Bibliographic record
Abstract
Glucagon-like peptide-1 (GLP-1) receptor agonists are effective treatments for type 2 diabetes as they stimulate insulin release and promote weight loss through appetite suppression. Their main side effect is nausea. All approved GLP-1 agonists are full agonists across multiple signalling pathways. However, selective engagement with specific intracellular effectors, or biased agonism, has been touted as a means to improve GLP-1 agonists therapeutic efficacy. In this review, I critically examine how GLP-1 receptor-mediated intracellular signalling is linked to physiological responses and discuss the implications of recent studies investigating the metabolic effects of biased GLP-1 agonists. Overall, there is little conclusive evidence that beneficial and adverse effects of GLP-1 agonists are attributable to distinct, nonoverlapping signalling pathways. Instead, G protein-biased GLP-1 agonists appear to achieve enhanced anti-hyperglycaemic efficacy by avoiding GLP-1 receptor desensitisation and downregulation, partly via reduced β-arrestin recruitment. This effect seemingly applies more to insulin release than to appetite regulation and nausea, possible reasons for which are discussed. At present, most evidence derives from cellular and animal studies, and more human data are required to determine whether this approach represents a genuine therapeutic advance. LINKED ARTICLES: This article is part of a themed issue on GLP1 receptor ligands (BJP 75th Anniversary). To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v179.4/issuetoc.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".