Youth-onset Type 2 Diabetes: An Overview of Pathophysiology, Prognosis, Prevention and Management
Bibliographic record
Abstract
PURPOSE OF REVIEW: This review explores the emerging evidence regarding pathogenesis, future trajectories, treatment options, and phenotypes of youth-onset type 2 diabetes (T2D). RECENT FINDINGS: Youth-onset T2D is increasing in incidence and prevalence worldwide, disproportionately affecting First Nations communities, socioeconomically disadvantaged youth, and people of colour. Youth-onset T2D differs in pathogenesis to later-onset T2D and progresses more rapidly. It is associated with more complications, and these occur earlier. While there are limited licensed treatment options available, the available medications also appear to have a poorer response in youth with T2D. Multiple interacting factors likely contribute to this rising prevalence, as well as the increased severity of the condition, including structural inequities, increasing obesity and sedentary lifestyles, and intergenerational transmission from in-utero exposure to maternal hyperglycemia and obesity. Youth-onset T2D is also associated with stigma and poorer mental health, and these impact clinical management. There is an urgent need to develop effective interventions to prevent youth-onset T2D and enhance engagement of affected youth. It is also critical to better understand the differing phenotypes of youth-onset T2D, to effectively target treatments, and to address intergenerational transmission in high-risk populations.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".