Direct Control of Brown Adipose Tissue Thermogenesis by Central Nervous System Glucagon-Like Peptide-1 Receptor Signaling
Bibliographic record
Abstract
We studied interscapular brown adipose tissue (iBAT) activity in wild-type (WT) and glucagon-like peptide 1 receptor (GLP-1R)-deficient mice after the administration of the proglucagon-derived peptides (PGDPs) glucagon-like peptide (GLP-1), glucagon (GCG), and oxyntomodulin (OXM) directly into the brain. Intracerebroventricular injection of PGDPs reduces body weight and increases iBAT thermogenesis. This was independent of changes in feeding and insulin responsiveness but correlated with increased activity of sympathetic fibers innervating brown adipose tissue (BAT). Despite being a GCG receptor agonist, OXM requires GLP-1R activation to induce iBAT thermogenesis. The increase in thermogenesis in WT mice correlates with increased expression of genes upregulated by adrenergic signaling and required for iBAT thermogenesis, including PGC1a and UCP-1. In spite of the increase in iBAT thermogenesis induced by GLP-1R activation in WT mice, Glp1r(-/-) mice exhibit a normal response to cold exposure, demonstrating that endogenous GLP-1R signaling is not essential for appropriate thermogenic response after cold exposure. Our data suggest that the increase in BAT thermogenesis may be an additional mechanism whereby pharmacological GLP-1R activation controls energy balance.
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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".