Metformin ameliorates the severity of experimental Alport syndrome
Bibliographic record
Abstract
Metformin is widely used for the treatment of type 2 diabetes, and increasing numbers of studies have shown that metformin also ameliorates tumor progression, inflammatory disease, and fibrosis. However, the ability of metformin to improve non-diabetic glomerular disease and chronic kidney disease (CKD) has not been explored. To investigate the effect of metformin on non-diabetic glomerular disease, we used a mouse model of Alport syndrome (Col4a5 G5X) which were treated with metformin or losartan, used as a control treatment. We also investigated the effect of metformin on adriamycin-induced glomerulosclerosis model. Pathological and biochemical analysis showed that metformin or losartan suppressed proteinuria, renal inflammation, fibrosis, and glomerular injury and extended the lifespan in Alport syndrome mice. Transcriptome analysis showed that metformin and losartan influenced molecular pathways-related to metabolism and inflammation. Metformin altered multiple genes including metabolic genes not affected by losartan. Metformin also suppressed proteinuria and glomerular injury in the adriamycin-induced glomerulosclerosis mouse model. Our results showed that metformin ameliorates the glomerular sclerosis and CKD phenotype in non-diabetic chronic glomerular diseases. Metformin may have therapeutic potential for not only diabetic nephropathy but also non-diabetic glomerular disease including Alport syndrome.
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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) | 0.001 | 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".