This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
New onset diabetes after transplant (NODAT) occurs commonly following kidney transplantation and is associated with an increase in recipient morbidity and mortality, primarily through the development of cardiovascular disease (
Abnormalities of glucose metabolism prior to transplant have been shown to predispose recipients to the development of NODAT, although consensus is lacking over which glycaemic parameters are best measured to assess this risk. In general populations, patterns of oral glucose tolerance test (OGTT) results are predictive of future progression to diabetes (
Similarly, current guidelines suggest a number of glycaemic parameters including fasting plasma glucose (FPG), HbA1c, and OGTT to be suitable tests for the detection of diabetes post-transplant (
In this single centre study from a metropolitan transplant referral hospital, we used routine OGTTs to prospectively determine the glycaemic status of kidney transplant recipients prior to and following transplantation between 2003 and 2018. Records were linked to the ANZDATA registry to obtain recipient factors and transplant outcomes. We hypothesised that OGTTs performed prior to and following kidney transplant would outperform FPG in identifying at-risk transplant candidates and KTRs with NODAT, respectively.
This single centre retrospective cohort study included all non-diabetic adult kidney transplant recipients transplanted at Royal Prince Alfred Hospital, Sydney, Australia, between 1st January 2003 and 31st March 2018. Patients with a diagnosis of diabetes prior to transplant, recipients of combined organ transplants (kidney and liver), patients with a functioning renal allograft
Results of pre- and post-transplant 2-h 75-g OGTT were obtained from the hospital Electronic Medical Record, the Departmental Database, and patient files.
The deidentified dataset was linked to the ANZDATA registry using deterministic record linkage (transplant centre, date of birth, date of transplant, and sex) to obtain recipient factors including ethnicity, primary kidney disease, history of prior kidney transplants, smoking history, weight, and comorbidities present at time of transplantation (coronary artery disease, peripheral vascular disease, diabetes mellitus, cerebrovascular disease, and chronic lung disease); and transplant characteristics including donor type, donor age, ischaemia time, HLA mismatch, delayed graft function, induction therapy, and transplant outcomes.
ANZDATA is a bi-national registry that collects demographic and kidney-related treatment and outcomes data for all dialysis and transplant patients within Australia and New Zealand. Data is provided on a yearly and voluntary basis by nephrology units with an opt-out system of consent. ANZDATA collection methods and validity have been previously described (
The study was conducted following approval by the institutional ethics committee under protocol 2019/ETH06370.
A 75-g OGTT was performed pre- and post-transplant for each participant, conducted according to American Diabetes Association (ADA) guidelines. On the basis of fasting plasma glucose (FPG) and 2-h plasma glucose (2hPG) levels, patients were categorised as having pre-transplant normoglycaemia (FPG <5.6 mmol/L and 2hPG <7.8 mmol/L), impaired fasting glucose (IFG, FPG ≥5.6 mmol/L to 6.9 mmol/L), or impaired glucose tolerance (IGT, 2hPG ≥7.8 mmol/L to 11.0 mmol/L). Patients with a new diagnosis of diabetes (FPG ≥7, or 2hPG ≥11.1) based on their pre-transplant OGTT were excluded from the primary analysis.
The glycaemic status of KTRs was censored at week 12 post-transplant. NODAT was determined by either a positive OGTT result (FPG ≥7, or 2hPG ≥11.1) performed at weeks 10–12 post-transplant, or by a clinical diagnosis defined as repeated elevations in fasting (≥7.0 mmol/L) or random/post-prandial (≥11.0 mmol/L) blood glucose levels throughout the post-transplant period that required ongoing treatment with antidiabetic medication at week 12 post-transplant. Patients not requiring antidiabetic medication and for whom the results of a 75g OGTT were not attainable were classified as having an unknown glycaemic state due to insufficient evaluation.
Data in the manuscript are expressed as means ± standard deviation for normally distributed data or median ± interquartile range for non-normally distributed data, and as frequencies for categorical variables.
Differences in continuous variables between groups were examined by analysis of variance (ANOVA) for normally distributed data, or by the non-parametric Kruskal-Wallis log rank test for non-normally distributed data. Categorical variables were compared using the Chi squared test. Cohen’s kappa was used to determine the agreement between the fasting and 2-h plasma glucose criteria for NODAT, and the correlation between fasting and subsequent 2-h glucose levels by the Pearson correlation coefficient. Receiver operating characteristic (ROC) curve analysis was conducted to identify the diagnostic utility of FPG value at time of OGTT in identifying pre and post-transplant dysglycaemia.
To ascertain the associations between patient factors and the development of NODAT we performed multivariate analysis using a generalised linear model with a logit link function. Variables were included if they were statistically associated with the outcome by univariate analysis (
Patient and graft survival were analysed by the Kaplan-Meier method and compared using the log-rank test for unadjusted survival, with Cox proportional hazard regression used for multivariate analyses.
For all analyses, a two-sided
As not all kidney transplant recipients at our centre underwent pre-transplant assessment with an OGTT, we conducted a sensitivity analysis to determine whether the association of post-transplant dysglycaemia and transplant outcomes remained consistent when the entire transplant cohort with known glycaemic status post-transplant were examined. This cohort consisted of an additional 114 KTRs who did not undergo pre-transplant assessment with an OGTT but had their post-transplant glycaemic status accurately determined by either a clinical diagnosis of NODAT or the results of an OGTT. A further 197 KTRs who had a pre-transplant diagnosis of diabetes were included as a third comparator.
A total of 1212 kidney only transplants were performed in our centre between January 2003 and the end of April 2018. We excluded 56 transplants performed with recipients whose usual residence was outside of Australia (
Flow diagram for enrolment and stratification of recipients according to pre- and post-transplant glycaemic status. (*114 transplant recipients who did not perform an OGTT pre-transplant had a known post-transplant glycaemic status and were included in the sensitivity analyses, in addition to 197 recipients with pre-transplant DM.) DM, diabetes mellitus; OGTT, oral glucose tolerance test; IGT, impaired glucose tolerance; IFG, impaired fasting glucose; NODAT, new onset diabetes after transplant.
Baseline characteristics of the final study population (
Characteristics of kidney transplant recipients stratified by post-transplant glycaemic status.
Normoglycaemic | IGT | NODAT | Unknown | p | |
---|---|---|---|---|---|
|
|
|
|
||
Age (mean (SD)) | 41.5 ± 13.5 | 49.7 ± 12.8) | 53.9 ± 11.4 | 47.7 ± 14.2 | <0.001 |
Age ≥ 50 (%) | 49 (27.7) | 69 (57.0) | 94 (65.7) | 70 (44.9) | <0.001 |
Gender | 0.967 | ||||
Male (%) | 116 (65.5) | 76 (62.8) | 91 (63.6) | 102 (65.4) | |
Female (%) | 61 (34.5) | 45 (37.2) | 52 (36.4) | 54 (34.6) | |
BMI (mean (SD)) | 25.4 ± 5.5 | 26.3 ± 4.6 | 26.2 ± 4.5 | 26.3 ± 4.9 | 0.326 |
BMI Category (%) | 0.593 | ||||
Underweight (<18.5) | 9 (5.1) | 4 (3.3) | 5 (3.5) | 6 (3.8) | |
Normal (≥18.5 to <25.0) | 84 (47.5) | 45 (37.2) | 58 (40.6) | 57 (36.5) | |
Overweight (≥25.0 to <30.0) | 52 (29.4) | 45 (37.2) | 46 (32.2) | 52 (33.3) | |
Obese (≥30) | 27 (15.3) | 25 (20.7) | 31 (21.7) | 34 (21.8) | |
Not Available | 5 (2.8) | 2 (1.7) | 3 (2.1) | 7 (4.5) | |
Racial Background (%) | 0.243 | ||||
Caucasian | 129 (72.9) | 93 (76.9) | 99 (69.2) | 116 (74.4) | |
Asian | 32 (18.1) | 21 (17.4) | 33 (23.1) | 17 (10.9) | |
Aboriginal/Torres Strait Islander | 1 (0.6) | 1 (0.8) | 4 (2.8) | 5 (3.2) | |
Other | 15 (8.4) | 6 (5.0) | 7 (4.9) | 18 (11.5) | |
Primary Renal Disease (%) | 0.068 | ||||
Glomerulonephritis | 93 (52.5) | 67 (55.4) | 69 (48.3) | 65 (41.7) | |
Polycystic Kidney Disease | 22 (12.4) | 23 (19.0) | 20 (14.0) | 26 (16.7) | |
Reflux Nephropathy/PUV | 13 (7.3) | 8 (6.6) | 9 (6.3) | 18 (11.5) | |
Hypertension | 17 (9.6) | 4 (3.3) | 18 (12.6) | 9 (5.8) | |
Other | 32 (18.1) | 19 (15.7) | 27 (18.9) | 38 (24.4) | |
RRT Prior To Transplant (%) | 0.327 | ||||
Haemodialysis | 109 (61.6) | 68 (56.2) | 88 (61.5) | 105 (67.3) | |
Peritoneal | 34 (19.2) | 32 (26.4) | 38 (26.6) | 32 (20.5) | |
Pre-emptive transplant | 34 (19.2) | 21 (17.4) | 17 (11.9) | 19 (12.2) | |
Living Donor (%) | 110 (62.1) | 65 (53.7) | 67 (46.9) | 55 (35.3) | <0.001 |
Prior Kidney Transplant (%) | 18 (10.2) | 8 (6.6) | 11 (7.7) | 25 (16.0) | 0.055 |
Smoking History (%) | 48 (27.0) | 36 (29.8) | 62 (43.3) | 70 (44.9) | 0.001 |
Prior Vascular Disease |
28 (15.8) | 21 (17.4) | 45 (31.5) | 26 (16.7) | 0.002 |
Induction Immunosuppression | |||||
IL-2 Receptor antibody (%) | 149 (84.2) | 109 (90.1) | 124 (86.7) | 114 (73.1) | 0.001 |
T cell depleting antibody (%) | 7 (4.0.) | 5 (4.1) | 4 (2.8) | 6 (3.8) | 0.931 |
B cell depleting antibody (%) | 4 (2.3) | 2 (1.7) | 3 (2.1) | 0 (0.0) | 0.335 |
Intravenous Immunoglobulin (%) | 17 (9.6) | 13 (10.7) | 15 (10.5) | 19 (12.2) | 0.937 |
Maintenance Immunosuppression | |||||
Tacrolimus v CSA (%) | 152 (88.4) | 97 (80.8) | 127 (89.4) | 148 (95.5) | 0.002 |
CNI Free (%) | 5 (2.8) | 1 (0.8) | 1 (0.7) | 1 (0.6) | 0.242 |
mTOR (%) | 49 (27.7) | 26 (21.5) | 34 (23.8) | 11 (7.1) | <0.001 |
Prednisolone (%) | 177 (100.0) | 121 (100.0) | 143 (100.0) | 155 (99.4) | 0.416 |
- Dose (mg) at 3 m (mean, SD) | 11.1 ± 5.5 | 10.7 ± 2.9 | 11.5 ± 8.1 | 11.1 ± 3.6 | 0.790 |
HLA MM (%) | 0.068 | ||||
1–2 | 63 (35.6) | 44 (36.4) | 48 (33.6) | 46 (29.5) | |
3–4 | 70 (39.5) | 41 (33.9) | 41 (28.7) | 47 (30.1) | |
5–6 | 44 (24.9) | 36 (29.8) | 54 (37.8) | 63 (40.4) | |
Rejection episode (any) (%) | 38 (21.5) | 25 (20.7) | 32 (22.4) | 37 (23.7) | 0.931 |
Early rejection (≤ 90 days post-transplant) (%) | 26 (14.7) | 15 (12.4) | 29 (20.3) | 32 (20.5) | 0.179 |
Delayed graft function (%) | 20 (11.3) | 17 (14.0) | 25 (17.5) | 38 (24.4) | 0.011 |
eGFR (CKD-EPI) | |||||
at 3 m (mean, SD) | 55.9 ± 18.5 | 53.1 ± 18.1 | 51.3 ± 16.5 | 48.7 ± 17.5 | 0.004 |
at 1 year (mean, SD) | 60.2 ± 18.8 | 52.2 ± 15.4 | 52.6 ± 18.6 | 51.2 ± 18.5 | <0.001 |
Coronary artery disease, peripheral vascular disease, or cerebrovascular disease.
The majority of patients received induction with intravenous methylprednisolone and basiliximab (83%), with antithymocyte induction (3.7%) and/or intravenous immunoglobulin (10.7%) administered to higher-immunologic risk recipients. Initial immunosuppression was with tacrolimus (89%) or cyclosporine (10%), mycophenolate (98%) and/or sirolimus/everolimus (20%); and all except one recipient received maintenance prednisolone. Tacrolimus trough concentrations of 10–12 ng/ml were targeted during the first 3 months post-transplant, and 5–8 ng/ml from month 3 onward depending on immunological risk.
Pre-transplant OGTTs were performed at a median of 367 (IQR: 166–714) days prior to transplantation. Dysglycaemia determined by OGTT before transplantation was common, affecting 27% of the cohort, with IGT (126, 21%) more prevalent than IFG (43, 7%); the remaining 428 tests (72%) were normal (
Results of oral glucose tolerance tests performed prior to and following kidney transplantation, stratified by post-transplant glycaemic status.
Normoglycaemic | IGT | NODAT | Unknown | p | |
---|---|---|---|---|---|
|
|
|
|
||
Pre-Transplant OGTT | |||||
Day pre-transplant (median [IQR]) | −282 [−551, −146] | −407 [−746, −211] | −367 [−672, −142] | −440 [−736, −227] | 0.002 |
FPG mmol/L [mean (SD)] | 4.77 (0.49) | 5.07 (0.59) | 5.07 (0.73) | 4.81 (0.56) | <0.001 |
2hPG mmol/L [mean (SD)] | 5.58 (1.49) | 6.54 (1.60) | 7.37 (1.90) | 5.98 (1.80) | <0.001 |
Glycaemic status pre-transplant | <0.001 | ||||
Normoglycaemic (%) | 151 (85.3) | 81 (70.0) | 74 (51.7) | 122 (78.2) | |
IFG (%) | 9 (5.1) | 16 (13.2) | 9 (6.3) | 9 (5.8) | |
IGT (%) | 17 (9.6) | 24 (19.8) | 60 (42.0) | 25 (16.0) | |
Post-Transplant OGTT | |||||
Day post-transplant (median [IQR]) | 77 [68, 92] | 72 [69, 91] | 73 [65, 88] | — | 0.312 |
FPG mmol/L (mean (SD)) | 4.95 (0.46) | 5.24 (0.58) | 5.76 (0.89) | — | <0.001 |
2hPG mmol/L (mean (SD)) | 6.21 (1.12) | 9.14 (0.95) | 13.08 (2.20) | — | <0.001 |
OGTT, oral glucose tolerance test; IGT, impaired glucose tolerance; IFG, impaired fasting glucose; NODAT, new onset diabetes after transplant.
Patients with pre-transplant dysglycaemia (IGT or IFG) were older (52 ± 12 years vs. 45 ± 14 years,
As elevated FPG levels have been advocated as a screening test to identify patients who would benefit from further investigation with an OGTT pre-transplant, we examined the predictive value of this approach. The mean FPG of patients in the normoglycaemic group was 4.8 ± 0.5 mmol/L compared to 5.2 ± 0.7 mmol/L in those with IGT (
Receiver operating characteristic (ROC) curves for
Fasting plasma glucose cut-off values for the detection of impaired glucose tolerance pre-transplant.
FPG (mmol/L) | Sensitivity (%) | Specificity (%) | FPR | FNR | PPV | NPV | Youden index |
---|---|---|---|---|---|---|---|
4.60 | 85 | 32 | 0.68 | 0.15 | 0.36 | 0.82 | 1.17 |
4.80 | 73 | 46 | 0.54 | 0.27 | 0.38 | 0.79 | 1.19 |
5.00 | 60 | 63 | 0.37 | 0.40 | 0.43 | 0.78 | 1.23 |
5.05 | 53 | 70 | 0.30 | 0.47 | 0.45 | 0.77 | 1.24 |
5.20 | 47 | 76 | 0.24 | 0.53 | 0.48 | 0.76 | 1.23 |
5.40 | 30 | 86 | 0.14 | 0.70 | 0.50 | 0.73 | 1.16 |
5.60 | 22 | 92 | 0.08 | 0.78 | 0.55 | 0.72 | 1.14 |
5.80 | 18 | 95 | 0.05 | 0.82 | 0.62 | 0.72 | 1.13 |
6.00 | 13 | 98 | 0.02 | 0.87 | 0.71 | 0.71 | 1.10 |
FPR, false positive ratio; FNR, false negative ratio; PPV, positive predictive value; NPV, negative predictive value.
Of the 597 KTRs assessed, post-transplant glycaemic status could be accurately determined in 441 cases by either a clinical diagnosis of NODAT (
Of the 143 KTRs with NODAT, 59 (41%) diagnoses were not established on clinical grounds and were detected by protocolised OGTT at 10 weeks post-transplant. Whilst all 59 patients met ADA diagnostic criteria by an elevated 2hPG, only 3 patients met FPG criteria (FPG ≥7 mmol/L). The concordance between the fasting and 2-h glucose criteria for the diagnosis of NODAT was poor (κ = 0.07).
In patients without clinical NODAT, post-transplant FPG levels were a poor indicator of KTRs likely to have dysglycaemia on formal testing (
Fasting plasma glucose cut-off values for the detection of dysglycaemia (IGT or NODAT) post-transplant.
FPG (mmol/L) | Sensitivity (%) | Specificity (%) | FPR | FNR | PPV | NPV | Youden index |
---|---|---|---|---|---|---|---|
4.60 | 92 | 20 | 0.80 | 0.08 | 0.54 | 0.72 | 1.12 |
4.80 | 85 | 35 | 0.65 | 0.15 | 0.57 | 0.70 | 1.20 |
5.00 | 74 | 51 | 0.49 | 0.26 | 0.60 | 0.66 | 1.24 |
5.15 | 66 | 67 | 0.33 | 0.34 | 0.67 | 0.66 | 1.33 |
5.20 | 66 | 67 | 0.33 | 0.34 | 0.67 | 0.66 | 1.33 |
5.40 | 50 | 81 | 0.19 | 0.50 | 0.73 | 0.62 | 1.31 |
5.60 | 40 | 90 | 0.10 | 0.60 | 0.80 | 0.60 | 1.30 |
5.80 | 28 | 95 | 0.05 | 0.72 | 0.85 | 0.57 | 1.23 |
6.00 | 18 | 98 | 0.02 | 0.82 | 0.92 | 0.55 | 1.17 |
FPR, false positive ratio; FNR, false negative ratio; PPV, positive predictive value; NPV, negative predictive value.
Of our cohort, 156 (26%) patients did not develop clinical NODAT and did not undergo post-transplant OGTT. This group were similar in age (47 ± 14 vs. 46 ± 13,
Covariates associated with the development of NODAT are shown in
Risk factors for the development of NODAT following univariate and multivariate analysis. BMI, body mass index; IGT, impaired glucose tolerance; IFG, impaired fasting glucose; NODAT, new onset diabetes after transplant.
Kaplan-Meier plots of graft survival, death-censored graft survival, and patient survival are shown in
Kaplan-Meier plots of
Univariate and multivariate Cox regression analysis of covariates associated with death post-transplant.
Crude HR (95% CI) | P (Wald’s Test) | Adjusted OR (95%CI) | P (Wald’s Test) | |
---|---|---|---|---|
NODAT |
2.29 (1.21–4.32) | 0.024 | 1.37 (0.69–2.72) | 0.369 |
Age at transplant | 1.05 (1.02–1.08) | <0.001 | 1.03 (1.01–1.06) | 0.034 |
Deceased donor | 2.74 (1.42–5.28) | 0.002 | 1.92 (0.97–3.81) | 0.061 |
Prior vascular disease |
3.94 (2.08–7.46) | <0.001 | 2.65 (1.35–5.28) | 0.006 |
NODAT, new onset diabetes after transplant.
normoglycaemia as reference group.
coronary artery disease, peripheral vascular disease, or cerebrovascular disease).
Five-years graft survival of all KTRs transplanted during the study period with a known post-transplant glycaemic status and 182 KTRs previously excluded because of known pre-transplant DM are shown in
In a large cohort of KTRs managed with contemporary immunosuppression, an OGTT conducted as part of pre-transplant candidate evaluation revealed unrecognised diabetes in 2% and IGT in 28%. Following transplantation, those with IGT incurred a greater than 3-fold higher incidence of NODAT as compared to their normoglycaemic peers. Elevated fasting glucose pre-transplant was not predictive of NODAT, nor did it identify a subset of candidates likely to manifest IGT or DM pre-transplant. These findings highlight the utility of routine pre-transplant OGTT to identify risk of NODAT, and thereby provide opportunities to recognise, discuss and potentially mitigate the negative impacts of NODAT on post-transplant survival. This data lends support to the 2020 KDIGO Guidelines on the management of Candidates for Kidney Transplantation where evaluation with a pre-transplant OGTT has been suggested for this purpose (
A secondary finding of our study was the utility of a protocolised, post-transplant OGTT to diagnose clinically inapparent NODAT and to identify KTRs with IGT. In addition to the 19% of KTRs with clinically apparent NODAT, OGTT detected NODAT in a further 14% yielding a total incidence of 33% in those who underwent thorough assessment. A further 121 KTRs exhibited IGT, thus use of post-transplant OGTT identified clinically unrecognised dysglycaemia in 42% of our cohort. Given the increase in cardiovascular risk associated with NODAT and IGT following kidney transplantation (
We recognise that widespread uptake of OGTTs has been limited by practical and economic constraints. For this reason, its use as a screening tool in transplant assessment has often been restricted to those with identified risk factors, such as a prior elevated FPG level (
The prevalence of pre-transplant dysglycaemia in our cohort is concordant with previously reported rates of IGT amongst kidney transplant candidates (
NODAT occurs commonly in KTRs although the reported incidence varies according to the diagnostic criteria employed, timing post-transplant, and the type of immunosuppression used. At month three post-transplant the incidence of recorded NODAT in our cohort was 24%, consistent with previous studies where protocolised OGTTs have been performed (
In this study, 41% of NODAT cases were not identified by routine surveillance of blood glucose levels and were only diagnosed by the use of a screening OGTT. We found FPG to not only lack sufficient sensitivity to identify patients with NODAT, but to poorly predict KTRs who would return an abnormal OGTT. Importantly, as the diagnosis of IGT in KTRs is clinically significant (
We confirmed well-known risk factors for NODAT such as increasing age and bring attention to the impact of smoking (
The development of NODAT is associated with an increased risk of adverse events, particularly cardiovascular morbidity and mortality (
This study presents the strongest evidence to date in support of the use of OGTTs to identify KTRs with or at risk of NODAT. However, there are certain limitations to our study. Firstly, we evaluated a predominantly Caucasian population, and caution should therefore be applied when extrapolating to other ethnicities. Secondly, the post-transplant glycaemic status could not be adequately ascertained for some patients. Whilst these patients did not have clinical NODAT, we cannot exclude the presence of occult dysglycaemia that would have been detected by an OGTT. Additionally, we were not able to report on the presence of some factors known to contribute to development of NODAT, such as hyperlipidaemia and a family history of diabetes. However, whilst these factors are no doubt important considerations in the assessment of risk, their absence does not detract from the utility presented by an OGTT.
Our findings, whilst supporting those of Caillard’s data from the cyclosporine era (
Deindetified data pertaining to this study will be made available to investigators upon reasonable request and submission of a research plan of sufficient scientific merit. Requests to access the datasets should be directed to
The studies involving human participants were reviewed and approved by Human Research Ethics Committee Royal Prince Alfred Hospital Sydney Local Health District. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.
JS participated in the research design, data analysis and writing of the paper. LA participated in research design and data analysis. KW and DG participated in research design. TY participated in research design and data analysis, and SC participated in the study conception, design, analysis, and writing of the paper. All authors read and approved the final manuscript.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
The authors acknowledge the technical assistance provided by the Sydney Informatics Hub, a Core Research Facility of the University of Sydney. Data is in this manuscript was supplied by the Australia and New Zealand Dialysis and Transplant Registry (ANZDATA). The interpretation and reporting of these data are the responsibility of the authors and in no way should be seen as an official policy or interpretation of the Australia and New Zealand Dialysis and Transplant Registry.
The Supplementary Material for this article can be found online at:
2hPG, 2-h plasma glucose; ANZDATA, Australia and New Zealand Dialysis and Transplant Registry; BMI, body mass index; CNI, calcineurin inhibitor; eGFR, estimated glomerular filtration rate; ESKD, end-stage kidney disease; FPG, fasting plasma glucose; IFG, impaired fasting glucose; IGT, impaired glucose tolerance; KTRs, kidney transplant recipients; mTORi, mammalian target of rapamycin inhibitor; NODAT, new onset diabetes after transplant; OGTT, oral glucose tolerance test.