Data-driven questionnaire-based cluster analysis of asthma in Swedish adults

Area of Science:

• Pulmonology
• Clinical Medicine
• Data Science

Background:

• Asthma is a heterogeneous respiratory disease with varying clinical presentations and treatment responses.
• Identifying distinct asthma phenotypes is crucial for personalized medicine and improved patient outcomes.
• Current phenotyping often relies on complex diagnostics, limiting accessibility in primary care.

Purpose of the Study:

• To identify and characterize distinct asthma phenotypes in a Swedish cohort using cluster analysis.
• To evaluate the clinical characteristics and disease burden associated with identified asthma phenotypes.
• To assess the potential utility of these phenotypes in primary care settings.

Main Methods:

• Cluster analysis was performed on self-reported data from 1291 Swedish asthma patients.
• Disease burden was assessed using the Asthma Control Test (ACT), mini Asthma Quality of Life Questionnaire (mini-AQLQ), exacerbation frequency, and asthma severity.
• Phenotype validation was conducted in an independent cohort of 748 individuals.

Main Results:

• Three asthma phenotypes were identified: early onset predominantly female, adult onset predominantly female, and adult onset predominantly male.
• The early onset, predominantly female phenotype exhibited a higher disease burden, frequent exacerbations, and greater inhaled corticosteroid use.
• The adult onset, predominantly male phenotype showed better asthma control (higher ACT and mini-AQLQ scores), fewer exacerbations, and milder disease.

Conclusions:

• Distinct asthma phenotypes can be identified using readily available clinical questionnaire data.
• These phenotypes differ in disease burden, exacerbation frequency, and symptom control.
• The identified asthma phenotypes may offer a practical approach for asthma management in primary care, especially where advanced diagnostics are limited.