Synergistic Combinations of Gut- and Pancreas-Hormone-Based Therapies: Advancements in Treatments for Metabolic Diseases
Bibliographic record
Abstract
Metabolic diseases, such as obesity, type 2 diabetes mellitus (T2DM), cardiovascular disease, and liver disease, have become increasingly prevalent around the world. As an alternative to bariatric surgery, glucagon-like peptide 1 (GLP-1) receptor agonists have been at the forefront of weight loss medication to combat these metabolic complications. Recently, there has been an exciting rapid emergence of new weight loss medications that combine GLP-1 receptor (GLP-1R) agonists with other gut- and pancreatic-derived hormones, such as glucose-dependent insulinotropic polypeptide (GIP) and glucagon (GCG) receptor agonists. Dual-agonist (GLP-1/GIP and GLP-1/GCG) and tri-agonist (GLP-1/GIP/GCG) administration generally result in greater weight loss, reduction of blood sugar and lipid levels, restoration of tissue function, and improvement in whole-body substrate metabolism compared to when GLP-1R agonists are used alone. The aim of this review is to summarize the recent literature of both preclinical and clinical studies on how these emerging gut-peptide therapies further improve weight loss and metabolic health outcomes for various metabolic diseases.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".