The Role of the Intestinal Immune System in Diet-induced Obesity and Insulin Resistance
Bibliographic record
Abstract
Chronic low-grade inflammation of metabolic tissues is thought to be a major driver of obesity related insulin resistance and metabolic disease. While changes to the immune profile in adipose tissue during obesity has been extensively studied, alterations to the intestinal immune system and the contribution of these cells to metabolic disease remain largely unknown. This thesis aims to elucidate the immunological changes within the intestine during obesity-related insulin resistance with focus on the adaptive component of the intestinal immune system. Indeed, our main findings indicated a shift towards a pro-inflammatory environment within the intestine during high fat diet feeding in mice mainly characterized by increased IFNγ producing Th1 and CD8+ T cells, and reductions in regulatory T cells and IgA+ B and plasma cells. Mechanistic interactions between diet, gut microbiota, intestinal barrier and the intestinal immune system were also explored to delineate the implication of the intestinal network on the overall manifestation of metabolic disease. Existing therapies for metabolic disease such as metformin and bariatric surgery protocols were shown to influence the immunological environment within the intestine, while gut-specific anti-inflammatory therapies, namely 5-aminosalicylic acid, demonstrated promise in ameliorating obesity-related insulin resistance in preclinical models.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".