The NLRP3 inflammasome regulates adipose tissue metabolism
Bibliographic record
Abstract
Adipose tissue regulates metabolic homeostasis by participating in endocrine and immune responses in addition to storing and releasing lipids from adipocytes. Obesity skews adipose tissue adipokine responses and degrades the coordination of adipocyte lipogenesis and lipolysis. These defects in adipose tissue metabolism can promote ectopic lipid deposition and inflammation in insulin-sensitive tissues such as skeletal muscle and liver. Sustained caloric excess can expand white adipose tissue to a point of maladaptation exacerbating both local and systemic inflammation. Multiple sources, instigators and propagators of adipose tissue inflammation occur during obesity. Cross-talk between professional immune cells (i.e. macrophages) and metabolic cells (i.e. adipocytes) promote adipose tissue inflammation during metabolic stress (i.e. metaflammation). Metabolic stress and endogenous danger signals can engage pathogen recognition receptors (PRRs) of the innate immune system thereby activating pro-inflammatory and stress pathways in adipose tissue. The Nod-like receptor protein 3 (NLRP3) inflammasome can act as a metabolic danger sensor to a wide range of pathogen- and damage-associated molecular patterns (PAMPs and DAMPs). Activation of the NLRP3 inflammasome facilitates caspase-1 dependent production of the pro-inflammatory cytokines IL-1β and IL-18. Activation of the NLRP3 inflammasome can promote inflammation and pyroptotic cell death, but caspase-1 is also involved in adipogenesis. This review discusses the role of the NLRP3 inflammasome in adipose tissue immunometabolism responses relevant to metabolic disease. Understanding the potential sources of NLRP3 activation and consequences of NLRP3 effectors may reveal therapeutic opportunities to break or fine-tune the connection between metabolism and inflammation in adipose tissue during obesity.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".