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Record W4414011712 · doi:10.1530/jme-25-0047

Aldosterone synthase inhibition: a novel bullet to fight cardiovascular–kidney–metabolic syndrome

2025· article· en· W4414011712 on OpenAlexaff
Jonatan Barrera‐Chimal, Anand Vaidya, Frédéric Jaisser

Bibliographic record

VenueJournal of Molecular Endocrinology · 2025
Typearticle
Languageen
FieldMedicine
TopicHormonal Regulation and Hypertension
Canadian institutionsHôpital Maisonneuve-Rosemont
FundersAgence Nationale de la Recherche
KeywordsAldosteroneAldosterone synthaseMetabolic syndromeKidneyATP synthaseEndocrinologyMedicineInternal medicineBioinformaticsChemistryBiologyEnzymeBlood pressureBiochemistryRenin–angiotensin systemObesity

Abstract

fetched live from OpenAlex

Aldosterone is synthesized by the CYP11B2 enzyme, primarily in the zona glomerulosa of the adrenal gland. It exerts its classical effects on sodium and water balance in the renal distal nephron through binding to the mineralocorticoid receptor (MR). Excess aldosterone production or overactivation of the MR outside the distal nephron leads to cardiac, renal, and vascular injury by increasing oxidative stress and activating the inflammatory and fibrotic pathways. MR antagonists (MRAs) have proved effective at decreasing organ damage and the deleterious effects of excess aldosterone/MR activation. However, MRAs do not fully block the non-genomic effects of aldosterone, which may contribute to residual risks. CYP11B2 inhibition has emerged as an additional therapeutic approach to decreasing the deleterious genomic and non-genomic effects of aldosterone. The development of specific aldosterone synthase inhibitors (ASi) has proved challenging due to the considerable similarity between aldosterone synthase and 11β-hydroxylase, an enzyme encoded by the CYP11B1 gene that catalyzes cortisol synthesis. In this review, we summarize the latest developments on preclinical evidence and clinical trials for ASi and explore the potential clinical advantages of ASi.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.813
Threshold uncertainty score0.649

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.014
GPT teacher head0.262
Teacher spread0.248 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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

Citations2
Published2025
Admission routes1
Has abstractyes

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