It’s in your breath: pediatric asthma diagnosis using CSI:FingerID compound annotation

Pediatric asthma poses diagnostic challenges due to its variable presentation. Breath analysis could be a game-changer in pediatric allergic asthma management. By identifying unique exhaled metabolic signatures using SESI-MS and CSI:FingerID, this cutting-edge approach distinguishes children with allergic asthma from healthy controls, aiding accurate diagnosis and personalized treatment.
The immune system of allergic asthma patients is over-sensitive and reacts to common allergens such as pollen. (Photo by Maria Hossmar on Unsplash.)

Allergic Asthma: when your immune system is overreactive

Asthma is a prevalent chronic condition in children in developed countries, and it is known for its highly variable presentation and clinical course. Unfortunately, there is no well-established diagnostic gold standard that is easy to apply​1​. The numbers of misdiagnosis are high, both underdiagnosis, and overdiagnosis. Allergic asthma is the most common asthma phenotype in children. The immune system of patients is over-sensitive and reacts to common allergens such as pollen or mold, causing inflammation in the airways or making existing inflammation even worse, leading to hyper-reaction. Exposure to allergens can cause allergy-induced asthma attacks. Improving the diagnosis and management of pediatric allergic asthma is a crucial need. To address this, breath analysis aims to non-invasively evaluate altered metabolism and disease-associated processes. By identifying exhaled metabolic signatures that distinguish children with allergic asthma from healthy controls, clinicians can more accurately diagnose and manage pediatric asthma.

SESI/HRMS: breathing into the mass spectrometer

Real-time breath analysis by secondary electrospray ionization (SESI) high-resolution mass spectrometry. (Picture from Singh et al. 2019​​​2​ published under CC BY 4.0)

Secondary electrospray ionisation high-resolution mass spectrometry (SESI/HRMS) is a powerful method for the detection of volatile compounds without the need for sample pretreatment. SESI ionization works by ionizing volatile analytes through proton transfer reactions between the electrospray solution and the sample. This method is particularly useful for online breath analysis, allowing real-time measurements without sample preparation​3​. Previous studies have demonstrated the potential of SESI/HRMS for identifying relevant exhaled organic compounds and for revealing altered molecular pathways associated with various respiratory diseases​3–5​. Note that the method is limited by the lacking chromatographic separation, which hinders the distinction of isomeric compounds. 

Detecting asthma from breath in children

In a cross-sectional study, researchers from the University Children’s Hospital Zurich, the University Hospital Zurich and the ETH Zurich collected exhaled breath samples from 48 allergic asthmatic children and 56 healthy control children​6​. All children were exhaling directly into the instrument (SuperSESI, FIT FossilionTech connected to a TripleTOF 5600+, AB Sciex). The researchers aimed to detect differentially expressed m/z features between the asthma patients and the control group. From a total of 2,315 m/z features detected in exhaled breath, 375 features exhibited significant differences between the two groups. Among these, 179 m/z features were upregulated, while 196 were downregulated in the allergic asthma group when compared to the control group. 

Annotating relevant features with CSI:FIngerID

For the 100 most significantly discriminative m/z features per study group, the researchers employed MS2 spectra analysis, which were also directly recorded from the exhaled breath. They used SIRIUS+CSI:FingerID​7,8​ to assign putative molecular formulae and chemical structures. They further screened the putatively identified within their biological context and subgrouped into metabolic pathways or chemical families. Additionally, pathway enrichment analysis​9​ was performed to further aid in the identification of all significant features, including those without MS2 spectra or compound annotation. In total, 134 m/z features were successfully assigned to compounds. The certainty of identification ranges from level 1 (confirmed structure with measured reference standard) to level 5 (exact mass), with CSI:FingerID identifications being level 3 (tentative candidates)​10​

The pathophysiology of allergic asthma

Aim of this study was not only the identification of an asthmatic breath profile, but also to link the identified molecules to relevant pathways and compound classes that are associated with the pathophysiology of allergic asthma.

Metabolic pathways elevated in the allergic asthma group. (Picture from Weber et al. 2023​​​6​ published under CC BY 4.0)

Elevated in allergic asthma: 

  • The researchers found the lysine metabolism to be the most significantly elevated pathway in asthmatic breath. Within this pathway, they unambiguously identified succinate and glutarate, which have been previously reported as associated with pediatric asthma in metabolomic studies using blood​11​, urine​12​, and breath​13​ samples. 
  • Also tyrosine metabolism is elevated. Previous metabolomic studies have reported increased levels of tyrosine in asthmatic children​12,14,15​, and high levels of tyrosine metabolism may be linked to inflammation and oxidative stress in asthma​16​
  • Fatty acid metabolites constitute the largest elevated group, with 20 different fatty acid metabolites showing elevated levels. All lysine metabolites also belong to this chemical family. These fatty acid metabolites have been previously reported to be decreased in chronic obstructive pulmonary disease exacerbations​3,17​.
Metabolic pathways decreased in the allergic asthma group. (Picture from Weber et al. 2023​​​6​ published under CC BY 4.0)

Diminished in allergic asthma: 

  • Arginine and proline metabolism, including arginine biosynthesis, are found to be diminished. Arginase is an important contributor to asthma pathophysiology​18​
  • Linoleic acid metabolism is another pathway that is diminished in allergic asthma. Linoleic acid has been reported to possess anti-inflammatory properties, but its effect on asthma remains controversial due to conflicting observations in clinical trials​19​. However, genetically predicted linoleic acid has been associated with a lower risk for asthma​20​.
  • Additionally, amides, including palmitoylethanolamide, are another group of compounds showing diminished levels in allergic asthma. Palmitoylethanolamide, in particular, is well-studied for its anti-inflammatory effects​21​.

Do you have allergic asthma?

The researchers assess the ability of breath profiles to classify samples as either allergic asthmatic or healthy using support vector machines (SVM)​22​, a popular machine learning technique. The SVM algorithm was trained and tested using a 10 times repeated stratified 10-fold cross-validation approach. The resulting classifier has an area under the receiver operating characteristic (ROC) curve of 0.83, which is a reasonably high predictive accuracy in distinguishing between allergic asthmatic and healthy breath samples. The metabolic profiles derived from breath analysis could have potential diagnostic applications in the context of allergic asthma.

During the cross-validation process, certain compounds frequently emerged as significant predictors. This small group of compounds not only demonstrates potential relevance to the pathophysiology of allergic asthma but also holds promise for integration into diagnostic models. Specifically, the two dicarboxylic acids, succinate and glutarate, as well as lysine metabolites, were among the promising candidates and were unambiguously identified.

New path for improved asthma diagnostics

This is the first online breath analysis study to detect pediatric allergic asthma. The researchers identified a large number of discriminative features, many of which were putatively identified using the SIRIUS+CSI:FingerID. Additionally, they identified a small subset of compounds that exhibited strong differentiating power, suggesting their potential use in predictive modeling for asthma diagnosis. The study highlights the potential of breath analysis

  • to provide valuable insights into the pathophysiology of allergic asthma in children,
  • to uncover unique metabolic signatures associated with the disease, and
  • to distinguishing children with allergic asthma from healthy controls.

This non-invasive approach shows promise for developing diagnostic tools that can aid in early detection and monitoring of asthma in children.

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The easy way to comprehensive structure elucidation​

SIRIUS is proven to be the best computational method for identifying molecules from tandem mass spectrometry data. SIRIUS is the umbrella application comprising molecular formula identification (ZODIAC), structure database search (CSI:FingerID), confidence score assignment (COSMIC) and compound class prediction (CANOPUS).​