Associations of cigarette smoking with memory decline and neurodegeneration among cognitively normal older individuals

Associations of cigarette smoking with memory decline and neurodegeneration among cognitively normal older individuals

Neuroscience Letters xxx (xxxx) xxxx Contents lists available at ScienceDirect Neuroscience Letters journal homepage: www.elsevier.com/locate/neulet...

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Neuroscience Letters xxx (xxxx) xxxx

Contents lists available at ScienceDirect

Neuroscience Letters journal homepage: www.elsevier.com/locate/neulet

Research article

Associations of cigarette smoking with memory decline and neurodegeneration among cognitively normal older individuals Peiliang Wua,1, Wenya Lia,1, Xueding Caia, Hanhan Yanb,*, Mayun Chena,**, for Alzheimer’s Disease Neuroimaging Initiative2 a b

Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, PR China Department of Respiratory Medicine, Ruian People’s Hospital, The Third Affiliated Hospital of Wenzhou Medical University, Ruian 325200, PR China

ARTICLE INFO

ABSTRACT

Keywords: Smoking Verbal memory Hippocampal volume Glucose metabolism β-amyloid Tau

Cigarette smoking is associated with a higher risk of Alzheimer’s disease (AD), but the underlying mechanisms remain to be clarified. In this study, we aimed to examine the effects of cigarette smoking on multiple AD biomarkers among older individuals with normal cognition (NC). Among 415 older individuals with NC from the Alzheimer’s disease Neuroimaging Initiative (ADNI) cohort, we examined the associations between smoking status (non-smokers vs smokers) and global cognition, verbal memory, hippocampal volumes, cerebral glucose metabolism and CSF AD pathologies. The primary findings of this study were: (1) In NC, smokers showed worse performance on verbal memory tests [Rey Auditory Verbal Learning Test (RAVLT) total learning score and delayed recall] than non-smokers; (2) Compared with non-smokers, smokers had significantly lower HpVR; (3) Smokers, relative to non-smokers, demonstrated lower levels of cerebral glucose metabolism as measured by FDG-PET; and (4) there were no significant differences in CSF AD pathologies (CSF Aβ42, t-tau or p-tau) between non-smokers and smokers. Longitudinal studies are needed to investigate the relationship between cigarettes smoking and changes in AD-related markers over time. Further, ADNI participants were highly educated and predominantly white. This may limit the generalizability of our results. In summary, among individuals with NC, cigarette smoking was associated with memory impairment, hippocampal atrophy and cerebral glucose hypometabolism, but not CSF AD pathologies.

1. Introduction

impairment, hippocampal atrophy, and reduced cerebral glucose metabolism [9–13]. Additionally, in postmortem studies, neuritic plaques, but not neurofibrillary tangles, were significantly higher in smokers than non-smokers among non-demented older people and patients with dementia [14]. However, some other investigations have yielded inconsistent findings regarding the associations of cigarette smoking with AD-related biomarkers [15,16]. In addition, no prior studies have investigated the associations of smoking status with CSF levels of βamyloid 42 (Aβ42), total-tau (t-tau) and phosphorylated-tau (p-tau) proteins in living human. More importantly, although there have been prior studies examining the relationships between smoking and these

Cigarette smoking has been considered as an important modifiable risk factor for cognitive decline and Alzheimer’s disease (AD) [1]. This notion is supported by previous epidemiological studies reporting that smoking is associated with an increased risk of cognitive decline and AD dementia [2–6]. However, the mechanisms by which smoking increases the risk for AD remain to be clarified. Increasing data have suggested that smoking affects the pathogenesis of AD through a variety of biological functions [7,8]. For instance, it has been reported that smoking is associated with verbal memory

⁎ Corresponding author at: Department of Respiratory Medicine, Ruian People’s Hospital, The Third Affiliated Hospital of Wenzhou Medical University, Ruian, 325200, Zhejiang, PR China. ⁎⁎ Corresponding author at: Department of Pulmonary and Critical Care Medicine, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou 325000, Zhejiang, PR China. E-mail addresses: [email protected] (H. Yan), [email protected] (M. Chen). 1 These authors contribute equally to this work. 2 Data used in preparation of this article were obtained from the Alzheimer’s disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wpcontent/uploads/how_to_apply/ADNI_Acknowledgement_List.pdf.

https://doi.org/10.1016/j.neulet.2019.134563 Received 7 June 2019; Received in revised form 27 September 2019; Accepted 14 October 2019 0304-3940/ © 2019 Elsevier B.V. All rights reserved.

Please cite this article as: Peiliang Wu, et al., Neuroscience Letters, https://doi.org/10.1016/j.neulet.2019.134563

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AD-related markers, no previous studies have attempted to bring these lines of work together in an effort to examine the effects of smoking on the neurocognitive and neurobiological functions. In the present study, we aimed to systematically examine the effects of smoking on a variety of AD-related markers (CSF AD pathologies, cerebral glucose metabolism, hippocampal volumes, verbal memory and global cognition) among older individuals with normal cognition (NC) from the Alzheimer’s disease Neuroimaging Initiative (ADNI) study.

Table 1 Demographic and clinical variables among smokers and non-smokers.

2. Materials and methods 2.1. Alzheimer’s disease neuroimaging initiative Cross-sectional data utilized in the preparation of this work were extracted from the Alzheimer’s disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). The ADNI study was launched in 2003 in an effort to examine whether neuropsychological measurements, neuroimaging markers and other fluid biomarkers can be combined to predict cognitive decline among individuals with normal cognition, MCI and mild AD dementia. The ADNI study was approved by institutional review boards in ADNI centers across the USA and Canada, and all subjects provided written informed consent.

Variables

Non-smokers (n = 296)

Smokers (n = 119)

p values

Age, years Education, years Females, n (%) APOE4 carriers, n (%) Blood glucose, mg/dl Triglyceride, mg/dl Total cholesterol, mg/dl History of Hypertension, n (%) MMSE score ADAS-Cog 11 score RAVLT total learning scorea RAVLT delayed recallb HpVRc FDG SUVRd CSF Aβ42 levelse, pg/ml CSF t-tau levelsf, pg/ml CSF p-tau levelsg, pg/ml AV45 SUVRh CSF sTREM2 levelsi, pg/ml

74.7 ± 5.89 16.4 ± 2.74 157 (53) 83 (28) 100 ± 20.4 136 ± 80.4 192 ± 37.5 134 (45.3)

75 ± 5.33 15.9 ± 2.66 49 (41) 31 (26) 100 ± 18.7 151 ± 86.6 192 ± 41.1 48 (40.3)

0.57 0.08 0.03 0.68 1 0.11 0.87 0.03

29.1 ± 1.1 5.9 ± 2.96 45.2 ± 9.6

28.9 ± 1.2 6.3 ± 3.1 42.2 ± 10.1

0.041 0.257 0.006

5.87 ± 2.3 4.92 ± 0.6 1.31 ± 0.11 197 ± 50 69.1 ± 33.4 30.5 ± 15.1 1.11 ± 0.18 4175 ± 2265

5.64 ± 2.3 4.76 ± 0.65 1.27 ± 0.12 209 ± 55 66.7 ± 30.1 29 ± 17.5 1.12 ± 0.176 4398 ± 2232

0.359 0.035 0.004 0.111 0.572 0.517 0.783 0.519

Abbreviations: MMSE; mini-mental state examination; ADAS-Cog11: Alzheimer’s disease Assessment Scale- Cognitive subscales; RAVLT: Rey Auditory Verbal Learning Test; HpVR: hippocampal/intracranial volume*103; FDG SUVR: fluorodeoxyglucose standardized uptake value ratio; Aβ42: βamyloid 42; t-tau: total tau; p-tau: phosphorylated-tau. sTREM2: soluble triggering receptor expressed on myeloid cells 2. Note: aThere were 413 individuals, including 296 non-smokers and 117 smokers. b There were 412 individuals, including 296 non-smokers and 116 smokers. c There were 372 individuals, including 266 non-smokers and 106 smokers. d There were 286 individuals, including 213 non-smokers and 73 smokers. e There were 277 individuals, including 205 non-smokers and 72 smokers. f There were 275 individuals, including 203 non-smokers and 72 smokers. g There were 276 individuals, including 204 non-smokers and 72 smokers. h There were 182 individuals, including 148 non-smokers and 34 smokers. i There were 224 individuals, including 168 non-smokers and 56 smokers.

2.2. Participants In this study, a total of 415 older individuals with NC was included. Individuals with NC had a Mini-Mental State Examination (MMSE) score of 24 or higher and a Clinical Dementia Rating (CDR) score of 0. Individuals were categorized into two groups: non-smokers (n = 296) and smokers (n = 119). Individuals were classified as non-smokers if they reported no history of cigarette smoking and as smokers if they reported a history of cigarette smoking. Seventy-nine out of 119 smokers had sufficiently detailed information about the quantity of smoking (1.19 ± 1.18 packs/day) and the length of smoking (22.8 ± 15 years) to calculate pack-years (26.4 ± 24.5). Smokers were further classified into heavy smokers (≥1 pack/day) and light smokers (< 1 pack/day). 2.3. Neuropsychological assessments

are given as pg/ml. Of the included 415 subjects, there were 277 subjects with CSF Aβ42 data, 275 subjects with CSF t-tau data and 276 subjects with CSF p-tau data. CSF soluble triggering receptor expressed on myeloid cells 2 (sTREM2) levels were examined using the MSD platform, which has been described previously [20]. The file “CSF soluble triggering receptor expressed on myeloid cells 2 (sTREM2) and progranulin (PGRN)” was downloaded from the ADNI database. Values are given as pg/ml.

In ADNI study, participants underwent a comprehensive neuropsychological assessment. In the present study, we selected four cognitive outcomes, including MMSE, Alzheimer’s disease Assessment Scale- Cognitive subscales (ADAS-Cog 11), Rey Auditory Verbal Learning Test (RAVLT) total learning and delayed recall scores. MMSE and ADAS-Cog 11 were used to examine global cognition, and RAVLT total learning and delayed recall scores were utilized to examine verbal episodic memory, which is one of the earliest and prominent symptoms in AD.

2.6. Measurement of cortical Aβ burden

2.4. Neurodegenerative markers

[18 F] florbetapir positron emission tomography (AV45 PET) was utilized to examine cortical Aβ burden as described at the ADNI website (http://www.adni-info.org). Standardized uptake value ratios (SUVRs) were measured by averaging across brain regions (anterior/posterior cingulate, frontal, lateral temporal, and lateral parietal regions) and then dividing by cerebellum.

The neuroimaging data, including hippocampal volumes on MRI and brain glucose metabolism on FDG-PET, were obtained from the ADNI database. Detailed neuroimaging methods and protocols used by ADNI have been described previously [17,18] and can be found at the ADNI website (adni.loni.usc.edu). In the present study, adjusted hippocampal volumes (HpVR: hippocampal/intracranial volume*103) were used in our analyses to adjust sex differences in head size.

2.7. Statistical analysis

2.5. Measurements of CSF biomarkers

Demographic and clinical variables were compared between two groups (Non-smokers vs Smokers) utilizing t-tests for continuous variables (age, years of education, blood glucose, triglyceride, total cholesterol, MMSE, ADAS-Cog11, RAVLT total learning score and delayed recall score, HpVR, FDG SUVR, CSF AD biomarkers, CSF sTREM2, and AV45 SUVR) and x2 tests for categorical variables (sex and APOE4 genotype). The t-tests suggested that four AD-related markers (MMSE

CSF Aβ42, t-tau and p-tau levels were determined as described previously [19]. In brief, the multiple xMAP Luminex platform and INNO-BIA AlzBio3 immunoassay kits were utilized to examine the levels of Aβ42, t-tau and p-tau in CSF [19]. The file “UPENNBIOMK_MASTER.csv” was downloaded from the ADNI database. Values 2

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Fig. 1. Correlations between smoking status and neuropsychological assessments among cognitively normal older individuals. Compared with non-smokers, smokers had lower MMSE scores (t = 2.06, Cohen’s d = 0.23, p = 0.041) and RAVLT total learning scores (t = 2.8, Cohen’s d = 0.31, p = 0.006). However, there were no significant differences in ADAS-Cog11 or RAVLT delayed recall between two groups (p > 0.05). Abbreviations: MMSE: mini-mental state examination; ADAS-Cog11: Alzheimer’s Disease Assessment Scale- Cognitive subscales; RAVLT: Rey Auditory Verbal Learning Test.

score, RAVLT total learning score, HpVR and FDG SUVR) were found to be associated with smoking status. We then conducted a multiple linear regression model for each AD-related marker (MMSE score, RAVLT total learning score, HpVR and FDG SUVR) in an effort to examine the effects of smoking status on these AD-related markers. All these models were adjusted for several potential confounders, including age, years of education, sex, APOE4 genotype, blood glucose, triglyceride, total cholesterol, and hypertension status. All statistical analyses were performed using R statistical software (v 3.6.0).

3.2. Relationships between smoking status and neuropsychological assessments As shown in Table 1 and Fig. 1, compared with non-smokers, smokers had lower MMSE scores (t = 2.06, Cohen’s d = 0.23, p = 0.041) and RAVLT total learning scores (t = 2.8, Cohen’s d = 0.31, p = 0.006). However, there were no significant differences in ADASCog11 or RAVLT delayed recall score between two groups (p > 0.05). 3.3. Relationships between smoking status and neurodegenerative markers

3. Results

As shown in Table 1 and Fig. 2, compared with non-smokers, smokers had significantly lower FDG SUVR (t = 2.94, Cohen’s d = 0.41, p = 0.004) and HpVR (t = 2.12, Cohen’s d = 0.25, p = 0.035).

3.1. Demographic data In the present study, there were 415 older individuals with NC, including 296 non-smokers and 119 smokers (Table 1). Compared with non-smokers, smokers were more likely to be male (p < 0.05). Smokers were less likely to have a history of hypertension. However, there were no significant differences in other demographic variables between two groups (all p > 0.05). Further, no significant differences in several vascular risk factors (blood glucose, triglyceride and total cholesterol) between two groups were observed (all p > 0.05).

3.4. Relationships between smoking status and AD-related biomarkers To examine whether CSF AD-related biomarkers differ between smokers and non-smokers, t-tests were utilized. As demonstrated in Fig. 3(A), there was no significant difference in AV45 SUVR between smokers and non-smokers (t = −0.28, Cohen’s d = −0.05, p = 0.783). 3

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Fig. 2. Correlations between smoking status and neurodegenerative markers among cognitively normal older individuals. Compared with non-smokers, smokers had lower FDG SUVR (t = 2.94, Cohen’s d = 0.41, p 0.004) and HpVR (t = 2.12, Cohen’s d = 0.25, p = 0.035). Abbreviations: HpVR: hippocampal/ intracranial volume*103; FDG SUVR: fluorodeoxyglucose standardized uptake value ratio.

As shown in Fig. 3(B), there were no significant differences in CSF Aβ42 levels between smokers and non-smokers (t = −0.161, Cohen’s d = −0.22, p = 0.111). As displayed in Fig. 3(C), no significant differences in CSF tau levels between smokers and non-smokers were observed (t = 0.57, Cohen’s d = 0.08, p = 0.57). Similarly, we did not find different levels of CSF p-tau between smokers and non-smokers (t = 0.65, Cohen’s d = 0.09, p = 0.517).

(rho = 0.005, p = 0.97), delayed recall(rho = 0.04, p = 0.72), HpVR (rho = −0.18, p = 0.13), AV45 (rho = −0.39, p = 0.1), FDG(rho = −0.02, p = 0.89), CSF Aβ42(rho = 0.26, p = 0.09), t-tau (rho = −0.28, p = 0.07), p-tau levels(rho = −0.14, p = 0.37) or sTREM2 (rho = −0.1, p = 0.56).

3.5. Relationships between smoking status and CSF sTREM2 levels

Further, we examined whether these markers differ between heavy smokers and light smokers. Similarly, there were no significant differences in MMSE (t = 1.08, Cohen’s d = 0.27, p = 0.285), ADAS-Cog 11 (t = −0.01, Cohen’s d = 0, p = 0.995), RAVLT total learning score (t = −1.03, Cohen’s d = −0.26, p = 0.307), delayed recall (t = −0.8, Cohen’s d = −0.21, p = 0.43), HpVR (t = 0.77, Cohen’s d = 0.22, p = 0.446), AV45 (t = 0.03, Cohen’s d = 0.02, p = 0.98), FDG (t = −0.4, Cohen’s d = −0.12, p = 0.69), CSF Aβ42 (t = −0.08, Cohen’s d = −0.03, p = 0.935), t-tau (t = −0.59, Cohen’s d = −0.2, p = 0.56), p-tau (t = 0.26, Cohen’s d = 0.08, p = 0.79) or sTREM2 levels (t = −1.64, Cohen’s d = −0.67, p = 0.128) between heavy smokers and light smokers.

3.8. AD-related markers among heavy smokers and light smokers

To examine whether CSF sTREM2 levels differ between smokers and non-smokers, t-tests were utilized. As shown in Fig. 4, we did not find different levels of CSF sTREM2 between smokers and non-smokers (t = −0.65, Cohen’s d = −0.1, p = 0.519). 3.6. Associations of smoking status with AD-related markers after adjusting for other potential confounders As shown in Table 2, multiple linear regression models with adjustment of several potential confounders (age, years of education, sex, APOE4 genotype, blood glucose, triglyceride, total cholesterol, hypertension) suggested that cigarette smoking was associated with RAVLT total learning scores (beta: −2.1, se = 1, p = 0.03). Further, compared with non-smokers, smokers had lower HpVR (beta = −0.14, se = 0.06, p = 0.03) and FDG SUVR (beta = −0.04, se = 0.02, p = 0.006). However, no significant difference in MMSE scores was found between non-smokers and smokers after controlling for other variables (p > 0.05).

4. Discussion The primary findings of the current study were: (1) Among older individuals with NC, smokers showed worse performance on a verbal memory test (RAVLT total learning score and delayed recall) than nonsmokers; (2) Compared with non-smokers, smokers had significantly lower HpVR; (3) Smokers, relative to non-smokers, demonstrated lower levels of cerebral glucose metabolism as measured by FDG-PET; and (4) there were no significant differences in CSF AD pathologies (CSF Aβ42, t-tau or p-tau) between non-smokers and smokers. The first finding that smokers had worse performance on a verbal memory task is in agreement with the previously published studies supporting a relationship between smoking and the impairment of verbal memory [9,13,21]. For instance, among individuals aged between 30 and 60, chronic smoking was found to be related to deficits in

3.7. Relationships between pack-years and AD-related markers In smokers with detailed smoking history information (n = 79), we further examined the relationships between pack-years and AD-related makers using Spearman’s correlation tests. However, the pack-years were not associated with MMSE (rho = 0.17, p = 0.13), ADASCog11(rho = −0.02, p = 0.86), RAVLT total learning score 4

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Fig. 3. Correlations between smoking status and AD-related markers among cognitively normal older individuals. There were no significant differences in AD-related markers between two groups (all p > 0.05). Abbreviations: SUVR: standardized uptake value ratio; Aβ42: β-amyloid 42; t-tau: total tau; p-tau: phosphorylated-tau.

The second finding that cigarette smoking was associated with reduced hippocampal volumes in older individuals with NC is also consistent with previous studies. For example, among young-to-middleaged individuals, Durazzo and colleagues found that chronic cigarette smoking was related to hippocampal atrophy, which was further deteriorated by increasing age [22]. Additionally, a large populationbased study of more than 2000 participants suggested that smokers have smaller hippocampal volumes than non-smokers [23]. In line with these findings from clinical studies, animal studies also revealed that tobacco smoke exposure reduces neurogenesis and facilitates gliogenesis in the dentate gyrus [24,25]. These findings demonstrate a relationship between smoking and hippocampus, which plays an important role in memory formation and is associated with risks of cognitive decline and AD [26,27]. The third finding that smokers had lower levels of cerebral glucose metabolism than non-smokers replicated a previous study using the ADNI database [13]. The literature on the effects of smoking on cerebral glucose metabolism is scarce. However, it has been reported that compared with non-smokers, smokers showed lower levels of cerebral blood flow, which is highly related to cerebral glucose metabolism [9]. These findings indicated that smoking may contribute to decreased cerebral perfusion, then leading to cerebral glucose metabolism deficit. However, further clinical and preclinical studies are needed to elucidate this notion. The fourth finding that smoking was not associated with CSF AD pathologies among individuals with NC is inconsistent with previous preclinical studies showing that cigarette smoke exposure contributes

Fig. 4. Correlations between smoking status and CSF sTREM2 levels among cognitively normal older individuals. There were no significant differences in CSF sTREM2 levels between two groups (p > 0.05). Abbreviations: sTREM2: soluble triggering receptor expressed on myeloid cells 2.

verbal memory as measured by California Verbal Learning Test-II [21]. Overall, previous investigations with individuals aged 60 years or older suggested that smokers perform worse than non-smokers on verbal memory tests [9]. Taken together, our findings further strengthen the notion that chronic smoking has adverse impacts on verbal memory. 5

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excitotoxicity, and loss of synapses and neurons [28–30]. Another possible mechanism is that cigarette smoke contains a large number of toxic compounders, which may directly or indirectly impair the function and structure of neurons [7,31]. Additionally, we cannot rule out the possibility that the effect of smoking on AD-related biomarkers may be dependent on the disease stages. Further cross-sectional studies should be conducted in MCI or AD patients. Further, longitudinal studies are also needed to examine the temporal relationship between smoking and AD-related biomarkers. Our findings should be interpreted with caution due to several potential limitations. First, given the cross-sectional design of this study, it prohibited us from examining the temporal association of smoking status with AD-related markers. Further prospective longitudinal studies are needed to examine the association of cigarettes smoking with changes in AD-related markers over time. Second, compared with a population-based cohort, ADNI participants were highly educated and predominantly white [32]. This may limit the generalizability of our findings. In conclusion, among individuals with NC, smoking history was associated with memory impairment, hippocampal atrophy and cerebral glucose hypometabolism, but not CSF AD pathologies.

Table 2 Summary of multiple linear regression models. Independent variables

Beta

Dependent variable: MMSE score Smoker vs Non-smoker −0.2 Age −0.01 Education 0.1 Female sex −0.34 APOE4 carriers −0.03 Blood glucose −0.001 Triglyceride 0.001 Total cholesterol −0.001 Hypertension −0.08 Dependent variable: RAVLT total learning Smoker vs Non-smoker −2.1 Age −0.4 Education 0.6 Female sex −5.3 APOE4 carriers 0.05 Blood glucose 0.04 Triglyceride 0.01 Total cholesterol −0.004 Hypertension −1.5 Dependent variable: HpVR Smoker vs Non-smoker −0.14 Age −0.05 Education −0.04 Female sex −0.18 APOE4 carriers −0.12 Blood glucose 0.0003 Triglyceride 0.001 Total cholesterol 0.001 Hypertension 0.03 Dependent variable: FDG SUVR Smoker vs Non-smoker −0.04 Age −0.003 Education 0.0002 Female sex 0.004 APOE4 carriers −0.03 Blood glucose −0.0007 Triglyceride 0.00003 Total cholesterol 0.0002 Hypertension −0.01

SE

t value

P values

0.12 0.01 0.02 0.12 0.12 0.003 0.001 0.002 0.11 score 1 0.08 0.17 0.99 1 0.02 0.01 0.01 0.9

−1.7 −0.96 4.9 −2.8 −0.25 −0.4 2 −0.95 −0.75

0.09 0.34 < 0.001 0.005 0.8 0.69 0.04 0.34 0.45

−2.1 −5.1 3.5 −5.3 0.05 1.9 1.3 −0.3 −1.7

0.03 < 0.001 < 0.001 < 0.001 0.96 0.06 0.18 0.77 0.1

0.06 0.005 0.01 0.06 0.07 0.001 0.0004 0.001 0.06

−2.2 −8.7 −3.3 −2.87 −1.8 0.22 1.55 1.9 0.46

0.03 < 0.001 0.001 0.004 0.07 0.83 0.12 0.06 0.65

0.02 0.001 0.003 0.01 0.01 0.0003 0.00008 0.0002 0.01

−2.8 −2.8 0.079 0.27 −1.67 −1.9 0.36 0.96 −0.95

0.006 0.006 0.9 0.79 0.1 0.06 0.72 0.34 0.34

Declaration of Competing Interest The authors declare that they have no conflict of interest. Acknowledgements Data collection and sharing for this project was funded by the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation;Araclon Biotech;BioClinica, Inc.;Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.;Cogstate;Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company;EuroImmun; F. HoffmannLa Roche Ltd. and its affiliated companyGenentech, Inc.;Fujirebio;GE Healthcare;IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.;Lumosity;Lundbeck;Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research;Neurotrack Technologies;Novartis Pharmaceuticals Corporation;Pfizer Inc.;Piramal Imaging;Servier;Takeda Pharmaceutical Company; andTransition Therapeutics. TheCanadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health(www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Abbreviations: MMSE: mini-mental state examination; RAVLT: Rey Auditory Verbal Learning Test; HpVR: hippocampal/intracranial volume*103; FDG SUVR: fluorodeoxyglucose standardized uptake value ratio.

to the elevations of sAPPβ levels, Aβ accumulation, neuritic plaques, and p-tau levels in the hippocampus and cerebral cortex of wild-type mice and AD mouse models [7,8]. Conversely, postmortem human studies have yielded inconsistent results regarding the relationship between cigarette smoking and AD pathologies. For instance, among non-demented older individuals and patients with dementia, Tyas and colleagues found that neuritic plaques, but not neurofibrillary tangles, were significantly higher in smokers than non-smokers in the hippocampus and cerebral cortex [14]. However, in the midfrontal cortex of AD patients, there were no significant differences in neuritic plaques or neurofibrillary tangles between non-smokers and smokers [16]. In the present study, no significant differences in CSF AD pathologies between non-smokers and smokers were found among individuals with NC. These inconsistencies regarding the relationship between smoking and AD pathologies may be due to a variety of differences between studies, including (1) sample size, which can vary from 108 to 277; (2) the sample, which can be cerebral tissue or CSF; (3) different methods used to determine the levels of AD pathologies. More importantly, the finding that there were no significant differences in CSF AD biomarkers between smokers and non-smokers may suggest that verbal memory impairment, hippocampal atrophy and brain glucose hypometabolism in smokers may be due to other non AD-related mechanisms. For example, one possible mechanism is that cigarette smoke exposure induces oxidative stress, then leading to inflammatory responses,

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