Efficacy and Safety of Balcinrenone in Combination with Dapagliflozin on Albuminuria in Participants with CKD
Bibliographic record
Abstract
Background: SGLT2is reduce albuminuria and the risk of CKD progression, and the MRA finerenone has similar effects in T2D with CKD. We assessed the albuminuria-lowering efficacy and safety of the novel MRA balcinrenone combined with the SGLT2i dapagliflozin. Methods: In this double-blind, active-controlled clinical trial (NCT06350123), adults with eGFR of 25–<60 mL/min/1.73 m2, UACR of >100–5000 mg/g, and potassium 3.5–5.0 mmol/L, were randomized 1:1:1 to combined balcinrenone/dapagliflozin 15/10 mg, balcinrenone/dapagliflozin 40/10 mg, or placebo/dapagliflozin 10 mg as adjunct to RAS inhibitors for 12 weeks, followed by an 8-week wash-out period. Primary endpoint was change in UACR from baseline to week 12. Results: Of 613 participants screened, 324 were randomized (mean age 65 years [SD 12]; mean eGFR 42 mL/min/1.73 m2 [SD 11], median UACR 365 mg/g [25th, 75th percentile 157, 825], and 56% taking SGLT2i). Balcinrenone/dapagliflozin 15/10 mg and balcinrenone/dapagliflozin 40/10 mg were superior in reducing UACR vs. placebo/dapagliflozin (Figure 1A). At week 12, UACR difference vs. placebo/dapagliflozin was –22.8% (90% CI –33.3 to –10.7; p=0.0038) for balcinrenone/dapagliflozin 15/10 mg and –32.8% (90% CI –42.0 to –22.1; p<0.001) for balcinrenone/dapagliflozin 40/10 mg. Minor changes were observed in potassium (Figure 1B). Investigator-reported adverse events of hyperkalaemia occurred in 6%, 7%, and 5% of participants in the balcinrenone/dapagliflozin 15/10 mg, balcinrenone/dapagliflozin 40/10 mg, and placebo/dapagliflozin groups, respectively. Conclusion: In participants with CKD, combined balcinrenone and dapagliflozin was superior to dapagliflozin in reducing albuminuria. The combination was well-tolerated with minor effects on potassium. Funding: Commercial Support - AstraZeneca
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".