Secretory IgA modifies the association between early-life gut microbiota trajectories and childhood nonatopic wheeze
Bibliographic record
Abstract
Aims Wheezing in childhood is common and evidence is accumulating for the role of the gut microbiome in the development of atopic wheeze. Changes to the early-life gut microbiota and secretory IgA (SIgA) production have been linked to childhood disease; however, their connection to nonatopic wheeze is unknown. The objectives of the present study were to evaluate the relationships between early-life gut microbiota trajectories, SIgA and childhood nonatopic wheeze. Methods Early-life gut microbiota, SIgA and child outcome data were collected as part of the Canadian Healthy Infant Longitudinal Development (CHILD) cohort study on 1203 children. Gut microbiota trajectories were categorised as C1–C1, C1–C2, C2–C1 and C2–C2 based on low (cluster 1; C1) or high (cluster 2; C2) Bacteroides abundance in faecal samples collected at 3 and 12 months. SIgA was assessed in faecal samples at 3 months. Results The main outcome was nonatopic wheeze before age 5 years (n=105). Logistic regression analysis showed the C1–C2 trajectory, of low Bacteroides abundance at 3 months but higher Bacteroides abundance at 12 months, to be associated with increased adjusted odds ratio (aOR) for nonatopic wheeze (aOR 1.74, 95% CI 1.13–2.67). This was further increased if the child was not exclusively breastfed and had high SIgA level in combination with the C1–C2 trajectory (OR 4.10, 95% CI 1.15–14.59). Conclusions Nonatopic wheeze is associated with a depletion of Bacteroides in infancy, and in children not exclusively breastfed the risk is further increased among those with high endogenous SIgA levels. These results highlight the importance of the interplay between the gut microbiota and immune system development during critical periods in early life and how it is linked to nonatopic wheeze in childhood.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".