Introduction

Nicotine addiction is the fundamental reason that individuals persist in using tobacco products, and this persistent tobacco use contributes to many diseases described in this report. The 1988 report, The Health Consequences of Smoking: Nicotine Addiction: A Report of the Surgeon General (U.S. Department of Health and Human Services [USDHHS] 1988, p. 9), describes the pharmacologic basis of tobacco addiction and arrives at three major conclusions:

1. Cigarettes and other forms of tobacco are addicting.
2. Nicotine is the drug in tobacco that causes addiction.
3. The pharmacologic and behavioral processes that determine tobacco addiction are similar to those that determine addiction to drugs such as heroin and cocaine.

Tobacco addiction remains a substantial problem in the United States and worldwide. Of those individuals who have ever tried smoking, about one-third become daily smokers (USDHHS 1994, p. 67). Of those smokers who try to quit, less than 5 percent are successful at any one time (Centers for Disease Control and Prevention [CDC] 2002, 2004). Although not all smokers become nicotine dependent, the prevalence of individuals diagnosed as nicotine dependent is higher than that for any other substance abuse disorder (Anthony et al. 1994; CDC 1995b; Kandel et al. 1997). Any efforts to reduce tobacco-related disease must take into account the addiction potential of a tobacco product.

Since the 1988 Surgeon General’s report was published, significant advances have been made in understanding the physiological effects of nicotine and the basis for addiction:

1. identifying specific genotypes and receptor subtypes that may contribute to and play an important role in nicotine addiction,
2. observing sensitivities and responses to nicotine in adolescents that might make them more susceptible to nicotine addiction than adults are and recognizing the different trajectories for the development of nicotine dependence,
3. developing a greater awareness of the important role of associative learning in addiction,
4. recognizing the strong associations between smoking and comorbid psychiatric disorders, and
5. achieving a better understanding of the relapse and recovery processes.

The goals of this chapter are to describe these advances and their implications and to discuss future directions.

Definition of Nicotine Addiction

The crux of understanding the pathophysiology of tobacco addiction and its measurement relies on the iden-tification of critical characteristics and the definition of addiction. This area continues to evolve, and significant gaps in research are evident. There is no established consensus on criteria for diagnosing nicotine addiction. However, researchers have identified several symptoms as indicators of addiction. The 1988 Surgeon General’s report lists the following general “criteria for drug dependence,” including nicotine dependence (USDHHS 1988, p. 7):

Primary Criteria

• Highly controlled or compulsive use
• Psychoactive effects
• Drug-reinforced behavior

Additional Criteria

• Addictive behavior, often involves:

  –

  stereotypic patterns of use

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  use despite harmful effects

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  relapse following abstinence

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  recurrent drug cravings

• Dependence-producing drugs often produce:

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  tolerance

  –

  physical dependence

  –

  pleasant (euphoriant) effects

These criteria are consistent with those for a diagnosis of dependence provided in the Diagnostic and Statistical Manual of Mental Disorders, 4th ed. (DSM-IV) (American Psychiatric Association [APA] 2000) and the International Classification of Diseases, Tenth Revision (ICD-10) (Table 4.1) (World Health Organization [WHO] 1992). The diagnosis of dependence using these diagnostic systems depends on the person experiencing a specific number of these symptoms. The relevance of some of these symptoms to nicotine addiction may be questionable because the DSM criteria are used across different drugs of abuse. For example, one symptom of addiction is that a great deal of time is spent in activities necessary to obtain the substance or recover from its effect. This criterion may not be as relevant to the diagnosis of nicotine addiction compared with other abused substances. Another prominent instrument that researchers have used to determine the degree or severity of dependence in smokers is the Fagerström Tolerance Questionnaire (FTQ) (Fagerström 1978; Fagerström and Schneider 1989), and a later, modified version, the Fagerström Test for Nicotine Dependence (FTND) (Heatherton et al. 1991). The items on these scales, which describe the extent of nicotine exposure, the impaired control over use, and the urgency for use, are listed in Table 4.2. The first item, time to first cigarette after waking, is by itself a stronger predictor of relapse than is any other self-report measure of dependence (Baker et al. 2007). The 1988 Surgeon General’s report describes the general characteristics and criteria for drug dependence, DSM-IV and ICD-10 describe the criteria necessary for diagnosis of dependence, and the FTQ and FTND can be used to determine the degree of dependence. The core features across these diagnostic methods include (1) repeated and compulsive self-administration; (2) impaired control over use (e.g., repeated unsuccessful attempts to stop use or continued use despite known harmful consequences); (3) high motivation to seek the drug, because of cravings, regulation of affect (e.g., smoking to ease a depressed mood, for relaxation, or for stimulation), or other reasons associated with the psychoactive effects of the drug; (4) judgment of greater value from use of the drug over other reinforcers or activities; and (5) manifestation of physical dependence, as evidenced by withdrawal or tolerance.

Table 4.1

Criteria for substance (nicotine) dependence.

Table 4.2

Questions, answers, and scoring for Fagerström Test for Nicotine Dependence and Fagerström Tolerance Questionnaire.

Despite acknowledgment of these core features, the current diagnostic criteria for nicotine addiction have certain limitations. Beginning in 2005, a group of scientists have worked to delineate the various issues surrounding the measurement of nicotine dependence. The results of this work were published in June 2009 (National Cancer Institute [NCI] 2009). These issues included the following:

1. whether nicotine addiction is categorical, dimensional, or emergent (changing over time) and, if emergent, whether different aspects of dependence are observed early or late in the process of dependence, for example, aspects more related to social, sensory, and associational learning versus a more physical dimension with a longer duration of drug use;
2. whether nicotine addiction is unidimensional or multidimensional and, if multidimensional, whether symptoms or dimensions warrant weighting or are additive;
3. whether a threshold of severity or a certain number or specific types of symptoms are needed for diagnosis of nicotine addiction;
4. whether motivations or cognitive processes for seeking a drug are important components of the addiction;
5. whether multiple profiles, patterns, and pathways of addiction exist; and
6. whether the quantity and frequency of use play a critical role in addiction.

Other current measures of nicotine addiction or tobacco dependence are shown in Table 4.3 that are beginning to consider and address some of the limitations of current definitions of addiction and that consider nicotine addiction to be comprised of more than one phenotype (expression of a trait on the basis of genetic and environmental influences). Developing valid measures of the various phenotypes of dependence is critical for researchthat(1) examines how these phenotypes are related to the trajectory and cessation of smoking behaviors and (2) determines whether these phenotypes are related to specific neurobiologic measures of addiction or to specific genes.

Table 4.3

Measures of nicotine addiction.

In this chapter, the terms “dependence” and “addiction” have been used interchangeably. For some disciplines, dependence has been primarily associated with physiological manifestations of repeated tobacco use, but compulsive drug seeking is typically at the core of both the technical term “dependence” and the more general term “addiction.” Furthermore, the terms “nicotine dependence” and “tobacco dependence” are used interchangeably. Nicotine is the drug in tobacco that leads to compulsive drug seeking or addiction. However, several lines of epidemiologic and laboratory evidence presented in this chapter indicate that tobacco-delivered nicotine is substantially more addictive than are pure nicotine forms. Other tobacco constituents, delivery methods, and processes may play a critical supporting role.

Factors contributing to nicotine or tobacco addiction include the following:

1. the effects of the product itself, including the addictive constituents, their pharmacokinetics and pharmacodynamics, and the design of the product that delivers the addictive constituents (see Chapter 3, “Chemistry and Toxicology of Cigarette Smoke and Biomarkers of Exposure and Harm”);
2. the response of the host, including genetic susceptibility and physiological response; and
3. the environmental setting that determines the availability of, accessibility to, and norms for use of the product.

Like the 1988 Surgeon General’s report on nicotine addiction, this chapter focuses primarily on the effects of the product and the response of the host.

Tobacco Constituents and Pharmacokinetics

Pharmacokinetics

Nicotine addiction depends on the amount of nicotine delivered and the way in which it is delivered, which can either enhance or reduce its potential for abuse: the faster the delivery, rate of absorption, and attainment of high concentrations of nicotine, the greater is the potential for addiction (Henningfield and Keenan 1993; deWit and Zacny 1995; Stitzer and de Wit 1998).

Nicotine can be readily absorbed in the lung, oral mucosa, and nose, and through the skin. Table 4.4 shows (1) the bioavailability and amount of nicotine absorbed per unit dose of products containing nicotine and (2) the time to reach maximum blood concentrations of nicotine (T_max). Figure 4.1 shows the concentrations of nicotine in venous blood and the peak concentrations across the products containing nicotine. The mean peak concentrations of nicotine are higher with use of tobacco products than with use of nicotine replacement products, and cigarette smoking produced both the highest peak concentration and most rapid rate of nicotine absorption. Venous concentrations of nicotine from smoking are lower than arterial concentrations. Ratios of arterial concentrations to venous concentrations ranged from 2.3 to 10 across studies (Henningfield et al. 1993; Gourlay and Benowitz 1997; Rose et al. 1999). What accounts for the variability in arterial to venous nicotine concentration ratios observed across studies is unclear but may be a function of the study procedures and cigarette brands that were tested. In one study, lower-than-expected arterial nicotine concentrations were observed. The low concentration was attributed to the distribution of nicotine into the lungs and the slow release of nicotine into arterial circulation (Rose et al. 1999). The greater reinforcing efficacy of rapid delivery of nicotine was therefore thought to be due to both direct effects on the CNS and to stimulation of nicotinic receptors in the lung. These results would also suggest that neuronal nicotinic receptors associated with reinforcing effects of nicotine may be sensitive to low concentrations of nicotine. Clearly, more studies are needed to resolve the issues related to arterial concentrations of nicotine and consequent physiological effects.

Table 4.4

Bioavailability and amount of nicotine absorbed per unit dose and time to maximum venous blood concentration of nicotine by product.

Figure 4.1

Venous blood concentrations of nicotine over time for various nicotine delivery systems. Source: Adapted from Fant et al. 1999b with permission from Elsevier, © 1999. Note: mg = milligrams; ng/mL = nanograms per milliliter; data table for above (more...)

Oral use of smokeless tobacco products results in high venous concentrations of nicotine equal to those for use of cigarettes. Although the T_max for delivery of nicotine in nasal spray appears to be less (faster) than that for smokeless tobacco products, the addiction potential may be higher for smokeless tobacco than for nicotine nasal spray, because the rate of nicotine absorption for smokeless tobacco is faster. Within 10 minutes after administration of a smokeless tobacco product, a nicotine boost of 10 nanograms per milliliter can be achieved (Holm et al. 1992) compared with two to three times longer after administration of nasal spray. However, the rise of arterial concentrations from nicotine nasal spray compared with smokeless tobacco is unknown. A further complication is that the rate and amount of nicotine absorption vary across smokeless tobacco products (Figure 4.2). This variability results from the processing and pH of the smokeless tobacco product. Cigarettes also vary in nicotine content. The tobacco plant, the curing process, and the additives can determine the pH of the tobacco and tobacco smoke (see Chapter 3, “Chemistry and Toxicology of Cigarette Smoke and Biomarkers of Exposure and Harm”).

Figure 4.2

Mean plasma nicotine concentrations after administration of each of four smokeless tobacco products or mint snuff. Source: Adapted from Fant et al. 1999a with permission from BMJ Publishing Group Ltd., © 1999. Note: ng/mL = nanograms per milliliter; (more...)

Nonetheless, although the pharmacokinetics of some smokeless tobacco products may overlap with those of medicinal nicotine products, medicinal products tend to have a slower rate and a lower amount of nicotine absorption than do the most popular brands of conventional smokeless tobacco products (Kotlyar et al. 2007). Among the medicinal nicotine products, nicotine nasal spray has the fastest rate of nicotine absorption, followed by nicotine gum, the nicotine lozenge, and the nico- tine patch.

Together, these results demonstrate that the nicotine pharmacokinetics associated with cigarette smoking is likely to lead to high potential for addiction, whereas medicinal nicotine products have relatively minimal potential for addiction. For example, the extent of liking, and therefore the addiction potential for these products, are related to the speed of nicotine delivery (Henningfield and Keenan 1993). Nicotine delivered through cigarette smoking and intravenously shows the greatest dose-related liking for the drug, and nicotine delivered transdermally is associated with the least liking (Henningfield and Keenan 1993; Stratton et al. 2001).

The pharmacokinetic profile of a drug can determine the user’s pattern of drug delivery. Cigarette smoking results in rapidly rising arterial concentrations of nicotine that reach the brain in about 10 to 19 seconds (Benowitz 1990). The peak levels decline quickly as nicotine is taken up by peripheral tissues, followed by an elimination of nicotine from the body (Benowitz et al. 1988). This profile enables the smoker to finely control the nicotine dose to obtain the desired effect and enables frequent doses. These characteristics facilitate the addiction potential of cigarettes (Benowitz 1999). In contrast, oral nicotine products such as smokeless tobacco result in a more gradual rate of nicotine absorption and the nicotine levels are more sustained, resulting in a reduced ability of the smoker to manipulate the nicotine dose and less frequent dosing. The nicotine patch is the extreme example of slow absorption and once-a-day dosing, which results in a minimal potential for addiction.

Nicotine metabolism may also play a role in the reinforcing effects of nicotine. Researchers have hypothesized that the rate of nicotine metabolism should be related to smoking behaviors and that faster elimination of nicotine is associated with increased smoking and nicotine dependence (Benowitz 1999). Although surprisingly few published studies have tested this hypothesis, the research evidence has given some support to it (see “Genetics” later in this chapter). However, the evidence is modest. The rate of nicotine metabolism accounts for less than 16 percent of variation in the number of cigarettes smoked per day (Benowitz et al. 2003; Johnstone et al. 2006), and there is no significant variance in the FTND (Benowitz et al. 2003; Johnstone et al. 2006; Kandel et al. 2007) or in scoring on the Horn-Russell Scale (Johnstone et al. 2006). Kandel and colleagues (2007) found no significant association between the rate of metabolism and the number of cigarettes per day or nicotine dependence as measured by the FTND in a sample of young (18 through 26 years of age), less dependent, light smokers (average of 12 cigarettes per day). Possible reasons for the apparent disconnect between rate of metabolism and nicotine dependence include the following: (1) The questionnaire measures of adult nicotine dependence used may not be the most sensitive measures of the rate of metabolism (Benowitz et al. 2003; Johnstone et al. 2006). (2) The rate of metabolism may be related to nicotine dependence only during the transition from experimentation to “addicted” smoking (Benowitz et al. 2003). (3) The rate of metabolism is not an important determinant of smoking behavior in young smokers because of a low level of smoking (Kandel et al. 2007).

One of the reasons metabolism per se may not be directly related to measures of nicotine dependence is that the pharmacokinetics of nicotine metabolism are one step removed from the pharmacodynamics of nicotine, that is, from the impact (1) on neurotransmitters in the reward pathway, (2) on central effects, as measured by electroencephalography and cerebral blood flow, and (3) on peripheral effects such as cardiovascular responses. Both central and peripheral effects contribute to subjective reactions to nicotine and the subsequent likelihood of continued smoking. (For discussion of the pharmacodynamics of nicotine in the brain, see “Pathophysiology of Nicotine Addiction” later in this chapter.)

The factors contributing to the high addiction potential of tobacco products are undoubtedly multiple and have complex interrelationships, making it a challenge to parse their relative contributions. In addition, smoking results in rapid delivery of nicotine by cigarette smoke and in exposure to chemicals other than nicotine that have central and sensory effects, including taste and draw resistance, as well as stimuli associated with smoking (Scherer 1999; Caggiula et al. 2002a; Rose 2006). Relatively few studies have been conducted outside the tobacco industry to determine how features of the cigarette are engineered to increase its addictive potential. However, tobacco industry documents suggest that more than nicotine dosing and pharmacokinetics are important in determining the overall addiction potential of modern cigarettes (Slade et al. 1995; Hurt and Robertson 1998; Wayne et al. 2004). (For description of design features that can enhance nicotine delivery and absorption rate, see Chapter 3, “Chemistry and Toxicology of Cigarette Smoke and Biomarkers of Exposure and Harm.”)

Components of Nicotine Addiction

What are the effects of nicotine, and how does it cause addiction? The factors that may contribute to addictive behaviors include (1) neuroadaptations that occur with the persistent use of nicotine (e.g., tolerance), (2) withdrawal symptoms experienced when intake of the drug is stopped, and (3) the effects of nicotine that reinforce dependence. The primary reinforcing effects can entail the rewarding (psychoactive or psychostimulant) effects of nicotine (positive reinforcement) and/or the alleviation of aversive or negative states or stimuli—for example, relief from withdrawal symptoms (negative reinforcement). Nicotine may also enhance the reinforcing values of other reinforcers or stimuli, which may also contribute to its reinforcing effects.

Strong learning processes also contribute to addictive behaviors. These learning processes include conditioning in which stimuli associated with drug use evoke responses that are similar to the effects from the drug or similar to withdrawal symptoms or that may modulate drug effects. One hypothesis is that incentive sensitization can occur, in which some of the conditioned stimuli (CSs) are given priority in the allocation of attention and become a strong source of motivation to seek the drug (Robinson and Berridge 2001). Incentive sensitization consists of neuroadaptations from repeated use of a drug that render brain-reward systems hypersensitive (sensitized) to drug-associated stimuli. Also, nicotine’s ability to be a secondary reinforcer of CSs to other reinforcers strengthens its addictive effects. Nicotine tolerance, withdrawal, and reinforcement in humans are examined in the next section, which is followed by a section on learning and conditioning in nicotine addiction.

Learning and Conditioning

Nicotine and Secondary Reinforcement

Perhaps as powerful as the direct effects of smoking and nicotine on neural functioning are the associative processes that develop with repeated tobacco use (Caggiula et al. 2002a; Hyman 2005). The classic conditioning paradigm provides an important conceptual and theoretical framework for consideration of the powerful associative learning processes that, according to Bevins and Palmatier (2004), develop in a specific manner. Smoking serves as the unconditioned stimulus (US), and the subjective and physiological effects of smoking and exposure to nicotine serve as unconditioned responses. Exteroceptive (environmental) and interoceptive (internal) stimuli that occur repeatedly in temporal proximity to smoking become CSs. CSs include smoking paraphernalia (e.g., an ashtray), sensory aspects of smoking (e.g., cigarette smell or taste), and/or situational cues (e.g., smoking in the car while driving to work). The acquired response evoked by CSs becomes a conditioned response. With longer-term smoking, conditioned responses include urges to smoke. Repeated pairings of these CSs with cigarette smoking result in the CSs alone (before smoking) triggering urges to smoke (to want and to seek a cigarette) (Niaura 2000; Berridge and Robinson 2003).

Nicotine as a Conditioned Stimulus

Bevins and Palmatier (2004) have extended the associative learning model of nicotine dependence by hypothesizing that nicotine also has important actions as a CS of smoking behavior (the conditioned response) (Figure 4.3). The traditional role of nicotine has been limited to serving as a US. As a CS, nicotine acquires new or additional affective properties through being paired repeatedly with other stimuli such as coffee. In other words, nicotine enhances the salience of these and numerous other stimuli, which strengthens the associative bond and increases smoking behavior.

Figure 4.3

Associative learning processes in nicotine addiction. Source: Adapted from Bevins and Palmatier 2004 with permission from Sage Publications Inc. Journals, © 2004. Note: Connections 1, 2, and 3 reflect the role of the unconditioned stimuli (USs), (more...)

Nicotine as a Modifier of Associative Processes

In addition to serving as a CS, nicotine modifies associative processes in conditioned and unconditioned manners (Bevins and Palmatier 2004). As a conditioned modulator (Figure 4.3), the interoceptive cuing of nicotine serves as a contextual stimulus that “sets the occasion” for an association between a discrete CS in the environment and smoking (Bevins and Palmatier 2004). The CS-US association is conditioned on the drug state (context). Examples include smoking while drinking alcohol to relax and smoking during a break at work to cope with distress. As an unconditioned modulator, nicotine may enhance the salience of other stimuli that have incentive values to the person (Bevins and Palmatier 2004). For example, as depicted in connector “a” (solid line) of Figure 4.3, nicotine enhances the incentive or reward value of alcohol, which has its own significant reward value. This “incentive amplification” is unconditioned because the effects of nicotine do not depend on a contingent association between smoking and the motivational stimulus. “Incentive amplification” by nicotine is not limited to other drug reinforcers. Nicotine also enhances the reinforcing effects of nondrug reinforcers, such as light stimulus (Palmatier et al. 2007).

Positive Reinforcement and Learning

The positive reinforcing action of nicotine is attributable in large part to its influence on the brain regions associated with reward processes (e.g., the mesolimbic dopaminergic system) (Balfour 2004). In a review of positive-reinforcement theory as applied to nicotine addiction, Glautier (2004) suggests several mechanisms underlying positive-reinforcement processes. Besides exerting a direct reinforcing action through its effects on core brain-reward centers, nicotine may enhance the reinforcing efficacy of smoking-related cues as a result of priming the smoker to selectively attend to those stimuli. In addition, nicotine acquires indirect reinforcing actions through its effect on other behaviors. According to Hogarth and Duka (2006), considerable evidence suggests that nicotine- conditioned effects are mediated by a smoker’s expectations of the effects of nicotine coupled with an appetitive emotional response that reflects the positive value of nicotine to the smoker (e.g., pleasure or relaxation).

Although clearly influential, positive reinforcement is not a likely primary motivational influence on persistence in smoking (Baker et al. 2004), except possibly in the case of occasional smoking (Shiffman and Paty 2006). Unlike expectations of negative reinforcement, such as smoking to relieve stress, expectations of positive reinforcement do not predict the likelihood of a relapse after smoking cessation. This finding indicates that positive-reinforcement processes may have less motivational significance for relapse than do negative-reinforcement processes (Wetter et al. 1994). Relapse is less likely to occur during positive-affect states than during negative-affect states (Shiffman et al. 1996c) (see “Trajectory of Recovery or Relapse” later in this chapter).

Negative Reinforcement and Learning

Negative reinforcement refers to processes by which smoking or nicotine reduces aversive states, such as pain, craving, difficulty concentrating, and the negative-affect states generally associated with nicotine withdrawal. Nicotine addiction is maintained in part because persons learn during the early stages of smoking that tobacco use allows them to escape aversive states associated with smoking abstinence or because they learn later that it helps them to avoid these aversive states (Eissenberg 2004). These states include irritability and an anxious or depressed mood. With continued use of cigarettes by smokers over time, the associative link between tobacco use and the relief of withdrawal-associated aversive states is strengthened. Tiffany and colleagues (2004) hypothesize that a crucial phase in the development of nicotine addiction may be the transition from experimental smoking to smoking to reduce the experience of negative states.

Baker and colleagues (2004) extend the conceptualization of this negative-reinforcement model of tobacco dependence by focusing on the role of negative affect. Within this reformulation of negative reinforcement, negative affect is the core symptom of the nicotine withdrawal syndrome that drives a person to smoke to relieve aversive states. Traditional negative-reinforcement models have emphasized the role of environmental cues, such as interpersonal conflict, but not internal cues, such as physiological symptoms that signal impending withdrawal-related states of negative affect. With repeated pairings of nicotine withdrawal and smoking to relieve withdrawal, persons addicted to nicotine may learn over time to detect internal cues at a level that is not in the immediate awareness of the smoker, especially cues associated with negative-affect states, regardless of whether they are related to withdrawal (Baker et al. 2004). Nicotine operates on both aversive withdrawal states and distress associated with external stressors, according to Baker and colleagues (2004). However, nicotine may be less effective in reducing negative affect associated with distress from external stressors. Consistent with this view, cigarette smoking has not been found to attenuate experimentally induced negative affect in the laboratory setting (Conklin and Perkins 2005). Studies of smoking in real-world settings have found little or no association between subjective negative affect and smoking behavior (Piasecki 2006). Baker and colleagues (2004) further hypothesize that smokers acquire a “motivational-processing sequence in which interoceptive signals of negative affect engage drug self-administration response sequences and may induce awareness of the desire or urge to use a drug without awareness of the affective origins or setting events for the desire” (p. 47).

The finding that negative-reinforcement processes may not be consciously accessible (Baker et al. 2004) could contribute to the difficulty smokers experience in trying to stop smoking. When negative affect or external stressors become sufficiently strong, the person becomes aware of them, and the negative affect leads to biases in information processing. One example is attentional bias for negative affect cues that trigger smoking (Baker et al. 2004). Although it is provocative, Baker and colleagues’ model of negative reinforcement should be viewed as provisional and requiring validation. Related individual differences, such as the inability to tolerate distress, may influence learning and conditioning processes (Brown et al. 2005). Using momentary ecological assessment, Shiffman and Waters (2004) found that rapid increases in negative affect exert especially strong influence in precipitating lapses to smoking (see “Trajectory of Recovery or Relapse” later in this chapter for a more detailed discussion of smoking relapse).

Environmental Context

Animal studies confirm the powerful role that environmental stimuli play in nicotine self-administration. When environmental stimuli are paired with nicotine self-administration, the extinguished drug-seeking behavior is reinstated (Le Foll and Goldberg 2005). The important role of environmental context in nicotine addiction is observed in its effect on relapse. (For more description, see “Trajectory of Recovery or Relapse” later in this chapter.) Studies of smokers consistently report an association among exposure to smoking cues, craving, and positive and negative affective states, which could be construed as emotional cues followed by return to smoking after an attempt to stop (Shiffman 1982; Marlatt and Gordon 1985). These studies, however, rely on subjectively recalled events that may be prone to several types of memory bias. Using methods of ecological momentary assessment in an electronic diary, Shiffman and colleagues (1996c) confirmed that lapses in smoking abstinence were strongly associated with being in situations in which smoking was permitted, cigarettes were available, and other persons were smoking.

Clinical studies have used a cue-exposure paradigm to explore the association of smoking cues with craving and physiological and behavioral responses. These studies are premised on the assumption that they yield insights into how environmental and internal stimuli play a role in provoking smoking and relapse (Niaura et al. 1988; Caggiula et al. 2001; Chiamulera 2005). Stimuli are presented in a variety of modes, including photographic and video, auditory, in vivo (presence of cigarette paraphernalia or smoking by another person), and the use of imagery (e.g., request to imagine specific situations). In a meta-analytic review, Carter and Tiffany (1999) found that exposure to smoking cues increased craving most reliably, followed in order by sweat gland activity and heart rate changes. Sweating and changes in heart rate probably reflect an increased arousal of the sympathetic nervous system. Other researchers have noted cue effects on an increase in reaction time (Sayette and Hufford 1994), cognitive interference on the Stroop test (Munafò et al. 2003), and similar paradigms used to assess attentional interference (Mogg and Bradley 2002). Imaging studies of cue responses also suggest that neural activity is greatest in brain areas involved in emotion and reward, including the prefrontal cortex, limbic lobe (anterior cingulate, posterior hippocampus, and right posterior amygdala), medial thalamus, and midbrain structures (ventral tegmentum) (Due et al. 2002; McClernon and Gilbert 2004; David et al. 2005).

Exposure to smoking cues among smokers also decreases the prepulse inhibition of the acoustic startle reflex, an effect associated with an increase in dopaminergic activity in the ventral tegmental brain region (Hutchison et al. 1999b). The effects of smoking cues on neural responses and craving are also moderated by factors such as the perceived availability of cigarettes. After cue exposure, craving increased more when there is an expectation of the opportunity to smoke (perceived availability) than when there is perceived unavailability (Carter and Tiffany 1999). Exposure to a cigarette cue under the condition of perceived availability is associated with an increase in activation of the ventromedial prefrontal cortex and a decrease in activation of the dorsolateral prefrontal cortex compared with exposure to a neutral cue (Wilson et al. 2004). This pattern of neural activation with corresponding increases in craving can be seen as setting the stage for behaviors that culminate in smoking.

Responses to cues in laboratory experiments are associated with responses to smoking cessation treatments. For example, acute increases in heart rate assessed among smokers in response to a cue exposure at the end of a treatment protocol were related to a later relapse to smoking (Niaura et al. 1989). An acute deceleration in heart rate assessed when smokers observed a cigarette being lit during the cue-exposure procedure also predicted relapse. Heart rate deceleration in this context may reflect a greater attention paid to the stimulus (e.g., a lit cigarette). Subsequently, Waters and colleagues (2004) found that a cue-provoked craving before treatment predicted relapse, but only among those who were treated with an active nicotine patch instead of a placebo patch, suggesting inconsistent results or uncertainty in the link between cue-provoked craving in the laboratory and relapse.

If cue-provoked responses assessed in the laboratory are associated with smoking relapse, then treatments that decrease or blunt these responses may increase the likelihood of successful smoking cessation. A review by Conklin and Tiffany (2002) suggests that conventional extinction-type treatments, such as exposure to smoking-related cues unaccompanied by the reinforcing effects of nicotine or exposure with response-prevention treatments, are ineffective in helping persons to stop smoking. This finding may relate to the possibility that stimulus-response pairing, if sufficiently strong, cannot be forgotten or unlearned (LaBar and LeDoux 2001; Conklin and Tiffany 2002; Niaura 2002). In addition, the large number of potential cues likely serves to maintain smoking behavior or the state-dependent learning processes. However, methods such as use of denicotinized cigarettes or antagonists (e.g., mecamylamine) show significant effects on reducing the rewarding value of smoking cues and have the potential to enhance smoking cessation (Rose and Behm 2004; Rose 2006). Other cognitive or behavioral methods based in modern learning theory may also show more promise in suppressing the stimulus-response bond. Pharmacologic treatments show some promise in decreasing cue reactivity. Compared with a placebo gum, nicotine polacrilex gum diminished the craving response to smoking cues more rapidly (Shiffman et al. 2003). In a study using the same cue-exposure paradigm, a more recent formulation of a rapid-release nicotine gum reduced craving more than did conventional nicotine polacrilex gum (Niaura et al. 2005). Nicotine polacrilex gum is an effective smoking cessation aid (Silagy et al. 2004). Its efficacy may be associated with its ability to diminish a cue-provoked craving.

Other rapid-release formulations of nicotine replacement therapy (NRT), including gum and nasal spray, may similarly help persons to stop smoking, in part because these formulations decrease the craving response to cues. In contrast, although slower-releasing NRT formulations (e.g., a nicotine patch) appear to lower absolute levels of craving, these formulations do not blunt cue-provoked craving (Tiffany et al. 2000; Waters et al. 2004). One study has suggested that treatment with bupropion blunts cue-provoked craving, but the study did not control for abstinence status (Brody et al. 2004a). Other nonnicotine compounds (e.g., naltrexone and olanzapine) also may blunt cue-provoked craving (Hutchison et al. 1999a, 2004). This finding suggests that the cue-exposure paradigm may be a useful screening tool for testing pharmacologic aids to smoking cessation; however, further studies need to be conducted to better understand why some medications affect cue-induced cravings and others do not.

Pathophysiology of Nicotine Addiction

Because nicotine is one of the primary constituents responsible for tobacco addiction, research to promote an understanding of the neurobiology of tobacco addiction focuses on the mechanisms mediating nicotine addiction. As noted previously, dependence on nicotine is characterized by both the persistence of a drug-taking behavior and the emergence of withdrawal symptoms on abrupt cessation of nicotine administration (Wikler 1973; Levine 1974; Stewart et al. 1984; Ludwig 1986; O’Brien et al. 1990; Hughes and Hatsukami 1992; Koob et al. 1993; Markou et al. 1993, 1998; APA 1994; Kenny and Markou 2001). Therefore, both the neurosubstrates (brain structures, pathways, and systems) mediating the reinforcing effects of acute administration of nicotine and those mediating the nicotine withdrawal syndrome are relevant to drug dependence. The physiological systems that develop adaptations to repeated nicotine administration and lead to the emergence of withdrawal signs on cessation of nicotine administration are likely to intersect with systems that mediate the acute effects of nicotine (Markou et al. 1998; Kenny and Markou 2001). That is, drug dependence develops as a neurobiologic adaptation to chronic drug exposure.

Accordingly, this section first reviews the systems and pathways mediating the reinforcing effects of nicotine and then discusses the neuroadaptations that occur because of chronic nicotine exposure. These neurobiologic adaptations mediate the tolerance to and effects of withdrawal from nicotine that are interlinked in most theoretical conceptualizations. Researchers have hypothesized that the sensitization to the locomotor-activating effects of drugs, including effects observed after repeated nicotine administrations, reflect a progressive augmentation in the motivation to self-administer the drug (Robinson and Berridge 1993). (The locomotor-activating effects consist of progressively increased locomotor responses to repeated drug-challenge injections.) However, no direct evidence suggests that sensitization to the locomotor-activating effects of nicotine reflects any aspect of dependence on nicotine. Therefore, sensitization is not covered in this section. If sensitization to the reinforcing effects of nicotine develops, it will most likely be relevant to early phases of tobacco use involving the acquisition of tobacco smoking as a continuing behavior.

The final discussion focuses on the comorbidity of nicotine dependence and psychiatric disorders in the context of shared substrates that mediate nicotine dependence and depression-like aspects of psychiatric disorders (Markou et al. 1998; Markou and Kenny 2002; Paterson and Markou 2007).

Neurosubstrates of Nicotine Reinforcement

The mesocorticolimbic brain system in the midbrain of mammals is composed of interconnected brain structures. This system has been shown to be critically involved in the effects of drugs of abuse (Koob 2008). Among the main components of this system are the dopaminergic neurons originating in the ventral tegmental area (VTA) and projecting to the nucleus accumbens and the frontal cortex. The activity of these VTA dopamine neurons is regulated by the release of the excitatory neurotransmitter glutamate from neuronal projections originating from several sites, including the nucleus accumbens and the frontal cortex. Other inputs that also regulate activity of the mesolimbic system are (1) γ-aminobutyric acid (GABA) inhibitory interneurons located within the VTA and the nucleus accumbens and (2) cholinergic projections from brainstem nuclei to the VTA. These cholinergic projections release the endogenous neurotransmitter acetylcholine, which acts on excitatory nAChRs located on glutamate and GABA neuronal terminals in the VTA (Figure 4.4). Extensive investigations over decades have conclusively demonstrated a critical role of the mesocorticolimbic system and its connections in several behavioral and affective responses to drugs of abuse.

Figure 4.4

Neural pathways for γ-aminobutyric acid, glutamate, dopamine, and excitatory neurotransmitters. Source: Markou 2006. Reprinted with permission from Wiley-Blackwell, © 2006. Note: The circular symbol with the reverse arrow attached to it (more...)

Dopamine and Nicotinic Acetylcholine Receptors

As with other drugs of abuse, it has been demonstrated that the mesolimbic dopaminergic system and nAChRs within that system are critically involved in the reinforcing properties of nicotine (Watkins et al. 2000; Picciotto and Corrigall 2002; Balfour 2004). Acute administration of nicotine increased the firing rate of dopaminergic neurons in the VTA (Grenhoff et al. 1986; Pidoplichko et al. 1997) and elevated dialysate levels of dopamine in the shell of the nucleus accumbens (Imperato et al. 1986; Damsma et al. 1989; Mifsud et al. 1989; Benwell and Balfour 1992; Pontieri et al. 1996; Nisell et al. 1997; Carboni et al. 2000). These effects of nicotine may occur through excitatory actions at nAChRs on the mesolimbic dopaminergic neurons in both the VTA and the nucleus accumbens and at nAChRs located on local neuronal circuitry within these brain regions (McGehee and Role 1996; Nisell et al. 1997; Teng et al. 1997). The nAChRs in the VTA play a more important role than those in the nucleus accumbens in the effects of nicotine on the release of dopamine from the nucleus accumbens (Nisell et al. 1994a,b, 1997).

Several findings support the conclusion that nAChRs located within the VTA are involved in nicotine reinforcement. Intravenous nicotine self-administration is a procedure that allows the assessment of the reinforcing effects of nicotine by measuring the number of infusions a rat chooses to receive intravenously through an indwelling permanent catheter by pressing a lever during one-hour daily sessions in a testing chamber. Each of four factors decreased intravenous nicotine self-administration in rats (Picciotto and Corrigall 2002). The factors were (1) injections of the competitive nAChR antagonist DHβE into the VTA (Williams and Robinson 1984) but not the nucleus accumbens (Corrigall et al. 1994), (2) development of lesions of the mesolimbic dopaminergic projections from the VTA to the nucleus accumbens (Corrigall et al. 1992), (3) development of cholinergic lesions of the brainstem pedunculopontine tegmental nucleus that project to the VTA (Lança et al. 2000), and (4) systemic administration of dopamine receptor antagonists (Corrigall and Coen 1991b). Studies suggest an involvement of the nAChR subtypes containing α4β2 in both the nicotine-induced release of dopamine and nicotine reinforcement (Picciotto et al. 1998; Schilström et al. 1998b; Watkins et al. 1999; Grillner and Svensson 2000; Sharples et al. 2000). In addition, mutant mice with hypersensitive α4 nAChRs show a 50-fold increase in sensitivity to the reinforcing effects of nicotine measured by a place-preference procedure (Tapper et al. 2004). A place-preference procedure assesses the rewarding effects of a drug by measuring the preference a rat exhibits for a compartment previously associated with the effects of a drug instead of a compartment associated with an injection of saline. The place-preference finding by Tapper and colleagues (2004) further indicates a critical role of α4 nAChRs in nicotine reinforcement. The α7 homomeric receptors may be involved in the reinforcing effects of nicotine. Methyllycaconitine, an antagonist with limited selectivity for the α7 nAChR, decreased the intravenous nicotine self-administration procedure in rats (Markou and Paterson 2001), although another study with rats showed no effects of this antagonist on nicotine- induced hyperactivity or nicotine self-administration (Grottick et al. 2000). Finally, both the α4β2 and α7 subtypes are implicated in the effects of nicotine on memory (Levin et al. 1999; Bancroft and Levin 2000) and the anxiolytic effects of nicotine (Gordon 1999; Cheeta et al. 2001), which also contribute to persistent tobacco use (USDHHS 1988).

Glutamate

Other mechanisms by which nicotine may elevate striatal dopamine levels include increases in excitatory glutamatergic inputs from the frontal cortex to the nucleus accumbens and/or excitatory glutamatergic inputs to VTA dopaminergic neurons projecting to the striatum. Nicotine increases the release of glutamate by agonist actions at excitatory presynaptic nAChRs on glutamatergic terminals in various brain sites, including the VTA (Fu et al. 2000; Grillner and Svensson 2000; Mansvelder and McGehee 2000), nucleus accumbens (Reid et al. 2000), prefrontal cortex (Gioanni et al. 1999), and hippocampus (Gray et al. 1996). In the VTA, nicotine acts at presynaptic α7 nAChRs located on glutamate neurons (neurons that release glutamate as the primary neurotransmitter). Activation of these α7 nAChRs on glutamate neurons (Mansvelder and McGehee 2000) increases the release of glutamate in the VTA. This activity, in turn, stimulates the release of dopamine in the nucleus accumbens (Nisell et al. 1994a,b; Schilström et al. 1998a,b; Fu et al. 2000; Mansvelder and McGehee 2000). That is, this increased release of glutamate acts at metabotropic and ionotropic glutamate receptors located on postsynaptic dopamine neurons (neurons that have dopamine as the primary neurotransmitter). Activation of these glutamate receptors leads to excitation of the dopamine neurons that results in increased release of dopamine in terminal brain sites where these neurons project, such as the nucleus accumbens, the amygdala, and the frontal cortex.

Ionotropic antagonists of N-methyl-d-aspartate receptors blocked (prevented) tolerance to the locomotor depressant effects of acute nicotine administration (Shoaib and Stolerman 1992; Shoaib et al. 1994) and blocked sensitization to the locomotor stimulant effects of chronic nicotine administration (Shoaib and Stolerman 1992). Most relevant to addiction is the finding that blockade of the postsynaptic metabotropic glutamate receptor subtype 5 (mGluR5) with 2-methyl-6-(phenyl-ethynyl)pyridine (MPEP) decreased intravenous nicotine self-administration in rats and mice (Paterson et al. 2003) and decreased the motivation to self-administer nicotine (Paterson and Markou 2005). These effects are likely mediated by decreasing the nicotine-stimulated release of dopamine in the mesolimbic system. At doses that blocked nicotine self-administration, MPEP had no effect on response for food (Paterson et al. 2003). The progressive-ratio schedule of reinforcement, which gradually increases the response requirements after each earned reward, allows the assessment of the motivation for reinforcers, such as nicotine or food, by evaluating the maximal number of responses emitted by the rat (i.e., breaking point) to receive a single intravenous infusion of nicotine or a single food reward. In this schedule, MPEP had a greater effect on motivation for nicotine than on motivation for food, even when the magnitudes of reinforcer value were equated to support equal breaking points for nicotine and food under baseline conditions (Paterson and Markou 2005). This selectivity of the MPEP effects for nicotine reinforcement versus food reinforcement suggests that MPEP selectively blocks the reinforcing effects of nicotine without affecting motor performance or food reinforcement. Furthermore, evidence suggests a potential role of ionotropic glutamate receptors in the effects of nicotine. Animals that self- administered nicotine chronically exhibited an increase in ionotropic glutamate receptor subunits in brain regions, such as the VTA and the frontal cortex, that are implicated in the reinforcing effects of nicotine (Wang et al. 2007).

γ-Aminobutyric Acid

GABA is the major inhibitory transmitter in the brain and is another transmitter system critically involved in the reinforcing effects of acute nicotine administration. Several factors inhibit the release of mesolimbic dopamine, including inhibitory GABA transmission on ascending afferents to dopaminergic VTA neurons from the pedunculopontine tegmental nucleus (Walaas and Fonnum 1980; Yim and Mogenson 1980), descending GABA-ergic inputs from the ventral pallidum and the nucleus accumbens, GABA interneurons within the VTA, and medium spiny GABA neurons in the nucleus accumbens (Walaas and Fonnum 1979; Heimer and Alheid 1991; Churchill et al. 1992; Dewey et al. 1992; Kalivas et al 1992; Klitenick et al. 1992; Sugita et al. 1992; Engberg et al. 1993). As suggested by this neuroanatomy and extensive electrophysiological studies, interactions between the GABA, dopaminergic, and glutamatergic systems in the VTA are complex (Mansvelder and McGehee 2000; Mansvelder et al. 2002). Glutamate afferents to the VTA excite dopamine neurons, and GABA-ergic afferents to the VTA inhibit dopamine neurons. Excitatory nAChRs are located on both glutamate and GABA-ergic neurons. The nAChRs on GABA neurons desensitize quickly to chronic administration of nicotine, but the nAChRs on glutamate neurons require higher doses of nicotine for desensitization. This delicate balance leads to a nicotine-induced increase in the release of dopamine in the nucleus accumbens, the terminal area of VTA neurons (Schilström et al. 1998b; Mansvelder and McGehee 2000). Similar transmitter interactions may also occur in other brain sites.

Increased GABA-ergic transmission abolishes both the nicotine-induced increases in dopamine in the nucleus accumbens and the reinforcing effects of nicotine (Dewey et al. 1999; Brebner et al. 2002). Systemic injections of γ-vinyl GABA (vigabatrin) increased GABA levels and decreased nicotine self-administration in rats (Paterson and Markou 2002). Vigabatrin is an irreversible inhibitor of GABA transaminase, the primary enzyme involved in GABA metabolism (Jung et al. 1977; Lippert et al. 1977). Systemic injections of vigabatrin also abolished the expression and acquisition of nicotine-induced conditioned place preference (Dewey et al. 1998). The administration of vigabatrin also lowered nicotine- induced increases in dopamine in the nucleus accumbens in both untreated rats and those receiving long-term treatment with nicotine in a dose- and time-dependent manner measured by in vivo microdialysis. In addition, vigabatrin abolished nicotine-induced increases in striatal dopamine in primates, as determined by PET scan (Brebner et al. 2002).

The use of receptor-selective agonists in animals suggests the involvement of GABA_B receptors in the reinforcing effects of nicotine. Systemic injections or microinjections of baclofen or CGP44532 [(3-amino-2[S]-hydroxypropyl)-methylphosphinic acid]—two GABA_B receptor agonists—into the nucleus accumbens shell, the VTA, or the pedunculopontine tegmental nucleus that sends cholinergic, GABA-ergic, and glutamatergic projections to the VTA decreased the reinforcing effects of nicotine (Shoaib et al. 1998; Corrigall et al. 2000, 2001; Fattore et al. 2002; Paterson et al. 2004). However, injections into the caudate-putamen did not have these effects. The decreases in nicotine self-administration persisted even after administration of CGP44532 for 14 days, indicating little tolerance to this effect of the GABA_B receptor agonist with this duration of treatment (Paterson et al. 2005b); that is, the reduction in nicotine self-administration persisted over time. The issue of tolerance is important because long-term administration of drug therapies is necessary to achieve smoking cessation. However, in studies of rats, vigabatrin and GABA_B receptor agonists also decreased response for food, although at doses higher than the threshold doses for inducing decreases in nicotine self-administration (Paterson and Markou 2002; Paterson et al. 2004, 2005b). These effects on response for food may reflect nonspecific effects on performance by GABA-ergic compounds or specific effects on food intake. The possibility of effects on food intake is intriguing, because weight gain associated with abstinence from smoking is often a concern for smokers, especially women, who want to stop smoking cigarettes.

Thus, increased GABA transmission through the activation of GABA_B receptors blocks the reinforcing effects of nicotine. However, a clinical study shows that one dose of baclofen had no effect on either the number of cigarettes smoked or the craving for nicotine (Cousins et al. 2001). Nevertheless, other clinical studies show that long-term administration of baclofen reduced abuse of cocaine and alcohol, as well as cue-induced brain activation (Ling et al. 1998; Addolorato et al. 2000, 2002a,b). Therefore, long-term treatment with these GABA-ergic drugs may first be required to reduce tobacco smoking.

Opioid, Endocannabinoid, and Serotonin Systems

The data on the possible role of opioid systems in the rewarding effects of nicotine remain inconclusive. Nicotine did not induce a conditioned place preference in μ-opioid receptor *NULL-mutant mice, but it did so in wild-type animals (Berrendero et al. 2002). Similarly, nicotine induced a conditioned place preference in wild-type but not in preproenkephalin *NULL-mutant mice. A nicotine-induced elevation in dopamine overflow in the nucleus accumbens was absent in *NULL mutants (Berrendero et al. 2005). However, systemic or intra-VTA administration of the opiate receptor antagonist naltrexone or the opiate receptor agonist d-Ala²,N-Me-Phe⁴-Gly-ol-enkephalin, respectively, had limited or no effects on nicotine self-administration in rats (Corrigall and Coen 1991a; Corrigall et al. 2000).

In humans, acute and short-term nicotine administration leads to the release of β-endorphins, endogenous opioid peptides that have reinforcing effects (Davenport et al. 1990; Boyadjieva and Sarkar 1997). Furthermore, in humans, the acute administration of naltrexone decreased the reinforcing value of nicotine in a procedure involving choice between puffs on nicotinized versus denicotinized cigarettes (i.e., compared with placebo, naltrexone significantly reduced the number of nicotine cigarette choices) (Rukstalis et al. 2005). This result is consistent with a previous finding that acute administration of naltrexone significantly decreased the total number of choice cigarettes smoked (e.g., subjects were given a choice to smoke four cigarettes in a two-hour period of time) (Epstein and King 2004). However, a randomized, double-blind trial of naltrexone for smoking cessation found only a nonsignificant trend toward increased cessation rates, and the effect disappeared at 12 months after cessation (Covey et al. 1999). Other clinical trials examining the effects of naltrexone versus placebo in smokers who were assigned nicotine patches to aid cessation have also observed no significant effects of naltrexone on improving treatment outcomes (King et al. 2006; O’Malley et al. 2006). Thus, the possible involvement of the opiate system in the reinforcing effects of nicotine remains at best unclear, and the use of opiate antagonists as treatments for dependence on tobacco smoking appears unwarranted. A Cochrane review in 2001 concluded that opioid antagonists failed to significantly increase long-term abstinence from smoking on the basis that the limited evidence was insufficient to support a conclusive finding on whether naltrexone is an aid to smoking cessation (David et al. 2006). Although one study suggested an effect of gender, women benefited more than men from treatment with naltrexone (King et al. 2006).

The evidence is much stronger for the role of serotonin in the reinforcing effects of nicotine. Acute administration of nicotine elevated extracellular serotonin in the nucleus accumbens (Schiffer et al. 2001) and the VTA (Singer et al. 2004). Serotonin was also implicated in a neurochemical sensitization to nicotine, which some researchers hypothesize to be relevant to aspects of nicotine dependence. The administration of the serotonin (5HT₂) receptor agonist (±)-2,5-dimethoxy-4-iodoamphetamine (Olausson et al. 2001) or the 5HT_2C receptor agonist (S)-2-(chloro-5-fluoro-indol-1-yl)-1-methylethyl-amine fumarate (Di Matteo et al. 2004) blocked the increased overflow of serotonin observed after a nicotine challenge in nicotine-treated rats. In addition, nicotine increased serotonin overflow in cortical areas (Toth et al. 1992; Ribeiro et al. 1993; Summers and Giacobini 1995; Singer et al. 2004) and in the dorsal hippocampus (Singer et al. 2004). In contrast, Balfour and Ridley (2000) found a decrease in the serotonin overflow after acute administration of nicotine. However, Singer and colleagues (2004) used anesthetized rats, and Balfour and Ridley (2000) used in vivo microdialysis in conscious rats. In addition, administration of nicotine for at least 20 days was associated with decreased serotonin levels in the dorsal hippocampus (Benwell and Balfour 1979; Balfour and Ridley 2000). However, nicotine administration for 14 days was associated with increased serotonin levels (Takada et al. 1995). Nicotine infusion into the ventromedial nuclei or the lateral hypothalamic area increased the release of serotonin in this area (Yang et al. 1999; Ramos et al. 2004). Together, the findings suggest that acute administration of nicotine increases serotonin levels but that long-term administration leads to decreases in serotonin levels that may mediate the affective aspects of nicotine dependence and withdrawal (Harrison et al. 2001).

Studies provide conflicting evidence on the role of cannabinoid subtype 1 (CB₁) receptors in modulating the reinforcing effects of nicotine. CB₁ knockout mice (i.e., mice genetically engineered to lack CB₁ receptors) self-administered nicotine (Cossu et al. 2001) but did not exhibit conditioned place preference to nicotine (Castañe et al. 2002). Furthermore, the CB₁ receptor antagonist rimonabant (SR141716) decreased nicotine seeking and self-administration of nicotine induced by the presentation of conditioned cues and also attenuated a nicotine-induced release of dopamine in the nucleus accumbens shell (Cohen et al. 2002, 2005; De Vries et al. 2005). Thus, the data from experimental studies of rodents on the role of the cannabinoid system are inconclusive and so are the clinical data (Le Foll and Goldberg 2005). However, an analysis of data pooled from three clinical trials of rimonabant compared with a placebo showed modest success at the end of treatment (Cinciripini et al. 2006).

Norepinephrine

Data also suggest a role of norepinephrine in the effects of nicotine. Acute nicotine administration increases extracellular norepinephrine in the nucleus accumbens, the hippocampus, and the cortex in rats (Brazell et al. 1991; Mitchell et al. 1993; Summers and Giacobini 1995; Benwell and Balfour 1997; Schiffer et al. 2001). Nicotine-evoked hippocampal release of norepinephrine in vivo was attenuated by α-bungarotoxin but was unaffected by either of the nAChR antagonists mecamylamine or DHβE, implicating α7 nAChRs, rather than α4β2 nAChRs associated with the release of norepinephrine in this region of the brain (Fu et al. 1999). However, norepinephrine release from hippocampal synaptosomes in rats was sensitive to mecamylamine, DHβE, and methyllycaconitine suggesting that the release of norepinephrine may not be specific to α7 nAChRs (Clarke and Reuben 1996). Additional studies suggest the role of norepinephrine in nicotine’s effects. Intravenous self-administration of nicotine increased norepinephrine concentrations in the amygdala and the hypothalamic paraventricular nucleus (Fu et al. 2001, 2003). In vitro studies indicated that nicotine increased release of norepinephrine in (1) prefrontal cortex slices of rats (Rao et al. 2003) and (2) locus coeruleus neurons of fetal rats grown in cultures (Gallardo and Leslie 1998).

Consistent with these neurochemical findings, short-term or long-term administration of reboxetine, the selective noradrenaline reuptake inhibitor, decreased nicotine self-administration in rats (Rauhut et al. 2002). However, reboxetine also decreased sucrose-maintained response, although to a lesser degree than nicotine-maintained response. Reboxetine acts as a noncompetitive nAChR antagonist, in addition to blocking noradrenaline reuptake (Miller et al. 2002). Thus, it is not conclusive that the effects of reboxetine on nicotine self-administration are attributable to its effects on noradrenaline reuptake rather than to its actions as an nAChR antagonist.

Bupropion, a smoking cessation aid, also inhibits reuptake of norepinephrine, as well as dopamine (Ferris et al. 1983). Administration of bupropion increased extracellular concentrations of dopamine and epinephrine in the nucleus accumbens, hypothalamus, and prefrontal cortex (Nomikos et al. 1989, 1992; Li et al. 2002). Furthermore, electrophysiological studies indicated that bupropion decreased the firing rates of dopamine neurons in the nucleus accumbens and noradrenergic neurons in the locus coeruleus but had no effect on firing of serotonin dorsal raphe neurons (neurons located in the dorsal raphe firing) (Cooper et al. 1994).

Despite the demonstrated effects of bupropion on neurotransmitter and receptor systems that appear to mediate the effects of nicotine, bupropion had inconsistent effects on nicotine self-administration in rats. Some studies showed a decrease in nicotine self-administration in fixed-ratio schedules of reinforcement but had no effects in a progressive-ratio schedule (Glick et al. 2002; Bruijnzeel and Markou 2003). In contrast, another study (Shoaib et al. 2003) indicated that repeated daily administration of bupropion increased nicotine self-administration in a fixed-ratio schedule, but the results were not significant (Shoaib et al. 2003). Finally, Rauhut and coworkers (2003) showed that low doses of bupropion increased and high doses of bupropion decreased nicotine self-administration and response for sucrose.

In summary, these findings suggest a strong effect of nicotine on transmission of norepinephrine, but bupropion, which inhibits the reuptake of both dopamine and norepinephrine, has inconsistent effects on nicotine self-administration in rodents. Thus, other properties of bupropion, such as relief from withdrawal symptoms, may contribute to its efficacy as an aid to smoking cessation.

Genetics

There is strong evidence for a genetic influence on smoking behavior. Since the last Surgeon General’s report on nicotine addiction (USDHHS 1988), knowledge has significantly increased in this area. For example, estimates of heritability have been determined for various phenotypes of smoking behavior. Studies of molecular genetic association and linkage studies were conducted to identify loci that influence these phenotypes. Furthermore, pharmacogenetic studies of smoking cessation were undertaken to increase understanding of interactions between genes and treatments. This research offers the possibility of interventions for smoking cessation that are tailored to individual genotypes.

Molecular Genetic Research on Smoking Behavior

Consistent evidence for the heritability of smoking behaviors led to molecular genetic studies designed to elucidate the specific genetic factors and biologic mechanisms involved in nicotine addiction. Two general scientific approaches to address this question include genetic linkage analysis and candidate gene studies. In linkage analysis, genetic variants or markers throughout the genome are tested within families (e.g., sibling pairs) and examined to identify markers that cosegregate with the trait of interest (e.g., nicotine dependence). This is a hypothesis-generating approach and does not require a priori knowledge about the biologic pathways involved. In contrast, studies of candidate genes, which are based on associations, use case-control methods to compare the prevalence of variants of candidate genes in two unrelated groups—for example, persons who are dependent on nicotine and those who are not dependent on nicotine. Although case-control studies have greater statistical power and are less costly than linkage analysis, such studies are not designed to identify novel genetic loci.

Cigarette smoking and nicotine dependence are complex traits arising from the interplay of multiple genetic and environmental influences. As mentioned previously (see “Definition of Nicotine Addiction” earlier in this chapter), definition of phenotype is a critical factor in genetic studies. Many genes are likely involved in smoking—for example, genes that influence the positive rewarding effects of nicotine, those that contribute to withdrawal symptoms and the negative reinforcing effects of nicotine (Pomerleau 1995), and those that determine general susceptibility to addiction (Nestler 2000). Interacting effects such as personality and environment are likely to also play an important role (Heath et al. 1995). Issues such as population heterogeneity (e.g., age, gender, and ethnicity) and bias (false positives results) introduced by ethnic admixture in study populations may also have a substantial impact on the outcome of association-based studies and may contribute to problems in replicating results (Munafò and Flint 2004).

Linkage Studies

Representative genetic linkage studies of smoking behavior phenotypes up to mid-2005 are shown in Table 4.5, although several of these report data from the same study samples (e.g., Collaborative Studies on the Genetics of Alcoholism and the Framingham Heart Study). Despite the success of linkage approaches in unraveling the genetic antecedents of disease (Menzel 2002), these initial findings about smoking behavior have not been consistent. Potential explanations include lack of refinement in phenotype definition and the relatively small sample sizes in some studies of smoking behavior. Subsequent studies have taken into account the complexity and heterogeneity of the nicotine dependence phenotype by using alternate measures, such as heavy smoking, severity of withdrawal, and history of smoking cessation (Li et al. 2006; Swan et al. 2006). These data suggest that different genetic loci are linked to different measures and support a multidimensional concept of the nicotine dependence phenotype (Swan et al. 2006).

Table 4.5

Genetic linkage studies of smoking behavior phenotypes.

In addition to linkage studies, investigations using an approach of genomewide association can also reveal promising novel candidate genes for nicotine dependence (Bierut et al. 2007). With advancements in genotyping technology, phenotype definition, and analytic approaches, both case-control studies and linkage analysis will likely identify an increasing number of associations with novel variants important in nicotine dependence (Li 2006). Examples include NTRK2 (Beuten et al. 2007), GABARAP (Lou et al. 2007), CHRNA5 (Saccone et al. 2007), and ANKK1 (Gelernter et al. 2006).

Candidate Gene Studies

A variety of plausible candidate genes have been examined for associations with smoking behavior. Most of these studies have focused on genetic variations in relevant neurotransmitter pathways, nicotine-metabolizing enzymes, or nAChRs. Genetic variants of relevant neurotransmitter pathways may have more generalized influences on addictive behaviors. Genes for nicotine-metabolizing enzymes and nAChRs may be specific for effects on nicotine dependence.

Nicotine Metabolism

To date, more than 20 published studies of candidate genes have investigated genes involved in the nicotine metabolism pathway (Table 4.6). Most of these studies investigated CYP2A6, for which researchers have identified functional genetic variants (Xu et al. 2002). Variants associated with *NULL activity (e.g., *2/*4) or reduced activity (*9/*12) are associated with reduced levels of the CYP2A6 enzyme and slower rates of nicotine metabolism, resulting in higher plasma nicotine levels from a given dose of nicotine (Malaiyandi et al. 2006). Thus, persons who carry these low activity alleles tend to have a lower risk of becoming smokers and, if they smoke, have slower rates of nicotine metabolism and tend toward reduced cigarette consumption compared with persons with a wild-type genotype (e.g., *1/*1). Furthermore, evidence from a meta-analysis that compared current versus former smokers suggests that the CYP2A6 alleles for reduced activity may increase the likelihood of smoking cessation (Munafò et al. 2004). However, the results are not consistent within or across all studies (Table 4.6). These inconsistencies may be attributable to relatively small sample sizes in some studies and differences in definition of phenotype and ethnic ancestry and genetic background.

Table 4.6

Studies of candidate genes for nicotine metabolism and smoking behavior.

Some studies investigated other cytochrome genes (CYP2D6 and CYP2E1), but evidence for a significant and reproducible role of these variants has not emerged, perhaps because the role of these enzymes in nicotine metabolism is limited.

Neuronal Nicotinic Receptors

Researchers have examined several genes for nAChR subunits to discover associations with smoking status (Table 4.7). The genes CHRNA4, CHRNA7, and CHRNB2 code for the α4, α7, and β2 subunits, respectively. However, because the functional relevance of variation in these genes is not known, these studies have explored associations of single nucleotide polymorphisms (SNPs) of unknown functional significance. To date, there is no evidence for associations of SNPs in the CHRNB2 gene with smoking behavior. However, two studies provide evidence for the role of CHRNA4 in nicotine dependence (Feng et al. 2004; Li et al. 2005). A small study of smokers with schizophrenia indicated that the CHRNA7 gene may be associated with smoking status. The relevance of this finding in the general population of smokers is unknown. However, studies have reported that these nAChR subtypes play a role in reinforcing the effects of nicotine and possibly withdrawal (see “Pathophysiology of Nicotine Addiction” earlier in this chapter). Moreover, because of the history of failure to replicate initial significant findings, these single studies require replication before the evidence can be considered to be confirmed.

Table 4.7

Studies of candidate genes for neuronal nicotine receptors and smoking behavior.

Recently, genomewide scans have revealed an association of novel genes, such as NRXN1 and NRXN3, with nicotine dependence (Bierut et al. 2007). In addition, genomewide association and candidate gene studies have identified associations of smoking behavior and nicotine dependence with SNPs in the CHRNA5/CHRNA3/CHRNB4 gene cluster and in CHRNB3, which code for the nicotinic receptor subunits α5, α3, β4 and β3, respectively (Saccone et al. 2007; Berrettini et al. 2008; Bierut et al. 2008; Grucza et al. 2008; Sherva et al. 2008; Stevens et al. 2008; Thorgeirsson et al. 2008; Weiss et al. 2008; Caporaso et al. 2009; Chen et al. 2009).

Dopaminergic and Serotonergic Neurotransmitter and Receptor Systems

A large number of candidate gene studies have investigated genes involved in the dopamine pathway, and most have investigated the gene for the dopamine receptor D2 (DRD2) (Table 4.8). Most studies of the DRD2 *TAQ1A polymorphism have reported an association with smoking behavior, typically smoking status, but a substantial number have shown no association. Moreover, the functional significance of the *TAQ1A polymorphism remains unclear, although there is some reported evidence for an association with the density of D2 receptors in the brain. One study investigated the functional DRD2-141C *INS/*DEL polymorphism and reported a significant association with smoking status (Yoshida et al. 2001). A modest number of studies have investigated other genes for dopamine receptors (DRD1, DRD4, and DRD5), DAT, and genes involved in dopamine synthesis and metabolism, including tyrosine hydroxylase (an enzyme that converts amino acid l-tyrosine to dihydroxyphenylalanine, a precursor of dopamine), DβH (an enzyme that converts dopamine to norepinephrine), and COMT (an enzyme that degrades dopamine).

Table 4.8

Studies of candidate genes for dopamine and smoking behavior.

Of the published studies of candidate genes involved in the serotonin pathway, eight investigated 5HTT, and three investigated TPH, which is involved in serotonin synthesis (Table 4.9). All but one study of the functional 5HTTLPR polymorphism found an association with smoking behavior. Three additional studies investigated the MAOA gene, which is involved in metabolism of both dopamine and serotonin and in norepinephrine pathways. Two of the three studies reported an association with smoking behavior that included both smoking status and cigarette consumption. Other studies of candidate genes are summarized in Table 4.10. Research is notably lacking on genes involved in glutamatergic and GABA-ergic mechanisms, despite basic research indicating the neurobiologic effects of nicotine on these systems. One study (Beuten et al. 2005) reports a significant association between a haplotype of SNPs in the GABA_B2 gene and nicotine dependence.

Table 4.9

Studies of candidate genes for serotonin and smoking behavior.

Table 4.10

Other studies of candidate genes for smoking behavior.

In summary, a few candidate genes appear to be associated with smoking behavior. Meta-analysis is a potentially powerful tool for assessing population-wide effects of candidate genes on complex behavioral phenotypes, such as smoking, although such meta-analysis requires that the phenotypes examined across studies are similar. It may also provide evidence for unrevealed diversity, such as heterogeneity in apparently similar populations (Munafò and Flint 2004). Despite the large number of studies reporting on the association between specific candidate genes and smoking behavior, one meta-analysis (Munafò et al. 2004) highlights the lack of depth of the research compared with the breadth that exists. The conclusion is that the “…evidence for a contribution of specific genes to smoking behavior remains modest” (p. 583). In this analysis, 5HTT and CYP2A6 were the only candidate genes for which there was evidence of an association with smoking behavior. Studies published more recently strongly indicate that SNPs in the CHRNA5/A3/B4 gene cluster are associated with smoking behavior and nicotine dependence (Berrettini et al. 2008; Bierut et al. 2008; Grucza et al. 2008; Sherva et al. 2008; Stevens et al. 2008; Thorgeirsson et al. 2008; Weiss et al. 2008; Caporaso et al. 2009; Chen et al. 2009; Saccone et al. 2009). Nonetheless, the relatively small effects and evidence for substantial heterogeneity between studies suggest that extreme care is necessary in the design of case-control studies of genetic association.

Prevalence and Trajectory Toward Nicotine Dependence

Genes appear to predispose persons to smoking initiation and persistence and possibly are related to the extent of difficulty a person has in smoking cessation. Genetic transmission may include inheritance of polymorphisms of specific genes that affect responses of the body and the brain to nicotine. These responses include the rate of metabolism of nicotine, receptor sensitivity to nicotine and to certain neurotransmitters, and the levels of neurotransmitters available at neural synapses. These individual differences in response to nicotine are likely to affect the trajectory toward the development of nicotine dependence. Characterization of differences in trajectories has primarily focused on the adolescent population, because most smokers begin smoking cigarettes during this period of life. The next section describes the prevalence of adolescent smoking to increase understanding of the scope for potential development of dependence, differences in trajectory patterns toward dependence, and determinants for developing nicotine addiction. Epidemiologic, laboratory, and clinical studies are described to elucidate the emerging science in this area.

Epidemiology of Tobacco Use and Nicotine Dependence in Adults

Prevalence of Cigarette Smoking and Nicotine Dependence

According to one study, the prevalence of current smoking among adults (aged ≥18 years), as assessed by the National Health Interview Survey, was approximately 19.8 percent, or 43.4 million U.S. adults (CDC 2008a). According to the survey, 77.8 percent of current smokers smoked every day and 22.2 percent smoked on some days. (Current smokers are defined as those who smoked ≥100 times during their lifetime and who are smoking every day or on some days.) This high prevalence of daily smokers indicates the highly addictive nature of cigarettes. More men (22.3 percent) than women (17.4 percent) reported current smoking. For the racial and ethnic groups, the lowest prevalence of smoking was among Asians (9.6 percent), and the highest prevalence was among American Indians and Alaska Natives (36.4 percent). Across educational levels and SES, the highest prevalence of smoking was among persons with low levels of education—44.0 percent of those with a General Educational Development diploma and 33.3 percent of those with 9 to 11 years of education, versus 6.2 percent of those with graduate degrees—and persons with the lowest levels of income—28.8 percent of adults living below the poverty level and 20.3 percent of those living at or above the poverty level. The prevalence of smoking was lowest among persons aged 65 years or older (10.2 percent) and highest among those aged 18 through 24 years (23.9 percent).

In adults, the diagnosis of nicotine dependence or addiction in population surveys has largely been based on DSM 3rd ed. (rev) (DSM-III-R), DSM-IV (APA 1987, 1994, 2000), and ICD-10 (WHO 1992) diagnostic criteria. The adult survey instruments used to make the diagnosis have included the National Institute of Mental Health Diagnostic Interview Survey and the Composite International Diagnostic Interview-Substance Abuse Module (Colby et al. 2000b). Researchers also have used data from other population surveys, such as the National Survey on Drug Use & Health to assess symptoms of tobacco dependence. That survey includes terms or phrases such as (1) “reported daily use of the product for two weeks or longer,” (2) “have tried to cut down on smoking,” (3) “unable to cut down or quit or experienced difficulty quitting,” (4) “felt a need for more tobacco for the same effect,” (5) “felt dependent,” or (6) “felt sick or experienced withdrawal symptoms when stopping smoking.” Results have been reported on the percentage of smokers who indicated one or more of these symptoms of dependence or experienced at least one of the withdrawal symptoms, psychoactive effects (e.g., “it relaxes or calms me”), or difficulty with smoking cessation, as a sign of potential tobacco dependence (CDC 1994, 1995a,b). Researchers have also used the presence of a specified number of these symptoms as a proxy measure for DSM-IV criteria for nicotine dependence (Kandel et al. 1997).

The prevalence of nicotine dependence based on these measures in population- or community-based samples from studies conducted in the United States are shown in Table 4.11. The variability in the prevalence of nicotine dependence can be mostly attributed to the characteristics of the population surveyed and the diagnostic tools used. The lifetime prevalence of DSM-III-R diagnosis of nicotine dependence in the general U.S. population ranges from 20 to 24 percent, and past-year prevalence of DSM-IV diagnosis of nicotine dependence is 9 to 13 percent. By virtually any measure, the prevalence of lifetime nicotine dependence is higher for cigarette smoking than for any other category of substance abuse (Anthony et al. 1994; Giovino et al. 1995). The results from Table 4.11 also illustrate that almost one-third of persons who have ever tried smoking cigarettes became dependent on nicotine.

Table 4.11

Lifetime and current prevalence of nicotine dependence in population studies in the United States.

Examination of self-reports of specific symptoms by adult daily or dependent smokers (Table 4.12) shows that in the majority of studies that assessed these symptoms, the least frequently reported symptoms include tolerance, withdrawal, and giving up activities as a result of tobacco use. The most frequently reported symptoms include efforts to reduce smoking and the inability to reduce smoking; feeling dependent; using more cigarettes than intended; and perhaps, continuing to smoke cigarettes despite experiencing problems. Therefore, the symptoms of nicotine dependence most likely to be reported among adults tend to be behavioral or a loss of control over smoking, and the least reported items appear to be physiological (e.g., symptoms of tolerance and withdrawal). In a study by Kandel and Chen (2000), a higher proportion of adolescents reported experiencing symptoms of tolerance (22.2 percent) and/or physical and psychological problems (27.0 percent) resulting from tobacco use, compared with the proportion of adults aged 18 through 49 years (14.4 and 20.3 percent, respectively) and adults aged 50 years or older (9.9 and 11.0 percent, respectively). These results may reflect either the cohort effect or the effect described previously as higher sensitivity in adolescents than that in adults to the effects of nicotine on physiological symptoms of dependence. However, on the basis of animal studies, withdrawal symptoms would be presumed to be fewer in adolescents than in adults (O’Dell et al. 2004, 2006), yet more adolescents are endorsing physical problems than do adults. As pointed out previously, factors other than withdrawal may be associated with higher endorsement of withdrawal symptoms among adolescents. Another possibility is that questions on physical dependence, particularly on tolerance, are not asked in a manner that is understood by or relevant to adult smokers.

Table 4.12

Prevalence of selected symptoms of nicotine dependence reported in selected studies.

The symptoms most frequently reported by adults appear to be less specific to the diagnosis of nicotine dependence. For example, Breslau and colleagues (1994) observed that (1) 88.6 percent of dependent smokers reported the symptom of dependence described as “smoking more than intended” (p. 747) and (2) 93.6 percent reported “unsuccessful attempts to quit” (p. 747). However, these items were also reported by a substantial percentage of nondependent smokers (47.9 and 25.2 percent, respectively) who smoked daily for a month or more during their lifetime but never met criteria for nicotine dependence. However, 87 percent of dependent smokers as opposed to only 12 percent of nondependent smokers reported one or more of the three physiological indicators of dependence (tolerance, withdrawal symptoms, and/or cigarette use to avoid withdrawal symptoms).

With regard to the onset of nicotine dependence relative to daily smoking, one study of data from the National Comorbidity Survey showed that the highest rates of becoming nicotine dependent, as defined by DSM-III-R, occurred in the first 16 years from the year after progression to daily smoking, whereas in the subsequent 10 years the progression to nicotine dependence declined and continued at a slower rate (Figure 4.5) (Breslau et al. 2001). Thus, nicotine dependence generally followed daily smoking, although 5.4 percent of nicotine dependence began before or in the same year as progression to daily smoking. In most cases, the onset of nicotine dependence occurred one or more years after the initiation of daily smoking. These results appear somewhat contrary to results described in “Prevalence of Symptoms and Diagnoses in Adolescence” earlier in this chapter, which indicates that dependence symptoms may occur even earlier in a person’s history of smoking. The discrepancies in results may be a function of how nicotine dependence was diagnosed or defined, that is, whether one was examining symptoms or a diagnosis of dependence or cohort effects.

Figure 4.5

Cumulative incidence curves of daily smoking and nicotine dependence in the National Comorbidity Survey. Source: Breslau et al. 2001. Reprinted with permission from American Medical Association, © 2001. Note: Participants included 4,144 daily (more...)

Trajectory of Recovery or Relapse

Studying recovery from smoking can provide valuable information on the nature of tobacco addiction and the factors affecting it. Every year about 45 percent of daily smokers in the United States stop smoking for 24 hours, but only 5 percent or less achieve long-lasting abstinence (CDC 2002, 2004). Thus, relapse is the principal limiting factor in the transition from smoking to nonsmoking status. This finding underscores the need to understand the nature of relapse and the factors affecting it.

Risk Factors

To promote more precise thinking about the time courses and interactive and cumulative effects of different types of influences on relapse, several reviews recommend an organizational framework for categorizing forces that influence a relapse (Shiffman et al. 1986; Shiffman 1989a; Piasecki et al. 2002). In general, such recommendations have proposed three factors as important influences on relapse: person factors, emergent processes, and situational instigators. Person factors are stable characteristics that preexist the attempt to stop smoking and endure (e.g., gender and history of or proneness to depression). Emergent processes are dynamic factors that unfold over time and emerge sometime during the postcessation period. Such processes tend not to be bound to context. For example, although these processes may arise in response to an episodic event such as stress, they can persist for days or weeks. Withdrawal is an example of a dynamic variable that arises gradually in response to falling blood concentrations of nicotine (Hughes et al. 1990b; Piasecki et al. 2003a). Although situational factors may affect withdrawal symptoms (McCarthy et al. 2006), the symptoms persist well beyond the situational influences and are not wholly explained by them. Situational instigators are factors such as cues, contexts, or events that give rise to short-lived (phasic) reactions lasting from seconds to hours. Such reactions might comprise affective reactions to a stressor, such as an argument, or to exposure to smoking cues, such as seeing someone smoke.

Thus, this organizational scheme reflects the instigator of the process, such as a contextual cue, as well as the time course of vulnerability associated with relapse. Such categorization is complex, because the distinction among the time courses of influences is somewhat arbitrary and various influences may interact (Piasecki et al. 2002; Gwaltney et al. 2005b). These influences are not mutually exclusive or independent, which adds to the complexity of this organizational method. For example, person factors may affect situational reactions or emergent patterns of symptoms. The categorization scheme described here is only one approach to conceptualizing the causes of relapse. This approach has, however, allowed researchers to identify factors that consistently predict relapse and is consistent with a greater body of research and theory showing that person factors, phasic reactions, and contexts powerfully affect behavior (Mischel 2004).

Person Factors

Cognitive and Attitudinal Influences

There is evidence that relatively stable attitudinal variables affect the vulnerability to relapse of smokers. For example, precessation assessments of expectations that smoking will alleviate distress (e.g., negative moods and stress) predict the subsequent likelihood of a relapse (Wetter et al. 1994; Brandon et al. 1999). In addition, multiple studies conclude that baseline measures of confidence in the ability to stop smoking can also predict outcomes (Condiotte and Lichtenstein 1981; Baer et al. 1986; Shiffman et al. 2000). Other findings indicate that confidence before attempts to stop smoking and positive expectations may interact to predict risk of relapse to smoking. Smokers with low confidence and high expectations for smoking reinforcement are especially likely to relapse (Shadel and Mermelstein 1993; Dijkstra and Brosschot 2003). Finally, high levels of motivation, based on health concerns (Nides et al. 1995; Dijkstra and Brosschot 2003) or other reasons (Turner and Mermelstein 2004), may foster cessation and protect against relapse. However, motivation tends to be less effective than other factors, such as level of tobacco dependence or self-efficacy, that is, self-confidence in the ability to stop smoking cigarettes (Hyland et al. 2004; University of Michigan 2006).

Other cognitive variables are less consistently related to lapse and relapse. For instance, expectations about the negative effects of smoking (e.g., risk of disease) appear to predict the motivation or intention to stop smoking but not the likelihood of a relapse (Wetter et al. 1994; Brandon et al. 1999). Also, one study found that a strong commitment to continuing abstinence from smoking was related to reduced rates of relapse, but this finding was obtained in a population that comprised persons who abused opiates and alcohol in addition to smokers, and this condition made the relevance to smoking per se unclear (Hall et al. 1990).

Finally, cognitive dimensions such as expectations or motivation are sometimes hard to classify. For example, motivational structures and attitudes may affect behavior over many years (Etter et al. 2003b; Beltman and Volet 2007). However, motivational phenomena change over time and can be affected by contextual factors (Beltman and Volet 2007; McCaul et al. 2007; Sanderson et al. 2008; Weiss-Gerlach et al. 2008). Therefore, cognitive and motivational factors are discussed both as person factors and emergent processes, with the distinction reflecting the time course of their emergence.

Other data show that the attentional salience of smoking cues also predicts vulnerability to relapse. Using the Stroop paradigm, researchers presented smoking-related and neutral words to 158 volunteers for a smoking cessation program (Waters et al. 2003b). Results show that if words related to smoking attracted the attention of smokers, an early relapse was more likely within a three-month follow-up interval. In theory, the attention-grabbing properties of words related to smoking reflect the motivational potency of smoking that could then account for the greater likelihood of a relapse.

Tobacco Dependence

Measures of tobacco dependence predict the likelihood that a smoker will achieve long-term abstinence from tobacco use. For instance, self-report measures of dependence tend to predict cessation and relapse (Breslau and Johnson 2000; Piper et al. 2004; Shiffman et al. 2004a). However, the various self-report measures of dependence often do not show good agreement with one another (Breslau and Johnson 2000; Moolchan et al. 2002). This finding is consistent with emerging evidence that nicotine dependence is multifactorial (Hudmon et al. 2003; Piper et al. 2004; Shiffman et al. 2004b). More recent evidence suggests that some dependence factors are more predictive of dependence than are others. In particular, self-report measures of tobacco dependence that assess heavy automatic smoking that is not discriminated on time or context are most consistently associated with heightened risk of relapse (Transdisciplinary Tobacco Use Research Center 2007; Piper et al. 2008). This finding is consistent with the observation that objective measures of a high rate of smoking, such as expired carbon monoxide levels and serum concentrations of cotinine, are often related to the likelihood of a relapse (Nørregaard et al. 1993; Faue et al. 1997; Kenford et al. 2002). Even in the best circumstances, however, measures of tobacco dependence account for only modest amounts of variation in risk of relapse. This finding is consistent with the notion that relapse is a function of multiple person factors, emerging processes, and contextual factors.

In addition to dependence, the sensitivity of a smoker to a nicotine reinforcement predicts a shorter latency to relapse (Perkins et al. 2002a). In contrast, formal laboratory measures of tolerance to the effects of nicotine do not appear to be significantly related to relapse (Perkins et al. 2002a).

Demographic and Lifestyle Variables

Studies have related numerous variables of demographic factors and lifestyle to vulnerability to relapse. For example, researchers have related an increased likelihood of relapse to younger age (Nides et al. 1995; Ockene et al. 2000; Hyland et al. 2004), a low SES or a low level of education (Nides et al. 1995; Ockene et al. 2000; Wetter et al. 2005b), being unmarried (Nides et al. 1995; Ockene et al. 2000), higher levels of tonic stress, and more stressors or the perception of a higher stress level (Swan et al. 1988; Wewers 1988; Cohen and Lichtenstein 1990; McKee et al. 2003). Of these factors, low SES and low educational status appear to be especially strong and consistent predictors of ability to abstain from smoking on a continuing basis (Mullen 2004; Wetter et al. 2005a; Fernández et al. 2006; Lee and Kahende 2007; Letourneau et al. 2007). In addition, some data from clinical trials and population samples indicate that women may be less likely than men to maintain abstinence from tobacco use (Hubert et al. 1987; Bjornson et al. 1995; Community Intervention Trial for Smoking Cessation 1995; Wetter et al. 1999; Smith et al. 2003; Hyland et al. 2004). However, such relationships are not consistently found across different data sets. For instance, as noted above, numerous data sets reveal that females are more likely to relapse to tobacco use than are males. However, a substantial number of studies fail to find such a relationship (Gritz et al. 1998; Killen et al. 2002; Westmaas and Langsam 2005; Velicer et al. 2007; Walsh et al. 2007). Besides the issue of consistency, additional topics deserve greater research attention. These topics include exploration of how the various person factors “work together” to affect the success or failure of smoking cessation. In addition, it is important to determine whether the different person factors are associated with different sorts of relapse mechanisms or processes; that is, regardless of the likelihood of relapse in different smoker groups, it is important to determine whether relapse processes “unfold” differently in such groups.

Research suggests that men and women may differ in sensitivity to environmental events. There is evidence, for instance, that environmental or conditioned cues related to use of nicotine, such as seeing information about nicotine dose, seeing others smoking, or receiving cues previously paired with nicotine, tend to elicit stronger motivational response to use the drug in women than in men (Pomerleau et al. 2005; Perkins et al. 2006; Leventhal et al. 2007; Walsh et al. 2007). These data agree with animal research data showing that nicotine-paired environmental cues are more effective in eliciting self-administration of nicotine in female rats than in male rats (Chaudhri et al. 2005). Complementary data suggest that men are more likely to be responsive to actual nicotine dose and other pharmacologic properties than are women (Perkins et al. 2006). If men are indeed more sensitive to nicotine’s pharmacologic properties than are women, this could explain why men who use NRT sometimes achieve higher levels of success with smoking cessation than do women who use NRT (Wetter et al. 1999; Perkins 2001; Cepeda-Benito et al. 2004) and why this finding did not hold for use of psychosocial interventions (Velicer et al. 2007).

Other studies show additional differences by gender. Data from study of a community-based population sample suggest that financial stressors may be more likely to inhibit smoking cessation in women than in men and that negative health events are more likely to prompt cessation in men (McKee et al. 2003). Other research shows that male smokers tend to be more reactive to relatively minor stressful events (i.e., hassles) than are women (Wetter et al. 1999; Delfino et al. 2001; Todd 2004). Although there is mounting evidence of differences by gender in reaction to nicotine or environmental cues (Perkins et al. 1999), and in motivation to use tobacco or nicotine, these differences have not been definitively linked with either relapse or differences by gender in relapse. Even less is known about the relationship of factors such as low SES or educational attainment to likelihood of smoking cessation (Wetter et al. 2005a).

One innovative approach to unraveling the complex interrelationships among the multiple person factors and relapse is to conduct classification or decision-tree analyses. These analyses have been used to determine whether categories of person factors (e.g., male versus female) comprise smoker subgroups that can be distinguished on the basis of their risk profiles for cessation failure. One example of this approach generated six subgroups of women smokers (Swan et al. 2004). For some subgroups, cessation failure appeared to be related to educational attainment and the number of previous attempts to stop smoking; for others, failure was more strongly related to body mass index and family history of depression (Swan et al. 2004). In contrast, male smokers comprised subgroups more highly distinguished by variables related to nicotine dependence, such as FTND score (Heatherton et al. 1991) and the number of years of smoking. In addition, male subgroups were distinguished on the basis of previous NRT and a history of depression. This type of classification or decision-tree analysis is useful because it has the potential to reveal factors that are highly predictive of cessation outcome in a subgroup of smokers, even if a factor is not important over an entire sample (Swan et al. 1997, 1999). Further research is needed to assess the replicability of such findings.

Psychiatric and Affective Dimensions

Some researchers have reported that the vulnerability to failure of smoking cessation or relapse to smoking is positively related to a history of depression, alcohol intake, a tendency toward negative affect, and an intolerance of psychological distress. As with most other individual differences, these relationships are either small in magnitude, inconsistent, or both. For example, both studies of population samples and clinical trials indicate that a history of depression or depressive symptoms predicts a greater likelihood of a relapse or a failure to stop smoking (Anda et al. 1990; Romans et al. 1993; Ferguson et al. 2003; Smith et al. 2003; Japuntich et al. 2007). However, one meta-analysis of data from 15 clinical trials failed to find such an effect (Hitsman et al. 2003). Studies in this meta-analysis generally excluded participants who were currently depressed or taking antidepressant medication.

One hypothesis is that if depression is correlated with vulnerability to relapse, the correlation may be attributable to the presence of two specific subpopulations of persons who have depression. Hitsman and colleagues (2003) observed that several studies have found a relationship between recurrent (multiple episode) depression and heightened risk of failure to stop smoking (Glassman et al. 1993; Covey et al. 1999; Brown et al. 2001). Haas and colleagues (2004) also found that a high rate of failure to stop smoking was associated with a history of multiple, but not single, episodes of depression. There also is evidence that current depression is more strongly associated with relapse than is past depression (Niaura et al. 2001; Japuntich et al. 2007; Turner et al. 2008). These results suggest that associations between depression and relapse may be attributable to subpopulations with depression, that is, those who are either currently depressed or who are prone to recurrent depression. These types of depression may be linked to risk of relapse because both were associated with recurrent or chronic negative mood (Niaura et al. 2001; Haas et al. 2004; Japuntich et al. 2007), and negative mood has repeatedly been linked with increased likelihood of relapse to smoking among persons with depression (Kahler et al 2002; Leventhal et al. 2008). However, it is possible that the heightened risk of relapse presented by current or recurrent depression is caused by other factors, such as poor coping skills or low self-efficacy.

There is also mixed evidence as to whether a history of alcohol abuse or dependence increases vulnerability to relapse to smoking. Some studies show an elevated risk of relapse (Hughes 1993; Breslau et al. 1996), but in others risk is not elevated (Covey et al. 1993; Hurt et al. 1995). Perhaps the best characterization of the evidence is the finding that active abuse of or dependence on alcohol constitutes a risk factor for relapse to smoking (Hurt et al. 1994; Kalman et al. 2001, 2002). However, there may be little or no risk if problems with alcohol are in remission (Hughes and Callas 2003). Evidence also shows that an active consumption of alcohol enhances the risk of relapse to smoking (Krall et al. 2002; McKee et al. 2003). Thus, the risk of relapse posed by alcohol use is not attributable to alcohol being a marker for a trait-like vulnerability to relapse but rather is attributable to the immediate (situational) effects of intoxication. However, there is modest evidence that another syndrome of disinhibition, attention-deficit/hyperactivity disorder, is associated with elevated risk of relapse to smoking (Humfleet et al. 2005).

An additional affective dimension that has been studied and may contribute to a heightened risk for relapse is the ability of a person to tolerate distress or to persist in a distressing task. Hence, several studies have associated measures of distress tolerance among smokers with the likelihood of or latency to relapse. In these studies, smokers with low vulnerability to relapse showed a greater persistence in tasks such as breathholding and mental arithmetic than did smokers with high vulnerability to relapse (Hajek et al. 1987; Brown et al. 2002). This finding provides evidence that characteristics such as an inability or unwillingness to tolerate distress is linked to a vulnerability to relapse. An inability to tolerate negative affect may be especially related to early relapse to smoking (Zvolensky et al. 2004; Brown et al. 2008).

In summary, the person factors that yield the strongest or most consistent prediction of relapse are measures of tobacco dependence and cognitive and attitudinal variables such as expectation of smoking reinforcement. In addition, measures of low SES and low educational attainment are also fairly consistently related to risk of relapse. Other relatively stable person factors are more modestly or inconsistently related to smoking relapse. Predictors of relapse vary from study to study, probably reflecting differences in the populations studied, different mixes of predictors included in the studies, and diverse methods and measures of the same target constructs. In addition, much of the variation in vulnerability to relapse is no doubt caused by other factors not measured in most studies—for example, exposure to episodic events and reactions to smoking cessation (Shiffman et al. 1996a,c; Kenford et al. 2002; Gwaltney et al. 2005a,b; McCarthy et al. 2006). Also, other variables may account for apparent direct associations between person factors and relapse. For instance, persons who drink heavily may be especially likely to socialize with other smokers, and an exposure to smokers may cause heightened risk of relapse.

Emergent Processes

Emergent processes are reflected in rapid changes in symptoms or behaviors that occur within several days before a lapse or relapse. Researchers have typically studied emergent processes across two temporal windows: one that begins at the time of smoking cessation and therefore captures initial responses to the event and a second that starts close in time to a lapse in cessation and captures changes in behaviors or symptoms leading up to the event. Both types of analyses provide evidence that emergent processes set the stage for smoking lapses and relapses. In general, emergent processes do not depend on the sort of treatment or cessation strategy used—for example, they occur in smokers receiving active or placebo pharmacotherapy.

Tobacco Withdrawal and Affective Symptoms

Perhaps the strongest evidence that emergent processes affect vulnerability to relapse comes from research on the tobacco withdrawal syndrome. Until recently, major reviews concluded there was little evidence that withdrawal symptoms were consistently related to the likelihood of a relapse (Hughes et al. 1990b; Patten and Martin 1996). However, in the decade ending in 2004, research has shed light on the nature of withdrawal, as well as its relationship to a relapse (Hughes 2007). First, in many smokers, perhaps most of those attempting to stop smoking, withdrawal symptoms are persistent and often remain elevated for months after an attempt to stop smoking (Gilbert et al. 1998, 2002; Piasecki et al. 1998, 2000). Second, withdrawal results (1) in great heterogeneity of symptoms, both in and across smokers, and (2) in volatile changes in affect and craving (see Figure 4.6 for craving pattern) (Piasecki et al. 2003a; McCarthy et al. 2006). Third, withdrawal results in vulnerability to more severe symptoms in reaction to environmental events than those that occur before smoking cessation (Figure 4.7) (McCarthy et al. 2006). In addition, research shows that some of these symptomatic effects of tobacco withdrawal are associated with an increased vulnerability to relapse. In general, smokers are more likely to relapse if withdrawal symptoms after smoking cessation are severe, increase in severity over time, or are highly variable (Piasecki et al. 1998, 2000, 2003b; McCarthy et al. 2006). Research also shows that withdrawal symptoms indicate vulnerability to relapse, as the result of either immediate increases in symptoms in response to abstinence from smoking or emergent changes in symptoms that occur across the days preceding a lapse in smoking cessation (Figure 4.8) (Piasecki et al. 2003b; McCarthy et al. 2006).

Figure 4.6

Individual estimated slopes in craving ratings over three weeks prequit, from just before to just after midnight on the quit date, and over three weeks postquit. Source: McCarthy et al. 2006. Note: The synthesized trajectories are based on multiple daily (more...)

Figure 4.7

Reactions for the three-week period before the quit date and the three-week period after the quit date. Source: McCarthy et al. 2006. Note: Data are from 70 smokers making a quit attempt. The y-axis depicts the magnitude of the average standardized coefficient (more...)

Figure 4.8

Withdrawal severity and lapse behavior among smokers who abstained for the first five days of a quit attempt. Source: Piasecki et al. 2003b. Note: Matched abstainers (MAs; n = 152) had never smoked during the follow-up period. Transient lapsers (TLs; (more...)

Withdrawal measures tap a variety of symptoms, but research suggests that self-reported craving and negative affect are the symptoms most predictive of relapse (West et al. 1989; Killen et al. 1991; Swan et al. 1996; Killen and Fortmann 1997; Piasecki et al. 1998; McCarthy et al. 2006). Other elements of the withdrawal syndrome, such as sleep disturbances or weight gain, are less consistent indices of a vulnerability to relapse (Wetter et al. 1995; Borrelli et al. 2001).

One piece of evidence that supports the role of withdrawal in precipitating relapse is research showing that withdrawal suppression appears to mediate the effects of pharmacologic treatments for smoking cessation (McCarthy et al. 2006; Shiffman et al. 2006). Statistical tests suggest that nicotine replacement and bupropion treatments reduce relapse risk to the extent that they suppress withdrawal symptoms. These studies suggest only partial mediation, however, consistent with the notion that other factors also influence relapse.

Although it is clear that emergent trends can set the stage for lapses and relapses to smoking, much remains to be learned about these associations. The time course by which emergent symptoms anticipate lapses, for example, needs more focused examination, because a gradual emergence of symptoms would permit the delivery of preventive interventions. Several studies show that craving and exacerbation of withdrawal symptoms precede lapses by several days (Piasecki et al. 2003b; McCarthy et al. 2006; Allen et al. 2008). However, as noted earlier in this section, other research shows that lapse-provoking increases in negative affect unfold within hours rather than days (Figure 4.9) (Shiffman and Waters 2004).

Figure 4.9

Negative affect in the days and hours preceding the first lapse for smokers who attributed their first lapse to a stressor or bad mood (stress trigger) or to some other type of event (other trigger). Source: Adapted from Shiffman and Waters 2004 with (more...)

Cognitive and Attitudinal Influences

Emergent cognitive and attitudinal processes may also enhance vulnerability to relapse. For instance, one study used real-time data recording to show that low abstinence and self-efficacy estimates, along with high expectations of smoking reinforcement, predicted a lapse to smoking that occurred on the following day (Gwaltney et al. 2005a). These effects were independent of scores for these measures on the day of smoking cessation, suggesting that the effects reflect emergent processes and not trait differences. Other research shows that persons who lapse to smoking appear to experience a marked dip in motivation during the week leading up to a lapse (Hedeker and Mermelstein 1996).

Timing and Motivational Significance of Emergent Processes

As previously noted, both symptomatic and attitudinal changes emerge across the period after smoking cessation and predict a relapse. Such changes may occur in the first few hours after cessation or in the hours or days just before a lapse to smoking (Figures 4.8 and 4.9) (Hedeker and Mermelstein 1996; Gwaltney et al. 2005a). Emergent symptoms may occur at any time during the postcessation period. However, research shows that symptoms that occur early in this period (e.g., in the first 24 hours) may be more tightly linked to outcomes than are later symptoms. For instance, in the Killen and Fortmann (1997) research, 2,600 smokers were entered into three studies through population-based recruitment. Across all three studies, craving ratings gathered early in the attempt to stop smoking (e.g., 24 hours postcessation) predicted relapses across the first year after cessation. Smokers with ratings in the highest quartile for craving were twice as likely to relapse as were smokers in the lowest quartile (31 versus 16 percent, respectively, averaged across all three studies). This research agrees with a wealth of other evidence that appearance of symptoms early in the attempt to stop smoking is negatively related to an ability to remain abstinent and to avoid a relapse (Killen et al. 1991; Doherty et al. 1995; McCarthy et al. 2006).

These data supported a large amount of evidence showing that various types of self-reports become markedly more predictive of ultimate outcomes as soon as persons have some experience in the attempt to stop smoking (Kenford et al. 2002; Gwaltney et al. 2005a). This evidence suggests that even though smokers have experience with abstinence from smoking and have memories of previous attempts to stop smoking, many are still unprepared for the forces unleashed by abstinence, which ultimately lead to a relapse.

Situational Instigators

A large body of research shows that lapses and relapse are associated with a limited set of contextual or situational features. Studies that use remote data collection techniques, in which data are gathered long after the lapse or relapse occurred, show that the contexts of lapses are characterized by features such as negative affect, urges to smoke, alcohol consumption, and cues to smoke (O’Connell and Martin 1987; Brandon et al. 1990). Research using real-time data acquisition shows that situations in which lapses occur (lapse situations) can be distinguished from temptations, that is, instances in which smoking did not actually occur, and from random occasions on the basis of the negative moods that occur in relation to lapses (Shiffman et al. 1996c). Negative moods are significantly more likely to co-occur with lapses than with temptations without smoking or to occur alone at randomly determined times. These negative moods tend to be strongly associated with reports of interpersonal stress such as arguments.

Shiffman and colleagues (1996c) also found that lapse situations can be distinguished from temptation situations and random occasions in that lapses are more likely to be accompanied by alcohol intake and strong urges to smoke that occur later in the day. Considerable additional evidence demonstrates that alcohol intake sets the stage for lapses (Borland 1990; Brandon et al. 1990). Also, both lapses and temptation situations tend to co-occur in the presence of other persons who are smoking. Thus, the availability of cigarettes and the modeling of smoking are associated with an increased desire to smoke. However, such cues do not reliably distinguish between the desire to smoke and the occurrence of smoking. Finally, the smoker is not a passive party in the progression to relapse. The data show that the execution of a coping response is more characteristic of temptation than of lapse occasions, suggesting that coping detoxifies temptation situations (Shiffman et al. 1996c).

Attesting to the powerful influence of contextual factors, recent research shows that smoking policies or the numbers of smokers in the person’s environment reliably predict likelihood of relapse or success in cessation (Letourneau et al. 2007). For instance, risk of relapse or rapidity of relapse is heightened by the number of smokers in a person’s social network, whether the person’s partner smokes (Mullen 2004; Letourneau et al. 2007; Macy et al. 2007; Solomon et al. 2007), and whether there are smoking restrictions at the person’s place of work or at home (Gilpin et al. 1999; Lee and Kahende 2007; Macy et al. 2007). However, some data suggest that bans in social contexts or restaurants may not be related to the success of cessation (Albers et al. 2007).

In general, research on situational indicators suggests that temptations to smoke and smoking lapses are contingent on internal symptoms of withdrawal (e.g., urges to smoke), alcohol use, and environmental signals of smoking, including the availability of cigarettes and the status of smoking restrictions. These findings are consistent with theories that drug availability and distress both constitute potent prods to motivation for drug use (Niaura et al. 1988; Skjei and Markou 2003; Baker et al. 2004).

Integration Across Relapse Influences

Person factors, situational cues, and emergent processes all influence risk of relapse. Moreover, research suggests that relapse risk reflects an interaction among these types of influences (Shiffman 1989a; Piasecki et al. 2002). For example, the intensity of the urge to smoke during temptation events predicts the likelihood of a lapse (Shiffman et al. 1997). However, this relationship depends on the level of urges to smoke reported on the day of smoking cessation. Thus, situational ratings are related to trait characteristics, such as tobacco dependence or emergent trends (e.g., withdrawal) that affect ratings for the urge to smoke on the day of smoking cessation. The relationship between the type of lapse situation and an emergent negative affect are shown in Figure 4.9. Data also show that the intensity of the urge to smoke during temptation episodes grows in the days leading up to a lapse (Shiffman et al. 1997). These data provide further evidence for the role of emergent processes in affecting situational reactions that, in turn, are related to lapse events. Finally, this same research shows that the level of the urge to smoke reported by persons on awakening predicted the likelihood of a lapse later on the same day. For some reason, urges to smoke in the morning, as opposed to urges reported at other times, tended to provide the most powerful predictions of lapses. In sum, research on the urge to smoke shows that the likelihood of a lapse reflects the interaction of trait factors, emergent processes, and situational cues.

Other data suggest interactive influences on the likelihood of a lapse. For example, Gwaltney and colleagues (2005b) found that persons who have low levels of trait-like self-efficacy at baseline show marked declines in self-efficacy in situations that produce strong urges or negative affect. Hence, trait measures capture a person’s vulnerability to succumb to situational challenges. A chief goal of future research is to elucidate how various types of influences on relapse interact to produce a relapse in a particular person at a particular time.

Evidence Summary

The 1988 Surgeon General’s Report, The Health Consequences of Nicotine Addiction, concluded that “nicotine is the drug in tobacco that causes addiction” (USDHHS 1988, p. 9). Studies show that animals self-administer or prefer nicotine over saline and that many people smoke to regulate blood concentrations of nicotine. For example, if smokers are given cigarettes with lower nicotine yields than their usual brands, they tend to smoke more intensely or to cover the filter ventilation holes to increase their nicotine intake. The VTA region of the brain and the mesocorticolimbic dopamine neurons originating in this brain site are primarily responsible for the positive reinforcing aspects of nicotine. An increase in levels of dopamine is mediated by nicotine directly stimulating nAChRs, primarily α7 homomeric and α4β2-containing nAChRs within the VTA, thus increasing activity of VTA neurons projecting to the nucleus accumbens and the frontal cortex. Nicotine stimulates α7 nAChRs on glutamatergic terminals that release glutamate, an excitatory neurotransmitter, which results in increased release of dopamine in the nucleus accumbens, amygdala, and frontal cortex. Nicotine also excites nAChRs on GABA-releasing terminals. Thus, levels of GABA, an inhibitory neurotransmitter, are also increased by nicotine. However, the interplay between the quick desensitization of nAChRs on the GABA neuron and the higher doses of nicotine required to desensitize nAChRs on the glutamate neuron result in a greater increase in dopamine levels.

The neurophysiology associated with withdrawal symptoms may be based on the type of symptoms experienced (e.g., somatic versus affective). It appears that nAChRs differ in their involvement in both the somatic and affective components of nicotine withdrawal and dependence. As seen in animal studies, β4 nAChRs play an important role in the somatic signs of withdrawal, whereas β2 nAChRs play an important role in the affective, but not somatic, aspects of withdrawal. The role of α4 nAChRs is unclear, but these receptors may play a role in both the affective and somatic withdrawal effects of nicotine addiction. The α7 nAChRs appear to be involved only in some of the somatic signs of nicotine withdrawal.

The amount and speed of nicotine delivery also plays a critical role in the potential for abuse of tobacco products. The speed and amount of nicotine delivered to the brain depend on the amount of nicotine in the product, the alkalinity of the product, and the route of administration. Nicotine, 3-(1-methyl-2-pyrrolidinyl)pyridine, is a volatile alkaloid in the tobacco plant, and its absorption and renal secretion is highly dependent on pH. Products with higher alkalinity are associated with greater amounts of nicotine in the nonionized or free base state, which can vaporize more easily into the gas phase, can be deposited directly on the lung tissues, and crosses cell membranes more rapidly than ionized nicotine. Tobacco products can contain constituents such as ammonia to increase the conversion of nicotine to the nonionized or free base state. Physical design features such as filter-tip ventilation also increase the free base fraction of nicotine (see Chapter 3, “Chemistry and Toxicology of Cigarette Smoke and Biomarkers of Exposure and Harm”). The fastest rate of nicotine delivery is through smoking cigarettes. Nicotine, when inhaled, enters the lungs, which present a large surface area of small airways and alveoli, undergoes dissolution in pulmonary fluid at a high pH, is transported to the heart, and then immediately passes to the brain. This rapid and bolus delivery of nicotine through cigarettes leads to greater control over the amount of nicotine delivered to the brain and results in higher abuse potential than do other tobacco- or nicotine-containing products.

Nicotine in the tobacco product and its kinetic profile are not the only factors that might contribute to a tobacco product’s potential for addiction. Other constituents may also serve as reinforcers or may enhance blood levels of nicotine or its effects. For example, animal studies have shown that nornicotine, a secondary tobacco alkaloid, functions as a reinforcer, but at less potency than nicotine. The effect of nornicotine in humans is unclear. Acetaldehyde, another constituent in tobacco smoke, which results from burning sugars and other materials in the tobacco leaf, may play a role in increasing the reinforcing effects of nicotine. In animal studies, acetaldehyde enhanced the acquisition of nicotine self-administration among adolescent rats but not adult rats. Extracts from flue-cured tobacco that appear to inhibit MAO activity in the brain may be another contributory factor to the reinforcing effects of cigarettes. Increased MAO inhibition results in increased levels of catecholamines. Current smokers have lower levels of MAO than do nonsmokers or former smokers.

Tobacco product design and ingredients contribute to the risk of addiction by reducing noxious effects such as the unpleasant taste of nicotine and unpleasant sensory effects (see Chapter 2, “The Changing Cigarette”). Such designs include ventilation to cool the smoke and ingredients such as menthol and chocolate that make nicotine inhalation more pleasant. Other nonnicotine factors can also contribute to addiction potential. These factors include the associative learning processes (internal and environmental cues linked with nicotine administration) that develop with repeated tobacco use. This associative learning can be as powerful as the direct effects of nicotine. For example, presenting smokers with sensory aspects of smoking without nicotine has resulted in a decrease in craving for cigarettes, a decreased subset of withdrawal symptoms, and short-term reinforcing efficacy similar to that of cigarettes containing nicotine (see Chapter 3, “Chemistry and Toxicology of Cigarette Smoke and Biomarkers of Exposure and Harm”).

Typically, smoking initiation occurs during adolescence. Research shows that adolescent smokers report some symptoms of dependence even at low levels of cigarette consumption, and animal studies show that sensitivity to nicotine in adolescents differs from that in adults. For example, results from the paradigms of self-administration and conditioned place preference in rats demonstrate that adolescence may be at a stage of development with higher sensitivity to nicotine exposure than that in adults. Using mixture modeling, longitudinal studies have identified multiple age-related trajectories of smoking behavior. These trajectories typically include smokers with early initiation of smoking and steep acceleration of smoking, persons who engage in experimental or light smoking, smokers with late initiation and accelerated progression of smoking, persons who stopped smoking, and those who never smoked. The group with early initiation and steeply escalating and persistent smoking has been associated with familial smoking, which reflects genetic and/or environmental risk factors, less parental support, and a risk for chronic heavy smoking in adulthood. Ethnic differences have also been observed for the age at initiation of smoking and the speed of progression in smoking. These studies showed that African Americans were more likely to have slower progression of smoking and a lower number of cigarettes smoked than do Whites. Studies that have looked at predictors for developing nicotine addiction or heavy smoking suggest the importance of both genes and environmental influences. Parental smoking, parental substance abuse disorders, and externalizing disorders (attention-deficit/hyperactivity, disruptive behavior, and alcohol and drug abuse) have been found to be predictive of nicotine dependence and/or daily smoking.

Initiation and persistence of smoking and nicotine dependence show strong heritability. Most coefficients of reported heritability range from less than 0.3 to more than 0.8 and vary on the basis of the smoking behavior phenotype examined and the social or environmental factors such as prevalence of smoking. The balance of evidence suggests that the risk of smoking initiation is influenced by both genetic and environmental factors, whereas the risk of smoking persistence may have a stronger genetic component. Although some genetic influences on smoking initiation and persistence are common, there are also separate and unique genetic influences for initiation and for persistence. Studies also suggest that the ability to stop smoking is under a strong genetic influence, and some consider this phenotype to be the key behavioral phenotype for nicotine dependence. Molecular genetic studies have been conducted to examine the specific genetic factors and biologic mechanisms involved in nicotine addiction. Most of the candidate gene studies have focused on genetic variation in nAChRs, relevant neurotransmitter pathways, or genes for nicotine-metabolizing enzymes. Candidate gene studies are association-based studies comparing prevalence of candidate gene variants in two unrelated groups—for example, nicotine-dependent versus nondependent persons. Examples of candidate gene variants that have been examined include nAChR subunits, such as CHRNA4 and CHRNA5; dopamine receptors D2 and D4 (DRD2 and DRD4) and dopamine transporter (DAT) genes; tryptophan hydroxylase, which is associated with serotonin biosynthesis; serotonin transporter 5HTTLPR, which is associated with genes that code for serotonin reuptake; MAOA and DβH genes, which affect norepinephrine pathways; genes in the endogenous opioid pathway (e.g., OPRM1); and genes involved in the metabolism of nicotine (e.g., CYP2A6).

To date, the only candidate genes with consistent evidence of an association with smoking behavior or nicotine dependence are CYP2A6 and 5HTT and SNPs in the CHRNA5/A3/B4 gene cluster. More research has been conducted on the effects of CYP2A6. Variants of P-450 CYP2A6 associated with *NULL or reduced activity are associated with reduced levels of the CYP2A6 enzyme and slower rates of nicotine metabolism, leading to higher plasma levels of nicotine for a given dose of nicotine. Persons who carry these variants with *NULL or reduced activity tend to have lower risk for becoming smokers, reduced cigarette consumption, and possibly higher likelihood of successful smoking cessation than that for persons with wild-type genotypes and higher rates of nicotine metabolism. Research in this area will be greatly enhanced when there is agreement in the field on phenotypes for smoking initiation, trajectory toward nicotine dependence, and nicotine dependence. One area of research that has provided promising initial evidence is the pharmacogenetics of treatment to aid in smoking cessation, which included examining genetic variations in drug-metabolizing enzymes and variations in drug targets to predict responses to treatment. It is important to recognize that although genes may play an important role in the various aspects of smoking behavior, the risk for smoking exists in persons without the gene variants, and it is predominantly exposure, rather than the host, that leads to smoking- related illnesses.

Studying recovery from smoking can provide valuable information on the nature of tobacco addiction and the factors that affect it. Relapse to smoking occurs at a high rate, and most smokers who ultimately relapse resume smoking early after the attempt to stop smoking. The risk for relapse is particularly high among those who lapse or engage in a single episode of smoking after their first day of cessation. The pattern of return to smoking varies across individuals. However, on average, a second lapse occurs within 24 hours of the first lapse, and lapse to relapse occurs three to five weeks after the cessation attempt. Several multidimensional factors may be associated with relapse. These factors include the expectations that the effects from smoking will be rewarding, confidence in the ability to stop smoking, educational status, and degree of tobacco dependence. Situational indicators suggest that temptations to smoke and smoking lapse and relapse are associated with alcohol use and environmental signals such as the sight of others smoking and the availability of cigarettes.

Evidence supports the relationship of tobacco withdrawal syndrome with vulnerability to relapse. Studies show three important findings for many smokers: (1) withdrawal symptoms are persistent and often severe for several months after an attempt to stop smoking, (2) the heterogeneity in withdrawal symptoms is great, and (3) features such as the severity, variability, and the course of withdrawal symptoms confer increased risk for relapse. Craving and negative affect are the withdrawal symptoms most predictive of relapse, including urges to smoke that are experienced immediately after awakening in the morning. Research suggests complex interrelationships within and across the different types of influences. Future research is needed to elucidate these interactions.

Conclusions

1. Nicotine is the key chemical compound that causes and sustains the powerful addicting effects of commercial tobacco products.
2. The powerful addicting effects of commercial tobacco products are mediated by diverse actions of nicotine at multiple types of nicotinic receptors in the brain.
3. Evidence is suggestive that there may be psychosocial, biologic, and genetic determinants associated with different trajectories observed among population subgroups as they move from experimentation to heavy smoking.
4. Inherited genetic variation in genes such as CYP2A6 contributes to the differing patterns of smoking behavior and smoking cessation.
5. Evidence is consistent that individual differences in smoking histories and severity of withdrawal symptoms are related to successful recovery from nicotine addiction.

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