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

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 airways diseases. Aims To conduct a systematic review evaluating (i) the diagnostic accuracy of VOCs in diagnosing airways diseases, (ii) understand the relationship between reported VOCs and biomarkers of type-2 inflammation, (iii) assess the standardisation of reporting according to STARD and TRIPOD criteria, (iv) review current methods of breath sampling and analysis Methods A PRISMA-oriented systematic search was conducted (January 1997–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 4 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 airways 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 airways 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.