Salsalate reduces atherosclerosis through AMPKβ1 in mice
Bibliographic record
Abstract
OBJECTIVE: Salsalate is a prodrug of salicylate that lowers blood glucose in people with type 2 diabetes. AMP-activated protein kinase (AMPK) is an αβγ heterotrimer which inhibits macrophage inflammation and the synthesis of fatty acids and cholesterol in the liver through phosphorylation of acetyl-CoA carboxylase (ACC) and HMG-CoA reductase (HMGCR), respectively. Salicylate binds to and activates AMPKβ1-containing heterotrimers that are highly expressed in both macrophages and liver, but the potential importance of AMPK and ability of salsalate to reduce atherosclerosis have not been evaluated. METHODS: mice with or without (-/-) germline or bone marrow AMPKβ1, respectively, were treated with salsalate, and atherosclerotic plaque size was evaluated in serial sections of the aortic root. Studies examining the effects of salicylate on markers of inflammation, fatty acid and cholesterol synthesis and proliferation were conducted in bone marrow-derived macrophages (BMDMs) from wild-type mice or mice lacking AMPKβ1 or the key AMPK-inhibitory phosphorylation sites on ACC (ACC knock-in (KI)-ACC KI) or HMGCR (HMGCR-KI). RESULTS: bone marrow. Reductions in atherosclerosis by salsalate were associated with reduced macrophage proliferation, reduced plaque lipid content and reduced serum cholesterol. In BMDMs, this suppression of proliferation by salicylate required phosphorylation of HMGCR and the suppression of cholesterol synthesis. CONCLUSIONS: These data indicate that salsalate suppresses macrophage proliferation and atherosclerosis through an AMPKβ1-dependent pathway, which may involve HMGCR phosphorylation and cholesterol synthesis. Since rapidly-proliferating macrophages are a hallmark of atherosclerosis, these data indicate further evaluation of salsalate as a potential therapeutic agent for treating atherosclerotic cardiovascular 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".