Chronic Smoking Is Associated With Differential

Neurocognitive Recovery in Abstinent Alcoholic Patients:

A Preliminary Investigation

Timothy C. Durazzo, Johannes C. Rothlind, Stefan Gazdzinski, Peter Banys, andDieter J. Meyerhoff

Background: Approximately 50 to 90% of individuals in North America seeking treatment foralcoholism are chronic smokers. A growing body of evidence suggests that chronic cigarette smokersshow a pattern of neurocognitive dysfunction similar to that observed in alcoholic patients. However,previous studies investigating neurocognitive recovery in abstinent alcoholic patients did not specif-ically consider the potential effects of chronic cigarette smoking.

Methods: This study comprehensively compared longitudinal neurocognitive changes over 6 to 9months of abstinence among 13 nonsmoking recovering alcoholic patients (ALC) and 12 activelysmoking ALC. The neurocognitive performance of the alcoholic groups was compared with non-smoking light-drinking controls (nonsmoking LD).

Results: Nonsmoking ALC exhibited a significantly greater magnitude of longitudinal improve-ment than smoking ALC on measures of cognitive efficiency, executive skills, visuospatial skills, andworking memory. Both nonsmoking ALC and smoking ALC demonstrated equivalent improvementon auditory-verbal learning, auditory-verbal memory, and processing speed. Nonsmoking LDshowed no significant changes in neurocognition over time. In cross-sectional comparisons at 6 to 9months of abstinence, nonsmoking ALC were superior to smoking ALC on measures of auditory-verbal learning, auditory-verbal memory, cognitive efficiency, executive skills, processing speed, andworking memory. The longitudinal and cross-sectional neurocognitive differences observed betweennonsmoking and smoking ALC remained significant after covarying for group differences in educa-tion, estimated premorbid intelligence alcohol consumption, and other potentially confoundingvariables. In smoking ALC, greater smoking severity was inversely related to longitudinal improve-ment on multiple neurocognitive measures.

Conclusions: These preliminary results suggest that chronic smoking may modulate neuro-cognitive recovery in abstinent alcoholic patients. More generally, chronic smoking may impactneurocognition in other conditions where is it a prevalent behavior.

Key Words: Alcoholism, Alcohol Dependence, Chronic Cigarette Smoking, Neurocognition,Recovery.

CHRONIC ALCOHOL USE DISORDERS (AUD;i.e., alcohol dependence and alcohol abuse) are

associated with abnormalities in several domains of neuro-cognitive functioning as well as gait, postural stability,and motor functioning (see Oscar-Berman, 2000; Rourke

and Loberg, 1996; Sullivan, 2003 for a review). Althoughthe nature and level of impairment may vary considerablyacross individuals, studies have consistently reported thatAUD are specifically associated with dysfunction incognitive efficiency (Glenn and Parsons, 1991, 1992;Lawton-Craddock et al., 2003; Nixon and Parsons, 1991;Nixon et al., 1995), executive skills (Bechara et al., 2001;Ihara et al., 2000; Moselhy et al., 2001; Sullivan et al.,2000b), learning and memory (Beatty et al., 1996; Brandtet al., 1983; Di Sclafani et al., 1995; Fama et al., 2004; Feinet al., 1990; Munro et al., 2000; Ratti et al., 1999; Rourkeand Grant, 1999), processing speed (Davies et al., 2005;Parsons, 1998; Schulte et al., 2005), visuospatial skills(Beatty et al., 1996; Munro et al., 2000; Sullivan et al.,2000b), working memory (Brandt et al., 1983; Ratti et al.,1999), and gait and postural stability (Sullivan, 2003;Sullivan et al., 1995, 2000b). In those who manifest neuro-cognitive dysfunction after acute detoxification, variablelevels of recovery are apparent with short-term (i.e., 1–5

From the San Francisco Veterans Administration Medical Center,San Francisco, California (TCD, JCR, SG, PB, DJM); the NorthernCalifornia Institute for Research and Education, San Francisco, Califor-nia (SG, DJM); the Department of Radiology, University of California,San Francisco, California (TCD, DJM); and the Department of Psy-chiatry, University of California, San Francisco, California (JCR, PB).

Received for publication December 7, 2006; accepted February 26,2007.

This project was supported by NIH AA10788 (DJM).

Reprint requests: Timothy C. Durazzo, PhD, San Francisco VeteransAdministration Medical Center, Center for Imaging of Neurodegenera-tive Disease (114M), 4150 Clement Street, San Francisco, CA 94121;Fax: 415-668-2864; E-mail: timothy.durazzo@ucsf.edu

Copyright r 2007 by the Research Society on Alcoholism

DOI: 10.1111/j.1530-0277.2007.00398.x

Alcohol Clin Exp Res, Vol 31, No 7, 2007: pp 1114–11271114

ALCOHOLISM: CLINICAL AND EXPERIMENTAL RESEARCH Vol. 31, No. 7July 2007

months; Mann et al., 1999; Munro et al., 2000), interme-diate-term (i.e., 6–12 months; Munro et al., 2000), andlong-term abstinence from alcohol (i.e., 41 year; Feinet al., 2006; Mann et al., 1999; Munro et al., 2000; Reedet al., 1992; Rourke and Grant, 1999; Sullivan et al.,2000a). However, residual dysfunction has been reportedto persist in several domains of functioning after short-term (Bates, 1997; Sullivan et al., 2000b; Wegner et al.,2001), intermediate-term (Di Sclafani et al., 1995;Rosenbloom et al., 2004), and long-term abstinence,particularly in the areas of executive skills, memory, vis-uospatial skills, and postural stability (Bates et al., 2002;Brandt et al., 1983; Munro et al., 2000; Rosenbloom et al.,2004; Wegner et al., 2001). Age, sex, treatment history,level of alcohol consumption, relapse history, nutritionalstatus, a family history of alcoholism, genetically basedresilience, comorbid psychiatric conditions, and concur-rent abuse of other substances may affect the magnitude ofneurocognitive dysfunction and recovery demonstrated inAUD (Fein et al., 1990; Fein and Landman, 2005;Lawton-Craddock et al., 2003; Munro et al., 2000; Oscar-Berman, 2000; Parsons, 1998; Parsons and Nixon, 1998;Reed et al., 1992; Rourke and Grant, 1999; Sullivan andPfefferbaum, 2005).Comorbid consumption of other substances such as

tobacco products is common in AUD. Approximately80% of alcohol-dependent individuals are chronic smok-ers (Hurt et al., 1994; Pomerleau et al., 1997; Rombergerand Grant, 2004), and 50 to 90% of individuals in NorthAmerica seeking treatment for AUD are active smokers(Durazzo et al., 2006b; Room, 2004). Chronic smoking inAUD is highly stable over extended periods (Culverhouseet al., 2005), and the majority of smoking individuals withAUD continue to smoke following treatment for AUD(Friend and Pagano, 2005) as well as during abstinencefrom alcohol (Currie et al., 2001). Chronic smoking alonein adults is associated with dysfunction in auditory-verballearning and memory (Fried et al., 2006; Hill et al., 2003;Schinka et al., 2003), prospective memory (Heffernanet al., 2005), working memory (Ernst et al., 2001; Georgeet al., 2002; Spilich et al., 1992), executive functions (Paulet al., 2006; Razani et al., 2004), psychomotor speed andcognitive flexibility (Kalmijn et al., 2002; Starr et al.,2007), visual search speeds (Richards et al., 2003), generalintellectual abilities (Deary et al., 2003), and posturalstability (Iki et al., 1994).We previously reported that smoking recovering

alcoholic patients (ALC) were inferior to nonsmokingrecovering ALC on measures of auditory-verbal learningand memory, cognitive efficiency, processing speed, andstatic postural stability after approximately 1 month ofabstinence from alcohol (Durazzo et al., 2006b). Theinferior performance of the smoking recovering ALCwas not a function of age, education, lifetime alcoholconsumption, and estimated premorbid intelligence, ormediated by other comorbid psychiatric or medical condi-

tions. In smoking recovering ALC, both higher nicotinedependence and number of cigarettes smoked per day wereassociated with poorer auditory-verbal memory, and long-er smoking duration was negatively correlated with per-formance on measures of executive skills, visuospatiallearning, cognitive efficiency, and postural stability. Thesecross-sectional results are consistent with those of Friendet al. (2005), who reported that nonsmoking ALC weresuperior to smoking ALC on tasks assessing set-shiftingand processing speed, and who observed that the combi-nation of excessive alcohol consumption and smokingpredicted poorer performance on these tasks. Further-more, Glass et al. (2006) found that both chronic AUDand smoking severity were inversely related to neurocog-nitive function, and that smoking severity (i.e., pack years)was a unique predictor of general intelligence and cogni-tive proficiency, an index of speed and accuracy.Previous longitudinal studies of neurocognitive recovery

in AUD did not consider smoking status as a potentialcontributing factor in the changes observed. Therefore, itis unknown whether nonsmoking and chronically smokingindividuals with AUD show equivalent rates and levels ofrecovery with sustained abstinence from alcohol. The goalof this study was to examine longitudinal changes inneurocognition among smoking and nonsmoking recover-ing ALC after 6 to 9 months of abstinence from alcohol.We tested the following hypotheses:

(1) Over 6 to 9 months of abstinence from alcohol, non-smoking ALC demonstrate a greater magnitude oflongitudinal improvement than smoking ALC on thedomains of cognitive efficiency, executive skills, learn-ing and memory, processing speed, visuospatial skills,and static postural stability.

(2). At 6 to 9 months of abstinence from alcohol, non-smoking ALC perform superior to smoking ALC inthe above domains.

(3). Greater smoking and drinking severity are inverselyrelated to longitudinal neurocognitive improvement.

METHODS

Participants

Alcoholic patients were recruited from the VA Medical CenterSubstance Abuse DayHospital and the Kaiser Permanente ChemicalDependence Recovery Program outpatient clinics in San Franciscofor a longitudinal study on the effects of abstinence from alcohol onbrain neurobiology and neurocognition. Nonsmoking light drinkingcontrols (nonsmoking LD) were recruited from the community. Atthe time of enrolment, all participants were between the ages of 28and 66, and had approximately 7 � 3 days of sobriety. From ourlongitudinal cohort, 13 nonsmoking ALC (1 female) and 12 smokingALC (1 female) first completed a comprehensive neuropsychologicalassessment battery after 33 � 9 days of sustained abstinence formalcohol (assessment 1). Nonsmoking ALC were re-evaluated after282 � 159 and smoking ALC after 194 � 38 days of abstinence fromalcohol (p5 0.08) (assessment 2). The interval between assessments1 and 2 was 247 � 163 for nonsmoking ALC and 178 � 57 forsmoking ALC (p5 0.17). Twenty-two nonsmoking LD (2 females)

1115CHRONIC SMOKING IN ABSTINENT ALCOHOLIC PATIENTS

were evaluated with the comprehensive battery and 8 of these par-ticipants were again studied after 334 � 129 days. The majority ofthe ALC participants also took part in the study of Durazzo et al.(2006b).

Primary inclusion criteria were current DSM-IV diagnosis ofalcohol dependence or abuse (American Psychiatric Association,1994), fluency in English, consumption of4150 alcoholic drinks permonth (1 alcoholic drink equivalent5 13.6 g pure alcohol) for atleast 8 years before enrolment for men, and consumption of 480drinks per month for at least 6 years before enrolment for women.Primary exclusion criteria are fully detailed in Durazzo et al. (2004).In summary, all participants were free of general medical, neurolog-ic, and psychiatric conditions known or suspected to influenceneurocognition, except for hepatitis C, hypertension, and unipolarmood disorders in ALC participants. Current or past unipolar mooddisorders (e.g., major depression, substance-induced mood disorder)were not exclusionary in ALC, given the reported high comorbiditywith both alcoholism (Gilman and Abraham, 2001) and chronic cig-arette smoking (Fergusson et al., 2003; Paperwalla et al., 2004). Allparticipants were screened for recent use of common illicit substanc-es and for recent ethanol consumption. No participant was positivefor the above-listed substances or ethanol at assessment 1 or 2, andno participant reported alcohol or substance use between enrolmentand assessment 2. All participants gave written informed consent,which had been approved by review boards of the University of Cal-ifornia, San Francisco and the San Francisco VA Medical Center.

Psychiatric/Behavioral Assessment

At the time of study enrolment, ALC participants completed theStructured Clinical Interview for DSM-IV Axis I disorders, PatientEdition, Version 2.0 (SCID-I/P; American Psychiatric Association,1994) and standardized questionnaires assessing depressive [BeckDepression Inventory (BDI); Beck, 1978] and anxiety symptomato-logy [State-Trait Anxiety Inventory form Y-2 (STAI); Spielbergeret al., 1977], lifetime alcohol consumption (Lifetime DrinkingHistory; Skinner and Sheu, 1982) and substance use (in-house ques-tionnaire assessing substance type, and quantity and frequency ofuse), and level of nicotine dependence via the Fagerstrom ToleranceTest for Nicotine Dependence (FTND) (Fagerstrom et al., 1991).Based on the Lifetime Drinking History, the average number ofalcoholic drinks per month were calculated for 1, 3, and 8 yearsbefore enrolment, average number of drinks per month over a life-time, number of lifetime years of regular drinking (i.e., duration forwhich the participant began consuming at least 1 alcoholic drink permonth), number of months of heavy drinking [i.e., total number ofmonths over lifetime that males drank in excess of 100 (80 forfemales) drinks per month], and age of onset of heavy drinking. Atboth assessment points, the total number of cigarettes smoked perday and the number of years of smoking at the current level wererecorded and pack years [i.e., (number of cigarettes per day/20)�number of years of smoking at level reported at enrolment]were calculated for smoking ALC participants.

Laboratory Tests

g-Glutamyltransferase (GGT), liver function panels, prealbumin,complete blood count, and common electrolytes were obtained atboth assessments. g-Glutamyltransferase level served as a marker ofrecent heavy alcohol consumption (Sillanaukee et al., 2000), and theplasma protein prealbumin provided information on nutritional sta-tus (Weinrebe et al., 2002).

Neurocognitive Assessment

Alcoholic patients completed comprehensive neuropsychologicaland motor/ataxia assessment (approximately 2.5 hours), which eval-

uated neurocognitive functions known to be adversely affected byalcohol dependence (Rourke and Grant, 1999) as well as chroniccigarette smoking. Alternate forms were used where available forassessment 2. Smoking ALC were allowed to smoke ad libitumbefore and during neurocognitive testing at both assessments. Thedomains assessed and the constituent measures were as follows:executive skills: Short Categories Test (Wetzel and Boll, 1987),Stroop Color–Word Test (Golden, 1978), Trail Making Test part B(Reitan and Wolfson, 1985), Wechsler Adult Intelligence Scale 3rded. (WAIS-III) (Wechsler, 1997)- Similarities, Wisconsin CardSorting Test-64: Computer Version 2-Research Edition (Heatonet al., 1993) nonperseverative errors, perseverative errors, andperseverative responses. Fine motor skills: Grooved Peg Board(Lafayette Instrument, Lafayette, IN). General intelligence: Ward-7Full Scale IQ (Axelrod et al., 2001; based on WAIS-III Arithmetic,Block Design, Digit Span, Digit Symbol, Information, PictureCompletion, and Similarities subtests). Learning and memory: Audi-tory-verbal: California Verbal Learning Test-II (Delis et al., 2000),Immediate Recall trials 1 to 5 (learning), Short and Long Delay FreeRecall (memory). Visuospatial: Brief Visual Memory Test—Revised(Benedict, 1997), Total Recall (learning), and Delayed Recall (mem-ory). Postural stability: Modified Fregly Ataxia Battery, SharpenedRomberg, eyes open and eyes closed trials (Fregly and Graybiel,1968). Processing speed: WAIS-III Digit Symbol, Stroop Color &Word (Golden et al., 1978), WAIS-III Symbol Search Trail MakingTest-A (Reitan and Wolfson, 1985). Visuospatial skills: WAIS-IIIBlock Design; Luria-Nebraska Item 99 (Golden et al., 1978). Work-ing memory:WAIS-III Arithmetic, WAIS-III Digit Span. Timed-testcomposite. Premorbid verbal intelligence was predicted with theAmerican National Adult Reading Test (Grober and Sliwinski,1991). For the Modified Fregly Ataxia battery, a measure of staticpostural stability, subjects first completed each task with eyes openand then with eyes closed. This task was administered as described inDurazzo et al. (2006b). The Fregly Ataxia eyes-open and eyes-closedtrials were log transformed due to the highly skewed distributions.Only the eyes-closed trial is reported in this study, as virtually allparticipants obtained the maximum score on the eyes-open trial. Forthe Luria-Nebraska Item 99, the correct number (maximum possi-ble5 8) was divided by the time required to complete the task. Thisratio was used due to the low ceiling (i.e., most participants achieveda score of 6 or better) for the correct number and the resultant highlyskewed distribution.

Raw scores for all neurocognitive measures, except the FreglySharpened Romberg and Luria-Nebraska Item 99 ratio, wereconverted to standardized scores via appropriate normative dataadjusted for age (BVMT-R, CVLT-II, Short Categories Test, StroopColor–Word Test, WAIS-III subtests) or age and education (TrailsA and B, WCST-64 variables). Standardized scores were trans-formed to z-scores for formation of the neurocognitive summaryscores for the above-listed domains. For the Fregly Sharpened Rom-berg, eyes-closed trial, and the Luria-Nebraska Item 99 ratio, rawscores were converted to z-scores based on the performance of the 22nonsmoking LD, as there are no appropriate norms for thesemeasures. The summary score of domains with multiple measuresrepresents the average of the individual z-scores of the constituentmeasures. A composite index, which consisted of all tests that weretimed, or in which the time to complete the task influenced the scoreachieved (herein referred to as ‘‘timed-test composite’’), was calcu-lated by averaging the individual z-scores of those measures.Timed tests included the Luria-Nebraska Item 99, Stroop word, col-or, and color–word tests, Trails A and B and WAIS-III Arithmetic,Block Design, Digit Symbol, Picture Completion, andSymbol Search. Higher scores on these measures reflect better speedand accuracy on predominantly nonverbal tasks. The timed-testcomposite is an approximation of the concept of cognitive efficiencydescribed by Glenn and Parsons (1992) and others (Nixon et al.,1998, 1995).

1116 DURAZZO ET AL.

Data Analyses

Longitudinal. We predicted that over 6 to 9 months of abstinencefrom alcohol, nonsmoking ALC demonstrate a greater magnitude oflongitudinal improvement than smoking ALC on the domains ofcognitive efficiency, executive skills, learning and memory, process-ing speed, visuospatial skills, and static postural stability. Thishypothesis was evaluated with a 2 (group)�2 (assessment point),general linear model, doubly multivariate analysis of repeateddependent measures multivariate analysis of variance (dMANOVA)(see Tabachnick and Fidell, 2001). This procedure allows for thesimultaneous evaluation of change in multiple dependent measuresobtained on different occasions. The doubly multivariate analysisprotects against the increased family-wise error rate that occurs whenseparately analyzingmultiple dependent measures, and it takes accountof relationships among dependent variables (in the combined ALCsample at assessment 1, the average Spearman’s correlation coeffi-cient among cognitive domains, excluding Fine Motor Skills andPostural Stability, was 0.518). Significant omnibus main effects forassessment (i.e., time) and group by assessment interactions werefurther evaluated with univariate repeated measures analysis ofvariance (ANOVA) and paired t-tests (2-tailed) for each domain.Separate repeated measures ANOVAs were conducted for the do-mains of fine motor skills and static postural stability. We view thesesensorimotor functions as distinct from neurocognitive processes, asevidenced by their general weak correlations among the neurocogni-tive domains evaluated in this study. All significant omnibus main ef-fects or interactions were repeated with education, AMNART score,average drinks per month over 3 and 8 years before enrolment andlifetime average drinks per month, interval between assessments, andduration of abstinence, as individual covariates, due to group differ-ences on these variables (see Table 1). Longitudinal changes in ne-urocognition for nonsmoking LD were evaluated separately fromALC, with a within-subjects design, general linear model, dMANO-VA. Absolute percent change for each domain was calculated as[(standardized score at assessment 2� standardized score at assess-ment 1)/(standardized score at assessment 1)�100]. Correspondingannualized percent changes were calculated by further dividing abso-lute percent changes by the assessment interval (in years). Positivechange values represent improved performance over time.

Cross-Sectional. We predicted at 6 to 9 months of abstinencefrom alcohol that nonsmoking ALC perform superior to smoking

ALC in the domains of cognitive efficiency, executive skills, learningand memory, processing speed, visuospatial skills, and static postur-al stability. This hypothesis was tested with a 1-way general linearmodel, multivariate analysis of covariance (MANCOVA). Educa-tion was used as a covariate in all cross-sectional comparisons, giventhe significantly lower education in smoking ALC relative to bothnonsmoking ALC and nonsmoking LD. The performance of non-smoking ALC and smoking ALC at assessment 2 was compared withthat of the 22 nonsmoking LD at their initial assessment. The largernonsmoking LD sample was considered more representative sampleof LD. Significant omnibus tests were further evaluated with univ-ariate ANOVA. Significant ANOVAs were followed up with pair-wise comparisons among nonsmoking LD, nonsmoking ALC, andsmoking ALC and corrected for multiplicity with a modified Bon-ferroni’s procedure, which adjusted alpha according to the numberof pairwise comparisons and the average correlation among the de-pendent measures of interest (Sankoh et al., 1997). In the combinedALC and nonsmoking LD sample, the average Spearman’s correla-tion coefficient among cognitive domains (excluding Fine MotorSkills and Postural Stability) was 0.508, which resulted in an adjusteda5 0.029 for pairwise comparisons among all 3 groups. Plannedcomparisons between nonsmoking ALC and smoking ALC werealso covaried for alcohol consumption variables, interval betweenassessments, and duration of abstinence due to group differences onthese variables (see Table 1). Alpha levels for these planned contrastswere adjusted using the modified Bonferroni’s procedure describedabove (adjusted a5 0.036).

Correlational. We hypothesized that greater smoking severity (insmoking ALC) and drinking severity (in both ALC groups) areinversely related to longitudinal cognitive improvement. Analysesexamining the relationships between smoking severity and perfor-mance of smoking ALC on individual domains were adjusted bythe 4 smoking severity measures (FTND, number of cigarettessmoked per day, smoking duration, and pack years) and by theintercorrelations between the smoking measures (adjusteda5 0.026). Relationships between longitudinal changes in neuropsy-chological performance (absolute percent change) and measures ofdrinking and smoking severity were examined with Spearman’scoefficients. Analyses investigating the relationships between drink-ing severity and longitudinal change of nonsmoking ALC andsmoking ALC on individual domains were adjusted by the 5 Life-time Drinking History variables (i.e., average drinks per month 1, 3,

Table 1. Demographics, Alcohol, and Cigarette Use Histories by Groups (Mean � SD)

Measure nsLD (n 5 20) nsALC (n 5 13) sALC (n 5 12) Post hoc tests (po0.05)

Age (y) 44.9 � 9.0 52.9 � 11 47.5 � 10.7 nsLDonsALCEducation (y) 16.1 � 2.4 15.7 � 2.3 13.0 � 2.1 sALConsLDAMNART 119 � 7 116 � 7 107 � 11 nsLD, nsALC4sALC1 y average drinks/mo 17 � 17 339 � 219 497 � 271 sALC4nsALC, nsLD3 y average drinks/mo 17 � 17 288 � 162 488 � 276 sALC4nsALC; sALC, nsALC4nsLD8 y average drinks/mo 17 � 16 227 � 157 463 � 151 sALC4nsALC; sALC, nsALC4nsLDLifetime average drinks/mo 17 � 15 124 � 56 302 � 112 sALC4nsALC; sALC, nsALC4nsLDLifetime years 27 � 7 36 � 12 30 � 11 sALC4nsALC; sALC, nsALC4nsLDMonths heavy drinking NA 207 � 123 286 � 111 NSAge onset heavy drinking (y of age) NA 31 � 12 21 � 12 sALC4nsALCFTND NA NA 6.0 � 1.8 NACigarettes/d NA NA 22 � 10 NAPack years NA NA 26 � 28 NASmoking duration (y) NA NA 24 � 14 NA

nsLD, nonsmoking light drinking control; nsALC, nonsmoking recovering alcoholic patients; sALC, smoking recovering alcoholic patients; AMNART,American National Adult Reading Test; 1-y average, average number of drinks per month over 1 y before study; 3 y average, average number of drinksper month over 3 y before study; 8 y average, average number of drinks per month over 8 y before study; Age onset of heavy drinking, chronologicalage at which individual began drinking 4100 drinks per month; Months heavy drinking, number of months drinking at 4100 drinks per month; Lifetimeaverage, average number of drinks per month over lifetime; Lifetime years, number of years of regular alcohol consumption over lifetime; FTND,Fagerstrom Tolerance Test for Nicotine Dependency; NA, not applicable; NS, not significant.

1117CHRONIC SMOKING IN ABSTINENT ALCOHOLIC PATIENTS

and 8 years before enrolment, lifetime average drinks per month,months heavy drinking) and by the intercorrelations between thesedrinking variables (adjusted a5 0.029). All statistical analyses wereconducted with S-Plus v6.2 and SPSS v14.0.

RESULTS

Participant Characterization (See Table 1)

Twenty of the 25 ALC participants were Caucasian, 4were African American, and 1 was Native American.Twenty of the 23 nonsmoking LD participants wereCaucasian, 2 were African American, and 1 was Asian.Nonsmoking ALC and smoking ALC were equivalent onage, but nonsmoking ALC had significantly greatereducation (p5 0.005) and AMNART score (p5 0.03).Smoking ALC relative to nonsmoking ALC consumedsignificantly more drinks per month over 3 (p5 0.04) and8 years (p5 0.02) before enrolment and over lifetime(po0.001). Smoking ALC also began drinking heavily(i.e., consuming 4100 drink equivalents per month) at ayounger age than nonsmoking ALC (p5 0.02). Nonsmok-ing LD were equivalent in age to smoking ALC, butsignificantly younger than nonsmoking ALC (p5 0.03).Nonsmoking LD were equivalent in education andAMNART to nonsmoking ALC, but had significantlygreater education (po0.001) and AMNART (p5 0.001)than smoking ALC. At study enrolment, all ALC partic-ipants met DSM-IV criteria for alcohol dependence withphysiological dependence. At enrolment, 3 nonsmokingALC participants met DSM-IV criteria for recurrentmajor depression and 1 for substance-induced (alcohol)mood disorder with depressive features. At assessment 2,the mood disorders in these nonsmoking ALC participantswere in full remission. No smoking ALC participant metthe criteria for a mood disorder at either assessment. Onenonsmoking ALC participant met the criteria for methyl-amphetamine abuse, and 1 smoking ALC met the criteriafor cannabis abuse; both were in early full remission atassessment 2. Two nonsmoking ALC participants hadmedically controlled hypertension and 1 was positive forthe hepatitis C antibody. In the smoking ALC group, 2had medically controlled hypertension and 2 were positivefor the hepatitis C antibody. Nonsmoking ALC and smok-ing ALC were equivalent on the BDI, STAI, GGT, alanineaminotransferase, hematocrit, hemoglobin, and electrolytelevels and erythrocyte and leukocyte counts at bothassessments, and these variables were generally in thenormal range for both groups. At assessment 1, smokingALC demonstrated a higher aspartate aminotransferaselevel than nonsmoking ALC, which normalized byassessment 2 and was not significantly different from non-smoking ALC at that time.All smoking ALC were actively smoking at the time of

both assessments, with essentially no change in their ciga-rette consumption between assessments. At assessmentpoint 1, the average FTND score for the smoking ALC

group was 6.0 � 1.8, which indicated a moderate to highlevel of nicotine dependence and they smoked 22 � 10 cig-arettes per day (see Table 1). At assessment point 2, theaverage FTND score for the smoking ALC group was6.0 � 1.9, and they smoked 22 � 11 cigarettes per day. Atthe time of enrolment, 8 of 13 nonsmoking ALC reportednever having used cigarettes consistently in their lifetime(i.e., more than 10 cigarettes in any 1 month). Five non-smoking ALC reported a previous history of chronicsmoking, with 2 participants quitting 3 years and 3 morethan 10 years before enrolment. No nonsmoking ALCparticipant began smoking during the course of the study.

Neurocognition, Motor Skills, and Postural Stability

Longitudinal Findings. In this analysis, we tested the hy-pothesis that over 6 to 9 months of sustained abstinencefrom alcohol, nonsmoking ALC demonstrate a greatermagnitude of longitudinal improvement than smokingALC in the domains of cognitive efficiency (i.e., timed-testcomposite), executive skills, learning and memory, pro-cessing speed, visuospatial skills, and static postural sta-bility. The omnibus dMANOVA (Wilks’ lambda) yieldeda significant group by assessment interaction [F(10, 14)5

2.86, p5 0.036]. ANOVAs showed significant group byassessment interactions for executive skills, time-testcomposite, visuospatial skills, and working memory(p5 0.002–0.024; see Fig. 1a–1d). Follow-up paired t-tests(2-tailed) indicated that nonsmoking ALC significantlyimproved between assessments on executive skills, timed-test composite, and working memory (po0.001–0.016),with a trend for improving visuospatial skills (p5 0.078),whereas smoking ALC only tended to improve on thetimed-test composite score (p5 0.078). The omnibus inter-action remained significant after separately covarying foreducation, AMNART, alcohol consumption variables,assessment interval, and duration of abstinence (allpo0.049) as did all significant univariate and follow-uptests (p5 0.005–0.045).The omnibus dMANOVA also indicated a significant

main effect for assessment point [F(10, 14)5 5.16, p5

0.003], with significant ANOVAs for auditory-verballearning, auditory-verbal memory, general intelligence,processing speed, time-test composite, and working mem-ory (po0.001–0.001; see Figs. 1b, 1d and 2a–2d), and atrend for visuospatial learning (p5 0.059). Follow-upt-tests showed that nonsmoking ALC significantlyimproved over time on all of these domains (po0.001–0.047), except visuospatial learning (p5 0.084). SmokingALC significantly improved only on auditory-verballearning, auditory-verbal memory, and processing speed(p5 0.002–0.036), with trends for general intelligence andtimed-test composite (both p5 0.074). The omnibus maineffect for assessment point remained significant afterseparately covarying for education, AMNART, alcoholconsumption variables, assessment interval, and duration

1118 DURAZZO ET AL.

of abstinence (all po0.01), as did all univariate and follow-up tests (p5 0.009–0.049). Examination of absolutepercent change (see Table 2) indicated that both groupsexperienced equivalent improvements on auditory-verballearning, and visuospatial memory, whereas smoking ALCshowed numerically greater improvement than nonsmok-ing ALC on auditory-verbal memory and static posturalstability. The main effects for general intelligence, timed-test composite, and working memory were primarilydriven by the longitudinal improvement of nonsmokingALC. The improvement of nonsmoking ALC on the gen-eral intelligence domain was mainly related to longitudinalincreases on WAIS-III nonverbal measures and the work-ing memory factor. The repeated measure ANOVAs forfine motor skills and static postural stability showed nointeractions or main effects.Cross-Sectional Findings. In this analysis, we tested the

primary hypothesis that at 6 to 9 months of abstinence

from alcohol, nonsmoking ALC perform superior tosmoking ALC in cognitive efficiency (i.e., timed-test com-posite), executive skills, learning and memory, processingspeed, visuospatial skills, and static postural stability. Theomnibus MANCOVA (Wilks’ lambda; covaried foreducation) comparing nonsmoking ALC, smoking ALC,and nonsmoking LD was significant [F(20, 70)5 1.75,p5 0.042], and group differences were apparent for audi-tory-verbal learning, auditory-verbal memory, executiveskills, processing speed, timed-test composite, and visuo-spatial memory (p5 0.004–0.05). Pairwise comparisonsrevealed that nonsmoking LD were superior to smokingALC on executive skills and visuospatial memory (bothp5 0.022). Unexpectedly, nonsmoking ALC performedsuperior to nonsmoking LD on processing speed(p5 0.017) and auditory-verbal learning (p5 0.002) withno other significant differences. In planned comparisons,nonsmoking ALC were superior to smoking ALC on

Fig. 1. (a) Longitudinal performance for the executive skills domain for nonsmoking and smoking recovering alcoholic patients (ALC). �Significant group byassessment interaction (p 5 0.016); #Nonsmoking ALC4smoking ALC at assessment 2 (p 5 0.004), mean � SE. (b) Longitudinal performance for the timed-test composite domain for nonsmoking and smoking recovering ALC. �Significant group by assessment interaction (p 5 0.016); @Significant main effect forassessment (po0.001); $Nonsmoking ALC4smoking ALC at assessment 1 (p 5 0.035); #Nonsmoking ALC4smoking ALC at assessment 2 (p 5 0.011),mean � SE. (c) Longitudinal performance for the visuospatial skills domain for nonsmoking and smoking recovering ALC. �Significant group by assessmentinteraction (p 5 0.04), mean � SE. (d) Longitudinal performance for the working memory domain for nonsmoking and smoking recovering ALC. �Significantgroup by assessment interaction (p 5 0.002); @Significant main effect for assessment (po0.001); $Nonsmoking ALC4smoking ALC at assessment 1(p 5 0.034); #Nonsmoking ALC4smoking ALC at assessment 2 (p 5 0.022), mean � SE.

1119CHRONIC SMOKING IN ABSTINENT ALCOHOLIC PATIENTS

auditory-verbal learning, auditory-verbal memory, execu-tive skills, processing speed, timed-test composite, visuo-spatial memory, and working memory (p5 0.004–0.022).The superior performance of nonsmoking ALC versussmoking ALC remained significant after additionalcovariance for AMNART score, duration of abstinence,assessment interval, and alcohol consumption variables(p5 0.008–0.048). The ANOVA for fine motor skills wassignificant, where both nonsmoking LD and nonsmokingALC were superior to smoking ALC (p5 0.01 and 0.026,respectively); however, the difference between nonsmok-ing ALC and smoking ALC was not significant after co-varying for 3 and 8 years average drinks per month. TheANOVA for static postural stability was not significant.Some studies (Ernst et al., 2001; Spilich et al., 1992) re-

ported inferior cross-sectional performance of formersmokers compared with never smokers in some neurocog-

nitive domains. We did not observe any cross-sectional orlongitudinal differences between former smokers and life-long nonsmokers in our smoking ALC group on any of thedomains evaluated. Additionally, nonsmoking ALC indi-viduals with mood disorders at assessment 1 showedequivalent rates of change in all domains relative tononsmoking ALC with no history of mood disorders.Furthermore, for nonsmoking LD, dMANOVA yieldedno significant longitudinal changes on any domain andsingle-sample t-tests for absolute and annualized percentchange were not significant for any domain. Thus, practiceeffects did not appear to contribute significantly to the lon-gitudinal findings among our ALC participants (Table 3).Correlational Findings. In the smoking ALC group,

many robust correlations were apparent between measuresof smoking severity and percent change in domain perfor-mance (see Table 4 and Fig. 3). Most notably, longitudinal

Fig. 2. (a) Longitudinal performance for the auditory-verbal learning domain for nonsmoking and smoking recovering alcoholic patients (ALC). @Significantmain effect for assessment (po0.001); $Nonsmoking ALC4smoking ALC at assessment 1 (p 5 0.002); #Nonsmoking ALC4smoking ALC at assessment 2(p 5 0.007), mean � SE. (b) Longitudinal performance for the auditory-verbal memory domain for nonsmoking and smoking recovering ALC. @Significant maineffect for assessment (po0.001); $Nonsmoking ALC4smoking ALC at assessment 1 (p 5 0.002); #Nonsmoking ALC4smoking ALC at assessment 2(p 5 0.008), mean � SE. (c) Longitudinal performance for the general intelligence domain for nonsmoking and smoking recovering ALC. @Significant maineffect for assessment (po0.001); $Nonsmoking ALC4smoking ALC at assessment 1 (p 5 0.018), mean � SE. (d) Longitudinal performance for the processingspeed domain for nonsmoking and smoking recovering ALC. @Significant main effect for assessment (p 5 0.001); $Nonsmoking ALC4smoking ALC atassessment 1 (p 5 0.004); #Nonsmoking ALC4smoking ALC at assessment 2 (p 5 0.006), mean � SE.

1120 DURAZZO ET AL.

improvement of executive skills in smoking ALC was neg-atively related to all measures of smoking severity, andgreater nicotine dependence showed the strongest and

greatest number of inverse relationships with improve-ment in function over time. No significant relationshipswere observed between alcohol consumption variables,clinical laboratory variables, and change in domain per-formance for either ALC group, which were generallyweak for both groups.

DISCUSSION

The main findings in this treatment-seeking, predomi-nantly Caucasian male Veteran sample of ALC were: (1)over 6 to 9 months of abstinence, nonsmoking ALC ex-hibited a significantly greater magnitude of longitudinalimprovement than smoking ALC on the domains of exec-utive skills, timed-test composite, visuospatial skills, andworking memory; (2) both nonsmoking ALC and smokingALC demonstrated significant longitudinal improvementin auditory-verbal learning, auditory-verbal memory, and

Table 2. Domain z-Scores, Self-Report Questionnaires, and Clinical Labo-ratories for Nonsmoking and Smoking Recovering Alcoholic Patients for

Assessments 1 and 2 (Mean � SD)

Measure nsALC (n 5 13) sALC (n 5 12)

Auditory-verbal learningAP1 1.18 � 0.78 0.01 � 0.80AP2 1.99 � 0.95 0.71 � 1.26% change 13.2 � 8.1 14.1 � 16.3

Auditory-verbal memoryAP1 0.94 � 0.65 � 0.11 � 0.94AP2 1.25 � 0.72 0.35 � 0.88% change 5.5 � 9.2 11.3 � 9.8

Executive skillsAP1 0.03 � 0.47 � 0.15 � 0.56AP2 0.63 � 0.61 � 0.27 � 0.69% change 13.1 � 16.8 � 1.9 � 11.3

Fine motor skillsAP1 � 0.44 � 0.78 � 1.19 � 0.91AP2 � 0.28 � 0.79 � 0.95 � 0.95% change 4.4 � 13.2 8.1 � 20.9

General intelligenceAP1 0.68 � 0.71 � 0.11 � 0.94AP2 1.26 � 0.63 0.15 � 1.24% change 10.7 � 7.6 5.2 � 9.1

Processing speedAP1 0.07 � 0.47 � 0.55 � 0.57AP2 0.51 � 0.46 � 0.31 � 0.69% change 9.4 � 11.8 5.3 � 8.0

Static postural stabilityAP1 0.32 � 0.46 � 1.52 � 1.78AP2 0.48 � 1.93 � 0.95 � 1.82% change 3.0 � 15.0 16.1 � 22.1

Timed-test compositeAP1 � 0.01 � 0.45 � 0.46 � 0.71AP2 0.46 � 0.41 � 0.36 � 0.79% change 10.0 � 9.0 3.1 � 5.7

Visuospatial learningAP1 � 0.18 � 0.99 � 0.95 � 1.35AP2 0.32 � 0.81 � 0.88 � 1.6% change 14.7 � 28.5 4.5 � 30.1

Visuospatial memoryAP1 � 0.13 � 0.98 � 1.21 � 1.34AP2 0.32 � 0.73 � 0.85 � 1.60% change 13.8 � 30.2 15.5 � 35.5

Visuospatial skillsAP1 � 0.26 � 0.79 � 0.39 � 0.68AP2 � 0.05 � 0.33 � 0.49 � 0.65% change 5.78 � 4.7 � 1.6 � 8.8

Working memoryAP1 0.38 � 0.61 � 0.20 � 0.91AP2 0.78 � 0.56 � 0.18 � 0.90% change 7.6 � 4.1 0.82 � 6.4

BDI (raw score)AP1 6.7 � 6.7 7.4 � 5.1AP2 4.8 � 3.7 5.1 � 3.6% change � 28.4 � 38.8 � 32.9 � 40.0

STAI (trait anxiety raw score)AP1 44.3 � 9.8 38.6 � 8.0AP2 36.1 � 7.9 36.9 � 10.7% change � 19.4 � 10.7 � 4.3 � 19.3

AP1, assessment 1; AP2, assessment 2; nsALC, nonsmoking recov-ering alcoholic patients; sALC, smoking recovering alcoholic patients; %change, absolute percent change; BDI, Beck Depression Inventory;STAI, State-Trait Anxiety inventory.

Table 3. Annualized Percent Change for All Groups (Mean � SD)

Domain nsLD (n 5 8) nsALC (n 5 13) sALC (n 5 12)

Auditory-verbal learning 5.33 � 8.79 25.76 � 22.00 26.00 � 28.20Auditory-verbal memory 0.92 � 2.44 10.61 � 14.35 23.88 � 21.94Executive skills �0.35 � 3.39 26.72 � 39.91 � 9.01 � 26.02Fine motor skills 2.29 � 3.48 5.66 � 21.09 10.59 � 42.72General intelligence 2.68 � 5.79 18.33 � 14.35 10.28 � 18.52Processing speed 1.91 � 3.28 13.60 � 12.03 9.51 � 15.36Static postural stability 1.10 � 2.99 2.01 � 7.01 8.08 � 21.70Timed-test composite 0.17 � 4.22 15.66 � 13.01 5.59 � 11.33Visuospatial learning 0.66 � 20.13 29.62 � 64.59 3.80 � 73.73Visuospatial memory 5.37 � 7.56 28.99 � 62.31 29.77 � 85.73Visuospatial skills 2.02 � 4.74 6.69 � 15.12 � 3.63 � 20.96Working memory 2.36 � 8.43 14.04 � 10.26 2.83 � 12.05

nsLD, nonsmoking light-drinking control; nsALC, nonsmoking recover-ing alcoholic patients; sALC, smoking recovering alcoholic patients.

Table 4. Relationships Between Measures of Cigarette Smoking Severityand Longitudinal Change in Domain Performance for Smoking ALC

Smoking severitymeasure Domain r� p-Value�

FTND Executive skills �0.85 o 0.001Time-test composite �0.79 0.001Auditory-verbal learning �0.69 0.007Processing speed �0.66 0.009Visuospatial learning �0.64 0.013

Pack years General intelligence �0.73 0.004Time-test composite �0.69 0.008Executive skills �0.66 0.010Fine motor skills �0.54 0.035Visuospatial learning �0.52 0.044

Cigarettes per day Fine motor �0.68 0.008Executive skills �0.66 0.015General intelligence �0.50 0.050Visuospatial learning �0.50 0.050

Smoking duration General intelligence �0.66 0.010Executive skills �0.59 0.022

�r-Values are Spearman’s coefficients; p-values � 0.026 are statisti-cally significant.

FTND, Fagerstrom Test of Nicotine Dependence.

1121CHRONIC SMOKING IN ABSTINENT ALCOHOLIC PATIENTS

processing speed; (3) at 6 to 9 months of abstinence, non-smoking ALC were superior to smoking ALC on thedomains of auditory-verbal learning, auditory-verbalmemory, cognitive efficiency, executive skills, processingspeed, and working memory; and (4) in smoking ALC,greater smoking severity was related to less longitudinalimprovement on multiple neurocognitive domains (seeTable 4). These longitudinal and cross-sectional findingsfor the ALC groups remained significant after covaryingfor differences in age, education, estimated premorbidintelligence, alcohol consumption, assessment interval, orduration of abstinence.Previous studies identified age, gender, length of absti-

nence, treatment history, or relapse history as potentialmodulators of neurocognitive recovery in alcoholicpatients (Fein et al., 1990; Fein and Landman, 2005;Munro et al., 2000; Oscar-Berman, 2000; Parsons, 1998;Parsons and Nixon, 1998; Reed et al., 1992; Rourke andGrant, 1999; Sullivan and Pfefferbaum, 2005). Our resultssuggest that chronic cigarette smoking in this sample ofabstinent alcoholic patients also modulates neurocognitiverecovery in multiple domains of functioning, and it does soin an adverse manner. The cross-sectional comparisons ofnonsmoking ALC and smoking ALC after 6 to 9 monthsof abstinence in this study are consistent with our previouscross-sectional findings from 1-month abstinent ALC,where nonsmoking ALC were superior to smoking ALCon measures of auditory-verbal learning, auditory-verbalmemory, cognitive efficiency, processing speed, and staticpostural stability (Durazzo et al., 2006b). Although smok-ing ALC in the present study improved significantly inseveral domains over the assessment interval, the magni-tude of their changes was not statistically greater thanthose in nonsmoking ALC. Consequently, nonsmokingALC continued to demonstrate superior performanceon auditory-verbal learning, auditory-verbal memory,

cognitive efficiency, executive skills, processing speed,and working memory at 6 to 9 months of abstinence.Overall, however, neither our nonsmoking ALC nor oursmoking ALC sample demonstrated clinically significantfunctional deficiencies after approximately 6 to 9 monthsof abstinence.In smoking ALC, all 4 measures of smoking severity

were related to longitudinal changes in at least 2 domains.The level of nicotine dependence (as represented by theFTND score) showed the strongest and most numerousrelationships with cognitive improvement (see Table 4).Specifically, the higher the FTND score, the lower thelongitudinal improvement of auditory-verbal learning,cognitive efficiency, executive skills (see Fig. 3), generalintelligence, processing speed, and visuospatial learning.These inverse relationships are in line with our cross-sectional results for 1-month abstinent smoking ALC,where greater nicotine dependence, higher number of cig-arettes smoked per day, and longer smoking duration wererelated to lower performance on multiple domains ofneurocognition (Durazzo et al., 2006b).Smoking ALC participants were allowed to smoke ad

libitum before, and during, the 2 to 2.5 hours assessment.The plasma half-life of nicotine in humans is approximate-ly 2 to 3 hours (Nakajima and Yokoi, 2005); however,based on a 2-hour half-life, nicotine levels will accumulate(i.e., 3 or more half-lives) in the body (nicotine has a highaffinity for brain tissue) with regular smoking during wak-ing hours (Hukkanen et al., 2005). This may explain whythe adverse effects of nicotine withdrawal on aspects ofneurocognition may not manifest for 8 to 12 hours orlonger (for a review, see Sacco et al., 2004). Therefore, wedo not believe that the effects of potential nicotine with-drawal confounded our longitudinal and cross-sectionalanalyses. The acute effects of nicotine administration andwithdrawal on neurocognition in alcoholic patients andother substance–abusing individuals are just beginning tobe investigated (see Ceballos et al., 2006).

Potential Mechanisms Underlying the Differential Neuro-cognitive Recovery in Nonsmoking and Smoking ALC

There are several possible mechanisms that maycontribute independently, or in concert, to the lesserneurocognitive recovery by the smoking ALC in this studyand their inferior performance in multiple cognitivedomains at 6 to 9 months of abstinence from alcohol.Chronic cigarette smoking is associated with significantlyincreased risk for atherosclerosis (Bolego et al., 2002), aswell as abnormalities in vascular endothelial function(Hawkins et al., 2002). These processes may impact thefunctional integrity of the cerebrovasculature and contrib-ute to the decreased regional cerebral blood flow (Dominoet al., 2004; Rose et al., 2003; Zubieta et al., 2001) and/orwhite matter disease (Ding et al., 2003; Fukuda andKitani, 1996) reported in chronic smokers.

Fig. 3. Relationship between longitudinal change in executive skills andlevel of nicotine dependence in smoking recovering alcoholic patients. FTND,Fagerstrom Test for Nicotine Dependency.

1122 DURAZZO ET AL.

Many toxic compounds exist in the particulate and gasphases of cigarette smoke (e.g., carbon monoxide, freeradicals, nitrosamines, polynuclear aromatic compoundsFowles et al., 2000), which may directly or indirectly com-promise brain tissue and function. For example, carbonmonoxide levels are significantly higher in smokers(Deveci et al., 2004), and its elevation is associated withlower effective hemoglobin concentrations, diminishedoxygen-carrying capacity of erythrocytes (Macdonaldet al., 2004), as well as decreased efficiency of themitochondrial respiratory chain (Alonso et al., 2004).Chronic smoking has also been equated to a type ofrepeated acute (mild) carbon monoxide poisoning (Alonsoet al., 2004), and is associated with nocturnal hypoxia(Casasola et al., 2002) as well as increased risk for chronicobstructive pulmonary disease and other conditions thatmay impair pulmonary function (Bartal, 2001). Decreasedpulmonary function has been associated with poorerneurocognition and increased subcortical atrophy amongcommunity-dwelling older adults (Sachdev et al., 2006).Cigarette smoke also contains high concentrations of freeradical species (e.g., reactive oxygen species) known topromote oxidative damage or stress of cellular structuresas well as macromolecules including membrane lipids, pro-teins, carbohydrates, and DNA (Moriarty et al., 2003).Late-myelinating areas such as the frontal and temporallobes may be particularly vulnerable to increased oxidativestress and cerebral hypoperfusion (see Bartzokis, 2004a,2004b), both of which have been described in chronicsmokers.We postulate that continued smoking during abstinence

from alcohol provides a sustained direct source of exoge-nous free radical species, carbon monoxide, and otherpotentially cytotoxic compounds. These noxious agents,in combination with potentially diminished cardiopulmo-nary function or cerebrovascular integrity, may adverselyaffect recuperation of the morphology or metabolism ofneural and glial tissue, particularly that comprising thefrontal–striatal–thalamic circuitry in smoking ALC. Inour neuroimaging studies of alcoholism, we observed sig-nificantly greater abnormalities in frontal and temporallobe morphology (Gazdzinski et al., 2005), in markers ofneuronal integrity in the frontal lobe (Durazzo et al.,2004), and in frontal lobe perfusion (Gazdzinski et al.,2006) in smoking ALC relative to nonsmoking ALC afterdetoxification. Smoking ALC also demonstrated signifi-cantly lower recovery of frontal markers of neuronalintegrity and frontal and temporal markers of cell mem-brane synthesis/turnover over approximately 1 month ofabstinence (Durazzo et al., 2006a). Additionally, weobserved smaller frontal GM volumes in actively drink-ing, treatment-naı̈ve alcoholic patients (Durazzo et al.,2007). In the present study, smoking ALC demonstratedsignificantly less longitudinal improvement than non-smoking ALC on measures of cognitive efficiency,executive skills, visuospatial skills, and working memory,

which are all suggested to be extensively subserved bydorsolateral and mesial frontal–striatal–thalamic circuitry(Cummings, 1998; Eslinger et al., 1995; Gazzaniga et al.,2002; Kolb and Whishaw, 2003; Mega and Cummings,1994; Saint-Cyr, 2003). Additionally, within the smokingALC group, higher scores on all measures of smokingseverity were inversely related to longitudinal improve-ment of executive skills. Chronic cigarette smoking-induced modulation of the morphology, biochemistry,and/or functioning of tissue comprising frontal–striatal–thalamic circuitry is also suggested by cross-sectionalstudies with nonalcoholic chronic smokers (see Brody,2006; Brody et al., 2004; Durazzo et al., 2006b and refer-ences therein). Additionally, chronic smoking may affectsome aspects of neurocognition through modulation ofmonoaminergic, cholinergic, glutamatergic, and GA-BAergic activity (see Bonvento et al., 2002; Brody, 2006;Das, 2003; Pitsikas et al., 2003), particularly in frontal–striatal–thalamic circuitry (Brody, 2006). Therefore, it isconceivable that the functional integrity of frontal–striatal–thalamic neural networks is altered in smokingALC relative to their nonsmoking counterparts. Finally, itis feasible that the brain regions adversely affected in AUD(e.g., frontal neocortical gray matter and white matter) arerendered more vulnerable to the effects of the potentiallynoxious compounds found in cigarette smoke (or vise-versa).

Study Limitations

The limitations of this study include the modest num-bers of participants. Although we statistically controlledfor factors that may have influenced our dependent mea-sures (e.g., education, premorbid intellectual functioning,drinking severity), it is possible that the smoking ALC per-formance was influenced by potential unrecorded groupdifferences in nutrition, exercise, overall physical health,and previous exposure to environmental cigarette smokeor to genetic predispositions. Additionally, the majority ofparticipants were males recruited from the San FranciscoVA Medical Center, which did not allow for the examin-ation of the potential effects of gender on our outcomemeasures. Verification of abstinence from alcohol wasbased on participant self-report. Although GGT, a mark-er of recent heavy drinking, was within normal limits atboth assessments, this does not preclude low-level alcoholuse between assessments.

Conclusions

In this cohort of treated alcoholic patients, we observedgreater improvement in nonsmoking ALC than smokingALC on several domains of neurocognitive function over6 to 9 months of abstinence from alcohol. The inferiorneurocognitive performance of smoking ALC comparedwith nonsmoking ALC observed at approximately 1month of abstinence from alcohol (Durazzo et al., 2006b)

1123CHRONIC SMOKING IN ABSTINENT ALCOHOLIC PATIENTS

persisted in smoking ALC in the same domains after 6 to 9months of sobriety. Although we covaried for factorsknown to influence neurocognition, it cannot be indisput-ably concluded that the observed longitudinal andcross-sectional findings were not influenced by group dif-ferences in premorbid neuropsychological functioning orgenetic predispositions. Irrespective of the etiology of theresults, our preliminary findings suggest that past and con-tinued chronic cigarette smoking in our abstinent alcoholicsubjects was associated with a lower level of neurocogni-tive recovery. Even though the results are preliminary,they suggest that consideration of smoking status is rele-vant to the assessment of neurocognitive recovery of othersamples with AUD. More generally, chronic smoking mayimpact neurocognition in other conditions where is it aprevalent behavior (e.g., schizophrenia spectrum andmood disorders). Finally, the mortality associated withcigarette smoking has been estimated to be greater thanthe mortality related to alcohol-induced diseases (Hurt etal., 1996). The cumulative findings from our brain mor-phological, perfusion, metabolite studies, and neurocogni-tive results from our group and others suggest that chroniccigarette smoking adversely affects brain neurobiologyand function. The foregoing, in conjunction with theknown mortality and morbidity associated with chronicsmoking, may give support to the growing clinical move-ment to encourage chronic smokers entering treatment forsubstance use disorders to consider concurrent participa-tion in a smoking cessation program.

ACKNOWLEDGMENTS

We thank Mary Rebecca Young, Bill Clift, ChristinaHall, and Dr. Donald Tusel at the San Francisco VAMed-ical Center and Drs. David Pating, Peter Washburn, andKaren Moise and their colleagues at Kaiser Permanente,San Francisco for their valuable assistance in recruitingparticipants, Derek Flenniken for database managementexpertise, and Dr. John Kornak, UCSF Department ofBiostatistics, for statistical consultation. We also wish toextend our gratitude to the study participants, who madethis research possible.

REFERENCES

Alonso JR, Cardellach F, Casademont J, Miro O (2004) Reversibleinhibition of mitochondrial complex IV activity in PBMC following

acute smoking. Eur Respir J 23:214–218.

American Psychiatric Association (1994) Diagnostic and Statistical

Manual of Mental Disorders. 4th ed. American Psychiatric Associa-tion, Washington, DC.

Axelrod BN, Ryan JJ, Ward CL (2001) Evaluation of seven-subtest short

forms of theWechsler Adult Intelligence Scale-III in a referred sample.

Arch Clin Neuropsychol 16:1–8.Bartal M (2001) Health effects of tobacco use and exposure. Monaldi

Arch Chest Dis 56:545–554.

Bartzokis G (2004a) Age-related myelin breakdown: a developmentalmodel of cognitive decline in Alzheimer’s Disease. Neurobiol Aging

25:5–18.

Bartzokis G (2004b) Quadratic trajectories of brain myelin content:

a unifying construct for neuropsychiatric disorders. Neurobiol Aging

25:49–62.Bates ME (1997) Stability of neuropsychological assessments early in

alcoholism treatment. J Stud Alcohol 58:617–621.

Bates ME, Bowden SC, Barry D (2002) Neurocognitive impairment

associated with alcohol use disorders: implications for treatment. Exp

Clin Psychopharmacol 10:193–212.

Beatty WW, Hames KA, Blanco CR, Nixon SJ, Tivis LJ (1996)

Visuospatial perception, construction and memory in alcoholism.

J Stud Alcohol 57:136–143.

Bechara A, Dolan S, Denburg N, Hindes A, Anderson SW, Nathan PE

(2001) Decision-making deficits, linked to a dysfunctional ventrome-

dial prefrontal cortex, revealed in alcohol and stimulant abusers.

Neuropsychologia 39:376–389.

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

Philadelphia.

Benedict R (1997) Brief Visuospatial Memory Test—Revised. Psycholog-

ical Assessment Resources Inc., Odessa, FL.

Bolego C, Poli A, Paoletti R (2002) Smoking and gender. Cardiovasc Res

53:568–576.Bonvento G, Sibson N, Pellerin L (2002) Does glutamate image your

thoughts? Trends in Neurosci 25:359–364.

Brandt J, Butters N, Ryan C, Bayog R (1983) Cognitive loss and recov-

ery in long-term alcohol abusers. Arch Gen Psychiatry 40:435–442.

Brody AL (2006) Functional brain imaging of tobacco use and depen-

dence. J Psychiatr Res 40:404–418.Brody AL, Mandelkern MA, Jarvik M E, Lee GS, Smith EC, Huang J,

Bota RG, Bartzokis G, London ED (2004) Differences between smok-

ers and nonsmokers in regional gray matter volumes and densities.

Biol Psychiatry 55:77–84.

Casasola GG, Alvarez-Sala JL, Marques JA, Sanchez-Alarcos JM,

Tashkin DP, Espinos D (2002) Cigarette smoking behavior and respi-

ratory alterations during sleep in a healthy population. Sleep Breath

6:19–24.

Ceballos NA, Tivis R, Lawton-Craddock A, Nixon SJ (2006) Nicotine

and cognitive efficiency in alcoholics and stimulant abusers: implica-

tions of smoking cessation for substance users in treatment. Subst Use

Misuse 41:265–281.

Culverhouse R, Bucholz KK, Crowe RR, Hesselbrock V, Nurnberger JI

Jr., Porjesz B, Schuckit M, Reich T, Beirut LJ (2005) Long-term

stability of alcohol and other substance dependence diagnoses and

habitual smoking. Arch Gen Psychiatry 62:753–760.

Cummings JL (1998) Frontal–subcortical circuits and human behavior.

J Psychosom Res 44:627–628.

Currie SR, Hodgins DC, el-Guebaly N, Campbell W (2001) Influence of

depression and gender on smoking expectancies and temptations in

alcoholics in early recovery. J Subst Abuse 13:443–458.Das U (2003) Can memory be improved? A discussion on the role of ras,

GABA, acetylcholine, NO, insulin, TNF-alpha, and long-chain poly-

unsaturated fatty acids in memory formulation and consolidation.

Brain Dev 25:251–261.

Davies SJ, Pandit SA, Feeney A, Stevenson BJ, Kerwin RW, Nutt DJ,

Marshall EJ, Boddington S, Lingford-Hughes A (2005) Is there

cognitive impairment in clinically ‘‘healthy’’ abstinent alcohol depen-

dence? Alcohol Alcohol 40:498–503.

Deary IJ, Pattie A, Taylor MD, Whiteman MC, Starr JM, Whalley LJ

(2003) Smoking and cognitive change from age 11 to age 80. J Neurol

Neurosurg Psychiatry 74:1003–1007.

Delis DC, Kramer JH, Kaplan E, Ober BA (2000) California Verbal

Learning Test. 2nd ed. The Psychological Corporation, San Antonio,

TX.Deveci S, Deveci F, Acik Y, Ozan A (2004) The measurement of exhaled

carbon monoxide in healthy smokers and non-smokers. Respir Med

98:551–556.

1124 DURAZZO ET AL.

Di Sclafani V, Ezekiel F, Meyerhoff DJ, MacKay S, Dillon WP, Weiner

MW, Fein G (1995) Brain atrophy and cognitive function in olderabstinent alcoholic men. Alcohol Clin Exp Res 19:1121–1126.

Ding J, Nieto FJ, Beauchamp NJ, Longstreth WT Jr., Manolio TA,

Hetmanski JB, Fried LP (2003) A prospective analysis of risk factorsfor white matter disease in the brain stem: the Cardiovascular Health

Study. 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 smokingtobacco cigarettes. Prog Neuropsychopharmacol Biol Psychiatry

28:319–327.

Durazzo TC, Cardenas VA, Studholme C, Weiner M, Meyerhoff DJ

(2007) Non-treatment-seeking heavy drinkers: effects of chronic ciga-rette smoking on brain structure. Drug Alcohol Depend 87:76–82.

Durazzo TC, Gazdzinski S, Banys P, Meyerhoff DJ (2004) Cigarette

smoking exacerbates chronic alcohol-induced brain damage: a prelim-inary metabolite imaging study. Alcohol Clin Exp Res 28:1849–1860.

Durazzo TC, Gazdzinski S, Banys P, Meyerhoff DJ (2006a) Brain

metabolite concentrations and neurocognition during short-term

recovery from alcohol dependence: preliminary evidence of the effectsof concurrent chronic cigarette smoking. Alcohol Clin Exp Res

30:539–551.

Durazzo TC, Rothlind JC, Gazdzinski S, Banys P, Meyerhoff DJ

(2006b) A comparison of neurocognitive function in nonsmoking andchronically smoking short-term abstinent alcoholics. Alcohol 39:1–11.

Ernst M, Heishman SJ, Spurgeon L, London ED (2001) Smoking history

and nicotine effects on cognitive performance. Neuropsychopharma-

cology 25:313–319.Eslinger PJ, Grattan LM, Geder L (1995) Impact of frontal lobe lesions

on rehabilitation and recovery from acute brain injury. Neurorehabil-

itation 5:161–182.Fagerstrom KO, Heatherton TF, Kozlowski LT (1991) Nicotine addic-

tion and its assessment. Ear Nose Throat J 69:763–765.

Fama R, Pfefferbaum A, Sullivan EV (2004) Perceptual learning in

detoxified alcoholic men: contributions from explicit memory, execu-tive function, and age. Alcohol Clin Exp Res 28:1657–1665.

Fein G, Bachman L, Fisher S, Davenport L (1990) Cognitive impair-

ments in abstinent alcoholics. West J Med 152:531–537.

Fein G, Landman B (2005) Treated and treatment-naive alcoholics comefrom different populations. Alcohol 35:19–26.

Fein G, Torres J, Price L, Di Sclafani V (2006) Cognitive performance in

long-term abstinent alcoholic individuals. Alcohol Clin Exp Res 30:1538–1544.

Fergusson DM, Goodwin RD, Horwood LJ (2003) Major depression

and cigarette smoking: results of a 21-year longitudinal study. Psychol

Med 33:1357–1367.Fowles J, Bates M, Noiton D (2000) The Chemical Constituents in Cig-

arettes and Cigarette Smoke: Priorities for Harm Reduction, pp 1–65.

Epidemiology and Toxicology Group, New Zealand.

Fregly AR, Graybiel A (1968) An ataxia test battery not requiring rails.Aerosp Med 39:277–282.

Fried PA, Watkinson B, Gray R (2006) Neurocognitive consequences of

cigarette smoking in young adults—a comparison with pre-drug per-

formance. Neurotoxicol Teratol 28:517–525.Friend KB, Malloy PF, Sindelar HA (2005) The effects of chronic

nicotine and alcohol use on neurocognitive function. Addict Behav

30:193–202.Friend KB, Pagano ME (2005) Changes in cigarette consumption and

drinking outcomes: findings from Project MATCH. J Subst Abuse

Treat 29:221–229.

Fukuda H, Kitani M (1996) Cigarette smoking is correlated with theperiventricular hyperintensity grade of brain magnetic resonance

imaging. Stroke 27:645–649.

Gazdzinski S, Durazzo TC, Jahng GH, Ezekiel F, Banys P, Meyerhoff

DJ (2006) Effects of chronic alcohol dependence and chronic cigarettesmoking on cerebral perfusion: a preliminary magnetic resonance

study. Alcohol Clin Exp Res 30:1–12.

Gazdzinski S, Durazzo TC, Studholme C, Song E, Banys P, Meyerhoff

DJ (2005) Quantitative brain MRI in alcohol dependence: preliminary

evidence for effects of concurrent chronic cigarette smoking on region-

al brain volumes. Alcohol Clin Exp Res 29:1484–1495.

Gazzaniga MS, Ivry RB, Mangun GR (eds) (2002) Cognitive Neurosci-

ence: The Biology of the Mind. 2nd ed. WW Norton & Company Inc.,

New York, NY.George TP, Vessicchio JC, Termine A, Sahady DM, Head CA, Pepper

WT, Kosten TR, Wexler BE (2002) Effects of smoking abstinence on

visuospatial working memory function in schizophrenia. Neuropsy-

chopharmacology 26:75–85.

Gilman SE, Abraham HD (2001) A longitudinal study of the order of

onset of alcohol dependence and major depression. Drug Alcohol

Depend 63:277–286.

Glass JM,AdamsKM,Nigg JT,WongMM, Puttler LI, BuuA, Jester JM,

Fitzgerald HE, Zucker RA (2006) Smoking is associated with neuro-

cognitive deficits in alcoholism. Drug Alcohol Depend 82:119–126.Glenn SW, Parsons OA (1991) Impaired efficiency in female alcoholics’

neuropsychological performance. J Clin Exp Neuropsychol 13:895–908.

Glenn SW, Parsons OA (1992) Neuropsychological efficiency measures

in male and female alcoholics. J Stud Alcohol 53:546–552.Golden CJ (1978) Stroop Color and Word Test. Stoelting Company,

Chicago, IL.

Golden CJ, Hammeke TA, Purisch AD (1978) Diagnostic validity of a

standardized neuropsychological battery derived from Luria’s neuro-

psychological tests. J Consult Clin Psychol 46:1258–1265.Grober E, Sliwinski M (1991) Development and validation of a model

for estimating premorbid verbal intelligence in the elderly. J Clin Exp

Neuropsychol 13:933–949.

Hawkins BT, Brown RC, Davis TP (2002) Smoking and ischemic stroke:

a role for nicotine? Trends Pharmacol Sci 23:78–82.Heaton RK, Staff PAR (1993) Wisconsin Card Sorting Test: Computer

Version-2, Research Edition. Psychological Assessment Resources Inc.,

Lutz, FL.

Heffernan TM, Ling J, Parrott AC, Buchanan T, Scholey AB, Rodgers J

(2005) Self-rated everyday and prospective memory abilities of ciga-

rette smokers and non-smokers: a web-based study. Drug Alcohol

Depend 78:235–241.

Hill RD, Nilsson LG, Nyberg L, Backman L (2003) Cigarette smoking

and cognitive performance in healthy Swedish adults. Age Ageing 32:

548–550.

Hukkanen J, Jacob P III, Benowitz N (2005)Metabolism and disposition

of kinetics of nicotine. Pharmacol Rev 57:79–115.

Hurt RD, Eberman KM, Croghan IT, Offord KP, Davis LJ Jr., Morse

RM, Palmen MA, Bruce BK (1994) Nicotine dependence treatment

during inpatient treatment for other addictions: a prospective inter-

vention trial. Alcohol Clin Exp Res 18:867–872.

Hurt RD, Offord KP, Croghan IT, Gomez-Dahl L, Kottke TE, Morse

RM, Melton LJ III (1996) Mortality following inpatient addictions

treatment. Role of tobacco use in a community-based cohort. JAMA

275:1097–1103.

Ihara H, Berrios GE, London M (2000) Group and case study of the

dysexecutive syndrome in alcoholism without amnesia. J Neurol

Neurosurg Psychiatry 68:731–737.Iki M, Ishizaki H, Aalto H, Starck J, Pyykko I (1994) Smoking habits

and postural stability. Am J Otolaryngol 15:124–128.

Kalmijn S, van Boxtel MP, Verschuren MW, Jolles J, Launer LJ (2002)

Cigarette smoking and alcohol consumption in relation to cognitive

performance in middle age. Am J Epidemiol 156:936–944.Kolb B, Whishaw IQ (2003) Fundamentals of Human Neuropsychology.

5th ed. Worth Publishers, New York, NY.

Lafayette Instrument (1989) Grooved Pegboard Test, Model 32025,

Lafayette, IN.Lawton-Craddock A, Nixon SJ, Tivis R (2003) Cognitive efficiency in

stimulant abusers with and without alcohol dependence. Alcohol Clin

Exp Res 27:457–464.

1125CHRONIC SMOKING IN ABSTINENT ALCOHOLIC PATIENTS

Macdonald G, Kondor N, Yousefi V, Green A, Wong F, Aquino-

Parsons C (2004) Reduction of carboxyhaemoglobin levels in thevenous blood of cigarette smokers following the administration of

carbogen. Radiother Oncol 73:367–371.

Mann K, Gunther A, Stetter F, Ackermann K (1999) Rapid recoveryfrom cognitive deficits in abstinent alcoholics: a controlled test–retest

study. Alcohol Alcohol 34:567–574.

Mega MS, Cummings JL (1994) Frontal–subcortical circuits and neuro-

psychiatric disorders. J Neuropsychiatry Clin Neurosci 6:358–370.Moriarty SE, Shah JH, Lynn M, Jiang S, Openo K, Jones DP, Sternberg

P (2003) Oxidation of glutathione and cysteine in human plasma asso-

ciated with smoking. Free Radic Biol Med 35:1582–1588.

Moselhy HF, Georgiou G, Kahn A (2001) Frontal lobe changes in alco-holism: a review of the literature. Alcohol Alcohol 36:357–368.

Munro CA, Saxton J, Butters MA (2000) The neuropsychological

consequences of abstinence among older alcoholics: a cross-sectionalstudy. Alcohol Clin Exp Res 24:1510–1516.

Nakajima M, Yokoi T (2005) Interindividual variability in nicotine

metabolism: C-oxidation and glucuronidation. Drug Metab Pharma-

cokinet 20:227–235.Nixon SJ, Parsons OA (1991) Alcohol-related efficiency deficits using an

ecologically valid test. Alcohol Clin Exp Res 15:601–606.

Nixon SJ, Paul R, Phillips M (1998) Cognitive efficiency in alcoholics

and polysubstance abusers. Alcohol Clin Exp Res 22:1414–1420.Nixon SJ, Tivis R, Parsons OA (1995) Behavioral dysfunction and cog-

nitive efficiency in male and female alcoholics. Alcohol Clin Exp Res

19:577–581.

Oscar-Berman M (2000) NIAAA Research Monograph No. 34:Neuropsychological Vulnerabilities in Chronic Alcoholism, in Review

of NIAAA’s Neuroscience and Behavioral Research Portfolio (Noronha

A, Eckardt M, Warren K, eds), pp 437–472. NIAAA, Bethesda, MD.Paperwalla KN, Levin TT, Weiner J, Saravay SM (2004) Smoking and

depression. Med Clin North Am 88:1483–1494.

Parsons OA (1998) Neurocognitive deficits in alcoholics and social

drinkers: a continuum? Alcohol Clin Exp Res 22:954–961.Parsons OA, Nixon SJ (1998) Cognitive functioning in sober social

drinkers: a review of the research since 1986. J Stud Alcohol 59:180–190.

Paul RH, Brickman AM, Cohen RA, Williams LM, Niaura R, Pogun S,

Clark CR, Gunstad J, Gordon E (2006) Cognitive status of young andolder cigarette smokers: data from the international brain database.

J Clin Neurosci 13:457–465.

Pitsikas N, Rigamonti AE, Cella SG, Muller EE (2003) The GABABreceptor and recognition memory: possible modulation of its behav-

ioral effects by the nitrergic system. Neurosci 118:1121–1127.

Pomerleau CS, Aubin HJ, Pomerleau OF (1997) Self-reported alcohol

use patterns in a sample of male and female heavy smokers. J AddictDis 16:19–24.

Ratti MT, Soragna D, Sibilla L, Giardini A, Albergati A, Savoldi F, Bo

P (1999) Cognitive impairment and cerebral atrophy in ‘‘heavy drink-

ers’’. Prog Neuropsychopharmacol Biol Psychiatry 23:243–258.Razani J, Boone K, Lesser I, Weiss D (2004) Effects of cigarette smoking

history on cognitive functioning in healthy older adults. Am J Geriatr

Psychiatry 12:404–411.

Reed RJ, Grant I, Rourke SB (1992) Long-term abstinent alcoholicshave normal memory. Alcohol Clin Exp Res 16:677–683.

Reitan RM, Wolfson D (1985) The Halstead-Reitan Neuropsychological

Test Battery: Theory and Interpretation. Neuropsychological Press,Tucson, AZ.

Richards M, Jarvis MJ, Thompson N, Wadsworth ME (2003) Cigarette

smoking and cognitive decline in midlife: evidence from a prospective

birth cohort study. Am J Public Health 93:994–998.Romberger DJ, Grant K (2004) Alcohol consumption and smoking sta-

tus: the role of smoking cessation. Biomed Pharmacother 58:77–83.

Room R (2004) Smoking and drinking as complementary behaviours.

Biomed Pharmacother 58:111–115.Rose JE, Behm FM, Westman EC, Mathew RJ, London ED, Hawk TC,

Turkington TG, Coleman RE (2003) PET studies of the influences of

nicotine on neural systems in cigarette smokers. Am J Psychiatry

160:323–333.Rosenbloom MJ, Pfefferbaum A, Sullivan EV (2004) Recovery of

short-term memory and psychomotor speed but not postural stability

with long-term sobriety in alcoholic women. Neuropsychology 18:

589–597.

Rourke SB, Grant I (1999) The interactive effects of age and length of

abstinence on the recovery of neuropsychological functioning in

chronic male alcoholics: a 2-year follow-up study. J Int Neuropsychol

Soc 5:234–246.

Rourke SB, Loberg I (1996) The neurobehavioral correlates of alcoholism,

in Neuropsychological Assessment of Neuropsychiatric Disorders (Grant

I, Adams K eds), pp 423–485. Oxford University Press, New York.Sacco KA, Bannon KL, George TP (2004) Nicotinic receptor mecha-

nisms and cognition in normal states and neuropsychiatric disorders.

J Psychopharmacol 18:457–474.Sachdev PS, Anstey KJ, Parslow RA, Wen W, Maller J, Kumar R,

Christensen H, Jorm AF (2006) Pulmonary function, cognitive

impairment and brain atrophy in a middle-aged community sample.

Dement Geriatr Cogn Disord 21:300–308.Saint-Cyr JA (2003) Frontal–striatal circuit functions: context, sequence

and consequence. J Int Neuropsychol Soc 9:103–128.

Sankoh AJ, Huque MF, Dubey SD (1997) Some comments on frequent-

ly used multiple endpoint adjustment methods in clinical trials. Stat

Medicine 16:2529–2542.

Schinka JA, Belanger H, Mortimer JA, Graves AB (2003) Effects of the

use of alcohol and cigarettes on cognition in elderly African American

adults. J Int Neuropsychol Soc 9:690–697.

Schulte T, Sullivan EV, Muller-Oehring EM, Adalsteinsson E, Pfeffer-

baum A (2005) Corpus callosal microstructural integrity influences

interhemispheric processing: a diffusion tensor imaging study. Cereb

Cortex 15:1384–1392.

Sillanaukee P, Massot N, Jousilahti P, Vartiainen E, Sundvall J, Olsson

U, Poikolainen K, Ponnio M, Allen JP, Alho H (2000) Dose response

of laboratory markers to alcohol consumption in a general population.

Am J Epidemiol 152:747–751.

Skinner HA, Sheu WJ (1982) Reliability of alcohol use indices. The life-

time drinking history and the MAST. J Stud Alcohol 43:1157–1170.Spielberger CD, Gorsuch RL, Lushene R, Vagg PR, Jacobs GA (1977)

State-trait anxiety self-evaluation questionnaire. Consulting Psychol-

ogists Press, Palo Alto, CA.

Spilich GJ, June L, Renner J (1992) Cigarette smoking and cognitive

performance. Br J Addict 87:1313–1326.

Starr JM, Deary IJ, Fox HC, Whalley LJ (2007) Smoking and cognitive

change from age 11 to 66 years: a confirmatory investigation. Addict

Behav 32:63–68.Sullivan E (2003) Compromised pontocerebellar and cerebellothalamo-

cortical systems: speculations on their contributions to cognitive

and motor impairment in nonamnesic. Alcohol Clin Exp Res

27:1409–1419.

Sullivan E, RosenbloomMJ, Deshmukh A, Desmond JE, PfefferbaumA

(1995) Alcohol and the cerebellum: effects on balance, coordination,

and cognition. Alcohol Health Res World 19:138–141.Sullivan EV, PfefferbaumA (2005)Neurocircuitry in alcoholism: a substrate

of disruption and repair. Psychopharmacology (Berlin) 180:583–594.

Sullivan EV, Rosenbloom MJ, Lim KO, Pfefferbaum A (2000a) Longi-

tudinal changes in cognition, gait, and balance in abstinent and re-

lapsed alcoholic men: relationships to changes in brain structure.

Neuropsychology 14:178–188.

Sullivan EV, Rosenbloom MJ, Pfefferbaum A (2000b) Pattern of motor

and cognitive deficits in detoxified alcoholic men. Alcohol Clin Exp

Res 24:611–621.

Tabachnick BG, Fidell LS (2001) Using Multivariate Statistics. 4th ed.

Allyn & Bacon, Needhan Heights, MA.Wechsler D (1997) Wechsler Adult Intelligence Scale: Administration and

ScoringManual. 3rd ed. The Psychological Corporation, San Antonio.

1126 DURAZZO ET AL.

Wegner AJ, Gunthner A, Fahle M (2001) Visual performance and re-

covery in recently detoxified alcoholics. Alcohol Alcohol 36:171–179.Weinrebe W, Graf-Gruss R, Schwabe R, Stippler D, Fusgen I (2002)

The two-factor method—a new approach to categorizing the clinical

stages of malnutrition in geriatric patients. J Am Geriatr Soc 50:2105–2107.

Wetzel L, Boll T (1987) Short Category Test, Booklet Format. Western

Psychological Services, Los Angeles.Zubieta J, Lombardi U, Minoshima S, Guthrie S, Ni L, Ohl LE,

Koeppe RA, Domino EF (2001) Regional cerebral blood flow

effects of nicotine in overnight abstinent smokers. Biol Psychiatry49:906–913.

1127CHRONIC SMOKING IN ABSTINENT ALCOHOLIC PATIENTS