EPA and DHA inhibit LDL-induced upregulation of human adipose tissue NLRP3 inflammasome/IL-1β pathway and its association with diabetes risk factors
Bibliographic record
Abstract
Elevated numbers of atherogenic lipoproteins (apoB) predict the incidence of type 2 diabetes (T2D). We reported that this may be mediated via the activation of the NLRP3 inflammasome, as low-density lipoproteins (LDL) induce interleukin-1 beta (IL-1β) secretion from human white adipose tissue (WAT) and macrophages. However, mitigating nutritional approaches remained unknown. We tested whether omega-3 eicosapentaenoic and docosahexaenoic acids (EPA and DHA) treat LDL-induced upregulation of WAT IL-1β-secretion and its relation to T2D risk factors. Twelve-week intervention with EPA and DHA (2.7 g/day, Webber Naturals) abolished baseline group-differences in WAT IL-1β-secretion between subjects with high-apoB (N = 17) and low-apoB (N = 16) separated around median plasma apoB. Post-intervention LDL failed to trigger IL-1β-secretion and inhibited it in lipopolysaccharide-stimulated WAT. Omega-3 supplementation also improved β-cell function and postprandial fat metabolism in association with higher blood EPA and mostly DHA. It also blunted the association of WAT NLRP3 and IL1B expression and IL-1β-secretion with multiple cardiometabolic risk factors including adiposity. Ex vivo, EPA and DHA inhibited WAT IL-1β-secretion in a dose-dependent manner. In conclusion, EPA and DHA treat LDL-induced upregulation of WAT NLRP3 inflammasome/IL-1β pathway and related T2D risk factors. This may aid in the prevention of T2D and related morbidities in subjects with high-apoB.Clinical Trail Registration 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".