Nicotine & Tobacco Research, Volume 16, Number 9 (September 2014) 1157–1166
Review
Cigarette Smoking and P300 Amplitude in Adults: A Systematic Review Dawson Hedges MD1,2, David P. Bennett2 1Department
of Psychology, Brigham Young University, Provo, UT; 2Neuroscience Center, Brigham Young University, Provo, UT
Corresponding Author: Dawson Hedges, MD, Department of Psychology, Brigham Young University, 1001 SWKT, Provo, UT 84317, USA. Telephone: 801-422-6357; Fax: 801-422-0602; E-mail: [email protected] Received February 23, 2014; accepted April 13, 2014
Abstract Design: Systematic review with meta-analysis and meta-regression. Data Sources: Medline, Cochrane Database of Systematic Reviews, PsychInfo, and Web of Science. Eligibility Criteria for Selecting Studies: Eligible studies contained P300 amplitudes obtained from either visual or auditory stimuli and standard deviations or errors in female and male subjects older than 18 years in a group of nonabstaining chronic cigarette smokers and a nonsmoking control group. Results: The 9 eligible studies comprised 13 relevant datasets containing 1,956 current cigarette smokers and 2,194 nonsmoking controls (N = 4,150). The P300 amplitude was smaller in cigarette smokers than in nonsmoking controls (Hedges’ g effect size = .365; 95% confidence interval [CI] = 0.196–0.534, p < .001). Meta-regression showed significant positive associations between the number of cigarettes smoked per day at the time of the study (slope estimate = .036, 95% CI = 0.016–0.056, p ≤ .001, length of smoking in years (slope estimate = .056, 95% CI = 0.005–0.102, p = .018), pack years (slope estimate = .018, 95% CI = 0.009–0.031 p = .009), and age (slope estimate = .068, 95% CI = 0.025–0.113, p = .002). Conclusions: P300 amplitude was smaller in cigarette smokers than in nonsmoking controls, and a possible dose–response relationship was apparent. Findings indicate a possible association between cigarette smoking and decreased P300 amplitude.
Introduction Cognitive decline in adulthood may lead to dementia. As many as 24 million people worldwide may have dementia (Reitz, Brayne, & Mayeux, 2011), and the number of people with dementia is expected to double every 20 years through at least 2040 (Reitz et al., 2011). However, some risk factors for cognitive decline are potentially modifiable (Kukull, 2006), including risk factors for vascular disease such as hypertension, type-2 diabetes, and possibly tobacco smoking (Ferri et al., 2005). A main cause of preventable mortality (Danaei et al., 2009), tobacco smoking is the most prevalent form of substance abuse worldwide (Guney, Genc, Kutlu, & Ilhan, 2009). Nicotine from tobacco smoking likely leads to addiction by affecting dopamine transmission in brain-reward systems, particularly in that nicotine increases dopamine release in the nucleus accumbens (Corrigall, 1991) and alters the firing pattern of ventral tegmental dopamine neurons (Grenhoff et al., 1986). Although the overall contribution of nicotine on dopaminergic neurons is not yet fully understood (Misfud, Hernandez, & Hoebel,
1989), dopamine release in the nucleus accumbens has been associated with the reward mechanisms in the brain (Koob & Bloom, 1988). Although identified as a risk factor for cognitive decline (Ferri et al., 2005), the effect of tobacco smoking on cognitive decline, however, is not clear. While tobacco smoking has been associated with a decreased risk for Alzheimer’s disease (Knott, Harr, & Mahoney,1999), tobacco smoking also has been implicated with worsened cognitive function in young (Fried, Watkinson, & Gray, 2006), middle-aged (Kalmijn, van Boxtel, Verschuren, Jolles, & Launer, 2002), and elderly people (Deary, Pattie, & Taylor, 2005). In a prospective study, cigarette smoking was associated with 2.3 times the risk for Alzheimer’s disease and 2.2 times the risk for other dementia (Ott et al., 1998). In a cross-sectional study, current cigarette smokers had poorer cognitive functioning than nonsmokers (Gons et al., 2011). Electroencephalographic (EEG) event-related potentials (ERPs) can be used to estimate cognitive function in that ERP waveforms occurring approximately 200 ms after a stimulus is given are associated with information processing (Pritchard, Sokhadze, & Houlihan, 2004). Recorded in response to an
doi:10.1093/ntr/ntu083 Advance Access publication May 20, 2014 © The Author 2014. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: [email protected].
1157
Downloaded from http://ntr.oxfordjournals.org/ at University of Pittsburgh on March 30, 2015
Objective: To determine the association between chronic tobacco cigarette smoking and P300 amplitude.
Cigarette smoking and P300 amplitude
1158
Methods Identification of Source Studies We identified potential source studies to June 2013 using combinations of the terms P300, smoking, cigarette smoking, and tobacco in Medline, PsychInfo, and Web of Knowledge electronic searches and the Cochrane Database of Systematic Reviews and by searching references lists in identified articles. Inclusion and Exclusion Criteria Inclusion criteria were published peer-reviewed studies in English reporting average (and standard deviation or standard error) P300 amplitude and subject number in adults ages 18 years or greater who chronically smoked cigarettes (greater than 1 year of smoking) compared to a control group unexposed to personal tobacco smoking. The exclusion criteria were studies in which the cigarette-smoking group or the control group had neuropsychiatric comorbidity and studies using subjects in nicotine withdrawal because P300 amplitude can be increased during nicotine withdrawal and after resumption of smoking following withdrawal (Kodoma et al., 1996). Because the acute administration of nicotine can affect the P300 (Pritchard et al., 2004), studies in which nicotine was acutely administered also were excluded. Studies in which chronic cigarettes smokers had abstained from smoking for greater than three hours before obtaining the P300 also were excluded because abstinence from cigarette smoking greater than this can increase P300 amplitude (Kodoma et al., 1996). If subjects from one study were included in another study, we used the study that had the larger number of subjects but did not include two studies that appeared to report data from the same subjects. Study Selection We reviewed titles and abstracts of identified potential source studies and evaluated full articles for those studies that appeared to possibly meet the study inclusion and exclusion criteria. Data Extraction First author, publication year, mean P300 latency and standard deviation or standard error, mean P300 amplitude and standard deviation or standard error, number in the tobacco-smoking group and number in the control group, percent female, selfreported length of tobacco smoking and number of pack years [the number of packs of cigarettes smoked per day multiplied by years of smoking or the number of cigarettes smoked daily divided by 20 and then multiplied by years of smoking (Gons et al., 2011)], current number of cigarettes smoked daily, average age of subjects in the entire study, and average age in the tobacco-smoking group and control group of each source study were extracted and included in an Excel spreadsheet. When available, because of its parietal distribution (Pritchard et al., 2004) where there is optimal recording reliability and less alteration by muscle and ocular artifacts (Anokhin, Todorov, Madden, Grant, & Heath, 1999), we used the P300 amplitude recorded at the midparietal region (Pz) according to the International 10–20 System for electrode placement (Pritchard et al., 2004), although if this was not available, we used frontal (Fz) or central (Cz) recordings.
Downloaded from http://ntr.oxfordjournals.org/ at University of Pittsburgh on March 30, 2015
infrequently presented stimulus (Mobascher et al., 2010), the P300 is a late-occurring positive event-related waveform in response to auditory, somatosensory, or visual stimuli (Guney et al., 2009). It is not known where in the brain the P300 is generated (Guney et al., 2009), and, in fact, the P300 may have multiple generators (Turetsky, Colbath, & Gur, 1998). Further, the P300 waveform is an objective, reliable, and noninvasive marker of cognitive function (Polich, 2007; Polich & Herbst, 2000), including the amount of attention allocated to a task (Kramer & Strayer, 1988) and working memory (Polich, 2007; Polich & Herbst, 2000). The P300 has been used in studies of age-related cognitive changes, dementia (Guney et al., 2009), and disease-associated cognitive deficits (Magnano, Aiello, & Piras, 2006). Further, the P300 may be more sensitive to cognitive change than is neuropsychological testing (Lai, Lin, Liou, & Liu, 2010). P300 amplitude reduction indicates decreased brain activation or processing dispersion (Magnano et al., 2006). The association of the P300 with working memory, attention allocation, and processing speed and its alteration in cognitive impairment possibly seen before changes occur in neuropsychological testing suggest that the P300 can be an important neurophysiological marker of cognitive function (Guney et al., 2009). As such, the P300 has been suggested to be a means of quantifying mental impairment in dementing diseases (Guney et al., 2009). In this regard, abnormal P300 waveforms have been associated with several neuropsychiatric disorders characterized by cognitive deficits, such as dementia (Goodin & Aminoff, 1986) and traumatic brain injury (Duncan, Kosmidis, & Mirskym, 2005). Some (Aşçioglu, Dolu, Gölgeli, Süer, & Özesmi, 2004) but not all (Knott, Bosman, Mahoney, Ilivitsky, & Quirt, 1999; Littel & Franken, 2011; Mobascher et al., 2010) studies report an association between cigarette smoking and an abnormal P300, and the association that may continue in former smokers (Neuhaus et al., 2006). Although tobacco smoking is a putative risk factor for dementia (Kukull, 2006), the association between tobacco smoking and alterations in the P300 is complex in that nicotine administration can affect cognition, such as shortening reaction time in highly learned tasks (Pritchard et al., 2004). Further, acute tobacco smoking tends to increase the P300 amplitude (Houlihan, Pritchard, & Robinson, 2001; Polich & Criado, 2006). Transdermal nicotine after overnight smoking abstinence also increases P300 amplitude (Knott, Bosman et al., 1999). Abstinence from tobacco smoking in chronic smokers can also increase P300 amplitude (Kodoma et al., 1996), further complicating the assessment of tobacco smoking on the P300 waveform. Although most studies have investigated the effects of acute tobacco smoking on the P300 (Polich & Ochoa, 2004), this meta-analysis focuses on the association between P300 amplitude and chronic cigarette smoking in nonabstaining chronic cigarette smokers compared to controls who have never smoked, thus attempting to identify an association between chronic exposure to cigarette smoking in the absence of any potential for acute nicotine exposure (Houlihan et al., 2001; Knott, Bosman, et al., 1999; Polich & Criado, 2006) or nicotine withdrawal (Kodoma et al., 1996) to increase the P300 amplitude. As such, the primary objective of this meta-analysis is to systematically review and quantify by meta-analysis the association between chronic cigarette smoking and the P300 amplitude in chronically cigarette-smoking groups and neversmoking controls and to identify whether any association is modified by the amount or duration of smoking, age, or sex.
Nicotine & Tobacco Research Statistical Analysis and Data Synthesis
Results The search strategy identified an initial 199 unique potential source studies. After reviewing titles and abstracts, there were 92 articles that potentially met inclusion and exclusion criteria. From a second search done 2 years later, we obtained an additional 40 articles that potentially could meet our inclusion and exclusion criteria. From all of the obtained articles, nine (Anokhin et al., 1999, 2000; Aşçioglu et al., 2004; Evans, Park, Maxfield, & Drobes, 2009; Guney et al., 2009; Kessels, Ruiter, & Jansma, 2010; Littel & Franken, 2007; Mobascher et al., 2010; Neuhaus et al., 2006) met the study inclusion and
Table 1. Demographic Characteristics of Source Studies Containing P300 Amplitude Data in Smoking and Nonsmoking Control Groups Smoking group Study and year
N
Anokhin et al. (1999a) Anokhin et al. (1999b) Anokhin et al. (1999c) Anokhin et al. (1999d) Anokhin et al. (2000) Aşçioglu et al. (2004) Evans et al. (2009) Littel and Franken (2007) Neuhaus et al. (2006) Mobascher et al. (2010a) Mobascher et al. (2010b) Guney et al. (2009) Kessels et al. (2010)
118 103 19 28 905 10 26 21 84 271 325 32 14
Percent
malea
100 0 100 0 NR NR 57.7 NR 50.0 48.0 43.4 56.3 57.1
Nonsmoking control group Age (y) NR NR NR NR NR NR 30.2 21.6 37.9 40.9 35.4 40.5 NR
N
Percent malea
111 137 22 13 979 10 22 21 110 722 722 32 15
100 0 100 0 NR NR 40.9 NR 42.7 39.6 43.4 50.0 46.7
Age (y) NR NR NR NR NR NR 25.0 19.6 40.5 35.4 35.4 37.1 NR
Note. NR = not reported. The Anokhin references (1999a, 1999b, 1999c, and 1999d) refer to Anokhin (1999), and the Mobascher references refer to Mobascher (2010). aPercent male in the tobacco-smoking group.
1159
Downloaded from http://ntr.oxfordjournals.org/ at University of Pittsburgh on March 30, 2015
The Cohen’s d effect size for P300 amplitude was calculated for each source study (Rosenthal, 1979) by dividing the mean P300 amplitude difference between groups by the pooled standard deviation. We used the J-correction factor to correct for small sample size to obtain the Hedges g effect size (Hedges & Olkin, 1985). In cases in which one source study reported data from two distinct groups that otherwise met all inclusion and exclusion criteria, we treated each group as a separate study. We then calculated a pooled effect size with an associated 95% CI and p value from all the source studies for the P300 amplitude using a random-effects model because the subject population in the source studies may have differed from each other due to between-study differences (Borenstein, Hedges, Higgins, & Rothstein, 2009). We assessed source-study heterogeneity using the χ2-based Q test with its associated p value and evaluated for potential publication bias using the Classic Failsafe-N test and the Trim and Fill test (Borenstein et al., 2009). For the meta-regressions, we calculated the slope of the relevant covariate against the effect size from each study that reported the particular covariate, using the method of moments to calculate the slope estimate because this is based on a random-effects model (Borenstein et al., 2009). For all statistical calculations, we used Comprehensive Meta-Analysis 2.0 (Biostat, Englewood, NJ).
exclusion criteria. The reasons for excluding retrieved studies were lack of a current cigarette-smoking group, lack of a nonsmoking control group, no P300 data reported, no error terms reported for the P300 data, comorbidity including alcoholism, neurological or medical disease that could affect brain function in either the cigarette-smoking or the nonsmoking group or both, adolescent subjects, smoking abstinence greater than three hours in smoking group, and use of snuff or transdermal nicotine patches instead of cigarette smoking. Of the included studies, one (Anokhin et al., 1999) reported data on four separate groups, which we included as four different studies, and one (Mobascher et al., 2010) reported data on two separate groups, which we included as two separate datasets, bringing the total number of identified source datasets to 13. The 13 source studies contained 1,956 cigarette-smoking subjects and 2,194 nonsmoking control subjects (Table 1). We also extracted when available the number of cigarettes smoked daily, length of smoking, pack years of smoking, percent male in the smoking group, and age of the smoking group (Table 2). Differences in effect sizes between the cigarette-smoking group and the non-cigarette-smoking group for P300 amplitude were statistically nonsignificant (p > .050) in five of the 13 datasets (Table 3), which comprised 360 total subjects out of the meta-analysis total of 4,150 subjects (8.67%). One study (Littel & Franken, 2007) had a significantly smaller effect size for P300 amplitude (42 total subjects out of 4,150 total metaanalysis subjects (1.08%) in the non-cigarette-smoking group compared to the cigarette-smoking group. The remaining seven studies, which comprised 3,748 total subjects out of the metaanalysis total of 4,150 (90.31%) had significantly (p ≤ .050) smaller effect sizes in the cigarette-smoking group than in the non-cigarette-smoking group (Table 3). The overall Hedges’ g effect size for the mean differences in P300 amplitude between cigarette-smoking and non-cigarette-smoking groups was .365 (95% CI = 0.196–0.534, p < .001, Table 3). The Q value was 57.320 (p < .001), indicating significant heterogeneity among the source studies. The Classic Failsafe Test showed that 323 missing studies would be needed to bring the p value of the Hedges’ g effect size to greater than .050.
Cigarette smoking and P300 amplitude Table 2. P300 Amplitude, Average Daily Number of Cigarettes, and Length of Smoking in the Source Studies Nonsmoking control group
Smoking group Amp
Num
Lth
PY
M%
Amp
Anokhin et al. (1999a) Anokhin et al. (1999b) Anokhin et al. (1999c) Anokhin et al. (1999d) Anokhin et al. (2000) Aşçioglu et al. (2004) Evans et al. (2009) Littel and Franken (2007) Neuhaus et al. (2006) Mobascher et al. (2010a) Mobascher et al. (2010b) Guney et al. (2009) Kessels et al. (2010)
14.6 15.0 14.0 13.0 15.0 4.8 0.7 12.7 14.2 4.2 4.8 13.4 10.9
NR NR NR NR NR 14 20 NR 15.2 21.5 8.0 26.2 6.8
NR NR NR NR NR NR 11.2 4.8 21.3 25.2 19.0 21.7 NR
100 NR NR NR NR NR NR NR 16.1 28.7 8.6 NR NR
NR 0 100 0 NR NR 58 NR 50 48 43 56 57
16.0 18.4 13.2 12.8 19.5 5.4 1.3 8.1 17.3 5.2 5.2 26.9 12.8
Note. Amp = P300 amplitude; Num = average number of cigarettes smoked per day; Lth = length of cigarette smoking (years); PY = pack years; M% = percent males in the smoking group; NR = not reported. The Anokhin references (1999a, 1999b, 1999c, and 1999d) refer to Anokhin, (1999), and the Mobascher references refer to Mobascher (2010). Table 3. Hedges’ g Effect for the Difference Between the Cigarette-Smoking Group and the Nonsmoking Group and Their SE and p Value for Each Individual Source Study Study and year Anokhin et al. (1999a) Anokhin et al. (1999b) Anokhin et al. (1999c) Anokhin et al. (1999d) Anokhin et al. (2000) Aşçioglu et al. (2004) Evans et al. (2009) Littel and Franken (2007) Neuhaus et al. (2006) Mobascher et al. (2010a) Mobascher et al. (2010b) Guney et al. (2009) Kessels et al. (2010) Summary
Hedges’ g effect size
SE
p value
0.189 0.367 −0.108 −0.028 0.519 0.316 0.571 −0.669 0.504 0.560 0.189 1.548 0.380 0.365
0.132 0.131 0.307 0.329 0.047 0.431 0.291 0.311 0.147 0.072 0.067 0.282 0.365
.173 .005 .724 .932
Review
Cigarette Smoking and P300 Amplitude in Adults: A Systematic Review Dawson Hedges MD1,2, David P. Bennett2 1Department
of Psychology, Brigham Young University, Provo, UT; 2Neuroscience Center, Brigham Young University, Provo, UT
Corresponding Author: Dawson Hedges, MD, Department of Psychology, Brigham Young University, 1001 SWKT, Provo, UT 84317, USA. Telephone: 801-422-6357; Fax: 801-422-0602; E-mail: [email protected] Received February 23, 2014; accepted April 13, 2014
Abstract Design: Systematic review with meta-analysis and meta-regression. Data Sources: Medline, Cochrane Database of Systematic Reviews, PsychInfo, and Web of Science. Eligibility Criteria for Selecting Studies: Eligible studies contained P300 amplitudes obtained from either visual or auditory stimuli and standard deviations or errors in female and male subjects older than 18 years in a group of nonabstaining chronic cigarette smokers and a nonsmoking control group. Results: The 9 eligible studies comprised 13 relevant datasets containing 1,956 current cigarette smokers and 2,194 nonsmoking controls (N = 4,150). The P300 amplitude was smaller in cigarette smokers than in nonsmoking controls (Hedges’ g effect size = .365; 95% confidence interval [CI] = 0.196–0.534, p < .001). Meta-regression showed significant positive associations between the number of cigarettes smoked per day at the time of the study (slope estimate = .036, 95% CI = 0.016–0.056, p ≤ .001, length of smoking in years (slope estimate = .056, 95% CI = 0.005–0.102, p = .018), pack years (slope estimate = .018, 95% CI = 0.009–0.031 p = .009), and age (slope estimate = .068, 95% CI = 0.025–0.113, p = .002). Conclusions: P300 amplitude was smaller in cigarette smokers than in nonsmoking controls, and a possible dose–response relationship was apparent. Findings indicate a possible association between cigarette smoking and decreased P300 amplitude.
Introduction Cognitive decline in adulthood may lead to dementia. As many as 24 million people worldwide may have dementia (Reitz, Brayne, & Mayeux, 2011), and the number of people with dementia is expected to double every 20 years through at least 2040 (Reitz et al., 2011). However, some risk factors for cognitive decline are potentially modifiable (Kukull, 2006), including risk factors for vascular disease such as hypertension, type-2 diabetes, and possibly tobacco smoking (Ferri et al., 2005). A main cause of preventable mortality (Danaei et al., 2009), tobacco smoking is the most prevalent form of substance abuse worldwide (Guney, Genc, Kutlu, & Ilhan, 2009). Nicotine from tobacco smoking likely leads to addiction by affecting dopamine transmission in brain-reward systems, particularly in that nicotine increases dopamine release in the nucleus accumbens (Corrigall, 1991) and alters the firing pattern of ventral tegmental dopamine neurons (Grenhoff et al., 1986). Although the overall contribution of nicotine on dopaminergic neurons is not yet fully understood (Misfud, Hernandez, & Hoebel,
1989), dopamine release in the nucleus accumbens has been associated with the reward mechanisms in the brain (Koob & Bloom, 1988). Although identified as a risk factor for cognitive decline (Ferri et al., 2005), the effect of tobacco smoking on cognitive decline, however, is not clear. While tobacco smoking has been associated with a decreased risk for Alzheimer’s disease (Knott, Harr, & Mahoney,1999), tobacco smoking also has been implicated with worsened cognitive function in young (Fried, Watkinson, & Gray, 2006), middle-aged (Kalmijn, van Boxtel, Verschuren, Jolles, & Launer, 2002), and elderly people (Deary, Pattie, & Taylor, 2005). In a prospective study, cigarette smoking was associated with 2.3 times the risk for Alzheimer’s disease and 2.2 times the risk for other dementia (Ott et al., 1998). In a cross-sectional study, current cigarette smokers had poorer cognitive functioning than nonsmokers (Gons et al., 2011). Electroencephalographic (EEG) event-related potentials (ERPs) can be used to estimate cognitive function in that ERP waveforms occurring approximately 200 ms after a stimulus is given are associated with information processing (Pritchard, Sokhadze, & Houlihan, 2004). Recorded in response to an
doi:10.1093/ntr/ntu083 Advance Access publication May 20, 2014 © The Author 2014. Published by Oxford University Press on behalf of the Society for Research on Nicotine and Tobacco. All rights reserved. For permissions, please e-mail: [email protected].
1157
Downloaded from http://ntr.oxfordjournals.org/ at University of Pittsburgh on March 30, 2015
Objective: To determine the association between chronic tobacco cigarette smoking and P300 amplitude.
Cigarette smoking and P300 amplitude
1158
Methods Identification of Source Studies We identified potential source studies to June 2013 using combinations of the terms P300, smoking, cigarette smoking, and tobacco in Medline, PsychInfo, and Web of Knowledge electronic searches and the Cochrane Database of Systematic Reviews and by searching references lists in identified articles. Inclusion and Exclusion Criteria Inclusion criteria were published peer-reviewed studies in English reporting average (and standard deviation or standard error) P300 amplitude and subject number in adults ages 18 years or greater who chronically smoked cigarettes (greater than 1 year of smoking) compared to a control group unexposed to personal tobacco smoking. The exclusion criteria were studies in which the cigarette-smoking group or the control group had neuropsychiatric comorbidity and studies using subjects in nicotine withdrawal because P300 amplitude can be increased during nicotine withdrawal and after resumption of smoking following withdrawal (Kodoma et al., 1996). Because the acute administration of nicotine can affect the P300 (Pritchard et al., 2004), studies in which nicotine was acutely administered also were excluded. Studies in which chronic cigarettes smokers had abstained from smoking for greater than three hours before obtaining the P300 also were excluded because abstinence from cigarette smoking greater than this can increase P300 amplitude (Kodoma et al., 1996). If subjects from one study were included in another study, we used the study that had the larger number of subjects but did not include two studies that appeared to report data from the same subjects. Study Selection We reviewed titles and abstracts of identified potential source studies and evaluated full articles for those studies that appeared to possibly meet the study inclusion and exclusion criteria. Data Extraction First author, publication year, mean P300 latency and standard deviation or standard error, mean P300 amplitude and standard deviation or standard error, number in the tobacco-smoking group and number in the control group, percent female, selfreported length of tobacco smoking and number of pack years [the number of packs of cigarettes smoked per day multiplied by years of smoking or the number of cigarettes smoked daily divided by 20 and then multiplied by years of smoking (Gons et al., 2011)], current number of cigarettes smoked daily, average age of subjects in the entire study, and average age in the tobacco-smoking group and control group of each source study were extracted and included in an Excel spreadsheet. When available, because of its parietal distribution (Pritchard et al., 2004) where there is optimal recording reliability and less alteration by muscle and ocular artifacts (Anokhin, Todorov, Madden, Grant, & Heath, 1999), we used the P300 amplitude recorded at the midparietal region (Pz) according to the International 10–20 System for electrode placement (Pritchard et al., 2004), although if this was not available, we used frontal (Fz) or central (Cz) recordings.
Downloaded from http://ntr.oxfordjournals.org/ at University of Pittsburgh on March 30, 2015
infrequently presented stimulus (Mobascher et al., 2010), the P300 is a late-occurring positive event-related waveform in response to auditory, somatosensory, or visual stimuli (Guney et al., 2009). It is not known where in the brain the P300 is generated (Guney et al., 2009), and, in fact, the P300 may have multiple generators (Turetsky, Colbath, & Gur, 1998). Further, the P300 waveform is an objective, reliable, and noninvasive marker of cognitive function (Polich, 2007; Polich & Herbst, 2000), including the amount of attention allocated to a task (Kramer & Strayer, 1988) and working memory (Polich, 2007; Polich & Herbst, 2000). The P300 has been used in studies of age-related cognitive changes, dementia (Guney et al., 2009), and disease-associated cognitive deficits (Magnano, Aiello, & Piras, 2006). Further, the P300 may be more sensitive to cognitive change than is neuropsychological testing (Lai, Lin, Liou, & Liu, 2010). P300 amplitude reduction indicates decreased brain activation or processing dispersion (Magnano et al., 2006). The association of the P300 with working memory, attention allocation, and processing speed and its alteration in cognitive impairment possibly seen before changes occur in neuropsychological testing suggest that the P300 can be an important neurophysiological marker of cognitive function (Guney et al., 2009). As such, the P300 has been suggested to be a means of quantifying mental impairment in dementing diseases (Guney et al., 2009). In this regard, abnormal P300 waveforms have been associated with several neuropsychiatric disorders characterized by cognitive deficits, such as dementia (Goodin & Aminoff, 1986) and traumatic brain injury (Duncan, Kosmidis, & Mirskym, 2005). Some (Aşçioglu, Dolu, Gölgeli, Süer, & Özesmi, 2004) but not all (Knott, Bosman, Mahoney, Ilivitsky, & Quirt, 1999; Littel & Franken, 2011; Mobascher et al., 2010) studies report an association between cigarette smoking and an abnormal P300, and the association that may continue in former smokers (Neuhaus et al., 2006). Although tobacco smoking is a putative risk factor for dementia (Kukull, 2006), the association between tobacco smoking and alterations in the P300 is complex in that nicotine administration can affect cognition, such as shortening reaction time in highly learned tasks (Pritchard et al., 2004). Further, acute tobacco smoking tends to increase the P300 amplitude (Houlihan, Pritchard, & Robinson, 2001; Polich & Criado, 2006). Transdermal nicotine after overnight smoking abstinence also increases P300 amplitude (Knott, Bosman et al., 1999). Abstinence from tobacco smoking in chronic smokers can also increase P300 amplitude (Kodoma et al., 1996), further complicating the assessment of tobacco smoking on the P300 waveform. Although most studies have investigated the effects of acute tobacco smoking on the P300 (Polich & Ochoa, 2004), this meta-analysis focuses on the association between P300 amplitude and chronic cigarette smoking in nonabstaining chronic cigarette smokers compared to controls who have never smoked, thus attempting to identify an association between chronic exposure to cigarette smoking in the absence of any potential for acute nicotine exposure (Houlihan et al., 2001; Knott, Bosman, et al., 1999; Polich & Criado, 2006) or nicotine withdrawal (Kodoma et al., 1996) to increase the P300 amplitude. As such, the primary objective of this meta-analysis is to systematically review and quantify by meta-analysis the association between chronic cigarette smoking and the P300 amplitude in chronically cigarette-smoking groups and neversmoking controls and to identify whether any association is modified by the amount or duration of smoking, age, or sex.
Nicotine & Tobacco Research Statistical Analysis and Data Synthesis
Results The search strategy identified an initial 199 unique potential source studies. After reviewing titles and abstracts, there were 92 articles that potentially met inclusion and exclusion criteria. From a second search done 2 years later, we obtained an additional 40 articles that potentially could meet our inclusion and exclusion criteria. From all of the obtained articles, nine (Anokhin et al., 1999, 2000; Aşçioglu et al., 2004; Evans, Park, Maxfield, & Drobes, 2009; Guney et al., 2009; Kessels, Ruiter, & Jansma, 2010; Littel & Franken, 2007; Mobascher et al., 2010; Neuhaus et al., 2006) met the study inclusion and
Table 1. Demographic Characteristics of Source Studies Containing P300 Amplitude Data in Smoking and Nonsmoking Control Groups Smoking group Study and year
N
Anokhin et al. (1999a) Anokhin et al. (1999b) Anokhin et al. (1999c) Anokhin et al. (1999d) Anokhin et al. (2000) Aşçioglu et al. (2004) Evans et al. (2009) Littel and Franken (2007) Neuhaus et al. (2006) Mobascher et al. (2010a) Mobascher et al. (2010b) Guney et al. (2009) Kessels et al. (2010)
118 103 19 28 905 10 26 21 84 271 325 32 14
Percent
malea
100 0 100 0 NR NR 57.7 NR 50.0 48.0 43.4 56.3 57.1
Nonsmoking control group Age (y) NR NR NR NR NR NR 30.2 21.6 37.9 40.9 35.4 40.5 NR
N
Percent malea
111 137 22 13 979 10 22 21 110 722 722 32 15
100 0 100 0 NR NR 40.9 NR 42.7 39.6 43.4 50.0 46.7
Age (y) NR NR NR NR NR NR 25.0 19.6 40.5 35.4 35.4 37.1 NR
Note. NR = not reported. The Anokhin references (1999a, 1999b, 1999c, and 1999d) refer to Anokhin (1999), and the Mobascher references refer to Mobascher (2010). aPercent male in the tobacco-smoking group.
1159
Downloaded from http://ntr.oxfordjournals.org/ at University of Pittsburgh on March 30, 2015
The Cohen’s d effect size for P300 amplitude was calculated for each source study (Rosenthal, 1979) by dividing the mean P300 amplitude difference between groups by the pooled standard deviation. We used the J-correction factor to correct for small sample size to obtain the Hedges g effect size (Hedges & Olkin, 1985). In cases in which one source study reported data from two distinct groups that otherwise met all inclusion and exclusion criteria, we treated each group as a separate study. We then calculated a pooled effect size with an associated 95% CI and p value from all the source studies for the P300 amplitude using a random-effects model because the subject population in the source studies may have differed from each other due to between-study differences (Borenstein, Hedges, Higgins, & Rothstein, 2009). We assessed source-study heterogeneity using the χ2-based Q test with its associated p value and evaluated for potential publication bias using the Classic Failsafe-N test and the Trim and Fill test (Borenstein et al., 2009). For the meta-regressions, we calculated the slope of the relevant covariate against the effect size from each study that reported the particular covariate, using the method of moments to calculate the slope estimate because this is based on a random-effects model (Borenstein et al., 2009). For all statistical calculations, we used Comprehensive Meta-Analysis 2.0 (Biostat, Englewood, NJ).
exclusion criteria. The reasons for excluding retrieved studies were lack of a current cigarette-smoking group, lack of a nonsmoking control group, no P300 data reported, no error terms reported for the P300 data, comorbidity including alcoholism, neurological or medical disease that could affect brain function in either the cigarette-smoking or the nonsmoking group or both, adolescent subjects, smoking abstinence greater than three hours in smoking group, and use of snuff or transdermal nicotine patches instead of cigarette smoking. Of the included studies, one (Anokhin et al., 1999) reported data on four separate groups, which we included as four different studies, and one (Mobascher et al., 2010) reported data on two separate groups, which we included as two separate datasets, bringing the total number of identified source datasets to 13. The 13 source studies contained 1,956 cigarette-smoking subjects and 2,194 nonsmoking control subjects (Table 1). We also extracted when available the number of cigarettes smoked daily, length of smoking, pack years of smoking, percent male in the smoking group, and age of the smoking group (Table 2). Differences in effect sizes between the cigarette-smoking group and the non-cigarette-smoking group for P300 amplitude were statistically nonsignificant (p > .050) in five of the 13 datasets (Table 3), which comprised 360 total subjects out of the meta-analysis total of 4,150 subjects (8.67%). One study (Littel & Franken, 2007) had a significantly smaller effect size for P300 amplitude (42 total subjects out of 4,150 total metaanalysis subjects (1.08%) in the non-cigarette-smoking group compared to the cigarette-smoking group. The remaining seven studies, which comprised 3,748 total subjects out of the metaanalysis total of 4,150 (90.31%) had significantly (p ≤ .050) smaller effect sizes in the cigarette-smoking group than in the non-cigarette-smoking group (Table 3). The overall Hedges’ g effect size for the mean differences in P300 amplitude between cigarette-smoking and non-cigarette-smoking groups was .365 (95% CI = 0.196–0.534, p < .001, Table 3). The Q value was 57.320 (p < .001), indicating significant heterogeneity among the source studies. The Classic Failsafe Test showed that 323 missing studies would be needed to bring the p value of the Hedges’ g effect size to greater than .050.
Cigarette smoking and P300 amplitude Table 2. P300 Amplitude, Average Daily Number of Cigarettes, and Length of Smoking in the Source Studies Nonsmoking control group
Smoking group Amp
Num
Lth
PY
M%
Amp
Anokhin et al. (1999a) Anokhin et al. (1999b) Anokhin et al. (1999c) Anokhin et al. (1999d) Anokhin et al. (2000) Aşçioglu et al. (2004) Evans et al. (2009) Littel and Franken (2007) Neuhaus et al. (2006) Mobascher et al. (2010a) Mobascher et al. (2010b) Guney et al. (2009) Kessels et al. (2010)
14.6 15.0 14.0 13.0 15.0 4.8 0.7 12.7 14.2 4.2 4.8 13.4 10.9
NR NR NR NR NR 14 20 NR 15.2 21.5 8.0 26.2 6.8
NR NR NR NR NR NR 11.2 4.8 21.3 25.2 19.0 21.7 NR
100 NR NR NR NR NR NR NR 16.1 28.7 8.6 NR NR
NR 0 100 0 NR NR 58 NR 50 48 43 56 57
16.0 18.4 13.2 12.8 19.5 5.4 1.3 8.1 17.3 5.2 5.2 26.9 12.8
Note. Amp = P300 amplitude; Num = average number of cigarettes smoked per day; Lth = length of cigarette smoking (years); PY = pack years; M% = percent males in the smoking group; NR = not reported. The Anokhin references (1999a, 1999b, 1999c, and 1999d) refer to Anokhin, (1999), and the Mobascher references refer to Mobascher (2010). Table 3. Hedges’ g Effect for the Difference Between the Cigarette-Smoking Group and the Nonsmoking Group and Their SE and p Value for Each Individual Source Study Study and year Anokhin et al. (1999a) Anokhin et al. (1999b) Anokhin et al. (1999c) Anokhin et al. (1999d) Anokhin et al. (2000) Aşçioglu et al. (2004) Evans et al. (2009) Littel and Franken (2007) Neuhaus et al. (2006) Mobascher et al. (2010a) Mobascher et al. (2010b) Guney et al. (2009) Kessels et al. (2010) Summary
Hedges’ g effect size
SE
p value
0.189 0.367 −0.108 −0.028 0.519 0.316 0.571 −0.669 0.504 0.560 0.189 1.548 0.380 0.365
0.132 0.131 0.307 0.329 0.047 0.431 0.291 0.311 0.147 0.072 0.067 0.282 0.365
.173 .005 .724 .932
