Role of  and  polymorphisms in oxidative stress derived cancer in Thais with and without dyslipidemia

Pharrunrat Tanaviyutpakdee; Krongtong Yoovathaworn; Jintana Sirivarasai; Suwannee Chanprasertyothin; Pachara Panpunuan; Krittaya Petchpoung; Aninthita Tatsaneeyapant; Thunyachai Sura; Sming Kaojarern; Piyamit Sritara

Journal & Issue Details

Asian Biomedicine

Journal Details

Format: Journal
eISSN: 1875-855X
First Published: 01 Jun 2007
Publication timeframe: 6 times per year
Languages: English

Oxidative stress can play a role in the pathogenesis of cancer [1]. Reactive species are generated endogenously, and from exogenous stimuli including xenobiotics. These reactive species are controlled by various cellular antioxidant systems [2, 3]. Hypercholesterolemia can induce the production of reactive oxygen species (ROS), such as superoxide anion, via enzymes including the oxidase for the reduced form of nicotinamide adenine dinucleotide phosphate (NADPH) and xanthine oxidase, and other sources of ROS from mitochondria [4]. Some investigators have reported an association between levels of plasma or serum lipids and lipoproteins, and various types of cancers [5, 6, 7]. Individual susceptibility to cancer can result from several host factors, especially differences in the activity of xenobiotic-metabolizing enzymes [8].

Cytochrome P450 (CYP) 2E1-induced toxicity is apparently mediated by the activation of a state of oxidative stress by a wide variety of xenobiotics [9, 10]. The activity of CYP2E1 can be induced by ethanol, obesity, diabetes, and polyunsaturated fatty acids [10, 11]. At present, more than 14 CYP2E1 genetic polymorphisms have been described [12], but only 3 point mutations have been extensively studied and shown to be linked to cancer risk [13]. Two point mutation polymorphisms of CYP2E1 in 5′-flanking regions sensitive to the restriction enzymes PstI (reference single-nucleotide polymorphism (SNP) rs3813867, G1293C) and RsaI (rs2031920, C1053T), as revealed by restriction fragment length polymorphism (RFLP) analyses, are in complete linkage disequilibrium, and have been associated with high-transcription and increased enzyme activity [14]. Another polymorphism in intron 6, sensitive to DraI, is T7632A (rs6413432) and is reported to enhance transcription of CYP2E1 [15]. CYP2E1 genetic polymorphisms effect the incidence of cancers [13].

Human NAD(P) H:quinone oxidoreductase 1 (NQO1) is a well-known phase II enzyme catalyzing diverse reactions that collectively result in broad protection against electrophiles, oxygen species, and superoxide anion radicals [16]. NQO1 also contributes to the maintenance of endogenous antioxidants [17]. The most widely studied NQO1 SNP is a C609T transition in exon 6 of NQO1 cDNA leading to 3 phenotypes: a wild-type phenotype with complete enzymatic activity, a heterozygous phenotype with around 3-fold decreased activity, and a homozygous mutant with only 2% to 4% full enzyme activity [18]. This SNP is rs1800566 in the National Center for Biotechnology Information database. To our knowledge, there are limited data on the association of hyperlipidemia and genetic polymorphisms of xenobiotic-metabolizing enzymes. Therefore, the present study sought to investigate the effect of genetic polymorphism of the 2 xenobiotic-metabolizing enzymes on the risk of cancer derived from oxidative stress in apparently healthy Thais with and without dyslipidemia, who have unintended daily exposure to various kinds of environmental chemicals.

Materials and methods

Study population

The study was approved by the Committee on Human Rights related to Research Involving Human Subjects, Faculty of Medicine, Ramathibodi Hospital, Mahidol University (no. MURA2008/809/S_1-2Aug₁₀). We enrolled 1380 employees at the state enterprise Electricity Generating Authority of Thailand (EGAT) after written informed consent to participate in the study was obtained from each participant. Participants lived and worked in Bangkok, and had the possibility of unintended exposed to various kinds and doses of environmental chemicals. Participants completed a self-administered questionnaire, underwent a physical examination, and provided a fasting blood sample. We collected 20 mL of blood from each participant by venipuncture into ethylenediaminetetraacetic acid (EDTA) -containing and heparinized tubes that were immediately centrifuged at 2000g. Buffy coat, erythrocytes, and plasma were separated and stored at −20°C until genotyping and biochemical measurements were conducted.

Determination of reduced glutathione using Ellman’s reagent

Whole blood (0.1 mL) was added to distilled water (1.9 mL) together with 3 mL of precipitating solution (100 mL containing 1.67 g glacial metaphosphoric acid, 0.2 g disodium EDTA, and 30 g sodium chloride). After standing for 5 min the mixture was filtered and filtrate (0.5 mL) was added to 0.3 M phosphate buffer, pH 6.4 (2 mL). Finally, we added 1 mM 5,5′-dithiobis-(2-nitrobenzoic acid) in 1% sodium citrate (0.25 mL), mixed the solutions well, and within 4 min the absorbance of the mixture was read at 412 nm. We compared the absorbances with appropriate blanks without blood [19].

Determination of glutathione peroxidase

Whole blood (0.1 mL) was added to distilled water (1.9 mL) to make a 1:20 hemolysate. Then, prepared solutions of 1 M Tris-HCl, 5 mM EDTA, pH 8.0 (100 μL), 0.1 M GSH (20 μL), 10 U/mL glutathione reductase (100 μL), and 2 mM NADPH (100 μL) ] were added to 10 μL of the hemolysate, together with 660 μL of distilled water and the mixture reaction preincubated at 37°C for 10 minutes. After the preincubation, the enzymatic reaction was initiated by addition of 10 L of freshly prepared 7 mM t-buty1 hydroperoxide. The decrease in the optical density at 340 nm was measured against a blank for 1 min [20].

Determination of superoxide dismutase

Components of extraction solution (3.5 mL of cold distilled water, 1 mL of ethanol, 0.6 mL of chloroform) were added to 0.5 mL of hemolysate, then mixed for 1 min. After centriftigation at 3000 rpm for 10 minutes at 4°C, supernatant was collected to determine superoxide dismutase (SOD) activity. Seven serial dilutions were prepared for each sample. The final volume (3 mL) was composed of the following: 200 μL of 0.1 M EDTA containing 1.5 mg sodium cyanide, 100 μL of 1.5 mM nitroblue tetrazolium (NBT), 50 μL of 0.12 mM riboflavin, portion of sample (10, 20, 40, 60, 80, 200, or 500 μL) and 0.067 M potassium phosphate buffer, pH 7.8 to make a final volume. All tubes were illuminated in a light box for 12 min (18 W fluorescence) and the optical density of the mixtures was measured at 560 nm. The resulting inhibition of NBT reduction versus the amount of SOD extract was plotted on a linear scale. The amount of SOD that gave half of this maximum (1 unit) was determined from the plot [21].

Determination of catalase

Erythrocytes were diluted at the ratio of 1:500 in 50 mM phosphate buffer pH 7.0 (2.0 mL) to make a hemolysate. Then 30 mMH₂0₂ (1.0 mL) was added in a continuous stream from a pipette to promote mixing and to start the reaction. The decrease in the optical density of the mixtures was measured at 240 nm for 30 s following a previously described method [22].

Determination of malondialdehyde

Malondialdehyde (MDA) was determined using HPLC method with fluorescence detection [23]. The coefficient of variation was 4% within runs and 3% between days. The detection limit was 0.25 μmο1/L and the method exhibited a linear response for MDA in range 1.5 to 15 μmο1/L, the calibration curve presented a high correlation coefficient (r² > 0.9), P = 0.001; n =10).

Determination of blood lipid profile

Serum cholesterol and triglyceride were analyzed using routine biochemical procedures at Ramathibodi Hospital. The classifications of lipid profiles were based on the criteria of the National Cholesterol Education Program [24]. Normolipidemia was defined as a triglyceride concentration <150 mg/dL and normal cholesterol concentration <200mg/dL. Dyslipidemias were defined as hypercholesterolemia (triglyceride <150 mg/dL, cholesterol >200 mg/dL), hypertriglyceridemia (triglyceride >150mg/dL, cholesterol <200mg/dL), and combined hyperlipidemia (triglyceride >150 mg/dL, cholesterol >200 mg/dL).

Genotype analysis

Genomic DNA was extracted from lymphocytes using a modified salting-out procedure [25] and frozen at −20°C until analysis. We conducted a TaqMan assay including a forward target-specific polymerase chain reaction (PCR) primer, a reverse primer, and TaqMan MGB probes labeled with a special dyes: FAM and VIC (Applied Biosystems, Waltham, MA, USA). The reaction mixture consisted of TaqMan Universal Master Mix (1×) and TaqMan-MGB probes for CYP2E1 *5B (rs3813867, rs2031920), CYP2E1 *6 (rs6413432), and NQO1 (rsl800566) in a total volume of 10 μL. The real-time PCR reaction protocol was 10 min at 95°C, 40 cycles of 15 s at 92°C, and 1 min at 60°C, using a 7500 Real-Time PCR System (Applied Biosystems). Data for specific probes and primers are available in the SNP500 database of the U.S. National Cancer Institute at http://snp500cancer.nci.nih.gov/ [26].

Statistical analyses

Statistical analyses were conducted using IBM SPSS Statistics for Windows, version 19.0 (Armonk, NY, USA). All biochemical parameters (glutathione peroxidase (GPx), SOD, catalase (CAT), MDA, and reduced glutathione (GSH)) are expressed as geometric means with 95% confidence intervals (95% CIs) and means with standard deviations (SD). Goodness of fit to normal distribution was determined using a Kolmogorov-Smirnov test. Non-normally distributed data was transformed into a log scale and retested for normal distribution before testing at the next step. A one-way analysis of variance and Mann-Whitney U test were conducted to compare difference in the means between 2 groups for normal and non-normally distributed data, respectively. A general linear model was applied to evaluate the significance of differences in the mean of parameters between more than 2 groups. Genotype frequencies were compared and tested for Hardy-Weinberg equilibrium using a Pearson χ² test. Logistic regression analyses were used to determine the association of oxidative stress markers with genes of interest and lipid profiles in the model adjusted for covariates (sex, smoking, and alcohol consumption). In this case, data were presented as an odds ratios (ORs) with 95% CIs. P < 0.05 was considered significant.

Results

Demographic characteristics of the study population

The demographic characteristics of 1380 EGAT employees (983 men and 397 women) were subdivided into 4 groups based on lipid profiles as summarized in Table 1.

Table 1

Demographic characteristics and distribution of the metabolic enzyme gene polymorphisms of the study population

Characteristics	Lipid profile

	Normolipidemia (n = 236)	Hypercholesterolemia (n = 638)	Hypertriglyceridemia (n = 84)	Combined hyperlipidemia (n = 422)	P P generated using a general-linear model: multivariate test, χ² test for genotype and lifestyle features
Age (years) Data = mean ± SD or n (%)	51.6±4.7	51.0±44	51.9±4.8	51.9±4.3	0.08
Weight (kg) Data = mean ± SD or n (%)	67.1 ±11.5	64.5 ± 10.8	69.3 ± 10.9	69.6 ±10.6	0.46
Height (cm) Data = mean ± SD or n (%)	164.4 ±8.2	163.3 ±7.6	164.3 ±7.7	164.8 ±7.5	0.16
BMI (kg/m²) Data = mean ± SD or n (%)	24.8 ±3.48	24.1 ±3.23	25.6±3.38 hypercholesterolemia or hypertriglyceridemia compare with normolipidemia	25.6 ±3.41	<.001 hypercholesterolemia or hypertriglyceridemia compare with normolipidemia
Cholesterol (mg/dL) Data = mean ± SD or n (%)	179.0 ±19.12	242.6 ±28.93 hypercholesterolemia or hypertriglyceridemia compare with normolipidemia	180.1 ±18.29	253.1 ±35.80 combined hyperlipidemia compared with normolipidemia	<.001 hypercholesterolemia or hypertriglyceridemia compare with normolipidemia ,0.02 combined hyperlipidemia compared with normolipidemia
Triglyceride (mg/dL) Data = mean ± SD or n (%)	93.3 ±32.40	98.9 ±30.08	224.5 ± 95.70 hypercholesterolemia or hypertriglyceridemia compare with normolipidemia	233.7 ±105.32 combined hyperlipidemia compared with normolipidemia	<0.001 hypercholesterolemia or hypertriglyceridemia compare with normolipidemia combined hyperlipidemia compared with normolipidemia
LDL-C (mg/dL) Data = mean ± SD or n (%)	113.9±1547	164.6 ± 30.13 hypercholesterolemia or hypertriglyceridemia compare with normolipidemia	95.8± 19.12	159.4 ±3642 combined hyperlipidemia compared with normolipidemia	<.001 hypercholesterolemia or hypertriglyceridemia compare with normolipidemia , 0.003 combined hyperlipidemia compared with normolipidemia
HDL-C (mg/dL) Data = mean ± SD or n (%)	50.9 ±10.31	56.6±11.56 hypercholesterolemia or hypertriglyceridemia compare with normolipidemia	43.2 ±9.93	47.3 ±9.71	<0.001 hypercholesterolemia or hypertriglyceridemia compare with normolipidemia
CYP2E1:CYP2E1 *5B (RsaI/PstI)
No. of c1c1 (WT)	175/176 (≈74)	450/453 (≈71)	54/55 (=65)	300/300 (71)	<0.001 significant difference between group of lipid profiles
No.of c1c2 (Het)	50/50 (21)	167/165 (=26)	26/26 (31)	110/110 (26)	0.20,0.25
No.of c2c2 (Var)	11/10 (≈4)	21/20 (=3)	4/3 (=4)	12/12 (3)	0.39,0.29
CYP2EI*6 (DraI)
No. of DD (WT)	148 (63)	380 (60)	48 (57)	263 (62)	<0.001 significant difference between group of lipid profiles
No. of DC (Het)	74 (31)	224 (35)	30 (36)	136 (32)	0.07
No. of CC (Var)	14 (6)	34 (5)	6 (7)	23 (6)	0.50
NQO1 NQO1 *2
No. of CC (WT)	71 (30)	210 (33)	30 (36)	127 (30)	0.10
No of CT (Het)	116 (49)	318 (50)	41 (48)	215 (51)	<0.001 significant difference between group of lipid profiles
No. of TT (Var)	49 (21)	110 (17)	13 (16)	80 (19)	0.41
Sex
Male	170 (72)	419 (66)	63 (75)	331 (78)	<0.001 significant difference between group of lipid profiles
Female	66 (28)	219 (34)	21 (25)	91 (22)	0.21
Smoking	33 (14)	64 (10)	11 (13)	82 (19)	<0.001 significant difference between group of lipid profiles
Nonsmoking	203 (86)	574 (90)	73 (87)	340 (81)	0.001
Alcohol drinking	115 (49)	304 (48)	43 (51)	248 (59)	<0.001 significant difference between group of lipid profiles
Alcohol abstinence	121 (51)	334 (52)	41 (49)	174 (41)	0.02*

BMI = Body Mass Index, Het = heterozygous, LDL-C = low density lipoprotein-cholesterol, HDL-C = high-density lipoprotein-cholesterol, Var = variant, WT = wild type

By comparison with normolipidemia, participants with different types of dyslipidemia (hypertriglyceridemia, hypercholesterolemia, and combined hyperlipidemia) showed significantly higher BMI, triglyceride, HDL-C, and LDL-C levels (all P ≤ 0.024). The frequency of CYP2E1 *5B c1c1 was approximately 71%, c1c2 was approximately 26%, and c2c2 was approximately 3%. The distribution of CYP2E1 *6 DD was 60%, DC was 34%, and CC was 6%. The distribution of NQO1 *2 CC was 32%, CT was 50%, and TT was 18%. The allele frequencies of CYP2E1*5B, CYP2E1*6, and NQ01*2 were c1 0.84, c2 0.16; D 0.78, C 0.22; C 0.57, and Τ 0.43, respectively. All allele frequencies were in Hardy-Weinberg equilibrium in all lipid profile groups (all P > 0.47). Different blood lipid levels were found in participants who carried the wild-type CYP2E1 (c1/c1) and (DD) alleles with P < 0.001. For NQO1, significant difference in blood lipid levels was found in participants who carried the heterozygous genotype (CT) with P < 0.001. The association of genetic variation and lipid profile on oxidative stress markers is presented in Table 2.

Table 2

Odds ratios for oxidative stress markers according to CYP2E1 and NQO1 polymorphism and lipid profile

Number (%)	Oxidative stress marker	Odds ratio (95% CI) Sex, smoking, and alcohol consumption were adjusted for odds ratio, GSH = reduced glutathione, GPx = glutathione peroxidase, SOD, superoxide dismutase, CAT = catalase, and MDA = malondialdehyde.	P
CYP2E1:PstI	Reference	1.0
Wild-type	GSH	0.996 (0.997-1.02)	0.66
n= 984 (71.3)	CAT	0.73 (0.28-1.91)	0.52
• Heterozygous	SOD	0.58 (0.33-1.03)	0.06
n= 351 (25.4)	GPx	2.53 (1.29-4.96)	0.007
	MDA	1.24 (0.79-1.97)	0.35
• Variant	GSH	1.00 (0.96-1.05)	0.94
n = 45 (3.3)	CAT	6.49 (0.86-48.8)	0.07
	SOD	0.38 (0.09-1.54)	0.17
	GPx	3.33 (0.65-17.15)	0.15
	MDA	0.81 (0.27-2.46)	0.72
CYP2E1:RsaI	Reference	1.0
• Wild-type	GSH	0.997 (0.98-1.02)	0.73
n= 979 (70.9)	CAT	0.60 (0.23-1.56)	0.29
• Heterozygous	SOD	0.59 (0.33-1.05)	0.07
n= 353 (25.6)	GPx	2.58 (1.31-5.05)	0.006
	MDA	1.27 (0.80-2.01)	0.31
• Variant	GSH	1.01 (0.97-1.05)	0.61
n = 48 (3.5)	CAT	7.22 (1.05-48.8)	0.004
	SOD	0.19 (0.05ndash;0.77)	0.20
	GPx	2.34 (048-114)	0.24
	MDA	1.21 (043-3.52)	0.71
CYP2E1:DraI	Reference	1.0
• Wild-type	GSH	0.99 (0.97-1.01)	0.26
n= 839 (60.8)	CAT	0.68 (0.28-1.65)	0.40
• Heterozygous	SOD	0.64 (0.38-1.09)	0.10
n= 464 (33.6)	GPx	1.79 (0.96-3.33)	0.07
	MDA	0.99 (0.79-1.97)	0.97
• Variant	GSH	0.99 (0.95-1.03)	0.58
n = 77 (5.6)	CAT	2.32 (042-13.0)	0.34
	SOD	0.78 (0.26-2.28)	0.64
	GPx	1.92 (0.53 −6.90)	0.32
	MDA	1.08 (045-2.59)	0.86
NQO1 *2	Reference	1.0
• Wild-type	GSH	1.02 (0.998-1.04)	0.08
n=438 (31.7)	CAT	1.45 (0.57-3.66)	0.44
• Heterozygous	SOD	1.31 (0.75-2.28)	0.35
n=690 (50.0)	GPx	0.90 (0.47-1.72)	0.74
	MDA	0.89 (0.56-1.40)	0.61
• Variant	GSH	1.02 (0.99-1.04)	0.19
n=252 (18.3)	CAT	0.75 (0.22-2.53)	0.65
	SOD	1.09 (0.53-2.25)	0.81
	GPx	1.98 (0.84-4.64)	0.12
	MDA	1.15 (0.64-2.05)	0.65
Cholesterol<200 mg/dL	Reference	1.0
n= 320 (23.2)
Cholesterol≥200 mg/dL	GSH	1.01 (0.99-1.03)	0.42
n= 1060 (76.8)	CAT	0.68 (0.26-1.79)	0.43
	SOD	0.85 (0.48-1.50)	0.57
	GPx	0.59 (0.30-1.17)	0.13
	MDA	1.85 (1.14-2.98)	0.01
Triglyceride < 150 mg/dL	Reference	1.0
n= 874 (63.3)
Triglyceride	GSH	0.99 (0.98-1.01)	0.56
≥200 mg/dL	CAT	0.42 (0.18-1.01)	0.05
n= 506 (36.7)	SOD	1.13 (0.68-1.89)	0.64
	GPx	1.05 (0.57-1.93)	0.87
	MDA	2.54 (1.66-3.89)	<0.01

By comparison with the wild type, significant associations were found in heterozygous CYP2E1 PstI with an OR 2.53 (P = 0.007) /RsaI with an OR 2.56 (P = 0.006) for GPx and in variant RsaI with an OR 7.22 (P = 0.004) for CAT. Significant associations between hyperlipidemia with MDA were observed (both P ≤ 0.01). The combined effect of genetic variation and lipid profile on oxidative stress status was analyzed.

No significant differences in GSH level, CAT activity, or GPx activity were found between subgroups. However, the level of GSH (25.66 ±8.66 mg/dL to 35.42 ± 1.76 mg/dL) tended to be higher in participants in the hyperlipidemia subgroup and participants bearing any variant alleles, than in participants bearing a wild-type allele with normolipidemia (28.00 mg/dL ± 6.52 to 32.66 ± 6.32 mg/dL).

Participants bearing any variant allele of either gene of interest tended to have a lower mean CAT activity (from 19.50 ± 9.95 kU/g hemoglobin (Hb) [95% CI] [6.5-39.06] to 37.17 ± 4.75 kU/g Hb [27.84-46.51]) than participants bearing the wild-type alleles of either gene of interest with normolipidemia (from 26.27 ± 1.68 kU/g Hb [22.97-29.57] to 37.06 ± 2.89 kU/g Hb [31.38-42.74]). Significant differences in SOD activity were found between the subgroups (Figure 1A-C) in combined hyperlipidemia subgroups and participants bearing any NQO1 alleles with PstI or RsaI variants. Participants with homozygous PstI or RsaI variants had the highest mean SOD activity (1.69 ± 0.51 U/g Hb [0.70-2.69] to 3.45 ± 0.93 U/g Hb [1.62-5.27]) (P = 0.01 and P = 0.03).

Participants in the hyperlipidemia subgroup and participants bearing any variant allele of either gene of interest tended to have a higher mean GPx activity (31.61 ± 11.56 U/g Hb [95% CI] [8.9-54.33] to 52.29 ± 15.98U/gHb [19.91-82.66]) than participants bearing the wild-type alleles of both genes of interest with normolipidemia (25.24 ± 6.67 U/g Hb [12.13-38.35] to 42.40 ± 3.86 U/g Hb [34.82-49.98]) although the differences were not significant.

Superoxide dismutase (SOD) activity (mU/g hemoglobin (Hb)) in participants with various CYP2E1 genotypes (A) PstI, (B) RsaI, and (C) DraI (solid bars = wild type, open bars = heterozygous, hatched bars = variant) paired with NQO1 genotypes (WT = wild type, Het = heterozygous, and Var = variant), and various blood lipid profiles. Design for each panel: upper left quadrant is normolipidemia, upper right quadrant is hypertriglyceridemia, lower left quadrant is hypercholesterolemia, and lower right quadrant is combined hyperlipidemia. DraIdenotes the allele with a polymorphic site recognized by this restriction enzyme localized in intron 6 at position T7632 A (rs6413432). PstI indicates the allele with a point mutation recognized by PstI in the 5′-flanking region at G1293C (rs3813867) and RsaIindicates the allele with a point mutation recognized by RsaI in the 5′-flanking region at C1053T (rs2031920). Error bars ± SE. *P < 0.05.

In the subgroup with combined hyperlipidemia. participants bearing any NQO1 allele with a RsaI variant showed significantly higher levels of MDA than participants bearing any NQO1 with a wild-type RsaI allele with P = 0.021. Participants bearing a heterozygous NQO1 allele with a DraIvariant had a significantly higher level of MDA than participants bearing any NQO1 with a wild-type DraIallele (P = 0.031). Comparison of the subgroups of hyperlipidemia showed participants bearing any variant alleles had a higher mean level of MDA (6.04 ± 2.74 (SE) μιmol/L. [range] [0.65-11.40] to 16.22 ± 5.68 μιmol/L [5.03-27.41]) than participants bearing a wild-type allele with normolipidemia (4.00 ± 3.49 μmol/L [-2.80-10.8] to 11.97 ± 2.55 μmol/L. [6.95-16.98]) (Figure 2A-C).

Malondialdehyde (MDA) level (μΜ) in participants with various CYP2E1 genotypes (A) PstI, (B) RsaI, and (C) DraI (solid bars = wild type, open bars = heterozygous, hatched bars = variant) paired with NQO1 genotypes (WT wild type, Het heterozygous, and Var variant), and various blood lipid profiles. Design for each panel : upper left quadrant is normolipidemia, upper right quadrant is hypertriglyceridemia, lower left quadrant is hypercholesterolemia, and lower right quadrant is combined hyperlipidemia. DraI denotes the allele with a polymorphic site recognized by this restriction enzyme localized in intron 6 at position T7632A (rs6413432). PstI indicates the allele with a point mutation recognized by PstI in the 5′-flanking region at G1293C (rs3813867) and RsaI indicates the allele with a point mutation recognized by RscaI inthe 5′-flanking region at C1053T (rs2031920). Error bars ± SE.*P<0.05.

Discussion

The present study sought to investigate the impact of genetic polymorphism and dyslipidemia on oxidative stress, which is related to cancer risk. Oxidative stress status was assessed by measuring biomarkers of oxidative damage and indirectly assessing antioxidant defensive systems in blood samples.

Five biomarkers of oxidative stress status were determined in 1,380 healthy participants to assess associations of hyperlipidemia and genetic polymorphisms of drug or xenobiotic-metabolizing enzymes. An increase in free radical generation or a decrease in antioxidant levels in living organisms, or both, suggest that these factors play a critical role in the etiology of carcinogenesis [27]. Genetic variation was also considered a biomarker of susceptibility. Genetic variation is not only an indicator of susceptibility to chemical exposure, but also a modifier of several events in progression from exposure to disease [28]

In the study population, the heterozygous and variant genotype distribution and allele frequency of CYP2E1 *5B was less common than CYP2E1 *6. The heterozygous form of NQO1 was the most prevalent found in this study. The frequency of the mutated allele CYP2E1*5B (c2) was 0.16 and CYP2E1*6 (C) was 0.22, and these frequencies were close to frequencies reported in the Thai population [29]. The mutated allele of NQO1 *2 (T) was 0.43 and was close to that found in Asian populations [30].

In the present study, the participants in all hyperlipidemia subgroups and bearing any variant alleles of CYP2E1 *5B, CYP2E1 *6, or NQO1 *2 tended to have higher blood levels of MDA suggesting that more free radicals might be generated in individuals from any subgroup with hyperlipidemia bearing any variant alleles of the genes of interest. Hayashi et al. [31] reported a 10-fold higher transcription activity of the c2/c2 allele of CYP2E1 compared with the c1/1c1 allele in a HepG2 cell line, suggesting that the transcription activity of the c2 allele must be more than that of c1. The c2 allele may mediate the susceptibility of an individual to oxidative stress.

Habitual smoking was a lifestyle feature of individuals with hypercholesterolemia and combined hyperlipidemia, as consistent with findings by Mari et al. [32]. Smoking with mild forms of hyperlipidemia was associated with an increase in some markers of oxidative stress. Interestingly, participants with hypercholesterolemia showed similar MDA levels irrespective of the genotype of the genes of interest and they also had a high level of HDL-C. These observations might be explained by the oxidative protection mechanism of HDL-C for LDL-C through HDL-associated enzymes, such as paraoxonase 1, lecithin-cholesterol acyltransferase, or platelet-activating factor acetylhydrolase [33]. Alcohol consumption was presumably another cause of oxidative stress resulting in a tendency toward higher MDA levels in participants in any of the subgroups with hyperlipidemia bearing variant alleles of any gene of interest.

Alcohol consumption was also a lifestyle feature of participants with hypercholesterolemia or combined hyperlipidemia. CYP2E1 is endogenously induced by ethanol consumption and a variety of xenobiotics and ROS [9, 10]. Participants in all lipid profile subgroups carrying the wild-type NQO1 allele with any allele of CYP2E1 tended to have a low blood level of MDA, possibly because NQO1 is a radical scavenger [34].

Participants with hypercholesterolemia and combined hyperlipidemia had high levels of LDL-C. The polyunsaturated fatty acids in cholesterol esters, phospholipids, and triglycerides are subjected to free radical-initiated oxidation and can participate in chain reactions that may amplify the extent of the damage they cause. Aldehydes and ketones such as MDA are breakdown products polyunsaturated fatty acid oxidation [35]. Furthermore, all participants in this study were classified as being overweight (BMI >24 kg/ m²) [36, 37], which poses a risk of comorbidity and related diseases. Obesity may cause a chronic overproduction of ROS [38], which are metabolized by the network of enzymatic and nonenzymatic antioxidant systems [39].

Our present study assessed 4 different biomarkers of antioxidant status, including the activity of SOD, CAT, GPx, and the level of GSH in circulating blood. Inconsistent findings for the level of GSH and the activity of these antioxidant enzymes were observed in all lipid profile groups. In summary, participants in the hyperlipidemia subgroup bearing any variant allele of either gene of interest tended to have a higher GSH level, and SOD and GPx activity, but lower CAT activity when compared with participants bearing a wild-type allele with normolipidemia. The high level of GSH in the participants in the hyperlipidemia subgroup bearing any variant allele is probably because of upregulated GSH synthesis as a result of free radical exposure [40]. CYP2E1 may be induced by alcohol consumption and smoking by individuals in the subgroup with hyperlipidemia, which suggests that upregulation of GSH synthesis might be an adaptive response to attenuate CYP2E1-dependent oxidative stress [41]. The high activity of SOD and GPx might be explained in a similar manner. Gpx is an essential enzyme for all cell types under normal or low levels of oxidative stress. CAT therefore plays a more important role in protecting cells against severe oxidant stress when compared with GPx [42].

Conclusion

Our data suggest that the presence of any variant alleles of CYP2E1 and NQO1 associated with hyperlipidemia may attenuate antioxidant status and affect levels of oxidative stress markers. This finding provides data supporting the antioxidant status and oxidative stress marker involvement of endogenous (genetic variation and lipid profile) and exogenous factors (smoking and alcohol consumption) in interindividual variation. The variation in genetic background and dyslipidemia can modify oxidative stress, an issue of interest and motivation to extrapolate to the well-being practices of individuals in everyday life. From a toxicological point of view, the present study also provides information regarding gene-gene and gene-environment interaction.

Figures & Tables

Odds ratios for oxidative stress markers according to CYP2E1 and NQO1 polymorphism and lipid profile

Number (%)	Oxidative stress marker	Odds ratio (95% CI) Sex, smoking, and alcohol consumption were adjusted for odds ratio, GSH = reduced glutathione, GPx = glutathione peroxidase, SOD, superoxide dismutase, CAT = catalase, and MDA = malondialdehyde.	P
CYP2E1:PstI	Reference	1.0
Wild-type	GSH	0.996 (0.997-1.02)	0.66
n= 984 (71.3)	CAT	0.73 (0.28-1.91)	0.52
• Heterozygous	SOD	0.58 (0.33-1.03)	0.06
n= 351 (25.4)	GPx	2.53 (1.29-4.96)	0.007
	MDA	1.24 (0.79-1.97)	0.35
• Variant	GSH	1.00 (0.96-1.05)	0.94
n = 45 (3.3)	CAT	6.49 (0.86-48.8)	0.07
	SOD	0.38 (0.09-1.54)	0.17
	GPx	3.33 (0.65-17.15)	0.15
	MDA	0.81 (0.27-2.46)	0.72
CYP2E1:RsaI	Reference	1.0
• Wild-type	GSH	0.997 (0.98-1.02)	0.73
n= 979 (70.9)	CAT	0.60 (0.23-1.56)	0.29
• Heterozygous	SOD	0.59 (0.33-1.05)	0.07
n= 353 (25.6)	GPx	2.58 (1.31-5.05)	0.006
	MDA	1.27 (0.80-2.01)	0.31
• Variant	GSH	1.01 (0.97-1.05)	0.61
n = 48 (3.5)	CAT	7.22 (1.05-48.8)	0.004
	SOD	0.19 (0.05ndash;0.77)	0.20
	GPx	2.34 (048-114)	0.24
	MDA	1.21 (043-3.52)	0.71
CYP2E1:DraI	Reference	1.0
• Wild-type	GSH	0.99 (0.97-1.01)	0.26
n= 839 (60.8)	CAT	0.68 (0.28-1.65)	0.40
• Heterozygous	SOD	0.64 (0.38-1.09)	0.10
n= 464 (33.6)	GPx	1.79 (0.96-3.33)	0.07
	MDA	0.99 (0.79-1.97)	0.97
• Variant	GSH	0.99 (0.95-1.03)	0.58
n = 77 (5.6)	CAT	2.32 (042-13.0)	0.34
	SOD	0.78 (0.26-2.28)	0.64
	GPx	1.92 (0.53 −6.90)	0.32
	MDA	1.08 (045-2.59)	0.86
NQO1 *2	Reference	1.0
• Wild-type	GSH	1.02 (0.998-1.04)	0.08
n=438 (31.7)	CAT	1.45 (0.57-3.66)	0.44
• Heterozygous	SOD	1.31 (0.75-2.28)	0.35
n=690 (50.0)	GPx	0.90 (0.47-1.72)	0.74
	MDA	0.89 (0.56-1.40)	0.61
• Variant	GSH	1.02 (0.99-1.04)	0.19
n=252 (18.3)	CAT	0.75 (0.22-2.53)	0.65
	SOD	1.09 (0.53-2.25)	0.81
	GPx	1.98 (0.84-4.64)	0.12
	MDA	1.15 (0.64-2.05)	0.65
Cholesterol<200 mg/dL	Reference	1.0
n= 320 (23.2)
Cholesterol≥200 mg/dL	GSH	1.01 (0.99-1.03)	0.42
n= 1060 (76.8)	CAT	0.68 (0.26-1.79)	0.43
	SOD	0.85 (0.48-1.50)	0.57
	GPx	0.59 (0.30-1.17)	0.13
	MDA	1.85 (1.14-2.98)	0.01
Triglyceride < 150 mg/dL	Reference	1.0
n= 874 (63.3)
Triglyceride	GSH	0.99 (0.98-1.01)	0.56
≥200 mg/dL	CAT	0.42 (0.18-1.01)	0.05
n= 506 (36.7)	SOD	1.13 (0.68-1.89)	0.64
	GPx	1.05 (0.57-1.93)	0.87
	MDA	2.54 (1.66-3.89)	<0.01

Demographic characteristics and distribution of the metabolic enzyme gene polymorphisms of the study population

Characteristics	Lipid profile

	Normolipidemia (n = 236)	Hypercholesterolemia (n = 638)	Hypertriglyceridemia (n = 84)	Combined hyperlipidemia (n = 422)	P P generated using a general-linear model: multivariate test, χ² test for genotype and lifestyle features
Age (years) Data = mean ± SD or n (%)	51.6±4.7	51.0±44	51.9±4.8	51.9±4.3	0.08
Weight (kg) Data = mean ± SD or n (%)	67.1 ±11.5	64.5 ± 10.8	69.3 ± 10.9	69.6 ±10.6	0.46
Height (cm) Data = mean ± SD or n (%)	164.4 ±8.2	163.3 ±7.6	164.3 ±7.7	164.8 ±7.5	0.16
BMI (kg/m²) Data = mean ± SD or n (%)	24.8 ±3.48	24.1 ±3.23	25.6±3.38 hypercholesterolemia or hypertriglyceridemia compare with normolipidemia	25.6 ±3.41	<.001 hypercholesterolemia or hypertriglyceridemia compare with normolipidemia
Cholesterol (mg/dL) Data = mean ± SD or n (%)	179.0 ±19.12	242.6 ±28.93 hypercholesterolemia or hypertriglyceridemia compare with normolipidemia	180.1 ±18.29	253.1 ±35.80 combined hyperlipidemia compared with normolipidemia	<.001 hypercholesterolemia or hypertriglyceridemia compare with normolipidemia ,0.02 combined hyperlipidemia compared with normolipidemia
Triglyceride (mg/dL) Data = mean ± SD or n (%)	93.3 ±32.40	98.9 ±30.08	224.5 ± 95.70 hypercholesterolemia or hypertriglyceridemia compare with normolipidemia	233.7 ±105.32 combined hyperlipidemia compared with normolipidemia	<0.001 hypercholesterolemia or hypertriglyceridemia compare with normolipidemia combined hyperlipidemia compared with normolipidemia
LDL-C (mg/dL) Data = mean ± SD or n (%)	113.9±1547	164.6 ± 30.13 hypercholesterolemia or hypertriglyceridemia compare with normolipidemia	95.8± 19.12	159.4 ±3642 combined hyperlipidemia compared with normolipidemia	<.001 hypercholesterolemia or hypertriglyceridemia compare with normolipidemia , 0.003 combined hyperlipidemia compared with normolipidemia
HDL-C (mg/dL) Data = mean ± SD or n (%)	50.9 ±10.31	56.6±11.56 hypercholesterolemia or hypertriglyceridemia compare with normolipidemia	43.2 ±9.93	47.3 ±9.71	<0.001 hypercholesterolemia or hypertriglyceridemia compare with normolipidemia
CYP2E1:CYP2E1 *5B (RsaI/PstI)
No. of c1c1 (WT)	175/176 (≈74)	450/453 (≈71)	54/55 (=65)	300/300 (71)	<0.001 significant difference between group of lipid profiles
No.of c1c2 (Het)	50/50 (21)	167/165 (=26)	26/26 (31)	110/110 (26)	0.20,0.25
No.of c2c2 (Var)	11/10 (≈4)	21/20 (=3)	4/3 (=4)	12/12 (3)	0.39,0.29
CYP2EI*6 (DraI)
No. of DD (WT)	148 (63)	380 (60)	48 (57)	263 (62)	<0.001 significant difference between group of lipid profiles
No. of DC (Het)	74 (31)	224 (35)	30 (36)	136 (32)	0.07
No. of CC (Var)	14 (6)	34 (5)	6 (7)	23 (6)	0.50
NQO1 NQO1 *2
No. of CC (WT)	71 (30)	210 (33)	30 (36)	127 (30)	0.10
No of CT (Het)	116 (49)	318 (50)	41 (48)	215 (51)	<0.001 significant difference between group of lipid profiles
No. of TT (Var)	49 (21)	110 (17)	13 (16)	80 (19)	0.41
Sex
Male	170 (72)	419 (66)	63 (75)	331 (78)	<0.001 significant difference between group of lipid profiles
Female	66 (28)	219 (34)	21 (25)	91 (22)	0.21
Smoking	33 (14)	64 (10)	11 (13)	82 (19)	<0.001 significant difference between group of lipid profiles
Nonsmoking	203 (86)	574 (90)	73 (87)	340 (81)	0.001
Alcohol drinking	115 (49)	304 (48)	43 (51)	248 (59)	<0.001 significant difference between group of lipid profiles
Alcohol abstinence	121 (51)	334 (52)	41 (49)	174 (41)	0.02*

References

1Halliwell B. Oxidative stress and cancer: have we moved forward? Biochem J. 2007; 401:1-11.1715004010.1042/BJ20061131HalliwellBOxidative stress and cancer: have we moved forward?Biochem J2007401111Search in Google Scholar

2Emerit J, Beaumont C, Trivin F. Iron metabolism, free radicals, and oxidative injury. Biomed Pharmacother. 2001; 55:333-9.1147858610.1016/S0753-3322(01)00068-3EmeritJBeaumontCTrivinFIron metabolism, free radicals, and oxidative injuryBiomed Pharmacother2001553339Search in Google Scholar

3Gupta S, Sodhi S, Mahajan V. Correlation of antioxidants with lipid peroxidation and lipid profile in patients suffering from coronary artery disease. Expert Opin Ther Targets. 2009; 13:889-94.1960692810.1517/14728220903099668GuptaSSodhiSMahajanVCorrelation of antioxidants with lipid peroxidation and lipid profile in patients suffering from coronary artery diseaseExpert Opin Ther Targets20091388994Search in Google Scholar

4M nzel T, Gori T, Bruno RM, Taddei S. Is oxidative stress a therapeutic target in cardiovascular disease? Eur Heart J. 2010; 31:2741-8.10.1093/eurheartj/ehq39620974801M nzelTGoriTBrunoRMTaddeiSIs oxidative stress a therapeutic target in cardiovascular disease?Eur Heart J20103127418Open DOI Search in Google Scholar

5Allampallam K, Dutt D, Nair C, Shetty V, Mundle S, Lisak L, et al. The clinical and biologic significance of abnormal lipid profiles in patients with myelodysplastic syndromes. J Hematother Stem Cell Res. 2000; 9: 247-55.1081353810.1089/152581600319469AllampallamKDuttDNairCShettyVMundleSLisakLet alThe clinical and biologic significance of abnormal lipid profiles in patients with myelodysplastic syndromesJ Hematother Stem Cell Res2000924755Search in Google Scholar

6Zhuang L, Kim J, Adam RM, Solomon KR, Freeman MR. Cholesterol targeting alters lipid raft composition and cell survival in prostate cancer cells and xenografts. J Clin Invest. 2005; 115:959-68.1577611210.1172/JCI200519935ZhuangLKimJAdamRMSolomonKRFreemanMRCholesterol targeting alters lipid raft composition and cell survival in prostate cancer cells and xenograftsJ Clin Invest200511595968Search in Google Scholar

7dos Santos CR, Domingues G, Matias I, Matos J, Fonseca I, de Almeida JM, et al. LDL-cholesterol signaling induces breast cancer proliferation and invasion. Lipids Health Dis. 2014; 13:16-25.10.1186/1476-511X-13-1624428917dos SantosCRDominguesGMatiasIMatosJFonsecaIde AlmeidaJMet alLDL-cholesterol signaling induces breast cancer proliferation and invasionLipids Health Dis2014131625Open DOI Search in Google Scholar

8Coon MJ, Vaz AD, Bestervelt LL. Cytochrome P450 2: peroxidative reactions of diversozymes. FASEB J. 1996; 10:428-34.8647341CoonMJVazADBesterveltLLCytochrome P450 2: peroxidative reactions of diversozymesFASEB J1996104283410.1096/fasebj.10.4.8647341Search in Google Scholar

9Tanaka E, Terada M, Misawa S. Cytochrome P4502E1: its clinical and toxicological role. J Clin Pharm Ther. 2000; 25:165-75.10.1046/j.1365-2710.2000.00282.xTanakaETeradaMMisawaSCytochrome P4502E1: its clinical and toxicological roleJ Clin Pharm Ther20002516575Open DOI Search in Google Scholar

10Ingelman-Sundberg M, Johansson I, Yin H, Terelius Y, Eliasson E, Clot P, Albano E. Ethanol inducible cytochrome P 4502E1: genetic polymorphism, regulation, and possible role in etiology of alcohol-induced liver disease. Alcohol. 1993; 10:447-52.10.1016/0741-8329(93)90063-TIngelman-SundbergMJohanssonIYinHTereliusYEliassonEClotPAlbanoEEthanol inducible cytochrome P 4502E1: genetic polymorphism, regulation, and possible role in etiology of alcohol-induced liver diseaseAlcohol19931044752Open DOI Search in Google Scholar

11Caro AA, Evans KL, Cederbaum AI. Oxidative stress, toxicology, and pharmacology of CYP2E1. Annu Rev Pharmacol Toxicol. 2004; 44:27-42.10.1146/annurev.pharmtox.44.101802.12170414744237CaroAAEvansKLCederbaumAIOxidative stress, toxicology, and pharmacology of CYP2E1Annu Rev Pharmacol Toxicol2004442742Open DOI Search in Google Scholar

12Ingelman-Sundberg M, Daly AK, Nebert DW. Human cytochrome P450 (CYP) allele nomenclature. [online] 2014. [cited 2015 Aug 1]; Available from http://www.cypalleles.ki.se/Ingelman-SundbergMDalyAKNebertDWHuman cytochrome P450 (CYP) allele nomenclature[online] 2014cited2015Aug1Available fromhttp://www.cypalleles.ki.se/Search in Google Scholar

13Danko IM, Chaschin NA. Association of CYP2E1 gene polymorphism with predisposition to cancer development. Exp Oncol. 2005; 27:248-56.16404342DankoIMChaschinNAAssociation of CYP2E1 gene polymorphism with predisposition to cancer developmentExp Oncol20052724856Search in Google Scholar

14Watanabe J, Hayashi S, Kawajiri K. Different regulation and expression of the human CYP2E1 gene due to the RsaI polymorphism in the 5′ flanking region. J Biochem. 1994; 116:321-6.752975910.1093/oxfordjournals.jbchem.a124526WatanabeJHayashiSKawajiriKDifferent regulation and expression of the human CYP2E1 gene due to the RsaI polymorphism in the 5′ flanking regionJ Biochem19941163216Search in Google Scholar

15Uematsu F, Ikawa S, Kikuchi S, Sagami I, Kanamura R, Abe T, et al. Restriction fragment length polymorphism of the human CYP2E1 (cytochrome P450IIE1) gene and susceptibility to lung cancer: possible relevance to low smoking exposure. Pharmacogenetics. 1994; 4: 58-63.791593510.1097/00008571-199404000-00002UematsuFIkawaSKikuchiSSagamiIKanamuraRAbeTet alRestriction fragment length polymorphism of the human CYP2E1 (cytochrome P450IIE1) gene and susceptibility to lung cancer: possible relevance to low smoking exposurePharmacogenetics199445863Search in Google Scholar

16Klaassen CD, Reisman SA. Nrf2 the rescue: effects of antioxidative electrophilic response on the liver. Toxicol Appl Pharmacol. 2010; 244:57-65.2012294610.1016/j.taap.2010.01.013KlaassenCDReismanSANrf2 the rescue: effects of antioxidative electrophilic response on the liverToxicol Appl Pharmacol20102445765Search in Google Scholar

17Nioi P, Hayes JD. Contribution of NAD(P) H:quinone oxidoreductase 1 to protection against carcinogenesis, and regulation of its gene by the Nrf2 basic-region leucine zipper and the arylhydrocarbon receptor basic helix-loop-helix transcription factors. Mutat Res. 2004; 555:149-71.10.1016/j.mrfmmm.2004.05.023NioiPHayesJDContribution of NAD(P) H:quinone oxidoreductase 1 to protection against carcinogenesis, and regulation of its gene by the Nrf2 basic-region leucine zipper and the arylhydrocarbon receptor basic helix-loop-helix transcription factorsMutat Res200455514971Open DOI Search in Google Scholar

18Siegel D, Ryder J, Ross D. NAD(P) H: quinone oxidoreductase 1 expression in human bone marrow endothelial cells. Toxicology Letters. 2001; 125:93-8.10.1016/S0378-4274(01)00426-XSiegelDRyderJRossDNAD(P) H: quinone oxidoreductase 1 expression in human bone marrow endothelial cellsToxicology Letters2001125938Open DOI Search in Google Scholar

19Beutler E, Duron O, Kelly BM. Improved method for the determination of blood glutathione. J Lab Clin Med. 1963; 61:882-8.13967893BeutlerEDuronOKellyBMImproved method for the determination of blood glutathioneJ Lab Clin Med1963618828Search in Google Scholar

20Beutler E. Glutathione peroxidase (GSH-Px). In: Beutler E, editor. Red cell metabolism. A manual of biochemical methods. 2nd ed. New York: Grane and Stratton; 1975, p. 71-73.BeutlerEGlutathione peroxidase (GSH-Px)InBeutlerERed cell metabolism. A manual of biochemical methods2New YorkGrane and Stratton19757173Search in Google Scholar

21Winterbourn C, Hawkins R, Brian M, Carrell R. The estimation of red cell superoxide dismutase activity. J Lab Clin Med. 1975; 85:337-41.803541WinterbournCHawkinsRBrianMCarrellRThe estimation of red cell superoxide dismutase activityJ Lab Clin Med19758533741Search in Google Scholar

22Beers RF Jr, Sizer IW. A spectrophotometric method for measuring the breakdown of hydrogen peroxide by catalase. J Biol Chem. 1952; 195:133-40.14938361BeersRFJrSizerIWA spectrophotometric method for measuring the breakdown of hydrogen peroxide by catalaseJ Biol Chem19521951334010.1016/S0021-9258(19)50881-XSearch in Google Scholar

23Khoschsorur GA, Winklhofer-Roob BM, Rabl H, Auer Th, Peng Z, Schaur RJ. Evaluation of a sensitive HPLC method for the determination of malondialdehyde, and application of the method to different biological materials. Chromatographia. 2000; 52:181-4.10.1007/BF02490453KhoschsorurGAWinklhofer-RoobBMRablHAuerThPengZSchaurRJEvaluation of a sensitive HPLC method for the determination of malondialdehyde, and application of the method to different biological materialsChromatographia2000521814Open DOI Search in Google Scholar

24Executive summary of the third report of The National Cholesterol Education Program (NCEP) Expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III). JAMA 2001; 285:2486-97.Executive summary of the third report of The National Cholesterol Education Program (NCEP) Expert panel on detection, evaluation, and treatment of high blood cholesterol in adults (adult treatment panel III)JAMA200128524869710.1001/jama.285.19.248611368702Search in Google Scholar

25Miller SA, Dykes DD, Polesky HF. A simple salting out procedure for extracting DNA from human nucleated cells. Nucleic Acids Res. 1988; 16:1215.334421610.1093/nar/16.3.1215MillerSADykesDDPoleskyHFA simple salting out procedure for extracting DNA from human nucleated cellsNucleic Acids Res19881612153347653344216Search in Google Scholar

26Packer BR, Yeager M, Stats B, Burdett L, Welch R, Beerman M, et al. SNP500 Cancer: a public resource for sequence validation and assay development for genetic variation in candidate genes. Nucleic Acids Res. 2004; 32:528-32.10.1093/nar/gkh005PackerBRYeagerMStatsBBurdettLWelchRBeermanMet alSNP500 Cancer: a public resource for sequence validation and assay development for genetic variation in candidate genesNucleic Acids Res20043252832Open DOI Search in Google Scholar

27Portakal O, Zkaya O, Inal ME, Bozan B, Kosan M, Sayek I. Coenzyme Q10 concentrations and antioxidant status in tissues of breast cancer patients. Clin Biochem. 2000; 33:279-84.1093658610.1016/S0009-9120(00)00067-9PortakalOZkayaOInalMEBozanBKosanMSayekICoenzyme Q10 concentrations and antioxidant status in tissues of breast cancer patientsClin Biochem20003327984Search in Google Scholar

28Mayeux R. Biomarkers: potential uses and limitations. Neuro RX. 2004; 1:182-810.1602/neurorx.1.2.182MayeuxRBiomarkers: potential uses and limitationsNeuro RX20041182853492315717018Open DOI Search in Google Scholar

29Sangrajrang S, Jedpiyawong A, Srivatanakul P. Genetic polymorphism of CYP2E1 and GSTM1 in a Thai population. Asian Pacific J Cancer Prev. 2006; 7: 415-19.SangrajrangSJedpiyawongA ivatanakul PSrGenetic polymorphism of CYP2E1 and GSTM1 in a Thai populationAsian Pacific J Cancer Prev2006741519Search in Google Scholar

30Nebert DW, Roe AL, Vandale SE, Bingham E, Oakley GG. NAD (P) H:quinone oxidoreductase (NQO1) polymorphism, exposure to benzene, and predisposition to disease: A HuGE review. Genet Med. 2002; 4:62-70.10.1097/00125817-200203000-00003NebertDWRoeALVandaleSEBinghamEOakleyGGNAD (P) H:quinone oxidoreductase (NQO1) polymorphism, exposure to benzene, and predisposition to disease: A HuGE reviewGenet Med20024627011882782Open DOI Search in Google Scholar

31Hayashi S, Watanabe J, Kawajiri K. Genetic polymorphism in 5′-flanking region change transcriptional regulation of human cytochrome P4502E1 gene. J Biochem 1991; 110:559-65.10.1093/oxfordjournals.jbchem.a123619HayashiSWatanabeJKawajiriKGenetic polymorphism in 5′-flanking region change transcriptional regulation of human cytochrome P4502E1 geneJ Biochem1991110559651778977Open DOI Search in Google Scholar

32Miri R, Saadati H, Ardi P, Firuzi O. Alterations in oxidatives stress biomarkers associated with mild hyperlipidemia and smoking. Food Chem. Toxicol. 2012; 50:920-6.10.1016/j.fct.2011.12.031MiriRSaadatiHArdiPFiruziOAlterations in oxidatives stress biomarkers associated with mild hyperlipidemia and smokingFood Chem. Toxicol201250920622227215Open DOI Search in Google Scholar

33Barter PJ, Nicholls S, Rye KA, Anantharamaiah GM, Navab M, Fogelman AM. Antiinflammatory properties of HDL. Circ Res. 2004; 95:764-72.1548632310.1161/01.RES.0000146094.59640.13BarterPJNichollsSRyeKAAnantharamaiahGMNavabMFogelmanAMAntiinflammatory properties of HDLCirc Res2004957647215486323Search in Google Scholar

34Siegel D, Gustafson DL, Dehn DL, Han JY, Boonchoong P, Berliner LJ, et al. NAD(P) H:quinone oxidoreductase 1: role as a superoxide scavenger. Mol Pharmcol. 2004; 65:1238-47.10.1124/mol.65.5.1238SiegelDGustafsonDLDehnDLHanJYBoonchoongPBerlinerLJet alNAD(P) H:quinone oxidoreductase 1: role as a superoxide scavengerMol Pharmcol20046512384715102952Open DOI Search in Google Scholar

35Steinberg D. Low density lipoprotein oxidation and its pathobiological significance. J Biol Chem 1997; 272:20963-66.926109110.1074/jbc.272.34.20963SteinbergDLow density lipoprotein oxidation and its pathobiological significanceJ Biol Chem199727220963669261091Search in Google Scholar

36WHO/IOTF/IASO. The Asian-Pacific perspective: redefining obesity and its treatment. Hong Kong: World Health Organization, International Obesity Task Force, International Association for the study of obesity 2000.WHO/IOTF/IASO. The Asian-Pacific perspective: redefining obesity and its treatmentHong Kong: World Health Organization, International Obesity Task Force, International Association for the study of obesity2000Search in Google Scholar

37James WPT, Chen C, Inoue S. Appropriate Asian body mass indices? Obesity Review. 2002; 3:139.10.1046/j.1467-789X.2002.00063.xJamesWPTChenCInoueSAppropriate Asian body mass indices?Obesity Review2002313912164464Open DOI Search in Google Scholar

38Furukawa S, Fujita T, Shimabukuro M, Iwaki M, Yamada Y, Nakajima Y, et al. Increased oxidative stress in obesity and its impact on metabolic syndrome. J Clin lnvest. 2004; 114:1752-61.10.1172/JCI21625FurukawaSFujitaTShimabukuroMIwakiMYamadaYNakajimaYet alIncreased oxidative stress in obesity and its impact on metabolic syndromeJ Clin lnvest200411417526153506515599400Open DOI Search in Google Scholar

39Aitken RJ, Roman SD. Antioxidant systems and oxidative stress in the testes. Oxid Med Cell Longevity. 2008; 1:15-24.10.4161/oxim.1.1.6843AitkenRJRomanSDAntioxidant systems and oxidative stress in the testesOxid Med Cell Longevity200811524271519119794904Open DOI Search in Google Scholar

40Halliwell BB, Poulsen HE. Cigarette smoke and oxidative stress. Berlin/Heidelberg: Springer Verlag. 2006; 5-47.HalliwellBBPoulsenHECigarette smoke and oxidative stressBerlin/HeidelbergSpringer Verlag200654710.1007/3-540-32232-9Search in Google Scholar

41Raza H, Prabu SK, Robin MA, Avadhani NG. Elevated mitochondrial cytochrome P450 2E1 and glutathione S transferase A4-4 in streptozotocin-induced diabetic rats: tissue-specific variations and roles in oxidative stress. Diabetes. 2004; 53:185-94.1469371410.2337/diabetes.53.1.185RazaHPrabuSKRobinMAAvadhaniNGElevated mitochondrial cytochrome P450 2E1 and glutathione S transferase A4-4 in streptozotocin-induced diabetic rats: tissue-specific variations and roles in oxidative stressDiabetes2004531859414693714Search in Google Scholar

42Yan H, Harding JJ. Glycation-induced inactivation and loss of antigenicity of catalase and superoxide dismutase. Biochem J. 1997; 328:599-605.10.1042/bj32805999371721YanHHardingJJGlycation-induced inactivation and loss of antigenicity of catalase and superoxide dismutaseBiochem J199732859960512189619371721Open DOI Search in Google Scholar

Asian Biomedicine

Role of CYP2E1 and NQO1 polymorphisms in oxidative stress derived cancer in Thais with and without dyslipidemia

Published Online: 31 Jan 2017

Volume & Issue: Volume 9 (2015) - Issue 5 (October 2015)

Page range: 601 - 611

Journal & Issue Details

Asian Biomedicine

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Figure 1A-C

Figure 2A-C

Figures & Tables

Figure 1A-C

Figure 2A-C

Odds ratios for oxidative stress markers according to CYP2E1 and NQO1 polymorphism and lipid profile

Demographic characteristics and distribution of the metabolic enzyme gene polymorphisms of the study population

References

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