Genetic susceptibility to asthma increases the vulnerability to indoor air pollution
Bibliographic record
Abstract
Introduction Indoor air pollution and maternal smoking during pregnancy are associated with respiratory symptoms in infants, but little is known about the direct association with lung function or interactions with genetic risk factors. We examined associations of exposure to indoor particulate matter with a 50% cut-off aerodynamic diameter of 10 µm (PM10) and maternal smoking with infant lung function and the role of gene–environment interactions. Methods Data from the Drakenstein Child Health Study, a South African birth cohort, were analysed (n=270). Lung function was measured at 6 weeks and 1 year of age, and lower respiratory tract infection episodes were documented. We measured pre- and postnatal PM10exposures using devices placed in homes, and prenatal tobacco smoke exposure using maternal urine cotinine levels. Genetic risk scores determined from associations with childhood-onset asthma in the UK Biobank were used to investigate effect modifications. Results Pre- and postnatal exposure to PM10as well as maternal smoking during pregnancy were associated with reduced lung function at 6 weeks and 1 year as well as with lower respiratory tract infection in the first year. Due to a significant interaction between the genetic risk score and prenatal exposure to PM10, infants carrying more asthma-related risk alleles were more susceptible to PM10-associated reduced lung function (pinteraction=0.007). This interaction was stronger in infants with Black African ancestry (pinteraction=0.001) and nonexistent in children with mixed ancestry (pinteraction=0.876). Conclusions PM10and maternal smoking exposures were associated with reduced lung function, with a higher susceptibility for infants with an adverse genetic predisposition for asthma that also depended on the infant's ancestry.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".