Metformin and salicylate synergistically activate liver AMPK, inhibit lipogenesis and improve insulin sensitivity
Bibliographic record
Abstract
Metformin is the mainstay therapy for type 2 diabetes (T2D) and many patients also take salicylate-based drugs [i.e., aspirin (ASA)] for cardioprotection. Metformin and salicylate both increase AMP-activated protein kinase (AMPK) activity but by distinct mechanisms, with metformin altering cellular adenylate charge (increasing AMP) and salicylate interacting directly at the AMPK β1 drug-binding site. AMPK activation by both drugs results in phosphorylation of ACC (acetyl-CoA carboxylase; P-ACC) and inhibition of acetyl-CoA carboxylase (ACC), the rate limiting enzyme controlling fatty acid synthesis (lipogenesis). We find doses of metformin and salicylate used clinically synergistically activate AMPK in vitro and in vivo, resulting in reduced liver lipogenesis, lower liver lipid levels and improved insulin sensitivity in mice. Synergism occurs in cell-free assays and is specific for the AMPK β1 subunit. These effects are also observed in primary human hepatocytes and patients with dysglycaemia exhibit additional improvements in a marker of insulin resistance (proinsulin) when treated with ASA and metformin compared with either drug alone. These data indicate that metformin-salicylate combination therapy may be efficacious for the treatment of non-alcoholic fatty liver disease (NAFLD) and T2D.
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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 | 0.000 |
| 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) | 0.002 | 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".