Urate lowering therapy to improve renal outcomes in patients with chronic kidney disease: systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: Hyperuricemia may contribute to renal injury. We do not know whether use of treatments that lower urate reduce the progression of chronic kidney disease (CKD) and cardiovascular disease. We performed a systematic review and meta-analysis of randomized controlled trials to assess the benefits and risks of treatments that lower urate in patients with stages 3-5 CKD. METHODS: We searched MEDLINE, EMBASE, CENTRAL, Web of Science and trial registers for randomized controlled trials (RCTs) without language restriction. Two authors independently screened articles, assessed risk of bias and extracted data. Data obtained included serum uric acid, serum creatinine or other estimates of glomerular filtration rate, incidence of end-stage renal disease (ESRD), systolic and diastolic blood pressure, proteinuria, cardiovascular disease and adverse events. RESULTS: From the 5497 citations screened, 19 RCTs enrolling 992 participants met our inclusion criteria. Given significant heterogeneity in duration of follow-up and study comparators, only trials greater than 3 months comparing allopurinol and inactive control were meta-analyzed using random effects models. Pooled estimate for eGFR was in favour of allopurinol with a mean difference (MD) of 3.2 ml/min/1.73 m(2), 95% CI 0.16-6.2 ml/min/1.73 m(2), p = 0.039 and this was consistent with results for serum creatinine. Statistically significant reductions in serum uric acid, systolic and diastolic blood pressure were found, favouring allopurinol. There were insufficient data on adverse events, incidence of ESRD and cardiovascular disease for analysis. CONCLUSIONS: Adequately powered RCTs are needed to establish whether treatments that lower urate have beneficial renal and cardiovascular effects.
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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.032 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".