Metabolomics Applied to Exhaled Breath Condensate in Childhood Asthma

Carraro, Silvia; Rezzi, Serge; Reniero, Fabiano; Héberger, Károly; Giordano, Giuseppe; Zanconato, Stefania; Guillou, Claude; Baraldi, Eugenio

doi:10.1164/rccm.200606-769oc

Cited by 217 publications

(199 citation statements)

References 29 publications

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“…Although NMR has a lower sensitivity compared to enzyme-linked immunosorbent assay (ELISA) and mass spectrometry, it is a non-destructive analytical method that requires minimal sample preparation and has rapid acquisition time of about 10 -15 min [ 104 ]. NMR-based metabolomics of EBC may have a potential use in characterising respiratory diseases as it was able to discriminate patients with COPD [ 104 ], asthma [ 106 ], stable and unstable cystic fibrosis [ 107 ] from healthy subjects. In addition, a recent NMR spectroscopic study has also shown that the repeated use of the EcoScreen ® condenser does not cause any carry-over effect and the disinfectant Milton does not alter EBC metabolomics [ 100 ], as opposed to another study that reported artificial signals when the Anacon condenser was used and disinfected with Descogen [ 108 ].…”

Section: Nmr Spectroscopy-based Metabolomicsmentioning

confidence: 99%

Exhaled breath condensate: a comprehensive update

Ahmadzai

Huang²,

Hettiarachchi³

et al. 2013

Clinical Chemistry and Laboratory Medicine

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Since the late 1990s, a surge in interest in the analysis of exhaled breath condensate (EBC) resulted in the American Thoracic Society and European Respiratory Society (ATS/ERS) organising a Task Force in 2001 to develop guidelines on EBC collection and measurement of biomarkers. This Task Force published their guidelines in 2005 based on literature and expert opinions at that time, and multiple shortcomings and knowledge deficits were also identified. The clinical application of EBC collection and its biomarkers are currently still limited by several of these knowledge gaps, hence further guidelines for standardisation are required to ensure external validity. Using related articles produced since the publication of the ATS/ERS Task Force report, this paper attempts to provide a comprehensive update to the original guideline and review the methodological shortcomings identified. This review can hopefully serve as a yardstick for future studies involving this emerging clinical tool.

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Section: Nmr Spectroscopy-based Metabolomicsmentioning

confidence: 99%

Exhaled breath condensate: a comprehensive update

Ahmadzai

Huang²,

Hettiarachchi³

et al. 2013

Clinical Chemistry and Laboratory Medicine

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show abstract

“…Carraro et al [10] first applied NMR-based metabolomics to EBC of asthmatic children, who were distinguished from healthy controls by the variation of the acetate signal. The authors speculated that such enhancement was due to an increased acetylation of proinflammatory proteins in the extracellular space of the airway environment.…”

Section: Phenotyping Ebcmentioning

confidence: 99%

Metabolomics of Exhaled Breath Condensate: A Means for Phenotyping Respiratory Diseases?

Maniscalco

Motta

2017

Biomark. Med.

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“…Model derived features were selected as those with a VIP score >= 1.0. PLSDA is commonly used in metabolomics investigations including multiple forms of cancer, cardiac ischemia, parkinson's disease and asthma (Nishiumi et al, 2010;Bodi et al, 2012;Bogdanov et al, 2008;Carraro et al, 2007;Chen et al, 2011;Duarte et al, 2010;Qiu et al, 2010). This technique has also been previously implemented in our lab investigating hemorrhagic shock (Lexcen, Lusczek, Witowski, Mulier, & Beilman, 2012).…”

Section: Partial Least Squares Discriminant Analysis (Plsda)mentioning

confidence: 99%

Optimal Algorithm for Metabolomics Classification and Feature Selection varies by Dataset

Determan¹

2014

IJB

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Metabolomics, the systematic identification and quantification of all metabolites in a biological system, is increasingly applied towards identification of biomarkers for disease diagnosis, prognosis and risk prediction. Applications of metabolomics extend across the health spectrum including Alzheimer's, cancer, diabetes, and trauma. Despite the continued interest in metabolomics there are numerous techniques for analyzing metabolomics datasets with the intent to classify group membership (e.g. Control or Treated). These include Partial Least Squares Discriminant Analysis, Support Vector Machines, Random Forest, Regularized Generalized Linear Models, and Prediction Analysis for Microarrays. Each classification algorithm is dependent upon different assumptions and can potentially lead to alternate conclusions. This project seeks to conduct an in depth comparison of algorithm performance on both simulated and real datasets to determine which algorithms perform best given alternate dataset structures. Three simulated datasets were generated to validate algorithm performance and mimic 'real' metabolomics data: (Han et al., 2011) independent null dataset (no correlation, no discriminatory variables), (Davis, Schiller, Eurich, & Sawyer, 2012) correlated null (no discriminating variables), (Guan et al., 2009) correlated discriminatory. This comparison is also applied to 3 open-access datasets including two Nuclear Magnetic Resonance (NMR) and one Mass Spectrometry (MS) dataset. Performance was evaluated based on the Robustness-Performance-Trade-off (RPT) incorporating a balance between model classification accuracy and feature selection stability. We also provide a free, open-source R Bioconductor package (OmicsMarkeR) that conducts the analyses herein. The proposed work provides an important advancement in metabolomics analysis and helps alleviate the confusion of potentially paradoxical analyses thereby leading to improved exploration of disease states and identification of clinically important biomarkers.

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Metabolomics Applied to Exhaled Breath Condensate in Childhood Asthma

Abstract: Metabolomics can be applied to exhaled breath condensate, leading to the characterization of airway biochemical fingerprints. The presence of acetylated compounds suggests new metabolic pathways that may have a role in asthma pathophysiology.

Cited by 217 publications

References 29 publications

Exhaled breath condensate: a comprehensive update

Exhaled breath condensate: a comprehensive update

Metabolomics of Exhaled Breath Condensate: A Means for Phenotyping Respiratory Diseases?

Optimal Algorithm for Metabolomics Classification and Feature Selection varies by Dataset

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