Serum cytokine profiles associated with early allograft dysfunction in patients undergoing liver transplantation

Authors

  • Benjamin H. Friedman,

    1. Penn Transplant Institute, Department of Surgery, University of Pennsylvania School of Medicine, Philadelphia, PA
    2. Division of Transplantation Immunology, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia/University of Pennsylvania School of Medicine, Philadelphia, PA
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  • Joshua H. Wolf,

    1. Penn Transplant Institute, Department of Surgery, University of Pennsylvania School of Medicine, Philadelphia, PA
    2. Division of Transplantation Immunology, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia/University of Pennsylvania School of Medicine, Philadelphia, PA
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  • Liqing Wang,

    1. Division of Transplantation Immunology, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia/University of Pennsylvania School of Medicine, Philadelphia, PA
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  • Mary E. Putt,

    1. Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA
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  • Abraham Shaked,

    1. Penn Transplant Institute, Department of Surgery, University of Pennsylvania School of Medicine, Philadelphia, PA
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  • Jason D. Christie,

    1. Department of Biostatistics and Epidemiology, University of Pennsylvania School of Medicine, Philadelphia, PA
    2. Division of Pulmonary, Allergy, and Critical Care Medicine, University of Pennsylvania School of Medicine, Philadelphia, PA
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  • Wayne W. Hancock,

    1. Division of Transplantation Immunology, Department of Pathology and Laboratory Medicine, Children's Hospital of Philadelphia/University of Pennsylvania School of Medicine, Philadelphia, PA
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  • Kim M. Olthoff

    Corresponding author
    1. Penn Transplant Institute, Department of Surgery, University of Pennsylvania School of Medicine, Philadelphia, PA
    • Penn Transplant Institute, Department of Surgery, University of Pennsylvania School of Medicine, 3400 Spruce Street, 2 Dulles Building, Philadelphia, PA 19104
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    • Telephone: 215-662-6136; FAX: 215-662-2244


  • This study was supported by funds from the Biesecker Center of the Children's Hospital of Philadelphia (to Wayne W. Hancock and Kim M. Olthoff) and the National Institute of Allergy and Infectious Diseases (grant 5-U01-AI-063589-05 to Jason D. Christie, Abraham Shaked, and Kim M. Olthoff).

Abstract

Early allograft dysfunction (EAD) occurring in the first week post-liver transplantation is associated with increased graft failure and mortality and is believed to be largely due to ischemia/reperfusion injury. We anticipated that the presence of EAD would be reflected by alterations in expression of serum proteins associated with an inflammatory response in the peri-operative period, and hypothesized that a specific pattern of expression might correlate with the development of EAD. The serum levels of 25 cytokines, chemokines, and immunoreceptors were measured by Luminex multiplex assays pre- and post-liver transplantation. Levels of each cytokine biomarker were compared in adult recipients with or without EAD at serial time points using samples collected pre-operatively and at 1, 7, 14, and 30 days post-transplant. EAD was defined according to standard criteria as maximum alanine transferase (ALT) or aspartate transferase (AST) levels on days 1-7 of >2000 U/ml, day 7 bilirubin level ≥10 mg/dl, or a day 7 international normalized ratio (INR) ≥1.7. Multivariable analyses showed that patients experiencing EAD had lower pre-operative IL-6 and higher IL-2R levels. Patients with EAD also showed higher MCP-1 (CCL2), IL-8 (CXCL8), and RANTES (CCL5) chemokine levels in the early post-operative period, suggesting up-regulation of the NF-kB pathway, in addition to higher levels of chemokines and cytokines associated with T cell immunity, including MIG (CXCL9), IP-10 (CXCL10) and IL-2R. These findings identify several possible biomarkers and pathways associated with EAD, that may guide future validation studies and investigation of specific cellular and molecular mechanisms of graft dysfunction. Furthermore, if validated, our findings may contribute to perioperative prediction of the occurrence of EAD and ultimately lead to identification of potential interventional therapies. Liver Transpl 18:166–176, 2012. © 2011 AASLD.

After deceased donor liver transplantation, 20% to 25% of the recipients develop early allograft dysfunction (EAD), a condition that is associated with significantly decreased graft and patient survival.1, 2 A current, simple definition of EAD was recently validated in a multicenter analysis, and EAD was characterized by early high aminotransferase levels, persistent cholestasis, and prolonged coagulopathy.2 Patients with 1 or more of these characteristics had a significant risk of graft loss or death in the first 6 months. Although the clinical parameters used to define EAD are usually indices of hepatocellular damage and synthetic impairment, the underlying mechanisms of EAD are still not clear.

The development of EAD is often thought to be secondary to ischemia/reperfusion (I/R) injury, which is associated with acute cellular damage, cell death, oxidative damage from the creation of reactive oxygen species, and a severe inflammatory response occurring within the liver.3-8 In addition, there are numerous clinical characteristics that may play a role. The extent of I/R injury may be related not only to the duration of the cold ischemia time but also to donor and recipient characteristics, including the graft quality, age, underlying illness, and surgical events. Although complex molecular events occur within the liver graft, these processes most likely result in the release of circulating proteins that can be measured in the peripheral blood. The overall state of the recipient also makes an important contribution to postoperative graft function, and the preoperative status of the intended recipient may be reflected by changes in the levels of inflammatory and/or immunologically associated proteins. Knowledge about serum biomarkers that are associated with poor graft function may promote our understanding of the innate molecular mechanisms underlying allograft dysfunction and posttransplant mortality due to graft failure. Such knowledge might also be useful in the development of new treatments for the prevention and therapy of EAD in liver transplant patients and new diagnostic approaches for the identification of patients or liver grafts at increased risk for developing EAD.

Several previous clinical studies have linked elevated perioperative or intraoperative cytokine production to increased rates of complications (eg, infection and rejection) in liver transplant patients, but none have studied the relationship of these patterns with EAD.7-9 We previously used Luminex assays to analyze serum cytokines and chemokines in lung transplant recipients, and we found that primary graft dysfunction was associated with significantly increased levels of monocyte chemoattractant protein 1 (MCP1) and interferon-inducible protein 10 (IP-10).10 In the current study, we measured the serum levels of 25 cytokines, chemokines, and immunoreceptors in liver allograft recipients. Our panel included proinflammatory cytokines involved in the activation of lymphocytes and neutrophils, anti-inflammatory and pleiotropic cytokines, and chemokines involved in the recruitment of neutrophils, lymphocytes, and monocytes.

Our primary goal was to assess the association of preoperative and postoperative cytokines and chemokines with the development of EAD, and our secondary goal was to assess temporal differences in these inflammatory agents between patients who developed EAD and patients who did not. We hypothesized that the presence of EAD would result in different levels of serum proteins in the perioperative period and that a specific pattern of expression would correlate with the development of EAD. Comparing patients who developed EAD and patients who did not, this report describes our preliminary findings of changes in serum proteins before and after liver transplantation. Although it is not possible to determine whether the acute changes seen in these serum proteins are results of dysfunction or markers of a pathogenic pathway leading to dysfunction, these explorations may provide clues to the mechanisms involved in EAD and lead to future validation and studies.

Abbreviations:

ALT, alanine aminotransferase; AST, aspartate aminotransferase; BMI, body mass index; CCL, chemokine (C-C motif) ligand; CXCL, chemokine (C-X-C motif) ligand; CXCR3, chemokine (C-X-C motif) receptor 3; EAD, early allograft dysfunction; GM-CSF, granulocyte-macrophage colony-stimulating factor; HCV, hepatitis C virus; IFN, interferon; IL, interleukin; INR, international normalized ratio; IP-10, interferon-inducible protein 10; I/R, ischemia/reperfusion; MCP1, monocyte chemoattractant protein 1; MIG, monokine induced by interferon-γ; MIP, macrophage inflammatory protein; NF-κB, nuclear factor kappa B; NS, not significant; RANTES, regulated upon activation, normal T cell expressed, and secreted; TNF-α, tumor necrosis factor α.

PATIENTS AND METHODS

Study Population

We performed a nested case-control study with a set of deceased donor liver recipient patients who displayed EAD criteria and underwent transplantation from June 2006 to July 2008. All 29 EAD cases from this time period were included, and they were population-matched with 44 controls to ensure balance in the ages of the recipients and donors, the hepatitis C virus (HCV) status, and the primary liver diseases. All recipients were initially placed on tacrolimus-based immunosuppression, and a steroid taper was begun postoperatively. Patients with preoperative renal dysfunction were also placed on mycophenolate mofetil.

As reported elsewhere,2 EAD in liver allograft recipients was defined with a validated clinical definition, which included 1 or more of the following: a maximum alanine aminotransferase (ALT) or aspartate aminotransferase (AST) level >2000 U/mL on posttransplant days 1 to 7, a bilirubin level ≥10 mg/dL on day 7, and an international normalized ratio (INR) ≥1.7 on day 7. Many EAD patients fit more than 1 of these criteria (Table 1).

Table 1. Patients With Different EAD-Defining Characteristics
EAD CriteriaPatients (%)
  • *

    Maximum level on postoperative days 1 to 7.

ALT/AST level >2000 U/mL*86
Bilirubin level ≥10 mg/dL10
INR ≥1.714
ALT/AST level >2000 U/mL* and bilirubin level ≥10 mg/dL7
ALT/AST level >2000 U/mL* and INR ≥1.73
Bilirubin level ≥10 mg/dL and INR ≥1.73
All 3 criteria3

Data Collection and Management

Informed consent for this study was obtained before transplantation, and it was approved by our institutional review board. Blood samples were obtained preoperatively on the day of transplantation and on days 1, 7, 14, 30, and 90 after transplantation. Because serum samples were not available for some patients for every time point, the numbers of EAD and non-EAD patients on days 1, 7, 14, 30, and 90 were lower than the overall numbers (Table 2). On average, there were 3.9 samples per non-EAD patient and 4.8 samples per EAD patient, and the overall average was 4.3 samples per patient. Serum was isolated from each blood sample and was stored at −80°C until its assessment by Luminex analysis. Clinical background variables describing the recipient and donor characteristics and operative data are presented in Table 3.

Table 2. Number of Serum Samples Available at Each Time Point for Comparisons of Non-EAD and EAD Patients
DaySerum Samples (n)
Non-EAD PatientsEAD Patients
04429
12522
72923
143324
302122
Table 3. Donor and Recipient Characteristics Before Surgery and in the First 7 Days After Surgery by the EAD Status
VariableNon-EAD Patients (n = 44)EAD Patients (n = 29)P Value
  • NOTE: P values >0.20 are designated as NS.

  • *

    The data are presented as medians and interquartile ranges.

  • Unknown for 2 patients, > IL at time of surgery.

  • Maximum level on postoperative days 1 to 7.

Donors   
 Age (years)*42 (24-55)47 (36-64)0.14
 BMI (kg/m2)*25.6 (22.7-29.1)28.1 (24.1-32.7)0.10
Recipients   
 Sex [n (%)]   
  Female9 (20%)8 (28%) 
  Male35 (80%)21 (72%)NS
 Race [n (%)]   
  White29 (66%)19 (65%) 
  Black11 (25%)8 (28%) 
  Other4 (9%)2 (7%)NS
 Age (years)*53 (47-60)57 (53-62)0.08
 MELD score*24.0 (19.0-27.5)23.0 (17.0-28.0)NS
 BMI (kg/m2)*25.7 (22.2-28.6)27.8 (25.6-29.3)0.031
 Ascites [n (%)]   
  Present30 (70%)16 (57%) 
  Absent13 (30%)12 (43%)0.045
 Primary diagnosis [n (%)]   
  HCV22 (50%)16 (55%) 
  Alcoholic cirrhosis10 (23%)3 (10%) 
  Cryptogenic cirrhosis5 (11%)3 (10%) 
  Primary sclerosing cholangitis3 (7%)2 (7%) 
  Nonalcoholic steatohepatitis1 (2%)2 (7%) 
  Other3 (7%)3 (10%)NS
 Cold ischemia time (minutes)*326 (279-408)366 (304-436)0.19
 ALT level*452 (248-760)2331 (1838-3043)<0.001
 AST level*622 (374-1156)3547 (2321-3848)<0.001
 Bilirubin level on day 7 (mg/dL)*1.60 (0.90-2.70)3.0 (1.50-6.70)0.010

Luminex Assays

A human 25-plex antibody bead kit (BioSource, Camarillo, CA) was used to measure the levels of 25 cytokines and chemokines in 50 μL of serum from each transplant patient at each available time point. The analytes included cytokines [interleukin-1β (IL-1β), IL-2, IL-4, IL-5, IL-6, IL-7, IL-8, IL-10, IL-12, IL-13, IL-15, IL-17, tumor necrosis factor α (TNF-α), interferon-α (IFN-α), IFN-γ, and granulocyte-macrophage colony-stimulating factor (GM-CSF)], cytokine receptors (IL-1RA and IL-2R or CD25), and chemokines [eotaxin or chemokine (C-C motif) ligand 11 (CCL11); IP-10 or chemokine (C-X-C motif) ligand (CXCL10); monokine induced by interferon-γ (MIG) or CXCL9; MCP1 or CCL2; macrophage inflammatory protein 1α (MIP1α) or CCL3; MIP1β or CCL4; and regulated upon activation, normal T cell expressed, and secreted (RANTES) or CCL5]. Data were collected with a Luminex 100 array reader (Luminex Corp., Austin, TX)

Statistical Analysis

Analyses were performed with R 2.9 (R Project for Statistical Computing). Demographic and clinical covariates at the baseline were described with either proportions or medians and interquartile ranges. Hypothesis tests comparing the distributions of these variables in EAD and non-EAD patients were performed with either a Wilcoxon signed-rank test or a chi-square test. The association between the preoperative cytokine levels and the EAD status was first assessed with logistic regression: each cytokine was dichotomized at its median into high and low levels. We then used random forest analysis to rank both the cytokine covariates and the clinical covariates as predictors of the EAD status.11 Random forest analysis takes repeated bootstrap samples to create training sets and fits a classification tree to these samples; the out-of-bag samples are used for the validation of the chosen model. Variables are ranked as potential predictors on the basis of importance scores, which reflect the frequency of their selection into the models along with the success of the models in the validation. Random forest analysis prevents overfitting, which can yield results with poor generalization in subsequent studies.12 Because random forest analysis does not yield a single interpretable model, to present our findings, we took 7 variables according to their importance in either the random forest analysis or initial univariate analyses and built a stepwise logistic regression model with the Akaike information criterion as the criterion for variable exclusion. For the 2 cytokines included in the final model, we assessed their association with the recipient body mass index (BMI) and the recipient age with the Spearman rank correlation, and we tested for differences in the distributions of the cytokines between patients with ascites and patients without ascites with a Wilcoxon rank-sum test and between patients with different diagnoses with a Kruskal-Wallis test. Differences in the distributions of the cytokines were compared between EAD and non-EAD patients at each time point with Wilcoxon rank-sum tests. A type 1 error of 0.05 was used as the criterion for statistical significance; because our study was exploratory, we did not make formal adjustments for multiple comparisons.

RESULTS

Patient Characteristics

Table 3 shows the donor ages and BMIs, the cold ischemia times of the deceased donor livers, the recipient characteristics at the baseline, and the posttransplant indicators of liver injury. EAD recipients had significantly higher BMI levels (P = 0.03) and were more likely to have at least 1 L of abdominal ascites (P = 0.045). Although these findings were not statistically significant, the ages of both the EAD recipients and their donor grafts appeared to be higher than those of the non-EAD recipients and their donor grafts. The cold ischemia time was 12% longer for the EAD patients versus the controls, but this difference likewise did not achieve statistical significance. The sex, race, primary diagnosis, and MELD score distributions were similar for the groups. The levels of ALT (P < 0.001), AST (P < 0.001), and bilirubin (P = 0.010) were significantly higher in EAD patients; this was expected because these criteria are part of the case definition.

Cytokine Levels

Table 4 demonstrates that the levels of a number of the cytokines were at or below the lower detection limit throughout much of the study. The cytokines for which 85% or more of the samples were at or below the lower detection limit included GM-CSF, IFN-α, IFN-γ, IL-13, IL-15, IL-17, IL-4, and IL-5.

Table 4. Lower Detection Limits for Cytokines and Fractions of All Assays at or Below the Limits
CytokineLower Detection Limit (pg/mL)Fraction of Assay at the Limit (%)
Eotaxin56
GM-CSF1285
IFN-α1289
IFN-γ1499
IL-102272
IL-12102
IL-131999
IL-152688
IL-171792
IL-2658
IL-424100
IL-51098
IL-61026
IL-71360
IL-83947
IL-1β1439
IL-1R300
IL-2R300
IP-1037
MCP1100
MIG42
MIP1α1261
MIP1β152
RANTES150
TNF-α277

Multivariate Analysis Indicates That Several Cytokines Are Preoperatively Associated With the Occurrence of EAD

We first examined the association of preoperative cytokine levels with the subsequent development of EAD. For the analysis, we removed IFN-γ and IL-4 from the 25 original cytokines because there was insufficient variation in their levels at the baseline, and we removed IL-1β because 13 values were not available. Table 5 indicates that in the initial univariate models of the dichotomized variables, IL-6 had an association with EAD: higher values of IL-6 were protective against the development of EAD. The odds ratio for developing EAD was 0.28 (95% confidence interval = 0.10-0.77) for patients with IL-6 levels above the median of 28 pg/mL instead of levels below the median.

Table 5. Univariate Association Between the EAD Status and the Preoperative Cytokine Levels
CytokineMedian (pg/mL)Odds Ratio (95% Confidence Interval)*P Value
  • NOTE: The cytokines were dichotomized at the median into high and low categories. The cytokines included in the stepwise modeling procedure are bolded. P values >0.20 are designated as NS.

  • *

    Odds of developing EAD versus no EAD with the high value versus the low value for each cytokine.

Eotaxin771.02 (0.40-2.61)NS
GM-CSF120.33 (0.10-1.14)0.08
IFN-α121.01 (0.26-3.96)NS
IL-10220.40 (0.13-1.27)0.12
IL-122130.93 (0.37-2.38)NS
IL-13191.52 (0.20-11.4)NS
IL-15260.62 (0.21-1.88)NS
IL-17170.54 (0.15-1.94)NS
IL-260.64 (0.25-1.66)NS
IL-5101.01 (0.16-6.46)NS
IL-6280.28 (0.10-0.77)0.01
IL-7131.95 (0.76-5.06)0.17
IL-8390.74 (0.74-1.17)NS
IL-1RA36221.48 (0.29-1.90)NS
IL-2R14132.36 (0.90-6.18)0.08
IP-10280.93 (0.37-2.38)NS
MCP15060.49 (0.19-1.29)0.15
MIG231.44 (0.55-3.77)NS
MIP1α120.98 (0.38-2.55)NS
MIP1β720.74 (0.29-1.90)NS
RANTES52471.86 (0.72-4.82)0.20
TNF-α20.56 (0.19-1.59)NS

The random forest analysis that used all the cytokine, demographic, and clinical variables as potential predictors ranked the recipient BMI and IL-6 most highly. In contrast, IL-10 and GM-CSF, which approached significance in the univariate models in Table 5, had very low rankings. For the stepwise model, we included the 7 variables from the random forest analysis with the largest importance scores. The candidates in the stepwise model included 5 cytokines (IL-6, IL-2R, RANTES, MCP1, and IP-10) along with the recipient BMI and the recipient age. (IL-10, IL-7, and GM-CSF were also included as potential predictors because of the evidence of a possible association in the univariate model [P < 0.20]). Variables were included in their dichotomized form (above the median versus below the median or, in the case of IL-10, above the level of detection versus at the level of detection). Table 6 shows that the final stepwise model included the recipient BMI, IL-6 level, and IL-2R level. In agreement with the univariate results, higher BMI values, higher preoperative IL-2R levels, and lower IL-6 levels were all associated with the development of EAD.

Table 6. Odds of Developing EAD According to Preoperative IL-6 and IL-2R Levels in the Multivariate Model
VariableOdds Ratio (95% Confidence Interval)*P Value
  • *

    Odds of developing EAD with a 1-unit increase in the BMI or with an increased value of IL-6 or IL-2R above the median versus values below the median.

BMI (kg/m2)1.15 (1.01-1.31)0.04
IL-6 (pg/mL)0.20 (0.06-0.64)0.01
IL-2R (pg/mL)3.77 (1.22-11.64)0.02

Table 7 further explores the association between the preoperative levels of IL-6 and IL-2R and patient characteristics that may be associated with the development of EAD. Lower levels of IL-6 were correlated with younger age in an association that approached significance (P = 0.05) and with the absence of ascites (P = 0.01). IL-6 also differed with the patient diagnosis (P = 0.027); patients with HCV tended to have the lowest levels of IL-6 at the baseline. Associations between IL-2R and patient characteristics did not achieve significance.

Table 7. Association of Preoperative Cytokines With Clinical Variables
VariableIL-6*P ValueIL-2R*P Value
  • NOTE: P values >0.20 are designated as NS.

  • *

    The data are presented as Spearman correlation coefficients for the BMI and age categories and as medians and interquartile ranges (pg/mL) for the ascites and primary diagnosis categories.

  • A test of Spearman correlation coefficients of 0 was used for the BMI and age categories, and a test of the equality of medians among the levels of the variables was used for the ascites and primary diagnosis categories.

BMI (kg/m2)0.065NS−0.01NS
Age (years)−0.230.050.10NS
Ascites    
 Present45.4 (19.1-253) 1360 (627-9418) 
 Absent21.2 (10-48.8)0.011413 (345-2381)0.12
Primary diagnosis    
 HCV19.2 (10.7-46.5) 1053 (403-3041) 
 Alcoholic cirrhosis38.1 (22.7-64.8) 1413 (555-3480) 
 Cryptogenic cirrhosis31.3 (19.5-87.8) 1639 (625-12,046) 
 Primary sclerosing cholangitis49.4 (27.4-65.9) 1910 (1165-9524) 
 Nonalcoholic steatohepatitis56.4 (36.4-174)0.032864 (1813-10,233)NS

Cytokines That Are Involved in Cellular Immune Responses and Cell Recruitment Are Increased in EAD

Because EAD by definition affects patients within the first week after liver transplantation, we expected that the levels of proinflammatory cytokines and chemokines would be increased in the sera of the EAD patients during this period. Figure 1 shows examples of proteins implicated in T cell responses and EAD. IL-2R, IP-10, MIG, and IL-7 have effects on T cell activation, chemoattraction, development, and survival. IL-2R trended toward higher levels in the EAD patients at all time points, and statistical significance was achieved on days 1, 7, and 14 (Fig. 1A). The chemokines IP-10 and MIG also displayed statistically significant differences on day 1 in the EAD patients versus the controls (P < 0.01; Fig. 1B,C). The IP-10 levels tended to be higher in the EAD patients across the study; the values on day 14 approached statistical significance (P = 0.07). As detailed in Table 4, more than half of the data were at the lower limits of detection, and there were a number of outliers in the non-EAD patients; the differences in IL-7 between the EAD and non-EAD patients approached significance at 90 days (P = 0.06; data not shown).

Figure 1.

Box plots of the serum levels of cytokines important to cellular immune responses by the day of study for EAD and non-EAD subjects: (A) IL-2R, (B) IP-10, and (C) MIG. Black squares represent medians, and boxes are interquartile ranges; circles represent outliers. P values <0.1 are displayed; note the natural logarithm scale.

Cytokines That Are Induced by Nuclear Factor Kappa B (NF-κB) Signaling Are Significantly Increased in EAD

Figure 2 displays the serum levels of 4 NF-κB–dependent cytokines. The levels of MCP1, RANTES, and IL-8 were significantly higher in the EAD patients versus the non-EAD patients on day 1 (Fig. 2A-C). Unlike the preoperative levels of IL-6, the postoperative levels of IL-6 were higher in the EAD patients versus the non-EAD patients on day 1, but the difference did not achieve significance (P = 0.07; Fig. 2D). The median IL-6 level was also higher in the EAD patients versus the non-EAD patients 2 weeks after transplantation, and the difference approached statistical significance (P = 0.09). The median serum levels of these cytokines tended to be higher on day 1 in the EAD patients versus the non-EAD patients. By day 7, the serum levels of MCP1, RANTES, IL-8 and IL-6 were no longer significantly different between the two groups.

Figure 2.

Box plots of the serum levels of NF-κB–stimulated chemokines whose levels were higher in EAD patients in the early postoperative period by the day of study for EAD and non-EAD subjects: (A) MCP1, (B) RANTES, (C) IL-8, and (D) IL-6. Black squares represent medians, and boxes are interquartile ranges; circles represent outliers. P values <0.1 are displayed; note the natural logarithm scale.

Association of Cytokines With Anti-Inflammatory Effects With EAD

Figure 3 shows 4 cytokines that were more abundant in the control patients. Figure 3A displays the serum levels of IL-1RA, an anti-inflammatory mediator that can block IL-1–mediated signaling.13 The serum IL-1RA levels were significantly higher in the EAD patients versus the non-EAD patients on days 1 and 14. IL-10 is another anti-inflammatory cytokine; its levels were marginally higher in the controls at the preoperative time point (Fig. 3B). The levels of GM-CSF and TNF-α were also higher in the sera of control patients (Fig. 3C,D). Unexpectedly, the level of TNF-α, a proinflammatory cytokine, was also significantly higher in the non-EAD patients on day 14 (P < 0.01).

Figure 3.

Box plots of the serum levels of cytokines that had anti-inflammatory effects or whose levels were higher in control patients by the day of study for EAD and non-EAD subjects: (A) IL-1RA, (B) IL-10, (C) GM-CSF, and (D) TNF-α. Black squares represent medians, and boxes are interquartile ranges; circles represent outliers. P values <0.1 are displayed; note the natural logarithm scale.

DISCUSSION

This study determined the preoperative and postoperative serum levels of peripheral blood cytokines, chemokines, and immunoreceptors in patients with or without EAD after liver transplantation. Correlations were found between the preoperative and postoperative expression of several cytokines and chemokines and the development of EAD, and several unique expression patterns of inflammatory and immunomodulatory proteins were identified.

Although it is often thought that EAD is mostly due to factors related to I/R injury in the graft, we also found an association with the preoperative levels of cytokines in the recipients who developed EAD after transplantation. Multivariate modeling identified a significant correlation between the development of EAD and the preoperative expression of IL-6. Lower levels of IL-6 were linked to a stronger likelihood of developing EAD, although it is unclear why patients had higher or lower levels of IL-6 before transplantation. Interestingly, studies in experimental I/R models have shown IL-6 to be hepatoprotective.14-16 In contrast, higher pretransplant IL-2R levels were a risk factor for the subsequent development of EAD and also correlated with postoperative serum elevations of IL-2R during EAD. These data suggest that a recipient's underlying illness and preoperative condition may contribute to the risk of developing EAD independently of the graft characteristics. The identification of high-risk recipients before transplantation would be clinically relevant because EAD is associated with increased morbidity and mortality. Although the correlation of preoperative IL-6 and IL-2 levels is interesting, further work with an independent validation population is needed to test the utility of these potentially predictive measures.

At several postoperative time points, the development of EAD correlated with higher levels of IL-2R and IL-7 (cytokines that are centrally involved in the activation or recruitment of host T cells) as well as IP-10 and MIG (chemokines whose signaling recruits T cells, natural killer cells, or B cells according to the biological context).17, 18 Hepatic I/R injury induces CD4-positive T cell infiltration, possibly via antigen-independent events, at least in some systems.19-21 The chemoattraction of chemokine (C-X-C motif) receptor 3 (CXCR3)–positive, CD4-positive T cells via IP-10 and MIG (both ligands for CXCR3) was observed after cold I/R in rat liver allografts.20 Likewise, studies in our laboratory using partial murine grafts and prolonged cold preservation times identified IP-10 as a potent signal leading to neutrophil and T cell recruitment and subsequent graft dysfunction.22 Therefore, these data support a concept established in previous studies: the expression of T cell–related chemokines in injured liver tissue is an early event that follows I/R injury. Determining whether the altered expression of these chemokines correlates with a true cellular infiltrate in EAD patients and determining the cellular makeup of such an infiltrate would require histopathology or cytometric studies of tissue biopsy samples, which were not possible in this study.

EAD was also associated with early postoperative increases in 4 proteins (MCP1, RANTES, IL-8, and IL-6) whose expression is driven by NF-κB activation. A link between NF-κB regulation and EAD was anticipated because the induction of the NF-κB–associated genes in Kupffer cells is a known early event after I/R injury and serves as a point of convergence for molecular signals inherent to many forms of chronic liver disease.23, 24 The activation of the NF-κB pathway in Kupffer cells leads to the expression of many cytokines and chemokines (including those identified in this analysis), which in turn induce the expression of many other inflammatory mediators in injured liver tissue.24, 25 During toxic liver injury and oxidative stress, the localized expression of MCP1 attracts monocytes and activated lymphocytes to injury sites.26 Likewise, IL-8 is secreted from Kupffer cells in response to I/R and causes sinusoidal neutrophil sequestration and resultant hepatocellular damage.27-30 Recently, a study of cytokine and chemokine responses in 26 patients after hepatectomy found that large increases in MCP1, IL-6, and IL-8 correlated with higher rates of postoperative complications (bile duct leaks, surgical site infections, and liver failure).31 Excessive activation of the NF-κB pathway may be detrimental to early liver allograft function after transplantation; alternately, NF-κB–regulated cytokines and chemokines may simply be markers of increased injury and diminished graft function.

Our study has several inherent limitations. Clearly, this descriptive case-control study is exploratory in nature; therefore, we cannot assign causality or mechanisms to our findings, nor can we recommend specific clinical interventions or actions on the basis of our conclusions. The sample size is the major limiting factor, and a prospective validation set is still needed for more definitive conclusions and for the confirmation of any clinical associations.

For the serum proteins that we have identified as showing significant changes associated with EAD, we are not able to state whether they represent the actual cause or effect of organ dysfunction or part of a new pathogenic process altogether. Further studies will be required to validate the idea that the identified factors are indeed part of a pathophysiological mechanism related to EAD. We used a previously validated and simple clinical definition of EAD based on clinical parameters during the first week after transplantation that correlate with worse outcomes.2 An assumption of this model is that EAD, which is due to I/R injury and donor/host characteristics, is a pathological process distinct from early acute rejection and HCV recurrence. We acknowledge that we are unable to truly distinguish between EAD, early acute rejection, and HCV recurrence without liver biopsy data; however, these events are highly unlikely to affect the parameters to the defined extent within the first week after the operation (except for hepatic artery thrombosis, which was excluded). In our center, it is relatively rare for patients to be biopsied within the first week, so biopsy data are not available for most patients.

We also recognize some limitations in the sample size and missed time points. We did not have serum samples for all patients at all time points. We caution that because the sample size changed across time, the power to detect a given effect size also changed. Negative results should be examined with caution, particularly for day 30, for which we had the smallest sample size. Also, many individual cytokine measurements were at or below the detectable minimum, and this led to skewed distributions for some of the cytokines, such as IL-10. Therefore, our inability to demonstrate differences in those mediators with 85% or more of the samples at or below the lower detection limit (GM-CSF, IFN-α, IFN-γ, IL-13, IL-15, IL-17, IL-4, and IL-5) should not be interpreted as evidence of a lack of pathophysiological involvement. There were differences in the clinical variables between the EAD and non-EAD groups, and we accounted for these in the multivariate analyses, which included preoperative clinical variables and cytokine levels. Because this is one of the first studies of cytokine levels associated with EAD in liver transplantation, we wanted to identify possible differences between patients with EAD and patients without EAD; for this reason, we did not make formal adjustments for multiple comparisons because doing so would have reduced our statistical power. However, because we assessed multiple biomarkers at numerous times, many hypotheses tests were performed, and there was a higher chance that some results were false positives. In a number of cases, patterns across time showed consistency, even when differences were statistically significant for only a subset of times, and this lends weight to our main conclusions. All patients were also on immunosuppressive therapy after transplantation, and this likely altered the expression of cytokines and chemokines; this is an effect that cannot be directly measured in humans in a controlled manner. Hence, this study was exploratory in nature, and larger studies that include more patients with EAD and graft failure are important for validating our findings.

Despite these limitations, this study provides evidence for several associations between specific inflammatory mediators and EAD that can guide future investigations. It is interesting that there were significant differences in the cytokine and chemokine profiles and associations with the clinical characteristics of EAD and non-EAD recipients both before and after transplantation because this highlights the dual importance of both donor and recipient factors. We also found that the majority of the cytokine and chemokine differences between the patients who developed EAD and the patients who did not occurred very early in the postoperative period; if these findings are validated, they may provide a brief window of opportunity for clinical decision making or interventions. However, we caution that our current studies are both exploratory and descriptive by design and are not intended to predict the clinical course or explain pathophysiological mechanisms. The molecules that we have identified may be promising targets for further investigation as mediators of liver injury after transplantation and may serve as potential clinical biomarkers for the prediction or early detection of EAD and the possibility of graft loss. The ultimate goal is the identification of potential pathways for therapeutic interventions so that we can prevent or minimize the development of EAD and thereby improve posttransplant graft function.

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