Insulin impairs regulatory T cell function: implications for obesity (P1020)
Bibliographic record
Abstract
Abstract Chronic inflammation is known to drive metabolic dysregulation in obesity and type 2 diabetes. Although the precise origin of the unchecked inflammatory responses in obesity is unclear, it is known that over-production of pro-inflammatory cytokines such as TNF-α by innate immune cells has a key role in the development of metabolic dysfunction. One key hallmark of obesity is high levels of the pancreatic hormone insulin, and we hypothesized that there may be an unknown link between hyperinsulinemia and chronic inflammation. Here we show that high levels of insulin impair the ability of regulatory T cells to suppress inflammatory responses via effects on the AKT/mTOR signaling pathway. Insulin strongly activates AKT/mTOR signalling in regulatory T cells, leading to specific inhibition of the production of the anti-inflammatory cytokine IL-10. As a result, insulin hinders the ability of regulatory T cells to suppress the production of TNF-α by macrophages. Regulatory T cells from the visceral adipose tissue of hyperinsulinemic, obese mice also have a decrease in IL-10 production and a parallel increase in production of IFN-γ. These data suggest that the hyperinsulinemia associated with obesity may contribute to the development of obesity-associated inflammation via a previously unknown effect on regulatory T cells function.
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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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".