Posttransplantation Diabetes Mellitus in FK-506-Treated Renal Transplant Recipients: Analysis of Incidence and Risk Factors. Transplantation 2001; 72: 1655.
Bibliographic record
Abstract
COMMENTARY Diabetes mellitus developing de novo following solid organ transplantation was reported in pioneering studies almost 40 years ago (1,2). The high doses of corticosteroids employed in those early reports often led to hyperglycemia that required insulin treatment. The introduction of azathioprine and the immunophilin-binding drugs permitted the use of lower doses of corticosteroids (1,2). Complete withdrawal of corticosteroid therapy subsequently became routine in some centers (1). However, posttransplantation diabetes has persisted because of intrinsic diabetogenic effects of cyclosporine and tacrolimus (FK-506) (1,2). Studies in the 1990s suggested a higher risk of diabetes in renal and liver transplant recipients treated with tacrolimus than in those receiving cyclosporine (1,3). Despite improved outcomes, ensuring long-term graft survival remains a challenge. Moreover, cardiovascular complications have emerged as the major cause of premature death in organ transplant recipients (4). Adverse effects of corticosteroids and immunophilin-binding drugs have been implicated in reduced graft and patient survival. Dose-dependent glucose intolerance and dyslipidemia are often accompanied by hypertension in transplant recipients. When present in combination, these risk factors confer a greatly increased risk of cardiovascular disease (5). Corticosteroids exacerbate this situation (1,2). The incidence of disordered glucose metabolism in transplant recipients reported in the literature has probably been underestimated. Diagnostic criteria differ between studies, often with only the more marked degrees of hyperglycemia being reported (1). In nontransplantation populations, even minor glucose intolerance is associated with an increased long-term risk of cardiovascular disease (6). Such dysglycemia often remains undetected. The category of impaired glucose tolerance, for example, can only be diagnosed by a 75-g oral glucose tolerance test. In this issue of Transplantation, Maes and colleagues (7) report a study using the recently revised diagnostic criteria (8,9). The new American Diabetes Association criteria, which are based on fasting blood glucose concentrations (8), were applied to 139 consecutive renal transplant recipients, none of whom had had recognized diabetes preoperatively. Maintenance immunosuppressive therapy comprised tacrolimus and low-dose methylprednisolone, the third agent being either mycophenolate mofetil or azathioprine (neither of which is regarded as having appreciable diabetogenic effects). With appropriate confirmatory tests (which are necessary if classic osmotic symptoms are absent) 32% met the revised criteria for diabetes during the first year posttransplantation. A further 15% had impaired fasting glucose (IFG). The latter category lies between normality and diabetes mellitus. Subjects with lesser degrees of glucose intolerance have a higher risk of progression to type 2 diabetes (and an increased risk of cardiovascular disease) (8,9). Whether the prevalence of type 2 diabetes will increase with a longer duration of follow-up is uncertain, particularly because some subjects had reverted to normal at one year (7). What are the factors that predict posttransplantation IFG and diabetes? Advanced age, a history of diabetes in a close relative, a personal history of glucose intolerance, and non-Caucasian ethnicity have been identified as risk factors in other retrospective studies (1,2). It is noteworthy that these characteristics also predispose to type 2 diabetes in the general population, obesity being a major factor in the majority of cases. Maes et al. confirmed these observations, also identifying cumulative corticosteroid dose and high trough blood concentrations of tacrolimus as risk factors for postoperative hyperglycemia. Hyperglycemia tended to be detected during episodes of acute rejection when high-dose methylprednisolone was given (and plasma glucose was perhaps monitored more closely). Other factors may be relevant to the emergence of posttransplantation hyperglycemia. Thus, in univariate analysis, a tendency to higher pretransplantation serum triglyceride concentration was associated with posttransplantation IFG or diabetes. Hypertriglyceridemia is common in dialysis patients, and in nontransplantation populations is regarded (along with low HDL cholesterol levels) as a prominent feature of the insulin resistance (or metabolic) syndrome of cardiovascular risk factors (9). This pattern of dyslipidemia has also been implicated in impaired long-term graft survival in organ transplant recipients. Interestingly, a lower cumulative dose of tacrolimus during the initial 3 months (resulting in similar or higher trough blood levels) was also associated with posttransplantation hyperglycemia. The study of Maes et al. highlights the continuing high incidence of posttransplantation disturbances of glucose metabolism. Use of oral glucose tolerance tests might well have identified additional subjects with impaired glucose tolerance. In nontransplantation populations, the 2-hr plasma glucose concentration following a 75-g oral glucose challenge appears to be a more reliable predictor of cardiovascular mortality than is fasting hyperglycemia (10). Clearly, vigilance is required; posttransplantation dysglycemia and other cardiovascular risk factors should be sought and treated, taking care to minimize the potential for drug interactions. What of alternatives to conventional triple immunosuppressive regimens? Reports of toxic effects of tacrolimus on insulin synthesis and secretion have not deterred investigators from exploring its use in pancreatic and islet transplantation (11,12). Fortunately, drugs with reduced potential to induce diabetes (e.g. sirolimus, mycophenolate mofetil) have allowed lower doses of tacrolimus to be used. Moreover, the detrimental effects of corticosteroids may be avoidable (4). For example, a corticosteroid-free regimen of low-dose tacrolimus, sirolimus, and the monoclonal antibody daclizumab, has recently been successfully employed in islet transplants by the Edmonton group (13). Tailored immunosuppressive therapy that seeks to maximize benefits while minimizing adverse effects by avoiding calcineurin inhibitors is another option that is currently being explored. However, this approach requires careful judgment, because rejection rates may increase (14,15). The need for additional well-designed clinical trials is clear (16).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".