Stevioside Improves Liver Insulin Resistance in Prediabetic Mice via IRS1/PI3K/AKT Signaling Pathway
Bibliographic record
Abstract
Prediabetes progression to type 2 diabetes mellitus (T2DM) can be effectively prevented by adequate dietary intervention via improved liver insulin resistance. Stevioside, as a natural and safe sweetener, has been shown to have antidiabetic properties. However, whether stevioside can enhance liver insulin resistance in prediabetes remains unclear. We therefore aimed to investigate the effect and molecular mechanisms of stevioside on liver insulin resistance in prediabetes. Prediabetic mice were induced using a high-fat diet and treated with stevioside for 8 weeks. The effects of stevioside on gene expression levels and signaling pathways in the mice liver were investigated by RNA-seq and gene enrichment analysis. We also treated the palmitic acid-induced insulin-resistance AML-12 cells with stevioside, and measured glucose uptake in insulin-stimulated cells with 2-NBDG. The expression levels of genes and proteins related to the insulin signaling pathway were detected using qRT-PCR and Western blotting. Stevioside improved glucose tolerance and plasma insulin levels in prediabetic mice with insulin resistance and enhanced liver function. Stevioside could improve liver insulin resistance via IRS1/PI3K/AKT signaling pathway in prediabetic mice. Similarly, stevioside decreased the level of p-IRS1 and increased the levels of p-PI3K p85α, AKT, and p-AKT in AML-12 cells. Stevioside could improve glucose tolerance in prediabetic mice with insulin resistance, and ameliorate liver insulin resistance by regulating the IRS1/PI3K/AKT signaling pathway. These results may support stevioside as a potential dietary approach for preventing prediabetes progression to T2DM.
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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 | 0.000 |
| 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".