Ectopic, hepatic GLP-1R agonism enhances the weight loss efficacy of GLP-1 analogues
Bibliographic record
Abstract
Abstract Objective Unimolecular triagonists drive substantial weight loss in patients with obesity (PwO) by engaging the glucagon-like peptide 1 (GLP-1) and glucose dependent insulinotropic polypeptide (GIP) receptors to reduce food intake (FI) and the hepatic glucagon (Gcg) receptor to enhance energy expenditure (EE). However, their development has been challenged by deleterious cardiovascular (CV) effects including increased heart rate (HR), elongated QTc, and arrhythmia mediated by GcgR agonism. GLP-1R monoagonists on the other hand improve both obesity and CV outcomes with negligible effects on EE. We sought to imbue peptide GLP-1R agonists with an EE enhancing effect by combining them with ectopic GLP-1R expression and agonism in hepatocytes. Methods We used an attenuated adenovirus (AAV) to induce the expression of a functional, liver-specific GLP-1R combined with traditional peptide agonist treatment to drive greater body weight loss via reduced energy intake and increased energy expenditure. Results Agonism of the ectopic GLP-1R with either semaglutide, a low internalization GLP-1R agonist (Sema584), or a dual GLP-1R/GIPR agonist in wild-type (WT) diet induced obese (DIO) mice led to enhanced EE and improved weight loss compared to agonist treatment alone. Conclusions This represents a novel mechanism for achieving polypharmacy to treat obesity. Highlights A Glp1r encoding AAV induces expression of a functional receptor mouse livers. Endogenous GLP-1R does not mediate semaglutide clearance. Ectopic GLP-1R mediates semaglutide clearance. Ectopic, hepatic Glp1r plus semaglutide enhances weight loss in mice. Ectopic, hepatic Glp1r plus a dual incretin agonist enhances weight loss in mice.
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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) | 0.002 | 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".