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

  • Kim DG van de Kant1Email author,

    Affiliated with

    • Linda JTM van der Sande1,

      Affiliated with

      • Quirijn Jöbsis1,

        Affiliated with

        • Onno CP van Schayck2 and

          Affiliated with

          • Edward Dompeling1

            Affiliated with

            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.
            http://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).
            http://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].

            http://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.
            http://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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            This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://​creativecommons.​org/​licenses/​by/​2.​0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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