Online breath analysis with SUPER SESI - HRMS for metabolic signatures in children with allergic asthma

Improving the diagnosis and management of pediatric asthma is necessary. Breath analysis offers a non-invasive approach to evaluate changes in metabolism and disease-related mechanisms. This study aimed to identify exhaled metabolic signatures using? SUPER SESI - HRMS that can distinguish children with allergic asthma from healthy controls.

Results:

The study included 48 people with allergic asthma and 56 healthy individuals. Through meticulous analysis, 375 significant mass-charge features were delineated, with 134 tentatively identified as metabolites. Many of these metabolites were found to participate in common metabolic pathways or chemical families. In particular, the study uncovered several pathways prominently represented by significant metabolites, including increased lysine degradation and suppression of two arginine pathways among asthmatic participants. In addition, supervised machine learning techniques were employed to assess the efficacy of respiratory profiling in distinguishing between asthmatic and healthy samples. Surprisingly, the results showed robust performance as evidenced by an area under the receiver operating characteristic curve of 0.83.

Discussion:

This study showcases the first online breath analysis conducted using SUPER SESI - HRMS on a pediatric population with allergic asthma, revealing distinct breath patterns that correlate with the condition. The findings unveil distinctive breath patterns intricately linked to the condition, shedding light on key metabolic pathways and chemical families.

Among the most notable revelations are the increase in pathways associated with the metabolism of lysine, tyrosine and several fatty acids, all of which have previously been implicated in pediatric asthma. Particularly noteworthy is the significant increase observed in lysine metabolism among asthmatic patients, with metabolites identified as succinate and glutarate directly implicated in the disease.

In addition, the study highlights a marked increase in tyrosine metabolism, with both human and microbiotic origins. In contrast, down-regulation was detected in the metabolism of arginine, proline and linoleic acid, along with decreased levels of anti-inflammatory metabolites such as palmitoylethanolamide (PEA). Curiously, despite inconsistent findings regarding oxidative stress markers such as aldehydes in all studies, their elevated presence was not observed in the asthmatic group, pointing to possible methodological or environmental influences.

It is important to note that machine learning analysis of metabolic profiles yielded promising results for diagnostic applications, with an impressive AUC of 0.83, underscoring the potential usefulness of these breath signatures in predictive models.

Conclusion:

For the first time, a significant number of metabolites present in the breath of children with allergic asthma were identified using online breath analysis. These metabolites were found to be different from those present in the breath of healthy children. Most of these metabolites are known to be associated with metabolic pathways and chemical families that play a role in the pathophysiological mechanisms of asthma. Furthermore, a small group of these volatile organic compounds exhibited high potential for future clinical diagnostic applications.