Classifying asthma control using salivary and fecal microbiome in children with moderate to severe asthma: results from the SysPharmPediA study

Abstract

Introduction: Uncontrolled asthma can lead to severe exacerbations and reduced quality of life. Research has shown that the microbiome may be linked with asthma characteristics, however, its role in asthma control has not been explored. We aimed to investigate whether the microbiome could be used to discriminate between uncontrolled and controlled asthma in children.

Methods: Saliva and feces samples were obtained from 143 children with moderate-to-severe asthma aged 6 to 17 years from the SysPharmPediA study [Abdel-Aziz and Neerincx et al. J Pers Med. 2021]. Patients were classified as controlled or uncontrolled asthmatics and their microbiome was compared using global (alpha/beta) diversity, differential abundance analysis (DAA, Analysis of compositions of microbiomes with bias correction) and machine learning (Recursive Ensemble Feature Selection (REFS)).

Results: Global diversity and DAA did not find significant differences between controlled and uncontrolled asthmatics. REFS detected a set of taxa, including Haemophilus and Veillonella, differentiating uncontrolled and controlled asthma with an average classification accuracy of 81% (saliva) and 85% (feces). These taxa showed enrichment in taxa previously associated with inflammatory diseases for both sampling compartments, and with COPD for the saliva samples.

Conclusions: Controlled and uncontrolled asthmatics could be differentiated in children based on their microbiome using machine learning, suggesting a link between asthma control and the microbiome. This suggests that the microbiome may be a potential target for a precision medicine approach to improve asthma control in children.