Perturbation of the Glutamate–Glutamine System in Alcohol
Dependence and Remission
Robert Thoma*,1,2, Paul Mullins1,3, David Ruhl1, Mollie Monnig1,4, Ronald A Yeo4, Arvind Caprihan1,
Michael Bogenschutz2, Per Lysne1,4, Scott Tonigan5, Ravi Kalyanam1and Charles Gasparovic1,4
1Mind Research Network, Albuquerque, NM, USA;2Department of Psychiatry, Center for Neuropsychological Services, University of New Mexico,
Albuquerque, NM, USA;3Bangor Imaging Center, School of Psychology, Bangor University, Gwynedd, UK;4Department of Psychology, University of
New Mexico, Albuquerque, NM, USA;5Center on Alcoholism, Substance Abuse, and Addictions (CASAA), Albuquerque, NM, USA
As acute ethanol exposure inhibits N-methyl-D-aspartate glutamate (Glu) receptors, sudden withdrawal from chronic alcohol use may
lead to an increased activation of these receptors with excitotoxic effects. In the longer term, brain levels of Glu and its metabolites, such
as glutamine (Gln), are likely to be chronically altered by alcohol, possibly providing a measure of overall abnormal Glu–Gln cycling.
However, few studies have assessed concentrations of these metabolites in clinical populations of individuals with alcohol use disorders.
Glu and Gln levels were compared in groups of 17 healthy controls and in 13 participants with alcohol dependence. Within the alcohol-
dependent group, seven participants had current alcohol use disorder (AUD), and six had AUD in remission for at least 1 year (AUD-R).
Neurometabolite concentrations were measured with proton magnetic resonance spectroscopy (1H-MRS) in a predominantly gray
matter voxel that included the bilateral anterior cingulate gyri. Tissue segmentation provided an assessment of the proportion of gray
matter in the1H-MRS voxel. The Drinker Inventory of Consequences (DrInC) and Form-90 were administered to all participants to
quantify alcohol consequences and use. Glu level was lower and Gln level was higher in the AUD and AUD-R groups relative to the
control group; creatine, choline, myo-inositol, and total N-acetyl groups, primarily N-acetylaspartate did not differ across groups. These
results were not confounded by age, sex, or proportion of gray matter in the1H-MRS voxel. Neurometabolite concentrations did not
differ between AUD and AUD-R groups. Subsequent regressions in the combined clinical group, treating voxel gray matter proportion as
a covariate, revealed that total score on the DrInC was positively correlated with Gln but negatively correlated with both Glu and gray
matter proportion. Regression analyses, including DrInC scores and smoking variables, identified a marginal independent effect of smoking
on Gln. The current findings of higher Gln and lower Glu in the combined AUD and AUD-R groups might indicate a perturbation of the
Glu–Gln cycle in alcohol use disorders. The absence of differences in mean Glu and Gln between the AUD and AUD-R groups suggests
that altered Glu–Gln metabolism may either predate the onset of abuse or persist during prolonged abstinence.
Neuropsychopharmacology (2011) 36, 1359–1365; doi:10.1038/npp.2011.20; published online 9 March 2011
Keywords: magnetic resonance spectroscopy; alcohol use disorders; alcoholism; glutamate; glutamine
Alcohol is one of the most commonly used recreational
drugs in society today, as almost half of Americans over the
age of 12 years (48.3%) report being current drinkers (US
Department of Health and Human Services, Substance
Abuse and Mental Health Services Administration, 2002).
Results from the 2001 National Household Survey on Drug
Abuse showed that approximately one-fifth of Americans
over the age of 12 years reported binge drinking at least
once in the last 30 days (ibid). In addition, roughly 14% of
the US population has met criteria for alcohol dependence
at some point, with 7% meeting criteria in the previous year
(APA, 1994). It has been estimated that approximately 14%
of those with alcohol use disorders have persistent cognitive
impairment (Rourke and Loberg, 1996).
Glutamate (Glu) metabolism abnormality is characteristic
of psychopathological disorder (Bustillo et al, 2011) and
traumatic brain injury (Yeo et al, 2011), and glutamatergic
neurotoxicity has been implicated as a major compo-
nent of the neurodegenerative effects of alcohol use
disorders. Acute exposure to alcohol inhibits ionotropic
Glu receptors, reducing the overall excitatory synaptic
transmission (Lovinger, 1993). This effect is strongest at the
N-methyl-D-aspartate receptor (NMDAR), though there is
Received 23 September 2010; revised 19 January 2011; accepted 20
*Correspondence: Dr RJ Thoma, Department of Psychiatry, Center
for Neuropsychological Services, 2400 Tucker NE, MSC 09 5030,
1 University of New Mexico, Albuquerque, NM 87131-0001, USA,
Tel: +1 505 272 8833, Fax: +1 505 272 8316,
Neuropsychopharmacology (2011) 36, 1359–1365
& 2011 American College of Neuropsychopharmacology.All rights reserved 0893-133X/11 $32.00
also evidence for involvement of a-amino-3-hydroxyl-5-
methyl-4-isoxazole-propionate- and kainate-type Glu re-
ceptors in certain brain regions (Lovinger, 1993; Frye and
Fincher, 2000). Chronic ethanol-induced inhibition leads to
a compensatory upregulation of NMDAR in cortical
neurons (Hoffman, 1995). This upregulation is thought to
result in hyperexcitability during alcohol withdrawal and,
hence, an increase in synaptic Glu concentrations in cortical
regions (Rossetti and Carboni, 1995; Rossetti et al, 1999). In
animal models, it has been shown that acute overstimula-
tion of post-synaptic ionotropic Glu receptors results in
excitotoxic neuronal death by disruption of cation and
water homeostasis, with chronic overstimulation leading to
apoptosis and increased oxidative stress (Coyle and
Puttfarcken, 1993). Thus, it has often been suggested (eg,
Tsai and Coyle, 1998) that NMDAR-based excitotoxicity
underlies the neuronal death and large-scale cortical volume
deficits observed in chronic alcoholics (Fein et al, 2002).
In addition to its role in ethanol toxicity, glutamatergic
receptor alteration, secondary to ethanol exposure, may
have a causal role in the development of alcohol addiction
(De Witte, 2004; Szumlinski et al, 2007; Zhu et al, 2007).
Ongoing research on small animal and tissue models of
alcohol use disorder (AUD) provides ample evidence for the
involvement of glutamatergic mechanisms in the develop-
ment of both deficits and addiction. Until recently, the role
of Glu in human AUD has most often been assessed
indirectly through analysis of the effects of glutamatergic
antagonists on symptoms of withdrawal and intoxication
(eg, Bisaga and Popik, 2000).
Proton magnetic resonance spectroscopy (1H-MRS), a
technique capable of measuring neurometabolite concen-
trations non-invasively, provides an ideal tool to directly
investigate Glu concentrations in AUD. Numerous studies
have used1H-MRS in human alcohol research (Meyerhoff
and Durazzo, 2008). To date, most
focused on the detection of chemical markers of neuronal
degradation (Jagannathan et al, 1996; Schweinsburg et al,
2001; Durazzo et al, 2004) and detection of ethanol itself
(Mendelson et al, 1990; Meyerhoff et al, 1996; Fein and
Meyerhoff, 2000). To our knowledge, only two studies have
reported1H-MRS data on Glu in a chronic AUD population.
One study reported no difference in Glx (a combined
measure of Glu and glutamine (Gln)) between 12 recently
abstinent (1 month) AUD individuals and eight control
subjects (Mason et al, 2006). Another study reported no
differences between 13 recently abstinent (2 weeks) AUD
men and 18 healthy controls in Glu concentrations in the
anterior cingulate gyrus or in the insula (Lee et al, 2007).
However, in the latter study, a higher ratio of Glu to total
creatine was found in the abstinent AUD group and this
ratio, along with Glu, was correlated with memory function.
None of the study assessed Gln concentrations.
In the present study, we sought to compare metabolic
alterations in both Glu and Gln in actively drinking AUD
participants, abstinent AUD participants, and healthy
controls using single-voxel1H-MRS. We also investigated
relationships of Glu and Gln with scores on the Drinker
Inventory of Consequences (DrInC; Miller et al, 1995), a
scale reflecting lifetime consequences of alcohol abuse, and
on the Form-90, a scale quantifying the frequency and
extent of the last 90 days of abuse.
1H-MRS studies have
SUBJECTS AND METHODS
Among the 17 alcohol-dependent participants recruited, 10
met criteria for active AUDs, and seven were in full
remission from alcohol use disorders (AUD-R) for at least
1 year. These participants were compared with a sample of
23 healthy controls recruited from the community. All
procedures were conducted with the approval of the Human
Research and Review Committee at the University Of New
Mexico School Of Medicine. All participants provided
informed consent. Basic demographic information on each
group is provided in Table 1. General inclusion criteria for
the study were as follows: (1) willingness to participate in all
study components; (2) ability to provide informed consent;
(3) ability to read, speak, and understand English at the
sixth grade level; (4) ability to provide at least one contact
person to assist with collateral interviews; (5) age between
21 and 45 years; (6) at least 48h after last drink; and (7) a
urine sample, free of the presence of cocaine, hallucinogens,
barbiturates, benzodiazepines, and opiates.
General exclusion criteria for the study were as follows:
(1) history of neurological disorder or disease; (2) history of
traumatic brain injury with loss of consciousness for more
than 5min; (3) mental retardation, dementia, or other
cognitive impairment of sufficient severity to render the
individual incapable of providing informed consent; or (4)
suicide attempt in the previous 6 months or current suicidal
ideation. As part of a brief health screening interview, self-
report of abnormal liver enzyme level or diagnosis of
hepatic disease was noted. One participant with a reported
history of abnormal liver enzymes was included in the
The AUD and AUD-R groups were recruited from the
University of New Mexico’s Alcohol and Substance Abuse
Program and Center for Alcoholism, Substance Abuse and
Addictions (CASAA). Diagnoses were established using the
Structured Clinical Interview for DSM Disorders (SCID),
AUD inclusion criteria were: (1) diagnosis of alcohol abuse
or dependence active within the past 1 month (ie, not in
early or sustained full remission) and (2) two or more days
of heavy drinking (five or more drinks for per occasion for a
man, four or more drinks per occasion for a woman) in the
last 30 days before screening. The AUD-R group met
diagnostic criteria for lifetime alcohol dependence in
sustained full remission, ie, not active in the previous year.
Specific AUD and AUD-R exclusion criteria were: (1)
presence of Axis-I schizophrenia spectrum disorders or
(2) a first-degree relative with schizophrenia or other
Table 1 Descriptive Demographic Statistics by Group for
Participants Included in Analysis of Glu and Gln Levels
MFMeanSDMeanSDCurrent NMean SD
AUD 5235.50 8.1612.292.49530.09.9
Perturbation of the Glu–Gln system
R Thoma et al
psychotic disorder. Healthy control participants were
recruited through advertisements in local newspapers or
from postings around the Albuquerque area. Control group
members reported no history of substance abuse or
dependence, with the exception of nicotine, or other
Axis-I psychopathology upon interview.
Study procedures were conducted at the CASAA and the
Mind Research Network in Albuquerque, New Mexico.
Clinical interviews typically took place 0–5 days before
imaging. AUD and AUD-R participants were administered
the DrInC (Miller et al, 1995) as a measure of negative
consequences from drinking. DrInC total score was
considered here as an estimate of long-term severity of
alcohol abuse. Recent consumption data for alcohol and
other substances were collected using the Form-90 (Miller
and Del Boca, 1994), a timeline follow-back interview in
which the participant reported his or her use of alcohol and
other substances starting at 90 days preceding the most
recent drink to the present. Drinks per drinking day
(DPDD) and percentage days drinking (PDD) were chosen
as the alcohol variables of interest in order to capture both
the intensity and frequency of recent drinking.
Magnetic resonance imaging and MRS data acquisition.
Magnetic resonance imaging and1H-MRS were performed
on a Siemens 3-Tesla TrioTIM scanner using the 12-channel
radiofrequency head coil. T1-weighted images were col-
lected in the sagittal plane using a five-echo 3-D MPRAGE
sequence (TR/echo time (TE)/TI¼2530/1.64, 3.5, 5.36, 7.22,
9.08/1200ms, flip angle¼71, field of view¼256?256mm,
matrix¼256?256, 1mm thick slice, 192 slices, GRAPPA
acceleration factor¼2). Using these images, a single
1H-MRS voxel was positioned in the bilateral medial frontal
cortex directly superior to the corpus callosum, containing
anterior cingulate, middle frontal, and superior frontal gyri
(Figure 1). A point-resolved spectroscopy sequence (TR/
TE¼1.5s/40ms, voxel size¼20?30?20mm, averages¼
192) was collected, using an TE of 40ms for improved
detection of Glu (Mullins et al, 2008). An unsuppressed
water sequence for use as a concentration reference and
eddy current correction in post processing was collected
with 16 averages and otherwise identical parameters for
each single-voxel spectrum.
MRS data analysis. Raw time-domain1H-MRS data from
4.0 to 1.0p.p.m. in the spectral dimension were analyzed
using LCModel (Provencher, 2001) with the unsuppressed
waterscan as a concentration reference. Parameterized
macromolecule intensities were included over the fitted
spectral region (the LCModel macromolecule intensity set
MM20). As a quality assurance measure, LCModel produces
a Cramer-Rao lower bound (CRLB) of the fit to the peak of
interest. If this value was greater than 20%, the fit was
deemed unreliable and excluded from analysis. Metabolite
concentrations in molality units of mmol/kg of tissue water
were computed for total creatine plus phosphocreatine
(Cre), total choline-containing compounds (Cho), myo-
inositol (Ins), total N-acetylaspartate plus N-acetyl-aspar-
tylglutamate (NAA), Glu, and Gln. T1-weighted images were
segmented into gray matter, white matter, and cerebrosp-
inal fluid (CSF) using SPM5. To calculate tissue and CSF
fraction within the spectroscopic voxel, the spatial coordi-
nates of the voxel and T1-weighted image were used to
register the voxel volume to the segmentation maps
generated from the T1-weighted image. Once this was
performed, the gray matter, white matter, and CSF pixels
from the segmentation maps that were included in the voxel
volume were summed and normalized by the total number
of pixels in the volume to arrive at the gray matter, white
matter, and CSF fractions in the voxel. Metabolite
concentrations were then computed, correcting for partial
volume and T1 and T2relaxation effects using methods
described previously (Gasparovic et al, 2006). Figure 2
shows a representative spectrum from a control participant.
Statistical analyses. All statistical analyses were carried out
using IBM SPSS, Version 16.0.1. w2-tests were used to
investigate the possible group differences on demographic
variables. An omnibus ANOVA was used to test the group
effect, with neurometabolites entered as dependent vari-
ables. Post-hoc t-tests using a Bonferroni adjustment were
then applied to identify which group differences accounted
for identified omnibus effects. Relationships between
substance use and metabolite levels were explored using
bivariate correlation and linear regression.
Figure 1Placement of 1H-MRS voxel in medial frontal/cingulate cortex.
Perturbation of the Glu–Gln system
R Thoma et al
Demographic information for participants whose data were
retained for Glu and Gln analyses (see below) is presented
in Table 1. Controls were somewhat younger than the AUD
and AUD-R groups, though this difference did not reach the
level of statistical significance in the current sample
(p¼0.31). A greater proportion of control participants
were female, but a w2-analysis showed this difference to be
nonsignificant (p¼0.36). The control group had signifi-
cantly more education than AUD and AUD-R groups
(po0.001), who did not differ from each other (p¼0.85).
The SCID gave evidence of extensive lifetime comorbid
psychopathology in AUD and AUD-R groups. In the healthy
control group, two participants met criteria for lifetime
nicotine dependence. In the alcohol use groups, criteria
were met for lifetime diagnosis of bipolar disorder (n¼1),
major depression (n¼7), dysthymia (n¼3), brief psychotic
disorder (n¼1), sedative abuse/dependence (n¼1), canna-
bis abuse/dependence (n¼7), stimulant abuse/dependence
(n¼5), opioid abuse/dependence (n¼2), cocaine abuse/
(n¼3), panic disorder (n¼3), agoraphobia (n¼1), social
phobia (n¼3), specific phobia (n¼1), obsessive compul-
sive disorder (n¼1), post-traumatic stress disorder (n¼3),
generalized anxiety disorder (n¼7), and nicotine abuse/
dependence (n¼12). One AUD-R participant reported a
history of abnormal liver enzymes and had the lowest Glu
level in the sample, but it was within two SDs of the mean.
MRS measures for each group are provided in Table 2.
For all neurometabolites except Gln, good quality spectra
were obtained for every participant. Gln values meeting the
CRLB threshold of 20% were obtained for 17 of 23 (74%)
controls and 13 of 17 (76%) of the AUD and AUD-R
participants, leaving 30 subjects for the analyses. A one-way
ANOVA, with Group as the independent variable and Gln
and Glu as dependent variables, revealed significant group
differences in both Gln (F(92, 30)¼5.052, p¼0.01), and Glu
(F(92, 30)¼3.48, p¼0.05). For Gln, post-hoc analysis using
Bonferroni correction for all possible comparisons revealed
significantly lower Gln levels in Controls than in AUD
(p¼0.03) and marginally lower Gln in Controls than in
AUD-R (p¼0.08). Gln concentration did not differ between
AUD and AUD-R (p¼0.99). Using a standard power
calculator (Lenth, 2006–2009), it was determined that a
mean difference of 1.588 in Gln level between AUD and
AUD-R would be necessary to detect a significant difference
with the current group sizes, a power of 0.6, and an alpha
level set at 0.05 in a two-tailed comparison. For Glu, post-
hoc analysis using Bonferroni correction for all possible
comparisons revealed higher Glu levels in Controls than in
AUD-R (p¼0.04), but no significant difference between
Controls and AUD (p¼0.54). AUD and AUD-R groups
showed no difference on Glu concentration (p¼0.77).
Using a standard power calculator (Lenth, 2006–2009), it
was determined that a mean difference of 1.301 in Glu level
between AUD and AUD-R would be necessary to detect a
significant difference with current group sizes, a power of
0.6, and an alpha level set at 0.05 in a two-tailed
Effects for sex, age, education, and voxel gray matter
concentration were explored by entering each separately
into the ANOVA as covariates; none approached the level of
significance. Although no group differences were hypothe-
sized, NAA (p¼0.29), Cho (p¼0.19), Cre (p¼0.69), and
Ins (p¼0.31) metabolite levels were tested using a post-hoc
Bonferroni correction in a one-way ANOVA. Again, none
approached the level of significance.
To test the extent to which AUD severity was related to
Glu–Gln perturbation, lifetime DrInC score was regressed
on those metabolite values. The overall regression was
significant (R-squared¼0.42, p¼0.02), with greater sever-
ity (higher DrInC score), predicting lower Glu level
(b¼?0.44, p¼0.05) and higher Gln level (b¼0.47,
p¼0.04). Although relationships were not predicted for
recent alcohol use, Form-90 DPDD and PDD measures were
tested in a similar model, and no values approached the
level of significance.
In a number of recent studies, cigarette smoking has been
shown to have an independent effect upon neurometabolite
levels (Licata and Renshaw, 2010; Mason et al, 2006;
Durazzo et al, 2004). To test whether smoking had an
Table 2 Neurometabolite Concentrations (mmol/kg Tissue
Water) as a Function of Group Membership
AUD (n¼7)AUD-R (n¼6)Control (n¼17)
Gln 5.372.695.043.08 3.090.85
Ins13.031.7513.470.63 12.46 1.67
Abbreviations: AUD, alcohol use disorder; AUD-R, alcohol use disorder in
remission; Cho, choline; Cre, creatine; Gln, glutamine; Glu, glutamate; Ins,
inositol; NAA, N-acetyl aspartate plus N-acetyl-aspartylglutamate.
An example of a single control subject’s spectra, with major
Perturbation of the Glu–Gln system
R Thoma et al
effect independent of alcohol use in the present data, Glu
and Gln were considered as dependent variables in separate
linear regressions. Form-90 estimates of cigarettes smoked
per day, number of days smoking, and lifetime weeks of
smoking were considered (along with DrInC score) as
independent predictors in separate regressions. None of the
Glu regressions approached the level of statistical signifi-
cance. Within the set of regressions for Gln, one approached
the level of overall significance. When Gln was regressed on
DrInC score and days of cigarette smoking, the regression
was marginally significant
F¼0.132, p¼0.084), with an independent effect for DrInC
score (b¼0.707, p¼0.033) and an effect approaching
significance at the 0.05 level for days of cigarette smoking
(b¼?0.523, p¼0.098). Hence, there is weak evidence for
an independent effect of frequency of cigarette smoking; the
effect is opposite to that of alcohol, with more frequent
smokers having lower Gln levels.
Post-hoc bivariate correlational analyses adjusted for
familywise error were used to investigate relationships
between DrInC score and other neurometabolite values
(NAA, Ins, Cre), with none approaching the level of
significance. When DrInC score was considered, with
respect to gray and white matter volumes corrected for
familywise error, higher DrInC score predicted reduced
voxel gray matter volume (r(17)¼?0.61, p¼0.04).
Lower Glu and higher Gln levels were identified in the
anterior cingulate gyrus in young adults with active AUD
and with AUD in sustained full remission of at least 1 year,
relative to a healthy control group. Hence, lower Glu levels
were associated with higher levels of Gln. The extent of
abnormality in these metabolites was negatively correlated
with the degree of reported lifetime consequences of
drinking, interpreted here as the severity of lifetime alcohol
use disorder for each subject.
Although glutamatergic hyperexcitability is a cause of
withdrawal symptoms and neural death associated with
abrupt withdrawal from chronic alcohol exposure (Tsai and
Coyle, 1998), abnormal extracellular levels of Glu have been
measured in animals long after the acute stage of with-
drawal (Rossetti and Carboni, 1995; Dahchour and De
Witte, 2003). Glu released from synapses is either tran-
siently bound to NMDA or other receptor sites or is rapidly
taken up by excitatory amino acid transporters, located pre-
and post synaptically on neurons or astrocytes (reviewed in
Gass and Olive, 2008). One of the primary fates of Glu taken
up by astrocytes is to be converted to Gln by Gln synthetase
before returning to the presynaptic neuron for conversion
back into Glu (Magistretti and Pellerin 1999). However, this
cycle is not strictly stoichiometric, and there are several
other pathways open to astrocytic or neuronal Glu not
directly involved in neurotransmission, including conver-
sion into glutathione and entrance into the energy cycle
(reviewed by McKenna, 2007).
Upregulation of excitatory transmission has received
much attention in the context of alcohol research (eg, Tsai
and Coyle, 1998), and a recent study has shown that
treatment with acamprosate, a possible NMDR modulator,
decreases the Glu/Cre ratio in the anterior cingulate gyrus of
AUD patients in early remission (Umhau et al, 2010). Our
results, however indicate lower Glu concentrations in AUD
subjects without treatment relative to control subjects. It is
not clear why a chronic upregulation of excitatory
neurotransmission should be accompanied by a decrease
in observed Glu concentrations. An alternative possibility is
that, given the higher extracellular levels of Glu during
withdrawal shown in animal models, there might be a
benefit of converting less Gln back into Glu, thereby,
diverting a potentially excitotoxic agent into a non-
excitotoxic metabolite. The observed shift toward main-
taining a larger pool of Gln and smaller pool of Glu may,
thus, represent a neuroprotective adaptation. However, this
conjecture is entirely speculative, and studies on animal
models of AUD, such as experiments measuring altered
activities of enzymes in the Gln–Glu cycle, would be
necessary to support it. It also is important to bear in mind
(cytosolic vs vesicular or neuronal vs glial) and extracellular
pools of Glu. Interestingly, the largest effects in the current
study were for Gln concentrations, which may be a more
accurate index of overall glutamatergic neurotransmission
than Glu (The ´berge et al, 2002; Rowland et al, 2005; Ongur
et al, 2008). Elevated Gln/Glu ratios have been observed in
first-episode schizophrenia (Bustillo et al, 2011), and
NMDAR abnormalities (hypofunction) are believed to be
important in the pathophysiology of schizophrenia.
The results of the present study suggest that the Glu–Gln
cycle remains abnormally long after alcohol cessation, and
thus may represent either a chronic effect of alcohol
dependence or a predisposing factor for susceptibility to
alcohol dependence. In this data set, Gln and Glu levels, as
well as voxel gray matter volume were significantly
correlated with the accumulated lifetime consequences of
drinking. This finding does not establish a causal role for
severity of alcohol use in brain abnormality, but future
research may pinpoint these measures as direct reflections
of chronic exposure to neurotoxic levels of alcohol.
Weaknesses of this study include a small sample size,
which particularly limits any conclusions regarding the
effects of cigarette smoking or other comorbid diagnoses on
neurometabolite levels. Cigarette smoking was confounded
with AUD status in this sample, making it impossible to
conclusively verify the source of abnormality. Smoking is
known to exert neurotoxic effects, and the present analyses
indicated a marginally significant, independent effect of
smoking beyond that of alcohol abuse. In addition, relative
inequalities in gender and age distribution across groups
limited our ability to test the effects (or lack thereof) of
those variables conclusively. Regarding health status, it is
possible to have elevated levels of ammonia or other
manifestations of liver damage without the patient’s
awareness of the condition. As the determination of liver
abnormality was based only on the self-report of the
subjects, some uncertainty remains regarding the extent to
which effects noted herein may be secondary to early stages
of liver disease. Finally, we note that the intensities of both
Glu and Gln fall in crowded regions of the
spectrum, in which they overlap not only with each other
but also with other metabolite and macromolecule inten-
sities. This overlap makes resolving Glu and Gln intensities
1H-MRS does not distinguish between intracellular
Perturbation of the Glu–Gln system
R Thoma et al
challenging and dependent on experimental factors, such as
spectral line width and the signal-to-noise ratio, as well as
several data processing factors, such as adequate modeling
of the expected spectral contributions, including macro-
molecules, and baseline artifacts.
In summary, the results of this study suggest a perturba-
tion of the Gln–Glu cycle in chronic alcoholism, the degree
of which correlates with the negative consequences of
alcohol abuse. Further studies, particularly human long-
itudinal studies and animal models, are recommended to
elucidate the precise nature and mechanisms of this
This paper was supported by grants to Dr Thoma (PI:
K23AA016544 & R21AA0173134) from the National In-
stitute on Alcohol Abuse and Alcoholism and by funding
from the Mind Research Network (DE-FG02-99ER62764).
The authors declare no conflict of interest.
American Psychiatric Association (1994). Diagnostic and Statis-
tical Manual of Mental Disorders: 4th edn. American Psychiatric
Association: Washington, DC.
Bisaga A, Popik P (2000). In search of a new pharmacological
treatment for drug and alcohol addiction: N-methyl-D-aspartate
(NMDA) antagonists. Drug and Alcohol Depend. 59: 1–15.
Bustillo JR, Chen H, Gasparovic C, Mullins P, Caprihan A, Qualls C
et al (2011). Glutamate as a marker of cognitive function in
schizophrenia: a proton spectroscopic imaging study at 4 Tesla.
Biol Psychiatry 69: 19–27.
Coyle JT, Puttfarcken P (1993). Oxidative stress, glutamate, and
neurodegenerative disorders. Science 262: 689–695.
Dahchour A, De Witte P (2003). Effects of acamprosate on
excitatory amino acids during multiple ethanol withdrawal
periods. Alcohol Clin Exp Res 27: 465–470.
De Witte P (2004). Imbalance between neuroexcitatory and
neuroinhibitory amino acids causes craving for ethanol. Addict
Behav 29: 1325–1339.
Durazzo TC, Gazdzinski S, Bayns P, Meyerhoff DJ (2004). Cigarette
smoking exacerbates chronic alcohol-induced brain damage: a
preliminary metabolite imaging study. Alcohol Clin Exp Res 28:
Frye G, Fincher A (2000). Sustained ethanol inhibition of native
AMPA receptors on medial septum/diagonal band (MS/DB)
neurons. Br J Pharmacol 129: 87–94.
Fein G, Meyerhoff DJ (2000). Ethanol in human brain by magnetic
resonance spectroscopy: correlation with blood and breath
levels, relaxation, and magnetization transfer. Alcohol Clin Exp
Res 24: 1227–1235.
Fein G, Sclafani V, Cardenas VA, Goldman H, Tolou-Shams M,
Meyerhoff DJ (2002). Cortical gray matter loss in treatment-
naive alcohol dependent individuals. Alcohol Clin Exp Res 26:
Gasparovic C, Song T, Devier D, Bockholt HJ, Caprihan A,
Mullins P et al (2006). Use of tissue water as a concentration
reference for proton spectroscopic imaging. Magn Reson Med
Gass J, Olive M (2008). Glutamatergic substrates of drug addiction
and alcoholism. Biochem Pharmacol 75: 218–265.
Hoffman PL (1995). Glutamate receptors in alcohol withdrawal-
induced neurotoxicity. Metab Brain Dis 10: 73–79.
Jagannathan NR, Desai NG, Raghunathan P (1996). Brain
metabolite changes in alcoholism: an in vivo proton magnetic
resonance spectroscopy (MRS) study. Magn Reson Imaging 14:
Lee E, Jang DP, Kim JJ, Kyoon AS, Sangjin P, In-Young K et al
(2007). Alteration of brain metabolites in young alcoholics
without structural changes. Neuroreport 18: 1511–1514.
Lenth RV (2006–2009). Java applets for power and sample size
[computer software]. Retrieved 16 December 2010 from http://
Licata SC, Renshaw PF (2010). Neurochemistry of drug action:
insights from proton magnetic resonance spectroscopic imaging
and their relevance to addiction. Ann N Y Acad Sci 1187: 148–171.
Lovinger DM (1993). High ethanol sensitivity of recombinant
AMPA-type glutamate receptors expressed in mammalian cells.
Neurosci Lett 159: 83–87.
Magistretti PJ, Pellerin L (1999). Astrocytes couple synaptic
activity to glucose utilization in the brain. News Physiol Sci. 14:
Mason GF, Petrakis IL, de Graaf RA, Gueorguieva E, Coric V,
Epperson CN et al (2006). Cortical gamma-amino butyric acid
levels and the recovery from ethanol dependence: preliminary
evidence of modification by cigarette smoking. Biol Psychiatry
McKenna M (2007). The glutamate-glutamine cycle is not
stoichiometric: fates of glutamate in brain. J Neurosci Res 58:
Mendelson JH, Woods BT, Chiu TM, Mello NK, Lukas SE, Teoh SK
et al (1990). In vivo proton magnetic resonance spectroscopy of
alcohol in human brain. Alcohol 7: 443–447.
Meyerhoff DJ, Durazzo TC (2008). Proton magnetic resonance
spectroscopy in alcohol use disorders: a potential new endo-
phenotype? Alcohol Clin Exp Res 32: 1146–1158.
Meyerhoff DJ, Rooney WD, Tokumitsu T, Weiner MW (1996).
Evidence of multiple ethanol pools in the brain: an in vivo
proton magnetization transfer study. Alcohol Clin Exp Res 20:
Miller WR, Del Boca FC (1994). Measurement of drinking behavior
using the Form 90 family of instruments. J Stud Alcohol Suppl 12:
Miller WR, Tonigan JS, Longabaugh R (1995). The Drinker
Inventory of Consequences (DrInC): An Instrument for Assessing
Monograph Series: Rockville, MD.
Mullins PG, Chen H, Xu J, Caprihan A, Gasparovic C (2008).
Comparative reliability of proton spectroscopy techniques
designed to improve detection of J-coupled metabolites. Magn
Reson Med 60: 964–969.
Ongur D, Jensen J, Prescot A, Stork C, Lundy M, Cohen B
et al (2008). Abnormal glutamatergic neurotransmission and
neuronal-glial interactions in acute mania. Biol Psychiatry
Provencher SW (2001). Automatic quantitation of localized in vivo
1H spectra with LCModel NMR in Biomedicine, Special Issue:
NMR Spectroscopy Quantitation 14: 260–264.
Rossetti Z, Carboni S, Fadda F (1999). Glutamate-induced increase
of extracellular glutamate through N-methyl-D-aspartate recep-
tors in ethanol withdrawl. Neuroscience 93: 1135–1140.
Rossetti ZL, Carboni S (1995). Ethanol withdrawal is associated
with increased extracellular glutamate in the rat striatum. Eur J
Pharmacol 283: 177–183.
Rourke S, Loberg I (1996). Neurobehavioral correlates of
alcoholism. In: Grant I, Adams K (eds). Neuropsychological
Assessment of Neuropsychiatric Disorders. Oxford, New York.
Rowland LM, Bustillo JR, Mullins PG, Jung RE, Lenroot R,
Landgraf E et al (2005). Effects of ketamine in anterior cingulate
Perturbation of the Glu–Gln system
R Thoma et al
glutamate metabolism in healthy humans: a 4-T proton MRS
study. Am J Psychiatry 162: 394–396.
Schweinsburg BC, Taylor MJ, Alhassoon OM, Videen JS, Brown
GG, Patterson TL et al (2001). Chemical pathology in brain white
matter of recently detoxified alcoholics: a 1H magnetic
resonance spectroscopy investigation of alcohol-associated
frontal lobe injury. Alcohol Clin Exp Res 25: 924–934.
Szumlinski KK, Diab ME, Friedman R, Henze L, Lominac K,
Bowers S (2007). Accumbens neurochemical adaptations pro-
duced by binge-like alcohol consumption. Psychopharmacology
The ´berge J, Bartha R, Drost DJ, Menon RS, Malla A, Takhar J et al
(2002). Glutamate and glutamine measured with 4.0 T Proton
MRS in never-treated patients with schizophrenia and healthy
volunteers. Am J Psychiatry 159: 1944–1946.
Tsai G, Coyle J (1998). The role of glutamatergic neurotransmission
in the pathophysiology of alcoholism. Annu Rev Med 49: 173–184.
Umhau JC, Momenan R, Schwandt ML, Singley E, Lifshitz M,
Doty L et al (2010). Effect of acamprosate on magnetic reso-
nance spectroscopy measures of central glutamate in detoxified
alcohol-dependent individuals: a randomized controlled experi-
mental medicine study. Arch Gen Psychiatry 67: 1069–1077.
US Department of Health and Human Services, Substance Abuse
and Mental Health Services Administration (2002). Results from
the 2001 National Household Survey on Drug Abuse: Volume I.
Summary of National Findings (Office of Applied Studies,
NHSDA Series H-17 ed.) (BKD461, SMA 02-3758) US Govern-
ment Printing Office: Washington, DC.
Yeo RA, Gasparovic C, Merideth F, Ruhl D, Doezema D, Mayer AR
(2011). A longitudinal proton magnetic resonance spectroscopy
study of mild traumatic brain injury. J Neurotrauma 28: 1–11.
Zhu W, Bie B, Pan ZZ (2007). Involvement of non-NMDA glutamate
receptors in central amygdala in synaptic actions of ethanol and
ethanol-induced reward behavior. J Neurosci 27: 289–298.
Perturbation of the Glu–Gln system
R Thoma et al