GLP-1 Improves Adipocyte Insulin Sensitivity Following Induction of Endoplasmic Reticulum Stress
Bibliographic record
Abstract
Glucagon-like peptide 1 (GLP-1) improves insulin resistance of adipose tissue in obese humans. However, the mechanism of this effect is unclear. Perturbation of endoplasmic reticulum (ER) homeostasis impairs insulin signaling. We hypothesized that GLP-1 could directly improve insulin signaling in ER-stressed adipocytes. Here, we examined the effects of GLP-1 on ER stress response in fat cells in an obese and insulin-resistant murine model. We found that GLP-1 analog liraglutide reduced ER stress related gene expression in visceral fat cells accompanied by improved systemic insulin tolerance. Consistently, GLP-1 decreased CHOP expression and increased insulin stimulated AKT phosphorylation (p-AKT) in thapsigargin, a ER stress inducer, treated white fat cells differentiated from visceral stromal vascular fraction. We further found blocking CHOP expression increased insulin stimulated p-AKT in ER-stressed fat cells. Of note, we found mTOR signaling pathway contributed to the expression of ATF4 and subsequently the CHOP expression in ER stress response, while GLP-1 inhibited mTOR activity as exemplified by elevated autophagosome formation and increased LC3II/LC3I ratio. These findings suggest that GLP-1 directly modulates the ER stress response partially via inhibiting mTOR signaling pathway, leading to increased insulin sensitivity in adipocytes.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".