Renal, Cardiovascular (CV), and Safety Outcomes of Canagliflozin (CANA) According to Baseline Albuminuria: A CREDENCE Secondary Analysis
Bibliographic record
Abstract
Background: Albuminuria is a risk factor for kidney disease progression and CV disease. We examined the relative and absolute effects of CANA by baseline albuminuria among CREDENCE participants. Methods: CREDENCE was a double-blind, randomized study of 4401 participants with eGFR 30-<90mL/min/1.73m2 and uACR >300-5000mg/g who demonstrated that CANA significantly reduced renal and CV outcomes, including the primary composite of end-stage kidney disease, doubling serum creatinine, or renal or CV death. We analyzed the effect of CANA on renal, CV, and safety outcomes by baseline uACR. Results: At baseline, 2348 (53.4%), 1547 (35.2%), and 506 (11.5%) participants had uACR ≤1000, >1000-<3000, ≥3000mg/g. Higher uACR was associated with higher event rates (Figure). CANA reduced renal and CV endpoints, with no statistical variation by uACR (all p heterogeneity >0.17). CANA led to a greater absolute reduction in renal events in those with higher uACR (number needed to treat to prevent 1 episode of the primary composite: 22 and 8 for uACR >1000-<3000 and ≥3000mg/g). Rates of renalrelated adverse events were lower with CANA, and the relative reduction was greater with higher uACR (p heterogeneity=0.003). CANA had no significant effect on acute kidney injury, volume depletion, hyperkalemia, urinary tract infections or hypoglycemia, with no differences by uACR (all p heterogeneity >0.12). Conclusions: CANA safely reduces renal and CV events in people with type 2 diabetes and substantial albuminuria, with the greatest absolute renal benefit in those with uACR of 3000-5000mg/g. Funding: Commercial Support - Janssen Scientific Affairs, LLC
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.010 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".