| Literature DB >> 33919626 |
Maria Michelle Papamichael1,2, Charis Katsardis3, Evangelia Sarandi4,5, Spyridoula Georgaki4, Eirini-Sofia Frima6, Anastasia Varvarigou6, Dimitris Tsoukalas2,4.
Abstract
Asthma in children remains a significant public health challenge affecting 5-20% of children in Europe and is associated with increased morbidity and societal healthcare costs. The high variation in asthma incidence among countries may be attributed to differences in genetic susceptibility and environmental factors. This respiratory disorder is described as a heterogeneous syndrome of multiple clinical manifestations (phenotypes) with varying degrees of severity and airway hyper-responsiveness, which is based on patient symptoms, lung function and response to pharmacotherapy. However, an accurate diagnosis is often difficult due to diversities in clinical presentation. Therefore, identifying early diagnostic biomarkers and improving the monitoring of airway dysfunction and inflammatory through non-invasive methods are key goals in successful pediatric asthma management. Given that asthma is caused by the interaction between genes and environmental factors, an emerging approach, metabolomics-the systematic analysis of small molecules-can provide more insight into asthma pathophysiological mechanisms, enable the identification of early biomarkers and targeted personalized therapies, thus reducing disease burden and societal cost. The purpose of this review is to present evidence on the utility of metabolomics in pediatric asthma through the analysis of intermediate metabolites of biochemical pathways that involve carbohydrates, amino acids, lipids, organic acids and nucleotides and discuss their potential application in clinical practice. Also, current challenges on the integration of metabolomics in pediatric asthma management and needed next steps are critically discussed.Entities:
Keywords: asthma; asthma phenotypes; biomarkers; metabolomics; pediatric
Year: 2021 PMID: 33919626 PMCID: PMC8072856 DOI: 10.3390/metabo11040251
Source DB: PubMed Journal: Metabolites ISSN: 2218-1989
Figure 1Genetic and non-genetic factors modulate the asthmatic phenotype which can be evaluated through metabolomics. Application of metabolomics in pediatric asthma include early diagnosis, asthma phenotyping through identification of unique metabolic fingerprints and treatment personalization and optimization.
Characteristics of pediatric asthma metabolomic studies reviewed from birth to school years.
| Author Year/Study | Follow-Up | Population | Group | Asthma or Bronchiolitis Diagnosis | Sample/ | Metabolites Isolated | Annotated Pathways | Conclusions |
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| Carraro et al., | 1 year | 292 mothers @ | wheezers | Parent’s symptom | Amniotic fluid | Steroid hormone | Amniotic fluid collected at delivery differed in neonates that experienced wheezing at 1 year than in non-wheezers. | |
| Chawes et al., 2019 [ | 6 years | Asthmatic mothers | Persistent wheezers or asthmatics in the first 6 years of life | Physician | Urine | Steroid, fatty acid metabolism | Metabolic profiles discriminated children developing asthma from healthy children. In both cohorts, urine metabolite levels measured at four weeks were related to asthma development before six years of age. | |
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| Chiu et al., 2018 [ | 1, 2, 3, 4 years | PATCH | Asthma | Physician | Urine | Purine and amino acid metabolism, nicotinamide/ nicotinate metabolism, methane metabolism and gut microbiota imbalance. | Metabolomic profiling provided a link of microbe-environment Interactions in the development of childhood. | |
| Barlotta et al., 2019 [ | 6 months, | Wheezers | BronchiolitisPhysician | Urine | Citric acid cycle, fatty acid and amino acid metabolism, | Metabolomic profiling of urine specimens from infants with bronchiolitis identified children at increased risk of developing recurrent wheezing. | ||
| Atzei et al., 2011 [ | N/A | Physician | Urine | Associated with RSV bronchiolitis: | Creatine metabolism and epigenetic regulation | 1H-NMR can be potentially applied to identify metabolic alterations in urine samples related to the differences in the inflammation of bronchioles. | ||
| Turi et al., 2018 [ | 1, 2, 3 years | Healthy | Physician | Urine | 11 metabolites were significantly different between | Citric acid cycle, amino acid metabolism, nicotinamide/ | Metabolomics may aid in prophylaxis against bronchiolitis in infants. | |
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| Carraro et al., 2018 [ | 3 years | Wheezing | Physician | Urine | Tryptophan metabolism, fatty acid metabolism and microbial derivatives | Urine metabolites distinguished between transient wheezers and early-onset asthma. | ||
| Smolinska et al., 2014 [ | 6 years | ADEM Study | Recurrent | Physician | VOC | High levels in early-onset asthma vs. transient wheezers: | Hydrocarbons produced during lipid peroxidation | VOCs profile in exhaled breath discriminated healthy, transient wheezing and true asthmatic children. |
| Klaassen et | 6 years | ADEM study | Recurrent wheezers | Physician | VOC | Hydrocarbons | VOCs profile plus Asthma Predictive Index (API) status improved asthma diagnosis at preschool age. VOCs could be a valuable monitoring tool for airway inflammation and in predicting asthma onset. | |
| Chiu et al., 2020 [ | N/A | Asthma | Physician | Plasma | Histadine metabolism, nicotinamide and pyruvate metabolism, phenylalanine metabolism, amino acid metabolism and products of microbial metabolism | Plasma pyruvate metabolism associated with Ig E production. Urinary branched-chain amino acids were associated with food allergic reactions. | ||
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| Saude et al., 2011 [ | N/A | Stable | Physician | Urine | Citric acid cycle, nicotinamide metabolism, lipid metabolism, | 1H-NMR can be used to differentiate stable asthma from controls and unstable asthma. | ||
| Tao et al., 2019 [ | N/A | Healthy | Physician | Urine | Citric acid cycle, purine metabolism, lipid and carbohydrate metabolism, amino acid and phenylalanine metabolism, pantothenate and Coenzyme A biosynthesis | Urine metabolomics discriminated asthma as well as controlled and uncontrolled sub-types and elucidated the biological mechanisms of pediatric asthma. | ||
| Dallinga et al., 2010 [ | N/A | Asthma | Physician | VOC | Hydrocarbons produced during | EBC samples and comparing VOCs differentiated children with asthma from healthy controls. | ||
| Gahleitner et al., 2013 [ | N/A | Asthma | Physician | VOC | Organic compounds from external sources. Used in food manufacturing (flavorings) and disinfectants. | VOCs discriminated between asthmatic and healthy children. | ||
Key: @: at; vs.: versus; VOCs: Volatile Organic Compounds; GC-MS: Gas-Chromatography-Mass Spectrometry; GC-TOF-MS: Gas-Chromatography-time of flight-Mass Spectrometry; LC-MS: Liquid Chromatography-Mass Spectrometry; NMR: 1H Nuclear Magnetic Resonance; EBC: Exhaled Breath Condensate; RSV: Respiratory Syncytial Virus; HRV: Human Rhino Virus; ARI: Acute Respiratory Infection; N/A: Not applicable; BCAA: Branched-Chain Amino Acids; Ig E: Immunoglobulin E.
Characteristics of metabolomic studies reviewed on pediatric asthma phenotypes.
| Asthma | Author Year/ Study Design | Population ( | Group Allocated | Asthma | Sample/ | Metabolites Isolated | Annotated Pathways | Conclusions |
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| Papamichael et al., 2019 [ | N/A | Physician | Urine | Tryptophan and tyrosine metabolism, lactic acidosis, catecholamine synthesis and alterations in gut microbiota. | Metabolomics is a promising approach in the research for novel biomarkers for asthma monitoring | ||
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| Kelly et al., | N/A | Physician | Plasma | 574 Metabolites isolated in mild-moderate asthmatics. | Metabolites common to AHR, pre and post-FEV1/FVC related to: Glycerophospholipids, linoleic acid and pyrimidine metabolism. | Metabolites and metabolomic profiles distinguished children with asthma by the degree of lung function as reflected by spirometric parameters, thus confirming the existence of an asthma severity metabolome. | |
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| Carraro et al., | Severe | Physician | EBC | Compounds related to: | Breathomics discriminated between severe, non-severe and healthy child asthmatics. | ||
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| Fitzpatrick et al., 2014 [ | Mild asthma | Physician | Plasma | Biosynthesis of purine /pyrimidines, phospho-glycerides, sphingo-lipid, glycolipids. Folate cycle, glutathione synthesis. | Metabolomics revealed that oxidative stress is a contributory factor to corticosteroid refractory severe | ||
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| Park et al., | Corticosteroid resistant | Physician | Urine | Metabolites discriminating | Tyrosine metabolism, catecholamine biosynthesis, and glutathione metabolism. | Putative biomarkers isolated using the metabolomics approach differentiated | |
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| Mattarucchi et al., | Atopic | Well-controlled | Physician | Urine | Histamine metabolism. | Metabolic profiling offers the potential of asthma characterization and identification of inflammation- |
Key: GC-MS: Gas-Chromatography-Mass Spectrometry; LC-MS: Liquid Chromatography-Mass Spectrometry; EBC: Exhaled Breath Condensate; PFTs: Pulmonary function tests; AHR: Airway Hyperresponsiveness; Ile-Pro: Isoleucyl-Proline: Cys-Gly: Cysteine-Glycine; ACQ: Asthma Control Questionnaire; N/A: Not Applicable; BCAA: Branched-Chain Amino Acids; Co A: Coenzyme A; FEV1: Forced Expiratory Volume in 1 second; FVC: Forced Vital Capacity; FEV1/FVC: Ratio of Forced Expiratory Volume in 1 second and Forced Vital Capacity; PEF: Peak Expiratory Flow; FEF 25–75%: Forced Expiratory Flow at 25–75% of the pulmonary volume; FeNO: Fractional exhaled Nitric Oxide.
Figure 2Summarizing diagram showing the application of metabolomics in patients with asthma at the intrauterine, pre-school stage and in children. Depending on the stage and the biospecimen type, metabolites can be potent predictive biomarkers or tools for asthma phenotyping.