How the infant gut microbiome shapes childhood allergic disease : data from the CHILD cohort study
Bibliographic record
Abstract
Allergic diseases are typically characterized by a Type 2 immune response, where immune cells are sensitized to an innocuous antigen, often at a compromised epithelial barrier. Re-exposure to the antigen then leads to a pro-inflammatory response. Symptoms can include hives, airway constriction, and dizziness; when this reaction is severe, it can be life-threatening. Therefore, identifying ways to prevent allergic diseases is paramount. Studies demonstrate that microorganisms and their genes in the infant gut (known as the gut microbiome) are associated with childhood allergic diseases. This is thought to be because the expansion of the human immune system during infancy coincides with gut microbial colonization. The gut microbiome may influence epithelial barrier integrity, immune cell gene expression, and the inflammatory state of the gut, potentially priming the immune environment before sensitization. However, the underlying relationship between the gut microbiome and allergies remains unclear. Thus, I hypothesize that the early-life gut microbiome influences the development of allergic outcomes, and that by defining the structure of the early-life stool metagenome, metabolome, and mycobiome, we can predict and ultimately prevent the development of allergic disease. To test this hypothesis, I used shotgun metagenomic and internal transcribed spacer 2 (ITS2) amplicon sequencing, together with metabolomics, to identify early life gut microbiome signatures associated with allergic diseases in the large Canadian Healthy Infant Longitudinal Development (CHILD) study. I discovered that 1-year microbiota maturation was negatively associated with four distinct pediatric allergies. Specifically, a core set of functional imbalances mediated the relationship between 1-year microbiota maturation and 5-year allergic diagnoses (βindirect=−2.28; p=0.0020). Next, in investigating antibiotic-associated perturbation of the gut environment and its link to allergies, I revealed that antibiotics during the first year of life, as opposed to later, were associated with increased atopic dermatitis (AD) risk (p<0.001). Finally, fungi were reliable and consistent biomarkers of the developing gut and a persistently infant-like mycobiome was linked to childhood AD (p=0.007). In conclusion, this thesis suggests that the compositional and functional makeup of the infant gut microbiome can be used to describe microbiome development and provides insight into the associations between the microbiome and childhood allergic disease.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".