doi: 10.1161/hq0701.092146
© 2001 American Heart Association, Inc.
Atherosclerosis and Lipoproteins |
Genetic Contributions to Plasma Total Antioxidant Activity
From the Department of Genetics, Southwest Foundation for Biomedical Research, San Antonio, Tex, and the Department of Medicine (B.D.M.), University of Maryland, Baltimore.
Correspondence to Dr X.L. Wang, Department of Genetics, Southwest Foundation for Biomedical Research, 7620 NW Loop 410, San Antonio, TX 78227-5301. E-mail xwang{at}darwin.sfbr.org
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Abstract |
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Abstract—Oxidative stress plays important roles in a wide spectrum of pathological processes, such as atherosclerosis. Although several environmental factors are documented to influence redox metabolism, relatively little is known about genetic effects. In the present study, we evaluated genetic contributions to variation in plasma total antioxidant status (TAS), a measure of peroxyl-scavenging capacity, in 1337 members of 40 Mexican American families. TAS levels were significantly lower in women than in men (1.675±0.004 versus 1.805±0.005 mmol/L, respectively; P<0.001), and there was a significant decline of TAS levels with age in men but not in women (P<0.01 for the interaction). Quantitative genetic analysis indicated the heritability of TAS levels to be 0.509±0.052; ie,
51% of the residual variance (after
covariate adjustment) in TAS levels was due to the additive effects of
genes (P<0.001). We have
further observed a significant gene-by-smoking interaction
(P<0.05). Additive genetic
effects account for 83% of the residual phenotypic variance in TAS
levels among smokers, but they account for only 49% in nonsmokers.
However, genes contributing to TAS variation are the same in smokers
and nonsmokers. Our study for the first time demonstrates that TAS, an
indicator of redox homeostasis, is under strong genetic control,
especially among smokers. With appropriate tools, such as genome
screening, it should be possible to localize genes that regulate redox
homeostasis and, ultimately, identify the DNA sequence variants
predisposing subjects to oxidative
damage.
Key Words: antioxidants • coronary disease • genetics • statistics • smoking
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Introduction |
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All aerobic organisms, including humans, derive most of their metabolic energy from the reduction of oxygen and, consequently, are susceptible to the damaging effects of the small amounts of O2-, OH, and H2O2 that are produced during the metabolism of oxygen. These 3 species, together with unstable intermediates in the peroxidation of lipids, are referred to as reactive oxygen species (ROS). ROS can react with nearly all biological macromolecules (lipids, proteins, nucleic acids, and carbohydrates). The initial reaction generates a second radical, which can, in turn, react with a second macromolecule to continue the chain reaction. As protection against the deleterious effects of free radicals, the human body has developed an antioxidant defense system that includes enzymatic, metal-chelating, and free radical–scavenging activities. They include superoxide dismutases (SODs), catalase, glutathione peroxidase, glutathione reductase, vitamins C and E, ß-carotene, and lipoic acid.1 2
Many diseases, such as atherosclerosis, appear to result from an overbalance between radical-generating, compared with radical-scavenging, systems, a condition called oxidative stress. In addition, oxidative stress has been proposed to be a major mechanism that accelerates the aging process,3 and various antioxidants have been tested as antiaging therapies in humans and in animal models with mixed results.4 Hyperglycemia or insulin-related metabolic disturbances in diabetes can induce excessive oxidative stress in type 1 and 2 diabetes.5 6 Neurodegenerative disorders, including Alzheimer’s disease, Parkinson’s disease, corticobasal degeneration, memory losses, Pick’s disease, and various dementias, are also associated with increased oxidative stress.7 8 9 Finally, ROS are a direct cause of DNA damage, which is often the trigger for carcinogenesis.
A number of mechanisms, which are not mutually exclusive, have been proposed to explain a possible connection between oxidative stress and coronary artery disease. ROS can damage endothelial cells in many ways, either directly or indirectly.10 ROS can promote endothelial apoptosis, leading to an increased tendency toward thrombosis and endothelial dysfunction.11 They can also increase endothelial permeability and thereby accelerate the accumulation of atherogenic factors, such as LDL, in the subendothelial cell space. They stimulate endothelial cell production of many adhesion molecules, so the vascular wall becomes prothrombotic and proatherogenic.12 Oxidative stress is also associated with stimulation of vascular smooth muscle cell apoptosis and necrosis and contributes to the formation of the necrotic core, a hallmark of an advanced unstable lesion. Therefore, ROS have been implicated to play a strong role in atherogenesis. However, clinical trials using antioxidants, such as vitamins C and E and ß-carotene, have produced conflicting results.13 14 This may reflect complex balanced chain reactions of oxidants and antioxidants in the redox system. It may also reflect a lack of appreciation of the specificity of oxidative stress and responses to antioxidants and the importance of the subcellular environment.
Although dietary factors play significant roles in ROS
production, genetic factors may also contribute to its
bioregulation. Liao et al15
have reported evidence of a common genetic pathway mediating oxidative
stress–induced inflammatory gene expression, and they have suggested a
major contributing gene. A G
A substitution at the fifth position of
intron 4 of the catalase gene was found to be responsible for
acatalasia in Japanese
individuals.16 Heterozygotes
have an intermediate level of catalase in the blood, and patients are
more likely to have infective diseases. Mutations occurring at the SOD1
gene are associated with reduced SOD activities and amyotrophic
lateral sclerosis.17 A DNA
variant at the SOD3 gene is associated with a reduced affinity for
heparin, which may compromise the ability of SOD3 to bind to the
vascular wall, thereby reducing its antioxidant
capacity.18 Moreover, we
have reported significant genetic contributions to the variance of
common SOD3 phenotypic
traits.19 However, these
genetic contributions are mostly confined to the known candidate
enzymes. The frequencies of these mutations are too rare to account for
population variances in ROS-related common diseases, such as
atherosclerosis.
We have begun a systematic study of the genetic control of oxidative stress, focusing on Mexican Americans in San Antonio, Tex. In the present study, we have explored genetic contributions to a global measure of total antioxidant status (TAS) in human plasma. TAS reflects the balance between oxidants and antioxidants in each system.20 Whereas the ROS-producing enzymes determine how many free radicals are produced, the antioxidant system determines whether these ROS are in excess, which classes of molecules they will oxidize, and what pathological changes they will leave behind. We hypothesize that oxidative stress and its subsequent pathological processes are under significant genetic regulation. Dissecting genetic contributions to TAS will lead us to discover potentially novel genes that contribute to both sides of the balance and that are critical to clinical or pathological outcomes.
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Methods |
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Subjects and Blood Samples
Samples were provided by participants in the San Antonio Family Heart Study (SAFHS), a study of the risk factors for cardiovascular disease in Mexican Americans.21 All first-, second-, and third-degree relatives of randomly ascertained probands were invited to participate, and the present study group was composed of 1337 individuals in 40 families. At the clinic visit, participants were subjected to an oral glucose tolerance test, and they provided information on health and lifestyle variables and a fasting blood sample. Plasma was isolated by low-speed centrifugation and was stored as single-use aliquots at -80°C, which were protected from oxidation and desiccation.22 Clinic procedures were approved by the Institutional Review Board of the University of Texas Health Science Center at San Antonio, and subjects gave written informed consent.
Covariates
Diabetes status (yes/no) was diagnosed by World
Health Organization
criteria23 or by whether the
subject was currently taking medication for diabetes. Information on
age, menopausal status, current smoking status, and medication usage
was obtained during the clinic interview.
Measurement of TAS
TAS levels reflect the overall antioxidant capacity
of the human plasma and summarize the pertinent
physiological and pathological conditions at the
time of sample collection. The assay was defined as the ability of
antioxidants in the plasma samples to prevent oxidation of
2,2'-azino-di-(3-ethylbenzthiazoline sulfonate)
(ABTS) by metmyoglobin and was quantified by
using a commercial kit (Total Antioxidant Status Assay kit,
Calbiochem) that was based on the method of
Miller et al.24 Antioxidant
ability of the sample was expressed relative to the standard
(6-hydroxy-2,5,7,8-tetramethylchroman-2-carboxylic acid) in
millimolar units. The interassay and intra-assay coefficients of
variation were 2.0% and 1.3%, respectively.
Statistical Genetic Analyses
In our analyses, we assume that variation in
TAS levels is jointly influenced by genes and the environment. We use a
variance decomposition approach, implemented in the statistical genetic
analysis software package SOLAR (Sequential Oligogenic Linkage
Analysis
Routines25 ), to estimate the
effects of genes, selected environmental covariates, and unmeasured
nongenetic factors on the variance in TAS levels. This approach,
developed according to methodology originally proposed by Hopper and
Mathews26 and Boehnke et
al,27 has been described in
detail elsewhere.25 It
models the phenotypic covariance (ie, 
) as the sum (
) of
the covariance due to the additive effects of genes (ie,
2
G2,
where is the matrix of kinship coefficients between all relative
pairs in a pedigree and
G2 is the
additive genetic variance) and the environment (ie,
IE2, where
I is an identity matrix with all diagonal elements equal to 1.0 and
off-diagonal elements equal to 0 and
E2 is the
variance due to unmeasured environmental factors) and allows us to
partition the phenotypic variance in TAS levels
(P2, where
subscript indicates phenotype) into components corresponding to
the additive genetic effects
(G2) and
nongenetic (ie, environmental) effects
(E2). Like
the components of the covariance, these variance components are
additive, such that
P2=G2+E2,
and we estimate the heritability (h2), or
proportion of the phenotypic variance attributable to additive genetic
effects, as
h2=G2/P2
and the proportion attributable to nongenetic factors as
e2=1-h2.
We conducted initial statistical genetic analyses to detect and measure the effects of genes and nongenetic factors on the variance in TAS levels. To accomplish this, we simultaneously estimated the values of the phenotypic mean±SD and h2, as well as the mean effects of age and sex terms. We also screened a number of selected environmental covariates for inclusion in this model. These included alcoholic beverage ingestion, diabetes, antidiabetic medication use, lipid-lowering medication use, hormone replacement therapy, oral contraceptive use, menopausal status, and cigarette smoking.
Multivariate extensions to the basic
quantitative genetic analyses used to detect and measure the
effects of genes and significant environmental covariates were used to
test also for the effects of genotype-by-covariate interactions
on variation in the TAS
levels.28 29 In
the absence of genotype-by-environment interaction (ie, the
null hypothesis), the genetic correlation between relatives for a trait
measured under 2 environments should be
G(1,2)=1.0, and the genetic variances in the
2 environments should be equal (ie,
G12=G22).
Rejection of the former hypothesis (ie,
G(1,2)
1.0) would imply that a different
gene or suite of genes is contributing to the variance in the TAS
levels in both environments, whereas rejection of the latter (ie,
G12G22)
would imply that the magnitude of the genetic effect is different in
the 2 environments.
Parameter estimates and their standard errors
are obtained by numerical maximization of the likelihood of models on
data from relatives in the SAFHS pedigrees. Statistical significance of
each parameter is assessed by means of likelihood ratio
tests,30 in which the
likelihood of a general unrestricted model wherein the values for all
parameters are estimated is compared with the likelihood of
alternate restricted models (hypotheses) in which the values for
parameters of interest are constrained to some
predetermined value (eg, 0 or 1). Obtaining degrees of freedom for
specific tests is explained in detail
elsewhere.25 26
Although P
0.10 served as the
covariate inclusion criterion during the initial screen,
P0.05 was required to declare
a genetic or genotype-by-environment effect to be
significant.
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Results |
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Effects of Age and Sex on TAS
The TAS levels in the total population appeared to be normally distributed (Figure 1
), and the mean±SEM value of TAS in this Mexican
American population was 1.728±0.004 mmol/L.
Table 1 gives some of the characteristics of the population
and indicates that TAS levels were higher in men than in women
(1.805±0.005 versus 1.675±0.004 mmol/L, respectively;
P<0.001). In addition, there
were significant effects of age in men. In men, mean TAS levels
decreased with increasing age, whereas TAS levels remained relatively
constant across age groups in women
(Figure 2). A formal test for statistical interaction
revealed that the effect of age on TAS levels was significantly greater
in men than in women
(P<0.01).
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Quantitative Genetic Analysis of TAS
Levels
Table 2 gives the results of quantitative genetic
analyses, which included several covariates (diabetes status,
diabetes medications, contraceptive hormone use, menopausal status, and
smoking status), in addition to age and sex. Normal quantitative
variation in TAS levels was moderately heritable in the SAFHS pedigrees
(Table 2). Seven variables, including sex, age,
age2, age-by-sex interaction, menopausal
status, oral contraceptive use, and smoking were identified by
our initial screening procedures as likely covariates of TAS level in
these subjects (P0.10).
Together, these covariates accounted for 25.8% of the total
phenotypic variance in TAS levels. Of the remaining 74.2%, the
additive effects of genes accounted for 50.9% (37.8% of the total
phenotypic variance), and 49.1% (36.4% of the total variance) was due
to random environmental (ie, unmeasured nonadditive genetic)
effects.
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Effects of Diabetes on TAS
Diabetes medication usage, but not diabetes status, was
a significant predictor of TAS variation. We hypothesized that diabetes
medication usage might be a surrogate indicator of diabetes severity
and that 2 other indicators of diabetes severity (diabetes duration and
fasting glucose levels) would be correlated with TAS levels in the
diabetic subjects (n=195). However, neither diabetes duration nor
fasting glucose level was a significant predictor of TAS variation in
diabetic patients (P=1 and
P=0.69, respectively) nor was
the fasting glucose level a significant predictor of TAS variation in
the entire population
(P=0.62).
Effects of Cigarette Smoking on TAS
Levels
The results of tests for gene-by-smoking effects and
covariate-by-smoking effects on variation in TAS levels in the SAFHS
pedigrees are summarized in
Table 3. Covariates included in
Table 3 are those satisfying the
P0.1 criterion. Likelihood
ratio tests again revealed significant effects
(P0.03) of sex, age,
age2, menopausal status, and oral
contraceptive use on variation in TAS levels. They also provided strong
evidence (P0.002) of
interactions between cigarette smoking and the following variables:
age, age-by-sex, and diabetic medication use; and they provided
tentative evidence (P=0.0672)
of a smoking-by-diabetes effect on TAS levels.
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A genotype-by-smoking effect on normal variation in TAS levels was detected also. The genetic variance in smokers was significantly greater than that in nonsmokers (P=0.0203), and the residual random environmental variance was significantly smaller (P=0.0129). Consequently, additive genetic effects accounted for a much greater proportion of residual phenotypic variance in smokers versus nonsmokers (ie, h2 was 83% and 49%, respectively).
Although the maximum likelihood estimate for the genetic
correlation between smokers and nonsmokers did not equal 1.0, it was
high (G=0.798) and not significantly
different from unity at the
P=0.05 level
(P=0.09). This suggests that
most, if not all, of the genetic effects on variation in TAS levels in
nonsmokers and smokers is due the same gene or suite of
genes.
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Discussion |
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TAS is a complex trait reflecting homeostasis of redox metabolism, which is highly relevant to nearly all pathological processes, including aging, neurodegenerative disorders, atherosclerosis, carcinogenesis, and inflammatory and immunological reactions. It is affected by the relative contributions of each antioxidant species and the stress of oxidative free radicals. The production and the consumption (by free radicals) of each specific antioxidant will affect the total antioxidant capacity. Although many environmental factors can affect the redox homeostasis, for the first time, the present study demonstrates that the TAS, an indicator of redox homeostasis, is under strong genetic control. Amid considerable environmental interferences, a genetic contribution to such a dynamic process at a level as high as 50.9% is particularly significant. Furthermore, on the basis of a dietary recall questionnaire that was designed to capture at least 85% of total macronutrient intake in this population, we found no significant effect on TAS levels for any of the dietary components tested (including percentage of calories as protein, carbohydrate, fat, and alcohol; data not shown). Although much attention has been given to environmental and dietary effects, the present results highlight the importance of investigating the genetic mechanisms responsible for variation in TAS.
There are several published methods for analyzing the total
antioxidant capacity as reviewed by Alho and
Leinonen.20 Apart from
ABTS-based analysis used in the
present study, total peroxyl radical–trapping potential is another
commonly used method.20
These assays are based on slightly different chemical reactions and are
possibly differentially affected by the various biologically important
antioxidants.20 However,
most methods, such as total peroxyl radical–trapping potential or TAS,
measure peroxyl-scavenging capacity, and a number of relevant
antioxidants, including sulfhydryl groups (mostly albumin),
urate, ascorbate, 
-tocopherol, and bilirubin, are
important contributors to this
capacity.24 Although the
presence of these antioxidants in plasma will not stop macromolecule
peroxidation, such as LDL oxidation in
plasma,31 32 33
the measure of TAS does represent a continuous spectrum in the
function of antioxidants and free radicals in plasma. It describes
residual antioxidant capacity after the consumption of free radicals in
plasma. For a given level of antioxidants in plasma, increased
production of ROS will result in a reduced TAS level. On the
other hand, increased antioxidant availability in plasma will enhance
TAS levels for a given ROS amount. Thus, TAS is a continuous measure of
oxidative stress in plasma that is relevant to oxidation-induced
pathological processes. It is further estimated that 25% to 35% of
the total antioxidant capacity is provided by uncharacterized
antioxidants. However, the relative contribution of each type of
antioxidant to overall antioxidant capacity and to each specific ROS is
unknown and may vary from subject to subject and from assay to assay.
Thus, dissecting genes contributing to TAS could lead us to discover
novel antioxidants that are biologically relevant.
It should also be noted that the measurement of neither total antioxidant capacity nor individual antioxidants has produced consistent results in the prediction of various diseases.13 14 34 Each measure of antioxidant status has its own limitations, including that used in the present study.20 35 36 Such limitations have severely restricted the clinical usage of oxidative stress–related markers. One of the limitations common to all methods is that we are measuring circulating plasma or serum antioxidant capacity and that this may not accurately reflect intracellular antioxidants in target areas, such as endothelial cells or vascular smooth muscle cells, where most atherogenic processes occur. It is known that intracellular and extracellular antioxidant pools are not fully exchangeable. For example, whereas Cu/Zn-SOD (SOD1) and Mn-SOD (SOD2) and are intracellular superoxide-scavenging enzymes, extracellular SOD (SOD3) is responsible for extracellular superoxide dismutation. Whereas oxidative stress occurs intracellularly, it also occurs extracellularly, such as in plasma with pathological relevance. The oxidatively modified lipoproteins, such as oxidized LDL in plasma, do cause endothelial dysfunction and, hence, atherogenesis.31 32 33 37 Therefore, antioxidants in plasma will have critical roles in scavenging these free radicals before they insult the vascular wall. Measurements of total antioxidant capacity in plasma will have significant implications for the pathological development and clinical manifestations of atherosclerotic lesions. Although intracellular and extracellular antioxidant pools are diversified and although separate studies are needed to dissect genetic contributions to intracellular antioxidant capacity, understanding the genetic regulation of extracellular antioxidant capacity will provide important clues to the intracellular antioxidant system. This may lead to the identification of gene variants that regulate intracellular antioxidation and that might be used as markers of the intracellular redox status. Given the potential inaccessibility to intracellular assessment of vascular wall antioxidant capacity, using functional genetic variants as a marker is an attractive alternative measure.
The observed significant age-sex interactions with the TAS are intriguing. There was a significant age-related decline in male subjects, whereas women showed relative age stability, except for a trend of increase from the mid 40– to mid 70–year range. Furthermore, men also had much higher TAS than did women at most age groups. Similar findings were also reported in a healthy Finnish population20 and a Chinese population.38 Although we can understand that the age-related decline in the male population represents a reduced antioxidant capacity with aging, it is somewhat difficult to explain why TAS levels are lower in females than in males. It is widely accepted that premenopausal females have a lower risk of atherosclerosis39 and that estrogens may also have antioxidant activities.40 Our findings have shown not only that oral contraceptives are associated with lower TAS levels but also that females have lower, rather than higher, total antioxidant capacity. Sex differences in TAS levels appear to be opposite the differences in the risk of coronary artery disease, raising an interesting question of whether females might be more susceptible to oxidative stress–related disorders. This is obviously a complex question with many confounding factors to affect the final outcome. Sex-specific differences in each of the contributing antioxidants could hold the key to the answer. On the other hand, the high TAS level in males could paradoxically reflect a high oxidative stress level in male subjects that has stimulated the compensatory upregulation of antioxidants.
The substantially higher additive genetic effect on residual
TAS phenotypic variance in smokers is not expected. Cigarette smoking
is a known environmental factor with a rich source of free radicals,
which can reduce antioxidant
capacity.41 42 43
We would expect cigarette smoking to have a strong effect on TAS, which
might disguise rather than enhance genetic contributions. However,
because plasma antioxidants are directly stressed by cigarette
smoke–derived free radicals, cigarette smoking could have triggered
biological antioxidant responses that are largely under genetic
control. It could also be possible that cigarette smoking has created a
more homogeneous environment so that the genetic
contribution becomes more readily detectable by the statistical model.
Indeed, the relationships between DNA sequence polymorphisms and
some phenotypic changes, such as hemostatic proteins, were strengthened
in
smokers.14 44 45
This is a novel finding, and clearly, more studies on tobacco-gene
interactions are needed to provide mechanistic information for such
regulations. A potential limitation of this finding is that it is
confounded by sex, which is also a strong effector of TAS
(Table 2
). Sixty percent of the 317 smokers were men,
whereas men represented only 41% of the total study
group.
Decreased antioxidant activity has previously been reported in some diabetic populations. The association between decreased TAS levels and the use of antidiabetic medications, although not diabetes itself, in the present study, may indicate a threshold effect to the extent that medication use reflects a subset of diabetics who have the largest cumulative exposure to hyperglycemia. However, 2 other surrogate indicators of diabetes severity (fasting glucose and diabetes duration) were not associated with TAS.
In summary, for the first time, we have documented that the additive effects of genes explain >50% of the phenotypic variance in plasma total antioxidant capacity. There is a significant gene-by-smoking effect, in which additive effects of genes explain as much as 83% of phenotypic variance in TAS levels in smokers, whereas the genetic contributions are significantly reduced to only 49% in nonsmokers. Further identification of the individual genes and, ultimately, the sequence variations responsible may provide insight in understanding the vastly inconsistent results from clinical trials using various antioxidants to prevent or treat "presumed oxidative stress."
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Acknowledgments |
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The study was supported by National Institutes of Health grant HL-45522.
Received February 5, 2001; accepted April 27, 2001.
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