Native low-density lipoproteins are priming signals of the NLRP3 inflammasome/interleukin-1β pathway in human adipose tissue and macrophages
Bibliographic record
Abstract
Elevated plasma numbers of atherogenic apoB-lipoproteins (apoB), mostly as low-density lipoproteins (LDL), predict diabetes risk by unclear mechanisms. Upregulation of the NLRP3 inflammasome/interleukin-1 beta (IL-1β) system in white adipose tissue (WAT) is implicated in type 2 diabetes (T2D); however, metabolic signals that stimulate it remain unexplored. We hypothesized that (1) subjects with high-apoB have higher WAT IL-1β-secretion than subjects with low-apoB, (2) WAT IL-1β-secretion is associated with T2D risk factors, and (3) LDL prime and/or activate the WAT NLRP3 inflammasome. Forty non-diabetic subjects were assessed for T2D risk factors related to systemic and WAT glucose and fat metabolism. Regulation of the NLRP3 inflammasome was explored using LDL without/with the inflammasome's priming and activation controls (LPS and ATP). LDL induced IL1B-expression and IL-1β-secretion in the presence of ATP in WAT and macrophages. Subjects with high-apoB had higher WAT IL-1β-secretion independently of covariates. The direction of association of LDL-induced WAT IL-1β-secretion to T2D risk factors was consistently pathological in high-apoB subjects only. Adjustment for IL-1β-secretion eliminated the association of plasma apoB with T2D risk factors. In conclusion, subjects with high-apoB have higher WAT IL-1β-secretion that may explain their risk for T2D and may be related to LDL-induced priming of the NLRP3 inflammasome.ClinicalTrials.gov (NCT04496154): Omega-3 to Reduce Diabetes Risk in Subjects With High Number of Particles That Carry "Bad Cholesterol" in the Blood-Full Text View-ClinicalTrials.gov.
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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".