Effects of Hyperuricemia on Renal Function of Renal Transplant Recipients: A Systematic Review and Meta-Analysis of Cohort Studies
Bibliographic record
Abstract
BACKGROUND: Hyperuricemia is an independent risk factor of nephropathy, but its role in renal transplant recipients (RTRs) is controversial. METHODS: Based on the methods of Cochrane systematic reviews, we searched MEDLINE (1948-2011.6), EMBASE (1956-2011.6), CBM (Chinese Biomedicine Database) (1978-2011.6) to identify cohort studies assessing the association between uric acid level and kidney allograft. Two authors independently screened the studies, assessed the risk of bias of included studies and extracted data. Unadjusted odds ratio (OR), mean difference (MD), adjusted hazard ratio (aHR) and their corresponding 95%CI were pooled to assess the effects of hyperuricemia on kidney allograft. RESULTS: Twelve cohort studies were included and the quality was moderate to high based on the NEWCASTLE-OTTAWA quality assessment scale. RTRs with hyperuricemia had lower eGFR (P<0.0001, 95%CI-16.34∼6.14) and higher SCr (P<0.00001, 95%CI 0.17∼0.31) than those with normal uric acid level. Meta-analysis showed that hyperuricemia was a risk factor of chronic allograft nephropathy (Unadjusted OR = 2.85, 95%CI 1.84∼4.38, adjusted HR = 1.65, 95%CI 1.02∼2.65) and graft loss (Unadjusted OR = 2.29, 95%CI 1.55∼3.39; adjusted HR = 2.01, 95%CI 1.39∼2.94). CONCLUSIONS: Current evidence suggests that hyperuricemia may be an independent risk factor of allograft dysfunction. Hyperuricemia may modestly increase the risk of poor outcomes of RTRs. Future research is needed to verify whether lowering uric acid level could improve the kidney function and prognosis of RTRs with hyperuricemia.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.022 | 0.003 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".