GLP-1 Agonism for Kidney Transplant Recipients: A Narrative Review of Current Evidence and Future Directions Across the Research Spectrum
Bibliographic record
Abstract
Purpose of Review: Diabetes is the most common cause of kidney disease in individuals that receive a kidney transplant, and those without pre-existing diabetes are at greater risk of developing diabetes following kidney transplant. A class of diabetes treatment medications called glucagon-like peptide-1 receptor agonists (GLP-1RA) has seen recent widespread use for people with diabetes or obesity, with efficacy for improved glycemic control, weight loss, and reduced risk of cardiovascular events. Given these benefits, and indications for use that often co-occur in kidney transplant recipients, use of GLP-1RAs warrants consideration in this population. Therefore, we sought to review the current literature to better understand the mechanisms of action, clinical application, and person-centred considerations of GLP-1RAs in kidney transplant recipients. Sources of Information: Original articles were identified between December 2023 and July 2024 from electronic databases including the Ovid MEDLINE database, PubMed, and Google Scholar using terms "kidney transplant," "GLP-1," "glucagon-like peptide-1 receptor agonist," and "diabetes." Methods: A comprehensive review of the literature was conducted to explore the relationship between GLP-1RAs and kidney transplant recipients. We reviewed the current state of evidence across the research disciplines of basic or fundamental science, clinical and health services research, and person-centred equity science, and highlighted important knowledge gaps that offer opportunities for future research. Key Findings: Numerous clinical studies have demonstrated the benefit of GLP-1RAs in people with and without diabetic kidney disease, including decreased risk of cardiovascular events. However, there is a paucity of high-quality randomized controlled trials and observational studies analyzing use of GLP-1RAs in kidney transplant recipients. Evidence of benefit in this population is therefore limited to small studies or inferred from research conducted in nontransplant populations. Growing evidence from preclinical and clinical studies may elucidate renoprotective mechanisms of GLP-1RAs and remove barriers to application of these drugs in the transplant recipient population. Individuals who are female, non-white, have lower socioeconomic status, and live in rural communities are at greater risk of diabetes and have lower uptake of GLP-1RAs. There is a need for clinical trials across diverse kidney transplant populations to estimate the efficacy of GLP-1RAs on important health outcomes. Limitations: The search strategy for this narrative review may not have been sensitive to identify all relevant articles. Our search was limited to English language articles.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".