Weil, E. Jennifer

Background Allografts from living donors survive longer than those from deceased donors but the role of HLA mismatching in living kidney donation is still in question. We examined the effect of HLA compatibility on kidney allograft survival from living donors by studying all first adult kidney transplants performed in the United States over 25 years. Methods Using the United Network for Organ Sharing data, we identified first kidney transplants between October 1, 1987, and December 31, 2013. Recipients were classified by their number of HLA mismatches and stratified by donor origin. Cox multivariate regression analyses adjusting for recipient and donor transplant characteristics were performed to determine impact of HLA compatibility on kidney allograft survival for all living donors and for living related and living unrelated subsets. Results There were 66 596 first adult transplants from living donors with 348 960 years of follow-up. We found a linear relationship between HLA mismatch and allograft survival. In adjusted analyses, among all living donors, 1 mismatch conferred a 44% higher risk, whereas 6 mismatches conferred a twofold higher risk of allograft failure. When using 0-mismatched full siblings as a reference, living-donor kidneys reduce the hazard of failure by approximately 34% when compared with deceased donors. Twenty-five years of transplant experience, stratified by donor source, was summarized and presented as a guide for allocation. Conclusions These data reinforce the importance of optimizing HLA matching to further improve survival in first adult kidney allografts in the future, especially in living unrelated donations, when possible.

OBJECTIVE Identification of patients at high risk for new-onset diabetes after kidney transplantation (NODAT) will facilitate clinical trials for its prevention. RESEARCH DESIGN AND METHODS We previously described a pretransplant predictive risk model for NODAT using seven pretransplant variables (age, planned use of maintenance corticosteroids, prescription for gout medicine, BMI, fasting glucose, fasting triglycerides, and family history of diabetes). We have now applied the initial model to a cohort of 474 transplant recipients from another center for validation. We performed two analyses in the validation cohort. The first was a standard model with variables derived from the original study. The second was a summary score model, in which the sum of dichotomized variables (all the variables dichotomized at clinically relevant cut points) was used to categorize, individuals into low (0–1), intermediate (2, 3), or high (4–7) risk groups. We also conducted a combined database analyses, merging the initial and validation cohorts (n = 792) to obtain better estimates for a prediction equation. RESULTS Although the frequency of several risk factors differed significantly between the two cohorts, the models performed similarly in each cohort. Using the summary score model, incidences of NODAT in low-risk, medium-risk, and high-risk groups in the initial cohort were 12, 29, and 56%, and in the validation cohort incidences were 11, 29, and 51%. CONCLUSIONS A pretransplant model for NODAT, including many type 2 diabetes risk factors, predicted NODAT in the validation cohort.

Because the negative consequences of new-onset diabetes mellitus after transplantation (NODAT) diminish the significant gains of kidney transplantation, it is imperative to develop clinical interventions to reduce the incidence of NODAT. In this review, we discuss whether intensive lifestyle interventions that delay or prevent type 2 diabetes mellitus may decrease the incidence of NODAT. We examine the literature pertaining to incidence and timing of onset of NODAT, as well as the risk factors and pathophysiology that NODAT shares with type 2 diabetes mellitus, namely pathways related to increased insulin resistance and decreased insulin secretion. Our central hypothesis is that NODAT results from the same metabolic risk factors that underlie type 2 diabetes mellitus. These risk factors are altered and enhanced by transplantation, “tipping” some transplant recipients with seemingly normal glucose homeostasis before transplant toward the development of NODAT. We describe the diabetogenic properties of transplant immunosuppressive drugs. We describe novel methods of prevention that are being explored, including resting the pancreatic β-cells by administration of basal insulin during the period immediately after transplant. On the basis of the current evidence, we propose that intensive lifestyle modification, adapted for individuals with chronic kidney disease or end-stage renal disease, as well as resting pancreatic β-cells during the immediate postoperative period, may lower the incidence of NODAT.

OBJECTIVE New-onset diabetes after kidney transplantation (NODAT) has adverse clinical and economic implications. A risk score for NODAT could help identify research subjects for intervention studies. RESEARCH DESIGN AND METHODS We conducted a single-center retrospective cohort study using pretransplant clinical and laboratory measurements to construct a risk score for NODAT. NODAT was defined by hemoglobin A1c (HbA1c) ≥6.5%, fasting serum glucose ≥126 mg/dL, or prescribed therapy for diabetes within 1 year posttransplant. Three multivariate logistic regression models were constructed: 1) standard model, with both continuous and discrete variables; 2) dichotomous model, with continuous variables dichotomized at clinically relevant cut points; and 3) summary score defined as the sum of the points accrued using the terms from the dichotomous model. RESULTS A total of 316 subjects had seven pretransplant variables with P < 0.10 in univariate logistic regression analyses (age, planned corticosteroid therapy posttransplant, prescription for gout medicine, BMI, fasting glucose and triglycerides, and family history of type 2 diabetes) that were selected for multivariate models. Areas under receiver operating curves for all three models were similar (0.72, 0.71, and 0.70). A simple risk score calculated as the sum of points from the seven variables performed as well as the other two models in identifying risk of NODAT. CONCLUSIONS A risk score computed from seven simple pretransplant variables can identify risk of NODAT.