Maternal autoimmune disease and risk of hospitalization for autoimmune disease, allergy, and cancer in offspring
Bibliographic record
Abstract
BACKGROUND: Children whose mothers have autoimmune disease may be at risk of developing immune-mediated disorders. We assessed the association between maternal autoimmune disease and risk of autoimmune disease, allergy, and cancer in offspring. METHODS: We analyzed a cohort of 1,011,623 children born in Canada between 2006 and 2019. We identified mothers who had autoimmune diseases and assessed hospitalizations for autoimmune disease, allergy, and cancer in offspring between birth and 14 years of age. We estimated hazard ratios (HR) for the association of maternal autoimmune disease with child hospitalization in adjusted Cox regression models. We used within-sibling analysis to control for genetic and environmental confounders. RESULTS: A total of 20,354 children (2.0%) had mothers with an autoimmune disease. Compared with no autoimmune disease, maternal autoimmune disease was associated with the risk of childhood hospitalization for autoimmune disease (HR 1.96, 95% CI 1.66-2.31) and allergy (HR 1.30, 95% CI 1.21-1.40), but was not significantly associated with cancer (HR 1.31, 95% CI 0.96-1.80). Type 1 diabetes, celiac disease, inflammatory arthritis, and systemic lupus erythematosus were among specific maternal autoimmune diseases most strongly associated with childhood hospitalization for autoimmune disease and allergy. The associations disappeared after controlling for genetic and environmental confounders in the within-sibling analysis. CONCLUSIONS: Maternal autoimmune disease is associated with an increased risk of autoimmune disease and allergy hospitalization in offspring, but the relationship appears to be confounded by genetic and environmental factors. Prenatal exposure to immunologic or pharmacologic products is not likely a direct cause of immune-mediated disease in children.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".