ABSTRACT
Background/Objective/Methods:
Glutathione-S-transferase Mu1 (GSTM1) and glutathione peroxidase 1 (GPX1) are known antioxidant enzymes that help protect cells from the oxidative damage that occurs from smoking. This study explored the correlation between GSTM1 and GPX1 levels between a group of smokers with the GSTM1 and GPX1 genes in the Saudi population and a control group and investigated the genetic risk factors in the group of smokers.
Results:
The control and smokers’ group (n = 50; aged 22.3 ± 3.1 years; BMI 24.6 ± 5.9 kg/m2) were genotyped using quantitative polymerase chain reaction (qPCR). In comparison with the control group, the smokers’ group displayed a different genotype disruption of GSTM1 and GPX1. Carriers of the homozygous (TT) genotype of GSTM1 had more than a twofold (OR = 2.71, 95% CI = 0.10–70.79, P = 1.000) smoking risk than the carriers of the heterozygous (CT) genotype. Those with the GPX1 gene showed no risk in the control and smokers’ groups. Smokers with the TT/GG combination (homozygous for GPX1 and normal for GPX1) were identified as high risk (OR = 2.58, 95% CI = 0.096–69.341).
Conclusion:
The main outcomes showed no significant association between genetic polymorphism of the GSTM1 and GPX1 genes and cigarette smoking in the Saudi Arabian population. However, the results showed a slight decrease in the number of GSTM1 and GPX1 gene modifications among smokers.
KEYWORDS: GPX1, GSTM1, polymorphism, Saudi population, smoking
INTRODUCTION
Smoking is considered a public health risk. It is one of the most preventable causes of death from noncommunicable diseases[1] and is one of the major public health concerns globally. Smoking kills more than 8 million people worldwide annually. Most smoking-related deaths are caused by direct smoking, while 1.2 million deaths are attributed to nonsmokers being exposed to second-hand smoke.[2] There was an estimated 1.76 million deaths from lung cancer in 2018; one of the main risk factors is tobacco use, with 20% of deaths caused by smoking.[3]
The risk of death from cancer is increased between 8 and 15 times in males and between 2 and 10 times in women when compared to nonsmokers, and smoking cessation has been shown to reduce risk, particularly in heavy smokers.[4] Several components in cigarette smoke are known carcinogens, including polycyclic aromatic hydrocarbons (PAHs), aromatic amines, and N-nitroso compounds.[5]
Glutathione-S-transferase Mu1 (GSTM1) is a glutathione-S-transferase (GST) superfamily member (GSTs). Through its engagement in the second phase of xenobiotic metabolism, this enzyme is known to play a significant role in xenobiotic metabolism, anticancer, and anti-infection.[6] GSTM1 is involved in the detoxification of metabolites of carcinogens found in the environment, such as cigarette smoke.[7] The GSTM1 gene has 10 exons and is found on chromosome 1p13.3. The homozygous deletion (null genotype) of the GSTM1 gene is a frequent mutation that has been linked to a loss of enzymic activity and an increased risk of cancer.[8]
Another enzyme involved in the neutralization of free radicals is glutathione peroxidase (GPX, EC no. 1.11.1.9). By converting glutathione (GSH) to oxidized glutathione (GSSG), GPXs, one of the antioxidant enzymes with peroxidase activity, catalyze the reduction of hydrogen peroxide (H2O2) and lipid hydroperoxides, thereby offering protection from oxidative damage.[9] Glutathione peroxidase 1 (GPX1) is the most abundant enzyme. GPX1 is an antioxidant and anti-inflammatory selenoprotein, having two exons within a 1.42-kb area on chromosome 3p21.[10]
Cigarette smoking and passive smoking are preventable risk factors which introduce a range of chronic diseases that raise morbidity and mortality rates. Exposure to cigarette smoke increases antioxidant enzymes and drug-metabolizing enzymes. By strengthening the body’s defense system, it might be possible to reduce the harm caused by smoke. However, the defense system cannot be sufficiently protected by those enzymes alone. Cigarette smoke contains a variety of chemically reactive molecular species, including reactive oxygen species (ROS) and radicals. Vitamins C (ascorbic acid [AA]) and E contain two small anti-oxidative molecules that act as defense mechanisms.[11]
The genus Glycyrrhiza includes licorice. Licorice is frequently employed in herbal medicine as well as being a natural sweetener. Numerous active components in licorice exhibit efficacy in a variety of biological and physiological processes.[12] A study by Abu-Awwad and his colleagues was conducted to determine how pomegranate and licorice beverages affect the metabolism of nicotine. According to their results, pomegranate and licorice beverages have an inducing effect on the hepatic cytochrome P450 enzymes, which speeds up the rate at which nicotine is metabolized.[13]
Tamarind (Tamarindus indica L.: T. indica), a member of the Leguminosae (Fabaceae) family, is a plant that typically grows in tropical climates and has long been used as a traditional remedy for ailments such as respiratory problems, fever, malaria, dysentery, diarrhea, and abdominal pain. Tamarind is also high in essential amino acids, phytochemicals, and vitamins, and its leaves and seeds have antioxidant and anti-inflammatory properties. The purpose of Saminan’s study was to determine whether Tamarind could help active smokers with FEV1 and airway obstruction. When compared to pretreatment, the FEV1 in the smoking group was significantly higher after tamarind consumption. Inactive smokers who took a T. indica L. supplement for 7 days experienced a significant increase in FEV1, indicating that this supplement may help lessen airway obstruction caused by smoking.[14]
Cigarette smoking is increasing in Saudi Arabia, starting from an early age in both the male and female population. There is a shortage of studies focusing on genetics and the effect of cigarette smoking. Our study aimed to investigate the genetic risk factors in smokers with the GSTM1 and GPX1 gene in the Saudi population.
Previous studies show that there is an association between the GSTM1 polymorphism and smoking. A meta-analysis was conducted by Liu and his colleagues to determine whether there is a link between GSTM1 gene polymorphism, coronary artery disease (CAD), and smoking. CAD was linked to the GSTM1 null genotype in groups of smokers. The combination of GSTM1 polymorphism and smoking may therefore increase CAD susceptibility.[15] Overall, the findings showed that the GSTM1 null genotype was linked to a greater risk of CHD and that this link might be influenced by smoking status.[16]
In Sciskalska and his colleagues’ study, a group of AP patients and healthy individuals were chosen to examine the effects of SNPs rs1050450 in the GPX1 gene and rs713041 in the glutathione peroxidase 4 (GPX4) gene on the activity of total GPX. The effects of AP on two groups—nonsmokers and smokers—were discovered to include decreased GPX activity in plasma and erythrocyte lysate, increased glutathione reductase activity, and decreased GSH concentration.[9]
Another study by Ho and his colleagues evaluated oxidative stress and the relationship of lipid peroxidation markers in both smokers and nonsmokers. The results showed that smokers’ blood levels of lead (Pb), alanine transaminase (ALT), thiobarbituric acid reactive substances, and oxidized LDL were considerably greater than those of nonsmokers.[17]
METHODOLOGY
Study subjects
In our study, both the smoker participants (n = 25) and matched controls (nonsmokers; n = 25) were recruited from King Abdulaziz University, Jeddah, Saudi Arabia.
Sample collection
A total of 50 Saudi adult male participants with ages ranging between 19 and 51 years were enrolled in this study. Five milliliters of blood was collected from each volunteer in an EDTA tube and immediately stored at −80°C until genotype analysis. The purpose of the study was explained to each volunteer before they signed written consent in accordance with the guidelines of the Ethical Committee from the Center of Excellence in Genomic Medicine Research (CEGMR). Using the standard procedure, the genomic DNA was isolated from the blood samples. A qPCR assay was then carried out on each gene by using 2× TaqMan Master Mix.
DNA extraction and quality control
In accordance with the manufacturer’s instructions, 5 ml of blood samples was withdrawn for genomic DNA isolation using the DNA Blood Mini Kit (Qiagen, Düsseldorf, Germany). The purity of the samples was assessed using the Thermo Scientific NanoDrop 2000 Spectrophotometer (Thermo Scientific™, USA) to evaluate the DNA sample concentrations. Nuclease-free water was used as a blank to calibrate the spectrophotometer before samples were taken using the micropipette.
Genotyping and QPCR
Specific primers covering GSTM1 SNP rs1056806 were designed by TaqMan® SNP (forward primer: 5′-TGTAAAACGACGGCCAGT-3′; reverse primer: 5′-CAGGAAACAGCTATGACC-3′). Primers covering GPX1 SNP rs1800668 were designed by TaqMan® SNP forward primer: 5′-TGTAAAACGACGGCCAGT-3′; reverse primer: 5′-CAGGAAACAGCTATGACC-3′ [see Table 1]. To amplify the extracted DNA, thermocycling conditions in an applied biosystem QuantStudio™ 12K Flex Real-Time PCR system were employed following the run method in the TaqMan® SNP Genotyping Assays user guide table [Table 2]. Every PCR reaction was carried out in an ultimate volume of 10 μl containing 5 μl of readymade Master mix buffer (Thermo Fisher Scientific), 0.5–1 mmol/l of Primers/probe mix, 2 mmol/l of H2O, and 2 mmol/l of DNA for one test and then transferred into a 96-well plate using a micropipette. Afterward, the PCR automatically determined genotypes and displayed data using Applied TaqMan™ Genotyper Software. This procedure was performed in the CEGMR in Jeddah, Saudi Arabia.
Table 1.
Real-time PCR primers
| Gene | Forward sequence | Reverse sequence |
|---|---|---|
| GSTM1 | 5′-TGTAAAACGACGGCCAGT-3′ | 5′-CAGGAAACAGCTATGACC-3′ |
| GPX1 | 5′-TGTAAAACGACGGCCAGT-3′ | 5′-CAGGAAACAGCTATGACC-3′ |
Table 2.
RT-PCR cycling parameters
| Step | Temperature (°C) | Duration (s) | Cycles |
|---|---|---|---|
| Enzyme activation | 95 | 20 | Hold |
| Denature | 95 | 3 | 40 |
| Anneal/Extend | 60 | 30 |
Statistical analysis
Data were analyzed by SPSS for Windows, Version 16.0. Chicago, SPSS Inc. For descriptive statistics, quantitative data are expressed as the mean [standard deviation (SD)]. Qualitative parameters were expressed as frequency and percentage. The Chi-square test was used to measure the relationship between qualitative variables. The t-test was used to compare the mean and SD of two sets of quantitative normally distributed data. In all cases, a P value of ≤0.05 was considered to be statistically significant.
RESULTS
Descriptive statistics of participants
Table 3 shows the descriptive statistics for the quantitative variables by age. The mean was 22.34 years with SD = 3.11 years, within the age range 19–35 years. The mean of height was 173.57 cm with SD = 7.40 cm, ranging from 155 cm to 196 cm. The mean of weight was 74.30 kg with SD = 19.30 kg, within the weight range of 47–142 kg. The BMI mean was 24.64 kg/m2 with SD = 5.95 kg/m2, ranging from 14.80 to 42.50 kg/m2. The mean of years smoked was 5.24 years with SD = 2.81 years, ranging from 1 to 11 years, and the mean of the number of cigarettes smoked per day was 12.56 with SD = 8.90 cigarettes, within the range between 1 and 30 cigarettes.
Table 3.
Descriptive statistics of participants
| Variable | Number | Minimum | Maximum | Mean±SD |
|---|---|---|---|---|
| Age (years) | 50 | 19.00 | 35.00 | 22.3400±3.11422 |
| Height (cm) | 50 | 155.00 | 196.00 | 173.5700±7.40657 |
| Weight (kg) | 50 | 47.00 | 142.00 | 74.3000±19.30237 |
| BMI (kg/m2) | 50 | 14.80 | 42.50 | 24.6420±5.95703 |
| Years smokeda (years) | 25 | 1.00 | 11.00 | 5.2400±2.81780 |
| Cigarettes per daya | 25 | 1.00 | 30.00 | 12.5600±8.90262 |
aExcludes cases and controls who had never smoked. SD: Standard deviation, cm: centimeter, kg: kilogram, BMI: body mass index
Genotype and allele frequencies of the GSTM1 variant
As shown in Table 4, the frequencies of the CC (normal), CT (heterozygous), and TT (homozygous) genotypes in GSTM1 were 76% (n = 19), 24% (n = 6) and 0.0%, respectively, in the smokers’ group. In the control group, the frequencies of CC, CT, and TT were 84% (n = 21), 12% (n = 3), and 4% (n = 1), respectively. These results were consistent with the expected genotype distributions, calculated using the Hardy–Weinberg equilibrium. The genotype frequencies in all subjects were consistent with the Hardy–Weinberg equilibrium (χ2 = 0.463, df = 1, P = 0.496).
Table 4.
Genotype and allele frequencies of GSTM1 variant
| Frequency | P | Odd ratio (95% CI) | Risk ratio (95% CI) | |||
|---|---|---|---|---|---|---|
|
| ||||||
| Control (n=25) | Smoker (n=25) | |||||
| CC | 84 (n=21) | 76 (n=19) | 1 (Reference) | 1 (Reference) | ||
| CT | 12 (n=3) | 24 (n=6) | 0.463b | 0.452 (0.099–2.065) | 0.635 (0.241–1.675) | |
| TT | 4 (n=1) | 0 | 1.000b | 2.721 (0.105–70.796) | 1.905 (1.419–2.558) | |
| Alleles | ||||||
| C | 89% | |||||
| T | 11% | |||||
| C | 88 (n=44) | 90 (n=45) | 1 (Reference) | 1 (Reference) | ||
| T | 12 (n=6) | 10 (n=5) | 0.751a | 0.815 (0.232–2.865) | 0.899 (0.456–1.773) | |
CC: Normal, CT: heterozygous, TT: homozygous. aTwo-sided χ2 test. bTwo-sided Fisher’s exact test. CI: Confidence intervals. OR odds ratio
The C allele represented 89%, while the T allele represented 11%. For the group of smokers, the percentages of the C and T alleles were 90% and 10%, respectively, while the percentages of the C and T alleles for the control group were 88% and 12%, respectively.
The genotype frequency of CT was increased in the smoker group, while TT was increased in the control group. Even though there was a slight increase in the smokers’ group, this variant genotype was not significantly associated with a risk connected with smoking.
GSTM1 variant with physical and biochemical characteristics in the control group and smokers’ group
An independent sample t-test was used for the variable of samples; the results are shown in Table 5. The subjects were divided according to alleles CC, CT, and TT. In CC, the mean age was 22.3 ± 3.16 and 22.8 ± 3.71 in the control group and smokers’ group, respectively, and showed no significant difference (P = 0.682). In CT, the mean age was 21.3 ± 1.21 and 21.7 ± 0.58 for the control group and smokers, respectively, with P = 0.651, again showing no significant difference. For the height mean in CC (175.0 ± 7.73 and 173.9 ± 6.69) for the control group and smokers’ group, respectively, there was no significant difference with P = 0.613. In CT, the mean height (169.7 ± 9.22 and 171.7 ± 7.64) for the control group and smokers’ group, respectively, showed no significant difference (P = 0.753). The mean weight in CC (74.1 ± 16.43 and 72.4 ± 18.94) for the control group and smokers’ group, respectively, showed no significant difference (P = 0.766). In CT, the mean weights (28.31 ± 7.07 and 22.9 ± 4.71) for the control group and smokers’ group, respectively, showed no significant difference (P = 0.226). The mean BMI in CC was 24.18 ± 5.11 and 24.03 ± 6.52, respectively, for the control group and smokers’ group and showed no significant difference (P = 0.913). In CT, the mean BMI (28.31 ± 7.07 and 22.90 ± 4.71), respectively, for the control group and smokers’ group again showed no significant difference (P = 0.226). The mean for tumor necrosis factor-α (TNF-α), matrix metalloproteinase 9 (MMP-9), and α-1 antitrypsin (AAT) in CC was 148.31 ± 201.19, 2,689.05 ± 2,314.36, and 6.04 ± 3.91, respectively, in the control groups and in the smokers’ groups was 159.30 ± 209.40, 2,607.38 ± 2,510.79, and 5.18 ± 4.18, respectively, with no significant differences (P value = 0.867, 0.915, 0.482). In CT, the mean for TNF-α, MMP-9, and AAT was 170.59 ± 193.67, 2,890.76 ± 2,840.16, and 5.89 ± 4.53, respectively, in the control and in the smokers’ groups the mean was 73.10 ± 37.75, 1,769.74 ± 515.55, and 3.24 ± 0.59, respectively, with no significant differences (P value = 0.287, 0.392, 0.210). These parameters were used in a previous study to examine these three macrophage inflammatory markers in smokers.[18]
Table 5.
GSTM1 variant with physical and biochemical characteristics in the control group and smokers’ group
| Variable | Allele | Control (mean±SD) | Smokers (mean±SD) | P |
|---|---|---|---|---|
| Age (years) | CC | 22.3±3.16 | 22.8±3.71 | 0.682 |
| CT | 21.3±1.21 | 21.7±0.58 | 0.651 | |
| TT | 0.00±0.00 | 0.00±0.00 | ||
| Height (cm) | CC | 175.0±7.73 | 173.9±6.69 | 0.613 |
| CT | 169.7±9.22 | 171.7±7.64 | 0.753 | |
| TT | 0.00±0.00 | 0.00±0.00 | ||
| Weight (kg) | CC | 74.1±16.43 | 72.4±18.94 | 0.766 |
| CT | 28.31±7.07 | 22.9±4.71 | 0.226 | |
| TT | 0.00±0.00 | 0.00±0.00 | ||
| BMI (kg/m2) | CC | 24.18±5.11 | 24.03±6.52 | 0.913 |
| CT | 28.31±7.07 | 22.90±4.71 | 0.226 | |
| TT | 0.00±0.00 | 0.00±0.00 | ||
| TNF-α | CC | 148.31±201.19 | 159.30±209.40 | 0.867 |
| CT | 170.59±193.67 | 73.10±37.75 | 0.287 | |
| TT | 0.00±0.00 | 0.00±0.00 | ||
| MMP-9 | CC | 2,689.05±2,314.36 | 2,607.38±2,510.79 | 0.915 |
| CT | 2,890.76±2,840.16 | 1,769.74±515.55 | 0.392 | |
| TT | 0.00±0.00 | 0.00±0.00 | ||
| AAT | CC | 6.04±3.91 | 5.18±4.18 | 0.482 |
| CT | 5.89±4.53 | 3.24±0.59 | 0.210 | |
| TT | 0.00±0.00 | 0.00±0.00 |
CC: Normal, CT: heterozygous, TT: homozygous, SD: standard deviation, cm: centimeters, Kg: kilogram, BMI: body mass index, TNF-α: tumor necrosis factor-α, MMP-9: matrix metalloproteinase 9, AAT: α-1 antitrypsin. A P value of ≤0.05 was considered statistically significant
The Associations between GSTM1 variant and the lifestyle of the control group and smokers’ group
The Chi-square test was used to test the associations between GSTM1 and the two groups (control and smokers). The results presented in Table 6 show the value of χ2 = 0.795, and the P value = 0.672 >0.05. Our study concluded that there were no associations between GSTM1 and drinking licorice, or taking tamarind and vitamin C in either the control group or smokers’ group. There is also no association between participating in sports/physical activity and GTSM1 (χ2 = 1.663 and the P value = 0.435).
Table 6.
Associations between GSTM1 variant and the lifestyle of the control group and smokers’ group
| Group | Licorice, tamarind, or vitamin C intake | Total | Physical activity | Total | ||||
|---|---|---|---|---|---|---|---|---|
|
|
|
|||||||
| Yes | No | Yes | No | |||||
| Control | CT | 2 | 4 | 6 | 4 | 2 | 6 | |
| TT | 0 | 0 | 0 | 0 | ||||
| CC | 1 | 18 | 19 | 10 | 9 | 19 | ||
| Total | 3 | 22 | 25 | 14 | 11 | 25 | ||
| Smoker | CT | 1 | 2 | 3 | 2 | 1 | 3 | |
| TT | 0 | 1 | 1 | 0 | 1 | 1 | ||
| CC | 8 | 13 | 21 | 13 | 8 | 21 | ||
| Total | 9 | 16 | 25 | 15 | 10 | 25 | ||
| Total | GSTM1 | CT | 3 | 6 | 9 | 6 | 3 | 9 |
| TT | 0 | 1 | 1 | 0 | 1 | 1 | ||
| CC | 9 | 31 | 40 | 23 | 17 | 40 | ||
| Total | 12 | 38 | 50 | 29 | 21 | 50 | ||
| χ2=0.795 | χ2=1.663 | |||||||
| df=2 | df=2 | |||||||
| P=0.672 | P=0.435 | |||||||
CC: Normal, CT: heterozygous, TT: homozygous, χ2 =Chi-square, df: degree of freedom. A P value of ≤0.05 was considered statistically significant
Genotype and allele frequencies of GPX1 variant
As shown in Table 7, the frequencies of GG (normal), GA (heterozygous), and AA (homozygous) genotypes in GPX1 were 64% (n = 16), 28% (n = 7), and 8% (n = 2), respectively, in the group of smokers. In the control group, the frequencies of GG, GA, and AA were 64% (n = 16), 32% (n = 8), and 4% (n = 1), respectively. Based on these results, the frequencies of G and A alleles were 79% and 21%, respectively. These results were consistent with expected genotype distributions. The genotype frequencies in all subjects were also consistent with the Hardy–Weinberg equilibrium (χ2 = 1.870, df = 1, P = 0.171).
Table 7.
Genotype and allele frequencies of GPX1 variant
| Frequency | P | Odd ratio (95% CI) | Risk ratio (95% CI) | |||
|---|---|---|---|---|---|---|
|
| ||||||
| Control (n=25) | Smoker (n=25) | |||||
| GG | 64 (n=16) | 64 (n=16) | 1 (Reference) | 1 (Reference) | ||
| GA | 32 (n=8) | 28 (n=7) | 0.833a | 1.143 (0.335–3.904) | 1.067 (0.593–1.918) | |
| AA | 4 (n=1) | 8 (n=2) | 1.000b | 0.500 (0.041–6.082) | 0.667 (0.130–3.428) | |
| Alleles | ||||||
| G | 79% | |||||
| A | 21% | |||||
| G | 80 (n=40) | 78 (n=39) | 1 (Reference) | 1 (Reference) | ||
| A | 20 (n=10) | 22 (n=11) | 0.807a | 0.886 (0.338–2.323) | 0.941 (0.571–1.549) | |
GG: Normal, GA: heterozygous, AA: homozygous. aTwo-sided χ2 test. bTwo-sided Fisher’s exact test. CI: Confidence intervals. OR: odds ratio
Allele frequencies in the subjects are presented in Table 7. The G allele in the control group represented 80% while the A allele represented 20%. For the group of smokers, the percentages of the G and A alleles were 78% and 22%.
The genotype frequency of GA was increased in the control group compared with the group of smokers, while AA was increased in the smoker group. Even though there was a slight increase in these variant genotypes, it was not significantly associated with an increased risk in smokers.
GPX1 variant with physical and biochemical characteristics in the control group and smokers’ group
An independent sample t-test was used for the variable of samples; the results are shown in Table 8. The subjects were divided according to alleles GG, GA, and AA. In GA, the mean age was 22.6 ± 2.99 and 22.31 ± 0.83 in the control group and smokers’ group, respectively, and showed no significant difference (P value = 0.685). In GG, the mean age was 21.88 ± 2.99 and 22.94 ± 4.25 for the control group and smokers’ group, respectively, with a P value = 0.442, again showing no significant difference. For the height mean in GA (176.7 ± 11.46 and 172.6 ± 6.16) in the control and smokers’ group, respectively, there was no significant difference with P value = 0.418. In GG, the mean height (172.2 ± 6.75 and 174.1 ± 7.05) for the control group and smokers’ group, respectively, also showed no significant difference (P = 0.438). The mean weight in GA (84.0 ± 27.8 and 73.8 ± 16.3) for the control group and smokers’ group, respectively, showed no significant difference. In GG, the mean weight was 72.9 ± 16.40 and 71.8 ± 20.4 for the control group and smokers’ group, respectively, also with no significant difference (P value = 0.86). The mean BMI in GA was 27.03 ± 8.24 and 24.75 ± 5.05, respectively, for the control group and smokers, with a P value = 0.532; again, there was no significant difference. In GG, the mean BMI (24.49 ± 4.6 and 23.8 ± 7.03) for the control group and smokers, respectively, showed no significant difference (P value = 0.722). The mean value of TNF-α, MMP-9, and AAT in GA was 59.20 ± 24.33, 1,249.61 ± 618.94, and 5.01 ± 3.22, respectively, in control groups, while for smokers, the mean value was 124.66 ± 162.52, 2,175.89 ± 1,691.05, and 4.13 ± 2.21, respectively, with no significant differences (P value = 0.313, 0.195, 0.52), respectively. In GG, the mean for TNF-α, MMP-9, and AAT was 208.10 ± 228.45, 3,522.99 ± 2,668.28, and 6.60 ± 4.39, respectively, in the control group and for smokers 118.76 ± 154.37, 2,182.66 ± 2,057.66, and 4.66 ± 3.77, respectively, with no significant association (P value = 0.204, 0.122, 0.17).
Table 8.
GPX1 variant with physical and biochemical characteristics in the control group and smokers’ group
| Variable | Allele | Control (mean±SD) | Smokers (mean±SD) | P |
|---|---|---|---|---|
| Age (years) | GG | 21.88±2.99 | 22.94±4.25 | 0.442 |
| GA | 22.6±2.99 | 22.31±0.83 | 0.685 | |
| AA | 0.00±0.00 | 0.00±0.00 | ||
| Height (cm) | GG | 172.2±6.75 | 174.1±7.05 | 0.438 |
| GA | 176.7±11.46 | 172.6±6.16 | 0.418 | |
| AA | 0.00±0.00 | 0.00±0.00 | ||
| Weight (kg) | GG | 72.9±16.40 | 71.8±20.4 | 0.86 |
| GA | 84.0±27.8 | 73.8±16.3 | 0.418 | |
| AA | 0.00±0.00 | 0.00±0.00 | ||
| BMI (kg/m2) | GG | 24.49±4.6 | 23.8±7.03 | 0.722 |
| GA | 27.03±8.24 | 24.75±5.05 | 0.532 | |
| AA | 0.00±0.00 | 0.00±0.00 | ||
| TNF-α | GG | 208.10±228.45 | 118.76±154.37 | 0.204 |
| GA | 59.20±24.33 | 124.66±162.52 | 0.313 | |
| AA | 0.00±0.00 | 0.00±0.00 | ||
| MMP-9 | GG | 3,522.99±2,668.28 | 2,182.66±2,057.66 | 0.122 |
| GA | 1,249.61±618.94 | 2,175.89±1,691.05 | 0.195 | |
| AA | 0.00±0.00 | 0.00±0.00 | ||
| AAT | GG | 6.60±4.39 | 4.66±3.77 | 0.17 |
| GA | 5.01±3.22 | 4.13±2.21 | 0.52 | |
| AA | 0.00±0.00 | 0.00±0.00 |
GG: Normal, GA: heterozygous, AA: homozygous, SD: standard deviation, cm: centimeters, Kg: kilogram, BMI: body mass index, TNF-α: tumor necrosis factor-α, MMP-9: matrix metalloproteinase 9, AAT: α-1 antitrypsin. A P value of ≤0.05 was considered statistically significant
The association between the GPX1 variant and the lifestyle of the control group and smokers’ group
In Table 9, the results of the associations between GPX1 and the lifestyle of the control and smokers’ groups showed a value of χ2 = 7.420, and a significant P value = 0.024 <0.05, so we conclude that there are associations between GPX1 and drinking licorice, taking tamarind and vitamin C in both groups. There were no associations for participation in sports (χ2 = 5.246 and P value = 0.073).
Table 9.
The associations between GPX1 variant and the lifestyle of the control group and smokers’ group
| Group | Licorice, tamarind, or vitamin C intake | Total | Physical activity | Total | |||||
|---|---|---|---|---|---|---|---|---|---|
|
|
|
||||||||
| Yes | No | Yes | No | ||||||
| Control | GA | 1 | 6 | 7 | 5 | 2 | 7 | ||
| AA | 1 | 1 | 2 | 2 | 0 | 2 | |||
| GG | 1 | 15 | 16 | 7 | 9 | 16 | |||
| Total | 3 | 22 | 25 | 14 | 11 | 25 | |||
| Smoker | GA | 5 | 3 | 8 | 6 | 2 | 8 | ||
| AA | 1 | 0 | 1 | 1 | 0 | 1 | |||
| GG | 3 | 13 | 16 | 8 | 8 | 16 | |||
| Total | 9 | 16 | 25 | 15 | 10 | 25 | |||
| Total | GPX1 | GA | 6 | 9 | 15 | 11 | 4 | 15 | |
| AA | 2 | 1 | 3 | 3 | 0 | 3 | |||
| GG | 4 | 28 | 32 | 15 | 17 | 32 | |||
| Total | 12 | 38 | 50 | 29 | 21 | 50 | |||
| χ2=7.420 | χ2=5.246 | ||||||||
| df=2 | df=2 | ||||||||
| P=0.024* | P=0.073 | ||||||||
GG: Normal, GA: heterozygous, AA: homozygous, χ2=Chi-square, df: degree of freedom. A P value of ≤0.05 was considered statistically significant. *Significant
Genotype combination of the GSTM1 and GPX1 polymorphisms in the control group and smokers’ group
To evaluate the effect of GSTM1 and GPX1 polymorphisms on the smokers’ and control groups in the Saudi population, genotype combinations were used. Nine genotypes were identified (CC/GG, CC/GA, CC/AA, CT/GG, CT/GA, CT/AA, TT/GG, TT/GA, and TT/AA) for RS1056806 and RS1800668 SNPs [see Table 10]. The frequency of combination in the genotypes of GSTM1/GPX1 and CC/GG was 48% (n = 12) and 56% (n = 14), respectively, for the smokers and control groups. In the control group, CC/GA was 24% (n = 6), CC/AA was 4% (n = 1), CT/GG was 4% (n = 1), CT/GA was 8% (n = 2), and TT/GG was 4% (n = 1). In the smokers’ group, CC/GA was 20% (n = 5), CC/AA was 8% (n = 2), CT/GG was 16% (n = 4), and CT/GA was 8% (n = 2). To summarize, the TT/GG (homozygous for GPX1 and normal for GPX1) combination was associated with high risk in smokers (OR = 2.58, 95% CI = 0.096–69.341).
Table 10.
Genotype combination of the GSTM1 and GPX1 polymorphisms in the control group and smokers’ group
| GSTM1/GPX1 | Frequencies (%) | P | Odd ratio (95% CI) | Risk ratio (95% CI) | |
|---|---|---|---|---|---|
|
| |||||
| Control (n=25) | Smokers (n=25) | ||||
| CC/GG | 56 (n=14) | 48 (n=12) | 1.00 (Reference) | 1.00 (Reference) | |
| CC/GA | 24 (n=6) | 20 (n=5) | 0.969a | 1.029 (0.250–4.235) | 1.013 (0.531–1.933) |
| CC/AA | 4 (n=1) | 8 (n=2) | 0.598b | 0.429 (0.034–5.333) | 0.619 (0.120–3.190) |
| CT/GG | 4 (n=1) | 16 (n=4) | 0.333b | 0.214 (0.021–2.187) | 0.371 (0.062–2.222) |
| CT/GA | 8 (n=2) | 8 (n=2) | 1.000b | 0.857 (0.104–7.043) | 0.929 (0.327–2.634) |
| CT/AA | 0 | 0 | 1.000b | 0 | 0 |
| TT/GG | 4 (n=1) | 0 | 1.000b | 2.586 (0.096–69.341) | 1.857 (1.301–2.651) |
| TT/GA | 0 | 0 | 1.000b | 0 | 0 |
| TT/AA | 0 | 0 | 1.000b | 0 | 0 |
aTwo sides χ2 test. bTwo-sided Fisher’s exact test. CI: Confidence interval. OR: Odd ratio
DISCUSSION
The research studied the correlation between GSTM1 and GPX1 levels between smokers and the control group and investigated the genetic risk factors in smokers with the GSTM1 and GPX1 gene in the Saudi population. Since many of the chemicals in cigarette smoke have negative effects on cells, metabolic detoxification is necessary to prevent toxicological reactions. A substantial number of genes are involved in the metabolism of possibly mutagenic compounds found in cigarette smoke. Many of these genes are polymorphic and have a role in chemical metabolism. GSTs are a wide family of enzymes that detoxify endogenous and exogenous toxic substrates, including tobacco-derived toxins. They also have peroxidase activity, suggesting that they may play a role in oxidative stress.[19]
Vitamin C plays an important role as a free radical scavenger and antioxidant agent. It significantly reduces the harmful effects of ROS, such as oxygen species and nitrogen species, that can cause oxidative damage to macromolecules like lipids, DNA, and proteins.[20] In this research, our results showed that the licorice, tamarind, or vitamin C intake P value for GSTM1 was 0.672 and for the GPX1 was 0.024, with an association between GPX1 and licorice, tamarind, and vitamin C intake. A study by Block et al.[21] investigated the effect of vitamin C serum concentration and GSTT1 and GSTM1 gene modifications. The study investigated 383 participants—smokers and nonsmokers; the authors concluded that the serum blood concentration of vitamin C was higher in participants with inactive GSTM1, but there was no statistical effect of the level of vitamin C and GSTM1 modifications. This was contrary to Cahill et al.[22] study which reported a significant interaction. The difference in results may be due to the higher proportion of suboptimal and deficient vitamin C concentrations in the Block et al.[21] study. Our study did not have enough subjects with low vitamin C concentrations or intakes to evaluate this interaction. The results indicated that there are no significant associations between GSTM1 and GSTP1 polymorphisms with an antioxidant level in blood.[23] According to some studies, licorice flavonoids effectively reduce pulmonary inflammation.[24] Licorice drinks also speed up the rate at which nicotine is metabolized by hepatic cytochrome P450 enzymes.[13] Saminan’s study indicates that licorice might minimize the airway obstruction caused by smoking.[14] Other findings demonstrate how AA intake affects the gene expression of antioxidant enzymes and enzymes that metabolize xenobiotics.[11]
Regarding links to obesity, our study showed that 48% of smokers were normal weight, 12% were obese, 28% were overweight, and 12% were underweight. As for the control group, 52% were normal weight, 12% were obese, 28% were overweight, and 8% were underweight. Overall, these findings are in accordance with findings reported by Ginawi and his colleagues: 24.9% of smokers were obese, 30.9% were within the normal weight range, and 7.4% were underweight. Of nonsmokers, obesity was present in 27.6%, 32.8% were within normal weight range, and 37.3% were underweight. In conclusion, obesity was more common in ex-smokers and less common in current smokers. Overall, current smokers were less likely to be obese in comparison to nonsmokers.[25] In a meta-analysis by Aubin et al.,[26] it was discovered that quitting smoking is linked to a mean gain of 4–5 kg in body weight after 12 months of abstinence, with the majority of weight gain occurring in the first three months after cessation. There is a significant range in weight change, with 16% of those who gave up smoking losing weight and 13% gaining more than 10 kg. Smokers who have a high level of nicotine dependence are more likely to put on weight while undergoing smoking cessation therapy and may need treatment in a clinic to prevent this.[27] A cross-sectional study by Alkeilani et al.[28] examined anthropometric measurements among 879 individuals aged 18–65, including water pipe smokers, cigarette smokers, dual smokers, and those who had never smoked. Lean mass, fat mass, and body fat percentage were used to calculate body composition using bioelectrical impedance analysis. The results showed that for smokers, 1.5% were underweight, 21.9% were normal weight, 39.8% were overweight, and 36.7% were obese. As for nonsmokers, 6.6% were underweight, 44.4% were normal weight, and 49.0% were overweight but not obese. The study concluded that the percentage of those who were obese was higher in smokers. Reduced calorific intake due to a central effect, impaired smell or taste, a change in food preference, a direct metabolic effect on calorie absorption or storage, and increased energy expenditure are all possible causal mechanisms, as nicotine speeds up metabolism. These findings align with a study by Ünsal et al.[29] which investigated polymorphisms of the GSTT1 and GSTM1 genes in 152 obese Turkish participants. The results reported no statistically significant relationship between obesity, BMI, and GSTT1 and GSTM1 genetic polymorphism. A study was conducted in Korea (Yang, 2017) to investigate if SNPs of the GSTM1 gene are susceptible to obesity in the Korean population. The findings showed that SNPs in the GSTM1 gene were not linked to obesity risk in the Korean population. The results of this study and those reviewed above were compatible with our findings of genetic polymorphism on GSTM1.
In addition, the results of our study showed that the frequencies of CC, CT, and TT genotypes in GSTM1 were 84%, 12%, and 4%, respectively, for the control group, and 76% and 24% for smokers. The results showed no significant difference between smoking and nonsmoking participants. In addition, no significant associations were found between GSTM1 and genetic polymorphism, although there is an increase in the heterozygous genotype in the smokers’ group compared to the control group, and a decrease in the homozygous genotype in the smokers’ group compared to the control. These findings are in accordance with findings reported by a case-control study by Cerliani et al.[30] that investigated the correlation between oncochematological disease (blood cancer) and GSTT1/GSTM1/CYP1A1 genetic polymorphisms and smoking and dietary habits. The results showed no statistically significant associations between smoking and dietary habits and genetic polymorphisms of GSTT1/GSTM1/CYP1A1 genes. Another genetic study by Kiyohara et al.[31] of 151 Japanese smokers and 421 nonsmokers investigated the correlation between CYP1A1 rs4646903 and GSTM1 and cigarette smoking among patients with systemic lupus erythematosus. The authors concluded that there was a lack of evidence of an association of GSTM1 genotypes with a smoking habit, although they acknowledged that the study population was small. Karahalil et al.[32] investigated the association between polymorphisms of the OGG1 Ser326Cys and GSTM1 genes and smoking on chromosomal damage of neonates. The authors found slightly higher polymorphism in the GSTM1 in neonates from smoking mothers compared with neonates from nonsmoking mothers, but this was not statistically significant. These studies were compatible with our results. In contrast, Gómez-Martín et al.[33] observed a small increase in the number of GSTM1 gene modifications among 317 smokers and concluded that smokers had a significantly decreased GSTM1 gene expression and an increase in gene modification. However, Gómez-Martín only investigated two copies of the GSTM1 gene and identified gene expression steps by using qPCR analytical methods. The authors predicted the modification rate of the GSTM1 gene based on the expression value. Inconsistency in the results of different studies might be due to a range of factors such as differences in ethnicity, gender, sample size, and study design.
CONCLUSIONS
GSTs constitute a superfamily of phase II detoxification enzymes that play a key role in cellular protection against environmental carcinogens, drugs, toxins, and by-products of oxidative stress. GSTM1 is one of the important genes belonging to this family and genetic polymorphisms in GSTM1 and GPX1 are responsible for several diseases and disorders, especially various types of cancer. On the other hand, smoking is considered one of the main sources of free radicals in the bloodstream worldwide.
The main outcomes of the current research showed that there is no significant association between genetic polymorphism of the GSTM1 and GPX1 genes and cigarette smoking in the Saudi population. However, the results found a slight increase in the number of GSTM1 and GPX1 gene modifications among smokers. Despite these limitations, the study design was relatively strong because the controls were recruited from the same cohort as the colon cancer patients. Also, the cases and controls were matched by age and sex. At the gene level, we found that the T allele in GSTM1 (RS1056806) was more associated with smoking risk factors.
These findings should be considered in the light of a number of limitations, including the small number of subjects which was limited to males at King Abdulaziz University. Large-scale genetic studies should be conducted in different ethnic communities.
Future and clinical implications
Further investigations should be carried out on the GSTM1 and GPX1 genes and other antioxidant genes with different SNPs and a wider range of samples and epigenetic alterations and an increased number of participants. Further validations should be sought, such as next-generation sequencing to yield more biomarkers, to assist in the association with GSTM1 and GPX1 levels between smokers and control groups in the Saudi population.
As a result, our study has established the causal role of the effect of smoking on health and suggests that cigarette-smoking habit is associated with the increase of genetic modifications. This finding has important therapeutic implications for health-care professionals who want to inform patients about the health risks of smoking and motivate them to quit. Furthermore, healthy dietary and consumption of antioxidant drink as Licorice and tamarind, as well as vitamin C effervescent, are beneficial in decreasing gene mutations and decreasing the incidence of various types of diseases. The noticeable point is that obesity and high BMI value increase the gene mutation and incidence of bodily disease.
Recommendations
GSTM1 and GPX1 gene modifications are associated with various types of disease, especially cancer disorders. It is recommended that more research about this gene is conducted in the Saudi Arabian population. Further investigation on polymorphism is needed to draw a firm conclusion about its association with smoking in different ethnicities.
Declarations
There are no ethical or financial issues, conflicts of interests, or animal experiments related to this research.
Availability of data and materials
All data analyzed during this study are included in this published article.
Ethics approval and consent to participate
The samples were used according to the guidelines of the Ethical Committee from the Center of Excellence in Genomic Medicine Research (CEGMR) approval code: 02CEGMR-Bioeth-2019. All participants signed an approval form stating that they were aware of the collection of samples and subsequent analysis.
Financial support and sponsorship
Nil.
Conflicts of interest
There are no conflicts of interest.
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Associated Data
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Data Availability Statement
All data analyzed during this study are included in this published article.