Management of type 2 diabetes, obesity, or nonalcoholic steatohepatitis with high‐dose GLP‐1 receptor agonists and GLP‐1 receptor‐based co‐agonists
Bibliographic record
Abstract
Type 2 diabetes (T2D), obesity, and nonalcoholic fatty liver disease/nonalacoholic steatohepatitis (NAFLD/NASH) share mutual causalities. Medications that may offer clinical benefits to all three conditions are being developed. Glucagon-like peptide-1 receptor agonists (GLP-1RAs) are approved for the management of T2D and obesity and there is great interest in evaluating higher doses of available GLP-1RAs and developing novel GLP-1RA-based co-agonists to provide greater reductions in glycated hemoglobin (HbA1c) and body weight as well as modifying NAFLD/NASH complications in clinically meaningful ways. High-dose GLP-1RAs and multi-hormonal strategies including GLP-1R agonism have either already been approved or are in development for managing T2D, obesity, or NASH. We provide a mechanistic outline with a detailed summary of the available clinical data and ongoing trials that are adjudicating the impact of high-dose GLP-1RAs, unimolecular, and multimolecular GLP-1R-based co-agonists in populations living with T2D, obesity, or NASH. The available trial findings are aligned with preclinical observations, showing clinical efficacy and safety thus providing optimism for the expansion of GLP-1R-based drug classes for managing the triad of T2D, obesity and NASH. Development, access, and wide-spread utilization of these new therapeutic approaches will offer important opportunities to markedly improve the collective global burden of T2D, obesity, and NASH.
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 | 0.001 | 0.000 |
| 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) | 0.005 | 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".