Alcohol 39 (2006) 1e11

A comparison of neurocognitive function in nonsmokingand chronically smoking short-term abstinent alcoholics

Timothy C. Durazzoa,b,*, Johannes C. Rothlinda,c, Stefan Gazdzinskia,b,Peter Banysa,c, Dieter J. Meyerhoffa,b,d

aSan Francisco Veterans Administration Medical Center, Center for Imaging of Neurodegenerative Disease, San Francisco, CA, USAbNorthern California Institute for Research and Education, San Francisco, CA, USA

cDepartment of Psychiatry, University of California, San Francisco, CA, USAdDepartment of Radiology, University of California San Francisco, San Francisco, CA, USA

Received 27 April 2006; received in revised form 14 June 2006; accepted 14 June 2006

Abstract

Approximately 70e90% of individuals in North America seeking treatment for alcoholism are chronic smokers. A growing body ofevidence suggests chronic cigarette smoking alone adversely affects neurocognition in adults. However, few studies on the neurocognitivefunction of short-term abstinent alcoholics have specifically considered the potential effects of chronic cigarette smoking. In this study, 20nonsmoking recovering alcoholics (nsRA) and 22 actively smoking recovering alcoholics (sRA) participants, matched on age and educa-tion, were contrasted on a comprehensive neurocognitive battery after 34 6 9 days of abstinence. nsRA were superior to sRA on measuresof auditory-verbal learning and memory, processing speed, cognitive efficiency, and static postural stability. These group differences werenot a function of group disparities in age, education, estimated premorbid verbal intelligence, lifetime alcohol consumption, or other mea-sured comorbid psychiatric or medical factors. In sRA, longer smoking duration was negatively correlated with executive skills, visuospatiallearning, general cognitive efficiency, and static postural stability. These results indicate that greater consideration of the potential neuro-biological effects of current chronic smoking on neurocognitive functioning is warranted in studies of alcoholism and other conditionswhere smoking is a common comorbid factor. � 2006 Elsevier Inc. All rights reserved.

Keywords: Alcoholism; Alcohol dependence; Chronic cigarette smoking; Neurocognition; Abstinence

1. Introduction

The adverse effects of chronic alcohol dependence onneurocognition, motor functions, and gait/balance and theirvariable recovery with abstinence from alcohol are welldocumented (Oscar-Berman, 2000; Reed et al., 1992;Rourke & Grant, 1999; Sullivan et al., 2000b, 2000c). How-ever, the vast majority of research investigating neurocogni-tive and motor dysfunction associated with chronic alcoholdependence did not consider the potential confounding ef-fects of chronic cigarette smoking. Approximately 80% ofalcohol-dependent individuals are smokers (Hurt et al.,1994; Pomerleau et al., 1997; Romberger & Grant, 2004),and 70e90% of individuals in North America seeking treat-ment for alcohol abuse are active smokers (Room, 2004). A

* Corresponding author. San Francisco Veterans Administration Med-

ical Center, Center for Imaging of Neurodegenerative Disease (114M),

4150 Clement St., San Francisco, CA 94116, USA. Tel.: þ1-415-221-

4810, ext. 4157; fax: þ1-415-668-2864.

E-mail address: timothy.durazzo@ucsf.edu (T.C. Durazzo).

0741-8329/06/$ e see front matter � 2006 Elsevier Inc. All rights reserved.

doi: 10.1016/j.alcohol.2006.06.006

growing body of evidence suggests chronic cigarette smok-ing, independent of alcohol or substance use disorders, ad-versely affects both neurocognition and motor function inadults. Specific dysfunction among active chronic smokershas been reported for auditory-verbal learning and memory(Hill et al., 2003; Schinka et al., 2003), prospective memory(Heffernan et al., 2005), working memory (Ernst et al.,2001; Spilich et al., 1992), executive functions (Razaniet al., 2004), visual search speeds (Richards et al., 2003),psychomotor speed and cognitive flexibility (Kalmijnet al., 2002), general intellectual abilities (Deary et al.,2003), and postural stability (Iki et al., 1994). Furthermore,adolescent daily smokers showed deficits in accuracy ofworking memory, with individuals who began smoking ata younger age demonstrating a greater level of impairment(Jacobsen et al., 2005). Prospective longitudinal researchwith nondemented adults suggests that chronic cigarettesmoking promotes abnormal rates of decline of verbalmemory in middle age (Richards et al., 2003) and generalcognitive functioning in the elderly (Ott et al., 2004), aswell as significantly increases the risk for various forms

2 T.C. Durazzo et al. / Alcohol 39 (2006) 1e11

of dementia, in particular Alzheimer’s disease (Launeret al., 1999; Merchant et al., 1999; Ott et al., 1998).

Recent neuroimaging studies of chronic smokers showedstructural and perfusion (i.e., blood flow) abnormalities,which may be contributing factors to the neurocognitiveand motor dysfunction reported. Specifically, magnetic reso-nance imaging indicated lower cortical gray matter volumesand densities in the prefrontal cortex, smaller left anteriorcingulate volume, and lower gray matter densities in the rightcerebellum of chronic smokers compared to nonsmokers(Brody et al., 2004), whereas computed tomography showedincreased generalized brain atrophy with advancing age (Ku-bota et al., 2001). Chronic smokers also demonstrated glob-ally decreased brain perfusion in 133Xe inhalation (Rogerset al., 1983; Yamashita et al., 1988) and single photon emis-sion computed tomography (Rourke et al., 1997) studies,with perfusion inversely related to cigarette pack years(Rourke et al., 1997).

Interestingly, the patterns of neurocognitive (Fein et al.,1990; Oscar-Berman, 2000; Tivis et al., 1995), brain struc-tural (Fein et al., 1990; Oscar-Berman, 2000; Sullivan et al.,2000a, 2000c, 2003; Tivis et al., 1995), and brain perfusion(Demir et al., 2002; Mampunza et al., 1995; Nicolas et al.,1993) abnormalities attributed to chronic alcohol depen-dence are very similar to that observed in chronic smokers(as described above). Thus, the neurocognitive and/or mo-tor dysfunction seen in alcoholics may be mediated, in part,by chronic cigarette smoking.

Few studies have addressed the neurobiological and neu-rocognitive consequences of comorbid chronic alcoholismand smoking, perhaps because the majority of treatment-seeking alcoholics are reported to be active smokers. Ina large community-recruited cohort of actively drinkingand abstinent alcoholics, Glass et al. (Glass et al., 2005)reported that both alcoholism and smoking severity wereinversely related to neurocognitive function, and smokingseverity (i.e., pack years) was a unique predictor of generalintelligence and cognitive proficiency (i.e., an index of bothspeed and accuracy). The authors suggested that the effectsof smoking might be most pronounced on measures that re-quire fast and flexible processing. Similarly, in a large com-munity-based group of actively drinking and abstinentalcoholics, Friend et al. (2005), found that both chronicityof alcohol misuse and cigarette smoking were inversely re-lated to measures of general intellectual functioning, setshifting, and processing speed; and the combination ofchronic alcohol and cigarette smoking had an additive ad-verse effect on cognitive functioning. Additionally, in thissample, nonsmoking alcoholics were superior to smokingalcoholics on measures of processing speed and set shifting.However, the authors indicated that only a small amount oftest score variance was accounted for by chronic alcoholismand smoking when used as individual or combined predic-tors. Rosenbloom et al. (2005) reported that 3-month absti-nent smoking alcoholics performed significantly worse onmeasures of verbal intelligence compared to age and

education equivalent controls, but no smoking effects wereapparent on tests of mental status/global cognitive function-ing, nonverbal intelligence, learning and memory, process-ing speed and set shifting. In a series of magnetic resonanceneuroimaging studies with 1-week abstinent, treatment-seeking alcohol-dependent individuals, we found thatchronic cigarette smoking compounded alcohol-inducedcortical gray matter loss (Gazdzinski et al., 2005), exacer-bated regional alcohol-induced abnormalities in brain me-tabolite markers of neuronal viability and cell membraneand/or myelin synthesis (Durazzo et al., 2004), and was pri-marily responsible for gray matter perfusion abnormalitiesin alcoholics (Gazdzinski et al., 2006). We also observedthat chronic smoking appears to adversely affect recoveryof regional metabolite markers of neuronal viability andcell membrane and/or myelin synthesis in alcohol-depen-dent individuals abstinent from alcohol for approximately1 month (Durazzo et al., 2006).

Considering these recent findings, and the fact that a sig-nificant number of smoking alcoholics continue to demon-strate a high level of nicotine dependence during abstinencefrom alcohol (Currie et al., 2001), it is possible that theeffects of chronic cigarette smoking may confound inter-pretability of research examining the neurocognition ofabstinent alcoholics. Specifically, the role of smoking asa mediator of the residual neurocognitive and motor dys-function previously reported during short- or long-termabstinence from alcohol remains unclear. Assessing thedifferential effects of smoking and alcoholism on neurocog-nition may simply seem like a scholarly exercise, withoutlittle practical research or clinical application, given thatthe majority of treatment-seeking alcoholics are reportedto be chronic smokers. However, our research cohort con-sists of only approximately 55% smokers. Thus, consider-ation of smoking status on neurocognition was clearlyindicated in this sample. In this study, we divided a groupof alcohol-dependent individuals in early stages of recoveryinto smoking recovering alcoholics (sRA) and nonsmokingrecovering alcoholics (nsRA). We predicted that sRA wouldperform significantly worse than those of nsRA on mea-sures of executive functions, learning and memory, process-ing speed, working memory, and static postural stability.We also postulated that within the sRA cohort, neurocogni-tive performance would be inversely related to measures ofsmoking severity.

2. Methods

2.1. Participants

Recovering alcoholics (RA) were recruited from the VAMedical Center Substance Abuse Day Hospital and the Kai-ser Permanente Chemical Dependence Recovery Programoutpatient clinics in San Francisco for a longitudinal studyon the effects of abstinence from alcohol on brain structure,metabolism, and function. All participants were between

3T.C. Durazzo et al. / Alcohol 39 (2006) 1e11

the ages of 28 and 66 years at the time of enrolment.Twenty nsRA (one female) and 22 sRA (one female) com-pleted a comprehensive neuropsychological assessmentbattery after 34 6 9 days of sustained abstinence. The ma-jority of RA participants first completed magnetic reso-nance studies 6 6 3 days after consumption of their lastalcoholic drink (as reported in Durazzo et al., 2004; Gaz-dzinski et al., 2005, 2006). Demographics, indices of alco-hol consumption, smoking severity, depressive and anxietysymptomatology as well as select clinical laboratory mea-sures are given in Table 1.

Primary inclusion criteria were current Diagnostic andStatistical Manual of Mental Disorders, 4th Edition (DSM-IV) diagnosis of alcohol dependence or alcohol abuse(American Psychiatric Association, 1994), fluency in En-glish, consumption of greater than 150 alcoholic drinks permonth (one alcoholic drink equivalent 5 12 oz of beer,5 oz of wine, or 1.5 oz of liquor, all corresponding to13.6 g pure alcohol) for at least 8 years prior to enrolmentfor men, and consumption of greater than 80 drinks permonth for at least 6 years prior to enrolment for women.

Table 1

Demographics, alcohol and cigarette use histories, self-report question-

naires, and select laboratory measures for nsRA and sRA (mean 6 S.D.)

Measure nsRA (n 5 20) sRA (n 5 22)

Age (years) 50.1 6 9.3 48.9 6 8.9

Education (years) 14.3 6 2.4 13.8 6 1.9

AMNART 110 6 9 114 6 9

1-year average drinks/month 421 6 252 401 6 191

3-year average drinks/month 382 6 216 359 6 166

8-year average drinks/month 315 6 197 324 6 151

Lifetime average drinks/month 204 6 133 261 6 116

Lifetime years 34 6 10 32 6 9

Age onset heavy drinking (years) 25 6 9 22 6 7

Months heavy drinking 238 6 97 276 6 108

Age onset of smoking na 27 6 12

FTND na 5.7 6 1.8

Cigarettes/day na 22 6 8

Pack years na 26 6 18

Smoking duration (years) na 22 6 13

BDI 6.0 6 6.2 10.4 6 9.0

STAI 41.1 6 11.4 45.0 6 12.7

GGT (institutional units) 41 6 28 40 6 24

Prealbumin (mg/dl) 26 6 7 28 6 4

sRA 5 smoking recovering alcoholics; nsRA 5 nonsmoking recovering

alcoholics; AMNART 5American National Adult Reading Test; 1-year

average 5 average number of drinks per month over 1 year prior to study;

3-year average 5 average number of drinks per month over 3 years prior to

study; 8-year average 5 average number of drinks per month over 8 years

prior to study; age onset of heavy drinking 5 chronological age at which

individual began drinking greater than 100 drinks per month; months heavy

drinking 5 number of months drinking at greater than 100 drinks per

month; lifetime average 5 average number of drinks per month over life-

time; lifetime years 5 number of years of regular alcohol consumption over

lifetime; age onset of smoking 5 age at which sRA participant began smoking

at the level reported at enrolment; FTND 5 Fagerstrom Tolerance Test for Nic-

otine Dependence; BDI 5 Beck Depression Inventory; STAI Y-2 5 State-trait

Anxiety Inventory-State; GGT 5 gamma-glutamylacidtransferase; local nor-

mal range 5 7e64; prealbumin local normal range 5 18e45; na 5 not

applicable.

Primary exclusion criteria are fully detailed in Durazzoet al. (2004). In summary, all participants were free of gen-eral medical, neurologic, and psychiatric conditions knownor suspected to influence neurocognition, except hepatitisC, hypertension, and unipolar mood disorders. Current orpast unipolar mood disorders (e.g., major depression, sub-stance-induced mood disorder) were not exclusionary, giventhe reported high comorbidity with both alcoholism (Gilman& Abraham, 2001) and chronic cigarette smoking (Fergus-son et al., 2003). Participants’ urine was tested for five com-mon illicit substances (i.e., THC, opiates, PCP, cocaine, andamphetamines), and they were evaluated for recent ethanolconsumption via breathalyzer prior to study. No participantwas positive for the above-listed substances or ethanol atthe first magnetic resonance study or neuropsychologicaltesting at approximately 1 month of abstinence. Thirteen of20 nsRA and 16 of 22 sRA participated in continued outpa-tient substance abuse treatment programs at the San Francis-co VA Medical Center after their baseline magneticresonance studies through the time of the comprehensiveneuropsychological assessment reported here. RA attendedthese programs 3e4 days per week and were given ran-dom-weekly drug screens. Breath alcohol levels were ac-quired randomly or in the case of suspected or obviousintoxication. No participant reported alcohol or substanceuse between enrolment and comprehensive neuropsycholog-ical assessment, and chart review confirmed that no partici-pant tested positive for illicit/nonprescribed substances oralcohol over this interval. All sRA were allowed to smokead libitum prior to and during the assessment. Prior to com-pleting any procedure, all participants gave written informedconsent, which was approved by review boards of the Univer-sity of California San Francisco and the San Francisco VAMedical Center and was in accordance with the 1983 Helsin-ki Declaration.

2.2. Psychiatric/behavioral assessment

At the time of enrolment, RA participants completed theStructured Clinical Interview for DSM-IV Axis I disorders,Patient Edition, Version 2.0 (American Psychiatric Asso-ciation, 1994), and standardized questionnaires assessingdepressive (Beck Depression Inventory [BDI]; [Beck,1978]) and anxiety symptomatology (State-Trait AnxietyInventory, form Y-2 [STAI]; [Spielberger et al., 1977]), life-time alcohol consumption (Lifetime Drinking History[LDH]; [Skinner & Sheu, 1982]), and substance use (in-house questionnaire assessing substance type, and quantityand frequency of use), and level of nicotine dependence viathe Fagerstrom Tolerance Test for Nicotine Dependence(FTND; Fagerstrom et al., 1991). From the LDH, we de-rived average number of drinks per month over 1, 3, and8 years prior to enrolment, average number of drinks permonth over lifetime, number of lifetime years of regulardrinking (i.e., duration for which the participant began con-suming at least one alcoholic drink per month), number of

4 T.C. Durazzo et al. / Alcohol 39 (2006) 1e11

months of heavy drinking (i.e., total number of months overlifetime in which the participant drank in excess of 100drinks per month), and age of onset of heavy drinking.The total number of cigarettes currently smoked per dayand the number of years of smoking at the current levelwere recorded and pack years (i.e., [number of cigarettesper day/20]� number of years of smoking at level reportedat enrolment) calculated for sRA.

2.3. Laboratory tests

Gamma-glutamyltransferase (GGT), prealbumin, com-plete blood count (e.g., erythrocyte and leukocyte count, he-matocrit, etc.), and common electrolytes (e.g., sodium,potassium, chloride) were obtained. GGT level was used asa marker of recent heavy alcohol consumption, and theplasma protein prealbumin provided information on nutri-tional status (Weinrebe et al., 2002).

2.4. Neurocognitive assessment

RA completed a comprehensive neuropsychological andmotor/ataxia test battery (approximately 2.5 h) that incor-porated standardized clinical and research instruments andevaluated neurocognitive functions known to be adverselyaffected by alcohol dependence (Rourke & Grant, 1999)as well as chronic cigarette smoking. The domains of func-tioning and constituent measures obtained were as follows:

1) Executive skills: Short Categories Test (Wetzel &Boll, 1987), Stroop Color-Word Test (Golden,1978), Trail Making Test part B (Reitan & Wolfson,1985), Wechsler Adult Intelligence Scale 3rdEdition (WAIS-III) (Wechsler, 1997) Similarities,Wisconsin Card Sorting Test-64 (WCST-64): Com-puter Version 2-Research Edition (Heaton & Staff,1993) perseverative errors, nonperseverative errors,perseverative responses.

2) Fine motor skills: Grooved Peg Board (LafayetteInstrument, Lafayette, IN).

3) General intelligence: Ward-7 Full Scale IQ (Axelrodet al., 2001; based on WAIS-III Arithmetic, BlockDesign, Digit Span, Digit Symbol, Information,Picture Completion, and Similarities subtests).

4) Learning and memory: Auditory verbal: CaliforniaVerbal Learning Test-II (CVLT-II; Delis et al.,2000), Immediate Recall trials 1e5 (learning), Shortand Long Delay Free Recall (memory). Visuospatial:Brief Visual Memory Test-Revised (Benedict, 1997),Total Recall (learning) and Delayed Recall (memory).

5) Postural stability: Modified Fregly Ataxia battery,Sharpened Romberg (Fregly & Graybiel, 1968;Sullivan et al., 2000a).

6) Premorbid verbal intelligence prediction: AmericanNational Adult Reading Test (Grober & Sliwinski,1991).

7) Processing speed: WAIS-III Digit Symbol, StroopColor & Word (Golden et al., 1978), WAIS-III

Symbol Search (Wechsler, 1997) Trail MakingTest-A (Reitan & Wolfson, 1985).

8) Visuospatial skills: WAIS-III Block Design (Wechs-ler, 1997).

9) Working memory: WAIS-III Arithmetic, WAIS-IIIDigit Span (Wechsler, 1997).

10) Timed-test composite (see below).

For the Modified Fregly Ataxia battery, subjects firstcompleted each task with eyes open and then with eyesclosed. For the Sharpened Romberg, a measure of staticpostural stability, participants were required to stand heel-to-toe, with arms crossed across the chest, for a maximumof 60 s. If participants were unable to maintain the requiredposition for at least 3 s on each of the four trials, the taskwas discontinued. If they successfully maintained this posi-tion for 60 s on any of the four trials they were given themaximum score of 60 for any remaining trials. For trialsin which the 60-s criteria was not achieved, but the partic-ipant was able to maintain the required position for at least3 s, the times were recorded and summed across all four tri-als to obtain the final total score. Maximum possiblescore 5 240 s. Raw scores for all neurocognitive measures,except the Fregly Sharpened Romberg, were converted tostandardized scores via appropriate age or age and educa-tion adjusted normative data. For the Fregly SharpenedRomberg, trials with eyes open and closed, raw scores wereused for all analyses, as there are no appropriate norms forthese measures. Standardized scores were transformed to z-scores for formation of the neurocognitive summary scoresfor the domains listed above. The summary score of do-mains where there were multiple measures represents theaverage of the individual z-scores of the constituent mea-sures. A composite index of all tests that were timed, orin which time to complete the task influenced the scoreachieved (herein referred to as timed-test composite), wascalculated by averaging the individual z-scores of thosemeasures. Tests included the Stroop word, color, andcolor-word tests, Trails A and B and WAIS-III Arithmetic,Block Design, Digit Symbol, Picture Completion, andSymbol Search. Higher scores on these measures reflectboth speed and accuracy on predominantly nonverbal tasks.Therefore, the timed-test composite is an approximation ofthe concept of general cognitive efficiency described byGlen and Parsons (Glenn & Parsons, 1992) and others (Nix-on et al., 1995, 1998). All behavioral and neurocognitivemeasures were administered by a doctoral level neuropsy-chologist (TCD) according to standardized procedures.

2.5. Data analyses

We compared nsRA and sRA on measures of executivefunctions, learning and memory, processing speed, timed-test composite, and working memory with multivariateanalysis of variance (MANOVA), multivariate analysis ofcovariance (MANCOVA), and follow-up t-tests using theleast significant difference method. A separate MANOVA

5T.C. Durazzo et al. / Alcohol 39 (2006) 1e11

was conducted for fine motor, general intellectual abilities,and visuospatial skills domains, as we had no a priori hy-potheses for these measures. The Fregly Sharpened Rom-berg, eyes-open and eyes-closed trials for sRA and nsRAwere analyzed with the ManneWhitney Test due to thenon-Gaussian distributions for these tasks.

To control for multiple comparisons, MANOVA/MAN-COVAs alpha levels (0.05) were adjusted by the number ofdomains used in the analysis. For correlational analysesbetween measures of drinking severity and neurocognition,alpha was adjusted conservatively by dividing 0.05 by the10-neurocognitive domains for each individual alcohol con-sumption variable (alpha 5 0.005). Analyses of relationshipsbetween measures of smoking severity and neurocognitionwere not corrected for multiple comparisons to elucidatepatterns of such relationships, as they have not been previ-ously reported in short-term abstinence alcoholics. Statis-tical analyses used SPSS v12.0 and S-plus v6.1.

3. Results

3.1. Participant characterization

All RA participants met DSM-IV criteria for alcohol de-pendence with physiological dependence at the time of en-rolment. Seventy-five percent were Caucasian, 13% wereAfrican American, 7% were Native American, and 5%were Latino. At the time of enrolment, five participants inthe nsRA group met DSM-IV criteria for recurrent majordepression and one for substance-induced (alcohol) mooddisorder with depressive features. In the sRA group, twoparticipants met criteria for recurrent major depressionand two for substance-induced (alcohol) mood disorderwith depressive features. In the sRA group, one participantmet criteria for opioid dependence, in sustained full remis-sion (240 months since last use) and another for currentcannabis abuse. No participant in the nsRA group met cri-teria for current or past substance abuse or dependence(other than alcohol). Three nsRA participants had hyperten-sion and two had hepatitis C. In the sRA group, two had hy-pertension and three had hepatitis C. One participant in thesRA group had mild-moderate COPD (emphysema). OnensRA and one sRA participant took trazodone (for sleepdisturbance) and one nsRA took oxycodone (for pain).The RA participants took no other medications known toinfluence neurocognition. The neurocognitive performancesof the subjects with the above medical conditions and med-ication usage were within one standard deviation of themeans of their respective group on all domains assessed;therefore, they were included in all analyses.

nsRA and sRA were not significantly different in age,education, estimated premorbid verbal intelligence, alcoholconsumption variables, STAI, GGT, prealbumin (seeTable 1), erythrocyte or leukocyte variables, or electrolytelevels. sRA demonstrated a trend for higher BDI scoresthan those of nsRA (P 5 .07). All sRA were actively

smoking at the time of enrolment with essentially nochange in their smoking levels between enrolment and neu-ropsychological assessment at approximately 1 month ofabstinence. sRA smoked 22 6 8 cigarettes per day(min 5 9, max 5 35), over 22 6 13 years (min 5 2,max 5 42), with 26 6 18 (min 5 2, max 5 70) cigarettepack years, with an FTND score of 5.7 6 1.8 (min 5 2,max 5 10), which indicated a moderate to high level of nic-otine dependence. At the time of enrolment, 15 of nsRA re-ported never having used cigarettes consistently (i.e., morethan 10 cigarettes in 1 month) in their life. Five nsRA re-ported a previous history of chronic smoking, with threequitting more than 8 years and two 3e5 years prior to en-rolment. No nsRA participant began smoking during thecourse of the study.

3.2. Group comparisons of neurocognition

In this analysis, we tested the hypothesis that at approx-imately 1 month of abstinence from alcohol, nsRA performsuperior to sRA on indices of executive functions, learningand memory, processing speed, cognitive efficiency, work-ing memory, and postural stability. The omnibus MANOVAwas significant [F(6, 35) 5 3.75, P 5 .006] (adjustedalpha 5 0.05/5 domains 5 0.01), with follow-up t-tests indi-cating that nsRA were superior to sRA on the domains ofauditory-verbal learning (P 5 .002), auditory-verbal mem-ory (P ! .001), processing speed (P 5 .02), and timed-testcomposite (P 5 .02), with a trend (P 5 .06) for better visuo-spatial learning in nsRA (see Table 2). No significant groupdifferences were observed for visuospatial memory, execu-tive skills, or working memory. MANOVA conducted forfine motor skills, general intelligence, and visuospatial skillsyielded no significant group differences on these domains

Table 2

Neurocognitive domain z-scores for nsRA and sRA (mean 6 S.D.)

Neurocognitive domain

nsRA

(n 5 20)

sRA

(n 5 22)

Effect size

(%)

Auditory-verbal learning 1.06 6 0.90 0.28 6 0.74** 15.0

Auditory-verbal memory 0.83 6 0.82 0.10 6 0.51** 21.0

Executive skills �0.35 6 0.44 �0.41 6 0.75 !1.0

Fine motor skills �0.90 6 0.72 �0.87 6 0.79 !1.0

General intelligence 0.41 6 0.66 0.27 6 0.89 !1.0

Processing speed �0.01 6 0.49 �0.35 6 0.58* 5.0

Timed-test composite 0.00 6 0.42 �0.36 6 0.60* 5.0

Visuospatial learning �0.02 6 0.87 �0.36 6 0.74 4.0

Visuospatial memory �0.32 6 1.00 �0.23 6 1.01 !1.0

Visuospatial skills �0.01 6 0.83 0.15 6 0.90 !1.0

Working memory 0.12 6 0.91 0.25 6 0.84 !1.0

sRA 5 smoking recovering alcoholics; nsRA 5 nonsmoking recover-

ing alcoholics.

Asterisks indicate statistically significant differences between nsRA

and sRA; *P ! .02, **P # .002. Effect size was calculated with eta

squared and represents the amount of variance in the dependent measures

accounted for by the factors (i.e., smoking status) in the model (see Levine

and Hullett, 2002 for review). Values were multiplied by 100 to yield

percent variance.

6 T.C. Durazzo et al. / Alcohol 39 (2006) 1e11

(see Table 2). The ManneWhitney Test for the FreglySharpened Romberg indicated that nsRA was superior tosRA performance on the eyes-closed trial (ManneWhitneyU 5 85.0, P 5 .01), with no significant group differences forthe eyes-open trial. See Table 3 for group performances onindividual measures.

Although groups did not differ significantly on any alco-hol consumption variable, sRA did consume approximately57 more drinks per month over lifetime than nsRA. To con-trol for the potential influence of the greater cumulativedose of alcohol over lifetime in sRA, we conducted a MAN-COVA with drinks per month over lifetime as acovariate for the domains of executive skills, learningand memory, processing speed, cognitive efficiency, andworking memory. The omnibus MANCOVA was significant[F(6, 34) 5 3.10, P 5 .01], as were the group differences for

Table 3

Individual measure z-scores for nsRA and sRA (mean 6 S.D.)

Measure

nsRA

(n 5 20)

sRA

(n 5 22)

BVMT-R Total Recall �0.02 6 0.87 �0.36 6 0.74

BVMT-R Delayed Recall �0.32 6 1.00 �0.23 6 1.01

CVLT-II Immediate Recall

Trails 1e5

1.06 6 0.90 0.28 6 0.74***

CVLT-II Short Delayed Free Recall 0.74 6 0.86 0.07 6 0.41***

CVLT-II Long Delayed Free Recall 0.79 6 0.89 0.20 6 0.65**

Grooved Peg Board Dominant Hand �0.95 6 0.89 �0.91 6 1.03

Grooved Peg Board Nondominant

Hand

�0.85 6 0.75 �0.86 6 0.76

Sharpened Romberg Eyes Open

(raw score)

233 6 29 237 6 32

Sharpened Romberg Eyes Closed

(raw score)

188 6 79 130 6 86*

Short Categories Test �0.60 6 0.90 �0.41 6 1.32

Stroop Word �0.26 6 0.54 �0.62 6 0.61**

Stroop Color �0.29 6 0.52 �0.74 6 0.55**

Stroop Color-Word Test �0.04 6 0.76 �0.62 6 1.37*

Trails Making Test A �0.28 6 0.76 �0.57 6 1.03

Trails Making Test B �0.03 6 0.81 �0.64 6 0.85*

WAIS-III Arithmetic 0.00 6 1.06 �0.09 6 0.88

WAIS-III Block Design �0.01 6 0.83 0.15 6 0.90

WAIS-III Digit Span 0.23 6 0.94 0.46 6 1.02

WAIS-III Digit Symbol 0.18 6 0.71 �0.33 6 0.94*

WAIS-III Information 0.51 6 0.86 0.75 6 0.82

WAIS-III Picture Completion 0.24 6 1.13 �0.33 6 0.91**

WAIS-III Similarities 0.75 6 0.89 0.68 6 0.91

WAIS-III Symbol Search 0.53 6 0.86 0.28 6 0.93

Ward-7 Full Scale IQ 0.41 6 0.66 0.27 6 0.89

WCST-64 total errors �0.30 6 0.91 �0.18 6 1.00

WCST-64 perseverative responses �0.35 6 0.82 �0.32 6 0.80

WCST-64 perseverative errors �0.45 6 0.81 �0.35 6 0.79

WCST-64 nonperseverative errors �0.19 6 0.83 �0.15 6 0.95

sRA 5 smoking recovering alcoholics; nsRA 5 nonsmoking recover-

ing alcoholics; BVMT-R 5 Brief Visual Memory Test-Revised; CVLT-

II 5 California Verbal Learning Test-II; WAIS-III 5 Wechsler Adult

Intelligence Scale 3rd Edition; WCST-64 5 Wisconsin Card Sorting

Test-64.

Asterisks indicate statistically significant differences between nsRA

and sRA; *P # .05, **P # .01, ***P # .001, significance levels not cor-

rected for multiple comparisons.

timed-test composite and auditory-verbal learning andmemory. However, the superior performance of nsRA onthe processing speed domain decreased to a trend(P 5 .08) when covaried for average number of drinks permonth over lifetime.

A few studies (e.g., Ernst et al., 2001; Spilich et al.,1992) reported inferior performance of former smokerscompared to never smokers in some areas of cognition.To ensure that the five former smokers in the nsRA groupdid not influence the above findings, analyses were repeatedwithout these participants, and the results were virtuallyidentical to the original findings. Furthermore, there wereno significant differences or trends between former smokersand life-long nonsmokers on the domains evaluated.

3.3. Relationships between neurocognitionand drinking severity, BDI, and STAI

Measures of drinking severity, BDI, and STAI scoresshowed no significant relationships with any of the neuro-cognitive domains evaluated for either nsRA or sRA.

3.4. Relationships between neurocognitionand smoking severity in sRA

For this analysis, we predicted that measures of smokingseverity in sRA (i.e., nicotine dependence, cigarettessmoked per day, pack years, smoking duration) are in-versely correlated with performance on composite neuro-psychological measures. As predicted, we found that insRA the FTND score and number of cigarettes per day wereinversely related to auditory-verbal memory (both r 5�0.37,P 5 .05), while smoking duration was negatively correlatedwith executive skills (r 5�0.63, P 5 .001; see Fig. 1), visuo-spatial learning (r 5�0.37, P 5 .05), timed-test composite(r 5�0.43, P 5 .02; see Fig. 2), and static postural stability(Sharpened Romberg eyes closed; r 5�0.57, P 5 .005).

Given that there were close correspondences betweenage of onset of heavy drinking (26 6 6 years) and age of

Smoking Duration (years)

0 5 10 15 20 25 30 35 40 45

Execu

tive S

kills (z-sco

re)

-3.0

-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0

1.5

r = -0.63, p = .001

Fig. 1. Relationship between smoking duration and executive skills in

smoking recovering alcoholics.

7T.C. Durazzo et al. / Alcohol 39 (2006) 1e11

onset of smoking at the current level (27 6 12 years) aswell as between years of heavy drinking (23 6 10) andsmoking duration (22 6 13) in sRA, we used multiple lin-ear regression to evaluate the potential independent andinteractive effects of smoking and drinking chronicity onneurocognition and postural stability in sRA. The predic-tors we chose were smoking duration, as it showed thestrongest inverse relationships with neurocognition, and av-erage drinks per month over lifetime as this measure incor-porates both dose and chronicity of alcohol consumption. Inthis exploratory analysis, significance levels were not ad-justed for multiple comparisons to elucidate prominent pat-terns. Smoking duration (controlled for average drinks permonth over lifetime) was a significant independent predic-tor of executive skills [t(1, 18) 5�3.25, P 5 .004] [Model,F(3, 18) 5 6.01, P 5 .009, R2 5 0.39] and processing speed[t(1, 18) 5�2.05, P 5 .03] [Model, F(3, 18) 5 5.81,P 5 .006, R2 5 0.51]. Lifetime average drinks per month(controlled for smoking duration) independently predictedworking memory [t(1, 18) 5�4.65, P ! .001] [Model,F(3, 18) 5 11.03, P ! .001, R2 5 0.52]. The interaction ofsmoking duration and lifetime average drinks per monthsignificantly predicted processing speed [t(1, 18) 5�2.76,P 5 .01] [Model, F(3, 18) 5 5.81, P 5 .006, R2 5 0.51],visuospatial learning [t(1, 18) 5�2.88, P 5 .009] [Model,F(3, 18) 5 6.30, P 5 .004, R2 5 0.49], and general intelli-gence [t(1, 18) 5�2.27, P 5 .04] [Model, F(3, 18) 5 6.90,P 5 .003, R2 5 0.54]. For each interaction, higher lifetimealcohol consumption and longer smoking duration wereinversely related to performance on these measures. Thiswas confirmed by examination of the slope of the regressionplane for each interaction via three-dimensional graphs (notpresented). Examination of other potential confounding/contributing factors including age, BDI score, cigarette packyears, and years of heavy drinking (all simultaneously enteredinto the multiple regression model) were not found to besignificantly independent or interactive predictors of thedomains evaluated.

Smoking Duration (years)

0 10 20 30 40 50

Tim

ed

-T

est C

om

po

site (z-sco

re)

-2.0

-1.5

-1.0

-0.5

0.0

0.5

1.0 r = -0.43, p = .02

Fig. 2. Relationship between smoking duration and timed-test composite

in smoking recovering alcoholics.

4. Discussion

This study identified inferior neurocognitive perfor-mance in sRA compared to nsRA at approximately 1 monthof abstinence from alcohol. sRA performed worse thannsRA on measures of auditory-verbal learning and memory,processing speed, cognitive efficiency, and postural stabil-ity. Overall, these findings were not a function of groupdifferences in age, education, or estimated premorbid intel-ligence, and they were not mediated by differences in life-time alcohol consumption or other psychiatric and medicalfactors evaluated in this study. Furthermore, groups werenot significantly different on measured general fund ofknowledge (WAIS-III Information), verbal abstraction(WAIS-III Similarities), or Full Scale IQ. The equivalencyof sRA and nsRA on these measures and demographic vari-ables mitigates the contribution of such factors known tosignificantly influence neurocognition (see Heaton et al.,1996 for review).

In the context of investigations of neuropsychologicaldysfunction in substance dependence, it is important to dis-tinguish between the effects of acute ingestion/intoxicationand withdrawal from the consequences of chronic exposureto the substance in question. Acute nicotine administrationhas been found to transiently improve some areas of cogni-tion, most prominently on measures of sustained attention,in healthy nonsmokers (see Sacco et al., 2004 for review).However, the effects of acute nicotine administration onneurocognition in alcoholics and other substance abusersare not clear (see Ceballos et al., 2006). The particulateand gas phases of cigarette smoke contain many toxic com-pounds (e.g., carbon monoxide, free radicals, nitrosamines,polynuclear aromatic compounds [Fowles et al., 2000]) thatmay directly or indirectly compromise brain tissue and leadto the morphologic abnormalities and neurocognitive ab-normalities reported in nonsubstance abusing smokers(see Durazzo et al., 2004, 2006 for detailed discussion ofthe possible biological mechanisms promoting the neuro-cognitive findings observed in this cohort of smokingalcoholics).

Measures of alcohol consumption severity, which in-cluded indexes of dose and chronicity, showed no associa-tion with neurocognition in either nsRA or sRA. Thesefindings are consistent with Beatty et al. (2000), who foundweak or no relationships between measures of alcohol dose,chronicity, and neurocognition in 3e6-week abstinent alco-holics. In sRA, both higher nicotine dependence and num-ber of cigarettes smoked per day were inversely related toauditory-verbal memory, and longer smoking durationwas negatively correlated with executive skills, visuospatiallearning, timed-test average, and postural stability. Our re-gression analyses indicated that the interaction of smokingduration with lifetime average drinks per month was a sig-nificant predictor of poorer performance in the domains ofprocessing speed, visuospatial learning, and general intelli-gence in sRA. These findings suggest that in sRA the

8 T.C. Durazzo et al. / Alcohol 39 (2006) 1e11

combination of chronic drinking and smoking negativelyimpact these neurocognitive functions in a synergistic fash-ion. Results from our correlation and regression analysesare consistent with those of Glass et al. (2005) who ob-served that higher smoking and drinking severity were in-versely related to measures of general intelligence andcognitive efficiency, but only smoking severity individuallypredicted both general intelligence and cognitive efficiency.Our findings are also consistent with Friend et al. (2005)who reported that the combination of chronic alcoholismand smoking predicted poorer performance on measuresof set shifting and processing speed and found nonsmokingalcoholics were superior to smoking alcoholics on tasksassessing set shifting and processing speed. In this study,sRA showed greater static postural instability than thoseof nsRA on the eyes closed, but not on the eyes-open con-dition of the Sharpened Romberg. These results are in linewith Iki et al. (1994) who reported significant postural swayon a force platform with eyes closed in nonalcoholicchronic smokers. Also, in sRA, longer smoking durationwas only related to worse performance on the SharpenedRomberg eyes-closed condition. Control of balance andpostural stability is a highly complex sensorimotor functionthat requires central integration of visual, vestibular, andsomatosensory information. With eyes closed, visual affer-ent input can no longer be integrated to assist with posturalstability (see Sullivan et al., 2005; Vouriot et al., 2004). Theobserved group differences may in part reflect greater func-tional compromise of cortical sensorimotor integration insRA and/or disruption of their reciprocal pathways connect-ing the cortex, thalamus, pons, and cerebellum (Sullivanet al., 2003).

All neurocognitive domains evaluated were generally inthe average range of functioning for nsRA and sRA, withthe exception of fine motor skills, which were in the low av-erage range for both groups. In fact, auditory-verbal learn-ing in nsRA was one standard deviation above the mean(84th percentile) relative to the CVLT-II normative sample.Numerous studies reported significant deficits in multipleareas of neurocognition after approximately 1 month of ab-stinence from alcohol relative to controls (e.g., Parsons,1998; Rourke & Grant, 1999; Sullivan et al., 2000c), but al-coholics may also show average performance on multiplemeasures when scores are based on test norms (e.g., Friendet al., 2005; Rosenbloom et al., 2005). It is estimated thatonly 50% of alcoholics demonstrate detectable neurocogni-tive dysfunction after 2e3 weeks of abstinence (Rourke &Loberg, 1996), which emphasizes the considerable individ-ual variability of effects of chronic alcoholism (and possi-bly comorbid chronic smoking) on neurocognition.

Limitations of this study include the relatively modestnumbers of participants. Additionally, since we did notsubject participants to the rigors of a comprehensive cog-nitive battery during detoxification, we are unable to deter-mine if the observed group differences in neurocognitionwere apparent at the time of enrolment. Therefore, the

observed smoking-associated neurocognitive differencesmay be premorbid or serve as a risk factor for the initia-tion of smoking. Similarly, we cannot exclude that poten-tial unrecorded group differences in nutrition, exercise,overall physical health, exposure to environmental ciga-rette smoke, and other genetic predispositions/vulnerabil-ities did not influence our findings. Additionally, the vastmajority of participants in this report were males, whichdid not permit the examination of the potential effects ofgender on our outcome measures. It should also be notedthat some domains where significant group differences ortrends were observed (i.e., auditory-verbal and visuospatiallearning) are not composite measures as they are based ona single test. The inferior performance of the sRA groupon measures of processing speed did not remain significantafter correcting for lifetime average drinks per month, al-though none of our measures of drinking severity was sig-nificantly correlated with any neurocognitive domain. Withnonparametric tests such as the ManneWhitney U Test, itis not possible to use covariates; consequently, we wereunable to control for group differences in lifetime averagedrinks per month. Although lifetime average drinks permonth were essentially uncorrelated with postural stabilityin sRA (r 5�0.017, P 5 .94), we cannot absolutely con-clude that the greater postural instability demonstratedby sRA in this analysis was not influenced by their greaterlifetime alcohol intake. Our naturalistic study design didnot allow for assessment of the potential short-term facil-itative effects of acute nicotine exposure on neurocogni-tion in the sRA cohort, who were permitted to smoke adlibitum during the assessment, and not monitored with re-gard to number of cigarettes smoked immediately prior toand/or during the neuropsychological assessment. As par-ticipants were allowed to smoke ad libitum prior to andduring the 2e2.5-h assessment, our findings were notlikely a function of nicotine withdrawal (the half-life ofnicotine in humans is approximately 2e3 h; Nakajima &Yokoi, 2005). Specific recording of biologic (e.g., breathCO, cotinine levels) and behavioral measures (e.g., timesince last cigarette smoked prior to assessment; numberof cigarettes smoked during assessment) may allow futureresearch to better assess the effects of acute nicotine con-sumption or withdrawal on neurocognitive function in thispopulation. Additionally, obtaining information on historyof chronic exposure to significant levels of environmentalcigarette smoke (e.g., parent(s) who smoked in the home,working in a bar) during childhood and/or adulthood mayassist in examining the effects of ‘‘second-hand smoke’’expose on adult human brain function. Finally, studies thatexamine the effects of concurrent alcoholism and chronicsmoking in a larger cohort of women are clearly indicated.

In conclusion, this well educated, predominantly Cauca-sian male, cohort of sRA showed significantly lower perfor-mance than nsRA on several neurocognitive domains offunctioning and static postural stability after 1 month of ab-stinence from alcohol. Although we controlled for factors

9T.C. Durazzo et al. / Alcohol 39 (2006) 1e11

known to influence neurocognition, it cannot be concludedunequivocally that the group differences observed were notpremorbid in nature. Irrespective of the etiology of theneurocognitive differences we observed, our findings, inaddition to other recent reports, indicate significant perfor-mance differences between smoking and nonsmoking RAin several domains of function, which suggests consider-ation of smoking status in the neurocognitive evaluationof RA is warranted.

Acknowledgments

This project was supported by NIH AA10788 (DJM).We thank Mary Rebecca Young, Bill Clift, Christina Hall,and Dr. Donald Tusel of the VA Substance Abuse Day Hos-pital and Drs. David Pating, Peter Washburn, and KarenMoise and their colleagues at the Kaiser Permanente Chem-ical Dependency Recovery Program for their valuable assis-tance in recruiting participants, Derek Flenniken for database management expertise, and Dr. John Kornak for statis-tical consultation. We also wish to extend our gratitude tothe study participants, who made this research possible.

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