Metabolic interventions as adjunctive therapies to insulin in type 1 diabetes: Current clinical landscape and perspectives
Bibliographic record
Abstract
Type 1 diabetes (T1D) is classically characterized as an autoimmune disease wherein the immune system erroneously attacks insulin-producing pancreatic β-cells, causing insulin insufficiency and severe metabolic dysregulation. However, intensive investigation and numerous clinical trials with immunotherapies have been largely unable to significantly alter the course of disease. Currently, there is no effective way to prevent or cure T1D, and insulin remains the cornerstone of T1D treatment. In recent years, a growing body of research suggests that β-cells actively contribute to the immune response and to disease development. Factors including glucotoxicity, lipotoxicity, inflammation, endoplasmic reticulum (ER) and oxidative stress can induce β-cell apoptosis and senescence, further promoting insulitis. Recent studies highlight the importance of targeting metabolic control for T1D management and treatment. Metabolic interventions, through their direct and indirect impacts on β-cells, have shown promise in preserving β-cell function. These interventions can reduce glucose toxicity, alleviate oxidative stress and inflammation, enhance insulin sensitivity, and indirectly mitigate the autoimmune responses. By preserving β-cell function, individuals with T1D attain better glycaemic control, reduced complication risks and exhibit improved overall metabolic health. Here, we provide an overview of insights from clinical studies, systematic reviews and meta-analyses that collectively demonstrate that adjunctive metabolic interventions can enhance glycaemic control, reduce insulin requirements and mitigate adverse effects associated with insulin monotherapy. They also show potential for halting disease progression, preserving residual β-cell function and improving long-term outcomes for newly diagnosed individuals. Future research should focus on optimizing these treatment strategies and establishing their long-term efficacy and safety.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".