Glucagon-Like Peptide-1 Receptor Knockout Mice Are Protected from High-Fat Diet-Induced Insulin Resistance
Bibliographic record
Abstract
Glucagon-like peptide-1 augments nutrient-stimulated insulin secretion. Chow-fed mice lacking the glucagon-like peptide-1 receptor (Glp1r) exhibit enhanced insulin-stimulated muscle glucose uptake but impaired suppression of endogenous glucose appearance (endoRa). This proposes a novel role for the Glp1r to regulate the balance of glucose disposal in muscle and liver by modulating insulin action. Whether this is maintained in an insulin-resistant state is unknown. The present studies tested the hypothesis that disruption of Glp1r expression overcomes high-fat (HF) diet-induced muscle insulin resistance and exacerbates HF diet-induced hepatic insulin resistance. Mice with a functional disruption of the Glp1r (Glp1r-/-) were compared with wild-type littermates (Glp1r+/+) after 12 wk on a regular chow diet or a HF diet. Arterial and venous catheters were implanted for sampling and infusions. Hyperinsulinemic-euglycemic clamps were performed on weight-matched male mice. [3-(3)H]glucose was used to determine glucose turnover, and 2[14C]deoxyglucose was used to measure the glucose metabolic index, an indicator of glucose uptake. Glp1r-/- mice exhibited increased glucose disappearance and muscle glucose metabolic index on either diet. This was associated with enhanced activation of muscle Akt and AMP-activated protein kinase and reduced muscle triglycerides in HF-fed Glp1r-/- mice. Chow-fed Glp1r-/- mice exhibited impaired suppression of endoRa and hepatic insulin signaling. In contrast, HF-fed Glp1r-/- mice exhibited improved suppression of endoRa and hepatic Akt activation. This was associated with decreased hepatic triglycerides and impaired activation of sterol regulatory element-binding protein-1. These results show that mice lacking the Glp1r are protected from HF diet-induced muscle and hepatic insulin resistance independent of effects on total fat mass.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".