Open Access

Clinical use of exhaled volatile organic compounds in pulmonary diseases: a systematic review

Respiratory Research201213:117

DOI: 10.1186/1465-9921-13-117

Received: 10 September 2012

Accepted: 5 December 2012

Published: 21 December 2012

Abstract

There is an increasing interest in the potential of exhaled biomarkers, such as volatile organic compounds (VOCs), to improve accurate diagnoses and management decisions in pulmonary diseases. The objective of this manuscript is to systematically review the current knowledge on exhaled VOCs with respect to their potential clinical use in asthma, lung cancer, chronic obstructive pulmonary disease (COPD), cystic fibrosis (CF), and respiratory tract infections. A systematic literature search was performed in PubMed, EMBASE, Cochrane database, and reference lists of retrieved studies. Controlled, clinical, English-language studies exploring the diagnostic and monitoring value of VOCs in asthma, COPD, CF, lung cancer and respiratory tract infections were included. Data on study design, setting, participant characteristics, VOCs techniques, and outcome measures were extracted. Seventy-three studies were included, counting in total 3,952 patients and 2,973 healthy controls. The collection and analysis of exhaled VOCs is non-invasive and could be easily applied in the broad range of patients, including subjects with severe disease and children. Various research groups demonstrated that VOCs profiles could accurately distinguish patients with a pulmonary disease from healthy controls. Pulmonary diseases seem to be characterized by a disease specific breath-print, as distinct profiles were found in patients with dissimilar diseases. The heterogeneity of studies challenged the inter-laboratory comparability. In conclusion, profiles of VOCs are potentially able to accurately diagnose various pulmonary diseases. Despite these promising findings, multiple challenges such as further standardization and validation of the diverse techniques need to be mastered before VOCs can be applied into clinical practice.

Keywords

VOCs Asthma COPD Lung cancer Cystic fibrosis Airway inflammation Biomarkers

Review

Introduction

Background and aim

Pulmonary diseases are important causes of morbidity in both adults and children [1, 2]. The diverse pulmonary diseases go along with clinical challenges. In adults, lung cancer is one of the leading causes of death worldwide. It is often diagnosed at an advanced stage when successful treatment is difficult [3]. Furthermore, chronic obstructive pulmonary disease (COPD) and asthma are prevalent lung diseases that account for a major burden on society in terms of morbidity and health care costs. Early diagnosis and close monitoring of both diseases are important for proper treatment decisions, optimal disease control and prognosis. However, the available clinical tools are not always fulfilling. In young children, a reliable asthma diagnosis is difficult as there are no tools available to discriminate between true asthmatics and children with transient, virus-induced symptoms. On account of these clinical challenges, there is a continuous search for techniques that can improve accurate diagnoses and management decisions. A potential non-invasive technique is the analysis of volatile biomarkers in exhaled breath, so called volatile organic compounds (VOCs). In this manuscript we systematically review the current knowledge on VOCs regarding their potential clinical use in pulmonary diseases.

The origin of exhaled volatile organic compounds

Asthma, COPD, Cystic Fibrosis (CF), and lung cancer are characterized by inflammation and oxidative stress. Monitoring of airway inflammation and oxidative stress can be helpful in the diagnosis and monitoring of these diseases. Current available techniques to directly measure inflammation and oxidative stress in the airways are bronchoscopy, bronchoalveolar lavage and biopsy. These techniques are too invasive for repeated routine use, especially in children. The need for non-invasive analysis of inflammation and oxidative stress in the lungs has led to increasing interest in exhaled breath analysis (Figure 1). Fractional exhaled Nitric Oxide (FeNO) is the most extensively studied marker in exhaled breath. Although the analysis of FeNO might be a helpful clinical tool in some pulmonary diseases, it has several limitations. For example in asthma, FeNO is especially a marker of allergic inflammation and therefore of limited use in non-allergic patients [4]. Consequently, additional exhaled biomarkers were studied. Next to non-volatile biomarkers that can be assessed in exhaled breath condensate, the analysis of exhaled VOCs gained popularity. VOCs are a diverse group of carbon-based chemicals that are volatile at room temperature. The source of exhaled VOCs can be exogenous or endogenous. Some VOCs can be taken up as pollutants from the environment via the skin or by inhalation or ingestion. Subsequently, these compounds are metabolized and exhaled. Other VOCs are formed in the body during several (patho)physiological processes [5, 6]. An important group of endogenously formed VOCs are hydrocarbons that are formed by lipid peroxidation. During the inflammatory process, Reactive Oxygen Species (ROS) are produced by inflammatory cells. Subsequently, ROS react with lipid membrane structures and cause degradation of polyunsaturated fatty acids. As a result several stable breakdown products including hydrocarbons are formed [5, 6]. Besides hydrocarbons, other VOCs can be identified, including nitrogen, oxygen or sulphur containing compounds. These VOCs can be formed by bacteria or during (patho)physiological processes in the liver, kidneys, and pancreas [5, 6]. As soon as VOCs are formed, they are either further oxidized into smaller components due to enhanced activity of enzymes (such as cytochrome P450 oxidase), or they directly enter the bloodstream [7]. Subsequently, VOCs are excreted into breath. Early findings of distinct VOCs in diseased people (e.g. with diabetes or cirrhosis) compared to healthy controls stimulated investigators to elucidate the clinical potential of exhaled VOCs in pulmonary diseases [5]. Since exhaled VOCs are formed during inflammatory processes, the analysis of VOCs may be a promising non-invasive technique to directly monitor inflammation and oxidative stress in the airways. This information might be of help in the diagnosis and monitoring of pulmonary diseases.
https://static-content.springer.com/image/art%3A10.1186%2F1465-9921-13-117/MediaObjects/12931_2012_1302_Fig1_HTML.jpg
Figure 1

Techniques to assess airway inflammation and oxidative stress. There are various methods to measure airway inflammation and oxidative stress ranging from completely non-invasive (exhaled breath analysis) to very invasive (open lung biopsy).

Technical analysis of exhaled breath

There are multiple techniques described to collect, detect, and analyze exhaled VOCs [6, 8, 9]. The most commonly used techniques are gas chromatography (GC), which is the gold standard, and the electronic nose (eNose). With the GC-technique, exhaled breath is firstly collected and temporarily stored (e.g. in inert bags or sorption tubes). After a desorption phase, individual VOCs can be assessed by GC usually followed by mass spectrometry (GC-MS) or flame ionization detection (GC-FID) [6]. The diverse VOCs are first separated based on their chemical properties and consecutively ionized and separated by their mass-to-charge (m/z) ratio (Figure 2). Breath samples can also be analyzed using an eNose [9]. The eNose consists of an array of nanosensors. When these sensors are exposed to a mixture of VOCs, a change in their electrical resistance is induced, leading to the production of a ‘breath-print’ (Figure 3). This breath-print represents the complex mixture of exhaled VOCs and can be used for pattern-recognition algorithms in multiple diseases [1013]. A limitation of the eNose is that it is unable to analyze individual VOCs. In addition to GC and the eNose, other techniques that are used to study VOCs in pulmonary diseases include; proton transfer reaction mass spectrometry (PTR-MS), selected ion flow tube mass spectrometry (SIFT-MS), ion mobility spectrometry (IMS), laser spectroscopy, colorimetric sensor array, and gold nano particles sensors (GNPs).
https://static-content.springer.com/image/art%3A10.1186%2F1465-9921-13-117/MediaObjects/12931_2012_1302_Fig2_HTML.jpg
Figure 2

Breath-print of VOCs by gas chromatography. With the gas chromatography (GC) technique, exhaled breath is collected and temporarily stored in e.g. gas-tight syringes, glass bulbs, inert bags, or metal containers. Once the VOCs are collected and temporarily trapped, they can be released for analysis. This is often performed by solvent or thermal desorption. Subsequently, the analysis of individual molecular components can be assessed by GC usually followed by mass spectrometry (GC-MS) or flame ionization detection (GC-FID). The diverse VOCs are separated and quantified by using their specific compound characteristics. Distinct VOCs have dissimilar progression rates and reach the end of the GC column at different time points; the retention time. Based on their retention time, VOCs can be identified in a mass-spectra library. The figure demonstrates an example of a chromatogram of a breath sample analyzed with GC. The retention time (in minutes) is stated on the x axis, while the y axis shows the relative abundance of various compound signals. Published in Robroeks et al. Pediatr Res 2010 [14].

https://static-content.springer.com/image/art%3A10.1186%2F1465-9921-13-117/MediaObjects/12931_2012_1302_Fig3_HTML.jpg
Figure 3

Breath-print of VOCs by the electronic Nose. Breath samples can also be analyzed using an eNose. The eNose consists of an array of nanosensors. When these sensors are exposed to a mixture of VOCs, a change in their electrical resistance is induced, leading to the production of a ‘breath-print’. This breath-print represents the complex mixture of exhaled VOCs and can be used for pattern-recognition algorithms in multiple diseases. A limitation of the eNose is that it is unable to analyze individual VOCs. In the figure two exhaled breath-prints analyzed with the eNose are demonstrated (purple line represents sample 1, green line represents sample 2). The y axis represents the change in resistance (Δ R/Rt = 0) of each of the 28 sensors (1–28). Courtesy: Paul Brinkman, Niki Fens, Peter Sterk, University of Amsterdam, the Netherlands.

Materials and methods

Data sources and search criteria

A systematic literature search was performed until July 2012 in PubMed, EMBASE, and the Cochrane Central Register of Controlled trials. Keywords/Mesh terms included: asthma, chronic obstructive pulmonary disease, COPD, cystic fibrosis, lung cancer, pulmonary disease, respiratory infection, combined with: volatile organic compounds, VOC, VOCs, exhaled breath or electronic nose. Reference lists were reviewed for additional references.

Study selection and data extraction

Figure 4 illustrates a flow-chart of the study selection [16]. Controlled, clinical studies, with full text in English, exploring the diagnostic and monitoring value of VOCs in asthma, COPD, CF, lung cancer and respiratory tract infections were included. In vitro studies were excluded. Data on study design, setting, participant characteristics, VOCs techniques, and outcome measures were extracted. Due to expected heterogeneity of studies, no single scale was used for excluding studies on basis of quality. Instead, per study, criteria that are of importance to examine the validity are described in Table 1.
https://static-content.springer.com/image/art%3A10.1186%2F1465-9921-13-117/MediaObjects/12931_2012_1302_Fig4_HTML.jpg
Figure 4

Flow-chart of literature search. Summary of evidence search and selection according to the Prisma flow-chart [16]. Abbreviations: VOCs = Volatile Organic Compounds.

Table 1

Characteristics of included studies on VOCs in pulmonary diseases

Author (year)

Design

Comparison

Sample size

Setting

Technique

Outcome measure

Ref.

Caldeira (2011)

Cross-sectional

Asthma vs. controls

35 children with asthma, 15 healthy controls

Hospital D. Pedro, Aveiro (Portugal)

GC-MS

Set of 44 VOCs

[17]

Caldeira (2012)

Cross-sectional

Asthma vs. controls

32 children with allergic asthma, 27 healthy controls

Hospital D. Pedro, Aveiro (Portugal)

GC-MS

VOCs profile

[18]

Dallinga (2010)

Cross-sectional

Asthma vs. controls

63 children with asthma, 57 healthy controls

Maastricht University MC

GC-MS

VOCs profile

[19]

(the Netherlands)

Dragonieri (2007)

Cross-sectional

Asthma vs. controls

10 patients with mild asthma, 10 patients with severe asthma, 20 healthy controls

Leiden University MC (the Netherlands)

eNose

VOCs profile

[10]

Ibrahim (2011)

Cross-sectional

Asthma vs. controls

35 patients with asthma, 23 healthy controls

Wythenshawe Hospital, Manchester (UK)

GC-MS

VOCs profile

[20]

Lärstad (2007)

Cross-sectional

Asthma vs. controls

13 patients with asthma, 14 healthy controls

Göteborg University (Sweden)

GC-FID

Ethane, Pentane, Isoprene

[21]

Montuschi (2010)

Cross-sectional

Asthma vs. controls

27 patients with asthma, 24 healthy controls

Istituto Dermopatico dell’ Immacolata, Rome (Italy)

eNose, GC-MS

VOCs profile

[22]

Olopade (1997)

Cross-sectional Short follow-up in acute asthma

Asthma vs. controls

12 patients with acute asthma, 11 patients with stable asthma, 17 healthy controls

University of Ilinois Hospital, Chicago (USA)

GC-FID

Pentane

[23]

Paredi (2000)

Cross-sectional

Asthma vs. controls

26 patients with asthma, 14 healthy controls

National Heart and Lung Institute, Imperial College, London (UK)

GC-FID

Ethane

[24]

Basanta (2010)

Cross-sectional

COPD vs. smokers

20 patients with COPD, 6 healthy smokers

Wytenshawe Hospital, Manchester (UK)

GC-DMS

VOCs profile

[25]

Cristescu (2011)

Cross-sectional

Emphysema vs. No emphysema

204 (former) smokers (43 with emphysema/COPD)

Radboud University, Nijmegen (the Netherlands)

PTR-MS

Mass-spectra

[26]

Fens (2009)

Cross-sectional

COPD vs. asthma vs. controls

30 patients with COPD, 20 patients with asthma, 20 non-smoking controls, 20 smoking controls

Academic MC Amsterdam; Haga Teaching Hospital, The Hague; Albert Schweitzer Hospital, Dordrecht (the Netherlands)

eNose

VOCs profile

[15]

Fens (2011)

Cross-sectional

COPD vs. asthma

40 patients with COPD, 21 patients with fixed asthma, 39 patients with classic asthma

Academic MC Amsterdam; Haga Teaching Hospital, The Hague; Albert Schweitzer Hospital, Dordrecht (the Netherlands)

eNose

VOCs profile

[27]

Hattesohl (2011)

Cross-sectional Follow up after treatment

COPD vs. controls

10 patients with COPD with AAT deficiency, 23 patients with COPD without AAT deficiency, 10 healthy controls

Phillipps University, Marburg (Germany)

eNose

VOCs profile

[28]

Hauschild (2012)

Cross-sectional

COPD vs. controls

30 patients with COPD, 54 patients with COPD + BC, 35 healthy controls

Max Planck Institute for Informatics, Saarbrücken (Germany)

IMS

VOCs profile

[29]

Paredi (2000)

Cross-sectional

COPD vs. controls

22 patients with COPD, 14 healthy controls

National Heart and Lung Institute, Imperial College, London (UK)

GC-FID

Ethane

[30]

Phillips (2012)

Cross-sectional

COPD vs. controls

119 patients with COPD, 63 healthy controls

Swansea University, Swansea (UK)

GC-MS

VOCs profile

[31]

Timms (2012)

Cross-sectional

COPD vs. asthma vs. controls

17 patients with COPD, 20 patients with asthma, 7 healthy controls

University of New South Wales, Sydney (Australia)

eNose

VOCs profile

[32]

Van Berkel (2010)

Cross-sectional

COPD vs. controls

66 patients with COPD, 15 steroid naïve COPD patients, 45 healthy controls

Maastricht University MC (the Netherlands)

GC-MS

VOCs profile

[33]

Barker (2006)

Cross-sectional

CF vs. controls

20 patients with CF, 20 healthy controls

Aachen CF center (Germany)

GC-MS

Set of 12 VOCs

[34]

Enderby (2009)

Cross-sectional

CF vs. asthma

16 patients with CF, 21 patients with asthma

University Hospital of North Staffordshire,Stoke-on-Trent (UK)

SIFT-MS

Hydrogen cyanide

[35]

Gilchrist (2012)

Cross-sectional

CF with- vs. CF without Ps. infection

8 CF patients with Ps. infection, 7 CF patients without Ps. infection

University Hospital of North Staffordshire,Stoke-on-Trent (UK)

SIFT-MS

Hydrogen cyanide

[36]

Kamboures (2005)

Cross-sectional

CF vs. controls

20 patients with CF, 23 healthy controls

University of California, Irvine (USA)

GC-MS

Carbonyl sulphide, Dimethyl sulphide, Carbon disulphide

[37]

McGrath (2000)

Cross-sectional Follow up after treatment

CF during- vs. CF after exacerbation vs. controls

12 patients with CF, 12 healthy controls

Queen’s University, Belfast (UK)

GC-MS

Isoprene

[38]

Paredi (2000)

Cross-sectional

CF vs. controls

23 patients with CF, 14 healthy controls

National Heart and Lung Institute, Imperial College, London (UK)

GC-FID

Ethane

[39]

Robroeks (2010)

Cross-sectional

CF vs. controls

48 patients with CF, 57 healthy controls

Maastricht University MC (the Netherlands)

GC-MS

VOCs profile

[14]

Shestivska (2011)

Cross-sectional

CF vs. controls

28 patients with CF, 9 healthy controls

Academy of Science of the Czech Republic, Prague (Czech Republic)

GC-MS

Methyl thiocyanate

[40]

Bajtarevic (2009)

Cross-sectional

LC vs. controls

285 patients with LC, 472 healthy controls

Innsbruck Medical University (Austria)

PTR-MS, GC-MS

VOCs profile

[41]

Buszewski (2012)

Cross-sectional

LC vs. controls

29 patients with LC, 44 healthy controls

Nicolaus Copernicus University, Torun (Poland)

GC-MS

Set of multiple VOCs

[42]

Crohns (2009)

Cross-sectional Follow up after treatment

LC vs. controls

11 patients with LC, 30 healthy controls

Tampere University Hospital (Finland)

GC-MS

Pentane

[43]

D’Amico (2010)

Cross-sectional

LC vs. no LC vs. controls

28 patients with LC, 28 patients with diverse lung diseases (e.g. COPD (n = 16), bronchitis), 36 healthy controls

C. Forlanini Hospital, Rome (Italy)

eNose (GC-MS)

VOCs profile

[44]

Di Natale (2003)

Cross-sectional

LC vs. controls

35 patients with LC, 9 post-surgical LC patients, 18 healthy controls

C. Forlanini Hospital, Rome (Italy)

eNose (GC-MS)

VOCs profile

[11]

Dragonieri (2009)

Cross-sectional

LC vs. COPD vs. controls

10 patients with NSCLC, 10 patients with COPD, 10 healthy controls

Leiden University MC (the Netherlands)

eNose

VOCs profile

[12]

Fuchs (2010)

Cross-sectional

LC vs. controls

12 patients with LC, 12 healthy smokers, 12 healthy controls

University Rostock (Germany)

GC-MS

Set of 10 volatile aliphatic aldehydes

[45]

Gaspar (2009)

Cross-sectional

LC vs. controls

18 patients with LC, 10 healthy controls

University of Lisbon (Portugal)

GC-MS

VOCS profile

[46]

Gordon (1985)

Cross-sectional

LC vs. controls

12 patients with LC, 9 healthy controls

Michael Reese Hospital, Chicago (USA)

GC-MS

Set of 22 VOCs

[47]

Kischkel (2010)

Cross-sectional

LC vs. controls

31 patients with LC, 31 healthy smokers, 31 healthy controls

University of Rostock (Germany)

GC-MS

Set of 42 VOCs

[48]

Ligor (2009)

Cross-sectional

LC vs. controls

65 patients with LC, 31 healthy controls

Innsbruck Medical University (Austria)

GC-MS

Set of 103 VOCs

[49]

Machado (2005)

Cross-sectional

LC vs. no LC vs. controls

28 patients with LC, 57 patients with diverse lung diseases (e.g. COPD (n = 12), asthma (n = 11), CBD), 50 healthy controls

Cleveland Clinic (USA)

eNose (GC-MS)

VOCs profile

[13]

Mazzone (2007)

Cross-sectional

LC vs. no LC vs. controls

49 patients with NSCLC, 73 patients with diverse lung diseases (e.g. COPD (n = 18), sarcoidosis), 21 healthy controls

Cleveland Clinic (USA)

Colorimetric sensor array

VOCs profile

[50]

Mazzone (2012)

Cross-sectional

LC vs. controls

92 patients with LC, 59 healthy smokers, 78 patients with diverse lung diseases (e.g. COPD (n = 8))

Cleveland Clinic (USA)

Colorimetric sensor array

VOCs profile

[51]

Peng (2009)

Cross-sectional

LC vs. controls

40 patients with LC, 56 healthy controls

Rambam Health Care Campus, Haifa (Israel)

GNPs GC-MS

VOCs profile

[52]

Peng (2010)

Cross-sectional

LC vs. controls

30 patients with PLC, 22 healthy controls

Rambam Health Care Campus, Haifa (Israel)

GNPs GC-MS

VOCs profile

[53]

Phillips (1999)

Cross-sectional

LC vs. no LC

108 patients with abnormal chest radiograph (60 patients with LC)

Penn State MC, Hershey (USA); Hammersmith Hospital, London (UK); St. Vincent’s MC, New York (USA)

GC-MS

VOCs profile

[54]

Phillips (2003)

Cross-sectional

LC vs. no LC vs. controls

178 patients with abnormal chest radiograph (87 patients with LC), 41 healthy controls

Charing Cross Hospital, London (UK); Columbia Presbyterian MC/New York University MC/St. Vincent’s MC, New York (USA); Penn State MC, Hershey (USA)

GC-MS

VOCs profile

[7]

Phillips (2007–2008)

Cross-sectional

LC vs. controls

193 patients with PLC, 211 (former) healthy smokers

Harper Hospital, Detroit; New York University MC/Columbia University MC/Weill Medical College of Cornell University, New York (USA); University of California, Los Angeles; Danbury Hospital, Connecticut (USA).

GC-MS

VOCs profile

[55], [56]

Poli (2005)

Cross-sectional Short follow-up in LC

LC vs. COPD vs. controls

36 patients with NSCLC, 25 patients with COPD, 35 healthy smokers, 50 healthy non-smokers

University of Parma (Italy)

GC-MS

Set of 13 VOCs

[57]

Poli (2008)

Follow-up

LC before vs. after surgery

36 patients with NSCLC, 50 healthy controls

University of Parma (Italy)

GC-MS

Set of 12 VOCs

[58]

Poli (2010)

Cross-sectional

LC vs. controls

40 patients with NSCLC, 38 healthy controls

University of Parma (Italy)

GC-MS

Set of 7 aldehydes

[59]

Preti (1988)

Cross-sectional

LC vs. controls

10 patients with LC, 16 healthy controls

University Hospital Pennsylvania, Philadelphia (USA)

GC-MS

Aniline, o-Toluidine

[60]

Rudnicka (2011)

Cross-sectional

LC vs. controls

23 patients with LC, 30 healthy controls

Nicolaus Copernicus University, Torun (Poland)

GC-MS

Set of 55 VOCs

[61]

Skeldon (2006)

Cross-sectional

LC vs. no LC vs. controls

12 patients with LC, 40 patients with diverse lung diseases, 58 healthy controls

Ninewells Hospital, Dundee (UK)

Laser absorption spectroscopy

Ethane

[62]

Song (2010)

Cross-sectional

LC vs. controls

43 patients with NSCLC, 41 healthy controls

Anhui Medical University, Hefei, Anhui (China)

GC-MS

1-butanol, 3-hydroxy-2-butanone

[63]

Steeghs (2007)

Cross-sectional

LC vs. controls

11 patients with LC, 57 healthy smokers

Radboud University, Nijmegen (the Netherlands)

PTR-MS

Mass-spectra

[64]

Ulanowska (2011)

Cross-sectional

LC vs. controls

137 patients with LC, 143 healthy controls

Nicolaus Copernicus University, Torun (Poland)

GC-MS

VOCs profile

[65]

Wehinger (2007)

Cross-sectional

LC vs. controls

17 patients with PLC, 170 healthy controls

Innsbruck Medical University (Austria)

PTR-MS

Mass-spectra

[66]

Westhoff (2009)

Cross-sectional

LC vs. controls

32 patients with LC, 54 healthy controls

Hemer Lung Hospital (Germany)

IMS

VOCs profile

[67]

Chapman (2012)

Cross-sectional

MPM vs. ARD vs. controls

20 patients with MPM, 18 patients with ARD, 42 healthy controls

St Vincent and Prince of Wales Hospital, Sydney (Australia)

eNose

VOCs profile

[68]

Gennaro (2010) Dragonieri (2012)

Cross-sectional

MPM vs. no MPM

13 patients with MPM, 13 subjects with long-term asbestos exposure, 13 healthy controls

University of Bari Aldo Moro, Bari (Italy)

eNose, GC-MS

VOCs profile

[69], [70]

Chambers (2009)

Cross-sectional

A. fumigatus vs. controls

32 patients with diverse lung diseases (e.g. asthma (n = 11), CF (n = 6), COPD (n = 3), 10 neutropenic patients, 14 healthy controls

University of Christchurch (New Zealand)

GC-MS

2-Pentylfuran

[71]

Hanson (2005)

Cross-sectional

VAP vs. no VAP

19 patients with + VAP score, 19 patients with - VAP score

University of Pennsylvania, Philadelphia (USA)

eNose

VOCs profile

[72]

Hockstein (2004)

Cross-sectional

VAP vs. no VAP

13 ventilated patients with VAP, 12 ventilated patients without VAP

University of Pennsylvania, Philadelphia (USA)

eNose

VOCs profile

[73]

Hockstein (2005)

Cross-sectional

VAP vs. no VAP

15 patients with + VAP score, 29 patients with - VAP score

University of Pennsylvania, Philadelphia (USA)

eNose

VOCs profile

[74]

Kanoh (2005)

Cross-sectional Short follow-up in ILD patients

ILD vs. controls

34 patients with ILD, 16 healthy controls

National Defense Medical College, Saitama (Japan)

GC-FID

Ethane

[75]

Kolk (2012)

Cross-sectional

TB vs. no TB

171 patients suspected of TB

Royal Tropical Institute, Amsterdam (the Netherlands); Desmond Tutu TB Centre, Cape Town (South Africa)

GC-MS

VOCs profile

[76]

Phillips (2007)

Cross-sectional

TB vs. no TB vs. controls

42 patients suspected of TB, 59 healthy controls

Bellevue Hospital, New York (USA)

GC-MS

VOCs profile

[77]

Phillips (2010)

Cross-sectional

TB vs. no TB

226 patients suspected of TB

University of California, San Diego (USA); University of Santo Tomas, Manila (Philippines), De La Salle University Hospital, Cavite (Philippines), East London Tuberculosis Service (UK)

GC-MS

VOCs profile

[78]

Phillips (2012)

Cross-sectional

TB vs. controls

130 patients with TB, 121 healthy controls

University of Santo Tomas, Manila (Philippines); De La Salle University Hospital, Cavite (Philippines); Homerton University Hospital, London (UK); Hinduja Hospital, Mumbai (India)

GC-SAW

VOCs profile

[79]

Syhre (2009)

Cross-sectional

TB vs. controls

10 patients with TB, 10 healthy controls

Otago University, Christchurch (New Zealand); Modilon Hospital, Madang (Papua New Guinea)

GC-MS

Methyl nicotinate

[80]

Scholpp (2002)

Cross-sectional

Critically ill patients vs. controls

65 critically ill patients (n = 19 with head injury, n = 13 with ARDS, n = 33 at risk of ARDS), 10 healthy controls

University Hospital of Freiburg (Germany)

GC-FID, GC-MS

Acetone Isoprene, n-Pentane

[81]

Schubert (1998)

Cross-sectional Short follow-up in VAP patients

ARDS vs. no ARDS

19 critically ill patients with ARDS, 18 critically ill patients without ARDS

University Hospital of Freiburg (Germany)

GC-FID, GC-MS

Acetone Isoprene, n-Pentane

[82]

Abbreviations: AAT deficiency = Alpha 1-antitrypsin deficiency; A. fumigatus = Aspergillus fumigatus; ARD = Benign Asbestos-Related Diseases; ARDS = Acute Respiratory Distress Syndrome; BC = Bronchial Carcinoma; CBD = Chronic pulmonary Beryllium Disease; CF = Cystic Fibrosis; Classic asthma = Asthmatics with reversible airway obstruction; COPD = Chronic Obstructive Pulmonary Disease; DMS = Differential Mobility Spectrometry; eNose = electronic Nose; FID = Flame Ionization Detector; Fixed asthma = Asthmatics with fixed airway obstruction; GC = Gas Chromatography; GNPs = Gold Nano Particles sensors; ILD = Interstitial Lung Disease (e.g. sarcoidosis, idiopathic pulmonary fibrosis, cryptogenic organizing pneumonia); IMS = Ion Mobility Spectrometry; LC = Lung Cancer; MC = Medical Centre; MPM = Malignant Pleural Mesothelioma; MS = Mass Spectrometry; NSCLC = Non-Small Cell Lung Cancer; OFD = On-Fiber-Derivatization; P. infection = Pseudomonas aeruginosa infection; PLC = Primary Lung Cancer; PTR-MS = Proton Transfer Reaction Mass Spectrometry; Ref. = Reference; SAW = surface acoustic wave; SIFT-MS = Selected Ion Flow Tube Mass Spectrometry; TB = pulmonary Tuberculosis; VAP = Ventilator Associated-Pneumonia; VOCs = Volatile Organic Compounds.

Data synthesis and analysis

Evidence data were pooled by study design; studies using: 1) single VOCs in diagnosing pulmonary diseases (Table 2); 2) VOCs profiles in diagnosing pulmonary diseases (Table 3); and 3) VOCs profiles in differential diagnosing pulmonary diseases (Table 4).
Table 2

Studies using single VOCs for the diagnosis of various pulmonary diseases (diseased vs. healthy controls)

Author (year)

Marker

Disease

N

Value

Unit

Diff.

Value marker

Unit

Controls

N

p-value

Ref.

Lärstad (2007)

Ethane

Asthma

13

N.S.

 

=

N.S.

 

Controls

14

p > 0.05

[21]

Pentane

  

N.S.

 

=

N.S.

   

p > 0.05

Isoprene

  

113

ppb

<

143

ppb

  

p < 0.05

Olopade (1997)

Pentane

Acute asthma

12

8.4 ± 2.9

nmol/L*

>

2.6 ± 0.2

nmol/L*

Controls

17

p < 0.05

[23]

Pentane

Stable asthma

11

3.6 ± 0.4

nmol/L*

=

2.6 ± 0.2

nmol/L*

Controls

17

p > 0.05

Paredi (2000)

Ethane**

Steroid naïve asthma

12

2.06 ± 0.30

ppb*

>

0.88 ± 0.09

ppb*

Controls

14

p < 0.01

[24]

Paredi (2000)

Ethane**

Steroid naïve COPD

12

2.77 ± 0.25

ppb*

>

0.88 ± 0.09

ppb*

Controls

14

p < 0.05

[30]

Barker (2006)

Pentane**

CF

20

0.36 (0.24-0.48)

ppb#

>

0.21 (0.13-0.29)

ppb#

Controls

20

p < 0.05

[34]

Dimethyl Sulphide**

  

3.89 (2.24-5.54)

ppb#

<

7.58 (5.73-9.43)

ppb#

  

p < 0.01

Ethane**

  

0.39 (−0.04-0.82)

ppb#

=

0.10 (−0.25-0.44)

ppb#

  

p > 0.05

Propane, methanol, ethanol, acetone, isoprene, benzene, toluene, limonene

  

-

 

=

-

   

p > 0.05

Kamboures (2005)

Carbonyl sulphide**

CF

20

- 110 ± 60

pptv#

>

- 250 ± 20

pptv#

Controls

23

p < 0.001

[37]

Dimethyl sulphide

  

4,780 ± 1,350

pptv#

=

3,920 ± 680

pptv#

  

p > 0.05

Carbon sulphide**

  

26 ± 38

pptv#

>

- 17 ± 15

pptv#

  

p < 0.05

McGrath (2000)

Isoprene

CF during exacerbation

12

125 ± 23

pmol·min·kg-1*

<

164 ± 20

pmol·min·kg-1*

Controls

12

p < 0.05

[38]

Isoprene

CF after exacerbation

12

188 ± 23

pmol·min·kg-1*

=

164 ± 20

pmol·min·kg-1*

Controls

12

p > 0.05

Paredi (2000)

Ethane**

Steroid naïve CF

23

1.99 ± 0.20

ppb*

>

0.82 ± 0.09

ppb*

Controls

14

p < 0.05

[39]

Shestivska (2011)

Methyl thiocyanate

CF

28

7 (2–21)

ppbv~

=

8 (5–8)

ppbv°°

Controls

9

p > 0.05

[40]

Bajtarevic (2009)

Isoprene

LC

220

81.5

ppb

<

105.2

ppb

Controls

441

p < 0.01

[41]

Acetone

  

458.7

ppb

<

627.5

ppb

  

p < 0.01

Methanol

  

118.5

ppb

<

142.0

ppb

  

p < 0.05

Buszewski (2012)

Acetone

LC

29

34.57-390.60

ppb°

?

44.20-531.45

ppb°

Controls

44

p < 0.05

[42]

Benzene

  

1.29-3.82

ppb°

?

1.38-14.97

ppb°

  

p < 0.05

Butanal

  

1.32-2.55

ppb°

>

1.35-1.87

ppb°

  

p < 0.01

2-Butanone

  

1.35-2.86

ppb°

?

1.35-3.18

ppb°

  

p < 0.01

Ethyl acetate

  

3.98-22.89

ppb°

>

1.12-8.22

ppb°

  

p < 0.01

Ethyl benzene

  

1.45-3.16

ppb°

?

2.22-18.38

ppb°

  

p < 0.01

2-Pentanone

  

3.25-8.77

ppb°

>

1.80-4.11

ppb°

  

p < 0.01

Propanal

  

1.56-3.74

ppb°

>

1.56-3.44

ppb°

  

p < 0.01

1-Propanol

  

4.37-13.15

ppb°

>

N.S.

ppb°

  

p < 0.01

2-Propanol

  

3.32-7.19

ppb°

>

3.21-4.17

ppb°

  

p < 0.01

2-Propenal

  

6.84-94.36

ppb°

>

5.10-9.57

ppb°

  

p < 0.05

Other VOCs

  

N.S.

ppb°

=

N.S.

ppb°

  

p > 0.05

Crohns (2009)

Pentane**

LC

11

1.73 (1.05-2.86)

ng/L#

>

0.83 (0.61-1.13)

ng/L#

Controls

30

p < 0.05

[43]

Fuchs (2010)

Pentanal**

LC

12

0.019 (0.011-0.031)

nmol/Lˆ

>

0.002 (0.000-0.011)

nmol/Lˆ

Controls

12

p < 0.05

[45]

Hexanal**

  

0.010 (0.008-0.026)

nmol/Lˆ

>

0.000 (0.000-0.001)

nmol/Lˆ

  

p < 0.05

Octanal**

  

0.052 (0.026-0.087)

nmol/Lˆ

>

0.011 (0.004-0.028)

nmol/Lˆ

  

p < 0.05

Nonanal**

  

0.239 (0.128-0.496)

nmol/Lˆ

>

0.033 (0.021-0.096)

nmol/Lˆ

  

p < 0.05

Acetaldehyde**, Propanal, butanal**, heptanal, decanal**

  

-

 

=

-

   

p > 0.05

Kischkel (2010)

Dimethyl sulphide**

LC

31

0.27 (0.00-0.27)

nmol/Lˆ

<

0.30 (0.00-0.31)

nmol/Lˆ

Controls

31

p < 0.01

[48]

Dimethyl formamide**

  

1855 (0.00-3340.88)

(counts)ˆ

>

0.00 (0.00-2954.13)

(counts)ˆ

  

p < 0.05

Butane**

  

0.00 (0.00-0.11)

nmol/Lˆ

>

0.18 (0.00-0.52)

nmol/Lˆ

  

p < 0.01

Butanal**

  

1.07 (0.38-3.51)

nmol/Lˆ

>

0.32 (0.00-1.40)

nmol/Lˆ

  

p < 0.001

Other VOCs (N = 38)

  

N.S.

  

N.S

   

p > 0.05

Poli (2005)

2-Methylpentane

NSCLC

36

139.5 (65.7-298.8)

10-12

>

27.7 (3.4-50.3)

10-12

Controls

50

p < 0.001

[57]

Pentane

  

647.5 (361.3-1112.5)

10-12

>

268.0 (107.7-462.7)

10-12

  

p < 0.001

Ethylbenzene

  

24.0 (13.6-32.6)

10-12

>

13.6 (10.8-15.1)

10-12

  

p < 0.01

Xylenes

  

68.9 (43.6-108.4)

10-12

>

31.1 (21.1-56.4)

10-12

  

p < 0.001

Trimethylbenzene

  

14.9 (9.3-22.1)

10-12

>

6.2 (4.7-11.0)

10-12

  

p < 0.01

Toluene

  

158.8 (118.7-237.5)

10-12

>

80.8 (58.9-140.0)

10-12

  

p < 0.001

Benzene

  

94.5 (62.2-132.2)

10-12

>

44.7 (27.7-68.6)

10-12

  

p < 0.001

Decane

  

568.0 (277.9-1321.6)

10-12

>

208.7 (14.3-405.5)

10-12

  

p < 0.001

Octane

  

61.0 (22.4-112.9)

10-12

>

20.2 (4.0-50.8)

10-12

  

p < 0.001

Pentamethylheptane

  

2.5 (1.2-9.7)

10-12

>

0.9 (0.1-2.6)

10-12

  

p < 0.001

Isoprene, heptane, styrene

  

-

 

=

-

   

p > 0.05

Poli (2008)

2-Methylpentane

NSCLC (3 yrs after surgery)

10

87.9 (35.5-278.9)

10-12

>

27.7 (3.4-50.3)

10-12

Controls

50

p < 0.05

[58]

Pentane

  

1569.0 (497.9-3214)

10-12

>

268.0 (107.7-462.7)

10-12

  

p < 0.001

Ethylbenzene

  

46.4 (38.6-90.9)

10-12

>

13.6 (10.8-15.1)

10-12

  

p < 0.001

Xylenes

  

56.2 (38.9-80.4)

10-12

>

31.1 (21.1-56.4)

10-12

  

p < 0.05

Trimethylbenzene

  

15.3 (11.7-22.3)

10-12

>

6.2 (4.7-11.0)

10-12

  

p < 0.001

Toluene

  

297 (202.6-297.0)

10-12

>

80.8 (58.9-140.0)

10-12

  

p < 0.001

Pentamethylheptane

  

8.8 (2.2-15.2)

10-12

>

0.9 (0.1-2.6)

10-12

  

p < 0.001

Isoprene

  

678.9 (359.8-1111.0)

10-12

<

3789 (1399–6589)

10-12

  

p < 0.01

Benzene, Heptane, Octane, Styrene

  

-

 

=

-

   

p > 0.05

Preti (1988)

O-toluidine

LC

10

N.S

 

>

N.S

 

Controls

16

p < 0.05

[60]

Aniline

  

N.S

 

=

N.S

   

p > 0.05

Rudnicka (2011)

Propane

LC

23

3.19-9.74

ppb°

>

3.45-5.96

ppb°

Controls

30

p < 0.05

[61]

2-Propenal

  

N.S

 

?

N.S

   

p < 0.05

Carbon disulfide

  

N.S

 

?

N.S

   

p < 0.05

Isopropyl alcohol

  

N.S

 

?

N.S

   

p < 0.05

Ethylbenzene

  

1.45–3.16

ppb°

<

2.22–18.38

ppb°

  

p < 0.05

Styrene

  

N.S

 

?

N.S

   

p < 0.05

Other VOCs (N = 49)

  

N.S

 

=

N.S

   

p > 0.05

Skeldon (2006)

Ethane**

LC

12

0.7 (0–7.6)

ppb~

=

1.9 (0–10.54)

ppb~

Controls

12

p > 0.05

[62]

Song (2010)

1-Butanol**

NSCLC

43

6.36 (12.93)

ng/Lˆ

>

2.18 (2.06)

ng/Lˆ

Controls

41

p < 0.001

[63]

3-Hydroxy-2-butanone**

  

8.28 (11.52)

ng/Lˆ

>

1.29 (2.01)

ng/Lˆ

  

p < 0.001

Ulanowska(2011)

Ethanol**

LC

137

466.9 (12.8-1520.1)

ppb°°

>

188.5 (4.5-479.5)

ppb°°

Controls

86

p < 0.05

[65]

Acetone**

  

358.6 (112.3-2653.7)

ppb°°

>

225.7 (41.6-753.4)

ppb°°

  

p < 0.05

Butane**

  

90.3 (6.1-421.3)

ppb°°

>

56.2 (5.2-165.7)

ppb°°

  

p < 0.05

Dimethyl sulphide**

  

11.9 (6.3-18.5)

ppb°°

>

9.3 (5.3-19.3)

ppb°°

  

p < 0.05

Isoprene**

  

100.3 (19.2-295.5)

ppb°°

>

70.8 (19.5-200.5)

ppb°°

  

p < 0.05

Propanal**

  

7.8 (5.5-33.8)

ppb°°

>

6.9 (5.6-9.1)

ppb°°

  

p < 0.05

1-Propanol**

  

54.8 (5.4-473.3)

ppb°°

>

6.6 (N.S.)

ppb°°

  

p < 0.05

2-Pentanone**

  

7.5 (4.4-53.2)

ppb°°

>

4.8 (4.6-5.1)

ppb°°

  

p < 0.05

Furan**

  

4.7 (3.1-7.0)

ppb°°

>

3.7 (3.0-5.3)

ppb°°

  

p < 0.05

o-Xylene**

  

22.1 (7.6-95.2)

ppb°°

>

17.4 (6.2-30.8)

ppb°°

  

p < 0.05

Ethylbenzene**

  

19.6 (4.6-89.3)

ppb°°

>

10.4 (8.6-14.0)

ppb°°

  

p < 0.05

Other VOCs (N ≈ 20)

  

-

 

=

-

   

p > 0.05

Wehinger (2007)

Formaldehyde

PLC

17

7.0 (15.5)

ppbˆ

>

3.0 (1.9)

ppbˆ

Controls

170

p < 0.001

[66]

Propanol

  

244.1 (236.2)

ppbˆ

>

94.1 (55.2)

ppbˆ

  

p < 0.001

Isoprene

  

52.1 (26.7)

ppbˆ

<

81.8 (56.1)

ppbˆ

  

p < 0.01

Acetone, o-Toluidine

  

-

 

=

-

   

p > 0.05

Gennaro (2010)

Cyclohexane**

MPM

13

251.79 (84%)

ng/L

>

33.08 (58%)

ng/L

Controls

13

p < 0.05

[70]

Other VOCs (N = 19)

  

-

 

=

-

   

p > 0.05

Syhre (2009)

Methyl nicotinate

TB

10

N.S

 

>

N.S

 

Controls

10

p < 0.01

[80]

Chambers (2009)

2-Pentylfuran***

A. fumigatus

17

Sens: 77, Spec: 78

%

>

Not detected

 

Controls

14

N.S.

[71]

Kanoh (2005)

Ethane**

ILD

34

8.5 ± 8.0

pmol/dL*

>

2.9 ± 1.0

pmol/dL*

Controls

16

p < 0.001

[75]

Scholpp (2002)

Acetone

ARDS

13

50.0 (19.6-72.3)

nmol/Lˆ

=

33.2 (20.8-38.6)

nmol/Lˆ

Controls

10

p > 0.05

[81]

Isoprene

  

2.18 (1.1-3.89)

nmol/L#

<

5.99 (3.53-8.45)

nmol/L#

  

p < 0.05

n-Pentane

  

1.00 (0.26-1.72)

nmol/Lˆ

>

0.12 (0.10-0.16)

nmol/Lˆ

  

p < 0.05

n-Pentane

At risk ARDS

33

0.49 (0.30-0.99)

nmol/Lˆ

>

0.12 (0.10-0.16)

nmol/Lˆ

Controls

10

p < 0.05

Schubert (1998)

Acetone

ARDS

19

149 (113–485)

nmol/m2 ≈

=

119 (52–270)

nmol/m2≈

No ARDS

18

p > 0.05

[82]

Isoprene

  

9.8 (8.2-21.6)

nmol/m2 ≈

<

21.8 (13.9-41.4)

nmol/m2≈

  

p < 0.05

 

n-Pentane

  

4.2 (3.7-9.3)

nmol/m2 ≈

=

5.1 (1.4-18.6)

nmol/m2≈

  

p > 0.05

 

Data are presented as; *mean ± SEM or SD; #mean (95% confidence interval);median; ˆmedian (25th-75th percentile); ~median (range); median (95% confidence interval); ° range; °° mean (range); median (relative standard deviation). ** Exhaled concentrations corrected for ambient concentrations (e.g. subtraction, VOCs filter). *** Sensitivity and specificity 2-Pentylfuran compared with gold standard (sputum). Diff. = Difference between diseased and controls: > elevated in diseased vs. controls, = no difference in diseased vs. controls, < decreased in diseased vs. controls. Abbreviations: A. fumigatus = Aspergillus fumigatus; ARDS = Acute Respiratory Distress Syndrome; CF = Cystic Fibrosis; COPD = Chronic Obstructive Pulmonary Disease; ILD = Interstitial Lung Disease (e.g. sarcoidosis, idiopathic pulmonary fibrosis, cryptogenic organizing pneumonia); LC = Lung Cancer; MPM = Malignant Pleural Mesothelioma; N = Sample size; N.S. = Not Stated; NSCLC = Non-Small Cell Lung Cancer; PLC = Primary Lung Cancer; Ref. = Reference.

Table 3

Studies using VOCs profiles for the diagnosis of various pulmonary diseases (diseased vs. healthy controls)

Author (year)

Disease

N

Discriminative

Controls

N

No. of markers

Sensitivity/Specificity (%)*

Ref.

Caldeira (2011)

Asthma

35

+

Controls

15

28

CVV: 88%

[17]

Caldeira (2012)

Asthma

32

+

Controls

27

9

98/93

[18]

Dallinga (2010)

Asthma

63

+

Controls

57

8 to 22

89 - 100/95 - 100

[19]

Dragonieri (2007)

Mild asthma

10

+

Controls

10

N.S.

CVV: 100% (M-distance 5.32)

[10]

Severe asthma

10

+

Controls

10

N.S.

CVV: 90% (M-distance 2.77)

Fens (2009)

Asthma

20

+

Non-smoking controls

20

N.S.

CVV: 95% (p < 0.001)

[15]

Asthma

20

+

Smoking controls

20

N.S.

CVV: 93% (p < 0.001)

Ibrahim (2011)

Asthma

35

+

Non-smoking controls

23

15

CVV: 83% (PPV: 0.85, NPV: 0.89)

[20]

Montuschi (2010)

Asthma

27

+

Controls

24

N.S.

DP: 88%

[22]

Timms (2012)

Asthma

20

+

Controls

7

N.S.

CVV: 70% (p = 0.047)

[32]

COPD

17

+

Controls

7

N.S.

M-distance: 3.601 (p < 0.01)

Cristescu (2011)

Emphysema

43

-

(Former) smoking controls

161

1

AUC: 0.56 (CI: 0.45-0.66)

[26]

Basanta (2010)

COPD

20

+

Smoking controls

6

N.S.

88/81

[25]

Fens (2009)

COPD

30

+/−

Smoking controls

20

N.S.

CVV: 66% (p < 0.01)

[15]

COPD

30

-

Non-smoking controls

20

N.S.

CVV: N.S.

Hattesohl (2011)

COPD

23

+/−

Controls

10

N.S.

CVV: 68% (p < 0.001)

[28]

Hauschild (2012)

COPD

84

+

Controls

35

120

87 - 98/71 - 86

[29]

Phillips (2012)

COPD

119

+

Controls

63

N.S.

79/64

[31]

Van Berkel (2010)

COPD

50

+

Controls

29

6 to 13

98 - 100/88 - 100

[33]

COPD (validation)

16

+

Controls (validation)

16

6

100/81

Robroeks (2010)

CF

48

+

Controls

57

22

100/100

[14]

Bajtarevic (2009)

LC

65

+

Controls

31

15 to 21

71 - 80/100 - 100

[41]

D’Amico (2010)

LC

28

+

Controls

36

N.S.

85/100

[44]

Di Natale (2003)

LC

35

+

Controls

18

N.S.

100/94

[11]

Dragonieri (2009)

NSCLC

10

+

Controls

10

N.S.

CVV: 90% (M-distance 2.96)

[12]

Gaspar (2009)

LC

18

+

Controls

10

10

100/100

[46]

Gordon (1985)

LC

12

+

Controls

9

22

DP > 80%

[47]

Ligor (2009)

LC

65

+/−

Controls

31

8

51/100

[49]

Machado (2005)

LC

14

+

Controls

20

N.S.

CVV: 72% (M-distance: 3.25)

[13]

Mazzone (2007)

NSCLC

49

-

Controls

21

N.S.

57/78

[50]

Peng (2009)

LC

40

+

Controls

56

42

2 PCA clusters: 100% discrimination

[52]

Peng (2010)

PLC

30

+

Controls

22

33

2 PCA clusters: 100% discrimination

[53]

Phillips (2003)

PLC

67

+

Controls

41

9

85/81

[7]

Phillips (2007–2008)

PLC

193

+

Controls

211

16 to 30

85 - 85/80 - 81

[55], [56]

Poli (2010)

NSCLC

40

+

Controls

38

7

90/92

[59]

Steeghs (2007)

LC

11

+

Controls

57

2

AUC: 0.81

[64]

Westhoff (2009)

LC

32

+

Controls

54

23

100/100

[67]

Chapman (2012)

MPM

10

+

Controls

32

N.S.

90/91

[68]

Dragonieri (2012)

MPM

13

+

Controls

13

N.S.

CVV: 85% (p < 0.001)

[69]

Phillips (2007)

Patients suspected of TB

42

+

Controls

59

N.S. (≈7)

100/100

[77]

Phillips (2012)

Patients with TB

130

+/−

Controls

121

8

71/72

[79]

*Sensitivity/Specificity (in %), unless stated otherwise. AUC = Area Under the ROC Curve; CF = Cystic Fibrosis; CI = 95% Confidence interval; COPD = Chronic Obstructive Pulmonary Disease; CVV = Cross-Validated accuracy-Value; DP = Diagnostic Performance; LC = Lung Cancer; M-distance = Mahalanobis-distance; MPM = Malignant Pleural Mesothelioma; N = Sample size; NPV = Negative Predictive Value; N.S. = Not Stated; NSCLC = Non-Small Cell Lung Cancer; PCA = Principal Component Analysis; PLC = Primary Lung Cancer; PPV = Positive Predictive Value; Ref. = Reference; TB = pulmonary Tuberculosis.

Table 4

Studies using VOCs profiles for the differential diagnosis of various pulmonary diseases

Author (year)

Disease I

N

Discriminative

Disease II

N

No. of markers

Sensitivity/Specificity (%)*

Ref.

D’Amico (2010)

LC

28

+

Other lung diseases

28

N.S.

93/79

[44]

Dragonieri (2007)

Mild asthma

10

+/−

Severe asthma

10

N.S.

CVV: 65% (M-distance 1.23)

[10]

Fens (2009)

Asthma

20

+

COPD

30

N.S.

CVV: 96% (p < 0.001)

[15]

Fens (2011) **

Fixed Asthma

21

+

COPD

40

N.S.

85/90 (CVV: 88%, p < 0.001)

[27]

Classic Asthma

39

+

   

91/90 (CVV: 83%, p < 0.001)

 

Ibrahim (2011)

Controlled Asthma

17

+

Uncontrolled asthma

18

13

89/88 (PPV: 0.89, NPV: 0.88)

[20]

Timms (2012)

Asthma

20

+

COPD

17

N.S.

CVV: 70% (p < 0.05)

[32]

Asthma

11

+

Asthma with GER

9

 

CVV: 85% (p < 0.05)

 

COPD

8

+/-

COPD with GER

9

 

CVV: 65 (p < 0.05)

 

Hattesohl (2011)

COPD without AAT deficiency

23

+/-

COPD with AAT deficiency

10

N.S.

CVV: 58% (M-distance: 2.27)

[28]

Dragonieri (2009)

LC

10

+

COPD

10

N.S.

CVV: 85% (M-distance: 3.73)

[12]

Machado (2005)

LC (validation)

14

+/-

No LC

62

N.S.

71/92

[13]

Mazzone (2007)

LC

49

+/−

No LC

94

N.S.

73/72

[50]

Mazzone (2012)

NSCLC

83

+

No LC

137

N.S.

70/86

[51]

Adenocarcinoma

50

+

No LC

137

 

80/86

 

Squamous cell

23

+

No LC

137

 

91/73

 

Adenocarcinoma

50

+

Squamous cell

22

 

90/83

 

Phillips (1999)

LC

60

+/−

No LC

48

22

72/67

[54]

Phillips (2003)

MLC

15

-

No MLC

91

9

67/37

[7]

Poli (2005)

NSCLC

36

+

No LC

110

13

72/94

[57]

Chapman (2012)

MPM

10

+

ARD

18

N.S.

90/83

[68]

Dragonieri (2012)

MPM

13

+

No MPM

13

N.S.

CVV: 81% (p < 0.001)

[69]

Hanson (2005)

+ VAP score

19

+

- VAP score

19

N.S.

R2 (to standard): 0.81 (p < 0.0001)

[72]

Hockstein (2004)

VAP

13

+

No VAP

12

N.S.

CVV: >80%

[73]

Hockstein (2005)

+ VAP score

15

+/−

- VAP score

29

N.S.

CVV: 66-70%

[74]

Kolk (2012)

TB

50

+

No TB

50

7

72/86

[76]

TB (validation)

21

+

No TB

50

7

62/84

Phillips (2007)

TB

23

+

No TB

19

N.S. (≈14)

96/79

[77]

Phillips (2010)

TB

N.S.

+

No TB

N.S.

N.S. (≈10)

84/65

[78]

*Sensitivity/Specificity (in %), unless stated otherwise. AAT deficiency = Alpha 1-antitrypsin deficiency; ARD = benign asbestos-related diseases; Classic asthma = Asthmatics with reversible airway obstruction; COPD = Chronic Obstructive Pulmonary Disease; CVV = Cross-Validated accuracy-Value; DP = Diagnostic Performance; Fixed asthma = Asthmatics with fixed airway obstruction; GER = Gastro-Esophageal Reflux; LC = Lung Cancer; M-distance = Mahalanobis-distance; MLC = Metastatic Lung Cancer; MPM = Malignant Pleural Mesothelioma; N = Sample size; NPV = Negative Predictive Value; N.S. = Not Stated; NSCLC = Non-Small Cell Lung Cancer; PPV = Positive Predictive Value; R2 = Coefficient of determination; TB = pulmonary Tuberculosis; VAP = Ventilator Associated-Pneumonia. ** External validation study of Fens 2009.

Results

Description of included studies

Seventy-three studies were included of which the characteristics are provided in Table 1. In total, nine studies described VOCs in asthma, seven in COPD, seven in CF, four compared asthma with COPD or CF, thirty-four in thoracic cancer (of which 6 studies included COPD patients in the control group), and twelve studies described VOCs in other pulmonary diseases. A total of 2,973 healthy controls and 3,952 patients were investigated; 417 asthmatic patients, 527 COPD patients, 188 CF patients, 1,575 lung cancer patients, 33 malignant pleural mesothelioma (MPM) patients, 139 subjects with an abnormal chest radiograph, 579 subjects suspected for pulmonary tuberculosis, and 494 patients with other (pulmonary) diseases (e.g. sarcoidosis, acute respiratory distress syndrome). Various techniques were described to collect and analyze exhaled VOCs. The most commonly used technique was gas chromatography (N = 50), usually combined with MS or FID. Fifteen studies analyzed VOCs using an eNose, whilst thirteen studies used PTR-MS, SIFT-MS, IMS, laser spectroscopy, colorimetric sensor array, and/or GNPs (some studies used multiple techniques). Forty-five studies were conducted in the last five years. An overview of findings per study can be found in Tables 2, 3, 4. The most important findings are summarized below.

Volatile organic compounds in asthma

Several studies found that an accurate asthma diagnosis was possible using profiles of VOCs (Table 3). Dragonieri and Fens et al. demonstrated that a VOCs profile could correctly classify asthmatic patients when using an eNose [10, 15]. Moreover, Montuschi and colleagues demonstrated that VOCs profiling using an eNose had higher diagnostic performance for asthma than exhaled nitric oxide or lung function [22]. Dallinga and Caldeira et al. demonstrated that VOCs profiling was able to accurately distinguish children with asthma from controls [1719]. With respect to differential diagnosis, it was demonstrated that an eNose VOCs profile was able to discriminate between asthma and COPD patients (Table 4) [15, 32]. An external validation study demonstrated that not only ‘classical’ asthmatic patients (with reversible airway obstruction) but also asthmatic patients with fixed airway obstruction could be distinguished from COPD patients [27]. As these latter two groups usually have similar symptoms and overlapping spirometry, differential diagnosis is often difficult. These findings imply that VOCs profiling is of additional value in the differential diagnosis of asthma and COPD.

Next to diagnosing asthma, VOCs might be useful in the assessment of asthma severity and control. Paredi et al. found elevated levels of exhaled ethane in steroid-naïve asthmatics compared to steroid-treated asthmatics (Table 2). Furthermore, ethane was higher in patients with severe asthma (FEV1 < 60%), compared to patients with mild asthma (FEV1 > 60%) [24]. In contrast, Dragonieri et al. reported that it was not possible to adequately distinguish mild and severe asthmatics using an eNose profile [10]. Regarding asthma control, higher exhaled pentane levels were found in asthmatic patients with an exacerbation compared to controls. Once the asthma exacerbation subsidized, pentane levels decreased to levels comparable to controls [23]. Moreover, Ibrahim demonstrated that VOCs profiles were able to diagnose sputum eosinophilia and identify patients with poor disease control [20]. Taken together, VOCs profiling might be useful for an asthma diagnosis, for differentiating asthma from COPD, and for assessing asthma control. The usefulness of VOCs profiles in assessing disease severity still needs to be established.

Volatile organic compounds in chronic obstructive pulmonary disease

Many COPD patients are diagnosed at an advanced stage of the disease, when benefits of interventions such as smoking cessation and drug therapy are less pronounced. An early diagnosis of COPD would be an advantage. Multiple research groups demonstrated that VOC profiles could accurately differentiate COPD patients from healthy (non-) smokers [25, 29, 31, 33]. In contrast, others found a limited performance of VOCs profiles to differentiate COPD patients from (former) smokers [15, 26]. Hattesohl et al. demonstrated that eNose derived VOCs profiles were not different between COPD patients with and without an alpha 1-antitrypsin (AAT) deficiency, after internal cross-validation. Moreover, cross-validated VOCs profiles of AAT deficiency patients did not differ after human recombinant AAT therapy [28].

Next to diagnostic purposes, VOCs might be useful to monitor severity and inflammation status in COPD patients. Elevated levels of ethane were found in steroid-naïve COPD patients and patients with low FEV1 values compared to steroid-treated patients and patients with higher FEV1 values [30]. Fens et al. demonstrated that VOCs profiles were associated with both cell counts and sputum markers of inflammatory cell activation (eosinophilic vs. neutrophilic) in COPD patients [83]. These findings indicate that VOCs profiles might monitor both type and activity of airway inflammation.

Volatile organic compounds in cystic fibrosis

In CF, there is less need for a new diagnostic tool as the sweat chloride test and genetic screening serve as gold standards. However, there is need for new tools regarding early detection of Pseudomonas (P.) aeruginosa and prediction and follow-up of exacerbations. Robroeks et al. demonstrated that a VOCs profile could accurately discriminate between CF patients with and without P. aeruginosa colonization [14]. Gilchrist et al. showed that exhaled hydrogen cyanide (a marker of P. aeruginosa) was elevated in CF children with P. aeruginosa colonization compared to CF children without colonization [36]. Accordingly, Enderby et al. demonstrated that exhaled hydrogen cyanide was elevated in children with CF compared to children with asthma [35]. Kamboures et al. demonstrated elevated levels of exhaled sulphides (produced by bacteria such as P. aeruginosa) in CF patients compared to controls [37]. In contrast, Shestivska et al. could not demonstrate different levels of exhaled methyl thiocyanate (also a marker of P. aeruginosa) in CF patients and controls [40].

Regarding monitoring disease control, McGrath et al. demonstrated that CF patients with an acute exacerbation had lower levels of exhaled isoprene compared to controls [38]. When these patients were treated with antibiotics, their isoprene levels increased to normal [38]. Moreover, elevated ethane levels were found in steroid-naïve CF patients compared to steroid-treated patients [39]. In addition, elevated pentane levels were found in CF patients with an exacerbation [34]. These data demonstrate that VOCs profiling can be useful for assessment and follow-up of exacerbations, and for a rapid detection of P. aeruginosa in CF patients.

Volatile organic compounds in thoracic oncology

The majority of lung cancer (LC) patients studied had non-small cell lung cancer (Table 1). Several studies demonstrated that a combination of VOCs, identified by GC-MS, could differentiate LC patients from controls [7, 41, 46, 47, 49, 52, 53, 55, 56, 59]. In general, the number of VOCs per model ranged from 7 to 33, with a sensitivity of 50-100% and a specificity of 80-100% (Table 3). These studies, together with studies investigating single VOCs, revealed that the discriminative VOCs were predominantly alkanes (e.g. pentane, butane, propane), alkane derivates (e.g. propanol, multiple aldehydes) and benzene derivates (e.g. ethyl-, propylbenzene) (Table 2) [42, 43, 45, 48, 57, 5963, 65, 66]. Although most VOCs levels were elevated, certain levels (e.g. of isoprene) were decreased in patients compared to controls [41, 58, 66]. The diagnostic potential of VOCs profiles in LC was also demonstrated by groups that used eNose and other sophisticated techniques [1113, 44, 5153, 64, 67]. Moreover, breath profiles were different in patients with dissimilar histology (adenocarcinoma vs. squamous cell carcinoma) [51]. Besides, Peng et al. demonstrated distinct VOCs profiles in patients with lung, colon, breast, and prostate cancer [53]. The important findings of VOC signatures of different cancer types, need to be confirmed in wider clinical studies.

Multiple studies investigated the potential of VOCs to discriminate between LC and other pulmonary diseases. Not single compounds (such as ethane), but a combination of multiple VOCs was able to distinct LC patients from patients with non-cancer pulmonary diseases (such as COPD, pleurisy, idiopathic fibrosis) with a reasonable accuracy (Table 4) [12, 13, 44, 50, 57, 62]. Phillips et al. demonstrated that primary LC could be reasonably diagnosed in subjects with an abnormal chest radiograph [7, 54]. However, VOCs had limited predictive value to stage LC patients [7, 63, 84].

Two studies described the potential of VOCs in evaluating treatment in LC patients. Poli et al. demonstrated that VOCs levels, except for isoprene, were unaffected one month after surgical resection of the tumor [58]. After three years, several VOCs either increased (e.g. pentane) or decreased (e.g. isoprene) compared to baseline [58]. However, most post-surgical VOCs levels remained higher compared to levels of controls. Likewise, Crohns et al. were not able to detect changes of pentane levels after radiotherapy, although they did demonstrate that higher pre-treatment levels predicted better survival [43].

Malignant pleural mesothelioma (MPM) is a rare tumor mainly caused by asbestos exposure. VOCs profiles were able to diagnose MPM in a group of subjects with long-term professional asbestos exposure [68, 69]. Moreover, de Gennaro et al. distilled cyclohexane as possible marker of MPM [70].

Smoking status can be an important influencing factor, especially in patients with LC and COPD. Smoke contains profuse amounts of VOCs and is associated with alterations in exhaled VOCs patterns. As high background of external VOCs caused by smoking can influence the accuracy of the diagnostic profile, most studies performed in LC and COPD took smoking status into account in their analysis [15, 25, 27, 31, 33, 43, 50, 5355, 59, 65].

Volatile organic compounds in other pulmonary diseases

VOCs were also studied in critically ill patients with acute respiratory distress syndrome (ARDS). Lower isoprene levels and elevated pentane levels were reported in ARDS patients compared to controls [81, 82]. These findings are in line with the findings that critically ill patients with ventilator-associated pneumonia (VAP) had decreased isoprene levels and increased pentane levels compared to patients without pneumonia [82]. In addition, VOCs profiles generated with the eNose had potential to diagnose this form of pneumonia [7274].

Kanoh et al. demonstrated that exhaled ethane was elevated in patients with an interstitial lung disease (including sarcoidosis and idiopathic pulmonary fibrosis) compared to controls, with highest levels in those with an active and progressive disease [75]. A small VOC, 2-pentylfuran, was commonly present in breath of patients with a chronic pulmonary disease (including asthma and CF) with Aspergillus fumigatus in their respiratory specimens, whereas this VOC was not detected in breath of controls [71]. Syhre et al. demonstrated elevated levels of exhaled methyl nicotinate (a volatile metabolite produced by M. tuberculosis) in patients with pulmonary tuberculosis (TB) compared to healthy controls [80]. In a group of patients with suspicion of TB, VOC patterns were able to distinguish patients with TB from those without active TB and healthy controls with a reasonable accuracy (Table 4) [7679].

Discussion

Conclusions from this review

A substantial increase in clinical studies on VOCs in pulmonary diseases was observed in the last decade. Initial studies on VOCs identified biomarkers in a traditional way by focusing on single compounds based on biological insight. Levels of several VOCs were demonstrated to be distinct in people with a pulmonary disease compared to controls. These markers mainly included alkanes for asthma and COPD; alkanes, alkenes and alkene-derivates for CF and analogous compounds plus benzene derivates and aldehydes in lung cancer. Due to overlap in markers, one may argue that a disease specific biomarker is not discovered yet. For example, ethane was not only elevated in asthmatic patients, but also in COPD and CF patients. Similarly, decreased levels of isoprene were found in both children with CF, asthmatic patients, lung cancer patients and in patients with ARDS. Despite the lack of a single discriminative biomarker, these studies did demonstrate that exhaled breath of patients with a particular lung disease is distinct from healthy controls. This finding evolved in a new hypothesis that pulmonary diseases are characterized by a distinctive breath-print that is not based on single markers, but on a profile of numerous VOCs. Instead of a knowledge based strategy, recent studies mainly support an inductive strategy to discover disease-specific VOCs profiles. Owing to recent technical and analytical advancements, hundreds of VOCs can be analyzed to characterize the breath-print of a pulmonary disease. Various research groups demonstrated that, either by using the eNose or GC, VOCs profiles of patients with several pulmonary diseases could be well distinguished from VOCs profiles of controls. Moreover, distinct VOCs profiles were found in patients with dissimilar pulmonary diseases. These promising results pave the way for the development of a non-invasive diagnostic tool based on exhaled VOCs.

Potential applications and advantages of using VOCs into clinical practice

Although current research mainly focused on the diagnostic potential of VOCs, there are multiple other conceivable applications of VOCs in the field of pulmonary diseases, such as:
  • (early) Diagnosing of pulmonary diseases (e.g. early asthma diagnosis in children).

  • Differential diagnosing (e.g. asthma versus COPD).

  • Phenotyping within a pulmonary disease (e.g. wheezing phenotypes in children).

  • Monitoring disease severity and control.

  • Predicting exacerbations and prognosis of a disease.

  • Evaluating treatment/surgery (e.g. checking compliance with prescribed medication).

  • Screening for different diseases in population based studies (e.g. predicting risk).

The advantages of VOCs profiling are evident. Although VOCs can have an exogenous origin, numerous VOCs are formed within the airways as a result of local inflammatory or neoplastic processes. Therefore, the analysis of exhaled VOCs can serve as a direct measure of lung status. Nevertheless, since VOCs are blood-borne they can also reflect other processes in the body and thus may assess different body functions in a flexible manner. Secondly, collection of breath samples is safe, non-invasive and easy to perform even in children and more severe patients. Breath collection does not require skilled medical staff and obtaining large quantities or repeated measurements are not as bothersome for patients compared to e.g. blood sampling, sputum induction or bronchoalveolar lavages. Moreover, the matrix of exhaled breath is less complex than blood or urine, eliminating the need for complicated work-up of samples. Finally, techniques to measure VOCs, such as GC-MS, are very sensitive to detect compounds and techniques such as the IMS allow real-time measurement of compounds in the body.

Challenges before VOCs can be used into clinical practice

Although VOCs profiling is a potential clinical tool, considerable work needs to be done before it can be applied into clinical practice. An important step that needs to be taken is extensive validation of the current available VOCs profiles. Next to (external) validation, standardization in collecting and analyzing VOCs is necessary to enhance inter-laboratory comparability. Due to the heterogeneity of the included studies (in study design, sampling and analytical techniques) a variety of results is reported, making it difficult to draw firm conclusions or to calculate an algorithm for the most important compounds in the diverse pulmonary diseases. In the standardization procedure, the influence of potential confounders needs to be explored before considering VOCs as a clinical tool [48]. As described before, exhaled VOCs can arise from various endogenous and exogenous sources. Consequently, numerous environmental-, subject- and analytical factors can influence the exhaled VOCs pattern (Table 5). For example, air pollution, smoking, eating, drinking and medication use can considerably affect the composition of exhaled VOCs [85]. Also the presence of bacteria can alter exhaled VOCs patterns (hence breath analysis could be used to identify bacterial infections and bacterial-induced diseases). This suggests that these factors should be taken into account when constructing a diagnostic tool on basis of VOCs patterns. Moreover, potential confounders on analytical level should be carefully studied. For example, during the offline-procedure of exhaled VOCs collection, Tedlar bags could release VOCs into the collected breath, and storage onto Tenax columns can disturb the composition of the breath. As the pool of exhaled VOCs arises from multiple endogenous and exogenous sources, the analysis of background samples and standardization of the analysis per technique is necessary. A list of recommendations for measurements composed by experts, as was provided for markers in exhaled breath condensate, will facilitate standardization [86]. Thirdly, more insight is needed in the physiological meaning and biochemical origin of endogenous formed VOCs. However, this might be difficult since, as described before, the origin of VOCs is blood-borne and therefore can be the result of widely different biochemical pathways (so the previously mentioned advantage of VOCs is a disadvantage as well). Fourthly, more research is needed on the potential of VOCs in differential diagnosis and monitoring purposes. Besides these four major ‘missions’, others aspects of VOCs assessment and analysis can be improved. Although breath samples are easy to collect, the analysis of VOCs is still quite cumbersome and time-consuming and requires trained personnel. Moreover, further refinement of sampling techniques, exploring advanced statistical techniques on the multi-data of VOCs to build diagnostic and prognostic models, and developing new tools that combine the strengths of the eNose (cheap, time efficient), IMS (real-time), and GC-MS (sensitive, compound identification) will facilitate the introduction of VOCs into clinical practice.
Table 5

Factors that can influence exhaled VOCs in pulmonary diseases

Source

Factor

Environment

Ambient VOCs (e.g. by air pollution)

Temperature of environment

Humidity of inhaled and exhaled air

Season

Subject

Clinical characteristics: e.g. age, gender, weight, length

Nutrition

Tobacco smoking

Medication use

Circadian rhythm

Non-pulmonary chronic diseases (liver impairment, diabetes, presence of bacteria)

Breathing pattern: e.g. exhaled flow, minute ventilation, breath hold

Overall lifestyle and physical condition

Analysis

Time and way of storage

Pre-concentration

Breath collection: mixed air or alveolar air

Collection method: e.g. tedlar bags, metal containers

 

Analytical method: e.g. eNose, GC-MS

Conclusions

As the current available tools are not always fulfilling, there is an increasing interest in non-invasive measurement of exhaled VOCs to improve the diagnosis and management of pulmonary diseases. Due to the complex pathophysiology of most pulmonary diseases, current research mainly focused on profiles of VOCs rather than on individual compounds. Promising findings were reported on VOCs profiles that were able to accurately diagnose and monitor various pulmonary diseases. However, multiple constraints including validation and standardization need to be resolved before VOCs can be applied into clinical practice. The rapid progress that is currently made in the field of VOCs will facilitate the imminent introduction of VOCs profiling as a non-invasive, additional tool to assist in diagnosing and monitoring of pulmonary diseases.

Abbreviations

AAT deficiency: 

Alpha 1-antitrypsin deficiency

A. fumigatus: 

Aspergillus fumigatus

ARD: 

Benign Asbestos-Related Diseases

ARDS: 

Acute Respiratory Distress Syndrome

CBD: 

Chronic pulmonary Beryllium Disease

CF: 

Cystic Fibrosis

COPD: 

Chronic Obstructive Pulmonary Disease

CVV: 

Cross-Validated accuracy-Value

DMS: 

Differential Mobility Spectrometry

eNose: 

electronic Nose

FID: 

Flame Ionization Detector

GC: 

Gas Chromatography

GER: 

Gastro-Esophageal Reflux

GNPs: 

Gold Nano Particles sensors

ILD: 

Interstitial Lung Disease

IMS: 

Ion Mobility Spectrometry

LC: 

Lung Cancer

MC: 

Medical Centre

M-distance: 

Mahalanobis-distance

MLC: 

Metastatic Lung Cancer

MPM: 

Malignant Pleural Mesothelioma

MS: 

Mass Spectrometry

NPV: 

Negative Predictive Value

NSCLC: 

Non-Small Cell Lung Cancer

OFD: 

On-Fiber-Derivatization

PCA: 

Principal Component Analysis

PLC: 

Primary Lung Cancer

PPV: 

Positive Predictive Value

PTR-MS: 

Proton Transfer Reaction Mass Spectrometry

SAW: 

Surface Acoustic Wave

SIFT-MS: 

Selected Ion Flow Tube Mass Spectrometry

TB: 

pulmonary Tuberculosis

VAP: 

Ventilator Associated-Pneumonia

VOCs: 

Volatile Organic Compounds.

Declarations

Acknowledgements

The authors would like to thank Ester Klaassen and Prof. Geertjan Wesseling for helpful comments on earlier drafts of this manuscript. No funding has been received for this manuscript.

Authors’ Affiliations

(1)
Department of Pediatric Pulmonology, School for Public Health and Primary Care (CAPHRI), Maastricht University Medical Center (MUMC)
(2)
Department of General Practice, CAPHRI, MUMC

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