Biomarkers
ISSN: 1354-750X (Print) 1366-5804 (Online) Journal homepage: http://www.tandfonline.com/loi/ibmk20
Urinary neutrophil gelatinase-associated lipocalin predicts graft loss after acute kidney injury in kidney transplant Juan C. Ramírez-Sandoval, Jonatan Barrera-Chimal, Perla E. Simancas, Alejandro Rojas-Montaño, Ricardo Correa-Rotter, Norma A. Bobadilla & Luis E. Morales-Buenrostro To cite this article: Juan C. Ramírez-Sandoval, Jonatan Barrera-Chimal, Perla E. Simancas, Alejandro Rojas-Montaño, Ricardo Correa-Rotter, Norma A. Bobadilla & Luis E. MoralesBuenrostro (2014) Urinary neutrophil gelatinase-associated lipocalin predicts graft loss after acute kidney injury in kidney transplant, Biomarkers, 19:1, 63-69 To link to this article: http://dx.doi.org/10.3109/1354750X.2013.867536
Published online: 11 Dec 2013.
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Date: 13 September 2015, At: 05:17
http://informahealthcare.com/bmk ISSN: 1354-750X (print), 1366-5804 (electronic) Biomarkers, 2014; 19(1): 63–69 ! 2014 Informa UK Ltd. DOI: 10.3109/1354750X.2013.867536
RESEARCH ARTICLE
Urinary neutrophil gelatinase-associated lipocalin predicts graft loss after acute kidney injury in kidney transplant Juan C. Ramı´rez-Sandoval1, Jonatan Barrera-Chimal1,2, Perla E. Simancas1, Alejandro Rojas-Montan˜o1, Ricardo Correa-Rotter1, Norma A. Bobadilla1,2, and Luis E. Morales-Buenrostro1 Instituto Nacional de Ciencias Me´dicas y Nutricio´n Salvador Zubira´n, Vasco de Quiroga 15, Mexico City, Mexico and 2Instituto de Investigaciones Biome´dicas, Universidad Nacional Auto´noma de Me´xico, Mexico City, Mexico Abstract
Keywords
Objective: Establish the prognostic value for graft loss of urinary neutrophil gelatinaseassociated lipocalin (uNGAL), kidney injury molecule-1 (uKIM-1), interleukin-18 (uIL-18), and heat shock protein 72 (uHsp72) in kidney transplant recipients (KTR) with acute kidney injury (AKI). Methods: Biomarkers were measured in 67 KTR with AKI caused by different entities. Results: After 1 year, 11 KTR with graft loss had higher uNGAL compared to KTR without loss (p50.001). There were no differences for uKIM-1, uIL-18 and uHsp-72. uNGAL 4200 ng/mL had 84% sensitivity and 86% specificity for graft loss (ROC AUC: 0.89, 95% CI: 0.81–0.97). uNGAL may be useful to predict graft loss after AKI.
Acute kidney injury, allograft failure, graft loss, NGAL, sensitivity and specificity, urinary biomarkers
Introduction Acute kidney injury (AKI) is a frequent complication in kidney transplant recipients (KTR) and it may be associated independently with increased risk of graft loss from any cause, both in the early post-transplant period as well as during later follow-up. The incidence of severe AKI requiring dialysis in KTR can be 45-fold higher in comparison with AKI of non-transplant patients (Mehrotra et al., 2012). Recently, several promising urinary biomarkers for early detection of AKI which increase before the rise in serum creatinine (sCr) have been identified. Some of these biomarkers are urinary neutrophil gelatinase-associated lipocalin (uNGAL) (Haase et al., 2009; Mishra et al., 2003; Mori et al., 2005), kidney injury molecule-1 (uKim-1) (Han et al., 2002; Ichimura et al., 1998; Vaidya et al., 2006, 2010; Zhang et al., 2007), interleukin-18 (uIL-18) (Fantuzzi et al., 1998; Haase et al., 2008; Melnikov et al., 2001; Parikh et al., 2005, 2006b; Vanmassenhove et al., 2013), and heat shock protein-72 (uHsp72) (Barrera-Chimal et al., 2011). Nevertheless, most studies have excluded KTR. A few studies have suggested that uNGAL concentration could predict delayed initial graft function (Heyne et al., 2012) or discriminate acute allograft rejection from other causes (Sureshkumar & Marcus, 2010) in KTR. Furthermore, there is evidence showing that urinary
Address for correspondence: Luis E. Morales-Buenrostro, MD, MMSc, PhD, Instituto Nacional de Ciencias Me´dicas y Nutricio´n Salvador Zubira´n, Vasco de Quiroga 15, Seccio´n XVI, ZP 14000, Mexico City, DF, Mexico. Tel: 152 5554870900 ext. 2505. E-mail: [email protected]
History Received 7 October 2013 Revised 13 November 2013 Accepted 17 November 2013 Published online 11 December 2013
biomarkers are helpful in the prediction of long-term allograft function after the early transplant period (Halawa, 2011). It is clear that there is an urgent need to identify better and earlier biomarkers to diagnose AKI. Additionally, these biomarkers could be of relevance for predicting graft loss in KTR after an AKI episode from different causes reflecting real daily practice. Currently, few studies have examined the usefulness of urinary biomarkers as outcome predictors in this context. This study was designed to assess whether uNGAL, uKIM-1, uIL-18, uHsp72 at the time of diagnosis of AKI after the early transplant period, can predict future graft loss in KTR with AKI.
Methods Study population
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A prospective cohort study of KTR with AKI was conducted at a tertiary National Health Institution located in Mexico City. We invited all consecutive KTR with AKI detected in the Emergency Department or in the outpatient clinic between April 2010 and July 2011. A urine sample was collected at the time of KTR enrollment for assessing biomarkers level. KTR were prospectively followed for 1 year after AKI episode and sCr was prospectively measured every three months in all patients. An attending nephrologist blinded to all the results of baseline biomarkers, performed adjudication of outcome at the end of follow-up. We excluded KTR with estimated GFR (eGFR) 530 mL/min/1.73 m2 within 3 or more months before AKI episode, kidney transplant in the last 6 months, unknown sCr value recorded within 3 months previous to AKI, anuria at AKI diagnosis, and denial to provide informed consent.
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The study was previously approved by our hospital0 s institutional biomedical research review board (Comite´ Institucional de Investigacio´n Biome´dica en Humanos, approval number 167) and all subjects provided written informed consent.
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Definitions AKI was diagnosed according to RIFLE criteria: rise in sCr by 1.5-fold or eGFR decrease425% from baseline (eGFR was calculated using the Modification of Diet in Renal Disease (MDRD) four-variable equation). We used the lowest known sCr value during the preceding 3 months as the baseline from patient’s clinical records. The primary clinical outcome variables were graft loss, incomplete recovery, and complete recovery at 12 months after AKI. Graft loss was defined by a permanent requirement for maintenance renal replacement therapy (RRT) or eGFR 15 mL/min/1.73 m2. Incomplete recovery was defined as failure of the sCr or eGFR to return to baseline and previous KDOQI chronic kidney disease stage and complete recovery was defined by the return of the sCr or eGFR to baseline or previous KDOQI CKD stage. As usual, patients were classified as ‘‘prerenal AKI’’, when the increase in sCr concentration was caused by factors that compromise renal perfusion, and sCr rapidly improved to baseline with volume repletion or improvement in cardiac output within 3 days of directed therapy. All AKI cases were evaluated by two certified nephrologists and each outcome was adjudicated or not as an AKI event. Data collection The KTR demographic data, etiology of AKI, comorbid conditions, RRT requirement, AKI recurrence, mortality, and renal outcome were prospectively collected. sCr was measured in all the patients at 1, 3, 6, 9 and 12 months after enrollment. Measurement of the biomarkers Fresh urine samples were collected and stored at 80 C until the urinary biomarkers assays were performed. Urine NGAL, Kim-1 and IL-18 levels were analyzed using commercially available enzyme-linked immune absorbent assay (ELISA) kits: uNGAL ELISA kit (BioPorto Diagnostics, KIT036, Gentofte, Denmark), uKim-1 ELISA kit (USCN Life Science Inc., 90785, Wuhan, China), uIL-18 ELISA Kit (Invitrogen, KHC0181, Carlsbad, CA), and uHsp72 ELISA kit (ENZO Life Sciences ADI-EKS-715, Ann Arbor, MI). All procedures were performed according to manufacturer’s instructions. Statistics Results are presented as mean standard deviation for normally-distributed data or median interquartile range (IQR) for non-normally distributed data. We divided the patients into three groups according to the primary clinical outcome (graft loss, incomplete recovery, and complete recovery). The differences among the groups were tested by analysis of variance with Bonferroni’s correction for multiple comparisons when variables were normally distributed or Kruskal–Wallis test was employed when variables were not normally distributed. To examine the predictive performance
Biomarkers, 2014; 19(1): 63–69
of urinary biomarkers to evaluate 1 year graft outcome, we plotted receiver operating characteristics (ROC) curve and used Kaplan–Meier curves according to high level versus low level biomarker (cutoff at the 75th percentile). The curves were compared by the log-rank test. The association of urinary biomarkers with graft-loss was examined by Cox proportional hazards regression analyses using backward elimination. Those variables identified as statistically significant in the univariate analysis were included in the multivariate model. All tests of significance were two-tailed, and differences were considered statistically significant at p value 50.05. All statistical analysis was performed by SPSS version 18.0 (SPSS Inc., Chicago, IL).
Results Baseline patient’s characteristics Of the 89 KTR identified with renal graft dysfunction between 26 April 2010 and 1 June 2011, 67 fulfilled the inclusion criteria of AKI and were enrolled in the study. Causes of exclusion were eGFR 530 mL/min/1.73 m2 within 3 o more months before AKI (8/22), unknown sCr within 3 months before AKI (8/22) and less than 6 months after transplant (6/22). Any patient had anuria at AKI diagnosis. Forty-six of these patients diagnosed with AKI (69%) were diagnosed at the emergency room in the context of diarrhea, fever, or respiratory symptoms. The other cases were asymptomatic and were diagnosed with AKI during the follow-up visit in the outpatient clinic (21 cases, 31%). All patients completed 1-year follow up. The median age was 40 years, 55% were females, and median time after transplantation was 2.7 years (Table 1). Seventy percent had received a kidney graft from living donor. Graft biopsy was obtained in all KTR within 6 weeks after AKI episode, including cases with vascular, obstruction or prerenal causes, according to institutional protocol. AKI causes, as previously defined, were: immunological rejection (20/67; 30%) and prerenal (20/67, 29%). Other causes (27/ 67, 40%): calcineurin-inhibitor toxicity (9), recurrence of primary glomerular disease-membranous glomerulopathy(2), obstructive uropathy (2), polyoma virus infection (3), non-steroidal anti-inflammatory drugs use (2), transplant renal artery stenosis (2), acute tubular necrosis (3) and kidney biopsy with no-specific cause of AKI and only interstitial fibrosis and tubular athrophy (4). KTR with clinical diagnosis of prerenal AKI and who experience recurrent episodes of slight SCr elevation (n ¼ 8/20) were biopsied during followup and no specific histopathologic diagnosis was evident. In general, the severity of illness of the patient cohort in this study was as follows: 42% KTR had systemic inflammatory response syndrome, only one patient was admitted to the Critical Care Unit during the course of hospitalization, and renal replacement therapy was provided 1 month after enrollment in 4 patients (9%). The median length of hospital admissions was 4 days (ranged from 1 to 30 days). Graft survival of KTR with AKI For further characterization of the patient cohort, we separately analyzed three groups of patients: those KTR who had graft loss (n ¼ 11, 16%), those with incomplete recovery
uNGAL and graft loss after AKI in kidney transplant
DOI: 10.3109/1354750X.2013.867536
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Table 1. Baseline characteristics of the studied population and the three groups patients: complete recovery, incomplete recovery and graft loss.
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Variablesa Median age [IQR], years Women, n (%) Causes of AKI, n (%) Prerenal Acute rejection Other Living donor, n (%) Time post kidney transplant [IQR], years Median baseline sCr [IQR], mg/dL eGFR before AKI [IQR], mL/min/1.73 m2 sCr at AKI diagnosis [S.D.], mg/dL Severity of AKI (RIFLE) Risk Injury Failure Diabetes mellitus, n (%) Infection, n (%) HLA mismatch (0/6), n (%) Calcineurin toxicity, n (%) Recurrent events of AKI after first episode eGFR 1 month after AKI eGFR 3 month after AKI eGFR 6 month after AKI eGFR 12 month after AKI
All KTR n ¼ 67 (100%)
Complete recovery, n ¼ 36 (54%)
Incomplete recovery, n ¼ 20 (30%)
40 [29–46] 30 (55)
43 [25–48] 18 (50)
(30) (30) (40) (70) [0.8–6.1] [0.8–1.7] [18–130] [1.3–3.0]
12 9 15 28 1.9 1.5 49 2.0
(80%) (7%) (13%) (18%) (43%) (55%) (15%)
33 2 1 5 16 20 5 13 42 43 42 44
20 20 27 47 2.7 1.4 50.1 2.0 54 5 9 12 29 37 10 16 41 40 41 42
(32–52) (31–51) (29–53) (28–52)
Graft loss, n ¼ 11 (16%)
p
40 [32–46] 8 (40)
36 [28–44] 4 (36)
0.35 0.64
(33) (25) (42) (77) [0.7–6] [1.2–1.8] [39–55] [1.7–2.4]
8 6 6 12 2.4 1.1 71 1.7
(40) (30) (30) (60) [0.8–4.7] [0.9–1.3] [50–79] [1.4–2.2]
0 5 6 7 8.4 1.7 43 5.4
(92%) (6%) (2%) (14%) (48%) (56%) (14%)
17 3 1 4 9 12 2 12 44 45 43 42
(85%) (14%) (1%) (20%) (45%)) (60%) (10%)
4 0 7 3 3 5 3 1 14 7 5 5
(35–52) (34–55) (34–53) (36–52)
(35–54) (32–54) (33–60) (26–54)
(71) (29) (63) [3.6–11.6] [1.5–2.7] [23–58] [4.2–8.1] (36%) (61%) (27%) (43%) (45%) (27%) (6–23) (4–14) (4–11) (4–6)
0.047 0.43 0.57 0.48 50.001 50.001 50.001 50.001 0.05 0.54 50.001 0.56 0.55 0.44 0.58 50.001 50.001 50.01 50.001 50.001
a
Values are % for categorical variables, mean standard deviation for normally distributed data or median interquartile range for non-normally distributed data. IQR, interquartile range; SD, standard deviation.
(n ¼ 20, 30%), and finally those that completely recovered their renal function, defined by return to baseline eGFR 1 year after the AKI episode (n ¼ 36, 54%) Graft-loss etiology was diverse: immunological rejection in five KTR (humoral rejection in three cases), acute tubular necrosis in two, obstructive uropathy in two with moderate interstitial fibrosis and tubular atrophy previous AKI episode, and intersticial fibrosis and tubular atrophy in two without evidence of rejection or other evident cause of injury. These two cases had previous biopsies (6 and 9 months before AKI) with minimal fibrosis. As expected, the graft-loss group had a significantly increased initial sCr at AKI diagnosis and 7 of 11 patients in this group evolved to ‘‘failure’’ category of the RIFLE score. The incomplete recovery group had a significant higher baseline eGFR compared to the other two groups. After the AKI episode, all patients had an eGFR under 60 mL/min/ 1.73 m2. For the groups with complete renal function recovery, almost all patients had the same SCr level one month after the AKI episode as the one observed at 1-year of follow up. We did not find differences between the three groups in relation to HLA mismatch, prevalence of diabetes mellitus, and frequency of infection. Urinary biomarkers and outcome at 12 months Figure 1 and Table 2 shows the measurement of renal function biomarkers according to outcome. Median uNGAL was 241 ng/mL in the graft loss group varying from 31 to 294 ng/mL and this mean value was significantly different from the complete and incomplete recovery groups (48 and 109 ng/mL, respectively, p: 0.002). Post-hoc test revealed significant differences between uNGAL from group with graft loss and incomplete recovery (p: 0.009). The other urinary
biomarkers assessed: Hsp-72, IL-18, and Kim-1 were not statistically different among the studied groups. Analysis of risk factors for graft loss To identify variables associated with graft loss, we first applied a univariate pair-matched Cox regression analysis as shown in Table 3. We found four variables associated with graft loss: sCr at AKI diagnosis, time after transplantation, baseline graft eGFR, and uNGAL percentile 475%. Other variables present at AKI diagnosis such as an episode of acute immunological rejection, AKI recurrence, presence of diabetes mellitus, delayed graft function history, and presence of infection were not risk factors for graft loss. All variables statistically significant in the univariate analysis were subjected to multivariate Cox regression analysis. Only baseline eGFR and NGAL percentile 475%, were identified as independent predictors of 1-year graft loss. Others such as sCr at AKI diagnosis were not associated with graft loss in multivariate analysis. NGAL percentile 475% had a hazard risk of 36.46 (95% CI 2.57–51.7, p 0.008). When the same analysis was performed using a different cutoff at median value (70 ng/mL), a uNGAL 450th percentile had a hazard risk of 5.22 (95% CI 1.3–27.7, p 0.009). The log-rank test based on the Kaplan–Meier curves showed a significant association between high uNGAL (475th percentile) and graft loss (p 0.001). As seen in Figure 2, a large proportion of the graft loss occurred in the first month after AKI. Prognostic performance of urinary NGAL The performance of urinary NGAL to predict graft failure was analyzed. Figure 3 shows the area under the curve
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Biomarkers, 2014; 19(1): 63–69
Figure 1. Urinary biomarkers according to outcome.
Table 2. Biomarkers according to outcome. Variables
Complete recovery
uNGAL, ng/mL (IQR) uKIM-1, ng/mL (IQR) uHsp72, ng/mL (IQR) uIL-18, pg/mL (IQR)
48 44 0.11 18
Incomplete recovery
(6–129) (22–70) (0.05–0.54) (5–121)
109 69 0.09 52
Graft loss
(14–200) (22–223) (0.02–0.61) (5–241)
241 158 0.02 102
(110–271) (18–247) (0.02–0.29) (85–272)
p 50.001a 0.24 0.73 0.11
a
Post-hoc Kruskal–Wallis revealed significant differences between graft loss group versus incomplete recovery group and graft loss group versus complete.
Table 3. Factors associated with graft failure in KTR in univariate Cox regression model. Univariate
a
sCr at AKI diagnosis Time after transplant (y) Basal eGFR (mL/min/1.73 m2) Immunological rejection uNGAL percentile 475%
Multivariate
HR
95% CI
p Value
HR
95% CI
p Value
2.95 1.20 1.20 1.85 43.2
1.9–4.9 1.07–1.35 1.07–1.35 0.52–6.55 5.52–340.74
0.01 0.03 50.001 0.34 50.001
1.89 1.08 1.03
1.16–3.06 0.88–1.31 1.01–1.08
0.24 0.44 0.017
36.46
2.57–51.72
0.008
a
AKI, acute kidney injury.
(AUC) for the ROC curve of uNGAL for graft failure. The uNGAL had a good performance to predict graft loss with an AUC of 0.89 (95% CI 0.81–0.97, p 0.0001). At a cutoff value of 200 ng/mL, the sensitivity and specificity for the prediction of graft failure were 83.9% and 90.9%,
respectively (95% CI 73.8–93.6 and 58.7–99.8%, LR 9.43). Nineteen KTR had an uNGAL value 4200 ng/mL (28%). In contrast, when we use sCr at AKI diagnosis to predict graft failure, the AUC for the ROC curve was 0.809 (95% CI 0.63–0.98, p 0.0001).
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DOI: 10.3109/1354750X.2013.867536
Figure 2. Kaplan–Maier plots of graft loss dependent on uNGAL stratified as high (475th percentile) versus low.
Figure 3. Area under the curve (AUC) for the receiver-operating characteristic curve (ROC) of uNGAL for graft loss.
Assessment of the studied population with AKIN definition and biomarker concentrations expressed as a ratio to the urinary creatinine concentration We finally performed a full analysis of our cases employing AKIN classification. According the AKIN criteria, we excluded reversible causes of AKI in KTR following adequate resuscitation, as is the case in patients with prerenal azotemia (20/67). Forty seven cases defined by AKIN classification were included. In these KTR, uNGAL was statistical different between the KTR with graft loss and the other two groups (p40.001). The AUC for the ROC curve of uNGAL for graft failure using AKIN definition was 0.80 (95% CI 0.65–0.92, p 0.003). We also analyzed the data adjusting for urinary creatinine concentration. Although normalization is less than ideal since creatinine is not in a steady state, we found similar results, with a significant difference between graft loss group and the others groups respect to uNGAL/uCr (358.5 mcg/g versus 23 mcg/g, p 0.001).
Discussion In this prospective cohort, we demonstrated that uNGAL determination at the time of an AKI event in a KTR is an independent predictor of graft loss. In addition, uNGAL was useful to stratify the risk of graft loss and may be useful as
uNGAL and graft loss after AKI in kidney transplant
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a prognostic marker in KTR with different causes of AKI who present an event at least 6 months after transplantation. uKIM-1, uIL-18, and uHsp72 were unable to predict graft failure in this population. This is the only study that has evaluated the potential use of AKI biomarkers to predict graft loss. Many studies that have explored the use of biomarkers for AKI prognosis have excluded KTR (Halawa, 2011). Notably, AKI is a common complication of KTR and could represent the major risk of graft failure (Nakamura et al., 2012). The most promising biomarkers for clinical diagnosis include a panel of uNGAL, uIL-18, and uKIM-1 (Devarajan, 2007). In the present study, we focused on the occurrence of AKI in the maintenance phase of a kidney transplant, and tested the usefulness of these biomarkers and uHSP72 to predict graft failure after an AKI episode. We decided to include different causes of AKI reflecting daily practice in our center. In our institution, after the initial 3 months of transplantation, we usually follow our KTR every 2 or 3 months and instruct them to come to our Unit in any unexpected health event. After detection of an AKI episode, patients are extensively studied and treated timely as possible. Despite this follow up, sixteen percent of KTR had graft loss, similar to other reports (Mehrotra et al., 2012). The high incidence of dialysis after AKI underscores the need to find prognostic factors in KTR with AKI. Urinary NGAL has been studied in various clinical situations. In kidney transplantation, uNGAL has been useful as prognostic of graft function immediately after transplantation (Hall et al., 2010; Hollmen et al., 2011; Parikh et al., 2006a). Recently, one study found that NGAL discriminate immunological rejection after early transplant period (Heyne et al., 2012). Our study suggest that urinary uNGAL and eGFR basal level are independent predictors for graft loss after an AKI episode and may be useful in clinical scenarios to guide treatment and diagnostic decisions. This study shows that uNGAL in KTR have a similar prediction to that reported in other populations without kidney transplant respect renal replacement therapy initiation (Haase et al., 2009). These results suggest that urinary biomarkers in KTR may have a similar behavior compared with patients without kidney transplant. Median uNGAL concentration in stable KTR has been described around 7.8 ng/mL and a uNGAL cut-off at 30 ng/mL allowed discerning KTR with AKI versus stable KTR (Heyne et al., 2012). We demonstrate that uNGAL cut-off of 200 ng/mL is sensitive and specific in predicting graft loss in unselected KTR with AKI. In our cohort, the area under the ROC curve for uNGAL was a better predictor of 1 year graft loss than sCr at AKI diagnosis. In our opinion, uNGAL can add information to the value of sCr as a predictor of graft loss. Another finding in our study is that IL-18, Hsp72 and Kim-1 were not useful for predict graft loss. Definition of AKI is based on sCr or urine output, reflecting loss of renal function. In clinical context, AKI diagnosis is detected until 24 to 48 h after injury. In outpatient scenario, it is impossible to know the exact beginning of the injury. IL-18 and Hsp-72 are very useful biomarkers for early diagnosis of AKI, both biomarkers may increase even days before SCr elevation
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(Barrera-Chimal et al., 2011). However, the biomarkers’ variability reported might be explained by different degrees, extension, and evolution of AKI in our cohort. Thus, urinary IL-18 and hSP72 levels may have been elevated days before enrollment, and could have fallen at the moment of the AKI diagnosis in some KTR. In patients with AKI induced by ischemic insult, Kim-1 elevation is seen several hours after AKI has occurred and remains elevated along the time, although Kim-1 was unable to stratify severe renal injury degrees (Han et al., 2009). In counterpart, it has been described that high urinary Kim-1 in stable KTR without AKI is an independent predictor of long-term graft loss (Van Timmeren et al., 2007). In our study, the graft loss group appears to have a higher urinary Kim-1 level with respect to other groups, but given the limited sample sizes, the difference did not attain statistical significance. One of major problems in conducting clinical studies of AKI in KTR is the absence of consensus definition. Although AKIN definition have attempted to exclude easily reversible causes of azotemia (such as volume depletion or urinary obstruction) in comparison with the RIFLE definition, both consensus criteria are mostly comparable (Joannidis et al., 2009). We defined AKI by RIFLE criteria according to our clinical practice in emergency department and included KTR with AKI, that after 48 h it was revealed a prerrenal or obstructive causes. However, when comparing results using AKIN criteria excluding reversible causes, uNGAL had the same significance in our clinical study. Recently, a study has described that the AKI group of KTR with AKI represented a high risk for graft failure, similar to the hazard risk that we report in our univariate analysis model (Nakamura et al., 2012). We did not find one variable for discriminate between KTR with complete recovery and KTR with incomplete recovery. In our cohort, we found that 46% of KTR had a detrimental effect in eGFR or graft loss only after an episode of AKI, underlined that we should make any effort in the prevention, diagnosis and early treatment of AKI to avoid graft loss. Some authors have proposed that subjects with advanced CKD show increased tubular excretion of biomarkers (e.g. NGAL) as consequence of a sustained production by ‘‘inflamed’’ tubular cells – forest fire theory (Mori & Nakao, 2007). Assuming that KTR with baseline chronic eGFR less than 30 mL/min might have a greater grade of tubular inflammation, interstitial fibrosis, or tubular atrophy, we decided to excluded those subjects to minimize potential confounding factors. This study does have some limitations. It is a singlecenter prospective cohort study, was based on the analysis of a relatively small number of recipients, and eight cases might have been missed a histologic diagnosis by a delay between AKI episode and graft biopsy. In spite of the small sample size, we found a significant association and a good prognostic value using uNGAL. Another potential limitation is that urinary biomarkers were assessed only one time, at diagnosis of AKI. Whether the observed effect would be comparable in KTR knowing basal biomarkers concentrations before the AKI event or during follow-up remains to be determined.
Biomarkers, 2014; 19(1): 63–69
Conclusions Our data demonstrate the ability of uNGAL to predict graft loss after an episode of AKI in KTR. uKIM-1, uIL-18 and uHsp72 did not have prognostic utility in this population. uNGAL may be useful tool in the follow-up of KTR with AKI.
Acknowledgements We are grateful to Rosalba Pe´rez for her technical assistance. The results presented in this article have not been published previously in whole or in part, except as an abstract presented at Kidney Week 2012: 45th Annual Meeting (San Diego, CA). J.C.R.S., R.C.R., N.A.B., L.E.M.B. conceived and design the study; J.C.R.S., J.B.C., P.E.S., A.R.M. included patients and 1 year follow-up; J.C.R.S., J.B.C., A.R.M., R.C.R., N.A.B., L.E.M.B. analyzed the data; J.B.C., P.E.S., N.A.B. contributed reagents or analysis tools; J.C.R.S., J.B.C., N.A.B., R.C.R., L.E.M.B. wrote the article.
Declaration of interest The authors declare no conflicts of interests. The authors alone are responsible for the content and writing of this article. This project was supported by a grant from Consejo Nacional de Ciencia y Tecnologı´a (CONACyT 181267 to NAB).
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DOI: 10.3109/1354750X.2013.867536
Ichimura T, Bonventre JV, Bailly V, et al. (1998). 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 273:4135–42. Joannidis M, Metnitz B, Bauer P, et al. (2009). Acute kidney injury in critically ill patients classified by AKIN versus RIFLE using the SAPS 3 database. Intensive Care Med 35:1692–702. Mehrotra A, Rose C, Pannu N, et al. (2012). Incidence and consequences of acute kidney injury in kidney transplant recipients. Am J Kidney Dis 59:558–65. Melnikov VY, Ecder T, Fantuzzi G, et al. (2001). Impaired IL-18 processing protects caspase-1-deficient mice from ischemic acute renal failure. J Clin Invest 107:1145–52. Mishra J, Ma Q, Prada A, et al. (2003). Identification of neutrophil gelatinase-associated lipocalin as a novel early urinary biomarker for ischemic renal injury. J Am Soc Nephrol 14: 2534–43. Mori K, Lee HT, Rapoport D, et al. (2005). Endocytic delivery of lipocalin-siderophore-iron complex rescues the kidney from ischemiareperfusion injury. J Clin Invest 115:610–21. Mori K, Nakao K. (2007). Neutrophil gelatinase associated lipocalin as the real-time indicator of active kidney damage. Kidney Int 71: 967–70. Nakamura M, Seki G, Iwadoh K, et al. (2012). Acute kidney injury as defined by the RIFLE criteria is a risk factor for kidney transplant graft failure. Clin Transplant 26:520–8. Parikh CR, Abraham E, Ancukiewicz M, Edelstein CL. (2005). Urine IL-18 is an early diagnostic marker for acute kidney injury and
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ISSN: 1354-750X (Print) 1366-5804 (Online) Journal homepage: http://www.tandfonline.com/loi/ibmk20
Urinary neutrophil gelatinase-associated lipocalin predicts graft loss after acute kidney injury in kidney transplant Juan C. Ramírez-Sandoval, Jonatan Barrera-Chimal, Perla E. Simancas, Alejandro Rojas-Montaño, Ricardo Correa-Rotter, Norma A. Bobadilla & Luis E. Morales-Buenrostro To cite this article: Juan C. Ramírez-Sandoval, Jonatan Barrera-Chimal, Perla E. Simancas, Alejandro Rojas-Montaño, Ricardo Correa-Rotter, Norma A. Bobadilla & Luis E. MoralesBuenrostro (2014) Urinary neutrophil gelatinase-associated lipocalin predicts graft loss after acute kidney injury in kidney transplant, Biomarkers, 19:1, 63-69 To link to this article: http://dx.doi.org/10.3109/1354750X.2013.867536
Published online: 11 Dec 2013.
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Date: 13 September 2015, At: 05:17
http://informahealthcare.com/bmk ISSN: 1354-750X (print), 1366-5804 (electronic) Biomarkers, 2014; 19(1): 63–69 ! 2014 Informa UK Ltd. DOI: 10.3109/1354750X.2013.867536
RESEARCH ARTICLE
Urinary neutrophil gelatinase-associated lipocalin predicts graft loss after acute kidney injury in kidney transplant Juan C. Ramı´rez-Sandoval1, Jonatan Barrera-Chimal1,2, Perla E. Simancas1, Alejandro Rojas-Montan˜o1, Ricardo Correa-Rotter1, Norma A. Bobadilla1,2, and Luis E. Morales-Buenrostro1 Instituto Nacional de Ciencias Me´dicas y Nutricio´n Salvador Zubira´n, Vasco de Quiroga 15, Mexico City, Mexico and 2Instituto de Investigaciones Biome´dicas, Universidad Nacional Auto´noma de Me´xico, Mexico City, Mexico Abstract
Keywords
Objective: Establish the prognostic value for graft loss of urinary neutrophil gelatinaseassociated lipocalin (uNGAL), kidney injury molecule-1 (uKIM-1), interleukin-18 (uIL-18), and heat shock protein 72 (uHsp72) in kidney transplant recipients (KTR) with acute kidney injury (AKI). Methods: Biomarkers were measured in 67 KTR with AKI caused by different entities. Results: After 1 year, 11 KTR with graft loss had higher uNGAL compared to KTR without loss (p50.001). There were no differences for uKIM-1, uIL-18 and uHsp-72. uNGAL 4200 ng/mL had 84% sensitivity and 86% specificity for graft loss (ROC AUC: 0.89, 95% CI: 0.81–0.97). uNGAL may be useful to predict graft loss after AKI.
Acute kidney injury, allograft failure, graft loss, NGAL, sensitivity and specificity, urinary biomarkers
Introduction Acute kidney injury (AKI) is a frequent complication in kidney transplant recipients (KTR) and it may be associated independently with increased risk of graft loss from any cause, both in the early post-transplant period as well as during later follow-up. The incidence of severe AKI requiring dialysis in KTR can be 45-fold higher in comparison with AKI of non-transplant patients (Mehrotra et al., 2012). Recently, several promising urinary biomarkers for early detection of AKI which increase before the rise in serum creatinine (sCr) have been identified. Some of these biomarkers are urinary neutrophil gelatinase-associated lipocalin (uNGAL) (Haase et al., 2009; Mishra et al., 2003; Mori et al., 2005), kidney injury molecule-1 (uKim-1) (Han et al., 2002; Ichimura et al., 1998; Vaidya et al., 2006, 2010; Zhang et al., 2007), interleukin-18 (uIL-18) (Fantuzzi et al., 1998; Haase et al., 2008; Melnikov et al., 2001; Parikh et al., 2005, 2006b; Vanmassenhove et al., 2013), and heat shock protein-72 (uHsp72) (Barrera-Chimal et al., 2011). Nevertheless, most studies have excluded KTR. A few studies have suggested that uNGAL concentration could predict delayed initial graft function (Heyne et al., 2012) or discriminate acute allograft rejection from other causes (Sureshkumar & Marcus, 2010) in KTR. Furthermore, there is evidence showing that urinary
Address for correspondence: Luis E. Morales-Buenrostro, MD, MMSc, PhD, Instituto Nacional de Ciencias Me´dicas y Nutricio´n Salvador Zubira´n, Vasco de Quiroga 15, Seccio´n XVI, ZP 14000, Mexico City, DF, Mexico. Tel: 152 5554870900 ext. 2505. E-mail: [email protected]
History Received 7 October 2013 Revised 13 November 2013 Accepted 17 November 2013 Published online 11 December 2013
biomarkers are helpful in the prediction of long-term allograft function after the early transplant period (Halawa, 2011). It is clear that there is an urgent need to identify better and earlier biomarkers to diagnose AKI. Additionally, these biomarkers could be of relevance for predicting graft loss in KTR after an AKI episode from different causes reflecting real daily practice. Currently, few studies have examined the usefulness of urinary biomarkers as outcome predictors in this context. This study was designed to assess whether uNGAL, uKIM-1, uIL-18, uHsp72 at the time of diagnosis of AKI after the early transplant period, can predict future graft loss in KTR with AKI.
Methods Study population
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A prospective cohort study of KTR with AKI was conducted at a tertiary National Health Institution located in Mexico City. We invited all consecutive KTR with AKI detected in the Emergency Department or in the outpatient clinic between April 2010 and July 2011. A urine sample was collected at the time of KTR enrollment for assessing biomarkers level. KTR were prospectively followed for 1 year after AKI episode and sCr was prospectively measured every three months in all patients. An attending nephrologist blinded to all the results of baseline biomarkers, performed adjudication of outcome at the end of follow-up. We excluded KTR with estimated GFR (eGFR) 530 mL/min/1.73 m2 within 3 or more months before AKI episode, kidney transplant in the last 6 months, unknown sCr value recorded within 3 months previous to AKI, anuria at AKI diagnosis, and denial to provide informed consent.
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The study was previously approved by our hospital0 s institutional biomedical research review board (Comite´ Institucional de Investigacio´n Biome´dica en Humanos, approval number 167) and all subjects provided written informed consent.
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Definitions AKI was diagnosed according to RIFLE criteria: rise in sCr by 1.5-fold or eGFR decrease425% from baseline (eGFR was calculated using the Modification of Diet in Renal Disease (MDRD) four-variable equation). We used the lowest known sCr value during the preceding 3 months as the baseline from patient’s clinical records. The primary clinical outcome variables were graft loss, incomplete recovery, and complete recovery at 12 months after AKI. Graft loss was defined by a permanent requirement for maintenance renal replacement therapy (RRT) or eGFR 15 mL/min/1.73 m2. Incomplete recovery was defined as failure of the sCr or eGFR to return to baseline and previous KDOQI chronic kidney disease stage and complete recovery was defined by the return of the sCr or eGFR to baseline or previous KDOQI CKD stage. As usual, patients were classified as ‘‘prerenal AKI’’, when the increase in sCr concentration was caused by factors that compromise renal perfusion, and sCr rapidly improved to baseline with volume repletion or improvement in cardiac output within 3 days of directed therapy. All AKI cases were evaluated by two certified nephrologists and each outcome was adjudicated or not as an AKI event. Data collection The KTR demographic data, etiology of AKI, comorbid conditions, RRT requirement, AKI recurrence, mortality, and renal outcome were prospectively collected. sCr was measured in all the patients at 1, 3, 6, 9 and 12 months after enrollment. Measurement of the biomarkers Fresh urine samples were collected and stored at 80 C until the urinary biomarkers assays were performed. Urine NGAL, Kim-1 and IL-18 levels were analyzed using commercially available enzyme-linked immune absorbent assay (ELISA) kits: uNGAL ELISA kit (BioPorto Diagnostics, KIT036, Gentofte, Denmark), uKim-1 ELISA kit (USCN Life Science Inc., 90785, Wuhan, China), uIL-18 ELISA Kit (Invitrogen, KHC0181, Carlsbad, CA), and uHsp72 ELISA kit (ENZO Life Sciences ADI-EKS-715, Ann Arbor, MI). All procedures were performed according to manufacturer’s instructions. Statistics Results are presented as mean standard deviation for normally-distributed data or median interquartile range (IQR) for non-normally distributed data. We divided the patients into three groups according to the primary clinical outcome (graft loss, incomplete recovery, and complete recovery). The differences among the groups were tested by analysis of variance with Bonferroni’s correction for multiple comparisons when variables were normally distributed or Kruskal–Wallis test was employed when variables were not normally distributed. To examine the predictive performance
Biomarkers, 2014; 19(1): 63–69
of urinary biomarkers to evaluate 1 year graft outcome, we plotted receiver operating characteristics (ROC) curve and used Kaplan–Meier curves according to high level versus low level biomarker (cutoff at the 75th percentile). The curves were compared by the log-rank test. The association of urinary biomarkers with graft-loss was examined by Cox proportional hazards regression analyses using backward elimination. Those variables identified as statistically significant in the univariate analysis were included in the multivariate model. All tests of significance were two-tailed, and differences were considered statistically significant at p value 50.05. All statistical analysis was performed by SPSS version 18.0 (SPSS Inc., Chicago, IL).
Results Baseline patient’s characteristics Of the 89 KTR identified with renal graft dysfunction between 26 April 2010 and 1 June 2011, 67 fulfilled the inclusion criteria of AKI and were enrolled in the study. Causes of exclusion were eGFR 530 mL/min/1.73 m2 within 3 o more months before AKI (8/22), unknown sCr within 3 months before AKI (8/22) and less than 6 months after transplant (6/22). Any patient had anuria at AKI diagnosis. Forty-six of these patients diagnosed with AKI (69%) were diagnosed at the emergency room in the context of diarrhea, fever, or respiratory symptoms. The other cases were asymptomatic and were diagnosed with AKI during the follow-up visit in the outpatient clinic (21 cases, 31%). All patients completed 1-year follow up. The median age was 40 years, 55% were females, and median time after transplantation was 2.7 years (Table 1). Seventy percent had received a kidney graft from living donor. Graft biopsy was obtained in all KTR within 6 weeks after AKI episode, including cases with vascular, obstruction or prerenal causes, according to institutional protocol. AKI causes, as previously defined, were: immunological rejection (20/67; 30%) and prerenal (20/67, 29%). Other causes (27/ 67, 40%): calcineurin-inhibitor toxicity (9), recurrence of primary glomerular disease-membranous glomerulopathy(2), obstructive uropathy (2), polyoma virus infection (3), non-steroidal anti-inflammatory drugs use (2), transplant renal artery stenosis (2), acute tubular necrosis (3) and kidney biopsy with no-specific cause of AKI and only interstitial fibrosis and tubular athrophy (4). KTR with clinical diagnosis of prerenal AKI and who experience recurrent episodes of slight SCr elevation (n ¼ 8/20) were biopsied during followup and no specific histopathologic diagnosis was evident. In general, the severity of illness of the patient cohort in this study was as follows: 42% KTR had systemic inflammatory response syndrome, only one patient was admitted to the Critical Care Unit during the course of hospitalization, and renal replacement therapy was provided 1 month after enrollment in 4 patients (9%). The median length of hospital admissions was 4 days (ranged from 1 to 30 days). Graft survival of KTR with AKI For further characterization of the patient cohort, we separately analyzed three groups of patients: those KTR who had graft loss (n ¼ 11, 16%), those with incomplete recovery
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Table 1. Baseline characteristics of the studied population and the three groups patients: complete recovery, incomplete recovery and graft loss.
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Variablesa Median age [IQR], years Women, n (%) Causes of AKI, n (%) Prerenal Acute rejection Other Living donor, n (%) Time post kidney transplant [IQR], years Median baseline sCr [IQR], mg/dL eGFR before AKI [IQR], mL/min/1.73 m2 sCr at AKI diagnosis [S.D.], mg/dL Severity of AKI (RIFLE) Risk Injury Failure Diabetes mellitus, n (%) Infection, n (%) HLA mismatch (0/6), n (%) Calcineurin toxicity, n (%) Recurrent events of AKI after first episode eGFR 1 month after AKI eGFR 3 month after AKI eGFR 6 month after AKI eGFR 12 month after AKI
All KTR n ¼ 67 (100%)
Complete recovery, n ¼ 36 (54%)
Incomplete recovery, n ¼ 20 (30%)
40 [29–46] 30 (55)
43 [25–48] 18 (50)
(30) (30) (40) (70) [0.8–6.1] [0.8–1.7] [18–130] [1.3–3.0]
12 9 15 28 1.9 1.5 49 2.0
(80%) (7%) (13%) (18%) (43%) (55%) (15%)
33 2 1 5 16 20 5 13 42 43 42 44
20 20 27 47 2.7 1.4 50.1 2.0 54 5 9 12 29 37 10 16 41 40 41 42
(32–52) (31–51) (29–53) (28–52)
Graft loss, n ¼ 11 (16%)
p
40 [32–46] 8 (40)
36 [28–44] 4 (36)
0.35 0.64
(33) (25) (42) (77) [0.7–6] [1.2–1.8] [39–55] [1.7–2.4]
8 6 6 12 2.4 1.1 71 1.7
(40) (30) (30) (60) [0.8–4.7] [0.9–1.3] [50–79] [1.4–2.2]
0 5 6 7 8.4 1.7 43 5.4
(92%) (6%) (2%) (14%) (48%) (56%) (14%)
17 3 1 4 9 12 2 12 44 45 43 42
(85%) (14%) (1%) (20%) (45%)) (60%) (10%)
4 0 7 3 3 5 3 1 14 7 5 5
(35–52) (34–55) (34–53) (36–52)
(35–54) (32–54) (33–60) (26–54)
(71) (29) (63) [3.6–11.6] [1.5–2.7] [23–58] [4.2–8.1] (36%) (61%) (27%) (43%) (45%) (27%) (6–23) (4–14) (4–11) (4–6)
0.047 0.43 0.57 0.48 50.001 50.001 50.001 50.001 0.05 0.54 50.001 0.56 0.55 0.44 0.58 50.001 50.001 50.01 50.001 50.001
a
Values are % for categorical variables, mean standard deviation for normally distributed data or median interquartile range for non-normally distributed data. IQR, interquartile range; SD, standard deviation.
(n ¼ 20, 30%), and finally those that completely recovered their renal function, defined by return to baseline eGFR 1 year after the AKI episode (n ¼ 36, 54%) Graft-loss etiology was diverse: immunological rejection in five KTR (humoral rejection in three cases), acute tubular necrosis in two, obstructive uropathy in two with moderate interstitial fibrosis and tubular atrophy previous AKI episode, and intersticial fibrosis and tubular atrophy in two without evidence of rejection or other evident cause of injury. These two cases had previous biopsies (6 and 9 months before AKI) with minimal fibrosis. As expected, the graft-loss group had a significantly increased initial sCr at AKI diagnosis and 7 of 11 patients in this group evolved to ‘‘failure’’ category of the RIFLE score. The incomplete recovery group had a significant higher baseline eGFR compared to the other two groups. After the AKI episode, all patients had an eGFR under 60 mL/min/ 1.73 m2. For the groups with complete renal function recovery, almost all patients had the same SCr level one month after the AKI episode as the one observed at 1-year of follow up. We did not find differences between the three groups in relation to HLA mismatch, prevalence of diabetes mellitus, and frequency of infection. Urinary biomarkers and outcome at 12 months Figure 1 and Table 2 shows the measurement of renal function biomarkers according to outcome. Median uNGAL was 241 ng/mL in the graft loss group varying from 31 to 294 ng/mL and this mean value was significantly different from the complete and incomplete recovery groups (48 and 109 ng/mL, respectively, p: 0.002). Post-hoc test revealed significant differences between uNGAL from group with graft loss and incomplete recovery (p: 0.009). The other urinary
biomarkers assessed: Hsp-72, IL-18, and Kim-1 were not statistically different among the studied groups. Analysis of risk factors for graft loss To identify variables associated with graft loss, we first applied a univariate pair-matched Cox regression analysis as shown in Table 3. We found four variables associated with graft loss: sCr at AKI diagnosis, time after transplantation, baseline graft eGFR, and uNGAL percentile 475%. Other variables present at AKI diagnosis such as an episode of acute immunological rejection, AKI recurrence, presence of diabetes mellitus, delayed graft function history, and presence of infection were not risk factors for graft loss. All variables statistically significant in the univariate analysis were subjected to multivariate Cox regression analysis. Only baseline eGFR and NGAL percentile 475%, were identified as independent predictors of 1-year graft loss. Others such as sCr at AKI diagnosis were not associated with graft loss in multivariate analysis. NGAL percentile 475% had a hazard risk of 36.46 (95% CI 2.57–51.7, p 0.008). When the same analysis was performed using a different cutoff at median value (70 ng/mL), a uNGAL 450th percentile had a hazard risk of 5.22 (95% CI 1.3–27.7, p 0.009). The log-rank test based on the Kaplan–Meier curves showed a significant association between high uNGAL (475th percentile) and graft loss (p 0.001). As seen in Figure 2, a large proportion of the graft loss occurred in the first month after AKI. Prognostic performance of urinary NGAL The performance of urinary NGAL to predict graft failure was analyzed. Figure 3 shows the area under the curve
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Biomarkers, 2014; 19(1): 63–69
Figure 1. Urinary biomarkers according to outcome.
Table 2. Biomarkers according to outcome. Variables
Complete recovery
uNGAL, ng/mL (IQR) uKIM-1, ng/mL (IQR) uHsp72, ng/mL (IQR) uIL-18, pg/mL (IQR)
48 44 0.11 18
Incomplete recovery
(6–129) (22–70) (0.05–0.54) (5–121)
109 69 0.09 52
Graft loss
(14–200) (22–223) (0.02–0.61) (5–241)
241 158 0.02 102
(110–271) (18–247) (0.02–0.29) (85–272)
p 50.001a 0.24 0.73 0.11
a
Post-hoc Kruskal–Wallis revealed significant differences between graft loss group versus incomplete recovery group and graft loss group versus complete.
Table 3. Factors associated with graft failure in KTR in univariate Cox regression model. Univariate
a
sCr at AKI diagnosis Time after transplant (y) Basal eGFR (mL/min/1.73 m2) Immunological rejection uNGAL percentile 475%
Multivariate
HR
95% CI
p Value
HR
95% CI
p Value
2.95 1.20 1.20 1.85 43.2
1.9–4.9 1.07–1.35 1.07–1.35 0.52–6.55 5.52–340.74
0.01 0.03 50.001 0.34 50.001
1.89 1.08 1.03
1.16–3.06 0.88–1.31 1.01–1.08
0.24 0.44 0.017
36.46
2.57–51.72
0.008
a
AKI, acute kidney injury.
(AUC) for the ROC curve of uNGAL for graft failure. The uNGAL had a good performance to predict graft loss with an AUC of 0.89 (95% CI 0.81–0.97, p 0.0001). At a cutoff value of 200 ng/mL, the sensitivity and specificity for the prediction of graft failure were 83.9% and 90.9%,
respectively (95% CI 73.8–93.6 and 58.7–99.8%, LR 9.43). Nineteen KTR had an uNGAL value 4200 ng/mL (28%). In contrast, when we use sCr at AKI diagnosis to predict graft failure, the AUC for the ROC curve was 0.809 (95% CI 0.63–0.98, p 0.0001).
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DOI: 10.3109/1354750X.2013.867536
Figure 2. Kaplan–Maier plots of graft loss dependent on uNGAL stratified as high (475th percentile) versus low.
Figure 3. Area under the curve (AUC) for the receiver-operating characteristic curve (ROC) of uNGAL for graft loss.
Assessment of the studied population with AKIN definition and biomarker concentrations expressed as a ratio to the urinary creatinine concentration We finally performed a full analysis of our cases employing AKIN classification. According the AKIN criteria, we excluded reversible causes of AKI in KTR following adequate resuscitation, as is the case in patients with prerenal azotemia (20/67). Forty seven cases defined by AKIN classification were included. In these KTR, uNGAL was statistical different between the KTR with graft loss and the other two groups (p40.001). The AUC for the ROC curve of uNGAL for graft failure using AKIN definition was 0.80 (95% CI 0.65–0.92, p 0.003). We also analyzed the data adjusting for urinary creatinine concentration. Although normalization is less than ideal since creatinine is not in a steady state, we found similar results, with a significant difference between graft loss group and the others groups respect to uNGAL/uCr (358.5 mcg/g versus 23 mcg/g, p 0.001).
Discussion In this prospective cohort, we demonstrated that uNGAL determination at the time of an AKI event in a KTR is an independent predictor of graft loss. In addition, uNGAL was useful to stratify the risk of graft loss and may be useful as
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a prognostic marker in KTR with different causes of AKI who present an event at least 6 months after transplantation. uKIM-1, uIL-18, and uHsp72 were unable to predict graft failure in this population. This is the only study that has evaluated the potential use of AKI biomarkers to predict graft loss. Many studies that have explored the use of biomarkers for AKI prognosis have excluded KTR (Halawa, 2011). Notably, AKI is a common complication of KTR and could represent the major risk of graft failure (Nakamura et al., 2012). The most promising biomarkers for clinical diagnosis include a panel of uNGAL, uIL-18, and uKIM-1 (Devarajan, 2007). In the present study, we focused on the occurrence of AKI in the maintenance phase of a kidney transplant, and tested the usefulness of these biomarkers and uHSP72 to predict graft failure after an AKI episode. We decided to include different causes of AKI reflecting daily practice in our center. In our institution, after the initial 3 months of transplantation, we usually follow our KTR every 2 or 3 months and instruct them to come to our Unit in any unexpected health event. After detection of an AKI episode, patients are extensively studied and treated timely as possible. Despite this follow up, sixteen percent of KTR had graft loss, similar to other reports (Mehrotra et al., 2012). The high incidence of dialysis after AKI underscores the need to find prognostic factors in KTR with AKI. Urinary NGAL has been studied in various clinical situations. In kidney transplantation, uNGAL has been useful as prognostic of graft function immediately after transplantation (Hall et al., 2010; Hollmen et al., 2011; Parikh et al., 2006a). Recently, one study found that NGAL discriminate immunological rejection after early transplant period (Heyne et al., 2012). Our study suggest that urinary uNGAL and eGFR basal level are independent predictors for graft loss after an AKI episode and may be useful in clinical scenarios to guide treatment and diagnostic decisions. This study shows that uNGAL in KTR have a similar prediction to that reported in other populations without kidney transplant respect renal replacement therapy initiation (Haase et al., 2009). These results suggest that urinary biomarkers in KTR may have a similar behavior compared with patients without kidney transplant. Median uNGAL concentration in stable KTR has been described around 7.8 ng/mL and a uNGAL cut-off at 30 ng/mL allowed discerning KTR with AKI versus stable KTR (Heyne et al., 2012). We demonstrate that uNGAL cut-off of 200 ng/mL is sensitive and specific in predicting graft loss in unselected KTR with AKI. In our cohort, the area under the ROC curve for uNGAL was a better predictor of 1 year graft loss than sCr at AKI diagnosis. In our opinion, uNGAL can add information to the value of sCr as a predictor of graft loss. Another finding in our study is that IL-18, Hsp72 and Kim-1 were not useful for predict graft loss. Definition of AKI is based on sCr or urine output, reflecting loss of renal function. In clinical context, AKI diagnosis is detected until 24 to 48 h after injury. In outpatient scenario, it is impossible to know the exact beginning of the injury. IL-18 and Hsp-72 are very useful biomarkers for early diagnosis of AKI, both biomarkers may increase even days before SCr elevation
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(Barrera-Chimal et al., 2011). However, the biomarkers’ variability reported might be explained by different degrees, extension, and evolution of AKI in our cohort. Thus, urinary IL-18 and hSP72 levels may have been elevated days before enrollment, and could have fallen at the moment of the AKI diagnosis in some KTR. In patients with AKI induced by ischemic insult, Kim-1 elevation is seen several hours after AKI has occurred and remains elevated along the time, although Kim-1 was unable to stratify severe renal injury degrees (Han et al., 2009). In counterpart, it has been described that high urinary Kim-1 in stable KTR without AKI is an independent predictor of long-term graft loss (Van Timmeren et al., 2007). In our study, the graft loss group appears to have a higher urinary Kim-1 level with respect to other groups, but given the limited sample sizes, the difference did not attain statistical significance. One of major problems in conducting clinical studies of AKI in KTR is the absence of consensus definition. Although AKIN definition have attempted to exclude easily reversible causes of azotemia (such as volume depletion or urinary obstruction) in comparison with the RIFLE definition, both consensus criteria are mostly comparable (Joannidis et al., 2009). We defined AKI by RIFLE criteria according to our clinical practice in emergency department and included KTR with AKI, that after 48 h it was revealed a prerrenal or obstructive causes. However, when comparing results using AKIN criteria excluding reversible causes, uNGAL had the same significance in our clinical study. Recently, a study has described that the AKI group of KTR with AKI represented a high risk for graft failure, similar to the hazard risk that we report in our univariate analysis model (Nakamura et al., 2012). We did not find one variable for discriminate between KTR with complete recovery and KTR with incomplete recovery. In our cohort, we found that 46% of KTR had a detrimental effect in eGFR or graft loss only after an episode of AKI, underlined that we should make any effort in the prevention, diagnosis and early treatment of AKI to avoid graft loss. Some authors have proposed that subjects with advanced CKD show increased tubular excretion of biomarkers (e.g. NGAL) as consequence of a sustained production by ‘‘inflamed’’ tubular cells – forest fire theory (Mori & Nakao, 2007). Assuming that KTR with baseline chronic eGFR less than 30 mL/min might have a greater grade of tubular inflammation, interstitial fibrosis, or tubular atrophy, we decided to excluded those subjects to minimize potential confounding factors. This study does have some limitations. It is a singlecenter prospective cohort study, was based on the analysis of a relatively small number of recipients, and eight cases might have been missed a histologic diagnosis by a delay between AKI episode and graft biopsy. In spite of the small sample size, we found a significant association and a good prognostic value using uNGAL. Another potential limitation is that urinary biomarkers were assessed only one time, at diagnosis of AKI. Whether the observed effect would be comparable in KTR knowing basal biomarkers concentrations before the AKI event or during follow-up remains to be determined.
Biomarkers, 2014; 19(1): 63–69
Conclusions Our data demonstrate the ability of uNGAL to predict graft loss after an episode of AKI in KTR. uKIM-1, uIL-18 and uHsp72 did not have prognostic utility in this population. uNGAL may be useful tool in the follow-up of KTR with AKI.
Acknowledgements We are grateful to Rosalba Pe´rez for her technical assistance. The results presented in this article have not been published previously in whole or in part, except as an abstract presented at Kidney Week 2012: 45th Annual Meeting (San Diego, CA). J.C.R.S., R.C.R., N.A.B., L.E.M.B. conceived and design the study; J.C.R.S., J.B.C., P.E.S., A.R.M. included patients and 1 year follow-up; J.C.R.S., J.B.C., A.R.M., R.C.R., N.A.B., L.E.M.B. analyzed the data; J.B.C., P.E.S., N.A.B. contributed reagents or analysis tools; J.C.R.S., J.B.C., N.A.B., R.C.R., L.E.M.B. wrote the article.
Declaration of interest The authors declare no conflicts of interests. The authors alone are responsible for the content and writing of this article. This project was supported by a grant from Consejo Nacional de Ciencia y Tecnologı´a (CONACyT 181267 to NAB).
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DOI: 10.3109/1354750X.2013.867536
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