Ju Chang-Chien1, Hsin-Yi Huang1, Hui-Ju Tsai2, Chi-Jen Lo3, Wan-Chen Lin1, Yu-Lun Tseng1, Shih-Ling Wang1, Hung-Yao Ho3,4,5, Mei-Ling Cheng3,4,6, Tsung-Chieh Yao1,7. 1. Division of Allergy, Asthma, and Rheumatology, Department of Pediatrics, Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Taoyuan, Taiwan. 2. Institute of Population Health Sciences, National Health Research Institutes, Zhunan, Taiwan. 3. Metabolomics Core Laboratory, Healthy Aging Research Center, Chang Gung University, Taoyuan, Taiwan. 4. Clinical Metabolomics Core Laboratory, Chang Gung Memorial Hospital, Taoyuan, Taiwan. 5. Department of Medical Biotechnology and Laboratory Science, Chang Gung University College of Medicine, Taoyuan, Taiwan. 6. Department of Biomedical Sciences, Chang Gung University College of Medicine, Taoyuan, Taiwan. 7. Community Medicine Research Center, Chang Gung Memorial Hospital at Keelung, Keelung, Taiwan.
Abstract
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.
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.
Authors: Jinpao Hou; Yuping Song; Agnes Sze Yin Leung; Man Fung Tang; Mai Shi; Evy Yiwei Wang; Joseph Gar Shun Tsun; Renee Wan Yi Chan; Gary Wing Kin Wong; Stephen Kwok-Wing Tsui; Ting Fan Leung Journal: Microbiol Spectr Date: 2022-05-12