Childhood allergic diseases, including asthma, rhinitis and eczema, are prevalent conditions that share strong genetic and environmental components. Diagnosis relies on clinical history and measurements of allergen-specific IgE. We hypothesize that a multi-omics model could accurately diagnose childhood allergic disease. We show that nasal DNA methylation has the strongest predictive power to diagnose childhood allergy, surpassing blood DNA methylation, genetic risk scores, and environmental factors. DNA methylation at only three nasal CpG sites classifies allergic disease in Dutch children aged 16 years well, with an area under the curve (AUC) of 0.86. This is replicated in Puerto Rican children aged 9–20 years (AUC 0.82). DNA methylation at these CpGs additionally detects allergic multimorbidity and symptomatic IgE sensitization. Using nasal single-cell RNA-sequencing data, these three CpGs associate with influx of T cells and macrophages that contribute to allergic inflammation. Our study suggests the potential of methylation-based allergy diagnosis.
Child maltreatment is associated with asthma in adults. We examined whether lifetime major depressive disorder (MDD) or lifetime generalised anxiety disorder (GAD) mediate an association between child maltreatment and current asthma among 81 105 British adults in the UK Biobank who completed a mental health survey and had complete data on child maltreatment, GAD, MDD, asthma, and relevant covariates but no diagnosis of chronic obstructive pulmonary disease. Child maltreatment was ascertained based on answers to the five questions in the Childhood Trauma Screener. Two mediators, lifetime MDD and GAD, were assessed based on the Composite International Diagnostic Interview Short Form (CIDI-SF). Current asthma was defined as physician-diagnosed asthma and wheeze or whistling in the chest in the previous year. Logistic regression was used for the multivariable analysis of child maltreatment and current asthma, and a mediation analysis was conducted to estimate the contributions of lifetime MDD and lifetime GAD to the child maltreatment-current asthma association. In a multivariable analysis, any child maltreatment was associated with asthma (adjusted odds ratio [aOR]=1.22, 95% confidence interval [CI]=1.15 to 1.28, p<0.01). In a mediation analysis adjusted for household income, educational attainment, smoking status, pack-years of smoking, and other covariates, lifetime GAD and lifetime MDD explained 21.8% and 32.5%, respectively, of the child maltreatment-current asthma association. Similar results were obtained after excluding current smokers and former smokers with ≥10 pack-years of smoking from the mediation analysis. Our findings suggest that GAD and MDD mediate an association between child maltreatment and asthma in adults, independently of smoking.
Age-related macular degeneration (AMD) is the principal cause of blindness in developed countries, and its prevalence will increase to 288 million people in 2040. Therefore, automated grading and prediction methods can be highly beneficial for recognizing susceptible subjects to late-AMD and enabling clinicians to start preventive actions for them. Clinically, AMD severity is quantified by Color Fundus Photographs (CFP) of the retina, and many machine-learning-based methods are proposed for grading AMD severity. However, few models were developed to predict the longitudinal progression status, i.e. predicting future late-AMD risk based on the current CFP, which is more clinically interesting. In this paper, we propose a new deep-learning-based classification model (LONGL-Net) that can simultaneously grade the current CFP and predict the longitudinal outcome, i.e. whether the subject will be in late-AMD in the future time-point. We design a new temporal-correlation-structure-guided Generative Adversarial Network model that learns the interrelations of temporal changes in CFPs in consecutive time-points and provides interpretability for the classifier's decisions by forecasting AMD symptoms in the future CFPs. We used about 30,000 CFP images from 4,628 participants in the Age-Related Eye Disease Study. Our classifier showed average 0.905 (95% CI: 0.886–0.922) AUC and 0.762 (95% CI: 0.733–0.792) accuracy on the 3-class classification problem of simultaneously grading current time-point's AMD condition and predicting late AMD progression of subjects in the future time-point. We further validated our model on the UK Biobank dataset, where our model showed average 0.905 accuracy and 0.797 sensitivity in grading 300 CFP images.
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