Cigarette Smoking Exacerbates Chronic Alcohol-Induced Brain Damage: A Preliminary Metabolite

Imaging StudyTimothy C. Durazzo, Stefan Gazdzinski, Peter Banys, and Dieter J. Meyerhoff

Background: Cigarette smoking is common among alcohol-dependent individuals. Nevertheless, previ-ous research has typically not accounted for the potential independent or compounding effects of cigarettesmoking on alcohol-induced brain injury and neurocognition.

Methods: Twenty-four 1-week-abstinent recovering alcoholics (RAs; 14 smokers and 10 nonsmokers) intreatment and 26 light-drinking controls (7 smokers and 19 nonsmokers) were compared on measures ofcommon brain metabolites in gray matter and white matter of the major lobes, basal ganglia, midbrain, andcerebellar vermis, obtained via multislice short–echo time proton magnetic resonance spectroscopic im-aging. Smoking and nonsmoking RAs were also contrasted on measures of neurocognitive functioning, aswell as laboratory markers of drinking severity and nutritional status.

Results: Chronic alcohol dependence, independent of smoking, was associated with lower concentra-tions of frontal N-acetylaspartate (NAA) and frontal choline-containing compounds, as well as lowerparietal and thalamic choline. Smoking RAs had lower NAA concentrations in frontal white matter andmidbrain and lower midbrain choline than nonsmoking RAs. A four-group analysis of covariance alsodemonstrated that chronic cigarette smoking was associated with lower midbrain NAA and choline andwith lower vermian choline. In smoking RAs, heavier drinking was associated with heavier smoking, whichcorrelated with numerous subcortical metabolite abnormalities. The 1-week-abstinent smoking and non-smoking RAs did not differ significantly on a brief neurocognitive battery. In smoking RAs, lower cere-bellar vermis NAA was associated with poorer visuomotor scanning speed and incidental learning, and innonsmoking RAs lower vermis NAA was related to poorer visuospatial learning and memory.

Conclusions: These human in vivo proton magnetic resonance spectroscopic imaging findings indicatethat chronic cigarette smoking exacerbates chronic alcohol-induced neuronal injury and cell membranedamage in the frontal lobes of RAs and has independent adverse effects on neuronal viability and cellmembranes in the midbrain and on cell membranes of the cerebellar vermis. Higher smoking levels areassociated with metabolite concentrations in select subcortical structures. Greater consideration of thepotential effects of comorbid cigarette smoking on alcohol-induced brain damage and other diseasesaffecting the central nervous system is warranted.

Key Words: Magnetic Resonance, Metabolites, Neurocognition, Alcoholism, Cigarette Smoking.

CHRONIC, HEAVY ALCOHOL consumption pro-duces abnormalities in brain morphology, neurome-

tabolism, and neurocognition (Oscar-Berman, 2000; Sulli-van, 2000). In alcoholics, the concurrent use of othersubstances, such as cocaine and nicotine, is well docu-

mented (Bjork et al., 2003; Degenhardt and Hall, 2003;Kampman et al., 2004). Therefore, the neurobiologic andneurocognitive abnormalities in alcohol-dependent individ-uals may be, at least in part, due to concurrent use of othersubstances. The most frequently used substances amongalcoholics are tobacco products: an estimated 80% ofalcohol-dependent individuals smoke regularly (Hurt et al.,1994; Pomerleau et al., 1997; Romberger and Grant, 2004),and 50–90% demonstrate nicotine dependence (Daeppenet al., 2000; Marks et al., 1997). Regular active cigarettesmoking in alcoholics is associated with a significantlyhigher quantity and frequency of alcohol consumption(John et al., 2003), particularly compared with nonsmokingor formerly smoking alcohol-dependent individuals (Yorkand Hirsch, 1995). Although the separate and interactiveeffects of concurrent cocaine dependence and chronic al-coholism on brain structure and metabolite levels havebeen investigated by magnetic resonance (O’Neill et al.,

From the San Francisco Veterans Administration Medical Center (TCD,SG, PB, DJM), San Francisco, California; Northern California Institute forResearch and Education (TCD), San Francisco, California; Department ofRadiology, University of California, San Francisco (SG, DJM), San Fran-cisco, California; and Department of Psychiatry, University of California SanFrancisco (PB), San Francisco, California.

Received for publication May 19, 2004; accepted September 1, 2004.Supported by National Institutes of Health, Grant AA10788 (DJM).Reprint requests: Timothy C. Durazzo, PhD, San Francisco Veterans Ad-

ministration Medical Center, MRS Unit (114M), 4150 Clement St., San Fran-cisco, CA 94121; Fax: 415-668-2864; E-mail: timothy.durazzo@med.va.gov.

Copyright © 2004 by the Research Society on Alcoholism.

DOI: 10.1097/01.ALC.0000148112.92525.AC

0145-6008/04/2812-1849$03.00/0ALCOHOLISM: CLINICAL AND EXPERIMENTAL RESEARCH

Vol. 28, No. 12December 2004

Alcohol Clin Exp Res, Vol 28, No 12, 2004: pp 1849–1860 1849

2001), the combined effects of cigarette smoking and alco-holism on central nervous system (CNS) function havereceived little research attention.

Several theories attempt to explain the concurrent heavyuse of alcohol and tobacco products: nicotine and alcoholmay potentiate each other’s rewarding properties (Nara-hashi et al., 2001; Rose et al., 2003), nicotine may partiallycounteract the adverse effects of alcohol on cognition andmotor incoordination (Prendergast et al., 2002), or paireduse of nicotine and alcohol may produce classically condi-tioned cue reactivity, leading to cravings for both sub-stances (Drobes, 2002). Finally, a genetic susceptibility forconcurrent active cigarette smoking and alcohol depen-dence has been proposed (Madden and Heath, 2002).

In humans, active cigarette smoking is associated with anincreased risk for atherosclerosis, ischemic and hemor-rhagic stroke, cardiovascular disease, peripheral vasculardisease, chronic obstructive pulmonary disease, variousforms of cancer, and lipid peroxidation secondary to pro-duction of oxygen-derived free radicals (Bolego et al., 2002;Garey et al., 2004; Hawkins et al., 2002). In a recent mag-netic resonance imaging (MRI) study, smokers demon-strated smaller cortical gray matter (GM) volumes anddensities in the bilateral prefrontal cortex, smaller left an-terior cingulate volumes, and lower GM densities in theright cerebellum compared with nonsmokers (Brody et al.,2004). Computed tomography studies have shown chroniccigarette smoking to be associated with increased brainatrophy in individuals aged 50 years and older (Hayee et al.,2003; Kubota et al., 1987). Active cigarette smoking isassociated with diminished neurocognitive performance.Affected neurocognitive domains include executive func-tions (Razani et. al., 2004), general intellectual abilities(Deary et al., 2003), memory (Hill et al., 2003; Schinka etal., 2003), and psychomotor speed and cognitive flexibility(Kalmijn et al., 2002). Furthermore, recent prospectivelongitudinal research with non-demented subjects suggeststhat cigarette smoking promotes an abnormal decline incognitive functioning (Ott et al., 2004) and significantlyincreases the risk for various forms of dementia, in partic-ular Alzheimer’s disease (Launer et al., 1999; Merchant etal., 1999; Ott et al., 1998).

Cigarette smoke contains more than 4000 compounds(Bartal, 2001; Bates et al., 1999), many of which are highlybioactive. Nicotine is one of the many highly concentratedbioactive substances found in cigarette smoke that mayhave adverse actions on CNS cellular or vascular function(Abou-Donia et al., 2003). Conversely, nicotine has neuro-protective properties in some in vitro animal models (Pren-dergast et al., 2000). Thus, it is uncertain whether nicotineor the many other bioactive components of cigarette smokelead to compromised function of cerebral nervous or glialtissue. Additionally, because the vast majority of empiricalresearch has been conducted with animals, the direct andindirect effects of chronic cigarette smoking on humanneuronal and glial function remain unclear.

Despite the high prevalence of cigarette smoking amongalcohol-dependent individuals and the known adversehealth consequences associated with smoking, few studieson the CNS effects of chronic alcoholism have accountedseparately for the effects of smoking and alcohol consump-tion, and, to our knowledge, there are no correspondingstructural or spectroscopic neuroimaging studies. Addition-ally, because it is unlikely that normal controls and alcohol-dependent participants in previous studies were matchedon the degree of smoking severity, smoking possibly con-founded the reported CNS findings in alcoholics.

Computed tomography and MRI studies have convinc-ingly demonstrated that chronic, heavy alcohol consump-tion is associated with brain volume loss. Magnetic reso-nance spectroscopy (MRS) enables measurement ofaspects of alcohol-induced brain damage that may accom-pany or precede alcohol-induced morphological changes.Proton MRS (1H MRS) allows noninvasive and concurrentquantitation of several brain metabolites from most brainregions. N-Acetylaspartate (NAA) is an amino acid that isfound in high concentrations in axons and dendrites ofneurons, particularly in pyramidal neurons (Moffett et al.,1991; Simmons et al., 1991), but is virtually absent in ma-ture glial cells. MRS-derived NAA concentration is thoughtto reflect neuronal viability (Vion-Dury et al., 1994); de-creased levels reflect neuronal loss, atrophied dendritesand/or axons, or derangements of neurometabolism (DeStefano et al., 1995; Hugg et al., 1996; Schuff et al., 2001).The 1H MRS signal from choline-containing metabolites(Cho) reflects compounds primarily involved in cell mem-brane breakdown and synthesis (Barker et al., 1994) andmay reflect cellular membrane turnover and density (Milleret al., 1996) and/or myelin breakdown products (Ross andBluml, 2001). In its bioactive form, myo-inositol (mI) is aconstituent of phosphatidylinositol, an important compo-nent of the phospholipid bilayer that constitutes all cellmembranes. It is also described as an astrocyte marker(Brand et al., 1993) and/or an osmolyte (Schweinsburg etal., 2000). The signal from creatine-containing metabolites(Cr) corresponds to the sum of concentrations of intracel-lular creatine and phosphocreatine, both of which are in-volved in the bioenergetics of neuronal and glial tissue(Ferguson et al., 2002). The first magnetic resonance studyto suggest neuronal damage in the frontal cortex of absti-nent alcoholics used MRS imaging (1H MRSI), a methodthat allows the simultaneous acquisition of spectra frommany voxels within a selected brain region (Fein et al.,1994). Subsequently, single-volume 1H MRS studies mea-sured metabolites primarily in the frontal lobes and cere-bellum of recovering alcoholics (RAs) after 3 to 40 days ofsobriety. These studies reported depressed NAA in thefrontal lobes (Bendszus et al., 2001; Jagannathan et al.,1996), thalamus (Jagannathan et al., 1996), and cerebellum(Parks et al., 2002; Seitz et al., 1999) of alcoholics, as wellas lower cerebellar choline (Bendszus et al., 2001; Parks etal., 2002) and increased thalamic mI (Schweinsburg et al.,

1850 DURAZZO ET AL.

2000) relative to light-drinking controls (LD). Lower con-centrations of NAA in frontal white matter (WM) and ofNAA, choline, and mI in the cerebellum correlated withlower neurocognitive and motor functioning (e.g., Bends-zus et al., 2001; Parks et al. 2002). None of these studies,however, controlled for the possible effects of smoking onbrain metabolite concentrations or neurocognition.

Therefore, it is unknown whether the full extent of atro-phy, cell membrane damage, derangement of neurome-tabolism, or neurocognitive dysfunction in RAs can in factbe attributed solely to chronic alcohol abuse/dependence orwhether smoking has a separate and independent effect onbrain metabolites and neurocognition. We used short–echotime multislice 1H MRSI and a brief neuropsychologicaltest battery in RAs abstinent for 1 week with the intent totest for effects of chronic alcohol consumption on brainmetabolites and neurocognitive function. In our variousstatistical analyses, we noted distinct differences in metab-olite concentrations between smoking and non-smokingRA. Therefore, we retrospectively divided our participants(i.e., RA and LD) into smokers and nonsmokers to enablethe simultaneous evaluation of potential independent andadditive effects of cigarette smoking and alcohol consump-tion on regional metabolite concentrations and neurocog-nition in RA. We tested the following primary hypotheses:

1. Compared with LDs, 1-week-abstinent RAs demon-strate neuronal dysfunction (indicated by lower NAA con-centrations) and cellular membrane damage (indicated bylower choline concentrations) in the cerebellum and GMand WM of the frontal lobes.

2. Smoking RAs show greater neuronal dysfunction andcellular membrane compromise than nonsmoking RAs andLDs, as evidenced by lower NAA and choline concentra-tions in the cerebellum and GM and WM of the frontallobes.

3. Neurocognitive performance in nonsmoking RAs issuperior to that in smoking RAs.

4. Frontal and parietal lobar and cerebellar NAA con-centrations are positively related to neurocognitive func-tioning in both smoking and nonsmoking RAs.

5. In smoking RAs, measures of smoking and drinkingseverity are positively related.

In additional exploratory analyses, we examined the re-lationships between measures of smoking severity and re-gional metabolite concentrations.

MATERIALS AND METHODS

Participants

Twenty-four male RAs were recruited from the San Francisco VeteransAdministration Medical Center Substance Abuse Day Hospital and theSan Francisco Kaiser Permanente Chemical Dependence Recovery Pro-gram. Twenty-six LDs served as controls (21 males and 5 females). LDswere part of a larger cohort recruited for a different study (Meyerhoff etal., 2004) from the community via postings on electronic poster boards inthe San Francisco Bay Area and used in a previous report comparingregional brain metabolite concentrations with those in heavy social drink-

ers (Meyerhoff et al., 2004). All participants were between the ages of 25and 66 years at the time of enrollment. Subjects were initially screened foreligibility via the inclusion and exclusion criteria listed below. Over theirlifetime, male LDs consumed less than or equal to an average of 45 (35 forfemales) standard alcoholic drinks per month [a standard drink is definedas containing approximately 13.6 g of pure ethanol (EtOH): 12 oz of beer,5 oz of wine, or 1.5 oz of liquor]. LDs had no history of past or currentalcohol abuse or dependence or 2 consecutive months of consuming morethan 100 drinks per month. LDs had no history of mood, anxiety, orthought disorders and met none of the exclusion criteria listed below forRAs.

Primary inclusion criteria for RAs were fluency in English, DSM-IVdiagnosis of alcohol dependence or alcohol abuse at the time of enroll-ment (all RAs met criteria for alcohol dependence with physiologicaldependence), consumption of more than 150 standard alcoholic drinks permonth for at least 8 years before enrollment for men, or consumption ofmore than 80 drinks per month for at least 6 years before enrollment forwomen.

Medical exclusion criteria for RAs and LDs were a current or pasthistory of intrinsic cerebral tumors, human immunodeficiency virus oracquired immune deficiency syndrome, cerebrovascular accident, aneu-rysm, arteriovenous malformations, peripheral vascular disease, uncon-trolled chronic hypertension (systolic blood pressure �180 mm Hg and/ordiastolic blood pressure �120 mm Hg), insulin-dependent diabetes, mod-erate or severe chronic obstructive pulmonary disease, non–alcohol-related seizures, significant exposure to known neurotoxins (e.g., tolueneor carbon tetrachloride), demyelinating and neurodegenerative diseases,Wernicke-Korsakoff syndrome, alcohol-induced persisting dementia, andtraumatic brain injury resulting in loss of consciousness for more than 5min. Laboratory-diagnosed hepatitis C was present in three of the smokingRAs and four of the nonsmoking RAs. Two participants in the smokingRA group and one in the nonsmoking RA group experienced hyperten-sion at the time of study. However, their hypertension was not severe andwas well controlled by antihypertensive medications.

Psychiatric exclusion criteria for RAs were a history of schizophrenia orother thought disorders, bipolar disorder, dissociative disorders, posttrau-matic stress disorder, obsessive compulsive disorder, panic disorder (withor without agoraphobia), major depression with mood-incongruent psy-chotic symptoms, current dependence and/or dependence during the 5years immediately before enrollment on any substance other than alcoholor nicotine, intravenous drug use during the 5 years immediately beforeenrollment, and current opioid agonist therapy. No RA participant metDSM-IV criteria for other substance abuse or dependence, other thannicotine, at any point during their life. RAs were urine-tested for illicitsubstances immediately before magnetic resonance studies (i.e., tetrahy-drocannabinol, opiates, phencyclidine, cocaine, and amphetamines). In-formed consent was obtained from all participants before the study, andall procedures were approved by the institutional review boards of theUniversity of California San Francisco and the San Francisco VeteransAdministration Medical Center. For their participation, RAs were com-pensated with gift certificates to a local retail store, and controls were paidwith checks. All behavioral and neurocognitive measures were adminis-tered by a doctoral-level neuropsychologist (TCD) according to standard-ized procedures.

Psychiatric/Behavioral Assessment and Smoking Classification

Participants completed the Clinical Interview for DSM-IV Axis I Dis-orders, Patient Edition, Version 2.0 (American Psychological Association,1994), and standardized questionnaires assessing alcohol withdrawal (Ad-diction Research Foundation Clinical Institute of Withdrawal Assessmentfor Alcohol), depression (Beck Depression Inventory; Beck, 1978) andtrait anxiety symptoms (State-Trait Anxiety Inventory, Y-2; STAI Y-2;Spielberger et al., 1977), lifetime alcohol consumption (Lifetime DrinkingHistory; LDH; Skinner and Sheu, 1982), substance use (in-house ques-tionnaire assessing substance type and quantity and frequency of use overlifetime), and degree of nicotine dependence (Fagerstrom Tolerance Test

CIGARETTE SMOKING AND ALCOHOL-INDUCED BRAIN DAMAGE 1851

for Nicotine Dependency; FTND; Fagerstrom et al., 1991). The LDHobtains quantity and frequency information about alcohol consumptionfrom the first age of regular drinking (defined as consuming at least onestandard drink per month) to the present. From the LDH, we calculatedthe average number of drinks per month over 1 and 3 years beforeenrollment, the average number of drinks per month over lifetime, thenumber of lifetime years of regular drinking, and the total amount of pureEtOH consumed over lifetime. For smoking RAs, the total number ofcigarettes currently smoked per day and the number of years of smokingat the current level was recorded, and the number of pack-years [(numberof cigarettes per day/20) � duration of smoking in years] was calculated.Nonsmoking RAs reported no cigarette use for at least 1 year beforeenrollment. Because LDs were recruited as part of a different study, theyhad no FTND assessment, but they were asked to complete an in-houseself-report questionnaire to classify their cigarette smoking frequencyaccording to the following scale: 0 � no smoking, 1 � approximately oncea month, 2 � two or three times per month, 3 � one or two times perweek, 4 � three or four times per week, 5 � nearly ever day, or 6 � at leastonce a day. LD participants endorsing 0 or 1 were considered to benonsmokers. On the basis of smoking status, the following groups wereformed: nonsmoking RAs (nsRA; n � 14), smoking RAs (sRA; n � 10),nonsmoking LDs (nsLD; n � 19, including 2 females), and smoking LDs(sLD; n � 7, including 3 females). In the sRA group, one participant metDSM-IV criteria for recurrent major depression and one for recurrentmajor depression with mood-congruent psychotic symptoms. Both weretaking antidepressants at the time of the study. Two participants in thensRA group met DSM-IV criteria for substance-induced mood disorderwith depressive features, and one met criteria for recurrent major depres-sion. None of these participants was taking antidepressants at the time ofthe study. Two sRA and three nsRA participants were prescribed chlor-diazepoxide (Librium®, Roche, Basel, Switzerland) for alcohol with-drawal at the time of study.

Neurocognitive Assessment

A brief neurocognitive battery (approximately 45 min in duration)assessed visuospatial learning and memory, as measured by the BriefVisual Memory Test-Revised (Benedict, 1997); working memory, mea-sured by WAIS-III Digit Span (Wechsler, 1997); visuomotor scanningspeed and incidental learning, measured by WAIS-III Digit Symbol(Wechsler, 1997); and estimated premorbid verbal intelligence, measuredby the American National Adult Reading Test (Grober and Sliwinski,1991). All neurocognitive tests were administered within 1 day of themagnetic resonance study in RAs. Raw scores for all neurocognitivemeasures were converted to standardized scores via appropriate norma-tive data. As LDs had been recruited as part of a different study, theneurocognitive assessment battery did not overlap sufficiently to permitmeaningful comparisons of cognitive measures in LDs and RAs.

Laboratory Tests

The following measures were obtained for RAs within 3 days of themagnetic resonance study: alanine aminotransferase (ALT), aspartateaminotransferase (AST), and �-glutamyltransferase (GGT) to evaluatealcohol-related or other hepatocellular injury; serum albumin and preal-bumin as indicators of nutritional status (Weinrebe et al., 2002); andhepatitis C and human immunodeficiency virus antibodies.

MRI Acquisition and Processing

Brain magnetic resonance data were obtained with a standard 1.5-Tscanner (Vision, Siemens Medical Systems, Inc., Iselin, NJ). StructuralMRI data were acquired with a double spin-echo sequence with TR/TE1/TE2 (repetition and echo times) of 5000/20/80 msec; 1 � 1 mm2 in-planeresolution; and 50 contiguous 3-mm-thick axial slices oriented along animaginary line connecting the anterior and posterior commissures, as seenon midsagittal scout MRI. A volumetric magnetization-prepared rapidgradient echo was acquired with TR/TE/TI of 9.7/4/300 msec, a 15° flip

angle, 1 � 1 mm2 in-plane resolution, and 1.5-mm-thick coronal partitionsoriented perpendicular to the main long axes of bilateral hippocampi, asseen on sagittal scout MRI. To assess clinically significant neuropathology,all participants’ MRI images were read by a clinical neuroradiologist, andWM signal hyperintensities were rated on a four-point scale: 0 � absent,1 � punctate foci, 2 � early confluence, and 3 � large confluent areas.The magnetization-prepared rapid gradient echo images were segmentedinto WM, GM, and cerebrospinal fluid by using probabilistic segmentationand an atlas-based method to identify and volume regions of interest(ROIs), which included the major lobes, subcortical nuclei, brainstem, andcerebellum. This method is described in detail in Meyerhoff et al. (2004)and in the references therein.

MRSI Acquisition and Processing

MRSI acquisition and processing methods are described in detail inMeyerhoff et al. (2004). In summary, MRI was followed by automatedhead shimming and a multislice 1H MRSI sequence with TR/TI/TE of1800/165/25 msec, imaging metabolites with three slices, each 15 mm thickwith a slice gap of at least 6 mm, a nominal in-plane resolution of 8 � 8mm2 (yielding a 1-ml nominal spectroscopic imaging voxel), and circulark-space sampling. The spectroscopic imaging slices were angulated paral-lel to the double spin-echo slices, covering primarily the major cerebrallobes, subcortical nuclei, midbrain, and cerebellar vermis. The total ac-quisition time was approximately 90 min for MRI and MRSI. Details ofthe acquisition sequence were previously published (Soher et al., 2000;Wiedermann et al., 2001). Multislice 1H MRSI data processing and anal-yses were applied offline with methods comprehensively described inMeyerhoff et al. (2004) to obtain metabolite concentrations for eachspectroscopic imaging (SI) voxel expressed in institutional units, hereinreferred to as concentrations. We did not report absolute metaboliteconcentrations in molar units, to avoid making possibly inaccurate as-sumptions about relaxation times affecting SI signals, or about concentra-tions of a metabolite such as creatine, which is often used as a concen-tration reference in single-volume MRS studies but which is quite variableacross brain regions. Results from all major processing steps were visuallyinspected to ensure proper software performance and satisfactory dataquality, as previously described in Meyerhoff et al. (2004).

MRI/1H MRSI Co-Processing

To calculate metabolite concentrations for GM and WM in each ROIidentified on MRI, the MRI dataset, segmented into ROIs and tissuetypes, was spatially co-registered to the 1H MRSI dataset and reduced toMRSI resolution, taking into account the MRSI point-spread function,chemical-shift displacement, and slice profile (Schuff et al., 2001). Thisoperation permitted computation of the tissue composition in each voxelof the spatially registered SI acquisition volume and of the metaboliteconcentration in each voxel. These concentrations were atrophy-correctedby using cerebrospinal fluid contribution and then averaged over all voxelsfrom a given ROI. SI voxels had an estimated effective spatial resolutionof approximately 1.5 ml. All procedures are described in detail in Meyer-hoff et al. (2004).

Study Design and Data Analyses

We conducted three separate data analyses. In analysis 1, regionalbrain metabolite data were analyzed, as was typical in previous studies, bycomparing the entire RA group with the entire LD group using indepen-dent t tests. In analysis 2, the sRA and nsRA groups were then directlycompared on their main outcome measures. Although the RAs and LDsand the sRAs and nsRAs did not significantly differ in age, we conserva-tively chose to use age as a covariate in these analyses because of theknown age effects on some brain metabolite concentrations. Becausesignificant metabolite concentration differences were observed betweensRAs and nsRAs, in analysis 3, we then tested for main effects andinteractions of cigarette smoking and chronic alcohol dependence onbrain metabolite concentrations by using univariate analyses of covari-

1852 DURAZZO ET AL.

ance, with age as the covariate, between all four groups (sRA, nsRA, sLD,and nsLD). For exploratory post hoc analyses, the following “families”were established, on the basis of tissue type and standard anatomicdivisions, to control for experiment-wise error rate for each individualmetabolite: (1) GM of the temporal, parietal, and occipital lobes; (2) WMof the temporal, parietal, and occipital lobes; (3) lenticular and caudatenuclei; (4) thalamus; and (5) midbrain. For analysis 1 and 2, significancelevels for individual metabolites were adjusted for the number of compo-nents in each family. Accordingly, GM family � � 0.05/(3 family compo-nents) � 0.017, WM family � � 0.017, lenticular and caudate family � �0.025, thalamus � � 0.05, and midbrain � � 0.05. For analysis 3, signifi-cance levels for main effects and interactions were adjusted for individualmetabolites by the number of components in each family (see above) andthe number of possible pairwise comparisons among groups, calculatedaccording to J(J � 1)/2, where J indicates number of groups; therefore,4 (4 � 1)/2 � 6. For analysis 3, the GM and WM family � � 0.05/(3 familycomponents � 6 possible pairwise comparisons) � 0.003, lenticular andcaudate family � � 0.004, thalamus � � 0.008, and midbrain � � 0.008.Significant main effects and interactions were further examined with t testsby using the least significant difference method, with age as a covariate.Spearman’s rank order was used to investigate correlations among mea-sures of smoking and drinking severity, neurocognitive performance, andmetabolite concentrations. In all non-planned analyses investigating therelationships among measures of drinking severity, neurocognitive perfor-mance, and metabolite concentrations in sRAs and nsRAs, the experiment-wise error rate was adjusted according to three measures of drinking severity,four neuropsychological measures, and the number of components in thefamily. Analyses of relationships between measures of smoking severity andmetabolite concentrations for sRAs and nsRAs were not corrected for mul-tiple comparisons to elucidate patterns of such relationships, because theyhave not been previously reported. All statistical analyses were conductedwith SPSS 11.5 for Windows (SPSS Inc., Chicago, IL).

RESULTS

Participant Characterization

The entire RA group (n � 24; age, 50 � 7 years) wasequivalent in age to the entire LD group (n � 26; 48 � 5years), but LDs (16 � 2 years) were better educated thanRA (13 � 3 years) [t(1,47) � 4.14; p � 0.001]. Table 1

shows the demographics of all four groups. The sRA andnsRA groups were not significantly different in age oreducation. However, when comparing all four groups, dif-ferences were observed for age [F(1,47) � 2.81; p � 0.05]and education [F(1,46) � 5.67; p � 0.002]: nsRAs wereolder and less educated than sLDs. Therefore, age was usedas covariate in all analyses comparing brain metaboliteconcentrations among all four groups.

The sRAs had a greater average number of alcoholic drinksconsumed per month over lifetime than nsRAs [t(1, 22) �1.75; p � 0.04], but the sRA and nsRA groups did not differon the average number of drinks per month over 1 and 3 yearsbefore enrollment or on lifetime years of regular drinking. Nosignificant differences were observed between sRAs andnsRAs on the total quantity of pure EtOH consumed overlifetime. The sRA and nsRA groups did not differ on GGT,AST, ALT, or prealbumin, whereas sRAs had greater serumalbumin than nsRAs [t(1,48) � �2.40; p � 0.02], but albuminvalues for both groups were within normal limits.

The sRAs smoked 21 � 8 cigarettes per day (minimum,6; maximum, 35) and smoked at this level for 23 � 13 years(minimum, 2 years; maximum, 42 years), and the number ofcigarette pack-years was 25 � 19 (minimum, 1; maximum,70). The sRA FTND score was 6 � 2 (minimum, 2; maxi-mum, 10), indicating a high level of dependence. In thesLD group, 43% (three of seven) smoked at least once perday, 14% (one of seven) smoked nearly every day, 29%(two of seven) smoked three or four times per week, and14% (one of seven) smoked two or three times per month.

A clinical neuroradiologist read all MRI data. An equalproportion of sRAs (8 of 13) and nsRAs (6 of 10) demon-strated WM signal hyperintensities on MRI. However, allnsRAs had punctate foci, whereas most sRAs had earlyconfluence of signal hyperintensities.

Table 1. Participant Demographics, Alcohol Consumption, and Laboratory Variables (Mean � SD)

Variable nsRA (n � 10) sRA (n � 14) nsLD (n � 19) sLD (n � 7)

Age (years) 53 � 7 48 � 7 50 � 4 45 � 7Education (years) 13 � 2 13 � 3 16 � 3 15 � 2AMNART 107 � 10 110 � 8 NA NABDI 16 � 9 17 � 12 NA NASTAI Y-2 48 � 11 50 � 16 NA NACIWA-Ar 4 � 4 4 � 6 NA NA1-year average 411 � 183 457 � 185 11 � 13 8 � 83-year average 420 � 176 448 � 185 11 � 13 7 � 7Lifetime average 208 � 129 304 � 135 12 � 11 20 � 14Lifetime years 37 � 6 31 � 8 29 � 5 24 � 7Total lifetime EtOH (kg) 1208 � 779 1541 � 937 56 � 50 76 � 51GGT (i.v.) 189 � 228 87 � 63 NA NAAST (i.u.) 53 � 51 36 � 9 NA NAALT (i.u.) 66 � 56 35 � 15 NA NAAlbumin (g/dl) 4.8 � 0.29 4.05 � 0.30 NA NAPrealbumin (mg/dl) 26.8 � 6.6 28.6 � 4.8 NA NA

AMNART, American National Adult Reading Test; BDI, Beck Depression Inventory; STAI Y-2, State-Trait Anxiety Inventory—State; CIWA-Ar, Addiction ResearchFoundation Clinical Institute of Withdrawal Assessment for Alcohol; 1-year average, number of drinks per month over 1 year before study; 3-year average, number ofdrinks per month over 3 years before study; lifetime average, number of drinks per month over lifetime; lifetime years, number of years of regular alcohol consumptionover lifetime; total lifetime ethanol (EtOH), total amount of pure EtOH consumed over lifetime; GGT, �-glutamylacidtransferase, local normal range 7–64 institutionalunits (i.u.); AST, aspartate aminotransferase, local normal range 5–35 i.u.; ALT, alanine aminotransferase, local normal range 7–56 i.u.; albumin local normal range3.3–5.2 g/dl; prealbumin local normal range 18–45 mg/dl; NA, not available.

CIGARETTE SMOKING AND ALCOHOL-INDUCED BRAIN DAMAGE 1853

Analysis 1: RA versus LD—Group Comparisons

The results from comparisons of the RA and LD groupsgenerally confirmed our first hypothesis. Specifically, RAsshowed lower NAA (�5%) and choline (�10%) than LDsin frontal GM and WM [both t(1,47) � 2.25; both p � 0.02].RAs also demonstrated lower choline in GM (�9%) andWM (�16%) of the parietal lobe [both t(1,47) � 2.75; bothp � 0.003] and thalami (�11%) [t(1,48) � 2.88; p � 0.003].Regional mI and creatine concentrations were not signifi-cantly different between RAs and LDs.

Analysis 2: sRA Versus nsRA—Group Comparisons. Asshown in Table 2 and as postulated in our second hypoth-

esis, sRAs demonstrated 10% lower NAA compared withnsRAs in the frontal WM [t(1,20) � 2.72; p � 0.007]. ThesRAs also exhibited 15% lower NAA and 21% lower cho-line in the midbrain [both t(1,16) � 2.18; both p � 0.02]than nsRAs. In addition, sRAs showed trends to decreasedNAA in the parietal GM [t(1,20) � 1.99; p � 0.03] andlenticular nuclei [t(1,20) � 1.99; p � 0.03]. Whereas sRAsdid not differ significantly from nsRAs on the duration ofregular drinking (i.e., onset age), sRAs had a higher aver-age number of drinks per month over lifetime (p � 0.04).To remove potential dose-related contributions of alcoholto the smoking-related group differences observed, we re-

Table 2. Regional Metabolite Concentrations (in Institutional Units) for nsRA, sRA, nsLD, and sLD (Mean � SD)

Metabolite Tissue type Region nsLD sLD nsRA sRA

Alcoholmain effect

p value

Smokingmain effect

p value

NAA GM Frontal 32.19 � 3.01# 31.06 � 1.66 30.62 � 2.72 29.46 � 3.36# 0.02 NSParietal 31.86 � 3.16 30.80 � 3.46 31.35 � 2.47 29.35 � 2.22 NS NSTemporal 26.69 � 2.88 25.31 � 4.46 25.18 � 3.18 23.07 � 4.07 NS NS

WM Frontal 31.02 � 4.17# 31.42 � 2.24$$ 30.63 � 2.55 27.64 � 2.65#$$ 0.009 NSParietal 30.42 � 3.02 28.64 � 2.88 28.40 � 3.30 27.81 � 2.90 NS NSTemporal 26.87 � 4.02 26.99 � 4.80 27.10 � 2.54 25.57 � 3.89 NS NSOccipital 30.51 � 2.43 29.75 � 2.37 29.93 � 2.80 29.41 � 3.38 NS NS

Subcortical Thalamic 35.83 � 3.52 36.15 � 4.86 35.06 � 3.92 33.99 � 4.67 NS NSCaudate 31.11 � 5.07 23.06 � 3.87 28.27 � 5.59 25.00 � 4.26 NS NSMidbrain 32.84 � 6.12 32.18 � 3.14 35.71 � 3.71* 30.40 � 6.00* NS 0.002Lenticular 30.02 � 4.66 32.37 � 1.24 30.46 � 3.36 27.66 � 3.82 NS NSCerebellar vermis 36.56 � 4.23 31.99 � 5.48 33.98 � 3.85 33.26 � 4.06 NS NS

Cho GM Frontal 6.22 � 0.61##& 5.55 � 0.66 5.68 � 0.78& 5.47 � 0.84## 0.01 NSParietal 5.12 � 0.64 4.84 � 0.75 4.72 � 0.46 4.44 � 0.51 NS NSTemporal 5.72 � 1.04 5.67 � 0.89 5.44 � 1.04 5.20 � 0.88 NS NS

WM Frontal 6.26 � 0.98### 6.39 � 1.13$% 5.65 � 0.87% 5.14 � 0.89###$ 0.001 NSParietal 5.66 � 1.08##&&& 5.08 � 0.76 4.56 � 0.52&&& 4.73 � 0.98## 0.002 NSTemporal 5.41 � 0.73 5.31 � 0.90 5.12 � 0.61 4.97 � 0.67 NS NSOccipital 4.62 � 0.69 4.39 � 0.79 4.32 � 0.49 4.14 � 0.64 NS NS

Subcortical Thalamic 7.16 � 0.97#% 7.21 � 0.91$^ 6.38 � 0.91%^ 6.32 � 1.17#$ 0.001 NSCaudate 6.67 � 1.31 5.81 � 1.77 6.30 � 0.41 5.39 � 1.62 NS NSMidbrain 8.13 � 1.53# 6.72 � 1.08^^ 8.73 � 1.02^^** 6.91 � 1.42#** NS 0.002Lenticular 6.02 � 1.03 5.81 � 0.76 5.92 � 1.15 5.21 � 0.93 NS NSCerebellar vermis 9.30 � 1.09@@ 7.48 � 1.18@@ 8.84 � 1.28 8.44 � 1.79 NS 0.04

mI GM Frontal 19.19 � 2.01 21.28 � 2.48 19.16 � 2.48 19.12 � 3.42 NS NSParietal 17.52 � 2.14 17.93 � 4.06 17.93 � 2.16 16.89 � 1.73 NS NSTemporal 17.49 � 3.09 17.99 � 2.99 18.64 � 3.27 18.44 � 3.13 NS NS

WM Frontal 17.82 � 3.29 19.33 � 1.63 17.22 � 1.90 16.62 � 2.61 NS NSParietal 18.14 � 2.96 18.17 � 2.95 16.83 � 1.19 17.39 � 2.79 NS NSTemporal 18.39 � 2.85 18.58 � 2.47 18.32 � 3.20 17.51 � 2.97 NS NSOccipital 16.81 � 2.34 17.97 � 1.76 16.87 � 2.50 17.39 � 2.52 NS NS

Subcortical Thalamic 20.84 � 2.81 21.41 � 4.96 20.20 � 2.06 19.46 � 3.69 NS NSCaudate 15.72 � 5.01 16.51 � 6.57 11.79 � 3.39 15.23 � 2.99 NS NSMidbrain 24.72 � 5.75 18.91 � 7.90 26.19 � 2.77 26.60 � 6.45 NS NSLenticular 16.97 � 3.62 19.31 � 3.15 17.22 � 2.06 16.10 � 3.15 NS NSCerebellar vermis 25.29 � 3.91 22.81 � 3.75 25.23 � 4.32 24.32 � 3.83 NS NS

Cr GM Frontal 21.53 � 1.95 22.54 � 1.46 21.65 � 2.04 21.19 � 2.65 NS NSParietal 20.05 � 2.30 20.01 � 2.44 20.57 � 2.25 19.55 � 1.97 NS NSTemporal 22.12 � 3.00 21.56 � 2.29 21.62 � 1.48 20.38 � 3.11 NS NS

WM Frontal 18.75 � 1.98 20.21 � 2.04 18.97 � 1.55 17.97 � 1.71 NS NSParietal 18.30 � 1.96 18.26 � 1.27 18.07 � 2.03 18.09 � 2.36 NS NSTemporal 20.75 � 2.39 19.75 � 2.20 21.19 � 2.76 20.16 � 2.07 NS NSOccipital 20.10 � 1.80 20.24 � 1.53 19.87 � 1.60 19.70 � 2.09 NS NS

Subcortical Thalamic 24.06 � 2.27 25.92 � 2.82 24.57 � 3.01 24.15 � 3.58 NS NSCaudate 22.81 � 3.30 20.27 � 1.67 24.79 � 2.35 22.38 � 3.87 NS NSMidbrain 27.76 � 3.77 26.29 � 5.74 30.57 � 3.24 29.41 � 4.07 NS NSLenticular 21.91 � 2.50 23.34 � 2.59 22.36 � 2.94 20.77 � 3.36 NS NSCerebellar vermis 33.55 � 3.54 29.39 � 5.43 32.27 � 3.34 31.64 � 4.77 NS NS

nsLD, nonsmoking light drinker; sLD, smoking light drinker; nsRA, nonsmoking recovering alcoholic; sRA, smoking recovering alcoholic; NS, nonsignificant; NAA,N-acetylaspartate; Cho, choline-containing metabolites; mI, myo-inositol; Cr, creatine-containing metabolites; GM, gray matter; WM, white matter.

# sRA � nsLD; $ sRA � sLD; * sRA � nsRA; & nsRA � nsLD; % nsRA � sLD; @ sLD � nsLD; ^ sLD � nsRA; one symbol, p � 0.05; two symbols, p � 0.01; threesymbols, p � 0.005.

1854 DURAZZO ET AL.

analyzed our data by using average number of drinks permonth over lifetime and total amount of pure EtOH con-sumed over lifetime as covariates. Additionally, althoughsRAs and nsRAs did not differ significantly on measures ofhepatocellular injury (i.e., GGT, AST, or ALT), these vari-ables were used as covariates, given the wide distribution ofscores within groups. Statistical significances of a priorihypotheses, post hoc tests, and reported trends remainedessentially unchanged when covaried for these variables.This indicated that the observed metabolite differencesbetween nsRAs and sRAs were primarily associated withthe effects of smoking and that smoking exacerbateschronic alcohol-induced brain metabolite abnormalities.

Contrary to our third hypothesis, there were no differ-ences between the nsRA and sRA groups on any neuro-cognitive measure. Furthermore, no differences were foundfor the American National Adult Reading Test, Beck De-pression Inventory, or STAI Y-2, indicating estimated pre-morbid IQ, and depressive and trait anxiety symptoms wereequivalent between nsRAs and sRAs.

Relationships Among Outcome Measures for sRAs andnsRAs. In sRAs, the average number of drinks per monthover lifetime and kilograms of pure EtOH consumed overlifetime were positively correlated with cigarette pack-years(both r � 0.45; both p � 0.05). This indicates that overlifetime, heavier drinking was associated with heaviersmoking, thus providing support for our fifth hypothesis.There were no significant correlations among measures ofdepressive and anxiety symptomatology (i.e., Beck Depres-sion Inventory and STAI) or indices of smoking severity.The FTND score was negatively correlated with thalamic (r� �0.65; p � 0.02) and lenticular (r � �0.54; p � 0.05)NAA and was positively related to thalamic choline (r �0.60; p � 0.02) and caudate mI (r � 0.71; p � 0.05).Similarly, the number of cigarettes per day was negativelycorrelated with thalamic and lenticular NAA (both r � �0.49;p � 0.05). Smoking duration was positively correlated withcaudate creatine (r � 0.88; p � 0.004), thalamic creatine (r �0.61; p � 0.02), and caudate mI (r � 0.71; p � 0.04).

Cerebellar vermis NAA was positively related to visuo-motor scanning speed and incidental learning (r � 0.59; p� 0.05), thus giving partial support to our fourth hypothe-sis. In sRAs, there were many additional correlationsamong regional metabolite concentrations and measures ofneurocognition, smoking, and drinking severity, but theydid not survive our rigorous correction for multiplecomparisons.

The nsRA Group. For nsRAs, cerebellar vermis NAA waspositively related to visuospatial learning (r � 0.72; p �0.02; Fig. 1) and visuospatial memory (r � 0.71; p � 0.02),partially supporting our fourth hypothesis. Similar to sRAs,correlations among regional metabolite concentrations andmeasures of neurocognition, smoking, and drinking werenot significant after correction for multiple comparisons.

Analysis 3: sRA, nsRA, sLD, and nsLD—Main Effects andInteractions of Alcohol and Smoking Status

Table 2 lists all metabolite concentrations by group, sig-nificant main effects, and follow-up comparisons.

N-Acetylaspartate. Main effects for alcohol status wereobserved for NAA in the frontal GM [F(1,47) � 6.03; p �0.02] and frontal WM [F(1,47) � 7.41; p � 0.009], withlower NAA concentrations in sRAs than nsLDs in thefrontal GM and WM and lower NAA in sRAs than nsLDsand sLDs in the frontal WM (Fig. 2). These findings pro-vide partial support for our second hypothesis. In addition,trends for alcohol main effects on NAA were observed fortemporal GM [F(1,41) � 4.98; p � 0.03] and parietal WM[F(1,46) � 6.31; p � 0.02] NAA, where sRAs demonstratedlower concentrations than nsRAs in the temporal GM, andsRAs and nsRAs had lower NAA than nsLDs in the pari-etal WM. Qualitatively, sRAs had the lowest NAA andcholine concentrations of all four groups in all lobar regionsand in nearly all subcortical structures.

Main effects for smoking status were found for midbrainNAA [F(1,32) � 11.70; p � 0.002], where sRAs showed lowerconcentrations relative to nsRAs. A trend for a smoking effectwas seen for caudate NAA [F(1,25) � 5.15; p � 0.03], withsRAs and sLDs demonstrating lower NAA than nsLDs.

Choline-Containing Metabolites. Main effects for alcoholstatus were observed for choline in frontal GM [F(1,47) �6.70; p � 0.01], where sRAs and nsRAs had lower concen-trations than nsLDs, and frontal WM [F(1,47) � 14.29; p �0.001], where sRAs demonstrated lower choline thannsLDs and sLDs, and nsRAs had lower choline than sLDs(Fig. 2), thus providing partial support for our secondhypothesis. Main effects for alcohol status were also foundfor parietal WM choline [F(1,47) � 11.29; p � 0.002],where sRAs and nsRAs had lower concentrations thannsLDs, and for thalamic choline [F(1,47) � 11.56; p �0.008], where both sRAs and nsRAs had lower levels thannsLDs and sLDs. A trend was observed for parietal GM

Fig. 1. Correlation of visuospatial learning with cerebellar NAA concentration innsRAs. BVMT-R, Brief Visual Memory Test-Revised; i.u., institutional unit.

CIGARETTE SMOKING AND ALCOHOL-INDUCED BRAIN DAMAGE 1855

choline [F(1,45) � 7.73; p � 0.008], with sRAs showinglower choline than nsLDs.

Main effects for smoking status were observed for cere-bellar vermis choline [F(1,45) � 4.50; p � 0.04], wheresLDs had lower choline than nsLDs, and for midbraincholine [F(1,38) � 11.50; p � 0.002], where sRAs had lowerconcentrations than nsRAs and nsLDs, and sLDs had lowercholine than nsRAs.

Myo-Inositol and Creatine-Containing Metabolites. No sig-nificant main effects or interactions were observed foreither metabolite after adjusting for multiple comparisons.

DISCUSSION

This study describes the effects of chronic alcohol depen-dence and cigarette smoking on regional brain metabolitesand neurocognition in 1-week-abstinent alcoholics. Ourresults confirm findings from previous research indicatingchronic alcohol-induced effects on regional brain metabo-lites. Additionally, in the absence of statistical interactions,these preliminary results suggest that cigarette smoking hasseparate and additive adverse effects on regional brainmetabolites, in particular on markers of neuronal functionand cellular membrane turnover. The major findings are asfollows: (1) chronic alcohol dependence (without control-ling for smoking status) was associated with lower NAAand choline concentrations in the frontal lobe and lowercholine concentrations in the parietal lobe and thalamus;(2) sRAs had lower concentrations of NAA in frontal WMand midbrain and lower midbrain choline relative tonsRAs; (3) chronic cigarette smoking was associated withlower NAA and choline concentrations in the midbrainand with lower choline in the cerebellar vermis; (4)sRAs, relative to all other groups, demonstrated thelowest concentrations of NAA and choline in frontal GMand WM, owing to additive adverse effects of chronicalcohol consumption and smoking; (5) nsRAs and sRAswere not significantly different on neurocognitive mea-sures; (6) lower cerebellar vermis NAA concentrationcorrelated with lower neurocognition in both nsRAs andsRAs; and (7) heavier drinking was associated withheavier smoking levels in sRAs, which in turn wereassociated with metabolite abnormalities.

Chronic Alcohol Dependence Was Associated With LowerRegional NAA and Choline Concentrations

Consistent with previous 1H MRS research in RAs ab-stinent for several weeks, 1-week-abstinent RAs had lowerfrontal NAA and choline relative to LDs and had lowerparietal and thalamic choline. Comparing results fromanalyses contrasting the entire RA group with the entireLD group (i.e., without considering effects of smoking)with results in this study that account for smoking effectssuggests, however, that previous MRS studies overesti-mated the specific effects of chronic alcohol consumptionon regional brain metabolites, particularly in the frontallobe and subcortical structures.

The sRA Group Demonstrated Lower Concentrations ofNAA in Frontal WM and Midbrain and Lower MidbrainCholine Relative to the nsRA Group

The significant group differences were likely a functionof the effects of smoking, because groups did not differ inage, the total quantity of pure EtOH consumed over life-time, and the number of drinks per month over 1 and 3years before enrollment in the study and because they didnot show differences in markers of nutritional functioningor hepatocellular injury. Additionally, after conservativelycontrolling for drinking severity and hepatocellular injury,the findings did not change appreciably, reinforcing thatthe metabolite differences observed between sRAs andnsRAs were related primarily to the effects of smoking.

Chronic Cigarette Smoking Was Associated With LowerNAA and Choline Concentrations in Subcortical BrainStructures

Both cigarette smoking and alcoholism seem to promotereductions of NAA and choline, suggesting neuronal lossand/or injury (via decreased NAA) as well as damage to cellmembranes and/or myelin (via decreased choline). How-ever, chronic smoking and drinking affect different brainregions. Chronic alcoholism is primarily associated withfrontal lobe damage, whereas cigarette smoking seems topromote a significant independent reduction of midbrainNAA and choline and vermian choline. Together with theinverse correlations of thalamic and lenticular NAA with

Fig. 2. Concentrations (institutional units;i.u.) of frontal WM NAA (left) and frontal WMcholine (right) in nsLDs, sLDs, nsRAs, andsRAs (mean � SD).

1856 DURAZZO ET AL.

measures of smoking severity in sRAs, our findings suggesta particular vulnerability of subcortical structures to theeffects of cigarette smoking.

The sRAs Showed the Lowest Frontal NAA and CholineConcentrations of All Groups

Although there were no statistically significant interac-tions, sRAs, compared with all other groups, consistentlyhad the lowest levels of NAA and choline in frontal GMand WM, indicating additive effects of chronic alcoholconsumption and smoking. This suggests that smoking ex-acerbates alcohol-induced neuronal and cellular membranedamage in frontal GM and WM. Additionally, in the sRAgroup, the inverse relationships between measures of smok-ing severity and thalamic and lenticular NAA indicategreater neuronal dysfunction in these brain structuresamong heavier smokers (i.e., a dose-dependent relation-ship). The significantly lower vermian choline in sLDs rel-ative to nsLDs indicates that smoking might have detrimen-tal effects on cerebellar vermis cellular membranes in LDs,even at the relatively low level of smoking encountered inthis control sample. Because less than 50% of participantsin the sLD group smoked daily, their smoking level waslikely not severe enough to significantly affect metabolitelevels in other brain regions.

The nsRAs and sRAs Were Not Significantly Different onNeurocognitive Measures

Previous research with smokers 45 years and older founddeficiencies in multiple neurocognitive functions relative tononsmoking individuals, and these were most evident withadvancing age (Hill et al., 2003; Schinka et al., 2003). In thisstudy, no differences were observed between sRAs andnsRAs on measures of visuospatial learning and memory,working memory, or visuomotor scanning speed and inci-dental learning. This absence of cognitive group differencesmay relate to sample size, the limited number of cognitivedomains assessed by the brief battery, and the relativelyyoung age of our RA group.

Lower Cerebellar Vermis NAA Was Related to LowerNeurocognition in nsRAs and sRAs

Our results are consistent with those of Bendszus et al.(2001), who studied alcoholics abstinent for 1 to 3 days, andthey further support the role of the cerebellum in highercognitive processes. Specifically, there is increasing evi-dence that the cerebellum and the frontocerebellar path-ways are critically involved in learning and executive func-tions and that these pathways are probably compromised bychronic alcoholism (Sullivan, 2003; Sullivan et al., 2003).

Heavier Drinking Was Associated With Heavier Smoking

The average number of drinks per month and the volumeof pure EtOH consumed over lifetime were both positively

correlated with pack-years. These findings are consistentwith previous studies (e.g., John et al., 2003) and indicatethat in sRAs, chronic alcohol consumption and cigarettesmoking coexist over an extended period. Similar to studiesthat show alcohol consumption damaging the brain in adose-dependent manner, smoking seems to be associatedwith dose-dependent brain injury.

Interpretation and Mechanisms of Smoking Effects onMetabolites

The many toxic compounds in cigarette smoke (e.g., tar,carbon monoxide, formaldehyde, and nitrosamines; Fowleset al., 2000) may directly compromise the neuronal andcellular membrane function of CNS tissue. Smoking mayalso have indirect effects on brain tissue, in particular onsubcortical structures, via cerebrovascular compromisesuch as atherosclerosis (Bolego et al., 2002; Iida et al.,2003) and nicotine-induced alterations of vascular endothe-lial function (Hawkins et al., 2002). Therefore, atheroscle-rosis and/or impaired vasomotor regulation may alter theperfusion of penetrating branches of the posterior cerebralartery (which perfuses the midbrain; Marinkovic et al.,2001), thereby reducing midbrain NAA and choline insmokers. Consistent with altered cerebrovascular function,healthy cigarette smokers, relative to nonsmokers, haveboth lower global cerebral (Yamamoto et al., 2003) andlimbic system blood flow (Domino et al., 2004; Rose et al.,2003; Zubieta et al., 2001). Additionally, cigarette smokinghas been specifically linked to WM disease of brainstemstructures (Ding et al., 2003) and to severity of WM signalhyperintensities (Fukuda and Kitani, 1996; Tsushima et al.,2002), presumably secondary to cerebrovascular compro-mise. The suggestion of greater overall severity of the MRIWM signal hyperintensities of our sRAs compared withnsRAs is consistent with this link. Although chronic, heavyalcohol consumption is linked to hypertension (Parekh andKlag, 2001), in our study, the small number of subjects withwell-controlled hypertension in the RA groups likely doesnot represent a significant confound for the metabolitedifferences observed among groups. With respect to nico-tine/alcohol interactions, rat models of short-term alcoholexposure show that nicotine protects cerebellar granularneurons in vitro (Tizabi et al., 2003) and olfactory bulb cellsin vivo (Penland et al., 2001) from alcohol-induced toxicity.Similarly, Prendergast et al. (2000) demonstrated that nic-otine protected rat hippocampal neurons from chronicalcohol-induced damage, which may be related to intracel-lular Ca2� regulation (Mulholland et al., 2003). However,Penland et al. (2001) found that animals concurrently ad-ministered nicotine and alcohol tended to show more neu-ronal damage in the perirhinal and entorhinal corticescompared to control and nicotine-only groups, which sug-gests that the combination of both substances results in anadditive adverse affect. Whereas these studies suggest bothprotective and damaging effects of nicotine on neural tissue

CIGARETTE SMOKING AND ALCOHOL-INDUCED BRAIN DAMAGE 1857

in the presence of high alcohol concentrations, our resultsin humans do not indicate that long-term chronic cigarettesmoking offers protection from alcohol-induced brain in-jury. To the contrary, our results suggest that cigarettesmoking exacerbates alcohol-induced neuronal and cellularmembrane dysfunction in the frontal lobes and, in addition,has independent damaging effects on vermian and mid-brain tissue. Finally, higher nicotine tolerance levels andcigarette consumption are associated with lower thalamicand lenticular NAA concentrations. Thus, previous brainMRS results obtained in chronic alcohol drinkers may bepartially confounded by chronic effects of smoking.

Limitations

A relatively small cohort with unbalanced group mem-bership and a limited assessment of cognitive function re-strict the generalizability of these preliminary findings. Dif-ferences in smoking severity between sRAs and sLDs mayhave affected the results of the four-group analysis of co-variance, in particular the lack of alcohol status � smokingstatus interactions. Finally, our RA sample did not includewomen, but we included women in the LD group to in-crease the overall smoking severity in this group. It is notclear whether cigarette smoking affects brain metabolitesor cognition differently as a function of gender.

In conclusion, our results replicate previous findings ofneuronal and cellular membrane damage in recently detox-ified RAs. Most notably, they provide preliminary indica-tions that cigarette smoking in RAs has significant anddose-dependent detrimental effects on region-specificbrain metabolite concentrations. Specifically, smoking sig-nificantly compounds alcohol-induced brain damage in thefrontal lobe of RAs and has independent adverse effects ontissues in subcortical structures. Prospective studies areneeded that match LDs and RAs on measures of smokingseverity to evaluate more comprehensively the independentand potentially interactive effects of smoking and alcoholdependence on brain metabolites, neurocognitive function,and their interrelationships. Moreover, the potential effectsof cigarette smoking should be given greater considerationin future studies of CNS pathology in which smoking is acomorbid factor.

ACKNOWLEDGMENTS

We thank Dr. Donald Tusel, Mary Rebecca Young, and BillClift of the San Francisco VA Substance Abuse Day Hospital andDr. David Pating, Karen Moise, and their colleagues at the SanFrancisco Kaiser Permanente Chemical Dependency RecoveryProgram for their valuable assistance in recruiting participants;Dr. Pratik Mukherjee for clinical interpretation of MRI data;Derek Flenniken for database assistance; and Dr. John Kornakfor statistical consultation. We also extend our gratitude to Dr.Mary-Anne Enoch for inspiring and illuminating discussionsalong the way.

REFERENCES

Abou-Donia MB, Abdel-Rahman A, Goldstein LB, Dechkovskaia AM,Shah DU, Bullman SL, Khan WA (2003) Sensorimotor deficits andincreased brain nicotinic acetylcholine receptors following exposure tochlorpyrifos and/or nicotine in rats. Arch Toxicol 77:452–458.

American Psychological Association (1994) Diagnostic and Statistical Man-ual of Mental Disorders (4th ed.) American Psychological AssociationPress, Washington, D.C.

Barker PB, Breiter SN, Soher BJ, Chathamn JC, Forder JR, SamphilipoMA, Magee CA, Anderson JA (1994) Quantitative proton spectroscopyof canine brain: in vivo and in vitro correlations. Magn Reson Med32:157–163.

Bartal M (2001) Health effects of tobacco use and exposure. MonaldiArch Chest Dis 56:545–554.

Bates C, Jarvis M, Connolly G (1999) Tobacco Additives: Cigarette Engi-neering and Nicotine Addiction, pp 1–23. Massachusetts Tobacco Con-trol Program, Boston.

Beck AT (1978) Depression Inventory. Center for Cognitive Therapy,Philadelphia.

Bendszus M, Weijers HG, Wiesbeck G, Warmuth-Metz M, Bartsch AJ,Engels S, Boning J, Solymosi L (2001) Sequential MR imaging andproton MR spectroscopy in patients who underwent recent detoxifica-tion for chronic alcoholism: correlation with clinical and neuropsycho-logical data. AJNR Am J Neuroradiol 22:1926–1932.

Benedict R (1997) Brief Visuospatial Memory Test–Revised. PsychologicalAssessment Resources, Odessa, FL.

Bjork JM, Grant SJ, Hommer DW (2003) Cross-sectional volumetricanalysis of brain atrophy in alcohol dependence: effects of drinkinghistory and comorbid substance use disorder. Am J Psychiatry 160:2038–2045.

Bolego C, Poli A, Paoletti R (2002) Smoking and gender. Cardiovasc Res53:568–576.

Brand A, Richter-Landsberg C, Leibfritz D (1993) Multinuclear NMRstudies on the energy metabolism of glial and neuronal cells. DevNeurosci 15:289–298.

Brody AL, Mandelkern MA, Jarvik ME, Lee GS, Smith EC, Huang JC,Bota RG, Bartzokis G, London ED (2004) Differences between smok-ers and nonsmokers in regional gray matter volumes and densities. BiolPsychiatry 55:77–84.

Daeppen JB, Smith TL, Danko GP, Gordon L, Landi NA, Nurnberger JIJr, Bucholz KK, Raimo E, Schuckit MA (2000) Clinical correlates ofcigarette smoking and nicotine dependence in alcohol-dependent menand women. The Collaborative Study Group on the Genetics of Alco-holism. Alcohol Alcohol 35:171–175.

Deary IJ, Pattie A, Taylor MD, Whiteman MC, Starr JM, Whalley LJ(2003) Smoking and cognitive change from age 11 to age 80. J NeurolNeurosurg Psychiatry 74:1003–1007.

Degenhardt L, Hall W (2003) Patterns of co-morbidity between alcoholuse and other substance use in the Australian population. Drug AlcoholRev 22:7–13.

De Stefano N, Matthews PM, Arnold DL (1995) Reversible decreases inN-acetylaspartate after acute brain injury. Magn Reson Med 34:721–727.

Ding J, Nieto FJ, Beauchamp NJ, Longstreth WT Jr, Manolio TA, Het-manski JB, Fried LP (2003) A prospective analysis of risk factors forwhite matter disease in the brain stem: the Cardiovascular HealthStudy. Neuroepidemiology 22:275–282.

Domino EF, Ni L, Xu Y, Koeppe RA, Guthrie S, Zubieta JK (2004)Regional cerebral blood flow and plasma nicotine after smoking to-bacco cigarettes. Prog Neuropsychopharmacol Biol Psychiatry 28:319–327.

Drobes DJ (2002) Cue reactivity in alcohol and tobacco dependence.Alcohol Clin Exp Res 26:1928–1929.

Fagerstrom KO, Heatherton TF, Kozlowski LT (1991) Nicotine addictionand its assessment. Ear Nose Throat J 69:763–765.

1858 DURAZZO ET AL.

Fein G, Meyerhoff DJ, Di Sclafani V, Ezekiel F, Poole N, MacKay S,Dillon WP, Constans J-M, Weiner MW (1994) 1H magnetic resonancespectroscopic imaging separates neuronal from glial changes in alcohol-related brain atrophy, in Alcohol and Glial Cells (NIAAA ResearchMonograph No. 27), pp 227–241, National Institutes of Health, Be-thesda, MD.

Ferguson KJ, MacLullich AM, Marshall I, Deary IJ, Starr JM, Seckl JR,Wardlaw JM (2002) Magnetic resonance spectroscopy and cognitivefunction in healthy elderly men. Brain (Pt 12) 125:2743–2749.

Fowles J, Bates M, Noiton D (2000) The Chemical Constituents in Ciga-rettes and Cigarette Smoke: Priorities for Harm Reduction, pp 1–65.Epidemiology and Toxicology Group, Institute of Environmental Sci-ence and Research Limited, Kenepuru Science Centre, Porirua, NewZealand.

Fukuda H, Kitani M (1996) Cigarette smoking is correlated with theperiventricular hyperintensity grade of brain magnetic resonance imag-ing. Stroke 27:645–649.

Garey KW, Neuhauser MM, Robbins RA, Danziger LH, Rubinstein I(2004) Markers of inflammation in exhaled breath condensate of younghealthy smokers. Chest 125:22–26.

Grober E, Sliwinski M (1991) Development and validation of a model forestimating premorbid verbal intelligence in the elderly. J Clin ExpNeuropsychol 13:933–949.

Hawkins BT, Brown RC, Davis TP (2002) Smoking and ischemic stroke:a role for nicotine? Trends Pharmacol Sci 23:78–82.

Hayee A, Haque A, Anwarullah AKM, Rabbani MG (2003) Smokingenhances age related brain atrophy—a quantitative study with com-puted tomography. Bangladesh Med Res Counc Bull 23:118–124.

Hill RD, Nilsson LG, Nyberg L, Backman L (2003) Cigarette smoking andcognitive performance in healthy Swedish adults. Age Ageing 32:548–550.

Hugg JW, Kuzniecky RI, Gilliam FG, Morawetz RB, Faught RE, Heth-erington HP (1996) Normalization of contralateral metabolic functionfollowing temporal lobectomy demonstrated by 1H magnetic resonancespectroscopic imaging. Ann Neurol 40:236–239.

Hurt RD, Eberman KM, Croghan IT, Offord KP, Davis LJ Jr, Morse RM,Palmen MA, Bruce BK (1994) Nicotine dependence treatment duringinpatient treatment for other addictions: a prospective interventiontrial. Alcohol Clin Exp Res 18:867–872.

Iida M, Iida H, Fujiwara H, Dohi S (2003) Effects of alcohol infusion onsmoking-induced cerebrovascular changes in rat in vivo. Alcohol 30:175–181.

Jagannathan NR, Desai NG, Raghunathan P (1996) Brain metabolitechanges in alcoholism: an in vivo proton magnetic resonance spectros-copy (MRS) study. Magn Reson Imaging 14:553–557.

John U, Meyer C, Rumpf HJ, Schumann A, Thyrian JR, Hapke U (2003)Strength of the relationship between tobacco smoking, nicotine depen-dence and the severity of alcohol dependence syndrome criteria in apopulation-based sample. Alcohol Alcohol 38:606–612.

Kalmijn S, van Boxtel MP, Verschuren MW, Jolles J, Launer LJ (2002)Cigarette smoking and alcohol consumption in relation to cognitiveperformance in middle age. Am J Epidemiol 156:936–944.

Kampman KM, Pettinati HM, Volpicelli JR, Oslin DM, Lipkin C, Spark-man T, O’Brien CP (2004) Cocaine dependence severity predicts out-come in outpatient detoxification from cocaine and alcohol. Am JAddict 13:74–82.

Kubota K, Matsuzawa T, Fujiwara T, Yamaguchi T, Ito K, Watanabe H,Ono S (1987) Age-related brain atrophy enhanced by smoking: a quan-titative study with computed tomography. Tohoku J Exp Med 153:303–311.

Launer LJ, Andersen K, Dewey ME, Letenneur L, Ott A, Amaducci LA,Chao LL, Rothlind J, Studholme C, Weiner MW (1999) Rates and riskfactors for dementia and Alzheimer’s disease: results from EURODEMpooled analyses. EURODEM Incidence Research Group and WorkGroups. Eur Stud Dement Neurol 52:78–84.

Madden PA, Heath AC (2002) Shared genetic vulnerability in alcohol andcigarette use and dependence. Alcohol Clin Exp Res 26:1919–1921.

Marinkovic S, Gibo H, Milisavljevic M, Cetkovic M (2001) Anatomic andclinical correlations of the lenticulostriate arteries. Clin Anat 14:190–195.

Marks JL, Hill EM, Pomerleau CS, Mudd SA, Blow FC (1997) Nicotinedependence and withdrawal in alcoholic and nonalcoholic ever-smokers. J Subst Abuse Treat 14:521–527.

Merchant C, Tang MX, Albert S, Manly J, Stern Y, Mayeux R (1999) Theinfluence of smoking on the risk of Alzheimer’s disease. Neurology52:1408–1412.

Meyerhoff D, Blumenfeld R, Truran D, Lindgren J, Flenniken D, Carde-nas V, Brayne C, Copeland JR, Dartigues JF, Kragh-Sorensen P, LoboA, Martinez-Lage JM, Stijnen T, Hofman A (2004) Effects of heavydrinking, binge drinking, and family history of alcoholism on regionalbrain metabolites. Alcohol Clin Exp Res 28:650–661.

Miller BL, Chang L, Booth R, Ernst T, Cornford M, Nikas D, McBride D,Jenden DJ (1996) In vivo 1H MRS choline: correlation with in vitrochemistry/histology. Life Sci 58:1929–1935.

Moffett JR, Namboodiri MA, Cangro CB, Neale JH (1991) Immunohis-tochemical localization of N-acetylaspartate in rat brain. Neuroreport2:131–134.

Mulholland PJ, Harris BR, Wilkins LH, Self RL, Blanchard JA, HolleyRC, Littleton JM, Prendergast MA (2003) Opposing effects of ethanoland nicotine on hippocampal calbindin-D28k expression. Alcohol 31:1–10.

Narahashi T, Soderpalm B, Ericson M, Olausson P, Engel JA, Zhang X,Nordberg A, Marszalec W, Aistrup GL, Schmidt LG, Kalouti U,Smolka M, Hedlund L (2001) Mechanisms of alcohol-nicotine inter-actions: alcoholics versus smokers. Alcohol Clin Exp Res (5 SupplISBRA) 25:152S–156S.

O’Neill J, Cardenas VA, Meyerhoff DJ (2001) Separate and interactiveeffects of cocaine and alcohol dependence on brain structures andmetabolites: quantitative MRI and proton MR spectroscopic imaging.Addict Biol 6:347–361.

Oscar-Berman M (2000) Neuropsychological vulnerabilities in chronicalcoholism, in Review of NIAAA’s Neuroscience and Behavioral ResearchPortfolio (NIAAA Research Monograph No. 34), pp 437–472. NationalInstitute of Alcohol Abuse and Alcoholism, Bethesda, MD.

Ott A, Andersen K, Dewey ME, Letenneur L, Brayne C, Copeland JR,Dartigues JF, Kragh-Sorensen P, Lobo A, Martinez-Lage JM, Stijnen J,Hofman A, Launer LJ (2004) Effect of smoking on global cognitivefunction in nondemented elderly. Neurology 62:920–924.

Ott A, Slooter AJ, Hofman A, van Harskamp F, Witteman JC, VanBroeckhoven C, van Duijn CM, Breteler MM (1998) Smoking and riskof dementia and Alzheimer’s disease in a population-based cohortstudy: the Rotterdam Study. Lancet 351:1840–1843.

Parekh RS, Klag MJ (2001) Alcohol: role in the development of hyper-tension and end-stage renal disease. Curr Opin Nephrol Hypertens10:385–390.

Parks MH, Dawant BM, Riddle WR, Hartmann SL, Dietrich MS, NickelMK, Price RR, Martin PR (2002) Longitudinal brain metabolic char-acterization of chronic alcoholics with proton magnetic resonance spec-troscopy. Alcohol Clin Exp Res 26:1368–1380.

Penland S, Hoplight B, Obernier J, Crews FT (2001) Effects of nicotine onethanol dependence and brain damage. Alcohol 24:45–54.

Pomerleau CS, Aubin HJ, Pomerleau OF (1997) Self-reported alcohol usepatterns in a sample of male and female heavy smokers. J Addict Dis16:19–24.

Prendergast MA, Harris BR, Mayer S, Littleton JM (2000) Chronic, butnot acute, nicotine exposure attenuates ethanol withdrawal-inducedhippocampal damage in vitro. Alcohol Clin Exp Res 24:1583–1592.

Prendergast MA, Rogers DT, Barron S, Bardo MT, Littleton JM (2002)Ethanol and nicotine: a pharmacologic balancing act? Alcohol Clin ExpRes 26:1917–1918.

Razani, J, Boone K, Lesser I, Weiss D (2004) Effects of cigarette smokinghistory on cognition functioning in healthy older adults. Am J GeriatrPsychiatry 12(4)404–411.

CIGARETTE SMOKING AND ALCOHOL-INDUCED BRAIN DAMAGE 1859

Romberger DJ, Grant K (2004) Alcohol consumption and smoking status:the role of smoking cessation. Biomed Pharmacother 58:77–83.

Rose JE, Behm FM, Westman EC, Mathew RJ, London ED, Hawk TC,Turkington TG, Coleman RE (2003) PET studies of the influences ofnicotine on neural systems in cigarette smokers. Am J Psychiatry 160:323–333.

Ross B, Bluml S (2001) Magnetic resonance spectroscopy of the humanbrain. Anat Rec 265:54–84.

Schinka JA, Belanger H, Mortimer JA, Graves AB (2003) Effects of theuse of alcohol and cigarettes on cognition in elderly African Americanadults. J Int Neuropsychol Soc 9:690–697.

Schuff N, Ezekiel F, Gamst A, Amend D, Capizzano A, Maudsley AA,Weiner MW (2001) Region and tissue differences of metabolites innormally aged brain using 1H magnetic resonance spectroscopic imag-ing. Magn Reson Med 45:899–907.

Schweinsburg BC, Taylor MJ, Videen JS, Alhassoon OM, Patterson TL,Grant I (2000) Elevated myo-inositol in gray matter of recently detox-ified but not long-term alcoholics: a preliminary MR spectroscopystudy. Alcohol Clin Exp Res 24:699–770.

Seitz D, Widmann U, Seeger U, Nagele T, Klose U, Mann K, Grodd W(1999) Localized proton magnetic resonance spectroscopy of the cere-bellum in detoxifying alcoholics. Alcohol Clin Exp Res 23:158–163.

Simmons ML, Frondoza CG, Coyle JT (1991) Immunocytochemical lo-calization of N-acetyl-aspartate with monoclonal antibodies. Neuro-science 45:37–45.

Skinner HA, Sheu WJ (1982) Reliability of alcohol use indices. TheLifetime Drinking History and the MAST. J Stud Alcohol 43:1157–1170.

Soher BJ, Vermathen P, Schuff N, Wiedermann D, Meyerhoff DJ, WeinerMW, Maudsley AA (2000) Short TE in vivo (1)H MR spectroscopicimaging at 1.5 T: acquisition and automated spectral analysis. MagnReson Imaging 18:1159–1165.

Spielberger CD, Gorsuch RL, Lushene R, Vagg PR, Jacobs GA (1977)Self-Evaluation Questionnaire. Consulting Psychologists Press, Inc.,Palo Alto, CA.

Sullivan E (2003) Compromised pontocerebellar and cerebellothalamo-cortical systems: speculations on their contributions to cognitive and

motor impairment in nonamnesic alcoholism. Alcohol Clin Exp Res27:1409–1419.

Sullivan EV (2000) Human brain vulnerability to alcoholism: evidencefrom neuroimaging studies, in Review of NIAAA’s Neuroscience andBehavioral Research Portfolio (NIAAA Research Monograph No. 34)(Noronha A, Eckardt M, Warren K eds), pp 473–508. National Instituteon Alcohol Abuse and Alcoholism, Bethesda, MD.

Sullivan EV, Harding AJ, Pentney RJ, Dlugos CA, Martin PR, Parks MH,Desmond JE, Chen SH, Pryor MR, De Rosa E, Pfefferbaum A (2003)Disruption of frontocerebellar circuitry and function in alcoholism.Alcohol Clin Exp Res 27:301–309.

Tizabi Y, Al-Namaeh M, Manaye KF, Taylor RE (2003) Protective effectsof nicotine on ethanol-induced toxicity in cultured cerebellar granulecells. Neurotoxicol Res 5:315–321.

Tsushima Y, Tanizaki Y, Aoki J, Endo K (2002) MR detection of micro-hemorrhages in neurologically healthy adults. Neuroradiology 44:31–36.

Vion-Dury J, Meyerhoff DJ, Cozzone PJ, Weiner MW (1994) What mightbe the impact on neurology of the analysis of brain metabolism by invivo magnetic resonance spectroscopy (editorial)? J Neurol 241:354–371.

Wechsler D (1997) Wechsler Memory Scale. 3rd ed. Psychological Corpo-ration, San Antonio, TX.

Weinrebe W, Graf-Gruss R, Schwabe R, Stippler D, Fusgen I (2002) Thetwo-factor method—a new approach to categorizing the clinical stagesof malnutrition in geriatric patients. J Am Geriatr Soc 50:2105–2107.

Wiedermann D, Schuff N, Matson GB, Soher BJ, Du AT, Maudsley AA,Weiner MW (2001) Short echo multislice MR spectroscopic imaging:metabolite distributions and reliability. Magn Reson Imaging 19:1073–1080.

Yamamoto Y, Nishiyama Y, Monden T, Satoh K, Ohkawa M (2003) Astudy of the acute effect of smoking on cerebral blood flow using99mTc-ECD SPET. Eur J Nucl Med Mol Imaging 30:612–614.

York JL, Hirsch JA (1995) Drinking patterns and health status in smokingand nonsmoking alcoholics. Alcohol Clin Exp Res 19:666–673.

Zubieta J, Lombardi U, Minoshima S, Guthrie S, Ni L, Ohl LE, KoeppeRA, Domino EF (2001) Regional cerebral blood flow effects of nicotinein overnight abstinent smokers. Biol Psychiatry 49:906–913.

1860 DURAZZO ET AL.