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Kidney Transplantation

Autosomal Dominant Polycystic Kidney Disease Is a Risk Factor for Posttransplantation Diabetes Mellitus: An Updated Systematic Review and Meta-analysis

Culliford, Alice MBChB1; Phagura, Nuvreen2; Sharif, Adnan MD1,3

Author Information
doi: 10.1097/TXD.0000000000000989


Autosomal dominant polycystic kidney disease (ADPKD) is the most common hereditary kidney disorder1 and the fourth leading cause of end-stage kidney disease (ESKD) across Europe.2 As per other individuals living with ESKD, kidney transplantation should be considered the renal replacement therapy of choice. Although ADPKD individuals with ESKD require special consideration as potential kidney transplant candidates, including assessment for native nephrectomy, cystic liver involvement, and/or screening for intracranial aneurysms, long-term patient and graft survival is equivalent for kidney transplant recipients with ADPKD compared with those with other causes of ESKD.3

However, metabolic disturbances have been associated with ADPKD4 and one of the risks identified for ADPKD individuals is an increased susceptibility for developing posttransplantation diabetes mellitus (PTDM). PTDM is a common medical complication after kidney transplantation and associated with increased risk for cardiovascular disease and all-cause mortality.5 International PTDM Consensus guidelines recommend identifying kidney transplant candidates at increased risk for PTDM and advocate preventative measures to attenuate risk for PTDM.6 However, published reports are inconsistent with regard to whether ADPKD is a risk factor for PTDM or not.7–20 In a systematic review and meta-analysis of 12 published cohort studies, the relative risk for development of PTDM was 1.92 (95% confidence interval, 1.36-2.70).21 However, reported studies that were included in this meta-analysis had significant heterogeneity and many used obsolete PTDM diagnostic criteria that are inconsistent with contemporary guidance. Therefore, the question as to whether ADPKD is a risk factor for development of PTDM after kidney transplantation remains unresolved. To investigate this risk further, the aim of this study was 2-fold: (1) to use contemporary diagnostic criteria to determine the incidence of PTDM in a large, single-center retrospective analysis of kidney transplant recipients stratified by ADPKD status and (2) to perform an updated systematic review and meta-analysis of cohort studies reporting PTDM incidence by ADPKD status.


Study Population

We performed a retrospective cohort study and analyzed all kidney transplant procedures between January 1, 2007, and June 30, 2018, at a single transplant center. We excluded recipients of multiple organ transplants and those with pre-existing diabetes at the time of kidney transplantation.

Data Sources

Local data were electronically extracted by the hospital informatics team for every patient, with manual data linkage to electronic patient records for diagnosis of PTDM. Acute rejection, 1-y creatinine, and patient and graft survival data were acquired and linked from National Health Service Blood and Transplant. Hospitalization data were acquired from Hospital Episode Statistics, an administrative data warehouse containing admissions to all National Health Service hospitals in England. It contains detailed records relating to individual patient treatments; with data extraction facilitated using codes on procedural classifications (Office of Population Censuses and Surveys Classification of Interventions and Procedures, 4th Revision) and medical classifications (World Health Organization International Classification of Disease, 10th Revision).

Diagnostic Criteria for PTDM

PTDM was diagnosed in accordance with International PTDM Consensus guidelines.6 In summary, PTDM was officially diagnosed if any of the following were recorded after 6 wk posttransplantation: (1) symptoms of diabetes plus random plasma glucose ≥200 mg/dL (11.1 mmol/L); (2) fasting plasma glucose ≥126 mg/dL (7.0 mmol/L); or (3) glycated hemoglobin (HbA1c) ≥6.5%. Either fasting or random glucose was tested at each clinic visit, with HbA1c performed on a quarterly basis from 3 mo after kidney transplantation. Patients started on antidiabetic therapy before 6 wk posttransplantation who were still on treatment at 6 wk were also classed as PTDM.

Immunosuppression Protocol

All patients received the same immunosuppression over the study period, with minimization of tacrolimus exposure in line with the Efficacy Limiting Toxicity Elimination–Symphony protocol.22 Induction therapy was with basiliximab (20 mg on days 0 and 4) and methylprednisolone (500 mg on day 0). Maintenance therapy included tacrolimus (target 12-h trough level 5–8 ng/L), mycophenolate mofetil (2 g daily with tapering to 1 g daily after 6 mo), and maintenance corticosteroids (20 mg daily weaned down to 5 mg daily by 3 mo). Biopsies were indication based in the context of transplant dysfunction (categorized as ≥20% creatinine rise or new-onset proteinuria). Biopsy data were classified in accordance to latest Banff criteria.23

Episodes of acute cellular rejection were treated with a bolus of corticosteroids, with T-cell depletion therapy for steroid-resistant rejection. Antibody-mediated rejection was treated with antibody removal by plasmapheresis ± intravenous immunoglobulin. Viral serology (eg, polyomavirus) and/or anti-HLA antibodies were checked by indication basis based on transplant dysfunction.

Definitions of Variables

Baseline and posttransplant data were extracted and classified from our database as follows. HLA mismatch levels were defined and graded in accordance to National Health Service Blood and Transplant classification used during this study period: level 1 (HLA mismatch 0), level 2 (HLA mismatch 0 doctor [DR] and 0/1 B), level 3 (HLA mismatch 0 DR and 2B, or 1 DR and 0/1 B), and level 4 (1 DR and 2B, or 2 DR). Matchability was calculated from a standardized pool of 10 000 recent donors, from which the number of blood group identical donors that recipients are well or favorably HLA-mismatched were counted. This number was converted to a standardized score between 1 and 10, which was used to categorize recipients into 1 of 3 matchability groups: easy (1–3), moderate (4–6), or difficult (7–10) to match. Determination of socioeconomic deprivation was based on the Index of Multiple Deprivation, a model calculated from multiple domains reflective of area socioeconomic deprivation, with 1 the most deprived to 5 the least deprived.


The primary outcome measure was development of PTDM after kidney transplantation. In addition, we looked at various secondary outcome measures including postoperative admission length of stay; rehospitalization episode within 90 d; any admission secondary to a cardiac, stroke, cancer, or infection-related episode; graft function (by estimated glomerular filtration rate in milliliters per minute); and patient or graft survival. Follow-up commenced at the time of transplant, with patients censored at the earliest of their final recorded follow-up or the end of follow-up for the study (October 13, 2018). For analysis of graft survival, patients who died with a working graft were censored at death.

Statistical Analysis

Initially, a range of demographic and transplant characteristics was compared between the follow-up groups. Normality of data was assessed using the Kolmogorov–Smirnov tests. Descriptive statistics were used to estimate the frequencies. Categorical variables are presented as number (%) and continuous variables as mean (± SD) or median (± interquartile range) dependent upon normality of distribution. Difference between groups was assessed with χ2 or 2-sided Fisher exact test for categorical variables and t test or Mann-Whitney U test to compare continuous variables. A P value of <0.05 and 0.001 in the statistical analysis was considered significant and highly significant, respectively. All analyses were performed using IBM SPSS 25 (IBM Corp., Armonk, NY).

Systematic Review and Meta-analysis

Two investigators (A.C., A.S.) independently searched published studies indexed in MEDLINE, EMBASE, and the Cochrane database from inception through September 2019 using a similar search strategy as utilized by Cheungpasitporn et al21 (described in Supplemental Appendix 1, In addition, a manual search was performed for additional relevant studies using references from all retrieved articles. The primary data extracted from each study included all PTDM event rates and total numbers for each patient cohort stratified by available ADPKD status. The inclusion criteria for studies were as follows: (1) randomized clinical trials or observational studies (case-control, cross-sectional, or cohort studies) published as original studies that evaluated the risk for PTDM in kidney transplant recipients with ADPKD; (2) studies that included extractable data for PTDM event rates and total at-risk numbers; and (3) a reference group composed of kidney transplant recipients who did not have ADPKD. No restrictions were applied for language. This study is reported in accordance with the Meta-Analyses and Systematic Reviews of Observational Studies checklist for reporting meta-analyses of observational studies (Supplemental Appendix 2,

Study eligibility was independently determined by the 2 investigators noted above, with any discrepancy resolved by mutual consensus. Review Manager 5.3 software (Cochrane Collaboration, London, United Kingdom) was used for data analysis. We first conducted a meta-analysis using all eligible studies identified in our systematic review. We subsequently performed an additional meta-analysis after limiting studies to those where diagnosis of PTDM was consistent with contemporary diagnostic classification.6 Given the high likelihood of interstudy variances, we used a random-effect model rather than a fixed-effect model to determine effect size by odds ratio (OR). Statistical heterogeneity was assessed using the Cochran Q test. This statistic was complemented by the I2 statistic, which quantifies the proportion of the total variation across studies due to heterogeneity rather than chance. A value of I2 of 0%–25% represents insignificant heterogeneity, 26%–50% low heterogeneity, 51%–75% moderate heterogeneity, and >75% high heterogeneity.


This study received institutional review board approval and was registered as an audit (audit identifier: CARMS-12578). The corresponding author had full access to all data. The data that support the findings of this study are available from the corresponding author upon reasonable request.


Study Cohort

In the study time period, a total of 1770 kidney transplant procedures were performed, of which 210 occurred in recipients with a known diagnosis of diabetes at the time of transplant surgery (including only 13 ADPKD patients) and were excluded from any further analysis. This left a final study cohort of 1560 nondiabetic kidney transplant recipients. From this cohort, 248 (15.9%) kidney transplant recipients had a diagnosis of ADPKD versus 1312 non-ADPKD recipients. Median follow-up for the study cohort was 4.7 y (interquartile range, 2.3–7.5 y). Table 1 outlines baseline characteristics comparing ADPKD versus non-ADPKD kidney transplant recipients.

Baseline demographics of kidney transplant recipients stratified by ADPKD status

Risk for PTDM and Other Posttransplant Outcomes

Comparing ADPKD versus non-ADPKD kidney transplant recipients, the risk for PTDM was not found to be significantly different (19.4% versus 14.9%, respectively; P = 0.085). We did not identify any significant difference in any other clinical outcome after kidney transplantation when comparing ADPKD versus non-ADPKD kidney transplant recipients (Table 2).

Clinical outcomes of kidney transplant recipients stratified by ADPKD status

Subgroup Analysis of ADPKD Patients Developing PTDM

Comparing ADPKD patients who developed PTDM versus ADPKD patients who did not, we identified the former at baseline to be older (53.9 versus 48.4 y, respectively; P = 0.003), borderline heavier body mass index (27.7 versus 26.5, respectively; P = 0.088), and less likely to be recipients of a living donor kidney (22.2% versus 43.1%, respectively; P = 0.011). There was no difference in other baseline variables.

We did observe a trend toward more rejection episodes within the first year after kidney transplantation in ADPKD patients who subsequently developed PTDM compared with those without any rejection (14.6% versus 5.2%, respectively; P = 0.081). There was no difference in weight gain over the first 6 or 12 mo for ADPKD patients who did or did not develop PTDM.

Systematic Review and Meta-analysis of Published Studies

Figure 1 highlights our Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow diagram outlining the systematic review and selection of studies for meta-analysis. Table 3 summarizes the main features of the 14 studies selected for meta-analysis after the systematic review. Only 2 new studies were available because the previous meta-analysis of 12 studies,21 which undertook quality assessment using the Newcastle-Ottawa scale, and therefore repeat quality assessment was not undertaken. Extracting the primary data for all PTDM event rates and total cohort numbers for patients stratified by ADPKD status, we synthesized the data from these studies with our data. We observed an increased OR of kidney transplant recipients with ADPKD developing PTDM (OR, 1.98; 95% confidence interval, 1.43-2.75) (see Forest plot in Figure 2) when all 14 studies were combined with our data. We then repeated the meta-analysis after restricting selected studies to those that diagnosed PTDM in accordance with contemporary diagnostic criteria aligned with international recommendations.6 After pooling our data to these selected 8 studies, we still observed an increased OR of kidney transplant recipients with ADPKD developing PTDM (OR, 1.81; 95% confidence interval, 1.16-2.83) (see Forest plot in Figure 3). Significant heterogeneity was observed in both Forest plots.

Main characteristics of included studies
Preferred Reporting Items for Systematic Reviews and Meta-Analyses flow chart of study inclusion and exclusion.
Forest plot of odds ratio for posttransplantation diabetes mellitus (PTDM) comparing autosomal dominant polycystic kidney disease (ADPKD) vs non-ADPKD kidney transplant recipients (all studies). CI, confidence interval.
Forest plot of odds ratio for posttransplantation diabetes mellitus (PTDM) comparing autosomal dominant polycystic kidney disease (ADPKD) vs non-ADPKD kidney transplant recipients (restricted studies). CI, confidence interval.


Previous work has reported heterogenous findings with regard to whether ADPKD is a risk factor for the development of PTDM. Although a positive association was identified after previous meta-analysis of published studies,21 the combined empirical data were methodologically flawed as the diagnosis of PTDM in many studies was not compatible with International Consensus Recommendations. Our retrospective analysis of single-center data, using recommended PTDM diagnostic criteria, did not identify a statistically significant difference in the incidence of PTDM comparing kidney transplant recipients with versus without ADPKD. No difference was observed in any other clinical outcomes between ADPKD and non-ADPKD kidney transplant recipients in agreement with previous studies. However, after pooling our results with existing empirical data extracted from published cohort studies, our meta-analysis did identify increased odds for kidney transplant recipients with ADPKD developing PTDM. This remained consistent regardless of whether the diagnosis of PTDM was heterogenous or robustly defined according to recommended classification.

It has been suggested that individuals with ADPKD have significant underlying insulin resistance25 or impaired insulin secretion,26,27 both of which are considered key pathophysiologic defects in the development of PTDM.5 Although multiple genes are indicated in the pathogenesis of ADPKD, the commonest mutations are in the genes PKD1 and PKD2 (encoding polycystin-1 and polycystin-2, respectively).1 Polycystin-1 and polycystin-2 are located on the primary cilia (an apical antenna-like organelle with an important role in mechanotransduction), where they are believed to transmit information from the external environment internally to the cell.1 Both polycystin molecules are expressed in organs that are integral to maintenance of glucose homeostasis outside of the kidney (eg, pancreas, liver, skeletal muscle)28 and could be implicated in glycemic dysregulation. Mutation of PKD1 has been shown to enhance glycolysis in cells in a mouse model of ADPKD and in kidneys from humans with ADPKD.29 The work from Rowe et al29 demonstrated a switch in glucose utilization to higher lactate production in murine PKD1−/− renal cells and supports a role for ciliary components in cellular glucose metabolism. In fact, as many as two-thirds of the type 2 diabetes susceptibility genes have documented entries in the ciliary proteome database.30 Therefore, a direct pathophysiologic link between ciliary dysfunction and susceptibility to PTDM could be postulated and requires further mechanistic investigation.

Paradoxically, there is some suggestion of reduced risk for diabetes among ADPKD individuals outside the setting of kidney transplantation but is limited to single-center experience. Pietrzak-Nowacka et al31 observed a lower prevalence of transplant-unrelated diabetes after analyzing 291 questionnaire respondents from the Polish Registry of ADPKD patients (prevalent OR, 0.18; 95% confidence interval, 0.07-0.47; P < 0.001). However, this contradicts work from the same group showing higher prevalence of metabolic syndrome31 and impaired β-cell function26 in ADPKD individuals with normal renal function. It could be hypothesized that polycystic kidneys have suppressed gluconeogenesis and observed that hyperinsulinemia is due to slower insulin breakdown in the kidneys with the context of reduced renal function. More studies are clearly required to investigate any underlying metabolic susceptibility to diabetes for ADPKD individuals, especially considering increased mortality is observed for patients with both ADPKD and type 2 diabetes in the context of moderate chronic kidney disease.32 However, existing literature that has been published remains limited, heterogenous, and conflicting, which prohibits any firm conclusions.

This is an important issue to investigate because identifying kidney transplant candidates at increased odds for developing PTDM is aligned with internal consensus recommendations.6 PTDM is a leading concern for kidney transplant recipients33 and is associated with adverse posttransplant complications.34 Risk awareness is important for patient counseling and can help reinforce the importance of lifestyle modification, which has recently been shown to reduce the risk of some adverse clinical metabolic outcomes.35 Kidney transplant recipients with ADPKD could be considered for adapted immunosuppression regimens to prevent the risk of PTDM, if deemed to be low-immunologic risk, but this strategy needs to personalized based on individualized risk. For example, meta-analyses of randomized controlled trials show steroid withdrawal and/or avoidance reduces the risk for PTDM but increases the risk for rejection,36 although risk for rejection may be attenuated with tacrolimus-based immunosuppression.37 However, the largest clinical trial of steroid maintenance versus withdrawal did not show any difference in PTDM incidence after 5 y post-kidney transplantation.38 Torres et al39 have also shown tacrolimus, mycophenolate mofetil, and steroid maintenance to be the optimal immunosuppression for achieving a PTDM/rejection balance in metabolically high-risk kidney transplant candidates. Although mammalian target of rapamycin inhibitors (eg, sirolimus and everolimus) were briefly considered beneficial at reducing cyst volume for ADPKD individuals,40 subsequent randomized controlled trials have shown it to be ineffective and/or harmful.41–43 Additionally, in a kidney transplantation setting, the use of the mammalian target of rapamycin inhibitor sirolimus has been associated with increased risk for PTDM44 and non–cancer-related mortality,45 thereby limiting its potential utility posttransplantation.46 Finally, belatacept has limited data in the setting of kidney transplantation for recipients with ADPKD and its link to development of PTDM in clinical trials is unclear.47 Therefore, in line with PTDM Consensus recommendations,6 it is important even for ADPKD patients that posttransplant immunosuppression is tailored with long-term patient and graft survival as the primary focus, with secondary focus on minimizing potential side effects and/or immunosuppression-related complications. Although strength of our analysis was the same immunosuppression protocol for all kidney transplant recipients, this means we are unable to ascertain the impact of different immunosuppressants on risk for PTDM in our cohort.

It is important to appreciate the other limitations of this analysis for correct interpretation of the data. Our data are likely to underestimate the incidence of PTDM because some kidney transplant recipients are repatriated back to their referral hospitals within 3 mo posttransplant. In contrast, it is possible that some recipients with preexisting diabetes before kidney transplantation were not appropriately identified and, therefore, led to inclusion in our analyzed cohort as an overestimate. Missing data are an inherent bias in epidemiologic analyses such as this, although long-term hospitalization and survival data are complete for the entire cohort because of record linkage with national data resources. There are confounders that have an impact on PTDM kidney transplantation that we are unable to appreciate (eg, lifestyle factors, family history of diabetes) and the lack of patient-level data precludes any detailed assessment of potential confounders which could impact upon risk for PTDM in our meta-analysis. Our single-center cohort is underpowered to determine true difference in PTDM incidence and justified our methodologic approach to pool data with other cohort studies. However, we are still not able to ascertain with clarity whether the observed PTDM risk for ADPKD kidney transplant recipients is independent of competing risk factors. We lacked mechanistic insight into this association, and further investigation exploring metabolic physiology parameters (eg, insulin sensitivity and insulin secretion) would be of interest. Finally, our retrospective study was of short duration to robustly assess difference in survival outcomes and further maturing of this database in the long term should provide more definitive answers.

To conclude, our retrospective, single-center study did not identify an association between ADPKD and PTDM, but after pooling our data with published cohort studies, we demonstrated increased odds for developing PTDM among kidney transplant recipients with ADPKD. We believe that these data are important because ADPKD is a common indication for kidney transplantation and PTDM is a common medical complication posttransplantation. Confirming ADPKD as a risk factor for PTDM should facilitate targeted patient counseling pretransplantation to encourage lifestyle modification. However, further studies are warranted to investigate the mechanism linking ADPKD and abnormal glucose metabolism, which may shed light on pathophysiologic pathways for both ADPKD and PTDM, respectively. This may allow future risk stratification and tailored immunosuppression for ADPKD kidney transplant candidates to minimize risk for PTDM while still optimizing patient and allograft outcomes.


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