Identifying the Impact of Early-Life Lower Respiratory Tract Infections on the Development of Preschool Asthma and Atopic Disease in the CHILD Study
Bibliographic record
Abstract
This dissertation provides an exploration of the association between lower respiratory tract infections (LRTIs) in early life and the development of preschool asthma and atopic diseases in Canadian children, utilizing a large bio-informatics dataset from the CHILD Study. By analyzing lung function parameters, inflammatory serum biomarkers, antibiotic usage, and gut microbiome profiles, this dissertation outlines associations that contribute to our understanding of these complex diseases. Early-life LRTIs were found to correlate with low neonatal lung function and changes in ventilation inhomogeneity, as assessed by spirometry and the Multiple Breath Washout test. LRTIs were linked with modifications in serum proteins and immune responses, shedding light on potential pathways leading to asthma development. This dissertation also confirmed a substantial role of systemic antibiotic exposure in early life, particularly when prescribed for respiratory indications, on the risk of preschool asthma. A similar pattern was observed for the development of atopic dermatitis (AD), where antibiotic-induced shifts in microbial populations and metabolic functions suggest a link between gut microbiome perturbations and allergic diseases. We emphasize the critical period of the first year of life, where antibiotic administration had a dose-response relationship with an increased AD risk. Moreover, the dissertation outlines specific microbiome alterations—such as decline of beneficial short chain fatty acid producing bacteria like Bifidobacterium and Eubacterium spp. and changes in functional pathways—linked to both AD and antibiotic use. These findings suggest potential biomarkers for predicting and possibly preventing AD development. In summary, this research challenges traditional hypotheses of LRTI impact on asthma and atopic disease, instead suggesting that the relationship between LRTIs and these diseases involves dynamic interactions with the microbiome. This paradigm shift offers new avenues for prevention and treatment strategies and highlights the importance of careful systemic antibiotic use in early childhood and its impact on long-term health outcomes.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".