NLRP3 inflammasome activation pathways and their contribution to the pathophysiology of chronic inflammatory and metabolic diseases
Bibliographic record
Abstract
When Jürg Tschopp's laboratory first described the inflammasome concept in 2002, few predicted that a single multiprotein complex would become one of the most intensely investigated targets in immunology and metabolic medicine. The NLRP3 inflammasome a cytosolic sensor that activates caspase-1 and triggers the release of interleukin-1β (IL-1β) and interleukin-18 (IL-18) — has since been implicated in type 2 diabetes, atherosclerosis, gout, Alzheimer's disease, and non-alcoholic steatohepatitis, among others. This research investigated the activation kinetics, signaling intermediates, and downstream inflammatory outputs of NLRP3 in human peripheral blood mononuclear cells (PBMCs) from patients with chronic inflammatory and metabolic conditions. PBMCs were isolated from 96 participants (48 patients with metabolic syndrome and 48 age-matched healthy controls) recruited at the Department of Molecular Immunology, Toronto Metropolitan University, between April 2023 and September 2024. Cells were stimulated ex vivo with lipopolysaccharide (LPS) and ATP, and NLRP3 expression, caspase-1 activity, and cytokine release were measured over a 72-hour time course. NLRP3 mRNA expression peaked at 24 hours in both groups but reached 14.8-fold elevation in the metabolic syndrome group versus 8.3-fold in controls (p < 0.001). IL-1β secretion was 1.9 times higher and IL-18 was 1.7 times higher in metabolic syndrome PBMCs. Caspase-1 activity correlated strongly with both NLRP3 expression (r = 0.824) and circulating C-reactive protein levels (r = 0.716). These results confirm that the NLRP3 inflammasome is hyperactivated in metabolic syndrome and suggest that its upstream regulators may represent therapeutic targets for managing chronic sterile inflammation.
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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 | 0.001 | 0.001 |
| 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".