An update review of post‐transplant diabetes mellitus: Concept, risk factors, clinical implications and management
Bibliographic record
Abstract
OBJECTIVE: Kidney transplantation is the gold standard therapeutic alternative for patients with end-stage renal disease; nevertheless, it is not without potential complications leading to considerable morbidity and mortality such as post-transplant diabetes mellitus (PTDM). This narrative review aims to comprehensively evaluate PTDM in terms of its diagnostic approach, underlying pathophysiological pathways, epidemiological data, and management strategies. METHODS: Articles were retrieved from electronic databases using predefined search terms. Inclusion criteria encompassed studies investigating PTDM diagnosis, pathophysiology, epidemiology, and management strategies. RESULTS: PTDM emerges as a significant complication following kidney transplantation, influenced by various pathophysiological factors including peripheral insulin resistance, immunosuppressive medications, infections, and proinflammatory pathways. Despite discrepancies in prevalence estimates, PTDM poses substantial challenges to transplant. Diagnostic approaches, including traditional criteria such as fasting plasma glucose (FPG) and HbA1c, are limited in their ability to capture early PTDM manifestations. Oral glucose tolerance test (OGTT) emerges as a valuable tool, particularly in the early post-transplant period. Management strategies for PTDM remain unclear, within sufficient evidence from large-scale randomized clinical trials to guide optimal interventions. Nevertheless, glucose-lowering agents and life style modifications constitute primary modalities for managing hyperglycemia in transplant recipients. DISCUSSION: The complex interplay between PTDM and the transplant process necessitates individualized diagnostic and management approaches. While early recognition and intervention are paramount, modifications to maintenance immunosuppressive regimens based solely on PTDM risk are not warranted, given the potential adverse consequences such as increased rejection risk. Further research is essential to refine management strategies and enhance outcomes for transplant recipients.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".