TOXICOLOGICAL SCIENCES, 144(2), 2015, 238–245
doi: 10.1093/toxsci/kfu311
Advance Access Publication Date: January 1, 2015
Modifying Effect of COMT Gene Polymorphism and a
Predictive Role for Proteomics Analysis in Children’s
Intelligence in Endemic Fluorosis Area in Tianjin,
China
Shun Zhang*,1, Xiaofei Zhang*,1, Hongliang Liu†, Weidong Qu‡,
Zhizhong Guan§, Qiang Zeng†, Chunyang Jiang*, Hui Gao*, Cheng Zhang*,
Rongrong Lei*, Tao Xia*, Zhenglun Wang*, Lu Yang*, Yihu Chen*, Xue Wu*,
Yushan Cui†, Linyu Yu†, and Aiguo Wang*,2
*Department of Environmental Health and MOE Key Lab of Environment and Health, School of Public Health,
Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, Hubei, †Tianjin
Center for Disease Control and Prevention, Tianjin 300011, Tianjin, ‡Department of Environmental Health and
MOE Key Lab of Public Health and Safety, School of Public Health, Fudan University, Shanghai 200032,
Shanghai and §Department of Pathology, Guiyang Medical College, Guiyang 550004, Guizhou, People’s
Republic of China
1
These authors contributed equally to this study.
2
To whom correspondence should be addressed at Department of Environmental Health and MOE Key Lab of Environment and Health, School of Public
Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Road, Wuhan 430030, Hubei, China. Fax: þ86 27 83692986.
E-mail: [email protected].
The authors certify that all research involving human subjects was done under full compliance with all government policies and the Helsinki
Declaration
ABSTRACT
Cumulative fluoride exposure has adverse influences on children’s intelligence quotient (IQ). In addition, catechol-Omethyltransferase (COMT) gene Val158Met polymorphism (rs4680) is associated with cognitive performance. This study
aimed to evaluate the associations of COMT polymorphism and alterations of protein profiles with children’s intelligence in
endemic fluorosis area. We recruited 180 schoolchildren (10–12 years old) from high fluoride exposure (1.40 mg/l) and
control areas (0.63 mg/l) in Tianjin City, China. The children’s IQ, fluoride contents in drinking water (W-F), serum (S-F), and
urine (U-F); serum thyroid hormone levels, COMT Val158Met polymorphism, and plasma proteomic profiling were
determined. Significant high levels of W-F, S-F, U-F, along with poor IQ scores were observed in the high fluoride exposure
group compared with those in control (all P < 0.05). S-F and U-F were inversely related with IQ (rs ¼ 0.47, P < 0.01; rs ¼ 0.45,
P ¼ 0.002). Importantly, higher fluoride exposure was associated with steeper cognitive decline among children with the
reference allele Val compared with those homozygous or heterozygous for the variant allele Met (95% CI, 16.80 to 2.55;
P interaction < 0.01). Additionally, 5 up-regulated protein spots related to cell immunity and metabolism were detected in
children with high fluoride exposure compared with the control. In conclusion, fluoride exposure was adversely associated
with children’s intelligence, whereas the COMT polymorphism may increase the susceptibility to the deficits in IQ due to
C The Author 2015. Published by Oxford University Press on behalf of the Society of Toxicology.
V
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ZHANG ET AL.
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fluoride exposure. Moreover, the proteomic analysis can provide certain basis for identifying the early biological markers
of fluorosis among children.
Key words: children’s IQ; COMT polymorphism; fluoride; plasma proteomic
Fluoride is widely dispersed in nature and is known for both
beneficial and detrimental effects on health. The primary adverse effects of chronic, excess intake of fluoride in drinking water are dental and skeletal fluorosis. Impairments in intellectual
function are also one of the hallmark effects, even at low levels
(Ozsvath, 2009), which is further demonstrated recently by a
systematic review and meta-analysis with regard to developmental fluoride neurotoxicity based on numerous epidemiologic studies (Choi et al., 2012). Moreover, the evidence of
fluoride neurotoxicity is also supported by animal experiments,
which showed that fluoride can penetrate the blood-brain
barrier and significantly influence the learning and memory in
laboratory animals and their offsprings (Gui et al., 2010; Liu et al.,
2010).
The reasons for cognitive deficits caused by excessive intake
of fluoride from drinking water are still poorly understood.
However, there is evidence that it may involve the alterations of
neurotransmitters metabolism in the central nervous system
(CNS). It is reported that fluoride is capable of changing the levels of dopamine, norepinephrine, epinephrine, serotonin,
homovanillic acid, and 5-hydroxyindoleacetic acid in the hippocampus and neocortex regions of the rat brain (Chirumari and
Reddy, 2007; Flora et al., 2009; Kaur et al., 2009; Reddy et al., 2014).
Of particular concern is dopamine, since it plays a prominent
role in modulating processes within the prefrontal cortex (PFC),
which is the central system of human cognitive activity and is
directly associated with human intelligence (Seamans and
Yang, 2004; Sternberg, 2000).
Availability of dopamine is regulated by 2 crucial proteins:
dopamine transporter and catechol-O-methyltransferase
(COMT), controlling reuptake and degradation, respectively. In
the PFC, COMT enzymatic activity is thought to be particularly
important for determining dopamine availability, due to the relative absence of dopamine reuptake transporter (Yavich et al.,
2007). The human COMT gene on chromosome 22q11.2 contains
a functional Val158Met polymorphism (rs4680), causing a valine
(Val) to methionine (Met) substitution at codon 158 that affects
the activity of the enzyme (Lachman et al., 1996). As a result, the
variant allele Met has one-fourth the enzymatic activity of the
reference allele Val, resulting in slower degradation and greater
availability of dopamine within the brain, especially in the PFC
(Chen et al., 2004; Lachman et al., 1996). As expected, numerous
studies have shown that individuals with Val allele performed
worse on neurocognitive tests than Met carriers due to lesser
dopamine available (Barnett et al., 2007). Fluoride has been
found to decrease the level of dopamine in brain (Flora et al.,
2009; Kaur et al., 2009; Reddy et al., 2014). Nonetheless, whether
children with Val allele are more susceptible to the cognitive
deficits following fluoride exposure is unclear. We sought to
test whether the Val158Met polymorphism is a modifier of fluoride exposure on intellectual impairments.
To date, a critical gap in the field of fluoride exposure-associated cognitive decline research is the identification of molecules
that will provide valuable insights into mechanisms of fluoride
neurotoxicity. Although such indicators can contribute to diagnostic, prognostic, and therapeutic determinations, the complexity of the nervous system and its distinctive peculiarities
have hindered their development (Gil and Pla, 2001). In this regard, emerging technologies such as proteomics have acquired
great interest in terms of its applications and may help to identify proteins involved in the formation of cognitive impairment.
Proteomic analysis has previously been applied to kidney and
brain of rats exposed to high fluoride. More importantly, it has
identified several proteins mainly related with cellular signaling, cell proliferation, metabolism, and oxidative stress, which
provide a valuable clue to explore the mechanism of fluorosis
(Ge et al., 2011; Xu et al., 2005). However, it has not been reported
for proteomic analysis in human plasma.
In the present study, therefore, we focused on associations
of COMT gene polymorphism and alterations of protein profiles
with children’s intelligence in endemic fluorosis area, with an
attempt to discover the modification of cognitive performance
by COMT gene polymorphism and to find the early biomarkers
of fluorosis in children.
MATERIALS AND METHODS
Study population. Endemic fluorosis is very serious in Jinnan
District, Tianjin City, China, fluoride concentration in drinking
water in this region is higher than the upper limit of 1 mg/l prescribed in Chinese Standards for Drinking Water Quality (GB
5749-2006) for a long period. In accordance with the principles
of matching social and natural factors such as educational
standard, economic situation, geological environments as much
as possible, we selected 2 areas with different fluoride concentrations in the groundwater by a stratified cluster randomly
sampling of this region. Taking the results of a pilot study into
consideration, we calculated the sample size for the present
study to achieve a 5% level of significance and 80% power. A
cross-sectional design was applied in the current study to
obtain a total of 180 children aged from 10 to 12 years among
the fifth grade in 2 primary schools: Gegu Second Primary
School (from an endemic fluorosis area, drinking water fluoride
>1 mg/l) and Shuanggang Experimental Primary School (from a
non-endemic fluorosis area, drinking water fluoride <1 mg/l),
located 18 km apart in Jinnan District, based on the geological
survey report of the Government of China. Each extended
household has one or more tube wells and members of
extended families rely on well water as their main source of
drinking water. None of these 2 sites was exposed to potential
neurotoxins that are recognized as contaminates affecting intelligence quotient (IQ) value, like arsenic in drinking water, nor
delimitated into endemic areas of iodine deficiency disorders.
All subjects were unrelated ethnic Han Chinese and residents in
Tianjin, which had similar physical and mental health status.
We excluded known neurological conditions including pervasive developmental disorders and epilepsy. Information about
age and other personal data (educational level of parents and
knowledge of fluorosis) were collected from self-completed,
structured questionnaire at the time of the check-up. The data
were collected in 2011.
The study protocols were approved by 2 collaborative institutions, namely the ethics review boards of Tongji Medical
College Ethics Committee and Tianjin Center for Disease
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Control and Prevention, and complied with Chinese regulations
and laws regarding research in human subjects. Written
informed consent was obtained from all parents and their
children.
Sample collection. The drinking water samples (10 ml) collected
from the tube wells of each child’s household were kept in clean
plastic bottles and analyzed within 1 week. Three fasting
venous blood samples were collected: 1 (1 ml) was drawn by
venipuncture into an anticoagulant vacuum tube and stored at
80 C for later genotyping; another 2 (2–2.5 ml) were drawn into
anticoagulant and anticoagulant-free vacuum tubes, respectively. After standing at room temperature for natural solidification for 30 min, the latter (2–2.5 ml) were centrifuged at
3000 rpm for 10 min at 4 C to isolate the plasma (for proteomic
analysis) and serum (for fluoride content and serum thyroid
hormones determination), respectively. Urine samples were collected in the early morning before breakfast and kept in 20 C
until used for analysis.
Fluoride concentrations in drinking water, serum, and urine. Fluoride
contents in drinking water (W-F), serum (S-F), and urine (U-F)
were measured using an ion analyzer EA940 with a fluoride
ion selective electrode (Shanghai constant magnetic electronic
technology Co, Ltd, China) according to the China standard
GB 7484-87. All reference solutions for the fluoride determinations were double-deionized water. Parallel samples were
set for determination and averages were taken. The
quantitation limits of this method for W-F, S-F, and U-F were
0.2, 0.012, and 0.5 mg/l, respectively. Recovery rate of this
method was in the range of 94.3%–106.4%. The intra- and interassay coefficients of variation for fluoride were 2.7% and 6.7%,
respectively.
Genotyping of COMT. The genomic DNA was isolated via commercial DNA Extraction Kit (Tiangen Biotech, Beijing, China).
Genotypes were examined by using polymerase chain reactionrestriction fragment length polymorphism methods as
previously described (Lee and Kim, 2011). To ensure laboratory
quality control, 2 independent readers interpreted the gel photographs and 10% of the samples were selected randomly and
genotyped again, with identical results.
Plasma proteomic. Plasma samples from 10 children of each area
(high fluoride and control) were collected for complete proteomic analysis. Two-dimensional electrophoresis (2-DE) was performed with the Amersham Biosciences IPGphor IEF system
and Ettan DALT Six electrophoresis unit (GE Healthcare
Amersham Biosciences, Little Chalfont, UK). Spot detection,
quantification, and image matching were analyzed by using the
Image Master 2D-Platinum software version 5.0 (GE Healthcare
Amersham Biosciences). Protein spots of interest were analyzed
on an ABI 4800 matrix-assisted laser desorption ionization-time
of flight/time of flight Proteomics Analyzer mass spectrometer
(Applied Biosystems). A database search against NCBI was performed with MASCOT software.
Serum thyroid hormone levels. Serum levels of total
triiodothyronine (T3), thyroxine (T4), and thyroid stimulating
hormone (TSH) were determined by standard radioimmunoassay kits (North Institute of Biological Technology, Beijing,
China). The extract samples were run on radioimmunoassay
in triplicates, and results were interpolated from the
standard curve with recoveries in the range of 95.0%–110.0%.
The intra-assay coefficients of variation for T3, T4, and TSH
were 5.8%, 3.6%, and 2.7%, and the inter-assay coefficient
variations were 6.2%, 5.3%, and 4.2%, respectively.
Children’s IQ scores. A Combined Raven’s Test for Rural China
(CRT-RC) was taken to evaluate the IQ of each child (Wang,
2007). All tests were administered at school by a trained examiner who was masked to participants’ drinking water fluoride
levels. The 7 categories of this test scores are as follows: 69
retarded (low); 70–79 borderline (below average); 80–89 dull normal (low average); 90–109 normal (average); 110–119 high normal (high average); 120–129 superior (good); and 130 very
superior (excellent).
Statistical analysis. Data from both high fluoride and control
regions were analyzed separately and then combined.
Normality test (Kolmogorov-Smirnov test) was executed for all
continuous variables. Genotype frequencies were calculated
and compared against expected counts with the v2 statistic to
test adherence to the principles of Hardy-Weinberg equilibrium.
Descriptive analyses were given for characteristics of the
study children, and differences according to the status were
evaluated. After descriptive analyses, associations among
exposure markers, IQ, and risk factors were evaluated using
partial correlation coefficients with an adjustment of age and
gender.
We estimate the associations between S-F or U-F levels and
IQ scores with 2 progressive methods. First, we performed a
general linear model aimed at analyzing the difference in mean
IQ scores across different levels of fluoride. Full ranges of S-F or
U-F concentrations were included as independent variable, age
as covariate variable, and IQ scores were seen as the dependent
variable.
In addition, multivariate models with ordinary least squares
regression directed to the associations of U-F levels with IQ
scores according to COMT polymorphism were carried out. Our
first step was to create a multivariate main effects model to
estimate the effects of U-F levels and COMT genotypes (categorized as val/val [reference], val/met, or met/met) on IQ (model
1). Next we estimated effects of U-F on IQ according to genotype
(3 categories) using stratified models (model 2). We then combined the COMT allele categories into 2 categories (val/val vs
val/met and met/met combined). In Model 3, we estimated the
effects of U-F on IQ stratified across these categories, and in
Model 4 we evaluated the interaction between U-F and COMT
polymorphism. The interaction term (U-F COMT category)
was modeled first in separate model to assess joint interactions.
Covariates in the regression model were chosen as follows:
First, the risk factors associated with U-F levels or IQ scores
were identified from the literature. Second, the variable
was considered to be a potential confounder if the variables
changed the R2 and/or main effect estimate by >10%. Covariates
included the indicator variables for age, gender, educational
levels of parents (primary and below, junior high school,
senior high school, and above), and continuous variables
for drinking water fluoride (mg/l) and levels of thyroid hormones (T3, T4, and TSH). All models were adjusted for the covariates above.
For 2-DE, among the resulting protein spots, only those differentially expressed with fold changes 1.5 were chosen, and
further analyzed using Student’s t test. The a-level for all statistical tests of significance was set at 5%. Statistical analyses were
performed using SPSS for windows software version 12.0 (SPSS
Inc, Chicago, Illinois).
ZHANG ET AL.
TABLE 1. Demographic, Baseline Lifestyle,
Information by Fluoride Exposure Status
Characteristics
High Fluoride
and
Phenotypic
Control
Pe
84
96
Age (years) (means, SD)a
11.37 6 0.19
11.34 6 0.40
11.50 6 0.51
11.32 6 0.32
11.50 6 0.50
11.23 6 0.25
0.63
0.87
Boys
Girls
Genderb
Boys
34 (40.48)
40 (41.67)
Girls
Years of residencea
50 (59.52)
11.32 6 1.85
56 (58.33)
11.25 6 1.26
0.98
Water fluoride (mg/l)c
1.40 (1.23–1.57)
0.63 (0.58–0.68)
0.01*
Serum fluoride (mg/l)a
0.18 6 0.11
0.06 6 0.03
<0.01**
Urinary fluoride (mg/l)a
Education levels of parents (%)b
2.40 6 1.01
1.10 6 0.67
<0.01**
Primary and below
12 (14.28)
12 (12.50)
Junior high school
36 (42.86)
40 (41.67)
36 (42.86)
44 (45.83)
Yes
75 (89.29)
86 (89.58)
No
9 (10.71)
10 (10.42)
Senior high school and above
Having knowledge of fluorosis (%)b
Thyroid hormones
T3 (ng/ml)a
0.97
0.92
2.22 6 0.36
2.15 6 0.42
T4 (ng/ml)a
85.65 6 16.80
90.79 6 18.85
0.06
TSH (lIU/ml)c
3.11 (2.60–4.09)
2.59 (2.24–3.16)
0.03*
COMTc,d
val/val
16 (19.05)
12 (12.50)
met/val
48 (57.14)
40 (41.67)
met/met
20 (23.81)
44 (45.83)
102.33 6 13.46
24 (28.57)
109.42 6 13.30
8 (8.33)
IQ scoresa
<90 (%)
0.29
<0.01**
<0.01**
<0.01**
Data collected in 2011.
a
Data were presented as mean 6 SD for parametrically distributed data.
b
Number (percentage) for categorical data.
Median (interquartile range) for nonparametrically distributed data.
c
d
Genotype frequencies for COMT conformed to the Hardy-Weinberg equilibrium
(all P > 0.05).
e
Differences for means were tested with Student’s t test, for medians were evaluated using Mann-Whitney U test, and for percentages were evaluated using v2
test, respectively.
*Significant (P < 0.05) difference.
**Significant (P < 0.01) difference.
RESULTS
Sociodemographic and Clinical Characteristics
Descriptive data of the children and fluoride contents are listed
in Table 1. The average age of children was 11.35 years, and
approximately one-third of the subjects were boys. Children in
the 2 groups were well matched for age and gender. According
to their age and exposure duration, children on average have
been exposed since birth to the wells used by the household. SF and U-F were significantly higher in children exposed to a
higher drinking W-F concentration. Interestingly, there is a
pretty clear trend that U-F > W-F > S-F, both in the 2 groups.
In regard to the thyroid hormones, we found that TSH in
the high fluoride group was notably increased (P ¼ 0.03),
whereas T4 was somewhat decreased but did not reach
statistical significance (P ¼ 0.06). However, the individual values
of thyroid hormones were all within the normal range. For
children exposed to high fluoride drinking water, the mean IQ
value was notably decreased, whereas the percentage of poor IQ
(IQ scores fell below the normal range of 90) was markedly
increased, when compared with those of control cohorts
(both P < 0.01).
241
TABLE 2. Partial Correlation Coefficients (rs) between Exposure
Markers, IQ, and Risk Factors in the Study Subjectsa
Variables
Serum Fluoride Urinary Fluoride IQ
rs
N (%)
|
Water fluoride
0.41
Serum fluoride
/
Urinary fluoride
0.59
Years of residence
0.46
Education levels
0.08
of parents
a
P
rs
<0.01**
/
<0.01**
<0.01**
0.33
0.73
0.59
/
0.37
0.05
P
rs
P
<0.01**
0.11 0.08
<0.01** 0.47 <0.01**
/
0.45 0.002**
<0.01**
0.06 0.41
0.44
0.21 0.017*
Adjusted for age and gender, if applicable.
*Significant (P < 0.05) difference.
**Significant (P < 0.01) difference.
For the COMT gene, 15.56% of children had the val/val
genotype, 48.89% had the met/val genotype, and 35.56% had the
met/met genotype, with variant allele frequency of 0.40. The
alleles for gene polymorphism conformed to the HardyWeinberg equilibrium (control group: v2 ¼ 0.375, P > 0.05; high
fluoride group: v2 ¼ 1.777, P > 0.05; total subjects: v2 ¼ 0.062,
P > 0.05), which indicated that samples selected were
representative.
Associations among Exposure Markers, IQ, and Risk Factors
Data from both high fluoride and control regions were combined for analysis. As shown in Table 2, S-F and U-F were positively correlated, and both increased with fluoride
concentration in drinking water. Among all the children, S-F
and U-F were positively correlated with years of residence. In
addition, IQ had a negative relationship to S-F and U-F (all
P < 0.05).
Associations between Fluoride Levels and IQ Scores
To further examine the dose-response relationship of S-F or U-F
levels with IQ scores, the children were assigned by their fluoride concentrations in serum and urinary and divided into 9
groups of about 20 in each. The mean value of S-F concentration
of the lowest group was 0.003 mg/l and of the highest group was
0.333 mg/l, whereas for U-F was 0.459 mg/l and 3.290 mg/l,
respectively. For each group, the IQ difference from mean value
was plotted against fluoride concentrations in serum and urinary (Fig. 1). From the first group to the ninth, mean IQ scores
declined about 16.40 (S-F) and 14.40 (U-F) points, respectively.
Besides, mean IQ scores of the last 5 groups were below the
mean value of total IQ scores. Manifestly, there was an inverse
dose-response relationship between S-F or U-F levels and IQ
scores.
Associations between Fluoride Levels and IQ Scores According to
COMT Genotype
The adjusted multivariate main effects of model (Table 3,
Model 1) indicated an inverse association between U-F and IQ.
Adjusted estimate (95% confidence interval [CI]) of the effect of
U-F levels on IQ scores was a decrease of 2.42 (4.59, 0.24)
points for each one increase in U-F (mg/l) (P ¼ 0.030).
In the models stratified by 3-category genotype (Table 3,
Model 2), the inverse association between U-F and IQ was stronger among children who carried the reference genotype (val/val)
than among children who carried the heterozygous or homozygous variant genotypes (met/val or met/met). Also, the
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FIG. 1. The relationships between serum and urinary fluoride concentrations
and IQ differences. Inverse dose-response relationships between serum or urinary fluoride levels and IQ scores were observed in the multiple linear regression models (all P < 0.05), which include full range of fluoride concentrations as
independent variables and age as covariate variable.
magnitude and direction of the association between U-F and IQ
were similar in children with the met/val and met/met genotypes for the COMT polymorphisms.
This suggested a recessive expression of the genotype,
which we modeled by creating categories of COMT variant genotypes (met/val þ met/met; n ¼ 152) versus COMT reference genotype (val/val, n ¼ 28).
In the subpopulation carrying the COMT reference genotype
(Model 3), 1 unit increase in U-F (1 mg/l) was associated with a
decrease of 9.67 points of IQ and was significant after controlling for covariates (P ¼ 0.003). Among children carrying variant
genotypes, 1 unit increase in U-F resulted in a decrease of 1.85
IQ points, but this was not statistically significant in this stratum. We also observed a significant interaction between U-F
and COMT genotype (Model 4). From the interaction model
including the cross product term (U-F COMT homozygous
reference), 1 unit increase in U-F was associated with 2.63 IQ
points decrease (P ¼ 0.002), after controlling for other variables.
Identification of Differentially Expressed Proteins
Seven differentially expressed protein spots (B1–B7) matched on
the 2-DE were detected in high fluoride group contrasted with
those in the control (Fig. 2). Of these high expression proteins,
B1 was not identified; whereas B3 and B4 were the same protein
(Table 4). The identified up-regulated proteins can be fallen
into 2 functional categories: (1) immune-associated proteins,
including Complement C1s subcomponent precursor, alpha1-B-glycoprotein (A1GB), and immunoglobulin light chain variable region and (2) metabolism-related proteins, including
hemopexin (HPX) protein and apolipoprotein E (APOE) precursor
light chain variable region.
DISCUSSION
A strong body of evidence suggests that exposure to high levels
of fluoride in drinking water associates with deficits in children’s intelligence (Choi et al., 2012; Tang et al., 2008).
Consistently, the present work demonstrated that the IQ scores
of children exposed to high fluoride drinking water were
significantly lower than those who lived in control area, and
this decreased intelligence was accompanied by elevated levels
of fluoride in serum and urine. It is well accepted that fluoride
contents in serum and urine, as internal exposure indicators,
integrate all sources and changes in exposure, and reflect the
actual exposure levels (Ding et al., 2011; Mehta, 2013). There is
also evidence that internal concentrations are crucial to understanding toxicity (Escher and Hermens, 2004; Mehta, 2013). In
support of this, our findings further showed that, across the full
range of serum and urinary fluoride, children’s IQ decreased
gradually with the increase of fluoride contents in serum and
urine, in a dose-dependent manner, even when the external
exposure levels were not relatively high. These are in agreement with a recent study (Ding et al., 2011) showing a very close
mean value of fluoride in drinking water (1.31 6 1.05 mg/l) to our
study.
Accumulating evidence has showed that COMT Val158Met
polymorphism is a striking candidate influencing intelligencerelated neural function (Winterer and Goldman, 2003). Our
study showed that the COMT genotype was correlated to total
IQ scores before and after adjusting for age and gender. In particular, we found that children with Val/Val homozygote
showed lower IQ scores than those with Met/Met homozygote
or Met/Val heterozygote, which coincides with the findings
from previous results. A meta-analysis of 16 studies (n > 9,000)
demonstrated that COMT polymorphism was markedly and
robustly associated with IQ scores (Barnett et al., 2008). Further
evidence for a contribution of COMT polymorphism to intelligence has been validated by brain-imaging studies in humans,
pharmacological, transgenic, and gene-knockout studies in animals (Goldman et al., 2009).
The steep decrease in IQ associated with increasing urinary
fluoride among the children with the reference allele (Val) of
COMT suggests that these children may be more susceptible to
the influences of fluoride on cognition and, more importantly,
this points toward COMT polymorphism as a modifier of the
effect of cumulative fluoride exposure on cognition. In animal
studies, fluoride has been shown to alter dopamine system.
Fluoride treatment is reported to reduce dopamine level in rat
brain with increased fluoride deposition (Reddy et al., 2014).
Additionally, in a combined study of fluoride and aluminum,
chronic post-weaning fluoride exposure was associated with
decreased dopamine in cerebrum, cerebellum, and medulla
oblongata of rat brain, and this effect was more pronounced in
animals given fluoride and aluminum together (Kaur et al.,
2009). Since the reference Val allele carriers with high enzymatic activity induce faster degradation and less availability of
dopamine within the brain, it is conceivable that the children
with Val allele had a higher risk for cognitive impairments after
fluoride exposure.
In order to better understand the molecular mechanisms
of fluoride neurotoxicity and to evaluate potential protein
biomarkers, a classical gel-based proteomic approach was
applied to determine the children’s plasma proteome profile
from the high fluoride and the control groups. The functional
significance and potential roles of the altered proteins in fluoride-exposed children are highlighted as follows: (1) immuneassociated proteins. Complement C1s subcomponent precursor is
a serine protease that combines with C1q and C1r to form C1
complex, which initiates the classical pathway activation of the
complement system (Wallis et al., 2010). It is known that complement components play central roles in mediation of inflammation and regulation of the immune response (Ricklin et al.,
2010), implying that fluoride exposure may cause immune
ZHANG ET AL.
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TABLE 3. Effect of Fluoride on IQ by COMT Genotype: Results from Stratified and Interaction Multivariate Regression
Modelsa
Model
1. Main effects model
2. Stratified model
3. Stratified model
4. Interaction modelb
a
b
Genotype
N
Combined
val/val
val/met
met/met
val/val
val/met þ met/met
val/val
val/met þ met/met
U-F (mg/l)
180
28
88
64
28
152
180
b (95% CI)
P
2.42 (4.59 to 0.24)
9.67 (16.80 to 2.55)
4.67 (9.75 to 0.41)
1.45 (4.05 to 1.15)
9.67 (16.80 to 2.55)
1.85 (4.07 to 0.38)
2.63 (4.14 to 1.12)
0.21 (3.28 to 2.86)
0.030*
0.003**
0.055
0.269
0.003**
0.104
0.002c,**
Multivariate regression models included age, gender, drinking water fluoride, education levels, T3, T4, and TSH as covariates.
The model assessing the interaction of U-F and COMT controlled for confounding by S-F.
c
P-values for interaction term coefficients.
*Significant (P < 0.05) difference.
**Significant (P < 0.01) difference.
FIG. 2. Representative 2-DE maps of differentially expressed plasma proteins. Seven differentially expressed protein spots (B1–B7) matched on the 2-DE were detected
in high fluoride group contrasted with those in the control.
TABLE 4. Differentially Expressed Plasma Proteins from Children With High Fluoride Exposure
RSNa
Protein
ANb
Score
Mrc
pId
Sequence Coverage (%)
B2
B3/B4
B5
B6
B7
Complement C1s subcomponent precursor
Alpha-1-B glycoprotein
Hemopexin
Apolipoprotein E precursor
Immunoglobulin light chain variable region
gi4502495
gi69990
gi13529281
gi4557325
gi18025688
125
180
251
303
100
78,174
52,479
29,068
36,246
12,420
4.86
5.65
6.45
5.65
5.11
9
19
37
29
25
a
RSN: reference spot number.
b
AN: access ID of NCBI database.
c
Mr: Molecular weight (Da).
d
pI: Isoelectric point.
response and inflammatory activation in children with fluorosis. Similar result was obtained in a recent study with regard to
Huntington’s disease that plasma complement components
elevation reveals neuroinflammatory activation (Dalrymple
et al., 2007). Another identified protein Immunoglobulin light chain
variable region, representing prototypical domain of the immunoglobulin superfamily, can mediate interactions with antigens
and effector molecules (Poljak et al., 1976). This may further support the role of inflammatory reaction in fluoride-exposed children. A1GB belongs to immunoglobulin superfamily but with a
hitherto unknown function (Ishioka et al., 1986). However, it was
present in the plasma of subjects with multiple sclerosis
(Rithidech et al., 2009), a disabling CNS disorder. Thus, it is worthy of further study to determine its association with pathogenesis of fluorosis. (2) Metabolism-related proteins. HPX is the
plasma protein with the highest binding affinity to heme. It is
mainly responsible for the transportation of heme thus preventing both heme-catalyzed oxidative damage and heme-bound
iron loss (Tolosano and Altruda, 2002). In the current study, the
increased level of plasma HPX may be accounted for oxidative
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TOXICOLOGICAL SCIENCES, 2015, Vol. 144, No. 2
stress arising from fluoride exposure. This is consistent with
previous in vivo and in vitro studies and epidemiologic investigations that fluoride is an oxidative stress inducer (Barbier et al.,
2010). Finally, APOE precursor is a key lipoprotein of lipoprotein
complexes that is expressed highest in the liver followed by the
brain. Although APOE have substantial effects on peripheral
lipid metabolism, it is still involved in diseases such as type III
hyperlipoproteinemia and atherosclerosis (Mahley and Rall,
2000).
Furthermore, it is interesting to note that majority of the
candidate markers, such as complement components, HPX and
APOE, have previously been implicated in Alzheimer’s disease
(AD) pathogenesis or related pathological processes or have
been found to be altered in AD, though many of which were
identified in core cerebrospinal fluid (Finehout et al., 2007; Roher
et al., 2009). Given that AD is a progressive neurodegenerative
disease characterized by cognitive deficits; one can hypothesize
that this abnormal expression of plasma proteins can contribute to or be a consequence of impaired cognition, or predict a
high risk of neurodegenerative diseases in children following
fluoride exposure. Actually, recent works in our laboratory and
others have showed that fluoride is capable of inducing
neurodegeneration in cognition-related brain regions (El-lethey
et al., 2010; Jiang et al., 2014; Wilson et al., 2012). However, due to
the number of samples included, our study should be regarded
as a preliminary study that makes further evaluation of the
identified candidate markers on a larger cohort of cases
necessary.
The present study has several limitations. First, the crosssectional observational design does not allow us to determine
temporal or causal associations between fluoride and cognition.
Second, the study has a relatively small sample size, which limits the power to assess effects of gene-environment interactions
on children’s IQ. Third, we did not evaluate the cognitive performance of children using other neurocognitive tests except
for CRT-RC. Despite the study limitations, this is the first geneenvironment study investigating the potential impact of COMT
single-nucleotide polymorphism (SNP) on the relationship
between children’s cognitive performance and exposure to elemental fluoride.
CONCLUSION
In summary, our data suggest that the intelligence of children is
affected by the COMT gene polymorphism and, in particular,
this SNP plays a role in modifying the effect of fluoride exposure
on cognition. Children with COMT reference allele had a higher
risk for cognitive impairments after fluoride exposure.
Additionally, proteomics analysis represents early specific
markers of developmental fluoride neurotoxicity. Hence, our
findings provide certain basis for clarifying the mechanisms
and identifying molecular targets of pharmacological interventions for potential delayed therapy.
FUNDING
The State Key Program of National Natural Science of China
(Grant No. 81430076), the National Natural Science
Foundation of China (Grant Nos. 81273021 and 81372934),
the Key Project of National High-tech R&D Program of China
(863 Program) (Grant No. 2013AA065204), the Foundation of
the Ministry of Science and Technology of China (Grant No.
2010DFB30530), and the Natural Science Foundation of
Hubei Province, China (Grant No. 2013CFB123).
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