Maternal atopic conditions and autism spectrum disorder: a systematic review
Bibliographic record
Abstract
Autism spectrum disorder (ASD) is a disabling neurodevelopmental condition with complex etiology. Emerging evidence has pointed to maternal atopy as a possible risk factor. It is hypothesized that maternal atopic disease during pregnancy can lead to increased levels of inflammatory cytokines in fetal circulation via placental transfer or increased production. These cytokines can then pass through the immature blood-brain barrier, causing aberrant neurodevelopment via mechanisms including premature microglial activation. The objective of this study is to systematically review observational studies that investigate whether a maternal history of atopic disease (asthma, allergy, or eczema/atopic dermatitis) is associated with a diagnosis of ASD in offspring. A search was conducted in Ovid MEDLINE, PsycINFO, and Embase databases for relevant articles up to November 2021; this was later updated in January 2022. Observational studies published in peer-reviewed journals were included. Data were synthesized and qualitatively analyzed according to the specific atopic condition. Quality assessment was done using the Newcastle-Ottawa Scale. Nine articles were identified, with all including asthma as an exposure, alongside four each for allergy and eczema. Findings were inconsistent regarding the association between a maternal diagnosis of either asthma, allergy, or eczema, and ASD in offspring, with variations in methodology contributing to the inconclusiveness. More consistent associations were demonstrated regarding maternal asthma that was treated or diagnosed during pregnancy. Evidence suggests that symptomatic maternal asthma during pregnancy could be associated with ASD in offspring, underscoring the importance of effective management of atopic conditions during pregnancy. Further research is needed, particularly longitudinal studies that use gold-standard assessment tools and correlate clinical outcomes with laboratory and treatment data.PROSPERO Registration Number and Date: CRD42018116656, 26.11.2018.
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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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".