Repurposing Metformin for Vascular Disease
Bibliographic record
Abstract
Metformin has been used as an oral anti-hyperglycaemic drug since the late 1950s; however, following the release in 1998 of the findings of the 20-year United Kingdom Prospective Diabetes Study (UKPDS), metformin use rapidly increased and today is the first-choice anti-hyperglycaemic drug for patients with type 2 diabetes (T2D). Metformin is in daily use by an estimated 150 million people worldwide. Historically, the benefits of metformin as an anti-diabetic and cardiovascular-protective drug have been linked to effects in the liver, where it acts to inhibit gluconeogenesis and lipogenesis, as well as reduce insulin resistance and enhance peripheral glucose utilization. However, direct protective effects on the endothelium and effects in the gut prior to metformin absorption are now recognized as important. In the gut, metformin modulates the glucagon-like peptide- 1 (GLP-1) - gut-brain axis and impacts the intestinal microbiota. As the apparent number of putative tissue and cellular targets for metformin has increased, so has the interest in re-purposing metformin to treat other diseases that include polycystic ovary syndrome (PCOS), cancer, neurodegenerative diseases, and COVID-19. Metformin is also being investigated as an anti-ageing drug. Of particular interest is whether metformin provides the same level of vascular protection in individuals other than those with T2D, including obese individuals with metabolic syndrome, or in the setting of vascular thromboinflammation caused by SARS-CoV-2. In this review, we critically evaluate the literature to highlight clinical settings in which metformin might be therapeutically repurposed for the prevention and treatment of vascular disease.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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) | 0.010 | 0.003 |
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".