Metformin suppresses vascular smooth muscle cell senescence by promoting autophagic flux
Bibliographic record
Abstract
INTRODUCTION: Vascular smooth muscle cell (VSMC) senescence in the vasculature results in vascular aging as well as age-related diseases, while metformin improves the inflamm-aging profile by enhancing autophagy. However, metformin's impact on VSMC senescence is largely undefined. OBJECTIVES: To test the hypothesis that metformin exerts an anti-senescence role by restoring autophagic activity in VSMCs and vascular tissues. METHODS: Animal models established by angiotensin II (Ang II) induction and physiological aging and senescent primary VSMCs from the aortas of elderly patients were treated with metformin. Cellular and vascular senescence were assessed by measuring the amounts of senescence-associated β-galactosidase and senescence markers, including p21 and p53. Autophagy levels were assessed by autophagy-related protein expression, transmission electron microscope, and autolysosome staining. In order to explore the underlying mechanism of the anti-senescence effects of metformin, 4D label-free quantitative proteomics and bioinformatic analyses were conducted, with subsequent experiments validating these findings. RESULTS: Ang II-dependent senescence was suppressed by metformin in VSMCs and vascular tissues. Metformin also significantly improved arterial stiffness and alleviated structural changes in aged arteries, reduced senescence-associated secretory phenotype (SASP), and improved proliferation and migration of senescent VSMCs. Mechanistically, the proteomic analysis indicated that autophagy might contribute to metformin's anti-senescence effects. Reduced autophagic flux was observed in Ang II-induced cellular and vascular senescence; this reduction was reversed by metformin. Specifically, metformin enhanced the autophagic flux at the autophagosome-lysosome fusion level, whereas blockade of autophagosome-lysosome fusion inhibited the anti-senescence effects of metformin. CONCLUSIONS: Metformin prevents VSMC and vascular senescence by promoting autolysosome formation.
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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) | 0.001 | 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".