ArticleJournal of translational medicine2017
Exhaled breath condensate metabolome clusters for endotype discovery in asthma.
Article in Journal of translational medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers, 1 of them a synthesis that pooled it.
What it found
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
26 citing papers in PubMed, 1 synthesis or guideline pooled it, 60 citations in OpenAlex.
- Methodological and applicability pitfalls of clinical prediction models for asthma diagnosis: a systematic review and critical appraisal of evidence.BMC medical research methodology · 2025Pooled it
- Artificial Intelligence in Asthma and COPD: Current Status and Future Potential.Journal of clinical medicine · 2026Review
- Molecular profiling of exhaled breath condensate in respiratory diseases.Annals of medicine · 2025Review
- Review
- Exhaled Breath Analysis: From Laboratory Test to Wearable Sensing.IEEE reviews in biomedical engineering · 2025Review
- Application and research progress of artificial intelligence in allergic diseases.International journal of medical sciences · 2025Review
- Artificial intelligence in pediatric allergy research.European journal of pediatrics · 2024Review
- An integrated metabo-lipidomics profile of induced sputum for the identification of novel biomarkers in the differential diagnosis of asthma and COPD.Journal of translational medicine · 2024Article
- Childhood asthma phenotypes and endotypes: a glance into the mosaic.Molecular and cellular pediatrics · 2023Review
- Metabolomics Applied to Pediatric Asthma: What Have We Learnt in the Past 10 Years?Children (Basel, Switzerland) · 2023Article
- Pharmacometabolomics of Asthma as a Road Map to Precision Medicine.Handbook of experimental pharmacology · 2023Article
- Determining asthma endotypes and outcomes: Complementing existing clinical practice with modern machine learning.Cell reports. Medicine · 2022Review
- Metabolomics in asthma: A platform for discovery.Molecular aspects of medicine · 2022Review
- Metabo-Endotypes of Asthma Reveal Differences in Lung Function: Discovery and Validation in Two TOPMed Cohorts.American journal of respiratory and critical care medicine · 2022Article
- Analyzing the Treatment of Patients with Acute Exacerbation of COPD with the Aid of Intelligent Diagnosis Method.Journal of healthcare engineering · 2022Article
- Asthma and the Missing Heritability Problem: Necessity for Multiomics Approaches in Determining Accurate Risk Profiles.Frontiers in immunology · 2022Review
- Microbiome Research and Multi-Omics Integration for Personalized Medicine in Asthma.Journal of personalized medicine · 2021Review
- Effects of ambient air pollution on emergency room visits of children for acute respiratory symptoms in Delhi, India.Environmental science and pollution research international · 2021Article
- Research Progress of Metabolomics in Asthma.Metabolites · 2021Review
- Global metabolome profiling of exhaled breath condensates in male smokers with asthma COPD overlap and prediction of the disease.Scientific reports · 2021Article
Corrections and comments
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Authors and funding
10 authors at 5 institutions in 3 countries.
Funding
Abstract
backgroundAsthma 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.
methodsHere 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.
resultsNMR 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.
conclusionAsthma clusters derived from NMR spectra of EBC show important clinical and chemical differences, suggesting this as a useful tool in asthma endotype-discovery.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.