Glucose Transporter 4 and Peroxisome Proliferator-Activated Receptor-Alpha Overexpression Association With Cardioprotective Effects of Myoinositol and Metformin Combination in Type 2 Diabetic Rat Model
Bibliographic record
Abstract
Background: Type 2 diabetes mellitus (DM) has many complications associated with increased morbidity and mortality. Cardiovascular complications are one of these serious complications. Therefore, there is a need for early and effective management to hamper them. This study aimed to evaluate the cardioprotective efficacy of metformin and myoinositol combination through exploring the associated underlying mechanisms in type 2 DM rat model. Methods: Type 2 DM model was induced in rats by streptozotocin and high-fat diet. Rats were treated with metformin (150 mg/kg) and/or myoinositol (50 mg/kg) by oral gavage once daily for 4 weeks. Immunohistochemistry was used to assess the drugs’ cardioprotective efficacy by estimating troponin T, nuclear factor kappa B (NF-kappaB) and tumor necrosis factor alpha (TNF-alpha). Quantitative real-time polymerase chain reaction and enzyme-linked immunosorbent assay were utilized to investigate peroxisome proliferator-activated receptor-alpha (PPARalpha) and glucose transporter 4 (GLUT-4) expression levels in the skeletal muscles, respectively. Results: This study found that metformin and myoinositol combination was associated with GLUT-4 and PPARalpha overexpression, together with better lipid profile than metformin alone in diabetic rats. Additionally, the combination of both drugs improved troponin C and decreased creatine kinase MB isoenzyme, NF-kappaB and TNF-alpha cardiac levels. Conclusion: The current study indicated that myoinositol in combination with metformin had better cardioprotective effect than metformin alone in type 2 DM. This favorable effect was exhibited through upregulation of GLUT-4 and PPARalpha receptors expression in skeletal muscles, thus increasing insulin sensitivity and improving lipid profile. J Endocrinol Metab. 2021;11(5):140-151 doi: https://doi.org/10.14740/jem765
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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) | 0.001 | 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".