Impact of oral immunotherapy on diversity of gut microbiota in food‐allergic children
Bibliographic record
Abstract
BACKGROUND: Food allergies (FAs) are an increasing public health concern, particularly in children. Oral immunotherapy (OIT) is an emerging treatment strategy under clinical investigation for desensitization of children with FA to food allergens. Dysbiosis of the gut microbiota has been implicated in FAs, and various factors influence its composition; however, the impact of OIT on the gut microbiota remains largely unexplored. OBJECTIVE: This study aimed to identify the changes in diversity of the gut microbiota following OIT in children with FA. METHODS: Thirty children with FA (mean age 3.93 years, age range 2.00-14.00) undergoing oral immunotherapy targeting legumes (lentils, peanuts, peas), tree nuts (cashews, hazelnuts, pistachios), animal products (milk, egg), and fish and shellfish (salmon, shrimp), as well as seven non-allergic controls (mean age 2.65 years, age range 0.25-5.00) participated in this study. Fecal samples were collected before and after OIT from children with FA, and once from controls. The gut microbiota was profiled using 16S rRNA sequencing, followed by diversity and differential abundance analyses. Alpha and beta diversities were compared, and differential abundance was assessed. RESULTS: Beta diversity analysis revealed small but significant differences in microbial composition between children with FA before and after OIT, and between controls and children with FA before OIT. Differential abundance analysis showed that OIT induced a reversion of the abundance levels of Bacteroidota and Verrucomicrobiota toward those observed in controls. CONCLUSION: To our knowledge, this is the first study to investigate the impact of OIT on the gut microbiota in children with different FAs for identifying potential microbial biomarkers and convincingly demonstrated their interrelation. These findings may help improve and personalize FA treatment.
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".