Associations Between Eczema and Attention Deficit Hyperactivity Disorder Symptoms in Children
Bibliographic record
Abstract
Background: Epidemiological studies suggest a link between eczema and attention deficit hyperactivity disorder (ADHD), but underlying mechanisms have not been examined. Objective: We aim to investigate the association between eczema and subsequent ADHD symptoms in the Growing Up in Singapore Towards healthy Outcomes cohort and explore the role of pro-inflammatory cytokines and gut microbiome. Methods: The modified International Study of Asthma and Allergies in Childhood questionnaire and Computerized Diagnostic Interview Schedule for Children Version IV were administered to assess reported eczema within the first 18 months and presence of ADHD symptoms at 54 months, respectively. Skin prick testing at 18 months, cytokines in maternal blood during pregnancy and cord blood and the mediating role of the gut microbiome at 24 months were assessed. Results: After adjusting for confounders, eczema with or without a positive skin prick test was associated with doubling the risk of ADHD symptoms. No differences in maternal and cord blood cytokines were observed in children with and without eczema, or children with and without ADHD. Gut microbiome dysbiosis was observed in children with eczema and children with ADHD. Children with eczema also had lower gut bacterial Shannon diversity. However, the relationship between eczema and ADHD was not mediated by gut microbiome. Conclusion: Early life eczema diagnosis is associated with a higher risk of subsequent ADHD symptoms in children. We found no evidence for underlying inflammatory mechanism or mediation by gut microbiome dysbiosis. Further research should evaluate other mechanisms underlying the link between eczema and ADHD. Clinical Trial Registration: [https://clinicaltrials.gov/ct2/show/NCT01174875], identifier [NCT01174875].
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".