The Influence of Diet and Sex on the Gut Microbiota of Lean and Obese JCR:LA-cp Rats
Bibliographic record
Abstract
There is an increased interest in the gut microbiota as it relates to health and obesity. The impact of diet and sex on the gut microbiota in conjunction with obesity also demands extensive systemic investigation. Thus, the influence of sex, diet, and flaxseed supplementation on the gut microbiota was examined in the JCR:LA-cp rat model of genetic obesity. Male and female obese rats were randomized into four groups (n = 8) to receive, for 12 weeks, either (a) control diet (Con), (b) control diet supplemented with 10% ground flaxseed (CFlax), (c) a high-fat, high sucrose (HFHS) diet, or (d) HFHS supplemented with 10% ground flaxseed (HFlax). Male and female JCR:LA-cp lean rats served as genetic controls and received similar dietary interventions. Illumine MiSeq sequencing revealed a richer microbiota in rats fed control diets rather than HFHS diets. Obese female rats had lower alpha-diversity than lean female; however, both sexes of obese and lean JCR rats differed significantly in β-diversity, as their gut microbiota was composed of different abundances of bacterial types. The feeding of an HFHS diet affected the diversity by increasing the phylum Bacteroidetes and reducing bacterial species from phylum Firmicutes. Fecal short-chain fatty acids such as acetate, propionate, and butyrate-producing bacterial species were correspondingly impacted by the HFHS diet. Flax supplementation improved the gut microbiota by decreasing the abundance of Blautia and Eubacterium dolichum. Collectively, our data show that an HFHS diet results in gut microbiota dysbiosis in a sex-dependent manner. Flaxseed supplementation to the diet had a significant impact on gut microbiota diversity under both flax control and HFHS dietary conditions.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".