IL-33 Reverses an Obesity-Induced Deficit in Visceral Adipose Tissue ST2+ T Regulatory Cells and Ameliorates Adipose Tissue Inflammation and Insulin Resistance
Bibliographic record
Abstract
Obesity is associated with insulin resistance and inflammation thought to be caused by a visceral adipose tissue (VAT)-localized reduction in immunoregulatory cells and increase in proinflammatory immune cells. We previously found that VAT regulatory T cells (Tregs) normally express high levels of IL-10 and that expression of this cytokine in VAT Tregs is specifically reduced in mice fed a high-fat diet. In this study, we further investigated the phenotype of VAT Tregs and found that the majority of IL-10-expressing Tregs in the VAT of lean mice also expressed the ST2 chain of the IL-33R. In addition to high expression of IL-10, ST2(+) Tregs in lean VAT expressed higher proportions of Th2-associated proteins, including GATA3 and CCR4, and Neuropillin-1 compared with ST2(-) Tregs. The proportion of ST2(+) Tregs in VAT was severely diminished in obese mice that had been fed a high-fat/sucrose diet, and this effect could be completely reversed by treatment with IL-33. IL-33 treatment also reversed VAT inflammation in obese mice and resulted in a reduction of hyperinsulinemia and insulin resistance. These data suggest that IL-33 contributes to the maintenance of the normal pool of ST2(+) Tregs in the VAT, and that therapeutic administration of IL-33 results in multiple anti-obesity effects, including the reversal of VAT inflammation and alleviation of insulin resistance.
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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) | 0.000 | 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) | 0.001 | 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".