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Keywords:

  • Hepatitis C virus;
  • liver transplantation

Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References

In the nontransplant setting diabetes mellitus is a risk factor for disease progression in patients with chronic hepatitis C virus (HCV) infection. The impact of early insulin resistance on the development of advanced fibrosis, even in the absence of clinically apparent diabetes mellitus, is not known.

Our aim was to determine whether the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) can be used to identify insulin-resistant patients at risk for rapid fibrosis progression.

Cohort study including patients transplanted for chronic HCV between January 1, 1995 and January 1, 2005.

One hundred sixty patients were included; 25 patients (16%) were treated for diabetes mellitus and 36 patients (23%) were prediabetic, defined as HOMA-IR >2.5.

Multivariate Cox regression analysis showed that insulin resistance (hazard ratio (HR) 2.07; confidence interval (CI) 1.10–3.91, p = 0.024), donor age (HR 1.33;CI 1.08–1.63, p = 0.007) and aspartate aminotransferase (HR 1.03;CI 1.01–1.05, p < 0.001) were significantly associated with a higher probability of developing advanced fibrosis, i.e. Knodell fibrosis stage 3 or 4, whereas steatosis (HR 0.94;CI 0.46–1.92, p = 0.87) and acute cellular rejection (HR 1.72;CI 0.88–3.36, p = 0.111) were not.

In conclusion, posttransplant insulin resistance is strongly associated with more severe recurrence of HCV infection. HOMA-IR is an important tool for the identification of insulin resistance among patients at risk for rapid fibrosis progression after liver transplantation for HCV.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References

Chronic hepatitis C infection is one of the leading causes of liver disease in the United States and is the most common indication for liver transplantation in the Western world. Population-based studies have shown that diabetes mellitus promotes fibrosis progression in the liver and is associated with the development of hepatocellular carcinoma (HCC) (1), particularly in patients who have concomitant hepatitis C virus (HCV) infection (2,3). Furthermore, diabetes mellitus has been shown to be a risk factor for graft loss (4) and mortality (5) in liver transplant patients with chronic HCV infection.

The impact of early insulin resistance on the subsequent development of advanced hepatic fibrosis, even in the absence of clinically apparent diabetes mellitus, is not known. It is possible that posttransplant insulin resistance, exacerbated by weight gain and immunosuppressive agents might promote fibrosis progression even before diabetes mellitus is manifest.

HCV infection may contribute to posttransplant insulin resistance through effects of HCV on insulin signaling on a molecular level. Proteins such as insulin receptor substrates mediate signaling of the insulin receptor. Tyrosine phosphorylation of the insulin receptor substrate by insulin is a crucial step in insulin action (6). Interestingly, in a mouse-model harboring the HCV-core gene, this step in the insulin pathway is affected (7).

Disturbances in insulin action and sensitivity may affect different pathways eventually leading to liver fibrosis (8). One of the most important features of insulin resistance is impaired suppression of hepatic glucose production (9). Loss of control over hepatic glucose output leads to hyperglycemia and compensatory hyperinsulinemia, which in turn may exert mitogenic and proliferative effects on hepatic cells (10,11).

In contrast to hepatic glyconeogenesis, pathways involved in hepatic lipogenesis initially remain insulin sensitive (12,13). However, hyperinsulinemia may stimulate expression of tumor necrosis factors-alpha (TNFα) and interleukin (IL)-6, leading to increased lipid synthesis and hepatic steatosis (14,15).

Although several animal studies have shed light on some of the mechanisms underlying the effects of the metabolic syndrome on liver fibrogenesis, the relative effect of changes in insulin levels, lipid metabolism and levels of hormones such as leptin and adiponectin and cytokines such as TNFα and IL-6 on fibrosis progression in patients with hepatitis C infection remains to be elucidated (16).

In this study, we investigated whether the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR) can be used to identify insulin-resistant patients at risk for rapid fibrosis progression after liver transplantation for chronic hepatitis C. We also studied whether serum adipokines and cytokines are of any additional value in identifying patients at risk for developing advanced fibrosis or cirrhosis after liver transplantation for hepatitis C.

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References

Design: Single-center cohort study

Participants: Consecutive chronic hepatitis C patients who underwent liver transplantation between January 1, 1995 and January 1,2005 in the Mayo Clinic in Rochester, MN. Patients entered the study at 4 months after liver transplantation. The study protocol was approved by the Institutional Review Board of the Mayo Clinic and was carried out in accordance with institutional guidelines. All participating patients gave informed consent.

Data assembly: Data were obtained on patient demographics (gender, ethnicity, age at transplantation), anthropomorphics (height, weight), donor demographics (donor age) and transplant procedure (cold and warm ischemic time). The body mass index (BMI) was calculated as weight divided by height squared. Patients underwent dual energy x-ray absorptiometry to assess body composition before transplantation. By employing two different x-ray energy sources, dual energy x-ray absorptiometry allows discrimination of two substances, in this case fat and lean tissue, and enables quantitation of the percentage body fat (17). Treatment of hepatitis C (treatment duration and response to treatment) was documented.

Analytical procedures: Biochemical data (glucose, insulin, cholesterol, triglycerides, high density lipoprotein [HDL], leptin, adiponectin, TNFα, IL-6, creatinine, bilirubin, sodium, aspartate aminotransferases (AST), alanine aminotransferase [ALT]), hematological data (hemoglobin, platelet count, international normalized ratio [INR]) and virological data (genotype, viral load), were measured in the certified Mayo Clinic laboratories.

Levels of hormones, cytokines and adipokines that had not been routinely measured at baseline, were measured in stored serum samples. Previous large cohort studies have shown that these measurements are valid after long-term storage (18,19).

The HOMA-IR was calculated using the following formula: [fasting insulin (μU/mL) × glucose (mmol/L)/22.5]. Glucose in mg/dL was multiplied by 0.055 to convert to mmol/L.

The Mayo Model for End-Stage Liver Disease (MELD) score was calculated as previously described (3.8 × log(bilirubin) + 11.2 × log(INR) + 9.57 × log(creatinine) + 6.43) (20).

The evaluation of HOMA-IR, cytokines and adipokines was at 4 months posttransplant, because at this time point it is still early enough for patients not having developed significant graft fibrosis and it is late enough for the largest acute effects of the transplantation procedure on homeostasis to have resolved. In addition, we also evaluated the effect of pretransplant diabetes mellitus on fibrosis progression.

Follow-up:  Liver biopsies were routinely performed at 1, 3 and 5 years after transplantation on a protocol basis regardless of biochemical profile and also when clinically indicated. Liver biopsies were scored by experienced pathologists, using the Knodell fibrosis score (21). In addition, steatosis was graded 0 to 3 as follows: grade 0 (none/minimal) representing <5%, grade 1 (mild) representing 5% to 33%, grade 2 (moderate) representing >33–66%, and grade 3 (severe) representing >66% (22).

Statistics:  Baseline characteristics were compared using Mann–Whitney and chi-square tests.

Independent samples t-tests were used to compare mean fibrosis stage at year 1, 3 and 5. In order to avoid bias, missing values were substituted by the previous value in the analysis presented in Figure 1.

image

Figure 1. Mean fibrosis stage at 1, 3 and 5 years after liver transplantation, for patients with normal insulin sensitivity and for insulin-resistant patients.

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The Kaplan–Meier method was used to estimate the effect of insulin resistance on occurrence of fibrosis over time. In this analysis, patients were classified according to the presence of insulin resistance (no insulin resistance vs. insulin-resistant, i.e. HOMA-IR >2.5 or treated for diabetes mellitus) at 4 months after transplantation.

Univariate and multivariate Cox regression analyses were applied to assess risk factors for the development of steatosis in the allograft. In addition, univariate and multivariate Cox regression analyses were applied to assess risk factors for the development of advanced fibrosis i.e. Knodell fibrosis stage 3 or 4. All of the following factors were evaluated in univariate Cox regression analysis: Age, gender, donor age, BMI, body fat percentage, insulin, glucose, HOMA-IR, IGF1, leptin, adiponectin, TNFα, IL-6, cholesterol, triglycerides, HDL cholesterol, albumin, bilirubin, INR, AST, ALT, creatinine, warm ischemia time, cold ischemia time, tacrolimus level, cyclosporine level, prednisone dose, HCV RNA, genotype, rejection.

Subsequently, a multivariate model was built, using variables that were significantly associated with progression to advanced fibrosis in univariate analysis, with a p-value <0,15. The variable ‘steatosis’ was added to the multivariate model because of the association with fibrosis progression and insulin resistance in previous studies. Steatosis was modeled as a time-dependent covariate to represent the ability of patients to change their steatosis score over time. Forward and backward stepwise analyses were used to determine the multivariate model with the best fit.

The model including the variables insulin resistance, donor age, AST, steatosis and acute cellular rejection, provided the best fit to the data. Importantly, insulin resistance was statistically significantly associated with development of advanced fibrosis in all multivariate models that were evaluated.

Since previous studies have shown that peginterferon treatment may influence fibrosis progression, we repeated the multivariate Cox regression analysis with and without peginterferon treatment as a time-dependent covariate. Including peginterferon treatment in the analysis did not change the results.

The results are reported as hazard ratios with 95% confidence intervals. The reported hazard ratios are the relative increases in hazard associated with increases of 10 years for the covariate donor age, 10 U/L for aspartate aminotransferases and one stage for steatosis. Multiple logistic regression analysis was used to assess which covariates were associated with insulin resistance. In this analysis, insulin resistance was defined as a dichotomous variable (yes/no): both patients with HOMA-IR >2.5 and patients receiving treatment for diabetes mellitus were defined as insulin-resistant. First, all of the following factors were evaluated in univariate logistic regression analysis: Age, gender, donor age, BMI, body fat percentage, IGF1, Leptin, Adiponectin, TNFα, IL-6, cholesterol, triglycerides, HDL-cholesterol, albumin, bilirubin, INR, AST, ALT, creatinine, warm ischemia time, cold ischemia time, tacrolimus level, cyclosporine level, prednisone dose, HCV RNA, genotype, rejection. Subsequently, a multivariate model was built, using variables that were significantly associated with insulin resistance in univariate analysis, with a p-value <0.15. The reported odds ratios are the relative increases in odds associated with increases of 1 g/dL for albumin, 10 ng/mL for leptin, 10 mg for mean prednisone and 1 kg/m2 for BMI. The results are reported as odds ratios with 95% confidence intervals.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References

Between January 1, 1995 and January 1, 2005, 220 liver transplantations were performed for chronic hepatitis C. We included all 160 patients who were alive with their first graft at 4 months after transplantation and who gave informed consent for their data to be used in the study. Of the 60 patients who were not included, 34 had graft failure within 4 months after liver transplantation, 4 did not give informed consent and 21 had undergone retransplantation because of graft failure.

Insulin resistance: At baseline, 61 patients (38%) had insulin resistance: 25 patients (16%) were being treated for diabetes mellitus and 36 patients (23%) were prediabetic, defined as HOMA-IR above 2.5, while not receiving treatment for diabetes mellitus.

Of the 25 patients who were treated for diabetes mellitus, 11 had new-onset posttransplant diabetes mellitus and 14 patients had a diagnosis of diabetes mellitus before transplantation. All patients with insulin resistance who had a medical need were managed with insulin. None were treated with insulin sensitizing agents.

There was no difference in progression to advanced fibrosis between patients with pretransplant diabetes mellitus and patients who developed diabetes mellitus within 4 months of transplantation: hazard ratio (HR) 0.87 (confidence interval (CI) 0.25–3.00, p = 0.83).

Insulin-resistant patients had higher serum leptin levels, lower serum albumin levels, higher bilirubin levels and higher transaminases than patients with normal insulin sensitivity. Furthermore, prediabetic patients had received higher prednisone doses during the first month after transplantation than patients without insulin resistance (45 ng/mL (interquartile range (IQR) 18–96) vs. 20 ng/mL (IQR 15–70)) (Table 1).

Table 1.  Baseline characteristics at month 4 after transplantation1
 No insulin resistance n = 99Insulin- resistantp-Value4 (Mann– Whitney/chi-square)
Prediabetic2 n = 36Diabetes mellitus3 n = 25
  1. 1Continuous variables are expressed as median (interquartile range).

  2. 2Prediabetic = patients not receiving antidiabetic treatment, but with HOMA-IR >2.5.

  3. 3Diabetes mellitus = patients receiving antidiabetic treatment.

  4. 4For noninsulin-resistant versus insulin-resistant (prediabetic or diabetes mellitus).

  5. 5Glycosylated hemoglobin was not routinely measured in noninsulin-resistant patients. The p-value is given for prediabetic patients versus patients with diabetes mellitus.

  6. 6Genotype was measured in 77% of the patients.

Age, years50 (45–56)51 (44–54)53 (47–60)0.71
Donor age, years41 (24–54)49 (35–59)53 (37–60)0.031
Male gender (%)69 (70)26 (72)17 (68)0.93
Body mass index, kg/m227 (24–31)29 (26–37)27 (23–30)0.51
Body fat percentage before OLT30 (23–37)29 (23–38)32 (27–38)0.68
Insulin, μU/mL4.9 (3.8–6.9)11.1 (9.6–14.7)8.6 (5.2–12.3)<0.001
Glucose, mg/dL100 (91–111)128 (104–175)111 (93–164)<0.001
Homeostasis Model Assessment of Insulin Resistance1.2 (0.9–1.7)3.5 (2.8–4.7)2.2 (1.2–4.7)<0.001
Glycosylated hemoglobin6.7 (5.3–9.2)7.6 (6.2–9.4)0.1555
Insulin like growth factor 1, ng/mL213 (136–322)156 (89–294)211 (135–353)0.23
Leptin, ng/mL8.1 (4.0–13.5)14.1 (8.3–25.0)12.3 (8.9–20.7)0.002
Adiponectin, μg/mL11.3 (7.3–16.6)13.7 (6.8–19.2)10.9 (9.0–20.9)0.178
TNFα, pg/mL2.0 (1.3–2.9)1.7 (1.5–2.5)2.2 (1.4–2.9)0.95
IL-6, pg/mL4.9 (2.5–15.0)4.8 (2.9–7.2)5.6 (4.5–15.0)0.94
Cholesterol, mg/dL166 (134–199)149 (121–167)170 (150–215)0.54
Triglycerides, mg/dL126 (93–193)164 (102–221)131 (108–185)0.21
High-density lipoprotein, mg/dL43 (36–49)36 (26–46)46 (31–59)0.23
Albumin, g/dL4.0 (3.6–4.2)3.8 (3.4–3.9)3.7 (3.2–4.1)0.002
Bilirubin, mg/dL0.8 (0.6–1.4)1.2 (0.8–2.3)0.8 (0.7–1.5)0.022
International normalized ratio1.0 (0.9–1.0)1.0 (0.9–1.1)1.0 (0.9–1.0)0.64
Aspartate aminotransferase, U/L47 (31–106)111 (55–187)81 (35–153)0.003
Alanine aminotransferase, U/L73 (41–133)149 (62–215)80 (42–254)0.015
Creatinine, mg/dL1.3 (1.1–1.6)1.2 (1.1–1.5)1.2 (1.1–1.6)0.28
Genotype (%)6   0.177
 153 (54)23 (64)16 (64) 
 211 (11)3 (8)0 (0) 
 310 (10)1 (3)2 (8) 
 41 (1)1 (3)2 (8) 
Warm ischemia, min44 (34–63)42 (32–58)48 (38–58)0.58
Cold ischemia, min435 (379–506)472 (390–540)453 (375–535)0.163
Mean tacrolimus level first month, ng/mL9.8 (8.6–11.1)9.9 (8.1–11.1)8.9 (7.5–10.7)0.33
Mean cyclosporin level first month, ng/mL218 (95–275)272 (147–290)288 (210–345)0.111
Mean prednisone dose first month, mg/day20 (15–70)45 (18–96)25 (14–62)0.009
Hepatitis C virus ribonucleic acid, 1106 IU/mL4.2 (0.4–8.3)7.2 (1.2–30)3.4 (0.4–6.7)0.67
Alcohol as contributory factor for liver disease (%)36 (36)13 (36)7 (28)0.73
Hepatocellular carcinoma pretransplantation (%)29 (29)12 (33)10 (40)0.58
Acute cellular rejection (%)29 (29)8 (22)7 (28)0.82

Multiple logistic regression analysis showed that lower serum albumin levels (OR 0.22; CI 0.09–0.58, p = 0.002) were statistically significantly associated with the presence of insulin resistance at 4 months after transplantation. In addition, there was a strong trend toward higher prednisone doses during the first month after liver transplantation (odds ratio [OR] 1.10; CI 1.00–1.20, p = 0.052) and a trend toward higher leptin levels (OR 1.28; CI 0.94–1.75, p = 0.116). The association with BMI was not statistically significant (OR 1.01; CI 0.94–1.10, p = 0.72).

Steatosis: During follow-up, 62 patients (39%) developed mild-to-severe steatosis of the allograft. Multivariate Cox regression analysis showed that genotype 3 (HR 2.99; CI 1.00–8.87, p = 0.049), insulin resistance (HR 2.16; CI 1.09–4.25, p = 0.026) and BMI (HR 1.09; CI 1.02–1.17, p = 0.008) were statistically significantly associated with the development of steatosis over time, whereas serum leptin was not (HR 1.08; CI 0.89–1.32, p = 0.44).

Fibrosis progression

Figure 1 shows the mean fibrosis scores at 1, 3 and 5 years after liver transplantation for insulin-resistant patients and for patients with normal insulin sensitivity. At year 1, mean fibrosis stages were 1.03 versus 0.73 (p = 0.044), at year 3 mean fibrosis stages were 1.66 versus 1.14 (p = 0.011) and at year 5 mean fibrosis stages were 1.78 versus 1.28 (p = 0.017) for insulin-resistant patients compared to patients with normal insulin sensitivity.

The median duration between OLT and the last liver biopsy that was taken, was 3.4 years (IQR 2.0–5.8) for insulin-resistant patients and 3.0 years (IQR 1.1–5.2) for patients with normal insulin sensitivity (p = 0.21). The mean fibrosis progression rates over these time periods were 0.87 units/year (SD 0.73) for insulin-resistant patients compared to 0.51 units/year (SD 1.11) for patients with normal insulin sensitivity (p = 0.015).

Figure 2 shows Kaplan–Meier curves demonstrating the proportion of patients with fibrosis stage 1 (Figure 2A), fibrosis stage 2 (Figure 2B), fibrosis stage 3 (Figure 2C) and fibrosis stage 4 (Figure 2D) over time.

image

Figure 2. Kaplan–Meier curves showing the proportion of patients progressing to Knodell fibrosis stage 1 (A), stage 2 (B), stage 3 (C) and stage 4 (D), over time. Please note that follow-up starts at 4 months after liver transplantation.

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During follow-up, 42 patients (26%) developed advanced fibrosis i.e. Knodell fibrosis score 3 or 4. The risk of developing advanced fibrosis over time was higher in insulin-resistant patients than in patients with normal insulin sensitivity at month 4 (5-year occurrence 43.0%; CI 26.6–59.4 vs. 20.6% CI 10.0–31.2, p = 0.016). In addition, there was a trend for more rapid progression toward advanced fibrosis for patients with pretransplant diabetes mellitus compared to patients without pretransplant diabetes mellitus (5-year occurrence 67.0%[CI 56.0–78.0] vs. 54.2%[CI 23.2–85.2], log rank p = 0.148).

Neither leptin, adiponectin, IGF-1, TNFα or IL-6 were associated with the development of advanced fibrosis in univariate Cox regression analysis. Univariate Cox regression analysis including only patients with insulin resistance showed that there was no statistically significant effect of glycosylated hemoglobin levels on the development of advanced fibrosis (HR 0.94; CI 0.77–1.15, p = 0.56).

Time-dependent Cox regression analysis of the whole cohort showed that insulin resistance (HR 2.10; CI 1.09–4.04, p = 0.022), donor age (HR 1.38; CI 1.10–1.74, p = 0.005) and aspartate aminotransferase (1.03; CI 1.01–1.05, p = 0.001) were statistically significantly associated with a higher probability of developing advanced fibrosis. The associations with acute cellular rejection (HR 1.75; CI 0.88–3.45, p = 0.110) and steatosis (HR 0.88; CI 0.42–1.86, p = 0.73) were not statistically significant (Table 2).

Table 2.  Time-dependent Cox regression analysis assessing risk factors for the development of advanced fibrosis
 Hazard ratio95% Confidence intervalp-Value
LowerUpper
  1. In this analysis, insulin resistance was defined as a dichotomous variable (yes/no): both patients with HOMA-IR >2.5 and patients receiving treatment for diabetes mellitus were defined as being insulin-resistant.

  2. The reported hazard ratios are the relative increases in hazard associated with increases of 10 years for the covariate donor age, 10 U/L for aspartate aminotransferases and 1 stage for steatosis.

Insulin resistance2.071.103.910.024
Donor age1.331.081.630.007
AST1.031.011.05<0.001 
Steatosis0.940.461.920.87
Acute cellular rejection1.720.883.360.111

Overall survival did not vary with normal insulin sensitivity (5-year survival 85.5% CI 77.5–93.5 vs. 78.1%[no insulin resistance vs. insulin resistance] CI 66.1–90.1, log rank p = 0.37). This was also true for graft survival (log rank p = 0.67, Figure 3).

image

Figure 3. Kaplan-Meier curves illustrating posttransplant graft survival for patients with and without insulin resistance are shown. The difference in graft survival was not statistically significant.

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Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References

Our study shows that not only patients treated for diabetes mellitus, but also prediabetic patients with an elevated HOMA-IR are at risk for rapid fibrosis progression after liver transplantation for hepatitis C.

The mechanism by which insulin resistance leads to increased allograft fibrosis is not fully known. One explanation may be that metabolic changes associated with insulin resistance, such as hyperlipidemia and hyperleptinemia, enhance fibrosis progression (23). In animal models, exogenous leptin has been shown to potentiate the activation of Kupffer cells and to upregulate the production of proinflammatory cytokines, such as TNFα (24,25). Several studies with human subjects suggest that increased serum leptin levels may be associated with higher fibrosis stages (26–28). However, although there was a trend toward higher leptin levels among patients with insulin resistance in our study, insulin resistance itself was a stronger predictor of the development of severe fibrosis than serum leptin. Nevertheless, if our cohort of patients had been larger, or if serial measurements had been available, the effect of leptin on fibrosis progression might have been more pronounced.

Another hypothesis for the effect of insulin resistance on fibrosis progression is based on the observation that both diabetes mellitus and chronic hepatitis C are associated with liver steatosis (29). Nonalcoholic steatohepatitis might therefore play a key role in the pathophysiology of liver injury observed in these patients. However, in our study, the risk of developing severe fibrosis was independent of the presence of hepatic steatosis. This suggests that insulin resistance itself is implicated in the formation of fibrosis and that there is no direct link between steatosis and fibrosis. Indeed, some studies suggest that insulin itself may have an enhancing effect on cell turnover (30), which may lead to fibrosis progression. Our findings are in line with the recent study by Conjeevaram and colleagues, who showed that the deleterious effect of insulin resistance on HCV treatment outcome was independent of the presence of steatosis (31).

Insulin resistance, as defined by HOMA-IR, is characterized by elevated serum glucose and elevated serum insulin. Interestingly, in our cohort, insulin-resistant patients with lower glycosylated hemoglobin levels did not show significantly lower fibrosis progression rates. Therefore, improvement of serum glucose levels alone, might not prevent fibrosis progression. The impact of insulin resistance on HCV progression might however be modifiable by using insulin sensitizing agents, aimed at improvement of not only serum glucose levels, but also of other aspects, such as serum insulin (32,33). Insulin sensitizing agents may therefore have a beneficial role, especially in patients in whom hepatic insulin resistance leads to loss of control over hepatic glucose production but where the beta-cell function is still partly intact.

Foxton et al. have found that the effect of insulin resistance on fibrosis progression is most pronounced in liver transplant patients with older donor livers (2). In our study, there was no interaction between donor age and insulin resistance, suggesting that insulin resistance and donor age are two distinct risk factors for rapid fibrosis progression which are additive, but are not synergistic with each other. In our study, initial prednisone treatment was a potentially modifiable risk factor associated with insulin resistance. Therefore, steroid-free immunosuppressive regimens may prove to be beneficial for patients with chronic hepatitis C. It is important to bear in mind that, although insulin resistance was an important predictor of more advanced fibrosis stage, this did not translate into an association of insulin resistance and poorer graft survival in our study. (5-year survival 85.5% vs 78.1% log rank p = 0.37). Studies with longer follow-up may be needed to confirm or exclude an effect of insulin resistance on graft survival.

In conclusion, HOMA-IR is an important tool to identify insulin-resistant patients at risk for rapid fibrosis progression after liver transplantation for hepatitis C. Serum adipokines and cytokines were of no additional value in identifying patients at risk for developing advanced fibrosis or cirrhosis. Treatment with insulin alone had no significant beneficial effect on the fibrosis progression rates, suggesting there might be a potential role for treatment with insulin sensitizing agents.

Acknowledgment

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References

BJV received a stipend from the Netherlands Organization for Health Research and Development (ZonMw, project number 1900120283) for his work as a clinical research trainee. During his stay at the Mayo Clinic in Rochester, MN, he was also supported by a travel grant from the Trustfund of the Erasmus University Rotterdam, the Netherlands (grant number 97030.10/07.0275). The sponsors did not have any influence on the design and conduct of the study; nor on collection, management, analysis and interpretation of the data; nor on preparation, review or approval of the manuscript.

References

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Results
  6. Discussion
  7. Acknowledgment
  8. References