Association of Parasomnia Symptoms with Risk of Childhood Asthma and the Role of Preterm Birth

Abstract

Purpose To examine whether parasomnia symptoms are associated with increased odds of childhood asthma and wheeze, and the role of preterm birth. Patients and Methods The Shanghai Children’s Allergy Study was cross-sectionally conducted in 31 kindergartens and 17 primary schools in Shanghai, China. After excluding the missing data of gestational week and child’s age, this study included a total of 16,487 individuals with a mean age of 7.74 years and 52.4% of males. The association between parasomnia symptoms and wheeze/asthma was assessed by univariate and multivariate analyses. The interaction effects of parasomnias and preterm birth were primarily evaluated by P for multiplicative interaction, and the relative excess risk due to interaction (RERI), attributable proportion due to interaction (AP), and synergy index (SI) were also measured. Results Parasomnias, especially rapid eye movement (REM) parasomnia symptoms, were associated with an increased risk of childhood wheeze/asthma, and the interaction between parasomnia and preterm birth exhibited an excess risk of current wheeze (RERI, 1.43; 95% CI, 0.41–2.45) and ever asthma (RERI, 0.75; 95% CI, 0.01–1.50). In the stratification analyses, the combination of parasomnia symptoms and preterm birth had higher odds of wheeze/asthma. And the odds of current wheeze (OR, 4.55; 95% CI, 1.69–12.25; p =0.003) and ever asthma (OR, 6.17; 95% CI, 2.36–16.11; p <0.001) were much higher in cumulative parasomnia symptoms plus very preterm birth. And sensitive analyses were further conducted in populations without sleep disordered breathing (SDB), and an allergen test subgroup, yielding similar results. Conclusion Parasomnia symptoms are associated with increased odds of childhood wheeze/asthma, and the odds were even higher in premature population. The findings suggest that parasomnia symptoms, as a developmental sleep disorder, are supposed to be closely watched among children who have asthma or are at risk for asthma, and that preterm children deserve more attention.

Use of feature importance statistics to accurately predict asthma attacks using machine learning: A cross-sectional cohort study of the US population

“…However, predicting the risk of an asthma attack is currently a largely qualitative process, and there is no widely used algorithm to help identify patients at risk [6]. Most studies on the subject have used univariate regression modeling to identify risk factors for asthma attacks, which do not determine the predictive performance of an optimal combination of factors in individual patients [7][8][9][10][11][12][13][14][15].…”