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Published in final edited form as: Drug Alcohol Depend. 2013 Sep 2;133(2):10.1016/j.drugalcdep.2013.08.020. doi: 10.1016/j.drugalcdep.2013.08.020

Interactive Effects of Chronic Cigarette Smoking and Age on Hippocampal Volumes

Timothy C Durazzo a,b,*, Dieter J Meyerhoff a,b, Sara Jo Nixon c
PMCID: PMC3870586  NIHMSID: NIHMS522055  PMID: 24051060

Abstract

Background

Previous cross-sectional MRI studies with healthy, young-to-middle-aged adults reported no significant differences between smokers and non-smokers on total hippocampal volume. However, these studies did not specifically test for greater age-related volume loss in the total hippocampus or hippocampal subregions in smokers, and did they did not examine relationships between hippocampal and subfield volumes and episodic learning and memory performance.

Methods

Healthy, young-to-middle-aged (45 ± 12 years of age) smokers (n = 39) and non-smokers (n = 43) were compared on total hippocampal and subfield volumes derived from high-resolution 4 Tesla MRI, emphasizing testing for greater age-related volume losses in smokers. Associations between hippocampal volumes and measures of episodic learning and memory were examined.

Results

Smokers showed significantly smaller volumes, as well as greater volume loss with increasing age than non-smokers in the bilateral total hippocampus and multiple subfields. In smokers, greater pack-years were associated with smaller volumes of the total hippocampus, presubiculum, and subiculum. In the entire cohort, performance on measures of learning and memory was related to larger total hippocampal and several subfield volumes, predominately in the left hemisphere.

Conclusions

Chronic cigarette smoking in this young-to-middle aged cohort was associated with smaller total hippocampal and subfield volumes, which were was exacerbated by advancing age. Findings also indicated an adverse smoking dose/duration response (i.e., pack-years) with total hippocampal and select subfield volumes. These hippocampal volume abnormalities in smokers may be related to the deficiencies in episodic learning and memory in young-to-middle-aged smokers reported in previous studies.

Keywords: cigarette smoking, MRI, hippocampus, hippocampal subfields, learning and memory, aging

1. INTRODUCTION

An extensive body of research describes the deleterious effects of chronic cigarette smoking on human cardiac and pulmonary functions, vascular systems, as well as its carcinogenic properties, principally in the elderly (Ambrose and Barua, 2004; Bartal, 2001; Boudreaux et al., 2003; Casasola et al., 2002). However, beyond cardiovascular and cerebrovascular risk factors for stroke, little research has been devoted to effects of chronic smoking on human brain morphology, particularly in young-to-middle-aged adults (i.e., 25–60 years of age). This age range contains the greatest proportion of the population in the United States population (U.S. Census Bureau, 2010), and the greatest number of smokers. Specifically, the prevalence of smoking in the 25–60 age range is approximately 23% compared to 10% in those greater than 60 years of age (Dube et al., 2010). Previous cross-sectional magnetic resonance imaging (MRI) studies comparing brain morphological measures in young-to-middle-aged smokers and non-smokers (mean ages from 28 to 50 years of age) reported smokers showed smaller volumes and/or lower gray matter (GM) densities in the dorsolateral frontal cortex, anterior and posterior cingulate cortex, mesial temporal lobe, posterior parietal lobe, thalamus, cerebellum, and components of the basal ganglia, as well as thinner orbitofrontal cortex (Brody et al., 2004; Gallinat et al., 2006; Kuhn et al., 2010; Liao et al., 2010; Yu et al., 2011). Greater pack years were associated with smaller anterior frontal, temporal, and cerebellar GM volume (Brody et al., 2004; Gallinat et al., 2006), as well as thinner orbitofrontal cortex (Kuhn et al., 2010). Chronic cigarette smoking during middle-age is robustly associated with increased risk for Alzheimer’s disease (AD) and other forms of dementia (Cataldo et al., 2010; Rusanen et al., 2010a, 2010b). Numerous MRI studies demonstrated significant volume loss in the total hippocampus (Chupin et al., 2009; Du et al., 2001; Fellgiebel et al., 2006; Pennanen et al., 2004; Schuff et al., 2001; Tapiola et al., 2006), and more recently, in hippocampal subfields (Apostolova et al., 2012; Apostolova et al., 2010a; Lim et al., 2012b; Mueller and Weiner, 2009), in those with mild cognitive impairment (MCI) and early-stage AD dementia. The rate of hippocampal atrophy in persons with MCI and AD is significantly greater than in cognitively normal elderly controls (Jack, Jr., et al., 2000), and an increased rate of hippocampal atrophy in the cognitively normal elderly has been proposed as a neuroimaging biomarker for MCI and early-stage AD dementia (Apostolova et al., 2012). Given that an abnormal rate of hippocampal atrophy in the elderly serves as a risk factor for MCI and early-stage AD dementia, it is of critical importance to determine if young-to-middle-aged chronic smokers manifest greater-than-normal age-related hippocampal volume loss. Previous cross-sectional MRI studies with young-to-middle-aged healthy, non-clinical cohorts reported no significant differences between smokers and non-smokers on total hippocampal volumes (Brody et al., 2004; Gallinat et al., 2006; Yu et al., 2011). However, these previous studies did not specifically test for greater age-related volume loss in the total hippocampus or hippocampal subregions (e.g., CA1, subiculum) in smokers, nor did they examine relationships between hippocampal and subfield volumes and episodic learning and memory performance. Accordingly, this study compared healthy, predominantly young-to-middle-aged smokers and non-smokers, on total hippocampal and subfield volumes derived from high-resolution MRI at 4 Tesla, and specifically tested for greater age-related volume loss in smokers.

We predicted that: 1) With increasing age, smokers show greater volume loss than non-smokers in total hippocampal volume, and in the CA1, CA2-3, CA4-dentate gyrus, and subiculum subfields; 2) Larger total hippocampal and subfield volumes are related to better performance on measures of learning and memory in the combined study cohort (i.e., smokers + non-smokers); 3) For smokers, greater pack-years and lifetime duration of smoking are related to smaller total hippocampal and subfield volumes.

2. METHODS

2.1. Participants

Eighty-two healthy, community-dwelling participants [43 non-smokers (eight females) and 39 smokers (six females)], were recruited via posters, electronic billboards, and word-of-mouth. Participants were between the ages of 25 and 68 and all were employed at the time of study (see Table 1 for demographics). Participants provided written informed consent according to the Declaration of Helsinki, and the consent document and procedures were approved by the University of California San Francisco and the San Francisco VA Medical Center.

Table 1.

Demographic and Clinical Measures

Variable Non-smokers (n = 43) Smokers (n = 39)
Age (years) 46.5 ± 11.0
min: 22.2; max: 68.8		 43.3 ± 12.6
min: 23.7; max: 64.1
Education (years) 16.1 ± 2.1
min: 12; max: 20		 14.8 ± 2.1*
min: 12; max: 20
AMNART 119 ± 9 117 ± 6
% Male 81 84
% Caucasian 58 62
Beck Depression Inventory 2.7 ± 2.9 5.2 ± 4.2*
STAI-trait 32.2 ± 7.8 33.0 ± 6.8
1-yr avg drinks/month 13.8 ± 15.5 21.4 ± 19.6
Lifetime average drinks/month 17.7 10.9 25.6 13.8*
FTND NA 4.7 ± 1.6
Cigarettes/day NA 18.3 ± 6.9
Total lifetime years of smoking NA 26.4 ± 11.5
Age started smoking daily NA 20.0 ± 7.8
min: 11; max: 47
Pack years NA 24.9 ± 15.9
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Note. Mean ± standard deviation;

*

p < .05; AMNART: American National Adult Reading Test; FTND: Fagerstrom Test for Nicotine Dependence; STAI: State-Trait Anxiety Inventory, trait-score.

Primary inclusion/exclusion criteria are fully detailed elsewhere (Durazzo et al., 2012a). In summary, participants were screened for history of neurologic (e.g., seizure disorder, neurodegenerative disorder, demyelinating disorder, closed head trauma with loss of consciousness), general medical (e.g., hypertension, myocardial infarction, Type-1 or 2 diabetes, cerebrovascular accident), and psychiatric (i.e., mood, thought, anxiety, substance/alcohol use disorders) conditions known or suspected to influence neurocognition or brain neurobiology. All females were pre-menopausal, by self-report. All non-smoking participants never smoked, or smoked less than 40 cigarettes during their lifetime and used no cigarette/tobacco in the 10 years prior to study. All smoking participants were actively smoking at the time of assessment and smoked at least 10 cigarettes per day for 5 years or more, with no periods of smoking cessation greater than 1-month in the 5-years prior to study. No smoker was engaged in any pharmacological/behavioral smoking nicotine cessation program or used other forms of tobacco at the time of study.

2.2. Medical, Psychiatric, Substance, Alcohol Consumption Assessment

Participants completed the screening section of the Structured Clinical Interview for DSM-IV Axis I disorders, Patient Edition, Version 2.0 (SCID-I/P; First et al., 1998), as well as an in-house questionnaire designed to screen for medical, psychiatric, neurological and developmental conditions that may affect neurocognition or brain neurobiology (see Durazzo et al., 2004). Participants completed standardized questionnaires assessing lifetime alcohol consumption (Lifetime Drinking History, LDH; Skinner and Sheu, 1982; Sobell et al., 1988) and substance use (in-house questionnaire assessing substance type, and quantity and frequency of use). From the LDH, we derived average number of drinks (defined as containing 13.6 grams of pure ethanol) per month over 1-year prior to enrollment and average number of drinks per month over lifetime. Participants also completed self-report measures of depressive (Beck Depression Inventory, BDI; Beck, 1978) and anxiety symptomatology (State-Trait Anxiety Inventory, form Y-2, STAI; Spielberger et al., 1977). Smokers also completed a measure of nicotine dependence level (Fagerström Test for Nicotine Dependence, FTND; Heatherton et al., 1991), and provided self-reported information regarding the total number of cigarettes currently smoked per day, the number of years of smoking at the current level, and the total number of years of smoking over lifetime. From this information, pack years (i.e., (number of cigarettes per day/20) x total number of years of smoking) were calculated for smokers. Approximately 30% of smokers and non-smokers reported intermittent “recreational” use (i.e., ≤ 3 episodes/month) of cannabis or cocaine during late adolescence or early adulthood; there was no significant difference in frequency of substance use between smokers and non-smokers. Prior to assessment, participants’ urine was tested for five common illicit substances (i.e., THC, opiates, PCP, cocaine, and amphetamines), and they were evaluated for recent ethanol consumption via breathalyzer. No participant was positive for the above common illicit substances or ethanol consumption at the time of assessment.

2.3. Magnetic Resonance Imaging (MRI) Acquisition and Processing

MRI data were acquired on a 4.0 Tesla Bruker MedSpec system using an 8-channel transmits-receive head coil (Siemens, Erlangen, Germany). A Magnetization Prepared Rapid Gradient (TR/TE/TI = 2300/3/950 ms, 7° flip angle, 1.0 × 1.0 × 1.0 mm3 resolution) sequence was used to acquire 3D sagittal T1-weighted images for morphological analyses. The publicly available Freesurfer (v5.1) segmentation and cortical surface reconstruction methods were used to obtain regional, bilateral cortical, subcortical GM (including the total hippocampus) morphometrics, and intracranial volume (ICV) (all in mm3; Dale et al., 1999; Fischl and Dale, 2000; Fischl et al., 2004, 1999). Volumetric results from FreeSurfer segmentation of the bilateral total hippocampus were shown to be highly correlated with those from manual tracing methods (Morey et al., 2009). Automated hippocampal subfield segmentation was performed via the FreeSurfer v5.1 pipeline, as described by Van Leemput and colleagues (Van Leemput et al., 2009), and volumes for the following bilateral hippocampal subfields were acquired: CA1, CA2-3, CA4-dentate gyrus, subiculum, presubiculum, fimbria, and tail. Segmentated/parcellated T1-weighted images for cortical, subcortical and hippocampal subfield regions for all participants were visually inspected by one of the authors (TCD) for accuracy, and any errors in segmentation/parcellation were manually edited and reprocessed as previously described (Durazzo et al., 2012b). Initial comparisons of smokers and non-smokers on total hippocampal and subfield volumes yielded a highly similar pattern of results for left and right hemispheres (data not shown). Therefore, in comparisons between smokers and non-smokers, volumes from the left and right hemispheres for the total hippocampus and subfields were summed, and analyses were conducted on these composite volumes.

2.4. Neurocognitive Assessment

Participants completed a comprehensive battery composed of well-normed measures that are commonly used in clinical and research settings (Strauss et al., 2006). Verbal intelligence was estimated with the American National Adult Reading Test (Grober and Sliwinski, 1991). Given the hippocampus subserves multiple aspects of learning and memory functions (Eldridge et al., 2005), we examined associations between hippocampal volumetrics and measures of auditory-verbal learning and memory (California Verbal Learning Test-II, CVLT-II; Delis et al., 2000) - Immediate Recall trials 1–5 (learning), average of Short and Long Delay Free Recall (memory), and visuospatial learning and memory (Brief Visual Memory Test, BVMT Revised; Benedict, 1997) - Total Recall (learning) and Delayed Recall (memory]. Results from comparisons of smokers and non-smokers on measures of learning, memory and other neurocognitive domains of functioning in this cohort are presented elsewhere (Durazzo et al., 2012a).

2.5. Statistical Analyses

Dependent measures were volumes of the total hippocampus, CA1, CA2-3, CA4-dentate gyrus, subiculum, presubiculum, fimbria and tail. To test our hypothesis of greater age-related volume loss in the total hippocampus and subfields in smokers, we employed multivariate analysis of covariance (MANCOVA), and specifically tested for a group (smoker vs. non-smoker) x age interaction. Covariates included ICV, education, and average lifetime drinks/month (groups were significantly different on education and lifetime drinks/month. (See Section 3.1.) In secondary analyses, to assess for potential non-linear age effects across the sample age-range, a quadratic age term was also added as a main/linear effect, and formed a group x age2 interaction factor. In secondary analyses, we also added BDI (groups were significantly different. (See Section 3.1) and any history of recreational substance use (binary variable) as predictors, because these factors may be related to hippocampal volume. Significant multivariate effects were followed-up with univariate tests. Although we had a priori predictions, alpha level (p = .05) for all univariate and pairwise comparisons between smokers and non-smokers were corrected for multiple comparisons via false discovery rate (Benjamini and Hochberg, 1995). Relative effect sizes for significant group x age interactions were calculated by dividing the estimated slope by the corresponding standard error (Judd and McClelland, 1989). Effect sizes for mean differences in pairwise comparisons between smokers and non-smokers on hippocampal and hippocampal subfield volumes were calculated with Cohen’s d (Cohen, 1988). Relationships between age and hippocampal and subfield volumes were examined separately for smokers and non-smokers with partial correlations, controlled for ICV. Group differences in the magnitudes of these correlations between smokers and non-smokers were tested with Fisher’s Z test (Glass and Hopkins, 1984), and p < .05 was considered statistically significant. Associations between measures of learning, memory and working memory, and total hippocampal, and subfield volumes for each hemisphere were examined with partial correlations, controlling for age, education, and ICV for the combined group, and for each group separately; p < .05 was considered statistically significant. Associations between total hippocampal and subfield volumes for each hemisphere and measures of smoking severity (i.e., pack-years, lifetime years of smoking, FTND score, age started daily smoking) were measured with partial correlations, controlling for age and ICV; p < .05 was considered statistically significant.

3. RESULTS

3.1. Participant Demographics and Clinical Variables

Smokers and non-smokers were equivalent on age, sex, ethnicity, anxiety symptomatology (STAI-Trait score), and 1-year average drinks consumed/month (see Table 1). Smokers had significantly fewer years of education, higher BDI scores, and more lifetime average drinks consumed/month. The average BDI score for both groups was in the normal range (i.e., < 10) and well below the cutoff for mild depressive symptomatology (Richter et al., 1998). The alcohol consumption of both groups did not approach a hazardous or clinically significant level of use [see (McKee et al., 2007; Mertens et al., 2005)].

3.2. Comparisons of Smokers and Non-smokers on Total Hippocampal and Subfield Volumes

3.2.1 Main effects and interactions

MANCOVA indicated age [F (8, 70) = 11.27, p < .001], ICV [F (8, 70) = 9.06, p < .001], group [F (8, 70) = 3.38, p = .002], and the group x age interaction [F (8, 70) = 2.47, p < .02] were significant omnibus predictors of hippocampal and subfield volumes. Education, average lifetime drinks/month, age2, and group x age2 were not significant predictors in the multivariate model (all p > .42). In secondary analyses, BDI and history of recreational substance use were not significant predictors (both p > .25), when simultaneously entered with age, education, average lifetime drinks/month, group and ICV.

Univariate tests (FDR corrected) showed significant group-by-age interactions for the total hippocampus (p =.027; ES = 2.73), CA1 (p = .033; ES = 2.27), CA2-3 (p = .027; ES = 2.45), CA4-dentate gyrus (p = .028; ES = 2.56), fimbria (p = .027; ES = 2.56), and tail (p = .027; ES = 2.49), with a trend for the subiculum (p = .059). In all of these areas, smokers demonstrated significantly greater age-related volume loss than non-smokers (see Figure 1 representative pattern across regions). For the total hippocampus, smokers exhibited a 33.13 mm3 greater volume loss for each year of advancing age than non-smokers (smokers −0.84% per year of age; non-smokers −0.39% per year of age).

Figure 1.

Figure 1

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Association between total hippocampal volume and age for groups

3.2.2 Pairwise comparisons

Smokers demonstrated smaller volumes than non-smokers in the total hippocampus (−6.3%), CA1 (−5.0%), CA2-3 (−6.5%), and CA4-dentate gyrus (−4.9%) (FDR corrected; all p < .05), with trends for the presubiculum and subiculum (both −4.1% p = .051). The mean differences between smokers and non-smokers on total hippocampal, CA1, CA2-3, and CA4-dentate gyrus showed moderate effect sizes (see Figure 2).

Figure 2.

Figure 2

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Group volumes (mean ± standard error of the mean) for total hippocampus, CA1, CA2-3, and CA4-dentate gyrus. ES = effect size for mean differences between groups (Cohen’s d)

3.3. Associations of Total Hippocampal and Subfield Volumes with Age for Non-smokers and Smokers

For both non-smokers and smokers, associations between age, hippocampal, and subfield volumes were similar for the left and right hemispheres, so correlations (partial correlations, controlled for ICV) between were computed on the summed left and right volumes. For smokers, age was strongly related to smaller volumes in the total hippocampus and all subregions. For non-smokers, the correlation between age and hippocampal volumes was statistically significant only for the total hippocampus and presubiculum. Importantly, except for the presubiculum and subiculum, the associations between age and volumes for smokers were of statistically greater in magnitude than those for non-smokers (see Table 2).

Table 2.

Associations between age and total hippocampal and subregion volumes for groups

Age Fisher’s Z-test (two tailed)
Region Non-smokers Smokers
Total hippocampus rp = −.49; p = .001 rp = −.76; p < .001 Z = 2.00; p =.046
CA1 rp = −.09; p = .54 rp = −.50; p = .002 Z = 1.97; p =.048
CA2-3 rp = −.19; p = .24 rp = −.60; p < .001 Z = 2.18; p = .028
CA4-dentate gyrus rp = −.23; p = .15 rp = −.62; p < .001 Z = 2.14; p = .032
Fimbria rp = −.05; p = .73 rp = −.51; p = .001 Z = 2.32; p = .020
Presubiculum rp = −.32; p = .04 rp = −.42; p = .009 Z = 0.51; p = .620
Subiculum rp = −.19; p = .22 rp = −.54; p = .001 Z = 1.79; p = .072
Tail rp = −.10; p = .55 rp = −.55; p < .001 Z = 2.32; p = .024
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Note. rp: partial correlation, controlled for intracranial volume

3.4. Associations of Total Hippocampal and Subfield Volumes with Measures of Learning and Memory

In the entire cohort (i.e., smokers + non-smokers), measures of learning and memory showed overall moderate-strength associations with predominately left hemisphere total hippocampal and hippocampal subfield volumes (see Table 3). Smokers and non-smokers showed similar directions and magnitudes of correlations.

Table 3.

Associations between measures of learning and memory and regional volumes in the combined sample of smokers and non-smokers

Region Auditory-verbal Memory Visuospatial Learning Visuospatial Memory
Left total hippocampus ns rp = .28, p = .041 rp = .29, p = .032
Left CA1 rp = .30, p = .022 rp = .33, p = .014 rp = .30, p = .027
Left CA2-3 rp = .27, p = .046 ns ns
Left CA4-dentate gyrus rp = .28, p = .043 ns ns
Left presubiculum ns rp = .32, p = .019 rp = .33, p = .013
Left subiculum ns rp = .31, p = .020 rp = 34, p = .012
Right tail ns rp = .30, p = .022 ns
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Note. rp = partial correlation, controlled for age, education and intracranial volume

3.5. Associations of Total Hippocampal and Subfield Volumes with Measures of Smoking Severity in Smokers

For smokers, associations between measures of smoking severity and volumes were similar for each hemisphere, so correlations (partial correlations, controlled for age and ICV) were based on summed left and right volumes. Greater pack-years showed moderate-to-strong associations with smaller volumes of the total hippocampus (r = −0.34; p = .047), presubiculum (r = −0.43; p = .009), and subiculum (r = −0.51; p = .002; see Figure 3). Greater level of nicotine dependence (i.e., FTND score) was related to smaller subiculum volume (r = −0.38; p = .027). The age participants started daily smoking and lifetime years of smoking were not significantly correlated with any hippocampal volume. Pack-years and FTND score were not significantly related.

Figure 3.

Figure 3

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Association between subiculum volume and pack-years in smokers

4. DISCUSSION

In this cohort of predominately young-to-middle-aged, healthy males, smokers demonstrated significantly greater volume loss with increasing age than non-smokers in the total hippocampus, CA1, CA2-3, CA4-dentate gyrus, fimbria, and tail. For the total hippocampus, smokers exhibited a more than two-fold greater volume loss across the age range than non-smokers, and smokers demonstrated similar magnitudes of greater-age-related volume loss for the CA1, CA2-3, CA4-dentate gyrus, fimbria, and tail subfields. Increasing age in smokers was strongly related to smaller total hippocampal and all subregion volumes, whereas increasing age was only moderately correlated with smaller total hippocampus and fimbria volumes in non-smokers. In both groups, the associations between age and volumes in all regions were highly linear. These significantly stronger relationships in smokers are consistent with the observed group x age interactions for the total hippocampus and several subfields, in which smokers showed greater age-related atrophy than non-smokers. Smokers also had significantly smaller mean volumes than non-smokers in many of the same regions (i.e., total hippocampal, CA1, CA2-3, and CA4-dentate gyrus) that also demonstrated greater age-related volume losses in smokers. These cross-sectional differences between smokers and non-smokers were not influenced by education, history of minor recreational substance use, depressive symptomatology (mean BDI for each group in the normal range) or the low, non-hazardous level of alcohol consumption demonstrated by both groups. In smokers, greater pack-years were related to smaller total hippocampal, subiculum and presubiculum volumes (after adjusting for age and ICV), which suggests an adverse dose/duration response between chronic smoking and hippocampal morphology in this cohort.

The young-to-middle-aged smokers in this study showed a 0.84% loss in total hippocampal volume per year of age, which was over twice the rate of non-smokers. Additionally, the magnitude of correlations between age and total hippocampal and subfield volumes was significantly greater in smokers than non-smokers (see Table 2). Taken together, the stronger association of increasing age with hippocampal volumes in smokers may reflect the cumulative adverse effects imposed by chronic cigarette smoking on hippocampal macrostructural integrity. Hippocampal volume loss is a hallmark MRI-based finding in amnestic MCI (aMCI), and cross-sectional studies reported that aMCI (65–85 years of age) demonstrate from 6 to 20% smaller total hippocampal volume than age-equivalent, healthy controls (Apostolova et al., 2012; Lim et al., 2012a, b; Troyer et al., 2012). The 6.3% smaller total hippocampal volume observed in smokers in this study corresponds to the lower range of the total hippocampal volume deficit observed in aMCI. Accelerated hippocampal atrophy is apparent in cognitively-normal elderly up to 3 years prior to meeting diagnostic criteria for MCI, and up to 6 years prior to onset of AD dementia (Apostolova et al., 2010b). Therefore, the accelerated age-related atrophy, together with the significantly smaller volumes of the total hippocampus and hippocampal subfields in these young-to-middle-aged smokers, may represent one of the potential neurobiological substrates linking midlife cigarette smoking to the reported increased risk of AD (e.g., Cataldo et al., 2010).

In the entire sample group (smokers and non-smokers combined), better performance on measures of learning and memory were related to larger volumes of the total hippocampus and most subfields, primarily in the left hemisphere. These findings are consistent with the reported association between measures of episodic learning and memory and volumes of the total hippocampal and hippocampal subfields in clinical and non-clinical populations (Bartels et al., 2007; Eldridge et al., 2005; Kuhn and Gallinat, 2013; Libon et al., 1998). In a recent study with this cohort (Durazzo et al., 2012a), smokers demonstrated significantly poorer performances than non-smokers on measures of auditory-verbal and visuospatial learning and memory, as well as in multiple other neurocognitive domains of function. Given the structure-function relationships observed in this study, the neurobiological mechanism contributing to the inferior performance of smokers on measures of auditory-verbal and visuospatial learning and memory in our previous report may be, at least partially, related to significantly smaller total hippocampal and subfield volumes.

While nicotine is the primary compound in tobacco products that promotes physiological dependence (Dani and De Biasi, 2001; Mansvelder et al., 2003, 2006; Vallejo et al., 2005; Volkow et al., 1999), the adverse effects of smoking on the human body appear to be predominately related to chronic exposure to the toxic combustion products in cigarette smoke, which promotes significant oxidative stress (OxS) in multiple organ systems, including the brain (Durazzo et al., 2010; Fowles et al., 2000; Haustein, 1999; Seet et al., 2011; Swan and Lessov-Schlaggar, 2007; Yang and Liu, 2003). Amplified OxS is suggested to serve as a major pathophysiologic mechanism contributing to cigarette smoking-related brain injury (e.g., lipid peroxidation, proteolysis) detected in animal models and human post-mortem studies (Anbarasi et al., 2005, 2006; Ho et al., 2012; Khanna et al., 2013; Rueff-Barroso et al., 2010; Sonnen et al., 2009; Tyas et al., 2003). In general, the brain is highly susceptible to OxS due to its high metabolism and high energy demand, poor energy reserve, and high vulnerability of cell membrane and macromolecule phospholipids to free radicals and other oxidizing agents (Anbarasi et al., 2006; Chalela et al., 2001; Kovacic, 2005; Mueller et al., 2001). Hippocampal neurons, particularly pyramidal neurons in the CA1 subfield, are highly vulnerable to OxS (Wang and Michaelis, 2010). For example, Ho and colleagues reported that rats exposed to daily cigarette smoke for approximately 8 weeks showed significantly increased levels of 8-hydroxyguanine, a marker of oxidative damage to RNA and DNA nucleosides, in the dentate and CA3 regions of the hippocampus (Ho et al., 2012); in the present study, mean volumes of CA2-3, and CA4-dentate gyrus were significantly smaller in smokers compared to non-smokers, and smokers also showed greater age-related volume losses in these regions. Sonnen and colleages (2009) conducted post-mortem comparisons of OxS levels human, elder active smokers and never-smokers (87 ± 6 years of age) without significant AD or microvascular pathology. Active-smokers showed significantly higher cortical F4-neuroprostane level, a measure of free radical-mediated lipid peroxidation in neurons. Thus, the persistently elevated level of OxS imposed by chronic cigarette smoking may serve as a primary mechanism contributing to the hippocampal volume deficits observed in this study, as well as the other neurobiological and neurocognitive abnormalities observed in clinical and non-clinical cohorts of chronic smokers across the lifespan (for review, see Azizian et al., 2009; Durazzo et al. 2010). See (Durazzo et al., 2010) for a more detailed discussion on OxS and other potential mechanisms contributing to neurobiological and neurocognitive dysfunction in chronic smokers.

This cross-sectional study has limitations that may influence the generalizability of the findings. The automated hippocampal subfield segmentation was based on a probabilistic atlas derived from sub-millimeter, ultra high-resolution MRI at 3T. Although the 1mm3 isotropic T1-weighted images in this study were acquired at the higher field strength of 4T, the acquisition was not specifically optimized for hippocampal subfield volumetry (Mueller et al., 2007); therefore, the images may not be of sufficient resolution to permit fully accurate delineation of subfield boundaries (e.g., boundary between CA1 and subiculum), and precise quantitation of the segmented subfield volumes. Additionally, as with all current automated subfield segmentation methods, they may be more influenced by normal and/or pathological variations of shape and tissue contrast, than manual segmentation methods (Mueller and Weiner, 2009). Therefore, the findings for hippocampal subfields in this study should be considered preliminary. Results may have also been influenced by factors not assessed in this study, such as subclinical biomedical conditions (e.g., hypertension, atherosclerosis), diet, physical activity, exposure to environmental cigarette smoke or genetic factors that are associated with hippocampal morphology (e.g., ApoE and brain derived neurotrophic factor genotypes). Finally, this study did not contain adequate numbers of females to assess sex effects on the hippocampal volumetrics obtained in this study.

In conclusion, compared to non-smokers, smokers exhibited greater accelerated age-related atrophy, as well as significantly smaller volumes of the total bilateral hippocampus and several hippocampal subfields. For smokers, greater cigarette pack-years were associated with smaller total hippocampal, presubiculum, and subiculum subfield volumes. Taken together, these findings indicate that active chronic cigarette smoking in this young-to-middle aged cohort may serve as a risk factor for hippocampal atrophy and related cognitive dysfunction, particularly with advancing age. Increased cerebral OxS may serve as a pathophysiological mechanism contributing to the cigarette-smoking related morphological abnormalities observed in this study and previous reports. Quantitation of in vivo markers of cerebral OxS (e.g., MR spectroscopy measurement of glutathione) is necessary to confirm if increased OxS is apparent in young-to-middle-aged smokers. Longitudinal morphometrics of hippocampal and other mesial temporal structures (e.g., entorhinal cortex, parahippocampal gyrus) at higher spatial resolution, with smoking and non-smoking samples that are equivalent in the proportion of males and females, are needed to better assess for sex effects, and determine if accelerated age-related regional atrophy in these regions during midlife serves as a potential neuroimaging biomarker for increased risk for development of MCI and/or AD.

Acknowledgments

Role of funding sources. The study sponsors had no role in the study design, in the collection, analysis and interpretation of data, in the writing of the report, and in the decision to submit the paper for publication.

This material is the result of work supported by the National Institute on Drug Abuse (DA24136 to TCD), Department of Defense (W81XWH-05-2-0094 to TCD), and National Institute on Alcohol and Alcoholism (AA10788 to Dieter J. Meyerhoff), and with resources and the use of facilities at the San Francisco Veterans Administration Medical Center, San Francisco CA. We thank Dr. Anderson Mon for assistance in MR data acquisition in early studied participants. We also wish to extend our gratitude to the study participants, who made this research possible

Footnotes

Author contribution. Dr. Durazzo was responsible for study concept and design, all data acquisition and processing, all statistical analyses, data interpretation and manuscript preparation. Drs. Nixon and Meyerhoff were involved with data interpretation and manuscript preparation. All authors approved the final manuscript.

Conflict of Interest. The Authors have no conflicts of interest to disclose.

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References

  1. Ambrose JA, Barua RS. The pathophysiology of cigarette smoking and cardiovascular disease: an update. J Am Coll Cardiol. 2004;43:1731–1737. doi: 10.1016/j.jacc.2003.12.047. [DOI] [PubMed] [Google Scholar]
  2. Anbarasi K, Vani G, Balakrishna K, Devi CS. Effect of bacoside A on membrane-bound ATPases in the brain of rats exposed to cigarette smoke. J Biochem Mol Toxicol. 2005;19:59–65. doi: 10.1002/jbt.20050. [DOI] [PubMed] [Google Scholar]
  3. Anbarasi K, Vani G, Balakrishna K, Devi CS. Effect of bacoside A on brain antioxidant status in cigarette smoke exposed rats. Life Sci. 2006;78:1378–1384. doi: 10.1016/j.lfs.2005.07.030. [DOI] [PubMed] [Google Scholar]
  4. Apostolova LG, Green AE, Babakchanian S, Hwang KS, Chou YY, Toga AW, Thompson PM. Hippocampal atrophy and ventricular enlargement in normal aging, mild cognitive impairment (MCI), and Alzheimer Disease. Alzheimer Dis Assoc Disord. 2012;26:17–27. doi: 10.1097/WAD.0b013e3182163b62. [DOI] [PMC free article] [PubMed] [Google Scholar]
  5. Apostolova LG, Morra JH, Green AE, Hwang KS, Avedissian C, Woo E, Cummings JL, Toga AW, Jack CR, Jr, Weiner MW, Thompson PM. Automated 3D mapping of baseline and 12-month associations between three verbal memory measures and hippocampal atrophy in 490 ADNI subjects. Neuroimage. 2010a;51:488–499. doi: 10.1016/j.neuroimage.2009.12.125. [DOI] [PMC free article] [PubMed] [Google Scholar]
  6. Apostolova LG, Mosconi L, Thompson PM, Green AE, Hwang KS, Ramirez A, Mistur R, Tsui WH, de Leon MJ. Subregional hippocampal atrophy predicts Alzheimer’s dementia in the cognitively normal. Neurobiol Aging. 2010b;31:1077–1088. doi: 10.1016/j.neurobiolaging.2008.08.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  7. Azizian A, Monterosso J, O’Neill J, London ED. Magnetic Resonance Imaging Studies of Cigarette Smoking. In: Henningfield JE, London E, Pogun S, editors. Nicotine Psychopharmacology. Springer-Verlag; Berlin Heidelberg: 2009. pp. 113–43. [Google Scholar]
  8. Bartal M. Health effects of tobacco use and exposure. Monaldi Arch Chest Dis. 2001;56:545–554. [PubMed] [Google Scholar]
  9. Bartels C, Kunert HJ, Stawicki S, Kroner-Herwig B, Ehrenreich H, Krampe H. Recovery of hippocampus-related functions in chronic alcoholics during monitored long-term abstinence. Alcohol Alcohol. 2007;42:92–102. doi: 10.1093/alcalc/agl104. [DOI] [PubMed] [Google Scholar]
  10. Beck AT. Depression Inventory. Center for Cognitive Therapy; Philadelphia: 1978. [Google Scholar]
  11. Benedict R. Brief Visuospatial Memory Test - Revised: Professional Manual. Psychological Assessment Resources, Inc; Odessa, FL: 1997. [Google Scholar]
  12. Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J Roy Stat Soc. 1995;57:289–300. [Google Scholar]
  13. Boudreaux ED, Francis JL, Carmack Taylor CL, Scarinci IC, Brantley PJ. Changing multiple health behaviors: smoking and exercise. Prev Med. 2003;36:471–478. doi: 10.1016/s0091-7435(02)00048-8. [DOI] [PubMed] [Google Scholar]
  14. Brody AL, Mandelkern MA, Jarvik ME, Lee GS, Smith EC, Huang JC, Bota RG, Bartzokis G, London ED. Differences between smokers and nonsmokers in regional gray matter volumes and densities. Biol Psychiatry. 2004;55:77–84. doi: 10.1016/s0006-3223(03)00610-3. [DOI] [PubMed] [Google Scholar]
  15. Casasola GG, Alvarez-Sala JL, Marques JA, Sanchez-Alarcos JM, Tashkin DP, Espinos D. Cigarette smoking behavior and respiratory alterations during sleep in a healthy population. Sleep Breath. 2002;6:19–24. doi: 10.1007/s11325-002-0019-y. [DOI] [PubMed] [Google Scholar]
  16. Cataldo JK, Prochaska JJ, Glantz SA. Cigarette smoking is a risk factor for Alzheimer’s Disease: an analysis controlling for tobacco industry affiliation. J Alzheimers Dis. 2010;19:465–480. doi: 10.3233/JAD-2010-1240. [DOI] [PMC free article] [PubMed] [Google Scholar]
  17. Chalela JA, Wolf RL, Maldjian JA, Kasner SE. MRI identification of early white matter injury in anoxic-ischemic encephalopathy. Neurology. 2001;56:481–485. doi: 10.1212/wnl.56.4.481. [DOI] [PubMed] [Google Scholar]
  18. Chupin M, Gerardin E, Cuingnet R, Boutet C, Lemieux L, Lehericy S, Benali H, Garnero L, Colliot O. Fully automatic hippocampus segmentation and classification in Alzheimer’s disease and mild cognitive impairment applied on data from ADNI. Hippocampus. 2009;19:579–587. doi: 10.1002/hipo.20626. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Cohen J. Statistical Power Analysis for the Behavioral Sciences. Lawrence Erlbaum Associates; Hillsdale, NJ: 1988. [Google Scholar]
  20. Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis. I segmentation and surface reconstruction. Neuroimage. 1999;9:179–194. doi: 10.1006/nimg.1998.0395. [DOI] [PubMed] [Google Scholar]
  21. Dani JA, De Biasi M. Cellular mechanisms of nicotine addiction. Pharmacol Biochem Behav. 2001;70:439–446. doi: 10.1016/s0091-3057(01)00652-9. [DOI] [PubMed] [Google Scholar]
  22. Delis DC, Kramer JH, Kaplan E, Ober BA. California Verbal Learning Test. 2. The Psychological Corporation; San Antonio, TX: 2000. [Google Scholar]
  23. Du AT, Schuff N, Amend D, Laakso MP, Hsu YY, Jagust WJ, Yaffe K, Kramer JH, Reed B, Norman D, Chui HC, Weiner MW. Magnetic resonance imaging of the entorhinal cortex and hippocampus in mild cognitive impairment and Alzheimer’s disease. J Neurol Neurosurg Psychiatry. 2001;71:441–447. doi: 10.1136/jnnp.71.4.441. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Dube SR, McClave A, James C, Caraballo R, Kaufmann R, Pechacek TF, et al. Vital signs: current cigarette smoking among adults aged ≥ 18 years --- United States, 2009. MMWR. 2010;59:1135–1140. [PubMed] [Google Scholar]
  25. Durazzo TC, Gazdzinski S, Banys P, Meyerhoff DJ. Cigarette smoking exacerbates chronic alcohol-induced brain damage: a preliminary metabolite imaging study. Alcohol Clin Exp Res. 2004;28:1849–1860. doi: 10.1097/01.alc.0000148112.92525.ac. [DOI] [PubMed] [Google Scholar]
  26. Durazzo TC, Meyerhoff DJ, Nixon SJ. Chronic cigarette smoking: implications for neurocognition and brain neurobiology. Int J Environ Res Public Health. 2010;7:3760–3791. doi: 10.3390/ijerph7103760. [DOI] [PMC free article] [PubMed] [Google Scholar]
  27. Durazzo TC, Meyerhoff DJ, Nixon SJ. A comprehensive assessment of neurocognition in middle-aged chronic cigarette smokers. Drug Alcohol Depend. 2012a;122:105–111. doi: 10.1016/j.drugalcdep.2011.09.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Durazzo TC, Mon A, Pennington D, Abe C, Gazdzinski S, Meyerhoff DJ. Interactive effects of chronic cigarette smoking and age on brain volumes in controls and alcohol-dependent individuals in early abstinence. Addict Biol. 2012b doi: 10.1111/j.1369-1600.2012.00492.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Eldridge LL, Engel SA, Zeineh MM, Bookheimer SY, Knowlton BJ. A dissociation of encoding and retrieval processes in the human hippocampus. J Neurosci. 2005;25:3280–3286. doi: 10.1523/JNEUROSCI.3420-04.2005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Fellgiebel A, Dellani PR, Greverus D, Scheurich A, Stoeter P, Muller MJ. Predicting conversion to dementia in mild cognitive impairment by volumetric and diffusivity measurements of the hippocampus. Psychiatry Res. 2006;146:283–287. doi: 10.1016/j.pscychresns.2006.01.006. [DOI] [PubMed] [Google Scholar]
  31. First MB, Spitzer RL, Gibbon M, Williams JBW. Structured Clinical Interview for DSM-IV Axis I Disorders - Patient Edition (SCID-I/P, Version 2.0, 8/98 revision) Biometrics Research Department; New York, NY: 1998. [Google Scholar]
  32. Fischl B, Dale AM. Measuring the thickness of the human cerebral cortex from magnetic resonance images. Proc Natl Acad Sci U S A. 2000;97:11050–11055. doi: 10.1073/pnas.200033797. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Fischl B, Destrieux C, Halgren E, Segonne F, Salat DH, Busa E, Seidman LJ, Goldstein J, Kennedy D, Caviness V, Makris N, Rosen B, Dale AM. Automatic parcellation of the human cerebral cortex. Cereb Cortex. 2004;14:11–22. doi: 10.1093/cercor/bhg087. [DOI] [PubMed] [Google Scholar]
  34. Fischl B, Sereno MI, Dale AM. Cortical surface-based analysis. II: inflation, flattening, and a surface-based coordinate system. Neuroimage. 1999;9:195–207. doi: 10.1006/nimg.1998.0396. [DOI] [PubMed] [Google Scholar]
  35. Fowles J, Bates M, Noiton D. The Chemical Constituents in Cigarettes and Cigarette Smoke: Priorities for Harm Reduction. Epidemiology and Toxicology Group; New Zealand: 2000. pp. 1–65. [Google Scholar]
  36. Gallinat J, Meisenzahl E, Jacobsen LK, Kalus P, Bierbrauer J, Kienast T, Witthaus H, Leopold K, Seifert F, Schubert F, Staedtgen M. Smoking and structural brain deficits: a volumetric MR investigation. Eur J Neurosci. 2006;24:1744–1750. doi: 10.1111/j.1460-9568.2006.05050.x. [DOI] [PubMed] [Google Scholar]
  37. Glass GV, Hopkins KD. Statistical Methods in Education and Psychology. Prentice-Hall, Inc; New York: 1984. [Google Scholar]
  38. Grober E, Sliwinski M. Development and validation of a model for estimating premorbid verbal intelligence in the elderly. J Clin Exp Neuropsychol. 1991;13:933–949. doi: 10.1080/01688639108405109. [DOI] [PubMed] [Google Scholar]
  39. Haustein KO. Smoking tobacco, microcirculatory changes and the role of nicotine. Int J Clin Pharmacol Ther. 1999;37:76–85. [PubMed] [Google Scholar]
  40. Heatherton TF, Kozlowski LT, Frecker RC, Fagerström KO. The Fagerström Test for Nicotine Dependence: a revision of the Fagerström Tolerance Questionnaire. Br J Addict. 1991;86:1119–1127. doi: 10.1111/j.1360-0443.1991.tb01879.x. [DOI] [PubMed] [Google Scholar]
  41. Ho YS, Yang X, Yeung SC, Chiu K, Lau CF, Tsang AW, Mak JC, Chang RC. Cigarette smoking accelerated brain aging and induced pre-Alzheimer-like neuropathology in rats. PLoS One. 2012;7:e36752. doi: 10.1371/journal.pone.0036752. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Jack C, Jr, Petersen RC, Xu Y, O’Brien PC, Smith GE, Ivnik RJ, Boeve BF, Tangalos EG, Kokmen E. Rates of hippocampal atrophy in normal aging, mild cognitive impairment, and Alzheimer’s Disease. Neurology. 2000;55:484–489. doi: 10.1212/wnl.55.4.484. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Judd CM, McClelland GH. Data Analysis: A Model-Comparison Approach. Harcourt Brace Jovanovich; San Diego, CA: 1989. [Google Scholar]
  44. Khanna A, Guo M, Mehra M, Royal W., 3rd Inflammation and oxidative stress induced by cigarette smoke in Lewis rat brains. J Neuroimmunol. 2013;254:69–75. doi: 10.1016/j.jneuroim.2012.09.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. Kovacic P. Unifying mechanism for addiction and toxicity of abused drugs with application to dopamine and glutamate mediators: electron transfer and reactive oxygen species. Med Hypotheses. 2005;65:90–96. doi: 10.1016/j.mehy.2005.01.031. [DOI] [PubMed] [Google Scholar]
  46. Kuhn S, Gallinat J. Segregating cognitive functions within hippocampal formation: a quantitative meta-analysis on spatial navigation and episodic memory. Hum Brain Mapp. 2013 doi: 10.1002/hbm.22239. epub ahead of print. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Kuhn S, Schubert F, Gallinat J. Reduced thickness of medial orbitofrontal cortex in smokers. Biol Psychiatry. 2010;68:1061–1065. doi: 10.1016/j.biopsych.2010.08.004. [DOI] [PubMed] [Google Scholar]
  48. Liao Y, Tang J, Liu T, Chen X, Hao W. Differences between smokers and non-smokers in regional gray matter volumes: a voxel-based morphometry study. Addict Biol. 2010;17:977–980. doi: 10.1111/j.1369-1600.2010.00250.x. [DOI] [PubMed] [Google Scholar]
  49. Libon DJ, Bogdanoff B, Cloud BS, Skalina S, Giovannetti T, Gitlin HL, Bonavita J. Declarative and procedural learning, quantitative measures of the hippocampus, and subcortical white alterations in Alzheimer’s disease and ischaemic vascular dementia. J Clin Exp Neuropsychol. 1998;20:30–41. doi: 10.1076/jcen.20.1.30.1490. [DOI] [PubMed] [Google Scholar]
  50. Lim HK, Hong SC, Jung WS, Ahn KJ, Won WY, Hahn C, Kim IS, Lee CU. Automated hippocampal subfield segmentation in amnestic mild cognitive impairments. Dement Geriatr Cogn Disord. 2012a;33:327–333. doi: 10.1159/000339588. [DOI] [PubMed] [Google Scholar]
  51. Lim HK, Hong SC, Jung WS, Ahn KJ, Won WY, Hahn C, Kim IS, Lee CU. Hippocampal shape and cognitive performance in amnestic mild cognitive impairment. Neuroreport. 2012b;23:364–368. doi: 10.1097/WNR.0b013e328351ddc9. [DOI] [PubMed] [Google Scholar]
  52. Mansvelder HD, De Rover M, McGehee DS, Brussaard AB. Cholinergic modulation of dopaminergic reward areas: upstream and downstream targets of nicotine addiction. Eur J Pharmacol. 2003;480:117–123. doi: 10.1016/j.ejphar.2003.08.099. [DOI] [PubMed] [Google Scholar]
  53. Mansvelder HD, van Aerde KI, Couey JJ, Brussaard AB. Nicotinic modulation of neuronal networks: from receptors to cognition. Psychopharmacology (Berl) 2006;184:292–305. doi: 10.1007/s00213-005-0070-z. [DOI] [PubMed] [Google Scholar]
  54. McKee SA, Falba T, O’Malley SS, Sindelar J, O’Connor PG. Smoking status as a clinical indicator for alcohol misuse in US adults. Arch Intern Med. 2007;167:716–721. doi: 10.1001/archinte.167.7.716. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Mertens JR, Weisner C, Ray GT, Fireman B, Walsh K. Hazardous drinkers and drug users in HMO primary care: prevalence, medical conditions, and costs. Alcohol Clin Exp Res. 2005;29:989–998. doi: 10.1097/01.alc.0000167958.68586.3d. [DOI] [PubMed] [Google Scholar]
  56. Mueller SG, Stables L, Du AT, Schuff N, Truran D, Cashdollar N, Weiner MW. Measurement of hippocampal subfields and age-related changes with high resolution MRI at 4T. Neurobiol Aging. 2007;28:719–726. doi: 10.1016/j.neurobiolaging.2006.03.007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Mueller SG, Trabesinger AH, Boesiger P, Wieser HG. Brain glutathione levels in patients with epilepsy measured by in vivo (1)H-MRS. Neurology. 2001;57:1422–1427. doi: 10.1212/wnl.57.8.1422. [DOI] [PubMed] [Google Scholar]
  58. Mueller SG, Weiner MW. Selective effect of age, Apo e4, and Alzheimer’s disease on hippocampal subfields. Hippocampus. 2009;19:558–564. doi: 10.1002/hipo.20614. [DOI] [PMC free article] [PubMed] [Google Scholar]
  59. Pennanen C, Kivipelto M, Tuomainen S, Hartikainen P, Hanninen T, Laakso MP, Hallikainen M, Vanhanen M, Nissinen A, Helkala EL, Vainio P, Vanninen R, Partanen K, Soininen H. Hippocampus and entorhinal cortex in mild cognitive impairment and early AD. Neurobiol Aging. 2004;25:303–310. doi: 10.1016/S0197-4580(03)00084-8. [DOI] [PubMed] [Google Scholar]
  60. Richter P, Werner J, Heerlein A, Kraus A, Sauer H. On the validity of the Beck Depression Inventory. A review Psychopathology. 1998;31:160–168. doi: 10.1159/000066239. [DOI] [PubMed] [Google Scholar]
  61. Rueff-Barroso CR, Trajano ET, Alves JN, Paiva RO, Lanzetti M, Pires KM, Bezerra FS, Pinho RA, Valenca SS, Porto LC. Organ-related cigarette smoke-induced oxidative stress is strain-dependent. Med Sci Monit. 2010;16:BR218–226. [PubMed] [Google Scholar]
  62. Rusanen M, Kivipelto M, Quesenberry CP, Jr, Zhou J, Whitmer RA. Heavy smoking in midlife and long-term risk of Alzheimer disease and vascular dementia. Arch Intern Med. 2010a;171:333–339. doi: 10.1001/archinternmed.2010.393. [DOI] [PubMed] [Google Scholar]
  63. Rusanen M, Rovio S, Ngandu T, Nissinen A, Tuomilehto J, Soininen H, Kivipelto M. Midlife smoking, apolipoprotein E and risk of dementia and Alzheimer’s disease: a population-based cardiovascular risk factors, aging and dementia study. Dement Geriatr Cogn Disord. 2010b;30:277–284. doi: 10.1159/000320484. [DOI] [PubMed] [Google Scholar]
  64. Schuff N, Du A-T, Amend D, Hsu Y, Laakso M, Jagust W, Chui H, Weiner M. MRI of Entorhinal cortex and hippocampus in Alzheimer’s disease, subcortical ischemic vascular dementia and mixed dementia. In: Iqbal K, Sisodia SS, Winblad B, editors. Alzheimer’s Disease: Advances in Etiology, Pathogenesis and Therapeutics. John Wiley & Sons, Ltd; London: 2001. pp. 229–236. [Google Scholar]
  65. Seet RC, Lee CY, Loke WM, et al. Biomarkers of oxidative damage in cigarette smokers: which biomarkers might reflect acute versus chronic oxidative stress? Free Radic Biol Med. 2011;50:1787–1793. doi: 10.1016/j.freeradbiomed.2011.03.019. [DOI] [PubMed] [Google Scholar]
  66. Skinner HA, Sheu WJ. Reliability of alcohol use indices. The Lifetime Drinking History and the MAST. J Stud Alcohol. 1982;43:1157–1170. doi: 10.15288/jsa.1982.43.1157. [DOI] [PubMed] [Google Scholar]
  67. Sobell LC, Sobell MB, Riley DM, Schuller R, Pavan DS, Cancilla A, Klajner F, Leo GI. The reliability of alcohol abusers’ self-reports of drinking and life events that occurred in the distant past. J Stud Alcohol. 1988;49:225–232. doi: 10.15288/jsa.1988.49.225. [DOI] [PubMed] [Google Scholar]
  68. Sonnen JA, Larson EB, Gray SL, Wilson A, Kohama SG, Crane PK, Breitner JC, Montine TJ. Free radical damage to cerebral cortex in Alzheimer’s disease, microvascular brain injury, and smoking. Ann Neurol. 2009;65:226–229. doi: 10.1002/ana.21508. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Spielberger CD, Gorsuch RL, Lushene R, Vagg PR, Jacobs GA. Self-Evaluation Questionaire. Consulting Psychologist Press; Palo Alto, CA: 1977. [Google Scholar]
  70. Strauss E, Sherman EMS, Spreen O. A Compendium of Neuropsychological Tests: Administration, Norms and Commentary. Oxford University Press; New York, NY: 2006. [Google Scholar]
  71. Swan GE, Lessov-Schlaggar CN. The effects of tobacco smoke and nicotine on cognition and the brain. Neuropsychol Rev. 2007;17:259–273. doi: 10.1007/s11065-007-9035-9. [DOI] [PubMed] [Google Scholar]
  72. Tapiola T, Pennanen C, Tapiola M, Tervo S, Kivipelto M, Hanninen T, Pihlajamaki M, Laakso MP, Hallikainen M, Hamalainen A, Vanhanen M, Helkala EL, Vanninen R, Nissinen A, Rossi R, Frisoni GB, Soininen H. MRI of hippocampus and entorhinal cortex in mild cognitive impairment: a follow-up study. Neurobiol Aging. 2006;29:31–38. doi: 10.1016/j.neurobiolaging.2006.09.007. [DOI] [PubMed] [Google Scholar]
  73. Troyer AK, Murphy KJ, Anderson ND, Craik FI, Moscovitch M, Maione A, Gao F. Associative recognition in mild cognitive impairment: relationship to hippocampal volume and apolipoprotein E. Neuropsychologia. 2012;50:3721–3728. doi: 10.1016/j.neuropsychologia.2012.10.018. [DOI] [PubMed] [Google Scholar]
  74. Tyas SL, White LR, Petrovitch H, Webster Ross G, Foley DJ, Heimovitz HK, Launer LJ. Mid-life smoking and late-life dementia: the Honolulu-Asia Aging Study. Neurobiol Aging. 2003;24:589–596. doi: 10.1016/s0197-4580(02)00156-2. [DOI] [PubMed] [Google Scholar]
  75. Vallejo YF, Buisson B, Bertrand D, Green WN. Chronic nicotine exposure upregulates nicotinic receptors by a novel mechanism. J Neurosci. 2005;25:5563–5572. doi: 10.1523/JNEUROSCI.5240-04.2005. [DOI] [PMC free article] [PubMed] [Google Scholar]
  76. Van Leemput K, Bakkour A, Benner T, Wiggins G, Wald LL, Augustinack J, Dickerson BC, Golland P, Fischl B. Automated segmentation of hippocampal subfields from ultra-high resolution in vivo MRI. Hippocampus. 2009;19:549–557. doi: 10.1002/hipo.20615. [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Volkow ND, Fowler JS, Ding YS, Wang GJ, Gatley SJ. Imaging the neurochemistry of nicotine actions: studies with positron emission tomography. Nicotine Tob Res. 1999;1(Suppl 2):S127–132. doi: 10.1080/14622299050011941. discussion S139–140. [DOI] [PubMed] [Google Scholar]
  78. Wang X, Michaelis EK. Selective neuronal vulnerability to oxidative stress in the brain. Front Aging Neurosci. 2010;2:12. doi: 10.3389/fnagi.2010.00012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Yang YM, Liu GT. Injury of mouse brain mitochondria induced by cigarette smoke extract and effect of vitamin C on it in vitro. Biomed Environ Sci. 2003;16:256–266. [PubMed] [Google Scholar]
  80. Yu R, Zhao L, Lu L. Regional grey and white matter changes in heavy male smokers. PLoS One. 2011;6:e27440. doi: 10.1371/journal.pone.0027440. [DOI] [PMC free article] [PubMed] [Google Scholar]

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