Dose-Responsive Effects of Iron Supplementation on the Gut Microbiota in Middle-Aged Women
Bibliographic record
Abstract
Oral iron supplementation is the first-line treatment for addressing iron deficiency, a concern particularly relevant to women who are susceptible to sub-optimal iron levels. Nevertheless, the impact of iron supplementation on the gut microbiota of middle-aged women remains unclear. To investigate the association between iron supplementation and the gut microbiota, healthy females aged 40–65 years (n = 56, BMI = 23 ± 2.6 kg/m2) were retrospectively analyzed from the Alberta’s Tomorrow Project. Fecal samples along with various lifestyle, diet, and health questionnaires were obtained. The gut microbiota was assessed by 16S rRNA sequencing. Individuals were matched by age and BMI and classified as either taking no iron supplement, a low-dose iron supplement (6–10 mg iron/day), or high-dose iron (>100 mg/day). Compositional and functional analyses of microbiome data in relation to iron supplementation were investigated using various bioinformatics tools. Results revealed that iron supplementation had a dose-dependent effect on microbial communities. Elevated iron intake (>100 mg) was associated with an augmentation of Proteobacteria and a reduction in various taxa, including Akkermansia, Butyricicoccus, Verrucomicrobia, Ruminococcus, Alistipes, and Faecalibacterium. Metagenomic prediction further suggested the upregulation of iron acquisition and siderophore biosynthesis following high iron intake. In conclusion, adequate iron levels are essential for the overall health and wellbeing of women through their various life stages. Our findings offer insights into the complex relationships between iron supplementation and the gut microbiota in middle-aged women and underscore the significance of iron dosage in maintaining optimal gut health.
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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 | 0.001 |
| 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) | 0.001 | 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".