COPD refers to a group of diseases that cause airflow blockage and breathing-related problems.
Chronic Obstructive Pulmonary Disease (COPD) refers to a group of diseases that cause airflow blockage and breathing-related problems. The Global Burden of Disease Study (2016) reported 251 million cases of COPD globally and contributes to around 5% of deaths (2015, 3.17 million)1, by the end of 2020 it is anticipated to be the third leading cause of death worldwide2. COPD is usually progressive, and exacerbations, which are typically related to viral or bacterial infections, are a major cause of lung function decline. Cigarette smoking is the most commonly encountered risk factor for COPD.
Asthma and COPD are both chronic inflammatory lung diseases that cause airflow limitation by bronchoconstriction. As such, they have many similarities but also important differences. Often, the nature of the inflammation present differs; in asthma the inflammation is commonly eosinophilic, whereas in COPD it is predominantly neutrophilic3. That said, there is significant overlap between the conditions and each encompasses multiple subgroups of patients with distinct clinical and pathophysiological features4.
As with asthma, COPD symptoms can arise from multiple mechanisms, so finding the most suitable treatments for each patient can be challenging. Hence, there has been an increasing call in recent years for exploring precision medicine approaches to COPD and a growing amount of research is going into developing tailored biological treatments for COPD5.
VOCs and COPD
Breath VOCs have great potential to be used in the diagnosis of COPD. Patients being tested for COPD often have impaired lung function and so their ability to successfully complete spirometry tests and other complex breath analyses is often limited. Since VOC analysis can be carried out using breath collected during regular tidal breathing, it is much easier to collect samples from patients.
In a study of 28 COPD patients, VOCs in exhaled breath were measured with GC-MS and electronic nose devices: a composite of 19 VOCs were found to be highly associated with eosinophil cationic protein (ECP) – a known biomarker of eosinophil activity – while another four were highly associated with myeloperoxidase (MPO) – an activity marker for neutrophilic cells6 (Figure 1). Analysis showed high sensitivity and specificity (AUC 1.00 for ECP, 0.96 for MPO) for the 12 mild COPD patients but not for 16 moderate COPD patients: since mild COPD is more commonly typified by airway inflammation while moderate COPD is less associated with inflammation and more associated with airway remodeling, this suggests that these VOCs may be used for measuring activity in airway inflammation.
A small proof-of-concept study7 of 14 patients showed that various breathomics approaches could be used to monitor treatment response in COPD with different breathprints detected from the same patients on beclomethasone/formoterol vs. fluticasone propionate/salmeterol. Larger studies are needed to explore the full potential of using breath tests to monitor COPD treatment.
A 2018 systematic review8 of existing studies applying breath analysis to respiratory diseases highlighted indole9, aromatic hydrocarbons, acetic acid, phenol10, hexanal, nonanal and decanal11, and 2-pentanone12 as VOCs with proposed associations with COPD. These VOCs have been used to create predictive models with between 70% and 92% correct classification rates. Studies have also suggested VOCs with correlative associations with sputum eosinophilia and frequency of exacerbations
Using Breath Biopsy® to distinguish between patients with COPD and lung cancer
Evidence suggests that breath VOCs collected and analysed using the Breath Biopsy platform can differentiate between COPD and other lung diseases. Figure 2 shows the performance of the platform in a population of 227 COPD patients and 269 lung cancer patients. Breath Biopsy® was able to distinguish lung cancer patients from those with COPD with a sensitivity of 91% and a specificity of 64%. The area under the receiver operator curve equals 0.80 ± 0.03.
To date, a relatively small number of studies have attempted to apply breathomics to the detection and stratification of COPD and those that have often lack the scale and statistical robustness to provide conclusive links between specific VOCs and the disease.
Despite this, early results using Breath Biopsy are promising. Non-invasive breath testing for COPD represents a key opportunity with an increasing number of patients being diagnosed worldwide and a growing need for diagnostic and monitoring approaches that can be deployed for patients with significant lung function impairment.
- World Health Organization, COPD Fact Sheet 2017 https://www.who.int/news-room/fact-sheets/detail/chronic-obstructive-pulmonary-disease-(copd)
- Global Strategy for Prevention, Diagnosis and Management of COPD, Global Initiative for Chronic Obstructive Lung Disease (2019) https://goldcopd.org/gold-reports/
- Buist, Similarities and differences between asthma and chronic obstructive pulmonary disease: treatment and early outcomes, Eur. Respir. J. (2003). doi.org/10.1183/09031936.03.00404903
- Kolsum et al., Clinical characteristics of eosinophilic COPD versus COPD patients with a history of asthma, Respir. Res. (2017). doi.org/10.1186/s12931-017-0559-0
- Pavord, Biologics and chronic obstructive pulmonary disease, J. Allergy Clin. Immunol. (2019). doi.org/10.1016/j.jaci.2018.04.020
- Fens et al., Exhaled air molecular profiling in relation to inflammatory subtype and activity in COPD, Eur. Respir. J. (2011), 38(6):1301 – 1309. doi.org/10.1183/09031936.00032911
- Montuschi et al., Exhaled 8-isoprostane as an in vivo biomarker of lung oxidative stress in patients with COPD and healthy smokers. Am J Respir Crit Care Med. (2000) 162:1175–7. doi.org/10.1164/ajrccm.162.3.2001063
- Finamore et al., Breath analysis in respiratory diseases, state-of-the-art and future perspectives. J. Molecular Diagnositcs. (2018). 19 (10): 47-61. doi.org/10.1080/14737159.2019.1559052
- Sinues et al., Breath analysis in real time by mass spectrometry in chronic obstructive pulmonary disease. (2014), Respiration; 87: 301-310. doi.org/10.1159/000357785
- Phillips et al., Machine learning methods on exhaled volatile organic compounds for distinguishing COPD patients from healthy controls. (2012). J Breath Res; 6:036003. doi.org/10.1088/1752-7155/6/3/036003
- Basanta et al., Exhaled volatile organic compounds for phenotyping chronic obstructive pulmonary disease: a cross-sectional study. (2012). Respir Res. 13:72. doi.org/10.1186/1465-9921-13-72
- Allers et al., (2016). Measurement of exhaled volatile organic compounds from patients with chronic obstructive
pulmonary disease (COPD) using closed gas loop GC-IMS and GCAPCI-MS. J Breath Res. 10:026006. doi.org/10.1088/1752-7155/10/2/026004