Magnesium status and clinical outcomes in kidney transplant recipients: Systematic review and meta-analysis
Bibliographic record
Abstract
Kidney transplant recipients (KTRs) remain at risk for metabolic, cardiovascular, and infectious complications. Magnesium abnormalities are common but their prognostic significance remains unclear. Objectives. To systematically review and meta-analyze the association between serum magnesium status and clinical outcomes in KTRs. Methods. We searched PubMed, Embase, Web of Science, Cochrane Library, and ClinicalTrials.gov (by May 31, 2025). Eligible studies were observational cohorts in adult KTRs reporting outcomes by magnesium categories. The primary outcome was all-cause mortality; secondary outcomes were cardiovascular events, graft outcomes, and infections. Data were synthesized using random-effects meta-analysis where ≥3 studies were available, complemented by Bayesian analysis. Risk of bias was assessed with the Newcastle–Ottawa Scale (NOS), and certainty of evidence was graded using GRADE. The protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO, ID: CRD420251055485). Results. Eight studies (n=7,026 KTRs) met the inclusion criteria. Five studies assessed all-cause mortality, and four provided adjusted estimates for pooling. Higher/normal magnesium (≥1.7–1.8 mg/dL) vs. lower was associated with increased mortality (HR 1.17, 95% CI 1.06–1.29; I²=59.5%). Sensitivity analysis excluding one null study yielded HR 1.18 (95% CI 1.03–1.34). Bayesian modeling produced consistent but uncertain estimates (posterior HR 1.22, 95% CrI 0.94–1.68; BF₁₀=0.88). Evidence for cardiovascular, graft, and infection outcomes was inconsistent and heterogeneous, precluding pooling. Certainty of evidence was low for the all-cause mortality outcome and very low for secondary outcomes. Conclusions. Current evidence from a small number of heterogeneous observational studies suggests that higher/normal serum magnesium status may be associated with a modest increase in all-cause mortality among KTRs. However, the certainty of evidence is low, and associations with cardiovascular, graft, and infection outcomes remain inconclusive. Prospective multicenter cohorts and interventional trials are needed to determine whether magnesium status is a causal and modifiable factor and to define the optimal post-transplant magnesium range.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.031 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".