Canagliflozin Across the Spectrum of Kidney Function and Albuminuria: Integrated Data from CANVAS and CREDENCE
Bibliographic record
Abstract
Background: People with type 2 diabetes mellitus (T2DM) and chronic kidney disease (CKD) are at very high risk of cardiovascular events and kidney failure. While canagliflozin reduces the risk of these outcomes, the consistency of this effect across all levels of estimated glomerular filtration rate (eGFR) and urinary albumin:creatinine ratio (UACR) remains uncertain. Methods: We pooled individual participant data from the CANVAS Program (n=10,142) and CREDENCE trial (n=4,401) to assess the effect of canagliflozin on a primary composite outcome of myocardial infarction, stroke, heart failure, doubling of serum creatinine, kidney failure, cardiovascular or kidney death. The effect of canagliflozin was assessed using Cox regression models with treatment by subgroup interaction terms stratified by trial. Results: 2,051/14,543 (14%) participants experienced the primary outcome over a median follow-up of 2.5 years. Overall, canagliflozin reduced the risk of the primary outcome (HR 0.77, 95% 0.70-0.84; Figure). The magnitude of relative benefit increased as eGFR declined (P-trend=0.0067; Figure) with some evidence of greater relative benefit at higher UACR (P-trend=0.057; Figure). Lower eGFR and higher UACR levels were independently associated with cardio-renal risk. Consequently, absolute risk reductions increased more than 5-fold across lower eGFR categories and more than 9-fold across higher UACR categories (Figure). Conclusions: Canagliflozin reduces the risk of cardio-renal outcomes in people with T2DM; the magnitude of relative and absolute protection varies by severity of CKD. Funding: Commercial Support - Janssen funded the CANVAS and CREDENCE trials. This analysis was not specifically funded and conducted independent of the trial sponsors.
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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 | 0.016 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".