Blockade of cannabinoid CB<sub>1</sub> receptors potentiates the anti‐fibrotic effects mediated by SGLT2 inhibition in a mouse model of diabetic nephropathy
Bibliographic record
Abstract
Background and purpose Diabetic nephropathy (DN) is a common complication of diabetes. Current treatments include renin‐angiotensin‐aldosterone system (RAAS) blockers and sodium‐glucose co‐transporter 2 (SGLT2) inhibitors. The cannabinoid CB 1 receptor is a potential therapeutic target. We explored combining CB 1 receptor inverse agonism and SGLT2 inhibition for treating DN, to offer better reno‐protection. Experimental approach C57BLKS‐Lepr db/db and control mice were fed a high‐protein diet for 9 weeks. After 5 weeks, db/db mice were either exposed to placebo, empagliflozin (SGLT2 inhibitor), monlunabant (CB 1 receptor inverse agonist) or a combination of both compounds (same dose) by daily oral gavage for 28 days. Diagnostic parameters for DN were analysed, along with markers of oxidative stress, inflammation and renal fibrosis. Key results Both single treatments improved albuminuria and albumin‐to‐creatinine ratios, but the combination was more effective. Similar results were seen for inflammatory oxidative stress markers. The combination showed additive protective effects on glomerular morphology, podocyte loss and proximal tubular cell injury. Dual treatment significantly reduced tubulointerstitial fibrosis compared to monotherapy and vehicle‐treated mice. Transcriptomic analysis identified the STAT3 signalling pathway as a key mediator, with decreased STAT3 phosphorylation observed with both treatments. Key mediators involved included angiopoietin 1 and fibroblast growth factor 20, which modulated the STAT3 pathway via CB 1 receptors and SGLT2, respectively. Conclusions and implications Taken together, these data strongly suggest that a poly‐pharmacological approach combining both SGLT2 inhibitors and CB 1 receptor inverse agonism represents a promising therapeutic strategy for managing DN, with better reno‐protection than mono‐therapies.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
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".