The role of mental disorders in precision medicine for diabetes: a narrative review
Bibliographic record
Abstract
This narrative review aims to examine the value of addressing mental disorders as part of the care of people with type 1 and type 2 diabetes in terms of four components of precision medicine. First, we review the empirical literature on the role of common mental disorders in the development and outcomes of diabetes (precision prevention and prognostics). We then review interventions that can address mental disorders in individuals with diabetes or at risk of diabetes (precision treatment) and highlight recent studies that have used novel methods to individualise interventions, in person and through applications, based on mental disorders. Additionally, we discuss the use of detailed assessment of mental disorders using, for example, mobile health technologies (precision monitoring). Finally, we discuss future directions in research and practice and challenges to addressing mental disorders as a factor in precision medicine for diabetes. This review shows that several mental disorders are associated with a higher risk of type 2 diabetes and its complications, while there is suggestive evidence indicating that treating some mental disorders could contribute to the prevention of diabetes and improve diabetes outcomes. Using technologically enabled solutions to identify mental disorders could help individuals who stand to benefit from particular treatments. However, there are considerable gaps in knowledge and several challenges to be met before we can stratify treatment recommendations based on mental disorders. Overall, this review demonstrates that addressing mental disorders as a facet of precision medicine could have considerable value for routine diabetes care and has the potential to improve diabetes outcomes.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".