Promising use of metformin in treating neurological disorders: biomarker-guided therapies
Bibliographic record
Abstract
Neurological disorders are a diverse group of conditions that affect the nervous system and include neurodegenerative diseases (Alzheimer's disease, multiple sclerosis, Parkinson's disease, Huntington's disease), cerebrovascular conditions (stroke), and neurodevelopmental disorders (autism spectrum disorder). Although they affect millions of individuals around the world, only a limited number of effective treatment options are available today. Since most neurological disorders express mitochondria-related metabolic perturbations, metformin, a biguanide type II antidiabetic drug, has attracted a lot of attention to be repurposed to treat neurological disorders by correcting their perturbed energy metabolism. However, controversial research emerges regarding the beneficial/detrimental effects of metformin on these neurological disorders. Given that most neurological disorders have complex etiology in their pathophysiology and are influenced by various risk factors such as aging, lifestyle, genetics, and environment, it is important to identify perturbed molecular functions that can be targeted by metformin in these neurological disorders. These molecules can then be used as biomarkers to stratify subpopulations of patients who show distinct molecular/pathological properties and can respond to metformin treatment, ultimately developing targeted therapy. In this review, we will discuss mitochondria-related metabolic perturbations and impaired molecular pathways in these neurological disorders and how these can be used as biomarkers to guide metformin-responsive treatment for the targeted therapy to treat neurological disorders.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".