The Soy Peptide Phe–Leu–Val Reduces TNFα-Induced Inflammatory Response and Insulin Resistance in Adipocytes
Bibliographic record
Abstract
Obesity-induced adipose inflammation plays a crucial role in the development of obesity-induced metabolic disorders such as insulin resistance and type 2 diabetes. In the presence of obesity, hypertrophic adipocytes release inflammatory mediators, including tumor necrosis factor-alpha (TNFα) and monocyte chemoattractant protein-1 (MCP-1), which enhance the recruitment and activation of macrophages, and in turn augment adipose inflammation. We demonstrate that the soy peptide Phe-Leu-Val (FLV) reduces inflammatory responses and insulin resistance in mature adipocytes. Specifically, the soy peptide FLV inhibits the release of inflammatory cytokines (TNFα, MCP-1, and IL-6) from both TNFα-stimulated adipocytes and cocultured adipocytes/macrophages. This inhibition is mediated by the inactivation of the inflammatory signaling molecules c-Jun N-terminal kinase (JNK) and IκB kinase (IKK), and the downregulation of IκBα in the adipocytes. In addition, soy peptide FLV enhances insulin responsiveness and increases glucose uptake in adipocytes. More importantly, we, for the first time, found that adipocytes express peptide transporter 2 (PepT2) protein, and the beneficial action of the soy peptide FLV was disrupted by the peptide transporter inhibitor GlySar. These findings suggest that soy peptide FLV is transported into adipocytes by PepT2 and then downregulates TNFα-induced inflammatory signaling, thereby increasing insulin responsiveness in the cells. The soy peptide FLV, therefore, has the potential to prevent obesity-induced adipose inflammation and 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) | 0.000 | 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) | 0.001 | 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".