Glucagon-like peptide-1 receptor co-agonists for treating metabolic disease
Bibliographic record
Abstract
BACKGROUND: Glucagon-like peptide-1 receptor (GLP-1R) agonists are approved to treat type 2 diabetes and obesity. They elicit robust improvements in glycemic control and weight loss, combined with cardioprotection in individuals at risk of or with pre-existing cardiovascular disease. These attributes make GLP-1 a preferred partner for next-generation therapies exhibiting improved efficacy yet retaining safety to treat diabetes, obesity, non-alcoholic steatohepatitis, and related cardiometabolic disorders. The available clinical data demonstrate that the best GLP-1R agonists are not yet competitive with bariatric surgery, emphasizing the need to further improve the efficacy of current medical therapy. SCOPE OF REVIEW: In this article, we discuss data highlighting the physiological and pharmacological attributes of potential peptide and non-peptide partners, exemplified by amylin, glucose-dependent insulinotropic polypeptide (GIP), and steroid hormones. We review the progress, limitations, and future considerations for translating findings from preclinical experiments to competitive efficacy and safety in humans with type 2 diabetes and obesity. MAJOR CONCLUSIONS: Multiple co-agonist combinations exhibit promising clinical efficacy, notably tirzepatide and investigational amylin combinations. Simultaneously, increasing doses of GLP-1R agonists such as semaglutide produces substantial weight loss, raising the bar for the development of new unimolecular co-agonists. Collectively, the available data suggest that new co-agonists with robust efficacy should prove superior to GLP-1R agonists alone to treat metabolic disorders.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".