Soluble Fiber Inulin Consumption Limits Alterations of the Gut Microbiota and Hepatic Fatty Acid Metabolism Caused by High-Fat Diet
Bibliographic record
Abstract
Diet shapes the gut microbiota which impacts hepatic lipid metabolism. Modifications in liver fat content are associated with metabolic disorders. We investigated the extent of dietary fat and fiber-induced alterations in the composition of gut microbiota and hepatic fatty acids (FAs). Mice were fed a purified low-fat diet (LFD) or high-fat diet (HFD) containing non-soluble fiber cellulose or soluble fiber inulin. HFD induced hepatic decreases in the amounts of C14:0, C16:1n-7, C18:1n-7 and increases in the amounts of C17:0, C20:0, C16:1n-9, C22:5n-3, C20:2n-6, C20:3n-6, and C22:4n-6. When incorporated in a LFD, inulin poorly affected the profile of FAs. However, when incorporated in a HFD, it (i) specifically led to an increase in the amounts of hepatic C18:0, C22:0, total polyunsaturated FAs (PUFAs), total n-6 PUFAs, C18:3n-3, and C18:2n-6, (ii) exacerbated the HFD-induced increase in the amount of C17:0, and (iii) prevented the HFD-induced increases in C16:1n-9 and C20:3n-6. Importantly, the expression/activity of some elongases and desaturases, as well as the gut microbiota composition, were impacted by the dietary fat and fiber content. To conclude, inulin modulated gut microbiota and hepatic fatty acid composition, and further investigations will determine whether a causal relationship exists between these two parameters.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".