Emerging Therapies Mimicking the Effects of Amylin and Glucagon-Like Peptide 1
Bibliographic record
Abstract
Current therapies for type 2 diabetesare frequently associated with inad-equate control of postprandial hy-perglycemia, weight gain, and, in the case of oral agents, loss of efficacy over time. A better understanding of physiological re-sponses to meals is leading to the devel-opment of new agents whose therapeutic action is based on the enhancement of gastrointestinal hormone action. These therapies are associated with slowing of gastric emptying, stimulation of insulin and inhibition of glucagon secretion, im-proved control of postprandial hypergly-cemia, and control of body weight. This review summarizes several limitations in the treatment of type 2 diabetes and de-scribes the mechanisms of action and clinical data obtained with amylin and glucagon-like peptide 1 (GLP-1) receptor agonists and dipeptidyl peptidase IV (DPP-IV) inhibitors for the treatment of diabetes. Despite considerable effort by patients and physicians, the results of treating type 2 diabetes are often disappointing. This re-view examines the limitations of current an-tihyperglycemic therapies and assesses the potential of the emerging class of agents that mimic or enhance the actions of amylin and GLP-1,which are both gastrointestinal pep-tide hormones that in concert with insulin and glucagon regulate fuel homeostasis and eating behavior (1–4). Several agents from this class have been recently approved for clinical use or are in the advanced stages of development. Their mechanisms of action and therapeutic effects, as described in peer-reviewed publications, will be dis-cussed.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".