Glucagon-like Peptide-1 Receptor Agonist Treatment in Type 1 Diabetes
Bibliographic record
Abstract
Type 1 diabetes (T1D) is characterized by a progressive decline of insulin production due to a marked destruction of pancreatic B cells. Intensive insulin therapy is the pillar of T1D management. More recently, continuous glucose monitoring devices, closed-loop systems (CLS) and smarter connected insulin pen systems have all significantly helped individuals to improve glycemic control. Despite these advances, however, more than three-quarters of the adult T1D population does not achieve recommended glycemic targets. In addition, aggressive insulin intensification potentiates weight gain and the risk of recurrent hypoglycemic events. Recent significant increase in rates of obesity has also led to a sharp increase in T1D patients who concurrently have adiposity-based chronic disease, increasing their insulin resistance and predisposition for cardiovascular events. While insulin will remain the basis of T1D management, there is an unmet need for individualized adjunctive therapeutic approaches focusing on the prevention of diabetic complications in addition to glycemic control. One such adjunctive therapy currently being explored in T1D are the glucagon-like peptide-1 receptor agonists (GLP-1 RAs), a popular and robust approach in Type 2 diabetes (T2DM) to mimic the natural endogenous GLP-1 incretin. This brief review will focus on the rationale and existing evidence for the use of GLP-1 RAs in the management of T1D.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".