Abstract
BACKGROUND: Despite the construction of the metagenome of the asthmatic
lung, limitations persist in sampling the bronchial airway. This study
analyzed extracellular vesicles (EVs) obtained from exhaled breath
condensate (EBC) to compare the distinct characteristics of the
microbiome in asthmatics with those in healthy controls and proposed a
diagnostic artificial intelligence-based model of asthma.
METHODS: We obtained the EBC from 58 healthy controls and 251 patients
with asthma. EVs were isolated from the EBC and analyzed. The extracted
16s rDNA was subjected to next generation sequencing. Taxonomic
profiling was conducted for all samples at the genus level. A
combination of artificial neural network (ANN) and gradient boosting
(GBM) was applied to selective EBC biomarkers.
RESULTS: The asthma group exhibited significantly higher alpha diversity
based on the results of the Chao1, Shannon, and Simpson indices. The
bacterial composition of patients with asthma different from that of the
controls. At the genus level, Sphingomonas , Akkermansia ,Methylophaga , Acidocella , and Marinobacter were
significantly more abundant in patients with asthma. The diagnostic
model using GBM and ANN demonstrated good performance with respective
areas under the curve of 0.832 and 0.769. Firmicutes and Proteobacteria
at the phylum level were common important features between the GBM and
ANN asthma models.
CONCLUSION: We demonstrated a distinct pattern in the microbiome of
patients with asthma, indicating the potential role of microbiome-based
diagnosis of asthma. To the best of our knowledge, this was the first
study to identify the microbiome in asthma using EBC-derived EVs.
Keyword: asthma; microbiome; exhaled breath condensate;
extracellular vesicles; metagenomics