Nonsteroidal and Steroidal Mineralocorticoid Antagonists
Bibliographic record
Abstract
Steroidal mineralocorticoid receptor antagonists (MRAs), such as spironolactone and eplerenone, have demonstrated substantial benefits in randomized controlled trials for patients with heart failure with reduced ejection fraction. However, their effectiveness in heart failure with mildly reduced ejection fraction and heart failure with preserved ejection fraction remains uncertain, and the implementation of this class has remained low, in part due to its side effects and tolerability profile. Emerging therapies that target the mineralocorticoid receptor and/or the production of aldosterone may offer alternative strategies to treat the aldosterone-mineralocorticoid receptor axis. For instance, the nonsteroidal MRA finerenone has shown efficacy in reducing cardiovascular and renal events in patients with type 2 diabetes mellitus and chronic kidney disease, as well as decreasing the combined endpoint of cardiovascular death and heart failure hospitalizations in heart failure with mildly reduced ejection fraction and heart failure with preserved ejection fraction populations. Large-scale, direct comparative outcome studies are currently lacking that compare steroidal MRAs vs emerging therapies. This review critically assesses the structural and mechanistic distinctions between nonsteroidal and nonsteroidal MRAs as well as mineralocorticoid receptor modulators and aldosterone synthase inhibitors; summarizes available evidence across heart failure, diabetes, and chronic kidney disease populations; and highlights ongoing and forthcoming research aimed at addressing key unanswered questions in this rapidly evolving therapeutic field.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".