introduction

Asthma is a heterogeneous disease, and a clear-cut characterization of its various phenotypes has historically remained daunting for both clinical practice and research purposes 1-3. Conventionally, asthma phenotypes have been defined based on timing of onset, atopic origin, eosinophilic inflammation, and presence of obesity, to name a few 4. Such phenotypic characterization has been described as primarily based on clinical insights and experiences of the attending clinician. However, it has been suggested that such asthma phenotyping is largely subjective as the classification may vary from clinician to clinician4,5. Additionally, asthma phenotyping has mostly been attempted in selected cohorts, example hospital-based asthma patients or those with severe asthma, with less data from population-representative samples.
The advancements being made by computational science at elucidating biological processes have been welcomed in the field of asthma, particularly in identifying asthma phenotypes 4-6. In this context, various features of asthma are inputted into algorithms that learn from unlabelled data, with less artefact bias, to produce meaningful asthma phenotypes. This data-driven approach is believed to be more objective and can, with relevant clinical inputs, produce phenotypes that are clinically meaningful 4,5,7. Characterizing asthma at a more granular level is in parallel with efforts towards precision medicine, subsequently enabling prevention and optimal, tailored management 8.
In this work, by including a broad range of clinical, biological, and epidemiological parameters that are relevant to asthma, we employed a novel machine learning approach to identify and describe asthma phenotypes in an adult representative sample in western Sweden.