Insights from CREDENCE trial indicate an acute drop in estimated glomerular filtration rate during treatment with canagliflozin with implications for clinical practice
Bibliographic record
Abstract
Canagliflozin slows the progression of chronic kidney disease in patients with type 2 diabetes and induces a reversible acute drop in estimated glomerular filtration rate (eGFR), believed to be a hemodynamic effect. Predictors of the initial drop and its association with long-term eGFR trajectories and safety outcomes are unknown. To assess this, we performed a post-hoc analysis of 4289 participants in the CREDENCE trial with type 2 diabetes and chronic kidney disease equally split into treatment and placebo groups who had eGFR measured at both baseline and week three. The eGFR was categorized at week three as greater than a 10% decline; between 0 and 10% decline; and no decline. Long-term eGFR trajectories and safety outcomes were estimated in each category of acute eGFR change by linear mixed effects models and Cox regression after adjustment for baseline characteristics and medications use. Significantly more participants in the canagliflozin (45%) compared to the placebo (21%) group experienced an acute drop in eGFR over 10%. An over 30% drop occurred infrequently (4% of participants with canagliflozin and 2% with placebo). The odds ratio for a drop in eGFR over 10% with canagliflozin compared to placebo was significant at 3.03 (95% confidence interval 2.65, 3.47). Following the initial drop in eGFR, multivariable adjusted long-term eGFR trajectories, as well as overall and kidney safety profiles, in those treated with canagliflozin were similar across eGFR decline categories. Thus, although acute drops in eGFR over 10% occurred in nearly half of all participants following initiation of canagliflozin, the clinical benefit of canagliflozin was observed regardless. Additionally, safety outcomes were similar among subgroups of acute eGFR drop.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.024 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".