Metab Brain Dis
DOI 10.1007/s11011-016-9904-0
ORIGINAL ARTICLE
Urinary metabolic profiling by 1H NMR spectroscopy in patients
with cirrhosis may discriminate overt but not covert
hepatic encephalopathy
Mark J. W. McPhail 1 & Sara Montagnese 2 & Manuela Villanova 2 & Hamza El Hadi 2 &
Piero Amodio 2 & Mary M. E. Crossey 1 & Roger Williams 3,4 & I. Jane Cox 3,4 &
Simon D. Taylor-Robinson 1
Received: 14 November 2015 / Accepted: 1 September 2016
# The Author(s) 2016. This article is published with open access at Springerlink.com
Abstract To date urinary metabolic profiling has been applied
to define a specific metabolic fingerprint of hepatocellular carcinoma on a background of cirrhosis. Its utility for the stratification of other complications of cirrhosis, such as hepatic encephalopathy (HE), remains to be established. Urinary proton
nuclear magnetic resonance (1H-NMR) spectra were acquired
and NMR data from 52 patients with cirrhosis (35 male; 17
female, median (range) age [60 (18–81) years]) and 17 controls
were compared. A sub-set of 45 patients (33 male; 12 female,
[60 (18–90) years, median model for end stage liver disease
(MELD) score 11 (7–27)]) were fully characterised by WestHaven criteria, Psychometric Hepatic Encephalopathy Score
(PHES) and electroencephalogram (EEG), and defined as overt
HE (OHE, n = 21), covert HE (cHE, n = 7) or no HE (n = 17).
Urinary proton nuclear magnetic resonance (1H-NMR) spectra
were analysed by partial-least-squares discriminant analysis
(PLS-DA). The results showed good discrimination between
patients with cirrhosis (n = 52) and healthy controls (n = 17)
(R2X = 0.66, R2Y = 0.47, Q2Y = 0.31, sensitivity-60 %,
specificity-100 %) as the cirrhosis group had higher 1methylnicotinamide with lower hippurate, acetate,
phenylacetylglycine and N-methyl nicotinic acid levels. While
patients with OHE could be discriminated from those with no
HE, with higher histidine, citrate and creatinine levels, the best
models lack robust validity (R2X = 0.65, R2Y = 0.48,
Q2Y = 0.12, sensitivity-100 %, specificity-64 %) with the sample size used. Urinary 1H-NMR metabolic profiling did not
discriminate patients with cHE from those without HE, nor
discriminate subjects on the basis of PHES/EEG result or
MELD score. In conclusion, patients with cirrhosis showed different urinary 1H-NMR metabolic profiles compared to healthy
controls and those with OHE may be distinguished from those
with no HE although larger studies are required. However, urinary 1H-NMR metabolic profiling did not discriminate patients
with differing grades of HE or according to severity of underlying liver disease.
Mark J.W. McPhail and Sara Montagnese joint first author.
Keywords Hepatic encephalopathy . Metabolic profiling .
Urinary biomarkers . Magnetic resonance spectroscopy .
Hippurate . Histidine
* I. Jane Cox
j.cox@researchinliver.org.uk
Introduction
1
Liver Unit, Division of Digestive Health, Department of Surgery and
Cancer, Imperial College London, St Mary’s Campus, London W2
1NY, UK
2
Department of Medicine DIMED, University of Padova,
Padova, Italy
3
Institute of Hepatology London, Foundation for Liver Research, 111
Coldharbour Lane, London SE5 9NT, UK
4
Faculty of Life Sciences & Medicine, King’s College London,
London, UK
Hepatic encephalopathy (HE) is a common complication of
cirrhosis, porto-systemic shunting and acute liver failure
(ALF) (Ferenci et al. 2002;Vilstrup et al. 2014). HE is a defining feature of ALF and the majority of patients with cirrhosis will experience an episode of HE at some point during their
illness. This may manifest as confusion, somnolence, poor
concentration or even coma. In some patients such overt
symptoms are lacking, but when psychometric tests are performed, significant impairment is revealed in attention,
Metab Brain Dis
concentration and executive function (Weissenborn et al.
2001;Weissenborn 2013). Such covert HE (cHE) is associated with progression to OHE with hospitalisation and has
been linked to shortened survival in some studies
(Weissenborn 2015).
The pathogenesis of HE is incompletely understood.
Hyperammonaemia is central to HE, but ammonia levels only
correlate with the cerebral oedema and outcome of patients
with ALF. In ALF, gut-derived ammonia is not detoxified in
the liver, due to failure of the urea cycle, causing large rises in
ambient ammonia levels. Following translocation across the
blood brain barrier, ammonia enters astrocytes and is converted to glutamine. Whether by glutamine-associated osmosis or
failure of mitochondria (due to ammonia toxicity), the astrocyte swells and symptoms related to cerebral oedema occur.
However, although present in patients with HE neither
hyperammonaemia nor cerebral oedema correlate strongly
with severity of HE in patients with cirrhosis and co-factors
are required to explain the clinical syndrome and brain dysfunction. These include the presence of inflammatory states,
such as sepsis (Tranah et al. 2013). Furthermore, the role of
the gut microbiota have recently been implicated in HE (Bajaj
et al. 2013), which have been explored in proof-of-principle
studies, assessing the effect of therapies, such as lactulose
(Bajaj et al. 2012) and rifaximin (Bajaj et al. 2011).
The role of zinc (Warthon-Medina et al. 2015) and manganese (Rivera-Mancia et al. 2011) have long been reported in
patients with cognitive dysfunction and liver disease. Low zinc
levels are common in acute and chronic liver disease and are
associated with increased GABAergic tone. Manganese is deposited in the basal ganglia in patients with cholestasis (including when secondary to parenteral nutrition) and in cirrhosis and
may contribute to the Parkinsonian phenotype observed in
chronic encephalopathy (Zeron et al. 2011).
Hyponatraemia is a common cofactor in cerebral oedema
in patients with acute liver failure and traumatic brain injury
where hypertonic saline solutions are indicated as prophylaxis
or treatment for raised intracranial pressure. In cirrhosis patients with hyponatraemia from diuretic use, dilution and
hepatorenal syndrome are also at increased risk of overt HE
(Iwasa and Takei 2015).
Present diagnostic pathways for HE involve bedside clinical assessment, psychometric evaluation (pencil-and-paper or
computerized test batteries), and electroencephalography
(EEG). While structural and functional magnetic resonance
imaging can elucidate profound changes associated with HE,
these powerful imaging modalities are not yet suitable for
routine clinical diagnostic use. A Bbiomarker^ for grading
HE would be of significant importance to the hepatology community. At present, plasma or capillary ammonia remains the
only plasma marker in routine use. This measurement has
significant false positive rates (although virtually no false negative rates), because of the multifactorial nature of HE and
relative instability of ammonia outside the body at ambient temperatures. Recent evidence suggests that the
gut microbiota can be interrogated by salivary microbe analysis, which reflects gut host-microbe interactions and inflammation, another important cofactor in the development of HE
(Bajaj et al. 2015).
Metabolic profiling (Nicholson et al. 2012) (also termed
metabonomics or metabotyping) involves analysis of
biofluids or tissues by measurement of low molecular weight
(<1 kDa) compounds using proton nuclear magnetic resonance (1H-NMR) spectroscopy or mass spectrometry (MS)
techniques (Nicholson et al. 1999). Alterations in the complex
spectral data sets can then be assessed using multivariate statistical techniques (Trygg et al. 2007) to determine scope of
the data sets for diagnosis, prognosis or response to intervention. Since the metabolic profile is typically comprised of
hundreds or thousands of signals, depending on the technique,
it might prove to be a highly valuable methodology in delivering personalised and highly discriminant prediction of response or diagnostic accuracy (Nicholson and Holmes 2006).
It has recently been shown that information about the gutliver-brain axis can be determined from urinary 1H-NMR
analysis (Williams et al. 2008;Williams et al. 2010). Urinary
metabolic profiles have been noted to be useful in classifying
patients with non-HE related complications of cirrhosis, such
as hepatocellular carcinoma (Ladep et al. 2014;Shariff et al.
2010;Shariff et al. 2011), but the relative contribution from
HE to the urinary metabolome remains under-characterised.
A recent proton magnetic resonance spectroscopy of metabolic profiling of serum in patients with HE, showed that
supervised modelling provided discrimination between
healthy controls and patients with cirrhosis (Jimenez et al.
2010). A predictive model was generated which displayed
strong discrimination between patients with and without
cHE. cHE patients displayed increased serum concentrations of glucose, lactate, methionine, trimethylamine-N-oxide
(TMAO), and glycerol, as well as decreased levels of choline,
branch amino acids, alanine, glycine, acetoacetate, NAC, and
lipid moieties. Defining metabolic change in urine samples of
patients with cHE would strengthen the validity of metabolic
profiling as a future diagnostic and research tool in patients
with HE.
In this study, the urinary 1H-NMR metabolic profile from a
well characterised group of patients with cirrhosis, in the presence and absence of HE, was interrogated with clinical and
diagnostic assessments to determine the utility of urinary metabolic phenotyping for assessing cHE.
Materials and methods
This study conformed to the ethical standards of the
Declaration of Helsinki and underwent review and approval
Metab Brain Dis
at Hammersmith and Queen Charlottes & Chelsea Local
Research Ethics Committee (ref 04/Q0406/161) and Padova
University Hospital Ethical Review Board (Protocollo 1385P,
and subsequent 2010 amendments).
to age and education-adjusted Italian norms (Amodio et al.
2008). Performance was classified as impaired if the sum of
the standard deviations for the individual tests, referred to as
Psychometric Hepatic Encephalopathy Score (PHES), was ≤
−4 (Amodio et al. 2008).
Patient selection
Neuropsychiatric status
Sixty-six consecutive patients with cirrhosis referred to the
outpatient clinic for Cognitive Disturbances in Medicine,
Padova University Hospital. Patients were excluded (4 out
of 66 screened) if they were actively misusing alcohol, had
significant cerebrovascular or cardiovascular disease, renal failure, neurological or psychiatric co-morbidity, previous liver transplant, were taking psychoactive drugs or
were unable/unwilling to participate (see Fig. 1 for flow chart
of recruitment; demographic and liver failure characteristics
are presented in Table 1, by degree of neuropsychiatric
impairment, vide infra). Twenty age-matched healthy volunteers served as controls.
Neurophysiological assessment
Patients underwent 10-min, eyes-closed EEG recording, in a
condition of relaxed wakefulness and in a quiet room, at
08:30–09:00 in the morning. Attention was paid to avoid somnolence, muscle or other types of artefact. The EEG was obtained by Brainquick 3200 digital equipment (Micromed,
Italy). A 21-channel cap was used, and the electrodes placed
according to the 10–20 International System (ground: Fpz,
reference Oz). Impedance was kept under 5 kΩ. Each channel
had its own analogue-to-digital converter, while signals were
digitally filtered in the 1.6–70 Hz range. Sampling frequency
was 256 Hz, with 12-bit analogue-to-digital conversion. One
continuous 80–100 s period of EEG tracing was selected (authors SM and/or PA) for subsequent spectral analysis by Fast
Fourier Transform. The following spectral parameters were
obtained: mean dominant frequency (MDF), which is an estimate of the background frequency of the EEG, and relative
power of the spectral bands delta (1–3.5 Hz), theta (4–7.5 Hz),
alpha (8–13 Hz) and beta (13.5–26.5). Spectral parameters
were obtained on the derivations P3-P4 (bi-parietal), and the
EEG was qualified as normal/abnormal, according to Amodio
and co-workers (Amodio et al. 1999).
Neuropsychiatric status on the day of study was classified as
unimpaired: no clinical evidence of HE and normal PHES;
covert HE: no clinical abnormalities but abnormal PHES
and/or abnormal EEG; overt HE: clinically evident neuropsychiatric disturbances [≥ grade II according to the West Haven
criteria (Conn 1977), applied by authors SM and/or PA]
(Vilstrup et al. 2014).
Nutritional status
This included measurement of weight/height, calculation of
the body mass index (BMI), and evaluation of body composition by the mid-arm circumference and the triceps skinfold
thickness. The mid-arm muscular area was then calculated and
the results scored according to reference percentiles
(Frisancho 1981). In addition, information was obtained to
calculate the Royal Free Hospital (RFH) nutritional screening
tool and patients qualified as being at high, medium or low
risk for malnutrition (Morgan et al. 2006).
No dietary exclusion was imposed on the participating subjects, but a detailed dietary and lifestyle history
was taken. This included current regular medications,
and any other drugs used intermittently; use of any
herbal remedies or medications, including pre- or
probiotics; usual and recent alcohol intake; smoking history;
exercise and normal dietary habits; and a 24 h dietary recall.
Participants were also directly questioned about their intake of
specific dietary components which may influence urinary
metabolic profiles.
66 paents screened
62 paents studied
Neuropsychological assessment
Psychometric performance was assessed, under standardized
conditions, using Number Connection Tests A and B, the
Digit Symbol, Line Tracing, and Serial Dotting tests
(Weissenborn et al. 2001). Results were scored with relation
52 paents studied
4 paents excluded
1 previous liver transplant
1 cerebrovascular disease
2 psychoacve medicaon
10 paent’s spectra excluded
2 very dilute
1 dominant hippurate peaks
4 dominant glucose peaks
1 ethanol peaks
2 unassigned resonances
Fig. 1 Flowchart of patient recruitement
Metab Brain Dis
Urinary metabolic profiling by 1H-NMR
spectroscopy
Samples were collected from random mid-stream urine between 10.00 and 16.00. The urine samples were centrifuged
at 2500 rpm for 20 min to remove precipitates and then stored
in siliconized microvials at -80 °C pending subsequent metabolic profiling using nuclear magnetic resonance (NMR)
spectroscopy techniques.
Samples were thawed and prepared as follows: 400 μL of
urine was mixed with 200 μL of buffer solution (0.2 M
Na 2 HPO 4 /0.2 M NaH 2 PO 4 , pH 7.4), and 60 μL of 3trimethylsilyl-(2,2,3,3-2H4)-1-propionate (TSP)/D2O solution
was added. The TSP served as an internal chemical shift reference (δ 0.00 ppm) and the D2O provided a field lock. The
buffered urine sample was left to stand and then centrifuged at
13,000 g for 10 min. A total of 550 μL of supernatant was
transferred into a 5 mm diameter glass NMR tube (Norell Inc.,
Landisville, NJ, USA).
1
H-NMR spectra were acquired at 25 °C using a pulsecollect sequence with water presaturation (JEOL 500 MHz
Eclipse + NMR spectrometer). Sixty-four data collects were
summated. A 90° pulse angle was used with an acquisition time
of 4.4 s and a total repetition time of 6.4 s. The 1H-NMR spectra
were pre-processed using the KnowItAll Informatics System
v7.8 (Bio-Rad, Philadelphia, PA). Free induction decays were
zero-filled by a factor of 2 and multiplied by an exponential
window function with a 0.3 Hz line-broadening factor prior to
Fourier transformation. All 1H-NMR spectra were phased and a
baseline correction applied manually. 1H-NMR spectral resonances were assigned on the basis of chemical shifts and coupling patterns and according to the literature (Bouatra et al.
2013;Holmes et al. 1997). 1H-NMR spectral analysis included
the range δ 0.20–10.00 ppm, excluding the region δ 4.50–
6.40 ppm, to remove the residual water and urea signal.
Data analysis
Principal Component Analysis (PCA) was performed to visualise any inherent clustering and identify outliers using the
Hotelling’s t ellipse for strong outliers at the 95 % confidence
interval. Orthogonal Projection to Latent Structure (OPLS)
analysis was performed to supervise class differences while
minimising variability unrelated to class. The R2 value was
calculated to give a measure of the goodness of fit or amount
of variability explained by the model. A cross-validated Q2
statistic (based on a 1:7 leave one out algorithm) was calculated as a quantitative measure of the predictability of the
model for the Y variable, where a positive Q2 indicated good
predictivity. In 2-class discriminant problems the Q2 value
may not be the best method for determining predictivity or
validity, so a number of other measures were also performed.
Permutation analysis allows assessment of whether overfitting is occurring (e.g. due to excess number of components
to generate high R2 and Q2). 999 random permutations were
calculated using partial least squares discriminant analysis
(PLS-DA) models using the same number of components as
orthogonal projection on latent structure-discriminant analysis
(OPLS-DA). The cross-validated-analysis-of-variance (CVANOVA) statistical assessment corresponds to a nullhypothesis of equal predictive residuals between the models
under investigation. A value <0.05 rejects the null hypothesis
and suggests the model fitted is superior to one chosen at
random. Sensitivity and specificity were calculated from the
Y predicted variable either back predicting via a leave-one-out
algorithm on the cross-validated data set (internal validation)
or by modelling on a random 50 % of the data set and
predicting the class of the remaining 50 % (external validation). The S-plot loadings (p v p(corr)) was investigated to
determine the metabolites contributing to class separation both
in terms of effect on the model (f(p)) or in terms of confidence
in predicting class differences (f(p(corr)). A leave one out
cross validation method was used for the sensitivity and specificity analysis.
The spectral regions corresponding to selected metabolite
peaks, as identified by the PCA and OPLS-DA loadings plots,
were normalised to the sum of the total spectral integral to
account for differences in concentration, and differences in
these relative metabolite signal levels were compared
between groups using the Student’s t test or one way
ANOVA (following logarithmic transformation if necessary) with correction for multiple comparisons by the
Tukey-Kramer method. In all cases a two-sided p-value
of <0.05 was considered significant.
Results
While 62 patients with cirrhosis and 20 controls were recruited to the study, the initial study cohort was revised to comprise
of 52 patients with cirrhosis and 17 controls, as there were
limitations in the urinary NMR data sets from 10 patients
[(very dilute samples (n = 2), dominant hippurate signals
(n = 1), dominant glucose or glucosamine peaks (n = 4), ethanol (n = 1) and additional unassigned resonances (n = 2)] and
three controls [very dilute sample (n = 1), baseline artefact
(n = 1), dominant hippurate (n = 1)].
Of these 52 patients, 40 were taking lactulose, 32
rifaximin and 14 branch-chain amino acids. The majority of patients (n = 35) were taking two or more agents.
Twelve patients were not on any anti-encephalopathy
medication.
Not all subjects completed all the clinical assessments and
therefore details from the final study cohort of 45 patients
(with complete clinical data and urinary NMR profiles) and
Metab Brain Dis
Table 1 Characteristics of
included patients for multivariate
analysis and with full clinical data
Variable
Patients with cirrhosis
OHE
cHE
No HE
N
Age median (range)
45
60 (18–90)
21
60(18–90)
7
67 (42–81)
17
43(58–74)
0.368*
Sex (M:F)
33:12
14:7
6:1
13:4
0.152^
MELD median (range)
Abnormal EEG (Y:N)
11 (7–27)
23:22
11(7–23)
17:4
10(7–12)
6:1
8(11–27)
0:17
0.176*
<0.002^
Abnormal PHES (Y:N)
16:29
14:7
1:6
0:17
<0.001^
BMI
25(18–37)
24(18–35)
32 (24–37)
25 (18–34)
0.101*
P-value
*Kruskall Wallis test, ^-χ2 test. OHE overt hepatic encephalopathy, cHE covert hepatic encephalopathy. MELD
model for end stage liver disease, BMI body mass index
17 controls are summarised in Table 1. Twenty-one of these
patients had OHE, 7 patients had cHE and 17 patients no HE.
Representative urinary 1H-NMR spectra from patients with
cirrhosis (with and without OHE) and healthy control are presented in Fig. 2A, B and C.
Unsupervised PCA was performed on the initial data set of
69 subjects (17 healthy controls and 52 patients with cirrhosis)
to help identify outliers. Clustering occurred for control subjects with significantly increased variation in the urinary NMR
spectral from patients with cirrhosis.
Fig. 2 Representative urinary NMR spectra from A, A' (hvb10) control;
B, B' (ptb14) EEG 0, PHES 0, overt 0; and C, C': (IP22) EEG, PHES 1,
overt 1. Peak assignments 1, added external reference standard (TSP); 2,
lactate; 3, alanine; 4, acetate; 5, pyruvate; 6, citrate; 7, dimethylamine; 8,
creatinine; 9, trimethylamine-N-oxide; 10, glycine; 11, hippurate; 12, formate; 13, histidine; 14, N-methyl nicotinic acid; 15, glutamate; 16,
acetylcarnitine; 17, phenylacetylglycine; 18, 1-methylnicotinamide
Metab Brain Dis
a
b
c
d
e
f
g
h
Metab Brain Dis
Multivariate analysis of urinary spectra comparing patients (PT)
with cirrhosis and healthy controls (HC) and between patients with OHE
and no HE. a Principal components analysis HC vs PT ((R2X = 0.674,
Q2 = 0.54). b Principal components analysis OHE v no HE
((R2X = 0.627, Q2 = 0.463). c Orthogonal projection to latent squares
discriminant analysis (OPLS-DA), HC v PT (R2X =0.66, R2Y = 0.479,
Q2 = 0.314, 84 % and specificity of 95 %). d OPLS-DA OHE v no HE,
(R2X =0.648, R2Y = 0.483, Q2 = 0.118, 100 % and specificity of 64 %).
e S-loadings plot for model C. f S-loadings plot for model D. g
Permutation analysis for PLSDA HC v PT. h Permutation analysis for
OHE v no HE
unsupervised modelling for patients with or without abnormal EEG.
Supervised PLS-DA and OPLS-DA models using patients
with normal or abnormal EEG did not discriminate between
these groups. For example, on 3-component PLS-DA,
R2X = 0.435, R2Y = 0.53, Q2 = 0.245 with a CV-ANOVA
of 1 indicating no validity or discriminatory ability. This did
not improve using alternative scaling methods.
A three-component PCA model adequately described the
variation in the urinary 1H-NMR data sets with acceptable
validity (R2X = 0.674, Q2 = 0.54). Principal component 2
separated healthy controls from patients with cirrhosis.
Three-component OPLS-DA analysis demonstrates excellent discriminant ability (R2X =0.66, R2Y = 0.47,
Q2 = 0.314). This gave a sensitivity of 84 % and specificity
of 95 %, CV-ANOVA p = 0.0002 with a validation permutation analysis suggesting a valid model (see Fig. 3) with y axis
crossing points of the permutations significantly different
from those of the constructed model. The cross validated
AUROC for the model for patients with cirrhosis versus
healthy controls was 0.92 (95 % CI 0.87–0.97).
Univariate analysis of selected metabolites is summarised in
Table 2 and confirm significant differences in alanine,
acetate, glycine, hippurate, N-methyl nicotinic acid,
phenylacetylglycine and 1-methonicotinamide between
patients with cirrhosis and controls.
Differences in the final study cohort of 45 patients with
cirrhosis were then considered with respect to more detailed
clinical categorisation:
OHE versus no OHE (either cHE or no HE)
Fig. 3
PLS-DA and OPLS-DA models using patients with OHE
(n = 21) versus patients with either cHE or no HE (n = 24)
did not discriminate between these groups. On PLS-DA, the
R2 was 0.446 and the Q2–0.21 for these models with a CV
ANOVA of 1 indicating no validity or discriminatory ability.
This did not improve on the removal of weak outliers.
OHE versus patients with cirrhosis and no HE
There was no clear clustering by PHES (n = 16 positive,
n = 29 negative) for the patients with cirrhosis using a threecomponent PCA model (R2X = 0.55, Q2 = 0.326). PLS-DA
and OPLS-DA models on patients with and without positive
PHES testing did not demonstrate any valid models (for example, three-component PLS-DA, R2X = 0.55, R2Y = 0.548,
Q2 = −0.383) with a CV-ANOVA of 1 indicating no validity
or discriminatory ability. Use of alternative scaling methods
such as mean centring alone, or Pareto-scaling (with or without logarithmic transformation) did not yield valid discriminatory models.
When comparing patients with OHE (n = 21) with patients
with no HE (n = 17), it was possible to generate valid models
to describe this difference in clinical phenotype. On PCA,
patients with HE tended to have less variance in urinary metabolic phenotype as shown in the clustering on the scores plot
for a three-component PCA (R2X = 0.719, Q2 = 0.103). A
three-component PLS-DA model could not robustly discriminate the urinary NMR spectra of patients with OHE from
patients with no HE with R2X = 0.648, R2Y = 0.483 and
Q2Y = 0.118 with a sensitivity of 100 % and specificity of
64 % (Fig. 3d). The relatively low Q2Y is suggestive of an
over-fitted model although the AUROC was high. The cross
validated AUROC for the model for patients with and without
OHE 0.86 (95%CI 0.72–0.95).
The primary metabolites responsible for these potential differences were increases in histidine and glutamate for patients
with OHE and increased N-methyl nicotinic acid (in patients
without HE). Taking N-methyl nicotinic acid levels alone,
these predicted the presence of OHE with 80 % sensitivity
and 65 % specificity, AUROC 0.722 (95 % CI 0.580–0.865,
p = 0.002). Univariate analysis of selected metabolite intensities is provided in Table 2. In the absence of a fully validated
model these markers should be tested in larger cohorts or
using other measurement modalities (such as LCMS).
EEG-positive versus EEG-negative patients
cHE versus patients with cirrhosis and no HE
PCA on the urinary 1H-NMR data for patients with EEG data
(n = 23 positive, n = 22 negative) similarly showed no clear
clustering. Further, no clear clustering was observed on
PLS-DA and OPLS-DA models using patients with cHE
(n = 7) versus patients with normal PHES and EEG (n = 17)
did not discriminate between these groups. On PLS-DA, the
PHES-positive versus PHES-negative patients
Table 2 The 1H-NMR spectral intensities of selected metabolites identified by urinary 1H-NMR spectroscopy for healthy controls, patients with cirrhosis and also patients with and without OHE. Data
are given as mean (SD), [arbitrary units, normalised to total spectral area, log transformed where necessary], (resonances used for univariate analysis are in bold)
Peak # from Fig. 2
Metabolite
Chemical shifts
ppm
Controls (n = 17)
1
2
3
4
TSP
Lactate
Alanine
Acetate
0
1.33 (d)
1.48 (d)
1.92 (s)
7.26(1.84)
7.50(2.09)
4.87(3.59)
5
6
7
Pyruvate
Citrate
Dimethylamine
2.37 (s)
2.57 (d), 2.70 (d)
2.72 (s)
8
9
10
Creatinine
Trimethylamine-N-oxide
Glycine
11
Hippurate
Cirrhosis
(n = 52)
P-value, controls
vs cirrhotics
No HE
(n = 21)
cHE
(n = 7)
OHE
(n = 17)
P-value,
No HE vs cHE
vs OHE
6.55(2.20)
6.19(2.30)
3.49(1.68)
0.235
0.041
0.031
N.A.
6.03(1.16)
5.42(2.04)
3.17(0.74)
6.02(1.60)
6.05(2.95)
3.11(1.51)
7.13(2.74)
6.70(2.29)
3.98(2.17)
0.222
0.232
0.231
1.98(0.27)
25.40(8.12)
6.01(0.15)
1.90(0.82)
26.80(15.5)
6.60(2.53)
0.694
0.718
0.373
2.01(1.23)
22.10(14.1)
7.23(1.99)
1.54(0.62)
29.00(12.3)
5.72(2.17)
1.97(0.51)
28.80(17.5)
6.19(1.85)
0.349
0.357
0.120
3.05 (s), 4.05 (s)
3.27 (s)
3.56 (s)
88.77(19.2)
22.00(8.44)
11.50(3.28)
75.2(28.2)
25.80(3.85)
8.64(3.58)
0.070
0.689
0.003
82.20(35.11)
19.80(27.7)
8.31(2.40)
78.6(30.8)
44.8(73.3)
8.05(4.84)
71.3(12.1)
25.3(29)
8.81(3.95)
0.459
0.306
0.838
20.70(13.1)
7.65(1.13)
<0.001
5.02(5.75)
13.3(7.67)
7.27(11.9)
0.232
12
Formate
3.98 (d), 7.56 (t),
7.65 (t), 7.84 (d)
8.46 (s)
0.83(0.62)
1.26(1.15)
0.149
1.36(1.01)
1.48(1.18)
1.17(1.32)
0.762
13
Histidine*
7.76 (s)
0.46(0.06)
0.47(0.03)
0.942
0.27(0.04)
0.21(0.07)
0.68(0.06)
0.052
14
15
16
17
N-methyl nicotinic acid
Glutamate
Acetylcarnitine*
Phenlyacetylglycine
18
1-methylnicotinamide
8.84 (m), 9.13 (s)
2.04 (m), 2.28 (m)
3.20 (s)
7.36 (m), 7.42 (m)
8.97 (m), 9.28 (s)
1.03(0.83))
7.23(0.82)
4.00(1.36)
12.23(4.68)
0.055(0.0066)
0.44(0.47)
7.47(1.75)
4.53(2.59)
9.01(5.66)
0.21(0.22)
<0.001
0.589
0.560
0.039
0.004
0.72(0.60)
8.01(2.14)
5.33(5.76)
6.92(4.03)
0.29(0.26)
0.39(0.26)
6.46(1.23)
3.73(1.35)
10.6(6.77)
0.10(0.21)
0.25(0.29)
7.48(1.50)
4.27(1.82)
9.87(5.98)
0.20(0.16)
0.004
0.098
0.499
0.164
0.103
*Provisional assignment
Metab Brain Dis
Metab Brain Dis
R2 was 0.383 and the Q2 was –0.33 for 2 component models
with a CV ANOVA of 1 indicating no validity or discriminatory ability. This did not improve on the removal of weak
outliers.
Nutritional status
Utilising a nutritional scoring system (0, 1, 2) for each patient
PLS-DA and OPLS-DA models using patients with any HE
(OHE or cHE) did not discriminate between these groups.
Only a subset of patients (n = 28) had a nutritional assessment
performed and these were split into two groups (low or intermediate (n = 9) versus high risk (n = 19) of malnutrition). On
PLS-DA the R2 was 0.378 and the Q2 was –0.15 for these
models with a CV ANOVA of 1, indicating no validity or
discriminatory ability.
Discussion
Consistent with previous studies, we illustrate that urinary 1HNMR metabolic profiling discriminates patients with cirrhosis
from healthy controls. In addition to a reduction in hippurate,
as previously shown in cirrhosis, compared to healthy controls
(Bajaj 2014;Shariff et al. 2011), in this cohort we have also
identified a reduction in alanine, acetate, glycine, N-methyl
nicotinic acid and phenylacetylglycine in cirrhosis patients
with a concomitant an increase in 1-metholnicotinamide. We
have further demonstrated that urinary 1H-NMR metabolic
profiling has the potential to detect a urinary metabolic phenotype associated with OHE (albeit with less validity), but the
technique has not been of value in characterising cHE.
However, in contrast to plasma NMR studies, urinary 1HNMR profiling did not correlate with severity of underlying
liver disease (expressed as MELD). When the differing clinical, neuropsychological or neurophysiological tests were included, this suggests that the urinary metabolic profile does
not discriminate between clinical grading, even with an improved sample size over other recent studies.
We did note a change in N-methyl nicotinic acid levels in
patients with cirrhosis and between those with or without
OHE. Urinary N-methyl nicotinic acid excretion was impaired
in patients with cirrhosis and further impaired in patents with
OHE. Whether nicotinamide metabolism is altered in cirrhosis
is a matter of debate (Pumpo et al. 2001) , although we have
not seen reports to date on changes in metabolism and urinary
levels in patients with HE. Our results favour the hypothesis of
reduction in methylation secondary to cirrhosis, leading to
reduced urinary N-methyl nicotinic acid secretion, in contrast
to the results from Pumpo et al. (Pumpo et al. 2001). Lower
urinary N-methyl nicotinic acid levels predicted the presence
of OHE and these nucleotide pathways should be further
evaluated. It may be that our cohort has a higher incidence
of HE than that of Pumpo et al. (Pumpo et al. 2001), given that
patients without HE had levels close to the normal range for
healthy controls.
Other investigators have focused on the effects of medication on the metabolome and microbiome. Rifaximin has been
shown to have profound effects on the serum metabolic profile
in patients with cHE (Bajaj et al. 2011). Particular changes
were seen in increases in both saturated and unsaturated fatty
acids after rifaximin administration. These were measured by
GCMS and are therefore not entirely comparable with our
results here. Of note, rifaximin itself did not modulate the
gut microbiome significantly, other than small changes in
two species (Eubacteriaceae and Veillonellaceae). This was
while highly significant changes in cognitive function were
observed. In patients undergoing planned withdrawal from
lactulose, cognition was linked to stool Prevotella spp. concentrations and to changes in choline metabolism, with
TMAO being the only urinary metabolite to be different in
those with HE recurrence (Bajaj et al. 2012).
The effect of gut microbial activity on cHE on the background of the changes already observed in patients with cirrhosis (Bajaj et al. 2012;Bajaj et al. 2013) may not be large,
which may partly explain why we did not observe differences
in the urinary metabolic profile in patients with cirrhosis with
or without cHE, but those with OHE demonstrated more
marked differences. Both of these previous studies had
high levels of patient characterisation (psychometric
evaluation, gut microbe pyrosequencing, urine and plasma
1
H-NMR spectroscopy and in vivo 1H cerebral magnetic resonance spectroscopy), but had small numbers of participants,
with only seven in the study of lactulose withdrawal (Bajaj
et al. 2012) and 20 in the study discussing rifaximin (Bajaj
et al. 2013). Our present study seems to point to cHE not being
responsible for a significant shift in the urinary metabolic profile, which is likely borne out by the small effect on the gut
microbiome, demonstrated in these previous studies.
In common with other top down spectroscopic techniques
urinary NMR gives an overview of several other reaction
pathways which may contribute to cognitive dysfunction in
these groups. Experimentally, induction of zinc deficiencyrelated encephalopathy is possible with oral ingestion
of histidine (increasing renal excretion of zinc). This may be
a cofactor in the encephalopathy we see in patients with HE. It
may be more likely that histidine is incorporated into
the brain in the context of hyperammonaemia causing
increased cerebral histamine levels (Fogel et al. 1990)
and subsequent neurotransmitter abnormalities (in particular increased GABAergic tone).
It is now understood that even modest increases in circulating ammonia occur in patients with OHE and increased levels
of glutamate secondary to this have been demonstrated by
NMR spectroscopy in plasma of patients with acute liver
Metab Brain Dis
failure. The kidney is a major ammonia detoxifying organ and
hence by-products of this reaction are likely to be seen in the
urine of patients with cirrhosis and HE (Shawcross et al. 2011).
It is unclear with methylation of nicotinic acid may be
singularly affected in HE. Our metabolite coverage does not
allow us to interrogate all other methylation reactions but cognitive dysfunction related to methylation has been described
classically in pellagra (Pitsavas et al. 2004) and in the
schizophrenias.
The patients in this cohort were predominantly ambulant
and without overt sepsis or other known precipitating causes.
As OHE in patients with cirrhosis is multifactorial in
aetiology, studies of the serum and urinary metabolic profile
of the specific effects of sepsis, gastrointestinal bleeding or
electrolyte disturbance would be of interest. Septic encephalopathy is substantially different from HE and it also occurs in
non-cirrhotic patients, so the mental state of cirrhosis patients
whose clinical condition is complicated by sepsis is difficult to
examine clearly. These patients were therefore excluded from
this study. Similarly, patients with major electrolyte imbalance, with the exception of hyponatremia, which is so closely
associated with encephalopathy, were excluded.
The main weakness of the study are the relatively small
sample size and single modality of metabonomics platform.
The validity statistics for the model of patients with OHE
versus no HE suggest that further characterisation of
larger validation cohorts would be needed to confirm
these finding and generate valid multivariate models with
more confidence in the discriminatory ability of these
markers. Tandem use of mass spectrometry may be useful to
better resolve the intensities of glutamine and glutamate and
give wider metabolite coverage.
We did explore whether nutritional state could confound
the urinary metabolic profile, but our findings demonstrated
no significant change. The significant changes between
healthy controls and patients with cirrhosis in terms of creatinine, creatine and glutamate suggest that changes in muscle
metabolism are being reflected in the urinary metabolic profile. However, within the cohort of patients with cirrhosis,
nutritional phenotype differences were not reflected in the
urinary NMR profile. Further direct measurements of changes
in muscle, serum and urine in the same patient cohort may be
of more use to make further conclusions.
In conclusion, the present data show that metabolic profiling of urine by NMR spectroscopy is not currently of value in
supporting a clearer diagnosis of covert HE in patients with
cirrhosis. Whether some of the potential biomarkers of nucleotide metabolism identified in the overt HE situation could be
quantified by other methods, such as mass spectrometry, requires further consideration. Examination of other matrices,
such as blood plasma, saliva or stool may give further profiling information for use in the diagnosis of HE and in the
understanding of underlying pathogenic mechanisms.
Acknowledgments We are grateful to the NIHR Biomedical Research
Unit at Imperial College London for infrastructure support. MJWM was
supported by the Wellcome Trust, UK. MMEC was supported by a
Fellowship from the Sir Halley Stewart Trust. We thank the Clinical
Pathology Laboratory, MRC Harwell for use of the NMR facility. The study
was part-funded by University of Padova grants to PA and SM.
Open Access This article is distributed under the terms of the Creative
Commons Attribution 4.0 International License (http://
creativecommons.org/licenses/by/4.0/), which permits unrestricted use,
distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the
Creative Commons license, and indicate if changes were made.
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