Effects of canagliflozin on serum potassium in people with diabetes and chronic kidney disease: the CREDENCE trial
Bibliographic record
Abstract
AIMS: Hyperkalaemia is a common complication of type 2 diabetes mellitus (T2DM) and limits the optimal use of agents that block the renin-angiotensin-aldosterone system, particularly in patients with chronic kidney disease (CKD). In patients with CKD, sodium‒glucose cotransporter 2 (SGLT2) inhibitors provide cardiorenal protection, but whether they affect the risk of hyperkalaemia remains uncertain. METHODS AND RESULTS: The CREDENCE trial randomized 4401 participants with T2DM and CKD to the SGLT2 inhibitor canagliflozin or matching placebo. In this post hoc analysis using an intention-to-treat approach, we assessed the effect of canagliflozin on a composite outcome of time to either investigator-reported hyperkalaemia or the initiation of potassium binders. We also analysed effects on central laboratory-determined hyper- and hypokalaemia (serum potassium ≥6.0 and <3.5 mmol/L, respectively) and change in serum potassium. At baseline, the mean serum potassium in canagliflozin and placebo arms was 4.5 mmol/L; 4395 (99.9%) participants were receiving renin-angiotensin system blockade. The incidence of investigator-reported hyperkalaemia or initiation of potassium binders was lower with canagliflozin than with placebo [occurring in 32.7 vs. 41.9 participants per 1000 patient-years; hazard ratio (HR) 0.78, 95% confidence interval (CI) 0.64-0.95, P = 0.014]. Canagliflozin similarly reduced the incidence of laboratory-determined hyperkalaemia (HR 0.77, 95% CI 0.61-0.98, P = 0.031), with no effect on the risk of hypokalaemia (HR 0.92, 95% CI 0.71-1.20, P = 0.53). The mean serum potassium over time with canagliflozin was similar to that of placebo. CONCLUSION: Among patients treated with renin-angiotensin-aldosterone system inhibitors, SGLT2 inhibition with canagliflozin may reduce the risk of hyperkalaemia in people with T2DM and CKD without increasing the risk of hypokalaemia.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".