A High‐Fat High‐Fructose Diet Dysregulates the Homeostatic Crosstalk Between Gut Microbiome, Metabolome, and Immunity in an Experimental Model of Obesity
Bibliographic record
Abstract
SCOPE: Ample evidence supports the prominent role of gut-liver axis in perpetuating pathological networks of high-fat high-fructose (HFF) diet induced metabolic disorders, however, the molecular mechanisms are still not fully understood. Herein, this study aims to present a holistic delineation and scientific explanation for the crosstalk between the gut and liver, including the potential mediators involved in orchestrating the metabolic and immune systems. METHODS AND RESULTS: An experimental obesity-associated metaflammation rat model is induced with a HFF diet. An integrative multi-omics analysis is then performed. Following the clues illustrated by the multi-omics discoveries, putative pathways are subsequently validated by RT-qPCR and Western blotting. HFF diet leads to obese phenotypes in rats, as well as histopathological changes. Integrated omics analysis shows that there exists a strong interdependence among gut microbiota composition, intestinal metabolites, and innate immunity regulation in the liver. Some carboxylic acids may contribute to gut-liver communication. Moreover, activation of the hepatic LPS-TLR4 pathway in obesity is confirmed. CONCLUSION: HFF-intake disturbs gut flora homeostasis. Crosstalk between gut microbiota and innate immune system mediates hepatic metaflammation in obese rats, associated with LPS-TLR4 signaling pathway activation. Moreover, α-hydroxyisobutyric acid and some other organic acids may play a role as messengers in the liver-gut axis.
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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 | 0.001 | 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) | 0.002 | 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".