Liraglutide Induces Brown Adipocyte Specific Genes in Mice Skeletal Muscle Tissue
Bibliographic record
Abstract
Obesity is a risk factor for type 2 diabetes, coronary artery disease, and stroke. Glucagon like peptide ‐1 (GLP‐1) is synthesized by the L cells of ileal mucosa and is released after nutrients ingestion to potentiate glucose stimulated insulin secretion. Liraglutide, a full agonist of the GLP‐1 receptor (GLP‐R), has long‐lasting effects due to its increased resistance to enzymatic degradation. Clinical data has demonstrated the protective effect of liraglutide on weight gain in type 2 diabetes subjects. However, the underlying mechanisms of this protective effect have not been identified. Brown adipose tissue plays a major role in control of energy balance in rodents, whether GLP‐1 activate brown fat differentiation has not been studied. C2C12 myoblasts are known to be able to differentiate into adipocytes after stimulation. By treating the undifferentiated C2C12 myoblast with 5nM liraglutide, we observed a significant induction of GLP‐1 receptor after 24 h. C2C12 cells were cultured to confluence, and then exposed to brown adipocyte differentiation medium in the presence or absence of 5nM liraglutide, the mRNA level of brown fat enriched genes including peroxisome proliferator‐activated receptor α (PPAR‐α) and cell death activator‐A (Cidea) were upregulated after 6 days of induction of differentiation compare to control cells. Induction of peroxisome proliferator‐activated receptor‐γ coactivator (PGC)‐1α mRNA was observed as early as 3 d post‐induction. In vivo study was performed by injecting C57 black/6 mice with liragludie daily for 5 weeks. Both mRNA and protein levels of uncoupling protein‐1(UCP‐1), a brown fat specific gene, are significantly induced in skeletal muscle tissue after liraglutide treatment. Lirglutide also upregulated other brown fat enriched gene including PGC‐1α, Cidea, and PPAR‐α. Our study indicates that liraglutide induces brown adipogenesis. GLP‐1 and its analogues are potential therapies for obesity and obesity related metabolic disorders. Support or Funding Information Central Michigan University Faculty Startup Fund.
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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) | 0.000 | 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 | 0.001 | 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".