The non-steroidal mineralocorticoid receptor blocker esaxerenone reduces glomerular hyperfiltration and albuminuria
Bibliographic record
Abstract
BACKGROUND: Diabetic kidney disease (DKD) is a major cause of chronic kidney disease, with glomerular hyperfiltration contributing to its progression. Esaxerenone, a non-steroidal mineralocorticoid receptor antagonist (MRA), reduces albuminuria, but its precise mechanism remains unclear. Mineralocorticoid receptor (MR) activation is implicated in tubuloglomerular feedback (TGF) dysregulation, and we hypothesized that MR inhibition attenuates albuminuria by mitigating glomerular hyperfiltration. METHODS: To investigate the effects of esaxerenone, we used aldosterone-induced MR activation rats and type 2 diabetic (db/db) mice. In vivo multiphoton imaging was performed to assess the single-nephron glomerular filtration rate (SNGFR) and arteriolar diameters. Macula densa (MD) cells were used to examine MR activation's impact on TGF. RESULTS: In aldosterone-infused rats, MR activation induced glomerular hyperfiltration via afferent arteriolar dilation, which was attenuated by esaxerenone. In db/db mice, esaxerenone reduced SNGFR and urinary albumin excretion while increasing urinary adenosine levels, effects reversed by A1aR blockade. In MD cells, MR activation increased nitric oxide (NO) production and reduced Na+-K+-2Cl- cotransporter membrane expression, both of which were mitigated by MRA or neuronal nitric oxide synthase inhibition. CONCLUSION: These findings suggest that esaxerenone restores TGF function via adenosine signalling, attenuating glomerular hyperfiltration and reducing albuminuria. This study provides novel insights into the albuminuria-lowering effects of MR blockade in DKD and supports the therapeutic potential of esaxerenone.
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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) | 0.002 | 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".