The Endothelin Antagonist Atrasentan Lowers Residual Albuminuria in Patients with Type 2 Diabetic Nephropathy
Bibliographic record
Abstract
Despite optimal treatment, including renin-angiotensin system (RAS) inhibitors, patients with type 2 diabetic nephropathy have high cardiorenal morbidity and mortality related to residual albuminuria. We evaluated whether or not atrasentan, a selective endothelin A receptor antagonist, further reduces albuminuria when administered concomitantly with maximum tolerated labeled doses of RAS inhibitors. We enrolled 211 patients with type 2 diabetes, urine albumin/creatinine ratios of 300-3500 mg/g, and eGFRs of 30-75 ml/min per 1.73 m(2) in two identically designed, parallel, multinational, double-blind studies. Participants were randomized to placebo (n=50) or to 0.75 mg/d (n=78) or 1.25 mg/d (n=83) atrasentan for 12 weeks. Compared with placebo, 0.75 mg and 1.25 mg atrasentan reduced urine albumin/creatinine ratios by an average of 35% and 38% (95% confidence intervals of 24 to 45 and 28 to 47, respectively) and reduced albuminuria≥30% in 51% and 55% of participants, respectively. eGFR and office BP measurements did not change, whereas 24-hour systolic and diastolic BP, LDL cholesterol, and triglyceride levels decreased significantly in both treatment groups. Use of atrasentan was associated with a significant increase in weight and a reduction in hemoglobin, but rates of peripheral edema, heart failure, or other side effects did not differ between groups. However, more patients treated with 1.25 mg/d atrasentan discontinued due to adverse events. After stopping atrasentan for 30 days, measured parameters returned to pretreatment levels. In conclusion, atrasentan reduced albuminuria and improved BP and lipid spectrum with manageable fluid overload-related adverse events in patients with type 2 diabetic nephropathy receiving RAS inhibitors.
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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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".