Update and Next Steps for Real-World Translation of Interventions for Type 2 Diabetes Prevention: Reflections From a Diabetes Care Editors’ Expert Forum
Bibliographic record
Abstract
The International Diabetes Federation estimates that 415 million adults worldwide now have diabetes and 318 million have impaired glucose tolerance. These numbers are expected to increase to 642 million and 482 million, respectively, by 2040. This burgeoning pandemic places an enormous burden on countries worldwide, particularly resource-poor regions. Numerous landmark trials evaluating both intensive lifestyle modification and pharmacological interventions have persuasively demonstrated that type 2 diabetes can be prevented or its onset can be delayed in high-risk individuals with impaired glucose tolerance. However, key challenges remain, including how to scale up such approaches for widespread translation and implementation, how to select appropriately from various interventions and tailor them for different populations and settings, and how to ensure that preventive interventions yield clinically meaningful, cost-effective outcomes. In June 2015, a Diabetes Care Editors' Expert Forum convened to discuss these issues. This article, an outgrowth of the forum, begins with a summary of seminal prevention trials, followed by a discussion of considerations for selecting appropriate populations for intervention and the clinical implications of the various diagnostic criteria for prediabetes. The authors outline knowledge gaps in need of elucidation and explore a possible new avenue for securing regulatory approval of a prevention-related indication for metformin, as well as specific considerations for future pharmacological interventions to delay the onset of type 2 diabetes. They conclude with descriptions of some innovative, pragmatic translational initiatives already under way around the world.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".