Evaluating biased agonism of glucagon‐like peptide‐1 ( <scp>GLP</scp> ‐1) receptors to improve cellular bioenergetics: A systematic review
Bibliographic record
Abstract
Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are highly effective antidiabetic and anti-obesity agents. Recent development and optimisation of these agents includes biased agonism (selective activation of a downstream signalling pathway) of the GLP-1 receptor, which has demonstrated greater weight-lowering effects and improved insulin response in treated persons relative to other GLP-1 RAs. We aim to synthesise current literature reporting on the effects of biased GLP-1 RAs and compare their effects on cellular-level bioenergetics compared to non-biased GLP-1 RAs. A systematic search was conducted on PubMed, Ovid, and Scopus databases from inception to April 2025 for studies reporting on the effects of GLP-1 receptor biased agonists on cellular bioenergetics. Primary studies reporting on the effects of biased GLP-1 RAs on cellular signal transduction and bioenergetic outcomes were sought for inclusion. Current literature to date suggests that GLP-1 receptor biased agonism contributes to improved bioenergetic outcomes. Biased agonism of the GLP-1 receptor was associated with increased cAMP production and accumulation, increased ERK1/2 phosphorylation, and decreased GLP-1 receptor internalisation and recycling. In addition, improved glucose control and insulin response were observed. GLP-1 receptor biased agonism may be associated with greater weight-lowering and glucose-lowering effects as well as improved safety and tolerability. Notwithstanding, additional studies are needed to parse the contributory effects of GLP-1 receptor biased agonism compared to agonism of GIP and glucagon receptors on anti-obesity and glucose-lowering effects as well as tolerability. Moreover, their effects on other metabolic outcomes are future research vistas.
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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 | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".