Nutritional profile of rodent diets impacts experimental reproducibility in microbiome preclinical research
Bibliographic record
Abstract
The lack of reproducibility of animal experimental results between laboratories, particularly in studies investigating the microbiota, has raised concern among the scientific community. Factors such as environment, stress and sex have been identified as contributors, whereas dietary composition has received less attention. This study firstly evaluated the use of commercially available rodent diets across research institutions, with 28 different diets reported by 45 survey respondents. Secondly, highly variable ingredient, FODMAP (Fermentable Oligo-, Di-, Mono-saccharides And Polyols) and gluten content was found between different commercially available rodent diets. Finally, 40 mice were randomized to four groups, each receiving a different commercially available rodent diet, and the dietary impact on cecal microbiota, short- and branched-chain fatty acid profiles was evaluated. The gut microbiota composition differed significantly between diets and sexes, with significantly different clusters in β-diversity. Total BCFA were highest (p = 0.01) and SCFA were lowest (p = 0.03) in mice fed a diet lower in FODMAPs and gluten. These results suggest that nutritional composition of commercially available rodent diets impact gut microbiota profiles and fermentation patterns, with major implications for the reproducibility of results across laboratories. However, further studies are required to elucidate the specific dietary factors driving these changes.
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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 | 0.045 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".