Patterns of Respiratory Symptoms and Asthma Diagnosis in School-Age Children: Three Birth Cohorts.

Abstract

Many studies used information on wheeze presence/absence to determine asthma-related phenotypes. We investigated whether clinically intuitive asthma subtypes can be identified by applying data-driven semi-supervised techniques to information on frequency and triggers of different respiratory symptoms. Partitioning Around Medoids clustering was applied to data on multiple symptoms and their triggers in school-age children from three birth cohorts: MAAS (n = 947, age 8 years), SEATON (n = 763, age 10) and ASHFORD (n = 584, age 8). 'Guided' clustering, incorporating asthma diagnosis, was used to select the optimal number of clusters. Five-cluster solution was optimal. Based on their clinical characteristics, including frequency of asthma diagnosis, we interpreted one cluster as 'Healthy'. Two clusters were characterised by high asthma prevalence (95.89% and 78.13%). We assigned children with asthma in these two clusters as 'persistent, multiple-trigger, more severe' (PMTS) and 'persistent, triggered by infection, milder' (PIM). Children with asthma in the remaining two clusters were assigned as 'mild-remitting wheeze' (MRW) and 'post-bronchiolitis resolving asthma' (PBRA). PBRA was associated with RSV bronchiolitis in infancy. In most children with asthma in this cluster wheezing resolved by age 5-6, and predominant symptoms were shortness of breath and chest tightness. Children in PBRA had the highest hospitalisation rates and wheeze exacerbations in infancy. From age 8 years (cluster derivation) to early adulthood (18-20 years), lung function was significantly lower, and FeNO and airway hyperreactivity significantly higher in PMTS compared to all other clusters. Patterns of coexisting symptoms identified by semi-supervised data-driven methods may reflect pathophysiological mechanisms of distinct subtypes of childhood wheezing disorders.