Immunometabolism of obesity and diabetes: microbiota link compartmentalized immunity in the gut to metabolic tissue inflammation
Bibliographic record
Abstract
The bacteria that inhabit us have emerged as factors linking immunity and metabolism. Changes in our microbiota can modify obesity and the immune underpinnings of metabolic diseases such as Type 2 diabetes. Obesity coincides with a low-level systemic inflammation, which also manifests within metabolic tissues such as adipose tissue and liver. This metabolic inflammation can promote insulin resistance and dysglycaemia. However, the obesity and metabolic disease-related immune responses that are compartmentalized in the intestinal environment do not necessarily parallel the inflammatory status of metabolic tissues that control blood glucose. In fact, a permissive immune environment in the gut can exacerbate metabolic tissue inflammation. Unravelling these discordant immune responses in different parts of the body and establishing a connection between nutrients, immunity and the microbiota in the gut is a complex challenge. Recent evidence positions the relationship between host gut barrier function, intestinal T cell responses and specific microbes at the crossroads of obesity and inflammation in metabolic disease. A key problem to be addressed is understanding how metabolite, immune or bacterial signals from the gut are relayed and transferred into systemic or metabolic tissue inflammation that can impair insulin action preceding Type 2 diabetes.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".