Crosstalk between intestinal microbiota, adipose tissue and skeletal muscle as an early event in systemic low‐grade inflammation and the development of obesity and diabetes
Bibliographic record
Abstract
Obesity is associated with a systemic chronic low-grade inflammation that contributes to the development of metabolic disorders such as cardiovascular diseases and type 2 diabetes. However, the etiology of this obesity-related pro-inflammatory process remains unclear. Most studies have focused on adipose tissue dysfunctions and/or insulin resistance in skeletal muscle cells as well as changes in adipokine profile and macrophage recruitment as potential sources of inflammation. However, low-grade systemic inflammation probably involves a complex network of signals interconnecting several organs. Recent evidences have suggested that disturbances in the composition of the gut microbial flora and alterations in levels of gut peptides following the ingestion of a high-fat diet may be a cause of low-grade systemic inflammation that may even precede and predispose to obesity, metabolic disorders or type 2 diabetes. This hypothesis is appealing because the gastrointestinal system is first exposed to nutrients and may thereby represent the first link in the chain of events leading to the development of obesity-associated systemic inflammation. Therefore, the present review will summarize the latest advances interconnecting intestinal mucosal bacteria-mediated inflammation, adipose tissue and skeletal muscle in a coordinated circuitry favouring the onset of a high-fat diet-related systemic low-grade inflammation preceding obesity and predisposing to metabolic disorders and/or type 2 diabetes. A particular emphasis will be given to high-fat diet-induced alterations of gut homeostasis as an early initiator event of mucosal inflammation and adverse consequences contributing to the promotion of extended systemic inflammation, especially in adipose and muscular tissues.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".