Metformin attenuates white matter microstructural changes in Alzheimer’s disease
Bibliographic record
Abstract
Objectives: A body of evidence suggests that individuals with diabetes face an elevated susceptibility to developing Alzheimer's disease when compared to the general populace. Prior research has indicated the potential of metformin to confer a protective influence in postponing dementia onset among diabetic patients. Nevertheless, data are scarce regarding the impact of metformin on microstructural alterations. The primary objective of this study is to explore the influence of metformin on white matter microstructural change in non-demented individuals with diabetes.Methods: We entered 113 non-demented diabetic subjects including 77 mild cognitive impairment (MCI), and 36 cognitively healthy individuals from Alzheimer's disease Neuroimaging Initiative (ADNI) which were then categorized as metformin users and non-users. We used the ANCOVA model to measure the association between metformin use and DTI values.Results: Results of the univariate model indicate that metformin users had a higher FA value left hippocampal cingulum (p = 0.003) and right internal capsule (p = 0.004). Moreover, the MD value of the right inferior frontal-occipital fasciculus was lower in those who used metformin compared to those not use it (p = 0.027).Conclusion: Our results revealed that metformin has protective effects on brain microstructural changes in elderly individuals with diabetes who do not exhibit signs of dementia. A comparison of the groups yielded compelling evidence of reduced neurodegeneration among those utilizing metformin.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".