Breath metabolomic profiling by nuclear magnetic resonance spectroscopy in asthma

Untargeted Metabolomic Analysis of Exhaled Breath Condensate Identifies Disease-Specific Signatures in Adults With Asthma

PURPOSE

An objective test for the auxiliary diagnosis of asthma is still lacking. The aim of this study was to discriminate asthma signatures via an untargeted metabolomic analysis of exhaled breath condensate.

MATERIALS AND METHODS

This study enrolled 19 patients diagnosed with asthma and 23 healthy volunteers. Samples of exhaled breath condensate (EBC) were collected from both groups. Untargeted metabolomic analyses of EBC were used to identify disease-specific signatures for asthma.

RESULT

There were 30 identifiable differentially expressed metabolites and 7 disordered metabolic pathways between the EBCs of asthmatic patients and healthy control subjects. The main differential pathways included biosynthesis of unsaturated fatty acids, HIF-1 signalling pathway, Glutathione metabolism, Ascorbate and aldarate metabolism, and fatty acid biosynthesis. The integrated machine learning method was used to construct an asthma EBC metabolomic signature model from four differential metabolites; 3,4'-dimethoxy-2'-hydroxychalcone, C17-sphinganine, (z)-6-octadecenoic acid, and 2-butylaniline. The model showed a high level of discrimination efficiency (area under curve (AUC) = 0.98), with robust validation through logistic regression (LR), random forest (RF), and support vector machine (SVM) (LR AUC = 0.98, RF AUC = 0.94, SVM AUC = 1.00). The discriminative ability of the EBC metabolomic signature model in both the training set (AUC = 1.0) and testing data (AUC = 0.817) was superior to that of FeNO (AUC = 0.515 and 0.567, respectively) and FEV1/FVC % predicted (AUC = 0.767 and 0.765, respectively). Among the four biomarkers, (z)-6-octadecenoic acid was significantly correlated with serum IgE.

CONCLUSION

The EBC metabolomic signature model demonstrated good feasibility for assisting in the diagnosis of asthma in adults.

A feasibility study on exhaled breath analysis using UV spectroscopy to detect COVID-19

A 23-subject feasibility study is reported to assess how UV absorbance measurements on exhaled breath samples collected from silicon microreactors can be used to detect COVID-19. The silicon microreactor technology chemoselectively preconcentrates exhaled carbonyl volatile organic compounds and subsequent methanol elution provides samples for analysis. The underlying scientific rationale that viral infection will induce an increase in exhaled carbonyls appears to be supported by the results of the feasibility study. The data indicate statistically significant differences in measured UV absorbance values between healthy and symptomatic COVID-19 positive subjects in the wavelength range from 235 nm to 305 nm. Factors such as subject age were noted as potential confounding variables.

Biomarkers in exhaled breath condensate as fingerprints of asthma, chronic obstructive pulmonary disease and asthma-chronic obstructive pulmonary disease overlap: a critical review

Asthma, chronic obstructive pulmonary disease (COPD) and asthma-COPD overlap are the third leading cause of mortality around the world. They share some common features, which can lead to misdiagnosis. To properly manage these conditions, reliable markers for early and accurate diagnosis are needed. Over the past 20 years, many molecules have been investigated in the exhaled breath condensate to better understand inflammation pathways and mechanisms related to these disorders. Recently, more advanced techniques, such as sensitive metabolomic and proteomic profiling, have been used to obtain a more comprehensive understanding. This article reviews the use of targeted and untargeted metabolomic methodology to study asthma, COPD and asthma-COPD overlap.

Metabolic Phenotypes in Asthmatic Adults: Relationship with Inflammatory and Clinical Phenotypes and Prognostic Implications

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.

Potential Metabolic Biomarkers in Adult Asthmatics

Asthma is the most common chronic airway inflammation, with multiple phenotypes caused by complicated interactions of genetic, epigenetic, and environmental factors. To date, various determinants have been suggested for asthma pathogenesis by a new technology termed omics, including genomics, transcriptomics, proteomics, and metabolomics. In particular, the systematic analysis of all metabolites in a biological system, such as carbohydrates, amino acids, and lipids, has helped identify a novel pathway related to complex diseases. These metabolites are involved in the regulation of hypermethylation, response to hypoxia, and immune reactions in the pathogenesis of asthma. Among them, lipid metabolism has been suggested to be related to lung dysfunction in mild-to-moderate asthma. Sphingolipid metabolites are an important mediator contributing to airway inflammation in obese asthma and aspirin-exacerbated respiratory disease. Although how these molecular variants impact the disease has not been completely determined, identification of new causative factors may possibly lead to more-personalized and precise pathway-specific approaches for better diagnosis and treatment of asthma. In this review, perspectives of metabolites related to asthma and clinical implications have been highlighted according to various phenotypes.

Associations of maternal and infant metabolomes with immune maturation and allergy development at 12 months in the Swedish NICE-cohort

Allergic diseases are the most common chronic diseases in childrenin the Western world, but little is know about what factors influence immune maturation and allergy development. We therefore aimed to associate infant and maternal metabolomes to T- and B-cell subpopulations and allergy diagnosis. We performed liquid chromatography-mass spectrometry based untargeted metabolomics on blood plasma from mothers (third trimester, n = 605; delivery, n = 558) and from the umbilical cord (n = 366). The measured metabolomes were associated to T- and B-cell subpopulations up to 4 months after delivery and to doctor´s diagnosed eczema, food allergy and asthma at one year of age using random forest analysis. Maternal and cord plasma at delivery could predict the number of CD24+CD38low memory B-cells (p = 0.033, n = 26 and p = 0.009, n = 22), but future allergy status could not be distinguished from any of the three measured metabolomes. Replication of previous literature findings showed hypoxanthine to be upregulated in the umbilical cord of children with subsequent asthma. This exploratory study suggests foetal immune programming occuring during pregnancy as the metabolomic profiles of mothers and infants at delivery related to infants' B-cell maturation.

Metabolomic differences of exhaled breath condensate among children with and without asthma

BACKGROUND

There remains an unmet need in objective tests for diagnosing asthma in children. The objective of this study was to investigate the potential of metabolomic profiles of exhaled breath condensate (EBC) to discriminate stable asthma in Asian children in the community.

METHODS

One hundred and sixty-five Asian children (92 stable asthma and 73 non-asthmatic controls) participating in a population-based cohort were enrolled and divided into training and validation sets. Nuclear magnetic resonance-based metabolomic profiles of EBC samples were analyzed by using orthogonal partial least squares discriminant analysis.

RESULTS

EBC metabolomic signature (lactate, formate, butyrate, and isobutyrate) had an area under the receiver operator characteristic curve (AUC) of 0.826 in discriminating children with and without asthma in the training set, which significantly outperformed FeNO (AUC = 0.574; P < .001) and FEV₁ /FVC % predicted (AUC = 0.569; P < .001). The AUC for EBC metabolomic signature was 0.745 in the validation set, which was slightly but not significantly lower than in the testing set (P = .282). We further extrapolated two potentially involved metabolic pathways, including pyruvate (P = 1.67 × 10^-3 ; impact: 0.14) and methane (P = 1.89 × 10^-3 ; impact: 0.15), as the most likely divergent metabolisms between children with and without asthma.

CONCLUSION

This study provided evidence supporting the role of EBC metabolomic signature to discriminate stable asthma in Asian children in the community, with a discriminative property outperforming conventional clinical tests such as FeNO or spirometry.

EBC metabolomics for asthma severity

Asthma is a heterogeneous disease with diverse severity and represents a considerable socio-economic burden. Exhaled Breath Condensate (EBC) is a biofluid directly obtained from the airway lining fluid non-invasively. We attempted to discriminate severe from mild-to-moderate asthma using EBC metabolomics based on both NMR and UHPLC-MS techniques. 36 patients were included in this study (15 patients with severe and 21 with mild-to-moderate asthma). EBC was collected and analyzed using both NMR and UHPLC-MS techniques for possible metabolites. Using PLS and oPLS analysis for the UHPLC-MS data, no metabolite was found to be sufficient for the discrimination of asthma severity. However, when another PLS-regression model was applied five metabolites were found to discriminate severe from mild-to-moderate asthma. Amino-acid lysine was the only metabolite that discriminated the two study groups using NMR data (p= 0.04, t-test with Welch's correction, AUC 0.66). EBC is an easily available biofluid which directly represents the lower airways but difficult-to-use for metabolomic analysis. Our study presents some encouraging findings for the discrimination of asthma severity subgroups using EBC metabolomics but more well-designed studies with a higher number of patients need to be conducted.

Metabolomics of Exhaled Breath Condensate by Nuclear Magnetic Resonance Spectroscopy and Mass Spectrometry: A Methodological Approach

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Respiratory diseases present a very high prevalence in the general population, with an increase in morbidity, mortality and health-care expenses worldwide. They are complex and heterogeneous pathologies that may present different pathological facets in different subjects, often with personal evolution. Therefore, there is a need to identify patients with similar characteristics, prognosis or treatment, defining the so-called phenotype, but also to mark specific differences within each phenotype, defining the endotypes.

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Biomarkers are very useful to study respiratory phenotypes and endotypes. Metabolomics, one of the recently introduced “omics”, is becoming a leading technique for biomarker discovery. For the airways, metabolomics appears to be well suited as the respiratory tract offers a natural matrix, the Exhaled Breath Condensate (EBC), in which several biomarkers can be measured. In this review, we will discuss the main methodological issues related to the application of Nuclear Magnetic Resonance (NMR) spectroscopy and Mass Spectrometry (MS) to EBC metabolomics for investigating respiratory diseases.

What did we learn from multiple omics studies in asthma?

More than a decade has passed since the finalization of the Human Genome Project. Omics technologies made a huge leap from trendy and very expensive to routinely executed and relatively cheap assays. Simultaneously, we understood that omics is not a panacea for every problem in the area of human health and personalized medicine. Whilst in some areas of research omics showed immediate results, in other fields, including asthma, it only allowed us to identify the incredibly complicated molecular processes. Along with their possibilities, omics technologies also bring many issues connected to sample collection, analyses and interpretation. It is often impossible to separate the intrinsic imperfection of omics from asthma heterogeneity. Still, many insights and directions from applied omics were acquired-presumable phenotypic clusters of patients, plausible biomarkers and potential pathways involved. Omics technologies develop rapidly, bringing improvements also to asthma research. These improvements, together with our growing understanding of asthma subphenotypes and underlying cellular processes, will likely play a role in asthma management strategies.

Priorities for future research into asthma diagnostic tools: A PAN-EU consensus exercise from the European asthma research innovation partnership (EARIP)

The diagnosis of asthma is currently based on clinical history, physical examination and lung function, and to date, there are no accurate objective tests either to confirm the diagnosis or to discriminate between different types of asthma. This consensus exercise reviews the state of the art in asthma diagnosis to identify opportunities for future investment based on the likelihood of their successful development, potential for widespread adoption and their perceived impact on asthma patients. Using a two-stage e-Delphi process and a summarizing workshop, a group of European asthma experts including health professionals, researchers, people with asthma and industry representatives ranked the potential impact of research investment in each technique or tool for asthma diagnosis and monitoring. After a systematic review of the literature, 21 statements were extracted and were subject of the two-stage Delphi process. Eleven statements were scored 3 or more and were further discussed and ranked in a face-to-face workshop. The three most important diagnostic/predictive tools ranked were as follows: "New biological markers of asthma (eg genomics, proteomics and metabolomics) as a tool for diagnosis and/or monitoring," "Prediction of future asthma in preschool children with reasonable accuracy" and "Tools to measure volatile organic compounds (VOCs) in exhaled breath."

Development of a new quantification method for organic acids in urine as potential biomarkers for respiratory illness

Asthma and chronic obstructive pulmonary disease (COPD) are common respiratory disorders that have similar clinical presentation and misdiagnosis may lead to improper treatment. There is a need for a better, non-invasive test for the differentiation of asthma and COPD. In this study, we developed a new validated LC-MS/MS method for 17 urinary organic acids that could serve as potential biomarkers. Human urine samples were collected from adults with asthma or COPD. LC-MS/MS was performed using the differential isotope labeling approach. 4-(Dimethylamino) phenacyl bromide (DmPA) was used for derivatization using two different carbon isotopes, allowing for the formation of internal standard for each metabolite. Gradient elution was employed on a C₁₈ column while the LC-MS/MS operated in the multiple reaction monitoring mode (MRM). Regulatory guidelines were used for method validation. Partial Least Squares Discriminative Analysis (PLS-DA) was applied to the log-transformed values of metabolites in each group of asthma and COPD subjects. Full validation in targeted metabolomics is scarce with usually limited number of metabolites, unlike fit-for-purpose approach. Due to the endogenous nature of the metabolites, numerous challenges were encountered during method development and validation, such as the lactic acid interference from the surrounding environment. The required specificity, accuracy and precision was successfully achieved. The method was fully validated, ensuring robustness and reproducibility when analyzing patient samples. The method was applied to analyze human urine samples and PLS-DA analysis showed differentiation of asthma and COPD subjects (R2 0.89, Q2 0.68). As targeted metabolomics is expanding to the clinical sphere, more validated methods/strategies are needed. Our work will expand the current knowledge-base regarding targeted metabolomics.

Clinical metabolomics of exhaled breath condensate in chronic respiratory diseases

Chronic respiratory diseases (CRDs) are complex multifactorial disorders involving the airways and other lung structures. The development of reliable markers for an early and accurate diagnosis, including disease phenotype, and prediction of the response and/or adherence to treatment prescribed are essential points for the correct management of CRDs. Beside the traditional techniques to detect biomarkers, "omics" sciences have stimulated interest in clinical field as they could potentially improve the study of disease phenotype. Perturbations in a variety of metabolic and signaling pathways could contribute an understanding of CRDs pathogenesis. In particular, metabolomics provides powerful tools to map biological perturbations and their relationship with disease pathogenesis. The exhaled breath condensate (EBC) is a natural matrix of the respiratory tract, and is well suited for metabolomics studies. In this article, we review the current state of metabolomics methodology applied to EBC in the study of CRDs.

Serum Metabolomics Analysis of Asthma in Different Inflammatory Phenotypes: A Cross-Sectional Study in Northeast China

BACKGROUND AND OBJECTIVE

Asthma as a chronic heterogeneous disease seriously affects the quality of life. Incorrect identification for its clinical phenotypes lead to a huge waste of medical resources. Metabolomic technique as a novel approach to explore the pathogenesis of diseases have not been used to study asthma based on their clear defined inflammatory phenotypes. This study is aimed to distinguish the divergent metabolic profile in different asthma phenotypes and clarify the pathogenesis of them.

METHODS

Participants including eosinophilic asthmatics (EA, n=13), noneosinophilic asthmatics (NEA, n=16), and healthy controls (HC, n=15) were enrolled. A global profile of untargeted serum metabolomics was identified with Ultra Performance Liquid Chromatography-Mass Spectrometry technique.

RESULTS

Multivariate analysis was performed and showed a clear distinction between EA, NEA, and HC. A total of 18 different metabolites were recognized between the three groups based on OPLS-DA model and involved in 10 perturbed metabolic pathways. Glycerophospholipid metabolism, retinol metabolism, and sphingolipid metabolism were identified as the most significant changed three pathways (impact > 0.1 and -log(P) > 4) between the phenotypes.

CONCLUSIONS

We showed that the different inflammatory phenotypes of asthma involve the immune regulation, energy, and nutrients metabolism. The clarified metabolic profile contributes to understanding the pathophysiology of asthma phenotypes and optimizing the therapeutic strategy against asthma heterogeneity.

Nuclear magnetic resonance spectroscopy as a new approach for improvement of early diagnosis and risk stratification of prostate cancer

Prostate cancer (PCa) is the second most common male cancer worldwide and the fifth leading cause of death from cancer in men. Early detection and risk stratification is the most effective way to improve the survival of PCa patients. Current PCa biomarkers lack sufficient sensitivity and specificity to cancer. Metabolite biomarkers are evolving as a new diagnostic tool. This review is aimed to evaluate the potential of metabolite biomarkers for early detection, risk assessment, and monitoring of PCa. Of the 154 identified publications, 27 and 38 were original papers on urine and serum metabolomics, respectively. Nuclear magnetic resonance (NMR) is a promising method for measuring concentrations of metabolites in complex samples with good reproducibility, high sensitivity, and simple sample processing. Especially urine-based NMR metabolomics has the potential to be a cost-efficient method for the early detection of PCa, risk stratification, and monitoring treatment efficacy.

Plasma metabolite profiles in children with current asthma

BACKGROUND

Identifying metabolomic profiles of children with asthma has the potential to increase understanding of asthma pathophysiology.

OBJECTIVE

To identify differences in plasma metabolites between children with and without current asthma at mid-childhood.

METHODS

We used untargeted mass spectrometry to measure plasma metabolites in 237 children (46 current asthma cases and 191 controls) in Project Viva, a birth cohort from eastern Massachusetts, USA. Current asthma was assessed at mid-childhood (mean age 8.0 years). The ability of a broad spectrum metabolic profile to distinguish between cases and controls was assessed using partial least squares discriminant analysis. We used logistic regression models to identify individual metabolites that were differentially abundant by case-control status. We tested significant metabolites for replication in 411 children from the VDAART clinical trial.

RESULTS

There was no evidence of a systematic difference in the metabolome of children reporting current asthma vs. healthy controls according to partial least squares discriminant analysis. However, several metabolites were associated with odds of current asthma at a nominally significant threshold (P < .05), including a metabolite of nicotinamide (N1-Methyl-2-pyridone-5-carboxamide (Odds Ratio (OR) = 2.8 (95% CI 1.1-8.0)), a pyrimidine metabolite (5,6-dihydrothymine (OR = 0.4 (95% CI 0.2-0.9)), bile constituents (biliverdin (OR = 0.4 (95%CI 0.1-0.9), taurocholate (OR = 2.0 (95% CI 1.2-3.4)), two peptides likely derived from fibrinopeptide A (ORs from 1.6 to 1.7), and a gut microbiome metabolite (p-cresol sulphate OR = 0.5 (95% CI 0.2-0.9)). The associations for N1-Methyl-2-pyridone-5-carboxamide and p-cresol sulphate replicated in the independent VDAART population (one-sided P values = .03-.04).

CONCLUSIONS AND CLINICAL RELEVANCE

Current asthma is nominally associated with altered levels of several metabolites, including metabolites in the nicotinamide pathway, and a bacterial metabolite derived from the gut microbiome.

Exhaled breath condensate metabolome clusters for endotype discovery in asthma

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.

Electronic supplementary material

The online version of this article (10.1186/s12967-017-1365-7) contains supplementary material, which is available to authorized users.

Allergic asthma: an overview of metabolomic strategies leading to the identification of biomarkers in the field

Allergic asthma is a prominent disease especially during childhood. Indoor allergens, in general, and particularly house dust mites (HDM) are the most prevalent sensitizers associated with allergic asthma. Available data show that 65-130 million people are mite-sensitized world-wide and as many as 50% of these are asthmatic. In fact, sensitization to HDM in the first years of life can produce devastating effects on pulmonary function leading to asthmatic syndromes that can be fatal. To date, there has been considerable research into the pathological pathways and structural changes associated with allergic asthma. However, limitations related to the disease heterogeneity and a lack of knowledge into its pathophysiology have impeded the generation of valuable data needed to appropriately phenotype patients and, subsequently, treat this disease. Here, we report a systematic and integral analysis of the disease, from airway remodelling to the immune response taking place throughout the disease stages. We present an overview of metabolomics, the management of complex multifactorial diseases through the analysis of all possible metabolites in a biological sample, obtaining a global interpretation of biological systems. Special interest is placed on the challenges to obtain biological samples and the methodological aspects to acquire relevant information, focusing on the identification of novel biomarkers associated with specific phenotypes of allergic asthma. We also present an overview of the metabolites cited in the literature, which have been related to inflammation and immune response in asthma and other allergy-related diseases.

Using omics approaches to understand pulmonary diseases

Omics approaches are high-throughput unbiased technologies that provide snapshots of various aspects of biological systems and include: 1) genomics, the measure of DNA variation; 2) transcriptomics, the measure of RNA expression; 3) epigenomics, the measure of DNA alterations not involving sequence variation that influence RNA expression; 4) proteomics, the measure of protein expression or its chemical modifications; and 5) metabolomics, the measure of metabolite levels. Our understanding of pulmonary diseases has increased as a result of applying these omics approaches to characterize patients, uncover mechanisms underlying drug responsiveness, and identify effects of environmental exposures and interventions. As more tissue- and cell-specific omics data is analyzed and integrated for diverse patients under various conditions, there will be increased identification of key mechanisms that underlie pulmonary biological processes, disease endotypes, and novel therapeutics that are efficacious in select individuals. We provide a synopsis of how omics approaches have advanced our understanding of asthma, chronic obstructive pulmonary disease (COPD), acute respiratory distress syndrome (ARDS), idiopathic pulmonary fibrosis (IPF), and pulmonary arterial hypertension (PAH), and we highlight ongoing work that will facilitate pulmonary disease precision medicine.

A European Respiratory Society technical standard: exhaled biomarkers in lung disease

Breath tests cover the fraction of nitric oxide in expired gas (F_eNO), volatile organic compounds (VOCs), variables in exhaled breath condensate (EBC) and other measurements. For EBC and for F_eNO, official recommendations for standardised procedures are more than 10 years old and there is none for exhaled VOCs and particles. The aim of this document is to provide technical standards and recommendations for sample collection and analytic approaches and to highlight future research priorities in the field. For EBC and F_eNO, new developments and advances in technology have been evaluated in the current document. This report is not intended to provide clinical guidance on disease diagnosis and management.Clinicians and researchers with expertise in exhaled biomarkers were invited to participate. Published studies regarding methodology of breath tests were selected, discussed and evaluated in a consensus-based manner by the Task Force members.Recommendations for standardisation of sampling, analysing and reporting of data and suggestions for research to cover gaps in the evidence have been created and summarised.Application of breath biomarker measurement in a standardised manner will provide comparable results, thereby facilitating the potential use of these biomarkers in clinical practice.

Diagnostic value of a pattern of exhaled breath condensate biomarkers in asthmatic children

BACKGROUND

Diagnosing asthma in children is a challenge and using a single biomarker from exhaled breath condensate (EBC) showed the lack of improvement in it.

OBJECTIVE

The aim of this study was to assess the diagnostic potential of a pattern of simple chemical biomarkers from EBC in diagnosing asthma in children in a real-life setting, its association with lung function and gastroesophageal reflux disease (GERD).

METHODS

In 75 consecutive children aged 5-7 years with asthma-like symptoms the following tests were performed: skin prick tests, spirometry, impulse oscillometry (IOS), exhaled NO (F_ENO), 24-hour oesophageal pH monitoring and EBC collection with subsequent analysis of pH, carbon dioxide tension, oxygen tension, and concentrations of magnesium, calcium, iron and urates.

RESULTS

No significant differences were found for individual EBC biomarkers between asthmatics and non-asthmatics (p>0.05 for all). A pattern of six EBC biomarkers showed a statistically significant (p=0.046) predictive model for asthma (AUC=0.698, PPV=84.2%, NPV=38.9%). None to moderate association (R² up to 0.43) between EBC biomarkers and lung function measures and F_ENO was found, with IOS parameters showing the best association with EBC biomarkers. A significantly higher EBC Fe was found in children with asthma and GERD compared to asthmatics without GERD (p=0.049).

CONCLUSIONS

An approach that involves a pattern of EBC biomarkers had a better diagnostic accuracy for asthma in children in real-life settings compared to a single one. Poor to moderate association of EBC biomarkers with lung function suggests a complementary value of EBC analysis for asthma diagnosis in children.

A Review of Analytical Techniques and Their Application in Disease Diagnosis in Breathomics and Salivaomics Research

The application of metabolomics to biological samples has been a key focus in systems biology research, which is aimed at the development of rapid diagnostic methods and the creation of personalized medicine. More recently, there has been a strong focus towards this approach applied to non-invasively acquired samples, such as saliva and exhaled breath. The analysis of these biological samples, in conjunction with other sample types and traditional diagnostic tests, has resulted in faster and more reliable characterization of a range of health disorders and diseases. As the sampling process involved in collecting exhaled breath and saliva is non-intrusive as well as comparatively low-cost and uses a series of widely accepted methods, it provides researchers with easy access to the metabolites secreted by the human body. Owing to its accuracy and rapid nature, metabolomic analysis of saliva and breath (known as salivaomics and breathomics, respectively) is a rapidly growing field and has shown potential to be effective in detecting and diagnosing the early stages of numerous diseases and infections in preclinical studies. This review discusses the various collection and analyses methods currently applied in two of the least used non-invasive sample types in metabolomics, specifically their application in salivaomics and breathomics research. Some of the salient research completed in this field to date is also assessed and discussed in order to provide a basis to advocate their use and possible future scientific directions.

Asthma Metabolomics and the Potential for Integrative Omics in Research and the Clinic

Asthma is a complex disease well-suited to metabolomic profiling, both for the development of novel biomarkers and for the improved understanding of pathophysiology. In this review, we summarize the 21 existing metabolomic studies of asthma in humans, all of which reported significant findings and concluded that individual metabolites and metabolomic profiles measured in exhaled breath condensate, urine, plasma, and serum could identify people with asthma and asthma phenotypes with high discriminatory ability. There was considerable consistency across the studies in terms of the reported biomarkers, regardless of biospecimen, profiling technology, and population age. In particular, acetate, adenosine, alanine, hippurate, succinate, threonine, and trans-aconitate, and pathways relating to hypoxia response, oxidative stress, immunity, inflammation, lipid metabolism and the tricarboxylic acid cycle were all identified as significant in at least two studies. There were also a number of nonreplicated results; however, the literature is not yet sufficiently developed to determine whether these represent spurious findings or reflect the substantial heterogeneity and limited statistical power in the studies and their methods to date. This review highlights the need for additional asthma metabolomic studies to explore these issues, and, further, the need for standardized methods in the way these studies are conducted. We conclude by discussing the potential of translation of these metabolomic findings into clinically useful biomarkers and the crucial role that integrated omics is likely to play in this endeavor.

Identifying biomarkers for asthma diagnosis using targeted metabolomics approaches

BACKGROUND

The diagnosis of asthma in children is challenging and relies on a combination of clinical factors and biomarkers including methacholine challenge, lung function, bronchodilator responsiveness, and presence of airway inflammation. No single test is diagnostic. We sought to identify a pattern of inflammatory biomarkers that was unique to asthma using a targeted metabolomics approach combined with data science methods.

METHODS

We conducted a nested case-control study of 100 children living in a peri-urban community in Lima, Peru. We defined cases as children with current asthma, and controls as children with no prior history of asthma and normal lung function. We further categorized enrollment following a factorial design to enroll equal numbers of children as either overweight or not. We obtained a fasting venous blood sample to characterize a comprehensive panel of targeted markers using a metabolomics approach based on high performance liquid chromatography-mass spectrometry.

RESULTS

A statistical comparison of targeted metabolites between children with asthma (n = 50) and healthy controls (n = 49) revealed distinct patterns in relative concentrations of several metabolites: children with asthma had approximately 40-50% lower relative concentrations of ascorbic acid, 2-isopropylmalic acid, shikimate-3-phosphate, and 6-phospho-d-gluconate when compared to children without asthma, and 70% lower relative concentrations of reduced glutathione (all p < 0.001 after Bonferroni correction). Moreover, a combination of 2-isopropylmalic acid and betaine strongly discriminated between children with asthma (2-isopropylmalic acid ≤ 13 077 normalized counts/second) and controls (2-isopropylmalic acid > 13 077 normalized counts/second and betaine ≤ 16 47 121 normalized counts/second).

CONCLUSIONS

By using a metabolomics approach applied to serum, we were able to discriminate between children with and without asthma by revealing different metabolic patterns. These results suggest that serum metabolomics may represent a diagnostic tool for asthma and may be helpful for distinguishing asthma phenotypes.

Noninvasive Recognition and Biomarkers of Early Allergic Asthma in Cats Using Multivariate Statistical Analysis of NMR Spectra of Exhaled Breath Condensate

Asthma is prevalent in children and cats, and needs means of noninvasive diagnosis. We sought to distinguish noninvasively the differences in 53 cats before and soon after induction of allergic asthma, using NMR spectra of exhaled breath condensate (EBC). Statistical pattern recognition was improved considerably by preprocessing the spectra with probabilistic quotient normalization and glog transformation. Classification of the 106 preprocessed spectra by principal component analysis and partial least squares with discriminant analysis (PLS-DA) appears to be impaired by variances unrelated to eosinophilic asthma. By filtering out confounding variances, orthogonal signal correction (OSC) PLS-DA greatly improved the separation of the healthy and early asthmatic states, attaining 94% specificity and 94% sensitivity in predictions. OSC enhancement of multi-level PLS-DA boosted the specificity of the prediction to 100%. OSC-PLS-DA of the normalized spectra suggest the most promising biomarkers of allergic asthma in cats to include increased acetone, metabolite(s) with overlapped NMR peaks near 5.8 ppm, and a hydroxyphenyl-containing metabolite, as well as decreased phthalate. Acetone is elevated in the EBC of 74% of the cats with early asthma. The noninvasive detection of early experimental asthma, biomarkers in EBC, and metabolic perturbation invite further investigation of the diagnostic potential in humans.

Metabolomic analysis identifies potential diagnostic biomarkers for aspirin-exacerbated respiratory disease

BACKGROUND

To date, there has been no reliable in vitro test to diagnose aspirin-exacerbated respiratory disease (AERD).

OBJECTIVE

To investigate potential diagnostic biomarkers for AERD using metabolomic analysis.

METHODS

An untargeted profile of serum from asthmatics in the first cohort (group 1) comprising 45 AERD, 44 patients with aspirin-tolerant asthma (ATA), and 28 normal controls was developed using the ultra-high-performance liquid chromatography (UHPLC)/Q-ToF MS system. Metabolites that discriminate AERD from ATA were quantified in both serum and urine, which were collected before (baseline) and after the lysine-aspirin bronchoprovocation test (Lys-ASA BPT). The serum metabolites were validated in the second cohort (group 2) comprising 50 patients with AERD and 50 patients with ATA.

RESULTS

A clear discrimination of metabolomes was found between patients with AERD and ATA. In group 1, serum levels of LTE₄ and LTE₄ /PGF₂ α ratio before and after the Lys-ASA BPT were significantly higher in patients with AERD than in patients with ATA (P < 0.05 for each), and urine baseline levels of these two metabolites were significantly higher in patients with AERD. Significant differences of serum metabolite levels between patients with AERD and ATA were replicated in group 2 (P < 0.05 for each). Moreover, serum baseline levels of LTE₄ and LTE₄ /PGF₂ α ratio discriminated AERD from ATA with 70.5%/71.6% sensitivity and 41.5%/62.8% specificity, respectively (AUC = 0.649 and 0.732, respectively P < 0.001 for each). Urine baseline LTE₄ levels were significantly correlated with the fall in FEV₁ % after the Lys-ASA BPT in patients with AERD (P = 0.008, r = 0.463).

CONCLUSIONS AND CLINICAL RELEVANCE

Serum metabolite level of LTE₄ and LTE₄ /PGF₂ α ratio was identified as potential in vitro diagnostic biomarkers for AERD using the UHPLC/Q-ToF MS system, which were closely associated with major pathogenetic mechanisms underlying AERD.

Noninvasive metabolic profiling for painless diagnosis of human diseases and disorders

Metabolic profiling provides a powerful diagnostic tool complementary to genomics and proteomics. The pain, discomfort and probable iatrogenic injury associated with invasive or minimally invasive diagnostic methods, render them unsuitable in terms of patient compliance and participation. Metabolic profiling of biomatrices like urine, breath, saliva, sweat and feces, which can be collected in a painless manner, could be used for noninvasive diagnosis. This review article covers the noninvasive metabolic profiling studies that have exhibited diagnostic potential for diseases and disorders. Their potential applications are evident in different forms of cancer, metabolic disorders, infectious diseases, neurodegenerative disorders, rheumatic diseases and pulmonary diseases. Large scale clinical validation of such diagnostic methods is necessary in future.

[Biomarkers in asthma]

Identifying new biomarkers in asthma is attractive but requires assessing their relevance and their reliability to clinical practice. Beyond fashion, the improvement in identification of new candidate biomarkers benefited of scientific and biologic progresses, biobanks and platforms robustly backed on longitudinal cohorts and registries. Paradoxically, the main issue is now to stress up the good question, in other words to correctly characterize the unmet needs in asthma that might benefit of a biomarker. Chronicity, variability, weakness of diagnostic tools and the heterogeneity of the disease are features of asthma claiming for identifying new biomarkers. Unmet needs in asthma encompass areas such as diagnosis, prognosis, management and follow-up, therapeutic guidance and phenotypic/endotypic identification. FEV1 is an available biomarker largely tested in asthma worth in most of these areas. Albeit, mandatory features required for a new biomarker to emerge, pro/con debates on those already existing and currently used methods for identifying new ones are worth explorations. We reviewed and summarized the current literature focusing biomarkers in asthma.

Prediction of asthma exacerbations in children by innovative exhaled inflammatory markers: results of a longitudinal study

BACKGROUND

In asthma management guidelines the primary goal of treatment is asthma control. To date, asthma control, guided by symptoms and lung function, is not optimal in many children and adults. Direct monitoring of airway inflammation in exhaled breath may improve asthma control and reduce the number of exacerbations.

AIM

1) To study the use of fractional exhaled nitric oxide (FeNO) and inflammatory markers in exhaled breath condensate (EBC), in the prediction of asthma exacerbations in a pediatric population. 2) To study the predictive power of these exhaled inflammatory markers combined with clinical parameters.

METHODS

96 asthmatic children were included in this one-year prospective observational study, with clinical visits every 2 months. Between visits, daily symptom scores and lung function were recorded using a home monitor. During clinical visits, asthma control and FeNO were assessed. Furthermore, lung function measurements were performed and EBC was collected. Statistical analysis was performed using a test dataset and validation dataset for 1) conditionally specified models, receiver operating characteristic-curves (ROC-curves); 2) k-nearest neighbors algorithm.

RESULTS

Three conditionally specified predictive models were constructed. Model 1 included inflammatory markers in EBC alone, model 2 included FeNO plus clinical characteristics and the ACQ score, and model 3 included all the predictors used in model 1 and 2. The area under the ROC-curves was estimated as 47%, 54% and 59% for models 1, 2 and 3 respectively. The k-nearest neighbors predictive algorithm, using the information of all the variables in model 3, produced correct predictions for 52% of the exacerbations in the validation dataset.

CONCLUSION

The predictive power of FeNO and inflammatory markers in EBC for prediction of an asthma exacerbation was low, even when combined with clinical characteristics and symptoms. Qualitative improvement of the chemical analysis of EBC may lead to a better non-invasive prediction of asthma exacerbations.

Systems biology of asthma and allergic diseases: a multiscale approach

Systems biology is an approach to understanding living systems that focuses on modeling diverse types of high-dimensional interactions to develop a more comprehensive understanding of complex phenotypes manifested by the system. High-throughput molecular, cellular, and physiologic profiling of populations is coupled with bioinformatic and computational techniques to identify new functional roles for genes, regulatory elements, and metabolites in the context of the molecular networks that define biological processes associated with system physiology. Given the complexity and heterogeneity of asthma and allergic diseases, a systems biology approach is attractive, as it has the potential to model the myriad connections and interdependencies between genetic predisposition, environmental perturbations, regulatory intermediaries, and molecular sequelae that ultimately lead to diverse disease phenotypes and treatment responses across individuals. The increasing availability of high-throughput technologies has enabled system-wide profiling of the genome, transcriptome, epigenome, microbiome, and metabolome, providing fodder for systems biology approaches to examine asthma and allergy at a more holistic level. In this article we review the technologies and approaches for system-wide profiling, as well as their more recent applications to asthma and allergy. We discuss approaches for integrating multiscale data through network analyses and provide perspective on how individually captured health profiles will contribute to more accurate systems biology views of asthma and allergy.

The metabolomics of airway diseases, including COPD, asthma and cystic fibrosis

Chronic obstructive pulmonary disease (COPD), asthma and cystic fibrosis (CF) are characterized by airway obstruction and an inflammatory process. Reaching early diagnosis and discrimination of subtypes of these respiratory diseases are quite a challenging task than other chronic illnesses. Metabolomics is the study of metabolic pathways and the measurement of unique biochemical molecules generated in a living system. In the last decade, metabolomics has already proved to be useful for the characterization of several pathological conditions and offers promises as a clinical tool. In this article, we review the current state of the metabolomics of COPD, asthma and CF with a focus on the different methods and instrumentation being used for the discovery of biomarkers in research and translation into clinic as diagnostic aids for the choice of patient-specific therapies.

Taking your breath away: metabolomics breathes life in to personalized medicine

Breath-based metabolomics (breathomics) is an exciting developing area of biotechnology that centers on the capture, identification, and quantification of volatile organic compound (VOC) patterns in human breath and their utilization as tools in the diagnosis of a broad spectrum of medical problems. With the age of personalized medicines demanding rapid bespoke diagnosis and treatment, this area of molecular diagnostics is beginning to see an upsurge in biotechnological advancement. Here, we discuss recent improvements and directions in the development of breath VOC analysis and diagnosis platforms that offer the potential for disease biomarker discovery and disease prognosis.

Metabolomics approach in allergic and rheumatic diseases

Metabolomics is the analysis of the concentration profiles of low molecular weight compounds present in biological fluids. Metabolites are nonpeptide molecules representing the end products of cellular activity. Therefore, changes in metabolite concentrations reveal the range of biochemical effects induced by a disease or its therapeutic intervention. Metabolomics has recently become feasible with the accessibility of new technologies, including mass spectrometry and high-resolution proton nuclear magnetic resonance, and has already been applied to several disorders. Indeed, it has the advantage of being a nontargeted approach for identifying potential biomarkers, which means that it does not require a preliminary knowledge of the substances to be studied. In this review, we summarize the main studies in which metabolomic approach was used in some allergic (asthma, atopic dermatitis) and rheumatic diseases (rheumatoid arthritis, systemic lupus erythematosus) to explore the feasibility of this technique as a novel diagnostic tool in these complex disorders.