Introduction

This chapter addresses evidence on smoking and health effects over a range of specific diseases and nonspecific but adverse consequences. Previous Surgeon General's reports have reviewed age-related macular degeneration, dental diseases, and diabetes. Since the last reviews were carried out on those topics, additional significant findings have been reported. Building on the reviews in the 2004, 2006, and 2010 reports, this chapter reassesses the state-of-the-evidence for these conditions giving consideration to more recent publications. Smoking and immunity, rheumatoid arthritis and systemic lupus erythematosus, and inflammatory bowel disease are being covered for the first time in this Surgeon General's report.

Eye Disease: Age-Related Macular Degeneration

Age-related macular degeneration (AMD) is the leading cause of blindness for persons 65 years of age and older in the United States (Congdon et al. 2004). Researchers have sought to identify modifiable risk factors and to test strategies to modify the natural history of AMD, but preventive therapy is not available for early AMD. Located at the center of the optical axis, the macula is a component of the retina and contains the fovea, a highly specialized area that is responsible for high-resolution vision. The retina consists of neural tissues, including photoreceptors, which convert energy from visible light to electrical signals and sends these signals to the brain for processing. Photoreceptors (rods and cones) in the retina have high metabolic requirements; they replace their outer segments daily. The metabolic functions of the retina are supported by retinal pigment epithelium (RPE), which phagocytizes an estimated 25,000–30,000 outer segment membranes per day. This high rate of activity is made possible by an exchange of nutrients and the removal of waste within the retinal blood supply through the choriocapillaris. RPE and its anchor, Bruch's membrane, form a blood-retinal barrier to this exchange. Thus, the complex of choriocapillaris, RPE, and Bruch's membrane serves as the nutritional source for the sensory retina. Researchers hypothesize that AMD stems from changes in each of the tissues in this complex.

AMD is an umbrella term for a variety of degenerative changes in the macula. The disease's early stages are characterized by pigmentary disturbances, development of drusen (deposits of extracellular material), and atrophic changes. The late stages are characterized by RPE atrophy; loss of photoreceptors (which occurs in atrophic AMD or geographic atrophy [GA]); and, less commonly, neovascular (NV) AMD. With NV AMD, new but unstable blood vessels develop in the choroid and grow under or through the RPE via breaks in Bruch's membrane. Leakage from these NV membranes may lead to detachment of RPE, hemorrhage, and the later formation of a disciform scar. The late stages of AMD are associated with loss of vision—classically the loss of central vision, which is critical for such activities as reading and performing near work (such as typing, cooking, and sewing).

With the discovery of highly significant associations between AMD and several complement pathway-associated genes, a coherent story for inflammation as the model for AMD pathogenesis is emerging (Anderson et al. 2010). Morphologic changes associated with AMD include thickening of Bruch's membrane, the formation of basal deposits within Bruch's membrane, and accumulation of drusen. Drusen accumulate within Bruch's membrane in the same area in which basal deposits form. A wide variety of complement-related molecules have been reported in drusen; some authorities regard these molecules as the byproduct of chronic local inflammatory processes. The dysregulation of the complement cascade is likely an early predisposing step in the development of drusen, but the role of the complement in advanced AMD, either NV or GA, is not yet clear. At least two types of drusen are recognized clinically based on their appearance. Small, hard drusen are a common feature of aging; while large, soft drusen are commonly found with aging, but are also a risk factor for the development of advanced AMD. Drusen can appear and disappear over time, however, making them unstable biomarkers for risk of AMD (Bressler et al. 1995; Klein et al. 1997). Moreover, most persons with large, soft drusen do not develop advanced AMD (Klein et al. 1997), and the epidemiologic patterns associated with advanced AMD may not be the same as those for drusen-defined early AMD. Thus, the specific phenotypes of early AMD that are most likely to progress to NV or GA AMD need further characterization. This lack of specificity should be considered when interpreting evidence of the association between smoking and early versus advanced AMD.

Dental Disease

Diseases of the teeth and their supporting structures have a significant impact on social, economic, and personal well-being. In 2009, more than $102 billion was spent on dental care in the United States (National Center for Health Statistics 2012), and acute dental conditions resulted in an estimated 1.6 million days of missed school and 2.4 million days of lost work annually (USDHHS 2000).

Diabetes

This section addresses type 2 diabetes mellitus. The prevalence of type 2 diabetes in the United States has increased dramatically during the past few decades, in parallel with the rapid rise in the country's prevalence of overweight and obesity. According to CDC (2011), 25.8 million Americans, or 8.3% of the population, had diabetes in 2010. About 1.9 million new cases of diabetes, mostly type 2, are diagnosed in U.S. adults (CDC 2011). The raw and age-adjusted prevalence of diabetes is substantially higher in minority populations. Among persons 20 years of age and older in 2007–2009, the non-age-adjusted prevalence of diabetes was 12.6% for non-Hispanic Blacks, 11.8% for Hispanics, 8.4% for Asian Americans, and 7.1% for non-Hispanic Whites (CDC 2011). Diabetes is a leading cause of cardiovascular mortality. Nearly two-thirds of people with diabetes die of cardiovascular disease (Nathan et al. 1997). Diabetes is also the leading cause of new cases of blindness, kidney failure, and nontraumatic lower-limb amputation (CDC 2011). Beyond its unfortunate consequences for quality of life, the economic cost of diabetes is high. In 2007, the estimated total cost of diagnosed diabetes in the United States was $174 billion, including $116 billion from direct medical costs and $58 billion from three indirect costs: disability, work loss, and premature mortality (CDC 2011).

A growing body of evidence from epidemiologic studies suggests that smoking is associated with increased risk of type 2 diabetes (Willi et al. 2007), and studies of pathogenesis also support a potential causal relationship between smoking and diabetes (Xie et al. 2009). However, type 2 diabetes is multifactorial in etiology. The rising prevalence worldwide is generally attributed to increasing overweight and obesity, which is now an important concern in both high- and low-income countries. In many high-income countries, the prevalence of diabetes has risen even as smoking rates have dropped (Chen et al. 2012).

The 2010 Surgeon General's report (USDHHS 2010) reviewed the evidence on the role of smoking in diabetes. This chapter reviews evidence on the association between active smoking and the incidence of diabetes and evaluates the extent to which the evidence supports a causal relationship between smoking and that disorder. Because of limited evidence, this chapter will not review the evidence on the effects of passive smoking on diabetes, the adverse effects of smoking on the development of diabetic complications, or the benefits of smoking cessation among people with diabetes. These topics were discussed in a comprehensive review by Tonstad (2009).

Epidemiologic Evidence

Description of the Literature Review

This systematic review and meta-analysis updates the literature from the 2007 review and meta-analysis by Willi and colleagues (2007) covering the association between active smoking and type 2 diabetes. The present review examined articles published between May 2007 (the cutoff date for the paper by Willi and colleagues [2007]) and January 2010. Using the same strategy as that employed by Willi and colleagues (2007), articles were identified through a search of PubMed and Embase. The search incorporated MeSH terms across three themes: smoking or cigarette; diabetes mellitus or glucose metabolism irregularity; and studies with a prospective design. For inclusion in the meta-analysis, studies had to meet several criteria:

• Report data from an original study (i.e., not just review articles)
• Focus on an adult population (i.e., 16 years of age or older)
• Incorporate level of smoking intensity or exposure to nicotine as a primary predictor or one of the cofactors for risk of diabetes, not just as a covariate or confounder

Studies were excluded if they met any of the following conditions:

• Included participants with previously diagnosed diabetes at the beginning of the study
• Used inappropriate comparison groups (i.e., a comparison group other than nonsmokers or former smokers)
• Could not provide original data after inquiries from investigators

If data from a study were reported in several publications, the most relevant or most recent publications were used to avoid double counting.

Methods

The present review abstracted and reviewed 25 studies (Cassano et al. 1992; Perry et al. 1995; Rimm et al. 1995; Kawakami et al. 1997; Njølstad et al. 1998; Sugimori et al. 1998; Uchimoto et al. 1999; Manson et al. 2000; Nakanishi et al. 2000; Strandberg and Salomaa 2000; Hu et al. 2001; Wannamethee et al. 2001; Will et al. 2001; Montgomery and Ekbom 2002; Sawada et al. 2003; Carlsson et al. 2004; Eliasson et al. 2004; Sairenchi et al. 2004; Foy et al. 2005; Lyssenko et al. 2005; Patja et al. 2005; Tenenbaum et al. 2005; Waki et al. 2005; Houston et al. 2006; Meisinger et al. 2006) from the meta-analysis by Willi and colleagues (2007). Two of the 25 studies (Cassano et al. 1992; Sawada et al. 2003) were excluded from the present review because smoking was used as only a confounder for risk of diabetes. The study by Perry and colleagues (1995) was also excluded, because similar results were reported in a later report (Wannamethee et al. 2001).

This review also abstracted and included 21 new studies (Burke et al. 2007; Cugati et al. 2007; Dehghan et al. 2007; Holme et al. 2007; Hur et al. 2007; Mozaffarian et al. 2007, 2009; Onat et al. 2007; Schulze et al. 2007; Hayashino et al. 2008; Lyssenko et al. 2008; Magliano et al. 2008; Nagaya et al. 2008; Nichols et al. 2008; Park et al. 2008; Chien et al. 2009; Cho et al. 2009; Cullen et al. 2009; Hippisley-Cox et al. 2009; Laaksonen et al. 2010; Yeh et al. 2010) that were not part of the meta-analysis by Willi and colleagues (2007). In addition, based on a careful review of reference lists from all relevant publications, 3 studies published before 2007 (Keen et al. 1982; Bonora et al. 2004; Harding et al. 2006) were included. Therefore, this updated meta-analysis included 46 studies about active smoking and risk of type 2 diabetes.

Study Characteristics

Table 10.8S depicts the characteristics of the 51 studies that were selected for the present meta-analysis. All were prospective cohort studies: 44 reported the incidence of diabetes as the sole outcome of interest, and 2 reported the incidence of diabetes plus impaired glucose tolerance (Carlsson et al. 2004; Houston et al. 2006). The association between smoking and risk of diabetes was the primary focus of 27 studies, and smoking was included as one of the cofactors in the other 19 studies.

The diagnosis of diabetes was ascertained by biologic measures in 28 studies, reported by patients or physicians in 11 studies, and determined by other methods (e.g., examination of hospital medical registries and insurance registries) in 7 studies. Because the definition of diabetes and the cutoff points used to establish its presence changed from 1980–2009, the fasting glucose thresholds varied across studies (15 studies did not explicitly describe criteria for this threshold):

• 140 milligrams (mg)/deciliter (dL) (7.8 millimoles per liter [mmol/L]) or higher for 6 studies
• 126 mg/dL (7.0 mmol/L) or higher for 20 studies
• 120 mg/dL (6.6 mmol/L) or higher for 1 study
• 110 mg/dL (6.1 mmol/L) or higher for 3 studies
• 100 mg/dL (5.6 mmol/L) or higher for 1 study

The meta-analysis included more than 3.9 million participants and 140,813 cases of diabetes, with the number of participants in the studies ranging from 241 to 2,540,753. Follow-up ranged from 3.5–30 years, with a median of 10 years. Two studies included only women, 13 included only men, and the remaining 31 included both men and women. Among these 31 studies, 6 reported results for both genders and for the total population, and 5 studies reported results separately by gender.

The studies in the meta-analysis adjusted for many risk measures, such as:

• Age (42 studies)
• Body mass index (33 studies)
• Intensity of physical activity (28 studies)
• Level of alcohol consumption (24 studies)
• Heredity or family history of diabetes (19 studies)
• Gender (19 studies)
• Level of education (13 studies)
• Diet (11 studies)
• Waist circumference or waist-to-hip ratio (11 studies)
• Race or ethnicity (6 studies)

Finally, statistical models in 24 of the studies controlled for biomarkers, such as fasting glucose, insulin, and lipid profile.

Risk of Diabetes: Smokers Compared With Nonsmokers

Based on 51 comparisons from the 46 studies, active smokers had an increased risk of developing type 2 diabetes compared with nonsmokers, with a pooled RR of 1.37 (95% CI, 1.31–1.44) (Figure 10.1). There was evidence of heterogeneity of the RRs across studies (Q statistic = 273.1; P <0.001; I² = 82%) that was statistically significant, given the extremely large number of participants in the analysis. Among the 51 comparisons, 40 showed a significantly increased risk of diabetes among smokers, 10 showed a nonsignificant association between smoking and risk of diabetes (in 8 of these studies the RR exceeded 1.00; in 2 it did not), and 1 showed a significant inverse association between smoking and risk of diabetes.

Figure 10.1

Adjusted relative risk (RR) of diabetes, current smokers compared with nonsmokers. Note: CI = confidence interval.

The pooled risk changed little (RR = 1.42; 95% CI, 1.34–1.51) when the two largest studies, which may have dominated the results (Will et al. 2001; Hippisley-Cox et al. 2009), were excluded in a sensitivity analysis (results not shown in a table or figure).

The Begg funnel plot was used to evaluate publication bias (Begg and Mazumdar 1994; Egger et al. 1997). Visual inspection revealed asymmetry and the possibility of publication bias, although the finding was not significant (p = 0.552) (Figure 10.2A). Therefore, a sensitivity analysis was conducted using the trim-and-fill procedure, which conservatively imputes studies to mirror the positive studies that cause asymmetry in funnel plots (Figure 10.2B). The pooled RR incorporating the imputed studies remained significant (RR = 1.26; 95% CI, 1.21–1.33).

Figure 10.2

Log relative risk of diabetes for current smokers, funnel plots without (A) and with 225 (B) trim-and-fill procedure. Note: lnrr = natural log of relative risk.

Subgroup Analysis

To explore potential heterogeneity, analyses were stratified by several key study characteristics and clinical factors (Table 10.9). In each stratified analysis, smokers demonstrated a significantly increased RR for diabetes. The quality of the study characteristics did not influence the results substantially. A stronger association between smoking and incident diabetes was found in studies in which blood glucose was measured to assess the presence of diabetes at baseline and endpoint, compared with studies that relied on reports by patients or physicians or on registry data. Studies that used higher fasting glucose thresholds as the definition of diabetes also showed a stronger association between smoking and diabetes.

Table 10.9

Stratified analyses of pooled relative risk (RR) for incident diabetes from smoking.

Dose-Response Analysis

To generate pooled estimates for the dose-response analysis, the meta-analysis examined studies in which measures of association were stratified by level of smoking intensity. These levels were categorized as never, former, light (0–19 cigarettes smoked/day in most studies; 0–15 cigarettes/day in some studies), and heavy (20 or more cigarettes/day in most studies; 15 or more in some studies). As shown in Table 10.9 and Figure 10.3, the RR increased with higher levels of smoking intensity. When compared with never smokers, former smokers had an RR of 1.14 (95% CI, 1.09–1.19). Compared with nonsmokers, light smokers had an RR of 1.25 (95% CI, 1.14–1.37), and heavy smokers had an RR of 1.54 (95% CI, 1.40–1.68).

Figure 10.3

Pooled relative risk of diabetes associated with various levels of smoking intensity. Note: Light smoking defined in most studies as current smoking of 0–19 cigarettes/day (0–15 in some studies), and heavy smoking defined in most studies (more...)

Smoking Cessation

A review by Filozof and colleagues (2004) found that smoking cessation improves insulin sensitivity in spite of short-term weight gain. In the Atherosclerosis Risk in Communities Study, the risk of incident type 2 diabetes for short-term quitters was above that for current smokers, but it then decreased to the level of never smokers at 12 years (Yeh et al. 2010). Another large cohort study found that smoking cessation reduced RRs for diabetes to those for never smokers after 5 years for women and 10 years for men (Will et al. 2001). Finally, a cohort study from Korea (Hur et al. 2007) showed that smoking cessation is followed by a decreasing risk of diabetes that reaches that of never smokers in the long term.

Immune Function and Autoimmune Disease

This section considers the evidence related to the adverse effects of smoking on the immune system and whether smoking is a cause or contributory cofactor in immunologically mediated diseases. This section also covers the current understanding of the cellular and molecular mechanisms by which smoking affects immunity (Holt and Keast 1977; Sopori 2002; Stampfli and Anderson 2009). Previous reports from the Surgeon General have not covered this topic in depth. Several reports have covered effects of smoking on respiratory immunity, most recently in 2010, and diseases for which the immune system plays a key role (Tables 10.10–10.12) (USDHHS 2010).

Table 10.10

Conclusions about the adverse effects of tobacco use and exposure to tobacco smoke on infectious diseases, from previous Surgeon General's reports.

Table 10.11

Conclusions about the adverse effects of tobacco use and exposure to tobacco smoke on asthma, from previous Surgeon General's reports.

Table 10.12

Conclusions about the adverse effects of tobacco use and exposure to tobacco smoke on chronic obstructive pulmonary disease, from previous Surgeon General's reports.

Overview of Innate and Adaptive Immune Defense

The immune system exerts its beneficial and detrimental effects via a complex, highly cross-regulated network of cellular and molecular defense mechanisms. Therefore, any discussion of the effects of smoking on immunity should consider the multiple and richly interconnected tiers of immunological defenses that include innate defense mechanisms, such as barrier functions, soluble defense molecules, and cellular defenses and adaptive immune responses (Figure 10.4).

Figure 10.4

Overview of immune defects caused by smoking in the lungs. Source: Stampfli and Anderson 2009. Reprinted with permission from Macmillan Publishers Ltd., © 2009. Note: Cigarette smoke has both proinflammatory and immune-suppressive effects on the (more...)

Immunity comprises an array of defense mechanisms that protect the host from infection by pathogens (Holt et al. 2008; Kohlmeier and Woodland 2009). Many of these defense mechanisms are mediated by protective inflammation. The immune system also plays a central role in internal homeostasis, guarding against malignant transformation to prevent cancer and responding to tissue damage after injury (Oppenheim and Yang 2005; Rock et al. 2010; Vesely et al. 2011). In health, the immune system does not normally damage host tissue or attack the diverse self-antigens in the human body because of a state of self-tolerance (Wing and Sakaguchi 2010). However, when turned against the host, immune mechanisms can contribute to an array of disorders, many of which have an inflammatory basis and, in extreme cases, provoke autoimmune diseases, such as systemic lupus erythematosus (SLE or lupus).

Conventionally, the immune system is divided into two broad tiers: innate immunity and adaptive immunity. Innate immunity represents a large family of effector mechanisms, including barrier functions, soluble defense molecules that provide nonspecific protection against harmful agents, and cellular defenses triggered by pattern recognition receptors (PRRs) that recognize conserved pathogen-associated molecular patterns (PAMPs) (Table 10.13) (Janeway 1989; Kawai and Akira 2011). Innate immune mechanisms play an important role in responding to tissue damage. These responses arise when so-called damage associated molecular patterns (DAMPs) that are normally cryptic, become exposed after injury and activate immune cells via PRRs (Matzinger 2002; Rock et al. 2010). The term “alarmin” describes collectively an array of structurally diverse host proteins that are released after tissue injury or during infection to mobilize host defense and tissue repair mechanisms. These innate immune mediators include interleukin-1alpha (IL-1α) and related members of the IL-1 family (e.g., IL-18, IL-33), S100 proteins, defensins, heat-shock proteins (HSPs), uric acid, antibacterial peptides, hepatoma-derived growth factor, eosinophil-derived neurotoxin, cathelicidins, nucleotides, interferons (IFNs), and high mobility group box 1 (HMGB1) (Oppenheim and Yang 2005).

Table 10.13

Pathogen-associated molecular patterns and their respective pattern recognition receptors.

The main cell types involved in mediating innate immunity are epithelial cells and leukocytes, especially granulocytes (neutrophils and eosinophils) and mononuclear lineage cells (monocyte and macrophage subpopulations) (Figure 10.5). Macrophages play a critical role in the destruction of pathogens and the removal of cell debris and dying cells (Metschnikoff 1887; Murray and Wynn 2011), but neutrophils and other cell types have important phagocytic activities (Walsh et al. 1999; Soehnlein and Lindbom 2010). Macrophage phagocytosis is usually accompanied by macrophage activation, which leads to a proinflammatory and procoagulant state. Other cells—notably natural killer (NK) cells and natural killer T (NKT) cells (Berzins et al. 2011; Sun and Lanier 2011), mast cells (Galli et al. 2005), and nuocytes (Neill et al. 2010), which lack conventional surface markers—constitute important components of innate immunity. Innate reactions characteristically can be activated very rapidly but do not hold a molecular memory of past immunologic exposures.

Figure 10.5

Diagram of innate and adaptive immunity. Source: Illustration created by Jake Nikota for this Surgeon General's report. Note: To illustrate the various aspects of the immune system, the figure is divided into physical barriers, innate, and adaptive immunity. (more...)

The main cell types involved in mediating adaptive immunity are T and B lymphocytes (T cells and B cells) (Figure 10.5). In contrast to innate immunity, adaptive immunity retains memory of past insults, such that if an individual encounters the same insults later in life, a much more rapid and amplified response can be mounted. T cells respond mostly to short peptide fragments of foreign proteins (antigens) presented in the context of major histocompatibility complex (MHC) Class I or II (MHC I or II) proteins (Zinkernagel and Doherty 1974). Antigens must usually be presented to T cells by specialized antigen-presenting cells, of which the dendritic cell subtypes are among the most important (Steinman and Cohn 1973; Banchereau and Steinman 1998). Presentation of soluble, mainly extracellular proteins is mediated by MHC II, whereas intracellular proteins (for example from an intracellular pathogen such as a virus) are presented on MHC I molecules. T cells, with the surface marker CD4, classically recognize MHC II, whereas CD8 positive (CD8+) lymphocytes recognize antigen on MHC I molecules. Human self-antigens are also presented on MHC I molecules constitutively but do not trigger immune reactions because of tolerance (i.e., protective processes). T cells exert their effects by differentiating into effector cells able to secrete cytokines and chemokines that regulate defensive inflammation. Some T lymphocytes differentiate into cytotoxic cells that kill cellular targets. B cells recognize unprocessed antigen in its natural configuration and exert their effects largely by producing one of the five major classes of immunoglobulin (Ig) antibodies. This process requires antigen presentation and help from CD4+ T cells, which are designated as CD4+ T helper (Th) cells.

The division between innate immunity and adaptive immunity is convenient but does not reflect the intimate interaction between the two. For example, stimulation of dendritic cells by PRR not only activates these cells to move to secondary lymphoid organs and present antigen but also shapes the nature of the subsequent immune reaction, as PRR stimulation induces specific costimulatory molecules and immune mediators that control T cell polarization and acquisition of effector function (Macagno et al. 2007).

Both overactivated or inappropriate innate and adaptive immune responses can directly damage the host, which leads to inflammation, tissue damage, and disease (Murray and Wynn 2011; Park et al. 2012). Mechanisms underlying these immune-driven pathologies are complex and may include innate and adaptive processes targeted at environmental agents, as well as autoreactive responses that drive autoimmune diseases. The latter are associated with a breakdown in tolerance. Tolerance is conventionally described as constituting central tolerance, where self-reactive lymphocytes are killed by apoptosis in the thymus, or peripheral tolerance, where potentially self-reactive cells survive but are rendered immunologically nonresponsive (Wing and Sakaguchi 2010; Zanoni and Granucci 2011).

Immune surveillance does more than just detect and eliminate or contain pathogens. The immune system is also very actively involved in maintaining homeostasis and continually surveys tissues to eliminate damaged cells and cells that are undergoing malignant transformation (Vesely et al. 2011).

With regard to the effects of cigarette smoking on immunity, a critical issue is the degree to which the marked effects of smoking on inflammation are considered immune effects. Because inflammation is an effector arm of immunity and subserves a defensive role in health, this chapter considers adverse effects of smoking on inflammation, particularly where inflammation is directly part of host defense.

Adverse Effects of Cigarette Smoke on Specific Cellular and Molecular Mechanisms

There is convincing evidence that cigarette smoke impacts a wide range of host defense functions (Sopori 2002; Barnes 2004; van der Vaart et al. 2004; Stampfli and Anderson 2009; Vesely et al. 2011). Some findings across studies have been inconsistent and controversial. These inconsistencies are likely caused in part by differences in key characteristics of study participants, such as smoking history, genetic susceptibility, and SES. Similar methodological issues can apply to animal models and in vitro systems, in which smoke exposure parameters, such as frequency, duration, and mode of smoke exposure (nose-only versus whole body exposure; sidestream smoke included or excluded) vary greatly among studies. Additionally, experimental systems do not fully represent the circumstance of human smoking, but the results are informative on particular elements of immune response and mechanisms.

The following discussion addresses the effects of cigarette smoke on individual components of the immune system, starting with how inhaled cigarette smoke impacts the respiratory epithelium, as these are the first cells to be exposed to cigarette smoke, followed by more classical innate immune cells, including mononuclear cells and macrophages, and finally adaptive immune cells. The cellular and molecular mechanisms discussed in this section are often studied in isolation in experimental investigations. However, the effects of smoking mediated through these mechanisms occur in the context of the full immune system, and every effect observed in an experimental system, even if statistically significant, may not affect overall immunity. As discussed above, the immune system exerts its effects via a complex, highly cross-regulated and often redundant network of cellular and molecular defense mechanisms. Therefore, moving from observations related to the effect of smoking on a particular element of the immune system to a broader interpretation can be problematic.

Respiratory Epithelium

Tobacco smoke reaches the respiratory epithelium at very high concentrations, so that many of the defined effects of smoking on immune effector pathways are exerted on this tissue (Figure 10.6). Far more than a simple barrier, epithelial cells are able to sense microbial agents through PRRs and exert direct antimicrobial effector function (Gribar et al. 2008). This innate immune recognition enables epithelial cells to respond to antigens and allergens, thereby initiating the first step in the host-pathogen interaction, triggering early host defense, and priming the adaptive immune response concurrently. Depending on the level of the airway, the airway epithelium is comprised of a variety of ciliate cells (not found below the bronchi) and secretory cells. This pattern is held in fixed ratio in health, but may change toward more secretory cells, particularly mucus secreting goblet cells as a result of infection. Similarly, the secreted mucins vary in rheology and composition in response to insults. Intercalated between and below the epithelial cells, per se, are antigen-presenting cells (e.g., dendritic cells) and several intraepithelial leukocytes (Lambrecht et al. 1998). The fluid that bathes the epithelium contains an abundance of antioxidants, antiproteases, and innate defense molecules (Widdicombe 1995). Cigarette smoke diminishes key antibacterial defense proteins within the airway lining fluid, including surfactant proteins, beta-defensins, secretory leucocyte protease inhibitors, and lysozymes (Shibata et al. 2008). The epithelium is also metabolically active and able to transform a range of xenobiotics in tobacco smoke. These enzyme systems are induced by cigarette smoke (Spira et al. 2004) and detoxify pollutants but can also convert some carcinogens to more active forms as reviewed in the 2010 Surgeon General's report.

Figure 10.6

The respiratory epithelium. Source: Holt et al. 2008. Reprinted with permission from Macmillan Publishers Ltd., on behalf of Cancer Research UK, © 2008. Note: Local immune cells in the two lung compartments showing capture of airborne antigens (more...)

The ciliated respiratory epithelium is a critical defensive barrier able to protect the host against harmful environmental agents. The epithelium forms a barrier via tight junctions between cells and protects by sweeping particles away in the overlying mucus gel layer. Cigarette smoke disrupts the tight bonds at the adherent junctions between epithelial cells, compromising the integrity of the physical epithelial barrier and leading to increased alveolar epithelial permeability (Boucher et al. 1980; Jones et al. 1980). This increased permeability of the respiratory epithelium exposes the lung tissue to inhaled substances, such as microbial agents and other environmental factors. Cigarette smoke further exerts a rapid and adverse effect on mucociliary clearance (Jones et al. 1980; Burns et al. 1989; Dye and Adler 1994).

In vitro studies have demonstrated that cigarette smoke extract activates epithelial cells to produce proinflammatory mediators such as IL-8 (Mio et al. 1997). Contrasting with these observations, cigarette smoke extract attenuates in vitro production of proinflammatory mediators by epithelial cells following stimulation with PAMPs, such as the Toll-like receptor 4 (TLR4) ligand lipopolysaccharide (LPS) (Laan et al. 2004). Cigarette smoke extract also suppresses type I IFN-mediated antiviral immunity following stimulation with double-stranded RNA, a mimic of viral replication, and rhinovirus in lung fibroblast and epithelial cells (Bauer et al. 2008a; Eddleston et al. 2011). In agreement with this observation, influenza-infected nasal epithelial cells from smokers produced less type I IFN than similarly infected nasal epithelial cells from nonsmokers (Jaspers et al. 2010). Moreover, in vitro exposure to cigarette smoke extract modulated rhinovirus-induced chemokine production by airway epithelial cells. Although the neutrophil chemoattractant IL-8 increased in response to rhinovirus infection, expression of chemokine (C-X-C motif) ligand 10 (CXCL-10)—also known as IP-10—and chemokine (C-C motif) ligand 5 (CCL5) was attenuated by cigarette smoke extracts (Eddleston et al. 2011).

Recent evidence from animal models suggests that smoke-exposed epithelium upregulates retinoic acid early transcript 1, the ligand for NK group 2D (NKG2D), rendering it susceptible to NKG2D-mediated cytotoxicity (Borchers et al. 2009). These results imply that aberrant NKG2D ligand expression in the pulmonary epithelium may contribute to the development of structural changes found in COPD and emphysema.

In summary, there is evidence that cigarette smoke activates the airway epithelium to produce proinflammatory mediators, but suppresses the response of the epithelium to viral and bacterial PAMPs. The majority of reports published to-date utilized in vitro exposure of epithelial cell lines or primary cells to cigarette smoke extract. Studies are required to validate these observations in smokers. There is further evidence that smoking compromises airway epithelial host defense by altering the lining fluid, usually by chemical or oxidative inactivation; paralyzing ciliary beating; and damaging the tight junctions between airway epithelial cells.

Alveolar Macrophages and Mononuclear Cells

Alveolar macrophages play a key role in sensing and eliminating hazardous agents due to their strategic positioning in the luminal space of the lung (Twigg 2004; Murray and Wynn 2011). In health, macrophages protect the host because of their ability to recognize, phagocytose, and destroy pathogens; clear cellular debris before triggering secondary inflammation; and release proresolution and healing growth factors. However, in disease, the destructive capacity of macrophages may damage host tissue, their failure to clear debris may perpetuate inflammation, and their adoption of certain phenotypes may promote tissue scarring and also exert strong immune suppression on lymphocytes. Partly for these reasons, alveolar macrophages have been discussed as central orchestrators of COPD (Barnes 2004).

Cigarette smoking increases the number of macrophages in the alveolar space of smokers and patients with COPD and activates macrophages to produce proinflammatory mediators, reactive oxygen species, and proteolytic enzymes (Hoidal and Niewoehner 1982; de Boer et al. 2000; Russell et al. 2002), providing a cellular mechanism that links smoking with inflammation and tissue damage. Moreover, cigarette smoking compromises the ability of alveolar macrophages to phagocytose bacteria and apoptotic cells (King et al. 1988; Berenson et al. 2006; Hodge et al. 2007, 2008). The process of removing moribund cells in a sterile manner before they proceed to full necrosis and rupture is called efferocytosis (Vandiver et al. 2006). With regard to COPD, a disease with a high number of apoptotic cells in the lungs, it is tempting to speculate that failure to clear dead cells may lead to secondary inflammation through the release of a number of DAMPs or alarmins, including HMGB1, adenosine triphosphate, HSPs, and IL-1α. Moreover, failure to engage anti-inflammatory and quiescence mechanisms that are usually activated during normal efferocytosis may further perpetuate cigarette smoke-induced inflammation (Liu et al. 2008a). Although of interest, further investigation is required to establish the relevance of these processes to cigarette smoke-induced inflammation and, importantly, human health.

As for the epithelium, smoke impairs PAMP sensing and signaling in humans and mice (Soliman and Twigg 1992; Drannik et al. 2004; Chen et al. 2007b; Gaschler et al. 2008). In a study by Gaschler and colleagues (2009), macrophages isolated from cigarette smoke-exposed mice expressed a skewed inflammatory mediators profile after stimulation with nontypeable Haemophilus influenza (NTHi), which caused attenuated levels of TNF-α. In parallel, levels of CCL2, CXCL10, and CCL9 increased significantly.

Nuclear factor-kappa B (NF-κB) is a key inflammatory and regulatory transcription factor. Smoking impairs NF-κB pathway activation in macrophages in response to innate immune stimuli—such as LPS from Gram-negative bacteria, the prototypical agonist of TLR4 (Laan et al. 2004; Birrell et al. 2008). This effect is dependent on oxidative stress, which promotes chemical modification of transduction intermediates in the TLR4/MyD88 pathway by carbonylation (Bozinovski et al. 2011).

Functional phenotyping and gene profiling studies support the division of macrophages into two polarized subsets (Biswas and Mantovani 2010). Mirroring the CD4 Th1/Th2 cell paradigm (Mosmann et al. 1986), macrophages have been divided into the classically activated M1 phenotype at one extreme and the alternatively activated M2 phenotype at the other. M1 and M2 polarization relates to broad transcriptional profiles induced by different activation signals, giving rise to macrophage populations with markedly different properties. M1 cells are classically induced by IFN-γ and are primed for TNF-α production and protease release. M2 polarization is associated with induction of acidic mammalian chitinase and is classically induced in response to helminthic parasites (Mantovani et al. 2005). The balance and intensity of this skewing has direct implications for immunity and disease because effective host defense requires a pathogen-appropriate macrophage activation program. At the level of macrophages, smoking alters the transcriptional profile of effector cells generating an intermediate phenotype. Smoking seems to favor neither of these subsets. Instead, smoking skews the inflammatory mediator profile while suppressing key effector functions, creating a distinctive activation state that distinguishes smokers from nonsmokers (Woodruff 2005; Shaykhiev et al. 2009). This state has been described as “partial M1 deactivation/partial M2 activation” of macrophages. As this skewing is at least partially reversible by reduced glutathione, it is dependent on oxidative damage of effector pathways, but the precise molecular pathways have not been elucidated. This skewing of polarity diminishes the effectiveness of macrophages as agents of host defense, promotes secondary inflammation with attendant proteolytic tissue destruction linked to emphysema, and promotes a profibrotic program related to fixed small airway airflow limitation. Moreover, mononuclear lineage cells are essential for tumor induction, growth, and metastasis under a wide variety of conditions (Condeelis and Pollard 2006). Therefore, smoking induces a partial M2-like state, as this phenotype is most closely associated with cancer. Established tumors have a large volume fraction of macrophages that adopt a strongly immunosuppressive state (tumor-associated macrophages) that prevents immune destruction of tumors. However, while smoking can induce M2-like gene programming, smoke-skewed macrophages are more likely to be polarized further in the direct tumor microenvironment.

In summary, there is clear evidence that exposure to cigarette smoke is associated with an increase in macrophages in the alveolar space in humans, as well as in experimental animals. Although cigarette smoke activates these macrophages to produce proinflammatory and tissue-damaging mediators, smoke paradoxically compromises the ability of alveolar macrophages to sense and phagocytose microbial agents and apoptotic cells.

NK and NKT Cells

NK cells are generally considered innate immune cells that play a critical role in host defense against microbial agents and tumors (Sun and Lanier 2011). Unlike T and B cells, NK cells do not express antigen-specific receptors that are generated through VDJ (variable, diversity, and joining gene segments) recombination (Murphy 2012). NK cells exert their effector function through direct cell cytotoxicity via the release of perforin and granzymes, Fas cell surface death receptor ligand-induced apoptosis, and proinflammatory cytokine and chemokine release (Hamerman et al. 2005; Swann et al. 2007). Defects in NK function in smokers were first observed in the 1970s (Ferson et al. 1979) and have been replicated since that time (Tollerud et al. 1989; Lu et al. 2006; Mian et al. 2008). The suppressive effect of smoke exposure on NK cell function and number persists after smoking cessation (Hersey et al. 1983).

NKT cells are a small population of thymus-derived T cells expressing an alpha-beta T cell receptor (TCR) (Berzins et al. 2011). Unlike conventional T cells, NKT cells recognize lipid antigens presented on cluster of differentiation (CD1), a small family of nonclassical MHC I-like proteins. NKT cells are viewed as regulatory lymphocytes that play an important role in promoting immunity to tumors, and microbial agents, and suppressing cell-mediated autoimmunity. Kim and colleagues (2008a) described a novel role of CD1-restricted NKT cell-dependent macrophage activation in the development of prolonged inflammatory changes after viral lung infection. However, the study did not address the impact of cigarette smoke on NKT cells after viral infection. Additionally, Vijayanand and colleagues (2007) observed only low numbers of NKT cells in airway biopsies, bronchoalveolar lavage, and induced sputum of subjects with COPD and healthy control subjects; no significant differences were observed between the two groups, an observation consequently confirmed by others.

In summary, evidence suggests that cigarette smoke suppresses NK cell function in humans, as well as in experimental models. In contrast, the effect of smoking on the NKT cell is understudied and clear conclusions cannot be drawn from the current literature.

Dendritic Cells

Lung dendritic cells are highly efficient antigen-presenting cells. Dendritic cells are indispensable to the initiation of T cell immunity (Mellman and Steinman 2001) and, via a range of PRRs, are an effective component of innate immune defense. Anatomically, dendritic cells are highly susceptible to the effects of smoking because they are usually located subjacent to the respiratory epithelium, although their cellular processes and some cells are also found in the airspace (Figure 10.6) (Jahnsen et al. 2006). Although they are highly efficient in mediating T cell activation, dendritic cells in lung tissue are normally quiescent and only trigger immune reactions after migrating to the T cell area of lymph nodes and upregulating co-stimulatory molecules. However, in diseased tissue, dendritic cells may become competent to activate T cells locally. As with other immune cells, subpopulations with distinct functions are known, and dendritic cells in the lung are often divided into myeloid and plasmacytoid dendritic cells (Tsoumakidou et al. 2008).

Dendritic cell-directed chemokine CCL20 is upregulated in the airways of people with COPD (Demedts et al. 2007), and functional studies in mice have shown that this chemokine contributes to the infiltration of dendritic cells after exposure to cigarette smoke (Bracke et al. 2006). Functionally, increased numbers of dendritic cells likely contribute to the adjuvant properties of cigarette smoke, in part through granulocyte macrophage colony-stimulating factor (GM-CSF)-dependent mechanisms (Trimble et al. 2008). Further evidence suggests that myeloid dendritic cells in the lung promote Th1 and Th17 responses in human emphysema (Shan et al. 2009), linking dendritic cells with disease progression in COPD. However, other evidence indicates that exposure to cigarette smoke decreases the number of dendritic cells in mice (Robbins et al. 2004, 2008). Functional studies suggest that exposure to cigarette smoke decreases the expression of costimulatory molecules and the Th1 cell-inducing cytokine IL-12 and IL-23 by dendritic cells, at least in smoke exposure models involving mice (Robbins et al. 2004, 2008; Kroening et al. 2008). These effects have been linked to immune skewing, predisposing to asthma (Vassallo et al. 2005).

A limited number of studies have investigated the effect of cigarette smoking on dendritic cells in the lungs of healthy smokers and people with COPD. As discussed by Tsoumakidou and colleagues (2008), different and seemingly conflicting results have been reported on the number and function of dendritic cells in COPD. For example, clinical studies show that smokers have a reduced number of dendritic cells in large airways (Rogers et al. 2008) and an increased number, with an immature phenotype, in small airways (Demedts et al. 2007). Complicating the interpretation of the clinical data further is the observation that the number of dendritic cells may expand and contract very rapidly, particularly in response to steroid treatments (Brokaw et al. 1998). In nonhuman primates, the Th1 cell-suppressing and Th2 cell-inducing activity of smoke was most pronounced when exposure commenced in utero (Wang et al. 2008b). In vitro, cigarette smoke extract augmented the production of IL-8 and suppressed the release of TNF-α, IL-6, and IFN-α by plasmacytoid dendritic cells. This effect appeared to be mediated via suppression of PI3K/Akt signaling in plasmacytoid dendritic cells (Mortaz et al. 2009). Mechanistically, both oxidative stress and nicotine have been associated with induction of a proinflammatory dendritic cell state (Vassallo et al. 2008).

In summary, dendritic cells play an important role in the initiation and perpetuation of innate and adaptive immune responses. The effect of cigarette smoke on dendritic cells is mixed. Some evidence indicates that the number of dendritic cells increases in response to exposure to cigarette smoke in humans and mice, although other reports are contradictory. Clinically, these differences may relate to the site that was studied, as smoke may affect dendritic cells differentially in the large and small airways. Data may also be influenced by current treatment with anti-inflammatory agents that are known to affect dendritic cells. Differences observed in animal studies may result from differences between exposure protocols.

Mast Cells

Mast cells are innate immune sentinel cells that reside in proximity to epithelia, blood vessels, nerves, smooth muscle cells, and mucus-producing glands (Galli et al. 2005). Upon activation, mast cells release a range of different bioactive molecules, including histamine. Research has long shown that mast cells are involved in defense against parasites and closely associated with allergic disorders, playing a critical role in type 1 hypersensitivity reactions (Bischoff 2007). Although evidence reveals that smokers have higher numbers of mast cells in their sputum compared with former smokers (Wen et al. 2010), the impact of cigarette smoke on the function of mast cells and their role in the pathogenesis of smoking-related disorders is poorly understood (Mortaz et al. 2011). In people with COPD, mast cell populations are associated with phenotype and severity (Andersson et al. 2010; Ballarin et al. 2012).

Nuocytes

Nuocytes are a novel lineage of immune effector cell and are distinguished by their lack of conventional surface markers (Neill et al. 2010). Nuocytes represent an innate effector cell that appears to play a critical role in type 2 immune responses. The effect of smoking on these cells is not known.

T Cells

T cells are centrally important immune initiators, regulators, and effectors (Castellino and Germain 2006; Zhu et al. 2010). CD4+ Th cells recognize processed extracellular antigen presented in the context of MHC II molecules and differentiate into armed effectors that are able to mediate effects via distinct cytokine secretion patterns. Mosman and colleagues (1986) were the first to describe Th1 cells and Th2 cells as two functional subsets of CD4+ T cells. This division is based on cytokine production: Th1 cells produce IL-2, IFN-γ, and GM-CSF; and Th2 cells produce IL-3, IL-4, IL-5, and IL-13 (Mosmann 1992). Th1 cell-polarized responses are associated with delayed type hypersensitivities, macrophage activation, and immunoglobulin G (IgG) responses. Th2 cell-polarized responses are linked to atopy, allergy, and immunoglobulin (IgE) production. While mechanisms that control Th1 cell-and Th2 cell-polarization are well-understood, research is expanding into other CD4+ T cell subsets, including Th9 and Th17, as well as regulatory T cells and T follicular helper cells (Zhu et al. 2010). CD8+ T cells respond to intracellular antigens presented on MHC I molecules and develop into cytokine-secreting and cytotoxic effectors. Because TCR binding to antigen is highly specific and few primary cells are matched to a given antigen, expansion of responsive clones is a central process in immunity. After expansion and execution of their effector function, the majority of T cells die by apoptosis, leaving only a small residual number of antigen-experienced memory cells.

Cigarette smoke has strong and direct effects on the gene expression profile of the T cell (Charlesworth et al. 2010). Evidence suggests that smoke affects T cell polarity—that is, the net pattern of cytokines and surface stimulator molecules expressed that in turn defines function. Studies have described effector populations within CD4+ Th1 (IFN-γ high) cells and Th17 (IL-17 high) cells (Barceló et al. 2008; Harrison et al. 2008; Chen et al. 2011; Shan et al. 2012). Although the concept of Th1 and Th17 or other subtypes of lymphocytes (and macrophages) is convenient, cytokine expression in effector populations is a stochastic process and polarization signifies a change in the statistical probability distribution profile of gene expression in populations of cells rather than the creation of distinct cellular entities. While oligoclonal expansion of CD4+ cells is documented in humans (Korn et al. 2005; Sullivan et al. 2005), the specificity of these T cells is not well-understood but is important nonetheless, because these cells may be directed against self-antigens, indicative of autoimmune processes, or environmental agents (e.g., viruses and bacteria) that may help to explain the relationship between viral infection and emphysema in experimental models (Kang et al. 2008).

The retention of CD8+ T cells in the lungs of chronic smokers warrants particular attention, as it is a hallmark of COPD (O'shaughnessy et al. 1997; Saetta et al. 1999). CD8+ T cells can kill cells through T cell-mediated cytotoxicity (Kagi et al. 1996), and these cells can activate alveolar macrophages to produce MMP-12 (Grumelli et al. 2004). MMP-12 is a potent elastin-degrading enzyme that has been linked to emphysema (Hautamaki et al. 1997). Evidence in mice exposed to cigarette smoke suggests that CD8+ T cells are required for inflammation and emphysematous destruction (Maeno et al. 2007). In mice that were chronically exposed to cigarette smoke, CD8+ T cells were oligoclonally expanded and persisted following cessation of smoke exposure (Motz et al. 2008). TCR analysis by polymerase chain reaction amplification followed by spectratyping showed preferential expansion of CD8+ T cells using Vβ7, Vβ9, and Vβ13. Similar oligoclonal expansion was observed in the lungs of smokers and persons with emphysema (Korn et al. 2005; Sullivan et al. 2005).

At present, the mechanisms responsible for this oligoclonal T cell expansion in animal models and human smokers remain unknown. It is not known whether specific antigens drive oligoclonal T cell expansions or, alternatively, whether cigarette smoke directly promotes a reduction in the breadth of the TCR repertoire. Speculatively, the decline in TCR repertoire may contribute to the increased viral infection susceptibility observed in smokers (USDHHS 2004).

Functional studies by Kalra and colleagues (2000) found that exposure to cigarette smoke induced a state of T cell anergy in rats via depletion of intracellular calcium ions. This observation critically contributes to the notion that cigarette smoke is an immunosuppressive agent (Sopori 2002). Subsequent studies in mice and humans did not confirm these observations (Zavitz et al. 2008). The impact of cigarette smoke on T cell responsiveness may be dose-dependent, as serum cotinine levels were markedly different between the two studies. Furthermore, TCR signaling may not be affected at moderate doses of cigarette smoke, and effects on innate immune cells—such as epithelial cells, macrophages, and NK cells—may predominate.

In summary, there is conclusive evidence that both CD4+ and CD8+ T cells accumulate in the lungs of smokers and persons with COPD. Although the evidence suggests that smoking has direct effects on the gene expression profiles of T cells and skews the T cell polarity toward Th1/Th17, major gaps remain in our understanding of cigarette smoke's effect on T cells. Little is known about the specificity and function of T cells accumulating in the lungs following exposure to cigarette smoke. Moreover, the mechanisms that contribute to T cell accumulation and their role in processes that contribute to the pathogenesis of smoking-related diseases remain poorly understood.

B Cells and Antibody-Mediated Autoimmunity

B cells are classically viewed as the cells that form antibodies (via plasma cells), but increasing evidence shows that B cells also regulate other immune effects through their ability to secrete cytokines (Harris et al. 2000; Lund and Randall 2010). Although dendritic cells are usually considered the main antigen-presenting cells, stimulated B cells are also highly effective antigen-presenting cells.

B cells are abundant in lungs of people with COPD, and in those of mice that have been chronically exposed to cigarette smoke (Hogg et al. 2004; Gosman et al. 2006; van der Strate et al. 2006). Immunologically, the formation of bronchial-associated lymphoid tissue is of particular interest, because it represents a tertiary lymphoid tissue. Bronchial-associated lymphoid tissue is a hallmark of Stages 3 and 4 of the COPD Global Initiative for Obstructive Lung Disease (Hogg et al. 2004). Similar structures are observed in mice following prolonged exposure to smoke, which provides an opportunity to study the immunologic structure of this function (van der Strate et al. 2006). At present, the role of bronchial-associated lymphoid tissue in the expression and progression of smoking-related diseases is not well-understood. It is also unclear whether the formation of bronchial-associated lymphoid tissue is a direct consequence of exposure to cigarette smoke or secondary to bacterial colonization of the lower respiratory tract.

Cigarette smoking decreases serum levels of all Ig classes, except for IgE (Holt 1987; Edwards 2009). Likewise, animal studies demonstrated that antibody responses to various antigens were reduced significantly as a consequence of chronic exposure to cigarette smoke. Mechanistically, cigarette smoke-exposed macrophages suppress B cell proliferation by producing secondary reactive oxidative metabolites (Hogg et al. 2004; Ishida et al. 2009).

A body of evidence asserts that COPD may be associated with pathogenic autoantibodies (Cosio et al. 2009). To date, three studies have described three autoimmune autoantibody responses: anti-elastin autoimmunity in smokers with emphysema (Lee et al. 2007b), autoantibodies to epithelium in COPD patients (Feghali-Bostwick et al. 2008), and induction of autoimmune emphysema by anti-endothelial antibodies in rats (Taraseviciene-Stewart et al. 2005). Mechanistically, evidence indicates that cigarette smoke serves as an adjuvant (Trimble et al. 2008), possibly because it is a potent inducer of GM-CSF production in the lungs. The role of these autoantibodies in the pathogenesis of COPD remains disputed, because autoantibodies are common in many inflammatory conditions but they may not be pathogenic. Little evidence exists on the systemic effects, despite the fact that proposed smoking-associated autoantigens, such as elastin, are ubiquitous. It is also unclear whether B cell follicles that form in the lungs of long-term smokers are the site of these autoantibody responses.

An emerging body of literature shows that B cell subsets modulate immune responses in an antibody-independent manner by producing cytokines that direct the T cell function (Lund and Randall 2010). Mirroring the Th1/Th2 cell paradigm, B cells have been termed B effector-1 cells and B effector-2 cells based on their cytokine expression profile (Harris et al. 2000). A third subtype of B regulatory cells (Breg or B10) has also been described that produces IL-10 (Yanaba et al. 2009). The effect of cigarette smoke on these B cell subtypes has not been studied.

In summary, there is clear evidence that the number of B cells is increased in people with COPD and in experimental animals after long-term exposure to cigarette smoke. Further evidence suggests that smoking decreases most Ig classes, except for IgE. Emerging literature posits that chronic smoke exposure is associated with the presence of autoantibodies against elastin, as well as the epithelium and the endothelium. However, more experimentation is required to investigate their contribution in perpetuating smoke-induced inflammation and in the pathogenesis of COPD.

Rheumatoid Arthritis and Systemic Lupus Erythematosus

The causes of autoimmune diseases, such as rheumatoid arthritis (RA) and SLE, remain elusive despite considerable research on risk factors and mechanisms. Although there are clearly genetic factors predisposing to these diseases, environmental factors also play a key role in their development (Klareskog et al. 2011; Salliot 2011; Sestak 2011). To date, there is a considerable body of literature on the effect of smoking on RA and a smaller number of studies exploring a role for smoking in SLE. These findings are supported on a mechanistic basis by the findings of studies on smoking and the immune system (see the “Immune Function and Autoimmune Diseases” section in this chapter). This topic, smoking and autoimmune diseases, has not been reviewed previously in the reports of the Surgeon General.

Inflammatory Bowel Disease

The predominant forms of inflammatory bowel disease (IBD) are Crohn's disease and ulcerative colitis. Crohn's disease is characterized by transmural inflammation occurring anywhere in the luminal gastrointestinal tract predominately affecting the ileum of the small intestine and the large intestine. In contrast, inflammation associated with ulcerative colitis is generally limited to the mucosal surface of the large intestine, although backwash ileitis can occur with active disease in the cecum.

The reported incidence of IBD is greater in North America and Northern Europe than in other regions of the world where incidence has been evaluated. The incidence of Crohn's disease in North America ranges between 4–16 cases per 100,000 compared with 4–10 in Northern Europe, 1–5 in Southern Europe, and 0–4 in Africa, Asia, and Latin America.

The incidence of ulcerative colitis per 100,000 ranges between 2–16 cases in North America, 3–20 in Northern Europe, 2–11 in Southern Europe, and 1–9 in Africa, Asia, and Latin America (Molodecky et al. 2012). Using the prevalence estimates for North America and a U.S. population of 300 million, the current prevalence of IBD in the United States is estimated at 1.6–1.7 million persons (Molodecky et al. 2012).

Although the incidence of IBD increased during the last century (Binder 2004), the causes of the increase are unknown. Over 160 genetic risk factors have been associated with Crohn's disease and ulcerative colitis (Jostins et al. 2012), but genes alone cannot explain the rapid increase in incidence. While increased diagnostic sensitivity may contribute to the increase, environmental factors, including cigarette smoking, may play a role.

Risk factors for IBD may include cigarette smoking, appendectomy, diet, infections and antibiotics to treat them, and socioeconomic factors (Ng et al. 2013). The environmental factors may interact with genetic risk factors in the development and response of the immune system. Environmental factors may also alter the intestinal microbiome, which also affects IBD (Erickson et al. 2012).

A personal history of cigarette smoking is the best described risk factor for IBD in adults. The first reports of a discordant relationship between active smoking and Crohn's disease and ulcerative colitis were published in the late 1970s and early 1980s (Samuelsson 1976; Mayberry et al. 1978; Harries et al. 1982; Jick and Walker 1983; Logan et al. 1984; Somerville et al. 1984) showing that ulcerative colitis patients were less likely to smoke on or after diagnosis, compared with controls, and more likely to be former smokers than controls. Crohn's disease patients were more likely to smoke on or after diagnosis than controls. A recent meta-analysis reported a protective association of smoking with ulcerative colitis (OR = 0.58) and an adverse association with Crohn's disease (OR = 1.75) (Mahid et al. 2006). A meta-analysis of prenatal and childhood exposure to secondhand cigarette smoke found no association with Crohn's disease or with ulcerative colitis (Jones et al. 2008). Persons with Crohn's disease, who continue to smoke, exhibit a greater need for use of immunosuppressant therapy, have higher rates of surgical resection, and greater frequency of postoperative recurrence after surgery and requirement for repeat resection (Birrenbach and Bocker 2004; Cosnes 2008). An intervention study demonstrated that Crohn's disease patients who stop smoking have a reduction in their need for immunosuppressants or surgery within the first year following cessation of cigarette smoking, when compared with those who continued to smoke. In contrast, cessation of cigarette smoking is sometimes associated with worsening disease activity in ulcerative colitis (Beaugerie et al. 2001; Cosnes 2004, 2008).

Epidemiologic Evidence

Studies for the current review were compiled by searching the MEDLINE database accessed through PubMed using the search phrase (smok* or tobacco) and (crohn or “ulcerative colitis” or “inflammatory bowel disease”). The search was performed on January 25, 2013, with no restriction on the date of publication, and 1,102 articles were identified. References cited in relevant reviews and meta-analyses (Cope et al. 1986; Calkins 1989; Thomas et al. 1998; Rubin and Hanauer 2000; Birrenbach and Bocker 2004; Wolf et al. 2004; Mahid et al. 2006; Jones et al. 2008; Bastida and Beltran 2011; Hovde and Moum 2012), and the studies that met the inclusion criteria were checked to identify articles not captured by the search.

Eight studies were excluded because the controls had irritable bowel syndrome or other gastrointestinal conditions (Burns 1986; Cope et al. 1986; Silverstein et al. 1989; Martins et al. 1996; Reif et al. 2000; de Saussure et al. 2007; Mahid et al. 2007; Lopez-Serrano et al. 2010). One study was excluded because the article could not be translated into English (Bures and Fixa 1985). When several articles reported on the same group of cases, the most recent article with the largest sample size and most rigorous control for confounding was included in the analysis (45 duplicates excluded).

Meta-analyses were performed using a random effects model accounting for the type of IBD, study design, and smoking definition. Smoking was classified as current at diagnosis of IBD (and corresponding age or date in controls); current at recruitment for prospective studies or when the questionnaire was administered after IBD diagnosis (or date of questionnaire administered to controls); and ever smoker at diagnosis, the time of the questionnaire, or unspecified smoking definition. Never smoker or not current smoking was used as the comparison. Former smoking was classified at the same time points as current smoking. Information on dose-response is described, but no meta-analyses were performed on dose-response relationships.

Seventy-two studies, which were reported in 75 articles, met the inclusion criteria (Table 10.20S). The case-control studies included hospital-based controls (often patients in orthopedic clinics or with fractures admitted through the emergency department), case-nominated controls, or controls that lived near the cases based on hospital or government records. Five prospective cohorts and 1 nested case-control study examined the incidence of Crohn's disease and ulcerative colitis by smoking status (Vessey et al. 1986; Logan and Kay 1989; Tragnone et al. 1993; Carlens et al. 2010; Higuchi et al. 2012; Chan et al. 2013). An additional case-control study enrolled only incident cases (Corrao et al. 1998). Three case series compared smoking among cases with nationally representative estimates (Srivasta et al. 1993; Tuvlin et al. 2007; van der Heide et al. 2011). The majority of studies were conducted in Europe and North America. Some studies included only women because the cases were originally collected to examine the relationship between oral contraceptives or hormone replacement therapy and disease (Vessey et al. 1986; Lashner et al. 1989, 1990; Logan and Kay 1989; Sandler et al. 1992; Katschinski et al. 1993; Boyko et al. 1994; Higuchi et al. 2012).

Current Smoking

Examining the 53 studies that reported on Crohn's disease, cases were more likely than controls to be current or ever smokers (RR = 1.6; 95% CI, 1.5–1.8). When studies that defined smoking as current at the time of recruitment into a prospective cohort or at the time of diagnosis or symptom onset in case-control studies were combined, the relationship between smoking and Crohn's disease was even greater (RR = 1.8; 95% CI, 1.6–2.2; N studies = 24; Figure 10.7); and the effect estimate did not differ meaningfully by study design. With restriction to studies assessing smoking on or before diagnosis and that adjusted for at least one factor in a multivariable model, the association increased (RR = 2.0; 95% CI, 1.6–2.6; N studies = 11). The 2006 meta-analysis by Mahid and colleagues reported an effect estimate of 1.8 (95% CI, 1.4–2.2; N studies = 9) for current smoking combining all studies identified without restriction by timing of current smoking or confounding control.

Figure 10.7

Relationship between smoking on or before the time of diagnosis and risk of Crohn's disease. Note: Weights are from random effects analysis. CI = confidence interval; ES = effect size.

Sixty-one studies reported on the relationship between current or ever smoking and ulcerative colitis. Ulcerative colitis cases were less likely to smoke than controls (RR = 0.54; 95% CI, 0.48–0.61). Restricting to the 28 studies that reported smoking on or before diagnosis, there was no meaningful change in the point estimate, although the CI was wider (RR = 0.56; 95% CI, 0.45–0.70) (Figure 10.8). With restriction to studies assessing smoking on or before diagnosis and adjusting for at least one factor, there was no meaningful difference compared with the unadjusted studies (RR = 0.49; 95% CI, 0.35–0.66; N studies = 16). The 2006 meta-analysis by Mahid and colleagues reported an effect estimate of 0.58 (95% CI, 0.45–0.75; N studies = 13) for current smoking among all studies identified without restriction to timing of current smoking or confounding control.

Figure 10.8

Relationship between smoking on or before the time of diagnosis and risk of ulcerative colitis. Note: Weights are from random effects analysis. CI = confidence interval; EGRCIBD-Japan = Epidemiology Group of the Research Committee of Inflammatory Bowel (more...)

When the U.S. studies were compared separately (Jick and Walker 1983; Boyko et al. 1987, 1994; Lashner et al. 1989, 1990; Sandler et al. 1992; Silverstein et al. 1994; Minocha and Raczkowski 1997; Tuvlin et al. 2007; Higuchi et al. 2012), the Crohn's disease and ulcerative colitis results were consistent with the analyses including all countries.

The descriptive epidemiology of IBD in Asia shows rising rates of ulcerative colitis during recent decades when smoking has also increased. When the studies conducted in Asia were considered separately both Crohn's disease (RR = 0.8; 95% CI, 0.6–1.1; N studies = 8) and ulcerative colitis (RR = 0.4; 95% CI, 0.3–0.6; N studies = 13) cases were less likely to smoke than controls (Stermer et al. 1985; Funakoshi et al. 1987; Higashi et al. 1991; Epidemiology Group of the Research Committee of Inflammatory Bowel Disease in Japan [EGRCIBD-Japan] 1994; Nakamura and Labarthe 1994; Reif et al. 1995, 2000; Fich et al. 1997; Naganuma et al. 2001; Firouzi et al. 2006; Jiang et al. 2007; Pugazhendhi et al. 2011; Habashneh et al. 2012; Kayahan et al. 2012). With restriction to studies assessing smoking on or before diagnosis and adjusting for at least one potential confounding factor, the association with Crohn's disease became statistically significant (RR = 0.5; 95% CI, 0.2–1.0; p = 0.04; N studies = 2) and remained similar to the unadjusted estimate for ulcerative colitis (RR = 0.3; 95% CI, 0.2 – 0.5; N studies = 5) (Figure 10.9).

Figure 10.9

Relationship between smoking on or before the time of diagnosis and risk of Crohn's disease or ulcerative colitis among case-control studies conducted in Asia. Source: Epidemiology Group of the Research Committee of Inflammatory Bowel Disease in Japan (more...)

Former Smoking

Seventy-one studies reported on the relationship between former smoking and Crohn's disease or ulcerative colitis (Table 10.21S). The effect estimates were elevated for both Crohn's disease (RR = 1.3; 95% CI, 1.1–1.5; N studies = 28) and ulcerative colitis (RR = 1.5; 95% CI, 1.3–1.8; N studies = 43). When studies that considered former smoking on or before diagnosis and adjusted for at least one factor were examined, the relationship for Crohn's disease was no longer statistically significant (RR = 1.2; 95% CI, 0.7–1.9; N studies = 6), but the relationship with ulcerative colitis remained statistically significant (RR = 1.7; 95% CI, 1.4–2.1; N studies = 14). The increase in risk of ulcerative colitis in former smokers may persist for as long as 20 years after cessation of smoking (Higuchi et al. 2012). The 2006 meta-analysis by Mahid and colleagues found similar relationships for Crohn's disease (RR = 1.3; 95% CI, 1.0–1.8; N studies = 9) and ulcerative colitis (RR = 1.8; 95% CI, 1.4–2.3; N studies = 13).

When the U.S. and Asian studies were considered separately, the Crohn's disease and ulcerative colitis results were consistent with the analyses including all countries.

Dose-Response

Seven studies reported a p-value associated with a test for trend for risk with the number of cigarettes smoked per day or week or pack-years (Jick and Walker 1983; Boyko et al. 1987; Funakoshi et al. 1987; Tobin et al. 1987; Logan and Kay 1989; Nakamura and Labarthe 1994; Higuchi et al. 2012). For Crohn's disease, two studies reported that heavy smokers or current smokers with more pack-years had increased incidence of disease (Logan and Kay 1989; Higuchi et al. 2012). Among former smokers, more pack-years of cumulative smoking was also associated with an increased incidence of Crohn's disease (Higuchi et al. 2012). A dose-response relationship with ulcerative colitis was also rarely reported. Three studies found that more cigarettes per day or pack-years among current smokers were associated with a decreased risk (Funakoshi et al. 1987; Logan and Kay 1989; Nakamura and Labarthe 1994), but two studies found that heavier current smoking was associated with an increased risk of ulcerative colitis (Jick and Walker 1983; Tobin et al. 1987). Among former smokers, heavier smoking was associated with increased risk of ulcerative colitis in two studies (Boyko et al. 1987; Nakamura and Labarthe 1994). For nine studies, the authors reported no dose-response relationship with the amount of current smoking for Crohn's disease or ulcerative colitis, but did not report a p-value from a test for trend (Boyko et al. 1987; Sorensen et al. 1987; Lindberg et al. 1988; EGRCIBD-Japan 1994; Silverstein et al. 1994; Corrao et al. 1998; Reif et al. 2000; Jiang et al. 2007; Carlens et al. 2010).

Chapter Conclusions

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