Short Term High Fat Diet Induces Obesity‐Enhancing Changes in Mouse Gut Microbiota That are Partially Reversed by Cessation of the High Fat Diet
Bibliographic record
Abstract
The gut microbiota is proposed as a "metabolic organ" involved in energy utilization and is associated with obesity. Dietary intervention is one of the approaches for obesity management. Changes in dietary components have significant impacts on host metabolism and gut microbiota. In the present study, we examined the influence of dietary fat intervention on the modification of gut mucosa-associated microbiota profile along with body weight and metabolic parameter changes. Male C57BL/6J mice (6-week old) were fed a low fat diet (10% kcal fat) as a control or a high fat diet (HFD 60% kcal fat) for 7 weeks. In another group, mice were fed HFD for 5 weeks followed by low fat control diet for 2 weeks (HFD + Control). At 7 weeks, body weight gain, blood glucose and hepatic triacylglycerol levels of mice fed a HFD were significantly higher than that of the control group and the HFD + Control group. There were significant differences in the diversity and predicted functional properties of microbiota in the cecum and colon mucosa between the control group and the HFD group. HFD feeding reduced the ratio of Bacteroidetes to Firmicutes, a microbiota pattern often associated with obesity. The HFD + Control diet partially restored the diversity and composition of microbiota in the cecum to the pattern observed in mice fed a control diet. These results suggest that short-term high fat diet withdrawal can restore metabolic changes and prevent excess body weight gain, however, long-term dietary intervention may be required to optimize the restoration of gut microbiota in mouse.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".