Metformin, monoacylglycerol lipase expression, cognition and emotion recognition in people with multiple sclerosis and comorbid type II diabetes: A case-control study
Bibliographic record
Abstract
Abstract Background Diabetes (DM), a common comorbidity, results in poorer cognition in people with multiple sclerosis (PwMS). Metformin may be a treatment option given cognitive benefits. Metformin represses monoacylglycerol lipase (Mgll), accompanied by improvements in cognition in animals. Aims To determine 1) whether metformin represses Mgll in humans, 2) if Mgll correlates with cognition/emotion recognition, and 3) if cognition differs between groups. Methods A convenience sample of seventeen PwMS and DM on metformin, 4 with MS and DM not on metformin, 10 with MS, and 21 healthy controls completed BICAMS and measures of premorbid ability, emotion recognition, mood and fatigue. Blood draw established Mgll levels. T-tests determined group differences in Mgll. Correlational analyses examined if Mgll correlated with cognition. ANCOVA evaluated differences in cognition/emotion recognition. Results Given small samples, we combined groups to determine if metformin impacted Mgll regardless of diabetes status. Significant differences in Mgll ( t = -2.07, p = .05), suggested that metformin suppresses Mgll. No relationship was found between Mgll and cognition/emotion recognition. Differences were found between PwMS and DM compared to controls in verbal learning ( F = 5.85, p = .02) and memory ( F = 5.62, p = .02). Conclusions Metformin suppresses Mgll in humans suggesting metformin be evaluated as a potential MS treatment. Mgll did not correlate with cognition possibly due to sample size or methodology. Combined impact of MS and DM negatively impacts cognition, supporting literature demonstrating that vascular comorbidity increases risk of cognitive dysfunction. Findings support pursuing clinical trials evaluating metformin efficacy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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 | 0.000 |
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 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".