Clustering lung function and symptom profiles for asthma risk stratification.

Asthma is a heterogeneous condition often studied through wheeze alone, yet the interplay between lung function and reported symptoms remains underexplored. To capture this heterogeneity, we applied Bayesian Profile Regression to data from school-age children in two prospective birth cohorts, integrating airway hyperresponsiveness, lung function, bronchodilator reversibility, allergic sensitisation, reported symptoms, and physician diagnosis. In the Manchester Allergy and Asthma Study (discovery cohort), five reproducible clusters were identified: HA-LLF (high asthma-low lung function), HA-NLF (high asthma normal lung function), LA-RLF (low asthma-reduced lung function), LA-NLF (low asthma normal lung function), and MA-NLF (moderate asthma normal lung function). The HA-LLF and HA-NLF clusters had very high asthma prevalence (80–100%), but differed markedly in lung function, airway responsiveness, bronchodilator reversibility, sensitisation, and symptom burden. The LA-RLF and LA-NLF clusters with low asthma prevalence (< 5%) displayed contrasting lung function profiles, while MA-NLF (~ 50% asthma prevalence) was largely defined by prominent symptoms such as chest tightness and shortness of breath. These subtypes were replicated in an independent cohort, Isle of Wight. Our findings demonstrate that integrating physiological, immunological, and symptom-based measures yields clinically meaningful asthma subtypes beyond wheeze-based definitions and may support more precise disease classification.