Knowler, William C.

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.

OBJECTIVE Fasting hyperinsulinemia in the presence of normoglycemia usually indicates insulin resistance and is characteristic of populations at high risk for developing NIDDM. Hyperinsulinemia predicts the development of impaired glucose tolerance and NIDDM in Pima Indians, a population with a high incidence of NIDDM. Insulin concentrations in population-based samples of children who have different risks of developing NIDDM later in life have not been reported previously. RESEARCH DESIGN AND METHODS We compared fasting insulin concentrations in two populations of nondiabetic children, 6–19 yr of age: Pima Indians from southern Arizona and Caucasians from Minnesota. RESULTS Insulin concentration varied with age, sex, glucose concentration, and relative weight. Mean fasting insulin concentration was 140.3 pM in Pima Indian males, 94.4 pM in Caucasian males, 171.5 pM in Pima Indian females, and 107.1 pM in Caucasian females. For each sex, the mean fasting insulin concentration, controlled for age, glucose, and relative weight, was significantly higher in the Pima Indians than in the Caucasians (P < 0.001). CONCLUSIONS From a young age, Pima Indian children have higher fasting insulin concentrations than Caucasian children. As hyperinsulinemia predicts subsequent NIDDM, these data suggest that the susceptibility to NIDDM is manifest at a young age as fasting hyperinsulinemia.