Background: Despite the well-documented role of cigarette smoke in the development of chronic obstructive pulmonary disease (COPD), lung cancer and cardiovascular disease, biomarkers for screening or monitoring disease progression and outcome remain elusive, particularly for COPD and lung cancer. Inflammatory cells and mediators are likely to be involved in the disease processes, but their importance is still poorly understood. The purpose of this study was to investigate early changes in immunological markers associated with smoking in healthy monozygotic twins without a detectable disease discordant for smoking, thereby minimising data variability due to genetic background.
Methods: Twenty-two monozygotic twin pairs, aged 31.5±6.3 years, entered the study. One of each twin pair was a smoker and the other a non-smoker. None of the subjects reported any diseases or clinically defined respiratory symptoms or airflow limitation. Each subject donated blood samples for determination of total leukocytes and subpopulations, lymphocyte subpopulation plus pro-inflammatory mediators (interleukin-8, tumour necrosis factor-α, soluble tumour necrosis factor-α receptors and C-reactive protein).
Results: We observed a significant increase in the number of circulating leukocytes and neutrophils in smokers compared to non-smokers. Smokers also had significantly higher numbers of B cells and CD4+ T cells, plus an increased CD4/CD8 ratio. The numbers of NK cells were statistically significant lower in smokers compared to non-smokers.
Conclusions: While the prognostic significance of these changes is uncertain, results suggest that smoking is associated with immune changes, independent of genetic background and environmental conditions.
Cigarette smoking is an important modifiable risk factor for human health, associated with the development and progression of cardiovascular (CVD) and pulmonary diseases, including chronic obstructive pulmonary disease (COPD) and lung cancer. Increasing evidence indicates that chronic inflammatory and immune responses play key roles in the development and progression of smoking-related disorders [1–5].
Studies of lung cancer patients have shown an impairment of host defenses together with enhanced immunosuppression. Cigarette smoke and nicotine are reported to have immunosuppressive properties in both men and animals as well as in in vitro models, an effect that may contribute to increased microbial infections and cancer incidence among smokers [6–10].
Distinct changes related to cigarette smoke are observed in the respiratory tract, such as bronchiolitis and fibrosis . Smoking associated changes in the respiratory immune system have been involved in the pathogenesis of COPD [1, 12], CVD  and lung cancer . Although cigarette smoking is a recognised risk factor associated with COPD, and smoking cessation is accompanied by a reduction in the rate of decline of lung function, much of the aetiology remains to be clarified [13, 14]. Interestingly, only 15%–20% of smokers develop COPD, suggesting that genetic predisposition and environmental factors play a role in the pathogenesis of the disease .
Van Eeden et al. suggested that COPD can develop into a systemic inflammatory disease . This process could be mediated by different mechanism during onset and progression of the disease .
Some evidence suggests that smoking may influence cardiovascular events through induction of inflammation [18–22], as atherosclerosis and coronary disease are now considered to be inflammatory diseases [19, 20].
The inflammatory response associated with tobacco smoking is characterised by an array of dysregulated cells, cytokines, and growth factors that are described as being conducive to the development of COPD, CVD and lung cancer via several possible pathways [5, 23, 24].
The purpose of this study was to investigate several immunological markers in pairs of smoking and non-smoking monozygotic twins, without a detectable disease, in order to identify early changes associated with tobacco smoking. The study was conducted with monozygotic twins, discordant for smoking, most of whom shared other environmental conditions and had very similar lifestyles. The choice of genetically identical monozygotic twins with similar environmental influences was intended to help understand potential modifications in the immune system related to smoking. Results showed a positive association between smoking status and a subset of biomarkers of biological effect (BoBE) related to inflammatory conditions and possible early stages of CVD.
Materials and methods
Study design and plan
A cross-sectional study of monozygotic twin pairs discordant for smoking was carried out. The study was conducted in accordance with the principles of Good Clinical Practice. The protocol was approved by the Polyclinic of “Tor Vergata” University of Rome Independent Ethics Committee and written informed consent was obtained from all subjects prior to the start of the study, according to the Declaration of Helsinki. Each subject attended the Polyclinic of “Tor Vergata” University on two occasions over 2 days. The first visit was for screening, during which the medical history and demographic data, such as gender, age, weight, body mass index (BMI) and lifestyle, were recorded. During the first visit, urinary cotinine levels were measured using the Accutest NicAlert™ test kit (Jant Pharmacal Corp., Encino, CA, USA), to avoid misclassification based on the self-reported smoker or non-smoker status, and saliva samples were collected for zygosity analysis, to verify the monozygosity status. The subjects were given instruction on the collection of 24 h urine samples and containers were given to them. At the second visit, all the enrolled subjects attended the Polyclinic for blood collection and other clinical evaluations, such as spirometry and measurement of exhaled carbon monoxide (CO). During the 2 days of the study, the usual smoking routine was recorded by each smoker in an ad hoc diary.
Twenty-four pairs of monozygotic twins (48 subjects), without a detectable disease, were enrolled for this study on volunteer base through the Italian twin association. Two pairs were excluded from the study: one pair because saliva analysis suggested they were dizygotic and the other pair because the smoker had a value of exhaled CO in the range of non-smokers (<9 ppm). The 44 selected subjects (22 twin pairs) were composed of 26 males and 18 females with a mean age of 31.5 years and a range of 23–46 years. According to the inclusion criteria for this study, smokers were defined as having an average daily ISO tar intake of ≥60 mg based on the number of cigarettes smoked per day multiplied by the machine smoked ISO tar yield of the brand smoked as declared on the pack. Non-smokers were defined as subjects who have never smoked or had stopped smoking for more than 10 years. The smokers were asked to smoke their usual number and brand of cigarettes during the study. They were free to cease smoking at any time during the study, but they would no longer be eligible for inclusion in the study. Non-smoker subjects were asked to remain non-smokers during the study. The use of pharmaceutical products containing cyclo-oxygenase inhibitors within the 2 weeks prior the start of the study or use of any other medicinal products that, according to the Principal Investigator’s judgement, could interfere with biomarker measurements were considered to be exclusion criteria. This included inhaled corticosteroids, immunosuppressive drugs or any other drugs affecting the immune system. Subjects who reported any serious illness or infections during the 6 months preceding the study including influenza, chronic inflammatory diseases, hypertension and CVD were also excluded. For further details, see Andreoli et al. .
Urine collection and biomarker analyses
The 24 h collection period started after the first morning void urine on the first day of the study and included the first morning void urine on second day. The 24 h urine samples were analysed for the following biomarkers of exposure (BoE): nicotine and nicotine glucuronide (total nicotine), cotinine and cotinine glucuronide (total cotinine), hydroxycotinine and hydroxycotinine glucuronide (total hydroxycotinine), 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) and NNAL-glucuronide (total NNAL). Urinary nicotine and its metabolites were analysed using solid phase extraction and liquid chromatography with tandem mass spectrometry based on the method described by St. Charles and colleagues . NNAL and its metabolites were analysed using a solid phase extraction and a LC/MS/MS method based on that of Xia and colleagues and Pan and colleagues [27, 28].
Blood collection and biomarker analyses in blood
Peripheral venous blood was obtained from all subjects for determination of leukocyte counts, lymphocyte subpopulations and selected proinflammatory proteins. Total and differential total leukocyte counts, such as neutrophils, lymphocytes, eosinophils, basophils and monocytes, were determined by flow cytometry. Lymphocyte subpopulations (CD45+), including CD3+CD45+ (total T cells), CD3+CD4+CD45+ (T helper cells), CD3+CD8+CD45+ (T cytotoxic/suppressor cells), CD3+CD4+CD8+CD45+ (double positive T cells), CD16+CD56+CD45+ (NK cells), CD19+CD45+ (B cells) were quantified by direct fluorescence method for whole blood and flow cytometry following supplier’s specification (BD FACScalibur flow cytometer, Becton Dickinson, San Jose, CA, USA).
Measurements of proinflammatory protein levels were performed at the Clinical Biochemistry Medicine Laboratory of the Polyclinic of “Tor Vergata”. Interleukin-8 (IL-8) and tumour necrosis factor-α (TNF-α) were measured in serum using commercially available ELISA kits from DRG Instruments GmbH (Marburg, Germany), while soluble TNF-α receptors 1 (sTNF R1) and 2 (sTNF R2) were measured using commercially available kits from IBL (Gesellschaft fur Immunchemie und Immunbiologie MBH, Hamburg, Germany). Serum C-reactive protein (CRP) levels were determined by a CRP (Latex) HS immunoturbidimetric test on an automatic analyser (Roche Hitachi Modular P800, Roche Diagnostics GmbH, Mannheim, Germany).
Other clinical evaluations
The zygosity test was performed in saliva by using the AmpFiSTRs Identifiler Kit (Applied Biosystems, Darmstadt, Germany), which amplifies 15 loci and amelogenin in a single tube and provides loci consistent with major worldwide STR databasing standards. Spirometry examinations were conducted according to the ERS/ATS 2005 guidelines. The forced expiratory volume in 1 s (FEV1) was determined on all subjects using a Master Screen-PFT pneumotachograph (Erich Jaeger GmbH, Hoechberg, Germany). Levels of CO in exhaled breath were determined using a Micro CO/Smoke Check instrument (Micro Medical Ltd, Rochester, Kent, UK).
The t-test for paired samples was used to evaluate differences between smokers and non-smokers . The proinflammatory cytokines were not normally distributed, the Wilcoxon-signed rank test was used to evaluate differences. For regression analyses, the linear correlation coefficients (r) between pairs of different variables were also calculated, using Pearson’s correlation analysis . All statistical analyses were carried out using SPSS V15.0 software.
The subjects’ age, smoking habits, FEV1 and levels of BoE for tobacco smoke constituents in 24 h urine are reported in Table 1. There was no significant difference in BMI between the smokers and non-smokers. None of the subjects reported any appreciable respiratory symptoms or airflow limitations: FEV1 was comparable for both groups (Table 1). Similarly, the percentage of predicted FEV1 was >100% for the two groups [107.8%±11.1 (mean±SD) for non-smokers versus 105.6%±11.0 for smokers]. As expected, the exhaled CO and urinary levels of all the BoE measured were significantly increased among smokers compared to non-smokers (p<0.001).
|Years of smoking||0||11.3±6.6|
|FEV1, % predicted||107.8±11.1||105.6±11.0|
|Exhaled CO, ppm||1.1±1.2||13.8±6.4a|
|Cotinine, μg/24 h||7.2±3.2||5701±2643a|
|Nicotine, μg/24 h||4.9±1.8||2597±1563a|
|Hydroxycotinine, μg/24 h||8.6±4.5||6691±5858a|
|Total nicotine equivalents, μg/24 h||18.7±5.9||13490±7177a|
|NNAL, ng/24 h||13.3±6.8||229±163a|
Data are presented as mean±SD. ap<0.001, t-test for paired samples (smoker twin vs. non-smoker twin). FEV1: forced expiratory volume in 1 s. NNAL: 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol.
Effect of cigarette smoking on circulating leukocyte populations
Peripheral blood total and differential leukocyte counts, and lymphocyte subpopulations, were determined for all subjects. As shown in Table 2, there was a statistically significant higher total leukocyte count in smokers compared to non-smokers (p<0.01), due to an increase in lymphocytes (2.2 vs. 2.0×109/L, p<0.05) and neutrophils (4.6 vs. 3.9×109/L, p<0.05). No changes in the percentage of leukocytes were observed.
|Total leucocytes, 109/L||6.3±1.5||7.6±1.9b|
|Total CD45+ cells/μL||1965±478||2232±515|
|Total T cells/μL||1383±402||1584±436a|
|CD4+ T cells/μL||809±289||992±293b|
|CD8+ T cells/μL||535±177||554±190|
|CD4+ CD8+ T cells/μL||16.5±17.5||14.9±20|
|Total T cells, %||69.8±6.9||70.6±8.2|
|CD4+ T cells, %||40.5±7.1||44.2±6.9b|
|CD8+ T cells, %||27.2±6.8||24.6±5.1a|
|CD4+ CD8+ T cells,%||0.77±0.97||0.55±0.96|
|B cells, %||12.9±4.4||15.1±4.6b|
|NK cells, %||16.5±6.3||13.2±7.3a|
Data are presented as mean±SD. ap<0.05, bp<0.01: t-test for paired samples (smoker twin vs. non-smoker twin).
Lymphocyte subpopulations where quantified by FACS analysis (Table 2). There was a significant increase in the number of total T cells, and in the number and percentage of CD4+ T cells and B cells in smokers compared with non-smoker twins. A significant reduction in the percentage of NK cells and CD8+ T cells was observed in smokers compared to non-smokers. A significant increase in CD4/CD8 ratio was also found in smokers when compared to non-smokers, mainly due to the increase in CD4+ T cells (Table 2).
In Table 3 the correlation coefficients (r) between leukocyte subsets and the number of cigarettes smoked per day or the BoE measured in 24 h urine are presented. For each pair in the correlation analysis, the exposure estimate was set as the independent variable and the leukocyte subset as the dependent variable. All subjects were included in this correlation analysis because valid estimates of exposure were available for each exposure parameter, even if the subject was not a smoker. Apart from the total leukocyte count, which correlated with all the estimates of exposure, there were few other correlations between these parameters. Total neutrophil counts were correlated with most BoE except for 24 h urinary nicotine excretion. As nicotine equivalents represent the molar sum of excreted nicotine and its major metabolites over the 24 h period, it should be a more reliable marker than any single nicotine metabolite alone, and this estimate of smoke exposure did show statistically significant correlations with total leukocytes, neutrophils and B lymphocytes but not with any T lymphocyte subpopulation nor with NK cells. The nicotine equivalents appear to be more robust as a measure of exposure than cigarettes per day, which only gave a statistically significant correlation with total leukocyte count and with B lymphocytes. The 24 h excretion of nicotine itself did show statistically significant correlations with total leukocyte, lymphocyte and several lymphocyte subset counts. The 24 h excretion of NNAL was also statistically significant correlated with total leukocyte and neutrophil counts.
|‘r’ Value for the exposure estimate usinga|
|Leucocyte variableb||Cigarettes/day||Nicotine||Cotinine||Hydroxy-cotinine||Nicotine equivalents||NNAL|
|Total T cells||0.157||0.377c||0.150||0.057||0.161||0.129|
|T helper cells||0.213||0.407c||0.221||0.148||0.234||0.209|
|T cytotoxic/suppressor cells||0.017||0.208||0.036||0.116||0.028||0.064|
|Double positive T cells||0.133||0.007||0.106||0.121||0.119||0.184|
aExposure estimates were used as the independent variable in each individual correlation analysis. All biomarker exposure estimates are based on total 24 h excretion measurements. Nicotine equivalents were calculated by converting the total of all 24 h metabolite measurements to the equivalent molar mass of nicotine; bFor the dependent variables in the correlation analyses, absolute counts were used for all leukocyte subsets; cp<0.05 using a two-tailed test with 42 degrees of freedom, based on Pearson’s correlation critical values.
Effect of cigarette smoking on proinflammatory proteins
Several proteins, including CRP, the cytokines IL-8, TNF-α and the soluble TNF receptors sTNFR1 and sTNFR2, were assessed in serum from smokers and non-smokers by ELISA. As shown in Table 4, no significant differences between the two groups were observed for any of the proteins measured but the median levels of CRP, IL-8, TNF-α and TNF-α/sTNFR1 ratio were slightly higher in smokers compared to non-smokers.
Data are presented as mean±SD. aWilcoxon signed rank test.
Smoking is a known risk factor for COPD, lung cancer and CVD, and inflammation is reported to be an aetiological factor for all three pathologies [1, 5, 31].
The purpose of this study was to investigate several immunological markers in pairs of healthy smoking and non-smoking monozygotic twins, sharing similar environmental conditions and lifestyles, in order to identify early changes associated with tobacco smoking. Results show that smoking is associated with statistically significant increases in the number of circulating leukocytes, neutrophils, T helper cells and B cells, and with increased CD4/CD8 ratio. Concurrently, the number of NK cells and CD8+ T cells were statistically significant lower in smokers compared to non-smokers. While the prognostic significance of these changes is uncertain, results suggest that smoking is associated with immune changes, independent of genetic background and environmental conditions.
While the use of biomarkers for the immune system is a rapidly expanding field in molecular epidemiology research, there is still a need to accumulate population distribution data regarding age, gender and ethnic group responses in individuals without detectable diseases. Many factors can contribute to biomarker variability including host and exposure factors. To overcome some of these limitations, and to reduce possible data dispersion due to differences in genetic background and lifestyle, we investigated the effects of cigarette smoking on several immune parameters in healthy smoking-discordant twins: one a smoker and the other a non-smoker.
The influence of smoke on leukocyte cell numbers and cell function has been demonstrated in several previous studies and our results are consistent with these findings [9, 32–35]. On the contrary, data concerning the influence of smoking on lymphocyte subpopulations are conflicting [35–38]. This may be due to the age of the population recruited, smoking habits, healthy status, etc. Changes in lymphocyte subpopulations may reflect progressive changes from non-smokers to healthy smokers to patients with COPD. Very interesting are the data reported by Vargas-Rojas and collaborators showing a polarisation of Th17 cells and their balance with Th1/Th2 and Treg subpopulations in COPD and current smokers, with Th17 and Treg cells showing a progressive increase from healthy subjects to current smokers to COPD group .
In our study, data on leukocyte counts and lymphocyte subpopulations were all within normal ranges, as should be expected in relatively young subjects without a detectable disease. In the present paper, some significant differences in circulating leukocytes between smoker and non-smoker twins were found, indicating that cigarette smoking affects cells of the immune system. The correlations observed between leukocyte subsets and 24 h nicotine excretion or nicotine equivalent measurements, suggest that smoke can directly or indirectly affect leukocyte subsets.
Lymphocytes have been designated by some investigators as the effector cells in COPD  and lung cancer pathogenesis . In our population, we found an increase in the number of total leukocytes, due to both lymphocytes (T helper and B cells) and neutrophils, and a decrease in the percentage of NK cells and T suppressor/cytotoxic cells. Total leukocyte and neutrophil counts also showed a positive correlation with individual nicotine metabolites, total nicotine equivalents and NNAL. These correlations were stronger than those with number of cigarettes smoked per day, suggesting that the BoE considered are more reliable for smoke exposure than number of cigarettes smoked per day. The observed increase in total T cells and CD4+ T cells in this study is in agreement with previous work [39, 40]. CD4+ T cells appear to be responsible for the observed higher counts in circulating T cells in peripheral blood of smokers. In agreement with Tollerud et al., CD4/CD8 ratio was increased in smokers . Other studies have reported an elevated proportion of T lymphocytes but a lower CD4/CD8 ratio in peripheral blood of smokers [41, 42]. The CD4/CD8 ratio may be important for disease induction and progression. An increase in this ratio suggests an altered balance in lymphocyte functional activity that, together with decreased NK cell numbers, has been proposed to increase the risk of tumour development and viral infections .
Systemic inflammation may cause endothelial cell activation and promote the prothrombotic state and atherosclerosis plaque production . Inflammation is regulated by cytokines, chemokines, and growth factors. Furthermore, it has been suggested that cigarette smoke might mediate oxidative stress by inducing an inflammatory response through the release of pro-inflammatory cytokines . Several mediators are involved in systemic inflammation and chronic lung diseases, including TNF-α, and IL-8. We did not observe any statistically significant changes in levels of pro-inflammatory cytokines, in agreement with published data which showed that similar level of TNF-α, sTNFR 1, sTNFR 2 and IL-8 are found in smokers and non-smokers without a detectable disease . In our study, however, there was a slight but not statistically significant increase in the mean levels of TNF-α, TNF-α/sTNFR 1 ratio and IL-8 between smokers and non-smokers. The increased ratio observed in smokers might favour increased TNF-α activity, further suggesting a slight increase in the level of systemic inflammation, consistent with the observed increase of total leukocytes, lymphocytes and neutrophils and the slight increase of CRP. It is possible that these increases might precede further changes which have been suggested to be indicative of increased atherosclerotic risk [46, 47].
Although our current data are limited, they suggest that smoking is associated with early immune changes, independent of genetic background and lifestyle. Our observations suggest that smokers may become more easily prone to chronic inflammation than non-smokers. Some smoker twins present an inflammatory status comparable to Step 1 Initial Response to Cigarette Smoke, recently proposed by Cosio et al. as the mechanisms leading to COPD, which contemplates three progressive steps where the last one depends on the previous ones . Of course, the relationship between these altered immunological parameters and progression to clinical signs and symptoms, especially those indicative of COPD, lung cancer or CVD, and whether they have prognostic significance, remains to be clarified. Longitudinal studies might help to shed light on the role of early immunological changes associated with smoking and their relationship with different smoking-related diseases.
The results of this study are of particular interest considering the young age of the subjects (mean age 31.5 years, range 23–46 years), the relatively low mean number of cigarettes smoked (cigarettes per day: 13.7; mean period: 11.3 years), and the independency of genetic background and environmental conditions. Due to the relatively small size of this study, before reaching any firm conclusion, it is necessary to verify our results in a greater number of subjects. Nevertheless, the use of immunological biomarkers in epidemiological studies can improve our understanding of the mechanisms underlying associations between environmental exposure and immune-mediated disorders.
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Financial support: British American Tobacco funded the study.
Employment or leadership: Cristina Andreoli, Antonella Bassi and Alfredo Nunziata were employees of British American Tobacco when this study was carried out.
Honorarium: None declared.
Competing interests: The funding organisation(s) played no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the report for publication.
6. Domagala-Kulawik J, Hoser G, Droszcz P, Kawiak J, Droszcz W, Chazan R. T-cell subtypes in bronchoalveolar lavage fluid and in peripheral blood from patients with primary lung cancer. Diagn Cytopathol 2001;25:208–13.10.1002/dc.2040Search in Google Scholar
7. Domagala-Kulawik J. Effects of cigarette smoke on the lung and systemic immunity. J Physiol Pharmacol 2008;59(Suppl 6):19–34.Search in Google Scholar
8. Hoser G, Domagala-Kulawik J, Droszcz P, Droszcz W, Kawiak J. Lymphocyte subsets differences in smokers and nonsmokers with primary lung cancer: a flow cytometry analysis of bronchoalveolar lavage fluid cells. Med Sci Monit 2003;9:310–5.Search in Google Scholar
10. Arimilli S, Damratoski BE, Prasad GL. Combustible and non-combustible tobacco product preparations differentially regulate human peripheral blood mononuclear cell functions. Toxicol In Vitro 2013;27:1992–2004.10.1016/j.tiv.2013.06.015Search in Google Scholar PubMed
11. Caminati A, Graziano P, Sverzellati N, Harari S. Smoking- related interstitial lung diseases. Pathologica 2010;102: 525–36.Search in Google Scholar
12. Zhu X, Gadgil AS, Givelber R, George MP, Stoner MW, Sciurba FC, et al. Peripheral T cell functions correlate with the severity of chronic obstructive pulmonary disease. J Immunol 2009;182:3270–7.10.4049/jimmunol.0802622Search in Google Scholar PubMed
13. Atsou K, Chouaid C, Hejblum G. Simulation-based estimates of effectiveness and cost-effectiveness of smoking cessation in patients with chronic obstructive pulmonary disease. PLoS One 2011;6:e24870.10.1371/journal.pone.0024870Search in Google Scholar PubMed PubMed Central
15. Saetta M, Di Stefano A, Turato G, Facchini FM, Corbino L, Mapp CE, et al. CD8+ T-lymphocytes in peripheral airways of smokers with chronic obstructive pulmonary disease. Am J Respir Crit Care Med 1998;157:822–6.10.1164/ajrccm.157.3.9709027Search in Google Scholar PubMed
18. Rudolph TK, Rudolph V, Baldus S. Contribution of myeloperoxidase to smoking-dependent vascular inflammation. Proc Am Thorac Soc 2008;1:5:820–3.10.1513/pats.200807-063THSearch in Google Scholar PubMed
20. Goldschmidt-Clermont PJ, Dong C, Seo DM, Velazquez OC. Atherosclerosis, inflammation, genetics, and stem cells: 2012 update. Curr Atheroscler Rep 2012;14:201–10.10.1007/s11883-012-0244-1Search in Google Scholar PubMed PubMed Central
21. Lavi S, Prasad A, Yang EH, Mathew V, Simari RD, Rihal CS, et al. Smoking is associated with epicardial coronary endothelial dysfunction and elevated white blood cell count in patients with chest pain and early coronary artery disease. Circulation 2007;115:2621–7.10.1161/CIRCULATIONAHA.106.641654Search in Google Scholar PubMed
22. Madjid M, Awan I, Willerson JT, Casscells SW. Leukocyte count and coronary heart disease: implications for risk assessment. J Am Coll Cardiol 2004;44:1945–56.10.1016/j.jacc.2004.07.056Search in Google Scholar PubMed
23. Lee G, Walser TC, Dubinett SM. Chronic inflammation, chronic obstructive pulmonary disease, and lung cancer. Curr Opin Pulm Med 2009;15:303–7.10.1097/MCP.0b013e32832c975aSearch in Google Scholar PubMed
24. Walser T, Cui X, Yanagawa J, Lee JM, Heinrich E, Lee G, et al. Smoking and lung cancer: the role of inflammation. Proc Am Thorac Soc 2008;5:811–5.10.1513/pats.200809-100THSearch in Google Scholar PubMed PubMed Central
25. Andreoli C, Gregg EO, Puntoni R, Gobbi V, Nunziata A, Bassi A. Cross-sectional study of biomarkers of exposure and biological effect on monozygotic twins discordant for smoking. Clin Chem Lab Med 2011;49:137–45.10.1515/CCLM.2011.009Search in Google Scholar PubMed
26. St. Charles FK, Krautter GR, Dixon M, Mariner DC. A comparison of nicotine dose estimates in smokers between filter analysis, salivary cotinine, and urinary excretion of nicotine metabolites. Psychopharmacol 2006;189:345–54.10.1007/s00213-006-0586-xSearch in Google Scholar PubMed PubMed Central
27. Xia Y, McGuffey JE, Bhattacharyya S, Sellergren B, Yilmaz E, Wang L, et al. Analysis of the tobacco-specific nitrosamine 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol in urine by extraction on a molecularly imprinted polymer column and liquid chromatography/atmospheric pressure ionization tandem mass spectrometry. Anal Chem 2005;77:7639–45.10.1021/ac058027uSearch in Google Scholar PubMed
28. Pan J, Song Q, Shi H, King M, Junga H, Zhou S, et al. Development, validation and transfer of a hydrophilic interaction liquid chromatography/tandem mass spectrometric method for the analysis of the tobacco-specific nitrosamine metabolite NNAL in human plasma at low picogram per milliliter concentrations. Rapid Commun Mass Spectrom 2004;18:2549–57.10.1002/rcm.1656Search in Google Scholar PubMed
30. Edwards AL. The correlation coefficient. In: An introduction to linear regression and correlation. San Francisco (CA): WH Freeman, 1976:33–46.Search in Google Scholar
31. Calverley PM, Scott S. Is airway inflammation in chronic obstructive pulmonary disease (COPD) a risk factor for cardiovascular events? COPD 2006;3:233–42.10.1080/15412550600977544Search in Google Scholar PubMed
32. Smith LA, Paszkiewicz GM, Hutson AD, Pauly JL. Inflammatory response of lung macrophages and epithelial cells to tobacco smoke: a literature review of ex vivo investigations. Immunol Res 2010;46:94–126.10.1007/s12026-009-8133-6Search in Google Scholar PubMed
35. Calapai G, Caputi AP, Mannucci C, Russo GA, Gregg E, Puntoni R, et al. Cardiovascular biomarkers in groups of established smokers after a decade of smoking. Basic Clin Pharmacol Toxicol 2009;104:322–8.10.1111/j.1742-7843.2008.00361.xSearch in Google Scholar PubMed
36. Roos-Engstrand E, Ekstrand-Hammarström B, Pourazar J, Behndig AF, Bucht A, Blomberg A. Influence of smoking cessation on airway T lymphocyte subsets in COPD. COPD 2009;6:112–20.10.1080/15412550902755358Search in Google Scholar PubMed
37. Hodge G, Mukaro V, Reynolds PN, Hodge S. Role of increased CD8/CD28(null) T cells and alternative co-stimulatory molecules in chronic obstructive pulmonary disease. Clin Exp Immunol 2011;166:94–102.10.1111/j.1365-2249.2011.04455.xSearch in Google Scholar PubMed PubMed Central
38. Vargas-Rojas MI, Ramírez-Venegas A, Limón-Camacho L, Ochoa L, Hernández-Zenteno R, Sansores RH. Increase of Th17 cells in peripheral blood of patients with chronic obstructive pulmonary disease. Respir Med 2011;105:1648–54.10.1016/j.rmed.2011.05.017Search in Google Scholar PubMed
39. Kim WD, Kim WS, Koh Y, Lee SD, Lim CM, Kim DS, et al. Abnormal peripheral blood T-lymphocyte subsets in a subgroup of patients with COPD. Chest 2002;122:437–44.10.1378/chest.122.2.437Search in Google Scholar PubMed
40. Tollerud DJ, Clark JW, Brown LM, Neuland CY, Mann DL, Pankiw-Trost LK, et al. The effects of cigarette smoking on T cell subsets. A population-based survey of healthy Caucasians. Am Rev Respir Dis 1989;139:1446–51.10.1164/ajrccm/139.6.1446Search in Google Scholar PubMed
42. MacCallum PK. Markers of hemostasis and systemic inflammation in heart disease and atherosclerosis in smokers. Proc Am Thorac Soc 2005;2:34–43.10.1513/pats.200406-036MSSearch in Google Scholar PubMed
43. Robbins CS, Dawe DE, Goncharova SI, Pouladi MA, Drannik AG, Swirski FK, et al. Cigarette smoke decreases pulmonary dendritic cells and impacts antiviral immune responsiveness. Am J Respir Cell Mol Biol 2004;30:202–11.10.1165/rcmb.2003-0259OCSearch in Google Scholar PubMed
44. Kode A, Yang SR, Rahman I. Differential effects of cigarette smoke on oxidative stress and proinflammatory cytokine release in primary human airway epithelial cells and in a variety of transformed alveolar epithelial cells. Respir Res 2006;7:132. Erratum in: Respir Res. 2008; 9:6.Search in Google Scholar
46. Mazer SP, Rabbani LE. Evidence for C-reactive protein’s role in vascular disease: atherothrombosis, immuno-regulation and CRP. J Thromb Thrombolysis 2004;17:95–105.10.1023/B:THRO.0000037664.77460.d8Search in Google Scholar
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