Diabetes and mood disorders: shared mechanisms and therapeutic opportunities
Bibliographic record
Abstract
OBJECTIVE: The objective of this manuscript is to provide a comprehensive and critical overview of the current evidence on the association between Diabetes mellitus (DM) and mood disorders [i.e., Major depressive disorder (MDD) and bipolar disorder (BD)], and therapeutic opportunities. METHODS: We searched in MEDLINE (via Ovid) for placebo-controlled clinical trials published in the last 20 years that assessed drug repurposing approaches for the treatment of DM or mood disorders. RESULTS: We found seven studies that aimed to verify the effects of antidepressants in patients diagnosed with DM, and eight studies that tested the effect of antidiabetic drugs in patients diagnosed with MDD or BD. Most studies published in the last two decades did not report a positive effect of antidepressants on glycemic control in patients with DM. On the other hand, antidiabetic drugs seem to have a positive effect on the treatment of MDD and BD. CONCLUSIONS: While effect of antidepressants on glycemic control in patients with DM is still controversial, the use of antidiabetic drugs may be a promising strategy for patients with MDD or BD. Prospective studies are still needed.Key pointsMood disorders in patients with DM affect glycemic control, potentially increasing mortality risk.The effect of antidepressants on glycemic control in patients with DM is still controversial. The coexistence of complicated DM and a mood disorders would require a careful, individualised, and comprehensive evaluation.Insulin resistance may increase the risk of depressive symptoms and is associated with worse outcomes in BD.The use antidiabetic drugs may be a promising strategy for patients with MDD or BD. However, prospective trials are needed to prove a potential antidepressant activity of antidiabetic drugs.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| 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".