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01.12.2017 | Research | Ausgabe 1/2017 Open Access

Journal of Translational Medicine 1/2017

Exhaled breath condensate metabolome clusters for endotype discovery in asthma

Zeitschrift:
Journal of Translational Medicine > Ausgabe 1/2017
Autoren:
Anirban Sinha, Koundinya Desiraju, Kunal Aggarwal, Rintu Kutum, Siddhartha Roy, Rakesh Lodha, S. K. Kabra, Balaram Ghosh, Tavpritesh Sethi, Anurag Agrawal
Wichtige Hinweise

Electronic supplementary material

The online version of this article (https://​doi.​org/​10.​1186/​s12967-017-1365-7) contains supplementary material, which is available to authorized users.
Anirban Sinha and Koundinya Desiraju contributed equally to this work

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.
Zusatzmaterial
Additional file 1: Figure S1. Dynamic adaptive binning achieved optimal binning on the spectra. Figure S2. Changes in error rates of the random forest model at different steps of optimization. Figure S3. Boxplots of annotated bins which are top predictors in random forest model showing difference between asthmatics and healthy controls (A) and a table of p values for compounds showing statistical significance (B). Table S1. Most important NMR bins according to the random forest model along with the compounds annotated at that particular position.
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