Abstract
Bronchial asthma is a chronic disease that affects individuals of all ages. It has a high prevalence and is associated with high morbidity and considerable levels of mortality. However, asthma is not a single disease, and multiple subtypes or phenotypes (clinical, inflammatory or combinations thereof) can be detected, namely in aggregated clusters. Most studies have characterised asthma phenotypes and clusters of phenotypes using mainly clinical and inflammatory parameters. These studies are important because they may have clinical and prognostic implications and may also help to tailor personalised treatment approaches. In addition, various metabolomics studies have helped to further define the metabolic features of asthma, using electronic noses or targeted and untargeted approaches. Besides discriminating between asthma and a healthy state, metabolomics can detect the metabolic signatures associated with some asthma subtypes, namely eosinophilic and non-eosinophilic phenotypes or the obese asthma phenotype, and this may prove very useful in point-of-care application. Furthermore, metabolomics also discriminates between asthma and other “phenotypes” of chronic obstructive airway diseases, such as chronic obstructive pulmonary disease (COPD) or Asthma–COPD Overlap (ACO). However, there are still various aspects that need to be more thoroughly investigated in the context of asthma phenotypes in adequately designed, homogeneous, multicentre studies, using adequate tools and integrating metabolomics into a multiple-level approach.
Highlights
Bronchial asthma is a chronic respiratory disease that affects individuals of all ages
2-palmatoylglycerol and cholesterol were decreased in BA when compared with ACO and COPD; in contrast, stearic acid expression was increased in BA in comparison with ACO and ACO—Asthma–COPD Overlap; BA—bronchial asthma; COPD—chronic obstructive pulmonary disease; EA—eosinophilic asthma; GC×GC-ToFMS—two-dimensional gas chromatography coupled to mass spectrometry with a high-resolution time-of-flight analyser; GC-TOF-MS—gas chromatography time of flight mass spectrometry; HPLC-QTOF—high-performance liquid chromatography with quadrupole flight time mass spectrometry; liquid chromatography coupled to mass spectrometry (LC–MS)—liquid chromatography–mass spectrometry; NA—neutrophilic asthma; NEA—non-eosinophilic asthma; PGA—paucigranulocytic asthma; UHLC/MS/MS—ultraHPLC/tandem mass spectrometry; UPLC-MS/MS—ultra performance liquid chromatography–tandem mass spectrometry
The reproducibility and temporal stability of metabolic signatures of asthma and asthma phenotypes can only be adequately gauged by comparing the different approaches, the various methods that were used (e.g., nuclear magnetic resonance “spectroscopy” (NMR), various forms of MS) and the analytical parameters incorporated into validation models in each study, as well as by assessing confounding factors, the specificity of each model that was used and data obtained from different organic fluids, among other aspects
Summary
Bronchial asthma is a chronic respiratory disease that affects individuals of all ages. Fast, targeted metabolomics and untargeted metabolomics are the two main study strategies in the area of metabolomics [12,13] Both provide important information about changes in metabolism and quantification of metabolites in many chronic pathological settings, with applications in the diagnosis, pathophysiology and management of diseases, including asthma [14]. On the one hand, targeted metabolomics is only concerned with identifying and semi- or fully quantifying pre-defined metabolites of interest, the non-targeted strategy offers far more comprehensive results as to the identification and quantification of metabolites, since it does not restrict analysis to previously defined target molecules [12] The latter is possibly the best way to characterise a disease from the metabolic point of view and identify new biomarkers [12,15]. Since this is a narrative review, and not a systematic review, this document does not aim to be an exhaustive, comprehensive analysis but rather a conceptual approach to the issue of metabolomics signatures and asthma phenotypes
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