Cardiometabolic and Kidney Protection in Kidney Transplant Recipients With Diabetes: Mechanisms, Clinical Applications, and Summary of Clinical Trials
Bibliographic record
Abstract
Kidney transplantation is the therapy of choice for patients with end-stage renal disease. Preexisting diabetes is highly prevalent in kidney transplant recipients (KTR), and the development of posttransplant diabetes is common because of a number of transplant-specific risk factors such as the use of diabetogenic immunosuppressive medications and posttransplant weight gain. The presence of pretransplant and posttransplant diabetes in KTR significantly and variably affect the risk of graft failure, cardiovascular disease (CVD), and death. Among the many available therapies for diabetes, there are little data to determine the glucose-lowering agent(s) of choice in KTR. Furthermore, despite the high burden of graft loss and CVD among KTR with diabetes, evidence for strategies offering cardiovascular and kidney protection is lacking. Recent accumulating evidence convincingly shows glucose-independent cardiorenal protective effects in non-KTR with glucose-lowering agents, such as sodium-glucose cotransporter-2 inhibitors and glucagon-like peptide-1 receptor agonists. Therefore, our aim was to review cardiorenal protective strategies, including the evidence, mechanisms, and rationale for the use of these glucose-lowering agents in KTR with diabetes.
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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".