Potent Anti-inflammatory Effects of the Adiponectin Receptor Agonist, ALY688
Bibliographic record
Abstract
Abstract Low circulating levels of adiponectin are associated with increased inflammation in a wide range of disease states. In particular, this applies to obesity-related diseases such as type 2 diabetes, heart failure and non-alcoholic fatty liver disease. Numerous studies have shown that adiponectin exerts anti-inflammatory and beneficial metabolic effects. Hence there has been tremendous interest in developing adiponectin-based therapeutics. Here we examined the effect of ALY688, a small peptide adiponectin receptor agonist, on lipopolysaccharide (LPS)-driven inflammation in vivo and in vitro. We show that ALY688 administration significantly diminished LPS-driven production of pro-inflammatory cytokines and chemokines (i.e., IL-1β, TNF-α, IL-12, IFN-β, MCP-1, KC, RANTES) in mice. Moreover, mice injected with ALY688 exhibited a rapid increase in the anti-inflammatory cytokine, TGF-β. Apart from an increase in total CD3+ T cells, there were no marked changes in the circulating immune cells following ALY688 injection. No alterations in cytokine profile was seen in normal mice given ALY688, suggesting that the effects of ALY688 are context-dependent. Mechanistically, we employed phospho-flow cytometry to demonstrate that circulating immune cells responded rapidly to ALY688 injection by upregulating p-AMPK and p-P38, two well established adiponectin downstream signaling events. These results were confirmed in human peripheral blood mononuclear cells (PBMCs), where ALY688 treatment led to a robust increase in phospho-AMPK and phospho-P38. Altogether, these results show that the adiponectin agonist, ALY688, has strong anti-inflammatory effects by triggering AMPK and P38 activation in vivo and in vitro.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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".