Effect of Metformin Use on Graft and Patient Survival in Kidney Transplant Recipients with Diabetes: A Systematic Review
Bibliographic record
Abstract
Background: Metformin is a widely used antihyperglycemic agent in diabetes, but its role in kidney transplant recipients (KTR) remains uncertain due to concerns about safety and unclear impact on clinically meaningful outcomes. We conducted a systematic review to evaluate the effect of metformin on graft and patient survival in KTRs. Methods: We systematically searched five databases (inception–April 2023) for studies evaluating metformin use in adult KTRs with type-2 diabetes mellitus (T2DM) or post-transplant diabetes mellitus (PTDM). Observational and interventional studies reporting graft failure or all-cause mortality were included. Risk of bias was assessed using ROBINS-I tool. Adjusted hazard ratios (aHR) were pooled using multilevel random-effects models. Results: Three observational cohort studies met inclusion criteria, encompassing 48,909 KTRs, including 18,113 living donor and 30,787 deceased donor transplants. Of these, 5,802 were metformin users and 43,107 were non-users. Metformin use was associated with a significantly lower risk of graft failure (aHR 0.48 [95% CI: 0.32–0.71], p<0.001; Figure 1A) and all-cause mortality (aHR 0.58 [95% CI: 0.34–1.00], p=0.049; Figure 1B) at one-year post-transplant. Safety outcomes, such as lactic acidosis or acute kidney injury, were underreported. Conclusion: Metformin use is associated with lower graft failure and mortality benefit in KTRs with diabetes. However, findings are based on observational data, and safety outcomes remain underreported. Further studies with robust designs are needed.Adjusted hazard ratios of metformin use for (A) graft failure and (B) all-cause mortality at one-year post-transplant
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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 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.010 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".