Association of Pretransplant Glycemic Control With Posttransplant Outcomes in Diabetic Kidney Transplant Recipients

We linked data on all kidney transplant recipients listed in the Scientific Registry of Transplant Recipients (SRTR) up to June 2007 to a list of individuals with chronic kidney disease (CKD) who underwent maintenance hemodialysis or peritoneal dialysis treatment from July 2001 to June 2006 in one of the outpatient dialysis facilities of a U.S.-based large dialysis organization (DaVita, Inc., prior to its acquisition of former Gambro dialysis facilities) using patients’ social security numbers. The study was approved by the Institutional Review Boards of both Los Angeles Biomedical Research Institute at Harbor-UCLA and DaVita Clinical Research. Inclusion criteria were patients who had been undergoing dialysis for at least 90 days, had a history of diabetes, and had at least one HbA_1c measurement in the first quarter of entry into the cohort.

The creation of the national DaVita dialysis patient cohort has been described previously (14,15). To minimize measurement variability, all repeated measures for each patient during any given calendar quarter, i.e., over a 13-week interval, were averaged, and the summary estimate was used in all models. Average values were obtained from up to 20 calendar quarters (q1–q20) for each laboratory and clinical measure for each patient for up to 6 years of follow-up. The first (baseline) studied quarter for each patient was the calendar quarter in which the patient’s dialysis vintage was >90 days. Demographic data and details of medical history were collected, with information on age, sex, race, type of insurance, marital status, height, posthemodialysis dry weight (to calculate averaged BMI), and dialysis vintage. Dialysis vintage was defined as the duration of time between the first day of dialysis treatment and the day of kidney transplantation.

All HbA_1c values were measured using the Roche Cobar Integra 800 whole-blood immune-turbidimetric assay (standardized according to the Diabetes Complications Control Trial/National Glycohemoglobin Standardization Program) performed by a single laboratory (DaVita Laboratory, Deland, FL). Most laboratory values were measured monthly, including serum urea, creatinine, albumin, calcium, phosphorus, bicarbonate, and total iron binding capacity. Serum ferritin and intact parathyroid hormone were measured at least quarterly. Hemoglobin was measured at least monthly in essentially all patients and weekly to biweekly in most patients. Most blood samples were collected predialysis, with the exception of postdialysis serum urea nitrogen to calculate urea kinetics. HbA_1c was usually measured quarterly or semiannually. We divided patients into seven a priori–defined categories based on HbA_1c values: <5%, ≥10%, and 1% increments in between, to examine the dose-response association between HbA_1c categories and outcome risk.

Data were summarized using proportions, means (±SD) or medians, and interquartile ranges as appropriate. We examined P values for trends across HbA_1c categories. For all-cause and cardiovascular mortality and graft failure, defined as reinitiation of dialysis treatment or retransplantation, time to event was used in all survival analyses. For DGF, defined as the need for any dialysis therapy in the first week after transplantation, time to event was not accounted for. Survival analyses to calculate hazard ratio (HR) and 95% CI of death or graft failure used Cox proportional hazards regression. In the mortality analyses, the patients were followed until event (death) or censoring (graft failure or end of follow-up period), whichever happened first. Our uncensored, all-cause mortality analysis patients were followed until event (death) or censoring for end of follow-up period, whichever happened first. In the graft failure analyses, the patients were followed until event (graft failure) or censoring (death or end of follow-up period), whichever happened first. In the combined outcome analyses, patients were followed until event (death or graft failure) or censoring (end of follow-up period), whichever happened first. Logistic regression models were used to estimate the odds ratio and 95% CI of posttransplant DGF.

For each regression analysis, four levels of multivariate adjustment were examined: I) an unadjusted model that included HbA_1c categories (reference, HbA_1c 6 to <7%) as the predictor; II) case mix–adjusted models that included the above plus age, sex, recipient race/ethnicity (African Americans and other self-categorized blacks, non-Hispanic whites, Asians, Hispanics, and others), dialysis vintage (<6 months, 6 months to 2 years, 2 to <5 years, and ≥5 years), primary insurance (Medicare, Medicaid, private, and others), marital status (married, single, divorced, widowed, and other or unknown), standardized mortality ratio of the dialysis clinic during entry quarter, dialysis dose as indicated by Kt/V (single pool), presence or absence of a hemodialysis catheter, residual renal function during the entry quarter and eight comorbidities (atherosclerotic heart disease, congestive heart failure, cancer, chronic obstructive pulmonary disease, cerebrovascular disease, hypertension, peripheral vascular disease, and tobacco use); III) malnutrition inflammation complex syndrome (MICS)–adjusted models, which included all of the above covariates plus 11 surrogates of nutritional status and inflammation measured during the last calendar quarter before transplantation, including BMI and 10 laboratory variables, i.e., normalized protein catabolic rate as an indicator of daily protein intake, also known as the normalized protein nitrogen appearance (nPNA), and serum or blood concentrations of total iron binding capacity, ferritin, phosphorus, calcium, bicarbonate, peripheral white blood cell count (WBC), lymphocyte percentage, albumin and hemoglobin; and IV) case mix–, MICS-, and transplant data–adjusted models included all of the above plus seven transplant-related variables: (1) donor type (deceased or living), (2) donor age, (3) donor sex, (4) panel-reactive antibody titer (last value prior to transplant), (5) number of HLA mismatches, (6) cold ischemia time, and (7) DGF (except when DGF was a dependent variable in our logistic regression models). Nonlinear association between pretransplant, time-averaged HbA_1c and posttransplant outcomes was assessed using fractional polynomials and restricted cubic splines. Based on goodness of fit of these models, we used a model with two degrees of freedom (knot in all models was 6.59). All analyses were carried out with SAS version 9.1 (SAS Institute, Inc., Cary, NC) and STATA version 11.1 (STATA Corporation, College Station, TX).

The original 5-year (July 2001– June 2006) national database of all DaVita patients included 164,789 adult subjects. Out of 65,386 DaVita patients who were identified in the SRTR database, 17,629 had undergone one or more kidney transplantations during their lifetime, of which 14,508 dialysis patients had undergone kidney transplantation for the first time. From these 14,508 dialyzed patients, we excluded patients without diabetes (n = 9,482) and patients who did not have HbA_1c measured (n = 2,154). We examined the remaining 2,872 patients who underwent first kidney transplantation during the observation period and who were followed until death, graft failure, loss of follow-up, or survival until 30 June 2007 (Supplementary Fig. 1). There were 331 deaths (11.5%) and 191 graft failures (6.7%) irrespective of subsequent deaths. The median follow-up time was 736 days (interquartile range, 353–1,216 days).

Table 1 shows the clinical, demographic and laboratory data of the 2,872 transplanted patients across seven categories of HbA_1c. Patients with higher HbA_1c were more likely to be women and to be younger, and to have lower dialysis duration, lower serum creatinine level, and higher WBC level. The crude rates of mortality, graft failure, and DGF were not different between the groups (Table 2).

Supplementary Table 1 shows the calculated HRs of all-cause and cardiovascular death and/or graft failure for different HbA_1c categories. Case mix–, MICS-, and transplant data–adjusted graft failure censored all-cause death HRs and 95% CIs for time-averaged pretransplant HbA_1c categories of 7 to <8%, 8 to <9%, 9 to 10%, and ≥10%, compared with 6 to <7% (reference), were 0.89 (0.59–1.36), 2.06 (1.31–3.24), 1.41 (0.73–2.74), and 3.43 (1.56–7.56), respectively. Fig. 1A shows cubic spline models for the association of the entire range of pretransplant HbA_1c with posttransplant mortality consistent with the findings in Supplementary Table 1. Similar trends were found for cardiovascular death. Fully adjusted cardiovascular death HRs and 95% CIs for time-averaged pretransplant HbA_1c categories of 7 to <8%, 8 to <9%, 9 to 10%, and ≥10%, compared with 6 to <7% (reference), were 0.38 (0.13–1.05), 1.78 (0.69–4.55), 1.59 (0.44–5.76), and 4.28 (0.85–21.64), respectively. Similar results were found when we adjusted for variables in fully adjusted model plus erythropoiesis-stimulating agent dose as sensitivity analysis (data not shown). Fig. 1B shows cubic spline models for the association of the entire range of pretransplant HbA_1c with posttransplant uncensored, all-cause mortality that were consistent with the findings in Supplementary Table 1. Fig. 1C shows cubic spline models for the association of the entire range of pretransplant HbA_1c with posttransplant cardiovascular mortality that were consistent with the findings in Supplementary Table 1. Fig. 1D shows cubic spline models for the association of the entire range of pretransplant HbA_1c with posttransplant graft failure, again consistent with the findings in Supplementary Table 1. We did not find any difference in the risk of all-cause death-censored graft failure with different pretransplant HbA_1c levels.

To examine the association of pretransplant HbA_1c levels with posttransplant DGF, multivariate logistic regression analyses were performed using the same covariates as in the Cox models. Compared with HbA_1c levels between 6 and <7%, patients with HbA_1c levels <5%, 5 to <6%, 7 to <8%, 8 to <9%, 9 to 10%, and ≥10% had similar risks of DGF (Supplementary Table 1).

In 2,872 kidney transplant recipients with comprehensive pretransplant data during hemodialysis treatment who were followed for up to 6 years posttransplantation, poor glycemic control appears to be associated with higher all-cause and cardiovascular mortality. Pretransplant HbA_1c level was not a predictor of either posttransplant graft failure or DGF. These findings may have important clinical implications, especially since they imply mild hyperglycemia during the dialysis period may not be a risk factor for negative posttransplant short-term and long-term outcomes.

It is currently not known what the ideal level of glycemic control is in dialysis patients (16,17). The literature on the relationship between glycemic control and survival in CKD populations is somewhat limited. In a cohort of 840 nondiabetic patients with moderate CKD who participated in the Modification of Diet in Renal Disease trial, HbA_1c was a predictor of all-cause mortality (18). However, a recent study was unable to demonstrate any association between HbA_1c and 1-year survival (7). This contrasts with several other observational studies. Wu et al. (19) studied 137 hemodialysis patients with type 2 diabetes and reported that the cumulative survival was lower in the group with poor glycemic control (13). In kidney transplant recipients, pretransplant diabetes, maximal glucose levels, and insulin treatment were independently associated with higher rates of mortality (1). However, until now, it has been unclear if poor glycemic control during the dialysis period affected posttransplant outcomes.

Based on our analyses, pretransplant, time-averaged HbA_1c levels ≥8% were associated with increased all-cause and cardiovascular mortality. There are several possible mechanisms that might explain the relationship between poor glycemic control during the dialysis period and worse posttransplant survival of kidney transplant recipients. Diabetes after transplantation is a predictor of mortality in some (20), but not all, studies (1). It is also associated with different risk factors of mortality, such as higher infection rates or other diabetes complications (21). Poor glycemic control might induce macrovascular complications, possibly secondary to the generation of advanced glycation end products, and hence shorten the survival of these patients. Moreover, in renal transplant recipients, high HbA_1c is associated with chronic inflammation (22), and the latter is a predictor of mortality and graft failure (23).

In our contemporary study, the pretransplant HbA_1c level was not a predictor of posttransplant graft failure or DGF. Similar results were found in studies conducted in kidney transplant recipients that examined new-onset diabetes after transplantation, which was not a predictor of graft loss in some studies (24). A potential explanation for the lack of association between poor pretransplant and posttransplant glycemic control and increased risk of allograft loss is the relatively short follow-up time. Poor glycemic control is associated with micro- and macrovascular complication, but the development of these complications in the allografts may take >6 years. It is possible that studies with longer follow-up time may find significant associations between poor glycemic control and graft loss.

Our study should be qualified for several potential limitations. Like all observational studies, ours too cannot prove causality. Repeated posttransplant measures of HbA_1c or other laboratory variables and immunosuppressive and other medical regimens were not available in the SRTR database, but in the full model, we did adjust for a number of transplant-related variables. A potential limitation of our study is that HbA_1c is not a good reflection of blood glucose levels in individuals with advanced chronic kidney disease. It may be difficult to accurately assess glycemic control in this population because of changes in erythrocyte survival in renal failure and the effects of erythropoiesis-stimulating agents on HbA_1c levels (25). We did not have access to data pertaining to death after graft loss, which is another important outcome. Patients who did not have measured HbA_1c were excluded from the analyses. The excluded patients may have been different from those included in our study, which may have biased our results. We have tested this hypothesis and the only clinically significant difference was in the number of graft failures (Supplementary Table 2). The proportion of graft failures was more than twice higher in excluded patients than the group included. It is possible that the lower HbA_1c levels in the included patients were the reason why we did not detect any association between high HbA_1c level and graft failure. To our knowledge this is the first study examining the association between pretransplant HbA_1c levels and posttransplant short- and long-term outcomes. Strengths of this study include the high number of patients, the relatively long follow-up time, and multilevel adjustment, which includes several important pretransplant measures.

In our large and contemporary national database of 2,872 kidney transplant recipients, a pretransplant, time-averaged HbA_1c ≥8% appears to be associated with higher all-cause and cardiovascular mortality. Pretransplant HbA_1c levels were not predictive of posttransplant graft failure or DGF. Clinical trials are needed to better define optimal target HbA_1c levels in dialysis patients.

This study was supported by a research grant from the National Institute of Diabetes and Digestive and Kidney Diseases of the National Institutes of Health (R01-DK-078106), a research grant from DaVita Clinical Research, and a philanthropic grant from Mr. Harold Simmons. M.Z.M. received grants from the National Developmental Agency (KTIA-OTKA-EU 7KP-HUMAN-MB08-A-81231) and the Research and Technological Innovation Fund. M.Z.M. was also supported by the Hungarian Kidney Foundation.

M.K. and A.R.N. are employees of DaVita, Inc. K.K.-Z. is the medical director of DaVita Harbor-UCLA/MFI (Long Beach, CA). No other potential conflicts of interest relevant to this article were reported.

M.Z.M. contributed to analyzing and interpreting data and writing the manuscript. E.H., J.H., M.K., and A.R.N. contributed to analyzing and interpretation of data. C.P.K. contributed to analyzing and interpretation of data and writing the manuscript. K.K.-Z. designed, organized, and coordinated the study; managed data entry; contributed to data analysis and interpretation of data; and wrote the manuscript.

Parts of this study were presented in abstract form at the American Society of Nephrology Renal Week, Philadelphia, Pennsylvania, 8–13 November 2011.

The authors express their sincere appreciation to the teammates in nearly 1,600 DaVita clinics who work every day not only to take care of patients but also to ensure the extensive data collection on which their work is based. The authors thank DaVita Clinical Research for providing the clinical data, analysis, and review for this research project and for advancing the knowledge and practice of kidney care.