• Diabetes mellitus;
  • hepatitis C virus (HCV);
  • meta-analysis;
  • observational studies;
  • renal transplantation (RT)


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

Hepatitis C virus (HCV) infection has a detrimental role on patient and graft survival after renal transplantation (RT). Some studies have also implicated HCV in the development of post-transplant diabetes mellitus (PTDM). We conducted a systematic review of the published medical literature of the relationship between anti-HCV seropositive status and DM after RT. The risk of DM occurrence in anti-HCV-positive and -negative patients after RT was regarded as the most reliable outcome end-point. We used the random effects model of DerSimonian and Laird to generate a summary estimate of the Odds Ratio (OD) of new onset DM in HCV-positive and -negative patients after kidney transplantation. Ten studies involving 2502 unique RT recipients were identified. The incidence of PTDM after RT ranged between 7.9% and 50%. The summary estimate for adjusted OR was 3.97 with a 95% confidence interval (CI) of 1.83–8.61 (p-value for homogeneity <0.0473). Thus, pooling of study results demonstrated the presence of a significant link between anti-HCV seropositive status and DM after RT. This relationship provides one potential explanation for the adverse effects of HCV on patient and graft survival after RT.


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

Liver disease has emerged as an important cause of morbidity and mortality after renal transplantation (RT) (1), with hepatitis C virus (HCV) the leading cause of liver disease among RT recipients (2). The impact of HCV infection on patient and graft survival after RT is a major concern (3). Recent studies with large size and adequate follow-up have confirmed a detrimental effect of HCV infection on long-term patient and graft survival after kidney transplantation (4).

Mortality due to liver disease and its attendant complications (cirrhosis and hepatocellular carcinoma [HCC]) is significantly higher in anti-HCV-positive than anti-HCV-negative patients after RT (4–6). Increased graft loss has been linked with the occurrence of de novo HCV-related glomerulonephritis (7), and a greater rate of proteinuria in RT recipients with HCV infection (8).

PTDM has become increasingly common after kidney transplantation and may adversely affect patient and graft survival (9–11). Accumulating evidence suggests a significant link between HCV and DM in non-transplant population (12–14), after orthotopic liver (OLT) (15–18), and renal transplantation (RT). These associations remain controversial as results vary among reports from single-center observational studies or clinical trials. Also, the relationship between prevalent DM and anti-HCV at the time of renal transplant has been addressed with conflicting results (19–20).

The primary aim of this study was to synthesize the available evidence on the relationship between post-transplantation DM and anti-HCV seropositive status in kidney transplant recipients by performing a systematic review and meta-analysis of the medical literature.

Patients and Methods

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

Search strategy and data extraction

Electronic searches of the National Library of Medicine's MEDLINE database, Current Contents, Cochrane Library and manual searches of selected specialty journals were performed to identify all pertinent literature. It has been previously demonstrated that an electronic search alone may not be sensitive enough (21). We searched MEDLINE (PubMed and OVID Technologies), EMBASE (OVID Technologies), Currents Contents (Institute for Scientific Information) and the Cochrane Library (Update Software). The key words ‘hepatitis C virus’, ‘diabetes mellitus’, ‘immunosuppressive agents’, ‘renal transplantation’ and their synonyms or related terms were used. Reference lists from qualitative topic reviews and published clinical trials were also searched. Our search was limited to human studies that were published in the English literature. All articles were identified by a search from 1980 to December 2004. Data extraction was conducted independently by two investigators (F.F., V.D.) and consensus was achieved for all data. Studies were compared to eliminate duplicate reports for the same patients, which included contact with investigators when necessary. Eligibility and exclusion criteria were pre-specified.

Criteria for inclusion

To be included in this systematic review, a clinical trial had to fulfill a set of criteria. It had to be published as a full paper; we included only those studies that enrolled adults with no history of diabetes before transplantation and followed them for at least 1 year after RT with <10% loss to follow-up. We included clinical trials (CTs), both case-control (CC) and cohort (Co) studies that provided data on PTDM in HCV-positive and -negative patients after RT. To be included, case-control and cohort studies must have compared all patients with PTD (cases or exposed cohort) with all, a matched set, or a random sample of others (control subjects or unexposed cohort) in whom PTDM did not develop. We attempted to include clinical trials that enrolled patients with PTDM and randomized them to intervention and control arms. The decision as to the inclusion or exclusion of clinical trials was not related to results.

Ineligible studies

Studies were excluded if they reported inadequate data on measures of response. Trials that were published only as abstracts or as interim reports were excluded; letters and review articles were not considered for this analysis. Clinical trials of end-stage renal disease (ESRD) patients on maintenance dialysis or not yet requiring dialysis were excluded. Clinical studies enrolling patients with combined transplant (kidney/pancreas or other organs) were excluded.


In all the trials included in this analysis, we extracted the cumulative incidence of PTDM and calculated its 95% confidence interval (CI) using the exact method of Pearson. We calculated odds ratios (ORs) and risk ratios (RRs) and their 95% CIs. When these calculations could not be conducted because too little information had been published, we noted the published p values or other statements of statistical significance.

In order to reduce the heterogeneity introduced by including studies that were different across important confounding variables, we selected the adjusted OR (95% CI) for PTDM after RT in anti-HCV-positive and -negative patients as our primary outcome measure in this systematic review. The adjusted OR (aOR) was specified by multivariate regression model in six (60%) of ten studies and controlled for age, gender, race, body mass index (BMI) at transplantation, family history of DM, type of immunosuppressive therapy and duration of post-RT follow-up. We calculated by univariate analysis the un-adjusted OR (and 95% CI) for PTDM after RT in anti-HCV-positive and -negative patients; this was the secondary outcome measure in our meta-analysis.

PTDM was diagnosed according to American Diabetes Association (ADA) guidelines (22). Diagnostic criteria include: fasting blood glucose levels higher than 126 mg/dL on two separate occasions; random blood sugar >160 mg/dL, confirmed by fasting blood sugar >126 mg/dL, and 2-h post-prandial blood sugar >200 mg/dL, confirmed by fasting blood sugar >126 mg/dL. Alternatively, DM was defined as the requirement of glucose-lowering medications (insulin or oral hypoglycemic agents for >1 month). In all studies, assessment of HCV seropositive status was made by serological technology (second- or third-generation enzyme-linked immunoadsorbent assay [ELISA]) aimed to detect anti-HCV antibody in serum. This meta-analysis was not supported by any grant or any pharmaceutical company.

Statistical methods

Prior to conducting statistical analysis, we constructed evidence tables and performed a qualitative assessment of heterogeneity by comparing key study features including demographic characteristics, duration of follow-up, concurrent immunosuppressive therapies etc. We then performed a meta-analysis using XYZ package, version ABC. Quantitative, pooled, summary estimates of ORs across individual studies were generated using the random effects model of DerSimonian and Laird (23). For the DerSimonian and Laird method (23), the hypothesis is that studies are a random sample from a population of studies (random sample). The mean of the population of studies is the ‘true effect’. We then performed a statistical test for heterogeneity and adopted a p-value of <0.05, as evidence of heterogeneity. The random effects model incorporates the heterogeneity of the studies. The overall treatment effect is estimated by a weighted average of the individual effects, with weights inversely proportional to the variance of the observed effects. Ninety-five percent confidence intervals (95% CI) for point estimates were computed using non-parametric re-sampling (bootstrap) methods, each with 1000 resamples. Chi-square test statistics were used to test for homogeneity across studies and Spearman correlation coefficients were used to assess the association between outcomes of interest (e.g. the reported size of the estimated intervention benefit) and variables thought to be potential sources of heterogeneity (e.g. various subjects and trial characteristics of interest). A two-sided value of p < 0.05 was considered to be statistically significant.


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

Literature review

Our electronic and manual searches identified 150 reports that were selected for full text review; the complete list of these reports is available on request. One hundred and forty-one (94%) papers were excluded because they did not fulfill the inclusion criteria. Nine reports, providing information on a total of 2502 unique patients enrolled in 10 clinical trials (Table 1), were included in our meta-analysis (24–32). One report (33) was excluded as it concerned the same patients already described in another survey (27). Another manuscript (28) gave complete information on two clinical trials (Table 1). An additional paper was not included as inclusion criteria were not satisfied (34). There was a 100% concordance between reviewers with respect to final inclusion and exclusion of studies reviewed on the basis of the predefined inclusion and exclusion criteria.

Table 1.  Baseline characteristics of studies included in the analysis
AuthorsStudy designReference number CountryPatients, n
  1. CC: case-control; Co: cohort study; R: retrospective; P: prospective; NS: not stated.

Gursoy M et al., 2000Co, R24Turkey247
Yildiz A et al., 2002CC, R25Turkey86
Bloom RD et al., 2002Co, R26U.S.427
Gentil MA et al., 2002CC, R27Spain354
Gentil MA et al., 2003 (1)Co, R28Spain330
Gentil MA et al., 2003 (2)Co, R28Spain240
Finni PES et al., 2004Co, R29Brazil66
Foo SM et al., 2004Co, R30Malaysia316
Gourishankar S et al., 2004Co, R31Canada386
Sens YAS et al., 2004CC, R32Brazil50

Patient characteristics

Some salient demographic and clinical characteristics of subjects enrolled in the included clinical trials are shown in Tables 1–5. All these studies were published in English from 1999 to 2004. Five studies (5/10 = 50%) were from Europe (Table 1), two (20%) and two (20%) were from Northern and Southern America, respectively; one (10%) study was made in Asia.

Table 2.  Baseline characteristics of studies included in the analysis
AuthorsAge, yearsMale (%)Immunosuppressive regimen
  1. Figures are given as post-transplant diabetes mellitus (PTDM) positive or negative patients, respectively NR: not reported (information needed was not available); CS: corticosteroids; CyA: cyclosporine; FK: tacrolimus; MMF: mycophenolate mofetil; AZA: azathioprine.

Gursoy M et al.29.9 ± 10.5NRNR
Yildiz A et al.44 ± 10/37 ± 1183.7/69.8CS, CyA (or FK), AZA
Bloom RD et al.49 ± 1.2/44.4 ± 0.679.4/56.3CS, CyA (or FK), MMF (or AZA)
Gentil MA et al.51.2 ± 10/40.6 ± 1246.4 /61.0%CS, CyA (or FK), AZA (or MMF)
Gentil MA et al. (1)38.9 ± 12NRCS, CyA, AZA
Gentil MA et al. (2)41.7 ± 13NRCS, FK, MMF
Finni PES et al.36.4 ± 15.548.5%CS, FK, MMF (or AZA)
Foo SM et al.50.5 ± 8.8/42 ± 11.171.4/57.3%CS, FK (or CyA)
Gourishankar S et al.43.162.4% (241/386)CS, CyA (or FK), MMF (or AZA)
Sens YAS et al.41.4±11/35.4±11.455.5/51.2%CS, FK (or CyA)
Table 3.  Baseline characteristics of studies included in analysis
AuthorsAnti-HCV positive recipients, n (%)PTDM recipients, n (%) Time after RT, months (mo)
  1. Figures are given as post-transplant diabetes mellitus (PTDM) positive or negative recipients, respectively NR: not reported (information needed was not available).

Gursoy M et al.40 (16.2)40 (16.2)39.1±19.6
Yildiz A et al.47 (54.6)43 (50)NR
Bloom RD et al.71 (16.6)63 (14.7)41.9±2.2 / 38.7±1.0
Gentil MA et al.177 (50)28 (7.9)44.4±21 / 43.7±23
Gentil MA et al.(1)96 (29.1)36 (10.9)95.6±47
Gentil MA et al.(2)27 (11.3)19 (7.9)32.1±17
Finni PES et al.23 (34.8)22 (33.3)9.5±1.5 / 9.8±1.2
Foo SM et al.89 (28.1)42 (13.3)125.5±60 / 85±61.5
Gourishankar S et al.11 (3.0)35 (9.8)44.2 (1–92)
Sens YAS et al.25 (50)9 (18)29.4±16/28.7±15.8
Table 4.  Baseline characteristics of studies included in analysis
AuthorsSerum creatinine, after RT (mg/dL)Race, Caucasian (%) Body weight at RT Living donor (%)
  1. Figures are given as post-transplant diabetes mellitus (PTDM) positive or negative recipients, respectively NR: not reported (information needed was not available); BMI: body mass index.

Gursoy M et al.NRNRNR77 (31.1%)
Yildiz A et al.1.3±0.25/1.34±0.26100%NR63 (73.2%)
Bloom RD et al.NR48%/44%83.1±2.4/73.6±1.0119 (27.8%)
Gentil MA et al.NRNR66.6±11/63.5±13NR
Gentil MA et al. (1)NRNR64.7±13NR
Gentil MA et al. (2)NRNR67.3±14NR
Finni PES et al.NR28 (66%)NR34 (52%)
Foo SM et al.NR0%57.6±10.9/ 52.3±11.6201 (63.6%)
Gourishankar S et al.2.1376 (97.4%)75.7±14.8/ 71.2±18.8122 (31.6%)
Sens YAS et al.1.7±0.4/1.7±0.533 (66%)NR12 (24%)
Table 5.  Baseline characteristics of studies included in the meta-analysis
Authors Patients with pre-RT DM (n)PTDM definition (definition category)* Family history for DM
  1. *(1) Glycemic threshold; (2) use of glucose-lowering medications NR: not reported (information needed was not available).

Gursoy M et al.01NR
Yildiz A et al.0135% (30/86)
Bloom RD et al.01NR
Gentil MA et al.02NR
Gentil MA et al. (1)02NR
Gentil MA et al.(2)02NR
Finni PES et al.01NR
Foo SM et al.01NR
Gourishankar S et al.01,20
Sens YAS et al.0130% (15/50)

As reported in Table 1, there were three (30%) case-control trials and seven retrospective, cohort studies. The mean age of subject cohort ranged from 29.9 ± 10.5 to 52.0 ± 9.2 years and the gender distribution ranged from 46.4% to 83.7% male (Table 2). The rate of anti-HCV positive RT recipients was between 3.0% and 54.6%, and the PTDM occurrence ranged between 7.9% and 50%. No information was given in the included studies to make distinction between type I and II DM after RT.

Summary estimates of outcome

Table 6 reports the adjusted odds ratio (aOR) for PTDM after RT, according to anti-HCV seropositive status. The summary estimate for aOR was 3.97 with a 95% CI of 1.83–8.61 (p-value for homogeneity <0.0473) (Figure 1). The results obtained by univariate analysis have been shown in Table 7; it reports the unadjusted Odds Ratio for developing PTDM after RT according to anti-HCV seropositive status. The summary estimate for unadjusted OR was 3.75 with a 95% CI of 1.94–7.25 (p-value for homogeneity <0.0001) (Figure 2). These data suggest that there is a lot of heterogeneity if we do not account for the potential confounders (OR by univariate analysis), but that the heterogeneity is almost minimal if we use adjusted OR. We have not done a meta-regression to explain the reasons for heterogeneity in the univariate analysis because of the small number of total studies included in this systematic review.

Table 6.  Anti-HCV seropositive status and diabetes mellitus after RT: multivariate analysis adjusted odds ratio (aOR) and 95% CI.
AuthorsaORs95% CIP
  1. Yildiz: OR adjusted for age, body mass index, high-dose steroid use, family history for DM and HLA-antigens.

  2. Bloom: OR adjusted for age, weight at transplantation, cumulative steroid dose, black race and tacrolimus use.

  3. Gentil: OR adjusted for age, gender, body mass index, time on dialysis and adult polycystic kidney disease (APKD). Gentil (1) and Gentil (2): OR adjusted for age, body mass index at transplantation and present steroid dose use. Gourishankar: OR adjusted for age, gender, deceased (vs. living) donor, immunosuppressive therapy, rejection episodes, hypertension, induction therapy, pre-transplant dialysis, transplant after 1998, CMV mismatch, male gender, polycystic kidney disease (PKD).

Yildiz A et al.17.442.48–122.60.004
Bloom RD et al.6.762.36–19.380.0001
Gentil MA et al.1.7780.768–4.125NS
Gentil MA et al. (1)5.652.6–12.00.0001
Gentil MA et al. (2)1.2320.255–5.964NS
Gourishankar S et al.3.41.02–11.20.047
Overall random effect model estimate3.971.83–8.61 

Figure 1. ORs (multivariate analysis) from each of the individual studies. Reference numbers are reported in parentheses. The vertical line represents the summary mean estimate of ORs for new onset DM after RT.

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Table 7.  Anti-HCV seropositive status and diabetes mellitus after RT: Univariate analysis odds ratio (OR) and 95% confidence intervals (95% CIs)
AuthorsORs95% CIP
Gursoy M et al.3.501.478–8.280.007
Yildiz A et al.4.361.87–10.10.002
Bloom RD et al.5.973.310–10.750.0001
Gentil MA et al.1.600.816–2.384NS
Gentil MA et al.(1)9.784.39–21.770.0001
Gentil MA et al.(2)0.9220.207–4.11NS
Sens YAS et al.2.310.508–10.48NS
Foo SM et al.1.1670.423–2.36NS
Gourishankar S et al.3.20.99–10.470.053
Finni PES et al.63.312.87–311.30.0001
Overall random effect model estimate3.751.94–7.25 

Figure 2. ORs (univariate analysis) from each of the individual studies. Reference numbers are reported in parentheses. The vertical line represents the summary mean estimate of ORs for new onset DM after RT.

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

Morbidity and mortality remain high after RT although newer immunosuppressive regimens have greatly improved patient and graft survival. Post-transplant diabetes mellitus is a frequent complication of solid-organ transplantation and is associated with impaired long-term renal allograft function and survival (9). A number of risk factors for new onset DM after RT have been identified including obesity, age, race, ethnicity, family history, donor source, acute rejection, corticosteroid dose and type of immunosuppressive agents (9). The great majority of these risk factors are not potentially modifiable. Recent reports have implicated anti-HCV seropositive status as a risk factor for PTDM after RT. However, this information has been mostly obtained from single-center studies with limited size (24–34).

This meta-analysis shows a significant relationship between HCV and DM after RT, both in univariate and multivariate analyses. Thus, the higher prevalence of DM in HCV-positive patients after RT may be a factor in the adverse impact of HCV infection on patient and graft survival after RT. This finding should be of particular interest to clinical nephrologists as HCV can be included in the subset of potentially modifiable risk factors for PTDM after RT (e.g. obesity and type of immunosuppressive regimen).

The data provided by this meta-analysis are in keeping with results obtained from other sources. Our rate of PTDM occurrence (2–50%) was noted in prior reviews (9). Analogous to other series (9), the wide range of PTDM incidence between studies may be explained by various reasons: criteria for PTDM diagnosis, heterogeneity between groups on background parameters (age, ethnicity) and type of immunosuppression (i.e. intravenous corticosteroids, high-dose CyA and FK-506). Using data from the United Renal Data System (URDS) on 11 659 Medicare beneficiaries who received their first kidney transplant in 1996–2000 (35), HCV was included among the risk factors for PTDM using Cox's proportional hazards (aRR [adjusted relative risk], 1.33; 95% CI, 1.15–1.55, p < 0.0001). However, this survey included only Medicare beneficiaries, who have a higher prevalence of risk factors for PTDM (i.e. greater age, body mass index and lower education) compared with the non-Medicare primary population. Additional data showed that PTDM may contribute substantially to the increased risk of death associated with the use of HCV positive donor kidneys (36).

The potential association between HCV and DM has not been extensively evaluated in ESRD. In a series of 2370 patients who underwent renal biopsy in Japan, the highest rate of HCV was observed in type II diabetic-related glomerulosclerosis (24 of 123; 19.5%) (37). Saxena et al. (38) observed a higher anti-HCV rate in dialysis patients with type II DM compared with non-diabetics (57.4%[31/54] vs. 35.3%[50/142], p < 0.011), but only univariate analysis with a small study group (n = 196) was performed. Additional investigations aimed at evaluating the relationship between HCV and DM in dialysis population are in progress.

PTDM has been linked to use of steroids, and calcineurin inhibitors. Lower rates of this complication have been documented in efficacy studies of combination therapy with alternative medications (e.g. MMF and sirolimus) (39–41). Gentil et al. (28) observed significant association between HCV and DM after RT only in patients who received standard triple therapy (study-1 patients, Tables 6–7); in their series, the link between HCV and PTDM was lost after complete withdrawal of steroids (28). Several investigators suggested an association between HCV and tacrolimus-induced DM after RT (26, 29, 31, 42), but this relationship was not noted in some reports (25, 30); also, tacrolimus doses at DM diagnosis were lower in HCV-positive than -negative RT recipients (0.12 ± 0.05 vs. 0.18 ± 0.02 [p < 0.05]) in the study by Sens et al. (32).

In some surveys (27, 30, 32), although PTDM was more frequent in anti-HCV-positive than negative recipients, no statistical relationship between HCV and post-transplant DM was obtained. This could be related to the type of immunosuppressive therapy and/or other factors, i.e. the limited number of anti-HCV positive RT recipients (resulting in a type II error), the low occurrence of PTDM and the short post-transplant follow-up. A successful antiviral therapy pre-RT, as shown by Gursoy et al. (24), and Kamar et al. (43), could also play a role.

Our multivariate analyses confirmed the role of age (25–28, 31) and body weight (26–28) in the DM occurrence after RT; however, the incomplete information on these covariates in some studies, as shown in Table 2, precluded more definitive conclusions.

The current meta-analysis has some shortcomings. As with all meta-analyses, this study has the potential limitation of publication bias. We postulated that the investigators who failed to find an association between DM and HCV would be less likely to report on the absence of such an association. We believe that obtaining information on the relationship between HCV and DM from as many sources as possible is a reliable approach to this issue. Second, the quality of the available trials was not very high (44). There were no CTs, information on some risk factors was incomplete and the adjustment of outcomes for potential confounders was not done in all studies. However, results by multivariate regression models, when available, mirrored those obtained by univariate analyses. In addition, multivariate analyses showed a low statistical heterogeneity. Third, individual data (e.g. ‘patient-level’ data) were not available; thus, it was impossible to perform our own adjustments. On the basis of the relative risk reported in each study, we have calculated our summary estimate for relative risk of mortality with HCV across the studies. Fourth, it was impossible to assess whether any studies included cases of type I PTDM; however, type I PTDM would appear to be a minority of cases, according to the clinical features listed in Table 2. Fifth, we used adjusted relative risk obtained by Cox model in most clinical trials. This approach takes into account both differential follow-up time as well as differential distribution of covariates in order to isolate the effect of HCV seropositive status per se. Finally, the clinical studies included in this meta-analysis had retrospective and observational design. Although much has been learned about the DM occurrence after RT, the available data is of limited nature because of the lack of carefully controlled studies providing prospectively pre-transplant baseline data and sequential follow-up. Prospective trials with appropriate virologic and clinical evaluation at baseline and over post-transplant follow-up are in progress.

No known mechanism can fully explain the development of PTDM during HCV. Chronic HCV adversely affects glucose metabolism by destruction of hepatocytes (45); in vitro and in vivo studies support the association between HCV and insulin resistance (and beta cell dysfunction) (46–49), and various mechanisms for HCV-associated insulin resistance have been cited including the down-regulation of hepatic molecules (insulin receptor substrates and cytokines) (50). HCV is able to replicate in the pancreas in a post-mortem study (51). Also, glucose intolerance is common in patients with liver diseases, because of abnormalities in peripheral insulin resistance and hepatic glycogen production (52). Many HCV positive patients have abnormal glucose tolerance masked by ESRD (27), and the immunosuppressive therapy after RT could result in the development of frank DM.

It has been suggested that HCV-induced insulin resistance likely plays a role in the development of liver fibrosis in non-diabetic patients with chronic hepatitis C (53); also, DM has been considered as a risk factor for HCC in the United States (54). In chronic hepatitis C, insulin resistance may affect the sustained response rate to antiviral therapy (peginterferon plus ribavirin) (55). Thus, various mechanisms support the increased risk of morbidity and mortality among HCV-infected RT recipients after DM occurrence.

In summary, this meta-analysis of observational studies demonstrated a significant and independent relationship between HCV and DM after RT. This association may be a factor in the detrimental consequences of HCV on patient and graft survival after RT. Clinical trials aimed to define the role of new immunosuppressive agents in lowering the DM occurrence in HCV-infected RT recipients are warranted. Studies assessing the benefits of antiviral therapy on the occurrence of new onset DM in RT recipients with HCV are under way.


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

We acknowledge Prof. M.A. Gentil, M.D., Prof. A. Yildiz, M.D. and Prof. S. Gourishankar, M.D., who kindly gave us detailed information about their studies included in the current meta-analysis.


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