The effects of atrasentan on urinary metabolites in patients with type 2 diabetes and nephropathy
Bibliographic record
Abstract
We assessed the effect of atrasentan therapy on a pre‐specified panel of 13 urinary metabolites known to reflect mitochondrial function in patients with diabetic kidney disease. This post‐hoc analysis was performed using urine samples collected during the RADAR study which was a randomized, double‐blind, placebo‐controlled trial that tested the effects of atrasentan on albuminuria reduction in patients with type 2 diabetes and nephropathy. At baseline, 4 of the 13 metabolites, quantified by gas‐chromatography mass spectrometry, were below detectable levels, and 6 were reduced in patients with eGFR < 60 mL/min/1.73 m 2 . After 12 weeks of atrasentan treatment in patients with eGFR < 60 mL/min/1.73 m 2 , a single‐value index of the metabolites changed by −0.31 (95%CI −0.60 to −0.02; P = .035), −0.08 (−12 to 0.29; P = .43) and 0.01 (−0.21 to 0.19; P = .913) in placebo, atrasentan 0.75 and 1.25 mg/d, respectively. The metabolite index difference compared to placebo was 0.13 (−0.17 to 0.43; P = .40) and 0.35 (0.05‐0.65; P = .02) for atrasentan 0.75 and 1.25 mg/d, respectively. These data corroborate previous findings of mitochondrial dysfunction in patients with type 2 diabetes, nephropathy and eGFR < 60 mL/min/1.73 m 2 , suggesting that atrasentan may prevent the progression of mitochondrial dysfunction common to this specific patient population. Future studies of longer treatment duration with atrasentan are indicated.
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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) | 0.002 | 0.001 |
| 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".