Chromatographic analysis of VOC patterns in exhaled breath from smokers and nonsmokers. (12th December 2017)

• Record Type: Journal Article
• Authors: Capone, Simonetta; Tufariello, Maria; Forleo, Angiola; Longo, Valentina; Giampetruzzi, Lucia; Radogna, Antonio Vincenzo; Casino, Flavio; Siciliano, Pietro
• Is Part Of: Biomedical chromatography. Volume 32:Number 4(2018)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2017-12-12
• Subjects: breath analysis -- exhaled VOCs -- smoking biomarkers -- SPME‐GC/MS; Chromatographic analysis -- Periodicals; Biology -- Periodicals; Medicine -- Periodicals; Biology -- Periodicals; Chromatography -- methods -- Periodicals; Medicine -- Periodicals; 543.089
• DOI: 10.1002/bmc.4132
• Languages: English
• ISSNs: 0269-3879

Abstract

Abstract: Cigarette smoking harms nearly every organ of the body and causes many diseases. The analysis of exhaled breath for exogenous and endogenous volatile organic compounds (VOCs) can provide fundamental information on active smoking and insight into the health damage that smoke is creating. Various exhaled VOCs have been reported as typical of smoking habit and recent tobacco consumption, but to date, no eligible biomarkers have been identified. Aiming to identify such potential biomarkers, in this pilot study we analyzed the chemical patterns of exhaled breath from 26 volunteers divided into groups of nonsmokers and subgroups of smokers sampled at different periods of withdrawal from smoking. Solid‐phase microextraction technique and gas chromatography/mass spectrometry methods were applied. Many breath VOCs were identified and quantified in very low concentrations (ppbv range), but only a few (toluene, pyridine, pyrrole, benzene, 2‐butanone, 2‐pentanone and 1‐methyldecyclamine) were found to be statistically significant variables by Mann–Whitney test. In our analysis, we did not consider the predictive power of individual VOCs, as well as the criterion of uniqueness for biomarkers suggests, but we used the patterns of the only statistically significant compounds. Probit prediction model based on statistical relevant VOCs‐patterns showed that assessment of smoking status is heavily time dependent. In a two‐class classifier model, it is possible to predict with high specificity and sensitivity if a subject is a smoker who respected 1 hour of abstinence from smoking (short‐term exposure to tobacco) or a smoker (labelled "blank smoker") after a night out of smoking (long‐term exposure to tobacco). On the other side, in our study "blank smokers" are more like non‐smokers so that the two classes cannot be well distinguished and the corresponding prediction results showed a good sensitivity but low selectivity.