Metformin to decrease COVID-19 severity and mortality: Molecular mechanisms and therapeutic potential
Bibliographic record
Abstract
The COVID-19 pandemic caused by the coronavirus SARS-CoV-2 has become a serious challenge for medicine and science. Analysis of the molecular mechanisms associated with the clinical manifestations and severity of COVID-19 has identified several key points of immune dysregulation observed in SARS-CoV-2 infection. For diabetic patients, factors including higher binding affinity and virus penetration, decreased virus clearance and decreased T cell function, increased susceptibility to hyperinflammation, and cytokine storm may make these patients susceptible to a more severe course of COVID-19 disease. Metabolic changes induced by diabetes, especially hyperglycemia, can directly affect the immunometabolism of lymphocytes in part by affecting the activity of the mTOR protein kinase signaling pathway. High mTOR activity can enhance the progression of diabetes due to the activation of effector proinflammatory subpopulations of lymphocytes and, conversely, low activity promotes the differentiation of T-regulatory cells. Interestingly, metformin, an extensively used antidiabetic drug, inhibits mTOR by affecting the activity of AMPK. Therefore, activation of AMPK and/or inhibition of the mTOR-mediated signaling pathway may be an important new target for drug therapy in COVID-19 cases mostly by reducing the level of pro-inflammatory signaling and cytokine storm. These suggestions have been partially confirmed by several retrospective analyzes of patients with diabetes mellitus hospitalized for severe COVID-19.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".