Why do young women smoke? VI. A controlled study of nicotine effects on attention: pharmacogenetic interactions

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In prior studies we found that young, female smokers manifest poorer performance than non-smokers on attention-related tasks and that these findings can be moderated by variation in nicotinic acetylcholine receptor (nAChR) genes. We predicted that under controlled conditions (1) nicotine would improve functioning on attentional tasks in smokers who previously manifested relatively poor performance, and that (2) smokers who carry genetic variations associated with poorer attention performance would derive greater benefit from nicotine. To test these hypotheses, 31 young female smokers, who participated in our previous study, performed the Matching Familiar Figures Test (MFFT), Tower of London Test and Continuous Performance Task (CPT) in a double-blind, within-between subject design, placebo or nicotine (4 mg as gum) serving as the within factor and genetic profile as the between factor. Repeated measures ANCOVA controlling for attention deficit symptomatology, substance abuse and nicotine dependence showed better performance under nicotine among participants with higher levels of attention deficit symptoms (MFFT errors: P=0.04; CPT commissions: P=0.01) and nicotine dependence (CPT stability of response: P=0.04) and greater consumption of caffeine (CPT stability of response: P=0.04). An interactive effect of genetic profile was demonstrated for SNP rs2337980 in CHRNA7. These findings suggest that nicotine may have stronger short-term facilitating effects on attention in women who have more attention deficit symptoms and consume more nicotine and caffeine. This effect may be modified by a specific genetic make-up. Such individuals may be at increased risk for nicotine addiction and for greater difficulties in smoking cessation.


It is estimated that 29% of the world's population aged 15 and over smoke cigarettes daily. Although smoking prevalence is four times higher among men than women the decrease observed in recent years in high-income countries1, 2 including Israel3is greater among men. In most European countries teenage girls are as likely to smoke as boys, if not more so.4 These findings replicate previous surveys.5, 6, 7 They indicate that in spite of growing awareness of the health hazards, cigarette smoking remains a significant public health problem. In high-income countries there is a specific concern among young women. In 2002, the Biological Psychiatry Laboratory, Department of Psychiatry, Hadassah-Hebrew University Medical Center launched the ‘Why Do Young Women Smoke?’ (WDYWS) project to identify factors that predispose to smoking initiation and nicotine dependence in young, Israeli women.8, 9, 10, 11, 12

It is well established that nicotine has a small but significant short-term, facilitating effect on attention.13, 14, 15, 16, 17 In pathological states characterized by cognitive deficits nicotine appears to have a stronger facilitating effect on cognitive function.18, 19, 20, 21, 22 Moreover, smoking occurs at higher rates than in the general population among individuals with schizophrenia and Attention Deficit/Hyperactivity Disorder (ADHD)23, 24 who may smoke more because nicotine helps them to cope with cognitive deficits that characterize these states. In the WDYWS sample, Yakir et al.10 showed that current and past smokers made more errors on tests of attention and response-inhibition than non-smokers and suggested that a priori attention deficits lead to a greater risk for developing nicotine dependence. On the basis of the role of nicotinic acetylcholine receptors (nAChRs) in mediating the physiological, behavioral and cognitive effects of nicotine,25 Rigbi et al.12 examined interactive effects of smoking status and nAChR gene variants. A plethora of studies have associated nAChR gene variation and nicotine dependence;26 fewer have sought association with cognitive function.27, 28, 29, 30 Rigbi et al.12 demonstrated a significant effect of SNPs and haplotypes in several nAchR genes on tests that reflect attention and response-inhibition and were shown by Yakir et al.10 to differentiate young women who smoked cigarettes regularly or had done so in the past from young women who had never smoked. In several cases there was an interaction with smoking status; in some a particular genetic variant had effects that were different in direction in the group of smokers (current or past) compared with young women who had never smoked.

The hypotheses of this study were that (1) exogenous nicotine administration would improve functioning on tasks that measure attention and response-inhibition in young women who smoke cigarettes on a regular basis and were found (10) to manifest relatively poor performance on these tasks, and that (2) nAChR gene variants found to modify cognitive performance among cigarette smokers (12) would interact with nicotine to alter cognitive performance in the direction of the main and previously observed interactive effects with smoking status.

Materials and methods


Participants were re-recruited from current smokers in the WDYWS sample who had undergone cognitive testing and (1) had error scores 5 on the Matching Familiar Figures Test (MFFT), which yielded the most robust differences between smokers and non-smokers in Yakir et al.10 (n=108), with priority for the 100 who had also been genotyped for nAchR gene variants;8 (2) continued to smoke currently; and, (3) were competent and willing to give written informed consent. Forty-four of the potential participants were ineligible because of smoking cessation (28), pregnancy/breastfeeding (5), substance abuse (4, self-report of using any drug apart from cannabis and/or using cannabis twice a week or more) or medical/psychiatric condition;7 15 refused to participate and 15 did not reply to the invitation; 34 were recruited of whom 31 completed both arms of the study.

Instruments of evaluation

Background measures

Questionnaires completed in the WDYWS project covering background information, life experience and smoking behavior and the Fagerström Tolerance Scale for Nicotine Dependence31 were re-administered to obtain information on variables subject to change during the 6 years since initial recruitment.

Cognitive battery

We used the CogScan battery (V4.0) provided by AnimaScan, Ashdod, Israel.32 The tests re-administered were those that yielded a significant association among smoking status, genetic profile and cognitive performance in Rigbi et al.12—the MFFT, Tower of London and Continuous Performance Task (CPT).

Subjective measures of attention deficit and impulsivity

Brown Attention Deficit Disorder Rating Scale for Adults, the Adult ADHD Rating Scale and the Barratt Impulsiveness Scale33, 34, 35 (see Supplementary Methods for detailed description and psychometric properties of the evaluation instruments above).


Genotypes were available from Greenbaum et al.8 for 24 participants. The genetic variants analyzed were those that remained significant in the regression models applied by Rigbi et al.,12 that is significantly predicted cognitive performance on the MFFT, Tower of London and CPT within the smokers group (Table 1) (see Supplementary Methods for detailed description of genotyping).

Table 1 Selected SNPs and haplotypes

Urine cotinine levels

Urine cotinine levels were assayed in the laboratories of the Association for Public Health Services, Tel-Aviv, Israel using DRI Cotinine Assay kits (Microgenics Corporation, Fremont, CA, USA). Cotinine is a nicotine metabolite (half-life 16 h) produced by the liver enzyme CYP2A6, widely used as a quantitative marker for nicotine exposure.36

Mode of nicotine delivery

Cigarettes are problematic as a mode of nicotine delivery because the amount of nicotine administered cannot be controlled and an effective placebo group cannot be created.13 Although nicotine patch contains more nicotine than gum and both patch and nicotine nasal spray have better bioavailability, nicotine gum (‘Nicotinell’ (Novartis, Basel, Switzerland) containing 4 mg of nicotine) was used for this study because time to reach maximum concentration can extend up to 12 h using a patch compared with 30 min when using a gum37 and the maximum nicotine concentration with nasal spray is smaller than with gum (5–8 ng ml−1 compared with 10–17 ng ml−1).37 Placebo gum was supplied by Novartis, Petah-Tikva, Israel (see Supplementary Methods for more details on the nicotine gum).


Participants were tested in counterbalanced placebo and nicotine conditions (mean interval: 9±4 days) and were randomized, double-blind, to receive nicotine or placebo in the first session. Participants were directed to avoid alcohol and/or cannabis 24 h before sessions and coffee, juices or fizzy drinks 15 min before as these may reduce the absorption of nicotine from the gum. Participants could smoke freely before the sessions. To control for nicotine abstinence, they were asked to smoke their last cigarette no earlier than 2 h before.38 Participants who had smoked their last cigarette more than 2 h before a session could choose between smoking a cigarette and quitting the session. Two participants chose to quit. One participant vomited after the administration of nicotine gum and was dropped from the experiment. At the beginning of each session, blood pressure (BP) and heart rate (HR) were recorded and urine samples were obtained (Figure 1). The participants then received a detailed explanation of how to chew the gum and possible side effects that might occur. After 30 min the gum was discarded. BP and HR were recorded and the participants performed the three cognitive tests (MFFT, Tower of London and CPT) consecutively in counterbalanced manner. When the test sessions were over, HR and BP were again recorded.

Figure 1

Study procedure.

Data analysis

Repeated measures ANCOVA (SPSS 15, SPSS, Chicago, IL, USA, 1989–2003) were used to test the main hypothesis regarding the effect of experimental condition on cognitive functioning. Within (experimental condition)-between (genetic variance) ANCOVA was used to test the secondary hypothesis of interaction between genetic variance and experimental condition on cognitive functioning. The Greenhouse–Geiser correction was applied for the within-subject effects. Cronbach's α coefficient was used for assessing reliability. Principal component analysis was used for data reduction. Factor loading was determined by absolute factor scores of 0.4 and above.39


Background variables

Descriptive statistics for relevant background variables are presented in Table 2. The sample consisted of smokers with medium–high nicotine dependence and 11 years mean duration of smoking, a low level of subjective ADHD symptoms and mild caffeine, cannabis and alcohol consumption. There was no significant difference between sessions for the mean time interval since last cigarette smoked (nicotine: 70.47±27.88; placebo: 76.87±32.91 min) and mean number of sleep hours before session (nicotine: 6.48±1.58; placebo: 6.95±1.29). Owing to the high inter-correlations between the subjective attention (the Adult ADHD Rating Scale, Brown Attention Deficit Disorder Rating Scale for Adults) and impulsivity (Barratt Impulsiveness Scale) measures (0.60, Cronbach α=0.61), principal component analysis was performed showing that all three measures were highly loaded on one factor with loadings >0.80. This was termed ‘Attention-Impulsivity Factor’ (A-I) and was scored by transforming the Brown Attention Deficit Disorder Rating Scale for Adults, the Adult ADHD Rating Scale and Barratt Impulsiveness Scale scores into z-scores and summing them into one composite score.39

Table 2 Background variables for the 31 participants who completed both arms of the study

Physiological measures

Figure 2 (a–c) shows systolic BP, diastolic BP and HR for both conditions within three time-points during the two experimental sessions. HR linear decrement was significant within the nicotine (Overall: F=4.17; df=2, 56; P=0.02; Contrast analysis: beginning of session-end of session time-points: F=8.36; df=1, 28; P=0.007) and placebo (Overall: F=15.52; df=2, 56; P<0.0001; Contrast analysis: beginning of session-after gum time-points: F=27.86; df=1, 28; P<0.0001; beginning-end of session time-points: F=17.66; df=1, 28; P<0.0001). Systolic and diastolic BP were not significant along time-points in both conditions. Comparison of time-points between conditions showed higher diastolic BP for nicotine after chewing the gum and at the end of the experiment (t=2.46, 2.74; df=30, 27; P=0.02, 0.01, respectively) and faster HR after chewing the gum (t=2.95, df=30; P=0.006). Mean urinary cotinine levels in the nicotine and placebo conditions were 690.88±550.15 and 680.47±419.44 ng ml−1, respectively. Both results were positive for urine cotinine based on the assay's cut-off value of 500 ng ml−1. No significant difference in cotinine levels was found across conditions.

Figure 2

Effect of condition on HR (a), systolic (b) and diastolic BP (c) within three time-points between experimental conditions. Comparison of time-points between conditions showed higher diastolic BP for nicotine after chewing the gum and at the end of the experiment and faster HR after chewing the gum. ** (a) and (c) effect is significant at P0.01. y axes (ac): mean±s.e. (The color reproduction of this figure is available on the html full text version of the manuscript.)

Effect of nicotine on attention and impulsivity

Descriptive statistics of the cognitive performance measures under nicotine and placebo are shown in Table 3. Repeated measures ANCOVA was performed with condition (nicotine/placebo) serving as the independent variable and the relevant operational measures in each of the three cognitive tasks serving as dependent measures. Each analysis controlled for (1) A-I factor because of the associations among smoking, ADHD and attention-related cognitive performance; (2) Fagerström Tolerance Scale for Nicotine Dependence (FTQ) score because nicotine gum might affect participants who are more addicted to nicotine differently compared with participants who are less addicted to it. In addition, univariate correlation analysis was performed between the cognitive measures and the following background variables: hours of sleep prior session; smoking latency prior session; urine continine level of session; age; number of cigarettes smoked per day; number of coffee cups consumed per day; cannabis consumption; and alcohol consumption. Any variable showing a significant association with the cognitive measures in both conditions (that is nicotine or placebo) was entered as an additional covariate (see Supplementary Table 1). Overall, there was no direct effect of condition; however, the following modifying effects were found (Supplementary Table 2 for all effects):

Table 3 Descriptive statistics of cognitive tests
  1. 1

    On the MFFT a significant condition by A-I factor effect on the number of errors was found (F(1, 28)=4.50, P=0.04). Inspection of the association between number of errors in each condition and the A-I factor score showed a significant correlation between the A-I score and the difference in errors between the conditions (r=0.38, P=0.03), indicating that nicotine administration decreases the number of errors more the higher the participants scored on the A-I factor (that is reported more symptoms of ADHD and impulsivity).

  2. 2

    On the CPT-Boring phase a significant condition by coffee consumption effect on the standard deviation of reaction time for correct responses (SD of RT) was found (F(1, 26)=7.23, P=0.01). Inspection of the association between SD of RT in each condition and coffee consumption showed a significant correlation between the coffee consumption and the difference in SD of RT between the conditions (r=0.40, P=0.02), indicating that nicotine administration decreases SD of RT (that is better performance) more the more coffee the participants consume.

  3. 3

    On the CPT-Loading phase a significant condition by FTQ score effect on SD of RT was found (F(1, 25)=4.68, P=0.04). Inspection of the association between SD of RT in each condition and the FTQ score showed a significant correlation between the FTQ and the difference in SD of RT between the conditions. However, this was limited to those participants who reported high nicotine dependence (FTQ8, r=0.64, P=0.02), indicating that nicotine administration decreases SD of RT (that is better performance) specifically for those participants reporting high nicotine dependence.

  4. 4

    Moreover, on the CPT-Loading phase a significant condition by A-I factor score effect on the number of commissions was found (F(1, 27)=6.98, P=0.01). Inspection of the association between number of commissions in each condition and the A-I factor score showed a significant correlation between the A-I score and the difference in commission between the conditions (r=0.38, P=0.03), indicating that nicotine administration decreases the number of commissions more the higher the participants scored on the A-I factor (that is reported more symptoms of ADHD and impulsivity).

Figure 3 (a–d) shows the nature of the modified effects of condition on cognitive performance. The modifying variables (x axis) are presented as dichotomous variables, despite of being analyzed as continuous variables, to clarify and simplify the effects found.

Figure 3

Effect of nicotine on (a) MFFT errors controlled for A-I Factor; (b) CPT-Boring SD of RT controlled for coffee consumption; (c) CPT-Loading SD of RT controlled for FTQ score and; (d) CPT-Loading commissions controlled for A-I factor. ANCOVA showed greater number of MFFT errors (a) and CPT commissions (d) under placebo within participants reporting greater ADHD symptomatology and greater level of unstable performance in the CPT under placebo within participants reporting of consuming more caffeine (b) and nicotine (c). Splits are based on ANCOVA results. All effects were significant at P0.05. y axes (ad): mean±s.e. (The color reproduction of this figure is available on the html full text version of the manuscript.)

Interaction with nAChR gene variants

The hypothesis tested was that participants who carry an nAChR genetic variant that was associated with worse cognitive performance in Rigbi et al.12 would benefit more from nicotine than participants who do not (Supplementary Table 3 shows the associations between the SNPs tested and background variables). Eleven SNPs and four haplotypes were tested under this hypothesis (Table 1) by entering as factors into the main hypotheses multivariate models. Figure 4 presents the significant condition by genotype interaction effect on MFFT errors that was found for SNP rs2337980 in CHRNA7 and is in the hypothesized direction. Controlling for A-I factor and FTQ scores (F(1, 19)=4.35, P=0.05), CC genotype carriers (n=8) made fewer errors under nicotine compared with placebo whereas CT/TT carriers (n=16; 10,6 respectively) were more indifferent to condition. Other SNPs/haplotypes did not reach a significant interaction effect with condition or could not be analyzed because of small samples within genotypes.

Figure 4

Interaction between condition and genotype in SNP A7-rs2337980 controlling for A-I factor in relation to MFFT errors. ANCOVA showed that participants carrying CC genotype make more MFFT errors under placebo compared with participants carrying CT/TT genotypes. Interaction is significant at P0.05. y axis: mean±s.e. (The color reproduction of this figure is available on the html full text version of the manuscript.)


The results of this study show that nicotine does not have an omnibus facilitating effect on the functioning of young female smokers on attention and response-inhibition-related tasks but improves performance of those who report more attention deficit symptoms, greater nicotine dependence and greater consumption of coffee. The findings are in accordance with the consensus that the facilitating effect of nicotine on cognition is greater and more salient among populations with attention deficits.18, 19, 20, 21, 22 Attention deficit symptoms, even below the clinical threshold, are significantly associated with risk for smoking.40, 41, 42 Caffeine, another CNS stimulant,43 was also reported to be associated with smoking44 and to enhance attention performance, although the results are less consistent than for nicotine.45

The interaction found between nicotine administration and genetic variation in CHRNA7 SNP rs2337980, where carriers of the CC variant benefited more from nicotine than CT/TT carriers, is as predicted by the findings of Rigbi et al.12 in which CC carriers manifested worse MFFT performance. Compared with the number of studies relating variation in nAChR receptor genes to smoking behavior and nicotine dependence,26 the number of studies relating such variations to attention deficit symptomatology42 or cognitive performance28, 29, 30 is rather small and shows mixed results. Thus, the genetic interplay between smoking, attention and nAChR variation requires additional research.

Our findings have several potential limitations. First, the sample was relatively small; thus, the possibility of splitting it into sub-groups of interest according to specific genetic variation, cognitive performance or level of modifying variables is very limited and associations do not withstand correction for multiple testing. Second, nicotine gum contains a smaller amount of nicotine than cigarettes;37 however, 4 mg is the maximum nicotine concentration available commercially. Moreover, placebo gum does not match perfectly in taste; however, overcoming the taste dissimilarity by adding an external flavoring agent,46 may affect the chemical/pharmacological properties of the gum and reduce participants’ compliance. Third, blood nicotine levels were not assessed. However, the physiological effects of nicotine on BP and HR indicate that the gum did cause the expected physiological effect compared with placebo.

Notwithstanding these limitations, the study sheds light on the interplay between attention-related cognitive performance, genetic make-up and smoking. Subject to verification in a larger sample, the results suggest that nicotine, as a short-term facilitating agent for attention and response-inhibition, may have stronger effects in individuals who have more attention deficit symptoms and tend to consume more CNS stimulants such as nicotine and caffeine. In addition, this effect can be modified by a specific genetic make-up that is related to poorer performance on attention and response-inhibition functions. Such individuals may be at greater risk for developing nicotine addiction and for experiencing of more difficulties in smoking cessation.


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This study was supported in part by a grant from the Professor Milton Rosenbaum Endowment Fund for Research in the Psychiatric Sciences (to YP).

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Correspondence to B Lerer.

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Supplementary Information accompanies the paper on the The Pharmacogenomics Journal website

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About this article


  • nicotine
  • nicotinic acetylcholine receptor genes
  • attention
  • response-inhibition
  • smoking
  • women

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