Review
Copyright ©The Author(s) 2015. Published by Baishideng Publishing Group Inc. All rights reserved.
World J Nephrol. Feb 6, 2015; 4(1): 57-73
Published online Feb 6, 2015. doi: 10.5527/wjn.v4.i1.57
Biomarkers in chronic kidney disease, from kidney function to kidney damage
Salvador Lopez-Giacoman, Magdalena Madero
Salvador Lopez-Giacoman, Magdalena Madero, Division of Nephrology, National Heart Institute, 14000 México City, México
Author contributions: All authors contributed to this work.
Open-Access: This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Correspondence to: Magdalena Madero, MD, Division of Nephrology, National Heart Institute, Juan Badiano No. 1, Tlalpan, D.F., 14000 México City, México. madero.magdalena@gmail.com
Telephone: +52-55-55736902 Fax: +52-55-55737716
Received: July 30, 2014
Peer-review started: July 30, 2014
First decision: September 16, 2014
Revised: October 28, 2014
Accepted: November 7, 2014
Article in press: November 10, 2014
Published online: February 6, 2015

Abstract

Chronic kidney disease (CKD) typically evolves over many years, with a long latent period when the disease is clinically silent and therefore diagnosis, evaluation and treatment is based mainly on biomarkers that assess kidney function. Glomerular filtration rate (GFR) remains the ideal marker of kidney function. Unfortunately measuring GFR is time consuming and therefore GFR is usually estimated from equations that take into account endogenous filtration markers like serum creatinine (SCr) and cystatin C (CysC). Other biomarkers such as albuminuria may precede kidney function decline and have demonstrated to have strong associations with disease progression and outcomes. New potential biomarkers have arisen with the promise of detecting kidney damage prior to the currently used markers. The aim of this review is to discuss the utility of the GFR estimating equations and biomarkers in CKD and the different clinical settings where these should be applied. The CKD-Epidemiology Collaboration equation performs better than the modification of diet in renal disease equation, especially at GFR above 60 mL/min per 1.73 m2. Equations combining CysC and SCr perform better than the equations using either CysC or SCr alone and are recommended in situations where CKD needs to be confirmed. Combining creatinine, CysC and urine albumin to creatinine ratio improves risk stratification for kidney disease progression and mortality. Kidney injury molecule and neutrophil gelatinase-associated lipocalin are considered reasonable biomarkers in urine and plasma to determine severity and prognosis of CKD.

Key Words: Chronic kidney disease, Estimated glomerular filtration rate, Kidney damage, New biomarkers, MicroRNA

Core tip: Until more accurate equations are developed the chronic kidney disease (CKD) epidemiology collaboration appears to be superior to other glomerular filtration rate (GFR) estimating equations. In circumstances where CKD requires confirmation estimated GFR based on the combined creatinine-cystatin C equation is recommended. The recent advances in molecular biology have resulted in promising biomarkers for CKD detection and prognosis; however more research is needed before applying them into clinical practice.


Citation: Lopez-Giacoman S, Madero M. Biomarkers in chronic kidney disease, from kidney function to kidney damage. World J Nephrol 2015; 4(1): 57-73
INTRODUCTION

Chronic kidney disease (CKD) has become a public-health problem. The definition of CKD was introduced by de National Kidney Foundation (NFK/KDOQI) in 2002 and latter adopted by the international group Kidney Disease Improving Global Outcomes (KDIGO) in 2004. The definition of CKD requires a decrease in kidney function with a glomerular filtration rate (GFR) of less than 60 mL/min per 1.73 m2 and/or kidney damage for 3 mo or more. Kidney damage refers to pathologic abnormalities documented by biopsy or imaging, alterations in urinary sediment or proteinuria (proteinuria/creatinuria > 200 mg/g, albuminuria/creatinuria > 30 mg/g)[1].

One important aspect about of classification of CKD is that it can usually be detected with non invasive testing. CKD classification is relevant as it has been associated with outcomes such as kidney disease progression, cardiovascular disease and all cause mortality. It is also important as it can allow therapeutic interventions in earlier stages to slow disease progression reduce complications related to decreased estimated GFR (eGFR), cardiovascular (CVD) risk and improve quality of life and survival[2-4]. GFR is the most important marker of kidney function. Unfortunately GFR cannot be easily measured in most clinical or research settings (see below), and therefore estimating equations are based on filtration markers such as serum creatinine (SCr) and cystatin C (CysC). Other biomarkers such as albuminuria may precede kidney function decline and have demonstrated to have strong associations with disease progression and outcomes. New potential biomarkers have arisen with the promise of detecting kidney damage prior to the commonly used markers of kidney disease. The aim of this review is to summarize the most recent findings of most biomarkers in CKD and its implications in clinical practice.

KIDNEY FUNCTION MEASUREMENT

Kidney function estimation was commonly made using SCr concentration, blood urea nitrogen (BUN) level and urine analysis[5]. However accumulating evidence has demonstrated that these biomarkers are not optimal to detect kidney disease in early stages[6-9]. The KDIGO recommends that CKD be diagnosed, classified, and staged by GFR[10]. In clinical practice GFR is crucial for diagnosis, management, drug dosing and prognosis, in addition to its utility for research and public health[11-13]. GFR is the volume of fluid filtered from the glomerular capillaries into the Bowman’s capsule per unit time[14,15]. GFR values are associated with age, sex and body surface and are 120 and 130 mL/min per 1.73 m2 in young men and women, respectively (GFR declines with age)[16-18].

mGFR

Establishing the true GFR is difficult because the filtration process simultaneously takes place in millions of glomeruli and filtrate composition and volume change when passing through the kidney. GFR is measured (mGFR) indirectly as the clearance of filtration markers that are eliminated by the kidney only by glomerular filtration. Clearance can be measured as either plasma or urinary methods that record the clearance of endogenous or exogenous substances by the kidney[11]. As such, an ideal substance is one that is freely filtered at the glomeruli and neither secreted nor reabsorbed by the renal tubules[15,18]. Inulin is an exogenous filtration marker derived from a fructose polymer and is a physiologically inert substance and is considered an ideal substance for mGFR[19,20]. Although inulin clearance is considered the gold-standard method for mGFR[20,21], the need for continuous infusion, multiple blood samples and urine collection, make it cumbersome and expensive to measure and has led to research of alternative methods with other biomarkers[10,21-24].

Other methods for mGFR have also been validated. Soveri et al[24] reported that kidney excretion of 51Cr-EDTA or iothalamate, and plasma removal of 51Cr-EDTA or iohexol, using inulin clearance as reference, were sufficiently accurate (P30 > 80%) methods to measure GFR[24]. Among these iohexol is the most recent biomarker for mGFR, it is a non-ionic and non radioactive contrast agent, its molecular weight is 821 Da, has a small extra renal clearance and could be measured only as plasma clearance without the need of urine collections[25]. Some of its other advantages are low expense, wide availability, stability in biologic fluids, and rare adverse reactions when given in a small dose (5 mL of 300 mg/mL iodine)[26,27]. In addition, iohexol does not require a continuous IV infusion and can be given as an intravenous bolus injection. It can be measured by several different techniques, the most used is the high-performance liquid chromatography (HPLC). However, HPLC requires a great deal of effort which limits its usefulness in the clinical setting[28]. Capillary electrophoresis (CE) a technique in which electrophoretic separations are performed in capillary tubes and is easier and faster than HPLC[29]. Shihabi et al[30] demonstrated that the iohexol determination by CE correlates well with HPLC.

However all these methods still require the need of continuous infusion or bolus administration of the marker (subcutaneous or intravenous) and like inulin, their complexity limits their application in clinical practice and epidemiological studies, mostly for the length of time that the procedure entails.

Routinely, GFR is usually estimated from prediction equations which are based on endogenous serum markers like creatinine or CysC in addition to demographic variables such as age, sex and race[13,16,31]. Measured GFR is reserved for situations where eGFR may be inaccurate such as patients in non-steady state, or individuals that possess different characteristics compared to those where the estimating equation was created such as old age, loss of muscle mass (malnutrition, amputation, paraplegia) obesity, chronic illness or in situations where precise GFR is important, like kidney definition[12,32-34].

GFR estimation

Given the limitations of creatinine as a marker of kidney function, implementation of prediction equations has been widely used to eGFR from endogenous filtration markers without the need of clearance calculation[32]. As mentioned above, SCr and CysC are the most commonly used endogenous filtration markers for eGFR.

Creatinine: SCr derives from creatine degradation with a weight of 113 Da[35]. It is freely filtered but is not reabsorbed or metabolized however a significant percentage of creatinine in the urine derives from proximal tubular secretion[16,36]. One of the requirements for utilizing estimating equations based on SCr is stable kidney function. In addition, non-GFR determinats, such as variation in production associated to dietary intake, or changes in muscle mass, variation in tubular secretion and extrarenal creatinine excretion (associated with advanced kidney disease) need to be accounted when utilizing creatinine[13,32,37,38].

Another important factor that limits the accuracy of equations is the variability in SCr measurement[39]. In a study that examined frozen samples from 554 participants, where creatinine was measured with different assays, the SCr changed on average 0.23 mg/dL. This difference can result in substantial variations in GFR estimation when the SCr concentration is relatively normal[40]. The recognition that small variations in SCr translates in significant changes in kidney function has prompted to standardize creatinine determinations throughout clinical laboratories. In 2006 a standard method was introduced as a reference and was used in combination with the isotope-dilution mass spectrometry method in order to achieve better consensus among methods[41,42].

CysC: CysC has come to light as another marker of kidney function during the past decade. However, its clinical use worldwide remains limited compared with that of SCr[43]. CysC is a non-glycosylated protein produced by all nucleated cells. CysC is freely filtered, reabsorbed and completely metabolized in tubular cells and therefore is not subjected to tubular secretion[44,45]. Compared to creatinine, CysC has a more stable rate of production with less intra variability; however CysC serum levels are also influenced by non GFR determinants, such as uncontrolled thyroid disease, corticosteroid use, age, sex, ethnicity, smoking and adipose tissue[46-48]. In a recent meta analyses, the reciprocal value of CysC was more closely related to GFR (correlation coefficient 0.82 vs 0.74) and higher area under de curve (0.93 vs 0.84)[49].

In addition, CysC predicts outcomes and the association is stronger than SCr. Shlipak et al[50] reported CysC level to have an important association with mortality across the GFR range, including individuals with GFR between 60 and 90 mL/min per 1.73 m2, grouped as “preclinical kidney disease”[50]. These findings have been reproduced in other studies in older adults where CysC has been shown to be a better predictor of adverse cardiovascular and non cardiovascular outcomes compared to to SCr[51-56]. Potential explanations for these findings may be accounted by the fact that compared to SCr, CysC is not influenced by muscle mass and reflect a better marker of GFR in this population[53]. In addition, these findings have also been reproduced in the general population and CysC estimated GFR has consistently provided a stronger association with outcomes than equations based on SCr eGFR[57].

Estimating equations

Since Effersoe in 1957 developed the first equation to estimate GFR[58], more de 20 equations have been developed. Most of the equations incorporate demographic and clinical variables[39]. The most commonly used equations include Cockroft Gault (CG)[59], 4-modification of diet in renal disease (MDRD)[60,61], 2009 CKDEPI[62] and more recently the equation that combines creatinine and CysC[63]. Since the standardization of creatinine, the CG equation is barely used in clinical practice[39].

CG: The CG formula was created almost thirty years ago in order to estimate creatinine clearance. It was developed in a population of white men and therefore the equation does not take into consideration sex, race and body surface area. Until recently, CG equation was solely utilized for drug dosing however the equation has been recently compared to the widely used equations with similar findings[59,64].

MDRD equation: The MDRD equation was developed in 1999 from a study including 1628 mostly white and non diabetic patients with CKD stages 3 and 4. The original equation included 6-variables and was further abbreviated in year 2000 to a four variable equation that included age, sex, ethnicity, and SCr[60]. In 2006 it was adapted to be used with standardized creatinine[61]. The four variable equation demonstrated to have similar performance compared to the six variable equation[65]. Although the MDRD has demonstrated to have high accuracy for individuals with CKD, the equation underestimates GFR in healthy individuals resulting in false positive diagnosis of CKD in this population[66].

CKD-epidemiology collaboration equation: The CKD epidemiology collaboration (CKD-EPI) was developed in 2009 and resulted from a study that included 8250 participants and was validated in similar cohort of 3900 subjects. Compared to the MDRD coh–ort, the CKD-EPI had higher GFR (68 mL/min per 1.73 m2vs 40 mL/min per 1.73 m2), younger age, included diabetics, blacks and kidney transplant recipients[39,62,67]. Linear regression was employed to estimate the logarithm of measured GFR from standardized SCr concentrations, gender, race, and age. The main objective for the CKD-EPI was to develop an equation that was superior to the MDRD, especially amongst those subjects with GFR > 60 mL/min per 1.73 m2. Indeed, the same variables were used in CKD-EPI and MDRD equations but CKD-EPI performed better in those with GFR > 60 mL/min per 1.73 m2. In subjects with GFR > 60 mL/min per 1.73 m2 the P30% was 88.3% (86.9%-89.7%) and 84.7% (83%-86.3%) for CKD-EPI and MDRD, respectively, while in subjects with GFR < 60 mL/min per 1.73 m2 the P30% for CKD-EPI was 79.9% (78.1%-81.7%) and for MDRD was 77.2% (75.5%-79%). Furthermore the CKD prevalence was estimated using the CKD-EPI and MDRD Study equations among 16032 adults from the NHANES cohort. Median eGFR by CKD-EPI was almost 10 mL/min per 1.73 m2 higher than by MDRD. As a result, the CKD-EPI resulted in a significantly lower estimated CKD prevalence than the MDRD equation in the g (11.6% vs 13.1%, respectively)[62].

CysC and combined CysC and creatinine equations: In order to overcome the imprecision of creatinine estimating equations, Stevens et al[48], developed three eGFR equations for CysC (using CysC alone, CysC with demographic factors, and CysC with SCr and demographic factors) and compared them with mGFR iothalamate and 51-EDTA in 3418 patients. The equation that included CysC with SCr yielded the most accurate GFR estimates (P30 of 89%)[48]. Segarra et al[68] found that CysC-based GFR equations performed better than the CKD-EPI equation in a study of 3114 hospitalized patients because creatinine generation is dependent on the presence of muscle mass and malnourishment[68]. Similarly CysC-based GFR was superior than the CKD-EPI equation in certain subgroups of patients in which SCr level may be insensitive to capture reduced kidney function such as patients with chronic liver disease, frail elders, AIDS and malignancy[69-74].

Inker et al[63] developed a new GFR estimating equation that was based on CysC alone or in combination with creatinine in a cohort of 5000 subjects and was further validated in a cohort of 1119 subjects with measured GFR. The authors developed two new equations involving CysC (2012 CKD-EPI cys, and 2012 CKD-EPI Cys-cr) and compared them to the 2009 CKD-EPI equation. Bias was not different between the three equations however precision and accuracy was improved with the combined CysC-cr equation. Also in subjects whose eGFRcr was of 45-59 mL/min per 1.73 m2, the combined equation reclassified correctly 17% to a no CKD category (GFR > 60 mL/min per 1.73 m2). The authors concluded that the combined equation performed better than equations based on either CysC or SCr and should be used in those subjects where CKD needs to be confirmed[63].

Ongoing studies include the eGFR-C study which is a prospective longitudinal cohort study of 1300 adults with stage 3 CKD that will be followed for 3 years with reference iohexol mGFR. The objective of the study is to evaluate the performance of GFR-estimating equations, including the new equations that incorporate CysC in addition to albuminuria, in order to monitor GFR progression in this populations. Data will be analyzed to assess the impact of race, proteinuria and diabetes on equation performance[75].

Equations, their performance and their implications

When we evaluate the performance of an equation we should take into account bias, precision, and accuracy. Bias has been defined as a median difference between the measured and estimating GFR, precision this is the repeatability or reproducibility of the measurement and accuracy is defined as percentage of eGFR within 30% of measured GFR. Accuracy is probably the best single measure for comparing equations because it incorporates bias and precision. The 2002 KDOQI guidelines concluded that an eGFR within 30% of an mGFR was satisfactory for clinical interpretation, and as a performance metric for accuracy, the guidelines recommended that > 90% of participants in the validation population have eGFR within 30% of the measured GFR (P30 > 90%)[76]. Although accuracy in GFR assessment has significantly improved and bias was decreased with the CKD-EPI equation, precision has not substantially improved. This imprecision is due to random error secondary to variation in non-GFR determinants and GFR measurement error, whilst bias reflects differences between the development and validation populations in measurement methods for GFR, assays for filtration markers, or the relationship of the surrogates to the non-GFR determinants of the filtration marker[13].

In one study conducted by Michels et al[77] that included 271 patients with a mean SCr of 1.2 mg/dL, the CG, MDRD, and CKD-EPI equations were compared with mGFR using the I-iothalamate filtration marker (median mGFR 78.2 mL/min per 1.73 m2), to assess the agreement between equations and examine whether the agreement was influenced by other known variables such as age, weight, body mass index and level of GFR. In general this study concluded that the CKD-EPI equation gives the overall best GFR estimation however the performance was close to MDRD[77].

One of the largest studies where MDRD and CKD-EPI were compared with the aim to assess performance was performed in a population of 12898 individuals from North America, Europe and Australia. The P30 ranged from 59%-95% and was higher for the CKD-EPI than for the MDRD equation in most studies, bias varied according to level of eGFR, was smaller for the CKD-EPI than for the MDRD equation at higher eGFR, but larger at lower eGFR. Table 1 shows the performance comparison of the equations in these populations. Authors from this study concluded that equations did not perform as well in regions outside North America, Europe, and Australia. In Asia and Africa, equations were less accurate (P30 ranged from 29%-94%). Equation performance can be improved by deriving local “race/ethnicity” coefficients; however, the new equations are more accurate in the Caucasian populations. The coefficients also do not seem to be generalizable beyond the local population presumably reflecting differences in SCr generation due to racial, ethnic, and regional variations in muscle mass and diet, and use of non standardized SCr[39].

Table 1 Performance comparison of creatinine-based estimated glomerular filtration rate in North America/Europe/Australia.
Ref.CountryPatients, nmGFReGFRResults
(value mL/min×1.73 m2, SD)(equation)1Bias (95%CI) mL/min×1.73 m22Precision (95%CI)3P30 (95%CI), %
Murata et al[180]United States5238I-Iothalamate, urine (55.9, SD 29.7)MDRD CKD-EPI-4.1 -0.7ND77.6 78.4
Levey et al[62]United States3896I-Iothalamate, urine and others (68, SD 36)MDRD CKD-EPI-5.5 (-5.0 to -5.9) -2.5 (-2.1 to -2.9)0.274 (0.265-0.283)4 0.250 (0.241-0.259)480.6 (79.5-82.0) 84.1 (83.0-85.3)
Lane et al[181]United States425I-Iothalamate, urine (50, IQR 29 to 69)MDRD CKD-EPI-1.0 -1.715.05 13.8575 80
Michels et al[77]The Netherlands271I-Iothalamate, urine (78.2, SD 33)MDRD CKD-EPI14.6 mL/min 12.3 mL/min19.96 12.1681.2 84.5
Tent et al[182]The Netherlands253 before donation, 253 after donationI-Iothalamate, urine (115, SD 20) and (73, SD 13)MDRD CKD-EPI MDRD CKD-EPI-22 mL/min (20-25) -14 mL/min (11-16) -15 mL/min (14-16) -11 mL/min (9-11)20 (14-26)5 18 (14-22)5 12 (9-15)5 12 (10-16)573 (68-79) 89 (85-93) 71 (65-76) 89 (85-93)
Kukla et al[183]United States107 on steroid-free early post tranplantation 81 on steroid-free at 1 yrI-Iothalamate, urine (55.5, SD 17) and (56.8, SD 17.7)MDRD CKD-EPI MDRD CKD-EPI8.23 13.30 2.40 6.9117.94 21.14 15.84 17.3471.7 58.5 75.0 66.7
White et al[184]Canada207Tc-DTPA, plasma (58, SD 22)MDRD CKD-EPI-7.4 -5.214.45 15.7579 (73-84) 84 (78- 88)
Pöge et al[185]Germany170Tc-DTPA, plasma (39.6, IQR 11.8 to 82.9)MDRD CKD-EPI4.49 8.0710.06 10.9671.8 64.1
Jones[186]Australia169Tc-DTPA, plasma (75, IQR 5 to 150)MDRD CKD-EPI-37 -1.57ND81 86
Cirillo et al[187]Italy356Inulina, plasma (71.5, SD 36.3)MDRD CKD-EPI-5.2 -0.914.96 13.2687.4 88.2
Eriksen et al[188]Norway1621Iohexol, plasma (91.7, SD 14.4)MDRD CKD-EPI1.3 (0.4-2.1) 2.9 (2.2-3.5)18.2 (17.2-19.5)5 15.4 (14.5-16.3)593 (91-94) 95 (94-96)
Redal-Baigorri et al[189]Denmark185Cr-EDTA, plasma (85.1, SD 20.3)MDRD CKD-EPI0.81 (IQR, -1.56 to 3.19) 1.16 (IQR, -0.76 to 3.09)16.496 13.37688.6 89.7

Thus far the new equation CKD-EPI Cys-cr has been evaluated in diverse populations. The berlin initiative study (BIS) included 610 older adults with a mean SCr level of 1.0 mg/dL, and mean CysC level of 1.15 mg/L. The study intended to assess the performance of the CKD-EPI Cys-cr equations compared to the mGFR by iohexol. A major finding of this study was that CysC had a stronger association with mGFR than creatinine and the best GFR estimation was derived from a combined Cys-cr equation (named BIS-2)[78]. The combined CKD-EPI Cys-cr equation performed well in Japanese and Chinese individuals[79-81]. One recent study compared the CKD-EPI Cys-cr and other four approved equations in a cohort of 788 adult Chinese patients and a Tc_DPTA mGFR of 76 mL/min per 1.73 m2. Compared to other equations, the CKD-EPI Cys-cr had less bias, (-4.11 mL/min per 1.73 m2) and higher accuracy (P30% of 77.03%)[80]. In a population of almost 700 kidney transplant recipients the performance of the CKD-EPI Cys-cr was superior showing less bias and better accuracy compared with 2009 CKD-EPI, using inulin mGFR as reference[82].

In addition, it is important to mention that the performance of the equations is affected not only by demographic and clinical factors but by the reference method considered as the gold standard to measure GFR in different populations[83-85].

From the epidemiological standpoint, CKD prevalence was assessed in diverse populations comparing the MDRD and CKE-EPI equation[62]. For example, the Atherosclerosis Risk in Communities Study reclassified 43.5% to a higher eGFR category compared with CKD stage 3 for MDRD[86]. The AusDiab (Australian Diabetes, Obesity and Lifestyle) study reclassified 266 participants identified as having CKD with MDRD to no CKD with CKD-EPI, decreasing the prevalence of CKD in adults > 25 year 1.9% in Australia[87]. The kidney early evaluation program included 116321 individuals where 17.5% and 2.7% were reclassified to higher or lower eGFR categories, respectively, when compared with MDRD[88].

Reclassifying subjects to a higher GFR has demonstrated to translate in a lower risk for outcomes. In a recent meta-analysis, the CKD-EPI and MDRD equations were compared with respect to CKD stage and risk prediction in a 1.1 million adults from distinct cohorts followed over seven years. Outcomes included mortality, cardiovascular mortality, and kidney failure. In this study CKD-EPI reclassified to a higher and lower estimated GFR category 24.4% and 0.6% respectively, compared with the MDRD, and when the CKD-EPI equation was used, the prevalence of CKD was reduced by 2.4 percent. Furthermore, in individuals with MDRD eGFR of 45-59 mL/min per 1.73 m2, the CKD-EPI creatinine equation reclassified 34.7% to eGFR of 60-89 mL/min per 1.73 m2 and 1.2% to eGFR of 30-44 mL/min per 1.73 m2. Individuals reclassified to a higher eGFR category had 0.80, 0.73, and 0.49 lower adjusted risks for death, cardiovascular disease, mortality, respectively, than those not reclassified. Overall net reclassification favored the CKD-EPI over the MDRD for the three outcomes[86].

Rule et al[89] evaluated the association of CKD risk factors (urine albumin, lipid profile, uric acid, hypertension, diabetes and smoking) with eGFR based on Cr and/or CysC and compared them with iothalamate mGFR in 1150 subjects with a mean age 65 year and mean mGFR of 80 mL/min per 1.73 m2. Authors concluded that the association between most of the risk factors was stronger for CysC than SCr and CysC was a better predictor for risk stratification and management of CKD than SCr eGFR[89].

These data demonstrates that the CKD-EPI equation is superior for GFR estimation leading to fewer false-positive diagnoses of CKD. In addition the CKD-EPI equation translates in a decreased prevalence of CKD and is associated with a more precise risk prediction for outcomes and prognosis. The KDIGO 2012 Clinical Practice Guideline for the Evaluation and Management of Chronic Kidney Disease, based on this evidence recommends that CKD be diagnosed, classified, and staged by eGFR and suggests CKD-EPI should be utilized as the preferred equation[1].

Other endogenous biomarkers for kidney function

Blood urea nitrogen: BUN increases as GFR declines however is less valuable than the SCr since the BUN can vary independently of the GFR. The production rate of urea is not stable and increases with rich protein diets or tissue breakdown such as bleeding, muscle trauma or steroid administration. On the other hand a very low protein diet or liver failure can decrease BUN without affecting GFR[32,90].

B2-microglobulin: B2-microglobulin (B2-M) is a small molecule of 11.8 kDa and constitutes a class I HLA, is present in all nucleated cells in the body, and has a large quantity of immune cells like lymphocytes and monocytes. It has the characteristic that it is freely filtered in the glomeruli and is reabsorbed and metabolized in the proximal tubule[91]. Levels of B2-M are elevated in kidney disease, in addition to other conditions such as malignancies, autoimmune diseases, infections and aging[92]. There is data to demonstrate that plasma B2-M is a good endogenous marker of GFR and that in the context of GFR decline the increase of serum B2-M occurs prior than SCr. B2-M has been associated with death in a cohort of 1034 elderly subjects and appeared to be superior than CysC, even after adjustment for known risk factors[93,94]. Lack of further studies in the last decade however has limited the utility of this biomarker in clinical practice.

KIDNEY DAMAGE

The kidney damage refers to pathologic abnormalities documented by biopsy or imaging, alterations in urinary sediment or proteinuria (proteinuria/creatinuria > 200 mg/g, albuminuria/creatinuria > 30 mg/g). Damage usually precedes alterations in functions. For instance it is known that albuminuria precedes the decrease in eGFR, hence the importance to count with markers of renal damage in stages that are blind for current markers of renal function decline. In theory this could facilitate early diagnosis, guide interventions and monitor disease progression.

Albuminuria

Albumin excretion rate (AER) can be determined in 24 h urine collections or in spot collections. Increases should be confirmed in at least two of three samples, within a period from 3 to 6 mo[11]. Microalbuminuria, or incipient nephropathy, is defined as an AER of 20-200 μg/min in timed samples, or 30-300 mg/24 h in 24 h samples, however spot collections are accurate enough that they can replace 24 h collections and these are now strongly recommended by the most recent guidelines[1,95].

The corresponding values that define microalbuminuria in a urine sample are AER > 30 mg/24 h or an albumin-creatinine ratio (ACR) of 30-300 mg/g (0.3-3 mg/mmol). Higher values indicate macroalbuminuria, also called clinical nephropathy[1]. Taking these values into account the prevalence of microalbuminuria in 4101 individuals of NHANES (1999-2000) with ACR 30-300 mg/g and ACR > 300 mg/g was 7.3% and 1.7% in men and 10.4% and 0.9% in females, respectively[96].

The threshold of ACR > 30 mg/g to define kidney damage has been validated as a risk factor for adverse events in different populations. In high risk patients for CKD, the ACR > 30 mg/g is has demonstrated to be a risk factor cardiovascular (CV) death and all cause mortality, progression of kidney disease, acute kidney injury (AKI) and kidney failure[97,98]. Likewise, these findings have been reproduced in low risk cohorts. In more than 1 million participants from 21 cohorts, ACR > 30 mg/g and ACR > 300 mg/g were associated with higher risk for death (HR of 1.6 and 2, respectively). Moreover the risk for CV mortality was two-fold higher with ACR > 30 mg/g compared to those with ACR of 5 mg/g and this risk persisted after adjustment for GFR and other known risk factors. This risk also applies to ACR levels < 30 mg/g. In study of Waheed et al[99], ACR of 10 mg/g compared to 5 mg/g was associated with all cause mortality. This however may not necessarily reflect kidney damage and may be a marker of endothelial dysfunction.

On the basis of the linear association of albuminuria with progression of CKD, end stage renal disease (ESRD), and all cause of mortality independent of eGRF, albuminuria staging has been added in the 2012 KDIGO guidelines.

Combination of biomarkers

Combining albuminuria with eGFR improves the prediction of CKD progression. This was demonstrated in the Nord-Trondelag Health (HUNT-2) study that included 65589 participants, where albuminuria and eGFR independently predicted kidney disease progression and the combination of both markers was superior to predict those subjects at highest risk for ESRD development[100]. In a large prospective cohort involving more than 26000 subjects, the authors evaluated whether combining eGFR creatinine, CysC, and urine ACR could improve risk prediction when compared with eGFR alone. In this cohort the adjusted mortality risk was six fold higher in patients with CKD identified by all three markers and was also three fold higher in patients with CKD defined by both eGFR Cys-cr, compared to those with CKD defined by eGFR creatinine alone. The risk for CKD progression to kidney failure was higher among patients with CKD defined by all three markers. The authors concluded that adding CysC to SCr and ACR was superior for prediction for kidney disease progression and death[101].

New biomarkers for kidney damage

Although albuminuria is a powerful biomarker, it may occur after the damage has occurred or may not be present in other types of kidney damage such as tubulointerstitial disease and hypertensive kidney disease. This has led to the search for new biomarkers that are also non-invasive and could better correlate with the etiology of the kidney disease. Moreover; early identification of patients with CKD could allow implementing early interventions to reduce CVD or CKD progression. In the next few paragraphs we describe the most promising biomarkers in CKD (Table 2) and its utility (Table 3).

Table 2 Novel biomarkers in chronic kidney disease.
Biomarker sourceRef.Population/type of studyCommentaries
u-LFABP UrinaryNielsen et al[190]227 newly diagnosed type 1 diabetic patients/longitudinalBaseline u-LFABP levels predicted development of microalbuminuria (HR = 2.3, 95%CI: 1.1-4.6), and predicted mortality (HR = 3.0, 95%CI: 1.3-7.0)
NAG UrinaryKern et al[191]87 type 1 diabetics with microalbuminuria and 174 controls/longitudinalBaseline NAG independently predicted microalbuminuria (OR = 1.86, P < 0.001) and macroalbuminuria (OR = 2.26, P < 0.001) but risk was attenuated in multivariate models
CTGF UrinaryNguyen et al[192]318 type 1 diabetic patients and 29 control subjects/cross sectionalU-CGTF was significantly higher in diabetic nephropathy than micro o normoalbuminuria. U-CGTF correlated with albuminuria and GFR
IL-18 Kidney tissueMiyauchi et al[193]12 type 2 diabetes with overt nephropathy and 7 patients with MCD/cross sectionalIL-18 expression in tubular cells was observed highly observed (83%) in patients with diabetes but only observed in 14.3% of MCD
ApoA-IV PlasmaBoes et al[194]177 non-diabetic patients with mild to modetare renal CKD/longitudinalBaseline ApoA-IV was a significant predictor of disease progression (HR = 1.062, 95%CI: 1.018-1.108) and patients with level above the median had significantly faster progression compared with patients with level below median (P < 0.0001)
CD14 mononuclear cells UrinaryZhou et al[195]16 patients with autosomal dominat polycystic kidney disease/longitudinalBaseline urinary CD14 mononuclear cells correlated with 2 yr change in total kidney volume in males
NGALBolignano et al[121]33 patients with glomerulonephritis and proteinuria > 1 g per day/cross sectionalu-NGAL was higher in glomerulonephritis compared with controls and significantly correlated with serum creatinine and urinary protein excretion
UrinarySmith et al[124]158 patients with CKD stages 3 and 4/longitudinalu-NCR was associated with a higher risk of death and initiation of renal replacement therapy
UrinaryBolignano et al[125]96 white patients with CKD/longitudinalBaseline urinary and serum NGAL were predictors of CKD progression
Urinary/serumShen et al[119]92 patients with chronic glomerulonephritis CKD stage 2-4, and 20 control subjects/longitudinals-NGAL levels were higher compared to controls and negatively correlated with the eGFR Patients with sNGAL level > 246 ng/mL had a poor 2 yr renal survival compared with the control group
SerumBhavsar et al[123]286 participants from the ARIC and 143 matched controls/longitudinalHigher quiartiles of NGAL (but no KIM-1) were associated with incident CKD
KIM-1 SerumKrolewski et al[111]107 diabetic type 1 with CKD 1-3 (AER > 500 mg/24 h)/longitudinalBaseline plasma KIM-1 levels correlated with rate of eGFR decline KIM-1 levels (> 97 pg/mL) correlated with progression to ESRD
UrinaryPeters et al[109]65 patients with Proteinuric IgAN and 65 control subjects/longitudinalIn patients with IgAN uKIM-1 excretion was significantly higher than controls uKIM-1 is independently predictor of ESRD
FGF-23Nakano et al[134]738 Japanese patients with CKD stages 1-5/longitudinalLevels of FGF-23 associated with kidney function decline or initiation renal replacement therapy
SerumFliser et al[137]227 non diabetic patients with CKD stages 1-4/longitudinalFGF-23 was an independent predictor of CKD progression
Lee et al[138]380 patients with type 2 diabetes/longitudinalLevels of FGF-23 was associated with increased risk of ESRD and was a significant risk factor for all cause mortality
Table 3 Utility of new biomarkers in chronic kidney disease.
BiomarkerOriginOutcome assessed
Urinary liver-type fatty acid-binding proteinProximal tubuleDiabetic Nephropathy: Microalbuminuria and mortality
Urinary N-Acetyl-b-O-glucosaminidaseProximal tubuleDiabetic Nephropathy: Albuminuria
Urinary connective tissue growth factorProximal tubuleDiabetic Nephropathy: Glomerular filtration rate decline
Interleukin-18TubulointerstitialDiabetic Nephropathy: Albuminuria
Apolipoprotein A-IVIntestinal enterocytesCKD: CKD Progression
Urinary CD14 mononuclear cellsPolycystic kidney disease: Kidney volume
Neutrophil gelatinase associated lipocalinProximal and distal tubuleGlomerulonephritis: GFR and proteinuria CKD: CKD progression, renal replacement therapy and mortality
Kidney injury molecule-1Proximal tubuleCKD: CKD progression and renal replacement therapy
Fibroblast growth factor-23Osteocytes and osteoblastsDiabetic Nephropathy and others CKD: CKD progression and mortality
Urinary retinol binding protein 4Proximal tubuleCongenital or acquired tubular dysfunction: Proximal tubule dysfunction
Kidney injury molecule

Kidney injury molecule (KIM-1) is a transmembrane protein is a type 1 transmembrane protein whose expression has been upregulated after kidne injury[102,103]. KIM-1 is an early biomarker for proximal tubular damage since it is expressed in the urine during the first 12 h of the tubular injury[104]. Experimental and clinical studies have demonstrated high KIM-1 expression in areas of fibrosis and inflammation. In murine models with polycystic kidney disease, KIM 1 is highly expressed in renal tubules, it associates with interstitial fibrosis in human allografts and in type 1 diabetes mellitus regression of microalbuminuria has been associated with lower urinary levels of KIM-1[105-108].

Persistent expression of KIM-1 has been associated to inflammation characterized by high monocyte chemoattractant protein-1 (MCP-1) levels[109]. In contrast, in experimental models, mice with mutant KIM-1 are protected from fibrosis and had lower inflammatory markers[110]. In a retrospective analysis of 107 diabetic type 1 with CKD stages 1-3 (AER > 500 mg/24 h) followed for 5-15 years, 63% of those subjects with higher KIM-1 levels (> 97 pg/mL) progressed to ESRD whereas only 20% of patients with lower levels progressed. In addition baseline plasma KIM-1 levels correlated with rate of eGFR decline after adjustment for baseline urinary albumin-to-creatinine ratio, eGFR, and Hb1Ac[111]. KIM-1 may represent a promising marker for the future. Larger studies however are still warranted before KIM-1 could be applied routinely in clinical practice.

Neutrophil gelatinase-associated lipocalin: Neutrophil gelatinase-associated lipocalin (NGAL) is a lipocalin iron-carrying protein of 25 kDa and is part of the well-defined super family of proteins called lipocalins, is expressed by tubular renal epithelial cells following tubulointerstitial injury[112-114]. NGAL has been an established marker for acute kidney injury however its role in CKD is less studied[115-119]. In patients with IgA nephropathy urinary NGAL level was higher compared to controls and was also associated with disease severity[120]. In patients with glomerular proteinuria above 1 g/24 h and in patients with polycystic kidney disease, NGAL levels were higher compared to controls and significantly correlated to SCr[121,122]. NGAL has also been associated to incident CKD progression in adults. In a community based population of 286 subjects, NGAL was evaluated as an independent risk factor for incident CKD. Those in the highest quartile of NGAL had a higher risk for incident CKD, effect that was attenuated after adjustment for creatinuria and albuminuria[123]. In a cohort of 158 adults with stage 3 or 4 CKD, urinary NGAL to creatinine ratio was associated with mortality and renal replacement therapy and this risk was independent of kidney and CV risk factors[124]. Similar results were found in a cohort of 96 CKD patients followed for 18.5 mo where plasma and urinary NGAL predicted CKD progression after adjustment for eGFR[125].

Thus far there is evidence to support that NGAL levels either in plasma or urine can predict kidney disease progression independent of GFR, however the data is limited by the number of participants and larger studies are needed before establishing this biomarker in clinical practice.

Fibroblast growth factor 23: Fibroblast growth factor 23 (FGF-23) is 32-kDa phosphaturic protein secreted by bone osteocytes. Among its functions is to promote phosphate excretion, decrease calcitriol production and suppress parathyroid hormone[126-128]. In CKD the increase of FGF-23 level precedes the decline in vitamin 1,25-(OH)2 vitamin D3 and the increase of PTH level. Although FGF-23 is higher in patients with moderate to severe CKD, there is data to support that the rise of FGF-23 occurs earlier in the disease. In the past decade several studies have found an association between high FGF-23 levels, kidney disease progression and mortality in subjects with CKD[129-132]. In a cohort of 227 non diabetic patients with CKD followed for more than 4 years, FGF-23 was an independent risk factor for kidney disease progression. Likewise Semba et al[133] in 701 healthy women (mean eGFR 60 mL/min × 1.73 m2), and Nakano et al[134] in 738 Japanese patients with CKD stages 1-5 (mean eGFR 35 mL/min × 1.73 m2) reported that increasing levels of FGF-23 associated with decline in kidney function or initiation renal replacement therapy after a follow-up of 2 and 4.4 years, respectively. In addition, in patients undergoing renal replacement therapy, elevated FGF-23 levels have been associated with CV outcomes such as left ventricular hypertrophy and increased risk of mortality[133-138]. It is important to mention that this association has been independent of phosphate levels and CKD stage.

Asymmetric dimethylarginine: Asymmetric dimethylarginine (ADMA) is an aminoacid of 202 Da, it is normally synthesized intracellularly and eliminated through the urine. One of its adverse effects is the inhibition of the nitric oxide synthases and this mechanism has been associated to adverse cardiovascular side effects[139,140]. As kidney function deteriorates ADMA levels increase and this has been associated to kidney parenchymal damage through the decrease in dimethylarginine-dimethylamino-hydrolase[141,142]. ADMA has been associated to CKD progression. In the diabetic and non diabetic population, ADMA levels are higher as GFR declines and are associated with rapid kidney function decline[143,144]. In a recent study of 164 CKD patients followed for one year, elevated ADMA and markers of oxidative stress were strong predictors of progression in patients with CKD stages 3-4[145]. Moreover, ADMA has been associated to death and CV events in the CKD population[146,147]. Some authors had considered ADMA to be the “missing link” between cardiovascular disease and CKD[139]. Whether counteracting the effects of ADMA in CKD should be explored as a strategy to prevent cardiorenal complications would need to be confirmed in larger studies.

MCP-1: MCP-1 belongs to the group of inflammatory chemokines[148,149]. Expression of MCP-1 is up regulated in kidney diseases that have a sustained inflammatory response, such as in diabetic nephropathy and lupus nephritis[150,151]. Studies have demonstrated glomerular and tubular kidney cells release MCP-1 in response to high glucose levels and urine levels of MCP-1 are increased in diabetic nephropathy[152,153]. Likewise MCP-1 levels in urine are over expressed in active lupus nephritis[151-154]. Emerging evidence suggest that MCP-1 has a significant role in the pathogenesis of many kidney diseases and urinary MCP-1 is a promising biomarker with diagnostic and prognostic implications[155-157].

Urine retinol-binding protein 4: Urine retinol-binding protein 4 (uRBP4) is a 21 KDa protein derived of plasma RBP4 (pRBP4), is an integrant of the lipocalin family and is produced mainly in the liver but also in the adipose tissue where it performs as an adipokine that has been linked to insulin resistance and obesity[158,159]. Unlike other biomarkers such as NGAL and KIM-1, uRBP4 is currently the most sensitive functional biomarker of proximal tubule. pRBP4 is filtered at the glomerulus and completely reabsorbed in the proximal tubule. In addition, it is known that variation levels of pRBP4 (secondary to nutrition, vitamin A levels, liver disease and infection) have small effect on uRBP 4 as a biomarker[160]. Sensitivity for uRBP4 however decreases as kidney function declines due to false positives that occur in the presence of glomerular disease[161]. This marker was been useful in several diseases related with proximal tubule dysfunction, either hereditary, such as Fanconi syndrome, dent type 1 syndrome and lowe syndrome[162], or acquired conditions that directly affect proximal tubule such as drug toxicity in human immunodeficiency virus, cadmium toxicity, plasma cell dyscrasias, AKI diagnosis and other renal tubulointerstitial diseases[163]. Amer et al[164] assessed the prognostic value in renal transplantation of a panel of urinary proteins in 221 patients at 1 year post transplant and reported that patients with glomerular lesions had higher albuminuria than patients with normal histology, and in patients with tubulointerstitial disease, uRBP4 has over expressed. In addition, uRBP4 was a risk factor for long term allograft loss and this risk was independent of kidney biopsy histology and albuminuria[164].

Future directions

Advances in technology during the last decade have enlightened our knowledge regarding genetic regulatory pathways. A fast growing arena are the microRNAs (miRNAs), the current number of miRNAs in humans are estimated to be between 700 and 1000, and they have been implicated in several physiological events as well pathologic process, including kidney disease[165]. miRNA have selective expression by different organs, and the kidney expresses mostly miRNA 192, 194, 204, 215 and 216 which have been implicated in proliferation, migration and structure of renal cells[166,167]. Little changes in these molecules have implications in kidney function, for instance it is know that deletion of the miRNA 30 family decreases renal cells, affects blood pressure and develop vascular damage and extensive fibrosis[168]. Other miRNAs are related with diverse pathophysiologic process, miRNA 155 is associated to blood pressure control through down regulation of type 1 angiotensin II receptor[169,170], miRNA 192 and 200 families are related to fibrotic damage in diabetic nephropathy manly by regulation of transforming growth factor beta[171], miRNA 15, 17 and 31 are associated with cystogenesis in polycystic kidney disease[172], and finally miRNA 142, 155 and 223 are increased in acute rejection related to activation of epithelial cells and blood mononuclear cells[173], and can discriminate between acute humoral rejection and cellular rejection[174]. MiRNA expression pathways have also been evaluated as diagnostic biomarkers in other pathologies. In a study of lupus nephritis patients miRNA 27 and 192 in urine could identified in renal biopsies of lupus patients with nephritis[175]. The knowledge of miRNA in health and disease remains with several questions concerning its regulation, production and specific target. In addition most studies have measured miRNA in tissue and therefore become cumbersome to measure in clinical practice. Studies evaluating its utility in plasma and urine are urgently needed. Nonetheless this is a rapidly growing field and future research may provide a better understanding of the pathophysiology in kidney disease and may reveal potential diagnosis and therapeutic options.

Not only in the area of proteomics (NGAL, KIM-1, etc.) and transcriptomics (miRNAs) have the kidney markers evolved, the latest piece added to the puzzle corresponds to metabolomics, and as it name points out, is the measure of end products of basic metabolic molecules. These end products could improve the utility of other type of biomarkers[176]. Currently, metabolomics in kidney disease have mainly been studied in uremia, renal cell carcinoma, glomerulonephritis, diabetes mellitus, polycystic kidney disease and drug related nephrotoxicity. For instance in patients with drug related nephrotoxicity, end products from amino acids and simple sugars increase in urine before tissular changes become apparent. The latter has been described with antibiotics[177], and immunosupression therapy, for example, the increase of metabolomic end products during the first month after cyclosporine predicts kidney damage[178]. Similarly metabolomics has been associated to several metabolic profiles (mainly amino acids, derivatives of sugar and phospholipids) that could be useful in the diagnosis and prognosis of different types of renal disease as diabetic nephropathy, IgA nephropathy and other glomerulonephritis, in addition to diagnosis, metabolomics offers a promising future in the area of pharmaco-metabolomics, which could lead to personalized therapeutic targets[179]. At this point metabolomics main limitation is related to problems with specificity and technical variability and is not ready to be implemented in clinical practice.

CONCLUSION

During the last century, SCr has been the most used biomarker to screen and diagnose kidney disease. SCr however has several limitations and should be utilized only in estimating equations. The CKD-EPI is more generalizable and performs better than the MDRD estimating equation, especially in the healthy population. More recently the GFR estimating equation that combines SCr and CysC has demonstrated to be superior than equations that use either SCr or CysC alone, and is recommended in specific conditions, such as when confirmation of CKD is required. Albuminuria remains one of the strongest risk factors for outcomes and the combination of SCr, CysC and urinary albumin to creatinine ratio improves risk stratification predicts CKD progression and mortality.

In the last decade several other promising biomarkers have emerged. However, although these biomarkers are highly sensitive and specific and have allowed an earlier diagnosis of kidney disease with promising results; none of them have been validated to make clinical decisions upon their positivity. These biomarkers should have the potential to indicate injury type or the specific site of harm. It is improbable however that one biomarker would be sufficient to guide intervention upon their result. Larger and long term studies are warranted before applying these biomarkers in clinical practice. The CKD Biomarkers Consortium has 15 ongoing studies with the aim to develop and validate novel biomarkers for CKD. In the meantime current biomarkers in CKD should be cautiously implemented acknowledging its strengths and limitations.

Footnotes

P- Reviewer: Camara NOS, Scherer A S- Editor: Ji FF L- Editor: A E- Editor: Liu SQ

References
1.  Lamb EJ, Levey AS, Stevens PE. The Kidney Disease Improving Global Outcomes (KDIGO) guideline update for chronic kidney disease: evolution not revolution. Clin Chem. 2013;59:462-465.  [PubMed]  [DOI]
2.  Levey AS, Coresh J. Chronic kidney disease. Lancet. 2012;379:165-180.  [PubMed]  [DOI]
3.  Pereira BJ. Optimization of pre-ESRD care: the key to improved dialysis outcomes. Kidney Int. 2000;57:351-365.  [PubMed]  [DOI]
4.  Obrador GT, Pereira BJ, Kausz AT. Chronic kidney disease in the United States: an underrecognized problem. Semin Nephrol. 2002;22:441-448.  [PubMed]  [DOI]
5.  Rehberg PB. Studies on Kidney Function: The Rate of Filtration and Reabsorption in the Human Kidney. Biochem J. 1926;20:447-460.  [PubMed]  [DOI]
6.  Bosch JP, Saccaggi A, Lauer A, Ronco C, Belledonne M, Glabman S. Renal functional reserve in humans. Effect of protein intake on glomerular filtration rate. Am J Med. 1983;75:943-950.  [PubMed]  [DOI]
7.  Waikar SS, Betensky RA, Bonventre JV. Creatinine as the gold standard for kidney injury biomarker studies? Nephrol Dial Transplant. 2009;24:3263-3265.  [PubMed]  [DOI]
8.  Bjornsson TD. Use of serum creatinine concentrations to determine renal function. Clin Pharmacokinet. 1979;4:200-222.  [PubMed]  [DOI]
9.  Bellomo R, Kellum JA, Ronco C. Defining acute renal failure: physiological principles. Intensive Care Med. 2004;30:33-37.  [PubMed]  [DOI]
10.  Gaspari F, Perico N, Remuzzi G. Application of newer clearance techniques for the determination of glomerular filtration rate. Curr Opin Nephrol Hypertens. 1998;7:675-680.  [PubMed]  [DOI]
11.  Soares AA, Eyff TF, Campani RB, Ritter L, Camargo JL, Silveiro SP. Glomerular filtration rate measurement and prediction equations. Clin Chem Lab Med. 2009;47:1023-1032.  [PubMed]  [DOI]
12.  Ruggenenti P, Gaspari F, Cannata A, Carrara F, Cella C, Ferrari S, Stucchi N, Prandini S, Ene-Iordache B, Diadei O. Measuring and estimating GFR and treatment effect in ADPKD patients: results and implications of a longitudinal cohort study. PLoS One. 2012;7:e32533.  [PubMed]  [DOI]
13.  Levey AS, Inker LA, Coresh J. GFR estimation: from physiology to public health. Am J Kidney Dis. 2014;63:820-834.  [PubMed]  [DOI]
14.  Mccance RA, Robinson JR. Evaluation of renal clearances. Proc R Soc Med. 1949;42:475-480.  [PubMed]  [DOI]
15.  Smith HW, Goldring W, Chasis H. The measurement of the tubular excretory mass, effective blood flow and filtration rate in the normal human kidney. J Clin Invest. 1938;17:263-278.  [PubMed]  [DOI]
16.  Stevens LA, Coresh J, Greene T, Levey AS. Assessing kidney function--measured and estimated glomerular filtration rate. N Engl J Med. 2006;354:2473-2483.  [PubMed]  [DOI]
17.  Ephrati P. [Kidney structure and function]. Harefuah. 1961;61:314-315.  [PubMed]  [DOI]
18.  Delanaye P, Schaeffner E, Ebert N, Cavalier E, Mariat C, Krzesinski JM, Moranne O. Normal reference values for glomerular filtration rate: what do we really know? Nephrol Dial Transplant. 2012;27:2664-2672.  [PubMed]  [DOI]
19.  Mandel EE, Jones FL, Willis MJ, Cargill WH. Renal excretion of creatinine and inulin in man. J Lab Clin Med. 1953;42:621-637.  [PubMed]  [DOI]
20.  Smith HW The reliability of inulin as a measure of glomerular filtration, in The Kidney: Structure and Function in Health and Disease (chap 9). Edited by Smith HW. New York: Oxford University Press 1951; 231-238.  [PubMed]  [DOI]
21.  Rahn KH, Heidenreich S, Brückner D. How to assess glomerular function and damage in humans. J Hypertens. 1999;17:309-317.  [PubMed]  [DOI]
22.  Brown SC, O’Reilly PH. Iohexol clearance for the determination of glomerular filtration rate in clinical practice: evidence for a new gold standard. J Urol. 1991;146:675-679.  [PubMed]  [DOI]
23.  Perrone RD, Steinman TI, Beck GJ, Skibinski CI, Royal HD, Lawlor M, Hunsicker LG. Utility of radioisotopic filtration markers in chronic renal insufficiency: simultaneous comparison of 125I-iothalamate, 169Yb-DTPA, 99mTc-DTPA, and inulin. The Modification of Diet in Renal Disease Study. Am J Kidney Dis. 1990;16:224-235.  [PubMed]  [DOI]
24.  Soveri I, Berg UB, Björk J, Elinder CG, Grubb A, Mejare I, Sterner G, Bäck SE. Measuring GFR: a systematic review. Am J Kidney Dis. 2014;64:411-424.  [PubMed]  [DOI]
25.  Aakhus T, Sommerfelt SC, Stormorken H, Dahlström K. Tolerance and excretion of iohexol after intravenous injection in healthy volunteers. Preliminary report. Acta Radiol Suppl. 1980;362:131-134.  [PubMed]  [DOI]
26.  Edelson J, Shaw D, Palace G. Pharmacokinetics of iohexol, a new nonionic radiocontrast agent, in humans. J Pharm Sci. 1984;73:993-995.  [PubMed]  [DOI]
27.  Nossen JO, Jakobsen JA, Kjaersgaard P, Andrew E, Jacobsen PB, Berg KJ. Elimination of the non-ionic X-ray contrast media iodixanol and iohexol in patients with severely impaired renal function. Scand J Clin Lab Invest. 1995;55:341-350.  [PubMed]  [DOI]
28.  Cavalier E, Rozet E, Dubois N, Charlier C, Hubert P, Chapelle JP, Krzesinski JM, Delanaye P. Performance of iohexol determination in serum and urine by HPLC: validation, risk and uncertainty assessment. Clin Chim Acta. 2008;396:80-85.  [PubMed]  [DOI]
29.  Rocco MV, Buckalew VM, Moore LC, Shihabi ZK. Capillary electrophoresis for the determination of glomerular filtration rate using nonradioactive iohexol. Am J Kidney Dis. 1996;28:173-177.  [PubMed]  [DOI]
30.  Shihabi ZK, Hinsdale ME. Serum iohexol analysis by micellar electrokinetic capillary chromatography. Electrophoresis. 2006;27:2458-2463.  [PubMed]  [DOI]
31.  Traynor J, Mactier R, Geddes CC, Fox JG. How to measure renal function in clinical practice. BMJ. 2006;333:733-737.  [PubMed]  [DOI]
32.  Levey AS. Measurement of renal function in chronic renal disease. Kidney Int. 1990;38:167-184.  [PubMed]  [DOI]
33.  Stevens LA, Levey AS. Measured GFR as a confirmatory test for estimated GFR. J Am Soc Nephrol. 2009;20:2305-2313.  [PubMed]  [DOI]
34.  Prigent A. Monitoring renal function and limitations of renal function tests. Semin Nucl Med. 2008;38:32-46.  [PubMed]  [DOI]
35.  Colls PC. Notes on Creatinine. J Physiol. 1896;20:107-111.  [PubMed]  [DOI]
36.  Shemesh O, Golbetz H, Kriss JP, Myers BD. Limitations of creatinine as a filtration marker in glomerulopathic patients. Kidney Int. 1985;28:830-838.  [PubMed]  [DOI]
37.  Branten AJ, Vervoort G, Wetzels JF. Serum creatinine is a poor marker of GFR in nephrotic syndrome. Nephrol Dial Transplant. 2005;20:707-711.  [PubMed]  [DOI]
38.  Preiss DJ, Godber IM, Lamb EJ, Dalton RN, Gunn IR. The influence of a cooked-meat meal on estimated glomerular filtration rate. Ann Clin Biochem. 2007;44:35-42.  [PubMed]  [DOI]
39.  Earley A, Miskulin D, Lamb EJ, Levey AS, Uhlig K. Estimating equations for glomerular filtration rate in the era of creatinine standardization: a systematic review. Ann Intern Med. 2012;156:785-95, W-270, W-271, W-272, W-273, W-274, W-275, W-276, W-277, W-278.  [PubMed]  [DOI]
40.  Coresh J, Astor BC, McQuillan G, Kusek J, Greene T, Van Lente F, Levey AS. Calibration and random variation of the serum creatinine assay as critical elements of using equations to estimate glomerular filtration rate. Am J Kidney Dis. 2002;39:920-929.  [PubMed]  [DOI]
41.  Myers GL, Miller WG, Coresh J, Fleming J, Greenberg N, Greene T, Hostetter T, Levey AS, Panteghini M, Welch M. Recommendations for improving serum creatinine measurement: a report from the Laboratory Working Group of the National Kidney Disease Education Program. Clin Chem. 2006;52:5-18.  [PubMed]  [DOI]
42.  Panteghini M, Myers GL, Miller WG, Greenberg N. The importance of metrological traceability on the validity of creatinine measurement as an index of renal function. Clin Chem Lab Med. 2006;44:1287-1292.  [PubMed]  [DOI]
43.  Shlipak MG, Mattes MD, Peralta CA. Update on cystatin C: incorporation into clinical practice. Am J Kidney Dis. 2013;62:595-603.  [PubMed]  [DOI]
44.  Grubb AO. Cystatin C--properties and use as diagnostic marker. Adv Clin Chem. 2000;35:63-99.  [PubMed]  [DOI]
45.  Kyhse-Andersen J, Schmidt C, Nordin G, Andersson B, Nilsson-Ehle P, Lindström V, Grubb A. Serum cystatin C, determined by a rapid, automated particle-enhanced turbidimetric method, is a better marker than serum creatinine for glomerular filtration rate. Clin Chem. 1994;40:1921-1926.  [PubMed]  [DOI]
46.  Knight EL, Verhave JC, Spiegelman D, Hillege HL, de Zeeuw D, Curhan GC, de Jong PE. Factors influencing serum cystatin C levels other than renal function and the impact on renal function measurement. Kidney Int. 2004;65:1416-1421.  [PubMed]  [DOI]
47.  Köttgen A, Selvin E, Stevens LA, Levey AS, Van Lente F, Coresh J. Serum cystatin C in the United States: the Third National Health and Nutrition Examination Survey (NHANES III). Am J Kidney Dis. 2008;51:385-394.  [PubMed]  [DOI]
48.  Stevens LA, Coresh J, Schmid CH, Feldman HI, Froissart M, Kusek J, Rossert J, Van Lente F, Bruce RD, Zhang YL. Estimating GFR using serum cystatin C alone and in combination with serum creatinine: a pooled analysis of 3,418 individuals with CKD. Am J Kidney Dis. 2008;51:395-406.  [PubMed]  [DOI]
49.  Dharnidharka VR, Kwon C, Stevens G. Serum cystatin C is superior to serum creatinine as a marker of kidney function: a meta-analysis. Am J Kidney Dis. 2002;40:221-226.  [PubMed]  [DOI]
50.  Shlipak MG, Katz R, Sarnak MJ, Fried LF, Newman AB, Stehman-Breen C, Seliger SL, Kestenbaum B, Psaty B, Tracy RP. Cystatin C and prognosis for cardiovascular and kidney outcomes in elderly persons without chronic kidney disease. Ann Intern Med. 2006;145:237-246.  [PubMed]  [DOI]
51.  Sarnak MJ, Katz R, Stehman-Breen CO, Fried LF, Jenny NS, Psaty BM, Newman AB, Siscovick D, Shlipak MG. Cystatin C concentration as a risk factor for heart failure in older adults. Ann Intern Med. 2005;142:497-505.  [PubMed]  [DOI]
52.  Shlipak MG, Sarnak MJ, Katz R, Fried LF, Seliger SL, Newman AB, Siscovick DS, Stehman-Breen C. Cystatin C and the risk of death and cardiovascular events among elderly persons. N Engl J Med. 2005;352:2049-2060.  [PubMed]  [DOI]
53.  Madero M, Sarnak MJ. Association of cystatin C with adverse outcomes. Curr Opin Nephrol Hypertens. 2009;18:258-263.  [PubMed]  [DOI]
54.  Madero M, Wassel CL, Peralta CA, Najjar SS, Sutton-Tyrrell K, Fried L, Canada R, Newman A, Shlipak MG, Sarnak MJ. Cystatin C associates with arterial stiffness in older adults. J Am Soc Nephrol. 2009;20:1086-1093.  [PubMed]  [DOI]
55.  Deo R, Sotoodehnia N, Katz R, Sarnak MJ, Fried LF, Chonchol M, Kestenbaum B, Psaty BM, Siscovick DS, Shlipak MG. Cystatin C and sudden cardiac death risk in the elderly. Circ Cardiovasc Qual Outcomes. 2010;3:159-164.  [PubMed]  [DOI]
56.  Fried LF, Katz R, Sarnak MJ, Shlipak MG, Chaves PH, Jenny NS, Stehman-Breen C, Gillen D, Bleyer AJ, Hirsch C. Kidney function as a predictor of noncardiovascular mortality. J Am Soc Nephrol. 2005;16:3728-3735.  [PubMed]  [DOI]
57.  Shlipak MG, Matsushita K, Ärnlöv J, Inker LA, Katz R, Polkinghorne KR, Rothenbacher D, Sarnak MJ, Astor BC, Coresh J. Cystatin C versus creatinine in determining risk based on kidney function. N Engl J Med. 2013;369:932-943.  [PubMed]  [DOI]
58.  Effersoe P. Relationship between endogenous 24-hour creatinine clearance and serum creatinine concentration in patients with chronic renal disease. Acta Med Scand. 1957;156:429-434.  [PubMed]  [DOI]
59.  Cockcroft DW, Gault MH. Prediction of creatinine clearance from serum creatinine. Nephron. 1976;16:31-41.  [PubMed]  [DOI]
60.  Levey AS, Bosch JP, Lewis JB, Greene T, Rogers N, Roth D. A more accurate method to estimate glomerular filtration rate from serum creatinine: a new prediction equation. Modification of Diet in Renal Disease Study Group. Ann Intern Med. 1999;130:461-470.  [PubMed]  [DOI]
61.  Levey AS, Coresh J, Greene T, Marsh J, Stevens LA, Kusek JW, Van Lente F. Expressing the Modification of Diet in Renal Disease Study equation for estimating glomerular filtration rate with standardized serum creatinine values. Clin Chem. 2007;53:766-772.  [PubMed]  [DOI]
62.  Levey AS, Stevens LA, Schmid CH, Zhang YL, Castro AF, Feldman HI, Kusek JW, Eggers P, Van Lente F, Greene T. A new equation to estimate glomerular filtration rate. Ann Intern Med. 2009;150:604-612.  [PubMed]  [DOI]
63.  Inker LA, Schmid CH, Tighiouart H, Eckfeldt JH, Feldman HI, Greene T, Kusek JW, Manzi J, Van Lente F, Zhang YL. Estimating glomerular filtration rate from serum creatinine and cystatin C. N Engl J Med. 2012;367:20-29.  [PubMed]  [DOI]
64.  Stevens LA, Nolin TD, Richardson MM, Feldman HI, Lewis JB, Rodby R, Townsend R, Okparavero A, Zhang YL, Schmid CH. Comparison of drug dosing recommendations based on measured GFR and kidney function estimating equations. Am J Kidney Dis. 2009;54:33-42.  [PubMed]  [DOI]
65.  Levey AS, Coresh J, Greene T, Stevens LA, Zhang YL, Hendriksen S, Kusek JW, Van Lente F. Using standardized serum creatinine values in the modification of diet in renal disease study equation for estimating glomerular filtration rate. Ann Intern Med. 2006;145:247-254.  [PubMed]  [DOI]
66.  Rule AD, Larson TS, Bergstralh EJ, Slezak JM, Jacobsen SJ, Cosio FG. Using serum creatinine to estimate glomerular filtration rate: accuracy in good health and in chronic kidney disease. Ann Intern Med. 2004;141:929-937.  [PubMed]  [DOI]
67.  Stevens LA, Schmid CH, Zhang YL, Coresh J, Manzi J, Landis R, Bakoush O, Contreras G, Genuth S, Klintmalm GB. Development and validation of GFR-estimating equations using diabetes, transplant and weight. Nephrol Dial Transplant. 2010;25:449-457.  [PubMed]  [DOI]
68.  Segarra A, de la Torre J, Ramos N, Quiroz A, Garjau M, Torres I, Azancot MA, López M, Sobrado A. Assessing glomerular filtration rate in hospitalized patients: a comparison between CKD-EPI and four cystatin C-based equations. Clin J Am Soc Nephrol. 2011;6:2411-2420.  [PubMed]  [DOI]
69.  Kim DJ, Kang HS, Choi HS, Cho HJ, Kim ES, Keum B, An H, Kim JH, Seo YS, Kim YS. Serum cystatin C level is a useful marker for the evaluation of renal function in patients with cirrhotic ascites and normal serum creatinine levels. Korean J Hepatol. 2011;17:130-138.  [PubMed]  [DOI]
70.  Pöge U, Gerhardt T, Stoffel-Wagner B, Klehr HU, Sauerbruch T, Woitas RP. Calculation of glomerular filtration rate based on cystatin C in cirrhotic patients. Nephrol Dial Transplant. 2006;21:660-664.  [PubMed]  [DOI]
71.  Sharawey MA, Shawky EM, Ali LH, Mohammed AA, Hassan HA, Fouad YM. Cystatin C: a predictor of hepatorenal syndrome in patients with liver cirrhosis. Hepatol Int. 2011;Epub ahead of print.  [PubMed]  [DOI]
72.  Bölke E, Schieren G, Gripp S, Steinbach G, Peiper M, Orth K, Matuschek C, Pelzer M, Lammering G, Houben R. Cystatin C - a fast and reliable biomarker for glomerular filtration rate in head and neck cancer patients. Strahlenther Onkol. 2011;187:191-201.  [PubMed]  [DOI]
73.  Stabuc B, Vrhovec L, Stabuc-Silih M, Cizej TE. Improved prediction of decreased creatinine clearance by serum cystatin C: use in cancer patients before and during chemotherapy. Clin Chem. 2000;46:193-197.  [PubMed]  [DOI]
74.  Jones CY, Jones CA, Wilson IB, Knox TA, Levey AS, Spiegelman D, Gorbach SL, Van Lente F, Stevens LA. Cystatin C and creatinine in an HIV cohort: the nutrition for healthy living study. Am J Kidney Dis. 2008;51:914-924.  [PubMed]  [DOI]
75.  Lamb EJ, Brettell EA, Cockwell P, Dalton N, Deeks JJ, Harris K, Higgins T, Kalra PA, Khunti K, Loud F. The eGFR-C study: accuracy of glomerular filtration rate (GFR) estimation using creatinine and cystatin C and albuminuria for monitoring disease progression in patients with stage 3 chronic kidney disease--prospective longitudinal study in a multiethnic population. BMC Nephrol. 2014;15:13.  [PubMed]  [DOI]
76.  Goolsby MJ. National Kidney Foundation Guidelines for chronic kidney disease: evaluation, classification, and stratification. J Am Acad Nurse Pract. 2002;14:238-242.  [PubMed]  [DOI]
77.  Michels WM, Grootendorst DC, Verduijn M, Elliott EG, Dekker FW, Krediet RT. Performance of the Cockcroft-Gault, MDRD, and new CKD-EPI formulas in relation to GFR, age, and body size. Clin J Am Soc Nephrol. 2010;5:1003-1009.  [PubMed]  [DOI]
78.  Schaeffner ES, Ebert N, Delanaye P, Frei U, Gaedeke J, Jakob O, Kuhlmann MK, Schuchardt M, Tölle M, Ziebig R. Two novel equations to estimate kidney function in persons aged 70 years or older. Ann Intern Med. 2012;157:471-481.  [PubMed]  [DOI]
79.  Horio M, Imai E, Yasuda Y, Watanabe T, Matsuo S. GFR estimation using standardized serum cystatin C in Japan. Am J Kidney Dis. 2013;61:197-203.  [PubMed]  [DOI]
80.  Zhu Y, Ye X, Zhu B, Pei X, Wei L, Wu J, Zhao W. Comparisons between the 2012 new CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration) equations and other four approved equations. PLoS One. 2014;9:e84688.  [PubMed]  [DOI]
81.  Guo X, Qin Y, Zheng K, Gong M, Wu J, Shou W, Cheng X, Xia L, Xu E, Li X. Improved glomerular filtration rate estimation using new equations combined with standardized cystatin C and creatinine in Chinese adult chronic kidney disease patients. Clin Biochem. 2014;47:1220-1226.  [PubMed]  [DOI]
82.  Masson I, Maillard N, Tack I, Thibaudin L, Dubourg L, Delanaye P, Cavalier E, Bonneau C, Kamar N, Morelon E. GFR estimation using standardized cystatin C in kidney transplant recipients. Am J Kidney Dis. 2013;61:279-284.  [PubMed]  [DOI]
83.  Matsuo S, Imai E, Horio M, Yasuda Y, Tomita K, Nitta K, Yamagata K, Tomino Y, Yokoyama H, Hishida A. Revised equations for estimated GFR from serum creatinine in Japan. Am J Kidney Dis. 2009;53:982-992.  [PubMed]  [DOI]
84.  Dai SS, Yasuda Y, Zhang CL, Horio M, Zuo L, Wang HY. Evaluation of GFR measurement method as an explanation for differences among GFR estimation equations. Am J Kidney Dis. 2011;58:496-498.  [PubMed]  [DOI]
85.  Horio M, Imai E, Yasuda Y, Watanabe T, Matsuo S. Modification of the CKD epidemiology collaboration (CKD-EPI) equation for Japanese: accuracy and use for population estimates. Am J Kidney Dis. 2010;56:32-38.  [PubMed]  [DOI]
86.  Matsushita K, Mahmoodi BK, Woodward M, Emberson JR, Jafar TH, Jee SH, Polkinghorne KR, Shankar A, Smith DH, Tonelli M. Comparison of risk prediction using the CKD-EPI equation and the MDRD study equation for estimated glomerular filtration rate. JAMA. 2012;307:1941-1951.  [PubMed]  [DOI]
87.  White SL, Polkinghorne KR, Atkins RC, Chadban SJ. Comparison of the prevalence and mortality risk of CKD in Australia using the CKD Epidemiology Collaboration (CKD-EPI) and Modification of Diet in Renal Disease (MDRD) Study GFR estimating equations: the AusDiab (Australian Diabetes, Obesity and Lifestyle) Study. Am J Kidney Dis. 2010;55:660-670.  [PubMed]  [DOI]
88.  Stevens LA, Li S, Kurella Tamura M, Chen SC, Vassalotti JA, Norris KC, Whaley-Connell AT, Bakris GL, McCullough PA. Comparison of the CKD Epidemiology Collaboration (CKD-EPI) and Modification of Diet in Renal Disease (MDRD) study equations: risk factors for and complications of CKD and mortality in the Kidney Early Evaluation Program (KEEP). Am J Kidney Dis. 2011;57:S9-16.  [PubMed]  [DOI]
89.  Rule AD, Bailey KR, Lieske JC, Peyser PA, Turner ST. Estimating the glomerular filtration rate from serum creatinine is better than from cystatin C for evaluating risk factors associated with chronic kidney disease. Kidney Int. 2013;83:1169-1176.  [PubMed]  [DOI]
90.  Dossetor JB. Creatininemia versus uremia. The relative significance of blood urea nitrogen and serum creatinine concentrations in azotemia. Ann Intern Med. 1966;65:1287-1299.  [PubMed]  [DOI]
91.  Mistry CD, O’Donoghue DJ, Nelson S, Gokal R, Ballardie FW. Kinetic and clinical studies of beta 2-microglobulin in continuous ambulatory peritoneal dialysis: influence of renal and enhanced peritoneal clearances using glucose polymer. Nephrol Dial Transplant. 1990;5:513-519.  [PubMed]  [DOI]
92.  Shinkai S, Chaves PH, Fujiwara Y, Watanabe S, Shibata H, Yoshida H, Suzuki T. Beta2-microglobulin for risk stratification of total mortality in the elderly population: comparison with cystatin C and C-reactive protein. Arch Intern Med. 2008;168:200-206.  [PubMed]  [DOI]
93.  Bianchi C, Donadio C, Tramonti G, Consani C, Lorusso P, Rossi G. Reappraisal of serum beta2-microglobulin as marker of GFR. Ren Fail. 2001;23:419-429.  [PubMed]  [DOI]
94.  Donadio C, Lucchesi A, Ardini M, Giordani R. Cystatin C, beta 2-microglobulin, and retinol-binding protein as indicators of glomerular filtration rate: comparison with plasma creatinine. J Pharm Biomed Anal. 2001;24:835-842.  [PubMed]  [DOI]
95.  Vassalotti JA, Stevens LA, Levey AS. Testing for chronic kidney disease: a position statement from the National Kidney Foundation. Am J Kidney Dis. 2007;50:169-180.  [PubMed]  [DOI]
96.  Nissenson AR, Collins AJ, Hurley J, Petersen H, Pereira BJ, Steinberg EP. Opportunities for improving the care of patients with chronic renal insufficiency: current practice patterns. J Am Soc Nephrol. 2001;12:1713-1720.  [PubMed]  [DOI]
97.  Coresh J, Astor BC, Greene T, Eknoyan G, Levey AS. Prevalence of chronic kidney disease and decreased kidney function in the adult US population: Third National Health and Nutrition Examination Survey. Am J Kidney Dis. 2003;41:1-12.  [PubMed]  [DOI]
98.  Gansevoort RT, Matsushita K, van der Velde M, Astor BC, Woodward M, Levey AS, de Jong PE, Coresh J. Lower estimated GFR and higher albuminuria are associated with adverse kidney outcomes. A collaborative meta-analysis of general and high-risk population cohorts. Kidney Int. 2011;80:93-104.  [PubMed]  [DOI]
99.  Waheed S, Matsushita K, Astor BC, Hoogeveen RC, Ballantyne C, Coresh J. Combined association of creatinine, albuminuria, and cystatin C with all-cause mortality and cardiovascular and kidney outcomes. Clin J Am Soc Nephrol. 2013;8:434-442.  [PubMed]  [DOI]
100.  Hallan SI, Ritz E, Lydersen S, Romundstad S, Kvenild K, Orth SR. Combining GFR and albuminuria to classify CKD improves prediction of ESRD. J Am Soc Nephrol. 2009;20:1069-1077.  [PubMed]  [DOI]
101.  Warnock DG, Muntner P, McCullough PA, Zhang X, McClure LA, Zakai N, Cushman M, Newsome BB, Kewalramani R, Steffes MW. Kidney function, albuminuria, and all-cause mortality in the REGARDS (Reasons for Geographic and Racial Differences in Stroke) study. Am J Kidney Dis. 2010;56:861-871.  [PubMed]  [DOI]
102.  Rees AJ, Kain R. Kim-1/Tim-1: from biomarker to therapeutic target? Nephrol Dial Transplant. 2008;23:3394-3396.  [PubMed]  [DOI]
103.  Ichimura T, Bonventre JV, Bailly V, Wei H, Hession CA, Cate RL, Sanicola M. Kidney injury molecule-1 (KIM-1), a putative epithelial cell adhesion molecule containing a novel immunoglobulin domain, is up-regulated in renal cells after injury. J Biol Chem. 1998;273:4135-4142.  [PubMed]  [DOI]
104.  Han WK, Bailly V, Abichandani R, Thadhani R, Bonventre JV. Kidney Injury Molecule-1 (KIM-1): a novel biomarker for human renal proximal tubule injury. Kidney Int. 2002;62:237-244.  [PubMed]  [DOI]
105.  van Timmeren MM, van den Heuvel MC, Bailly V, Bakker SJ, van Goor H, Stegeman CA. Tubular kidney injury molecule-1 (KIM-1) in human renal disease. J Pathol. 2007;212:209-217.  [PubMed]  [DOI]
106.  Schröppel B, Krüger B, Walsh L, Yeung M, Harris S, Garrison K, Himmelfarb J, Lerner SM, Bromberg JS, Zhang PL. Tubular expression of KIM-1 does not predict delayed function after transplantation. J Am Soc Nephrol. 2010;21:536-542.  [PubMed]  [DOI]
107.  Kuehn EW, Park KM, Somlo S, Bonventre JV. Kidney injury molecule-1 expression in murine polycystic kidney disease. Am J Physiol Renal Physiol. 2002;283:F1326-F1336.  [PubMed]  [DOI]
108.  Vaidya VS, Niewczas MA, Ficociello LH, Johnson AC, Collings FB, Warram JH, Krolewski AS, Bonventre JV. Regression of microalbuminuria in type 1 diabetes is associated with lower levels of urinary tubular injury biomarkers, kidney injury molecule-1, and N-acetyl-β-D-glucosaminidase. Kidney Int. 2011;79:464-470.  [PubMed]  [DOI]
109.  Peters HP, Waanders F, Meijer E, van den Brand J, Steenbergen EJ, van Goor H, Wetzels JF. High urinary excretion of kidney injury molecule-1 is an independent predictor of end-stage renal disease in patients with IgA nephropathy. Nephrol Dial Transplant. 2011;26:3581-3588.  [PubMed]  [DOI]
110.  Humphreys BD, Xu F, Sabbisetti V, Grgic I, Naini SM, Wang N, Chen G, Xiao S, Patel D, Henderson JM. Chronic epithelial kidney injury molecule-1 expression causes murine kidney fibrosis. J Clin Invest. 2013;123:4023-4035.  [PubMed]  [DOI]
111.  Krolewski AS, Niewczas MA, Skupien J, Gohda T, Smiles A, Eckfeldt JH, Doria A, Warram JH. Early progressive renal decline precedes the onset of microalbuminuria and its progression to macroalbuminuria. Diabetes Care. 2014;37:226-234.  [PubMed]  [DOI]
112.  Goetz DH, Holmes MA, Borregaard N, Bluhm ME, Raymond KN, Strong RK. The neutrophil lipocalin NGAL is a bacteriostatic agent that interferes with siderophore-mediated iron acquisition. Mol Cell. 2002;10:1033-1043.  [PubMed]  [DOI]
113.  Berger T, Togawa A, Duncan GS, Elia AJ, You-Ten A, Wakeham A, Fong HE, Cheung CC, Mak TW. Lipocalin 2-deficient mice exhibit increased sensitivity to Escherichia coli infection but not to ischemia-reperfusion injury. Proc Natl Acad Sci USA. 2006;103:1834-1839.  [PubMed]  [DOI]
114.  Mishra J, Mori K, Ma Q, Kelly C, Barasch J, Devarajan P. Neutrophil gelatinase-associated lipocalin: a novel early urinary biomarker for cisplatin nephrotoxicity. Am J Nephrol. 2004;24:307-315.  [PubMed]  [DOI]
115.  Mishra J, Ma Q, Prada A, Mitsnefes M, Zahedi K, Yang J, Barasch J, Devarajan P. Identification of neutrophil gelatinase-associated lipocalin as a novel early urinary biomarker for ischemic renal injury. J Am Soc Nephrol. 2003;14:2534-2543.  [PubMed]  [DOI]
116.  Haase-Fielitz A, Bellomo R, Devarajan P, Story D, Matalanis G, Dragun D, Haase M. Novel and conventional serum biomarkers predicting acute kidney injury in adult cardiac surgery--a prospective cohort study. Crit Care Med. 2009;37:553-560.  [PubMed]  [DOI]
117.  Constantin JM, Futier E, Perbet S, Roszyk L, Lautrette A, Gillart T, Guerin R, Jabaudon M, Souweine B, Bazin JE. Plasma neutrophil gelatinase-associated lipocalin is an early marker of acute kidney injury in adult critically ill patients: a prospective study. J Crit Care. 2010;25:176.e1-176.e6.  [PubMed]  [DOI]
118.  Viau A, El Karoui K, Laouari D, Burtin M, Nguyen C, Mori K, Pillebout E, Berger T, Mak TW, Knebelmann B. Lipocalin 2 is essential for chronic kidney disease progression in mice and humans. J Clin Invest. 2010;120:4065-4076.  [PubMed]  [DOI]
119.  Shen SJ, Hu ZX, Li QH, Wang SM, Song CJ, Wu DD, He JL, Guan JC, Shan JP. Implications of the changes in serum neutrophil gelatinase-associated lipocalin and cystatin C in patients with chronic kidney disease. Nephrology (Carlton). 2014;19:129-135.  [PubMed]  [DOI]
120.  Ding H, He Y, Li K, Yang J, Li X, Lu R, Gao W. Urinary neutrophil gelatinase-associated lipocalin (NGAL) is an early biomarker for renal tubulointerstitial injury in IgA nephropathy. Clin Immunol. 2007;123:227-234.  [PubMed]  [DOI]
121.  Bolignano D, Coppolino G, Campo S, Aloisi C, Nicocia G, Frisina N, Buemi M. Urinary neutrophil gelatinase-associated lipocalin (NGAL) is associated with severity of renal disease in proteinuric patients. Nephrol Dial Transplant. 2008;23:414-416.  [PubMed]  [DOI]
122.  Bolignano D, Coppolino G, Campo S, Aloisi C, Nicocia G, Frisina N, Buemi M. Neutrophil gelatinase-associated lipocalin in patients with autosomal-dominant polycystic kidney disease. Am J Nephrol. 2007;27:373-378.  [PubMed]  [DOI]
123.  Bhavsar NA, Köttgen A, Coresh J, Astor BC. Neutrophil gelatinase-associated lipocalin (NGAL) and kidney injury molecule 1 (KIM-1) as predictors of incident CKD stage 3: the Atherosclerosis Risk in Communities (ARIC) Study. Am J Kidney Dis. 2012;60:233-240.  [PubMed]  [DOI]
124.  Smith ER, Lee D, Cai MM, Tomlinson LA, Ford ML, McMahon LP, Holt SG. Urinary neutrophil gelatinase-associated lipocalin may aid prediction of renal decline in patients with non-proteinuric Stages 3 and 4 chronic kidney disease (CKD). Nephrol Dial Transplant. 2013;28:1569-1579.  [PubMed]  [DOI]
125.  Bolignano D, Lacquaniti A, Coppolino G, Donato V, Campo S, Fazio MR, Nicocia G, Buemi M. Neutrophil gelatinase-associated lipocalin (NGAL) and progression of chronic kidney disease. Clin J Am Soc Nephrol. 2009;4:337-344.  [PubMed]  [DOI]
126.  Shimada T, Kakitani M, Yamazaki Y, Hasegawa H, Takeuchi Y, Fujita T, Fukumoto S, Tomizuka K, Yamashita T. Targeted ablation of Fgf23 demonstrates an essential physiological role of FGF23 in phosphate and vitamin D metabolism. J Clin Invest. 2004;113:561-568.  [PubMed]  [DOI]
127.  Nakai K, Komaba H, Fukagawa M. New insights into the role of fibroblast growth factor 23 in chronic kidney disease. J Nephrol. 2010;23:619-625.  [PubMed]  [DOI]
128.  Ito N, Fukumoto S, Takeuchi Y, Takeda S, Suzuki H, Yamashita T, Fujita T. Effect of acute changes of serum phosphate on fibroblast growth factor (FGF)23 levels in humans. J Bone Miner Metab. 2007;25:419-422.  [PubMed]  [DOI]
129.  Marsell R, Grundberg E, Krajisnik T, Mallmin H, Karlsson M, Mellström D, Orwoll E, Ohlsson C, Jonsson KB, Ljunggren O. Fibroblast growth factor-23 is associated with parathyroid hormone and renal function in a population-based cohort of elderly men. Eur J Endocrinol. 2008;158:125-129.  [PubMed]  [DOI]
130.  Isakova T, Wahl P, Vargas GS, Gutiérrez OM, Scialla J, Xie H, Appleby D, Nessel L, Bellovich K, Chen J. Fibroblast growth factor 23 is elevated before parathyroid hormone and phosphate in chronic kidney disease. Kidney Int. 2011;79:1370-1378.  [PubMed]  [DOI]
131.  Larsson T, Nisbeth U, Ljunggren O, Jüppner H, Jonsson KB. Circulating concentration of FGF-23 increases as renal function declines in patients with chronic kidney disease, but does not change in response to variation in phosphate intake in healthy volunteers. Kidney Int. 2003;64:2272-2279.  [PubMed]  [DOI]
132.  Gonzalez-Parra E, Gonzalez-Casaus ML, Galán A, Martinez-Calero A, Navas V, Rodriguez M, Ortiz A. Lanthanum carbonate reduces FGF23 in chronic kidney disease Stage 3 patients. Nephrol Dial Transplant. 2011;26:2567-2571.  [PubMed]  [DOI]
133.  Semba RD, Fink JC, Sun K, Cappola AR, Dalal M, Crasto C, Ferrucci L, Fried LP. Serum fibroblast growth factor-23 and risk of incident chronic kidney disease in older community-dwelling women. Clin J Am Soc Nephrol. 2012;7:85-91.  [PubMed]  [DOI]
134.  Nakano C, Hamano T, Fujii N, Matsui I, Tomida K, Mikami S, Inoue K, Obi Y, Okada N, Tsubakihara Y. Combined use of vitamin D status and FGF23 for risk stratification of renal outcome. Clin J Am Soc Nephrol. 2012;7:810-819.  [PubMed]  [DOI]
135.  Gutiérrez OM, Mannstadt M, Isakova T, Rauh-Hain JA, Tamez H, Shah A, Smith K, Lee H, Thadhani R, Jüppner H. Fibroblast growth factor 23 and mortality among patients undergoing hemodialysis. N Engl J Med. 2008;359:584-592.  [PubMed]  [DOI]
136.  Jean G, Terrat JC, Vanel T, Hurot JM, Lorriaux C, Mayor B, Chazot C. High levels of serum fibroblast growth factor (FGF)-23 are associated with increased mortality in long haemodialysis patients. Nephrol Dial Transplant. 2009;24:2792-2796.  [PubMed]  [DOI]
137.  Fliser D, Kollerits B, Neyer U, Ankerst DP, Lhotta K, Lingenhel A, Ritz E, Kronenberg F, Kuen E, König P. Fibroblast growth factor 23 (FGF23) predicts progression of chronic kidney disease: the Mild to Moderate Kidney Disease (MMKD) Study. J Am Soc Nephrol. 2007;18:2600-2608.  [PubMed]  [DOI]
138.  Lee JE, Gohda T, Walker WH, Skupien J, Smiles AM, Holak RR, Jeong J, McDonnell KP, Krolewski AS, Niewczas MA. Risk of ESRD and all cause mortality in type 2 diabetes according to circulating levels of FGF-23 and TNFR1. PLoS One. 2013;8:e58007.  [PubMed]  [DOI]
139.  Ueda S, Yamagishi S, Kaida Y, Okuda S. Asymmetric dimethylarginine may be a missing link between cardiovascular disease and chronic kidney disease. Nephrology (Carlton). 2007;12:582-590.  [PubMed]  [DOI]
140.  Ueda S, Yamagishi S, Okuda S. New pathways to renal damage: role of ADMA in retarding renal disease progression. J Nephrol. 2010;23:377-386.  [PubMed]  [DOI]
141.  Vallance P, Leone A, Calver A, Collier J, Moncada S. Accumulation of an endogenous inhibitor of nitric oxide synthesis in chronic renal failure. Lancet. 1992;339:572-575.  [PubMed]  [DOI]
142.  Raptis V, Kapoulas S, Grekas D. Role of asymmetrical dimethylarginine in the progression of renal disease. Nephrology (Carlton). 2013;18:11-21.  [PubMed]  [DOI]
143.  Ravani P, Tripepi G, Malberti F, Testa S, Mallamaci F, Zoccali C. Asymmetrical dimethylarginine predicts progression to dialysis and death in patients with chronic kidney disease: a competing risks modeling approach. J Am Soc Nephrol. 2005;16:2449-2455.  [PubMed]  [DOI]
144.  Lajer M, Tarnow L, Jorsal A, Teerlink T, Parving HH, Rossing P. Plasma concentration of asymmetric dimethylarginine (ADMA) predicts cardiovascular morbidity and mortality in type 1 diabetic patients with diabetic nephropathy. Diabetes Care. 2008;31:747-752.  [PubMed]  [DOI]
145.  Eiselt J, Rajdl D, Racek J, Vostrý M, Rulcová K, Wirth J. Asymmetric dimethylarginine and progression of chronic kidney disease - a one-year follow-up study. Kidney Blood Press Res. 2014;39:50-57.  [PubMed]  [DOI]
146.  Tripepi G, Mattace Raso F, Sijbrands E, Seck MS, Maas R, Boger R, Witteman J, Rapisarda F, Malatino L, Mallamaci F. Inflammation and asymmetric dimethylarginine for predicting death and cardiovascular events in ESRD patients. Clin J Am Soc Nephrol. 2011;6:1714-1721.  [PubMed]  [DOI]
147.  Zoccali C, Bode-Böger S, Mallamaci F, Benedetto F, Tripepi G, Malatino L, Cataliotti A, Bellanuova I, Fermo I, Frölich J. Plasma concentration of asymmetrical dimethylarginine and mortality in patients with end-stage renal disease: a prospective study. Lancet. 2001;358:2113-2117.  [PubMed]  [DOI]
148.  Van Coillie E, Van Damme J, Opdenakker G. The MCP/eotaxin subfamily of CC chemokines. Cytokine Growth Factor Rev. 1999;10:61-86.  [PubMed]  [DOI]
149.  Melgarejo E, Medina MA, Sánchez-Jiménez F, Urdiales JL. Monocyte chemoattractant protein-1: a key mediator in inflammatory processes. Int J Biochem Cell Biol. 2009;41:998-1001.  [PubMed]  [DOI]
150.  Wada T, Furuichi K, Sakai N, Iwata Y, Yoshimoto K, Shimizu M, Takeda SI, Takasawa K, Yoshimura M, Kida H. Up-regulation of monocyte chemoattractant protein-1 in tubulointerstitial lesions of human diabetic nephropathy. Kidney Int. 2000;58:1492-1499.  [PubMed]  [DOI]
151.  Wada T, Yokoyama H, Su SB, Mukaida N, Iwano M, Dohi K, Takahashi Y, Sasaki T, Furuichi K, Segawa C. Monitoring urinary levels of monocyte chemotactic and activating factor reflects disease activity of lupus nephritis. Kidney Int. 1996;49:761-767.  [PubMed]  [DOI]
152.  Chow F, Ozols E, Nikolic-Paterson DJ, Atkins RC, Tesch GH. Macrophages in mouse type 2 diabetic nephropathy: correlation with diabetic state and progressive renal injury. Kidney Int. 2004;65:116-128.  [PubMed]  [DOI]
153.  Takebayashi K, Matsumoto S, Aso Y, Inukai T. Aldosterone blockade attenuates urinary monocyte chemoattractant protein-1 and oxidative stress in patients with type 2 diabetes complicated by diabetic nephropathy. J Clin Endocrinol Metab. 2006;91:2214-2217.  [PubMed]  [DOI]
154.  Noris M, Bernasconi S, Casiraghi F, Sozzani S, Gotti E, Remuzzi G, Mantovani A. Monocyte chemoattractant protein-1 is excreted in excessive amounts in the urine of patients with lupus nephritis. Lab Invest. 1995;73:804-809.  [PubMed]  [DOI]
155.  Tam FW, Sanders JS, George A, Hammad T, Miller C, Dougan T, Cook HT, Kallenberg CG, Gaskin G, Levy JB. Urinary monocyte chemoattractant protein-1 (MCP-1) is a marker of active renal vasculitis. Nephrol Dial Transplant. 2004;19:2761-2768.  [PubMed]  [DOI]
156.  Grandaliano G, Gesualdo L, Ranieri E, Monno R, Montinaro V, Marra F, Schena FP. Monocyte chemotactic peptide-1 expression in acute and chronic human nephritides: a pathogenetic role in interstitial monocytes recruitment. J Am Soc Nephrol. 1996;7:906-913.  [PubMed]  [DOI]
157.  Yoshimoto K, Wada T, Furuichi K, Sakai N, Iwata Y, Yokoyama H. CD68 and MCP-1/CCR2 expression of initial biopsies reflect the outcomes of membranous nephropathy. Nephron Clin Pract. 2004;98:c25-c34.  [PubMed]  [DOI]
158.  Naylor HM, Newcomer ME. The structure of human retinol-binding protein (RBP) with its carrier protein transthyretin reveals an interaction with the carboxy terminus of RBP. Biochemistry. 1999;38:2647-2653.  [PubMed]  [DOI]
159.  Yang Q, Eskurza I, Kiernan UA, Phillips DA, Blüher M, Graham TE, Kahn BB. Quantitative measurement of full-length and C-terminal proteolyzed RBP4 in serum of normal and insulin-resistant humans using a novel mass spectrometry immunoassay. Endocrinology. 2012;153:1519-1527.  [PubMed]  [DOI]
160.  Bernard AM, Vyskocil AA, Mahieu P, Lauwerys RR. Assessment of urinary retinol-binding protein as an index of proximal tubular injury. Clin Chem. 1987;33:775-779.  [PubMed]  [DOI]
161.  Bernard A, Vyskocyl A, Mahieu P, Lauwerys R. Effect of renal insufficiency on the concentration of free retinol-binding protein in urine and serum. Clin Chim Acta. 1988;171:85-93.  [PubMed]  [DOI]
162.  Norden AG, Scheinman SJ, Deschodt-Lanckman MM, Lapsley M, Nortier JL, Thakker RV, Unwin RJ, Wrong O. Tubular proteinuria defined by a study of Dent’s (CLCN5 mutation) and other tubular diseases. Kidney Int. 2000;57:240-249.  [PubMed]  [DOI]
163.  Norden AG, Lapsley M, Unwin RJ. Urine retinol-binding protein 4: a functional biomarker of the proximal renal tubule. Adv Clin Chem. 2014;63:85-122.  [PubMed]  [DOI]
164.  Amer H, Lieske JC, Rule AD, Kremers WK, Larson TS, Franco Palacios CR, Stegall MD, Cosio FG. Urine high and low molecular weight proteins one-year post-kidney transplant: relationship to histology and graft survival. Am J Transplant. 2013;13:676-684.  [PubMed]  [DOI]
165.  Akkina S, Becker BN. MicroRNAs in kidney function and disease. Transl Res. 2011;157:236-240.  [PubMed]  [DOI]
166.  Tian Z, Greene AS, Pietrusz JL, Matus IR, Liang M. MicroRNA-target pairs in the rat kidney identified by microRNA microarray, proteomic, and bioinformatic analysis. Genome Res. 2008;18:404-411.  [PubMed]  [DOI]
167.  Ardekani AM, Naeini MM. The Role of MicroRNAs in Human Diseases. Avicenna J Med Biotechnol. 2010;2:161-179.  [PubMed]  [DOI]
168.  Li JY, Yong TY, Michael MZ, Gleadle JM. Review: The role of microRNAs in kidney disease. Nephrology (Carlton). 2010;15:599-608.  [PubMed]  [DOI]
169.  Xu CC, Han WQ, Xiao B, Li NN, Zhu DL, Gao PJ. [Differential expression of microRNAs in the aorta of spontaneously hypertensive rats]. Shengli Xuebao. 2008;60:553-560.  [PubMed]  [DOI]
170.  Martin MM, Lee EJ, Buckenberger JA, Schmittgen TD, Elton TS. MicroRNA-155 regulates human angiotensin II type 1 receptor expression in fibroblasts. J Biol Chem. 2006;281:18277-18284.  [PubMed]  [DOI]
171.  Kato M, Zhang J, Wang M, Lanting L, Yuan H, Rossi JJ, Natarajan R. MicroRNA-192 in diabetic kidney glomeruli and its function in TGF-beta-induced collagen expression via inhibition of E-box repressors. Proc Natl Acad Sci USA. 2007;104:3432-3437.  [PubMed]  [DOI]
172.  Pandey P, Brors B, Srivastava PK, Bott A, Boehn SN, Groene HJ, Gretz N. Microarray-based approach identifies microRNAs and their target functional patterns in polycystic kidney disease. BMC Genomics. 2008;9:624.  [PubMed]  [DOI]
173.  Anglicheau D, Sharma VK, Ding R, Hummel A, Snopkowski C, Dadhania D, Seshan SV, Suthanthiran M. MicroRNA expression profiles predictive of human renal allograft status. Proc Natl Acad Sci USA. 2009;106:5330-5335.  [PubMed]  [DOI]
174.  Wilflingseder J, Regele H, Perco P, Kainz A, Soleiman A, Mühlbacher F, Mayer B, Oberbauer R. miRNA profiling discriminates types of rejection and injury in human renal allografts. Transplantation. 2013;95:835-841.  [PubMed]  [DOI]
175.  Dai Y, Sui W, Lan H, Yan Q, Huang H, Huang Y. Comprehensive analysis of microRNA expression patterns in renal biopsies of lupus nephritis patients. Rheumatol Int. 2009;29:749-754.  [PubMed]  [DOI]
176.  Zhao YY, Lint RC. Metabolomics in nephrotoxicity. Adv Clin Chem. 2014;65:69-89.  [PubMed]  [DOI]
177.  Portilla D, Schnackenberg L, Beger RD. Metabolomics as an extension of proteomic analysis: study of acute kidney injury. Semin Nephrol. 2007;27:609-620.  [PubMed]  [DOI]
178.  Han SY, Mun KC, Choi HJ, Kwak CS, Bae JH, Suh SI, Park SB, Kim HC, Chang EJ. Effects of cyclosporine and tacrolimus on the oxidative stress in cultured mesangial cells. Transplant Proc. 2006;38:2240-2241.  [PubMed]  [DOI]
179.  Zhao YY. Metabolomics in chronic kidney disease. Clin Chim Acta. 2013;422:59-69.  [PubMed]  [DOI]
180.  Murata K, Baumann NA, Saenger AK, Larson TS, Rule AD, Lieske JC. Relative performance of the MDRD and CKD-EPI equations for estimating glomerular filtration rate among patients with varied clinical presentations. Clin J Am Soc Nephrol. 2011;6:1963-1972.  [PubMed]  [DOI]
181.  Lane BR, Demirjian S, Weight CJ, Larson BT, Poggio ED, Campbell SC. Performance of the chronic kidney disease-epidemiology study equations for estimating glomerular filtration rate before and after nephrectomy. J Urol. 2010;183:896-901.  [PubMed]  [DOI]
182.  Tent H, Rook M, Stevens LA, van Son WJ, van Pelt LJ, Hofker HS, Ploeg RJ, van der Heide JJ, Navis G. Renal function equations before and after living kidney donation: a within-individual comparison of performance at different levels of renal function. Clin J Am Soc Nephrol. 2010;5:1960-1968.  [PubMed]  [DOI]
183.  Kukla A, El-Shahawi Y, Leister E, Kasiske B, Mauer M, Matas A, Ibrahim HN. GFR-estimating models in kidney transplant recipients on a steroid-free regimen. Nephrol Dial Transplant. 2010;25:1653-1661.  [PubMed]  [DOI]
184.  White CA, Akbari A, Doucette S, Fergusson D, Knoll GA. Estimating glomerular filtration rate in kidney transplantation: is the new chronic kidney disease epidemiology collaboration equation any better? Clin Chem. 2010;56:474-477.  [PubMed]  [DOI]
185.  Pöge U, Gerhardt T, Stoffel-Wagner B, Sauerbruch T, Woitas RP. Validation of the CKD-EPI formula in patients after renal transplantation. Nephrol Dial Transplant. 2011;26:4104-4108.  [PubMed]  [DOI]
186.  Jones GR. Use of the CKD-EPI equation for estimation of GFR in an Australian cohort. Pathology. 2010;42:487-488.  [PubMed]  [DOI]
187.  Cirillo M, Lombardi C, Luciano MG, Bilancio G, Anastasio P, De Santo NG. Estimation of GFR: a comparison of new and established equations. Am J Kidney Dis. 2010;56:802-804.  [PubMed]  [DOI]
188.  Eriksen BO, Mathisen UD, Melsom T, Ingebretsen OC, Jenssen TG, Njølstad I, Solbu MD, Toft I. Cystatin C is not a better estimator of GFR than plasma creatinine in the general population. Kidney Int. 2010;78:1305-1311.  [PubMed]  [DOI]
189.  Redal-Baigorri B, Stokholm KH, Rasmussen K, Jeppesen N. Estimation of kidney function in cancer patients. Dan Med Bull. 2011;58:A4236.  [PubMed]  [DOI]
190.  Nielsen SE, Sugaya T, Hovind P, Baba T, Parving HH, Rossing P. Urinary liver-type fatty acid-binding protein predicts progression to nephropathy in type 1 diabetic patients. Diabetes Care. 2010;33:1320-1324.  [PubMed]  [DOI]
191.  Kern EF, Erhard P, Sun W, Genuth S, Weiss MF. Early urinary markers of diabetic kidney disease: a nested case-control study from the Diabetes Control and Complications Trial (DCCT). Am J Kidney Dis. 2010;55:824-834.  [PubMed]  [DOI]
192.  Nguyen TQ, Tarnow L, Andersen S, Hovind P, Parving HH, Goldschmeding R, van Nieuwenhoven FA. Urinary connective tissue growth factor excretion correlates with clinical markers of renal disease in a large population of type 1 diabetic patients with diabetic nephropathy. Diabetes Care. 2006;29:83-88.  [PubMed]  [DOI]
193.  Miyauchi K, Takiyama Y, Honjyo J, Tateno M, Haneda M. Upregulated IL-18 expression in type 2 diabetic subjects with nephropathy: TGF-beta1 enhanced IL-18 expression in human renal proximal tubular epithelial cells. Diabetes Res Clin Pract. 2009;83:190-199.  [PubMed]  [DOI]
194.  Boes E, Fliser D, Ritz E, König P, Lhotta K, Mann JF, Müller GA, Neyer U, Riegel W, Riegler P. Apolipoprotein A-IV predicts progression of chronic kidney disease: the mild to moderate kidney disease study. J Am Soc Nephrol. 2006;17:528-536.  [PubMed]  [DOI]
195.  Zhou J, Ouyang X, Cui X, Schoeb TR, Smythies LE, Johnson MR, Guay-Woodford LM, Chapman AB, Mrug M. Renal CD14 expression correlates with the progression of cystic kidney disease. Kidney Int. 2010;78:550-560.  [PubMed]  [DOI]