ORIGINAL ARTICLE

Breath volatile analysis from patients diagnosed with harmfuldrinking, cirrhosis and hepatic encephalopathy: a pilot study

Tanzeela Yasmin Khalid • Ben De Lacy Costello • Richard Ewen •

Paul White • Simon Stevens • Fiona Gordon • Peter Collins • Anne McCune •

Achuth Shenoy • Sharan Shetty • Norman Mark Ratcliffe • Chris Simon Probert

Received: 26 November 2012 / Accepted: 26 February 2013

� Springer Science+Business Media New York 2013

Abstract Hepatic encephalopathy (HE) is a neuropsy-

chiatric state potentially complicating cirrhosis following

the accumulation of toxic compounds that cross the blood–

brain barrier and affect brain function; the compounds may

undergo alveolar gas exchange and be partially excreted by

exhalation. Thus breath analysis as a non-invasive means

of diagnosing HE, cirrhosis and harmful drinking was

investigated in a pilot study. One litre samples of breath

were collected from patients with alcohol-related cirrhosis

(n = 34) with HE (n = 11) and without HE (n = 23), non-

alcoholic cirrhosis without HE (n = 13), harmful drinkers

without cirrhosis (n = 7), and healthy controls (n = 15) in

a hospital setting. Breath compounds trapped on adsorbent

tubes were released via thermal desorption and analysed by

gas chromatography mass spectrometry for separation and

detection. Multivariate discriminant analysis was used to

identify volatile organic compounds to differentiate

patients according to disease status and build models for

disease classification. HE was correctly identified in

90.9 % of alcoholic cirrhotic patients and liver cirrhosis in

100 % of alcoholic patients. In patients without clinical

HE, alcohol was correctly predicted as the cause of cir-

rhosis in 78.3 % of patients and non-alcoholic causes of

cirrhosis were correctly determined in 69.2 %. Non-alco-

holic cirrhosis, alcoholic cirrhosis, and harmful drinking

could be discriminated from healthy controls with a sen-

sitivity of 92.3, 97.1 and 100 %, respectively. Breath vol-

atile analysis has the potential to aid the diagnosis of HE

and a range of liver disorders.

Keywords Volatile organic compound � Gas

chromatography � Mass spectrometry � Breath test � Liver �Hepatic encephalopathy

Abbreviations

DLR Diagnostic likelihood ratio

GC Gas chromatography

HE Hepatic encephalopathy

MS Mass spectrometry

VOC Volatile organic compound

DMS DimethysulfideElectronic supplementary material The online version of thisarticle (doi:10.1007/s11306-013-0510-4) contains supplementarymaterial, which is available to authorized users.

T. Y. Khalid � C. S. Probert

Department of Gastroenterology, Institute of Translational

Medicine, University of Liverpool, Liverpool, UK

T. Y. Khalid � B. D. L. Costello � R. Ewen � S. Stevens �N. M. Ratcliffe (&)

Institute of Biosensor Technology, University of the West of

England, Coldharbour Lane, Bristol, Frenchay BS16 1QY, UK

e-mail: Norman.Ratcliffe@uwe.ac.uk

P. White

Faculty of Environment and Technology, University of the West

of England, Bristol, UK

F. Gordon � P. Collins � A. McCune � A. Shenoy � S. Shetty

University Hospitals Bristol NHS Trust, Bristol, UK

A. Shenoy

Colchester Hospital NHS Foundation Trust, Essex, UK

S. Shetty

Dudley Group of Hospitals NHS Foundation Trust, Dudley, UK

C. S. Probert

Clinical Science at South Bristol, Bristol Royal Infirmary,

Bristol, UK

123

Metabolomics

DOI 10.1007/s11306-013-0510-4

1 Introduction

The burden of chronic liver disease is increasing inexora-

bly. In the West, this is due to the rapid rise in metabolic

syndrome as well as alcohol misuse (Davies 2012). In the

developing world, the main cause remains hepatitis C.

Patients presenting with established cirrhosis typically

have physical signs of portal hypertension, such as ascites

and spider naevi, but many patients are discovered to have

liver damage as result of ‘routine’ blood tests. Clinical

history taking, with reference to risk factors such as alcohol

misuse and recreational drug abuse, and further blood tests

(for viral serology and auto-antibodies) may give clues to

the aetiology of liver disease. Liver imaging, using

sonography and elastography, are often used to determine

the liver anatomy and rigidity, in support of a diagnosis of

cirrhosis. However, a percutaneous or even transjugular

liver biopsy remains the gold standard for the confirmation

of cirrhosis, the staging and grading of pre-cirrhotic disease

and diagnosis of other disorders. In short, the evaluation of

liver disease is costly, time-consuming and, potentially,

hazardous; given the rise in prevalence of liver disease, an

inexpensive, non-invasive means of assessing liver disease

would be clinically attractive.

The diseased liver may decompensate. Most cases in the

West occur on a background of chronic liver disease,

although hepatitis and acetaminophen (paracetamol) over-

dose may cause acute hepatic failure. Part of the syndrome

of decompensation is hepatic encephalopathy (HE); how-

ever in patients with alcoholic cirrhosis, the diagnosis of

HE may be confused with acute intoxication, alcohol

withdrawal and Wernicke’s encephalopathy. The manage-

ment of each of these conditions is distinctly different and

so a precise diagnosis is crucial.

Hepatic encephalopathy is a neuropsychiatric syndrome

with symptoms varying depending on the severity of the

condition. Personality changes with mild confusion, euphoria,

irritability, lethargy, inability to perform mental tasks with

impaired consciousness are early signs followed by increasing

loss of intellectual and motor functions and, ultimately, coma.

HE occurs when the liver decompensates and substances that

would normally be detoxified by the liver accumulate and then

cross the blood–brain barrier causing neurotoxicity and

abnormal functioning of the brain.

Hepatic encephalopathy has a poor prognosis: the mor-

tality rate is 58 % after 1 year and 77 % after 3 years in

cirrhotic patients (Bustamante et al. 1999). However, HE is

treatable and is easiest to treat if diagnosed at an early

stage.

There is no ‘gold-standard’ method for diagnosing HE.

Current tools include electroencephalography, assessment

of smooth pursuit eye movements, critical flicker frequency

tests, magnetic resonance imaging, single-photon emission

computed tomography and psychometric testing. The tests

used are complex and have limited availability (Tan et al.

2009). Breath analysis is increasingly being exploited to

aid clinical diagnosis (Amann and Smith 2005). Volatile

metabolites excreted in the breath have been associated

with conditions including small intestinal bacterial over-

growth (Ford et al. 2009) and asthma (American Thoracic

Society & European Respiratory Society 2005) and may

therefore serve as markers of disease. Breath testing is non-

invasive offering an attractive, and patient-friendly,

evaluation.

As stated earlier HE results from the accumulation of

compounds not cleared by the liver; these compounds in

the blood may cross the pulmonary alveolar membrane if

volatile to be exhaled and may offer a non-invasive means

of identifying the presence of this condition.

Ammonia plays a major role in the pathogenesis of HE,

however attempts to use breath ammonia measurements for

diagnosis have failed (Adrover et al. 2012; DuBois et al.

2005) probably because most exhaled ammonia is gener-

ated within the oral cavity by bacterial and/or enzymatic

activity on nitrogenous substrates (Smith et al. 2008;

Spanel et al. 2006). The smell of hepatic fetor is caused,

mainly by dimethyl sulphide (DMS), however this is not

pathognomonic of HE (Tangerman et al. 1994). In 2008

Van den Velde et al. reported specific malodorous com-

pounds in the breath of liver patients to assess how they

differed from those present in halitosis of oral origin. Some

breath gases do not contribute to bad breath but neverthe-

less are involved in clinical states (e.g. nitric oxide) and

could provide useful information for the diagnosis of dis-

ease. Recently, the same group assessed the entire breath

composition seeking biomarkers of cirrhosis; 24 models of

8 compounds (from a total of 20 differentiating VOCs)

appeared to discriminate between healthy controls and

patients with cirrhosis with 83 % sensitivity and 100 %

specificity (Dadamio et al. 2012). An extensive literature

search has revealed that no studies have investigated

whether HE can be diagnosed on the basis of VOCs

excreted on the breath.

This study investigated breath analysis as a diagnostic

tool for HE and aimed to determine whether patients with

liver cirrhosis or harmful drinking can be identified based

on their breath volatile organic compounds (VOCs).

2 Materials and methods

2.1 Participants

Participants were recruited over a 6-month period from

Bristol Royal Infirmary after giving written consent. Con-

sent was obtained when the patients were competent; in the

T. Y. Khalid et al.

123

case of those with HE, this meant during low grade disease,

and or re-consenting them when they had recovered from

moderate HE. Samples were collected from 47 patients

with cirrhosis (Table 1); the diagnosis was established by

histology (n = 41) and ultrasonography (irregular echo-

genic liver with splenomegaly and/or collaterals) in six.

Eleven samples were taken from alcoholic cirrhotic

patients with clinically diagnosed HE at the time of sam-

pling. Samples were also obtained from harmful drinkers

without cirrhosis (n = 7) undergoing inpatient detoxifica-

tion treatment, all had been drinking prior to admission,

and had no clinical evidence of chronic liver disease or

alcoholic hepatitis based on their Maddrey Score. Control

samples were obtained from hospital staff (n = 15) without

a history of harmful drinking (Table 1); the main role of

this group was as a comparator for those with harmful

drinking without evidence of cirrhosis. These groups were

well matched for age (40 and 43 years for healthy controls

and harmful drinkers, respectively).

Ethical approval for the study was granted by Central

and South Bristol Research Ethics Committee.

2.2 Assessment of HE

The presence and degree of HE were assessed using the West

Haven Criteria and Glasgow coma scale (Mullen 2007).

Simple psychometric tests were performed on all cirrhotic

patients; these included drawing a 5-pointed star, writing

their name clearly, completing an abbreviated mental test

and two different trail-making tests. The clinician then

determined whether the patient had HE. Breath samples were

collected within an hour of the clinical assessment. Partici-

pants were excluded if they had used methadone, illicit, or

psychoactive drugs in the last month. Patients receiving

benzodiazepines for alcohol withdrawal were not excluded.

2.3 Sampling of breath volatiles

A breath-sampling device, extensively described in the sup-

plementary (Supplementary data Fig. 1a–e), was developed

in-house to concentrate breath volatiles onto adsorbent tubes.

The tubes were packed with two types of graphitized carbon

(Sigma-Aldrich, Dorset, UK) to trap VOCs from C3-C12

(Carbopack B) and C12-C20 (Carbopack C). Prior to sample

collection, the tubes were pre-conditioned at 380 �C for

90 min in a helium flow of 30 ml/min. To avoid contami-

nation, each tube was capped before and after sample col-

lection. During transportation to and from the hospital the

tubes were sealed using brass Swagelock� storage caps with

PTFE ferrules.

2.4 Breath gas sampling

A new mouthpiece fitted with a one-way non-re-breathing

valve was attached to the device for each participant. The

wide-bore mouthpiece served as a 180 ml breath reservoir

presenting low resistance. A pre-conditioned adsorbent

tube was inserted into the side of the device. Participants

fully exhaled; the first portion of breath was removed and

alveolar air retained; subsequently an aliquot was pumped

across the adsorbent tube. The procedure was repeated until

one litre of air was sampled; this was performed without

difficulty by all participants. Tubes were sealed and the

captured VOCs analysed within 18 h using an ATD 50

automated thermal desorption system coupled to a Clarus

500 quadrupole gas chromatography (GC) mass spec-

trometry (MS) instrument (Perkin Elmer, Beaconsfield,

UK) fitted with a 60 meter Zebron ZB-624 capillary GC

column (0.25 mm I.D., 1.4 lm film thickness) from Phe-

nomenex (Macclesfield, UK). The composition of the

column consisted of 94 % methyl polysiloxane and 6 %

cyanopropyl-phenyl. All samples were collected at least

1 h after eating or drinking.

2.5 Recovery and analysis of breath VOCs

Adsorbent tubes were purged with helium (99.999 % pur-

ity, BOC, Guildford, UK) at 35 ml/min for 10 min at

ambient temperature to remove oxygen and moisture.

Thereafter tubes were heated to 250 �C for 10 min with

Table 1 Demographics for patient and control populations

Diagnosis No. of patients Age range in years (mean) Sex (M:F)

Alcoholic cirrhosis 34 34–73 (50) 20:14

With HEa 11 43–59 (49) 9:2

Without HE 23 34–73 (51) 11:12

Non-alcoholic cirrhosisb 13 38–61 (51) 11:2

Harmful drinkers 7 35–56 (43) 6:1

Healthy 15 20–59 (40) 5:10

a HE was graded using the West Haven Criteria; 6 patients had grade 1, 4 patients had grade 2 and 1 patient had grade 3 HEb Aetiology of cirrhosis was hepatitis C in the non-alcoholic cirrhosis group

Breath test for liver disease

123

helium (60 ml/min) to desorb the VOCs and concentrate

them onto an electrically cooled cold-trap (Air Monitoring

Trap, Perkin Elmer), held at 5 �C. The cold-trap was rap-

idly heated to 310 �C (rate of 99 �C/s) to desorb the VOCs

and transfer them to the GC column via a transfer line

heated to 250 �C (Supplementary Table 1). The GC tem-

perature program used to separate the VOCs was: ini-

tial oven temperature held at 35 �C for 1 min, ramped

(7 �C/min) to 100 �C, followed by a ramp (4 �C/min) to

200 �C, and finally ramped (25 �C/min) to 250 �C with a

4 min hold at 250 �C: total run time 41.29 min. The mass

spectrometer was run in electron impact ionization mode at

70 eV, scanning mass ions 10–300 at 0.3 scan/s (0.05 s

inter-scan delay), with a 3.50 min delay at the start of the

run. The detector voltage was set to 400 V.

2.6 Reliability tests

The reliability of the method was quantified from assessing

the intra-subject variation in VOC profiles by enumerating

the percentage agreement in presence and absence of vol-

atiles in breath samples from seven donors each sampled at

two time points (Supplementary Table 2). Low intra-sub-

ject variations in breath composition were reported using

the adsorbent tube breath sampling device.

2.7 Identification and quantification of breath VOCs

Chromatograms (Supplementary Fig. 2) were obtained and

analyzed using Turbomass (Version 5.0.0; Perkin Elmer).

All peaks three times above the signal-to-noise ratio were

reported. Peaks were identified by the NIST (National

Institute of Standards and Technology, Maryland, USA)

2005 MS search program. A forward and reverse match of

800/1000 and above was used for identification. Peaks that

could not be identified by the required match criteria were

labelled as ‘unknowns’ with their retention times recorded;

these were compared across all samples using retention

times and mass spectra for cross-matched assignations.

2.8 Statistical analysis

Peaks three times above the noise were analysed in a VOC

presence/absence evaluation. Statistical analyses were per-

formed using SPSS (version 17. Chicago: SPSS Inc., USA).

Pearson’s v2 test was performed to identify VOCs associated

with case groups; a significance level of B0.05 was adopted.

Statistically significant VOCs were used as variables in mul-

tivariate discriminant analysis models (Fisher 1936). Multi-

variate discriminant analysis was used to identify breath

VOCs that differentiate between groups. The significance of

each discriminant model was assessed using the Wilks’

Lambda statistic and v2 test. Measures of predictive accuracy

were cross-validated using the ‘leave-one-out’ procedure. The

sensitivity, specificity, predictive values and diagnostic like-

lihood ratios were calculated for each discriminant analysis

rule (Altman 1991; Landis and Koch 1977). The study is

primarily designed as a hypothesis generating study with an

exploratory descriptive protocol. Accordingly, statistical

analysis proceeds without explicit familywise controls on the

Type I error rate so as to not miss potentially important bio-

markers, see for instance (Bender and Lange 2001). In this

study the comparison-wise Type I error rate is controlled at the

alpha = 0.05 significance level and scrutiny of discovered

effects are subject to replication in a follow-up study.

3 Results

385 different compounds were identified in total, with an

average of 75 VOCs detected per subject.

3.1 Distinguishing HE in alcoholic cirrhosis

Thirteen VOCs were associated with the presence of HE and

two with the absence of HE in alcoholic cirrhotics by the v2

test (Table 1a in online supplement). Multivariate discrimi-

nant analysis was used to determine which VOCs allowed

differentiation of alcoholic cirrhotics with and without HE.

Two VOCs contributed to the group separation: the presence

of a compound eluting at 36.72 min likely to be iso-

thiocyanato-cyclohexane (Supplementary Fig. 3), was asso-

ciated with the presence of HE, while methyl vinyl ketone was

associated with the absence of HE.

A discriminant model (Wilks’ K = 0.53, v2 = 19.5,

df = 2, p \ 0.001) was built based on the presence or

absence of these compounds. Fisher’s discriminant score

was given by:

Y ¼ 2:039þ 3:202X1 � 4:21X2

where the presence or absence was substituted with a ‘‘1’’ or

‘‘0’’ respectively for the following VOCs within a sample:

X1 = methyl vinyl ketone, X2 = isothiocyanato-cyclohexane.

A positive result indicated HE was not present, a neg-

ative result indicated it was (Fig. 1a). The model classified

88.2 % of samples correctly (with 90.9 % sensitivity,

87.0 % specificity) for HE in alcoholic cirrhotic patients.

Classification results were the same under leave-one-out

cross-validation.

3.2 Distinguishing alcoholic from non-alcoholic

cirrhosis

Eleven VOCs were associated with alcoholic cirrhosis

compared with non-alcoholic cirrhosis and 8 volatiles with

non-alcoholic cirrhosis (Table 1b in online supplement).

T. Y. Khalid et al.

123

Undecane and an unknown compound eluting at

10.61 min contributed to group separation of alcoholic and

non-alcoholic cirrhosis in patients without HE and produced

a discrimination model (Wilks’ K = 0.68, v2 = 12.6,

df = 2, p = 0.002). Undecane was associated with alcoholic

cirrhosis while unknown 10.61 was associated with non-

alcoholic cirrhosis. Fisher’s discriminant score was given by:

Y ¼ 2:285X1 � 1:889X2 � 0:346

in which: X1 = undecane and X2 = unknown 10.61.

Fig. 1 Box-and-whiskers plots of the Fisher’s linear discriminant

scores obtained for disease classification. Scores are given for

classifying a the presence or absence of HE in alcoholic cirrhosis,

b alcoholic and non-alcoholic causes of cirrhosis, c the presence or

absence of cirrhosis in alcoholic patients, d harmful levels of drinking

in seemingly healthy people, e non-alcoholic cirrhosis cases from

healthy, and f alcoholic cirrhosis cases from healthy

Breath test for liver disease

123

A positive result indicated alcoholic cirrhosis, while a

negative result suggested hepatitis C was the cause (Fig. 1b).

The model had an overall predictive accuracy of 75.0; 78.3 %

for alcoholic cirrhosis and 69.2 % for non-alcoholic cirrhosis;

which fell to 65.2 and 69.2 %, respectively under leave-one-

out cross-validation.

3.3 Distinguishing alcoholic cirrhosis from harmful

drinking

Ten VOCs were associated with alcoholic cirrhotics and

twenty-seven were associated with harmful drinkers with-

out cirrhosis (Table 1c in online supplement). Discriminant

analysis found 1-methyl-4-(1-methylethenyl)-benzene

(p-cymenene) and two unknown VOCs eluting at retention

times of 9.39 and 34.69 min contributed to the group

separation of alcoholic patients with and without cirrhosis

and produced a discriminant model (Wilks’ K = 0.39,

v2 = 35.1, df = 3, p \ 0.001). Unknown 9.39 and

unknown 34.69 were positively associated with harmful

drinkers while 1-methyl-4-(1-methylethenyl)-benzene was

positively associated with alcoholic cirrhosis. Fisher’s

discriminant score was given by:

Y = 7:785X1 þ 7:785X2 � 4:526X3 � 3:262

in which: X1 = unknown 9.39, X2 = unknown 34.69,

X3 = 1-methyl-4-(1-methylethenyl)-benzene.

A positive result indicated harmful drinking with no

cirrhosis, while a negative result indicated cirrhosis

(Fig. 1c).

The discriminant test correctly classified 92.9 % of

alcoholic patients with and without cirrhosis (100.0 % of

alcoholic cirrhotics, 85.7 % of harmful drinkers). The

corresponding percentages under leave-one-out cross-vali-

dation were 88.2 and 85.7 %, respectively.

3.4 Distinguishing harmful drinkers from healthy

controls

Two VOCs were associated with healthy controls and 54

with harmful drinking (Table 1d in online supplement).

Key VOCs found to discriminate healthy cases from

harmful drinkers include octanal, a compound tentatively

identified as 2,6-dimethyl-7-octen-2-ol, and an unknown

compound eluting at a retention time of 10.43 min. These

VOCs significantly contributed to group separation (Wilks’

K = 0.2, v2 = 33.9, df = 3, p \ 0.001). Fisher’s dis-

criminant score was given by:

Y = 13:779X1 þ 9:449X2 þ 7:454X3 � 13:083

in which: X1 = unknown 10.43, X2 = 2,6-dimethyl-7-octen-

2-ol, X3 = octanal.

A positive result indicated harmful drinking and a

negative result indicated the sample was from a healthy

control (Fig. 1d); the model gave 100 % correct classifi-

cation of samples falling to 86.4 % under leave-one-out

cross-validation (71.4 % sensitivity, 93.3 % specificity).

3.5 Distinguishing non-alcoholic cirrhosis

from healthy controls

Only one VOC, methyl vinyl ketone, was associated with

the healthy group in comparison to 45 significantly asso-

ciated with the non-alcoholic cirrhotic group (Table 1e in

online supplement).

Multivariate discriminant analysis found that 1-methyl-2-

(1-methylethyl)-benzene (o-cymene), methyl vinyl ketone

and an unknown compound eluting at a retention time of

10.61 min allowed for the discrimination of non-alcoholic

cirrhotic patients from healthy controls and produced a dis-

criminant model (Wilks’ K = 0.22, v2 = 37.1, df = 3,

p \ 0.001). Fisher’s discriminant score was given by:

Y ¼ 8:721X1 þ 6:985X2 þ 5:33X3 � 3:521

in which: X1 = unknown 10.61, X2 = 1-methyl-2-(1-

methylethyl)-benzene, X3 = methyl vinyl ketone.

A positive score indicated non-alcoholic cirrhosis and a

negative result suggested the sample was from a healthy

person (Fig. 1e). The model had an overall predictive

accuracy of 96.4 % for both original and leave-one-out

cross-validated grouped cases; 92.3 % of non-alcoholic

cirrhotics and 100 % of healthy controls were correctly

classified by the test.

3.6 Distinguishing alcoholic cirrhosis from healthy

controls

Three compounds were associated with the healthy group

in comparison to 47 associated with the alcoholic cirrhosis

group (Table 1f in online supplement).

Key VOCs associated with alcoholic cirrhosis were

found to discriminate these patients from healthy cases

including heptane, 1-methyl-2-(1-methylethyl)-benzene,

phellandrene, and 2-methylhexane. These volatiles signif-

icantly contributed to the group separation (Wilks’

K = 0.21, v2 = 69.9, df = 4, p \ 0.001). Fisher’s dis-

criminant score was given by:

Y ¼ 6:777X1 þ 9:818X2 þ 7:062X3 þ 3:079X4 � 15:053

in which: X1 = heptane, X2 = 1-methyl-2-(1-methyleth-

yl)-benzene, X3 = phellandrene, X4 = 2-methylhexane.

A positive score indicated alcoholic cirrhosis, while a

negative result indicated healthy (Fig. 1f). The discrimi-

nant model gave 95.9 % correct classification of both

T. Y. Khalid et al.

123

original and leave-one-out cross-validated grouped cases;

97.1 % of alcoholic cirrhotic patients and 93.3 % of heal-

thy controls were correctly classified by the test.

Statistics to evaluate the diagnostic ability of the col-

lective multivariate discriminant analysis rules are given in

Table 2.

4 Discussion

This is the most detailed study of VOCs exhaled by HE

patients, cirrhotics and harmful drinkers to date. Models

based on several breath VOCs shows there is the potential

to differentiate between these groups.

4.1 Breath test for diagnosis of HE in alcoholic

cirrhotic patients

In patients with alcoholic cirrhosis, patients with HE can be

identified from those without HE, on the basis of VOCs in

breath. Two breath volatiles provided a model for diag-

nosing HE. Methyl vinyl ketone was associated with the

absence of HE and a compound eluting at a retention time

of 36.72 min, possibly isothiocyanato-cyclohexane, was

associated with the presence of HE.

There is a biochemical rationale for the presence or absence

of certain compounds in HE. In decompensated cirrhosis, as

demonstrated by HE, there is a loss of synthetic function

during impaired metabolism. Isothiocyanato-cyclohexane is a

widespread environmental pollutant (Gallego et al. 2007); we

speculate that in hepatic decompensation its catabolism is

impaired leading to increased detection on the breath of HE

patients.

The discriminant analysis model for HE had a positive

predictive value of 77 % and negative predictive value

of 95 %. This implies that the discriminant model was

over-sensitive for HE; however, it is possible that some of

these ‘false positives’ were patients with minimal HE

whose diagnosis was missed. This is supported by reports

of minimal HE being present in 30–84 % of cirrhotic

patients (Dhiman et al. 1995; Gitlin et al. 1986; Quero and

Schalm 1996). We did not have the facilities to undertake

laboratory assessment of minimal HE and it is plausible

that some patients without clinical signs actually had

minimal HE. Another limitation was the relatively small

number of patients with clinical HE that were studied

(n = 11). Future multicentre studies are needed to deter-

mine whether specific volatiles are correlated to the grade

of HE. It would be important to ascertain whether minimal

HE can be identified from breath samples.

Other research groups that studied VOCs in the blood

of patients with HE found elevated levels of metha-

nethiol, 3-methylbutanal and ammonia (Al Mardini et al.

1984; Chen et al. 2008; Goldberg et al. 1981; Ong et al.

2003). In rats, a reversible coma can be induced with

methanethiol (Al Mardini et al. 1984) and blood meth-

anethiol concentrations were reported to be significantly

risen in many HE patients compared to healthy individ-

uals although, not in all cases (McClain et al. 1980).

However, similar concentrations were found in deeply

comatosed patients and in those showing only mild

cerebral dysfunction. Methanethiol was not detected on

the breath of any subjects in this study and other studies

have reported that breath levels of this compound do not

reflect its levels in the blood but are likely to result from

production by bacteria within the mouth. Blom and

Tangerman (1988) carried out in vitro experiments

demonstrating that the free –SH group can rapidly react

with blood, covalently binding to proteins in serum and

is readily oxidised to sulphate by enzymatic mediation in

Table 2 Statistics to assess the discriminative power of the classification models based on the presence or absence of discriminatory breath

volatiles

Classification test for:

HE in

alcoholic

cirrhosis

Alcoholic

cause of

cirrhosis

Non-alcoholic

(hepatitis C) cause

of cirrhosis

Presence of

cirrhosis in

alcoholics

Discriminating

harmful drinkers

from healthy

Discriminating non-

alcoholic cirrhosis

from healthy

Discriminating

alcoholic cirrhosis

from healthy

Sensitivity 0.91 0.78 0.69 1.00 1.00 0.92 0.97

Specificity 0.87 0.69 0.78 0.86 1.00 1.00 0.93

PPV 0.77 0.82 0.64 0.97 1.00 1.00 0.97

NPV 0.95 0.64 0.82 1.00 1.00 0.94 0.93

Positive

DLR

6.97 2.54 3.18 7.00 30.0 139.33 14.56

Negative

DLR

0.10 0.31 0.39 0.00 0.06 0.08 0.03

Breath test for liver disease

123

red blood cells. This prevents its diffusion across the

alveoli-capillary membrane into the alveolar air. Further-

more, the concentrations of methanethiol are not always

found to be elevated in the breath of cirrhotic patients

compared to normal controls undermining its contribution

to fetor hepaticus (Kaji et al. 1978; Tangerman et al.

1983).

The characteristic odour of fetor hepaticus has already

been shown to be due to DMS and is not characteristic to

just HE as the smell is present in cirrhotic patients without

HE. Kaji et al. (1978), observed that the fasting levels of

DMS were significantly elevated in the breath of cirrhotic

patients compared to healthy controls, but there was no

clear correlation in the levels found for HE patients. A later

study by Tangerman et al. (1994) upheld these findings and

confirmed that fetor hepaticus was only due to the presence

of DMS and that the levels of DMS in the breath did not

correlate with clinical signs of HE but instead were sig-

nificantly related to the presence of shunting. Furthermore,

elevated levels of this compound are not specific to liver

cirrhosis and can also arise as a result of an unknown

metabolic disorder (Tangerman and Winkel 2007) and

hypermethioninemia (Mudd et al. 1995).

Our analytical method is capable of detecting DMS, as

observed by using standards, however it was not observed

in breath, although other organo-sulfides were. Ammonia

levels are likely to be elevated in HE, however we rarely

observed it. Ammonia detection by GCMS is challenging

due to its low b.p. and co-elution with water, and as stated

earlier there are problems with confounding production

from bacteria in the oral cavity.

4.2 Breath test to discriminate between alcoholic

and non-alcoholic cirrhosis

Different liver pathologies are expected to give rise to

different VOC profiles. Millonig et al. (2010) reported

differences between patients with non-alcoholic fatty liver

disease, alcoholic fatty liver disease and liver cirrhosis

(viral or alcohol-related). They were able to discriminate

between the different liver disease groups using diagnostic

algorithms based on the concentrations of several breath

marker compounds. We compared alcoholic cirrhosis

without HE (n = 23) to cirrhosis caused by hepatitis C

(n = 13). The discriminant analysis model correctly pre-

dicted group membership for 69.2 % of hepatitis C cir-

rhotics and 78.3 % of alcoholic cirrhotics. Attempts to

differentiate patients with different etiologies of liver cir-

rhosis gave a higher proportion of misclassified samples

(25 % incorrect) than other models, which suggests that

alcoholic and non-alcoholic cirrhosis share similar meta-

bolic disturbances.

4.3 Breath test for determination of cirrhosis

in alcoholic patients

Heavy drinkers often experience general health problems

prior to the onset of alcoholic liver cirrhosis and require

hospital admission. Breath VOCs of patients with cirrhosis

were compared to harmful drinkers without cirrhosis and

significant differences were found between the two groups

to allow for differentiation. There have been reports of

elevated levels of volatile sulphur compounds and fatty

acids in the breath of cirrhotic patients (Chen et al. 1970;

Kaji et al. 1978). However, our study, like that of Solga

et al. (2006) did not confirm this, but found that the pres-

ence of seven hydrocarbons, two alcohols, a ketone and a

sulphur-containing compound were associated with the

cirrhotic group. Assuming that alcohol increases the pro-

duction of oxygen free-radicals that cause oxidative stress

(Lieber 1997; Probert et al. 2009); then in both cases

up-regulation of alcohol-induced cytochrome P450 would

be expected to produce similar compounds such as

hydrocarbons. We observed more saturated hydrocarbons

in the breath of non-cirrhotic harmful drinkers, likely to be

a result of more recent alcohol-induced lipid peroxidation

(Moscarella et al. 1984).

The origin of 1-methyl-4-(1-methylethenyl)-benzene is

open to speculation. Chronic alcohol ingestion has been

reported to increase hepatic aromatase activity which aro-

matizes androgens into estrogens (Purohit 2000). Alcoholic

cirrhotic patients had a higher prevalence of benzene

compounds and lower prevalence of cyclohexenes than

harmful drinkers with no cirrhosis. This suggests that there

may be enhanced aromatase activity in the cirrhotics due to

extensive alcohol abuse that could be responsible for

changes in metabolism.

The presence of three VOCs enabled the differentiation

of alcoholic patients with and without cirrhosis. Our model

correctly categorized 100 % of alcoholic cirrhotics and

correctly diagnosed the absence of cirrhosis in 85.7 % of

harmful drinkers. The one misclassified harmful drinker

presented with evidence of cirrhosis on ultrasound 2 years

later. None of the other patients in this group presented

with cirrhosis in the intervening 2 years.

4.4 Breath test for detection of harmful drinking

Harmful drinkers without cirrhosis could be distinguished

from healthy controls on the basis of exhaled VOCs. A

model was built to differentiate harmful drinkers from

healthy controls with 100 % accuracy and under leave-one-

out cross-validation this fell to 86.4 % correct classifica-

tion. If these findings are confirmed in a larger study this

could lead to the development of a breath test to identify

harmful drinkers. Alcohol disturbs gastrointestinal function

T. Y. Khalid et al.

123

in high doses inhibiting gastric motility and acid secretion,

potentially leading to bacterial overgrowth (Bode and Bode

1997). Alcohol may also cause mucosal damage, increas-

ing permeability leading to toxin-induced liver damage

(Bode and Bode 1997; Fukui et al. 1991). This sequence of

events may explain why harmful drinking can lead to the

production of different VOCs.

Octanal, a compound tentatively identified as 2,

6-dimethyl-7-octen-2-ol, and an unknown hydrocarbon

eluting at a retention time of 10.43 min, were found more

commonly amongst harmful drinkers than healthy controls

allowing for differentiation. Ethanol-induced oxidative

stress may explain this, via P450 induction and catabolism

of fatty acids to make these compounds.

4.5 Breath tests for discrimination of alcoholic

and non-alcoholic cirrhosis cases from healthy

controls

Van den Velde et al. (2008) sampled the breath of cirrhotic

patients and healthy individuals with the aim of identifying

compounds that could aid in the diagnosis of liver cirrhosis.

However, this study only reported odorous compounds

associated with halitosis but non-odorous compounds could

be better markers for cirrhosis so this was investigated.

Given that different aetiologies of cirrhosis may give rise to

different VOCs, non-alcoholic and alcoholic cirrhotic cases

were separated. Two models were built to differentiate

these groups of patients from healthy controls. The model

for non-alcoholic cirrhosis correctly categorized 92.3 % of

patients based on the presence or absence of three VOCs;

that for alcoholic cirrhosis correctly categorized 97.1 % of

patients based on the presence or absence of four VOCs.

1-methyl-2-(1-methylethyl)-benzene helped differentiate

liver cirrhosis from healthy cases in both the non-alcoholic

and alcoholic cirrhosis testing models. This suggests it may

be a marker of cirrhosis regardless of aetiology and that

alcoholic and non-alcoholic cirrhosis may share common

pathological processes. The presence of heptane and

2-methylhexane in the alcoholic group can be explained by

increased production following oxidative stress and

enhanced peroxidation of unsaturated fatty acids.

4.6 Discussion on confounding factors

Background air levels of VOCs were not subtracted from

the breath samples due to a number of reasons. The main

problem with applying a correction factor for background

air is that the complexity of pulmonary adsorption and

exhalation of substances is not accounted for, which are

subject to large inter-individual variability. As a result

subtraction methods may lead to artificially generated sta-

tistical significance. Furthermore, correcting for background

air requires the subject to be in equilibrium with their

ambient air and the time taken to achieve this is not known.

The use of this method may therefore increase the uncer-

tainty of the results, especially given that the room air VOC

levels can fluctuate greatly over short periods of time. The

sampling efficiency and recovery of analytes from sorbent

traps will also vary between ambient air and breath air

samples due to differences in water content (Ali et al. 1989).

The variation in background air VOCs is expected to be

randomly distributed between samples from various subject

groups, suggesting that it is unlikely to interfere with the

outcome of the analyses (Van Berkel et al. 2008).

Other confounding/lifestyle factors including diet,

alcohol, certain medications, smoking, exercise and per-

sonal hygiene practices may influence VOCs. However it is

difficult, if not impossible, to correct for all these factors

and any correction may adversely impact on the ecological

validity of findings. It is also notoriously difficult to obtain

data about alcohol consumption; patients may appear

inaccurate when describing the amount, type and time of

drinking. However an attempt was made to collect

and report this data along with details of medications and

co-morbidities (Supplementary Table 5). We acknowledge

that donor characteristics, such as treatment and co-mor-

bidities, are high dimension data and may contribute to

partial confounding with group affiliation which is

unavoidable in an observational study of this nature. These

problems are common to many proof of concept studies.

However, these problems in this instance are partly miti-

gated from our use of prior reasoned and carefully selected

challenging comparisons (e.g. alcoholic versus non-alco-

holic cirrhosis, or the determination of cirrhosis in drink-

ers) which are not unduly affected by extreme between

groups covariate imbalances.

Discriminant analysis allowed for a number of groups to be

discriminated based on the presence or absence of a small

subset of breath VOCs; here it was used to differentiate

between 2 clinically separate groups for a range of conditions

with a positive score representing one group and a negative

representing another. There was insufficient data at present to

obtain a robust classifier to produce one model for all clini-

cally different groups; such a complex discriminant analysis

would be possible from a much larger study. Given this, our

methodological approach addressed one clinical question with

each discriminant model so if for example a cirrhotic patient is

admitted into hospital in a confused state then the test for HE

could be applied. Alternatively, if a patient is admitted for an

alcohol ‘detox’, then the test for cirrhosis versus harmful

drinking could be applied. The data analysis plan developed

for this study utilised standard and well-established tech-

niques. Leave-one-out cross-validation has been used to help

establish the within-sample internal validity of conclusions.

We acknowledge that the development of statistical models

Breath test for liver disease

123

based on small sample sizes may suffer from chance idio-

syncratic sample features due to the high ratio for the number

of VOCs relative to sample size. This implies that the external

validity of findings should be judged against a confirmatory

study.

Conditions associated with cirrhosis may have influenced

our findings e.g. pulmonary gas exchange abnormalities can

be present in patients with advanced liver disease (Hourani

et al. 1991; Martinez et al. 2001). Hepatopulmonary syndrome

is one such complication characterised by abnormal dilation

of pulmonary capillary vessels (Rodriguez-Roisin and Kro-

wka 2008). Significant ascites and/or hepatomegaly have been

found to reduce lung volumes in cirrhotics (Angueira and

Kadakia 1994). The high cardiac output of cirrhotic patients

may account for the alveolar-capillary diffusion impairment

to oxygen in advanced cirrhosis (Degano et al. 2009). Pres-

ence of any of these pulmonary gas exchange abnormalities

will affect breath VOCs.

Time consuming ATD-GCMS laboratory based methods

have been undertaken to identify VOCs with good disease

discriminating properties in this study. If these results are

confirmed in a larger study, a sensor system with targeted

sensitivity to key compounds could be developed to pro-

vide direct breath measurements, rapidly, with high

throughput and low running costs.

5 Concluding remarks

This data suggests that breath analysis may be a useful tool

for the diagnosis of HE, cirrhosis and harmful drinking.

Breath sampling is simple to perform with a non-invasive,

portable device. A larger quantitative study of HE patients

that includes more patients of varying disease severity

would be valuable. In the future, breath analysis may be

useful in GPs’ surgeries to support the management of

harmful drinkers as well as helping to select patients in

need of secondary care.

Acknowledgments The authors would like to thank the University

of the West of England for funding and all participants that took part

in this pilot study.

Funding Tanzeela Khalid was funded by a 3-year PhD studentship by

the University of the West of England, Bristol to carry out this work.

Conflict of interest There are no conflicts of interest to disclose.

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