PBI-4547 Reverses Diabetes and Metabolic Syndrome through Regulation of Lipid/Glucose Metabolism, ß-Oxidation and Fibrosis in Liver, and White Adipose Tissue in ob/ob Mice
Bibliographic record
Abstract
Introduction and Aims: Given the expected rise in the prevalence of obesity and type 2 diabetes, hepatic steatosis will, if not already, become an epidemic. The aim of this study was to determine the mechanism of action of PBI-4547 on diabetic complications. Methods: ob/ob mice (6 weeks old) were treated with vehicle or PBI-4547 (10, 25 and 50 mg/kg, oral once a day) from day 1 through 105. Liver and white adipose tissue (WAT) histology, blood glucose, serum triglyceride and adiponectin levels were examined. Gene expression of pro-inflammatory/fibrotic markers, metabolic and mitochondrial fatty acid transport and oxidation (FAO) regulators, and PPAR gene expression were determined in liver and WAT. Results: Blood glucose, cholesterol and triglyceride levels were strongly reduced by PBI-4547. ob/ob mice displayed severe liver steatosis which was completely reversed with PBI-4547. PBI-4547 significantly reduced fibrosis, inflammatory cell infiltration, and adipocyte size in WAT. Serum level of adiponectin was reduced in ob/ob mice and increased with PBI-4547. In liver, PBI-4547 reduced TNF-α, collagen I, III and IV, α-SMA, fibronectin, MMP-2, TIMP1, ICAM, PDGFα, TGFβ1, PPARδ and PPARγ, FASN, but increased mitochondrial FA transport and oxidation (ACOX-1, CD36, FABP4, CPT1β, PDK4). In WAT, PBI-4547 reduced TNF-α, collagen types I, IV and VI, MMP-2, MCP-1, F4/80, increased PPARα and PPARγ, and also increased ACOX-1, CD36, FASN, FABP4, COX4I1, SREBF1, PGC1-α, UCP-1 (by 160X, suggesting browning of adipose tissue), CPT1α, CPT1β and PDK4. Furthermore, PBI-4547 increased adiponectin and its receptors, and abolished vaspin gene expression. Conclusions: PBI-4547 is a potential novel therapy for diabetes and hepatic steatosis through its pleiotropic activities on glucose and lipid metabolism, by improving mitochondrial dysfunction, by inducing browning of adipose tissue, and by regulating adipokines. Disclosure L. Gagnon: Employee; Self; Prometic Biosciences Inc.. Stock/Shareholder; Self; Prometic Life Sciences Inc. A. Laverdure: Employee; Self; Prometic Biosciences Inc.. Stock/Shareholder; Self; Prometic Life Sciences Inc. F. Sarra-Bournet: Employee; Self; Prometic Biosciences Inc.. Stock/Shareholder; Self; Prometic Life Sciences Inc. M. Cloutier: Employee; Self; Prometic Biosciences Inc.. Stock/Shareholder; Self; Prometic Life Sciences Inc. A. Felton: Employee; Self; Prometic Biosciences Inc.. Stock/Shareholder; Self; Prometic Life Sciences Inc. M. Tremblay: Employee; Self; Prometic Biosciences Inc.. Stock/Shareholder; Self; Prometic Life Sciences Inc. J. Richard: Employee; Self; Prometic Biosciences Inc.. Stock/Shareholder; Self; Prometic Life Sciences Inc. L. Gervais: Employee; Self; Prometic Biosciences Inc.. Stock/Shareholder; Self; Prometic Life Sciences Inc. P. Laurin: Employee; Self; Prometic Biosciences Inc.. Stock/Shareholder; Self; Prometic Life Sciences Inc.. Board Member; Self; Prometic Life Sciences Inc. F.A. Leblond: Employee; Self; Prometic Biosciences Inc.. Stock/Shareholder; Self; Prometic Life Sciences Inc. B. Grouix: Employee; Self; Prometic Biosciences Inc.. Stock/Shareholder; Self; Prometic Life Sciences Inc..
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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 | 0.000 | 0.001 |
| 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".