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Record W4407998111 · doi:10.1097/mnh.0000000000001067

Targeting aldosterone to improve cardiorenal outcomes: from nonsteroidal mineralocorticoid receptor antagonists to aldosterone synthase inhibitors

2025· review· en· W4407998111 on OpenAlexaff
Wryan Helmeczi, Gregory L. Hundemer

Bibliographic record

VenueCurrent Opinion in Nephrology & Hypertension · 2025
Typereview
Languageen
FieldMedicine
TopicHormonal Regulation and Hypertension
Canadian institutionsOttawa HospitalUniversity of OttawaMcMaster University
Fundersnot available
KeywordsMineralocorticoid receptorEplerenoneAldosterone synthaseAldosteroneSpironolactoneMedicineMineralocorticoidKidney diseaseInternal medicinePharmacologyEndocrinologyBlood pressureRenin–angiotensin system

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: Aldosterone dysregulation plays a major role in the pathogenesis of hypertension, cardiovascular disease, and kidney disease. Traditionally, steroidal mineralocorticoid receptor (MR) antagonists, namely spironolactone and eplerenone, have been the only available options to target aldosterone. Over recent years, a host of promising novel aldosterone-targeted pharmacologic agents have been developed thereby providing new options to mitigate aldosterone-mediated cardiovascular and kidney disease. RECENT FINDINGS: Recently, a number of nonsteroidal MR antagonists (finerenone, esaxerenone, and ocedurenone) and highly specific aldosterone synthase inhibitors (baxdrostat, lorundrostat, dexfadrostat, and vicadrostat) have been developed. The early clinical data for these novel medications looks promising regarding their efficacy in improving blood pressure control, preventing adverse cardiovascular outcomes, and slowing chronic kidney disease progression. Moreover, they appear to be generally safe and well tolerated. SUMMARY: In the coming years, nonsteroidal MR antagonists and aldosterone synthase inhibitors are likely to play an increasingly large role in routine medical practice to help improve cardiovascular and kidney outcomes.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.895
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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.

Opus teacher head0.060
GPT teacher head0.356
Teacher spread0.295 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations10
Published2025
Admission routes1
Has abstractyes

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