Renoprotective Effects of Atorvastatin in Diabetic Mice: Downregulation of RhoA and Upregulation of Akt/GSK3
Bibliographic record
Abstract
Potential benefits of statins in the treatment of chronic kidney disease beyond lipid-lowering effects have been described. However, molecular mechanisms involved in renoprotective actions of statins have not been fully elucidated. We questioned whether statins influence development of diabetic nephropathy through reactive oxygen species, RhoA and Akt/GSK3 pathway, known to be important in renal pathology. Diabetic mice (db/db) and their control counterparts (db/+) were treated with atorvastatin (10 mg/Kg/day, p.o., for 2 weeks). Diabetes-associated renal injury was characterized by albuminuria (albumin:creatinine ratio, db/+: 3.2 ± 0.6 vs. db/db: 12.5 ± 3.1*; *P<0.05), increased glomerular/mesangial surface area, and kidney hypertrophy. Renal injury was attenuated in atorvastatin-treated db/db mice. Increased ROS generation in the renal cortex of db/db mice was also inhibited by atorvastatin. ERK1/2 phosphorylation was increased in the renal cortex of db/db mice. Increased renal expression of Nox4 and proliferating cell nuclear antigen, observed in db/db mice, were abrogated by statin treatment. Atorvastatin also upregulated Akt/GSK3β phosphorylation in the renal cortex of db/db mice. Our findings suggest that atorvastatin attenuates diabetes-associated renal injury by reducing ROS generation, RhoA activity and normalizing Akt/GSK3β signaling pathways. The present study provides some new insights into molecular mechanisms whereby statins may protect against renal injury in diabetes.
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 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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".