Maternal history of asthma modifies the risk of childhood persistent asthma associated with maternal age at birth: Results from a large prospective cohort in Canada

Relation of Maternal Pre-Pregnancy Factors and Childhood Asthma: A Cross-Sectional Survey in Pre-School Children Aged 2-5 Years Old

Background and Objectives: Asthma constitutes a constant, prolonged, inflammation-related pulmonary disorder in childhood with serious public health concerns. Several maternal risk factors can enhance the prevalence of its development in this stage of life; however, the currently available data remain contradictory and/or inconsistent. We aim to evaluate the potential impacts of mothers' sociodemographic, anthropometric and prenatal and perinatal factors on the prevalence of developing asthma in pre-school children. Materials and Methods: This is a retrospective cross-sectional survey, which includes 5133 women and their matched pre-school children. Childhood asthma was diagnosed using validated questionnaires. Statistical analysis was accomplished to evaluate whether maternal sociodemographic, anthropometric and prenatal and perinatal factors can increase the probability of childhood asthma in pre-school age. Results: A prevalence of 4.5% of childhood asthma was recorded in pre-school age. Maternal age and pre-pregnancy overweight and obesity, caesarean section, gestational diabetes and hypertension and not breastfeeding were associated with childhood asthma after adjustment for multiple confounding factors. Conclusion: Our research showed that several maternal factors increase the prevalence of childhood asthma in pre-school age. Suitable and effective health policies and strategies should be taken into account to confront the predominant maternal factors that increase its prevalence in pre-school age.

Interleukin 1 receptor-like 1 rs13408661/13431828 polymorphism is associated with persistent post-bronchiolitis asthma at school age

Aim

Interleukin (IL) 1 receptor‐like 1, encoded by the IL1RL1 gene, is a receptor for IL‐33. In European birth cohorts, IL1RL1 rs102082293, rs10204137 (rs4988955), rs13424006 and rs13431828 (rs13048661) variations were associated with asthma at school age. In a Dutch multi‐centre study, IL1RL1 rs1921622 variation was associated with severe bronchiolitis. We evaluated the associations of these five IL1RL1 variations with asthma and lung function at school age after hospitalisation for bronchiolitis in infancy.

Methods

Follow‐up data, including impulse oscillometry at age 5–7 and flow‐volume spirometry at age 11–13 years, and the IL1RL1 genotype data were available for 141 children followed until 5–7 and for 125 children followed until 11–13 age years after bronchiolitis in infancy. The IL1RL1 rs10204137 and rs4988955, and the IL1RL1 rs13048661 and rs13431828, are 100% co‐segregating in the Finnish population.

Results

The variant IL1RL1 rs13048661/13431828 genotype was constantly associated with increased asthma risk by various definitions at 5–7 and 11–13 years of ages. The result was confirmed with analyses adjusted for current confounders and early‐life environment‐related factors. Statistical significances were lost, when maternal asthma and atopic dermatitis in infancy were included in the model.

Conclusion

IL1RL1 rs13048661/13431828 variation was associated with post‐bronchiolitis asthma outcomes at school age.

Machine Learning for Predicting the Risk for Childhood Asthma Using Prenatal, Perinatal, Postnatal and Environmental Factors

The prevalence rate for childhood asthma and its associated risk factors vary significantly across countries and regions. In the case of Morocco, the scarcity of available medical data makes scientific research on diseases such as asthma very challenging. In this paper, we build machine learning models to predict the occurrence of childhood asthma using data from a prospective study of 202 children with and without asthma. The association between different factors and asthma diagnosis is first assessed using a Chi-squared test. Then, predictive models such as logistic regression analysis, decision trees, random forest and support vector machine are used to explore the relationship between childhood asthma and the various risk factors. First, data were pre-processed using a Chi-squared feature selection, 19 out of the 36 factors were found to be significantly associated (p-value < 0.05) with childhood asthma; these include: history of atopic diseases in the family, presence of mites, cold air, strong odors and mold in the child's environment, mode of birth, breastfeeding and early life habits and exposures. For asthma prediction, random forest yielded the best predictive performance (accuracy = 84.9%), followed by logistic regression (accuracy = 82.57%), support vector machine (accuracy = 82.5%) and decision trees (accuracy = 75.19%). The decision tree model has the advantage of being easily interpreted. This study identified important maternal and prenatal risk factors for childhood asthma, the majority of which are avoidable. Appropriate steps are needed to raise awareness about the prenatal risk factors.

Childhood food insecurity and incident asthma: A population-based cohort study of children in Ontario, Canada

BACKGROUND

Childhood food insecurity has been associated with prevalent asthma in cross-sectional studies. Little is known about the relationship between food insecurity and incident asthma.

METHODS

We used administrative databases linked with the Canadian Community Health Survey, to conduct a retrospective cohort study of children <18 years in Ontario, Canada. Children without a previous diagnosis of asthma who had a household response to the Household Food Security Survey Module (HFSSM) were followed until March 31, 2018 for new asthma diagnoses using a validated administrative coding algorithm. We used multivariable Cox proportional hazard models to examine the association between food insecurity and incident asthma, and adjusted models sequentially for clinical and clinical/socioeconomic risk factors. As additional analyses, we examined associations by HFSSM respondent type, severity of food insecurity, and age of asthma diagnosis. Moreover, we assessed for interaction between food security and child's sex, household smoking status, and maternal asthma on the risk of incident asthma.

RESULTS

Among the 27,746 included children, 5.1% lived in food insecure households. Over a median of 8.34 years, the incidence of asthma was 7.33/1000 person-years (PY) among food insecure children and 5.91/1000 PY among food secure children (unadjusted hazard ratio [HR] 1.24, 95% CI 1.00 to 1.54, p = 0.051). In adjusted analyses associations were similar (HR 1.16, 95% CI 0.91 to 1.47, p = 0.24 adjusted for clinical risk factors, HR 1.24, 95% CI 0.97 to 1.60, p = 0.09 adjusted for clinical/socioeconomic factors). Associations did not qualitatively change by HFSSM respondent type, severity of food insecurity, and age of asthma diagnosis. There was no evidence of interaction in our models.

CONCLUSIONS

Food insecure children have numerous medical and social challenges. However, in this large population-based study, we did not observe that childhood food insecurity was associated with an increased risk of incident asthma when adjusted for important clinical and socioeconomic confounders.