RAGE Regulates the Metabolic and Inflammatory Response to High-Fat Feeding in Mice
Bibliographic record
Abstract
In mammals, changes in the metabolic state, including obesity, fasting, cold challenge, and high-fat diets (HFDs), activate complex immune responses. In many strains of rodents, HFDs induce a rapid systemic inflammatory response and lead to obesity. Little is known about the molecular signals required for HFD-induced phenotypes. We studied the function of the receptor for advanced glycation end products (RAGE) in the development of phenotypes associated with high-fat feeding in mice. RAGE is highly expressed on immune cells, including macrophages. We found that high-fat feeding induced expression of RAGE ligand HMGB1 and carboxymethyllysine-advanced glycation end product epitopes in liver and adipose tissue. Genetic deficiency of RAGE prevented the effects of HFD on energy expenditure, weight gain, adipose tissue inflammation, and insulin resistance. RAGE deficiency had no effect on genetic forms of obesity caused by impaired melanocortin signaling. Hematopoietic deficiency of RAGE or treatment with soluble RAGE partially protected against peripheral HFD-induced inflammation and weight gain. These findings demonstrate that high-fat feeding induces peripheral inflammation and weight gain in a RAGE-dependent manner, providing a foothold in the pathways that regulate diet-induced obesity and offering the potential for therapeutic intervention.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".