Urate-Lowering Therapy Ameliorates Kidney Function in Type 2 Diabetes Patients With Hyperuricemia
Bibliographic record
Abstract
BACKGROUND: Hyperuricemia often causes kidney dysfunction which increases serum urate, forming a vicious cycle in the kidney. In this study, urate-lowering therapy was demonstrated in type 2 diabetic patients with hyperuricemia to evaluate the effect on diabetic nephropathy. METHODS: Type 2 diabetic patients with hyperuricemia (n = 34) were treated by urate-lowering drugs. Serum urate levels, estimated glomerular filtration rate (eGFR), blood pressure, HbA1c, and urinary albumin-to-creatinine ratio (UACR) were measured for 52 weeks. The parameters at the endpoint when serum urate decreased to below 6.0 mg/dL and at 52 weeks were compared to the initial levels at week 0. RESULTS: Serum urate level decreased to the endpoint in all patients and was maintained at under 6.0 mg/dL throughout the observation period. eGFR significantly increased at the endpoint and also at 52 weeks. Overall UACR did not change after 52 weeks; however, the treatment decreased UACR significantly in patients with no microalbuminuria. There was a negative relationship between the change of serum urate levels and the change of eGFR, and a negative relationship between the baseline UACR and the change of UACR when patients with macroalbuminuria were excluded. There were no changes in HbA1c levels and blood pressure before and after the treatment. CONCLUSIONS: There were significant improvements in kidney function by lowering serum urate levels to under 6.0 mg/dL and the effect was maintained for at least 52 weeks. This treatment may be one strategy to slow the progression of nephropathy in type 2 diabetic patients with hyperuricemia.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".