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Decrease in urinary creatinine in acute kidney injury influences diagnostic value of urinary biomarker-to-creatinine ratio in rats
Toxicology 290 (2011) 241–248
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Decrease in urinary creatinine in acute kidney injury influences diagnostic value of urinary biomarker-to-creatinine ratio in rats Yutaka Tonomura a,b,∗ , Takeki Uehara a , Emi Yamamoto a , Mikinori Torii a , Mitsunobu Matsubara b a b
Drug Safety Evaluation, Drug Developmental Research Laboratories, Shionogi & Co., Ltd., Osaka, Japan Division of Molecular Medicine, Center for Translational and Advanced Animal Research, Tohoku University School of Medicine, Sendai, Japan
a r t i c l e
i n f o
Article history: Received 8 September 2011 Accepted 3 October 2011 Available online 8 October 2011 Keywords: Acute kidney injury Correction Urinary biomarker Urinary creatinine Urine flow rate
a b s t r a c t Recent research has revealed several useful urinary biomarkers of renal dysfunction such as acute kidney injury (AKI). For adequate evaluation of altered urinary biomarkers, it is necessary to consider the influence of varied urine flow rate (UFR). Calculation of the excretion rate of a urinary biomarker (UFR-correction) is the gold standard for the correction of UFR variation. An alternative method that is widely used is to calculate the ratio of the biomarker level to urinary creatinine (Ucr-correction). To date, the equivalence between these two methods has been examined only in a steady state situation such as diabetic nephropathy, and the urinary biomarkers examined have been limited to proteinuria and albuminuria. Therefore, we comprehensively addressed the relationship between Ucr-correction and UFR-correction of ten urinary biomarkers N-acetyl--d-glucosaminidase (NAG), lactate dehydrogenase (LDH), total protein, albumin, kidney injury molecule-1, neutrophil gelatinase-associated lipocalin, clusterin, 2 -microglobulin, cystatin-c and glutathione S-transferase-␣ in non-steady state situations such as AKI. All ten urinary biomarkers showed larger amplitude increases in AKI by Ucr-correction than by UFR-correction in linear regression analysis. Moreover, receiver operating characteristic curves analysis suggested that, at least for the biomarkers NAG and LDH, Ucr-correction had higher diagnostic power than UFR-correction. We observed a decrease in the Ucr excretion in AKI that was accompanied by a reduction in creatinine clearance and reduced mRNA expression of the renal organic cation transporter-2, which is known to function as a transporter for creatinine. These results may provide a mechanistic explanation for the phenomena obtained in Ucr-correction. In conclusion, while Ucr-correction could overestimate the degree of AKI, it could also provide higher diagnostic power for AKI than UFR-correction. We should take into consideration of these backgrounds when using the Ucr-correction. © 2011 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Highly sensitive biomarkers enable the detection of pathophysiological alterations in diseases and of adverse drug effects at early stages. The kidney is one of the targets of iatrogenic injury that is caused by cardiovascular bypass surgery and several drug therapies (Han et al., 2009; Izzedine et al., 2009; Kintzel, ˜ and Pascual, 1996; Parikh et al., 2006). Plasma cre2001; Liano atinine (Pcr) is a widely established parameter for monitoring of renal conditions, and has been adopted as one of the global criteria of acute kidney injury (AKI) (Bellomo et al., 2004; Mehta et al., 2007). However, it is known that Pcr levels increase relatively late in renal injury (Bagshaw, 2010; Herget-Rosenthal
∗ Corresponding author at: Drug Safety Evaluation, Drug Developmental Research Laboratories, Shionogi & Co., Ltd., 3-1-1 Futaba-cho, Toyonaka, Osaka, 561-0825, Japan. Tel.: +81 6 6331 8590; fax: +81 6 6332 6385. E-mail address: [email protected] (Y. Tonomura). 0300-483X/$ – see front matter © 2011 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.tox.2011.10.001
et al., 2004), and that injured sites in the nephron segment cannot be distinguished by Pcr analysis (Schrier, 2010). To resolve these problems, several urinary biomarkers, such as N-acetyl-d-glucosaminidase (NAG), kidney injury molecule-1 (KIM-1), neutrophil gelatinase-associated lipocalin (NGAL), interleukin-18 and liver type fatty acid binding protein, have been recently demonstrated to be useful for the detection of renal dysfunction in humans (Han et al., 2009; Liangos et al., 2007; Parikh et al., 2006; Portilla et al., 2008). Additionally, numerous animal studies have indicated the usefulness of urinary biomarkers for renal dysfunction, such as KIM-1, NGAL, total protein (uTP), albumin (uALB), 2 -microglobulin (B2M), cystatin-c (CysC), clusterin (CLU), trefoil factor-3 (TFF3), glutathione S-transferase-␣ (GST␣) and lactate dehydrogenase (LDH) (Dieterle et al., 2010; Ozer et al., 2010; Tonomura et al., 2009, 2010; Vaidya et al., 2010; Yu et al., 2010). In these studies, receiver operating characteristic (ROC) curve analyses suggested that most of these urinary biomarkers have higher diagnostic power than Pcr. On the other hand, raw data of urinary biomarkers could lead to a misunderstanding of
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Table 1 Study design and results of the renal function tests of experiment I. Compound CDDP
Dose levels (mg/kg) 0 0.1 0.3 1
Pcr (mg/dL)
Ccr (mL/min)
Representative histopathological findings
0.2 0.2 0.2 0.5
± ± ± ±
0.0 0.0 0.0 0.1**
2.40 2.03 2.97 0.58
± ± ± ±
0.24 0.14 0.23 0.07**
No changes No changes Tubular necrosis Tubular degeneration, necrosis and regeneration
CBP
0 1 3 10
0.2 0.2 0.2 0.2
± ± ± ±
0.0 0.0 0.0 0.0
2.25 2.51 2.60 2.61
± ± ± ±
0.20 0.38 0.14 0.28
No changes No changes No changes No changes
BEA
0 2 6 20
0.2 0.2 0.2 0.1
± ± ± ±
0.0 0.0 0.0 0.0*
2.52 1.76 2.85 3.24
± ± ± ±
0.36 0.54 0.49 0.45
No changes No changes No changes Tubular necrosis and regeneration
CSA
0 10 30 100
0.2 0.2 0.2 0.3
± ± ± ±
0.0 0.0 0.0 0.0**
2.07 2.01 1.68 1.09
± ± ± ±
0.47 0.58 0.43 0.27
No changes No changes Tubular degeneration and regeneration Tubular degeneration and regeneration
GMC
0 10 30 100
0.2 0.2 0.2 0.3
± ± ± ±
0.0 0.0 0.0 0.0
3.33 3.02 3.28 1.75
± ± ± ±
0.18 0.49 0.26 0.22*
No changes No changes No changes Tubular degeneration and necrosis
PAN
0 4 12
DOX
0 0.1 0.3 1
0.2 ± 0.0 0.2 ± 0.0 0.5 ± 0.1** 0.2 0.2 0.2 0.2
± ± ± ±
0.0 0.0 0.1 0.0
2.29 ± 0.30 2.56 ± 0.32 1.02 ± 0.25* 2.90 3.11 2.19 1.96
± ± ± ±
0.10 0.42 0.29 0.13
No changes Glomerular degeneration Glomerular degeneration No changes No changes No changes No changes
Data are means ± SE. * and ** indicate statistically significant differences between the treated and the control groups that were analyzed using Dunnett’s multiple comparison test (p < 0.05 and p < 0.01, respectively). Each rat was treated with each compound once a day for 14 days and was necropsied on the day following the final treatment. CDDP, CBP, BEA, GMC, PAN and DOX were intravenously administered. CSA was orally administered. Each group contains five animals. Abbreviations – Pcr: plasma creatinine; Ccr: creatinine clearance; CDDP: cisplatin; CBP: carboplatin; BEA: bromoethylamine; CSA: cyclosporine A; GMC: gentamicin; PAN: puromycin aminonucleoside; and DOX: doxorubicin.
renal conditions. For instance, a decrease in the urine flow rate (UFR) enhances urinary biomarker concentration, and vice versa. UFR variation has been attributed to physiological and pathophysiological causes. Physiologically, urine is diluted or concentrated to maintain the water balance in the kidney; pathophysiologically, renal tubular obstruction in renal dysfunction decreases glomerular filtration, leading to oliguria. Therefore, in order to avoid any misunderstanding, it is necessary to consider the influence of UFR variation on urinary biomarker change. Calculation of the excretion rate of a urinary biomarker (i.e., the production of urinary biomarker concentration and UFR; UFR-correction) is the gold standard for correction of UFR variation (Kestenbaum and de Boer, 2010; Marshall, 1991; Moore et al., 1997). Alternatively, calculation of the ratio of a urinary biomarker to urinary creatinine (Ucr) (the quotient of urinary biomarker concentration divided by Ucr; Ucr-correction), e.g., the albumin-to-creatinine ratio, has also been widely used as a corrective method by virtue of its simplicity. The reason why Ucr can reflect UFR variation is based on the assumption that Ucr concentration is inversely proportional to UFR according to the formula; UFR × Ucr = Pcr × creatinine clearance (Waikar et al., 2010). Therefore, in order for Ucr-correction to be equivalent to UFR-correction, Ucr-correction requires a condition under which the production of UFR and Ucr is constant. Such a condition can be defined as when the production of Pcr and creatinine clearance (Ccr) is constant (Waikar et al., 2010). To date, several studies have demonstrated that Ucr-correction values strongly correlate with the gold standard UFR-correction values in a steady state situation such as diabetic nephropathy (Harvey et al., 1999; Hutchison et al., 1988). However, the biomarkers examined have been limited to proteinuria and albuminuria. Furthermore, there have only been a few studies of other urinary biomarkers in non-steady state
situation such as AKI. Therefore, the purpose of this study was to comprehensively investigate the influence of AKI on Ucr-correction values of ten urinary biomarkers and to characterize mechanistic backgrounds of relationship between Ucr-correction and the gold standard UFR-correction. 2. Materials and methods 2.1. Chemicals Cisplatin (CDDP) and gentamicin (GMC) were purchased from Sequoia Research Products, Ltd. (Pangbourne, UK). Carboplatin (CBP), bromoethylamine (BEA), cyclosporine A (CSA) and doxorubicin (DOX) were purchased from Kemprotec Ltd. (Middlesbrough, UK). Puromycin aminonucleoside (PAN) was purchased from Sigma-Aldrich (St. Louis, MO, USA). 2.2. Experimental design All experimental procedures were conducted after approval of the study by the Institutional Animal Care and Use Committee of Shionogi Research Laboratories. Male Crl:CD(SD) rats were purchased from Charles River (Kanagawa, Japan) and housed under a 12-h light/dark cycle. All animals were allowed free access to food and water. This study consisted of two experiments. Experiment I was conducted by the Toxicogenomics Informatics Project in Japan. The experimental design is summarized in Table 1. Briefly, for experiment I, CDDP, CBP, BEA, GMC, PAN and DOX powder were dissolved in saline, and CSA powder was dissolved in corn oil at the required concentration just prior to use. Solutions of CDDP (0.1, 0.3 and 1 mg/kg, 5 mL/kg), CBP (1, 3 and 10 mg/kg, 5 mL/kg), BEA (2, 6 and 20 mg/kg, 10 mL/kg), GMC (10, 30 and 100 mg/kg, 10 mL/kg), PAN (4 and 12 mg/kg, 10 mL/kg) and DOX (0.1, 0.3 and 1 mg/kg, 10 mL/kg) were intravenously administered every day for 2 weeks, using 5 animals for each dose of each chemical. The CSA solution (10, 30 and 100 mg/kg, 5 mL/kg, n = 5, respectively) was orally administered every day for 2 weeks. The animals were 6 weeks old at the start of drug administration. Necropsy was performed 15 days after first administration of drugs (8 weeks old). Experiment II was conducted at Shionogi & Co., Ltd. The experimental design is summarized in Table 3. Briefly, for experiment II, the CDDP solution was prepared as the same
Y. Tonomura et al. / Toxicology 290 (2011) 241–248 method as that used for experiment I. A single intravenous administration of saline (5 mL/kg, n = 10) or CDDP solution (1 and 4 mg/kg, 5 mL/kg, n = 5 and 10, respectively) was performed. The animals were 8 weeks old at the time of administration. Necropsy was performed on 72 h after administration.
2.3. Urinalysis and blood chemistry examination Urine samples were collected over a period of 4 h from 8-week-old treated and control rats (15 days after first administration, experiment I; 72 h after administration, experiment II) using individual metabolic cages. Blood samples were obtained from the vena cava at necropsy, using a vacuum blood-collecting tube containing heparin lithium under ether anesthesia (experiment I), or containing heparin sodium under pentobarbital sodium anesthesia (experiment II). Plasma samples were separated by centrifugation. Urinalysis and blood chemistry examinations were performed using the following methods: NAG, LDH, uTP, CysC, Ucr and Pcr were measured using an automatic analyzer (Hitachi High-Technologies Co., Tokyo, Japan); KIM-1, NGAL, uALB, CLU, GST␣ (Meso Scale Discovery, Gaithersburg, MD, USA) and B2M (Mitsubishi Chemical Medience, Tokyo, Japan) were measured using commercial enzyme-linked immunoabsorbent assay kits. Ucr-correction value and UFR-correction values of each urinary biomarker were calculated using the following formulae:
Ucr-correction value = Parameter/Ucr; UFR-correction value = Parameter × UFR; Ccr and Ucr excretion were calculated using the following formulae: Ccr = (Ucr × UFR)/Pcr; Ucr excretion = Ucr × UFR.
2.4. Examination of mRNA levels of organic cation transporter-2 transporter in the kidney A portion of the kidney was quickly obtained from three rats per group following exsanguinations, was immersed in RNAlater (Ambion Inc., Austin, TX, USA) overnight at 4 ◦ C and was then frozen at −80 ◦ C until use. Thereafter, kidney samples were homogenized in a RLT buffer of the RNeasy Mini kit (Qiagen, Venlo, Netherlands), and total RNA was isolated according to the manufacturer’s instructions. Changes in organic cation transporter-2 (Oct2) gene expression in the kidney were analyzed by microarray analysis using the GeneChip RAE 230 2.0 (Affymetrix, Santa Clara, CA, USA) using three animals for each group. Microarray Analysis Suite 5.0 (Affymetrix) was used to quantify microarray signal intensities. Microarray analyses were conducted by Toxicogenomics Informatics Project in Japan.
2.5. Statistical analysis Statistical significance was determined using Welch’s t-test for the comparison of two groups and Dunnett’s multiple comparison test for the comparison of three and four groups using Prism (GraphPad Software Inc., San Diego, CA, USA). ROC curves were denoted using a non-parametric method. ROC analysis was performed using JMP Pro 9.0 (SAS Institute, Cary, NC, USA). Non-AKI and AKI samples were used as negative and positive samples, respectively.
3. Results 3.1. Induction of AKI In order to induce AKI, we repeatedly administered two or three different doses of CDDP, CBP, BEA, CSA, GMC, PAN or DOX to rats every day for 2 weeks (experiment I, Table 1). Induction of AKI was confirmed by increase in Pcr and/or decrease in Ccr. Pcr and Ccr have been adopted as clinical parameters of AKI by the global criteria of Acute Dialysis Quality Initiative and/or Acute Kidney Injury Network (Bellomo et al., 2004; Mehta et al., 2007). Groups that received high doses of CDDP, CSA, GMC or PAN displayed AKI, showing a statistically significant increase in Pcr and/or decrease in Ccr compared to the corresponding control group, and exhibiting pathological alterations 15 days after first administration of the drug (Table 1). In contrast, although the high-dose BEA group, the middle-dose CDDP and CSA groups, and the low-dose PAN group, showed pathological alterations, these groups were not classified as AKI because they showed no increase in Pcr and/or decrease in Ccr compared to controls (Table 1).
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3.2. Influence of AKI on the Ucr-correction values of NAG and LDH In non-AKI, the Ucr-correction value is equivalent to the UFRcorrection value when the formula: UFR × Ucr = Pcr × Ccr is used, and the condition of constant production of UFR and Ucr is met (Waikar et al., 2010). We first performed regression analysis using non-AKI data of NAG and LDH. In non-AKI, the Ucr-correction values of NAG and LDH were linearly correlated to their UFR-correction values (Fig. 1a and b). In contrast, in AKI, the Ucr-correction values of NAG and LDH showed larger amplitude increases than their UFR-correction values. Regarding diagnostic accuracy, in ROC curve analyses the Ucr-correction displayed higher sensitivity, specificity and area under the curve than the UFR-correction for both NAG and LDH data (Fig. 1c and d, Table 2). 3.3. Relationship between Ucr-correction and UFR-correction for other urinary biomarkers To examine whether or not the large amplitude increase obtained by Ucr-correction for NAG and LDH might also be a feature of other urinary biomarkers in AKI, we newly prepared an AKI rat model by a single treatment with low or high-dose CDDP (experiment II, Table 3). AKI was confirmed in the high-dose CDDP group 72 h after administration, by increase in Pcr and decrease in Ccr. This group additionally showed pathological alterations (Table 3). On the other hand, there were no alterations in these AKI parameters, and no pathological alterations were observed, in the low-dose CDDP group 72 h after administration (Table 3). We then compared Ucr-correction values of the biomarkers; uTP, uALB, KIM-1, B2M, CysC, CLU, NGAL and GST␣ with their gold standard UFRcorrection values, in the control and the two CDDP groups, using the same method as that used for NAG and LDH in experiment I. The high-dose CDDP group showed larger amplitude increases in the Ucr-correction values of all eight of these urinary biomarkers compared to their UFR-correction values (Fig. 2a–h). 3.4. Background of the large amplitude increases in urinary biomarkers obtained by Ucr-correction in AKI To examine the background of the large amplitude increases observed for the urinary biomarkers by Ucr-correction in AKI, we examined Ucr excretion levels and renal transporter levels using data from rats treated with CDDP, CBP, BEA, CSA, GMC, PAN or DOX in experiment I (Table 1). First, a decrease in the Ucr excretion was observed in AKI compared to non-AKI (Fig. 3a). Second, Ccr was seen to be decreased in AKI compared to non-AKI (Fig. 3b). Third, organic cation transporter-2 (OCT2 or Oct2) mRNA levels were found to be decreased in the kidney of the high- and middle-dose CDDP and CSA groups, and of the high-dose GMC and PAN groups (Fig. 3c). Finally, a significant positive correlation between Ucr excretion and renal Oct2 mRNA levels was observed, although the correlation coefficient was low (Fig. 3d). In experiment II, decreases in the Ucr excretion and Ccr were observed in AKI compared to non-AKI as well as experiment I (Ucr excretion, 436 ± 26 mg/h in non-AKI vs. 340 ± 22 mg/h in AKI on 72 h after CDDP administration, p < 0.05, n = 15 and 10, respectively; Ccr, 2.87 ± 0.83 mL/min in non-AKI vs. 0.97 ± 0.38 mL/min in AKI on 72 h after CDDP administration, p < 0.01, n = 15 and 10, respectively. Data are means ± SE). 4. Discussion In this study, we examined the influence of AKI on urinary biomarker-to-creatinine ratios. For this examination, seven nephrotoxicants, CDDP, CBP, BEA, CSA, GMC, PAN and DOX, were separately administrated to rats for induction of AKI in experiment I. Induction of AKI was confirmed in the high-dose CDDP, CSA, GMC
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Fig. 1. Comparison of equivalence and diagnostic accuracy of Ucr- and UFR-correction values of NAG and LDH in AKI and non-AKI. Linear regression analysis was used to analyze the relationship between Ucr- and UFR-correction values of NAG (a) and LDH (b) in AKI (open circles, AKI rats with altered Pcr and/or Ccr, n = 18) and non-AKI (black circles, control rats and rats without altered Pcr and Ccr, n = 112). The insets in each figure show a magnification of the regression lines at low correction values (black circles, control rats, n = 35; gray circles, rats without altered Pcr and Ccr, n = 77). The diagnostic accuracy of Ucr-correction values (black circles, n = 130) and UFR-correction values (gray circles, n = 130) of NAG (c) and LDH (d) were examined using ROC curve analysis.
Table 2 Indices for diagnostic accuracy of ROC curve analyses. Index
NAG
Sensitivity (%) Specificity (%) AUC
LDH
Ucr-correction
UFR-correction
Ucr-correction
UFR-correction
72.2 90.2 0.887
61.1 70.5 0.739
77.8 99.1 0.838
77.8 92.0 0.792
Abbreviations – ROC: receiver operating characteristic; NAG: N-acetyl--d-glucosaminidase; LDH: lactate dehydrogenase; Ucr: urinary creatinine; UFR: urine flow rate; and AUC: area under curve.
and PAN groups by increase in Pcr and/or decrease in Ccr. Using these AKI rats, we first examined the influence of AKI on urinary NAG-to-creatinine ratio and urinary LDH-to-creatinine ratio. Urinary NAG and LDH were adopted as suitable model biomarkers. These enzymes pre-exist in normal renal tubules and leak into the tubular lumen when renal tubular injury occurs (Guder and
Ross, 1984; Tonomura et al., 2010, in press). Therefore, it is not necessary to consider the degree of transcription and translation of biomarkers, and the condition of glomerular sieves regarding these two enzymes. In this study, Ucr-correction values of NAG and LDH exhibited larger amplitude increases than their UFR-correction values in linear regression analysis, suggesting that Ucr-correction
Table 3 Study design and results of the renal function tests of experiment II. Compound
Dose levels (mg/kg)
PCr (mg/dL)
Ccr (mL/min)
Representative histopathological findings
CDDP
0 1 4
0.3 ± 0.0 0.3 ± 0.0 0.6 ± 0.0**
2.80 ± 0.18 3.01 ± 0.57 0.97 ± 0.12**
No changes No changes Tubular degeneration, necrosis, regeneration
Data are means ± SE. ** indicates a statistically significant difference between the treated and the control groups that were analyzed using Dunnett’s multiple comparison test (p < 0.01). Each rat was treated once with CDDP and necropsied 72 h after administration. CDDP was intravenously administered. The control and the high-dose groups contain ten animals, and the low-dose group contains five animals. Abbreviations – Pcr: plasma creatinine; Ccr: creatinine clearance; and CDDP: cisplatin.
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Fig. 2. Examination of the relationship between Ucr- and UFR-correction values of other urinary biomarkers in AKI and non-AKI. Linear regression analysis was used to analyze the relationship between Ucr- and UFR-correction values of uTP (a), uALB (b), KIM-1 (c), B2M (d), CysC (e), CLU (f), NGAL (g) and GST␣ (h) in AKI (high-dose CDDP group; open circles, n = 10) and non-AKI (control and low-dose CDDP groups; black circles, n = 15). The insets in each figure show a magnification of the regression lines at low correction values (black circles, control group, n = 10; gray circles, low-dose CDDP group without altered Pcr and Ccr, n = 5).
values could overestimate AKI. On the other hand, ROC curve analysis suggested that, at least for NAG and LDH, Ucr-correction values had higher diagnostic power than UFR-correction values. To comprehensively examine whether or not other urinary biomarkers would also show large amplitude increases following calculation of their Ucr-correction values in AKI, we newly prepared an AKI rat model by CDDP treatment (experiment II) and measured urinary biomarkers uTP, uALB, KIM-1, B2M, CysC, CLU, NGAL and GST␣. The levels of these biomarkers are known to increase when AKI is present (Tonomura et al., 2010). Similar to NAG and LDH, all eight of the urinary biomarkers exhibited large amplitude increases by Ucrcorrection. Therefore, a large amplitude increase by Ucr-correction is likely to be a common phenomenon for increase-type urinary biomarkers, regardless of the mechanism by which the level of the urinary biomarker was increased: abnormality in transglomerular passage (uALB) (D’Amico and Bazzi, 2003); inhibition of reabsorption in proximal tubules (uALB, B2M, CysC and NGAL) (Amsellem et al., 2010; Christensen et al., 2009; D’Amico and Bazzi, 2003; Dieterle et al., 2010; Gauthier et al., 1984; Kuwabara et al., 2009;
Portman et al., 1986; Tenstad et al., 1996); release into the extracellular space after induction of protein expression (KIM-1, CLU and NGAL) (Bailly et al., 2002; Hidaka et al., 2002; Ichimura et al., 1998; Kuwabara et al., 2009) leakage of pre-existing protein from the renal tubules into the lumens (NAG, LDH and GST␣) (Guder and Ross, 1984; Rozell et al., 1993). To elucidate the background of the large amplitude increases and the enhanced diagnostic power of Ucr-correction, we addressed the characteristics of Ucr in AKI. We observed a decrease in the Ucr excretion in AKI rats in experiments I and II. Considering that Ucr-correction values were calculated by dividing urinary biomarker concentration by Ucr concentration, the large amplitude increase and enhanced diagnostic power of Ucr-correction in AKI was attributed to a synergistic effect of increased urinary biomarker excretion and decreased Ucr excretion. Conversely, in the case of decrease-type urinary biomarkers such as TFF3 (Yu et al., 2010), Ucr-correction would counteract the decrease in the level of such a biomarker because the Ucr excretion would decrease along with the decrease in such a urinary biomarker in AKI. Furthermore, in a
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Fig. 3. Examination of the background of the large amplitude increase in the Ucr-correction value in AKI. The Ucr excretion in AKI was compared to that in non-AKI (a). Transverse lines indicate the mean value of the Ucr excretion. The statistical significance of the difference between AKI and non-AKI was determined using Welch’s t-test **p < 0.01. Ccr was compared between AKI and non-AKI (b; n = 18 and 112, respectively). The statistical significance of the difference between AKI and non-AKI was determined using Welch’s t-test **p < 0.01. Transverse lines indicate the mean value of Ccr. Renal Oct2 mRNA levels were assessed in rats treated with CDDP, CBP, BEA, CSA, GMC, PAN or DOX and in control rats (c; n = 3 in each group). Data are expressed as means ± SE. The statistical significance of differences between the treated and control groups was determined using Dunnett’s multiple comparison test, *p < 0.05 and **p < 0.01. Pearson’s correlation coefficient (R) between Ucr excretion and renal Oct2 mRNA levels was calculated (d; n = 78).
situation where the urinary biomarker remains at a constant level, a decrease in the Ucr excretion would lead to an increase in the Ucr-correction value. Therefore, when using Ucr-correction, the direction of alterations of the levels of the urinary biomarker and Ucr should be taken into consideration. Regarding the mechanism that underlies the decrease in the Ucr excretion in AKI, we observed a reduction in Ccr (experiments I and II) and renal Oct2 mRNA levels (experiment I) in AKI rats. The decrease in Ccr simply implied a decrease in the transglomerular passage of creatinine. The reduction in renal Oct2 mRNA levels suggested a decrease in creatinine secretion from renal tubules into lumens, since OCT2 has been reported to be localized on the basolateral side of renal tubules and imports creatinine from blood into the intracellular space (Karbach et al., 2000; Urakami et al., 2004). This suggestion was supported by a significant positive correlation between Ucr excretion and renal Oct2 mRNA levels in this study.
However, since the correlation coefficient was low, a decrease in creatinine import from blood is not sufficient to completely explain the decrease in creatinine secretion from renal tubules. The multidrug and toxin extrusion-2 (MATE2) protein has been reported to function as a transporter for creatinine (Masuda et al., 2006). MATE2 is expressed on the apical side of renal tubules and would export creatinine into the extracellular space, i.e., on the urine side. Unfortunately, MATE2 has not been identified in rats. Therefore, further study is needed to understand the relationship between tubular creatinine secretion and Ucr excretion in AKI. In this study, we further observed a decrease in body weight in AKI rats in experiment I (305.9 ± 3.3 g in non-AKI vs. 260.1 ± 7.5 g in AKI 15 days after first drug administration of CDDP, CBP, BEA, CSA, GMC, PAN or DOX, p < 0.01, n = 112 and 18, respectively. Data are means ± SE). This result indicated that muscle mass decreased in AKI and that a decrease in the Ucr excretion in AKI might be partially caused by a
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decrease in creatinine as a result of creatine metabolism (Hosten, 1990). In addition to corrective methods that use Ucr and UFR, corrective methods based on urinary specific gravity or urinary osmolality have also been reported in non-AKI situations (Gyamlani et al., 2003; Moore et al., 1997). However, urinary specific gravity and urinary osmolality are determined not only by urine dilution or concentration but also by the level of urinary solutes such as glucose, Na+ and K+ . The filtered and reabsorbed level of urinary solutes is thought to be affected by AKI due to renal tubular dysfunction and to a decrease in the glomerular filtration rate. Since urinary gravity and urinary osmolality are composed of multiple factors and are more complex than Ucr, further studies are required before using correction based on urinary specific gravity or urinary osmolality in AKI. In conclusion, the present study is the first to comprehensively examine the influence of AKI on the urinary biomarker-tocreatinine ratio and to indicate the merits and demerits of Ucr-correction: the merit is that it can sensitively detect AKI without timed urine collection; the demerit is that it does not provide a true evaluation of AKI. In an emergency medicine field site in human, sensitive detection rather than true understanding of disease symptoms is essential for quick treatment, whereas the overestimation of AKI cause a mistake in the decision-making for new drug development in preclinical toxicity study. Therefore, we should take into consideration of these backgrounds when using the Ucr-correction. Conflict of interest statement The authors declare that there are no conflicts of interest. Acknowledgments Experiment I and the microarray analyses were conducted by the Toxicogenomics Informatics Project in Japan, which was supported by a grant from the Ministry of Health, Labour and Welfare of Japan (H14-toxico-001). We thank Takako Miyoshi for preparation of the sections used for pathological examination, and Shingo Takagi for useful suggestions regarding this study. References Amsellem, S., Gburek, J., Hamard, G., Nielsen, R., Willnow, T.E., Devuyst, O., Nexo, E., Verroust, P.J., Christensen, E.I., Kozyraki, R., 2010. Cubilin is essential for albumin reabsorption in the renal proximal tubule. J. Am. Soc. Nephrol. 21, 1859–1867. Bagshaw, S.M., 2010. Acute kidney injury: diagnosis and classification of AKI: AKIN or RIFLE? Nat. Rev. Nephrol. 6, 71–73. Bailly, V., Zhang, Z., Meier, W., Cate, R., Sanicola, M., Bonventre, J.V., 2002. Shedding of kidney injury molecule-1, a putative adhesion protein involved in renal regeneration. J. Biol. Chem. 277, 39739–39748. Bellomo, R., Ronco, C., Kellum, J.A., Mehta, R.L., Palevsky, P., 2004. Acute Dialysis Quality Initiative workgroup: Acute renal failure—definition, outcome measures, animal models, fluid therapy and information technology needs: the Second International Consensus Conference of the Acute Dialysis Quality Initiative (ADQI) Group. Crit. Care 8, R204–R212. Christensen, E.I., Verroust, P.J., Nielsen, R., 2009. Receptor-mediated endocytosis in renal proximal tubule. Pflugers Arch. 458, 1039–1048. D’Amico, G., Bazzi, C., 2003. Pathophysiology of proteinuria. Kidney Int. 63, 809–825. Dieterle, F., Perentes, E., Cordier, A., Roth, D.R., Verdes, P., Grenet, O., Pantano, S., Moulin, P., Wahl, D., Mahl, A., End, P., Staedtler, F., Legay, F., Carl, K., Laurie, D., Chibout, S.D., Vonderscher, J., Maurer, G., 2010. Urinary clusterin, cystatin C, beta2-microglobulin and total protein as markers to detect drug-induced kidney injury. Nat. Biotechnol. 28, 463–469. Gauthier, C., Nguyen-Simonnet, H., Vincent, C., Revillard, J.P., Pellet, M.V., 1984. Renaltubular absorption of beta 2 microglobulin. Kidney Int. 26, 170–175. Guder, W.G., Ross, B.D., 1984. Enzyme distribution along the nephron. Kidney Int. 26, 101–111. Gyamlani, G.G., Bergstralh, E.J., Slezak, J.M., Larson, T.S., 2003. Urinary albumin to osmolality ratio predicts 24-hour urine albumin excretion in diabetes mellitus. Am. J. Kidney Dis. 42, 685–692.
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Toxicology journal homepage: www.elsevier.com/locate/toxicol
Decrease in urinary creatinine in acute kidney injury influences diagnostic value of urinary biomarker-to-creatinine ratio in rats Yutaka Tonomura a,b,∗ , Takeki Uehara a , Emi Yamamoto a , Mikinori Torii a , Mitsunobu Matsubara b a b
Drug Safety Evaluation, Drug Developmental Research Laboratories, Shionogi & Co., Ltd., Osaka, Japan Division of Molecular Medicine, Center for Translational and Advanced Animal Research, Tohoku University School of Medicine, Sendai, Japan
a r t i c l e
i n f o
Article history: Received 8 September 2011 Accepted 3 October 2011 Available online 8 October 2011 Keywords: Acute kidney injury Correction Urinary biomarker Urinary creatinine Urine flow rate
a b s t r a c t Recent research has revealed several useful urinary biomarkers of renal dysfunction such as acute kidney injury (AKI). For adequate evaluation of altered urinary biomarkers, it is necessary to consider the influence of varied urine flow rate (UFR). Calculation of the excretion rate of a urinary biomarker (UFR-correction) is the gold standard for the correction of UFR variation. An alternative method that is widely used is to calculate the ratio of the biomarker level to urinary creatinine (Ucr-correction). To date, the equivalence between these two methods has been examined only in a steady state situation such as diabetic nephropathy, and the urinary biomarkers examined have been limited to proteinuria and albuminuria. Therefore, we comprehensively addressed the relationship between Ucr-correction and UFR-correction of ten urinary biomarkers N-acetyl--d-glucosaminidase (NAG), lactate dehydrogenase (LDH), total protein, albumin, kidney injury molecule-1, neutrophil gelatinase-associated lipocalin, clusterin, 2 -microglobulin, cystatin-c and glutathione S-transferase-␣ in non-steady state situations such as AKI. All ten urinary biomarkers showed larger amplitude increases in AKI by Ucr-correction than by UFR-correction in linear regression analysis. Moreover, receiver operating characteristic curves analysis suggested that, at least for the biomarkers NAG and LDH, Ucr-correction had higher diagnostic power than UFR-correction. We observed a decrease in the Ucr excretion in AKI that was accompanied by a reduction in creatinine clearance and reduced mRNA expression of the renal organic cation transporter-2, which is known to function as a transporter for creatinine. These results may provide a mechanistic explanation for the phenomena obtained in Ucr-correction. In conclusion, while Ucr-correction could overestimate the degree of AKI, it could also provide higher diagnostic power for AKI than UFR-correction. We should take into consideration of these backgrounds when using the Ucr-correction. © 2011 Elsevier Ireland Ltd. All rights reserved.
1. Introduction Highly sensitive biomarkers enable the detection of pathophysiological alterations in diseases and of adverse drug effects at early stages. The kidney is one of the targets of iatrogenic injury that is caused by cardiovascular bypass surgery and several drug therapies (Han et al., 2009; Izzedine et al., 2009; Kintzel, ˜ and Pascual, 1996; Parikh et al., 2006). Plasma cre2001; Liano atinine (Pcr) is a widely established parameter for monitoring of renal conditions, and has been adopted as one of the global criteria of acute kidney injury (AKI) (Bellomo et al., 2004; Mehta et al., 2007). However, it is known that Pcr levels increase relatively late in renal injury (Bagshaw, 2010; Herget-Rosenthal
∗ Corresponding author at: Drug Safety Evaluation, Drug Developmental Research Laboratories, Shionogi & Co., Ltd., 3-1-1 Futaba-cho, Toyonaka, Osaka, 561-0825, Japan. Tel.: +81 6 6331 8590; fax: +81 6 6332 6385. E-mail address: [email protected] (Y. Tonomura). 0300-483X/$ – see front matter © 2011 Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.tox.2011.10.001
et al., 2004), and that injured sites in the nephron segment cannot be distinguished by Pcr analysis (Schrier, 2010). To resolve these problems, several urinary biomarkers, such as N-acetyl-d-glucosaminidase (NAG), kidney injury molecule-1 (KIM-1), neutrophil gelatinase-associated lipocalin (NGAL), interleukin-18 and liver type fatty acid binding protein, have been recently demonstrated to be useful for the detection of renal dysfunction in humans (Han et al., 2009; Liangos et al., 2007; Parikh et al., 2006; Portilla et al., 2008). Additionally, numerous animal studies have indicated the usefulness of urinary biomarkers for renal dysfunction, such as KIM-1, NGAL, total protein (uTP), albumin (uALB), 2 -microglobulin (B2M), cystatin-c (CysC), clusterin (CLU), trefoil factor-3 (TFF3), glutathione S-transferase-␣ (GST␣) and lactate dehydrogenase (LDH) (Dieterle et al., 2010; Ozer et al., 2010; Tonomura et al., 2009, 2010; Vaidya et al., 2010; Yu et al., 2010). In these studies, receiver operating characteristic (ROC) curve analyses suggested that most of these urinary biomarkers have higher diagnostic power than Pcr. On the other hand, raw data of urinary biomarkers could lead to a misunderstanding of
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Table 1 Study design and results of the renal function tests of experiment I. Compound CDDP
Dose levels (mg/kg) 0 0.1 0.3 1
Pcr (mg/dL)
Ccr (mL/min)
Representative histopathological findings
0.2 0.2 0.2 0.5
± ± ± ±
0.0 0.0 0.0 0.1**
2.40 2.03 2.97 0.58
± ± ± ±
0.24 0.14 0.23 0.07**
No changes No changes Tubular necrosis Tubular degeneration, necrosis and regeneration
CBP
0 1 3 10
0.2 0.2 0.2 0.2
± ± ± ±
0.0 0.0 0.0 0.0
2.25 2.51 2.60 2.61
± ± ± ±
0.20 0.38 0.14 0.28
No changes No changes No changes No changes
BEA
0 2 6 20
0.2 0.2 0.2 0.1
± ± ± ±
0.0 0.0 0.0 0.0*
2.52 1.76 2.85 3.24
± ± ± ±
0.36 0.54 0.49 0.45
No changes No changes No changes Tubular necrosis and regeneration
CSA
0 10 30 100
0.2 0.2 0.2 0.3
± ± ± ±
0.0 0.0 0.0 0.0**
2.07 2.01 1.68 1.09
± ± ± ±
0.47 0.58 0.43 0.27
No changes No changes Tubular degeneration and regeneration Tubular degeneration and regeneration
GMC
0 10 30 100
0.2 0.2 0.2 0.3
± ± ± ±
0.0 0.0 0.0 0.0
3.33 3.02 3.28 1.75
± ± ± ±
0.18 0.49 0.26 0.22*
No changes No changes No changes Tubular degeneration and necrosis
PAN
0 4 12
DOX
0 0.1 0.3 1
0.2 ± 0.0 0.2 ± 0.0 0.5 ± 0.1** 0.2 0.2 0.2 0.2
± ± ± ±
0.0 0.0 0.1 0.0
2.29 ± 0.30 2.56 ± 0.32 1.02 ± 0.25* 2.90 3.11 2.19 1.96
± ± ± ±
0.10 0.42 0.29 0.13
No changes Glomerular degeneration Glomerular degeneration No changes No changes No changes No changes
Data are means ± SE. * and ** indicate statistically significant differences between the treated and the control groups that were analyzed using Dunnett’s multiple comparison test (p < 0.05 and p < 0.01, respectively). Each rat was treated with each compound once a day for 14 days and was necropsied on the day following the final treatment. CDDP, CBP, BEA, GMC, PAN and DOX were intravenously administered. CSA was orally administered. Each group contains five animals. Abbreviations – Pcr: plasma creatinine; Ccr: creatinine clearance; CDDP: cisplatin; CBP: carboplatin; BEA: bromoethylamine; CSA: cyclosporine A; GMC: gentamicin; PAN: puromycin aminonucleoside; and DOX: doxorubicin.
renal conditions. For instance, a decrease in the urine flow rate (UFR) enhances urinary biomarker concentration, and vice versa. UFR variation has been attributed to physiological and pathophysiological causes. Physiologically, urine is diluted or concentrated to maintain the water balance in the kidney; pathophysiologically, renal tubular obstruction in renal dysfunction decreases glomerular filtration, leading to oliguria. Therefore, in order to avoid any misunderstanding, it is necessary to consider the influence of UFR variation on urinary biomarker change. Calculation of the excretion rate of a urinary biomarker (i.e., the production of urinary biomarker concentration and UFR; UFR-correction) is the gold standard for correction of UFR variation (Kestenbaum and de Boer, 2010; Marshall, 1991; Moore et al., 1997). Alternatively, calculation of the ratio of a urinary biomarker to urinary creatinine (Ucr) (the quotient of urinary biomarker concentration divided by Ucr; Ucr-correction), e.g., the albumin-to-creatinine ratio, has also been widely used as a corrective method by virtue of its simplicity. The reason why Ucr can reflect UFR variation is based on the assumption that Ucr concentration is inversely proportional to UFR according to the formula; UFR × Ucr = Pcr × creatinine clearance (Waikar et al., 2010). Therefore, in order for Ucr-correction to be equivalent to UFR-correction, Ucr-correction requires a condition under which the production of UFR and Ucr is constant. Such a condition can be defined as when the production of Pcr and creatinine clearance (Ccr) is constant (Waikar et al., 2010). To date, several studies have demonstrated that Ucr-correction values strongly correlate with the gold standard UFR-correction values in a steady state situation such as diabetic nephropathy (Harvey et al., 1999; Hutchison et al., 1988). However, the biomarkers examined have been limited to proteinuria and albuminuria. Furthermore, there have only been a few studies of other urinary biomarkers in non-steady state
situation such as AKI. Therefore, the purpose of this study was to comprehensively investigate the influence of AKI on Ucr-correction values of ten urinary biomarkers and to characterize mechanistic backgrounds of relationship between Ucr-correction and the gold standard UFR-correction. 2. Materials and methods 2.1. Chemicals Cisplatin (CDDP) and gentamicin (GMC) were purchased from Sequoia Research Products, Ltd. (Pangbourne, UK). Carboplatin (CBP), bromoethylamine (BEA), cyclosporine A (CSA) and doxorubicin (DOX) were purchased from Kemprotec Ltd. (Middlesbrough, UK). Puromycin aminonucleoside (PAN) was purchased from Sigma-Aldrich (St. Louis, MO, USA). 2.2. Experimental design All experimental procedures were conducted after approval of the study by the Institutional Animal Care and Use Committee of Shionogi Research Laboratories. Male Crl:CD(SD) rats were purchased from Charles River (Kanagawa, Japan) and housed under a 12-h light/dark cycle. All animals were allowed free access to food and water. This study consisted of two experiments. Experiment I was conducted by the Toxicogenomics Informatics Project in Japan. The experimental design is summarized in Table 1. Briefly, for experiment I, CDDP, CBP, BEA, GMC, PAN and DOX powder were dissolved in saline, and CSA powder was dissolved in corn oil at the required concentration just prior to use. Solutions of CDDP (0.1, 0.3 and 1 mg/kg, 5 mL/kg), CBP (1, 3 and 10 mg/kg, 5 mL/kg), BEA (2, 6 and 20 mg/kg, 10 mL/kg), GMC (10, 30 and 100 mg/kg, 10 mL/kg), PAN (4 and 12 mg/kg, 10 mL/kg) and DOX (0.1, 0.3 and 1 mg/kg, 10 mL/kg) were intravenously administered every day for 2 weeks, using 5 animals for each dose of each chemical. The CSA solution (10, 30 and 100 mg/kg, 5 mL/kg, n = 5, respectively) was orally administered every day for 2 weeks. The animals were 6 weeks old at the start of drug administration. Necropsy was performed 15 days after first administration of drugs (8 weeks old). Experiment II was conducted at Shionogi & Co., Ltd. The experimental design is summarized in Table 3. Briefly, for experiment II, the CDDP solution was prepared as the same
Y. Tonomura et al. / Toxicology 290 (2011) 241–248 method as that used for experiment I. A single intravenous administration of saline (5 mL/kg, n = 10) or CDDP solution (1 and 4 mg/kg, 5 mL/kg, n = 5 and 10, respectively) was performed. The animals were 8 weeks old at the time of administration. Necropsy was performed on 72 h after administration.
2.3. Urinalysis and blood chemistry examination Urine samples were collected over a period of 4 h from 8-week-old treated and control rats (15 days after first administration, experiment I; 72 h after administration, experiment II) using individual metabolic cages. Blood samples were obtained from the vena cava at necropsy, using a vacuum blood-collecting tube containing heparin lithium under ether anesthesia (experiment I), or containing heparin sodium under pentobarbital sodium anesthesia (experiment II). Plasma samples were separated by centrifugation. Urinalysis and blood chemistry examinations were performed using the following methods: NAG, LDH, uTP, CysC, Ucr and Pcr were measured using an automatic analyzer (Hitachi High-Technologies Co., Tokyo, Japan); KIM-1, NGAL, uALB, CLU, GST␣ (Meso Scale Discovery, Gaithersburg, MD, USA) and B2M (Mitsubishi Chemical Medience, Tokyo, Japan) were measured using commercial enzyme-linked immunoabsorbent assay kits. Ucr-correction value and UFR-correction values of each urinary biomarker were calculated using the following formulae:
Ucr-correction value = Parameter/Ucr; UFR-correction value = Parameter × UFR; Ccr and Ucr excretion were calculated using the following formulae: Ccr = (Ucr × UFR)/Pcr; Ucr excretion = Ucr × UFR.
2.4. Examination of mRNA levels of organic cation transporter-2 transporter in the kidney A portion of the kidney was quickly obtained from three rats per group following exsanguinations, was immersed in RNAlater (Ambion Inc., Austin, TX, USA) overnight at 4 ◦ C and was then frozen at −80 ◦ C until use. Thereafter, kidney samples were homogenized in a RLT buffer of the RNeasy Mini kit (Qiagen, Venlo, Netherlands), and total RNA was isolated according to the manufacturer’s instructions. Changes in organic cation transporter-2 (Oct2) gene expression in the kidney were analyzed by microarray analysis using the GeneChip RAE 230 2.0 (Affymetrix, Santa Clara, CA, USA) using three animals for each group. Microarray Analysis Suite 5.0 (Affymetrix) was used to quantify microarray signal intensities. Microarray analyses were conducted by Toxicogenomics Informatics Project in Japan.
2.5. Statistical analysis Statistical significance was determined using Welch’s t-test for the comparison of two groups and Dunnett’s multiple comparison test for the comparison of three and four groups using Prism (GraphPad Software Inc., San Diego, CA, USA). ROC curves were denoted using a non-parametric method. ROC analysis was performed using JMP Pro 9.0 (SAS Institute, Cary, NC, USA). Non-AKI and AKI samples were used as negative and positive samples, respectively.
3. Results 3.1. Induction of AKI In order to induce AKI, we repeatedly administered two or three different doses of CDDP, CBP, BEA, CSA, GMC, PAN or DOX to rats every day for 2 weeks (experiment I, Table 1). Induction of AKI was confirmed by increase in Pcr and/or decrease in Ccr. Pcr and Ccr have been adopted as clinical parameters of AKI by the global criteria of Acute Dialysis Quality Initiative and/or Acute Kidney Injury Network (Bellomo et al., 2004; Mehta et al., 2007). Groups that received high doses of CDDP, CSA, GMC or PAN displayed AKI, showing a statistically significant increase in Pcr and/or decrease in Ccr compared to the corresponding control group, and exhibiting pathological alterations 15 days after first administration of the drug (Table 1). In contrast, although the high-dose BEA group, the middle-dose CDDP and CSA groups, and the low-dose PAN group, showed pathological alterations, these groups were not classified as AKI because they showed no increase in Pcr and/or decrease in Ccr compared to controls (Table 1).
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3.2. Influence of AKI on the Ucr-correction values of NAG and LDH In non-AKI, the Ucr-correction value is equivalent to the UFRcorrection value when the formula: UFR × Ucr = Pcr × Ccr is used, and the condition of constant production of UFR and Ucr is met (Waikar et al., 2010). We first performed regression analysis using non-AKI data of NAG and LDH. In non-AKI, the Ucr-correction values of NAG and LDH were linearly correlated to their UFR-correction values (Fig. 1a and b). In contrast, in AKI, the Ucr-correction values of NAG and LDH showed larger amplitude increases than their UFR-correction values. Regarding diagnostic accuracy, in ROC curve analyses the Ucr-correction displayed higher sensitivity, specificity and area under the curve than the UFR-correction for both NAG and LDH data (Fig. 1c and d, Table 2). 3.3. Relationship between Ucr-correction and UFR-correction for other urinary biomarkers To examine whether or not the large amplitude increase obtained by Ucr-correction for NAG and LDH might also be a feature of other urinary biomarkers in AKI, we newly prepared an AKI rat model by a single treatment with low or high-dose CDDP (experiment II, Table 3). AKI was confirmed in the high-dose CDDP group 72 h after administration, by increase in Pcr and decrease in Ccr. This group additionally showed pathological alterations (Table 3). On the other hand, there were no alterations in these AKI parameters, and no pathological alterations were observed, in the low-dose CDDP group 72 h after administration (Table 3). We then compared Ucr-correction values of the biomarkers; uTP, uALB, KIM-1, B2M, CysC, CLU, NGAL and GST␣ with their gold standard UFRcorrection values, in the control and the two CDDP groups, using the same method as that used for NAG and LDH in experiment I. The high-dose CDDP group showed larger amplitude increases in the Ucr-correction values of all eight of these urinary biomarkers compared to their UFR-correction values (Fig. 2a–h). 3.4. Background of the large amplitude increases in urinary biomarkers obtained by Ucr-correction in AKI To examine the background of the large amplitude increases observed for the urinary biomarkers by Ucr-correction in AKI, we examined Ucr excretion levels and renal transporter levels using data from rats treated with CDDP, CBP, BEA, CSA, GMC, PAN or DOX in experiment I (Table 1). First, a decrease in the Ucr excretion was observed in AKI compared to non-AKI (Fig. 3a). Second, Ccr was seen to be decreased in AKI compared to non-AKI (Fig. 3b). Third, organic cation transporter-2 (OCT2 or Oct2) mRNA levels were found to be decreased in the kidney of the high- and middle-dose CDDP and CSA groups, and of the high-dose GMC and PAN groups (Fig. 3c). Finally, a significant positive correlation between Ucr excretion and renal Oct2 mRNA levels was observed, although the correlation coefficient was low (Fig. 3d). In experiment II, decreases in the Ucr excretion and Ccr were observed in AKI compared to non-AKI as well as experiment I (Ucr excretion, 436 ± 26 mg/h in non-AKI vs. 340 ± 22 mg/h in AKI on 72 h after CDDP administration, p < 0.05, n = 15 and 10, respectively; Ccr, 2.87 ± 0.83 mL/min in non-AKI vs. 0.97 ± 0.38 mL/min in AKI on 72 h after CDDP administration, p < 0.01, n = 15 and 10, respectively. Data are means ± SE). 4. Discussion In this study, we examined the influence of AKI on urinary biomarker-to-creatinine ratios. For this examination, seven nephrotoxicants, CDDP, CBP, BEA, CSA, GMC, PAN and DOX, were separately administrated to rats for induction of AKI in experiment I. Induction of AKI was confirmed in the high-dose CDDP, CSA, GMC
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Fig. 1. Comparison of equivalence and diagnostic accuracy of Ucr- and UFR-correction values of NAG and LDH in AKI and non-AKI. Linear regression analysis was used to analyze the relationship between Ucr- and UFR-correction values of NAG (a) and LDH (b) in AKI (open circles, AKI rats with altered Pcr and/or Ccr, n = 18) and non-AKI (black circles, control rats and rats without altered Pcr and Ccr, n = 112). The insets in each figure show a magnification of the regression lines at low correction values (black circles, control rats, n = 35; gray circles, rats without altered Pcr and Ccr, n = 77). The diagnostic accuracy of Ucr-correction values (black circles, n = 130) and UFR-correction values (gray circles, n = 130) of NAG (c) and LDH (d) were examined using ROC curve analysis.
Table 2 Indices for diagnostic accuracy of ROC curve analyses. Index
NAG
Sensitivity (%) Specificity (%) AUC
LDH
Ucr-correction
UFR-correction
Ucr-correction
UFR-correction
72.2 90.2 0.887
61.1 70.5 0.739
77.8 99.1 0.838
77.8 92.0 0.792
Abbreviations – ROC: receiver operating characteristic; NAG: N-acetyl--d-glucosaminidase; LDH: lactate dehydrogenase; Ucr: urinary creatinine; UFR: urine flow rate; and AUC: area under curve.
and PAN groups by increase in Pcr and/or decrease in Ccr. Using these AKI rats, we first examined the influence of AKI on urinary NAG-to-creatinine ratio and urinary LDH-to-creatinine ratio. Urinary NAG and LDH were adopted as suitable model biomarkers. These enzymes pre-exist in normal renal tubules and leak into the tubular lumen when renal tubular injury occurs (Guder and
Ross, 1984; Tonomura et al., 2010, in press). Therefore, it is not necessary to consider the degree of transcription and translation of biomarkers, and the condition of glomerular sieves regarding these two enzymes. In this study, Ucr-correction values of NAG and LDH exhibited larger amplitude increases than their UFR-correction values in linear regression analysis, suggesting that Ucr-correction
Table 3 Study design and results of the renal function tests of experiment II. Compound
Dose levels (mg/kg)
PCr (mg/dL)
Ccr (mL/min)
Representative histopathological findings
CDDP
0 1 4
0.3 ± 0.0 0.3 ± 0.0 0.6 ± 0.0**
2.80 ± 0.18 3.01 ± 0.57 0.97 ± 0.12**
No changes No changes Tubular degeneration, necrosis, regeneration
Data are means ± SE. ** indicates a statistically significant difference between the treated and the control groups that were analyzed using Dunnett’s multiple comparison test (p < 0.01). Each rat was treated once with CDDP and necropsied 72 h after administration. CDDP was intravenously administered. The control and the high-dose groups contain ten animals, and the low-dose group contains five animals. Abbreviations – Pcr: plasma creatinine; Ccr: creatinine clearance; and CDDP: cisplatin.
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Fig. 2. Examination of the relationship between Ucr- and UFR-correction values of other urinary biomarkers in AKI and non-AKI. Linear regression analysis was used to analyze the relationship between Ucr- and UFR-correction values of uTP (a), uALB (b), KIM-1 (c), B2M (d), CysC (e), CLU (f), NGAL (g) and GST␣ (h) in AKI (high-dose CDDP group; open circles, n = 10) and non-AKI (control and low-dose CDDP groups; black circles, n = 15). The insets in each figure show a magnification of the regression lines at low correction values (black circles, control group, n = 10; gray circles, low-dose CDDP group without altered Pcr and Ccr, n = 5).
values could overestimate AKI. On the other hand, ROC curve analysis suggested that, at least for NAG and LDH, Ucr-correction values had higher diagnostic power than UFR-correction values. To comprehensively examine whether or not other urinary biomarkers would also show large amplitude increases following calculation of their Ucr-correction values in AKI, we newly prepared an AKI rat model by CDDP treatment (experiment II) and measured urinary biomarkers uTP, uALB, KIM-1, B2M, CysC, CLU, NGAL and GST␣. The levels of these biomarkers are known to increase when AKI is present (Tonomura et al., 2010). Similar to NAG and LDH, all eight of the urinary biomarkers exhibited large amplitude increases by Ucrcorrection. Therefore, a large amplitude increase by Ucr-correction is likely to be a common phenomenon for increase-type urinary biomarkers, regardless of the mechanism by which the level of the urinary biomarker was increased: abnormality in transglomerular passage (uALB) (D’Amico and Bazzi, 2003); inhibition of reabsorption in proximal tubules (uALB, B2M, CysC and NGAL) (Amsellem et al., 2010; Christensen et al., 2009; D’Amico and Bazzi, 2003; Dieterle et al., 2010; Gauthier et al., 1984; Kuwabara et al., 2009;
Portman et al., 1986; Tenstad et al., 1996); release into the extracellular space after induction of protein expression (KIM-1, CLU and NGAL) (Bailly et al., 2002; Hidaka et al., 2002; Ichimura et al., 1998; Kuwabara et al., 2009) leakage of pre-existing protein from the renal tubules into the lumens (NAG, LDH and GST␣) (Guder and Ross, 1984; Rozell et al., 1993). To elucidate the background of the large amplitude increases and the enhanced diagnostic power of Ucr-correction, we addressed the characteristics of Ucr in AKI. We observed a decrease in the Ucr excretion in AKI rats in experiments I and II. Considering that Ucr-correction values were calculated by dividing urinary biomarker concentration by Ucr concentration, the large amplitude increase and enhanced diagnostic power of Ucr-correction in AKI was attributed to a synergistic effect of increased urinary biomarker excretion and decreased Ucr excretion. Conversely, in the case of decrease-type urinary biomarkers such as TFF3 (Yu et al., 2010), Ucr-correction would counteract the decrease in the level of such a biomarker because the Ucr excretion would decrease along with the decrease in such a urinary biomarker in AKI. Furthermore, in a
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Fig. 3. Examination of the background of the large amplitude increase in the Ucr-correction value in AKI. The Ucr excretion in AKI was compared to that in non-AKI (a). Transverse lines indicate the mean value of the Ucr excretion. The statistical significance of the difference between AKI and non-AKI was determined using Welch’s t-test **p < 0.01. Ccr was compared between AKI and non-AKI (b; n = 18 and 112, respectively). The statistical significance of the difference between AKI and non-AKI was determined using Welch’s t-test **p < 0.01. Transverse lines indicate the mean value of Ccr. Renal Oct2 mRNA levels were assessed in rats treated with CDDP, CBP, BEA, CSA, GMC, PAN or DOX and in control rats (c; n = 3 in each group). Data are expressed as means ± SE. The statistical significance of differences between the treated and control groups was determined using Dunnett’s multiple comparison test, *p < 0.05 and **p < 0.01. Pearson’s correlation coefficient (R) between Ucr excretion and renal Oct2 mRNA levels was calculated (d; n = 78).
situation where the urinary biomarker remains at a constant level, a decrease in the Ucr excretion would lead to an increase in the Ucr-correction value. Therefore, when using Ucr-correction, the direction of alterations of the levels of the urinary biomarker and Ucr should be taken into consideration. Regarding the mechanism that underlies the decrease in the Ucr excretion in AKI, we observed a reduction in Ccr (experiments I and II) and renal Oct2 mRNA levels (experiment I) in AKI rats. The decrease in Ccr simply implied a decrease in the transglomerular passage of creatinine. The reduction in renal Oct2 mRNA levels suggested a decrease in creatinine secretion from renal tubules into lumens, since OCT2 has been reported to be localized on the basolateral side of renal tubules and imports creatinine from blood into the intracellular space (Karbach et al., 2000; Urakami et al., 2004). This suggestion was supported by a significant positive correlation between Ucr excretion and renal Oct2 mRNA levels in this study.
However, since the correlation coefficient was low, a decrease in creatinine import from blood is not sufficient to completely explain the decrease in creatinine secretion from renal tubules. The multidrug and toxin extrusion-2 (MATE2) protein has been reported to function as a transporter for creatinine (Masuda et al., 2006). MATE2 is expressed on the apical side of renal tubules and would export creatinine into the extracellular space, i.e., on the urine side. Unfortunately, MATE2 has not been identified in rats. Therefore, further study is needed to understand the relationship between tubular creatinine secretion and Ucr excretion in AKI. In this study, we further observed a decrease in body weight in AKI rats in experiment I (305.9 ± 3.3 g in non-AKI vs. 260.1 ± 7.5 g in AKI 15 days after first drug administration of CDDP, CBP, BEA, CSA, GMC, PAN or DOX, p < 0.01, n = 112 and 18, respectively. Data are means ± SE). This result indicated that muscle mass decreased in AKI and that a decrease in the Ucr excretion in AKI might be partially caused by a
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decrease in creatinine as a result of creatine metabolism (Hosten, 1990). In addition to corrective methods that use Ucr and UFR, corrective methods based on urinary specific gravity or urinary osmolality have also been reported in non-AKI situations (Gyamlani et al., 2003; Moore et al., 1997). However, urinary specific gravity and urinary osmolality are determined not only by urine dilution or concentration but also by the level of urinary solutes such as glucose, Na+ and K+ . The filtered and reabsorbed level of urinary solutes is thought to be affected by AKI due to renal tubular dysfunction and to a decrease in the glomerular filtration rate. Since urinary gravity and urinary osmolality are composed of multiple factors and are more complex than Ucr, further studies are required before using correction based on urinary specific gravity or urinary osmolality in AKI. In conclusion, the present study is the first to comprehensively examine the influence of AKI on the urinary biomarker-tocreatinine ratio and to indicate the merits and demerits of Ucr-correction: the merit is that it can sensitively detect AKI without timed urine collection; the demerit is that it does not provide a true evaluation of AKI. In an emergency medicine field site in human, sensitive detection rather than true understanding of disease symptoms is essential for quick treatment, whereas the overestimation of AKI cause a mistake in the decision-making for new drug development in preclinical toxicity study. Therefore, we should take into consideration of these backgrounds when using the Ucr-correction. Conflict of interest statement The authors declare that there are no conflicts of interest. Acknowledgments Experiment I and the microarray analyses were conducted by the Toxicogenomics Informatics Project in Japan, which was supported by a grant from the Ministry of Health, Labour and Welfare of Japan (H14-toxico-001). We thank Takako Miyoshi for preparation of the sections used for pathological examination, and Shingo Takagi for useful suggestions regarding this study. References Amsellem, S., Gburek, J., Hamard, G., Nielsen, R., Willnow, T.E., Devuyst, O., Nexo, E., Verroust, P.J., Christensen, E.I., Kozyraki, R., 2010. Cubilin is essential for albumin reabsorption in the renal proximal tubule. J. Am. Soc. Nephrol. 21, 1859–1867. Bagshaw, S.M., 2010. Acute kidney injury: diagnosis and classification of AKI: AKIN or RIFLE? Nat. Rev. Nephrol. 6, 71–73. Bailly, V., Zhang, Z., Meier, W., Cate, R., Sanicola, M., Bonventre, J.V., 2002. Shedding of kidney injury molecule-1, a putative adhesion protein involved in renal regeneration. J. Biol. Chem. 277, 39739–39748. Bellomo, R., Ronco, C., Kellum, J.A., Mehta, R.L., Palevsky, P., 2004. Acute Dialysis Quality Initiative workgroup: Acute renal failure—definition, outcome measures, animal models, fluid therapy and information technology needs: the Second International Consensus Conference of the Acute Dialysis Quality Initiative (ADQI) Group. Crit. Care 8, R204–R212. Christensen, E.I., Verroust, P.J., Nielsen, R., 2009. Receptor-mediated endocytosis in renal proximal tubule. Pflugers Arch. 458, 1039–1048. D’Amico, G., Bazzi, C., 2003. Pathophysiology of proteinuria. Kidney Int. 63, 809–825. Dieterle, F., Perentes, E., Cordier, A., Roth, D.R., Verdes, P., Grenet, O., Pantano, S., Moulin, P., Wahl, D., Mahl, A., End, P., Staedtler, F., Legay, F., Carl, K., Laurie, D., Chibout, S.D., Vonderscher, J., Maurer, G., 2010. Urinary clusterin, cystatin C, beta2-microglobulin and total protein as markers to detect drug-induced kidney injury. Nat. Biotechnol. 28, 463–469. Gauthier, C., Nguyen-Simonnet, H., Vincent, C., Revillard, J.P., Pellet, M.V., 1984. Renaltubular absorption of beta 2 microglobulin. Kidney Int. 26, 170–175. Guder, W.G., Ross, B.D., 1984. Enzyme distribution along the nephron. Kidney Int. 26, 101–111. Gyamlani, G.G., Bergstralh, E.J., Slezak, J.M., Larson, T.S., 2003. Urinary albumin to osmolality ratio predicts 24-hour urine albumin excretion in diabetes mellitus. Am. J. Kidney Dis. 42, 685–692.
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