Identification of Multiple Pulmonary Diseases Using Volatile Organic Compounds Biomarkers in Human Exhaled Breath

Exploration and Study on the Identification of Various Pulmonary Diseases Using Volatile Organic Compounds Biomarkers in Human Exhaled Breath

The goal of this observational study is to develop an advanced expiratory algorithm model utilizing exhaled breath volatile organic compound (VOC) marker molecules. This model aims to accurately diagnose mutiple pulmonary diseases. The primary objectives it strives to accomplish are:

1. To assess the diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in diagnose several common pulmonary diseases.
2. To assess the diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in diagnose more pulmonary diseases.

Study Overview

• Status: Recruiting
• Conditions: Bronchiectasis; Emphysema; Pulmonary Fibrosis; Lung Cancer; Pulmonary Arterial Hypertension; Lung Injury; Interstitial Lung Disease; Bronchitis; COPD; Pulmonary Embolism; Pulmonary Tuberculosis; Bronchial Asthma; Lung Infection; Preserved Ratio Impaired Spirometry; Pulmonary Abscess; Cystic Fibrosis of the Lung
• Intervention / Treatment: Other: Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) system

Detailed Description

This is a prospective, cross-sectional, observational cohort study aimed at recruiting 10,000 participants with multiple pulmonary disease, including lung cancer, lung infection, chronic obstructive pulmonary disease (COPD), bronchitis, pulmonary fibrosis, pulmonary embolism, pulmonary arterial hypertension, tuberculosis, lung abscess, emphysema, radioactive lung injury, cystic fibrosis of the lung, Bronchial Asthma, Bronchiectasis, interstitial lung disease (ILD), preserved ratio impaired spirometry (PRISm) etc . Exhaled breath samples from these participants will be collected and analyzed using Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) system. Upon obtaining the μGC-PID results, a comprehensive evaluation of the diagnostic capabilities of exhaled breath samples in differentiating various pulmonary diseases will be performed, leveraging clinical diagnostic results, CT examination data, and clinical data.

• Study Type: Observational
• Enrollment (Estimated): 10000

Contacts and Locations

• Study Contact: Name: Hengrui Liang, MD; Phone Number: +86 15625064712; Email: hengrui_liang@163.com

Study Locations

• China
  • Guangdong
    • Guangzhou, Guangdong, China, 510140
      • Recruiting
      • The First Affiliated Hospital of Guangzhou Medical University
      • Contact: Hengrui Liang, MD; Phone Number: +86 15625064712; Email: hengrui_liang@163.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• Sampling Method: Probability Sample

Study Population

Patients with abnormal lung CT images within the past six months, including lung cancer, lung infection, chronic obstructive pulmonary disease (COPD), bronchitis, pulmonary fibrosis, pulmonary embolism, pulmonary arterial hypertension, tuberculosis, lung abscess, emphysema, radioactive lung injury, cystic fibrosis of the lung, Bronchial Asthma, Bronchiectasis, interstitial lung disease (ILD), preserved ratio impaired spirometry (PRISm), etc .

Description

Inclusion Criteria:

• Males or females, age must be 18 years old or above.
• Patients must meet the CT imaging diagnostic criteria for different lung diseases, and patients must be able to provide electronic versions of CT image data.
• Patients must have a clear clinical diagnosis.
• All participants must sign a written informed consent form.

Exclusion Criteria:

• Pregnant women.
• Individuals with a history of cancer other than lung disease.
• Individuals who have undergone organ transplants or non-autologous (allogeneic) bone marrow or stem cell transplants.
• Individuals with other severe organic diseases or mental illnesses.
• Individuals with metabolic diseases such as diabetes, hyperlipidemia, etc.
• Any other condition that researchers deem unsuitable for participation in this clinical trial.

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
pulmonary disease; Individuals with abnormalities in lung CT imaging and clinically diagnosed with lung cancer, lung infection, chronic obstructive pulmonary disease (COPD), bronchitis, pulmonary fibrosis, pulmonary embolism, pulmonary arterial hypertension, tuberculosis, lung abscess, emphysema, radioactive lung injury, cystic fibrosis of the lung, Bronchial Asthma, Bronchiectasis, interstitial lung disease (ILD), preserved ratio impaired spirometry (PRISm) etc .	Other: Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) system; Exhaled breath samples from these participants will be collected and analyzed to detect volatile organic compound molecules in human exhaled breath by GC-MS and μGC-PID
normal individual; Individuals with no abnormalities detected in lung CT imaging.	Other: Gas chromatography-mass spectrometry(GC-MS) and micro Gas Chromatography-photoionisation detector (μGC-PID) system; Exhaled breath samples from these participants will be collected and analyzed to detect volatile organic compound molecules in human exhaled breath by GC-MS and μGC-PID

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
The diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in the diagnosis of several common pulmonary diseases.	The diagnostic performance of the exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model will be compared with clinical diagnosis and CT/LDCT diagnosis, including sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).	2 years

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
The diagnostic accuracy of an exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model in the diagnosis of more pulmonary diseases.	The diagnostic performance of the exhaled breath VOC-assisted diagnostic artificial intelligence (AI) model will be compared with clinical diagnosis and CT/LDCT diagnosis, including sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV).	2 years

Other Outcome Measures

Outcome Measure	Measure Description	Time Frame
Establish an exhaled breath VOC model for predicting specific gene mutations in some lung diseases.	Establish an exhaled breath VOC model for predicting specific gene mutations in some lung diseases. And evaluate the prediction accuracy by comparing the results of specific gene testing	2 years

Collaborators and Investigators

• Sponsor: ChromX Health
• Collaborators: Peking Union Medical College Hospital; Shanghai Chest Hospital; The First Affiliated Hospital of Guangzhou Medical University; First People's Hospital of Foshan; Sichuan Cancer Hospital and Research Institute; Fifth Affiliated Hospital of Guangzhou Medical University; Liwan District Central Hospital; Guangzhou Development Zone Hospital; Huangpu District Hongshan Street Community Health Service Center; Huangpu District Chinese Medicine Hospital; Huangpu District Jiufo Street Community Health Service Center; Huangpu District Xinlong Town Central Hospital; Huangpu District Yonghe Street Community Health Service Center; Huangpu District Lianhe Street Second Community Health Service Center
• Investigators: Study Chair: Jianxing He, MD, The First Affiliated Hospital of Guangzhou Medical University

Publications and helpful links

General Publications

• GBD Chronic Respiratory Disease Collaborators. Prevalence and attributable health burden of chronic respiratory diseases, 1990-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet Respir Med. 2020 Jun;8(6):585-596. doi: 10.1016/S2213-2600(20)30105-3.
• Ratiu IA, Ligor T, Bocos-Bintintan V, Mayhew CA, Buszewski B. Volatile Organic Compounds in Exhaled Breath as Fingerprints of Lung Cancer, Asthma and COPD. J Clin Med. 2020 Dec 24;10(1):32. doi: 10.3390/jcm10010032.
• van de Kant KD, van der Sande LJ, Jobsis Q, van Schayck OC, Dompeling E. Clinical use of exhaled volatile organic compounds in pulmonary diseases: a systematic review. Respir Res. 2012 Dec 21;13(1):117. doi: 10.1186/1465-9921-13-117.
• Wang J, Janson C, Gislason T, Gunnbjornsdottir M, Jogi R, Orru H, Norback D. Volatile organic compounds (VOC) in homes associated with asthma and lung function among adults in Northern Europe. Environ Pollut. 2023 Mar 15;321:121103. doi: 10.1016/j.envpol.2023.121103. Epub 2023 Jan 21.
• V A B, Subramoniam M, Mathew L. Noninvasive detection of COPD and Lung Cancer through breath analysis using MOS Sensor array based e-nose. Expert Rev Mol Diagn. 2021 Nov;21(11):1223-1233. doi: 10.1080/14737159.2021.1971079. Epub 2021 Aug 27.

Study record dates

Study Major Dates

• Study Start (Actual): June 30, 2024
• Primary Completion (Estimated): December 30, 2026
• Study Completion (Estimated): June 30, 2027

Study Registration Dates

• First Submitted: July 18, 2024
• First Submitted That Met QC Criteria: July 25, 2024
• First Posted (Actual): July 30, 2024

Study Record Updates

• Last Update Posted (Actual): March 26, 2025
• Last Update Submitted That Met QC Criteria: March 23, 2025
• Last Verified: March 1, 2025

More Information

Terms related to this study

• Keywords: Volatile Organic Compounds; Pulmonary Disease; Human Exhaled Breath; micro Gas Chromatography-photoionisation; detector (μGC-PID) system
• Additional Relevant MeSH Terms: Vascular Diseases; Cardiovascular Diseases; Wounds and Injuries; Pathologic Processes; Genetic Diseases, Inborn; Immune System Diseases; Respiratory Tract Infections; Infections; Respiratory Tract Diseases; Digestive System Diseases; Infant, Newborn, Diseases; Bronchial Diseases; Lung Diseases, Obstructive; Respiratory Hypersensitivity; Hypersensitivity, Immediate; Hypersensitivity; Pancreatic Diseases; Embolism and Thrombosis; Gram-Positive Bacterial Infections; Bacterial Infections; Bacterial Infections and Mycoses; Suppuration; Thoracic Injuries; Actinomycetales Infections; Mycobacterium Infections; Hypertension, Pulmonary; Abscess; Pulmonary Arterial Hypertension; Lung Diseases; Asthma; Pulmonary Embolism; Pulmonary Fibrosis; Fibrosis; Embolism; Lung Diseases, Interstitial; Emphysema; Cystic Fibrosis; Lung Injury; Tuberculosis; Tuberculosis, Pulmonary; Bronchiectasis; Lung Abscess; Bronchitis
• Other Study ID Numbers: MLD001

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No