Prediction of allergic disease trajectories from birth up to adolescence.

backgroundAllergic diseases often develop jointly during early childhood. Potential disease trajectories and relevant early-life factors have been described, yet existing prediction approaches mostly focus on single allergic diseases cross-sectionally. Models addressing allergic multimorbidity and disease trajectories are lacking. We aim to predict allergic disease trajectories from birth up to adolescence using early-life factors.

methodsPreceding research using data from 4646 adolescents of the German birth cohorts GINIplus and LISA identified seven allergic disease trajectories up to the age of 15 years. A set of predictors comprising parental and perinatal factors, early allergic or respiratory symptoms, lifestyle and environmental factors was used with an XGBoost machine learning approach to perform multiclass classification. In a subsample (N = 2109), polygenic risk scores (PRS) for asthma, allergic rhinitis, atopic dermatitis, and any allergy were added to the predictor set.

resultsOur approach revealed moderate classification success (multiclass area under the curve (AUC) = 0.69). A macro-averaged sensitivity of 0.26 and specificity of 0.89 were obtained. The most important predictors were early-life skin rash, respiratory symptoms, and air pollution. In the sub-analysis, the PRS were among the factors with high importance, but the prediction performance in external test data was not improved.

conclusionsOur prediction success was comparable to established prediction scores while accounting for multiple allergic disease trajectories and using solely early-life factors. This study cannot yet provide reliable individual-level prediction in a clinical setting but can inform development of future work on this.