Maternal diabetes amplifies the influence of maternal asthma and smoke exposure on the development of asthma in offspring
Bibliographic record
Abstract
Perinatal programming is an emerging theory for the fetal origins of chronic disease. Maternal asthma and environmental tobacco smoke (ETS) are two of the best-known triggers for the perinatal programming of asthma, while the potential role of maternal diabetes has not been widely studied. The goal of this study was to determine if maternal diabetes contributes to the perinatal programming of asthma, and if so, whether its effect is additive or synergistic with respect to ETS exposure and maternal asthma. We studied 3,574 Canadian children, aged 7-8 yr, enrolled in a population-based birth cohort. Standardized questionnaires were completed by the children’s parents, and data were analyzed by multivariate logistic regression. Asthma was reported in 442 children (12.4%). Asthmatic children were more likely to have mothers, but not fathers, with diabetes. In children without maternal history of diabetes, ETS exposure increased the risk of child asthma by 1.4-fold (adjusted odds ratio, 1.40; 95% confidence interval, 1.13-1.73), while maternal asthma increased risk by 3.6-fold (3.59; 2.71-4.76). In children born to diabetic mothers, these effects were amplified to 5.7-fold (5.68; 1.18-27.37) and 11.3-fold (11.30; 2.26-56.38), respectively. There was no independent effect of maternal diabetes after adjusting for maternal asthma and ETS exposure (OR 0.65, 95%CI 0.16-2.56). Maternal diabetes contributes to the perinatal programming of child asthma by amplifying the detrimental effects of ETS exposure and maternal asthma.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".