Early Change in Albuminuria with Canagliflozin (CANA) Predicts Kidney and Cardiovascular (CV) Outcomes
Bibliographic record
Abstract
Background: The association between early changes in albuminuria and kidney and CV events is primarily based on trials of renin-angiotensin system blockade. It is unclear whether this association is similar with sodium-glucose cotransporter 2 inhibitors. Methods: In this post-hoc analysis of the CREDENCE trial in patients with type 2 diabetes and chronic kidney disease, we assessed the effect of CANA versus placebo on albuminuria at week 26, and the association of early changes in urinary albumin:creatinine ratio (UACR) for the first 26 weeks with kidney and CV outcomes using multivariable Cox regression. Kidney and CV outcomes were defined as (1) endstage kidney disease, doubling of serum creatinine or death due to kidney disease, (2) major adverse cardiovascular events (MACE) and (3) hospitalization for heart failure (HHF) or CV death. Results: This analysis included 3836 participants (87.2%) with complete data for early changes in UACR. CANA lowered UACR by 31% (95%CI 27-36%) at week 26 and increased the likelihood of achieving a 30% UACR reduction (OR 2.69, 95%CI 2.35-3.07). We observed log-linear associations of early changes in UACR during 26 weeks with kidney and CV outcomes (all p trend <0.001; Table). Each 30% UACR reduction was independently associated with a lower hazard for clinical outcomes, overall and in each treatment arm (all p <0.001).Table:: Adjusted HRs (95% Cls) of early changes in UACR at week 26 for kidney and CV outcomesConclusions: In people with type 2 diabetes and CKD, canagliflozin results in early and sustained reductions in albuminuria, which was independently associated with longterm kidney and cardiovascular outcomes.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".