Specific loss of GIPR signaling in GABAergic neurons enhances GLP-1R agonist-induced body weight loss
Bibliographic record
Abstract
OBJECTIVES: Dual incretin agonists are among the most effective pharmaceutical treatments for obesity and type 2 diabetes to date. Such therapeutics can target two receptors, such as the glucagon-like peptide-1 (GLP-1) receptor and the glucose-dependent insulinotropic polypeptide (GIP) receptor in the case of tirzepatide, to improve glycemia and reduce body weight. Regarding body weight effects, GIPR signaling is thought to involve at least two relevant mechanisms: the enhancement of food intake reduction and the attenuation of aversive effects caused by GLP-1R agonists. Although it is known that dual GLP-1R-GIPR agonism produces greater weight loss than GLP-1R agonism alone, the precise mechanism is unknown. METHODS: To address this question, we used mice lacking GIPR in the whole body, GABAergic neurons, or glutamatergic neurons. These mice were given various combinations of GLP-1R and GIPR agonist drugs with subsequent food intake and conditioned taste aversion measurements. RESULTS: A GIPR knockout in either the whole body or selectively in inhibitory GABAergic neurons protects against diet-induced obesity, whereas a knockout in excitatory glutamatergic neurons had a negligible effect. Furthermore, we found that GIPR in GABAergic neurons is essential for the enhanced weight loss efficacy of dual incretin agonism, yet, surprisingly, its removal enhances the effect of GLP-1R agonism alone. Finally, GIPR knockout in GABAergic neurons prevents the anti-aversive effects of GIPR agonism. CONCLUSIONS: Our findings are consistent with GIPR research at large in that both enhancement and removal of GIPR signaling are metabolically beneficial. Notably, however, our findings suggest that future obesity therapies designed to modulate GIPR signaling, whether by agonism or antagonism, would be best targeted towards GABAergic neurons.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".