ABSTRACT
BACKGROUND: Early identification of children at risk of asthma can have significant clinical implications for effective intervention and treatment. This study aims to disentangle the relative timing and importance of early markers of asthma. METHODS: Using the CHILD Cohort Study, 132 variables measured in 1754 multi-ethnic children were included in the analysis for asthma prediction. Data up to 4 years of age was used in multiple machine learning models to predict physician-diagnosed asthma at age 5 years. Both predictive performance and variable importance was assessed in these models. RESULTS: Early-life data (≤1 year) has limited predictive ability for physician-diagnosed asthma at age 5 years (area under the precision-recall curve (AUPRC) < 0.35). The earliest reliable prediction of asthma is achieved at age 3 years, (area under the receiver-operator curve (AUROC) > 0.90) and (AUPRC > 0.80). Maternal asthma, antibiotic exposure, and lower respiratory tract infections remained highly predictive throughout childhood. Wheezing status and atopy are the most important predictors of early childhood asthma from among the factors included in this study. CONCLUSIONS: Childhood asthma is predictable from non-biological measurements from the age of 3 years, primarily using parental asthma and patient history of wheezing, atopy, antibiotic exposure, and lower respiratory tract infections. IMPACT: Machine learning models can predict physician-diagnosed asthma in early childhood (AUROC > 0.90 and AUPRC > 0.80) using ≥3 years of non-biological and non-genetic information, whereas prediction with the same patient information available before 1 year of age is challenging. Wheezing, atopy, antibiotic exposure, lower respiratory tract infections, and the child's mother having asthma were the strongest early markers of 5-year asthma diagnosis, suggesting an opportunity for earlier diagnosis and intervention and focused assessment of patients at risk for asthma, with an evolving risk stratification over time.
ABSTRACT
Introduction: Decreased sleep duration and increased screen time as early as preschool age may contribute to overweight and obesity. The effects of bedtime together with nocturnal sleep duration remain unclear with a paucity of data evaluating these associations longitudinally. We aim to evaluate the independent and joint effects of sleep duration, sleep bedtime, and screen time at 3 years of age on BMI status, particularly overweight and obesity by age 5 years. Methods: Data from 2185 participants of the CHILD Cohort Study were analyzed longitudinally using generalized estimating equations (GEE). Models included changes in overweight/obesity status from 3 to 5 years of age as outcome, and nocturnal sleep duration, bedtime, and daily screen time at 3 years of age as explanatory variables. The joint effects of nocturnal sleep time and excess screen time, late bedtime on overweight/obesity were subsequently analyzed. Results: The median nocturnal sleep time at 3 and 5 years of age was 11.0 hours/night [IQR 10.5, 11.5]. A total of 14.5% children went to bed after 9PM at 3 years and 7.2% at 5 years. Median screen time was 1.0 hr/day [IQR 1.0, 2.0] at both ages. Longitudinal analyses showed that sleeping less than 10.5 hours at age 3 years was associated with 46% greater odds of overweight/obesity by age 5 years (OR 1.46, 95% CI 1.07, 2.00). The risk was higher when coupled with late bedtime after 9pm (OR 1.60, 95% CI 1.12, 2.31). Children with both short nocturnal sleep duration and excess screen time (>1hr/day) had twice the associated risk of overweight/obesity by age 5 years (OR 1.96, 95% CI 1.34, 2.88). Conclusion: Nocturnal sleep duration and screen time are modifiable risk factors in young children, which may have important implications for obesity prevention as early as infancy.
ABSTRACT
Background: Respiratory infections in infancy are associated with the development of allergic asthma and atopy. Delineating whether symptomatic infections are a marker of atopic predisposition or contribute to atopic development is important for preventive strategies. We hypothesized that early, severe lower respiratory tract infections (LRTIs) may be a risk factor for the development of atopic disease. Objective: Our aim was to determine whether clinically defined, moderate-to-severe LRTIs in infancy are associated with the development of atopic dermatitis and allergic sensitization at preschool age. Methods: LRTI timing and severity in the first 18 months of life was defined by using the Canadian Healthy Infant Longitudinal Development study questionnaires. Polysensitization and atopic dermatitis were determined by standardized skin prick testing and structured clinical assessments. Longitudinal associations between LRTI severity and clinical outcomes at ages 3 years and 5 years were determined by adjusted repeated measures generalized estimation equations. Results: Moderate-to-severe LRTIs were associated with increased odds of polysensitization (odds ratio = 1.91 [95% CI = 1.16-3.15]; P = .014) and atopic dermatitis (odds ratio = 2.19 [95% CI 1.41-3.39]; P < .001) as compared with the odds in children with no history of LRTI in the first 18 months of life. The association between moderate-to-severe LRTI and polysensitization or atopic dermatitis remained robust after adjusting for sex; study site; breast-feeding duration; and mother, father, or both-parent atopy or asthma. Conclusions: These results highlight severe infant LRTI as an important risk factor for allergic and atopic disease (ie, polysensitization and atopic dermatitis), and they suggest that this risk is independent of maternal in utero environment, both-parent history of asthma, and both-parent genetic predisposition.
