Adiponectin and Adiponectin Receptors in Atherosclerosis
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Adiponectin is an abundantly secreted hormone that communicates information between the adipose tissue, and the immune and cardiovascular systems. In metabolically healthy individuals, adiponectin is usually found at high levels and helps improve insulin responsiveness of peripheral tissues, glucose tolerance, and fatty acid oxidation. Beyond its metabolic functions in insulin-sensitive tissues, adiponectin plays a prominent role in attenuating the development of atherosclerotic plaques, partially through regulating macrophage-mediated responses. In this context, adiponectin binds to its receptors, adiponectin receptor 1 (AdipoR1) and AdipoR2 on the cell surface of macrophages to activate a downstream signaling cascade and induce specific atheroprotective functions. Notably, macrophages modulate the stability of the plaque through their ability to switch between proinflammatory responders, and anti-inflammatory proresolving mediators. Traditionally, the extremes of the macrophage polarization spectrum span from M1 proinflammatory and M2 anti-inflammatory phenotypes. Previous evidence has demonstrated that the adiponectin-AdipoR pathway influences M1-M2 macrophage polarization; adiponectin promotes a shift toward an M2-like state, whereas AdipoR1- and AdipoR2-specific contributions are more nuanced. To explore these concepts in depth, we discuss in this review the effect of adiponectin and AdipoR1/R2 on 1) metabolic and immune responses, and 2) M1-M2 macrophage polarization, including their ability to attenuate atherosclerotic plaque inflammation, and their potential as therapeutic targets for clinical applications.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.001 |
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 it