About this Research Topic
Breathomics is a branch of metabolomics that analyzes various volatile organic compounds (VOCs) from exhaled breath samples. It has been rapidly growing as a non-invasive diagnostic tool to probe or infer the pathogenic or physiological status of the human body, often yielding crucial information for disease diagnostics. Unlike traditional diagnostic methods that often require invasive procedures or complex laboratory analyses, breath analysis offers a simple, cost-effective, and patient-friendly approach that can be easily integrated into routine clinical practice. The non-invasive nature of breath sample collection makes breathomics particularly attractive for disease screening, monitoring, and personalized medicine. Understanding the link between breath molecules and diseases has gained significant advances due to recent developments in more reliable detection techniques and standardized breath sample collection methods. Additionally, the recent rapid progress in algorithm development, including machine learning and artificial intelligence, has unveiled intriguing associations between breath VOCs and diseases that were previously convoluted by multiple factors. With successful clinical applications of gastrointestinal and respiratory diagnostics, breath analysis has expanded to broader fields such as thoracic diseases, neurological disorders, pharmacokinetics, and more. The surge in registered clinical trials employing breath analysis and breathomics underscores the growing significance and potential of this innovative approach in modern healthcare.
The objective of this Research Topic is to provide a comprehensive overview of the recent advancements in breathomics, with a particular focus on exploring the association between exhaled breath VOCs and diseases. The aim is to delve deeper into the scientific underpinnings of this emerging field and to assess its potential in modern healthcare.
Focusing on the following four areas:
• Investigating the mechanism of breath biomarkers and diseases, including assessing unique VOC signatures associated with different diseases, potential VOC profiles for early disease screening, and influences of various factors such as diet, lifestyle, and environmental exposure on breath VOC composition.
• Advancements in breath analysis technologies, including novel analytical techniques for the detection and quantification of VOCs in exhaled breath and applications of standardized protocols for breath sample collection, storage, and analysis.
• Machine learning and AI-driven data processing in breathomics, including the development of predictive models for disease classification and risk prediction, integration of multi-omics data with breathomics data for comprehensive disease profiling, and validation strategies for assessing the robustness and generalizability of AI-driven models.
• Clinical applications of breathomics, including evaluation of breathomics-based diagnostic tools in clinical settings, monitoring disease progression and treatment response through longitudinal breath analysis or pharmacokinetics studies, and exploring the potential of breathomics in personalized medicine and disease screening.
Topic editor Meixiu Sun is employed by WIM Spirare Health Technology Limited. The other Topic Editors declare no potential conflicts of interest with regards to the Research.
The objective of this Research Topic is to provide a comprehensive overview of the recent advancements in breathomics, with a particular focus on exploring the association between exhaled breath VOCs and diseases. The aim is to delve deeper into the scientific underpinnings of this emerging field and to assess its potential in modern healthcare.
Focusing on the following four areas:
• Investigating the mechanism of breath biomarkers and diseases, including assessing unique VOC signatures associated with different diseases, potential VOC profiles for early disease screening, and influences of various factors such as diet, lifestyle, and environmental exposure on breath VOC composition.
• Advancements in breath analysis technologies, including novel analytical techniques for the detection and quantification of VOCs in exhaled breath and applications of standardized protocols for breath sample collection, storage, and analysis.
• Machine learning and AI-driven data processing in breathomics, including the development of predictive models for disease classification and risk prediction, integration of multi-omics data with breathomics data for comprehensive disease profiling, and validation strategies for assessing the robustness and generalizability of AI-driven models.
• Clinical applications of breathomics, including evaluation of breathomics-based diagnostic tools in clinical settings, monitoring disease progression and treatment response through longitudinal breath analysis or pharmacokinetics studies, and exploring the potential of breathomics in personalized medicine and disease screening.
Topic editor Meixiu Sun is employed by WIM Spirare Health Technology Limited. The other Topic Editors declare no potential conflicts of interest with regards to the Research.
Keywords: breath analysis, breathomics, volatile organic compounds (VOC), disease diagnostics, machine learning, artificial intelligence
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