Aldosterone, Mineralocorticoid Receptor Activation, and CKD: A Review of Evolving Treatment Paradigms
Bibliographic record
Abstract
Mineralocorticoid receptor (MR) activation is involved in propagating kidney injury, inflammation, and fibrosis and in the progression of chronic kidney disease (CKD). Multiple clinical studies have defined the efficacy of MR antagonism in attenuating progressive kidney disease, and the US Food and Drug Administration recently approved the nonsteroidal mineralocorticoid receptor antagonist (MRA) finerenone for this indication. In this review, we consider the basic science and clinical applicability of MR antagonism. Because hyperkalemia constitutes a constraint to implementing evidence-based MR blockade, we review MRA-associated hyperkalemia in the context of finerenone and discuss evolving mitigation strategies to enhance the safety and efficacy of this treatment. Although the FIDELIO-DKD and FIGARO-DKD clinical trials focused solely on patients with type 2 diabetes mellitus, we propose that MR activation and the resulting inflammation and fibrosis act as a substantive pathogenetic mediator not only in people with diabetic CKD but also in those with CKD without diabetes. We close by briefly discussing both recently initiated and future clinical trials that focus on extending the attributes of MR antagonism to a wider array of nondiabetic kidney disorders, such as patients with nonalbuminuric CKD.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".