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Record W4406106519 · doi:10.1111/dom.16154

Metabolic interventions as adjunctive therapies to insulin in type 1 diabetes: Current clinical landscape and perspectives

2025· review· en· W4406106519 on OpenAlexafffund
Juliana Podobnik, Kacey J. Prentice

Bibliographic record

VenueDiabetes Obesity and Metabolism · 2025
Typereview
Languageen
FieldMedicine
TopicDiabetes Management and Research
Canadian institutionsUniversity of Toronto
FundersCanadian Institutes of Health ResearchServierUniversity of Toronto
KeywordsMedicineType 2 diabetesAdjunctive treatmentPsychological interventionIntensive care medicineDiabetes mellitusInternal medicineEndocrinologyPsychiatry

Abstract

fetched live from OpenAlex

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.

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 imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.009
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0020.003
Science and technology studies0.0000.001
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0050.001

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.

Opus teacher head0.084
GPT teacher head0.414
Teacher spread0.331 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations6
Published2025
Admission routes2
Has abstractyes

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