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Copyright 2014 American Medical Association. All rights reserved.
Brain Nicotinic Acetylcholine Receptor Availability
and Response to Smoking Cessation Treatment
A Randomized Trial
Arthur L. Brody, MD; Alexey G. Mukhin, MD, PhD; Michael S. Mamoun, MD; Trinh Luu, MS; Meaghan Neary,BS;
Lidia Liang, BS; Jennifer Shieh, BS; Catherine A. Sugar, PhD; Jed E. Rose, PhD; Mark A. Mandelkern, MD, PhD
IMPORTANCE Cigarette smoking leads to upregulation of nicotinic acetylcholine receptors
(nAChRs) in the human brain, including the common α4β2* nAChR subtype. While subjective
aspects of tobacco dependence have been extensively examined as predictors of quitting
smoking with treatment, no studies to our knowledge have yet reported the relationship
between the extent of pretreatment upregulation of nAChRs and smoking cessation.
OBJECTIVE To determine whether the degree of nAChR upregulation in smokers predicts
quitting with a standard course of treatment.
DESIGN, SETTING, AND PARTICIPANTS Eighty-one tobacco-dependent cigarette smokers
(volunteer sample) underwent positron emission tomographic (PET) scanning of the brain
with the radiotracer 2-FA followed by 10 weeks of double-blind, placebo-controlled
treatment with nicotine patch (random assignment). Pretreatment specific binding volume of
distribution (V
S
/f
P
) on PET images (a value that is proportional to α
4
β
2
* nAChR availability)
was determined for 8 brain regions of interest, and participant-reported ratings of nicotine
dependence, craving, and self-efficacy were collected. Relationships between these
pretreatment measures, treatment type, and outcome were then determined. The study
took place at academic PET and clinical research centers.
MAIN OUTCOMES AND MEASURES Posttreatment quit status after treatment, defined as a
participant report of 7 or more days of continuous abstinence and an exhaled carbon
monoxide level of 3 ppm or less.
RESULTS Smokers with lower pretreatment V
S
/f
P
values (a potential marker of less severe
nAChR upregulation) across all brain regions studied were more likely to quit smoking
(multivariate analysis of covariance, F
8,69
= 4.5; P< .001), regardless of treatment group
assignment. Furthermore, pretreatment average V
S
/f
P
values provided additional predictive
power for likelihood of quitting beyond the self-report measures (stepwise binary logistic
regression, likelihood ratio χ
2
1
=19.8;P< .001).
CONCLUSIONS AND RELEVANCE Smokers with less upregulation of available α
4
β
2
* nAChRs
have a greater likelihood of quitting with treatment than smokers with more upregulation. In
addition, the biological marker studied here provided additional predictive power beyond
subjectively rated measures known to be associated with smoking cessation outcome. While
the costly, time-consuming PET procedure used here is not likely to be used clinically, simpler
methods for examining α
4
β
2
* nAChR upregulation could be tested and applied in the future
to help determine which smokers need more intensive and/or lengthier treatment.
TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT01526005
JAMA Psychiatry. doi:10.1001/jamapsychiatry.2014.138
Published online May 21, 2014.
Supplemental content at
jamapsychiatry.com
Author Affiliations: Department of
Research, VA Greater Los Angeles
Healthcare System, Los Angeles,
California (Brody, Mamoun, Luu,
Neary, Liang, Shieh, Mandelkern);
Department of Psychiatry, University
of California, Los Angeles (Brody,
Sugar); Department of Psychiatry,
Duke University,Durham, North
Carolina (Mukhin, Rose); Department
of Biostatistics, University of
California, Los Angeles (Sugar);
Department of Physics, University of
California, Irvine (Mandelkern).
Corresponding Author: Arthur L.
Brody,MD, Department of Psychiatry,
University of California, Los Angeles,
300 UCLA Medical Plaza, Ste 2200,
Los Angeles, CA 90095 (abrody
@ucla.edu).
Research
Original Investigation
E1
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While the health risks1,2 and societal costs
3-5
of ciga-
rette smoking are well documented, the preva-
lence of smoking among adults in the United States
remains high at approximately 20%.
6,7
Although most smok-
ers endorse a desire to quit,
8
very few (<5%) will do so in a given
year without treatment, and only about 20% to 25% will achieve
abstinence even with 6 months or more of gold-standard
treatment.
9-14
Therefore, there continues to be a vital need to
improve outcomes for cigarette smokers seeking treatment.
15
Prior research examining prediction of response to smok-
ing cessation treatments has focused primarily on clinical vari-
ables, with the most commonly reported predictors of out-
come being levels of nicotine dependence,
16-21
craving,
22,23
and
self-efficacy.
24-28
Greater severity of nicotine dependence has
been associated with poorer treatment outcome for nicotine
patch,
16,21
bupropion hydrochloride,
18,19
and group
psychotherapy
20
as well as in naturalistic settings with
no specific treatment.
17
Similarly, low craving
22,23
and high
self-efficacy
24-28
(self-confidence) have been repeatedly
demonstrated to be predictors of successful treatment
outcome,
27,29,30
especially in situations where smokers are at risk
for relapse. Other factors, such as desire to quit,
31
low negative
affect,
32
no history of depression,
33
low anger,
34
slow nicotine
metabolism,
35
absence of lapses during early treatment,
36
and
reduction in smoking over time,
37
have also been found to pre-
dict a positive response to treatment. Thus, clinical factors have
been extensively examined for their value in predicting re-
sponse to smoking cessation treatments; however, to our knowl-
edge, there are no published studies examining brain receptor
availability as a predictor of smoking cessation outcome.
Upregulation of β
2
-containing nicotinic acetylcholine re-
ceptors (nAChRs) is one of the most well-established effects
of smoking on the brain. Recent studies using single-photon
emission computed tomography (CT)
38-40
and positron emis-
sion tomography (PET)
41-43
have demonstrated significant up-
regulation of these receptors in smokers compared with non-
smokers in all brain regions studied other than the thalamus.
These in vivo studies were an extension of much prior re-
search, including human postmortem brain tissue studies dem-
onstrating that long-term smokers haveinc reased nAChRden-
sity compared with nonsmokers and former smokers.
44,45
Additionally, many studies of laboratory animals have dem-
onstrated upregulation of markers of nAChR density in re-
sponse to long-term nicotine administration.
46-50
For this study, we sought to determine whether the de-
gree of pretreatment α
4
β
2
* nAChR upregulation in cigarette
smokers is associated with smoking cessation outcomes with
a standard nicotine patch taper. In a smaller prior PET study
by our group,
51
we found possible associations that did not
reach statistical significance between lower levels of a PET
marker for α
4
β
2
* nAChR availability and improved outcome
across 3 smoking cessation treatment groups. Therefore, we
hypothesized that smokers with less pretreatment upregula-
tion of available α
4
β
2
* nAChRs would have a greater likeli-
hood of quitting smoking with the nicotine patch taper than
smokers with more upregulation. We also sought to deter-
mine whether pretreatment α
4
β
2
* nAChR availability pro-
vided additional predictive power beyond previously re-
ported clinical predictors (severity of nicotine dependence,
16-21
craving,
22,23
and self-efficacy
24-27
).
Methods
Participants and Screening Methods
Eighty-one treatment-seeking adult smokers completed the
study and had usable data. These participants underwent a
baseline screening visit, rating scale administration, pretreat-
ment PET/CT scanning with the radiotracer 2-FA (for labeling
α
4
β
2
* nAChRs), and double-blind, placebo-controlled treat-
ment with nicotine patch taper (see eFigure in Supplement for
details of numbers of screening failures and attrition).
Participants were recruited using the same methods as in
prior reports,
41,51
with the central inclusion criteria being to-
bacco dependence at the time of study initiation, smoking 10
to 40 cigarettes per day, and general good health. Exclusion
criteria were pregnancy, use of a medication or presence of a
medical condition that might affect the brain at the time of
scanning, or any history of an Axis I mental illness or sub-
stance abuse or dependence.
During the baseline screening visit, rating scales were ad-
ministered to verify participant reports and characterize smok-
ing history,which included the Smoker’s Profile Form (contain-
ing demographic variables and a detailed smoking history),
Fagerström Test for Nicotine Dependence (FTND),
52
Hamilton
Depression Rating Scale,
53
Hamilton Anxiety Rating Scale,
54
and
screening questions from the Structured Clinical Interview for
DSM-IV Axis I Disorders, Patient Edition, version 2.0.
55
An ex-
haled carbon monoxide (CO) level was obtained (Micro-
Smokerlyzer; Bedfont Scientific Ltd) to verify smoking status
(CO ≥8 ppm). A breathalyzer test (AlcoMatePro), urine toxicol-
ogy screen (Test Country I-Cup Urine Toxicology Kit), and urine
pregnancy test (for women of childbearing potential; TestCoun-
try Cassette Urine Pregnancy Test) were performed to support
the participant’s report of no current alcohol or drug depen-
dence and no pregnancy. The study was approved by the insti-
tutional review board and radiation safety committee of the VA
Greater Los Angeles Healthcare System, and participants pro-
vided written informed consent.
Abstinence Period and PET Protocol
One week after the baseline screening session, participants un-
derwent PET/CT scanning with the same abstinence and 2-FA
bolus-plus-continuous-infusion PET/CT protocol as in our re-
cent studies (see eAppendix in Supplement for details).
41,51
Briefly, participants underwent 2 nights of smoking/nicotine ab-
stinence, followed by a bolus-plus-continuous-infusion PET/CT
session during which PET/CTd atawere collected for 3 hours fol-
lowing a 4-hour radiotracer uptake period. During the uptake
period, the Urge to Smoke (UTS)
56
craving scale (an analog scale
with 10 craving-related questions rated 0-6) and Self-efficacy
Rating Scale
57,58
(ratings from 0-100) were administered.
Treatment for Cigarette Smoking
Within a week of PET/CT scanning, participants were ran-
domly assigned
59
to treatment with either active transder-
Research Original Investigation Nicotinic Acetylcholine Receptor and Smoking
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and evaluation of adverse effects. Participants assigned to the
active patch group received a standard course of treatment be-
ginning with 21-mg/d patches for 4 weeks followed by 14-mg/d
patches for 2 weeks and 7-mg/d patches for 2 weeks, while the
placebo patch group underwent an identical patch regimen with-
out nicotine. Participants were encouraged to minimize or elimi-
nate cigarette use when they initiated treatment and to choose
a quit date 2 weeks after treatment initiation. If participants
lapsed into smoking during treatment, they were encouraged
to pick another quit date within the following week. After 8
weeks of patch treatment, participants were seen for study vis-
its on 2 consecutive weeks (10 weeks total treatment). Al-
though we recognize that combining medication with psycho-
therapy would have resulted in an enhanced smokingcessation
rate,
8,70,71
no formal psychotherapy was provided so that the re-
lationship between pretreatment brain nAChR availability and
nicotine or placebo patch response could be isolated.
At the final study visit, a participant report of 7 or more
days of continuous abstinence from any tobacco use and an
exhaled CO level of 3 ppm or less were used as criteria for hav-
ing quit smoking. These criteria are similar to recent recom-
mendations for documenting smoking abstinence
72,73
and are
comparable to criteria used in many treatment studies.
9,13,35
Participants who initiated treatment but dropped out of the
study were classified as nonquitters in accordance with re-
cent recommendations
72,74
and use
75
of this classification. At
the conclusion of the medication or placebo trial, all partici-
pants were offered open-label treatment with nicotine patch
to assist in smoking cessation and to address (at least partly)
ethical concerns
76
about the use of placebo treatment in this
study.
PET Image Analysis
After decay and motion correction, each participant’s PET/CT
scan was coregistered to his or her magnetic resonance imaging
scan using PMOD version 3.0 software (http://www.pmod.com
/technologies). Regions of interest (ROIs) were drawn on mag-
netic resonance images using PMOD and transferred to the
coregistered PET (Figure 1). Most regions were delineated au-
tomatically using the Functional Magnetic Resonance Imaging
of the Brain Software Library program FIRST, which created au-
tomated drawings through model-based segmentation. These
automated regions were generated from conditional probabili-
ties based on shape and intensity
77
from each participant’s mag-
netic resonance imaging scans and included the following re-
gions bilaterally: nucleus accumbens, amygdala, caudate,
hippocampus, globus pallidus, and putamen. In addition, hand-
drawn ROIs consisted of representative slices of the prefrontal
cortex (middle frontal gyrus) bilaterally and the whole brain-
stem. These ROIs were chosen based on having a range of nAChR
densities, while the thalamus was specifically excluded from
analysis because it is known not to have significant upregula-
tionofα
4
β
2
* nAChRs in smokers. To preserve power, mean val-
ues of bilateral ROIs were used, so a total of 8 ROI values for each
participant were used for statistical analysis. Placement of ROIs
was visually inspected for each PET frame to minimize effects
of coregistration errors and movement; ROI placement proce-
dures were repeated if there was a noticeable problem.
Specific binding volume of distribution (designated as V
S
/f
P
based on standard nomenclature
78
) was calculated for each ROI
and used for all ROI-based analyses because this value is pro-
portional to α
4
β
2
* nAChR availability (see eAppendix in Supple-
ment for details of this calculation).
Statistical Analysis
Means and standard deviations were determined for demo-
graphic, rating scale, and smoking-related variables for the en-
tire study sample and subgroups based on treatment type. Base-
line data were compared between the nicotine and placebo patch
subgroups using ttests for continuous data and Fisher exact tests
for categorical data to confirm the success of randomization. For
verifying the effect of treatment on smoking-related variables,
repeated-measures analyses of variance were performed, with
the smoking-related variables (cigarettes per day and exhaled
CO levels) as repeated measures and treatment subgroup (nico-
tine vs placebo patch) as the between-subject factor.
To determine the relationship between α
4
β
2
* nAChR avail-
ability, treatment type, and quit status, an overall multivari-
ate analysis of covariance (ANCOVA) was performed using V
S
/f
P
values for the 8 ROIs as the measures of interest, subgroup (pla-
cebo or nicotine patch) and quit status as factors, and age as a
nuisance covariate (based on prior research indicating that
nAChR densities decline with age
41,79,80
). Follow-up ANCOVAS
were performed for the ROIs separately with the same vari-
ables as in the overall multivariate ANCOVA. For descriptive
purposes, mean V
S
/f
P
values for quitters and nonquitters were
compared with available values from nonsmoking control par-
ticipants in a previous study,
41
and percentage of upregula-
tion for these 2 groups was calculated.
For determining whether PET V
S
/f
P
data improve the abil-
ity to predict treatment response beyond self-report mea-
sures, binary logistic regression was used, as in prior
studies.
16,18,28,81
For this analysis, quit status was the outcome
variable and pretreatment PET V
S
/f
P
values (mean of all ROIs
based on the preceding analysis, which did not reveal regional
differences), severity of nicotine dependence (FTND score), sub-
jective UTS craving ratings, and self-efficacy ratings were the
independent variables. To specifically determine whether the
PET data provided additional predictive powerbeyond the well-
studied measures, a stepwise logistic regression was per-
formed with the 3 self-report measures entered first followed
by the PET V
S
/f
P
data (along with the same analysis in reverse
order). Statistical tests were performed using PASW/SPSS Sta-
tistics version 21.0 statistical software (SPSS, Inc).
Results
Baseline Demographic and Rating Scale Data
At baseline, the study sample was middle-aged, roughly half
female, and approximately half white, with some college edu-
cation and minimal anxiety and depressive symptoms (Table 1).
Participants smoked roughly three-quarters of a pack of ciga-
rettes per day and were moderately nicotine dependent. Study
subgroups based on randomly assigned treatment type (n = 44
randomized to nicotine patch and n = 41 included in analysis
Research Original Investigation Nicotinic Acetylcholine Receptor and Smoking
E4 JAMAPsychiatry Published online May 21, 2014 jamapsychiatry.com
Copyright 2014 American Medical Association. All rights reserved.
Downloaded From: http://archpsyc.jamanetwork.com/ by a SCELC - University of Southern California User on 05/21/2014
Copyright 2014 American Medical Association. All rights reserved.
and evaluation of adverse effects. Participants assigned to the
active patch group received a standard course of treatment be-
ginning with 21-mg/d patches for 4 weeks followed by 14-mg/d
patches for 2 weeks and 7-mg/d patches for 2 weeks, while the
placebo patch group underwent an identical patch regimen with-
out nicotine. Participants were encouraged to minimize or elimi-
nate cigarette use when they initiated treatment and to choose
a quit date 2 weeks after treatment initiation. If participants
lapsed into smoking during treatment, they were encouraged
to pick another quit date within the following week. After 8
weeks of patch treatment, participants were seen for study vis-
its on 2 consecutive weeks (10 weeks total treatment). Al-
though we recognize that combining medication with psycho-
therapy would have resulted in an enhanced smokingcessation
rate,
8,70,71
no formal psychotherapy was provided so that the re-
lationship between pretreatment brain nAChR availability and
nicotine or placebo patch response could be isolated.
At the final study visit, a participant report of 7 or more
days of continuous abstinence from any tobacco use and an
exhaled CO level of 3 ppm or less were used as criteria for hav-
ing quit smoking. These criteria are similar to recent recom-
mendations for documenting smoking abstinence
72,73
and are
comparable to criteria used in many treatment studies.
9,13,35
Participants who initiated treatment but dropped out of the
study were classified as nonquitters in accordance with re-
cent recommendations
72,74
and use
75
of this classification. At
the conclusion of the medication or placebo trial, all partici-
pants were offered open-label treatment with nicotine patch
to assist in smoking cessation and to address (at least partly)
ethical concerns
76
about the use of placebo treatment in this
study.
PET Image Analysis
After decay and motion correction, each participant’s PET/CT
scan was coregistered to his or her magnetic resonance imaging
scan using PMOD version 3.0 software (http://www.pmod.com
/technologies). Regions of interest (ROIs) were drawn on mag-
netic resonance images using PMOD and transferred to the
coregistered PET (Figure 1). Most regions were delineated au-
tomatically using the Functional Magnetic Resonance Imaging
of the Brain Software Library program FIRST, which created au-
tomated drawings through model-based segmentation. These
automated regions were generated from conditional probabili-
ties based on shape and intensity
77
from each participant’s mag-
netic resonance imaging scans and included the following re-
gions bilaterally: nucleus accumbens, amygdala, caudate,
hippocampus, globus pallidus, and putamen. In addition, hand-
drawn ROIs consisted of representative slices of the prefrontal
cortex (middle frontal gyrus) bilaterally and the whole brain-
stem. These ROIs were chosen based on having a range of nAChR
densities, while the thalamus was specifically excluded from
analysis because it is known not to have significant upregula-
tionofα
4
β
2
* nAChRs in smokers. To preserve power, mean val-
ues of bilateral ROIs were used, so a total of 8 ROI values for each
participant were used for statistical analysis. Placement of ROIs
was visually inspected for each PET frame to minimize effects
of coregistration errors and movement; ROI placement proce-
dures were repeated if there was a noticeable problem.
Specific binding volume of distribution (designated as V
S
/f
P
based on standard nomenclature
78
) was calculated for each ROI
and used for all ROI-based analyses because this value is pro-
portional to α
4
β
2
* nAChR availability (see eAppendix in Supple-
ment for details of this calculation).
Statistical Analysis
Means and standard deviations were determined for demo-
graphic, rating scale, and smoking-related variables for the en-
tire study sample and subgroups based on treatment type. Base-
line data were compared between the nicotine and placebo patch
subgroups using ttests for continuous data and Fisher exact tests
for categorical data to confirm the success of randomization. For
verifying the effect of treatment on smoking-related variables,
repeated-measures analyses of variance were performed, with
the smoking-related variables (cigarettes per day and exhaled
CO levels) as repeated measures and treatment subgroup (nico-
tine vs placebo patch) as the between-subject factor.
To determine the relationship between α
4
β
2
* nAChR avail-
ability, treatment type, and quit status, an overall multivari-
ate analysis of covariance (ANCOVA) was performed using V
S
/f
P
values for the 8 ROIs as the measures of interest, subgroup (pla-
cebo or nicotine patch) and quit status as factors, and age as a
nuisance covariate (based on prior research indicating that
nAChR densities decline with age
41,79,80
). Follow-up ANCOVAS
were performed for the ROIs separately with the same vari-
ables as in the overall multivariate ANCOVA. For descriptive
purposes, mean V
S
/f
P
values for quitters and nonquitters were
compared with available values from nonsmoking control par-
ticipants in a previous study,
41
and percentage of upregula-
tion for these 2 groups was calculated.
For determining whether PET V
S
/f
P
data improve the abil-
ity to predict treatment response beyond self-report mea-
sures, binary logistic regression was used, as in prior
studies.
16,18,28,81
For this analysis, quit status was the outcome
variable and pretreatment PET V
S
/f
P
values (mean of all ROIs
based on the preceding analysis, which did not reveal regional
differences), severity of nicotine dependence (FTND score), sub-
jective UTS craving ratings, and self-efficacy ratings were the
independent variables. To specifically determine whether the
PET data provided additional predictive powerbeyond the well-
studied measures, a stepwise logistic regression was per-
formed with the 3 self-report measures entered first followed
by the PET V
S
/f
P
data (along with the same analysis in reverse
order). Statistical tests were performed using PASW/SPSS Sta-
tistics version 21.0 statistical software (SPSS, Inc).
Results
Baseline Demographic and Rating Scale Data
At baseline, the study sample was middle-aged, roughly half
female, and approximately half white, with some college edu-
cation and minimal anxiety and depressive symptoms (Table 1).
Participants smoked roughly three-quarters of a pack of ciga-
rettes per day and were moderately nicotine dependent. Study
subgroups based on randomly assigned treatment type (n = 44
randomized to nicotine patch and n = 41 included in analysis
Research Original Investigation Nicotinic Acetylcholine Receptor and Smoking
E4 JAMAPsychiatry Published online May 21, 2014 jamapsychiatry.com
Copyright 2014 American Medical Association. All rights reserved.
Downloaded From: http://archpsyc.jamanetwork.com/ by a SCELC - University of Southern California User on 05/21/2014
Copyright 2014 American Medical Association. All rights reserved.
in nicotine patch subgroup; n = 44 randomized to placebo
patch and n = 40 included in analysis in placebo patch sub-
group) (eFigure in Supplement) did not differ on any demo-
graphic variables or rating scale scores (Table 1).
Effects of Treatment on Smoking-Related Variables
As expected, treatment was associated with a decrease for the
entire study sample in number of cigarettes per day (mean [SD],
−57.8% [43.6%]; F
1,79
= 106.4; P< .001) and exhaled CO level
(mean [SD], −36.6% [42.7%]; F
1,79
= 44.0; P< .001). Sub-
group × time interactions corresponding to differential change
in cigarettes per day and exhaled CO level were not signifi-
cant (F
1,79
= 0.5, P= .50; and F
1,79
=2.2,P= .16, respectively),
but the nicotine patch subgroup had greater numerical reduc-
tions in these measures than the placebo patch subgroup
(Table 1). Twenty of the 81 participants met criteria for quit-
ting smoking, and active nicotine patch treatment was asso-
ciated with a higher percentage of quitters than placebo patch
treatment (34.1% vs 15.0%, respectively; Fisher exact test,
P= .04) (Table 1).
Pretreatment V
S
f
P
Values and Smoking Cessation
The overall multivariate ANCOVA revealed a significant main
effect of quit status (F
8,69
= 4.5; P< .001), resulting from quit-
ters having lower pretreatment V
S
/f
P
values than nonquitters
(Table 2 and Figure 2). In follow-up ANCOVAs, all ROIs had sig-
nificant associations with quit status (F
1,80
= 10.4-24.9; P= .002
to <.001), indicating that the relationship between pretreat-
Table 1. Demographic, Rating Scale, and Smoking-Related Variables for the Study Sample
and Subgroups Randomly Assigned to Placebo or Nicotine Patch Treatment
Variable
Study Sample
(N = 81)
Patch-Treated Subgroup
a
Placebo
(n = 40)
Nicotine
(n = 41)
Age, mean (SD), y 40.7 (12.6) 42.7 (11.9) 38.6 (13.1)
Female, No. (%) 37 (45.7) 18 (45.0) 19 (46.3)
White, No. (%) 39 (48.1) 17 (42.5) 22 (53.7)
Education, mean (SD), y 14.5 (2.2) 14.3 (2.2) 14.8 (2.2)
Hamilton Anxiety Rating Scale score, mean (SD) 2.5 (2.8) 2.2 (2.1) 2.8 (3.4)
Hamilton Depression Rating Scale score, mean (SD) 2.5 (2.9) 2.3 (2.5) 2.7 (3.2)
FTND score, mean (SD) 4.4 (2.1) 4.6 (2.0) 4.3 (2.2)
Longest quit period, mean (SD), y 1.0 (1.6) 0.9 (1.5) 1.1 (1.7)
Cigarettes, No./d
Pretreatment 15.4 (4.5) 15.4 (4.7) 15.3 (4.3)
Posttreatment 6.8 (8.0)
b
7.5 (7.1)
b
6.3 (8.7)
b
Change in cigarettes/d with treatment, mean (SD), % −57.8 (43.6) −52.7 (41.4) −62.7 (45.6)
Exhaled CO, mean (SD), ppm
Pretreatment 13.8 (6.3) 14.5 (6.6) 13.1 (6.0)
Posttreatment 8.7 (7.2)
b
10.5 (7.8)
b
6.9 (6.2)
b
Change in exhaled CO with treatment, mean (SD), % −36.6 (42.7) −28.7 (38.6) −44.2 (45.5)
Smokers who quit with treatment, No. (%) 20 (24.7) 6 (15.0) 14 (34.1)
c
Abbreviations: CO, carbon monoxide;
FTND, FagerströmTest for Nicotine
Dependence.
a
No significant differences were
found for baseline demographic or
rating scale variables between
smokers randomly assigned to the
placebo vs nicotine patch treatment
subgroups (ttests for continuous
variables and Fisher exact tests for
categorical variables).
b
P< .001 for within-group changes in
cigarettes per day and exhaled CO
from before to after treatment
(paired ttest).
c
P= .04 for difference between
placebo and nicotine patch
subgroups in percentage of quitters
(Fisher exact test).
Table 2. Pretreatment Specific Binding Volumeof Distribution for Brain Regions of Interest for Nonquitters and Quitters in the Total Study Group
and the Placebo and Nicotine Patch Subgroups
Brain Region
V
S
/f
P
, Mean (SD)
a
Total Group Placebo Patch Nicotine Patch
Nonquitters
(n = 61)
Quitters
(n = 20)
Nonquitters
(n = 34)
Quitters
(n=6)
Nonquitters
(n = 27)
Quitters
(n = 14)
Amygdala 5.5 (2.7) 2.7 (1.2) 5.9 (2.6) 2.6 (1.2) 5.0 (2.8) 2.8 (1.3)
Brainstem 10.9 (3.4) 6.7 (1.9) 11.5 (3.5) 6.2 (1.7) 10.1 (3.1) 7.0 (2.0)
Caudate 7.1 (2.4) 5.2 (1.5) 7.6 (2.6) 4.7 (1.3) 6.6 (2.1) 5.5 (1.6)
Globus pallidus 10.2 (3.4) 6.0 (1.8) 10.9 (3.4) 5.6 (1.7) 9.5 (3.3) 6.1 (1.8)
Hippocampus 6.7 (3.0) 3.8 (1.3) 7.2 (2.9) 3.5 (1.4) 6.1 (3.1) 3.9 (1.3)
Nucleus accumbens 7.7 (3.6) 4.6 (1.8) 8.1 (3.4) 4.1 (1.3) 7.2 (3.9) 4.8 (2.0)
Putamen 9.7 (3.6) 5.9 (1.7) 10.3 (3.5) 5.4 (1.4) 8.8 (3.6) 6.1 (1.8)
Prefrontal cortex 6.9 (3.3) 4.0 (1.5) 7.5 (3.0) 3.6 (1.5) 6.2 (3.5) 4.2 (1.6)
Abbreviation: V
S
/f
P
, specific binding volume of distribution.
a
All values are presented as the mean of left and right regions of interest, where
applicable. The overall multivariate analysis of covariance examining the
relationship between pretreatment V
S
/f
P
values, treatment type, and quit
status revealed a significant main effect of quit status (F
8,69
= 4.5; P< .001),
resulting from quitters having lower pretreatment V
S
/f
P
values than
nonquitters. In post hoc analyses of covariance, all of the individual regions of
interest had significant associations with quit status (F
1,80
= 10.4-24.9;
P= .002 to <.001) but no significant interaction between treatment type and
quit status.
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ment nAChR availability and quitting was not region specific.
The interaction between treatment type and quit status was
not significant (F
8,69
=0.8;P= .70), indicating that the rela-
tionship between pretreatment nAChR availabilityand quit sta-
tus was not dependent on treatment type. For the brainstem
and prefrontal cortex, quitters had means of 20% and 29% up-
regulation of nAChR availability, respectively, compared with
available data from previously scanned nonsmoking control
participants,
41
while nonquitters had 66% and 80% upregu-
lation in these respective regions.
Pretreatment Variables and Smoking Cessation
For the logistic regression analysis, the overall test was signifi-
cant(χ
2
4
= 30.7; P< .001), indicating that the combination of PET
and clinical factors has high value in predicting treatment out-
come (Table 3). For the individual variables, pretreatment PET
V
S
/f
P
values (P< .001), UTS craving scores (P= .003), and self-
efficacy scores (P= .02) were all associated with quit status,
while FTND score did not reach statistical significance (P= .25).
In comparing respective mean values of these predictors, quit-
ters compared with nonquitters had lower pretreatment PET
Figure 2. Mean Pretreatment Positron Emission Tomographic Images From the Study Subgroups Demonstrating Higher 2-FA Binding at Baseline
in Nonquitters Compared With Quitters
Nonquitters (n
=
27) Quitters (n
=
14) Nonquitters (n
=
34) Quitters (n
=
6)
Nicotine patch subgroup Placebo patch subgroup
Mean magnetic
resonance image
16
VS/fP
0
Mean pretreatment positron emission tomographic scans are shown for
nonquitters and quitters treated with nicotine patch and for those treated with
placebo patch. Positron emission tomographic images were spatially
normalized to the group mean magnetic resonance imaging scan.
V
S
/f
P
indicates specific binding volume of distribution.
Table 3. Logistic Regression Analyses of Rating Scale Scores, Specific Binding Volume of Distribution, and the Combined Model of Rating Scale Scores
Plus Specific Binding Volume of Distribution for the Prediction of Quit Status With Treatment
a
Variable
Rating Scale Score V
S
/f
P
Rating Scale Score + V
S
/f
P
χ
2
(df)PValue χ
2
(df)PValue χ
2
(df)PValue
Wald χ
2
FTND score 0.1 (1) .77 0.002 (1) .96
UTS craving scale score 4.9 (1) .03 2.6 (1) .11
Self-efficacy 1.6 (1) .21 0.4 (1) .51
V
S
/f
P
17.1 (1) <.001 10.5 (1) .001
Model χ
2
11.0 (3) .01 26.4 (1) <.001 30.7 (4) <.001
Abbreviations: FTND, FagerströmTest for Nicotine Dependence; UTS, Urge to
Smoke; V
S
/f
P
, specific binding volume of distribution.
a
Logistic regression analyses of quit status as determined by rating scale scores
alone, V
S
/f
P
values alone, and all measures combined. Comparison likelihood
ratio χ
2
test results were as follows: for rating scale score + V
S
/f
P
vs rating
scale score, χ
2
1
=19.8,P< .001; for rating scale score + V
S
/f
P
vs V
S
/f
P
,χ
2
3
= 4.3,
P= .23. Likelihood ratio tests show that V
S
/f
P
significantly increases the
predictive power of rating scale scores but that rating scale scores do not
significantly supplement the predictive power of V
S
/f
P
values.
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V
S
/f
P
values (4.9 vs 8.1),lower UTS scores (2.2 vs 3.4), lower FTND
scores (4.0 vs 4.6), and higher self-efficacy scores (60 vs 46). Fur-
thermore, in the stepwise logistic regression, pretreatment PET
V
S
/f
P
values provided additional predictive power beyond the
self-report measures alone (for comparing the fit of the nested
models: likelihood ratio χ
2
1
= 19.8; P< .001).
Discussion
Cigarette smokers with less severe upregulation of available
brain α
4
β
2
* nAChRs have an improvedchance of quitting smok-
ing with treatment than smokers with more severe upregula-
tion. This finding was present in smokers treated with nicotine
and placebo patch and is consistent with a preliminary indica-
tion in a prior report by our group examining smaller groups of
smokers treated with cognitive behavioral therapy, bupro-
pion, or pill placebo.
51
Furthermore, the degree of α
4
β
2
* nAChR
upregulation (a biological phenomenon) was significantly as-
sociated with quitting even after adjusting for known associa-
tions between subjectively rated symptoms (severity of nico-
tine dependence, craving, and self-efficacy) and quit status,
indicating a very strong association between the biological mea-
sure and quitting. Prior research indicates that the level of up-
regulation of α
4
β
2
* nAChR availability may primarily reflect the
extent of nicotine exposure
51
; therefore, the biological mea-
sure determined here may indicate that markers of brain nico-
tine exposure may be highly useful in predicting smoking ces-
sation treatment response. This hypothesis is supported byprior
research indicating that plasma and salivary markers of greater
nicotine exposure are associated with worse treatment
response.
82,83
Findings here were also widespread throughout
the brain, including all ROIs studied, which is consistent with
prior research demonstrating significant upregulation of nAChR
densities in all brain regions studied other than the thalamus.
41
Predictors of response are helpful for treatment planning
in smoking cessation programs because smokers with poorer
projected outcomes may need more intensive and/or lengthier
treatment than smokers with better projected outcomes.
84,85
While the costly,time- consumingPET procedure used here is
not likely to be used clinically, simpler PET or single-photon
emission CT methods with shorter scanning times (ie, <1 hour,
as is common with brain imaging
86-88
) could be tested and ap-
plied to help guide treatment for cigarette smoking in the fu-
ture. Our study indicates that smokers with greater upregu-
lation of nAChRs may require higher medication doses (eg,
higher doses of nicotine patch or patch plus another form of
nicotine replacement) or more intensive psychotherapy than
smokers with less upregulation. In addition, these methods of
predicting treatment response could be tested for other medi-
cations that affect nAChRs, such as other forms of nicotine re-
placement or the α
4
β
2
* nAChR partial agonist varenicline
tartrate,
89,90
or for combination treatment including psycho-
therapy (as is commonly used in clinical practice
8,91
).
A central limitation of the study was sample size. Al-
though this study was relatively large for a PET experiment of
this type, relatively few smokers (15%) quit with placebo patch
treatment. While this low quit rate with placebo patch was ex-
pected, the small number of quitters in this subgroup pre-
cluded a definitive determination of the interaction between
nAChR availability, treatment type, and quit status. How-
ever, it should be noted that quitters and nonquitters in both
treatment subgroups had similar nAChR availabilities (Table2)
and that findings here were consistent with a prior study in
which pill placebo was one of the interventions.
51
Another limi-
tation of the study was the absence of follow-up beyond the
acute phase of treatment, given that smokers who quit with
short-term (several-month) treatment may relapse over lon-
ger periods.
92
Because of this limitation, results here should
be interpreted with caution regarding long-term smoking ces-
sation outcomes. A third limitation was that participants were
not excluded for previous history of nicotine patch use, which
could have affected the blinding. Additionally, while our find-
ings have been consistent for otherwise healthy moderate
smokers, future studies could include smokers with more com-
plex psychiatric and drug or alcohol dependence histories or
lighter (<10 cigarettes/d) or heavier (>40 cigarettes/d) smok-
ing for even greater generalizability.
Conclusions
Cigarette smokers with less upregulation of available brain
α4β2* nAChRs have an improved chance of quitting smoking
than smokers with more upregulation. This association was sig-
nificant even after controlling for known associations be-
tween subjectively rated symptoms (severity of nicotine de-
pendence, craving, and self-efficacy) and quit status, indicating
a very strong association between this biological measure and
quitting. Because prior research demonstrates that the ex-
tent of α4β2* nAChR upregulation is a marker for brain nico-
tine exposure, this study indicates that markers for brain nico-
tine exposure may be highly useful in the future for predicting
smoking cessation treatment responses.
ARTICLE INFORMATION
Submitted for Publication: October 11, 2013; final
revision received December 30, 2013; accepted
January 24, 2014.
Published Online: May 21, 2014.
doi:10.1001/jamapsychiatry.2014.138.
Author Contributions: Drs Brody and Mandelkern
had full access to all of the data in the study and
take responsibility for the integrity of the data and
the accuracy of the data analysis.
Study concept and design: Brody,Mukhin, Rose,
Mandelkern.
Acquisition, analysis, or interpretation of data:
Brody, Mukhin, Mamoun, Luu,Near y, Liang, Shieh,
Sugar, Mandelkern.
Drafting of the manuscript: Brody,Luu, Mandelkern.
Critical revision of the manuscript for important
intellectual conte nt: Brody, Mukhin, Mamoun,
Neary, Liang, Shieh, Sugar, Rose, Mandelkern.
Statistical analysis: Brody,Sugar, Mandelkern.
Obtained funding: Brody.
Administrative, technical, or material support:
Brody, Mamoun, Luu,Near y, Liang, Shieh, Rose.
Study supervision: Brody, Mamoun, Mandelkern.
Conflict of Interest Disclosures: Dr Rose is principal
investigator and Dr Mukhin is a coinvestigator on a
grant from Philip Morris Inc for research unrelated to
this study.Dr Rose also has a patent purchase
agreement with Philip Morris International for
nicotine inhalation technology unrelated to this
study.No other disclosures were reported.
Funding/Support: This study was supported by
grant R01 DA20872 from the National Institute on
Nicotinic Acetylcholine Receptor and Smoking Original Investigation Research
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Drug Abuse (Dr Brody), grant 19XT-0135 from the
Tobacco-RelatedDisease Research Program (Dr
Brody), and Clinical Science Research and
Development Merit Review Award I01 CX000412
from the Office of Research and Development, US
Department of Veterans Affairs (Dr Brody).
Role of the Sponsor: The funders had no role in the
design and conduct of the study; collection,
management, analysis, and interpretation of the
data; preparation, review, or approvalof the
manuscript; and decision to submit the manuscript
for publication.
Additional Contributions: Shahrdad Lotfipour,PhD,
University of California, Los Angeles, collected data
on which the regional nondisplaceable volume of
distribution values were calculated; he received no
compensation from the funders for this contribution.
REFERENCES
1. Mokdad AH, Marks JS, Stroup DF, Gerberding JL.
Actual causes of death in the United States, 2000.
JAMA. 2004;291(10):1238-1245.
2. Bartal M. Health effects of tobacco use and expo-
sure. Monaldi Arch Chest Dis. 2001;56(6):545-554.
3. Leistikow BN, Martin DC, Milano CE. Fire injuries,
disasters, and costs from cigarettes and cigarette
lights: a global overview. PrevMed. 2000;31
(2, pt 1):91-99.
4. Leistikow BN. The human and financial costs of
smoking. Clin Chest Med. 2000;21(1):189-197, x-xi.
5. Leistikow BN, Miller TR. The health care costs of
smoking. N Engl J Med. 1998;338(7):471, author
reply 472.
6. Centers for Disease Control and Prevention.
Cigarette smoking among adults: United States,
2007. MMWR Morb Mortal Wkly Rep. 2008;57(45):
1221-1226.
7. Brown DW. Smoking prevalence among US
veterans. J Gen Intern Med. 2010;25(2):147-149.
8. Fiore MC, Bailey WC, Cohen SJ, et al. Treating
Tobacco Use and Dependence: Clinical Practice
Guideline. Rockville, MD: US Dept of Health & Hu-
man Services; 2000.
9. Hurt RD, Sachs DP, Glover ED, et al.
A comparison of sustained-release bupropion and
placebo for smoking cessation. N Engl J Med.1997;
337(17):1195-1202.
10. Killen JD, Fortmann SP,Davis L , StrausbergL ,
Varady A. Do heavy smokers benefit from higher
dose nicotine patch therapy? Exp Clin Psychophar-
macol. 1999;7(3):226-233.
11. Killen JD, Fortmann SP,Schatzberg AF, et al.
Nicotine patch and paroxetine for smoking
cessation. J Consult Clin Psychol. 2000;68(5):883-
889.
12. Hughes JR, Lesmes GR, Hatsukami DK, et al.
Are higher doses of nicotine replacement more
effective for smoking cessation? Nicotine TobRes.
1999;1(2):169-174.
13. Jorenby DE, Leischow SJ, Nides MA, et al.
A controlled trial of sustained-release bupropion, a
nicotine patch, or both for smoking cessation.
N Engl J Med. 1999;340(9):685-691.
14. Holmes S, Zwar N, Jiménez-Ruiz CA, et al.
Bupropion as an aid to smoking cessation: a review
of real-life effectiveness. Int J Clin Pract. 2004;58
(3):285-291.
15. Ray R, Schnoll RA, Lerman C. Nicotine
dependence: biology, behavior, and treatment.
Annu Rev Med. 2009;60:247-260.
16. Westman EC, Behm FM, Simel DL, Rose JE.
Smoking behavior on the first day of a quit attempt
predicts long-term abstinence. Arch Intern Med.
1997;157(3):335-340.
17. Hymowitz N, Cummings KM, Hyland A, Lynn
WR, Pechacek TF, Hartwell TD.Predictors of
smoking cessation in a cohort of adult smokers
followed for five years. TobControl.1997;6
(suppl 2):S57-S62.
18. Dale LC, Glover ED, Sachs DP, et al. Bupropion
for smoking cessation : predictors of successful
outcome. Chest. 2001;119(5):1357-1364.
19. Paluck EC, McCormack JP, Ensom MH, Levine M,
Soon JA, Fielding DW.Outcomes of bupropion
therapy for smoking cessation during routine clinical
use. Ann Pharmacother. 2006;40(2):185-190.
20. Kozlowski LT,Porter CQ, Orleans CT, Pope MA,
Heatherton T.Predic ting smoking cessation with
self-reported measures of nicotine dependence:
FTQ, FTND, and HSI. Drug Alcohol Depend. 1994;34
(3):211-216.
21. Batra A, Collins SE, Torchalla I, Schröter M,
Buchkremer G. Multidimensional smoker profiles
and their prediction of smoking following a
pharmacobehavioral intervention. J Subst Abuse
Treat. 2008;35(1):41-52.
22. Waters AJ, Shiffman S, Sayette MA, Paty JA,
Gwaltney CJ, Balabanis MH. Cue-provoked craving
and nicotine replacement therapy in smoking
cessation. J Consult Clin Psychol. 2004;72(6):1136-
1143.
23. Berlin I, Singleton EG, Heishman SJ. Predicting
smoking relapse with a multidimensional versus a
single-item tobacco craving measure. Drug Alcohol
Depend. 2013;132(3):513-520.
24. Haaga DA, Stewart BL. Self-efficacy for
recovery from a lapse after smoking cessation.
J Consult Clin Psychol. 1992;60(1):24-28.
25. Li WW, Froelicher ES. Predictors of smoking
relapse in women with cardiovascular disease in a
30-month study: extended analysis. Heart Lung.
2008;37(6):455-465.
26. Shiffman S, Balabanis MH, Paty JA, et al.
Dynamic effects of self-efficacy on smoking lapse
and relapse. Health Psychol. 2000;19(4):315-323.
27. Gwaltney CJ, Shiffman S, Balabanis MH, Paty
JA. Dynamic self-efficacy and outcome
expectancies: prediction of smoking lapse and
relapse. J Abnorm Psychol. 2005;114(4):661-675.
28. Schnoll RA, James C, Malstrom M, et al.
Longitudinal predictors of continued tobacco use
among patients diagnosed with cancer. Ann Behav
Med. 2003;25(3):214-222.
29. Gwaltney CJ, Shiffman S, Norman GJ, et al.
Does smoking abstinence self-efficacy vary across
situations? identifying context-specificity within the
Relapse Situation Efficacy Questionnaire. J Consult
Clin Psychol. 2001;69(3):516-527.
30. Martinez E, Tatum KL, Glass M, et al. Correlates
of smoking cessation self-efficacy in a community
sample of smokers. Addict Behav. 2010;35(2):175-
178.
31. Wiggers LC, Stalmeier PF, Oort FJ, Smets EM,
Legemate DA, de Haes JC. Do patients’ preferences
predict smoking cessation? Prev Med. 2005;41(2):
667-675.
32. Shiffman S, Balabanis MH, Gwaltney CJ, et al.
Prediction of lapse from associations between
smoking and situational antecedents assessed by
ecological momentary assessment. Drug Alcohol
Depend. 2007;91(2-3):159-168.
33. Japuntich SJ, Smith SS, Jorenby DE, Piper ME,
Fiore MC, Baker TB. Depression predicts smoking
early but not late in a quit attempt. Nicotine Tob
Res. 2007;9(6):677-686.
34. al’Absi M, Carr SB, Bongard S. Anger and
psychobiological changes during smoking
abstinence and in response to acute stress:
prediction of smoking relapse. Int J Psychophysiol.
2007;66(2):109-115.
35. Schnoll RA, Patterson F, Wileyto EP,Tyndale RF,
Benowitz N, Lerman C. Nicotine metabolic rate
predicts successful smoking cessation with
transdermal nicotine: a validation study. Pharmacol
Biochem Behav. 2009;92(1):6-11.
36. Kenford SL, Fiore MC, Jorenby DE, Smith SS,
Wetter D, Baker TB. Predicting smoking cessation:
who will quit with and without the nicotine patch.
JAMA. 1994;271(8):589-594.
37. Hyland A, Levy DT, Rezaishiraz H, et al.
Reduction in amount smoked predicts future
cessation. Psychol Addict Behav. 2005;19(2):221-
225.
38. Mamede M, Ishizu K, Ueda M, et al. Temporal
change in human nicotinic acetylcholine receptor
after smoking cessation: 5IA SPECT study.J Nucl
Med. 2007;48(11):1829-1835.
39. Staley JK, Krishnan-Sarin S, Cosgrove KP,et al.
Human tobacco smokers in early abstinence have
higher levels of beta2* nicotinic acetylcholine
receptors than nonsmokers. J Neurosci. 2006;26
(34):8707-8714.
40. Cosgrove KP, Batis J,Bois F, et al.
beta2-Nicotinic acetylcholine receptor availability
during acute and prolonged abstinence from
tobacco smoking. Arch Gen Psychiatry. 2009;66
(6):666-676.
41. Brody AL , Mukhin AG,La Charite J, et al.
Up-regulation of nicotinic acetylcholine receptors in
menthol cigarette smokers. Int J Neuropsychophar-
macol. 2013;16(5):957-966.
42. Mukhin AG, Kimes AS, Chefer SI, et al. Greater
nicotinic acetylcholine receptor density in smokers
than in nonsmokers: a PET study with
2-18F-FA-85380. J Nucl Med. 2008;49(10):1628-
1635.
43. Wüllner U, Gündisch D, Herzog H, et al.
Smoking upregulates alpha4beta2* nicotinic
acetylcholine receptors in the human brain.
Neurosci Lett. 2008;430(1):34-37.
44. Benwell ME, Balfour DJK, Anderson JM.
Evidence that tobacco smoking increases the
density of (-)-[3H]nicotine binding sites in human
brain. J Neurochem. 1988;50(4):1243-1247.
45. Breese CR, Marks MJ, Logel J, et al. Effect of
smoking history on [3H]nicotine binding in human
postmortem brain. J Pharmacol Exp Ther.1997;282
(1):7-13.
46. Zhang X, Tian JY, Svensson AL, Gong ZH,
Meyerson B, Nordberg A. Chronic treatments with
Research Original Investigation Nicotinic Acetylcholine Receptor and Smoking
E8 JAMAPsychiatry Published online May 21, 2014 jamapsychiatry.com
Copyright 2014 American Medical Association. All rights reserved.
Downloaded From: http://archpsyc.jamanetwork.com/ by a SCELC - University of Southern California User on 05/21/2014
Copyright 2014 American Medical Association. All rights reserved.
tacrine and (-)-nicotine induce different changes of
nicotinic and muscarinic acetylcholine receptors in
the brain of aged rat. J Neural Transm.2002;109(3):
377-392.
47. Shoaib M, Schindler CW, Goldberg SR, Pauly JR.
Behavioural and biochemical adaptations to
nicotine in rats: influence of MK801, an NMDA
receptor antagonist. Psychopharmacology (Berl).
1997;134(2):121-130.
48. Pauly JR, Marks MJ, Robinson SF, van de Kamp
JL, Collins AC. Chronic nicotine and mecamylamine
treatment increase brain nicotinic receptor binding
without changing alpha 4 or beta 2 mRNA levels.
J Pharmacol Exp Ther. 1996;278(1):361-369.
49. Pauly JR, Stitzel JA, Marks MJ, Collins AC. An
autoradiographic analysis of cholinergic receptors
in mouse brain. Brain Res Bull. 1989;22(2):453-459.
50. Yates SL, Bencherif M, Fluhler EN, Lippiello PM.
Up-regulation of nicotinic acetylcholine receptors
following chronic exposure of rats to mainstream
cigarette smoke or alpha 4 beta 2 receptors to
nicotine. Biochem Pharmacol. 1995;50(12):2001-
2008.
51. Brody AL, Mukhin AG, Shulenberger S, et al.
Treatment for tobacco dependence: effect on brain
nicotinic acetylcholine receptor density. Neuropsy-
chopharmacology. 2013;38(8):1548-1556.
52. Heatherton TF, Kozlowski LT, Frecker RC,
Fagerström KO. The Fagerström Test forNicotine
Dependence: a revision of the Fagerström
ToleranceQuestionnaire. Br J Addict. 1991;86(9):
1119-1127.
53. Hamilton M. Development of a rating scale for
primary depressive illness. Br J Soc Clin Psychol.
1967;6(4):278-296.
54. Hamilton M. Diagnosis and rating of anxiety. Br
J Psychiatry. 1969;(special publication 3):76-79.
55. 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).
New York: Biometrics Research, New York State
Psychiatric Institute; 1995.
56. Jarvik ME, Madsen DC, Olmstead RE,
Iwamoto-Schaap PN, Elins JL, Benowitz NL.
Nicotine blood levels and subjective craving for
cigarettes. Pharmacol Biochem Behav. 2000;66(3):
553-558.
57. Condiotte MM, Lichtenstein E. Self-efficacy and
relapse in smoking cessation programs. J Consult
Clin Psychol. 1981;49(5):648-658.
58. McCarthy DE , Piasecki TM, Lawrence DL,
Jorenby DE, Shiffman S, Baker TB. Psychological
mediators of bupropion sustained-release
treatment for smoking cessation. Addiction. 2008;
103(9):1521-1533.
59. Stout RL, Wir tz PW, Carbonari JP, Del Boca FK.
Ensuring balanced distribution of prognostic factors
in treatment outcome research. J Stud Alcohol
Suppl. 1994;12:70-75.
60. Rose JE, Herskovic JE, Trilling Y,Jarvik ME .
Transdermal nicotine reduces cigarettecraving and
nicotine preference. Clin Pharmacol Ther. 1985;38
(4):450-456.
61. Levin ED, Conners CK, Sparrow E, et al. Nicotine
effects on adults with attention-deficit/
hyperactivity disorder. Psychopharmacology (Berl).
1996;123(1):55-63.
62. Shiffman S, Ferguson SG. The effect of a
nicotine patch on cigarette craving over the course
of the day: results from two randomized clinical
trials. Curr Med Res Opin. 2008;24(10):2795-2804.
63. Penetar DM, Kouri EM, Gross MM, et al.
Transdermal nicotine alters some of marihuana’s
effects in male and female volunteers. Drug Alcohol
Depend. 2005;79(2):211-223.
64. Benowitz NL. Pharmacology of nicotine:
addiction, smoking-induced disease, and
therapeutics. Annu Rev Pharmacol Toxicol. 2009;
49:57-71.
65. Ossip DJ, Abrams SM, Mahoney MC, Sall D,
Cummings KM. Adverse effects with use of nicotine
replacement therapy among quitline clients.
Nicotine Tob Res.2009;11(4):408-417.
66. Hatsukami D, Mooney M, Murphy S, LeSage M,
Babb D, Hecht S. Effects of high dose transdermal
nicotine replacement in cigarette smokers.
Pharmacol Biochem Behav. 2007;86(1):132-139.
67. Griffith SD, Shiffman S, Heitjan DF. A method
comparison study of timeline followback and
ecological momentary assessment of daily cigarette
consumption. Nicotine Tob Res.2009;11(11):1368-
1373.
68. Toll BA, Cooney NL, McKee SA, O’Malley SS. Do
daily interactive voice response reports of smoking
behavior correspond with retrospective reports?
Psychol Addict Behav. 2005;19(3):291-295.
69. Toll BA, Cooney NL, McKee SA, O’Malley SS.
Correspondence between Interactive Voice
Response (IVR) and Timeline Followback (TLFB)
reports of drinking behavior. Addict Behav. 2006;31
(4):726-731.
70. Brandon TH. Behavioral tobacco cessation
treatments: yesterday’s news or tomorrow’s
headlines? J Clin Oncol. 2001;19(18)(suppl):64S-
68S.
71. Richmond RL, Kehoe L, de Almeida Neto AC.
Effectiveness of a 24-hour transdermal nicotine
patch in conjunction with a cognitive behavioural
programme: one year outcome. Addiction.1997;92
(1):27-31.
72. Hughes JR, Keely JP, Niaura RS, Ossip-Klein DJ,
Richmond RL, Swan GE. Measures of abstinence in
clinical trials: issues and recommendations. Nicotine
Tob R es . 2003;5(1):13-25.
73. Perkins KA, Karelitz JL, Jao NC. Optimal carbon
monoxide criteria to confirm 24-hr smoking
abstinence. Nicotine TobRes. 2013;15(5):978-982.
74. West R, Hajek P, Stead L, Stapleton J. Outcome
criteria in smoking cessation trials: proposal for a
common standard. Addiction. 2005;100(3):299-
303.
75. Rigotti NA, Gonzales D, Dale LC, Lawrence D,
Chang Y; CIRRUS Study Group.A randomized
controlled trial of adding the nicotine patch to
rimonabant for smoking cessation: efficacy, safety
and weight gain. Addiction. 2009;104(2):266-276.
76. Hughes JR. Ethical concerns about non-active
conditions in smoking cessation trials and methods
to decrease such concerns. Drug Alcohol Depend.
2009;100(3):187-193.
77. Patenaude B, Smith SM, Kennedy DN,
Jenkinson M. A Bayesian model of shape and
appearance for subcortical brain segmentation.
Neuroimage. 2011;56(3):907-922.
78. Innis RB, Cunningham VJ, Delforge J, et al.
Consensus nomenclature for in vivo imaging of
reversibly binding radioligands. J Cereb Blood Flow
Metab. 2007;27(9):1533-1539.
79. Rogers SW, Gahring LC, Collins AC, Marks M.
Age-related changes in neuronal nicotinic
acetylcholine receptor subunit α4 expression are
modified by long-term nicotine administration.
J Neurosci. 1998;18(13):4825-4832.
80. Mitsis EM, Cosgrove KP, Staley JK, et al.
[123I]5-IA-85380 SPECT imaging of beta2-nicotinic
acetylcholine receptor availability in the aging
human brain. AnnNYAcadSci. 2007;1097:168-170.
81. Japuntich SJ, Leventhal AM, Piper ME, et al.
Smoker characteristics and smoking-cessation
milestones. Am J Prev Med. 2011;40(3):286-294.
82. Paoletti P, Fornai E, Maggiorelli F, et al.
Importance of baseline cotinine plasma values in
smoking cessation: results from a double-blind
study with nicotine patch. Eur Respir J. 1996;9(4):
643-651.
83. Nørregaard J, Tønnesen P, Petersen L.
Predictors and reasons for relapse in smoking
cessation with nicotine and placebo patches. Prev
Med. 1993;22(2):261-271.
84. Shiffman S, Dresler CM, Hajek P, Gilburt SJ,
Targett DA,Strahs KR. Efficac y of a nicotine lozenge
for smoking cessation. Arch Intern Med. 2002;162
(11):1267-1276.
85. Baker TB, Piper ME, McCarthy DE, et al;
Transdisciplinary Tobacco Use Research Center
(TTURC) Tobacco Dependence. Time to first
cigarette in the morning as an index of ability to quit
smoking: implications for nicotine dependence.
Nicotine Tob Res.2007;9(suppl 4):S555-S570.
86. Newberg AB, Lariccia PJ, Lee BY, Farrar JT, Lee
L, Alavi A. Cerebral blood flow effects of pain and
acupuncture: a preliminary single-photon emission
computed tomography imaging study.J Neuroim-
aging. 2005;15(1):43-49.
87. Novak B, Milcinski M, Grmek M, Kocmur M.
Early effects of treatment on regional cerebral
blood flow in first episode schizophrenia patients
evaluated with 99Tc-ECD-SPECT. Neuro Endocrinol
Lett. 2005;26(6):685-689.
88. Takagi S, TakahashiW, Shinohara Y, et al.
Quantitative PET cerebral glucose metabolism
estimates using a single non-arterialized
venous-blood sample. Ann Nucl Med. 2004;18(4):
297-302.
89. Coe JW, Brooks PR, Vetelino MG, et al.
Varenicline: an alpha4beta2 nicotinic receptor
partial agonist for smoking cessation. J Med Chem.
2005;48(10):3474-3477.
90. West R, Baker CL, Cappelleri JC, Bushmakin
AG. Effect of varenicline and bupropion SR on
craving, nicotine withdrawal symptoms, and
rewarding effects of smoking during a quit attempt.
Psychopharmacology (Berl). 2008;197(3):371-377.
91. Abrams DB, Niaura RS, Brown RA, Emmons KM,
Goldstein MG, Monti PM. The TobaccoDependence
Treatment Handbook: A Guide toBest Practices.
New York, NY: Guilford Press; 2003.
92. Hatsukami DK, Stead LF, Gupta PC. Tobacco
addiction. Lancet. 2008;371(9629):2027-2038.
Nicotinic Acetylcholine Receptor and Smoking Original Investigation Research
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