Impact of Dietary Fat on Murine Gut Microbiota and Fatty Liver
Bibliographic record
Abstract
Overconsumption of fat‐rich diets is a potential risk for obesity and metabolic disease such as non‐alcoholic fatty liver disease (NAFLD). Gut microbiota is an important factor that plays a role in regulating energy homeostasis and body weight control. An obesity‐associated gut microbiota can be induced by a high‐fat diet (HFD). Dietary intervention is one of the approaches for obesity management. The aim of our study was to investigate the impact of dietary fat intervention on the composition of gut microbiota. Male C57BL/6J mice were fed a control (10% kcal fat) or a HFD (60% kcal fat) for 7 weeks. A third group of mice was fed a HFD for 5 weeks followed by a control diet for 2 weeks (HFD + Control). DNA was extracted from cecal and colonic mucosa samples for amplification of V4 region of bacterial 16S rRNA genes and subjected to Illumina sequencing. Bioinformatics analyses were performed using QIIME. Statistical difference was determined by one‐way ANOVA and Permutational MANOVA at P < 0.05. At the end of 7 week trial, body weight gain of the control or the HFD+Control group was significantly lower than that of the HFD. Significant differences were observed on richness, abundance, diversity and functional properties of the gut microbiota in cecal and colonic mucosa between the control and the HFD group. The HFD+Control diet partially shifted microbiota profile to the control and reduced hepatic lipid accumulation. In conclusion, our results suggested that 5‐week HFD induced obesity‐associated microbiota. Withdrawal of HFD could prevent excess body weight gain and attenuated fatty liver while only partially restored the composition, diversity and profile of gut microbiota. Support or Funding Information The study was supported, in part, by NSERC, CIHR and St. Boniface Hospital Research Centre.
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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 | 0.001 |
| 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".