Effects of Canagliflozin (CANA) on Cardiovascular (CV), Kidney, and Albuminuria Outcomes by Diabetes Duration: Pooled Analysis From the CANVAS Program and CREDENCE
Bibliographic record
Abstract
Background: Type 2 diabetes (T2DM) is a progressive disease and increasing duration is associated with heightened risk of morbidity and mortality. CANA reduced CV and kidney events in patients (pts) with T2DM and CV risk or nephropathy. We assessed the effects of CANA on CV and kidney outcomes and albuminuria progression by disease duration. Methods: This post hoc analysis pooled patient-level data from the CANVAS Program (N=10,142) and the CREDENCE trial (N=4401) to examine the effect of CANA vs placebo on CV events (major adverse cardiovascular events [MACE] and CV death or hospitalization for heart failure), kidney events (doubling of serum creatinine [dSCr] and end-stage kidney disease or dSCr), a composite of CV and kidney events, and albuminuria progression and regression (change in albuminuria class [normo-, micro-, macro-] plus ≥30% urinary albumin to creatinine ratio change) in pts with diabetes duration of <5, ≥5 to ≤10, >10 to ≤15, and >15y. HRs and 95% CIs were estimated using a Cox proportional hazards model, stratified by duration of diabetes. Results: Overall, there were 1528, 3333, 4148, and 5523 pts with diabetes duration of <5, ≥5 to ≤10, >10 to ≤15, and >15y. CANA reduced the risk of CV, kidney, composite cardiorenal outcomes, with no statistical heterogeneity amongst subgroups by diabetes duration. Similarly, CANA reduced the rate of albuminuria progression and increased rate of albuminuria regression across diabetes duration subgroups (Figure).Figure.: Effects of CANA vs PBO on CV and kidney outcomes by diabetes duration.Conclusions: CANA reduced the risk of CV, kidney, and composite cardiorenal events consistently, regardless of diabetes duration. Results show within 5 years of developing T2DM, CANA positively impacts on albuminuria, an important consideration in primary care setting.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.020 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".