Exercise training modifies gut bacterial composition in normal and diabetic mice (LB434)
Bibliographic record
Abstract
Background: Intestinal microbiota are increasingly recognized as potential modifiers of whole‐body energy metabolism. Previous research has described changes in bacterial composition in diabetes; this study examines whether these differences can be altered with lifestyle treatment. Methods: Six‐week‐old diabetic (db/db) and control littermates (db+) were randomized into sedentary or exercise training groups for 6 weeks (n=9‐10/treatment). All animals consumed an identical, chow‐based diet. Exercise consisted of low‐intensity treadmill running (5d/week), and was ceased 48h prior to sacrifice. Cecal matter was collected from individual mice at sacrifice. Total bacterial DNA was extracted and quantified, and bacterial species were detected and profiled using qPCR and group specific primers. Genomic data is reported as the log transformed 16S rRNA copy number normalized for the amount of cecal matter analyzed. Results: Compared to db+ mice, db/db had higher Clostridium Difficile and lower Clostridium Perfringens (P蠄0.05). However, exercise training increased Clos. Perfringens levels in both db+ and db/db (P<0.001); in this way, exercise mitigated the diabetes‐induced reduction in Clos. Perfringens such that exercised db/db mice had levels (5.30 ± 0.09) similar to sedentary db+ (5.19 ± 0.06; P=0.30) and approached that of exercised db+ mice (5.49 ± 0.05; P=0.094). Exercise training independently reduced Bacteroidetes (P=0.009) and increased Clostridium Leptum (P蠄0.05) levels in both db+ and db/db animals. Finally, exercise training increased Bifidobacterium in db+ animals but not db/db mice (P<0.001 for interaction). Conclusion: Diabetes is associated with alterations in the gut bacteria profile, particularly in the Firmicutes species. Exercise training exerts independent effects on bacterial composition in both diabetic and non‐diabetic animals, and may rescue some diabetes‐associated alterations.
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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 | 0.001 | 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) | 0.003 | 0.001 |
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".