# Quantitative Computed Tomographic Clusters in C‐BIOPRED Asthma Cohort: Association with Sputum Proteomics

## TL;DR

This study uses CT scans to identify four asthma types based on lung structure and connects them to specific biological processes in sputum.

## Contribution

The study introduces qCT clustering to define asthma phenotypes and links them to sputum proteomic pathways.

## Key findings

- Four distinct asthma phenotypes were identified using qCT clustering based on airway and lung characteristics.
- Sputum proteomics revealed unique molecular pathways for each cluster, such as apoptosis and complement activation.
- Clusters 1 and 4 showed elevated sputum eosinophilia and distinct proteomic profiles linked to airway remodeling.

## Abstract

Severe asthma exhibits heterogeneity in airflow obstruction, driven by airway remodeling and air trapping, which can be noninvasively assessed via quantitative computed tomography (qCT). This study aimed to identify asthma phenotypes by clustering qCT measurements of airway dimensions, lung volumes, and densitometry, and to elucidate the underlying molecular pathways through sputum proteomics. We applied consensus clustering to qCT data from 239 asthma patients (severe and mild/moderate) and 68 healthy controls from the Chinese C‐BIOPRED cohort. Four distinct qCT clusters emerged: cluster 1, characterized by luminal dilation, severe air trapping, and reduced lung density; cluster 2, with thickened airway walls and luminal narrowing without air trapping; cluster 3, showing mild luminal dilation, preserved lung volumes, and optimal spirometry; and cluster 4, featuring airway wall thickening, luminal narrowing, severe air trapping, and profound airflow obstruction. Sputum eosinophilia was elevated in clusters 1 and 4. Proteomics revealed upregulated pathways in apoptosis execution and cornified envelope formation in cluster 1, while clusters 2 and 4 exhibited enhanced complement activation, fibrin formation, plasma lipoprotein assembly, and insulin‐like growth factor (IGF) transport regulation. These findings delineate qCT‐derived phenotypes and their associated underlying mechanisms of airway remodeling and airflow obstruction in severe asthma.

Using quantitative computed tomography (qCT) clustering in the C‐BIOPRED asthma cohort of 239 patients, four distinct phenotypes were identified based on airway dimensions, lung volumes, and densitometry, revealing variations in airway remodeling, air trapping, and airflow obstruction compared with healthy controls. Cluster 1 showed airway dilation with severe air trapping and reduced lung density; cluster 2 featured thickened airway walls and luminal narrowing without air trapping; cluster 3 displayed mild airway dilation, preserved lung volumes, and optimal spirometry; and cluster 4 exhibited airway wall thickening, luminal narrowing, severe air trapping, and profound airflow obstruction. Sputum eosinophilia was elevated in clusters 1 and 4. Sputum proteomics linked cluster 1 to upregulated apoptotic execution and cornified envelope formation pathways, while clusters 2 and 4 showed enhanced complement activation, fibrin formation, plasma lipoprotein assembly, and IGF transport regulation, providing insights into underlying molecular mechanisms of severe asthma heterogeneity.