Blood biomarkers of smoking induced oxidative dna damage and oxidant-antioxidant

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Blood biomarkers of smoking-induced oxidative DNA
damage and oxidant/antioxidant status in healthy males
Journal: Inhalation Toxicology
Manuscript ID UIHT-2018-0146
Manuscript Type: Research Article
Date Submitted by the
Author:
14-Dec-2018
Complete List of Authors: Mahrous, Mai; Faculty of Medicine, Cairo University, Cairo, Egypt.,
Department of Forensic Medicine and Clinical Toxicology.
El-Barrany, Usama; Faculty of Medicine, Cairo University, Cairo, Egypt.,
Ismail, Manal; Faculty of Medicine, Cairo University, Cairo, Egypt.,
Gaballah, Iman; Faculty of Medicine, Cairo University, Cairo, Egypt.,
Rashed, Laila; Faculty of Medicine, Cairo University, Cairo, Egypt.,
Department of Medical Biochemistry and Molecular Biology.
URL: http:/mc.manuscriptcentral.com/uiht
Inhalation Toxicology

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1 Blood biomarkers of smoking-induced oxidative DNA damage and oxidant/antioxidant
2 status in healthy males
3 Mai Mohamed Mahrous1, Usama Mohamed El-Barrany1, Manal Mohy El-Din Ismail1, Iman
4 Fawzy Gaballah1 and Laila Ahmed Rashed2
5 1Department of Forensic Medicine and Clinical Toxicology, Faculty of Medicine, Cairo
6 University, Cairo, Egypt. 2Department of Medical Biochemistry and Molecular Biology,
7 Faculty of Medicine, Cairo University, Cairo, Egypt.
8 Corresponding author: Mai Mohamed Mahrous. Department of Forensic Medicine and
9 Clinical Toxicology, Faculty of Medicine, Cairo University, Cairo, Egypt.
10 mai_mahrous2@hotmail.com mai_mahrous2@kasralainy.edu.eg
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1 Blood biomarkers of smoking-induced oxidative DNA damage and oxidant/antioxidant
2 status in healthy males
3 Abstract
4 Background: Smoking enhances oxidative stress by causing an oxidant/antioxidant
5 imbalance in the body leading to deleterious effects on various tissues and organs. 8-hydroxy-
6 2'-deoxyguanosine is a predominant form and widely used biomarker of free radical-induced
7 oxidative lesions.
8 Objectives: This study aimed to estimate blood 8-OHdG level in smokers and its relation
9 with some lifestyle determinants as well as to assess smoking effects on oxidant/antioxidant
10 status.
11 Participants and methods: The current work included 90 male participants classified into 3
12 groups; 20 non-smokers, 30 passive smokers and 40 active smokers. Data were collected by
13 answering a questionnaire. Venous blood samples were withdrawn then 8-OHdG, cotinine,
14 superoxide dismutase (SOD) and total antioxidant capacity (TAC) levels were estimated in
15 serum using ELISA kits, while malondialdehyde (MDA) was estimated by colorimetry.
16 Results: There were highly statistically significant higher levels of 8-OHdG, cotinine and
17 MDA and lower levels of SOD and TAC in active smokers compared to both passive and
18 non-smokers (p < 0.001) whereas, no statistically significant difference between passive and
19 non-smokers was detected (p > 0.05). There were no significant associations between 8-
20 OHdG level and smoking habits, age, exercise, tea and coffee consumption as well as body
21 mass index (BMI) among the 3 studied groups.
22 Conclusion: Smoking induces oxidative stress not only through the production of reactive
23 oxygen radicals but also through weakening of the antioxidant defense systems. Further
24 studies are required to reach a consensus on the background of 8-OHdG and understand
25 which factors determine it and to further differentiate between passive and non-smokers.
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1 Keywords: Smoking, Oxidative, Stress, Antioxidant, 8-OHdG.
2
3 Introduction
4 Cigarette smoking is one of the most common habits of the modern world. It is reported by
5 the World Health Organization (WHO) that 30% of the world's population over the age of 15
6 smoked cigarettes (Kulikowska & Czerw, 2015).
7 Oxidative stress caused by smoking reflects an imbalance between the systemic
8 manifestation of reactive oxygen species (ROS) and a biological system's ability to promptly
9 detoxify the reactive intermediates or to repair the subsequent harm. Disturbances in the
10 typical redox condition of cells can cause lethal impacts through the creation of peroxides and
11 free radicals that harm all segments of the cell, including lipids, proteins and DNA
12 (Andersson, 2017). The potential damage caused by free radicals found in tobacco smoke is
13 minimized by biological antioxidant mechanisms including enzymatic and non-enzymatic
14 reactions (Mahapatra et al., 2008). Antioxidant enzymes such as superoxide dismutase are
15 an important line of defense against oxidative cell damage preventing lipid peroxidation and
16 overproduction of malondialdehyde (Kamceva et al., 2016).
17 Damage to DNA is indicated by increased levels of 8-OHdG, a repair product of the
18 oxidation of guanine in DNA. It can be detected in human tissues, blood or urine and is
19 considered a reliable and pivotal biomarker of generalized and cellular oxidative stress (Liu
20 et al., 2018). Also, it is an important biomarker for various pathological conditions such as
21 aging, carcinogenesis, neurodegenerative and cardiovascular diseases as estimated by
22 quantitative analytical techniques in blood and urine (Valavanidis et al., 2009).
23 Cotinine, a metabolite of nicotine, is a specific biomarker of tobacco smoking
24 exposure. Using this biomarker increases the measured precision for the association between
25 8-OHdG and smoking (Lu et al., 2014).
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1 Therefore, the current study aimed to investigate the effects of smoking on oxidative
2 DNA damage by estimating blood 8-OHdG level and assessing its relation with some
3 lifestyle determinants. Oxidant stress marker (MDA) and antioxidant defense enzyme (SOD)
4 as well as cotinine and TAC levels were also measured.
5
6 Participants and methods
7 The current study is a prospective case control study in which healthy male participants were
8 randomly selected and interviewed after taking their informed consent as well as obtaining the
9 approval of the study design from the Research Ethics Committee at Kasr Alainy, Faculty of
10 Medicine.
11 The study represented collaboration between Forensic Medicine and Clinical
12 Toxicology Department and Medical Biochemistry Department, Faculty of Medicine, Cairo
13 University, Egypt. The study was conducted according to the declaration of Helsinki and the
14 protocol was approved by Ethics Research Board of both departments (World Medical
15 Association, 2013).
16 A premade Arabic questionnaire was answered; it included questions about socio-
17 demographic data, smoking status, space ventilation, exercise, tea, coffee, energy drinks and
18 alcohol consumption, present, past and family history of diseases.
19 Inclusion criteria
20 Ninety male participants, aged 20-60 years, were enrolled in the study. According to Lodovici
21 et al. (2005), participants were classified according to their smoking history into 3 groups:
22  Non-smokers (20 cases)  never smoked and not exposed to environmental tobacco
23 smoking (ETS).
24  Passive smokers (30 cases)  non-smokers but exposed to ETS in poorly ventilated areas
25 for > 3 hours/day in the year.
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1  Active smokers (40 cases)  > 3 cigarettes/day for over 1 year.
2 Exclusion criteria
3  Medical disorders  diabetes, autoimmune diseases, cardiovascular and neurodegenerative
4 diseases, hepatic and renal diseases, infections, cancer, chronic skin ulcerations as well as
5 endocrine and hematologic disorders.
6  Medications  steroids, non-steroidal anti-inflammatory, diuretics, anticonvulsants,
7 antidepressants, antibiotics as well as vitamin supplementation.
8  Participants receiving mutagenic and carcinogenic drugs, chemotherapy and/or
9 radiotherapy.
10  Participants living near or working in industrial areas.
11 Study measurements and estimations
12 Body weight and height were measured for all participants. Peripheral venous blood samples
13 were withdrawn (3 ml) into EDTA tubes. The collected whole blood was centrifuged for
14 approximately 20 min at 1000-3000 revolutions per minute (rpm) within 30 min after
15 collection. The resulting serum was collected carefully and stored at -80 °C. 8-OHdG,
16 cotinine, SOD and TAC levels were estimated in serum using ELISA kits, while MDA was
17 estimated by colorimetry.
18
19 ELISA kits
20 Human 8-OHdG ELISA kit (Cat No. MBS267161) was provided by MyBiosource Company,
21 USA. According to manual instructions provided by the applied kit, double-sandwich ELISA
22 technique was used where the pre-coated antibody was human 8-OHdG monoclonal antibody
23 and the detecting antibody was polyclonal antibody which was biotin labeled. Samples and
24 biotin labeling antibody were added into ELISA plate wells and washed out with phosphate-
25 buffered saline (PBS) followed by avidin-peroxidase conjugates which were added in order.
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1 The reactant was washed out thoroughly by PBS, tetramethylbenzidine (TMB) substrate was
2 then used for coloring. It turned blue in peroxidase and finally yellow under the action of
3 acid. Human cotinine ELISA kit (Cat No. MBS019457) was provided by MyBiosource
4 Company, USA. While Human SOD and TAC ELISA kits (Cat No. BYEK1111) were
5 provided by Chongqing Biospes Company, China. According to manual instructions provided
6 by the applied kits, purified anti-substance antibody was pre-coated onto well plates and the
7 horseradish peroxidase (HRP) conjugated anti-substance antibody was used as detection
8 antibodies. Standards, test samples and HRP conjugated detection antibody were added to the
9 wells subsequently, mixed and incubated, then, the unbound conjugates were washed away
10 with wash buffer. Tetramethylbenzidine substrates (A and B) were used to visualize HRP
11 enzymatic reaction. It is catalyzed by HRP to produce a blue color product that changes into
12 yellow after adding acidic stop solution. The density of yellow was proportional to the
13 substance amount of sample captured in plate.
14 Calculation: Absorbance of the optical density (O.D.) was read at 450 nm in a microplate
15 reader and then the concentration of substance was calculated.
16 𝑅𝑒𝑙𝑎𝑡𝑖𝑣𝑒 𝑂.𝐷. 450 = 𝑂.𝐷. 450 𝑜𝑓 𝑒𝑎𝑐ℎ 𝑤𝑒𝑙𝑙 ― 𝑂.𝐷. 450 𝑜𝑓 𝑏𝑙𝑎𝑛𝑘/𝑐𝑜𝑛𝑡𝑟𝑜𝑙 𝑤𝑒𝑙𝑙
17 The standard curve can be plotted as the relative O.D. 450 of each standard solution (Y)
18 versus the respective concentration of the standard solution (X). A line was drawn to connect
19 each coordinate point of standard solution. Sample concentrations were found by checking
20 sample O.D. reading. It is recommended to employ the professional curve software (e.g.
21 curve expert 1.3) to analyze and compute the results.
22
23 Colorimetric Method
24 Malondialdehyde colorimetric kit (Cat No. MD 25 29) was provided by Biodiagnostic
25 Company, Egypt. According to manual instructions provided by the applied kit, thiobarbituric
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1 acid (TBA) reacted with MDA in acidic medium at 95 °C for 30 min to form TBA reactive
2 product.
3 Calculation: The absorbance of sample (A sample) was read against blank and standard against
4 water at 534 nm, the resultant pink color was stable for 6 hours and linearity up to 100
5 nmol/ml. Malondialdehyde in sample was calculated as follows:
6 Serum =
A sample
A standard
x 10 nmol/ml
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8 Statistical analysis of data
9 Microsoft excel 2013 was used for data entry after coding and the statistical package for social
10 science (SPSS) version 21 (SPSS, Armonk, New York: International Business Machines
11 Corporation) was used for data analysis. Simple descriptive statistics (arithmetic mean and
12 standard deviation) were used for summary of quantitative data and frequencies were used for
13 qualitative data. Bivariate relationship was displayed in cross tabulations and comparison of
14 proportions was performed using the Chi-square test. Independent t-test, One-way analysis of
15 variance (ANOVA) and post hoc tests were used to compare normally distributed quantitative
16 data. The level of significance was set at probability (p) value ≤ 0.05 and p value < 0.001 was
17 considered as highly significant. Correlations between variables were done using Pearson
18 correlation coefficient (r) which was able to describe both correlation direction (positive or
19 negative according to the sign) and power (weak correlation if < 0.5, moderate correlation from
20 0.5 and 0.7 and strong correlation if > 0.7) (Dawson & Trapp, 2004).
21
22 Results
23 The current study included 90 male participants, aged 20-60 years, who were interviewed and
24 classified into 3 groups (20 non-smokers, 30 passive smokers and 40 active smokers).
25 Demographic characteristics of the studied groups were shown in Table 1.
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1 Lifestyle factors of the studied groups were demonstrated in Table 2. No significant
2 difference between exercise-practicing and non-practicing participants was detected. There
3 were increased tea and coffee consumption rates in active smokers with highly significant (p <
4 0.001) and significant (p = 0.052) values respectively. Active smokers had the lowest BMI
5 while non-smokers had the highest ones with a highly significant difference (p < 0.001).
6 As shown in Table 3, there were higher levels of 8-OHdG, cotinine and MDA and
7 lower levels of SOD and TAC in active smokers compared to passive and non-smokers with a
8 highly significant difference between the studied groups (p < 0.001).
9 When pairwise comparison (Post hoc test) was performed, a highly significant
10 difference was detected between active and both passive and non-smokers (p < 0.001) whereas,
11 no significant difference between passive and non-smokers was observed (p > 0.05) (Table 4).
12 Concerning smoking habits in active smokers, the mean values were provided in Figure
13 1.
14 No significant correlations were observed between 8-OHdG level and smoking habits as
15 shown in Table 5.
16 As shown in Table 6, there were no significant associations between 8-OHdG level and
17 age, exercise, tea and coffee consumption, as well as BMI among the 3 studied groups (p >
18 0.05).
19
20 Discussion
21 Oxidative stress caused by tobacco smoke can result in damage to lipids, proteins and DNA
22 leading to cellular dysfunction or ultimately cell death (Black et al., 2016). Sensitive and
23 specific biomarkers for antioxidant status/oxidative stress are essential to better understand the
24 role of antioxidants and oxidative stress in human health and diseases, thereby maintaining
25 health and establishing effective defense strategies against oxidative stress (Yeum et al., 2011).
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1 In the current study, active smokers represented 62.5% and 37.5% in urban and rural
2 areas respectively. No significant difference was detected between the studied groups. This
3 finding was in agreement with that of Völzke et al. (2006) who reported the presence of a
4 higher percent of smokers among urban compared to rural communities. This was explained by
5 the more stressful life in urban areas. Internal migration may further explain this finding where
6 more smokers, than non-smokers, move from rural to urban communities rather than vice versa.
7 However, researchers in Canada reached a different conclusion, stating that an increased
8 number of cigarette smokers in rural areas may be present due to fewer restrictions concerning
9 smoking in these areas (Li et al., 2009).
10 In the current work, while passing from non- to active smokers, the physical work
11 increased while mental work decreased with a significant difference between the studied groups
12 (p = 0.016). These results were in agreement with Kim et al. (2015) who investigated smoking
13 and possible related factors among Korean male workers and found that smoking rate in manual
14 workers was higher than in non-manual due to less effective antismoking policies in manual
15 workers. Daily smoking was more common in the absence of both workplace rules against
16 smoking and smoking cessation programs.
17 In the present study, the percent of active smokers with high educational level was the
18 lowest (7.5%) with a significant difference between the studied groups (p = 0.016). These
19 results were in accordance with Schnohr et al. (2004) and Han et al. (2010) and may be
20 explained by the fact that high educational level increased the awareness of smoking dangers
21 and reduced environmental and occupational exposure.
22 In the current work, there was significant increase in tea and coffee consumption rates
23 in active smokers. These results were in agreement with Chao (2015) who stated that many
24 people associate drinking caffeine and smoking because of the habit of doing both together.
25 Non-smokers metabolize caffeine at a lower rate than smokers, which means that recent quitters
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1 will need half the amount of caffeine they drank as a smoker. Caffeinated drinks, especially
2 coffee, often act as a smoking trigger. Moreover, Treur et al. (2016) investigated the
3 association between smoking and caffeine consumption comparing data from two different
4 countries: the Netherlands (coffee-drinking) and the United Kingdom (tea-drinking). They
5 found that smoking initiation and persistence were associated with consuming more caffeine
6 per day.
7 In the current study, there was a highly significant difference between the studied
8 groups regarding their BMI; active smokers had the lowest BMI while non-smokers had the
9 highest ones (p < 0.001). These results were in agreement with Gastaldelli et al. (2010) who
10 reviewed the effect of smoking on lipid metabolism and body weight and stated that smokers
11 tend to be thinner and have lower BMI than non-smokers because nicotine increases the basal
12 metabolic rate and energy expenditure. Furthermore, Dare et al. (2015) conducted a study in
13 the United Kingdom to demonstrate the relationship between smoking and obesity in middle-
14 aged adults and showed that smokers were less likely to be obese than non-smokers with a
15 highly significant difference between them (p < 0.001). This was attributed to the fact that
16 nicotine is associated with reduced appetite and weight, less efficient absorption and storage of
17 calories and increased thermogenesis (Chiolero et al., 2008).
18 In the present work, there were highly significant higher levels of 8-OHdG, cotinine and
19 MDA and lower levels of SOD and TAC in active smokers compared to both passive and non-
20 smokers whereas, there was no significant difference between passive and non-smokers. These
21 results were in accordance with those of Pilger et al. (2001) who conducted a study of urinary
22 8-OHdG excretion in healthy participants. The results showed 18% increase in 8-OHdG in
23 active smokers compared to passive and non-smokers. Passive smokers showed higher urinary
24 cotinine levels compared to non-smokers but this difference did not reach statistical
25 significance. On the same base, Lodovici et al. (2005) conducted a study on lifestyle
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1 determinants of 8-OHdG levels in human leukocyte DNA in Italy and stated that 8-OHdG and
2 cotinine levels were higher in active smokers compared to non-smokers; on the contrary, the
3 difference between ETS-exposed and non-smokers was not significant. Chávez et al. (2007)
4 measured serum levels of MDA and found that they were higher in smokers than in non-
5 smokers but with no significant difference. Reddy et al. (2012) and Jenifer et al. (2015)
6 demonstrated the effect of smoking on blood and salivary SOD levels in smokers and non-
7 smokers. According to the former, there was a significant decrease in SOD levels of smokers
8 with increased duration and frequency of smoking. Whereas, according to the latter, SOD levels
9 were higher in smokers than in non-smokers and this may be a defense mechanism to
10 counteract the increased ROS production induced by smoking. Bakhtiari et al. (2015)
11 conducted a cross-sectional study to investigate the influence of cigarette smoke on salivary
12 TAC. The mean salivary TAC in non-smokers (0.741 ± 0.123 U/ml) was statistically significant
13 higher than in smokers (0.529 ± 0.167 U/ml) (p < 0.001). The explanation was that low
14 antioxidant values in smokers were due to the presence of a high amount of free radicals in
15 cigarette smoke generating an oxidative stress causing an exhaustion of the antioxidants of the
16 body. In contrary, Nagler (2007) showed that salivary TAC was significantly higher in smokers
17 compared to non-smokers.
18 Discrepancy between the results of the above mentioned studies was explained as
19 follows: increased oxidative stress leads to an induction in the activities of antioxidants as a
20 part of compensatory and defense mechanisms to protect the organism from oxidative lesions.
21 These mechanisms prevent the accumulation of free radicals present in cigarette smoke and the
22 lipid peroxidation products, such as MDA. Therefore, it could be concluded that in moderate
23 smokers, antioxidants appear to effectively prevent oxidative damage avoiding
24 oxidant/antioxidant imbalance. At a point, this compensatory effect is overcome and toxic
25 effects are initiated (Chávez et al., 2007; Mizrak et al., 2015).
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1 In the current study, there were no significant correlations between 8-OHdG level and
2 smoking habits in active smokers. Similarly, Pilger et al. (2001) and Lodovici et al. (2005)
3 observed no correlation between 8-OHdG levels and the number of cigarettes smoked/day.
4 According to Suzuki et al. (2003), serum 8-OHdG levels were positively correlated with the
5 number of cigarettes smoked/day among all participants, but this correlation did not reach
6 statistical significance in males. Whereas, according to Irie et al. (2005), the average number
7 of cigarettes smoked/day were positively correlated to urinary 8-OHdG levels among all
8 participants as well as in males (p = 0.015 and 0.009 respectively). Discrepancy between the
9 results of various studies can be explained by variations in sample size, sample composition,
10 methods of measurement of 8-OHdG and statistical methods. Moreover, Cao et al. (2015)
11 investigated smoking-promoted oxidative DNA damage and its correlation with lung
12 carcinogenesis. A significant correlation was found between the levels of 8-OHdG and smoking
13 index. These findings emphasize the importance of smoking in oxidative stress.
14 In the current work, there were no significant correlation between 8-OHdG level and
15 age. Participants > 60 years were excluded from the study so that we could study the level of 8-
16 OHdG without the effect of aging. Lodovici et al. (2005) and Tamae et al. (2009) studied the
17 influence of age, smoking and other lifestyle factors on 8-OHdG where data from male
18 subjects, 18-60 years were analyzed. No association was noticed between 8-OHdG level and
19 age. On the other hand, Yao et al. (2004) studied the levels of urinary 8-OHdG and
20 investigated its association with cigarette smoking where healthy Chinese male volunteers, 23-
21 70 years were enrolled in the study and found a significant association between 8-OHdG level
22 and age (p = 0.032). This observation confirms the fact that oxidative DNA damage increases
23 with age.
24 In the present study, there was no significant difference between 8-OHdG and exercise.
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1 Black et al. (2016) conducted a study on socio-demographic and lifestyle factors of 8-OHdG
2 and its associations with metabolic syndrome in the Netherlands. Physical activity was assessed
3 using the International Physical Activity Questionnaire covering vigorous activity, moderate
4 walking and sitting activities over the past 7 days. No significant association was found
5 between 8-OHdG level and physical activity. On the other hand, Sato et al. (2003) studied the
6 effect of exercise in healthy male subjects on 8-OHdG in leukocyte DNA. They showed that 8-
7 OHdG levels of the physically active subjects were lower than those of the sedentary subjects
8 (p = 0.0109). However, Han et al. (2010) found that urinary 8-OHdG level was higher in
9 subjects who exercised regularly than those who did not. Exercise may enhance active DNA
10 repair enzymes resulting in excretion of 8-OHdG residues.
11 During oxidative metabolism, it is estimated that 4-5% of the oxygen consumed by
12 respiration is converted to oxygen free radicals. Thus, as oxygen consumption increases during
13 exercise, there is a consequent increase in free radical generation. Chronic exercise seems to
14 reduce the oxidative stress by increasing antioxidant defense mechanisms (Clarkson &
15 Thompson, 2000).
17 tea and coffee consumption rates among the studied groups. These results were in accordance
18 with Han et al. (2010) where they showed that tea and coffee consumption did not affect
19 urinary 8-OHdG levels. Whereas, Lodovici et al. (2005) showed that tea and coffee
20 consumption had a significant protective effect on 8-OHdG levels (p = 0.041) as they might
21 partially protect against and modify smoking-induced oxidative DNA damage. Moreover, Raza
22 & John (2008) demonstrated the effects of tea polyphenols on oxidative stress and apoptosis
23 and concluded that they have strong inhibitory activity on the free radical-induced oxidation.
25 BMI among the studied groups. Similarly, Pilger et al. (2001), Lodovici et al. (2005), Tamae
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1 et al. (2009), Han et al. (2010) and Black et al. (2016) found no significant correlation
2 between 8-OHdG and BMI level. Kasai et al. (2001) studied the influence of smoking and
3 BMI on urinary 8-OHdG. Negative correlation existed between 8-OHdG level and BMI (p =
4 0.0032). This may be related to the fact that lean persons have a higher metabolic rate than
5 obese and therefore have higher oxidative stress.
6
7 Conclusion
8 The results of the current study seem to support the hypothesis that active smoking is
9 associated with increased oxidative stress and decreased antioxidant defense. It elevates
10 blood 8-OHdG, a reliable biomarker for oxidative DNA damage. Hence, it is recommended
11 to strengthen tobacco control measures, promote healthy lifestyle as well as conduct further
12 work to further understand which lifestyle factors determine basal oxidative stress and 8-
13 OHdG level as well as to carry out individual exposure assessments instead of relying on the
14 reports of the participants in relation to smoking to further differentiate between passive and
15 non-smokers taking into consideration gender and individual variations as well as
16 methodological and analytical differences. It is also important to investigate the effect of
17 dietary patterns.
18
19 Disclosure of interest
20 The authors report no conflict of interest.
21
22 Funding
23 All ELISA and colorimetric kits were supplied via funding from Cairo University, Egypt.
24
25 References
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1  Andersson KE. (2017). Oxidative stress and its possible relation to lower urinary tract
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4 total antioxidant capacity. J Dent Res Dent Clin Dent Prospects 9(4):281–284.
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13 oxidant/antioxidant balance in healthy subjects. Am J Ther 14(2):189–193.
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15 weight, body fat distribution, and insulin resistance. Am J Clin Nutr 87(4):801–809.
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1  Han Y, Donovan M, Sung F. (2010). Increased urinary 8-hydroxy-2'-deoxyguanosine
2 excretion in long-distance bus drivers in Taiwan. Chemosphere 79:942–948.
3  Irie M, Tamae K, Iwamoto-Tanaka N, et al. (2005). Occupational and lifestyle factors
4 and urinary 8-hydroxydeoxyguanosine. Cancer Sci 96(9):600–606.
5  Jenifer HD, Bhola S, Kalburgi V, et al. (2015). The influence of cigarette smoking on
6 blood and salivary superoxide dismutase enzyme levels among smokers and non-
7 smokers: A cross sectional study. J Tradit Complement Med 5(2):100–105.
8  Kamceva G, Arsova-Sarafinovska Z, Ruskovska T, et al. (2016). Cigarette smoking and
9 oxidative stress in patients with coronary artery disease. Maced J Med Sci 4(4):636–640.
10  Kasai H, Iwamoto-Tanaka N, Miyamoto T, et al. (2001). Lifestyle and urinary 8-
11 hydroxydeoxyguanosine, a marker of oxidative DNA damage: Effects of exercise,
12 working conditions, meat intake, body mass index, and smoking. Jpn J Cancer Res
13 92(1):9–15.
14  Kim B, Pang D, Park Y, et al. (2015). Heavy smoking rate trends and related factors in
15 Korean occupational groups: Analysis of KNHANES 2007 - 2012 data. BMJ Open
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17  Kulikowska-Karpińska E, Czerw K. (2015). Estimation of 8-hydroxy-2'-deoxyguanosine
18 concentration in the urine of cigarette smokers. Wiad Lek 68(1):32–38.
19  Li FX, Robson PJ, Ashbury FD, et al. (2009). Smoking frequency, prevalence and
20 trends, and their socio-demographic associations in Alberta, Canada. Can J Public
21 Health 100(6):453–458.
22  Liu ZH, Cai Y, He, J. (2018). High serum levels of 8-OHdG are an independent predictor
23 of post-stroke depression in Chinese stroke survivors. Dove Med Press 2018(14):587–
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1  Lodovici M, Caldini S, Luceri C, et al. (2005). Active and passive smoking and lifestyle
2 determinants of 8-oxo-7,8-dihydro- 2'-deoxyguanosine levels in human leukocyte DNA.
3 Cancer Epidemiol Biomarkers Prev 14(12):2975–2977.
4  Lu C, Ma Y, Chen P, et al. (2014). Oxidative stress of office workers relevant to tobacco
5 smoking and inner air quality. Int J Environ Res Public Health 11:5586–5597.
6  Mahapatra SK, Das S, Dey SK et al. (2008). Smoking induced oxidative stress in serum
7 and neutrophil of the university students. Al Ameen J Med Sci 1(1):20–31.
8  Mizrak S, Turan V, Caglayan O, et al. (2015). The effect of long term pre/postnatal
9 low/high dose nicotine exposure on tissue oxidant/antioxidant status and DNA damage
10 in rats. Drug Res 65:432–436.
11  Nagler RM. (2007). Altered salivary profile in heavy smokers and its possible
12 connection to oral cancer. Int J Biol Markers 22(4):274–280.
13  Pilger A, Germadnik D, Riedel K, et al. (2001). Longitudinal study of urinary 8-
14 hydroxy-2′-deoxyguanosine excretion in healthy adults. Free Rad Res 35(3):273–280.
15  Raza H, John A. (2008). In vitro effects of tea polyphenols on redox metabolism,
16 oxidative stress, and apoptosis in PC12 cells. Ann NY Acad Sci 1138:358–365.
17  Reddy S, Swapna LA, Ramesh T, et al. (2012). Influence of cigarette smoking on blood
18 and salivary superoxide dismutase levels among smokers and non-smokers. J Invest Clin
19 Dent 3(4):298–303.
20  Sato Y, Nanri H, Ohta M, et al. (2003). Increase of human MTH1 and decrease of 8-
21 hydroxydeoxyguanosine in leukocyte DNA by acute and chronic exercise in healthy
22 male subjects. Biochem Biophys Res Commun 305(2):333–338.
23  Schnohr C, Højbjerre L, Riegels M, et al. (2004). Does educational level influence the
24 effects of smoking, alcohol, physical activity, and obesity on mortality? A prospective
25 population study. Scand J Public Health 32(4):250–256.
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1  Suzuki K, Ito Y, Ochiai J, et al. (2003). The relationship between smoking habits and
2 serum levels of 8-OHdG, oxidized LDL antibodies, Mn-SOD and carotenoids in rural
3 Japanese residents. J Epidemiol 13(1):29–37.
4  Tamae K, Kawai K, Yamasaki S, et al. (2009). Effect of age, smoking and other lifestyle
5 factors on urinary 7-methylguanine and 8-hydroxydeoxyguanosine. Cancer Sci
6 100(4):715–721.
7  Treur JL, Taylor AE, Ware JJ, et al. (2016). Associations between smoking and caffeine
8 consumption in two European cohorts. Addiction 111(6):1059–1068.
9  Valavanidis A, Viachogianni T, Fiotakis C. (2009). 8-hydroxy-2′-deoxyguanosine (8-
10 OHdG): A critical biomarker of oxidative stress and carcinogenesis. J Environ Sci
11 Health C Environ Carcinog Ecotoxicol Rev 27(2):120–139.
12  Völzke H, Neuhauser H, Moebus S, et al. (2006). Urban-rural disparities in smoking
13 behavior in Germany. BMC Public Health 6:146.
14  World Medical Association. (2013). WMA Declaration of Helsinki - Ethical principles
15 for medical research involving human subjects. 64th WMA General Assembly, Fortaleza,
16 Brazil. https://www.wma.net/policies-post/wma-declaration-of-helsinki-ethical-
17 principles-for-medical-research-involving-human-subjects/
18  Yao QH, Mei SR, Weng QF, et al. (2004). Determination of urinary oxidative DNA
19 damage marker 8-hydroxy-2′-deoxyguanosine and the association with cigarette
20 smoking. Talanta 63(3):617–623.
21  Yeum K, Russell R, Aldini G. (2011). Antioxidant activity and oxidative stress: An
22 overview. In: Biomarkers for antioxidant defense and oxidative damage. John Wiley &
23 Sons. Ames, Iowa, USA, Chapter 1, p:3.
24 Table caption
25 Table 1. Baseline demographic characteristics of the studied groups.
26 Table 2. Lifestyle factors of the studied groups.
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1 Table 3. Mean levels of the measured biochemical parameters among the studied groups.
2 Table 4. Pairwise comparison between the studied groups concerning levels of the measured
3 biochemical parameters.
4 Table 5. Correlation between 8-OHdG level and smoking habits in active smokers.
5 Table 6. Comparison between 8-OHdG level and age as well as lifestyle factors among the
6 studied groups.
7
8 Figure caption
9 Figure 1. Mean values of smoking habits in active smokers.
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Table 1. Baseline demographic characteristics of the studied groups.
Variable
Non-smokers
(n = 20)
Passive smokers
(n = 30)
Active smokers
(n = 40)
p value
Age (years)
(mean ± SD)
31.7 ± 14.12 34.57 ± 12.47 32.58 ± 9.34 0.655
Residence n % n % n %
 Urban 15 75 13 43.3 25 62.5
 Rural 5 25 17 56.7 15 37.5
0.069
Occupation n % n % n %
 Physical 11 55 24 80 35 87.5
 Mental 9 45 6 20 5 12.5
0.016*
Educational level n % n % n %
 Low 3 15 10 33.3 16 40
 Moderate 8 40 13 43.3 21 52.5
 High 9 45 7 23.3 3 7.5
0.016*
* Significant p ≤ 0.05
SD: standard deviation
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Table 2. Lifestyle factors of the studied groups.
Variable
Non-smokers
(n = 20)
Passive smokers
(n = 30)
Active smokers
(n = 40)
p value
Exercise n % n % n %
 No 19 95 29 96.7 35 87.5
 Yes 1 5 1 3.3 5 12.5
0.319
Tea (cups/day)
(mean ± SD)
2.06 ± 1.66 2.75 ± 2.16 5.13 ± 3.82 < 0.001**
Coffee (cups/day)
(mean ± SD)
0.16 ± 0.37 0.08 ± 0.21 0.61 ± 1.4 0.052*
BMI (kg/m2)
(mean ± SD)
29.19 ± 4.79 26.81 ± 4.49 24.43 ± 4.34 < 0.001**
* Significant p ≤ 0.05
** Highly significant P < 0.001
SD: standard deviation
Page 21 of 26
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Table 3. Mean levels of the measured biochemical parameters among the studied groups.
Variable
Non-smokers
(n = 20)
Passive smokers
(n = 30)
Active smokers
(n = 40)
Total
(n = 90)
p value
8-OHdG (ng/ml)
(mean ± SD)
0.77 ± 0.39 0.84 ± 1.18 3.59 ± 1.98
2.04 ± 2.03
< 0.001**
Cotinine (ng/ml)
(mean ± SD)
1.47 ± 1.06 1.67 ± 1.77 13.9 ± 5.26
7.06 ± 7.16
< 0.001**
MDA (nmol/ml)
(mean ± SD)
28.24 ± 24.82 24.33 ± 20.09 96.69 ± 25.22
57.3 6 ± 42.37
< 0.001**
SOD (U/ml)
(mean ± SD)
1.79 ± 0.65 1.88 ± 0.63 0.62 ± 0.28
1.3 ± 0.79
< 0.001**
TAC (U/ml)
(mean ± SD)
18.99 ± 5.47 18.41 ± 5.97 7.41 ± 2.2
13.65 ± 7.19
< 0.001**
** Highly significant p < 0.001
8-OHdG: 8-hydroxy-2′-deoxyguanosine; MDA: malondialdehyde; SOD: superoxide dismutase;
TAC: total antioxidant capacity
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Table 4. Pairwise comparison between the studied groups concerning levels of the
measured biochemical parameters.
Dependent
variable
(I) Group (J) Group
Mean
difference
(I-J)
p value
Active smokers -2.81850 0.000**
Non-smokers
Passive smokers -0.07233 0.985
Passive smokers
Non-smokers 0.07233 0.985
Non-smokers 2.81850 0.000**
8-OHdG
(ng/ml)
Active smokers
Passive smokers 2.74617 0.000**
Non-smokers
Passive smokers
Non-smokers 2.81850 0.000**
Cotinine
(ng/ml)
Active smokers
Non-smokers
Passive smokers 3.9050 0.834
Passive smokers
Non-smokers -3.9050 0.834
Non-smokers 68.4550 0.000**
MDA
(nmol/ml)
Active smokers
Active smokers 1.16475 0.000**
Non-smokers
Passive smokers
Non-smokers -1.16475 0.000**
SOD (U/ml)
Active smokers
Passive smokers -1.25775 0.000**
Non-smokers
Passive smokers 0.58767 0.895
Passive smokers
Non-smokers -0.58767 0.895
Non-smokers -11.58075 0.000**
TAC (U/ml)
Active smokers
Passive smokers -10.99308 0.000**
** Highly significant p < 0.001
8-OHdG: 8-hydroxy-2′-deoxyguanosine; MDA: malondialdehyde; SOD: superoxide dismutase;
TAC: total antioxidant capacity
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Table 5. Correlation between 8-OHdG level and smoking habits in active smokers.
Active smokers (n = 40)
8-OHdG
(ng/ml)
Number of cigarettes
smoked/day
Duration of
smoking/years
Smoking index
(pack-year)
Correlation
Coefficient (r)
0.219 0.135 0.058
p value 0.174 0.405 0.720
8-OHdG: 8-hydroxy-2′-deoxyguanosine
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Table 6. Comparison between 8-OHdG level and age as well as lifestyle factors among the studied groups.
Non-smokers (n = 20) Passive smokers (n = 30) Active smokers (n = 40)
8-OHdG
(ng/ml)
Age
(years)
Exercise
Tea
(cups/day)
Coffee
(cups/day)
BMI
(kg/m2)
Age
(years)
Exercise
Tea
(cups/day)
Coffee
(cups/day)
BMI
(kg/m2)
Age
(years)
Exercise
Tea
(cups/day)
Coffee
(cups/day)
BMI
(kg/m2)
Correlation
Coefficient
(r)
0.033 - 0.252 0.249 0.061 -0.200 - -0.070 -0.167 0.346 0.164 - 0.233
0.179
0.042
p value 0.889 0.536 0.285 0.290 0.797 0.288 0.621 0.713 0.377 0.061 0.312 0.192 0.148
0.269
0.796
8-OHdG: 8-hydroxy-2′-deoxyguanosine; BMI: body mass index
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187x78mm (300 x 300 DPI)
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