Type 2 diabetes in children and adolescents: Challenges for treatment and potential solutions
Bibliographic record
Abstract
Historically perceived as a disease mainly affecting adults, the prevalence of type 2 diabetes mellitus (T2DM) among children and adolescents has been rising, mirroring the increasing rates of childhood obesity. Currently, youth-onset T2DM poses a significant public health challenge globally. Treating youth-onset T2DM poses numerous critical challenges, namely limited and inadequate therapeutic options, and difficulties with conducting therapeutic studies. As a result, current treatment guidelines are based on adult studies and expert consensus. Few prominent guidelines on the treatment of youth-onset T2DM have been published recently, i.e., by the American Diabetes Association (ADA) 2024, National Institute for Healthcare and Excellence United Kingdom (NICE UK) 2023, International Society Paediatric and Adolescents Diabetes (ISPAD) 2022, Australasian Paediatric Endocrine Group (APEG) 2020 and Diabetes Canada 2018. This review first explores the unique aspects of youth-onset T2DM. It then summarises the different treatment guidelines, discusses the different treatment modalities based on available evidence and identifies any gaps. The review also explores challenges in the treatment of youth-onset T2DM with potential solutions and discusses recent trials on the treatment of youth-onset T2DM. Continued research aims to optimise treatment, improve outcomes, and alleviate the burden of T2DM on youths.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".