Preventing Early Renal Loss in Diabetes (PERL) Study: A Randomized Double-Blinded Trial of Allopurinol—Rationale, Design, and Baseline Data
Bibliographic record
Abstract
OBJECTIVE Higher serum uric acid (SUA) is associated with diabetic kidney disease (DKD). Preventing Early Renal Loss in Diabetes (PERL) evaluates whether lowering SUA with allopurinol slows glomerular filtration rate (GFR) loss in people with type 1 diabetes (T1D) and mild to moderate DKD. We present the PERL rationale, design, and baseline characteristics. RESEARCH DESIGN AND METHODS This double-blind, placebo-controlled, multicenter trial randomized 530 participants with T1D, estimated GFR (eGFR) of 40–99.9 mL/min/1.73 m2, SUA ≥4.5 m/dL, and micro- to macroalbuminuric DKD or normoalbuminuria with declining kidney function (NDKF) (defined as historical eGFR decline ≥3 mL/min/1.73 m2/year) to allopurinol or placebo. The primary outcome is baseline-adjusted iohexol GFR (iGFR) after 3 years of treatment plus a 2-month washout period. RESULTS Participants are 66% male and 84% white. At baseline, median age was 52 years and diabetes duration was 35 years, 93% of participants had hypertension, and 90% were treated with renin-angiotensin system inhibitors (median blood pressure 127/71 mmHg). Median HbA1c was 8%, SUA 5.9 mg/dL, iGFR 68 mL/min/1.73 m2, and historical eGFR slope −3.5 mL/min/1.73 m2/year. Compared with participants with albuminuria (n = 419), those with NDKF (n = 94) were significantly older (56 vs. 52 years), had lower HbA1c (7.7 vs. 8.1%) and SUA (5.4 vs. 6.0 mg/dL), and had higher eGFR (82 vs. 74 mL/min/1.73 m2) and historical eGFR loss (−4.7 vs. −2.5 mL/min/1.73 m2/year). These differences persisted when comparing groups with similar rates of historical eGFR loss. CONCLUSIONS PERL will determine the effect of allopurinol on mild to moderate DKD in T1D, with or without albuminuria. Participants with normoalbuminuria and rapid GFR loss manifested many DKD risk factors of those with albuminuria, but with less severity.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".