Effects of Add-on Fluvastatin Therapy in Patients with Chronic Proteinuric Nephropathy on Dual Renin-Angiotensin System Blockade
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: This open, prospective, randomized trial aimed to assess the effects of statins in chronic kidney disease patients on optimized antiproteinuric treatment with combined angiotensin-converting enzyme inhibition and angiotensin receptor blockade. DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS: After 1-month benazepril therapy followed by 1-month benazepril-valsartan combined therapy (run-in), 186 consenting patients with residual proteinuria >0.5 g/24 h were randomized to 6-month benazepril-valsartan therapy alone or combined with fluvastatin. Between-groups changes in proteinuria (primary outcome), serum lipids, and GFR were compared by ANCOVA. Analyses were blinded and by intention to treat. RESULTS: During the run-in, proteinuria decreased more on benazepril-valsartan than on benazepril alone. Proteinuria reduction correlated with concomitant reduction in total, LDL, and HDL cholesterol, and apolipoprotein B and apolipoprotein A levels. After randomization, median proteinuria similarly decreased from 1.2 (0.6 to 2.2) to 1.1 (0.5 to 1.7) g/24 h on fluvastatin and from 1.5 (0.8 to 2.7) to 1.0 (0.5 to 2.4) g/24 h on benazapril-valsartan therapy alone. Fluvastatin further reduced total and LDL cholesterol and apolipoprotein B versus benazepril-valsartan alone, but did not affect serum triglycerides and GFR. Treatment was well tolerated. CONCLUSIONS: In chronic kidney disease patients with residual proteinuria despite combined angiotensin-converting enzyme inhibitor and angiotensin receptor blockade therapy, add-on fluvastatin does not affect urinary proteins, but further reduces serum lipids and is safe. Whether combined angiotensin-converting enzyme inhibitor, angiotensin receptor blockade, and statin therapy may improve cardiovascular outcomes in this high-risk population is worth investigating.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".