In paediatric asthma, symptoms tend to appear during the first 6 years of life and may be associated with different disease phenotypes and endotypes, each responding differently to specific therapy. Therefore, asthma is more commonly used as a concept term comprising a set of non-specific symptoms (wheezing, dyspnoea, and dry cough), while proper identification of the pathophysiological origin of the associated symptoms is considered more important. This identification allows the delivery of a more targeted therapy to the patient, consequently reducing the risk of exacerbations.1 For instance, persistent eosinophilic asthma with a characteristic Type 2 T helper cell inflammation is usually responsive to inhaled corticosteroid therapy, while neutrophilic phenotypes do not usually respond as well.
However, the currently available diagnostic tools are unable to determine these specific phenotypes at the point of care, and hence there is a need for new and improved asthma biomarkers to be implemented in clinical practice. Breathomics, the measurement of metabolites in the exhaled breath,2 is currently being hypothesised as a possible technology to solve this problem, and several studies concerning measurement of volatile organic compounds (VOC) in exhaled breath have been published and significantly revised.3,4 These reviews underlined the promising results of electronic nose (eNose) technologies as fast, portable, and sufficiently sensitive instruments for analysing VOC in exhaled breath samples.
For this reason, eNose breathomics was presented at this year’s European Academy of Allergy and Clinical Immunology (EAACI) Congress in Munich, Germany as a technology that can improve asthma diagnosis.5 In short, exhaled breath condensate samples collected from paediatric patients were processed and analysed using eNose breathomics technology. A multivariate analysis was performed and a hierarchical model was developed to segment different VOC profiles, creating two well-defined clusters. The results showed that individuals with persistent asthma who required corticosteroid therapy were significantly agglomerated in a single cluster, thus highlighting that breathomics may be useful in identifying Type 2 T helper cell eosinophilic asthma phenotypes. Moreover, the diagnostic values were shown to surpass those from spirometry with bronchodilation, which is currently the most widely used technology to corroborate an asthma diagnosis.
Despite these promising results, external validation studies are still needed to completely understand the effectiveness of breathomics in a real clinical context. Furthermore, an eventual standardisation of the methods and procedures for exhaled breath sample processing is required, among other methodological questions that still need answering. Nevertheless, breathomics may be the solution to achieve one more goal in the gargantuan but honourable mission that has been assigned to researchers and clinicians alike: to improve asthma diagnosis and deliver the best possible treatment for patients.
