Effect of Urate-lowering Therapies on Renal Disease Progression in Patients with Hyperuricemia
Bibliographic record
Abstract
OBJECTIVE: To evaluate the association between hyperuricemia and renal disease progression in a real-world, large observational database study. METHODS: We conducted a population-based retrospective cohort study identifying 111,992 patients with hyperuricemia (> 7 mg/dl) from a large medical group. The final cohort were ≥ 18 years old, urate-lowering therapy (ULT)-naïve, and had the following laboratory results available: at least 1 glomerular filtration rate (GFR) level before the index date and at least 1 serum uric acid (sUA) level and GFR in the followup 36-month period. The cohort was categorized into 3 groups: never treated (NoTx), ULT time receiving therapy of < 80% (< 80%), and ULT time receiving therapy of ≥ 80% (≥ 80%). Outcomes were defined as a ≥ 30% reduction in GFR from baseline, dialysis, or GFR of ≤ 15 ml/min. A subanalysis of patients with sUA < 6 mg/dl at study conclusion was performed. Cox proportional hazards regression model determined factors associated with renal function decline. RESULTS: A total of 16,186 patients met inclusion criteria. There were 11,192 NoTx patients, 3902 with < 80% time receiving ULT, and 1092 with ≥ 80% time receiving ULT. Factors associated with renal disease progression were age, sex, hypertension, diabetes, congestive heart failure, hospitalizations, rheumatoid arthritis, and higher sUA at baseline. Time receiving therapy was not associated with renal outcomes. Patients who achieved sUA < 6 mg/dl had a 37% reduction in outcome events (p < 0.0001; HR 0.63, 95% CI: 0.5-0.78). CONCLUSION: Hyperuricemia is an independent risk factor for renal function decline. Patients treated with ULT who achieved sUA < 6 mg/dl on ULT showed a 37% reduction in outcome events.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".