Iron Supplements Modulate Colon Microbiota Composition and Potentiate the Protective Effects of Probiotics in Dextran Sodium Sulfate-induced Colitis
Bibliographic record
Abstract
BACKGROUND: Iron is an important nutrient for both the host and colonizing bacteria. Oral iron supplementation may impact the composition of the microbiota and can be particularly damaging to patients suffering from inflammatory bowel disease (IBD). However, patients with IBD may require iron supplementation to treat their anemia. METHODS: We fed mice with diets supplemented with ferrous sulfate at different doses (5, 50, and 500 mg of iron/kg chow) and with different iron formulations (ferrous sulfate, ferrous bisglycinate and ferric ethylenediaminetetraacetic acid [FEDTA]), and analyzed the effects on the composition of the gut microbiota by 16S ribosomal RNA gene sequencing. Using the dextran sodium sulfate (DSS)-induced colitis mouse model, we investigated the effects of iron supplementation in colitis severity, as well as the use of the probiotic Escherichia coli Nissle 1917 (EcN) in combination with iron supplementation. RESULTS: Iron supplementation at different doses induced shifts in the gut microbial communities and inferred metabolic pathways. However, depending on the iron formulation used in the diets, iron supplementation during dextran sodium sulfate-induced colitis was either beneficial (ferrous bisglycinate) or highly detrimental (FEDTA). Finally, the beneficial effect of the probiotic EcN in the dextran sodium sulfate-induced colitis model was potentiated by oral iron supplementation with ferrous sulfate. CONCLUSIONS: These results show that the iron formulations used to treat iron deficiency influence the gut microbiota and colitis in mice and suggest that distinct iron compounds may be of particular relevance to patients with IBD. In addition, the beneficial action of probiotics in IBD may be enhanced by oral iron supplementation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".