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. 2017 Dec 22;15(1):262.
doi: 10.1186/s12967-017-1365-7.

Exhaled breath condensate metabolome clusters for endotype discovery in asthma

Anirban Sinha  1 , Koundinya Desiraju  2   3 , Kunal Aggarwal  2 , Rintu Kutum  2   3 , Siddhartha Roy  4 , Rakesh Lodha  5 , S K Kabra  5 , Balaram Ghosh  2 , Tavpritesh Sethi  6   7 , Anurag Agrawal  8   9   10
Affiliations

Exhaled breath condensate metabolome clusters for endotype discovery in asthma

Anirban Sinha et al. J Transl Med. .

Abstract

Background: Asthma is a complex, heterogeneous disorder with similar presenting symptoms but with varying underlying pathologies. Exhaled breath condensate (EBC) is a relatively unexplored matrix which reflects the signatures of respiratory epithelium, but is difficult to normalize for dilution.

Methods: Here we explored whether internally normalized global NMR spectrum patterns, combined with machine learning, could be useful for diagnostics or endotype discovery. Nuclear magnetic resonance (NMR) spectroscopy of EBC was performed in 89 asthmatic subjects from a prospective cohort and 20 healthy controls. A random forest classifier was built to differentiate between asthmatics and healthy controls. Clustering of the spectra was done using k-means to identify potential endotypes.

Results: NMR spectra of the EBC could differentiate between asthmatics and healthy controls with 80% sensitivity and 75% specificity. Unsupervised clustering within the asthma group resulted in three clusters (n = 41,11, and 9). Cluster 1 patients had lower long-term exacerbation scores, when compared with other two clusters. Cluster 3 patients had lower blood eosinophils and higher neutrophils, when compared with other two clusters with a strong family history of asthma.

Conclusion: Asthma clusters derived from NMR spectra of EBC show important clinical and chemical differences, suggesting this as a useful tool in asthma endotype-discovery.

Keywords: Asthma; Endotype; Exhaled breath condensate; Metabolomics; NMR spectroscopy.

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Figures

Fig. 1
Fig. 1
Boruta algorithm identified 16 bins as important to differentiate asthmatics from healthy controls. a Variable importance (y-axis) of all 162 bins (x-axis) was shown as box and whiskers plots. Confirmed, putative and rejected variables were shown in green, yellow and red respectively. Importance of randomized variables was shown in blue. b Important bins highlighted by a box in a were enlarged and shown. Previously reported peaks corresponding to ammonia were highlighted by a box
Fig. 2
Fig. 2
Comparison of the clinical features between the three clusters, neutrophils (a), eosinophils (b), exhaled nitric oxide (c), exacerbation ratios (d). Data were presented as box and whiskers plots showing median and interquartile range. *p < 0.05
Fig. 3
Fig. 3
Boxplots of annotated bins which were statistically different (one way ANOVA p value < 0.05) between the three clusters
See this image and copyright information in PMC

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