Metformin: Repurposing Opportunities for Cognitive and Mood Dysfunction
Bibliographic record
Abstract
BACKGROUND: Cognitive deficits differentially affect individuals with type 2 diabetes mellitus (T2DM) and mood disorders. Accumulating evidence implicates disturbances in metabolism as salient to cognitive function. Thus, the mitigation of metabolic disturbances may preserve or ameliorate cognitive function. This review aims to evaluate available evidence investigating the effects of metformin on cognitive function as well as summarize putative mechanistic properties related to these clinical effects. METHODS: A PubMed search was conducted using the search words including, but not limited to: metformin, Major Depressive Disorder, type 2 diabetes mellitus, and cognitive dysfunction. All English language articles published from 1990 to July 2014 were reviewed. RESULTS: Extant preclinical and clinical data have been mixed, wherein both cognitive disruption and pro-cognitive effects have been reported with the administration of metformin. Sound mechanistic evidence supports metformin as a treatment; however, the heterogeneity of study designs has contributed to an inability to arrive at an unequivocal conclusion regarding metformin effects upon cognition. CONCLUSION: Available evidence does not provide a robust signal for improvement in cognition in either mood disorder or T2DM samples. Notwithstanding, it is premature to label metformin as a "no-go" agent for further testing and development for cognitive dysfunction. A well designed, proof-of-concept trial of metformin investigating its possible cognitive effects in mood disorders is therefore warranted.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".