The non-steroidal mineralocorticoid receptor antagonist finerenone is a novel therapeutic option for patients with Type 2 diabetes and chronic kidney disease
Bibliographic record
Abstract
Despite strong preclinical data supporting the use of mineralocorticoid receptor antagonists (MRAs) to provide cardiorenal protection in rodent models of diabetes, the clinical evidence of their utility in treating chronic kidney disease (CKD) has been limited. Two major clinical trials (FIDELIO-DKD and FIGARO-DKD) including more than 13,000 patients with albuminuric CKD and Type 2 diabetes randomized to placebo or finerenone (MRA) have recently provided exciting results showing a significant risk reduction for kidney and cardiovascular outcomes. In this review, we will summarize the major findings of these trials, together with post-hoc and pooled analyses that have allowed evaluation of the efficacy and safety of finerenone across the spectrum of CKD, revealing significant protective effects of finerenone against kidney failure, new-onset atrial fibrillation or flutter, new-onset heart failure, cardiovascular death, and first and total heart-failure hospitalizations. Moreover, we will discuss the current evidence that supports the combined use of MRAs with sodium-glucose co-transporter-2 inhibitors, either by providing an additive cardiorenal benefit or by decreasing the risk of hyperkalemia. Although the mechanisms of protection by finerenone have only been partially explored in patients, rodent studies have shed light on its anti-inflammatory and anti-fibrotic effects in models of kidney disease, which is one of the main drivers for testing the efficacy of finerenone in non-diabetic CKD patients in the ongoing FIND-CKD trial.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".