Adiponectin primes human monocytes into alternative anti-inflammatory M2 macrophages
Bibliographic record
Abstract
Altered macrophage kinetics is a pivotal mechanism of visceral obesity-induced inflammation and cardiometabolic risk. Because monocytes can differentiate into either proatherogenic M1 macrophages or anti-inflammatory M2 macrophages, approaches that limit M1 while promoting M2 differentiation represent a unique therapeutic strategy. We hypothesized that adiponectin may prime human monocytes toward the M2 phenotype. Adiponectin promoted the alternative activation of human monocytes into anti-inflammatory M2 macrophages as opposed to the classically activated M1 phenotype. Adiponectin-treated cells displayed increased M2 markers, including the mannose receptor (MR) and alternative macrophage activation-associated CC chemokine-1. Incubation of M1 macrophages with adiponectin-treated M2-derived culture supernatant resulted in a pronounced inhibition of tumor necrosis factor-alpha and monocyte chemotactic protein-1 secretion. Activation of human monocytes into M2 macrophages by adiponectin was mediated, in addition to AMP-activated protein kinase and peroxisome proliferator-activated receptor (PPAR)-gamma, via PPAR-alpha. Furthermore, macrophages isolated from adiponectin knockout mice demonstrated diminished levels of M2 markers such as MR, which were restored with adiponectin treatment. We report a novel immunoregulatory mechanism through which adiponectin primes human monocyte differentiation into anti-inflammatory M2 macrophages. Conditions associated with low adiponectin levels, such as visceral obesity and insulin resistance, may promote atherosclerosis, in part through aberrant macrophage kinetics.
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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".