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. 2021 Aug 31;7(3):00030-2021.
doi: 10.1183/23120541.00030-2021. eCollection 2021 Jul.

A systematic review of the diagnostic accuracy of volatile organic compounds in airway diseases and their relation to markers of type-2 inflammation

Wadah Ibrahim  1   2   3 , Sushiladevi Natarajan  1   2   3 , Michael Wilde  4 , Rebecca Cordell  4 , Paul S Monks  4 , Neil Greening  1   2 , Christopher E Brightling  1   2 , Rachael Evans  1   2 , Salman Siddiqui  1   2   5
Affiliations

A systematic review of the diagnostic accuracy of volatile organic compounds in airway diseases and their relation to markers of type-2 inflammation

Wadah Ibrahim et al. ERJ Open Res. .

Abstract

Background: Asthma and COPD continue to cause considerable diagnostic and treatment stratification challenges. Volatile organic compounds (VOCs) have been proposed as feasible diagnostic and monitoring biomarkers in airway diseases.

Aims: To 1) conduct a systematic review evaluating the diagnostic accuracy of VOCs in diagnosing airway diseases; 2) understand the relationship between reported VOCs and biomarkers of type-2 inflammation; 3) assess the standardisation of reporting according to STARD and TRIPOD criteria; 4) review current methods of breath sampling and analysis.

Methods: A PRISMA-oriented systematic search was conducted (January 1997 to December 2020). Search terms included: "asthma", "volatile organic compound(s)", "VOC" and "COPD". Two independent reviewers examined the extracted titles against review objectives.

Results: 44 full-text papers were included; 40/44 studies were cross-sectional and four studies were interventional in design; 17/44 studies used sensor-array technologies (e.g. eNose). Cross-study comparison was not possible across identified studies due to the heterogeneity in design. The commonest airway diseases differentiating VOCs belonged to carbonyl-containing classes (i.e. aldehydes, esters and ketones) and hydrocarbons (i.e. alkanes and alkenes). Although individual markers that are associated with clinical biomarkers of type-2 inflammation were recognised (i.e. ethane and 3,7-dimethylnonane for asthma and α-methylstyrene and decane for COPD), these were not consistently identified across studies. Only 3/44 reported following STARD or TRIPOD criteria for diagnostic accuracy and multivariate reporting, respectively.

Conclusions: Breath VOCs show promise as diagnostic biomarkers of airway diseases and for type-2 inflammation profiling. However, future studies should focus on transparent reporting of diagnostic accuracy and multivariate models and continue to focus on chemical identification of volatile metabolites.

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Conflict of interest statement

Conflict of interest: W. Ibrahim has nothing to disclose. Conflict of interest: S. Natarajan has nothing to disclose. Conflict of interest: M. Wilde has nothing to disclose. Conflict of interest: R. Cordell has nothing to disclose. Conflict of interest: P.S. Monks has nothing to disclose. Conflict of interest: N. Greening has nothing to disclose. Conflict of interest: C.E. Brightling has nothing to disclose. Conflict of interest: R. Evans has nothing to disclose. Conflict of interest: S. Siddiqui has nothing to disclose.

Figures

FIGURE 1
FIGURE 1
PRISMA flow chart illustrating article selection (modified from Moher et al. [65]); for more information see www.prisma-statement.org.
FIGURE 2
FIGURE 2
Distribution of study designs, and breath collection and analysis technologies across the identified studies.
FIGURE 3
FIGURE 3
Top panel: The precise cellular metabolic processes that underpin the majority of reported VOCs described in breath using GC-MS have yet to be determined. Bottom panel: Simplified illustration of the different causes of lipid peroxidation and the resultant formation of aldehydes and alkanes as well as other VOC classes, which have been consistently described across several breath volatile association studies in asthma and COPD (table 1 and the online supplementary material).
FIGURE 4
FIGURE 4
Risk of bias and applicability concerns using QUADAS-2 tool. Green: low risk; yellow: unclear; red: high concern.
FIGURE 5
FIGURE 5
Qualitative assessment of breath sampling and analytical technologies level on a nine-point technology readiness level (TRL) scale (adapted from TRL guidance published by the Nuclear Decommissioning Authority, NDA), comparative to existing biomarkers used in clinical practice for airway disease stratification. GC: gas chromatography; MS: mass spectrometry; IMS: ion mobility spectrometry; FENO: exhaled nitric oxide fraction.
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