• Biomarkers;
  • calcineurin-inhibitor toxicity;
  • kidney injury;
  • nephrotoxicity


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Disclosure
  9. References

Chronic kidney disease (CKD) occurs frequently after liver transplantation (LT) and is associated with significant morbidity and mortality. Thus, there is a pressing need to identify characteristics and biomarkers diagnostic of CKD to enable early diagnosis allowing preemptive interventions, as well as mechanistic insights into the progression from kidney injury to irreversible kidney failure. We analyzed 342 patients who had baseline glomerular filteration rate (GFR) >60 at the time of LT and are now >3 years post-LT. Risk factors for post-LT CKD were compared between three different groups defined by current GFR: >90 (n = 40), 60–90 (n = 146) and <60 (n = 156) mL/min. Age, cyclosporine use and pre-LT GFR were independently associated with new onset CKD. A subset (n = 64) without viral/immune disease or graft dysfunction underwent multianalyte plasma proteomic evaluations for correlation with CKD. Plasma proteomic analysis of two independent cohorts, test (n = 22) and validation (n = 42), identified 10 proteins highly associated with new onset CKD. In conclusion, we have identified clinical characteristics and a unique plasma proteomic signature correlating with new onset CKD after LT. These preliminary results are currently being validated in a prospective, multicenter study to determine if this signature precedes the onset of CKD and resolves with early interventions aimed at preserving kidney function.


alanine aminotransferase


area under the ROC


blood urea nitrogen


chronic kidney disease


calcineurin inhibitor






cystatin C


estimated glomerular filtration rate


glomerular filteration rate


fatty-acid binding protein




kidney injury molecule-1


liver transplantation


multianalyte panel


neutrophil gelatinase-associated lipcalin


receiver operating curve


trefoil factor


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Disclosure
  9. References

Renal injury resulting in chronic kidney disease (CKD) remains a major issue in the success of liver transplantation (LT). The causes of post-LT CKD are multifactorial, including advanced age, diabetes, hypertension, hepatitis C virus, pre- and intraoperative kidney injury and calcineurin inhibitor (CNI)-induced nephrotoxicity. Within 12–24 months postoperatively, more than half of liver transplant recipients have stage III CKD glomerular filteration rate (GFR < 60) and up to 10% have stage IV (GFR < 30; Ref. 1). Regardless of the pathways that lead to CKD, an elevated serum creatinine, the hallmark of renal dysfunction, is a lagging indicator of renal injury in LT recipients. By the time serum creatinine is elevated, there is already significant and largely irreversible damage to native kidney tissue and function. However, serum creatinine is often the marker relied upon in isolation because biopsies of native kidneys are rarely performed and other measures of GFR, often creatinine-based, may be inaccurate in this population. Thus, the ability to consider interventions before progression of renal disease is impeded by the lack of early, sensitive and minimally invasive markers of renal injury.

Biomarkers have been proposed that are potentially more sensitive and specific compared to conventional metrics of kidney function. A significant amount of interest has recently been directed toward clinical applications of blood and urine proteomic biomarkers of acute kidney injury in the general population, such as cystatin C (CyC), neutrophil gelatinase-associated lipocalin (NGAL), interleukin-19 (IL-19), α1-microglobulin, β2-microglobulin, trefoil factor 3 (TFF-3) and fatty-acid binding proteins (FABPs), with significantly less focus on markers of chronic kidney disease, early or advanced (2,3). Preliminary studies have also suggested that some of these biomarkers can be extrapolated to LT recipients (4,5), whereas others have reasonably questioned whether these immune-based biomarkers of kidney transplant injury are associated with native kidney dysfunction in the context of LT (6). Therefore, the aims of this study were to identify clinical characteristics in conjunction with the discovery of plasma proteomic markers linked to new onset CKD after LT.

Materials and Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Disclosure
  9. References

Patient population

This study involved a stepwise approach in identifying and characterizing our LT population, with and without CKD, and subsequently performing proteomic analyses on subsets to determine markers of new onset CKD. First, our LT database was probed for all de novo LT recipients followed at our center for at least 3 years post-LT. Patients were excluded if they had abnormal renal function (GFR < 60) at the time of transplant, were less than 3 years posttransplant, or had received combined liver–kidney or had undergone retransplantation. These patients were consecutively seen in the outpatient LT clinics at Northwestern. Second, clinical characteristics, immunosuppressive therapies and laboratory values were collected to determine variables associated with the different stages of CKD (GFR >90, 60–90, <60). Third, we consecutively consented all patients from the larger group for proteomic testing who met further refined criteria: CNI monotherapy; no liver dysfunction or history of viral (hepatitis B or C) or autoimmune disease (autoimmune hepatitis, primary biliary cirrhosis and primary sclerosing cholangitis). This refined subset was specifically chosen to eliminate potential confounders (graft dysfunction and viral/immune disease) and thus select patients only differentiated by the presence or absence of CKD for the final proteomic analysis.

Plasma proteomic assays

In the refined test and validation subsets, multianalyte plasma proteomic panel analyses were performed using a proprietary Luminex Bead technology and assay platform (Rules Based Medicine, Austin, TX, USA) testing two different multianalyte panels (MAPs). For discovery, we used the Human Discovery MAP® v1.0 (189 proteins). To screen for known kidney injury molecules we used the Human Kidney MAP™ v1.0 (13 proteins). Of note, for all GFR estimates, the isotope dilution mass spectrometry (IDMS) reference measurement-modified (MDRD) equation was used. Informed consent was obtained at all stages and the study was approved by our institutional review board.

Statistical methods

Categorical and continuous variables were statistically compared using parametric (Chi-squared, t-test) and nonparametric (Fisher's exact test, Wilcoxon–Mann–Whitney test) tests as appropriate. For correlations between the results of the MAPs and CKD, two separate analyses were performed, using either GFR as a dichotomous measure (< or ≥60) or as a continuous measure. The advantages of dichotomous measure analyses are that they are the standard used in the field allowing for comparisons and used clinically to define CKD stages. The advantages of continuous metric analyses are that the renal function deteriorates in a continuous fashion over time and thus correlations made to a continuous metric are more likely representative of markers in clinical practice. The p < 0.05 was considered statistically significant.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Disclosure
  9. References

Three hundred and forty-two LT recipients with pretransplant estimated glomerular filtration rate (eGFR) >60 mL/min (calculated by the MDRD equation), all on either cyclosporine or tacrolimus, met inclusion/exclusion criteria. They were first categorized into three groups based on their mean eGFR >3 years post-LT (Table 1). Older age, lower pre-LT eGFR and the use of cyclosporine rather than tacrolimus were all statistically associated with more advanced stages of CKD post-LT (p < 0.0001).

Table 1.  Demographics of patients based on degree of CKD after LT
CharacteristicsGroup 1: eGFR >90 mL/min (n = 40)1Group 2: eGFR 60–90 mL/min (n = 146)1Group 3: eGFR <60 mL/min (n = 156)1p-Values
  1. 1The three groups are determined by the eGFR at the time of current analysis.

  2. 2Trough drug level (ng/mL) measured within a time window of <3 months of the time of current analysis. CyA = cyclosporin; eGFR = estimated glomerular filtration rate; LT = liver transplantation.

Mean eGFR pre-LT (mL/min)96.990.782.3<0.0001
Mean LT follow-up (yrs)
Age (mean)45.348.854.9<0.0001
Gender (female)23%25%35%0.0984
Race (Caucasian)65%74%80%0.1177
Weight (kg) mean77.583.385.70.0959
HTN (%)23%27%37%0.3292
DM (%)23%14%20%0.3292
CyA use (%)5%6%22%<0.0001
FK506 use (%)95%94%78%0.0005
Recent CyA trough28778750.13
Recent FK506 trough24.

A refined subset (n = 64, normal graft function and no viral/immune disease) of the 342 recipients was divided into two independent cohorts (22 test, 42 validation). Samples were independently collected for each set at two different times and tested in two separate assay runs over 6 months apart with the plasma proteomic Discovery and Kidney Injury MAPs. Table 2 displays the patient characteristics of both cohorts. Other than renal parameters as expected, the patients in the post-LT GFR <60 group were older (test and validation sets) and more recently transplanted (test set only). Other demographics or clinical parameters, such as the presence of hypertension and diabetes, were not different between the higher versus lower post-LT GFR groups.

Table 2.  Nonimmune nonviral subset characteristics
GroupGroup #1: CKD (GFR <60)1Group #2:control (GFR ≥ 60)1p-Values
  1. For continuous variables, we report mean and 95% confidence interval and for discrete variables we report counts in each class. Listed p-values reflect significances of the differences between the CKD and Control groups. For continuous traits, the p-values were calculated using the Mann–Whitney U test, whereas for binary traits (Sex and CNI) we used Fisher's exact test. ALT = alanine aminotransferase; BUN = blood urea nitrogen; CyA = cyclosporin; eGFR = estimated glomerular filtration rate; LT = liver transplantation; TAC = tacrolimus.

  2. 1The two groups are determined by the eGFR at the time of current analysis and plasma collection.

  3. 2Trough drug level (ng/mL) measured within a time window of <3 months of the time of current analysis and plasma collection.

Test setN = 14N = 8 
 eGFR pre-LT (mL/min)84.792.10.02
 Mean LT follow-up (years)3.5 ± 1.76.5 ± 2.50.003
 Age (years)60.0 ± 4.851.1 ± 6.80.01
 Sex (male, %male)7 (50%)5 (63%)0.67
 CNI (#TAC/CyA)13/16/20.5
 eGFR46.2 ± 4.379.3 ± 7.90.0001
 BUN26.4 ± 3.815.0 ± 2.80.0008
 Cr (mg/dL)1.53 ± 0.151.05 ± 0.150.001
 ALT (U/L)37.4 ± 7.426.9 ± 8.60.16
 Recent CyA trough273750.61
 Recent TAC trough24.15.20.39
Validation setN = 19N = 23 
 eGFR pre-LT (mL/min)
 Mean LT follow-up (yrs)6.5 ± 1.75.2 ± 1.20.3
 Age (years)62.5 ± 3.654.3 ± 3.60.002
 Sex (male, %male)10 (53%)17 (74%)0.2
 CNI (#TAC/CyA)17/221/21
 eGFR43.4 ± 5.676.7 ± 7.93e-8
 BUN26.3 ± 2.816.4 ± 2.52e-5
 Cr (mg/dL)1.77 ± 0.601.07 ± 0.086e-5
 ALT (U/L)30.9 ± 11.632.7 ± 9.10.5
 Recent CyA trough273810.16
 Recent FK506 trough24.05.30.11

For the plasma MAPs, two separate analyses were performed. The first analysis studied associations of MAPs with eGFR as a continuous measure. Analysis of continuous eGFR identified 10 proteins (Table 3) significantly associated with eGFR in both cohorts, of which four are present in the specific MAP kidney injury panel (CyC, α1-microglobulin, β2-microglobulin and TFF-3), and six are present only in the MAP discovery panel (fatty acid binding protein, chromogranin A, apolipoprotein CIII, IL-16, CD40 and factor VII). For the second analysis, a dichotomous status variable was defined based on eGFR: control (status = 0) for eGFR ≥60 and CKD (status = 1) for eGFR <60. The analysis of dichotomous status identified nine proteins significantly associated in both test and validation data (Table 4): α1-microglobulin, β2-microglobulin, factor VII, apolipoprotein CIII, apolipoprotein H, chromogranin A, CyC, IL-16 and TFF-3. Of note, plasma levels of previously identified acute kidney injury markers, kidney injury molecule-1 (KIM-1) and NGAL, were not different among the GFR groups.

Table 3.  Screening results for eGFR as a continuous measure
  1. We list the 10 proteins whose association with eGFR is significant at the level p < 0.05 in both Test and Validation cohorts. In column headings, meaning of the prefixes is as follows: cor, robust correlation; Z, Fisher Z statistic corresponding to the correlation; p, correlation p-value; q, estimate of the local false discovery rate (FDR). The suffix .T refers to the Test cohort, suffix .V refers to the Validation cohort and suffix .M refers to a combined (meta-) analysis. eGFR = estimated glomerular filtration rate; FABP = fatty-acid binding protein; TFF3 = trefoil factor 3.

FactorVII–0.45–0.54–2.1–3.8–4.20.0362.00E-042.90E-050.240.0044 0.00031 
Table 4.  Screening results for eGFR as a dichotomous measure (< or ≥60 mL/min eGFR)
  1. We list the nine proteins whose association with status is significant at the level p < 0.05 in both Test and Validation cohorts. In column headings, meaning of the prefixes is as follows: cor, robust correlation; Z, Fisher Z statistic corresponding to the correlation; p, correlation p-value; q, estimate of the local false discovery rate (FDR). The suffix .T refers to the Test cohort, suffix .V refers to the Validation cohort and suffix .M refers to a combined (meta-) analysis. IL 16 = interleukin 16; TFF3 = trefoil factor 3.

Factor VII0.550.422.
Apolipoprotein CIII0.570.382.
Chromogranin A0.480.
IL 160.430.4222.83.40.0480.00560.000720.260.0740.0056
Apolipoprotein H0.540.312.623.20.010.0480.00120.120.170.0085

We next used the test cohort to train a predictor of eGFR using the 10 associated proteins and age from the continuous analysis, as the associations between the marker proteins and eGFR were overall stronger than those in the dichotomous analysis (Tables 3 and 4). Because of the relatively small size of the test cohort, we used a gene voting predictor based on univariate linear models. Although this predictor may not have optimal accuracy, it is more robust as a predictor than, for example, multivariate regression-based predictors. A scatter plot of observed versus predicted eGFR in the Validation cohort is shown in Figure 1. The predicted eGFR explains 43% of the variance of the observed GFR (Pearson correlation 0.66, p-value 7e-7). The receiver operating curve (ROC) of the predictor is shown in Figure 1(B). Area under ROC (AUC) is 0.78. Because the variable (protein) selection for the predictor took into account the Validation data, the reported accuracy may be higher than if these 10 proteins were used to predict eGFR in an independent data set. To obtain a lower bound on prediction accuracy, we also trained the same predictor on Test data, with variable selection based on Test data only. The “pure-Test” predictor explains 33% of variance in the Validation cohort (p-value 4e-5, AUC 0.74). This proportion of explained variance and AUC can be considered a lower bound on the performance of the predictor in independent data. The protein markers listed in Tables 3 and 4 exhibit negative correlations with eGFR; the higher the eGFR, the lower the marker expression level measured.


Figure 1. (A) Observed (y-axis) versus predicted (x-axis) eGFR in the validation cohort. The predictor is trained on the test data. The predictor explains 43% of the variance of the observed eGFR. (B) Receiver operating curve of the predictor. AUC is 0.78.

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  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Disclosure
  9. References

From the clinical and biomarker analyses, we have made several observations regarding CKD after LT. We demonstrated that before transplantation, older age, cyclosporine use and the presence of even mild GFR impairment are associated with the development of late onset CKD (>3 years; Stage 3 or higher) after LT. Next, in a select population without the influence of confounding variables (graft inflammation, viral/autoimmune disease), we have discovered and made an initial validation of a potentially novel plasma proteomic CKD signature. Interestingly, values of well-accepted kidney injury biomarkers, KIM-1 and NGAL, did not correlate with CKD. Although these markers may signify the presence of acute kidney injury and if measured early post-LT could be predictive of future CKD, they do not appear to be useful as markers of the chronic or progressive state. Overall, this multianalyte signature should now be tested in a prospective clinical study to determine if it has any value as a predictive assay of early onset CKD that might allow for individualized therapeutic strategies.

The development of an elevated creatinine, particularly >1.5 mg/dL in 1 year, and diminished GFR have significant clinical implications for LT recipients. The relevance and significance of identifying predictive biomarkers for CKD is foreshadowed by the increasing prevalence of older LT recipients and the fact that the liver allocation system favors recipients with pre-LT renal dysfunction (Model for End Stage Liver Disease score). Our study demonstrates that these two factors (age and pre-LT eGFR) are highly associated with the development of new onset CKD. Even mildly diminished GFR (82.3 mL/min) at the time of LT was associated with later onset post-LT CKD. Moreover, GFR is likely to be overestimated in the pre-LT population. As such, these recipient subsets (older age and mildly diminished pre-LT GFR) are ideal patients to prospectively follow with biomarkers that could predict CKD and allow objective testing of early interventions to prevent progression. Finally, the data show that the use of cyclosporine was associated with more CKD than tacrolimus, similar to other reports suggesting more intense systemic vasoconstriction and hypertension with cyclosporine (7). However, we would caution making conclusions based on our work as cyclosporine was used in only a small percentage of our patients during an earlier era of transplantation and there was no correlation with drug exposure and CKD.

Kidney injury biomarkers have become an attractive measure of acute injury or early renal impairment for use in pharmaceutical drug trials and in the clinical management of patients at higher risk for renal disease. The vast majority of these markers, such as KIM-1 and NGAL, are elevated in acute tubular injury, contrast-induced nephrotoxicity or perioperative renal injury (2). However, there have been few reports of correlation with these molecules and the development of CKD (8). Although KIM-1 and NGAL might be elevated in renal allografts and affected potentially by both drug-induced and alloimmune/inflammatory injury, our observations indicate that they are not useful markers of chronic native kidney injury in LT recipients. Prospective studies determining the validity of these established acute kidney markers of early post-LT (intraoperative or immediately postoperative) in predicting subsequent CKD are needed, as they may only elevate early, decrease after the acute phase and be no longer useful in the chronic, progressive state that we studied in the present work.

An important question is whether any proteomic signature for CKD in LT recipients is a surrogate marker of decreased glomerular filtration or reflects the underlying physiology of CKD. In our case, both observations may be correct. Blood levels of low molecular weight proteins such as chromogranin A, CyC and the microglobulins are known to be GFR-dependent and yet they have been shown (e.g. children undergoing cardiovascular surgery or screening adults with CKD) to be significantly more sensitive markers than creatinine. In contrast, a study of Factor VII levels showed no differences between hemodialysis patients and normal controls and another study concluded it was a biomarker for preeclampsia (9). Levels of apoliprotein A1 were reported as lower in hemodialysis than control patients and this was linked to increased cardiovascular risk. TFF-3 is made in the kidney tubules and is markedly reduced in the urine of rats with acute kidney injury, although blood levels have not been studied in any system (5). Early urinary elevations of IL-16 were recently shown to predict delayed graft function in kidney transplants and there is no evidence that it is cleared by the kidney (10). Finally, an observational study of soluble CD40 levels in diabetic nephropathy patients showed no correlation with the decline of GFR (11).

Another question is whether elevations of any of these markers in the first year posttransplant will be predictive of future CKD. We are not making such a claim. The important contribution of this present work is establishing the proof of principle for a biomarker-based approach by identifying a candidate plasma proteomic signature for CKD in LT. A clinical trial with a prospective, serial monitoring study design is required to determine if it is also predictive of CKD. We also acknowledge that if our signature is not predictive of CKD, then its value relative to serum creatinine and eGFR may be minimal unless we prove it is more sensitive for early diagnosis as suggested already for several of our candidates.

Mechanistically, a number of the overexpressed proteins are established markers of kidney injury in other clinical situations, including CyC, α-1 microglobulin, β-2 microglobulin and TFF-3, but are now validated here in the LT population with CKD (3,12). Interestingly, other proteins not currently known and not present in the kidney injury MAP panel were also demonstrated to be highly statistically associated with CKD in the test set and confirmed independently in the validation set using the Discovery panel. CD40 may mediate inflammatory signals in renal tubular epithelial cells (13) and factor VII has been found to be elevated, among a number of other proteins, in chronic renal insufficiency (14). The associations found between IL-16, chromogranin A and CKD have not been previously established, although we speculate that these are related to the tissue inflammation and repair processes that are naturally linked to progressive kidney injury mechanisms. Apolipoprotein H, otherwise known as β-2-glycoprotein-1, has been previously detected in the urine in patients with various renal diseases and may help distinguish glomerular and tubular renal dysfunction (15). In the blood, apolipoprotein H binds and neutralizes negatively charged phospholipid macromolecules and is thought to protect from inappropriate activation of the coagulation cascade. Apolipoprotein CIII, also higher with inflammation, and FABP could be elevated due to reduced renal metabolism of these plasma proteins but they also have been linked to the hyperlipidemia associated with CKD (16,17). In fact, the association made here of two apolipoprotein family members as markers for CKD in LT is consistent with the recent findings for a third family member, apolipoprotein AI. In the only other biomarker study of CKD in LT, O’Riordan found elevated plasma levels of apolipoprotein AI using SELDI-TOF mass spectrometry though they did not validate their results in an independent cohort and SELDI-TOF is a difficult platform for clinical biomarker work (18). More recently, two papers demonstrated that either missense mutations (19) or single nucleotide polymorphisms in apolipoprotein AI (20) are highly correlated with Focal Segmental Glomerulosclerosis and CKD in African Americans. Note that in these genetic cases, the levels of the protein were low. Although there would have to be another genetic mutation in our Caucasian LT patients, it may explain why we do not find the protein elevated in our study. The mechanism of the link to CKD in the African American population with apolipopotein AI mutations is presently unknown but taken with our data suggests that some unifying mechanism of renal injury linked to apolipoprotein metabolism is involved.

This study has several limitations. Samples were not collected in a prospective fashion. Only one time “snapshot” samples were performed in the presence of CKD rather than before its onset. Thus, we cannot make any conclusions on their predictive or prognostic values. As this was a discovery study, it was our intent to identify candidate biomarkers for validation in future prospective studies of serial collections. We also recognize that patient characteristic data were retrospectively collected and only patients seen in our transplant clinics had biomarker assays performed, which are both subject to selection bias. However, we did not preselect the patients for study and only collected samples during consecutive follow-up visits. Finally, we cannot determine if the proteomic signatures for CKD are specifically related to CNI therapy versus other factors (age, preexisting renal disease, diabetes and hypertension) common in this population, although CNI nephrotoxicity is likely the major culprit. Distinguishing these causes would require renal biopsy which is rarely performed after LT (none in our cohorts) and would be difficult to interpret given the likelihood of multiple contributors to renal injury. However, we did exclude patients with viral or autoimmune disease as proteomic signatures may be affected by these etiologies and, importantly, makes inflammatory CKD related to viral or immune disease unlikely.

In summary, we report a candidate plasma proteomic signature for new onset CKD after LT. It should now be validated in larger, serial biomarker studies to determine if it is predictive of early and impending CKD after LT. In addition, therapeutic strategies (i.e. reduction/elimination of CNI therapy, angiotensin inhibitors and antifibrotic agents) aimed at protecting renal function in this population could be targeted at a specific population considered at highest risk for CKD based on clinical (age and pre-LT GFR) and protein signature characteristics. As such, we are currently conducting a multicenter study of CKD after LT addressing the issues of biomarker prediction of renal injury and applications for therapeutic interventions.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Disclosure
  9. References

This study was funded in part by the Molly Baber Research Fund (D.R.S., S.M.K., P.L., S.H. and T.M.), U19 AI063603–06 (D.R.S., S.M.K., P.L., S.H. and T.M.), U01 AI084146–01 (M.A., D.R.S., E.W., J.L., J.F. and S.G.) and U01 A1084146 Clinical Trials in Organ Transplantation, a collaborative clinical research project headquartered at the National Institute of Allergy and Infectious Diseases (M.A., D.R.S., E.W., J.L., J.F. and S.G.).


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Disclosure
  9. References

The authors of this manuscript have no conflicts of interest to disclose as described by the American Journal of Transplantation.


  1. Top of page
  2. Abstract
  3. Introduction
  4. Materials and Methods
  5. Results
  6. Discussion
  7. Acknowledgments
  8. Disclosure
  9. References
  • 1
    Gonwa TA, Mai ML, Melton LB, et al. End-stage renal disease (ESRD) after orthotopic liver transplantation (OLTX) using calcineurin-based immunotherapy: Risk of development and treatment. Transplantation 2001; 72: 19341939.
  • 2
    Haase M, Bellomo R, Devarajan P, Schlattmann P, Haase-Fielitz A. Accuracy of neutrophil gelatinase-associated lipocalin (NGAL) in diagnosis and prognosis in acute kidney injury: A systematic review and meta-analysis. Am J Kidney Dis 2009; 54: 10121024.
  • 3
    Yu Y, Jin H, Holder D, et al. Urinary biomarkers trefoil factor 3 and albumin enable early detection of kidney tubular injury. Nat Biotechnol28: 470477.
  • 4
    Biancofiore G, Pucci L, Cerutti E, et al. Cystatin C as a marker of renal function immediately after liver transplantation. Liver Transpl 2006; 12: 285291.
  • 5
    Niemann CU, Walia A, Waldman J, et al. Acute kidney injury during liver transplantation as determined by neutrophil gelatinase-associated lipocalin. Liver Transpl 2009; 15: 18521860.
  • 6
    Boudville N, Salama M, Jeffrey GP, Ferrari P. The inaccuracy of cystatin C and creatinine-based equations in predicting GFR in orthotopic liver transplant recipients. Nephrol Dial Transplant 2009; 24: 29262930.
  • 7
    Textor SC, Wiesner R, Wilson DJ, et al. Systemic and renal hemodynamic differences between FK506 and cyclosporine in liver transplant recipients. Transplantation 1993; 55: 13321339.
  • 8
    Ko GJ, Grigoryev DN, Linfert D, et al. Transcriptional analysis of kidneys during repair from AKI reveals possible roles for NGAL and KIM-1 as biomarkers of AKI-to-CKD transition. Am J Physiol Renal Physiol298: F1472F1483.
  • 9
    Dusse LM, Carvalho MG, Cooper AJ, Lwaleed BA. Plasma factor VII: A potential marker of pre-eclampsia. Thromb Res 127: e15e19.
  • 10
    Alachkar N, Ugarte R, Huang E, et al. Stem cell factor, interleukin-16, and interleukin-2 receptor alpha are predictive biomarkers for delayed and slow graft function. Transplant Proc 42: 33993405.
  • 11
    Lajer M, Tarnow I, Michelson AD, et al. Soluble CD40 ligand is elevated in type 1 diabetic nephropathy but not predictive of mortality, cardiovascular events or kidney function. Platelets 21: 525532.
  • 12
    Vincent C, Dennoroy L, Revillard JP. Molecular variants of beta 2-microglobulin in renal insufficiency. Biochem J 1994; 298(Pt 1): 181187.
  • 13
    Laxmanan S, Datta D, Geehan C, Briscoe DM, Pal S. CD40: A mediator of pro- and anti-inflammatory signals in renal tubular epithelial cells. J Am Soc Nephrol 2005; 16(9): 27142723.
  • 14
    Shlipak MG, Fried LF, Stehman-Breen C, Siscovick D, Newman AB. Chronic renal insufficiency and cardiovascular events in the elderly: Findings from the Cardiovascular Health Study. Am J Geriatr Cardiol 2004; 13: 8190.
  • 15
    Flynn FV, Lapsley M, Sansom PA, Cohen SL. Urinary excretion of beta 2-glycoprotein-1 (apolipoprotein H) and other markers of tubular malfunction in “non-tubular” renal disease. J Clin Pathol 1992; 45: 561567.
  • 16
    Chan DT, Dogra GK, Irish AB, et al. Chronic kidney disease delays VLDL-apoB-100 particle catabolism: Potential role of apolipoprotein C-III. J Lipid Res 2009; 50: 25242531.
  • 17
    Pelsers MM. Fatty acid-binding protein as marker for renal injury. Scand J Clin Lab Invest Suppl 2008; 241: 7377.
  • 18
    O’Riordan A, Johnston O, McMorrow T, et al. Identification of Apolipoprotein AI as a serum biomarker of chronic kidney disease in liver transplant recipients, using proteomic techniques. Proteomics Clin Appl 2008; 2: 13381348.
  • 19
    Tzur S, Rosset S, Shemer R, et al. Missense mutations in the APOL1 gene are highly associated with end stage kidney disease risk previously attributed to the MYH9 gene. Hum Genet 2010; 128: 345350.
  • 20
    Genovese G, Friedman DJ, Ross MD, et al. Association of trypanolytic ApoL1 variants with kidney disease in African Americans. Science 2010; 329: 841845.