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Record W4407284937 · doi:10.1093/jcag/gwae059.075

A75 EARLY-LIFE IRON SUPPLEMENTATION SHAPES GUT MICROBIOTA AND MODULATES RECOVERY FROM GUT DYSBIOSIS

2025· article· en· W4407284937 on OpenAlexaffabout
T Maumy, Manuela M. Santos

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2025
Typearticle
Languageen
FieldMedicine
TopicDiet and metabolism studies
Canadian institutionsCentre Hospitalier de l’Université de Montréal
Fundersnot available
KeywordsGut floraDysbiosisIron supplementationDietary ironBiologyImmunologyPhysiologyMedicineIron deficiencyInternal medicine

Abstract

fetched live from OpenAlex

Abstract Background Iron is an essential micronutrient, but its supplementation carries significant risks to intestinal health. Excess free iron in the gut fosters pathogenic bacterial growth, disrupting the microbiota and increasing susceptibility to future intestinal stresses. Early life represents a critical window for microbiota establishment, yet little is known about the long-term effects of iron supplementation during this period. This raises concerns about how early iron exposure may impact the gut’s resilience to future stressors, such as gut dysbiosis, especially given the prevalence of intestinal stress and the widespread availability of over-the-counter iron supplements. Aims Our study aims to investigate how early-life iron excess influences the gut microbiota and its capacity to recover from two distinct dysbiosis models: 1- Colitis-induced dysbiosis via dextran sulfate sodium (DSS) 2- Antibiotic-induced dysbiosis via a metronidazole and ciprofloxacin cocktail. We hypothesize that early exposure to excess iron impairs microbiota recovery compared to exposure during adult life. Methods Using C57BL/6 mice, we compared the outcomes of iron-sufficient and iron-excess diets initiated at weaning and continued into adulthood. After a 5 week exposure and an iron-sufficient period of two weeks, dysbiosis was induced, followed by an 8-week recovery phase, with frequent stool sampling for 16S rRNA sequencing to track gut microbiota composition. A bioinformatic pipeline was developed to analyze microbiota recovery and the influence of iron exposure. Parallel experiments on adult mice were conducted to explore the role of age in recovery outcomes. Results Preliminary data indicate that the colitis model was effective, with disease severity scores confirming a stronger inflammatory response in mice exposed to excess iron during early life. Young mice in the iron-excess group showed heightened susceptibility to DSS-induced colitis compared to their iron-sufficient counterparts. Our bioinformatics workflow has been optimized for other datasets, showing reduced computational time while maintaining analytical precision. Conclusions These findings suggest that early-life iron excess impairs gut resilience, potentially predisposing individuals to heightened responses to gut stresses like colitis or antibiotic exposure. Ongoing 16S analysis will further delineate recovery patterns, providing critical insights into how iron supplementation during infancy may influence long-term intestinal health and informing future dietary recommendations for young infants. Funding Agencies Institut du Cancer de Montreal, Centre de recherche du CHUM, University of Montreal

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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.

Opus teacher head0.007
GPT teacher head0.229
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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