Objectives: Posttransplant diabetes mellitus is a common and serious metabolic complication after renal transplant. Patients with uremia are known to have abnormal glucose metabolism characterized by insulin resistance and defects in insulin secretion, which are ameliorated to some extent with renal replacement therapy and more so with renal transplant. However, the diabetogenicity of calcineurin inhibitors compounds this state of dysglycemia and promotes the development of diabetes in some patients. It is not clear whether pretransplant dysglycemia is a risk factor for posttransplant diabetes mellitus and, if so, which between insulin resistance and pancreatic β-cell dysfunction is a major determinant in predicting posttransplant diabetes mellitus. Here, we examined the roles of the pretransplant oral glucose tolerance test, glycated hemoglobin (HbA1c) levels, and homeostatic model assessment-derived insulin resistance and beta-cell function in the prediction of posttransplant diabetes mellitus and the course of these indexes posttransplant. Our aim was to examine the correlations between these factors and their changes posttransplant with the development of posttransplant diabetes mellitus.

Materials and Methods: Pretransplant fasting blood was drawn from patients for plasma glucose, insulin, C-peptide, and HbA1c levels, which was followed by a 2-hour oral glucose tolerance test. After transplant, patients were followed for 6 months to detect post-transplant diabetes mellitus. Serum insulin, C-peptide, and glycated hemoglobin levels were reexamined in patients with posttransplant diabetes mellitus at 1 and 6 months.

Results: Twenty-one patients (29%) developed post-transplant diabetes mellitus. Pretransplant HbA1c was associated with development of posttransplant diabetes mellitus (odds ratio 27.04) on logistic regression. Homeostatic model assessment-derived insulin resistance improved significantly at 6 months posttransplant, whereas beta-cell function remained lower than pretransplant levels in patients with posttransplant diabetes mellitus.

Conclusions: Pretransplant HbA1c may be used as a predictive marker for posttransplant diabetes mellitus. Insulin resistance but not beta-cell function improves in patients with posttransplant diabetes mellitus at 6 months posttransplant.

Key words : Blood glucose/metabolism, End-stage renal disease, Kidney transplantation/adverse effects, Risk assessment, Type 2 diabetes mellitus etiology

Introduction

Posttransplant diabetes mellitus (PTDM) is a common and serious metabolic complication after renal transplant that contributes to a high risk of death, major cardiovascular events, graft failure, and increased medical costs.¹ Several independent risk factors have been identified, such as older age, black race, family history of type 2 diabetes mellitus, obesity, hepatitis C virus (HCV) infection in the pretransplant period, as well as weight gain and corticosteroid, calcineurin inhibitor, or proliferation signal inhibitor use in the posttransplant period.² However, there are limited data regarding the role of pretransplant insulin and glucose metabolism in the development of PTDM.^3-7 Patients with uremia are known to have insulin resistance, which can be ameliorated to a certain extent by renal replacement therapy, implicating the role of uremic toxins.³ As insulin resistance progresses, there must be concomitant insulin overproduction to maintain normal glucose metabolism, failure of which may be interpreted as a relative insulin deficiency. There is conflicting evidence for the presence of beta-cell dysfunction in chronic kidney disease (CKD).^4-6 Although a successful renal transplant should improve the insulin sensitivity, its effect may be masked to a certain extent by the adverse effects of immunosuppressive therapy. Previous studies have not conclusively shown whether insulin resistance or beta-cell function is the predominant defect in end-stage renal disease and which of these 2 persists in patients with PTDM.

In this study, we investigated abnormalities in insulin and glucose metabolism in patients with prevalent end-stage renal disease who were undergoing renal transplant. We aimed to determine whether these abnormalities correlated with the development of PTDM. In addition, by measuring glucose metabolism parameters in patients with PTDM, we also aimed to delineate the relative contribution of insulin resistance and beta-cell dysfunction to PTDM.

Materials and Methods

This study was approved by the institute’s ethics committee and was conducted in accordance with the Helsinki Declaration of 1975. Patients gave written informed consent.

This study included consecutive patients who were undergoing work-up for living-donor renal transplant from January 2013 to March 2014 at a tertiary care center in northern India. Criteria for inclusion were patients above 18 years of age with end-stage kidney disease not due to diabetes. Patients on steroid therapy during a period of 2 weeks preceding transplant, patients who had deceased-donor renal transplants, and patients with diabetes mellitus were excluded. Information collected during study enrollment included detailed history regarding age, cause of CKD, modality of renal replacement therapy, length on dialysis, and history of treated or untreated hepatitis B virus (HBV) and HCV coinfection. Anthropometric measurements, including height, weight, and waist circumference in patients in light clothing, were also made by the same examiner. Weight was measured using a standard spring-type weighing scale (1 day after dialysis in patients on hemodialysis and after draining dialysate on a dry abdomen in patients on peritoneal dialysis). Waist circumference was taken at the level of the umbilicus, to the nearest 0.5 cm. Body mass index (BMI) was measured by the standard formula (weight/height², in kg/m²). Fasting blood samples were collected for plasma glucose, insulin, and C-peptide levels. Patients were instructed to fast for at least 8 hours and not more than 12 hours after a moderate meal. Blood samples were also collected for glycated hemoglobin A1c (HbA1c) estimation. Patients were administered 75 g of glucose, and blood sample was drawn 2 hours later, for plasma glucose measurements (2-h plasma glucose). Plasma glucose was estimated with Roche auto analyzer (modular P800, Roche Diagnostics, Indianapolis, IN, USA). Blood sampling was done 1 week before planned transplant surgery.

Glycated hemoglobin A1c measurement
Blood samples for HbA1c were collected in EDTA vials and estimated by the NycoCard rapid in vitro test (Alere, Waltham, MA, USA). NycoCard HbA1c is a boronate affinity assay. The reagent contains agents that lyse erythrocytes and precipitate hemoglobin specifically, as well as a blue boronic acid conjugate that binds cis-diols of glycated hemoglobin. An aliquot of the reaction mixture was added to the test device. After excess conjugates were washed, the precipitate was evaluated by measuring the blue (glycated) and the red (total hemoglobin) color intensity with the NycoCard Reader II. The ratio between these is proportional to the percentage of HbA1c in the sample.

Serum insulin measurement
Blood samples for insulin levels were collected in EDTA vials and estimated by electrochemilu-minescence immunoassay. Insulin in the test sample, along with biotinylated monoclonal insulin-specific antibody and monoclonal insulin-specific antibody labeled with a ruthenium complex form a sandwich complex. After addition of streptavidin-coated microparticles, the complex, becomes bound to the solid phase via interaction of biotin and streptavidin. The reaction mixture was aspirated into the measuring cell, where the microparticles were magnetically captured onto the surface of the electrode. Application of a voltage to the electrode then induced a chemiluminescent emission that was multiplied by a photomultiplier.

Serum C-peptide measurement
Blood for C-peptide estimation was collected in EDTA vials and estimated by ST AIA-PACK C-peptide test (TOSOH India Pvt Ltd, India). ST AIA-PACK C-peptide is a 2-site immunoenzymometric assay that is performed entirely in the AIA-PACK test cups. C-peptide present in the test sample gets bound with antibody immobilized on a magnetic solid phase and enzyme-labeled monoclonal antibody in the AIA-PACK test cups. The magnetic beads were washed to remove unbound enzyme-labeled mono-clonal antibody and then incubated with a fluorogenic substrate (4-methylumbelliferyl phosphate). The amount of enzyme-labeled antibody that binds to the beads is directly proportional to the C-peptide concentration in the sample.

Homeostatic model assessment-derived parameters
The homoeostatic model assessment (HOMA) estimates steady-state beta-cell function and insulin sensitivity as percentages of a normal reference population.8 These measures correspond well, but are not necessarily equivalent, to non-steady-state estimates of beta-cell function and insulin sensitivity derived from stimulatory models such as the hyperinsulinemic clamp, the hyperglycemic clamp, the intravenous glucose tolerance test (IVGTT; acute insulin response, minimal model), and the oral glucose tolerance test (OGTT; change in insulin/glucose over 30 min). An updated HOMA model (HOMA2), published in 1998, takes into account variations in hepatic and peripheral glucose resistance.8 The HOMA calculator released in 2004 provides quick and easy access to the HOMA2 model to derive estimates of beta-cell function and insulin sensitivity. The HOMA calculator used in our study is available from the Diabetes Trial Unit (version 2.2.3, University of Oxford).8 The formulas are as follows: insulin resistance (IR) = (FI × FG)/22.5; beta-cell function = (20 × FI)/FG minus 3.5; insulin sensitivity = 100/IR, where FI is fasting insulin (μIU/ml) and FG is fasting glucose (mmol/L).

Posttransplant care
In the posttransplant period, patients were screened for PTDM (according to international consensus guidelines) weekly for the first month posttransplant and then monthly for the next 6 months.⁹ Patients were required to have PTDM at 1 month or more after renal transplant if they continued to have insulin or oral hypoglycemic agent requirements or had elevated fasting plasma glucose/postprandial plasma glucose (according to the consensus guidelines). Posttransplant diabetes mellitus was treated with the use of insulin, sulfonylureas, metformin, and dipeptidylpeptidase-4 inhibitors, in doses according to assessments by treating physicians. For immunosuppression, all patients were given intravenous methylprednisolone 500 mg intraoperatively, followed by oral prednisolone at a dose of 0.5 mg/kg/day from transplant day 1. Steroid dose was tapered at 2.5 mg/week to reach 5 mg by 12 weeks. Induction immunosuppression (either antithymocyte globulin or basiliximab) was given to selected patients depending on their immunologic risks. All patients were maintained on tacrolimus, mycophenolate mofetil, and prednisolone. Tacrolimus concentrations were monitored using trough concentrations with therapeutic tacrolimus trough levels of 10 to 12 ng/mL in the first week, 8 to 10 ng/mL over the next 3 weeks, and 5 to 8 ng/mL beyond the first 1 month. Tacrolimus concentrations were measured with microparticle enzyme immunoassay. At 1 and 6 months posttransplant, patients diagnosed with PTDM were again tested for serum insulin, C-peptide, and HbA1c levels using the methods described above.

Statistical analyses
The statistical analysis was carried out using Statistical Package for Social Sciences (SPSS Inc., Chicago, IL, USA; version 16.0 for Windows). All quantitative variables were estimated using measures of central location (mean, median) and measures of dispersion (standard deviation and standard error). Normality of the data was checked by measures of skewness and Kolmogorov-Smirnov tests of normality. For normally distributed data, means were compared using t tests for 2 groups and one-way analyses of variance were used for more than 2 groups. For skewed data, the Mann-Whitney test was applied. For more than 2 groups, the Kruskal-Wallis test was applied. Qualitative or categorical variables were described as frequencies and proportions. Proportions were compared using chi-square or Fisher exact test, whichever applicable. To find the relation between 2 variables, Pearson correlation coefficient or Spearman correlation were calculated. All statistical tests were performed at a significance level of P = .05.

Results

During the study period, 200 patients underwent renal transplant at our institution, with 151 patients meeting the inclusion criteria. Of these, 72 patients were enrolled for the study (56 had insufficient data, 9 dropped out of the transplant program, 4 died before transplant, 5 did not receive transplants during the duration of the study, and 5 did not provide consent for the study). The enrollment of study participants is shown in Figure 1.

Pretransplant and peritransplant baseline characteristics
Of the 72 study participants, 54 were male and 18 were female patients, with mean age of 33.85 years. Most patients (94%) were on hemodialysis, with a mean dialysis duration of 10 months. Seven patients had chronic HCV infection, and 1 had HBV infection. Most patients were not obese (mean BMI of 20.47 kg/m²), with as many as one-fourth of the study participants having a BMI < 18 kg/m² (Table 1).

Forty-one patients (57%) had related donor graft procedures (39 haplomatched donors, 2 fully matched donors). Thirty one recipients (43%) had grafts from unrelated donors (mostly spouses). Induction immunosuppressive agents were used in 22 patients (16 with basiliximab and 6 with rabbit antithymocyte globulin). Mean serum creatinine was 1.09 mg/dL at 1 month posttransplant. Over the 6-month follow-up posttransplant, 7 patients developed biopsy-proven acute rejection (BPAR) (5 with acute cellular rejection and 2 with antibody-mediated rejection). All cases of BPAR occurred within the first 2 weeks, and none had a recurrence of BPAR in the 6-month follow-up.

Twenty-one patients developed PTDM during the study period, giving an incidence rate of 29%. Patients who developed PTDM did not differ significantly from the group without PTDM in terms of age, sex, length of dialysis, and anthropometric variables (height, weight, waist and hip circumference, and BMI). Four of the seven patients with HCV infection developed PTDM. Peritransplant factors (such as the use of induction therapy, incidence of acute rejection, and presence of related versus unrelated donors) also did not differ significantly between the groups with and without PTDM (Table 2).

Pretransplant oral glucose tolerance test
Four patients had impaired fasting glucose, and 23 patients had impaired glucose tolerance alone (37% of the study population). Three of the 4 patients with impaired fasting glucose developed PTDM (75%), whereas 8 of the 23 patients with impaired glucose tolerance alone developed PTDM (34%). The mean fasting plasma glucose was 7 mg/dL higher in the group who later developed PTDM, which was significant (88.33 vs 81.49 mg/dL; P = .014). The 2-hour plasma glucose after 75 g OGTT was 22 mg/dL higher in the group who later developing PTDM (151.67 vs 129.33 mg/dL; P = .002).

Pretransplant glycated hemoglobin A1c
The mean pretransplant HbA1cin the group who developed PTDM was 6.23% (in the prediabetic range), whereas it was 5.88% in the patients who did not develop PTDM (significantly different; P = .002). Figure 2 depicts HbA1cacross the groups with and without PTDM.

Fasting insulin and C-peptide levels
Higher pretransplant insulin and C-peptide levels were noted in the patients who developed PTDM later; however, differences were significant only for insulin levels (8.60 vs 6.28 μU/mL, P = .009) but not for C-peptide levels (5.15 vs 4.69 ng/mL; P = .353) (Table 3).

Homeostatic model assessment-derived estimates of glucose metabolism
Pretransplant insulin resistance (HOMA-IR) was higher (1.28 vs 0.81, P = .001) and its reciprocal, insulin sensitivity, was lower (95.53% vs 136.53%; P = .001) in the patients who developed PTDM. Pancreatic beta-cell function was also higher in this group, although the difference was not significant (131.29 vs 105.48; P = .08). Table 3 shows the differences in the pretransplant glucose metabolism parameters between the patients who developed PTDM and those who did not.

Glycated hemoglobin A1c and homeostatic model assessment-derived glucose metabolism parameters in patients with posttransplant diabetes mellitus at 1 and 6 months
Glycated hemoglobin A1c in patients with PTDM was expectedly higher at 1 and 6 months compared with pretransplant HbA1c levels, although differences did not reach significance (6.35% vs 6.12%; P = .10). Among HOMA-derived parameters, insulin sensitivity improved and insulin resistance decreased between 1 and 6 months after transplant, showing a significant trend (P < .001) (Figure 3). Beta-cell function was lowest at 1 month posttransplant and improved at 6 months, although it was still lower than the pretransplant values. The trend to improvement in beta-cell function at 6 months versus at 1 month was significant (P < .001), whereas the overall reduction in beta-cell function compared with pretransplant values was not significant (P = .15) (Table 4).

A logistic regression analysis was conducted to predict development of PTDM using pretransplant fasting plasma glucose, 2-hour plasma glucose, fasting insulin, C-peptide, and HbA1c levels as predictors. A test of the full model against a constant-only model was statistically significant, indicating that the predictors as a set reliably distinguished those who developed PTDM from those who did not (π2= 20.464; P < .001, with df = 5). Prediction success overall was 79.2%. The Wald criterion demonstrated that HbA1c had the most significant contribution to prediction (odds ratio of 27.694). This would mean that each unit rise of pretransplant HbA1c made it 27 times more likely that the patient would develop PTDM. A pretransplant HbA1c of 5.95% or more had a sensitivity of 71% and specificity of 49% in predicting PTDM. A receiver operating characteristic curve drawn with HbA1c on the y axis and development of PTDM on the x axis demonstrated an area under the curve of 0.726. The other parameters (2-h plasma glucose, fasting insulin, and C-peptide) were not significant on analysis.

Discussion

Posttransplant diabetes mellitus is a common metabolic complication, affecting up to 20% of kidney transplant recipients, and is an important contributor to cardiovascular morbidity and mortality. Several pretransplant risk factors have been identified, including older age, long-term dialysis, nonwhite ethnicity, metabolic syndrome, hypertriglyceridemia, hypomagnesemia, untreated HCV and HBV infections, and, more recently, genetic polymorphisms affecting pancreatic beta-cell function, especially the KCNJ11 polymorphisms.⁹ Here, we evaluated the correlation of pretransplant glucose metabolism parameters in patients with end-stage renal disease versus development of PTDM. The demographic profile of our study participants is representative of most Indian transplant centers,¹⁰ with younger age (mean age of 33.8 y), short dialysis period (10 months), and predominant use of hemodialysis as the modality of renal replacement therapy. In addition, most patients in our study were lean, with one-third having a BMI of less than 18.5 kg/m² and only a single participant with a BMI of more than 25 kg/m².

The incidence of PTDM in our study was 29%. This is comparable to other studies published after the currently used International Consensus definition of PTDM, such as Vincenti and associates, who reported an approximately 30% incidence of PTDM at the end of 6 months.11 In an earlier study from our center, the incidence of PTDM was reported to be 19.5%.¹²

Patients who went on to develop PTDM were not different from those who did not with respect to age, sex preponderance, basic disease, time on dialysis, anthropometric parameters (weight, waist and hip circumference, and BMI), and transplant-related factors (related versus unrelated donors, use of induction, and incidence of acute rejection). Pretransplant HCV and HBV infections have been found to be associated with PTDM.9 The mechanism of this association is not fully understood, but insulin resistance is thought to increase in HCV-infected transplant recipients, with no effect on beta-cell function.^13,14 In the present study, we observed that 50% of those with pretransplant HCV or HBV infections developed PTDM compared with 26% in the group without these infections. However, because the absolute number of patients with HCV/HBV infections was small (8 of 72 patients), this association did not reach statistical significance.

The present study evaluated measures of glucose metabolism and beta-cell function in the pre-transplant period in all participants. Four patients had impaired fasting glucose and 23 had impaired glucose tolerance, resulting in prevalence of prediabetes of 31%, which is much higher than in the nontransplant Indian population (8% to 14% in the ICMR-InDIAB study¹⁵). However, prediabetes prevalence of 41% has been reported in CKD patients by Hornum and associates in 2010.¹⁶ Most patients with prediabetes in our study had impaired glucose tolerance, with few patients having an impaired fasting glucose. Previous studies in nonuremic populations have postulated that elevated fasting glucose may signify defective beta-cell function or severe hepatic insulin resistance, whereas impaired glucose tolerance may implicate marked insulin resistance or insulin resistance in the muscle.^17,18 Thus, our findings would suggest a greater role for uremia-mediated insulin resistance in causing prediabetes in end-stage renal disease. Pretransplant fasting plasma glucose and 2-hour plasma glucose after 75 g OGTT were both significantly elevated in the patient group who later developed PTDM compared with the other patients. The OGTT test in the pretransplant period identified more patients with prediabetes than would have been identified by use of fasting sample alone. These results were similar to the findings of Bergrem and associates,³ although their mean 2-hour plasma glucose was lower than in our study.

Although a number of techniques are available for making definitive and accurate estimates of insulin resistance, such as hyperinsulinemic-euglycemic clamp, insulin suppression tests, and IVGTT, these are not suitable for routine clinical use.¹⁹ The use of HOMA is the most commonly used method for assessment of insulin resistance and beta-cell function. It has been validated in patients with decreased glomerular filtration rate and has been found to correlate closely with IVGTT. With the use of the insulin indexes calculated from HOMA, higher pretransplant HOMA-IR was associated with the development of PTDM in our study. Fasting insulin, C-peptide, and beta-cell function were higher than results shown in the pretransplant period in patients who subsequently developed PTDM. In the posttransplant period, insulin sensitivity and insulin resistance improved significantly between 1 and 6 months and also when compared with pretransplant values (albeit not significantly). Beta-cell function was at a minimum at 1 month and improved to some extent (significant trend) at 6 months, although not to the pretransplant value. The improvement in insulin sensitivity is likely because of amelioration of uremic insulin resistance. It is more evident at 6 months, when calcineurin inhibitors and steroids are administered at lower doses and when other postoperative stressors active at 1 month are no longer operative. It is known that a hyperbolic relationship exists between insulin sensitivity and secretion, ie, as the insulin sensitivity goes down, beta-cell function increases. However, with severe insulin resistance, the beta-cell function cannot keep up to maintain normal glucose values. Therefore, a higher beta-cell function in the pretransplant period might signify the islet cell response to peripheral insulin resistance.²⁰ In addition, in our study, lower beta-cell function at 6 months posttransplant compared with pretransplant values, in the face of improved insulin sensitivity, may indicate that beta-cell dysfunction is more contributory to PTDM. A study by Nagaraja and associates implicated beta-cell dysfunction and not HOMA-IR with the development of PTDM.⁴ Nam and associates also corroborated the view that insulin sensitivity improves in patients after transplant, including those who later develop PTDM (again similar to our findings), thus providing strength to the view that insulin secretory defects, and not insulin resistance, contribute to the development of PTDM.⁵ In the nontransplant population, it has emerged that, whereas insulin resistance contributes to progression from normal to impaired glucose tolerance, impaired beta-cell function drives the progression toward subsequent type 2 diabetes mellitus.^17,18 It is not entirely clear whether a corollary can be drawn in the case of transplant populations.

Pretransplant HbA1c was higher in patients who later went on to develop PTDM. In fact, the mean HbA1c in this group was 6.23%, which is again in the prediabetes range. Pretransplant HbA1c levels remained significantly associated with PTDM on logistic regression analysis. In a study of 204 patients, Tatar and associates found a positive correlation between pretransplant HbA1c levels and the development of PTDM, although the mean HbA1c level in their study was 4.9 ± 0.5, much lower than in our study.⁷ A similar study by Tokodai and associates of 119 Japanese patients showed that pretransplant HbA1c was an important predictor for the development of PTDM, again with much lower values of mean HbA1c (5.2% vs 4.9% in the groups with and without PTDM).²¹ It is possible that an interplay of factors, such as uremia, twice weekly hemodialysis, and South Asian ethnicity, could have contributed to elevated HbA1c in our population. South Asian ethnicity is a well-recognized risk factor for the development of type 2 diabetes mellitus, attributable to several pathogenetic mechanisms, including greater visceral fat and insulin resistance, early beta-cell dysfunction, high carbo-hydrate diet, and lower physical activity, compared with other ethnicities.²² However, HbA1c measure-ment is fraught with several confounding factors in patients on hemodialysis.²³ Nevertheless, the diagnostic accuracy, as well a precision in the estimation of chronic hyperglycemia, make this measurement a preferred tool in the general population. Therefore, the use of HbA1c measure-ments pretransplant merits further study for inclusion into risk scoring systems for predicting PTDM.

Prediction of PTDM assumes an important role in tailoring immunosuppression to strike a balance between optimal glycemic control and low risk of acute rejection. Early steroid withdrawal, within 1 week, is associated with lower incidence of PTDM but only when the calcineurin inhibitor used is cyclosporine and not tacrolimus.²⁴ Later withdrawal of steroids, at 3 to 6 months, does not have any advantage with respect to incidence of PTDM.²⁵ Corticosteroid withdrawal is associated with higher acute rejection rates and comparable long-term allograft function. Among the maintenance immuno-suppressive agents, tacrolimus is more diabetogenic than cyclosporine (as shown in the DIRECT study), although the trough levels in that study were much higher than currently used doses.¹¹ Tacrolimus to cyclosporine conversion has also been tried in patients with PTDM, but the evidence is limited. A pilot study previously performed at our institution showed lesser insulin requirements and higher resolution rates of PTDM when patients were converted from tacrolimus to cyclosporine.¹² The latest expert recommendations do not support conversion of calcineurin inhibitors or steroid minimization with a view to decrease PTDM incidence.⁹ With regard to newer immunosuppressive agents, a recent meta analysis indicated a lower risk of PTDM with calcineurin inhibitor-minimizing and avoidance strategies, such as the use of belatacept and tofacitinib.²⁶

Prediabetes in hemodialysis patients, besides increasing risk of PTDM, also correlates with higher mortality while on dialysis, due to its association with proinflammatory markers and poor nutritional status.²7 It remains to be seen whether achieving euglycemia in patients on kidney transplant wait lists improves survival. Patients with HBV and HCV infections and prediabetes may be considered for the treatment of these infections to ameliorate this risk.

To the best of our knowledge, no other study has yet looked into OGTT, HOMA-derived glucose metabolism parameters, and HbA1c for prediction of PTDM, especially in a relatively younger South Asian population with low BMI.

Limitations
The main limitation of our study was its small sample size. The duration of follow-up in our study was only 6 months; therefore, some late-onset cases of PTDM may have been missed. We included cases of PTDM from 1 month posttransplant. Current recommendations propose identifying PTDM after stable immunosuppression and renal function are achieved (ie, at 6 weeks or later). Therefore, it is possible that some cases of persistent postoperative hyperglycemia may have also been included in the study. In addition, because of our small number of PTDM patients, it was not possible to analyze separately the effects of insulin and oral antidiabetic agents on HOMA-derived parameters.

Conclusions

Pretransplant glucose metabolism parameters correlate with the development of PTDM in renal transplant recipients. Insulin resistance, more than beta-cell dysfunction, appears to be associated with development of PTDM. Pretransplant OGTT and HbA1c can be used to predict patients at higher risk for development of PTDM. Further large studies could confirm these findings and help in developing risk scores for PTDM, with greater applicability and predictive accuracy than the existing risk scores.

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