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Clusters based on immune markers in a Lithuanian asthma cohort study
Edita Gasiuniene1, Laura Tamasauskiene1, Ieva Janulaityte2
1Department of Immunology and Allergology, Lithuanian University of Health Sciences, Kaunas, Lithuania.
Summary
This study identified three distinct asthma phenotypes using computer analysis. These phenotypes are characterized by factors like age of onset, atopy, eosinophil levels, sex, and obesity, offering new insights into asthma classification.
Area of Science:
- Pulmonary Medicine
- Computational Biology
- Immunology
Background:
- Asthma phenotyping is typically hypothesis-driven and univariate.
- Computer algorithms can identify novel, hypothesis-free relationships among diverse characteristics.
- Understanding distinct asthma phenotypes is crucial for targeted treatment.
Purpose of the Study:
- To identify distinct asthma phenotypes using a data-driven approach.
- To analyze demographic, clinical, and immunological data for phenotype discovery.
- To move beyond traditional univariate phenotyping methods.
Main Methods:
- Cluster analysis was performed on data from 170 adult asthma patients.
- Data included spirometry, blood markers (IgE, periostin, IL-33), BMI, and clinical history.
- Hierarchical clustering was validated using Dunn criterion and clValid package.
Main Results:
- Three asthma phenotypes were identified through cluster analysis.
- Phenotype 1: Early-onset, atopic, eosinophilic asthma (male, high IL-33/periostin).
- Phenotype 2: Late-onset, eosinophilic asthma (female, low IL-33/periostin).
- Phenotype 3: Late-onset, obese, neutrophilic asthma (female, airway obstruction, very low IL-33/periostin).
Conclusions:
- A data-driven approach reveals distinct asthma phenotypes.
- These phenotypes differ in onset, atopy, inflammation type, sex, and obesity.
- Findings support a more personalized approach to asthma management.
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