Dietary Vitamin B6 Deficiency Impairs Gut Microbiota and Host and Microbial Metabolites in Rats
Bibliographic record
Abstract
Vitamin B6 plays a crucial role as a cofactor in various enzymatic reactions but bacteria-produced vitamin B6 is not sufficient to meet host requirements. Our objective was to assess the impact of diet-derived vitamin B6 on gut microbiota and host serum metabolomics. Sprague–Dawley rats (n = 47) were fed a control, low B6 (LB6) or high B6 (HB6) diet for six weeks. Serum and cecal samples were collected for biochemical, metabolomics and gut microbiota profiling. There was a significant sex effect for gut microbiota and several metabolic markers. Bodyweight and percent body fat were significantly reduced in LB6 compared to control and HB6 rats. Microbial beta-diversity differed significantly between LB6 and the control and HB6 rats in both sexes. Lachnospiraceae_NK4A136_group and Bacteroides were the primary taxa driving the difference between LB6 and control. There was a significant separation of cecal and serum metabolites of LB6 compared to control and HB6 rats. In the cecum, arginine biosynthesis was impaired, while vitamin B6 metabolism, lysine degradation and nicotinate and nicotinamide metabolism were impaired in serum metabolite profiles. Cecal propionate and butyrate were significantly reduced in LB6 rats irrespective of sex. Host vitamin B6 deficiency but not excess significantly alters gut microbial composition and its metabolites.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".