Anti-obesity effects of GIPR antagonists alone and in combination with GLP-1R agonists in preclinical models
Bibliographic record
Abstract
Glucose-dependent insulinotropic polypeptide (GIP) receptor (GIPR) has been identified in multiple genome-wide association studies (GWAS) as a contributor to obesity, and GIPR knockout mice are protected against diet-induced obesity (DIO). On the basis of this genetic evidence, we developed anti-GIPR antagonistic antibodies as a potential therapeutic strategy for the treatment of obesity and observed that a mouse anti-murine GIPR antibody (muGIPR-Ab) protected against body weight gain, improved multiple metabolic parameters, and was associated with reduced food intake and resting respiratory exchange ratio (RER) in DIO mice. We replicated these results in obese nonhuman primates (NHPs) using an anti-human GIPR antibody (hGIPR-Ab) and found that weight loss was more pronounced than in mice. In addition, we observed enhanced weight loss in DIO mice and NHPs when anti-GIPR antibodies were codosed with glucagon-like peptide-1 receptor (GLP-1R) agonists. Mechanistic and crystallographic studies demonstrated that hGIPR-Ab displaced GIP and bound to GIPR using the same conserved hydrophobic residues as GIP. Further, using a conditional knockout mouse model, we excluded the role of GIPR in pancreatic β-cells in the regulation of body weight and response to GIPR antagonism. In conclusion, these data provide preclinical validation of a therapeutic approach to treat obesity with anti-GIPR antibodies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".