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Neurotoxicology and Teratol

Chronic low-level mercury exposure, BDNF polymorphism, and

associations with cognitive and motor function

Diana Echeverria a,b,*, James S. Woods a,b, Nicholas J. Heyer a, Dianne S. Rohlman c,

Federico M. Farin b, Alvah C. Bittner Jr. a,b, Tingting Li b, Claire Garabedian a

a Battelle Centers for Public Health Research and Evaluation, Seattle, WA, United Statesb Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, United States

c Center for Research on Occupational and Environmental Toxicology (CROET), Oregon Health and Science University, United States

Received 6 October 2004; received in revised form 22 August 2005; accepted 22 August 2005

Abstract

Potential cognitive and motor effects from exposure to elemental mercury (Hg0) were examined in the presence and absence of a

polymorphism (Val66Met) in brain-derived neurotrophic factor (BDNF). A group of 194 male dentists (DDs) and 233 female dental

assistants (DAs) were occupationally exposed to mercury and had no history of kidney or nervous system disorders. Acute exposure was

measured using spot urinary Hg (HgU) concentrations (average 3.32 and 1.98 Ag/l, respectively) and indices of chronic occupational

exposure (26.3 and 14.9 years, respectively, weighted for historical exposures). The BDNF status was 68% and 66% wild type, 26% and 30%

single substitution, and 5% and 4% full mutation for DDs and DAs, respectively. DDs and DAs were evaluated separately. Regression

analyses controlled for age, premorbid intelligence, alcohol consumption, and education.

Statistically significant adverse associations with HgU ( p <.05) were found for nine measures among DDs (Digit Span Forward, Digit and

Spatial SpanBackward, Visual Reproduction, Finger TappingDominant, Alternate, and Alternate Partialed, Hand Steadiness, and Tracking), and eight

measures among DAs (Digit SpanForward, Visual Reproduction, Pattern DiscriminationRate, Symbol DigitRate, Trailmaking B, Finger

TappingDominant and Alternate Partialed, and Hand Steadiness). The BDNF status was associated with four measures in DDs and three measures in

DAs. Joint effects were found for Finger TappingAlternate and Alternate Partialed in DDs and Hand Steadiness and Trailmaking B in DAs. Joint

effects were additive in all cases. Performance on verbal intelligence and reaction time were not associated with either HgU or BDNF status.

A test of threshold effect for the association of Hand Steadiness with HgU demonstrated no lower boundary in both DDs and DAs. No

associations were observed with estimates of chronic mercury exposure. Our findings are applicable to exposure levels of the general

population and identify a potentially vulnerable group with a BDNF polymorphism.

D 2005 Published by Elsevier Inc.

Keywords: Mercury; Threshold; Short-term memory; Attention; Motor function; BDNF polymorphism

1. Introduction

Brain-derived neurotrophic factor (BDNF) is a protein

produced by a gene located on chromosome 11. It is a pro-

survival factor induced by cortical neurons that regulates

survival of striatal neurons in the brain and has recently

0892-0362/$ - see front matter D 2005 Published by Elsevier Inc.

doi:10.1016/j.ntt.2005.08.001

* Corresponding author. Battelle CPHRE, 1100 Dexter Avenue, Seattle,

WA 98109, United States. Tel.: +1 206 528 3131; fax: +1 206 528 3550.

E-mail address: echeverr@Battelle.org (D. Echeverria).

been shown to play a critical role in activity-dependent

neuroplasticity underlying learning and memory in the

hippocampus. It also regulates differentiation in the periph-

eral nervous system (PNS) and central nervous system

(CNS) [15]. It is hypothesized that a single nucleotide

substitution of valine (val) to methionine (met) (G to A at

nucleotide 196), in the BDNF gene at codon 66 (val66met),

is sufficient to suppress secretion of BDNF protein. The

substitution may be a single (val–met) or a double (met–

met) polymorphism substitution. The BDNF polymorphism

has been associated with abnormal intracellular trafficking

ogy 27 (2005) 781 – 796

D. Echeverria et al. / Neurotoxicology and Teratology 27 (2005) 781–796782

and regulated secretion of BDNF in cultured hippocampal

neurons transected with the met allele.

The met allele has also has been correlated with impaired

episodic memory in humans [24,38,55]. However, a direct

effect of BDNF alleles on hippocampal processing of

memory has only recently been demonstrated. Hariri et al.

[38] studied the relationship of the BDNF genotype and

hippocampal activity during episodic memory processing

using functional magnetic resonance imaging and a declar-

ative memory task in healthy individuals. The results

demonstrated that met carriers had reduced hippocampal

activity in comparison with val homozygotes during

encoding and retrieval processes. It is noteworthy that the

interaction between the BDNF polymorphism and the

hippocampal response during encoding accounted for 25%

of the total variation in recognition memory performance.

These findings reveal a specific genetic mechanism that

could explain substantial normal variation in human

declarative memory.

A central question of this study is whether the presence

of a BDNF polymorphism alters expected associations

between occupational elemental mercury (Hg0) exposure

and CNS effects that may potentially be affected by BDNF,

particularly for reduced attention and memory loss, as well

as depression and anxiety [20,31].

1.1. Mercury toxicity

The CNS is the critical target organ of Hg0. While there

is little debate regarding the potential for toxicity from high-

dose exposures to Hg0 consistent with urinary Hg levels

exceeding 50 Ag/l [20], there is no consensus with respect

to an acceptable level of Hg0 exposure. The toxicity of

Hg0 as reported in clinical reports [30,31,37,59], occupa-

tional studies [4], and amalgam exposure studies among

dentists [19,36,63] has prompted evaluations of its

pharmacokinetics [18], absorption and excretion [4,18–

20,30,31,36,37,42,52,59,63,68]. Due to its lipophilic prop-

erties and low vapor pressure (0.005 mm Hg0 at 37 -C),76–80% of Hg0 vapor is absorbed through the lungs [41].

After entering the blood, the dissolved metallic vapor is

oxidized primarily in erythrocytes, through mercurous into

mercuric ions by the hydrogen peroxide-catalase pathway

(i.e., Hg0YHg1+YHg2+) [20,33]. In vivo, the rate-limiting

step is linked to the production of hydrogen peroxide [54],

which stimulates the uptake of mercury vapor in the red

cells. The oxidation process is dose-dependent until

concentrations approach saturation in blood (244 ng/ml)

[39,54]. Hg in blood is well below saturation levels in

dentists, ranging from 1.2 to 14 ng/ml [1], ensuring the

validity of using a urinary Hg level as an indicator of

exposure. Hydrogen peroxide catalase is also the predom-

inant oxidative pathway in the kidney [86] and brain [53].

Unoxidized Hg0 readily passes the blood–brain barrier as

the circulation time from the lung to the brain is faster than

the oxidation rates. Once in the brain, final oxidation

proceeds and the divalent form (Hg2+) is complexed and

retained.

Laboratory results from controlled radioactive Hg0

inhalation studies in humans show that the brain retains

Hg0 approximately 21 days [41], providing ample time for

accumulation in the CNS due to repeated exposure.

Methylmercury (CH3Hg+) is considered more toxic than

Hg0 since the methyl group increases solubility in the

blood, thereby increasing distribution and subsequent

bioavailability [27]. In monkeys, the half-life in the blood

is shorter (14 days), while the half-life in the brain is longer,

ranging between 38 and 56 days for doses between 10 and

50 Ag/kg/day [70]. Once in the CNS, both forms are

oxidized to Hg2+ where the distribution in the brain for both

species has been found to be similar [88], though

demethylation is slow. Thus, the differences between

methyl and Hg0 effects on the CNS are most likely one

of half-life, distribution and dose. While we recognize the

distinction between these forms of Hg, we rely on the best

evidence of impairment among clinical patients and work-

ers exposed to Hg0, and humans, primates and rodents

exposed to CH3Hg+ to form our hypothesis and design the

test battery. Below, we discuss clinical manifestations of

mercurialism first, followed by a discussion of our rationale

for the test battery and known Hg0 effects on behavioral

and sensory domains.

1.2. Characterizing health effects of mercury

Exposures to 1–3 mg/m3 Hg0 trigger pulmonary

distress as well as clinical CNS effects [10,35]. Classic

signs of mercurialism [87,91] include (1) psychosomatic

symptoms, (2) alterations in affect or emotional lability, (3)

insidious loss of mental capacity (progressively affecting

memory, logical reasoning or intelligence), and (4) motor

effects (in the arms, progressing to coordination, imbal-

ance, and cerebella ataxia, and tremor in muscles that are

highly enervated and perform fine motor control of

extremities). The mercury toxicity literature provides

impressive consistency within these four domains of

mercurialism across occupational studies assessing HgU

at higher levels between 50 and 200 Ag/l [2,3,40,51,82,93]and at lower levels in dental population studies

[9,13,21,32,36,62,74,79,85,95].

The diversity in clinical effects indicates more than one

mechanism of toxicity and involvement of more than one

area of the brain. For example, exposure to Hg0 may

interfere with the limbic system associated with mood and

memory, as well as the motor strip and cerebellum

associated with movement, and peripherally insult axons

associated with vibration sensitivity. Although we antici-

pate subtle effects at dental exposure levels, our overall

hypothesis reflects this range of possible effects. Note that

the potential effect of a BDNF polymorphism on

associations between mood and symptoms and Hg0

exposure has already been discussed in a companion

D. Echeverria et al. / Neurotoxicology and Teratology 27 (2005) 781–796 783

paper [40] and is not covered here. For this study, we

evaluate whether the BDNF polymorphism alters potential

cognitive and motor association with Hg exposure. We

hypothesize that exposure to Hg0 may (1) adversely affect

cognitive skills requiring visual memory, working mem-

ory, and attention, (2) reduce motor speed and hand

coordination, and (3) decrease sensitivity to vibration and

visual contrast. We hypothesize that these are selective

effects, leaving language and retrograde memory intact

[91]. We also measured nerve conduction velocities (NCV)

in order to distinguish PNS from CNS-based affects in

motor skills.

1.3. Cognitive skills and Hg0 exposure

Linear exposure-effect relationships between Hg0 and

cognitive performance are reproducible [2,3,51,82,93].

Frank neurotoxicity is generally observed among subjects

with urinary Hg levels above 300 Ag/l [81]. Exposures justabove 100 Ag/l Hg have consistently resulted in reduced

performance of the following behavioral domains: attention

(Digit Span forward), working memory (Digit Span back-

ward) [46,82,93], Digit Scanning [82], and Visual Memory

[6,26], verbal concept formation (Wechsler Adult Intelli-

gence Scale (WAIS) Similarities [26,35]); abstract reason-

ing (the Raven Progressive Matrices [6,26]); and visual–

spatial function (Block-Design [6,26]) and visuomotor

speed (Symbol-Digit [6,26,43]). Though not well estab-

lished, several of these studies suggest that attention/

memory loss may be more permanent than induced tremor

and slowed motor responses, since the latter appear more

reversible post-exposure [16,26,31,82]. Reduced perform-

ance on attention and memory tests and the WAIS

Similarities tasks have also been reported in workers with

Hg0 levels between 30 and 50 Ag/l when compared to

referents [66,76,83]. However, determinations of a lower

threshold for cognitive effects is complicated by mixed

results among several chloralkali worker studies at low

exposure levels ranging between 0.025 and 0.076 mg/m3

(10 and 19.9 Ag/l in blood) [47,65,78]. In one study,

symptoms, mood, and tremor were impaired among

workers exposed for 14 years, but memory and psycho-

motor function were unaffected. In contrast, two alternate

studies reported that excessive fatigue, memory disturban-

ces, and confusion were observed among workers exposed

for 7 [78] and 13.5 [47] years, but tremor, coordination,

and reaction time remained unaffected. These conflicts

may be resolved by studying more uniform dental

populations who have similar economic, education, and

training backgrounds. Detection of subtle effects would be

further facilitated by using unexposed dentists as controls.

Both results from dental studies conducted by this research

group [9,13,21,32,47,95] and other dental studies

[62,74,79,85] report declines in cognitive performance at

higher urinary Hg levels justifying a reevaluation at very

low exposure levels.

1.4. Motor function and Hg0 exposure

Declines in motor function were first reported as finger

tremor among felters and hand tremor among chloralkali

workers [81]. These findings were later confirmed with

more sophisticated acceleration tremor and surface electro-

myography (EMG) measures [16,58]. EMG/PNS distur-

bances [6] have also been detected among workers with

mean urinary Hg levels of 93.4 Ag/l (S.D.=30.4). At

lower exposures, declines in performance on hand steadi-

ness [9,13], finger-tapping speed [45,75], and manual

dexterity [16,45,93] have also been reported. Even work-

ers with remote exposures (30 years earlier) to high

urinary Hg levels (171 Ag/l) [3,50] retained residual

slowed peroneal and ulnar motor NCVs in the absence

of CNS effects. The latter two PNS studies [3,50] reported

mild polyneuropathies defined by abnormal NCVs, includ-

ing prolonged distal latencies and smaller sensory evoked

response potentials.

Corroborative electrophysiologic studies in dentists

reported similar slowing in ulnar nerve as well as sural

sensory (of 2 ms�1) and median motor (of 2 ms�1)

conduction velocities, though velocities were within the

range of the control participants [79,85]. In rodents, motor

slowing and poor coordination have also been associated

with developmental exposure to CH3Hg+ resulting in brain

mercury levels of 0.04 Ag/gm, a level not thought to

produce adverse effects in humans [11]. These animals

were tested on an operant behavioral task termed ‘‘differ-

ential reinforcement of high rate (DRH)’’ which requires

several responses on a lever within a fixed period of time

to receive a food pellet reward. Collectively, these studies

support an evaluation of motor function at even lower

levels of exposure as a threshold level of effect remains to

be determined.

1.5. Sensory function and Hg0 exposure

A common mechanism of action at the cellular level for

methyl and inorganic mercury indicates that the sensory

system is a critical area to be evaluated [14]. In humans,

excessive exposure could produce a range of effects

including blindness, deafness, cerebral palsy, mental defi-

ciency, and delayed motor development. In primates, excess

exposure could also result in structural damage to the visual

cortex and cerebellum expressed as functional deficits in

vision and tremor [17]. Well-controlled exposure studies in

primates also indicate visual, auditory, and somatic effects

[71–73]. Among humans with urinary Hg0 levels exceeding

50 Ag/l, reduced vibration sensitivity has been observed in

defense workers [43] and in Brazilian miners [48]. This

suggests that the sensory system may be a responsive

indicator of mercury toxicity. This study will provide new

information on potential Hg effects for visual contrast

sensitivity [56] and will reevaluate the sensitivity of

vibration thresholds [29].

D. Echeverria et al. / Neurotoxicology and Teratology 27 (2005) 781–796784

1.6. Assessment of BDNF status and Hg0 Exposure

This study examines whether the BDNF polymorphism

can alter expected current and cumulative Hg0 exposure-

effect associations across cognitive and motor domains in a

group of dental professionals. The study evaluates potential

declines in performance in otherwise healthy but occupa-

tionally exposed dentists and dental assistants. Potential

associations between urinary mercury levels and neuro-

behavioral performance scores are considered meaningful if

the association complies with a statistically significant linear

‘‘exposure-effect’’ curve ( p <0.05) [16].

2. Methods

2.1. The study population

In 1998, licensed dentists in Washington State (n =3750)

were mailed a packet that included a letter of introduction,

an informed consent, a screening questionnaire, and a urine

kit. A total of 2675 urine samples were returned, with 1488

male dentists meeting eligibility criteria. These criteria

included (1) uninterrupted employment for 5 consecutive

years prior to testing, (2) absence of health conditions that

could alter performance or alter excretion of HgU including,

but not limited to, all CNS disorders, head trauma, diabetes,

other kidney disease (e.g., lithiasis, pyelonephritis, ortho-

static proteinuria), endocrine disorders, and cancer, as well

as (3) no history of chelation therapy. Eligibility was further

restricted to male dentists due to the very small number of

female respondents.

The mean HgU level among dentists was 2.5 Ag/l(range=0–67). The mean HgU among eligible participants

was 2.32 Ag/l (standard deviation [S.D.], 1.49). Between

1999 and 2001, a stratified sample of 193 male dentists

covering the range of screening HgU levels was recruited. In

addition, 233 female dental assistants were recruited from

the practices of participating dentists. This method of

recruiting dental assistants was employed to obtain a full

distribution of exposures within this group. The male dentist

mean (S.D.) urinary Hg level was 3.32 (4.87). The female

dental assistant mean (S.D.) urinary Hg level was 1.98

(2.29).

2.2. Test procedures

Study participants would arrive at the study center, sign

an informed consent, provide a urine (¨50 ml) sample, and

take an alcohol breath test before completing the Neuro-

quest computerized questionnaire and the Behavioral

Evaluation for Epidemiologic Studies (BEES) [23] test

battery. Both of these tools are extensively described

elsewhere [23] and are briefly discussed below.

The BEES test battery was designed to evaluate a range

of behavioral domains with sufficient redundancy and

sensitivity to allow researchers to distinguish between

domains that are sensitive or resistant to the insults being

evaluated. For this study, tests were specifically selected for

(1) their sensitivity to Hg exposure, (2) their statistical

properties, and (3) their compliance with recommendations

by the World Health Organization [84] and the Agency for

Toxic Substances and Disease Registry [7]. The BEES test

battery was administered using a touch-screen computer and

included the following tests: attention (Digit and Spatial

SpanForward, Trailmaking A); working memory (Digit and

Spatial SpanBackward); sustained attention (Vigilance); visual

memory (Pattern Memory); perception (Pattern Discrimi-

nation); visuomotor speed (Symbol-Digit Substitution);

cognitive flexibility (Trailmaking B); reaction time (Simple

and Choice Reaction Time); finger speed (Finger Tapping);

and tracking (Adaptive Tracking).

The BEES battery was supplemented with additional

standardized tests and control tests to strengthen our

interpretation of results. They include:

Visual memory (Wechsler Memory Scale-Revised,

WMS-R Visual Reproduction) [89]: This test is a subtest

of the WMS-R in which the subject is asked to draw four

figures as accurately as possible after viewing each one

for 10 s. Score: Sum of correct elements.

Manual dexterity (Hand Steadiness Battery) [34]: This

test measures intentional hand steadiness. The task

requires participants to hold a pointer for 15 s at the

center of a series of holes with decreasing diameters,

while instructed not to touch the sides of the holes.

Scores: Number of hits and cumulative contact time for

each of the 7 holes. Time: 3 min.

Peripheral nervous system (Nerve Conduction Veloc-

ities)(NCVs) [3,51,78]: Motor and sensory nerve con-

duction velocity measures of the ulnar nerve and sural

nerves were performed using standard techniques. The

sural nerve, which avoids use of the median nerve and

interference from hand wrist disorders involving the

carpal tunnel, was our preferred measure. However, we

were unable to complete a sufficient number of the sural

nerve tests and, therefore, only report results for the ulnar

nerve NCVs. We evaluate the integrity of the nerve and

can inform interpretation of potential effects in manual

coordination tasks. Scores: Latency, velocity, and ampli-

tude. Time: 15 min.

Control tests evaluate abilities that are resistant to

environmental effects and, therefore, are useful in control-

ling for variation in individual ability. Our control tests

include the Reading test of the Wide Range Achievement

Test 3 (WRAT-3 [92]), the BEES Vocabulary [23] test, and

the Test of Nonverbal Intelligence-3 (TONI-3) [12]. Taken

together, these tests assess general intelligence ( g).

The WRAT-3 Reading test [92] required the examinee to

read and pronounce 42 words in isolation (i.e., not in a

meaningful context) and served as a word recognition test.

D. Echeverria et al. / Neurotoxicology and Teratology 27 (2005) 781–796 785

The BEES Vocabulary [23] is based on words used in the

Armed Forces Qualifying Test (AFQT), comparable to the

list used by the Neurobehavioral Evaluation System [49].

The task has the subject view twenty-five words sequen-

tially presented on the computer screen and select the

correct synonym from a set of four additional words.

Vocabulary is not expected to be affected by Hg exposure

and is used as an index of stable CNS function or pre-

morbid intelligence [77,90]. Score: Number correct. Time:

4 minutes.

The TONI-3 [12] is a control test that attempts to

measure general intelligence independent of verbal ability

(based on aptitude, abstract reasoning, and problem solv-

ing). Minimal instructions are given and the examinee

responds by pointing to the correct answer. Form A,

consisting of 45 trials, was used in our study. Score:

Number correct. Time: 16 min.

A Visual Acuity [64] test was employed to control for

individual differences in vision which might impact some of

the above tests. The Snellen Test of Visual Acuity [64] is

based on character recognition using a standardized chart

consisting of lines of letters. The letter on the top line is the

largest; those on the bottom line are the smallest. To test one’s

ability to see at far distances, the participant is positionedwith

their forehead pressed against the viewer and reads aloud the

largest to the smallest line of letters they can see until they

cannot correctly identify the letters. The right and left eyes are

tested separately. Score: Scoring was done on a line-by-line

basis. A line was considered read if more than half of the

letters (i.e., 4 of 5) are identified correctly.

The Neuroquest computerized questionnaire collects

information on demographic and personal habits, including

the use of alcohol, fish consumption, vitamins and supple-

ments, medical and pregnancy histories, work histories, a

symptoms checklist (45 symptoms) expanded from the Q16

[80], and a computerized version of the Profile of Mood

States [57]. Histories of medical conditions are grouped into

categories, including physical injury, major operations,

digestive, circulatory, sensory, kidney, endocrine, immune,

nervous system, and emotional problems. Within each

category, the use of medications is indicated.

Blood samples were collected to measure and control for

organic mercury and lead exposures. Buccal swab samples

were collected for DNA analysis. Examiners were trained

with supervised practice using proxy participants. Written

manuals were produced for all procedures and provided to

examiners.

2.3. Urinary mercury analyses

Fifty milliliters of urine samples were divided into

aliquots for mercury and creatinine determinations. Analysis

of total mercury was performed by continuous-flow, cold

vapor spectrofluorometry, as previously described [67].

Urinary creatinine concentrations were measured using a

standard colorimetric procedure (Sigma #555-A). HgU

levels were calculated as both Ag/l and Ag/gm creatinine.

No significant differences between the two measures were

observed. The natural log of spot HgU levels (ln Ag/l) wasused in our analyses as it more accurately reflects biological

mechanisms.

2.4. Chronic and peak mercury exposure indices

Chronic exposure was calculated for each job by taking

the product of (1) the mean number of amalgam place-

ments and removals/week, (2) a weighting for the type of

amalgam used (1 for pre-encapsulated amalgams, and 2 for

office-mixed), (3) a weighting for the time period of the

job (1�1992, 1.5 = 1985–1992, 1.75�1972–1982,

2.0�1970), and (4) the duration of the job. The subject’s

chronic exposure index was calculated by taking the

square root of the sum of these calculations across all

jobs. Finally, age was covaried out of this index to remove

expected collinearity, allowing both variables to be used

simultaneously in regression models.

The peak index was calculated for each subject by

multiplying the maximum number of placements and

removals by the amalgam type with the time-period

weightings for each job. The job with the highest index

value was selected. The weighting systems used in these

calculations were derived from measurements of urinary Hg

levels among dental professionals since 1975 [61] and from

expert industrial hygiene opinion.

2.5. Genomic assay

BDNF genotyping was performed at the Functional

Genomics Laboratory of the Center for Ecogenetics and

Environmental Health at the University of Washington,

employing a 5V-Nuclease TaqMan Detection System-based

assay as previously described. BDNF polymorphism was

scored as F0_ for wild type, F1_ for a single substitution, andF2_ for a double substitution. Based on the very small

number of double substitutions analyses combine the single

and double substitutions into a single ‘‘any substitution’’

category.

2.6. Statistical analysis

The BEES behavioral test battery was designed to

optimize the statistical properties of its scores. It is also

designed to measure distinct components of tasks74 that

more accurately capture the behavioral skill being measured.

For example, in the reaction time tests, the response time is

separated into the components of Flift_ and Fmove._ Thesemeasures separately reflect the cognitive response time (lift)

and the motor response time (move), both necessary to

complete the task.

We have also added a derivedmeasure that used regression

analysis to co-vary out the influence of simple tasks on more

complex tasks, providing a more direct measure of the

Table 1

Study population

Means Males Females p value

N =194 N =233

Age at evaluation 49.0 (7.8) 36.0 (9.1) 0.00

BEES Vocabulary N Corr. (n =25) 10.2 (1.8) 8.2 (2.4) 0.00

Income in $1000s 166 (106) 56.1 (51.9) 0.00

WRAT3-Reading (Sum Score) 52.6 (4.8) 42.8 (3.0) 0.00

Highest academic education score 7.0 (1.0) 4.8 (1.0) 0.00

TONI-3 34.43 (6.6) 28.56 (7.6) 0.00

Visual acuity left 0.17 (0.56) 0.15 (0.55) ns

Visual acuity right 0.13 (0.43) 0.16 (0.51) 0.00

Snell equivalent right 1.55 (1.8) 1.78 (1.96) ns

Snell equivalent left 1.48 (1.84) 2.22 (2.27) ns

Number of caffeinated drinks

per week

10.2 (14.0) 9.1 (9.2) ns

Number alcohol drinks

per week

3.9 (5.2) 1.9 (2.5) 0.00

Maximum alcohol

consumption per week

5.5 (1.9) 3.3 (1.8) 0.00

Number cigarettes per day 0.20 (1.1) 0.90 (3.3) 0.00

Number of fish meals per week 1.7 (0.8) 1.9 (0.7) 0.00

Percentages:

Caucasian 96% 87% 0.00

English as first language (%) 98% 97% ns

Right handed 97% 78% 0.00

Physical injury 61% (n =118) 47% (n =111) 0.00

Repetitive trauma 43% (n =84) 33% (n =77) ns

Bone fracture 24% (n =44) 14% (n =32) ns

Eat fish 99% 92% 00

D. Echeverria et al. / Neurotoxicology and Teratology 27 (2005) 781–796786

additional cognitive requirements of the complex task. For

example, our Finger-Tapping test includes dominant, non-

dominant, and alternating tapping tasks. By co-varying out

performance on the dominant (practiced) and non-dominant

(less practiced) tasks from performance on the alternating

tapping task, we obtain a measure of the additional cognitive

resources needed to coordinate alternate taps without the

added variance of tapping speed for each hand. Finger

TapAlternate Partial becomes a complex coordination measure

involving feedback from each finger tap and the associated

response inhibition of reduced speed necessary to coordinate

alternate taps.

Hypotheses were tested based upon evidence of a linear

Fexposure-effect_ relationship using a fixed regression

model. This model included HgU, BDNF status, age,

alcohol consumption (number of drinks/week), pre-morbid

intelligence (BEES vocabulary score) [23], and education

(highest level achieved). The fixed model for dentists

excluded education as it was a constant (all dentists

achieved the highest level on our scale) and we tested and

omitted Fleft-handedness_ given it had no effect on

associations with mercury. The model controls for factors

that are known to affect behavior [5]. Secondary analyses

for Hand Steadiness [34] and NCV [51] tasks employed an

expanded fixed model that included a dichotomous variable

to indicate a history of hand injury, repetitive trauma, or

arthritis that could alter performance on hand coordination

tasks. In addition, skin temperature was included in the

model for NCV results, as variation in this temperature

affects normal nerve conduction rates. This correction is

standard practice for clinical evaluations employing NCV

tests.

The slope, standard error, partial correlation, and p-value

for all associations between outcomes measures and HgU,

BDNF, and their interaction term are reported for p-values

<0.10. Associations with a significance level above p=0.05

are included to capture observed directionality at these very

low exposures. However, only associations with p-values

<0.05 are considered to be significant indicators of possible

adverse behavioral effect.

Our analyses also addressed concerns about unwanted

alpha error. Bonferroni corrections do not take into account

the functional groupings of human performance and ignores

even consistent trends across scores when they do not meet

significance. To address this problem, our hypotheses were

tested on two levels. First, a priori effects (those specifically

known to be sensitive to mercury) were listed and compared

to observed effects. Second, directionality of all associa-

tions within behavioral domains were evaluated for p-

values <0.10 for consistency of directionality in the

expected direction (declining performance with increased

exposure).

Finally, an effect threshold was calculated for the

association between Hand Steadiness scores and HgU. This

was based upon historical sensitivity of performance on this

test to mercury and its occupational relevance to dentists.

The threshold was calculated using a branched non-linear

regression model that forced the exposure-effect response to

have a slope equal to zero below the threshold, and a log-

linear slope above the threshold.

3. Results

3.1. Univariate comparisons among dentists (DDs) and

dental assistants (DAs)

Demographic, health, and genetic distributions for the

two study groups are provided in Table 1. The decision to

analyze male dentists (DDs) separately from female dental

assistants (DAs) was supported by observed differences

between the two groups for many socio-economic variables

know to influence performance on behavioral tasks. These

variables include age, income, education level, the BEES

Vocabulary [23], the WRAT-3 Reading test [92], the Test of

Nonverbal Intelligence-3 (TONI-3) [12], and alcohol con-

sumption. Our control tests were not related to HgU. Our

analysis employed the BEES Vocabulary [23] score as the

preferred estimate of pre-morbid intelligence for three

reasons: (1) it is an accepted control measure [90] widely

used in the neurobehavioral literature; (2) it was the only

control measure that had no missing values; and (3) it was

more independent of education, an important covariate in

D. Echeverria et al. / Neurotoxicology and Teratology 27 (2005) 781–796 787

our model, compared to the WRAT-3 (R =0.41 vs. 0.53).

Visual acuity was similar for both groups.

Expected differences in lifetime histories of physical

injury (61% vs. 47%) were also observed between DDs and

DAs, respectively. These were attributable to a repetitive

trauma (43% vs. 33%) or bone fracture from a sports injury

(24% vs. 14%). However, only NCVlatency and Vibration

Sensitivity were associated by with these conditions. Func-

tional motor tests were not affected.

Measures estimating occupational exposure and personal

exposure to mercury are presented in Table 2. Based on

HgU levels, DDs incur, on average, at least one third more

occupational exposure than dental assistants. In contrast,

personal dental amalgam exposure was comparable in both

groups. Both genders also had remarkably similar distribu-

tions of BDNF polymorphisms (31% VS. 34%).

3.2. Behavioral measures among dentists and dental

assistants

Table 3 presents each test measure displayed with its

untransformed unit, transformed unit, their respective means

and standard deviations, the average duration of performance

for each test within our test battery and finally, test–retest

reliability and required sample size. The reliability and

associated sample sizes are given for the test as administered

on a small sample of 20 subjects (unrelated to this study) who

repeated the battery three times within 1 week [90]. Seven out

of eleven tests evaluated had reliabilities >0.80, demonstrat-

ing a good degree of efficiency. Only 3 subtest measures out

of 25 demonstrated reliabilities below 0.70. These included

the BEES [23] Digit SpanForward, BEES [23] Choice

ReactionMove, and the BEES [23] VigilanceNumber correct.

The poor reliability of the Vigilance test was attributable to a

small variance where scores converge with repetition con-

tributing to low reliability as well as natural variation in

attention.

3.3. Regression analyses

Table 4 presents the organization of measures by domain

and Table 5 presents the multiple regression model

Table 2

Mean (S.D.) urinary mercury levels and prevalence of BDNF polymorphisms

Means Males

N =194

Urinary mercury (Ag/l) 3.32 (4.87

Years employed in dentistry 26.27 (10.2

Number of mercury amalgam restorations 16.03 (15.6

Chronic index(Yrs*type*# restorations/wk *decade) 1150.7 (176

Peak exposure(Max # restorations/wk *type*decade) 602.5 (609

Number of personal amalgam in mouth 5.3 (4.3)

Recent amalgam restorations in <6 months 0.02 (0.1)

BDNF: Wild type 68%

Single substitution 26%

Double substitution 5%

coefficients for HgU and the BDNF polymorphism by

domains for measures that had a statistical relationship at

p <0.10. The distribution of findings was not identical for

DDs and DAs.

Within the domain of attention, scores for BEES [23]

Digit SpanForward achieved statistical significance at p <0.05

with exposure to HgU in dentists and dental assistants but

not with BDNF status. This was the only measure out of the

three in this domain to achieve statistical significance.

Both measures in the domain working memory (BEES

[23] Digit and Spatial SpanBackward) achieved statistical

significance with exposure to HgU among dentists but not

dental assistants. The association between the BEES [23]

Digit SpanBackwards and BDNF also met statistical signifi-

cance among dental assistants but only demonstrated a

non-significant trend in the expected direction among

dentists. Therefore, only one joint additive effect of

exposure of HgU and the BDNF polymorphism was

identified in this domain.

Performance scores for the WMS-R Visual Reproduction

[89] test also achieved statistical significance with exposure

to HgU in dentists and dental assistants but not with BDNF

status. Performance scores on the other test of Visual

Memory, the BEES [23] Pattern MemoryNumber Correct, were

only sensitive to the BDNF polymorphism in dentists.

Therefore, though there was a suggestion of a potential

relationship with BDNF status for the three visual memory

measures among DDs and one among DAs, only one

achieved statistical significance, and only among DDs.

Performance scores for BEES [23] VigilanceNumber of Hits,

the only measure of sustained attention, demonstrated a

weak trend in the expected direction in DAs with respect to

BDNF status.

The remaining cognitive domains of perception, visuo-

motor performance and cognitive flexibility had one

measure, BEES [23] Trailmaking B, adversely affected by

HgU among dental assistants. There also was a joint effect

for exposure to HgU and the BDNF polymorphism for this

measure.

Cognitive test scores that were not associated with either

Hg exposure or BDNF status in either group include

performance in the domains of reaction time (Simple and

Females p value

N =233

) 1.98 (2.29) 0.00

0) 14.93 (7.87) 0.00

6) 12.34 (10.77) 0.00

5.8) 365.4 (620.7) 0.00

.5) 250.9 (310.1) 0.00

5.0 (4.3) ns

0.10 (0.3) ns

66% ns

30% ns

4% ns

Table 3

Performance measures: crude scores and transformed units of performance, test duration, reliability, and minimum sample size

Test measure Untransformed

units

Transformed

units

Untransformed mean (S.D.) Transformed mean (S.D.) Parameters

DDS

mean

DDS

S.D.

DA

mean

DA

S.D.

DDS

mean

DDS

S.D.

DA

mean

DA

S.D.

Minimum Reliability N required

(r2=0.1)

Digit spanaforward Total corr digits Ln (Digit span) 5.52 1.32 5.15 1.30 1.60 0.29 1.67 0.26 1.6 0.65 49

Digit spanabackward Total corr digits Ln (Digit span) 3.89 1.40 3.49 1.23 1.19 0.35 1.30 0.34 1.1 0.78 69

Spatial spanaforward Total corr spaces Ln (Spatial span) 5.34 1.49 5.09 0.94 1.65 0.25 1.61 0.20 1.5

Spatial spanaBackward Total corr spaces Ln (Spatial span) 4.98 1.78 4.49 1.11 1.58 0.24 1.47 0.27 2.0

Visual reproductionsbN correct Sum score Ln (Score) 36.33 3.35 33.03 4.81 3.62 0.95 3.51 0.16 4.0 0.79 46

Pattern memoryaN correct N corr (max=25) Ln (Number correct) 18.9 1.09 18.2 1.13 2.93 0.17 2.90 0.18 7.0 0.75 41

Pattern memoryarate S/item 100*N. corr/median s 4.46 1.52 4.63 1.62 0.424 0.145 0.393 0.138 7.0 0.79 35

VigilanceaHits N Corr (max=25) Ln (Number correct) 43.50 1.51 43.45 1.30 3.77 0.131 3.77 0.11 2.1 0.42 92

Pattern discriminationaN Correct N Corr (max=25) Ln (Number correct) 23.8 0.52 23.6 0.67 3.17 0.07 3.16 0.09 2.4 0.71 32

Pattern discriminationaRate S/item 100*N corr/medians 6.54 1.90 5.98 2.00 0.364 0.106 0.394 0.132 2.4 0.86 24

Symbol digitaRate S(9 substitutions) 10/median ms 2.92 0.55 2.88 0.54 3.43 0.67 3.47 0.68 1.7 0.83 36

Trailmakinga A S 10/total s 21.01 5.58 19.01 4.56 0.476 0.127 0.526 0.129 0.4 0.79 47

Trailmakinga B S 10/total s 42.02 12.71 44.05 13.96 0.238 0.072 0.227 0.072 0.7 0.81 49

SwichingaRate s/switch Mean switch rate 1.96 0.41 1.80 0.41 0.742 0.127 0.807 0.152 1.0 0.86

Simple reaction timeaLift Mean ms 100/median ms 330.0 42.24 348.0 48.72 0.303 0.039 0.287 0.041 0.5 0.86 24

Choice reaction timeaLift Mean ms 10*corr./median ms 379.1 41.7 394.1 43.35 0.422 0.045 0.406 0.043 0.5 0.85 25

Simple reaction timeaMove Mean ms 100/median ms 205.867 0.30 200.0 71.6 0.486 0.159 0.500 0.179 1.0 0.70

Choice reaction timeaMove Mean ms 10*corr./median ms 216.8 62.20 202.5 63.00 0.738 0.212 0.789 0.212 1.0 0.57

Finger tappingaDominant Taps/10 s Tap1.5/1000 69.65 11.14 65.89 13.83 0.62 0.10 0.57 0.12 0.2 0.86 35

Finger tappingaNon-dominant Taps/10 s Tap1.5/1000 68.17 8.86 63.58 10.80 0.60 0.08 0.54 0.09 0.2 0.85 56

Finger tappingaAlternate Taps/10 s Tap1.5/1000 70.40 25.34 58.02 25.52 0.63 0.23 0.47 0.21 0.2 0.78 78

Hand steadinessFactor Factor (All hits

and time)

0.32 0.88 0.28 1.10 1.8 0.75 92

Tracking Scorea Median frequency 0.78 0.11 0.63 0.15 2.0 0.70 29

Vibration sensitivity Threshold score Ln (Threshold score) 14.20 8.00 10.43 2.56 2.63 0.40 2.0.2 0.22 3.0 0.80 40

Nerve conduction latency ms 3.83 1.67 3.30 1.72 3.0

Nerve conduction amplitude 7.89 5.63 7.92 5.92 3.0

a BEES.b WMS-R.

D.Echeverria

etal./Neurotoxico

logyandTera

tology27(2005)781–796

788

Table 4

Organization of measures into domains

Domain (Measures) Test measure

Attention (3) Digit spanaForwardSpatial spanaForwardTrailmaking Aa

Working memory (2) Digit spanaBackwardSpatial spanaBackward

Visual memory (3) Visual reproductionsbN Correct

Pattern memoryaN Correct

Pattern memoryaRateSustained attention (1) VigilanceaHitsPerception (2) Pattern discriminationaN Correct

Pattern discriminationaRateVisuo-motor Processing (1) Symbol digitaRateCognitive flexibility (n =2) Trailmaking Ba

SwitchingaRateComplex coordination (1) Finger tappingAlternate Partial

Information processing (2) Simple reaction timeaLiftChoice Reaction TimeaLift

Manual coordination skills (7) Finger tappingaDominant

Finger tappingaNon-dominant

Finger tappingaAlternateSimple reaction timeaMove

Choice reaction timeaMove

Hand steadinessFactorTracking scorea

Sensory test (1) Vibration sensitivity

Peripheral nerve integrity (2) Conduction Latency

a BEES.b WMS-R.

D. Echeverria et al. / Neurotoxicology and Teratology 27 (2005) 781–796 789

Choice Reaction Time), attention (Trailmaking A), and

cognitive flexibility (Switching Ability).

The domain of manual coordination skill, which had

seven measures, was of particular a priori interest to us. Four

of these measures among dentists and two among dental

assistants achieved statistically significant associations with

exposure to HgU. Joint effects of exposure to HgU and

BDNF status among the manual coordination tests were

limited to Finger TappingAlternate in DDs and Hand Steadi-

ness in DAs.

Further, a joint effect was also found among dentists for

performance of BEES [23] Finger TappingAlternate Partial,

which reflects the cognitive decision to alternate finger taps.

The same relationship among DAs was weaker, where only

exposure to HgU achieved statistical significance.

Though not shown, it is important to note that the

associations between BDNF status and the Hand Steadiness

task monotonically decreased as the subject progressed from

the first and largest hole to the last and smallest hole. Both

the number of hits and contact times showed the same trend.

The correlations for the number of hits from largest to

smallest hole were 0.27, 0.24, 0.03, 0.00, 0.02, 0.00, and

0.01. Corresponding contact time correlations were 0.45,

0.17, 0.06, 0.02, 0.04, 0.01, and 0.01. The only statistically

significant correlation was for the largest hole. This trend is

atypical of a true association because we would expect

declines in manual coordination to be most highly correlated

with smaller holes.

We also observed adverse correlations between HgU and

Nerve Conduction Velocity (Beta=0.18, p =0.03) in DAs.

When we removed participants with a history of hand wrist

disorders, arthritis, or injury to the hand, the association was

not statistically significant. The observed correlation

between BDNF status and NCV in DDs also disappeared

when similar participants were removed.

Vibration Sensitivity thresholds did not achieve statisti-

cally significant associations with HgU or BDNF.

Finally, we address the problem of our observations

being attributable to chance due to multiple testing. In this

paper, we report on 27 outcomes (see Table 4). Among

dentists, we observed nine significant associations

( p <0.05) with HgU—all showing decreased performance

with increased exposure. The probability of observing this

many significant outcomes (binomial distribution with

n =27, k =9 and p =0.05) is 4�10�6. Given this distribu-

tion, the probability that all nine associations would go in

the expected direction (binomial distribution with n =9, k =9

and p =0.5) is 2�10�3. Among dental assistants, we

observed eight significant outcomes, all in the expected

direction. The probability of observing this many significant

outcomes (binomial distribution with n =27, k =8 and

p =0.05) is 3.3�10�5, with a probability of 4�10�3 that

they would all be in the expected direction.

With respect to our a priori hypotheses, we expected

to observe associations between HgU and measures of

memory and manual coordination. Among DDs, we

observed significance for three of six memory outcomes

( p =2�10�3), and four out of seven manual coordina-

tion outcomes ( p =2�10�4). Among DAs, the corre-

sponding numbers for memory and manual coordination

were one out of six with p =0.23, and two out of seven

with p=0.04.

Not shown in our tables is the impressive consistency of

movement in the expected direction even among outcomes

that had weaker relationships with HgU ( p>0.10). Only

four of these measures moved in the unexpected direction.

3.4. Mercury exposure and a determination of behavioral

threshold levels

Fig. 1 shows the underlying distribution of HgU in the

current dental population in Washington State (n =2196) in

comparison to that of a national sample that were collected

in 1990–1996 (n =6925 dentists). The respective mean

urinary levels of our dentists and dental assistants were

lower at 3.32 (S.D.=4.87) Ag/l and 1.98 (S.D.=2.29) Ag/l.These urinary levels are comparable to two general

population estimates of exposure conducted on a military

cohort [44] and a New York City urban sample [25].

Fig. 2 presents our evaluation for a lower threshold of

adverse effect using Hand Steadiness Factor1, an occupation-

ally relevant task pertinent to dental populations. Using a

segmented non-linear test, we found no threshold of

mercury exposure, supporting the argument that no safe

Table 5

Multiple regression modela coefficients demonstrating associations with urinary mercury (Ag/l) and the BDNFPolymorphism

DDs (n =194) DAs (n =233)

B S.E. Beta p b S.E. Beta b

Associations with urinary mercury (Ag/l) (HgU)Attention (n=3)

Digit spanbForward �0.43 0.18 �0.16 0.02 �0.17 0.09 �0.13 0.05

Working memory (n=3)

Digit spanbBackward �0.45 0.23 �0.16 0.05 ns

Spatial spanbBackward �0.26 0.13 �0.16 0.04 ns

Visual memory (n=3)

Visual reproductioncN Correct �0.03 0.01 �0.11 0.04 �0.04 0.02 �0.14 0.03

Perception (n=2)

Pattern discriminationbRate ns �0.03 0.01 �0.13 0.05

Perceptual speed (n=1)

Symbol digitbRate �0.16 0.09 �0.13 0.06 �0.01 0.01 �0.13 0.03

Cognitive flexibility (n=2)

Trailmakingb B ns �0.01 0.005 �0.15 0.02

Complex coordination (n=1)

Finger tapbAlternate Partial �0.30 0.14 �0.15 0.03 �0.22 0.11 �0.13 0.05

Manual coordination skills (n=7)

Finger tapbDominant �0.04 0.02 �0.17 0.02 �0.04 0.01 �0.18 0.00

Finger tapbAlternate �0.07 0.03 �0.21 0.00 ns

Hand steadinessFactor1 0.50 0.20 0.30 0.01 0.28 0.13 0.70 0.03

Trackingb score �0.03 0.01 �0.14 0.04 ns

Sensory test

Vibration sensitivityTLV �0.08 0.04 �0.09 0.09 �0.06 0.03 �0.14 0.06

Peripheral nerve integrity: nerve conduction velocities

NC velocity latencyd ns 0.54 0.24 0.18 0.03

NC velocity latencye ns 0.27 0.42 0.10 ns

Associations with BDNFPolymorphism

Working Memory (n=2)

Digit spanbBackward �0.74 0.41 �0.13 0.07 �0.72 0.37 �0.14 0.05

Visual memory (n=3)

Visual reprodcN Correct ‘ 0.04 �0.10 0.07 �0.11 0.06 �0.11 0.08

Pattern memorybRate �0.04 0.02 �0.13 0.06 ns

Pattern memorybN Correct �0.05 0.02 �0.17 0.02 ns

Sustained attention (n=1)

VigilancebHits ns �0.02 0.01 �0.11 0.09

Cognitive flexibility (n=2)

Trailmakingb B ns �0.69 0.30 �0.14 0.04

Complex coordination (n=1)

Finger tapbAlternate Partial �0.38 0.15 �0.18 0.01 �0.26 0.14 �0.12 0.06

Manual coordination skills (n=7)

Finger tapbDominant ns �0.03 0.02 �0.12 0.07

Finger tapbAlternate �0.07 0.03 �0.15 0.04 ns

Hand steadinessFactor1 �0.88 0.52 �0.20 0.09 �1.09 0.51 �0.42 0.04

D. Echeverria et al. / Neurotoxicology and Teratology 27 (2005) 781–796790

Table 5 (continued)

DDs (n =194) DAs (n =233)

B S.E. Beta p b S.E. Beta b

Associations with urinary mercury (Ag/l) (HgU)Peripheral nerve integrity: nerve conduction velocities (n=2)

NC velocity latencyd �0.50 0.23 (�0.17) 0.03 ns

NC velocity latencye ns ns

a Regression models for DDs control for age, alcohol consumption, and BEES vocabulary. Regression models for DAs also include level of education.b BEES.c WMS-R.d Analyses included all DDs and results were in the unexpected direction noted by (I).e Analysis was restricted to 111 males without a history of repetitive trauma (84 subjects were removed).

D. Echeverria et al. / Neurotoxicology and Teratology 27 (2005) 781–796 791

level exists that prevents potential effects in motor

coordination (see Fig. 2).

4. Discussion

The results of this study present statistically significant

declines for several domains of cognitive and motor

performance related to HgU concentrations below 4 Ag/l,but verbal intelligence and reaction time remain intact.

Associations occurred at mean HgU levels of 3.32 (4.87)

Ag/l in dentists and 1.98 (2.29) Ag/l in dental assistants, an

exposure level that is within the range of urinary mercury

levels in the general population. In both groups, there was a

sufficient range in exposure from unexposed (zero) to 16 Ag/l to detect subtle behavioral effects. This study also found

adverse relationships between performance and BDNF

status limited to a small number of measures.

The results of this study are best understood in the

context of two distinct occupational populations. DDs are all

males and have a uniformly high level of education and

-2 0 2 4 6 8 10 12 14 16 18

Urinary Mercury (HgU in u

0

200

400

600

800

1000

1200

1400

1600

1800

2000

2200

2400 WA Stmeran

Nationmeran

New Ymeran

US Mimeran

Nu

mb

er o

f P

eop

le

=

=

=

=

Fig. 1. The distributions of urinary mercury levels for two dental populations are c

levels for Washington State dentists are lower than levels for a US national sam

symbolized by triangles, are comparable to mean urinary mercury levels of 3.32 (S

this study.

rigorous professional training consistent with observed high

scores for BEES [23] vocabulary and for the TONI-3 [12].

Consistent with their occupation, they all have a high socio-

economic status. The uniformity of this population enhances

detection of subtle changes in performance associated with

very low Hg exposures or BDNF status. On the other hand,

their high level of training may mask other effects and

improve test-taking ability.

In contrast, DAs are all female and have more variable

and lower mean levels of socio-economic status, education,

and training. Variations in these important determinants of

performance may hide small changes associated with Hg

exposure or BDNF status among DAs. They also incur

significantly lower observed levels and durations of

exposure to mercury compared to DDs.

4.1. Cognitive exposure-effect relationships within DDs and

DAs

Adverse Hg-related exposure-effects were observed

among both DDs and DAs for the BEES [23] Digit

20 22 24 26 28 30

g/l)

ate Dentists, 1998 (n = 2,196)an(sd) = 2.51 (3.01) ug/lge = 0 - 67 µg/l

al Dental Population, 1990-1996 (n = 6,925)an (sd) = 5.27 (6.51) ug/lge = 0 - 104 µg/l

ork City Residential Population99

an = 1.7 ug/lge = 0.09 -17.8 µg/l

litary Population98

an = 3.1 ug/lge = 0.0 - 34.0 µg/l

ompared to mean urinary levels reported for two general populations. HgU

ple of dentists. The two general population mean urinary mercury levels,

.D.=4.87) Ag/l for dentists and 1.98 (S.D.=2.29) Ag/l for dental assistants in

DDS

0.0 1.0

Ln HgU in µg/l

Ln

Han

d S

tead

ines

s Fa

cto

r

2.0 3.0 0.0 1.0Ln HgU in µg/l

2.0 3.0

.8

.0

-.8

.8

.0

-.8

DAs

Fig. 2. The graphs plot Hand SteadinessFactor against increasing urinary

mercury. The lack of a lower threshold of effect was confirmed using a

segmented (branched) non-linear test. The model estimated s, a lower

threshold of effect, at ¨0.0000 Ag/l mercury in urine. Note that the model

sets P=k when HgU is less than s and P=k +b(ln HgU) when HgU is

greater than s where s must be greater than zero.

D. Echeverria et al. / Neurotoxicology and Teratology 27 (2005) 781–796792

Span Forward in the domain of attention, and for the WMS-R

Visual Reproduction Test [89] in the domain of visual

memory. Both of these associations are consistent with our a

priori hypotheses. We also observed an association with the

derived measure of BEES [23] Finger tapAlternate Partial. This

measure, designed to evaluate the coordination required to

alternate finger taps, appears sensitive to exposure to

mercury. The fact that these results were observed in both

DDs and DAs, two distinct populations increase the validity

of these findings.

Several adverse relationships between performance and

exposure to mercury were also observed among DDs

alone. These include both BEES [23] Digit and Spatial

SpanBackward, which are measures of working memory.

This result was likely related to reduced attention and

memory that was also observed in DDs.

Unique to DAs, adverse exposure-effect relationships

were found on performance scores for the BEES [23]

Pattern Discrimination, the BEES [23] Symbol-Digit, and

the BEES [23] Trailmaking B tests within the domains of

perception, visuomotor processing, and cognitive flexibility.

The pattern of these results, dispersed across several

domains, was distinct from that for DDs. These unique

observations may be due to the lower level of training

among DAs compared to DDs.

Adverse associations between cognitive performance and

the BDNF polymorphism were not replicated between the

DDs and DAs for any measure. Reduced performance on

Pattern MemoryNumber Correct was associated with a BDNF

polymorphism among DDS and for the BEES [23] Digit

SpanBackward and Trailmaking B among DAs. Note that each

task has visual processing components that require encoding

and some retrieval. These associations within related

domains of memory are also consistent with one report

from an independent study. That study observed an

association between the BDNF polymorphism and reduced

hippocampal activity using functional magnetic resonance

imaging while participants performed a memory task of

encoding and retrieving [38]. Our results are consistent with

these findings.

It is interesting to note that in the case of Trailmaking B

among DAs cognitive performance is associated with both

mercury and BDNF. The observed joint-effect was strictly

additive (no interaction is seen) and suggests that there may

be independent mechanisms of action.

4.2. Consistency with literature on cognitive effects of

exposure to mercury

The cognitive results observed among subjects with HgU

levels as low as 3.0 Ag/l extends previously observed linear

exposure-effect relationships [2,3,51,82] to a very low

threshold of effect, partially replicating previous observa-

tions reported in a smaller cohort of 58 dental personnel

exposed to <4 Ag/l HgU [22]. Adverse relationships for

WMS-R Visual Reproduction [89] and Trailmaking B [69]

test were replicated but we also report one additional

significant decline in performance for the BEES [23]

Symbol-DigitRate among DAs that was not found earlier.

In contrast, this study did not replicate declines in

Trailmaking A and BEES [23] Switching Ability scores.

The differences in results are likely attributable to the dental

pool, size, test battery, and test conditions. Unlike the first

study in which participants were enrolled and tested over 2

days at a professional meeting, subjects in the current study

were more rigorously selected and tested in controlled

laboratory conditions using a touch screen test battery.

Our results are also in general agreement with results

from earlier dental studies that report adverse relationships

at slightly higher exposures [9,62,74,79,85,95]. One dental

study [62] examined 98 dentists and 54 non-dentist controls

in Singapore, where mean exposures of 16.7 Ag/m3 Hg0 in

air were associated with differences in attention: Digit

SpanForward, working memory: Digit SpanBackward, visual

memory: Visual Reproduction, logical memory: Logical

Memory Delayed Recall, visuomotor processing: Symbol-

Digit, and cognitive flexibility: Trail Making B. Two earlier

dental studies [79,85] also found adverse relationships with

visual–constructional memory performance scores. In the

first study [85] DDs with elevated mercury levels scored

significantly less well on the Bender Visual Motor Gestalt

Test (Bender-Gestalt) which is a drawing (copying) test. In

the second study [79], DAs performance scores declined on

a Recurrent Figures task but not on the WAIS, Rey’s

Auditory–Verbal Learning Test (AVLT), or Paced Auditory

Serial Addition Test (PASAT). Collectively, the results of

these dental studies provide similar evidence of adverse

relationships between exposure to mercury and declines in

performance on tests although this study reports effects at

lower exposure levels.

4.3. Associations between manual coordination skills and

exposure to mercury

Hg-related declines in performance were also observed

among both groups for the BEES [23] Finger TapDominant,

D. Echeverria et al. / Neurotoxicology and Teratology 27 (2005) 781–796 793

measuring finger speed, and Hand SteadinessFactor1, meas-

uring manual coordination. Both associations were consis-

tent with our a priori hypotheses. They were observed in

both groups of DDs and DAs, increasing the validity of

these relationships.

There also was an adverse association with BEES [23]

Tracking, which requires complex perceptual–motor skills

to track a moving target using a joystick. Performance on

tracking tasks involves multiple brain regions, including

anterior cortical and subcortical motor regions, and posterior

visual–perceptual regions. Because there was evidence of

diminished manual coordination skill on other motor tests

among DDs, but no evidence of reduced reaction times on

either the BEES [23] Simple or Choice Reaction Time

scores, the decline in Tracking ability may likely be related

to reduced attention as well as diminished manual skill.

It is noteworthy that declines in motor performance

among DAs were not as uniformly affected by exposure to

mercury as among DDs. Four out seven measures declined

with exposure to mercury whereas only two measures were

affected among DAs. The reduced sensitivity among DAs

may be attributable to greater variance in dexterity skills

that could reduce detection of effects. For example, the

coefficients of variation (CV=S.D./mean) were consis-

tently greater for DAs on most motor tasks. For Finger

TappingDominant and Alternate, the CVs in DDs and DAs

were, respectively, 0.16 vs. 0.21 and 0.13 vs. 0.17. For

Tracking scores, the respective CVs were 0.14 vs. 0.24 and

could account for the lack of significance.

4.4. Consistency with literature on motor effects from

exposure to mercury

As described in the Introduction, functional declines in

scores for Hand Steadiness [9,13,34], Finger Tapping

[45,75], vibration sensitivity, and manual dexterity

[16,58,76,81,93] have all been reported at relatively low

exposure levels (<26 Ag/l) [13,39]. This is consistent with

our own results for comparable tests of motor function [9]

and when using the NES2 Finger Tapping [49] and the same

Hand Steadiness Battery [34]. The converging evidence

supports our conclusions that performance related to manual

coordination skills is vulnerable to mercury exposure.

4.5. Associations between manual coordination skills and

the BDNF polymorphism

The two associations between BDNF polymorphism and

Finger TappingAlternate and Hand SteadinessFactor1 were not

expected [28]. We were able to explain these findings by

examining how performance on these motor tests depended

on other cognitive abilities. When we co-varied out the effect

of finger speed in both hands from Finger TappingAlternate,

the resultant residual term Finger TappingAlternate Partial,

remained associated with the BDNF polymorphism in

DDs. The analysis suggests that it is the complex

coordination required to alternate finger taps that is most

likely affected by the BDNF polymorphism. This observa-

tion is also consistent for both DDs and DAs. Similarly, the

only statistically significant association with the first and

largest hole for the Hand SteadinessFactor1 task suggests that

BDNF was primarily associated with the attention required

to first learning the task. In addition, other important motor

function tests, such as manual Tracking, were not

significantly associated with BDNF polymorphism. There-

fore, we do not believe our results demonstrate new

adverse associations between the BDNF polymorphism

and motor function.

4.6. Limitations and strengths

The lack of association with any index of cumulative

exposure or even past peak exposure was not expected.

Associations with peak exposures were anticipated, as

residual declines in peroneal and ulnar motor NCVs and

tremor have been observed in workers with remote (40 years

earlier), albeit high (mean HgU=171 Ag/l), mercury

exposures [2,3,50]. Also, given that DDs have an average

of 18 years of mercury-related exposure, it would be

expected that chronic effects might be more prevalent. We

expect that the lack of observed effects may be related to

inaccurate estimates of past exposures, compounded by

current circulating mercury levels acting as a surrogate for

past mercury exposure intensities (duration already being

removed by controlling for age). Of course, it is also

possible that, at these low levels, current circulating

exposure dominates the actual effects associated with

mercury.

In previous studies, we have estimated body burden by

chelating DDs and measuring mobilized Hg in urine over a

6-h period immediately following administration of the

water-soluble chelator, 2,3-dimercaptopropane-1-sulfonate

(DMPS, Dimaval) [8,94]. This procedure yielded HgU

excretions that are consistently ¨10-fold greater in dental

groups [22,60,96] representing a stable fraction about 17–

20% of stored mercury. While pronounced associations

observed in this study were for circulating HgU (mean¨1–

5 Ag/l) [22], additional associations related to mobilized

HgU were observed. The use of a chronic exposure index

clearly has limitations compared to a biological marker, but

human subject protection issues have limited the use of

chelation on otherwise healthy subjects in the United States.

We also know little about potential differences in effects

from distinct dose rates associated with continuous very

low-dose mercury exposure from personal amalgam in one’s

mouth, and intermittent higher occupational exposures from

working with amalgam. Both sources of exposure contribute

to circulating HgU. However, independent of the rate of

exposure, or whether the exposure is chronic or acute, the

results of multiple studies in different populations have

shown a broad distribution of behavioral effects associated

with HgU.

D. Echeverria et al. / Neurotoxicology and Teratology 27 (2005) 781–796794

Despite these limitations, the strength of our results was

to demonstrate behavioral relationships for domains con-

sistent with a priori expectations across two diverse dental

populations. The fact that specific measures within these

domains were not always consistently affected for DDs and

DAs may be partially explained by both differences between

the groups and the duration of exposure. The consistency of

observed declines in performance with increasing HgU,

given that urinary mercury levels were unknown to

participants and researchers at the time of examination, is

also difficult to explain by bias or chance. Our ability to

observe these relationships was also facilitated by conduct-

ing exposure-effect analyses within a well-defined group.

Employing comparisons with an external, unexposed con-

trol group would introduce systematic and unwanted error in

the analysis.

We also observed associations with the BDNF poly-

morphism in a small number of performance measures.

However, the relationship between BDNF status and manual

coordination skills was not expected and was otherwise

explained by factors unrelated to exposure.

Lastly, we report new evidence for potential declines in

working memory (BEES [23] Digit SpanBackwards), visual

memory (WMS-R Visual Reproduction), as well as in

cognitive flexibility (BEES [23] Trailmaking B), that are

associated with both exposure to elemental mercury and the

BDNF polymorphism in an additive manner. The results are

also applicable to health research in the general population,

particularly as reported mercury levels overlap with that in

the general population [25,44] and the prevalence of BDNF

polymorphisms are common as reported in this study

population. Investigators are further encouraged to identify

vulnerable groups where one can distinguish the impact of

personal genetic factors from the toxic effects of mercury in

the environment.

Acknowledgements

This work was supported in part by the following: Grant

5 RO1 DE11712 from the National Institute of Dental and

Craniofacial Research; Grant P30ES07033 from the

National Institute of Environmental Health Sciences for

the University of Washington Center for Ecogenetics and

Environmental Health; and grant P42ES04696 from the

University of Washington Superfund Program Project. The

Wallace Research Foundation provided additional funding.

References

[1] I. Akesson, A. Schutz, R. Attewell, S. Skerfving, P. Glantz, Status of

mercury and selenium in dental personnel: impact of amalgam work

and own fillings, Arch. Environ. Health 46 (2) (1991) 102–109.

[2] J.W. Albers, G.D. Cavender, S.P. Levine, G.D. Langolf, Asympto-

matic sensorimotor polyneuropathy in workers exposed to elemental

mercury, Neurology 32 (1982) 1168–1174.

[3] J.W. Albers, L.R. Kallenbach, L.J. Fine, G.D. Langolf, R.A. Wolfe,

P.D. Donofrio, A.G. Alessi, K.A. Stolp-Smith, M.B. Bromberg, and

the Mercury Workers Study Group, Neurological abnormalities

associated with remote occupational elemental mercury exposure,

Ann. Neurol. 24 (5) (1988) 651–659.

[4] W.K. Anger, Neurobehavioral tests used in NIOSH supported

worksite studies, 1973–1983, Neurobehav. Toxicol. Teratol. 7

(1985) 359–368.

[5] W.K. Anger, O.J. Sizemore, S.J. Grossmann, J.A. Glasser, R. Letz, R.

Bowler, Human neurobehavioral research methods: impact of subject

variables, Environ. Res. 73 (1997) 18–41.

[6] G. Angotzi, M.G. Cassitto, D. Camerino, R. Cioni, E. Desideri, A.

Franzinelli, R. Gori, F. Loi, E. Sartorelli, Relationship between

mercury exposure and health in workers of a mercury distillation

plant in the province of Siena, G. Ital. Med. Lav. 6 (1980) 463–480.

[7] R.W. Amler, W.K. Anger, O.J. Sizemore, Adult Environmental

Neurobehavioral Test Battery (AENTB), US Department of Health

and Human Services, Agency for Toxic Substances and Disease

Registry, Atlanta, GA, 1995.

[8] H.V. Aposhian, Mobilization of mercury and arsenic in humans by

sodium 2,3-dimercapto-1-propane sulfonate (DMPS), Environ. Health

Perspect. 106 (Suppl. 4) (1998) 1017–1025.

[9] A. Bittner, D. Echeverria, J.S. Woods, N. Heyer, M. Martin, N.

Mahurin, Behavioral effects of low level exposure to mercury Hg

among dental professionals: a cross-study evaluation of psychomotor

effects, Neurotoxicol. Teratol. 20 (4) (1998) 429–439.

[10] R.E. Bluhm, J.A. Breyer, R.G. Bobbitt, L.W. Welch, A.J.J. Wood,

R.A. Branch, Elemental mercury vapor toxicity, treatment, and

prognosis after acute intensive exposure in chloralki plant workers: I.

History of Hyperchloraemia and genetouria symptoms, Hum. Exp.

Toxicol. 11 (1992) 211–215.

[11] M. Bornhausen, H.R. Musch, H. Greim, Operant behavior perform-

ance changes in rats after prenatal methylmercury exposure, Toxicol.

Appl. Pharmacol. 56 (1980) 305–310.

[12] B. Brown, R. Sherbenou, S. Johnsen, Test of Non-verbal Intelligence

TONI-3, Pro-ed, Austin, TX, 1997.

[13] K. Bueller, Motor effects of low-level Hg0 exposure in dentists.

Master’s Thesis, Department of Environmental Health, School of

Public Health, University of Washington, Seattle, WA, 1994.

[14] T.M. Burbacher, P.M. Rodier, B. Weiss, Methylmercury developmen-

tal neurotoxicity: a comparison of effects in humans and animals,

Neurotoxicol. Teratol. 12 (1990) 191–202.

[15] J. Burkhalter, H. Fiumelli, I. Allaman, J.Y. Chatton, J.L. Martin,

Brain-derived neurotropic factor stimulates energy metabolism in

developing cortical neurons, J. Neurosci. 23 (2003) 8212–8220.

[16] D.B. Chaffin, B.D. Dinman, J.M. Miller, R.G. Smith, D.H. Zentine,

An Evaluation of the Effects of Chronic Mercury Exposures on

EMG and Psychomotor Functions. Contract No. HSM-099-71-62,

National Institute for Occupational Safety and Health, Cincinnati,

1973.

[17] L. Chang, K. Reuhl, Mercury in human and animal health, in: J. Rose

(Ed.), Trace Elements in Health: a Review of Current Issues, 1983,

pp. 132–149.

[18] T.W. Clarkson, The pharmacology of mercury compounds, Annu. Rev.

Pharmacol. 12 (1972) 375–406.

[19] T.W. Clarkson, L. Friberg, J.B. Hursh, M. Nylander, The prediction of

intake of mercury vapor from amalgams, Biological Monitoring of

Toxic Metals, Plenum Press, New York, 1988, pp. 247–260.

[20] T.W. Clarkson, L. Magos, G.J. Myers, The toxicology of mercury-

current exposures and clinical manifestations, N. Eng. J. Med. 349

(2003) 1731–1737.

[21] D. Echeverria, N.J. Heyer, M.D. Martin, C.A. Naleway, J.S. Woods,

A.C. Bittner, Behavioral effects of low-level exposure to Hg0 among

dentists, Neurotoxicol. Teratol. 17 (1995) 161–168.

[22] D. Echeverria, H.V. Aposhian, J.S. Woods, N.J. Heyer, M.M.

Aposhian, A.C. Bittner Jr., R.K. Mahurin, M. Cianciola, Neuro-

behavioral effects from exposure to dental amalgam Hg0: new

D. Echeverria et al. / Neurotoxicology and Teratology 27 (2005) 781–796 795

distinctions between recent exposure and Hg body burden, The

FASEB Journal 12 (1998) 971–980.

[23] D. Echeverria, N.J. Heyer, A.C. Bittner Jr., D. Rohlman, J.S. Woods,

Test– retest reliability and factor stability of the behavioral evaluation

for epidemiology studies test battery, Percept. Mot. Skills 95 (2002)

845–867.

[24] M.F. Egan, M. Kojima, J.H. Callicott, T.E. Goldberg, B.S. Kolachana,

A. Bertolino, E. Zaitsev, B. Gold, D. Goldman, M. Dean, B. Lu, D.R.

Weinberger, The BDNF val66met polymorphism affects activity-

dependent secretion of BDNF and human memory and hippocampal

function, Cell 112 (2003) 257–269.

[25] P. Factor-Litvak, G. Hasselgren, D. Jacobs, M. Begg, J. Kline, J. Geier,

N. Mervish, S. Schoenholtz, J. Graziano, Mercury derived from dental

amalgams and neuropsychologic function, Environ. Health Perspect.

111 (2003) 719–723.

[26] M. Forzi, M.G. Cassito, C. Bulgheron, V. Foa, Psychological

measures in workers occupationally exposed to mercury vapours: a

validation study, in: M. Horvath (Ed.), Adverse Effects of Environ-

mental Chemicals and Psychotropic Drugs, vol. 2, Elsevier Scientific

Publishing Company, Amsterdam, 1976, pp. 165–171.

[27] L. Friberg, N.K. Mottet, Accumulation of methylmercury and

inorganic mercury in the brain, Biol. Trace Elem. Res. 21 (1989)

201–206.

[28] Y. Ge, S.M. Belcher, K.E. Light, Alterations of cerebellar mRNA

specific for, BDNF., p75NTR, and TrkB receptor isoforms occur within

hours of ethanol administration to 4-day-old rat pups, Dev. Brain Res.

151 (2004) 99–109.

[29] F. Gerr, D. Hershman, R. Letz, Vibrotactile threshold measurement for

detecting neurotoxicity: reliability and determination of age- and

height-standardized normative values, Arch. Environ. Health 45

(1990) 148–154.

[30] H.B. Gerstner, J.E. Huff, Clinical toxicology of mercury, J. Toxicol.

Environ. Health 2 (1977) 491–526.

[31] H.B. Gerstner, J.E. Huff, Selected case histories and epidemiologic

examples of human mercury poisoning, Clin. Toxicol. 11 (2) (1977)

131–150.

[32] D. Gonzales-Ramirez, R.M. Mairino, M.Z. Zuniga-Charles, K.M.

Hurlbut, P. Junco-Munoz, M. Aposhian, R.C. Dart, J.H.D. Gama,

D. Echeverria, J.S. Woods, H.V. Aposhian, Sodium 2,3-dimercap-

topropane-1-sulfonate challenge test for mercury in humans: II.

Urinary mercury, porphyrins, and neurobehavioral changes of dental

workers in Monterrey, Mexico, J. Pharmacol. Exp. Ther. 272

(1995) 264–274.

[33] S. Halbach, T.W. Clarkson, Enzymatic oxidation of mercury vapor by

erythrocytes, Biochim. Biophys. Acta 523 (1978) 522–531.

[34] Hand Motor Steadiness Battery, The Lafayette Instrument Company,

Purdue, IN, 2000.

[35] H. Hanninen, Behavioral effects of occupational exposure to mercury

and lead, in: J. Juntunen (Ed.), Occupational Neurology, Acta Neurol.

Scand., Suppl 92, 66, 1982, pp. 167–175.

[36] M. Hanson, J. Pleva, The dental amalgam issue. A review, Experientia

(1991) 47.

[37] R.J. Hargreaves, J.G. Evans, I. Janota, L. Magos, J.B. Cavanagh,

Persistent mercury in nerve cells 16 years after metallic mercury

poisoning, Neuropathol. Appl. Neurobiol. 14 (1988) 443–452.

[38] A.R. Hariri, T.E. Goldberg, V.S. Mattay, B.S. Kolachana, J.H.

Callicott, M.F. Egan, D.R. Weinberger, Brain-derived neurotrophic

factor val66met polymorphism affects human memory-related hippo-

campal activity and predicts memory performance, J. Neurosci. 23

(2003) 6690–6694.

[39] R. Henderson, H.P. Shotwell, L.A. Krause, Analysis for total, ionic,

and elemental mercury in urine as a basis for biological standard, Am.

Ind. Hyg. Assoc. J. 38 (1974) 576–580.

[40] N.J. Heyer, D. Echeverria, A.C. Bittner Jr., F.M. Farin, C.C.

Garabedian, J.S. Woods, Chronic low-level mercury exposure, BDNF

polymorphism, and associations with self-reported symptoms and

mood, Toxicol. Sci. 81 (2004) 354–363.

[41] J.B. Hursh, M.G. Cherian, T.W. Clarkson, J.L. Vostal, R. Vander

Mallie, Clearance of mercury (Hg-197, Hg-203) vapor inhaled by

human subjects, Arch. Environ. Health 1976 (Nov./Dec.) 302–309.

[42] M.M. Joselow, L.T. Goldwater, Absorption and excretion of mercury

in man: XII. Relationship between urinary mercury and proteinia,

Arch. Environ. Health 15 (1967) 155–159.

[43] L.R. Kallenbach (1988). Health evaluation and reported symptoms in

workers with past occupational exposure to elemental mercury. PhD

thesis, University of Michigan.

[44] A. Kingman, T. Albertini, L.J. Brown, Mercury concentrations in

urine and whole blood associated with amalgam exposure in a US

military population, J. Dent. Res. 77 (1998) 461–471.

[45] G.D. Langolf, D.B. Chaffin, R. Henderson, H.P. Whittle, Evaluation

of workers exposed to elemental mercury using quantitative tests of

tremor and neuromuscular functions, Am. Ind. Hyg. Assoc. J. 39

(1978) 976–984.

[46] G.D. Langolf, T.J. Smith, R. Henderson, H. Whittle, Measurements of

neurological functions in the evaluations of exposure to neurotoxic

agents, Ann. Occup. Hyg. 24 (3) (1981) 293–296.

[47] S. Langworth, O. Almkvist, E. Soderman, B.O. Wikstrom, Effects of

occupational exposure to mercury vapour on the central nervous

system, Br. J. Ind. Med. 49 (1992) 545–555.

[48] J. Lebel, D. Mergler, M. Lucotte, M. Amorin, S. Dolbec, Early

neurobehavioral effects of methyl mercury exposure in the Amazon: a

preliminary study, Presented at the International Neurotoxicology

Congress, ‘‘Neurotoxicity of mercury: indicators of effects of low level

exposures’’. Hot Springs, Arkansas, 1994.

[49] R. Letz, The NES2 User’s Manual, Neurobehavioral Systems, Inc.,

Winchester, MA, 1993.

[50] R. Letz, F. Gerr, D. Cragle, R.C. Green, J. Watkins, A.T. Fidler,

Residual nuerological deficits 30 years after occupational exposure to

elemental mercury, Neurotoxicology 21 (4) (2000) 459–474.

[51] S.P. Levine, G.D. Cavender, G.D. Langolf, J.W. Albers, Elemental

mercury exposure: peripheral neurotoxicity, Br. J. Ind. Med. 39 (1982)

136–139.

[52] H.B. Lovejoy, Z.G. Bell, T.R. Vizena, Mercury exposure evaluations

and their correlation with urine mercury excretions: 4. Elimination of

mercury by sweating, J. Occup. Med. 15 (1973) 590–591.

[53] L. Magos, S. Halbach, T.W. Clarkson, Role of catalase in the oxidation

of mercury vapor, Biochem. Pharmacol. 27 (1978) 1373–1377.

[54] L. Magos, T.W. Clarkson, A.R. Hudson, The effects of dose of

elemental mercury and first-pass circulation time on exhalation and

organ distribution of inorganic mercury in rats, Biochim. Biophys.

Acta 99 (1) (1989) 85–89.

[55] J. Marx, Minor variation in growth-factor gene impairs human

memory, Science 299 (2003) 639–640.

[56] R.E. Maser, V.K. Nelson, E.B. Bass, Q. Manjo, J.S. Donoman, S.F.

Kelsey, B.J. Becker, T.S. Orchard, Measuring diabetic neuropathy

assessment and comparison of clinical examination and quantitative

sensory testing, Diabetes Care 12 (1989) 270–275.

[57] D.M. McNair, M. Lorr, L.F. Droppleman, EITS Manual—Profile of

Mood States, Educational and Industrial Testing Service, San Diego,

CA, 1971.

[58] J.M. Miller, D.B. Chaffin, R.G. Smith, Subclinical psychomotor and

neuromuscular changes in workers exposed to inorganic mercury, Am.

Ind. Hyg. Assoc. J. 36 (1975) 725–733.

[59] J. Milne, A. Christophers, P. DeSilva, Acute mercurial pneumonitis,

Br. J. Ind. Med. 27 (1970) 334–338.

[60] M. Molin, A. Schutz, S. Skerfving, G. Sallsten, Mobilized mercury in

subjects with varying exposure to elemental mercury vapour, Int.

Arch. Occup. Environ. Health 63 (3) (1991) 187–192.

[61] C. Naleway, R. Sakaguchi, E. Mitchell, T. Muller, W.A. Ayer, J.J.

Hefferren, Urinary mercury levels in US dentists, 1975–1983: review

of Health Assessment Program, JADA 111 (1985) 37–42.

[62] C.H. Ngim, S.C. Foo, K.W. Boey, J. Jeyaratnam, Chronic neuro-

behavioral effects of elemental mercury in dentists, Br. J. Ind. Med. 49

(1992) 782–790.

D. Echeverria et al. / Neurotoxicology and Teratology 27 (2005) 781–796796

[63] M. Nylander, J. Weiner, Mercury and selenium concentrations and

their inter-relationships in organs from dental staff and the general

population. Published Dissertation, Institute of Environmental Medi-

cine, Karolinska Institutet, Stockholm, 1990, pp.124.

[64] Optec 2000/25000 Vision Testing System, Stereo Optical, Inc,

Chicago, IL 60641, 1998.

[65] L. Piikivi, H. Hanninen, Subjective symptoms and psychological

performance of chlor-alkali workers, Scand. J. Work, Environ. &

Health 15 (1) (1989) 69–74.

[66] L. Piikivi, H. Hanninen, T. Martelin, P. Mantere, Psychological

performance and long-term exposure to mercury vapors, Scand. J.

Work, Environ. & Health 10 (1984) 35–41.

[67] S.D. Pingree, P.L. Simmonds, J.S. Woods, Effects of 2,3-Dimercapto-

1-propanesulfonate Acid (DMPS) on Tissue and Urine Mercury

Levels following Prolonged Methylmercury Exposure in Rats,

Toxicol. Sci. 61 (2001) 224–233.

[68] J.K. Piotrowski, B. Trojanowska, E.M. Mogilnicka, Excretion

kinetics and variability of urinary mercury in workers exposed to

mercury vapour, Int. Arch. Occup. Environ. Health 35 (1975)

245–256.

[69] R.M. Reitan, Theoretical and methodological bases of the Halstead-

Reitan neuropsychological test battery, in: I. Grant, K. Adams (Eds.),

Neuropsychological Assessment of Neuropsychiatric Disorders,

Oxford University Press, New York, 1986.

[70] D.C. Rice, Brain and tissue levels of mercury after chronic

methylmercury exposure in the monkey, J. Toxicol. Environ. Health

27 (1989) 189–198.

[71] D.C. Rice, Delayed neurotoxicity in monkeys exposed developmen-

tally to methylmercury, Neurotoxicology 10 (1989) 645–650.

[72] D.C. Rice, S.G. Gilbert, Effects of developmental exposure to methyl

mercury on spatial and temporal visual function in monkeys, Toxicol.

Appl. Pharmacol. 102 (1990) 151–163.

[73] D.C. Rice, S.G. Gilbert, Exposure to methylmercury from birth to

adulthood impairs high-frequency hearing in monkeys, Toxicol. Appl.

Pharmacol. 115 (1992) 6–10.

[74] K.A. Ritchie, W.H. Gilmour, E.B. Macdonald, F.J.T. Burke, D.A.

McGowan, I.M. Dale, R. Hammersley, R.M. Hamilton, V. Binnie,

D. Collington, Health and neuropsychological functioning of

dentists exposed to mercury, J. Occup. Environ. Med. 59 (5)

(2002) 287–293.

[75] H. Roels, R. Lauwerys, J.P. Buchet, A. Bernard, A. Barthels, M.

Oversteyns, J. Gaussin, Comparison of renal function and psychomo-

tor performance in workers exposed to elemental mercury, Int. Arch.

Occup. Environ. Health 50 (1982) 77–93.

[76] H. Roels, J.P. Gennart, R. Lauwerys, J.P. Buchet, J. Malchaire, A.

Bernard, Surveillance of workers exposed to mercury vapour:

validation of a previously proposed biological threshold limit value

for mercury concentration in urine, Am. J. Ind. Med. 7 (1985) 45–71.

[77] D.S. Rohlman, S.R. Bailey, M. Brown, M. Blanock, W.K. Anger, L.

McCauley, Establishing stable test performance in tests from the

Behavioral Assessment and Research System (BARS), Neurotoxicol-

ogy 21 (5) (2000) 715–724.

[78] F. Schuckmann, Study of preclinical changes in workers exposed to

inorganic mercury in chloralkali plants, Int. Arch. Occup. Environ.

Health 44 (1979) 193–200.

[79] I.M. Shapiro, D.R. Cornblath, A.J. Sumner, B. Uzzell, L.K. Spitz,

I.I. Ship, P. Bloch, Neurophysiological and neuropsychological

function in mercury-exposed dentists, The Lancet 1 (8282) (1982)

1147–1150.

[80] A. Smargiassi, E. Bergamaschi, A. Mutti, M.T. Cella, Predictive

validity of the Q16 questionnaire: a comparison between reported

symptoms and neurobehavioral tests, Neurotoxicology 19 (1998)

703–708.

[81] R.G. Smith, A.J. Vorwald, L.S. Patil, T.F. Mooney, Effects of exposure

to mercury in the manufacture of chlorine, Am. Ind. Hyg. Assoc. J. 31

(1970) 687–700.

[82] T.J. Smith, G.D. Langolf, J. Goldberg, Effects of occupational

exposure to elemental mercury on short term memory, Br. J. Ind.

Med. 40 (1983) 413–419.

[83] L. Soleo, M.L. Urbano, V. Petrera, L. Ambrosi, Effects of low

exposure to inorganic mercury on psychological performance, Br. J.

Ind. Med. 47 (1990) 105–109.

[84] The World Health Organization, Operational Guide for the WHO

Neurobehavioral Core Test Battery. Draft 25.5, Office of Occupational

Health, Geneva, 1986.

[85] B.P. Uzzell, J. Oler, Chronic low-level mercury exposure and neuro-

psychological functioning, J. Clin. Exp. Neuropsychol. 8 (5) (1986)

581–593.

[86] M.A. Verschoor, R.F.M. Herber, R.L. Zielhuis, Urinary mercury levels

and early changes in kidney function in dentists and dental assistants,

Community Dent. Oral Epidemiol. 16 (1988) 148–152.

[87] F.G. Vroom, M. Greer, Mercury vapor intoxication, Brain 95 (1972)

305–318.

[88] K. Warvinge, J. Hua, M. Berlin, Mercury distribution in the rat brain

after mercury vapor exposure, Toxicol. Appl. Pharmacol. 117 (1992)

46–52.

[89] D. Wechsler, The Wechsler Memory Scale-Revised, Psychological

Corporation, New York, 1987.

[90] R.F. White, S.P. Proctor, Research and clinical criteria for develop-

ment of neurobehavioral test batteries, J. Occup. Med. 1992

(February) 140–148.

[91] R.F. White, R.G. Feldman, S.P. Proctor, Neurobehavioral effects of

toxic exposures, in: R.F. White (Ed.), Clinical Syndromes in Adult

Neuropsychology: the Practioner’s Handbook, Elsevier, Amsterdam,

1992.

[92] G.S. Wilkinson, Wide Range Achievement Test-Reading, 3rd edR,Jastak Associates, Wilmington, DE, 1993.

[93] A.M. Williamson, R.K. Teo, J. Sanderson, Occupational mercury

exposure and its consequences for behavior, Int. Occup. Environ.

Health 50 (1982) 273–286.

[94] J.S. Woods, Porphyrin metabolism as indicator of metal exposure and

toxicity, in: R.A. Goyer, M.G. Cherian (Eds.), Handbook of

Experimental Pharmacology: Toxicology of Metals—Biochemical

Aspects, vol. 115, Springer-Verlag, Berlin, 1995, pp. 19–52.

[95] J.S. Woods, M.D. Martin, C.A. Naleway, D. Echeverria, Urinary

porphyrin profiles as a biomarker of mercury exposure: studies on

dentists with occupational exposure to mercury vapor, J. Toxicol.

Environ. Health 40 (1993) 235–246.

[96] D. Zander, U. Ewers, I. Freier, A. Brockhaus, Mercury exposure of

the population: IV. Mercury exposure of male dentists, female

dentists and dental aides, Zent.bl. Hyg. Umweltmed. 192 (5) (1992)

447–454 (Article in German).