EXCLI Journal 2004;3:58-67 – ISSN 1611-2156 
received: 26. August 2004, accepted: 9. September 2004, published: 13. September 2004 
 
Original article: 
 
Smoking, genetic polymorphisms of glutathione S-transferases and 
biological indices of inflammation and cellular adhesion  
in the STANISLAS study 
 
Mohammed Habdous1, Bernard Herbeth1,2*, Nadia Haddy1, Anne Ponthieux1, 
Hermann M. Bolt3, Gérard Siest1,2, Ricarda Thier3, Daniel Lambert1, 
Sophie Visvikis1,2
 
1INSERM U525, 30 rue Lionnois, F-54000 Nancy, France. 2Centre de Médecine 
Préventive, 2 rue Jacques Parisot, F-54501 Vandoeuvre-les-Nancy, France, Tel: 
+33.3.83.44.87.18, Fax: +33.3.83.44.87.21, e-mail: Bernard.herbeth@cmp.u-nancy.fr (* 
corresponding author). 3Institut für Arbeitsphysiologie an der Universität Dortmund 
(IfADo), Ardeystr. 67, D-44139 Dortmund, Germany. 
 
 
ABSTRACT 
 
A recent clinical study has focused on: 1- the interaction between genetic variants of 
glutathione S-transferases M1 and T1 (GSTM1 and GSTT1) and smoking on the risk of 
cardiovascular diseases, 2- the potential capacity of GSTM1 and T1 genotypes in 
modifying the effect of smoking on inflammation and endothelial function. In this 
study, we investigated whether carriage of these 2 polymorphisms altered the smoking 
impact on biological indices of inflammation and cellular adhesion. White blood cell 
count (WBC), albumin, C-reactive protein (CRP), interleukine-6 (IL-6), tumor necrosis 
factor-alpha (TNF-α), L-selectin, E-selectin, P-selectin and intracellular adhesion 
molecule-1 (ICAM-1) were measured in 189 non-smokers and 76 smokers (aged 20-55 
years) genotyped for the GSTM1 and T1 polymorphisms. Accounting for age and sex, 
smokers lacking GSTM1 had a higher WBC count, CRP and ICAM-1 levels as 
compared to the other groups; interaction term between smoking and genotype being 
significant (p≤0.05). Conversely, non-smokers lacking GSTM1 had a higher levels of 
TNF-α; the test for interaction being significant (p≤0.05). No significant interaction was 
found between smoking and GSTT1 genotypes, considering the 9 biological indices. 
However, significantly lower levels of IL-6 were noticed for non-smokers with GSTT1-
0 null allele (p≤0.05). Our study confirms previous results showing that GSTM1 
polymorphism could modulate the interrelationships between smoking and biological 
markers of inflammation and endothelial function. 
 
Keywords: smoking, inflammation, adhesion molecule, glutathione S-transferase 
polymorphism 
 
 
 58 
INTRODUCTION 
 
Numerous genetic variants of human 
drug-metabolizing enzymes, involved in 
either the activation or detoxification of 
carcinogenic compounds in tobacco 
smoke, influence the individual 
susceptibility to smoking related diseases, 
including atherosclerosis. The glutathione 
S-transferases (GST) are a polymorphic 
supergene family that detoxifies 
carcinogens and pro-atherogenic 
compounds generated by oxidative stress, 
in particular those found in tobacco 
smoke. In addition, it has been shown that 
the GST enzymes are expressed in human 
atherosclerotic plaques (Hayes et al., 
1995; Hayes et al., 2000; Ketterer, 1998). 
Several polymorphisms of these enzymes 
have been identified, including GST mu 
(GSTM1-1) and theta (GSTT1-1). Two 
functionally different alleles of GSTM1-1 
and GSTT1-1 genes have been described 
(GSTM1-0 or M1-1 and GSTT1-0 or T1-
1). Individuals with null alleles GSTM1-0 
and GSTT1-0 express no activity of the 
respective isoenzyme, and may be unable 
to efficiently eliminate electrophilic 
intermediates (Hayes et al., 1995; Hayes 
et al., 2000). 
 
Recent studies have reported altered 
allelic frequencies of the polymorphic 
genes GSTM1-1 and T1-1 as potential risk 
factors for the development of 
cardiovascular diseases (de Waart et al., 
2001; Li et al., 2000; Li et al., 2001; 
Masetti et al., 2003; Olshan et al., 2003).  
Thus, GST genetic polymorphisms may 
have an influence on the development of 
these diseases, possibly through the 
pivotal role of GST in the cellular 
metabolism of cytotoxic products 
generated in part by tobacco smoke 
constituents, including carcinogenic 
metabolites and reactive oxygen species 
(ROS) (Dusinska et al., 2001; Ketterer, 
1998; Onaran et al., 2001). Cigarette 
smoking has been firmly established as an 
independent risk factor for vascular 
diseases. However, the pathogenic effects 
of tobacco smoke on the atherosclerosis 
development remains unclear. Recently, 
studies have implicated oxidative stress 
modifications of low density lipoprotein 
and DNA damage of target tissues by 
cigarette smoke as a fundamental step in 
atherogenesis (Binkova et al., 2001; de 
Maat et al., 2002). It is also tempting to 
speculate that smoking-induced pro-
inflammatory markers are responsible for 
smooth muscle cells (SMC) proliferation 
and endothelial dysfunction, thereby 
contributing to atherosclerosis plaque 
formation. Recent evidence suggests that 
ROS may function as second messengers 
in cytokines or some growth factor-
mediated intracellular signal transduction 
pathways. In particular, ROS are reported 
to stimulate SMC growth and proto-
oncogene expression (Nishio et al., 1998). 
 
ROS and pro-oxidants in tobacco smoke 
could lead to regulation responses 
mediated by inflammatory markers such 
as C-reactive protein (CRP), interleukin-6 
(IL-6) and tumor necrosis factor-alpha 
(TNF-α), altering the physiological 
properties of vascular cells (Libby et al., 
2002; Saadeddin et al., 2002). Because the 
GSTs have been shown to protect cells 
from the toxicity of ROS and pro-oxidant 
compounds, including α,β-unsaturated 
carbonyls (He et al., 1998; Ketterer, 
1998), they have been proposed as an 
important defense mechanism against 
atherogenesis. Moreover, previous studies 
have identified interactions between 
smoking and two GST genes, GSTM1 and 
T1, in relation to coronary artery disease, 
lower extremity arterial disease and 
carotid artery intimal-medial thickness (de 
Waart et al., 2001; Li et al., 2000; Li et al., 
2001; Masetti et al., 2003; Olshan et al., 
2003). Recently, Miller et al. (Miller et al., 
2003) showed that GSTT1 and GSTM1 
 59 
could reduce the potential for oxidative 
damage and therefore modify 
inflammatory response to oxidative stress 
in the ARIC study. Smokers carrying 
GSTM1-0 null allele had the highest level 
of CRP, fibrinogen, von Willebrand 
factors, intercellular adhesion molecule-1 
(ICAM-1) and vascular cell adhesion 
molecule-1 (VCAM-1). Conversely, 
smokers with GSTT1-1 functional allele 
had the highest level of CRP and 
fibrinogen. 
 
Thus, the purpose of the present study was 
to assess the interactions between genetic 
variation in two GST and smoking on 9 
biomarkers of inflammation and cell 
adhesion (WBC, albumin, CRP, IL-6, 
TNF-α, L-selectin, E-selectin, P-selectin 
and ICAM-1) in a sub-sample of healthy 
French individuals of the STANISLAS 
cohort. 
 
MATERIALS AND METHODS 
 
Subjects 
The sample population including 189 non-
smokers and 76 smokers (males and 
females, aged 20-55 years) was taken 
from the Stanislas cohort, a longitudinal 
family study recruited at 1994-95 and 
having a 5 years check up (Siest et al., 
1998). Individuals included in this study 
had to be born of French parents. 
Moreover, they had to live in the east of 
France (particularly in two administrative 
departments: Vosges and Meurthe et 
Moselle). For this specific study, subjects 
were selected with the following criteria: 
to be present at the second (1999-2000) 
health screening; free from serious and/or 
chronic illnesses, especially diabetes 
mellitus or cardiovascular, hepatic or renal 
failure; without treatment with lipid-
lowering drugs or antidiabetic agent. 
Volunteers having aspartate 
aminotransferase (AST), alanine 
aminotransferase (ALT) or gamma-
glutamyltransferase (GGT) activities > 
200 U/L, orosomucoid or haptoglobin > 3 
g/L, cholesterol or triglyceride > 10 
mmol/L, CRP > 30 mg/L, or glucose > 7 
mmol/L were excluded. This study was 
approved by the Local Ethics Committee 
of Nancy (France), and each subject gave 
a written informed consent. 
Blood sample and data collection 
Venous blood samples were collected by 
venipuncture after an overnight fast. 
Blood samples were centrifuged (1,500 g 
for 15 min at 4°C) within 2h after 
collection, and resulting EDTA plasma 
samples for IL-6 and TNF-α 
determination, and serum samples for L-
selectin, E-selectin, P-selectin and ICAM-
1 level determination were promptly 
frozen at -196°C in liquid nitrogen until 
analysis. Aliquots and biological samples 
were stored in the Biobank of the Centre 
de Médecine Préventive (Siest et al., 
1998; Visvikis et al., 1998). Data 
collection included measurements of basic 
blood constituents, functional tests, 
physical examination, questionnaires on 
life-style and personal medical history. 
Medication was assessed by patient 
interview during the blood sampling. 
Information on alcohol and smoking 
consumption were collected by self-
administered questionnaires. 
Analytical methods 
White blood cell counts were determined 
on a MAX’M analyzer (Beckman-Coulter, 
Roissy, France). Serum concentrations of 
albumin, glucose, total cholesterol, 
triglycerides, and activities of AST, ALT 
and GGT were measured with 
commercially available kits on an 
AU5021 apparatus (all from Merck, 
Darmstadt, Germany) on fresh aliquots 
within 2 hours. Serum CRP, orosomucoid 
and haptoglobin were measured by 
immunonephelometry on a Behring 
Nephelometer Analyser (BN II, Dade-
 60 
Behring, Marburg, Germany) with 
Behring reagents (Reuil-Malmaison, 
France) within two hours after sampling. 
Circulating levels of ICAM-1, E-selectin, 
P-selectin, L-selectin, IL-6 and TNF-α 
were measured with commercially 
available enzyme-linked immunosorbant 
assay (R&D System, Abington UK) in 
serum samples stored in liquid nitrogen 
until use (storage period between 21 and 
39 weeks, mean: 31 weeks). The intra- 
and inter-assay coefficients of variation 
for inflammatory markers were as 
follows: CRP: 4.0% and 6.1%, IL-6: 
11.1% and 16.5%, TNF-α: 8.8% and 
16.7%, L-selectin: 8.9% and 11.7%, E-
selectin: 9.4% and 14.9%, P-selectin: 
5.8% and 7.0%, ICAM-1: 6.6% and 8.6%, 
respectively. 
 
GST genotyping 
DNA extraction was performed according 
to the “salting out” method, after 
validation of the procedure and DNA 
stability (Visvikis et al., 1998). GSTM1 
and GSTT1 genotypes were identified 
using an allele-specific multiplex PCR 
assay. Genotyping of GSTM1 and GSTT1 
was carried out using a multiplex-PCR 
method (Abdel-Rahman et al., 1996). The 
multiplex-PCR protocol consisted of 4 
min at 94°C; 45 cycles of 30 s min at 
94°C, 30 min at 60°C, 60 s at 72°C and 5 
min at 72°C. PCR products were 
separated on 3% agarose gel via 
electrophoresis, and visualized using 
ethidium bromide staining. The multiplex 
PCR method used for both GSTM1 and 
GSTT1 polymorphism determination 
allows distinguishing between carriers of 
at least one functional allele (homozygous 
functional genotype + heterozygous) and 
carriers of no functional allele 
(homozygous null genotype). 
Statistical analysis 
Statistical analyses were performed by 
using the SAS software version 8.01 (SAS 
Institute Inc., USA). Since the distribution 
of serum concentrations of CRP, Il-6, 
TNF-α, ICAM-1, E-selectin, P-selectin, 
and L-selectin were skewed, log10-
transformations were applied for setting 
purpose. Levels of these markers were 
presented as geometric means. Smoking 
exposure was defined as current or non-
current smokers (dichotomous); GST 
genotypes as 2 categories: GSTM1-1 or 
GSTT-1, if the subject had at least one 
functional allele and GSTM1-0 or 
GSTT1-0, if the subject had no functional 
allele (homozygous null genotype). 
Differences in biological indices between 
non-smokers and smokers were assessed 
by using 1-way analysis of variance after 
adjustment for age and sex. Two-way 
analysis of variance with interaction was 
used to determine whether the influence of 
smoking on biological analytes varied 
between genotype groups. The 
significance of difference between 
genotype groups in non-smokers and in 
smokers was assessed by ANOVA 
accounting for age and sex. GSTM1-
GSTM1-smoking interactions were not 
investigated because of too few subjects in 
these groups. In all analyses, as we did not 
adjust for multiple testing, p≤0.05 was 
considered as statistically significant; 
moreover, p≤ 0.10 was indicated and 
discussed. 
 
RESULTS 
 
Characteristics of non-smokers and 
smokers are shown in table 1. Subjects 
were of middle age; smokers were 
significantly younger and included more 
males than non-smokers (p≤0.05). 
Accounting for age and sex, smokers had 
a significantly increased WBC count, Il-6 
and ICAM-1 concentrations than non-
smokers (p≤0.001, p≤0.10 (borderline 
significant) and p≤0.001, respectively). 
Levels of albumin, CRP, TNF-α, L-
selectin, E-selectin and P-selectin, and 
 61 
genotype frequencies for GSTM1 and 
GSTT1 did not significantly differ 
between smokers and non-smokers. 
 
 
Table 1: Clinical characteristics of the 265 subjects 
 
 Non-smokers n = 189 
Smokers 
n = 76 
Age (years) 38.3 ±11.11 34.6 ± 12.0* 
Male sex (%) 46.6 63.2* 
Tobacco (cig/day) 0.0 ± 0.0 13.8 ± 9.7*** 
White blood cells (103/mm3) 6.75 ± 1.46 7.80 ± 2.34*** 
Albumin (g/l) 46.9 ± 2.5 47.1 ± 2.4 
CRP (mg/l) 0.81 (0.26-2.49)2 0.88 (0.30-2.59) 
IL-6 (ng/l) 0.85 (0.38-1.93) 1.05 (0.40-2.72)° 
TNF-α (ng/l) 1.23 (0.42-3.59) 1.06 (0.36-3.14) 
L-selectin (mg/l) 1011 (717-1426) 1030 (747-1421) 
E-selectin (mg/l) 44.5 (23.9-82.4) 48.9 (26.6-89.7) 
P-selectin (mg/l) 125 (89-174) 132 (94-186) 
ICAM-1 (mg/l) 244 (197-303) 278 (204-379)*** 
GSTT1-0 (%) 20.1 25.0 
GSTM1-0 (%) 52.4 42.9 
1 Arithmetic means adjusted for age and sex ± SD 
2 Geometric means adjusted for age and sex (range of 1 SD) 
° p≤0.10, * p≤0.05, *** p≤0.001: difference between non-smokers and smokers, ANOVA or χ2 test. 
 
 
Tables 2 and 3 show geometric means 
adjusted for age and sex for all analytes 
according to smoking status and GST 
genotypes (GSTM1 and T1, respectively); 
males and females being analyzed 
together. Using 2-factors ANOVA, 
interaction terms between GSTM1 
genotypes and smoking status were 
statistically significant for WBC count, 
and CRP, TNF-α and ICAM-1 
concentrations (all p≤0.05, Table 2). 
Accounting for age and sex, smokers with 
the GSTM1-0 null allele had a higher 
WBC count, CRP and ICAM-1 levels as 
compared to the other groups. In the 
smoker group, inter-genotype differences 
were significant for WBC count and CRP 
levels (p≤0.05) and borderline significant 
for ICAM-1 concentrations (p≤0.10). 
Conversely, in non-smokers, subjects 
lacking GSTM1 had the highest levels of 
TNF-α (p≤0.01) and P-selectin (p≤0.05) 
as compared to those with GSTM1-1 
functional allele. For the 9 analytes, 
interaction terms between GSTT1 
genotypes and smoking status were not 
significantly different by using 2-factors 
ANOVA (Table 3). In smokers, no 
significant difference was noticed between 
GSTT1-0 and GSTT1-1 alleles. 
Conversely, in non-smokers, subjects with 
GSTT1-1 functional allele had a higher Il-
6 and TNF-α levels as compared to those 
with GSTT1-0 null allele (p≤0.05 and 
p≤0.10 (borderline significant), 
respectively) (Table 3). 
 
 
 
 
 
 
 
 62 
Table 2: Biological indices of inflammation and cellular adhesion according to GSTM1 genotype 
and smoking status 
 
 Non-smokers Smokers 
 GSTM1-0 n = 99 
GSTM1-1 
n = 90 
GSTM1-0 
N = 33 
GSTM1-1 
N = 43 
Smoking / 
GSTT1 
Interaction2
WBC (103/mm3) 6.72 ± 1.641 6.77 ± 1.25 8.41 ± 2.85 7.32 ± 1.77* 0.018 
Albumin (g/l) 46.9 ± 2.6 46.9 ± 2.4 47.7 ± 2.8 46.7 ± 2.0 0.098 
CRP (mg/l) 0.76 (0.25-2.24)3 0.87 (0.27-2.77) 1.16 (0.34-4.00) 0.71 (0.28-1.74)* 0.035 
IL-6 (ng/l) 0.90 (0.40-2.04) 0.80 (0.35-1.82) 1.14 (0.44-2.95) 0.86 (0.32-2.27) 0.878 
TNF-α (ng/l) 1.55 (0.57-4.23) 0.96 (0.34-2.71)** 1.01 (0.33-3.09) 1.11 (0.35-3.58) 0.043 
ICAM1, mg/l 241 (197-296) 248 (198-312) 297 (209-422) 265 (202-347)° 0.033 
L-selectin, mg/l 990 (672-1460) 1035 (776-1379) 1094 (810-1478) 984 (705-1373) 0.097 
E-selectin, mg/l 45.2 (23.8-85.6) 44.1 (24.3-80.0) 54.9 (27.6-109.2) 45.1 (26.5-76.6) 0.284 
P-selectin, mg/l 131 (99-173) 119 (82-173)* 130 (91-186) 135 (96-189) 0.134 
1 arithmetic means adjusted for age and sex ± SD 
2 test of smoking/GSTM1 genotype interaction p values 
3 geometric means adjusted for age and sex (range of 1 SD) 
° p≤0.10, * p≤0.05 ** p≤0.01: difference between GSTM1 genotype in non smokers and smokers, ANOVA 
test on values adjusted for age and sex. 
 
 
Table 3: Biological indices of inflammation and cellular adhesion according to GSTT1 genotype 
and smoking status 
 
 Non-smokers Smokers 
 GSTT1-0 N = 38 
GSTT1-1 
N = 151 
GSTT1-0 
N = 19 
GSTT1-1 
N = 57 
Smoking / 
GSTT1 
Interaction2
WBC (103/mm3) 6.61 ± 1.441 6.78 ± 1.47 7.32 ± 1.90 7.95 ± 2.48 0.416 
Albumin (g/l) 46.4 ± 2.6 47.1 ± 2.4 46.6 ± 2.7 47.4 ± 2.3 0.877 
CRP (mg/l) 0.77 (0.32-1.85)3 0.82 (0.25-2.65) 0.82 (0.32-2.08) 0.89 (0.28-2.78) 0.948 
IL-6 (ng/l) 0.66 (0.30-1.44) 0.91 (0.40-2.07)* 0.97 (0.39-2.38) 1.07 (0.41-2.85) 0.413 
TNF-α (ng/l) 0.94 (0.35-2.52) 1.32 (0.45-3.89)° 1.03 (0.32-3.26) 1.07 (0.37-3.12) 0.383 
ICAM1 (mg/l) 1003 (675-1491) 1013 (728-1409) 1051 (702-1572) 1024 (765-1369) 0.744 
L-selectin (mg/l) 44.2 (25.1-77.8) 44.7 (23.8-84.2) 51.5 (26.1-101.2) 48.3 (26.8-87.0) 0.697 
E-selectin (mg/l) 123 (83-181) 125 (91-173) 143 (104-194) 130 (91-185) 0.289 
P-selectin (mg/l) 251 (204-309) 243 (196-302) 266 (183-385) 283 (212-378) 0.243 
1 arithmetic means adjusted for age and sex ± SD 
2 test of smoking/GSTT1 genotype interaction, p values 
3 geometric means adjusted for age and sex (range of 1 SD) 
° p≤0.10, * p≤0.05: difference between GSTT1 genotype in non smokers and smokers, ANOVA test on 
values adjusted for age and sex. 
 
DISCUSSION 
 
This study provides limited evidence that 
GSTM1 polymorphism might modify the 
effect of smoking on biological indices of 
inflammation and cellular adhesion. We 
observed that smokers lacking GSTM1 
had increased levels of CRP and ICAM-1, 
and a higher WBC count compared to 
smokers with GSTM1-1 functional allele 
and non-smokers. A significant interaction 
was found between smoking and GSTM1 
genotypes for WBC, CRP, TNF-α and 
ICAM-1 concentrations, suggesting that: 
1- the effects of smoking and GSTM1 
genotypes were dependant and 2- the 
changing levels of inflammatory proteins 
were closely linked to smoking. Cigarette 
 63 
smoking is a major risk factor for 
developing coronary artery disease, 
causing not only an endothelial 
dysfunction but also enhancing ROS 
production (Cai et al., 2000). 
Consequently, increased levels of 
inflammatory markers such as cytokines 
and cell adhesion molecules were 
currently found in smokers (Butkiewicz et 
al., 2000; Cai et al., 2000; Saadeddin et 
al., 2002; Zhang et al., 2002). Patterns of 
expression of inflammatory proteins were 
intimately related to generalized 
inflammatory response of vascular cells to 
smoking. 
 
One interesting result of our study was the 
GSTM1 genotype differences in TNF-α 
and P-selectin levels observed in non-
smokers: subjects carrying GSTM1-1 
functional allele had lower TNF-α and P-
selectin levels compared to those with 
GSTM1-0 null allele. In order to explain 
this difference, we postulate that subjects 
with GSTM1-0 null allele were less 
protected against oxidative stress than 
those with GSTM1-1 functional allele. 
Indeed, the GSTM1 enzyme has been 
shown to protect cells against endogenous 
compounds of oxidative stress, including 
epoxides, redox-cycling products and 
carbonyls (Hayes et al., 1995; He et al., 
1998; Ketterer, 1998). Several studies 
provide evidences that DNA adducts 
levels were consistently increased in 
individuals lacking GSTM1 (Butkiewicz 
et al., 2000; Godschalk et al., 2001; Izzotti 
et al., 2001; Scarpato et al., 1997), 
suggesting that GSTM1 genotypes might 
influence the individual susceptibility to 
oxidative damages (Dusinska et al., 2001; 
Onaran et al., 2001). One of the 
consequences of oxidative stress is the 
activation of the transcriptional factor 
complex NF-κB, which leads to the 
production of inflammatory proteins as 
TNF-α and P-selectin. Expression of 
inflammatory proteins could be modulated 
by the glutathione system (Rahman et al., 
2000). 
 
The biological indices of inflammation 
and cell adhesion were slightly higher in 
smokers than in non-smokers, regardless 
of the GSTT1 genotypes. As previously 
mentioned, cigarette smoking increases 
oxidative stress which participates in 
vascular dysfunction, enhancing 
inflammatory markers expression. When 
stratified by GSTT1 genotypes, no 
difference in inflammatory proteins levels 
was found between smokers. Surprisingly, 
we observed a significant association 
between GSTT1 genotypes and pro-
inflammatory cytokines in non-smokers. 
Subjects with the GSTT1-1 functional 
allele had higher levels of Il-6 and TNF-α 
than those carrying the GSTT1-0 null 
allele, suggesting that the non-expression 
of GSTT1 was associated with an 
attenuated inflammatory profile. No 
interaction was found between GSTT1 
genotypes and smoking, suggesting that 
the GSTT1-genotype differencies 
observed in non-smokers would be closely 
related to biological effect of GSTT1. A 
recent case-control study showed that 
individuals expressing GSTT1 have an 
elevated risk of smoking- and 
occupational-related diseases (Buzio et al., 
2003; de Waart et al., 2001; Li et al., 
2000; Li et al., 2001; Masetti et al., 2003; 
Olshan et al., 2003). This finding was 
consistent with the hypothesis that GSTT1 
catalyses the bioactivation of some 
substrates to cyto- and geno-toxic 
metabolites, in combination with ROS 
production (Hayes et al., 1995; Ketterer, 
1998; Thier et al., 1993). Therefore it was 
not surprising to find the increasing levels 
of IL-6 and TNF-α in subjects expressing 
GSTT1, which might reflect the 
compensatory biological responses of 
vascular cells to deleterious effects of 
oxidative stress. 
 
 64 
As mentioned by Miller et al. (Miller et 
al., 2003), some limiting factors could 
influence the results of this study and their 
interpretation such as cross-sectional 
design, diet, infection, pre-analytical and 
analytical factors, and unidentified 
confounding factors. Given the multitude 
of biological variables studied, these 
associations found between GST 
genotypes and inflammatory proteins may 
have been obtained by chance. However, 
we did not use the Bonferroni 
adjustments, and we decided simply to 
describe the data found and to discuss 
possible interpretations (Perneger, 1998). 
Despite the small size of our sample 
population, our results were in agreement 
with those found by Miller et al. (Miller et 
al., 2003) and provided evidence that 
GSTM1 and GSTT1 influence markers of 
inflammation and endothelial function in 
combination with smoking. We also 
examined the effects of smoking and 
combined genotypes (i.e GSTM1-
0/GSTT1-0 and GSTM1-0/GSTT1-1) on 
biological indices of inflammation and 
cell adhesion. However, these analyses 
and their interpretation are limited by two 
factors: 1- the power of study was 
severely decreased due to small number of 
individuals with both “at risk” genotypes 
and 2- GSTM1 and GSTT1 genotypes 
exhibit different patterns of interaction 
based on this study and the earlier studies 
of Li R team (Li et al., 2000; Li et al., 
2001; Miller et al., 2003; Olshan et al., 
2003). Therefore, the most appropriate 
and logical way to combine genotypes is 
not obvious. 
 
Recent epidemiological studies provide 
evidences that GSTM1 and GSTT1 
genotypes were associated with an 
increased risk of smoking-related 
diseases, including coronary artery 
disease, lower extremity arterial disease 
and carotid artery intimal-medial 
thickness (de Waart et al., 2001; Li et al., 
2000; Li et al., 2001; Masetti et al., 2003; 
Olshan et al., 2003). Indeed, Li R et al. (Li 
et al., 2000; Li et al., 2001) were the first 
studies providing evidences that risk of 
coronary artery diseases outcome is 
increased in lifelong smokers with 
GSTM1-0 null allele and GSTT1-1 
functional allele. De Waart et al. (de 
Waart et al., 2001) reported that the 2-year 
progression of common carotid intima-
media thickness was clearly more 
increased in lifelong smokers with 
GSTM1-0 null allele. Moreover, studies 
(Butkiewicz et al., 2000; Dusinska et al., 
2001; Godschalk et al., 2001; Izzotti et al., 
2001; Onaran et al., 2001; Scarpato et al., 
1997; Thier et al., 1993) showed that 
oxidative damages and DNA alterations in 
atherosclerotic lesions were increased in 
individuals with the GSTM1-0 null allele 
and GSTT1-1 functional allele, suggesting 
that smokers with these GST genotypes 
develop progression of atherosclerosis at 
an increased rate. 
 
Data from epidemiological studies suggest 
a protective role of GSTT1-0 allele and 
deleterious effect of GSTT1-1 functional 
allele, and were in line with our findings. 
Thus, GST genetic polymorphisms could 
modulate the individual susceptibility to 
develop the inflammatory-related diseases 
such as cardiovascular diseases. 
Consequently, individual differences in 
GST genetic polymorphisms and 
expression could be of considerable 
importance for inflammatory processes. 
This opens an interesting research field 
concerning the relevance of GST 
polymorphisms for inflammation. 
 
Acknowledgements: We are indebted to 
the participants of the Stanislas cohort 
who made this study possible, and to the 
medical staff and the laboratory of the 
Centre de Médecine Préventive of 
Vandoeuvre-lès-Nancy, France. We 
particularly are grateful to Catherine Sass 
 65 
for her implication in the management of 
the Stanislas Cohort, to Monique Balland, 
Monique Vincent-Viry, Dominique 
Aguillon, Maryvonne Chaussard, Colette 
Voirin, and Chantal Lafaurie for data 
collections, laboratory analyzes and 
Biobank management, to Michéle Pfister 
and Suzanne Droesch for analyte 
measurements, and to the staff of the 
IfADo institute (Institut für 
Arbeitsphysiologie an der Universität 
Dortmund) for genotype determination 
and technical assistance, particularly 
Brigitte Kullik, Doris Dannappel and 
Silke Hankinson. We also thank Joseph 
Henny for his technical assistance in 
management of the Biobank of the Centre 
de Médecine Préventive. The Stanislas 
cohort study is supported by the Caisse 
Nationale d’Assurance Maladie des 
Travailleurs Salariés (CNAM), the Institut 
National de Santé et de Recherche 
Médicale (INSERM), the Région 
Lorraine, the Communauté Urbaine de 
Nancy, the Université Henri Poincaré 
Nancy I, Bayer-Pharma, Hoffmann-
LaRoche, Beckmann-Coulter, 
Biomérieux, Daiichi Pure Chemicals, 
Randox, Dade-Behring. Travel funds were 
made available from the Deutsche 
Forschungsgemeinschaft through SFB 475 
of the University of Dortmund. 
 
 
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