<scp>Glucagon‐like peptide</scp> ‐1 receptor agonists as adjunctive treatment for type 1 diabetes: Renewed opportunities through tailored approaches?
Bibliographic record
Abstract
Exogenous insulin has been the mainstay treatment for individuals living with type 1 diabetes (T1D). Although there has been tremendous growth in both pharmacological and technological advancements, insulin monotherapy has proven to be insufficient for maintaining optimal glycaemic targets for most adults with T1D. At present, there is still no breakthrough for the treatment of T1D. Adjunctive pharmacotherapies might therefore complement insulin management to achieve better glycaemic control, while possibly offering additional benefits. Recent interest in re-purposing glucagon-like peptide-1 receptor agonists (GLP-1RAs), a leading antihyperglycaemic medication class approved for type 2 diabetes, has prompted the field to seek extended potential for the T1D population. The adjunctive use of GLP-1RAs has been at the forefront of T1D research, albeit with some conflicting trial findings to date. However, the potential of GLP-1 agonism for T1D may have been underestimated, possibly from missed opportunities or categorized effects. Moreover, some GLP-1RAs have demonstrated extra-pancreatic potential with emerging multi-organ protection involving the heart, kidneys, liver and brain in varied cohorts, which may bode well for the growing T1D profile of comorbid complications. This narrative review aims to summarize and critically appraise the current evidence-based literature from large-scale randomized controlled trials and closed-loop system pilot studies that examined GLP-1RAs as adjunctive therapy for T1D. Furthermore, we outline uncharted opportunities with GLP-1 agonism using versatile approaches in selected T1D populations that may inspire and re-direct future research in this field.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".