Treatment of diabetes with glucagon-like peptide-1 gene therapy
Bibliographic record
Abstract
IMPORTANCE OF THE FIELD: Glucagon-like peptide (GLP)-1 receptor agonists are in widespread clinical use for the treatment of diabetes. While effective, these peptides require frequent injections to maintain efficacy. Therefore, alternative delivery methods including gene therapy are currently being evaluated. AREAS COVERED IN THIS REVIEW: Here, we review the biology of GLP-1, evidence supporting the clinical use of the native peptide as well as synthetic GLP-1 receptor agonists, and the rationale for their delivery by gene therapy. We then review progress made in the field of GLP-1 gene therapy for both type 1 and type 2 diabetes. WHAT THE READER WILL GAIN: Efforts to improve the biological half-life of GLP-1 receptor agonists are discussed. We focus on the development of both viral and non-viral gene delivery methods, highlighting vector designs and the strengths and weaknesses of these approaches. We also discuss the utility of targeting regulated GLP-1 production to tissues including the liver, muscle, islet and gut. TAKE HOME MESSAGE: GLP-1 is a natural peptide possessing several actions that effectively combat diabetes. Current delivery methods for GLP-1-based drugs are cumbersome and do not recapitulate the normal secretion pattern of the native hormone. Gene therapy offers a useful method for directing long-term production and secretion of the native peptide. Targeted production of GLP-1 using tissue-specific promoters and delivery methods may improve therapeutic efficacy, while also eliminating the burden of frequent injections.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 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) | 0.004 | 0.003 |
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".