Longitudinal disease-associated gut microbiome differences in infants with food protein-induced allergic proctocolitis
Bibliographic record
Abstract
BACKGROUND: Complex interactions between the gut microbiome and immune cells in infancy are thought to be part of the pathogenesis for the marked rise in pediatric allergic diseases, particularly food allergies. Food protein-induced allergic proctocolitis (FPIAP) is commonly the earliest recognized non-immunoglobulin E (IgE)-mediated food allergy in infancy and is associated with atopic dermatitis and subsequent IgE-mediated food allergy later in childhood. Yet, a large prospective longitudinal study of the microbiome of infants with FPIAP, including samples prior to symptom onset, has not been done. RESULTS: Here, we analyzed 954 longitudinal samples from 160 infants in a nested case-control study (81 who developed FPIAP and 79 matched controls) from 1 week to 1 year of age by 16S rRNA ribosomal gene sequencing as part of the Gastrointestinal Microbiome and Allergic Proctocolitis (GMAP) study. We found key differences in the microbiome of infants with FPIAP, most strongly a higher abundance of a genus of Enterobacteriaceae and a lower abundance of a family of Clostridiales during the symptomatic period. We saw some of these significant taxonomic differences even prior to symptom onset. There were no consistent longitudinal differences in richness or stability diversity metrics between infants with FPIAP and healthy controls. CONCLUSIONS: This study is the first to identify differences in the infant gut microbiome in children who develop FPIAP, some even before they develop symptoms, and provides a foundation for more mechanistic investigation into the pathogenesis of FPIAP and subsequent food allergic diseases in childhood. Video abstract.
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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 | 0.001 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".