Original ArticlesAcute Kidney Injury /Acute Renal Failure

Association between Peritransplant Kidney Injury Biomarkers and 1-Year Allograft Outcomes

Isaac E. Hall, Mona D. Doshi, Peter P. Reese, Richard J. Marcus, Heather Thiessen-Philbrook and Chirag R. Parikh
CJASN August 2012, 7 (8) 1224-1233; DOI: https://doi.org/10.2215/CJN.00310112

Summary

Background and objectives Current tools to predict outcomes after kidney transplantation are inadequate. The objective of this study was to determine the association of perioperative urine neutrophil gelatinase-associated lipocalin and IL-18 with poor 1-year allograft function (return to dialysis or estimated GFR<30 ml/min per 1.73 m2).

Design, setting, participants, & measurements Neutrophil gelatinase-associated lipocalin and IL-18 from early post-transplant urine was measured in this prospective, multicenter study of deceased-donor kidney transplant recipients. The outcome of poor allograft function at 1 year relative to these biomarkers using multivariable logistic regression and net reclassification improvement was examined. Also, the interaction between delayed graft function and the biomarkers on the outcome were evaluated, and the change in biomarkers over consecutive days related to the outcome using trend tests was examined.

Results Mean age for the 153 recipients was 54 ± 13 years. Delayed graft function occurred in 42%, and 24 (16%) recipients had the 1-year outcome. Upper median values for neutrophil gelatinase-associated lipocalin and IL-18 on the first postoperative day had adjusted odds ratios (95% confidence interval) of 6.0 (1.5–24.0) and 5.5 (1.4–21.5), respectively. Net reclassification improvement (95% confidence interval) was significant for neutrophil gelatinase-associated lipocalin and IL-18 at 36% (1%–71%) and 45% (8%–83%), respectively. There was no significant interaction between biomarkers and delayed graft function on the outcome. Change in biomarkers moderately trended with the outcome.

Conclusions Perioperative urine neutrophil gelatinase-associated lipocalin and IL-18 are associated with poor 1-year allograft function, suggesting their potential for identifying patients for therapies that minimize the risk of additional injury.

Introduction

Peritransplant ischemic kidney injury may contribute to progressive allograft dysfunction, but clinicians have limited tools at the time of transplantation to predict outcomes (1,2). Because the degree of structural allograft injury is challenging to assess even with biopsy, the transplant community relies on injury risk factors (cold ischemia time) in conjunction with surrogates of repair potential (donor age, comorbidities, and terminal creatinine) and immunogenicity (HLA mismatch, etc.) to gauge kidney allograft prognosis. Several prognostic tools have been developed using these clinical variables at various time points after surgery (3–7), although they are limited in terms of accuracy, ease of use, delay in obtaining results, and/or cost. In addition, delayed graft function (DGF) is often interpreted as early clinical evidence of peritransplant allograft injury, but the term has varied definitions in the literature (8). The risk of DGF also depends on recipient characteristics and clinical judgment (when defined as the use of dialysis after transplant). For these reasons, novel methods that can be implemented early after transplant to predict allograft outcomes are needed and could help guide transplant care.

Kidney injury biomarkers have the potential to evaluate allograft injury noninvasively, and multiple proteins that quantify ischemia reperfusion injury specific to the kidney have been identified. Two of the most promising biomarkers are neutrophil gelatinase-associated lipocalin (NGAL) and IL-18. Previous studies show that these biomarkers are released into the urine at the time of renal tubular cell injury, and both biomarkers can be measured through noninvasive methods (9,10). The availability of biomarkers like NGAL and IL-18 could permit transplant teams to noninvasively assess early allograft injury to facilitate clinical decisions and potentially preserve long-term allograft function. If consistently shown as effective predictors, these biomarkers may also serve as important tools in biomarker-guided clinical trials for transplant drug development.

We previously showed that kidney injury biomarkers were strongly associated with DGF. NGAL and IL-18 measured from perioperative recipient urine significantly improved the prediction of DGF, the rate of recovery of early allograft function, and 3-month allograft function (11–13). However, it is unknown whether these biomarkers are associated with important recipient outcomes beyond 3 months. Given that DGF itself can be considered a proxy for early allograft injury and is a risk factor for later allograft loss, it is also unknown whether urinary biomarkers add long-term prognostic value for recipients that do not subsequently experience DGF. Additionally, patterns of change in urinary biomarker concentrations after transplant have not been explored and might offer insights into whether degrees of kidney injury lead to maladaptive repair.

The current study is an expansion of our previous cohort (11). The primary aim was to determine whether perioperative urine NGAL and IL-18 were associated with poor 1-year allograft function. We also sought to (1) evaluate potential interaction between injury biomarkers and DGF on the outcome and (2) explore how patterns of change in urinary biomarker concentrations during the early perioperative period relate to the outcome.

Materials and Methods

This prospective, multicenter cohort study was approved by the institutional review boards of all participating centers. Incident deceased-donor kidney transplant recipients who were at least 18 years old were recruited into the study over a period of 2 years. Written informed consent was obtained from all patients before enrollment, and adherence to the principles of the Declaration of Helsinki was maintained. We excluded recipients of pre-emptive transplants, live-donor transplants, one recipient with primary nonfunction related to technical/surgical causes, and three recipients with no outcome information at 1 year (lost to follow-up). Apart from consistency with our earlier cohort, the primary reason for excluding pre-emptive transplants was to limit the effects of significant residual renal function on 1-year outcomes.

Outcomes

The primary outcome was defined as the return to dialysis by 1 year or an estimated GFR<30 ml/min per 1.73 m2 at 1 year. GFR was estimated by the four-variable Modification of Diet in Renal Disease study equation based on 1-year serum creatinine values that were measured by participating centers during routine patient care (14). Those patients who died within 1 year were treated as having the primary outcome if they had returned to dialysis before death or the last available serum creatinine resulted in an estimated GFR<30 ml/min per 1.73 m2. All clinical variables and outcomes, including death and acute rejection, were confirmed by chart review.

Data and Sample Collection with Laboratory Measurements

We obtained baseline characteristics of donors and recipients as well as transplant information from all available medical records. DGF was defined as any dialysis in the first week after transplant. In those patients without DGF, slow graft function and immediate graft function were defined as a relative decrease in serum creatinine from 0 hour (immediately after transplant) to the seventh postoperative day (POD) of ≤70% or >70%, respectively (15). Other variable definitions were consistent with United Network for Organ Sharing forms (16). Decisions regarding induction and immunosuppressive therapy and use of dialysis were made by the clinicians at each center.

We collected 10 ml urine every 6 hours immediately after surgery for a total of four samples. We then collected urine samples on the mornings of the first and second PODs. We recorded daily urine output and serum creatinine beginning on the day of transplantation until the seventh POD or discharge (if hospitalized less than 7 days). All samples were centrifuged at 5,000×g for 10 minutes to remove cellular debris, aliquoted into barcoded cryovials, and stored at −80°C.

ELISA methods for NGAL and IL-18 were performed as previously published (17,18). The intra- and interassay variability for NGAL and IL-18 were less than 10%. Urine creatinine was measured by a quantitative colorimetric assay kit (Sigma, St. Louis, MO). Serum creatinine was measured by a modified Jaffé method (kinetic colorimetric assay) standardized against isotope dilution mass spectrometry, with intra- and interassay variability ranging from 0.7% to 2.3% (19). All laboratory measurements were performed by personnel blinded to patient information.

Statistical Analyses

Analyses were assessed at a two-tailed α=0.05. We compared donor, recipient, and clinical characteristics as well as biomarker concentrations between recipients with and without the primary outcome using t or Mann–Whitney Wilcoxon tests for continuous variables and chi-squared or Fisher exact tests for categorical variables. We used logistic regression to determine the association between biomarkers and the primary outcome. Biomarkers were included as categorical variables (more than median value) or natural log-transformed continuous values. We adjusted for clinical variables that predict DGF, including donor and recipient age (years), cold ischemia time (hours), and urine output <1 L on the first POD (11). Each biomarker was assessed individually in the model without combining biomarkers. To determine if the association between biomarkers and the primary outcome differed by DGF status, interaction terms between each biomarker (more than median value) and DGF (yes/no) were evaluated separately in the same models.

We calculated net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indices to determine the prognostic benefit of individually adding the biomarkers (above or below the median values) to the baseline clinical model (20). We defined predicted outcome risk categories as low (<10% occurrence), intermediate (10%–20%), and high (>20%). The purpose for using NRI is to enumerate reclassification in the desired direction resulting from the addition of a new marker to a predictive model, whereas the purpose for using IDI is to calculate the expansion in separation of events and nonevents after adding the new marker to the model. Traditionally, changes in model C statistics (analogous to changes in areas under the receiver-operating characteristic curve) have been used to describe the use of new biomarkers in relation to current prediction models, but these changes are difficult to interpret and often minor even for robust, new predictors (21). In contrast, NRI and IDI provide more intuitive and meaningful summary measures of risk reclassification and discrimination.

We used integrated predictiveness curves to display the distribution of risk in the cohort and assess the classification performance of the biomarkers (22). The predictiveness curves plot the estimated risk of the outcome (on the y axis) for each individual (ranked from lowest to highest risk on the x axis) based on the logistic regression model for the outcome using each biomarker (log-transformed first POD value) adjusted for donor and recipient age, cold ischemia time, and urine output <1 L on the first POD. Integrative predictiveness curves are valuable for determining the proportion of a cohort that would be considered low or high risk for a particular outcome at various risk thresholds.

Finally, we compared outcome rates in recipients with different patterns of change in urine biomarker concentrations (log-transformed) over the first 2 days using Jonckheere–Terpstra trend tests. The three patterns of change were continuous decreases over both days, continuous increases over both days, or an inconsistent pattern (increase and then decrease or decrease and then increase). SAS 9.2 statistical software for Windows (SAS Institute, Cary, NC) and R 2.12.1 (R Foundation for Statistical Computing, Vienna, Austria) were used for all analyses.

Results

Cohort

Of 172 patients enrolled, 153 patients were available for analysis after exclusions (15 patients had pre-emptive transplants, 3 patients were lost to follow-up, and 1 patient had primary nonfunction caused by surgical complications). Poor 1-year allograft function occurred in 24 (16%) patients; 11 patients returned to dialysis, and 13 patients had a GFR<30 ml/min per 1.73 m2 at 1 year. There were also 13 deaths and 18 acute rejections by 1 year. There were no significant differences in demographic or other clinical characteristics for donors or recipients between those patients with and without the primary outcome, with the notable exceptions of DGF and immediate graft function (Table 1).

View this table:
Table 1.

Summary of baseline and clinical characteristics in transplant donors and recipients

Biomarkers and Outcomes

Urine NGAL and IL-18 showed modest correlation at each time point and were 0.37 (P<0.001) on the first POD.(the morning after surgery). By this time point, important differences in biomarker concentrations were evident between recipients who did and did not subsequently develop poor 1-year allograft function. First POD median (10th–90th percentile) values were higher for those patients with versus without the outcome for urine NGAL (911 [133–3143] versus 370 [29–2381] ng/ml, P=0.02), urine NGAL/urine creatinine (751 [104–3995] versus 286 [32–1901] ng/mg, P=0.003), and urine IL-18/urine creatinine (328 [0–1992] versus 116 [0–940] pg/mg, P=0.04). Median first POD urine IL-18 values were 186 (48.5–1239) pg/ml in those patients with the outcome compared with 113 (0–781) pg/ml in those patients without the outcome (P=0.08). No values for serum creatinine during the first 7 days after transplant were statistically different between the two groups (data not shown). However, discharge GFR was lower for those patients with the outcome (13.4 [6.5–39.9] versus 18.2 [7.0–59.3] ml/min per 1.73 m2, P=0.05).

With logistic regression, only urine NGAL and IL-18 on the first POD remained significantly associated with poor 1-year allograft function after adjustment for baseline clinical variables (Table 2). Urine NGAL and IL-18 concentrations above the median values for the cohort on the first POD (422 ng/ml and 137 pg/ml, respectively) were associated with 6.0- and 5.5-fold increased adjusted odds of the outcome, respectively.

View this table:
Table 2.

Association of biomarkers and other predictors with the primary outcome

Comparing predicted risk categories and actual primary outcome occurrences between the baseline clinical model and the same model plus urine NGAL or urine IL-18 on the first POD resulted in very large and statistically significant NRI and IDI indices (Table 3). The NRI (95% confidence interval) for NGAL was 36% (1%–71%), and it was 45% (8%–83%) for IL-18. As an example of improved reclassification, among the 28 recipients without the primary outcome who were initially categorized as high risk by the clinical model alone, adding urine NGAL appropriately reclassified 15 patients into lower risk categories (3 patients as intermediate and 12 patients as low risk). Of note, adding DGF to the baseline clinical model (no biomarkers) resulted in an NRI of 5.0% (−21% to 31%) and an IDI of 2.4% (0.1%–4.7%). Adding serum creatinine (at any time point in the first week or change between time points) or discharge GFR to the baseline clinical model did not significantly improve reclassification for the primary outcome (data not shown).

View this table:
Table 3.

Reclassification of primary outcome risk after adding urine neutrophil gelatinase-associated lipocalin or IL-18 to the baseline clinical model

Effect of DGF on the Injury–Outcome Relationship

We compared biomarker concentrations among recipients with and without the outcome stratified by DGF status and found no statistically significant differences (data not shown). Next, we compared outcome rates after classifying recipients as above or below the median NGAL and IL-18 concentrations on the first POD. Those recipients with urine biomarker levels above the medians had higher rates of the outcome than recipients with levels below the median values (22% versus 6%, P=0.007 for NGAL; 22% versus 7%, P=0.01 for IL-18), and results were similar after stratifying by DGF status (Figure 1). Of note, interaction terms for each biomarker and DGF were not statistically significant with respect to the outcome (NGAL × DGF, P=0.75; IL-18 × DGF, P=0.93).

Figure 1.
Figure 1.

Occurrence of primary outcome by urine biomarkers on the first postoperative day. Primary outcome rates are significantly different by biomarker medians for all recipients (P=0.007 for neutrophil gelatinase-associated lipocalin [NGAL], P=0.01 for IL-18) and recipients without delayed graft function (DGF; P=0.02 for NGAL, P=0.04 for IL-18). There was no significant difference for recipients with DGF. There was no significant interaction between biomarkers and DGF with respect to the primary outcome.

Biomarker Predictiveness

Figure 2 shows the estimated integrated predictiveness curves for urine NGAL and IL-18 on the first POD adjusted for the same clinical predictors, indicating that the two biomarkers have similar predictive abilities. The horizontal dashed line in Figure 2 indicates the overall outcome prevalence (16%) for the cohort. These curves show the distribution of risks by plotting the calculated risk for the outcome (by multivariable logistic regression) for each patient after ordering the cohort from lowest to highest risk. Using predictiveness curves, we can determine the proportion of individuals that meet various risk thresholds. For example, if we define high risk of poor 1-year allograft function as a risk threshold≥0.25 (y axis), approximately 83% of the cohort is below this threshold using the multivariable model with NGAL, thus identifying 17% of patients as high risk. Similarly, 12% of patients would be high risk using the model with IL-18.

Figure 2.
Figure 2.

Predictiveness curves for urine NGAL and IL-18 (log-transformed) on the first day after transplant adjusted for donor age (years), recipient age (years), cold ischemia time (hours), and urine output <1 L on the first day after transplant. The vertical axis is the predicted risk of the outcome (return to dialysis or estimated GFR<30 ml/min per 1.73 m2 at 1 year) based on the multivariable logistic regression model for each biomarker. The horizontal dashed line indicates the overall outcome prevalence (16%). The horizontal axis represents each individual in the cohort ordered from lowest to highest predicted risk.

Change in Biomarkers

The change in urine biomarkers over the first 2 days after transplant is graphically displayed in Figure 3. All three time points (0 hour, first POD, and second POD) were available in 119 recipients for NGAL and 123 recipients for IL-18. Increasing NGAL concentrations resulted in a significant step-wise increase in primary outcome rates (P=0.02), whereas the trend in outcome rates approached significance for IL-18 (P=0.08).

Figure 3.
Figure 3.

Risk of return to dialysis or GFR<30 ml/min per 1.73 m2 by 1 year by patterns of change in urine NGAL over the first 48 hours after transplant. (A) Urine NGAL (log-transformed units of nanograms per milliliter) or (B) urine IL-18 (log-transformed units of picograms per milliliter) from 0 hours to the first postoperative day (POD) and from the first POD to the second POD. Upper left, middle, and right images depict recipients with values that decreased for both days, changed in the opposite directions over both days, and increased for both days, respectively. Individuals who experienced the outcome are represented by black lines. P values for the bar graphs were calculated by Jonckheere–Terpstra trend tests.

Discussion

We have shown that urine NGAL and IL-18 offer an early, noninvasive method of assessing the severity of allograft injury and that these biomarkers are associated with poor 1-year allograft function. Urine NGAL and IL-18 concentrations on the first POD were significantly higher among recipients who subsequently returned to chronic dialysis or had an estimated GFR below 30 ml/min per 1.73 m2 at 1 year. Higher biomarker concentrations (above the median) also conveyed increased adjusted odds for the outcome and led to substantial improvements in reclassification for the outcome as determined by the NRI and IDI. Our data provide some insight regarding DGF in that early injury biomarkers might help stratify risk for long-term outcomes, even in those recipients who do not require dialysis in the first week. Additionally, this study is the first to show that early changes in urine biomarkers of ischemic kidney injury over consecutive days are associated with longer-term kidney function.

We obtained comparable results for urine NGAL and IL-18 in this study; however, we do not assume them to be interchangeable given the relatively modest correlation between the two injury biomarkers, notwithstanding the statistical significance of this correlation. It is likely that different biomarkers assess different aspects and/or areas of kidney injury, which may be important with regard to long-term outcomes. Our findings are novel and specific to deceased-donor kidney transplantation, but they are congruent with an emerging body of literature in the nontransplant field showing that clinically important injury to kidneys is associated with long-term renal outcomes (2,23) and that kidney injury itself is detectable by noninvasive biomarkers (24). NGAL, a 25-kDa protein normally expressed at low levels in multiple tissues, has been extensively studied in this context and originates from epithelial cells in the distal nephron (10). The precursor to IL-18 is cleaved by caspase-1 to the 18-kDa biologically active cytokine that seems to be involved in several early inflammatory reactions (9). In kidney allografts, severe ischemic injury may have deleterious effects through multiple mechanisms. In the early post-transplant period, ischemia reperfusion injury likely causes increased expression of antigens, thereby increasing the risk of rejection. Additionally, ischemia reperfusion injury may reduce nephron number/mass, leading to hyperfiltration injury and progressive renal function deterioration (25). Later, the severity of ischemic injury to an allograft may determine whether the repair process restores renal epithelial function or produces fibrosis (26).

Our results are likely to be generalizable to most US centers given the multicenter design and diverse study population. Although the DGF rate in the current study was greater than the national average of 22% (27), this difference may be explained by the exclusion of lower-risk individuals (i.e., pre-emptive transplants). Our 1-year death and allograft failure rates are comparable with recent national data on allograft survival in deceased-donor kidney transplantation (28).

We evaluated changes in urine NGAL and IL-18 concentrations to address the concept that maladaptive renal repair leads to progressive loss of kidney function over time. It is important to note that nearly all recipients make enough urine for biomarker measurement, even those recipients ultimately diagnosed with DGF (less than 1 ml is required). The response to injury is a balance between functional repair and fibrosis. We hypothesize that sustained or worsening injury after severe ischemia (as evident by consistently increasing urine NGAL and IL-18 concentrations over the first 2 days after the insult) (Figure 3) leads to progressive fibrosis/scarring, whereas epithelial cell regeneration and functional repair is more likely after transient and mild injury. The underlying mechanisms linking these injury biomarkers to poor outcomes should be confirmed with studies that involve allograft biopsies and measurement of gene expression related to fibrosis, which might provide a helpful link to potential therapies for ischemia reperfusion injury in kidney transplantation.

AKI biomarker research is a rapidly evolving field with fairly recent expansion into transplantation (29). The work by Nauta et al. (30) described the prognostic use of several biomarkers, including NGAL, kidney injury molecule-1 (KIM-1), albumin, and others, measured from 24-hour urine collections to predict changes in GFR and kidney allograft failure. All individual biomarkers that they examined predicted graft failure to a modest degree, although not independent from albuminuria. Importantly, however, biomarker excretion was measured, on average, 5–9 years after transplant; therefore, it likely represented chronic, ongoing allograft damage as opposed to AKI from ischemia reperfusion. We previously showed that urine KIM-1 immediately after transplant was not useful for predicting DGF and 3-month allograft function in the initial cohort of 91 recipients (11). Thus, we did not undertake KIM-1 measurements for the participants added to this cohort. Likewise, given the modest or complete lack of use for predicting DGF and 3-month allograft function using urine cystatin C and serum levels of NGAL, IL-18, and cystatin C (12,13), we did not pursue measurements of these potential biomarkers, urine albumin, or total protein levels to predict 1-year outcomes in the full cohort.

In contrast to biomarker assessment after transplant, the work by Hollmen et al. (31) showed the use of NGAL measurements before transplant in deceased donors. Urine NGAL was an independent risk factor for prolonged DGF and associated with significant differences in 1-year allograft survival. These data by Hollmen et al. support the idea that measurable early kidney injury is detrimental to subsequent renal function and that attempts to more precisely assess kidney injury as early as possible in the transplant process will improve clinical prognostic accuracy, as also suggested by our present findings.

Sample size is a key limitation to our study. We were unable to adjust for additional variables with logistic regression, perform many potentially important subgroup analyses, analyze the effect of combining biomarkers, and fully address interaction and effect modification between biomarkers, DGF, and the outcome. As with any observational study, additional confounding is possible, although we attempted to adjust for this confounding by blinded biomarker measurement and chart review for outcomes. Although there were no formal study indications for reinitiating dialysis during follow-up, we used a composite outcome that included estimates of GFR by objective serum creatinine measurement at 1 year. Our findings may not be generalizable to live-donor kidney transplantation, where ischemia reperfusion injury is less severe and urinary biomarker concentrations are lower (32,33). Additionally, the participating centers did not perform protocol transplant biopsies, limiting our ability to link biomarkers to histopathology, although that is our goal for future studies.

Based on these findings, noninvasive peritransplant kidney injury biomarker levels may be predictive of poor 1-year allograft function. Creatinine-based markers soon after transplant seem to be less useful for this purpose. As a possible clinical implication, urine biomarkers like NGAL and IL-18 could be considered for assessing allograft injury severity when deciding which recipients might benefit most from more frequent outpatient follow-up, protocol biopsies, or medical regimens aimed at sparing the allograft from additional injury and fibrosis (e.g., calcineurin inhibitor minimization or belatacept). Noninvasive biomarkers could also be considered for determining eligibility for clinical trials or tested as surrogate markers in clinical trials of new therapies intended to improve long-term outcomes after kidney transplantation.

Disclosures

None.

Acknowledgments

We thank Dr. Prasad Devarajan and Dr. Michael Bennett (University of Cincinnati College of Medicine) and Christine Simpson (Yale Center for Clinical Investigation) for their assistance in biomarker measurement.

The Donaghue Foundation Clinical and Community Health Grant and National Institutes of Health Grant F32 DK088395-01 (to I.E.H.) supported this research.

Part of these results was presented in abstract and oral presentation format at the American Transplant Congress Annual Meeting in May of 2011 (Philadelphia, PA).

Granting agencies had no role in study design, implementation, analysis, interpretation, or manuscript creation. I.E.H. and C.R.P. had full access to all data and take responsibility for the integrity and accuracy of all analyses. C.R.P. is coinventor on the IL-18 patent issued to the University of Colorado, which has not been commercialized.

Footnotes

  • Published online ahead of print. Publication date available at www.cjasn.org.

  • Received January 9, 2012.
  • Accepted April 23, 2012.

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Association between Peritransplant Kidney Injury Biomarkers and 1-Year Allograft Outcomes
Isaac E. Hall, Mona D. Doshi, Peter P. Reese, Richard J. Marcus, Heather Thiessen-Philbrook, Chirag R. Parikh
CJASN Aug 2012, 7 (8) 1224-1233; DOI: 10.2215/CJN.00310112
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