The Brief Addiction Science Information Source (BASIS)

ASHES, Vol. 6 (1) –Modest lifetime cigarette use affects women’s breast cancer risk.

There are numerous factors that can lead to breast cancer. These influences include age, family history, exposure to hormones and lifestyle habits such as smoking and drinking alcohol. In this week’s ASHES, we review a study by Croghan et al. (2009). The study compared the prevalence of non-milk duct or lobule related breast cancer among women who reported smoking more than 100 cigarettes during their lifetime and women who smoked less than 100 cigarettes or never smoked.

Methods

• Croghan et al. collected data from 8,927 female patients seen at the Mayo Clinic Breast Clinic between August 1, 1993 and November 31, 2003
• The final sample of 8,097 women included: 
• 831 current smokers;
• 714 former smokers; and
• 6,552 women who never smoked.
• Researchers excluded women who:
• were diagnosed with non-breast-related cancers; 
• were diagnosed with breast cancers originating in the milk-producing ducts or lobules of the breast; or
• could not be linked with the Mayo Clinic Diagnostic Index. 
• Patients completed a self-administered questionnaire. If patients made return visits to the clinic, researchers included only their first completed survey in the analyses.
• To test the effect tobacco smoke has on the probability of a breast cancer diagnosis, researchers compared patients with no cancer to patients with breast cancer using Pearson chi-squared tests and multivariate logistic regression with stepwise elimination. In each case, researchers used a two-sided test with a 0.05 significance level.

Table 1: Predictive Model of Breast Cancer using Logistic Regression (Adapted from Croghan et al, 2009)

 

Results

• Croghan et al., using logistic regression found, after controlling for other factors, that having a personal history of ever smoking more than 100 cigarettes increased the odds of a breast cancer diagnosis by 25% (CI = 1.07, 1.46). 

Limitations

• Self-report of previous smoking behavior provides a limited amount of data.
• Self-report might be affected by recall bias.
• The use of a multivariate model includes shared variance; researchers did not specifically analyze the unique variance* due to cigarette smoking.

Conclusion 

The researchers admit that it is difficult to assess the effects of lifestyle habits such as smoking and drinking. More research is needed to clarify the influence of lifestyle on the development of breast cancer. Nevertheless, Croghan et al. observed that smoking at least 100 cigarettes at any point during life increases a woman’s risk for developing breast cancer. This study supports a cautionary note: it is possible that modest levels of substance use can lead to adverse health issues.

-Ingrid Maurice

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References 

Croghan, I. T., Pruthi, S., Hays, J. T., Cha, S., Johnson, R. E., Kosel, M., et al. (2009). The role of smoking in breast cancer development: an analysis of a Mayo Clinic cohort. The Breast Journal, 15(5), 489-495.

*Note: Researchers did not test the effect of cigarette smoking on the development of breast cancer independently from other variables listed in Table 1.

ASHES, Vol. 5(10) – Crushing virtual cigarettes to aid real world smoking cessation

There are a variety of treatments used to encourage smokers to stop smoking. However, despite the treatment options, many smokers still struggle to quit (Center for Disease Control and Prevention, 1999). Perhaps smoking cessation treatment could benefit by expanding the variety of treatments available; a particularly novel intervention is Virtual Reality (VR) treatment (Baumann & Sayette, 2006; Carter, Bordnick, Traylor, Day, & Paris, 2008). This week the ASHES reviews a study of the benefits of VR in combination with psychosocial treatment to reduce cigarette smoking (Girard, Turcotte, Bouchard, & Girard, 2009).

Methods

• Researchers recruited 91 healthy adults, aged 18-65, from Quebec, Canada via local ads. Participants were not interested in quitting and smoked ≥10 cigarettes per day.
• Researchers provided participants with brief psychosocial treatment and randomly assigned participants to either experimental (i.e., crushing cigarettes; n = 46) or control (i.e., crushing balls, n = 45) VR sessions.

• Clinic visits (weeks 1, 2, 3, 4, 6, 8, 10 and 12): During each 30-minute session, a nurse collected each participant’s smoking journal; performed a carbon monoxide test to confirm abstinence; administered a revised version of the Fagerström test (Heatherton, Kozlowski, Frecker, & Fagerström, 1991) for nicotine dependence (only at weeks 1, 4, and 12); and conducted a brief counseling session.    

• VR sessions (weeks 1, 2, 3 and 4): Additional 30-minute sessions during which participants in each condition repeatedly simulated grasping the assigned object or crushing it.    

• The researchers used an intent-to-treat analysis to analyze all the participants’ results regardless of treatment drop out.

Results

• Researchers tested the program’s impact on the Fagerström test using a repeated measures ANOVA. Figure 1 shows, a significant group (grasping ball or crushing cigarette ) by time (Pretreatment, End of VR, End of program) interaction F (2, 178) = 3.75, p < 0.05.

• Contrast analysis revealed that the Cigarette condition was statistically better than the Ball condition at reducing nicotine dependence between week 4, F (1, 89) = 33.34, p < 0.001, and the end of the study F (1, 89) = 55.18, p < 0.001.    

• At week 4, there was no significant difference for abstinence between the Ball condition (2%) and the Cigarette condition (9%) (Χ² (1) = 1.84, ns). However, the difference was significant at week 12 between the Ball condition (2%) and the Cigarette condition (15%) (Χ² (1) = 4.79, p < 0.05).
• On average, participants in the Ball condition dropped out of the study significantly earlier than participants in Cigarettes condition (5.89 weeks completed, SD = 4.66; 8.26 weeks, SD = 4.17; respectively; t(89) = 2.56, p < 0.05).

Figure 1 - Fagerström test for Nicotine Dependence

Click image to enlarge, or adjust your browser's zoom setting.
*Total number of nicotine dependent behaviors (six items) endorsed for a total score between 0 (minimum dependence) and 10 (maximum dependence).  

Limitations

• The control group had a higher drop-out rate and, thusly, a greater proportion of early final ratings. Without information on the tendency of drop-outs to show greater or less improvement than completers, it is not possible to gauge the extent to which the outcomes were affected by the intent-to-treat procedure.
• The study uses self-report for some of its measures.
• The study does not explore why crushing cigarettes was more effective than grasping balls for smoking cessation.

Discussion

Compared to the control group, participants whose treatment included crushing cigarettes in a VR environment had lower rates of nicotine dependence, higher cessation rates, and were more likely to remain in the treatment program. What remains illusive about these results is why crushing cigarettes in a VR environment helped participants improve. Future studies should analyze the mechanisms that moderate and mediate these outcomes (e.g., the objective or goal of the treatment session or the subjective states stimulated by crushing cigarettes) utilized in VR. However, these results indicate that VR, combined with more traditional treatment programs, can influence treatment outcomes favorably. Mental health professionals might consider adapting their current treatment programs to include a controlled VR environment.

-Tasha Chandler

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References

Baumann, S. B., & Sayette, M. A. (2006). Smoking cues in a virtual world provoke craving in cigarette smokers. Psychology of Addictive Behavior, 20(4), 484-489.

Carter, B. L., Bordnick, P., Traylor, A., Day, S. X., & Paris, M. (2008). Location and longing: The nicotine craving experience in virtual reality. Drug and Alcohol Dependence, 95(1-2), 73-80.

Center for Disease Control and Prevention. (1999). Cigarette smoking among adults: United States. Morbidity & Mortality Weekly Report, 48, 993-996.

Girard, B., Turcotte, V., Bouchard, S., & Girard, B. (2009). Crushing virtual cigarettes reduces tobacco addiction and treatment discontinuation. Cyberpsychology & Behavior, 12(5), 477-483.

Heatherton, T. F., Kozlowski, L. T., Frecker, R. C., & Fagerström, K.-O. (1991). The Fagerström Test for Nicotine Dependence: A revision of the Fagerström Tolerance Questionnaire. British Journal of Addiction, 86(9), 1119-1127.

ASHES, Vol. 5(9) –Thinking of quitting smoking "cold turkey?" Chew on it for a while.

Most smokers interested in quitting do not feel ready for an abrupt cessation, often referred to as “cold turkey.” Most smokers prefer to gradually quit by reducing the number of cigarettes they smoke (Cheong, Yong, & Borland, 2007). This method, however, fails more often than not (Levinson, Shapiro, Schwartz, & Tursky, 1971). It has been hypothesized that nicotine–replacement therapy might aid smoking cessation by permitting smokers to gradually reduce the number of cigarettes they smoke. This week’s ASHES reviews a randomized double-blind placebo-controlled clinical trial that tests the efficacy of nicotine gum to assist cessation (Shiffman, Ferguson, & Strahs, 2009).

Methodology

• Investigators enrolled 3297 participants (57% women) using print and radio advertisements. The investigators randomized participants to nicotine gum or placebo treatment.
• Participants self-selected their nicotine gum dosage (2 mg (n = 1636) vs. 4 mg (n = 1661))
• A user-guide instructed participants to add an additional hour of abstinence and substitute one additional piece of gum daily. Once smokers had quit completely (i.e., for 24 hours), clinicians instructed participants to gradually reduce use of gum consistent with the current FDA-approved labeling.
• The investigators compared the number of smokers who achieved and maintained abstinence—self-reported, verified by two CO readings <= 10 ppm in average—for the placebo condition and for each dose level of nicotine gum.

Results

• As Figure 1 illustrates, smokers using nicotine gum were more likely to achieve initial cessation (i.e., no smoking for 24 hours) compared to the placebo group. They also were more likely to maintain abstinence 28 days and six months later (p < .05).
• Participants using nicotine gum were more likely to experience an adverse event (e.g., nausea, hiccups, heartburn) than participants using placebo gum (48% vs. 37%, p < .001)

Figure 1. Initial cessation and continuous smoking abstinence percentages.

                          Initial abstinence  28-day abstinence   6-month abstinence

Limitations

• Low absolute cessation rates
• Specific sample (i.e., smokers who are ready to quit on a relatively time-limited program) restricts the ability to generalize these results to other groups.
• The absence of a procedural measure of fidelity reveals that it is not clear, how precisely smokers followed the instructions.
• As Table 1 shows, the study encountered non-completers; it is not evident whether these completers were systematically different from completers; therefore, it is not possible to determine whether the treatment yielded differential impact upon the participants both within and across groups.

Table 1. Number of non-completers per condition, summarized across 2mg and 4 mg self-selected groups

	Placebo (N = 1648)	Nicotine gum (N = 1649)
28-day follow-up: number of drop outs	238	262
6-month follow-up: number of drop outs	8	21

Conclusions

Nicotine gum is widely used to help smokers maintain abstinence (Silagy, Lancaster, Stead, Mant, & Fowler, 2004). This study is the first to investigate the effect of nicotine gum on graduated abstinence from smoking. Although the absolute rate of quitting was low, it is comparable to similar “cold turkey” studies (Shiffman et al., 2002). The results suggest that, for smokers quitting gradually, using nicotine gums facilitates tobacco abstinence compared to a placebo. Investigators should conduct more studies to compare its safety and effectiveness against other smoking cessation methods and to include fidelity measure to ensure the participants followed the instructions.

References

Cheong, Y., Yong, H.-H., & Borland, R. (2007). Does how you quit affect success? A comparison between abrupt and gradual methods using data from the International Tobacco Control Policy Evaluation Study. Nicotine & Tobacco Research, 9(8), 801-810.

Levinson, B. L., Shapiro, D., Schwartz, G. E., & Tursky, B. (1971). Smoking elimination by gradual reduction. Behavior Therapy, 2(4), 477-487.

Shiffman, S., Ferguson, S. G., & Strahs, K. R. (2009). Quitting by Gradual Smoking Reduction Using Nicotine Gum: A Randomized Controlled Trial. American Journal of Preventive Medicine, 36(2), 96-104.e101.

Shiffman, S., Rolf, C. N., Hellebusch, S. J., Gorsline, J., Gorodetzky, C. W., Chiang, Y.-K., et al. (2002). Real-world efficacy of prescription and over-the-counter nicotine replacement therapy. Addiction, 97(5), 505-516.

Silagy, C., Lancaster, T., Stead, L., Mant, D., & Fowler, G. (2004). Nicotine replacement therapy for smoking cessation. Cochrane Database Systematic Reviews, 3, CD000146.

 

ASHES, Vol. 5(8) - Lessons Learned from an Evaluation of a Smoking Cessation Program

Adolescent smoking rates in tobacco-growing states are higher than the national average (Eaton et al., 2008). Adolescents from these communities face unique social pressures that deter them from quitting smoking. The Adolescent Cessation of Tobacco: Independent of Nicotine (ACTION) program was designed to help adolescents who live in tobacco-growing communities stop using tobacco by prompting them to reflect on social pressures not to quit. This week’s ASHES reviews a recent study (Stein-Seroussi, Stockton, Brodish, & Meyer, 2009) which evaluates the efficacy of ACTION, and, in doing so, highlights several practical issues that can hamper efforts to evaluate school-based intervention programs. 

Methods
•    Participants were 268 adolescent smokers recruited from 14 schools in Kentucky, North Carolina, and Ohio. 
•    Assignment to a program was by school. Seven schools received the ACTION program and seven received the comparison program. Investigators randomly assigned ACTION to the schools, using stratification within state to assure comparable school assignments.
•    There were three assessment periods: pre-test (up to one week before the programs began); post-test (no more than thee days after the programs ended); and follow-up (three months after the programs ended).
•    261 students contributed useable baseline data; 220 (88%) of them contributed post-test data and 181 (69%) contributed follow-up data. 
•    At each assessment period, students completed a self-report of tobacco abstinence online and provided saliva samples to test for continine (a metabolite of nicotine). Salivary cotinine can be detected up to 72 h after cigarette and smokeless tobacco use.
•    There were two abstinence outcomes: 3-day (self-report of 3-day abstinence confirmed by negative continine sample) and 7-day (self-report of 7-day abstinence confirmed by negative continine sample).

Data Analysis
•    The authors report results from two types of analyses: intent-to-treat (ITT) and using all available data. Here we report results from the latter set of analyses, which are less conservative.
•    The authors used mixed model analyses to account for the intra-class correlation (ICC), or non-independence that resulted from student membership in particular classrooms and schools.

Results
•    Many participants falsely reported abstinence (61% at post-test and 38% at follow-up). Results reported here only include continine-verified abstinence.
•    At post-test, six students in the ACTION condition (5.6%) and nine students in the comparison condition (7.4%) achieved 3-day abstinence. Four students in the ACTION condition (3.7%) and three in the comparison condition (2.5%) achieved 7-day abstinence. Group differences were not significant for 3-day abstinence (OR=1.05; CI = 0.35-3.20) and 7-day abstinence (OR=1.56; CI = 0.32-7.71).
•    At follow-up, nine students in the ACTION condition (11.1%) and four students in the comparison condition (4%) achieved 3-day abstinence. Nine in the ACTION condition (11.1%) and three in the comparison condition (3%) achieved 7-day abstinence. Group differences were significant for both 3-day abstinence (OR=4.46; CI = 1.02-19.52; p < 0.05) and 7-day abstinence (OR=4.44; CI = 1.01-19.49; p < 0.05). See Figure 1.

Figure 1: Proportion of participants who achieved and failed to achieve 3-day verified abstinence at follow-up, as a function of program completed. Based on data provided by Stein-Seroussi et al. (2009).        
Limitations
•    The original protocol called for enrollment of 336 students. However, only 268 students (80%) actually participated. This sample size reduced statistical power and, consequently, the ability to detect significant group differences.
•    Also, according to the original protocol, participants were supposed to be interested in voluntarily participating in a study of smoking cessation. However, in several schools, participation was offered as an alternative to suspension for using tobacco on school grounds. Therefore, roughly 17% of participants indicated at the outset that they were not at all motivated to quit smoking. This might have diminished the effectiveness of both smoking cessation programs and limited the likelihood of detecting between-group differences.
•    Randomizing within classes (rather than schools) would have provided more power to detect significant effects. However, this would come at the expense of potential contamination between groups.
•    Program fidelity was low. Videotapes of the ACTION sessions revealed that on average, teachers only covered about half of the intended content. They also introduced their own content.
•    Results suggest that the effects of the ACTION program might become stronger over time. This claim is difficult to evaluate, however, because there was only one follow-up assessment at 90 days.


Conclusions
The group differences in abstinence rates at follow-up suggest that the ACTION program has promise as a smoking cessation program for adolescents from tobacco-growing communities. Future research in this area should seek to (1) increase statistical power (by enrolling larger samples and decreasing attrition rates), (2) decrease within-group variability (by ensuring program fidelity and standardizing participant enrollment procedures), and (3) increase the length of the follow-up period.

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References

Eaton, D. K., Kann, L., Kinchen, S., Shanklin, S., Ross, J., Hawkins, J., et al. (2008). Youth risk behavior surveillance--United States, 2007. MMWR Surveill Summ, 57(4), 1-131.

Stein-Seroussi, A., Stockton, L., Brodish, P., & Meyer, M. (2009). Randomized controlled trial of the ACTION smoking cessation curriculum in tobacco-growing communities. Addictive Behaviors, 34(9), 737-743.

ASHES Vol. 5(7) - Lighting up to feel thin: How body dissatisfaction and negative affect contribute to the urge to smoke among college women

The urge to smoke is a complex phenomenon that cannot be explained by nicotine dependence alone. A range of psychological factors—thoughts, feelings, and behavior patterns—also play an important role in smoking behavior. Among women in particular, smoking appears to be coupled with deep-seated concerns about body image (for a review, see Lopez Khoury, Litvin, & Brandon, 2009). This association can take different forms. For example, women might smoke cigarettes as a direct form of weight control. Less directly, women might light up as a way to cope with the shame of failing to meet cultural standards of thinness (Fiissel & Lafreniere, 2006). Indeed, reducing negative affect appears to be a potent motivator for smoking among women (Cepeda-Benito & Reig Ferrer, 2000). This week’s ASHES reports on a study that tested the hypothesis that among women smokers, the urge to smoke sometimes results from attempts to manage distressing feelings about body shame (Lopez Khoury et al., 2009).

Methods
•    Participants were 133 female college student smokers who abstained from smoking for at least 1 hour before the study. They were recruited through advertisements, fliers, the psychology participant pool, and in-class announcements. They received either course credit or $30 for participating.
•    Past work (cited by Lopez Khoury et al., 2009) demonstrates that asking women to try on swimsuits in front of a mirror is an effective way to produce feelings of body shame/dissatisfaction. Therefore, researchers randomly assigned participants to one of two conditions:
o    Evaluating a purse and comparing it with other purses shown in photographs or
o    Trying on and evaluating a swimsuit and comparing it with other swimsuits modeled in advertisements.
•    At baseline and again immediately following conditioning, participants completed a series of measures about their body dissatisfaction, negative affect, and the urge to smoke.
•    The authors hypothesized that women who tried on the swimsuit would experience more body dissatisfaction and a stronger urge to smoke compared with the comparison group. They also hypothesized that increased negative affect would mediate this relationship.

Results
•    Body dissatisfaction: The swimsuit manipulation succeeded in making participants feel more dissatisfied with their bodies F (1, 132) = 19.74, p <.001.
•    Negative affect: As Figure 1 shows, the swimsuit manipulation also resulted in a spike in negative affect. An ANCOVA controlling for baseline negative affect revealed a significant effect of condition, F (1, 132) = 21.83, p < 0.001, eta squared = 0.15, with participants who tried on the swimsuit reporting a greater increase in negative affect than their control-group counterparts.

•    Urge to smoke: Those who tried on the swimsuit also reported a stronger urge to smoke in anticipation of relieving negative affect, F (1, 132) = 4.16, p < 0.05, although this effect was smaller (eta squared = 0.04).                   
•    Mediation: Formal tests of the size of the indirect effect of negative affect were conducted using the Sobel test and the bootstrap approach. Results of both procedures were statistically significant (p's < 0.05). This indicates that the swimsuit manipulation increased the urge to smoke by increasing negative affect.

Limitations
•    This study used an experimental manipulation of body dissatisfaction, which results in high internal validity. The manipulation also has a good deal of ecological validity: that is, trying on swimsuits and other garments in front of mirrors is something women do often. However, some aspects of the study design—particularly the laboratory setting and the repeated assessments of body dissatisfaction and negative affect—limit the sense of authenticity.
•    Because the sample consisted of college women, it is unclear whether the results would generalize to non-college women or to men. Other mechanisms of smoking urge may be more powerful than body dissatisfaction and negative affect relief in these groups.
•    The authors measured smoking urges but not actual smoking behavior.

Conclusions
This study indicates that feeling shameful about one’s body helps account for smoking urges among college women. These results add to our understanding of the psychological and social barriers to smoking cessation. In doing so, this research helps to explain why nicotine-dependence treatments alone are often insufficient for smoking cessation.

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References

Cepeda-Benito, A., & Reig Ferrer, A. (2000). Smoking Consequences Questionnaire—Spanish. Psychology of Addictive Behaviors, 14, 219–230.

Fiissel, D. L., & Lafreniere, K. D. (2006). Weight control motives for cigarette smoking: Further consequences of the sexual objectification of women? Feminism & Psychology, 16, 327–344.

Lopez Khoury, E. N., Litvin, E. B., & Brandon, T. H. (2009). The effect of body image threat on smoking motivation among college women: Mediation by negative affect. Psychology of Addictive Behaviors, 23, 279-286.

ASHES, Vol. 5(6) – Resisting Tempting Foods and Cigarette Smoking.

Many smokers are concerned about gaining weight if they quit smoking. Doctors recommend that individuals concentrate on smoking cessation rather than dieting until they are certain that they will not relapse (Fiore et al., 2008). Imagine you are a cigarette smoker in front of a table full of tempting sweets that you are not allowed to eat. When you finally leave the room, will you be tempted to have a cigarette? This week the ASHES provides a critical review of a study by Shmueli and Prochaska (2009) that raises a similar question and then explains why it might be difficult to moderate food consumption and avoid smoking at the same time.

Method

• Investigators measured participants’ expired alveolar air to assess baseline levels of carbon monoxide (i.e., CO); CO levels serve as an indicator of recent tobacco smoking.
• Experimenters randomly assigned 97 smokers to two independent groups: one exposed to sweets and the other exposed to vegetables. An experimenter brought each participant to a room, where there was a plate of either sweets (i.e., cookies, and brownies) or vegetables (i.e., radishes and broccoli).
• Researchers required the participants to sit at the table for five minutes and did not allow them to eat anything from the plate. During the five-minute period, a bell rang at random intervals and at each ring the participant had to smell the plate of food and think about the temptation of eating the food.
• After a 10 minute break, participants provided investigators with another CO sample and also answered if they smoked during the break.

Results

• The agreement level between self-reported smoking and CO test was good, but not perfect (k = .88). All but one of the discrepancy cases involved participants who reported smoking during the break, but the CO reading did not confirm this.
• Participants who resisted sweets were more likely to smoke (based on an increase in CO level) during the break (n = 25, 53%) than those who resisted vegetables (n = 17, 34%), X²(97) = 3.65, p < .05. However, the effect size was small (i.e., φ =.19) and the self reported smoking rates of 51.1% and 42.0% for the sweets and vegetables groups were not significantly different.

Limitations

• Although statistically significant, the effect of “sweets” vs. “vegetables” resistance was small and was not supported by self-reported smoking.
• Participants in the study were not trying to inhibit smoking; therefore the self-control strength model was not directly tested.

Conclusion
The authors of this paper explain the results in terms of a self-control strength model (Muraven & Baumeister, 2000). According to this model, a person has limited resources for self-control. Therefore, after resisting temptation to eat a plate of food it becomes more difficult to resist smoking. This study supports the advice not to quit smoking and attempt a diet at the same time. However, the observed effect was not substantial and not supported by the self-reported results. Other studies (e.g., Drobes, 2002) have shown successful concurrent treatment for alcoholism and smoking. Despite the reported limitations, Shmueli and Prochaska present an interesting methodology for studying self-control and developing effective interventions for promoting quitting behavior. 

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References

Drobes, D. J. (2002). Concurrent Alcohol and Tobacco Dependence: Mechanisms and Treatment. Alcohol Research & Health, 26(2), 136-142.

Fiore, M. C., Jean, C. R., Baker, T. B., Bailey, W. C., Benowitz, N. L., & Curry, S. J. (2008). Treating tobacco use and dependence: 2008 update.

Muraven, M., & Baumeister, R. F. (2000). Self-regulation and depletion of limited resources: does self-control resemble a muscle? Psychol Bull, 126(2), 247-259.

Shmueli, D., & Prochaska, J. J. (2009). Resisting tempting foods and smoking behavior: Implications from a self-control theory perspective. Health Psychology, 28(3), 300-306.

 

ASHES, Vol. 5(5) – The Effects of Smoking and Smoking Abstinence on Verbal and Visuospatial Working Memory Capacity

Smoking can cause numerous health problems. However, despite this fact, many people start and continue to smoke cigarettes. Often, smokers argue that smoking actually helps them concentrate and think more clearly. Is it actually so? Working memory is one of the mental systems involved in the thinking process. This week the ASHES reviews a study by Greenstein & Kassel (2009), which examines the effect of smoking on working memory. Working memory is a complex mental system that is involved in performing tasks that require both storage and processing information. How does smoking and smoking abstinence affect working memory capacity? Previous studies examining this question have produced inconsistent results (e.g., showing higher or lower performance for smokers and nonsmokers on different tasks) depending on the task itself. The study under review uses highly reliable, well-validated working memory tasks to examine the effect of smoking and smoking abstinence on verbal and visiospatial working memory capacity.

Methods

• Researchers recruited 49 male middle-aged participants, 26 smokers and 23 nonsmokers.
• All participants completed two experimental sessions (1 week apart).
  • During one session, smokers abstained from smoking for 12 hours or more. During the next session, investigators instructed smokers to smoke as they usually would.  
  • During each session, participants completed two tasks to measure their memory capacity.  
    1. OSPAN – a measure of verbal working memory capacity.
       •  Participants solved a math problem while simultaneously trying to recall a series of letters.   
       • Investigators calculated their performance as a proportion of correct responses multiplied by 100 to get an index that ranged from 0 to 100.   
    2. SSPAN – a measure of spatial working memory capacity. 
    • Participants recalled sequences of red-squared locations while performing a symmetry judgment task (deciding whether the presented figures were symmetrical or asymmetrical along the vertical axis).
• In addition, researchers measured demographic characteristics and cognitive abilities to assure the initial similarities in basic academic skills between smokers and nonsmokers (no differences were found).      

Results

• Investigators removed 15 participants (9 smokers and 6 nonsmokers) from the analyses, because of low performance in math or symmetry tasks (less than 80% accuracy). This ensured that the analyses included only those participants who were attempting to solve both tasks.
• Smokers manifested better OSPAN performance (verbal working memory task) when they were abstinent compared with when they were not abstinent. Also, nonsmokers performed better on this task than did smokers (see Figure 1).
• There were no significant differences between any groups for the SSPN (spatial working memory task) performance.
  

Figure 1. OSPAN (Operation Span Task) performance (* p < .05).

 
Limitations

• Is working memory actually the same as “concentration”? These two concepts are not necessary identical. 
• The study used a limited sample size.
• Participants were males only.

Conclusion

The study claims to test the conventional wisdom that smoking helps concentration. Rather then testing concentration, however, the authors examined the more specific effects of smoking on working memory capacity. The results demonstrate that smoking might cause different effects on different working memory domains. Specifically, (a) smokers’ verbal working memory performance is less effective compared to nonsmokers’, (b) smokers’ verbal working memory capacity is less effective after smoking than during abstinence, and (c) smoking does not affect visiospatial working memory span. These results suggest that smoking actually can impair some of the memory functions necessary for complex tasks (e.g., comprehension, learning, and reasoning). The authors also interpret their results as a demonstration that there is no evidence that smoking helps the ability to concentrate. This particular conclusion, however, seems to contradict the “conventional wisdom” and other scientific results. For example, a recent study (Zabala et al. 2009) showed that smoking facilitated cognitive functioning (including attention and working memory tasks performance) among psychotic patients. The contradiction between these studies might be due to the differences between psychotic and non-psychotic participants. To test this hypothesis and to resolve the contradiction, we need additional studies.

-Julia Braverman

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References

 Greenstein, J., & Kassel, J. (2009). The effects of smoking and smoking abstinence on verbal and visuospatial working memory capacity. Experimental and Clinical Psychopharmacology, 17(2), 78-90.

Zabala, A., Eguiluz, J., Segarra, R., Enjuto, S., Ezcurra, J., Pinto, A., et al. (2009). Cognitive performance and cigarette smoking in first-episode psychosis. European Archives of Psychiatry and Clinical Neuroscience, 259(2), 65-71

ASHES, Vol. 5(4) – To Pee or not to Pee? Testing Urine to Predict Lung Cancer.

Some lifelong smokers do not develop lung cancer (Peto et al., 2000). The risk of developing lung cancer might vary as a function of smokers’ uptake and metabolism of carcinogens in tobacco smoke. NNK, a known carcinogen in rodents, is metabolically converted to NNAL, a human carcinogen (Hecht, 1998). Another common tobacco metabolite found in humans is cotinine; researchers often measure cotinine to determine exposure to tobacco smoke. This week’s ASHES reviews a study that examines the relationship between the urine levels of two carcinogen metabolites (NNAL and cotinine) and the risk for developing lung cancer (Yuan et al., 2009).

Methods:

• Participants were drawn from two cohorts:

          1. Shanghai Cohort (Ross et al., 1992):

• At baseline, researchers assessed tobacco use and collected urine samples among 18,244 participants. 
• 259 participants were diagnosed with lung cancer.  
• Participants with lung cancer (cases) were matched to cancer free participants (controls) based on the following criteria: age within 2 years, date of urine collection within 1 month, and residential neighborhood at baseline.  

          2. Singapore Cohort (Hankin et al., 2001):

• At baseline, researchers assessed tobacco use among 63, 257 participants. The current research team requested urine samples from all surviving participants.
  • 99 participants were diagnosed with lung cancer.
  • Controls were matched to cases based on the following criteria: gender, dialect group, baseline age within 3 years, date of baseline interview within 2 years, and date of urine collection within 6 months.

• Table 1 shows the exclusion of participants for the current study.
• Cases had a mean age of 65.6 (SD=6.8) years; controls matched at the time of cases’ cancer diagnosis had a mean age of 65.5 (SD=6.9) years.
  • Urine samples were taken pre-lung cancer diagnosis, with an average of 4.0 (SD=2.5) years prior. 
• The dependent measures were NNAL and cotinine levels.  

Table 1: Study Sample Cases and Matched Controls.
 

*Click image to enlarge, or adjust your browser's zoom setting.

Results:

• Figure 1 shows the odds ratios of the joint effects of cotinine and NNAL levels on the risk of developing lung cancer.
  • Compared to the low NNAL-low cotinine tertile, all odds ratios showed that the other tertiles were at a significantly increased risk for developing lung cancer. 
  • Participants in the highest tertile (high NNAL and cotinine levels) had an 8.47 fold (95% CI, 3.69-19.46) increased risk for developing lung cancer compared to participants in the lowest tertile (low NNAL and cotinine levels). 

Figure 1: Odds Ratios of NNAL and Cotinine Levels Risk for Developing Lung Cancer (adapted from Yuan et al., 2009). 

*Click image to enlarge, or adjust your browser's zoom setting.
Note. Cot stands for cotinine. Ca stands for cases. Co stands for controls.
The tertile cutoff values of NNAL for the Shanghai cohort were ≤0.105, 0.106 to 0.209, and ≥ 0.210 pmol/mg creatinine; for the Singapore cohort, ≤ 0.468, 0.469 to 0.819, and ≥ 0.820 pmol/mg creatinine.
ORs were adjusted for age, year of interview, year of sample collection, gender and dialect group, cohort location, number of curettes smoked per day, and number of years smoking.

Limitations:

• This study uses self-report to determine smoking history.
• The concentration of NNK varies in cigarette brands (e.g., lower NNK concentrations in cigarettes from the Shanghai cohort), and brands were not controlled in this study.
• In the Shanghai sample, there was significantly less intense smoking behavior in the control group compared with the case group at baseline; consequently, the results might emerge from smoking intensity and not metabolic activity.

Discussion:

The results of this study show that the extensive animal data (Hecht, 1998) suggesting a link between NNK and cancer also might apply to humans. However, more research is needed to confirm the relationship between the metabolites reviewed above and the risk for developing lung cancer. This study suggests that measuring metabolite levels might offer a more accurate way to assess the risk of developing lung cancer for smokers than an assessment based on self-reported smoking history.

-Tasha Chandler

What do you think? Please use the comment link below to provide feedback on this article.

References:

Hankin, J. H., Stram, D. O., Arakawa, K., Park, S., Low, S. H., Lee, H. P., et al. (2001). Singapore Chinese Health Study: Development, validation, and calibration of the quantitative food frequency questionnaire. Nutrition and Cancer, 39(2), 187-195.

Hecht, S. S. (1998). Biochemistry, biology, and carcinogenicity of tobacco-specific N-nitrosamines. Chemical Research in Toxicology, 11(6), 559-603.

Peto, R., Darby, S., Deo, H., Silcocks, P., Whitley, E., & Doll, R. (2000). Smoking, smoking cessation, and lung cancer in the UK since 1950: Combination of national statistics with two case-control studies. BMJ (British Medical Journal), 321(7257), 323-329.

Ross, R. K., Yuan, J. M., Yu, M. C., Wogan, G. N., Qian, G. S., Tu, J. T., et al. (1992). Urinary aflatoxin biomarkers and risk of hepatocellular carcinoma. Lancet, 339(8799), 943-946.

Yuan, J. M., Koh, W. P., Murphy, S. E., Fan, Y., Wang, R., Carmella, S. G., et al. (2009). Urinary levels of tobacco-specific nitrosamine metabolites in relation to lung cancer development in two prospective cohorts of cigarette smokers. Cancer Research, 69(7), 2990-2995.

ASHES, Vol. 5(3) - The No-Nicotine Blues: Investigating Why Some Smokers Feel Depressed While Trying to Quit.

Recent research has identified a correlation between nicotine withdrawal and depression. Scientists suggest that this association is related to changes in the function of serotonin transporter proteins, known to play a role in regulating mood, during periods of nicotine abstinence.  This week’s ASHES reviews an animal study investigating both the biological mechanisms underlying the appearance of depressive signs, and a potential treatment (Semba & Wakuta, 2008).

Methods

• Researchers implanted adult male rats with a pump that administered either nicotine or a neutral solution (control) at a constant rate for a period of 12 days.
• A separate group of rats also received bupropion treatment at 15 or 30mg/kg/day in addition to the nicotine or control treatments.
• Researchers removed the pumps and either (1) decapitated rats on the last day of treatment or (2) allowed the rats to enter into a withdrawal state, which lasted for an additional two days.
• Researchers prepared slides of rats’ brain tissue, including the dorsal raphe nuclei.
• in situ hybridization was performed to locate the mRNA that codes for serotonin transporter proteins.
• Researchers used computer software to create photographic images from the in situ slides.
• Scientists measured the relative optical density of the slides; this distinguishes areas positive for serotonin transporter mRNA from the rest of the brain tissue because this transporter appears darker than areas that do not contain it.
• Scientists compared the relative optical densities of the experimental and control groups’ slides using a t-test as the statistical tool.

Results

• As Figure 1 shows, expression of serotonin transporter coding mRNA in the dorsal raphe nuclei of rats exposed to nicotine for 12 days was significantly decreased (-24%) when compared to the control, t (15) = 3.18, p <0.01, immediately after nicotine administration was stopped.
• Two days following the termination of nicotine administration scientists identified a lesser, but still significant decrease (-14.9%), expression compared to the control, t (14) = 2.36, p<0.05.
• As Figure 2 shows, rats who were co-administered bupropion showed smaller decreases in serotonin transporter coding mRNA; the higher the dose, the smaller the decrease observed.

Figure 1: Expression of serotonin transporter mRNA in the dorsal raphe nucleus of male rats--both after twelve days of either saline or nicotine treatment, and after two days withdrawal from treatment (Adapted from Semba & Wakuta, 2008).

Figure 2: Effects of Bupropion on expression of serotonin transporter mRNA in the dorsal raphe nucleus of male rats (Adapted from Semba & Wakuta, 2008).

 

Limitations

• Findings from animal models do not always generalize to human beings.  The way nicotine effects the expression of serotonin transporter mRNA might vary across species.
• The synthesis of mRNA, which codes for serotonin transporter proteins, and the actual expression of these proteins is not always the same.
• Several similar studies have found no significant difference between control and experimental conditions. However, these studies used different tissue preparations and/or adolescent rats instead of adult rats. These differences in design could account for differences in results observed.
• Researchers are unsure how long the decrease in the expression of transporter coding mRNA lasts, and whether or not the change is permanent due to the fact that the in situ hybridization was not performed in live tissue.
• Biological processes and subjective states might not be consistent. In other words, reduced serotonin function might not cause depressed mood in rats as it does in humans.

Conclusions
Chronic exposure to nicotine can reduce the amount of serotonin transporter protein coding mRNA present in the dorsal raphe nuclei of rats. This reduction might underlie the feelings of depression that humans sometimes report while trying to abstain from smoking, making it very difficult for them to stop using products containing nicotine. Co-administration of Bupropion appears to help relieve this circumstance, and might be a useful tool to help smokers quit.

What do you think? Please use the comment link below to provide feedback on this article.

References

Semba, Jun'ichi; Wakuta, Mak (2008). Chronic effect of nicotine of serotonin transporter mRNA in the raphe nuclei of rats: Reversal by co-administration of bupropion. Psychiatry and Clinical Neurosciences, 62(4), 435-441.

ASHES, Vol. 5(2) - Advertising the Smoking while Drinking Phenomena

The tendency of people, especially young adults, to smoke while drinking is well documented in the scientific literature (e.g., Falk, Yi, & Hiller-Sturmhofel, 2006).  The tobacco industry suggested using the associations between smoking and drinking to “leverage …the correlation… in our marketing efforts” (p.1; Troutman & R.J. Reynolds Tobacco Co, 1990).  This week’s ASHES reviews a study that explores how frequently tobacco companies link alcohol to cigarettes in their advertisements. 

Methods
Belstock, Connolly, Carpenter, & Tucker (2008) reviewed each 2003 and 2004 issue of Maxim, FHM, Cosmopolitan, and Ebony. 

• The target audience of these magazines is 18-24 year olds for each magazine was (40.6%, 42.8%, 30.9%, and 18.9% of readers respectively).

The authors examined all alcohol and cigarette advertisements, using content analysis (κ = 0.79–1.0) to determine whether the advertisement:

• Was for alcohol, and pictured a bar or a club in the advertisement,
• Was for cigarettes, and pictured a bar or a club in the advertisement,
• Was for cigarettes, and contained a reference to drinking (e.g., a drink in the model’s hand) in the advertisement, or
• Was for alcohol, and contained a reference to smoking (e.g., a cigarette in the model’s hand) in the advertisement.

Results

• There were 114 unique cigarette advertisements and 317 unique alcohol advertisements.
• Of the cigarette advertisements, 32 (28.1%) referred to alcohol.  Of the alcohol advertisements, none referred to cigarettes.
• Table 1 shows that cigarette and alcohol advertisements were not statistically significant regarding their likelihood to show a bar or nightclub in the picture that appeared in the magazine.

Table 1. Characteristics of Cigarette and Alcohol Advertisements

Adapted from Belstock et al. (2008).
Click image to enlarge, or adjust your browser's zoom setting.

Limitations

• The researchers only reviewed four magazines during a two-year period. In addition to the limited magazine sample, tobacco companies use other advertising media, so the data reviewed might not be a representative sample of tobacco marketing.

Conclusion
Some tobacco advertisements referred to alcohol, but no alcohol advertisements referred to cigarettes.  This imbalanced relationship might be because alcohol advertisers consider an alcohol-cigarette pairing unfavorable, while tobacco advertisers consider it favorable.  It might be more favorable for tobacco advertisers because people smoke more while drinking (Falk et al., 2006), and unfavorable for alcohol advertisers because tobacco is less effective at changing people’s opinion of alcohol.

What do you think?  Comments can be addressed to Leslie Bosworth.

References
Belstock, S. A., Connolly, G. N., Carpenter, C. M., & Tucker, L. (2008). Using alcohol to sell cigarettes to young adults: A content analysis of cigarette advertisements. Journal of American College Health, 56(4), 383-389.

Falk, D. E., Yi, H.-Y., & Hiller-Sturmhofel, S. (2006). An epidemiologic analysis of co-occuring alcohol and tobacco use and disorders: Findings from the National Epidemiologic Survey on Alcohol and Related Conditions. Alcohol Research & Health, 29(3), 162-171.