Oral iron supplementation after antibiotic exposure induces a deleterious recovery of the gut microbiota
Bibliographic record
Abstract
BACKGROUND: Oral iron supplementation is commonly prescribed for anemia and may play an important role in the gut microbiota recovery of anemic individuals who received antibiotic treatment. This study aims to investigate the effects of iron supplementation on gut microbiota recovery after antibiotics exposure. RESULTS: Mice were subjected to oral antibiotic treatment with neomycin and metronidazole and were fed diets with different concentrations of iron. The composition of the gut microbiota was followed throughout treatment by 16S rRNA sequencing of DNA extracted from fecal samples. Gut microbiota functions were inferred using PICRUSt2, and short-chain fatty acid concentration in fecal samples was assessed by liquid-chromatography mass spectrometry. Iron supplementation after antibiotic exposure shifted the gut microbiota composition towards a Bacteroidetes phylum-dominant composition. At the genus level, the iron-supplemented diet induced an increase in the abundance of Parasutterella and Bacteroides, and a decrease of Bilophila and Akkermansia. Parasutterella excrementihominis, Bacteroides vulgatus, and Alistipes finegoldii, were more abundant with the iron excess diet. Iron-induced shifts in microbiota composition were accompanied by functional modifications, including an enhancement of the biosynthesis of primary bile acids, nitrogen metabolism, cyanoamino acid metabolism and pentose phosphate pathways. Recovery after antibiotic treatment increased propionate levels independent of luminal iron levels, whereas butyrate levels were diminished by excess iron. CONCLUSIONS: Oral iron supplementation after antibiotic therapy in mice may lead to deleterious changes in the recovery of the gut microbiota. Our results have implications on the use of oral iron supplementation after antibiotic exposure and justify further studies on alternative treatments for anemia in these settings.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".