align="center" valign="middle" >0.001*</td></tr><tr><td align="center" valign="middle" >BUN (mg/dl)</td><td align="center" valign="middle" >22.6 &#177; 13.1</td><td align="center" valign="middle" >67.6 &#177; 32.4</td><td align="center" valign="middle" >3.052</td><td align="center" valign="middle" >0.001*</td></tr><tr><td align="center" valign="middle" >Na (mmol/L)</td><td align="center" valign="middle" >134.1 &#177; 3.9</td><td align="center" valign="middle" >135.1 &#177; 6.8</td><td align="center" valign="middle" >1.114</td><td align="center" valign="middle" >0.275</td></tr><tr><td align="center" valign="middle" >K (mmol/L)</td><td align="center" valign="middle" >4.0 &#177; 0.7</td><td align="center" valign="middle" >4.2 &#177; 0.500</td><td align="center" valign="middle" >0.873</td><td align="center" valign="middle" >0.393</td></tr><tr><td align="center" valign="middle" >UA (mg/dl)</td><td align="center" valign="middle" >6.0 &#177; 0.7</td><td align="center" valign="middle" >7.9 &#177; 1.5</td><td align="center" valign="middle" >2.501</td><td align="center" valign="middle" >0.011*</td></tr><tr><td align="center" valign="middle" >Albumin (mg/dl)</td><td align="center" valign="middle" >2.2 &#177; 0.9</td><td align="center" valign="middle" >2.7 &#177; 0.9</td><td align="center" valign="middle" >1.112</td><td align="center" valign="middle" >0.275</td></tr><tr><td align="center" valign="middle" >TP(mg/dl)</td><td align="center" valign="middle" >5.4 &#177; 1.1</td><td align="center" valign="middle" >5.6 &#177; 1.1</td><td align="center" valign="middle" >0.638</td><td align="center" valign="middle" >0.536</td></tr><tr><td align="center" valign="middle" >ALT (U/L)</td><td align="center" valign="middle" >16.1 &#177; 6.8</td><td align="center" valign="middle" >11.9 &#177; 4.5</td><td align="center" valign="middle" >2.080</td><td align="center" valign="middle" >0.037*</td></tr><tr><td align="center" valign="middle" >Protein/creatinine ratio</td><td align="center" valign="middle" >2.8 &#177; 1.4</td><td align="center" valign="middle" >5.4 &#177; 6</td><td align="center" valign="middle" >0.833</td><td align="center" valign="middle" >0.438</td></tr></tbody></table></table-wrap><p>Hg = Hemoglobin; INR = International Normalized Ratio; BUN = Blood Urea Nitrogen; TP = Total Protein.</p><table-wrap id="table3" ><label><xref ref-type="table" rid="table3">Table 3</xref></label><caption><title> Comparison between GA and GB as regards CysC-based eGFR using various equations</title></caption><table><tbody><thead><tr><th align="center" valign="middle"  rowspan="2"  ></th><th align="center" valign="middle"  colspan="3"  >Group A (N = 13)</th><th align="center" valign="middle"  colspan="3"  >Group B (N = 7)</th><th align="center" valign="middle"  rowspan="2"  >Z</th><th align="center" valign="middle"  rowspan="2"  >p Value</th></tr></thead><tr><td align="center" valign="middle" >Min</td><td align="center" valign="middle" >Median</td><td align="center" valign="middle" >Max</td><td align="center" valign="middle" >Min</td><td align="center" valign="middle" >Median</td><td align="center" valign="middle" >Max</td></tr><tr><td align="center" valign="middle" >GFR_iohexol</td><td align="center" valign="middle" >9</td><td align="center" valign="middle" >136</td><td align="center" valign="middle" >200</td><td align="center" valign="middle" >12</td><td align="center" valign="middle" >26</td><td align="center" valign="middle" >82</td><td align="center" valign="middle" >−3.051</td><td align="center" valign="middle" >0.001*</td></tr><tr><td align="center" valign="middle" >Stevens</td><td align="center" valign="middle" >17</td><td align="center" valign="middle" >100</td><td align="center" valign="middle" >123</td><td align="center" valign="middle" >12</td><td align="center" valign="middle" >22</td><td align="center" valign="middle" >93</td><td align="center" valign="middle" >−2.899</td><td align="center" valign="middle" >0.002*</td></tr><tr><td align="center" valign="middle" >Grubb</td><td align="center" valign="middle" >10</td><td align="center" valign="middle" >127</td><td align="center" valign="middle" >162</td><td align="center" valign="middle" >7</td><td align="center" valign="middle" >13</td><td align="center" valign="middle" >115</td><td align="center" valign="middle" >−2.895</td><td align="center" valign="middle" >0.002*</td></tr><tr><td align="center" valign="middle" >CKD_EPI_CYST</td><td align="center" valign="middle" >15</td><td align="center" valign="middle" >110</td><td align="center" valign="middle" >124</td><td align="center" valign="middle" >11</td><td align="center" valign="middle" >16</td><td align="center" valign="middle" >115</td><td align="center" valign="middle" >−2.736</td><td align="center" valign="middle" >0.005*</td></tr></tbody></table></table-wrap><p>Z: Mann Whitney Test.</p><p>non-substantial relation among all the used equations and measured GFR at moderate to severe tubular affection (p &gt; 0.05). For all patients, a strong significant statistical correlation between all equations and measured mGFR, with comparable correlation coefficients (R = 0.799, p = 0.0001) was found, as illustrated in <xref ref-type="table" rid="table4">Table 4</xref> and <xref ref-type="fig" rid="fig1">Figure 1</xref>.</p><p><xref ref-type="table" rid="table5">Table 5</xref> presented the comparison between Iohexol clearance and different methods of eGFR in all patients and after patient’s division according to the degree of tubular affection by renal biopsy. Our results revealed that eGFR by cystatin-based equations (Stevens, Grubb, and CKD_EPI_CYST) underestimate mGFR, when compared to Iohexol clearance with statistical significance in all patients (by Z = −3.280%, −2.878%, −3.280%, respectively) and cases with mild tubular affection (by Z = −3.11%, −2.657%, −2.972%, respectively) (p &lt; 0.05), but with non-statistical significance in moderate to severe tubular affection category (B), p &gt; 0.05.</p><table-wrap id="table4" ><label><xref ref-type="table" rid="table4">Table 4</xref></label><caption><title> Correlations among various eGFR estimate techniques and iohexol clearance as mGFR a gold standard measure: (mild tubular affection, moderate to severe, and all patients)</title></caption><table><tbody><thead><tr><th align="center" valign="middle"  rowspan="2"  >GFR_iohexol</th><th align="center" valign="middle"  colspan="3"  >Group A</th><th align="center" valign="middle"  colspan="3"  >Group B</th><th align="center" valign="middle"  colspan="3"  >All patients</th></tr></thead><tr><td align="center" valign="middle" >S</td><td align="center" valign="middle" >G</td><td align="center" valign="middle" >ESK</td><td align="center" valign="middle" >S</td><td align="center" valign="middle" >G</td><td align="center" valign="middle" >ESK</td><td align="center" valign="middle" >S</td><td align="center" valign="middle" >G</td><td align="center" valign="middle" >ESK</td></tr><tr><td align="center" valign="middle" >R</td><td align="center" valign="middle" >0.490</td><td align="center" valign="middle" >0.485</td><td align="center" valign="middle" >0.601*</td><td align="center" valign="middle" >0.667</td><td align="center" valign="middle" >0.714</td><td align="center" valign="middle" >0.464</td><td align="center" valign="middle" >0.799**</td><td align="center" valign="middle" >0.799**</td><td align="center" valign="middle" >0.799**</td></tr><tr><td align="center" valign="middle" >P-Value</td><td align="center" valign="middle" >0.089</td><td align="center" valign="middle" >0.093</td><td align="center" valign="middle" >0.030</td><td align="center" valign="middle" >0.102</td><td align="center" valign="middle" >0.071</td><td align="center" valign="middle" >0.294</td><td align="center" valign="middle" >0.0001</td><td align="center" valign="middle" >0.0001</td><td align="center" valign="middle" >0.0001</td></tr><tr><td align="center" valign="middle" >Number</td><td align="center" valign="middle" >13</td><td align="center" valign="middle" >13</td><td align="center" valign="middle" >13</td><td align="center" valign="middle" >7</td><td align="center" valign="middle" >7</td><td align="center" valign="middle" >7</td><td align="center" valign="middle" >20</td><td align="center" valign="middle" >20</td><td align="center" valign="middle" >20</td></tr></tbody></table></table-wrap><p>S = Stevens, G = Grubb, CEC = CKD_EPI_CYST, R = Spearmanns correlation coefficient.</p><table-wrap id="table5" ><label><xref ref-type="table" rid="table5">Table 5</xref></label><caption><title> Comparison of Iohexol clearance mGFR and various techniques of eGFR in all patients and after patient division based on degree of tubular affection (mild tubular affection, moderate to severe) by renal biopsy</title></caption><table><tbody><thead><tr><th align="center" valign="middle"  rowspan="2"  ></th><th align="center" valign="middle"  colspan="5"  >Group A</th><th align="center" valign="middle"  colspan="5"  >Group B</th><th align="center" valign="middle"  colspan="5"  >All patients</th></tr></thead><tr><td align="center" valign="middle" >Mdn</td><td align="center" valign="middle" >Min</td><td align="center" valign="middle" >Max</td><td align="center" valign="middle" >Z</td><td align="center" valign="middle" >p</td><td align="center" valign="middle" >Mdn</td><td align="center" valign="middle" >Min</td><td align="center" valign="middle" >Max</td><td align="center" valign="middle" >Z</td><td align="center" valign="middle" >p</td><td align="center" valign="middle" >Mdn</td><td align="center" valign="middle" >Min</td><td align="center" valign="middle" >Max</td><td align="center" valign="middle" >Z</td><td align="center" valign="middle" >p</td></tr><tr><td align="center" valign="middle" >GFR_iohexol</td><td align="center" valign="middle" >136</td><td align="center" valign="middle" >9</td><td align="center" valign="middle" >200</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" >26</td><td align="center" valign="middle" >12</td><td align="center" valign="middle" >82</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td><td align="center" valign="middle" >105</td><td align="center" valign="middle" >9</td><td align="center" valign="middle" >200</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr><tr><td align="center" valign="middle" >Stevens</td><td align="center" valign="middle" >100</td><td align="center" valign="middle" >17</td><td align="center" valign="middle" >123</td><td align="center" valign="middle" >−3.111</td><td align="center" valign="middle" >0.002</td><td align="center" valign="middle" >22</td><td align="center" valign="middle" >12</td><td align="center" valign="middle" >93</td><td align="center" valign="middle" >−0.420</td><td align="center" valign="middle" >0.674</td><td align="center" valign="middle" >77</td><td align="center" valign="middle" >12</td><td align="center" valign="middle" >123</td><td align="center" valign="middle" >−3.280</td><td align="center" valign="middle" >0.001</td></tr><tr><td align="center" valign="middle" >Grubb</td><td align="center" valign="middle" >127</td><td align="center" valign="middle" >10</td><td align="center" valign="middle" >162</td><td align="center" valign="middle" >−2.657</td><td align="center" valign="middle" >0.008</td><td align="center" valign="middle" >13</td><td align="center" valign="middle" >7</td><td align="center" valign="middle" >115</td><td align="center" valign="middle" >−1.101</td><td align="center" valign="middle" >0.271</td><td align="center" valign="middle" >88</td><td align="center" valign="middle" >7</td><td align="center" valign="middle" >162</td><td align="center" valign="middle" >−2.878</td><td align="center" valign="middle" >0.004</td></tr><tr><td align="center" valign="middle" >CKD_EPI_CYST</td><td align="center" valign="middle" >110</td><td align="center" valign="middle" >15</td><td align="center" valign="middle" >124</td><td align="center" valign="middle" >−2.971</td><td align="center" valign="middle" >0.003</td><td align="center" valign="middle" >16</td><td align="center" valign="middle" >11</td><td align="center" valign="middle" >115</td><td align="center" valign="middle" >−0.420</td><td align="center" valign="middle" >0.674</td><td align="center" valign="middle" >85</td><td align="center" valign="middle" >11</td><td align="center" valign="middle" >124</td><td align="center" valign="middle" >−3.280</td><td align="center" valign="middle" >0.004</td></tr></tbody></table></table-wrap><p>Z: Wilcoxon Test; Mdn = Median.</p><p>Our results showed no independent association between any of the estimated GFR results and Iohexol clearance. Stevens eGFR had the highest-level bias 33.9 compared with CKD_EPI_CYST eGFR (28) and Grubb eGFR (22.85), <xref ref-type="table" rid="table6">Table 6</xref>, and <xref ref-type="fig" rid="fig2">Figure 2</xref>.</p></sec><sec id="s4"><title>4. Discussion</title><p>GFR is commonly used to assess kidney function. It is most often calculated in clinical practice utilizing endogenous surrogate indicators. The most often utilized endogenous marker is serum creatinine. Serum cyst-C is a relatively recent endogenous indicator that has the benefit of being produced continuously via all nucleated body cells and being catabolized almost entirely at the proximal tubule. Serum cyst-C had been found in clinical investigations to be an accurate diagnostic of GFR, [<xref ref-type="bibr" rid="scirp.121695-ref16">16</xref>].</p><p>The CKD Epidemiology (CKD-EPI) formula, introduced in 2009, appears to be better accurate in calculating GFR than prior ones. Because creatinine procedures were not standard throughout the intervening institutions, resulting in discrepancies in creatinine readings, all of these formulas lack appropriate validation at the GFR at that they were used. Lastly, Cr-depend GFR estimates have numerous disadvantages and are dependent on numerous variables, and the precision of these formulas is hotly debated [<xref ref-type="bibr" rid="scirp.121695-ref17">17</xref>].</p><p>Cyst-C has been suggested as a new endogenous GFR biomarker. Although newer research has questioned these findings, serum cyst-C level appears to be</p><table-wrap id="table6" ><label><xref ref-type="table" rid="table6">Table 6</xref></label><caption><title> Multivariate linear regression and bland altman analysis</title></caption><table><tbody><thead><tr><th align="center" valign="middle"  rowspan="2"  ></th><th align="center" valign="middle"  colspan="6"  >Multivariate Linear Regression Analysis (MLRA)</th></tr></thead><tr><td align="center" valign="middle" >(Constant)</td><td align="center" valign="middle" >Stevens</td><td align="center" valign="middle"  colspan="2"  >Grubb</td><td align="center" valign="middle"  colspan="2"  >CKD EPI CYST</td></tr><tr><td align="center" valign="middle" >B</td><td align="center" valign="middle" >−8.045</td><td align="center" valign="middle" >3.222</td><td align="center" valign="middle"  colspan="2"  >−0.760</td><td align="center" valign="middle"  colspan="2"  >−0.179</td></tr><tr><td align="center" valign="middle" >T</td><td align="center" valign="middle" >−0.211</td><td align="center" valign="middle" >0.900</td><td align="center" valign="middle"  colspan="2"  >−0.410</td><td align="center" valign="middle"  colspan="2"  >−0.097</td></tr><tr><td align="center" valign="middle" >p Value</td><td align="center" valign="middle" >0.836</td><td align="center" valign="middle" >0.384</td><td align="center" valign="middle"  colspan="2"  >0.688</td><td align="center" valign="middle"  colspan="2"  >0.924</td></tr><tr><td align="center" valign="middle"  colspan="7"  >Bland Altman Analysis:</td></tr><tr><td align="center" valign="middle"  colspan="2"  ></td><td align="center" valign="middle" >Stevens</td><td align="center" valign="middle" >Grubb</td><td align="center" valign="middle" >CKD EPI CYST</td><td align="center" valign="middle" >Kroskal wallis</td><td align="center" valign="middle" >p value</td></tr><tr><td align="center" valign="middle"  colspan="2"  >Bias</td><td align="center" valign="middle" >33.9</td><td align="center" valign="middle" >22.85</td><td align="center" valign="middle" >28.65</td><td align="center" valign="middle" >0.642</td><td align="center" valign="middle" >0.725</td></tr><tr><td align="center" valign="middle"  colspan="2"  >Precision</td><td align="center" valign="middle" >41.6</td><td align="center" valign="middle" >40.1</td><td align="center" valign="middle" >40.8</td><td align="center" valign="middle" ></td><td align="center" valign="middle" ></td></tr></tbody></table></table-wrap><p>unaffected by muscle mass, gender, aging, or dietary condition. Inflammation, fever, or other factors may not affect serum cystatin C levels. Furthermore, it appears to be a more accurate GFR indicator in diseases such as liver cirrhosis, diabetes mellitus, and the geriatric. Because of these qualities, several people have recommended cyst-C as a better exact measure of GFR than Cr, especially in persons of minor GFR impairment; however, these investigations are not only scarce but also conflicting and cover a small number of individuals [<xref ref-type="bibr" rid="scirp.121695-ref18">18</xref>].</p><p>Notwithstanding the theoretical benefits of cyst-C and the more refined formulae, the dispute persists, and no formula has been securely developed to measure GFR at any phase. As a result, the need for updated formulas is mostly owing to the lack of accuracy in estimating GFR, especially when the gold standard techniques of GFR assessment differ from one research to another [<xref ref-type="bibr" rid="scirp.121695-ref19">19</xref>]. Several formulae have been established based on creatinine and cystatin C. In this context, recent research wherein renal function was assessed using Iohexol clearance as the gold standard of GFR and Cr or cyst-C formulas is noteworthy [<xref ref-type="bibr" rid="scirp.121695-ref20">20</xref>].</p><p>In terms of demographic data, our analysis found no statistically significant difference among the 2 studied categories (A and B). eGFR estimated by Cystatin C-based equations had a strong correlation with mGFR estimated by Iohexol with comparable correlation coefficients (R), which is consistent with several studies, including one by Godwill et al., who found that cyst-C levels were substantially linked with assessed GFR [<xref ref-type="bibr" rid="scirp.121695-ref21">21</xref>]. Also, our findings matched those of Abdallah et al., who discovered a substantial association between the Cystatin C-based formula in the examined CKD patients and the measured GFR in the same patients [<xref ref-type="bibr" rid="scirp.121695-ref22">22</xref>].</p><p>Stevens et al. conducted a pooled analysis in which they estimated GFR utilizing serum Cyst-C alone and in conjunction with serum Cr, correlated significantly with GFR measured by Iothalamate but also to produce more reliable estimations, a formula combining serum cyst with serum Cr, age, gender, and race was proposed [<xref ref-type="bibr" rid="scirp.121695-ref10">10</xref>].</p><p>In a separate investigation, Inker et al. evaluated the efficacy of the Cyst_CKD_ EPI formula alone and in contrast to the combined Cr-cyst-C formula, finding that the combined formula provided a highly precise and accurate assessment of GFR [<xref ref-type="bibr" rid="scirp.121695-ref12">12</xref>]</p><p>In accordance with our findings, Hojs et al. found that cystatin-based equations underestimated measured GFR and lacked accuracy [<xref ref-type="bibr" rid="scirp.121695-ref20">20</xref>]; nevertheless, these findings contrast other research by Gupta et al., who reported cyst-based equations overestimated measured GFR [<xref ref-type="bibr" rid="scirp.121695-ref23">23</xref>]. Our findings also revealed a substantial degree of bias between cystatin C-based equations and Iohexol clearance, with a non-statistically significant tendency toward larger bias with Steven’s equation and the least bias with Grubb’s equation.</p><p>Steven’s equation was compared to other several equations in a study by Harman et al. found ten research that looked at 14 different cyst-C based estimating formulae: Grubb et al., Arnal-Dade, Macisaac et al. Stevens formula demonstrated the least bias and the maximum accuracy versus observed GFR utilizing kidney or Cl<sub>p</sub> of contrast media, radioactive elements, or inulin (2013) [<xref ref-type="bibr" rid="scirp.121695-ref23">23</xref>].</p><p>Another research by Chudleigh et al. evaluated the performance of multiple cystatin-based equations and discovered that all models underestimated GFR, with the Stevens equation showing less bias than the Rule and Perkins equations but higher bias than the Tan and MacIsaac equations [<xref ref-type="bibr" rid="scirp.121695-ref24">24</xref>].</p><p>Sharma et al. discovered that the diagnostic accuracy of several cystatin C equations varied with GFR in their investigation. This problem must be addressed when using these equations in clinical practice and in future research on eGFR equations [<xref ref-type="bibr" rid="scirp.121695-ref25">25</xref>].</p><p>According to Rule et al., the various methodologies (urinary inulin clearance, plasma 99mTc-DTPA clearance, and plasma Iohexol clearance) employed as a GFR assessments gold standard reference could potentially contribute to part of the among-investigation variations, where variations in GFR assessment procedures are likely to be a substantial origin of diversity, [<xref ref-type="bibr" rid="scirp.121695-ref26">26</xref>].</p><p>Furthermore, Delanaye et al. believe that a significant cause of variance is the lack of established calibration for cyst-C testing. On comparing various cyst-C procedures, where considerable discrepancies have been recorded, and therefore when employing cystatin C-based equations, it is vital to understand that cystatin C estimations vary depending on whether the test is performed using a turbidimetric or nephelometric approach [<xref ref-type="bibr" rid="scirp.121695-ref27">27</xref>]. Other results of the present investigation include a strong relationship between the degree of tubular affection and Hb level, which was shown to be lower in group B patients compared to those in group A.</p><p>Patients with varied etiologies were studied, and it was shown that the prevalence of anemia was closely linked to a decline in GFR [<xref ref-type="bibr" rid="scirp.121695-ref28">28</xref>]. The present investigation also demonstrated that patients in category B (moderate to severe tubular affection) had higher levels of serum uric acid, which is consistent with a study by Zhou et al. that found hyperuricemia to be a marker for tubulointerstitial lesions [<xref ref-type="bibr" rid="scirp.121695-ref29">29</xref>].</p></sec><sec id="s5"><title>5. Conclusion</title><p>According to our findings, GFR calculated using cystatin-based equations underestimates GFR when compared to GFR evaluated using iohexol. Only in moderate tubular affection and with the CKD EPI CYST equation is there a substantial association between GFR evaluated by cystatin-based equations and gold standard GFR iohexol. The Stevens equation had the greatest bias, whereas the Grubb equation had the least bias. Although cystatin-based equations have demonstrated a high level of correlation with measured GFR, they are still regarded as imprecise and cannot be established as equal to calculated GFR or as a gold standard for GFR estimate.</p></sec><sec id="s6"><title>Acknowledgements</title><p>Approval of the study design was obtained from the Institutional Review Board (IRB) unit and the Research Ethical Committee in the faculty of Medicine; Ain shams University. The proposed study methods were presented to all subjects, an oral and informed written permission consent document was signed by those who agreed to participate before sample collection. Ethically compliant with the Helsinki Ethical Declaration.</p></sec><sec id="s7"><title>Conflicts of Interest</title><p>The authors reported no possible conflicts of interest.</p></sec><sec id="s8"><title>Funding</title><p>Funded by the authors.</p><p>Ethically compliant with the Helsinki Ethical Declaration and the committee of ethics of Ain.</p><p>Shams University Hospitals.</p><p>Data Available on reasonable request from Dr Cherry Reda via her email.</p></sec><sec id="s9"><title>Contributions</title><p>Research idea and study design: MAI; data acquisition: NN; data analysis/interpretation: MAI, CRK; supervision or mentorship: MAI, CRK. CRK takes responsibility that this study has been reported honestly, accurately and transparently, and accepts accountability for the overall work by ensuring that questions pertaining to the accuracy or integrity of any portion of the work are appropriately investigated and resolved.</p></sec><sec id="s10"><title>Cite this paper</title><p>Ibrahim, M.A., Nagdi, N. and Kamel, C.R. (2022) Value of Cystatin C Based Equations for Assessment of Glomerular Filtration Rate in Relation to Renal Biopsy. Open Journal of Nephrology, 12, 426-440. https://doi.org/10.4236/ojneph.2022.124043</p></sec></body><back><ref-list><title>References</title><ref id="scirp.121695-ref1"><label>1</label><mixed-citation publication-type="other" xlink:type="simple">Vaidya, S.R., Aeddula, N.R. and Doerr, C. (2021) Chronic Renal Failure (Nursing). StatPearls Publishing, Treasure Island.</mixed-citation></ref><ref id="scirp.121695-ref2"><label>2</label><mixed-citation publication-type="other" xlink:type="simple">Sanyaolu, A., et al. (2018) Epidemiology and Management of Chronic Renal Failure: A global Public Health Problem. Biostatistics and Epidemiology International Journal, 1, 11-16. https://doi.org/10.30881/beij.00005</mixed-citation></ref><ref id="scirp.121695-ref3"><label>3</label><mixed-citation publication-type="other" xlink:type="simple">Johansen, K., et al. (2021) US Renal Data System 2020 Annual Data Report: Epidemiology of Kidney Disease in the United States. American Journal of Kidney Diseases, 77, A7-A8.</mixed-citation></ref><ref id="scirp.121695-ref4"><label>4</label><mixed-citation publication-type="other" xlink:type="simple">Perico, N. and Remuzzi, G. (2012) Chronic Kidney Disease: A Research and Public Health Priority. Nephrology Dialysis Transplantation, 27, iii19-iii26. https://doi.org/10.1093/ndt/gfs284</mixed-citation></ref><ref id="scirp.121695-ref5"><label>5</label><mixed-citation publication-type="other" xlink:type="simple">Carrero, J.J., Hecking, M., Chesnaye, N.C. and Jager, K.J. (2018) Sex and Gender Disparities in the Epidemiology and Outcomes of Chronic Kidney Disease. Nature Reviews Nephrology, 14, 151-164. https://doi.org/10.1038/nrneph.2017.181</mixed-citation></ref><ref id="scirp.121695-ref6"><label>6</label><mixed-citation publication-type="other" xlink:type="simple">Barsoum, R.S. (2013) Burden of Chronic Kidney Disease: North Africa. Kidney International Supplements, 3, 164-166. https://doi.org/10.1038/kisup.2013.5</mixed-citation></ref><ref id="scirp.121695-ref7"><label>7</label><mixed-citation publication-type="other" xlink:type="simple">Kar, S., Paglialunga, S. and Islam, R. (2018) Cystatin C Is a More Reliable Biomarker for Determining eGFR to Support Drug Development Studies. The Journal of Clinical Pharmacology, 58, 1239-1247. https://doi.org/10.1002/jcph.1132</mixed-citation></ref><ref id="scirp.121695-ref8"><label>8</label><mixed-citation publication-type="other" xlink:type="simple">Naik, G.S., et al. (2019) Complex Inter-Relationship of Body Mass Index, Gender and Serum Creatinine on Survival: Exploring the Obesity Paradox in Melanoma Patients Treated with Checkpoint Inhibition. The Journal for ImmunoTherapy of Cancer, 7, Article No. 89. https://doi.org/10.1186/s40425-019-0512-5</mixed-citation></ref><ref id="scirp.121695-ref9"><label>9</label><mixed-citation publication-type="other" xlink:type="simple">Benoit, S.W., Ciccia, E.A. and Devarajan, P. (2020) Cystatin C as a Biomarker of Chronic Kidney Disease: Latest Developments. Expert Review of Molecular Diagnostics, 20, 1019-1026. https://doi.org/10.1080/14737159.2020.1768849</mixed-citation></ref><ref id="scirp.121695-ref10"><label>10</label><mixed-citation publication-type="other" xlink:type="simple">Pottel, H., et al. (2017) Estimating Glomerular Filtration Rate for the Full Age Spectrum from Serum Creatinine and Cystatin C. Nephrology Dialysis Transplantation, 32, 497-507. https://doi.org/10.1093/ndt/gfw425</mixed-citation></ref><ref id="scirp.121695-ref11"><label>11</label><mixed-citation publication-type="other" xlink:type="simple">Larsson, A., Hansson, L.-O., Flodin, M., Katz, R. and Shlipak, M. (2011) Calibration of the Siemens Cystatin C Immunoassay Has Changed over Time. Clinical Chemistry, 57, 777-778. https://doi.org/10.1373/clinchem.2010.159848</mixed-citation></ref><ref id="scirp.121695-ref12"><label>12</label><mixed-citation publication-type="other" xlink:type="simple">Kitporntheranunt, M. and Manolertthewan, W. (2019) Correspondence to: Reference Intervals for Serum Cystatin C in the Second and Third Trimester of Thai Pregnant Women. Journal of the Medical Association of Thailand, 102, 46-55.</mixed-citation></ref><ref id="scirp.121695-ref13"><label>13</label><mixed-citation publication-type="other" xlink:type="simple">Stevens, L.A., et al. (2008) Estimating GFR Using Serum Cystatin C Alone and in Combination with Serum Creatinine: A Pooled Analysis of 3,418 Individuals with CKD. American Journal of Kidney Diseases, 51, 395-406. https://doi.org/10.1053/j.ajkd.2007.11.018</mixed-citation></ref><ref id="scirp.121695-ref14"><label>14</label><mixed-citation publication-type="other" xlink:type="simple">Grubb, A., et al. (2005) 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. Clinical Chemistry, 51, 1420-1431. https://doi.org/10.1373/clinchem.2005.051557</mixed-citation></ref><ref id="scirp.121695-ref15"><label>15</label><mixed-citation publication-type="other" xlink:type="simple">Inker, L.A., et al. (2012) Estimating Glomerular Filtration Rate from Serum Creatinine and Cystatin C. The New England Journal of Medicine, 367, 20-29. https://doi.org/10.1056/NEJMoa1114248</mixed-citation></ref><ref id="scirp.121695-ref16"><label>16</label><mixed-citation publication-type="other" xlink:type="simple">Stolz, A., et al. (2010) Evaluation of Sample Bias for Measuring Plasma Iohexol Clearance in Kidney Transplantation. Transplantation, 89, 440-445. https://doi.org/10.1097/TP.0b013e3181ca7d1b</mixed-citation></ref><ref id="scirp.121695-ref17"><label>17</label><mixed-citation publication-type="other" xlink:type="simple">Vincenti, F., et al. (2005) Costimulation Blockade with Belatacept in Renal Transplantation. The New England Journal of Medicine, 353, 770-781. https://doi.org/10.1056/NEJMoa050085</mixed-citation></ref><ref id="scirp.121695-ref18"><label>18</label><mixed-citation publication-type="other" xlink:type="simple">Waldman, M., et al. (2007) Adult Minimal-Change Disease: Clinical Characteristics, Treatment, and Outcomes. Clinical Journal of the American Society of Nephrology, 2, 445-453. https://doi.org/10.2215/CJN.03531006</mixed-citation></ref><ref id="scirp.121695-ref19"><label>19</label><mixed-citation publication-type="other" xlink:type="simple">Lopez-Giacoman, S. and Madero, M. (2015) Biomarkers in Chronic Kidney Disease, from Kidney Function to Kidney Damage. World Journal of Nephrology, 4, 57-73. https://doi.org/10.5527/wjn.v4.i1.57</mixed-citation></ref><ref id="scirp.121695-ref20"><label>20</label><mixed-citation publication-type="other" xlink:type="simple">Trimarchi, H., et al. (2012) Creatinine- vs. Cystatin C-Based Equations Compared with 99mTcDTPA Scintigraphy to Assess Glomerular Filtration Rate in Chronic Kidney Disease. Journal of Nephrology, 25, 1-13. https://doi.org/10.5301/jn.5000083</mixed-citation></ref><ref id="scirp.121695-ref21"><label>21</label><mixed-citation publication-type="other" xlink:type="simple">Chew-Harris, J., Saleem, M., Florkowski, C. and George, P. (2008) Cystatin C-A Paradigm of Evidence Based Laboratory Medicine. Clinical Biochemist Reviews, 29, 47-62.</mixed-citation></ref><ref id="scirp.121695-ref22"><label>22</label><mixed-citation publication-type="other" xlink:type="simple">Eriksen, B., et al. (2010) Cystatin C Is Not a Better Estimator of GFR than Plasma Creatinine in the General Population. Kidney International, 78, 1305-1311. https://doi.org/10.1038/ki.2010.321</mixed-citation></ref><ref id="scirp.121695-ref23"><label>23</label><mixed-citation publication-type="other" xlink:type="simple">Trimarchi, H., et al. (2014) Proteinuria, Tc-DTPA Scintigraphy, Creatinine-, Cystatin- and Combined-Based Equations in the Assessment of Chronic Kidney Disease. ISRN Nephrology, 2014, Article ID: 430247. https://doi.org/10.1155/2014/430247</mixed-citation></ref><ref id="scirp.121695-ref24"><label>24</label><mixed-citation publication-type="other" xlink:type="simple">Godwill, O. and Ntuen, N. (2013) Use of Serum Cystatin C in Assessment of Early Deterioration of Renal Function in Type 2 Diabetic Industrial Workers in Port Harcourt, Nigeria. IOSR Journal of Dental and Medical Sciences, 8, 27-35. https://doi.org/10.9790/0853-0822735</mixed-citation></ref><ref id="scirp.121695-ref25"><label>25</label><mixed-citation publication-type="other" xlink:type="simple">Abdallah, E., Waked, E., Nabil, M. and El-Bendary, O. (2014) Cystatin C as a Marker of GFR in Comparison with Serum Creatinine and Formulas Depending on Serum Creatinine in Adult Egyptian Patients with Chronic Kidney Disease.</mixed-citation></ref><ref id="scirp.121695-ref26"><label>26</label><mixed-citation publication-type="other" xlink:type="simple">Hari, P., Ramakrishnan, L., Gupta, R., Kumar, R. and Bagga, A. (2014) Cystatin C-Based Glomerular Filtration Rate Estimating Equations in Early Chronic Kidney Disease. Indian Pediatrics, 51, 273-277. https://doi.org/10.1007/s13312-014-0400-5</mixed-citation></ref><ref id="scirp.121695-ref27"><label>27</label><mixed-citation publication-type="other" xlink:type="simple">Cheuiche, A., Queiroz, M., Azeredo-Da-Silva, A.L. and Silveiro, S. (2019) Performance of Cystatin C-Based Equations for Estimation of Glomerular Filtration Rate in Diabetes Patients: A Prisma-Compliant Systematic Review and Meta-Analysis. Scientific Reports, 9, Article No. 1418. https://doi.org/10.1038/s41598-018-38286-9</mixed-citation></ref><ref id="scirp.121695-ref28"><label>28</label><mixed-citation publication-type="other" xlink:type="simple">Sharma, A., Yasin, A., Garg, A. and Filler, G. (2011) Diagnostic Accuracy of Cystatin C Based eGFR Equations at Different GFR Levels in Children. Clinical Journal of the American Society of Nephrology, 6, 1599-1608. https://doi.org/10.2215/CJN.10161110</mixed-citation></ref><ref id="scirp.121695-ref29"><label>29</label><mixed-citation publication-type="other" xlink:type="simple">Rule, A., Bergstralh, E., Slezak, J., Bergert, J. and Larson, T. (2006) Glomerular Filtration Rate Estimated by Cystatin C among Different Clinical Presentation. Kidney International, 69, 399-405. https://doi.org/10.1038/sj.ki.5000073</mixed-citation></ref><ref id="scirp.121695-ref30"><label>30</label><mixed-citation publication-type="other" xlink:type="simple">Delanaye, P., et al. (2008) Analytical Study of Three Cystatin C Assays and Their Impact on Cystatin C-Based GFR-Prediction Equations. Clinica Chimica Acta, 398, 118-124. https://doi.org/10.1016/j.cca.2008.09.001</mixed-citation></ref><ref id="scirp.121695-ref31"><label>31</label><mixed-citation publication-type="other" xlink:type="simple">Alagoz, S., et al. (2020) Prevalence of Anemia in Predialysis Chronic Kidney Disease: Is the Study Center a Significant Factor? PLOS ONE, 15, e0230980. https://doi.org/10.1371/journal.pone.0230980</mixed-citation></ref><ref id="scirp.121695-ref32"><label>32</label><mixed-citation publication-type="other" xlink:type="simple">Zhou, J., et al. (2014) Plasma Uric Acid Level Indicates Tubular Interstitial Lesions at Early Stage of IgA Nephropathy. BMC Nephrology, 15, Article No. 11. https://doi.org/10.1186/1471-2369-15-11</mixed-citation></ref></ref-list></back></article>