Effects of Canagliflozin on Cardiovascular, Renal, and Safety Outcomes by Baseline Loop Diuretic Use: Data from the CREDENCE Trial
Bibliographic record
Abstract
Background: Canagliflozin (CANA) reduces the risk of cardiovascular (CV) events and kidney failure in people with type 2 diabetes mellitus (T2DM) and chronic kidney disease (CKD). Inherent in its mechanism of action is enhanced natriuresis and osmotic diuresis. It is unclear if the efficacy or safety of CANA is modified by concomitant diuretic use. Methods: CREDENCE randomized participants with T2DM and CKD to CANA or matching placebo. The primary outcome was a composite of end-stage kidney disease, doubling of serum creatinine, CV or renal death. We estimated effects on key efficacy and safety outcomes by baseline use of loop diuretics. Results: Of 4401 CREDENCE participants, 955 (21.7%) received loop diuretics at baseline. These participants were older (mean age 63.5 vs 62.7 y; P=0.01), with a longer diabetes duration (17.0 vs 15.5 y), lower eGFR (49.7 vs 58.0 mL/min/1.73m2), and were more like to have a history of heart failure (27.6 vs 11.3%; all P<0.0001). Unadjusted event rates were higher in those using loop diuretics (Figure). Effects of CANA on the primary outcome and other CV and renal outcomes were consistent irrespective of loop diuretic use. The risk of renal-related adverse events, acute kidney injury, and volume depletion was not elevated by loop diuretic use (data not shown; all Pinteraction>0.05).Figure.: Effect of canagliflozin on cardiovascular and renal outcomes by baseline use of loop diureticsConclusions: CANA reduces the risk of CV and renal outcomes in people with T2DM and CKD irrespective of baseline use of loop diuretics, without additional adverse effects. 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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.005 | 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".