Journal of Gerontology: MEDICAL SCIENCES
2008, Vol. 63A, No. 6, 635–641
Copyright 2008 by The Gerontological Society of America
Trace Element Levels and Cognitive Function in
Rural Elderly Chinese
Sujuan Gao,1 Yinlong Jin,6 Frederick W. Unverzagt,2 Feng Ma,6 Kathleen S. Hall,2
Jill R. Murrell,3 Yibin Cheng,6 Jianzhao Shen,1 Bo Ying,6 Rongdi Ji,6
Janetta Matesan,1 Chaoke Liang,6 and Hugh C. Hendrie2,4,5
Background. Trace elements are involved in metabolic processes and oxidation-reduction reactions in the central
nervous system and could have a possible effect on cognitive function. The relationship between trace elements measured
in individual biological samples and cognitive function in an elderly population had not been investigated extensively.
Methods. The participant population is part of a large cohort study of 2000 rural elderly Chinese persons. Six cognitive
assessment tests were used to evaluate cognitive function in this population, and a composite score was created to
represent global cognitive function. Trace element levels of aluminum, calcium, cadmium, copper, iron, lead, and zinc
were analyzed in plasma samples of 188 individuals who were randomly selected and consented to donating fasting blood.
Analysis of covariance models were used to assess the association between each trace element and the composite cognitive
score adjusting for demographics, medical history of chronic diseases, and the apolipoprotein E (APOE) genotype.
Results. Three trace elements—calcium, cadmium, and copper—were found to be significantly related to the
composite cognitive score. Increasing plasma calcium level was associated with higher cognitive score ( p , .0001).
Increasing cadmium and copper, in contrast, were significantly associated with lower composite score ( p ¼ .0044 and
p ¼ .0121, respectively). Other trace elements did not show significant association with the composite cognitive score.
Conclusions. Our results suggest that calcium, cadmium, and copper may be associated with cognitive function in the
elderly population.
Key Words: Trace element—Cognitive function—Calcium—Copper—Cadmium.
T
RACE elements are chemical elements that are needed
in minute quantities for the proper growth, development, and physiology of the human organism. Trace
elements are routinely involved in metabolic processes
and oxidation-reduction reactions in the central nervous
system (CNS) and could have a possible effect on cognitive
function. For example, elevated lead levels are shown to be
associated with alterations in ionic, cholinergic, and
dopaminergic neurotransmission in the CNS (1). Iron is
necessary to ensure oxygenation and to produce energy in
the cerebral parenchyma and for the synthesis of neurotransmitters and myelin (2). Whereas calcium is hypothesized to impact brain dopamine synthesis (3), copper’s
impact on cognition is believed to be its interaction with
amyloid-b (Ab) peptide by causing Ab aggregation in the
brain (4).
The relationship between trace elements measured in
dietary intake or in drinking water and cognitive function in
elderly persons has been examined previously. Results of
the effect of aluminum on cognitive function or Alzheimer’s
disease were inconsistent (5,6). There were reports of
a positive relationship between cognitive function and
dietary intake of zinc and iron in healthy elderly adults
(7), but no associations were found when zinc and iron were
measured in drinking water (8,9). Dietary copper intake in
persons with high saturated and trans fat was found to be
a risk factor for cognitive decline (10), and calcium levels in
drinking water have a positive association with cognitive
function (8,9). It is recognized that trace element levels
measured in dietary intake based on food frequency
questionnaire may not take into account plant soil variation
for certain elements, and that elements measured in drinking
water may not accurately reflect trace element levels in each
individual. Biochemical measures from individuals are
preferred measures of trace elements. However, in studies
of populations from developed countries, biological measures of trace element levels often reflect intake from
multivitamin supplements, which may be confounded
by health conditions and socioeconomic status of the
individuals.
The rural elderly Chinese population represents an
opportunity for studying the relationship between trace
element levels and cognitive function. The rural Chinese are
unusually stable with most living in the same village
throughout their entire life and consuming food that is
locally grown. In addition, it is rare for these villagers to
take dietary supplements. In this article we report the
association between seven trace element levels measured in
plasma samples and cognitive function in a study of elderly
Chinese persons.
635
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Departments of 1Medicine, 2Psychiatry, 3Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis.
4
Indiana University Center for Aging Research, Indianapolis.
5
Regenstrief Institute, Inc., Indianapolis, Indiana.
6
Institute for Environmental Health and Related Product Safety, Chinese Center for Disease Control and Prevention, Beijing.
636
GAO ET AL.
METHODS
Cognitive Assessment
Cognitive assessment was conducted in face-to-face interviews using the Community Screening Instrument for
Dementia (CSID), the Consortium to Establish a Registry
for Alzheimer’s Disease (CERAD) Word List Learning
Test, the CERAD Word List Recall Test (12), the IU Story
Recall, Animal Fluency test (13), and the IU Token test
(14,15). The CSID was developed as a screening tool for
dementia in populations with various cultural backgrounds
and literacy levels. Details of the instrument have been
published elsewhere (16). CSID scores range from 0 to 30.
The CERAD Word List Learning test is one of the measures
from the CERAD neuropsychological assessment battery,
which was designed to assess cognitive skills in the elderly
population. It consists of a 10-item, three-trial word list in
which free recall is taken after each learning trial and after
a brief delay (approximately 5 minutes). Score is the total
number of words recalled across the three learning trials
(range 0–30) and at delay (range 0–10). The IU Story Recall
task was created by the research team to be suitable to the
Chinese culture and the rural population. The examiner
reads the story out loud to the participant, who attempts to
recall it verbatim immediately and again after a brief delay.
The story has 14 units of information that are gist scored
(range 0–14). The Animal Fluency test is a measure of
executive function in which a participant names as many
Blood Samples
Fasting blood samples from a random 10% of participants
were collected in the morning using 10-mL purple top
(EDTA) Vacutainer tubes manufactured in the United
States. A study participant was allowed to take part in the
study even if he or she refused a blood draw. If a study
participant refused a blood draw, those interviewed during
the same morning would be asked for consent of donating
blood to provide a replacement sample. The samples were
centrifuged at 897.5 3 g for 20 minutes. Plasma samples
were stored in a 208C freezer for trace element analyses.
Trace Element Measures
The levels of seven trace elements, aluminum (Al),
calcium (Ca), cadmium (Cd), copper (Cu), iron (Fe), lead
(Pb), and zinc (Zn), in plasma samples were determined by
inductively coupled plasma-mass spectrometry (ICP-MS;
Micromass UK Ltd., Manchester, U.K. ) after microwave
digestion (Microwave; CEM, Matthews, NC), according to
published methods (18,19).
Apolipoprotein E Genotype
Blood spots on filter paper were collected from all study
participants at the end of the interview. Apolipoprotein E
APOE genotype was determined by eluting DNA from a
dried blood spot (20) followed by HhaI digestion of
amplified products (21).
Collection of Other Risk Factors
Other risk factors collected during the interview include
age, gender, whether the participant attended school and
number of years of schooling, marital status, household
composition, participant’s birthplace and migration history,
alcohol consumption and smoking history, and history of
cancer, Parkinson’s disease, diabetes, hypertension, stroke,
heart attack, head injury, and bone fracture. Participants’
height, weight, and blood pressure were also measured
(twice) during the interview. Body mass index (BMI) was
derived from height and weight measurements. The average
of the two blood pressure measures was used in our analyses.
Statistical Analyses
Participants’ characteristics were compared between
those who had trace element measures and those who did
not have trace element measures using Student’s t test for
continuous variables and Fisher’s exact test for categorical
variables. Because of the wide range of cognitive function in
the elderly population, individual cognitive tests are subject
to floor and ceiling effects. To minimize such effects and
other sources of measurement error, we created a composite
cognitive score by using the average of the standardized
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Study Population
The parent study for this analysis includes 2000 Chinese
persons 65 years old or older from four counties in China.
Two counties are in Sichuan province in southwestern
China and the other two are in Shandong province in eastern
China. Details on the site selection process were described
elsewhere (11). Briefly, prior to final site selection, Chinese
investigators traveled to candidate sites collecting demographic information ensuring that the local elderly population was large enough to provide a sample of 500 elderly
participants in each site. Samples of grain (corn, rice, and
wheat), soil, and water from each candidate site were
collected and analyzed for trace element levels so that sites
with toxic levels were excluded. In addition, sites with
known endemic diseases including Keshan disease, Kashin–
Beck disease, goiter, cretinism, and fluorosis were excluded
from consideration.
For each village included in the study, Chinese investigators and a team of interviewers who were employees of
provincial and county centers for disease control traveled
to the area, established a temporary headquarters, and conducted a complete census of residents 65 years old or older
in the area. They enrolled eligible residents by going doorto-door, obtaining informed consent before conducting the
interview, and collecting biological samples. Five hundred
participants from each of the four counties in China were
interviewed between December 2003 and May 2005. The
study was approved by Indiana University (IU) Institutional
Review Board and the Institute for Environmental Health
and Related Safety, Chinese Center for Disease Control and
Prevention.
animals as possible in 60 seconds. The IU Token test is a
brief measure of language comprehension and working
memory, and the score is the number correct across all
12 commands (range 0–24) (14). The validity of the CSID,
CERAD Word List Learning and Recall, and the Animal
Fluency tests has been previously established in the Chinese
population and elsewhere (17). These cognitive tests were
described in more detail previously (11).
TRACE ELEMENTS AND COGNITION
Table 1. Demographic Characteristics of Participants With
Plasma Trace Element Measures (N ¼ 188) and the
Remaining Participants in the Cohort (N ¼ 1812)
Participant Characteristics
Without Trace
Elements
(N ¼ 1812)
p Value
69.2 (4.1)
50.0
46.8
22.9 (3.5)
72.5 (5.6)
54.0
36.7
21.8 (3.5)
,.0001
.2917
.0065
,.0001
142.9 (24.4)
82.7 (12.0)
17.5
44.7
51.1
146.0 (25.0)
83.7 (12.7)
16.5
43.4
45.9
.1007
.2606
.6974
.7459
.1779
History of (%)
Cancer
Parkinson’s disease
Diabetes
Hypertension
Stroke
Heart attack
Head injury
Fracture
0.5
0.5
3.7
16.0
1.1
3.2
4.8
2.1
0.7
0.9
2.5
17.0
3.3
3.5
5.8
2.6
.9999
.9999
.3298
.7601
.1181
.9999
.7404
.9999
Note: SD ¼ standard deviation; APOE ¼ apolipoprotein E.
scores of the six cognitive tests (CSID, IU Story Recall test,
Animal Fluency test, CERAD Word Listing Learning Test,
CERAD Word List Recall Test, and the IU Token test).
Shapiro–Wilk test was used to test the normality assumption
of the composite z score. Cronbach’s alpha was used to
examine whether the composite score appropriately summarized the performances of all individual cognitive tests.
Analyses of covariance (ANCOVA) models were used to
examine the associations between each trace element and
the composite z score adjusting for covariates. Because
many participants’ cadmium level reached the lower detection limit, we also grouped participants into low and high
groups; all those reaching detection limit were included in
the low cadmium group, and the rest were included in the
high cadmium group. The ANCOVA model was used to
examine whether composite z scores were different between
the two cadmium groups controlling for a set of covariates.
For each trace element, we also fitted quadratic effect
models to detect a potential nonlinear association between
the trace element level and the composite z score. Additional
ANCOVA models were also conducted examining interaction effects between any two of the seven trace
elements in a pairwise fashion. Significance level was set
at .05 for all comparisons.
RESULTS
Two hundred one participants donated blood samples,
with 49, 52, 50, and 50 samples from each of the four study
sites. Of these, 188 samples had a sufficient amount of
plasma for biochemical analyses of trace element levels. The
demographic characteristics of those who had trace element
measures and remaining participants who did not have trace
element measures are presented in Table 1. Participants with
trace element measures were significantly younger, had
a higher percentage of education (ever attending school),
and had significantly higher BMI than the remaining cohort.
These 188 participants did not differ significantly from the
remaining cohort on gender composition, alcohol consumption, smoking status, history of medical conditions included
in Table 1, or APOE genotypes.
In Table 2, we provide descriptive statistics on each of the
seven trace element levels. For cadmium and lead, we also
include toxicity limits set by the World Health Organization
(WHO) and the Centers for Disease Control and Prevention
(CDC) (22,23), respectively, and the number of participants
in our sample whose measurements exceeded these limits.
The composite z score in the subsamples with trace
element measures had an approximately normal distribution
(p ¼ .5319) and ranged from 1.872 to 2.377 (mean, 0.25;
standard deviation [SD] 0.74), with higher scores indicating
better cognitive function. Cronbach’s alpha was 0.84,
indicating that the composite z score adequately summarized
results of the individual tests. In Figure 1, bivariate scatter
plots of the composite z score versus each trace element are
presented. Significant correlations were observed between
the z score and calcium, cadmium, and copper. One
individual’s lead level seems to be considerably larger than
the rest of sample value and above the toxicity plasma lead
level set by the CDC. Therefore, we excluded this lead value
from subsequent analyses.
In Table 3, we present results from seven ANCOVA
models examining the association between each trace
Table 2. Summary of Plasma Trace Element Levels in Our Sample
Trace Elements (N ¼ 188)
Summary Measures
Mean
SD
Minimum
Median
Maximum
Toxic limit
No. of participants above limit
Al (mg/L)
Ca (mg/L)
Cd (lg/L)
Cu (mg/L)
Fe (mg/L)
Pb (lg/L)*
Zn (mg/L)
0.97
0.38
0.28
0.96
2.49
52.07
8.77
16.89
52.19
71.72
1.75
2.16
0.05
0.05
10.96
5.00y
13
0.65
0.29
0.07
0.61
2.49
3.64
3.43
0.59
2.44
27.84
3.91
6.27
0.30
1.15
39.34
100.0z
1
7.88
2.38
1.85
7.51
14.87
Notes: *N ¼ 187 because one individual with a Pb measure of 125.2 lg/L is excluded from this analysis.
World Health Organization (22).
z
Centers for Disease Control and Prevention (23).
SD ¼ standard deviation.
y
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Mean age, y (SD)
Female, %
Ever attended school, %
Mean body mass index,
kg/m2 (SD)
Systolic blood pressure (SD)
Diastolic blood pressure (SD)
APOE e4 carriers, %
Consume alcohol, %
Smoker, %
With Trace
Elements
(N ¼ 188)
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GAO ET AL.
638
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Figure 1. Scatter plots of the composite z score versus each trace element.
TRACE ELEMENTS AND COGNITION
639
Table 3. Results of Analysis of Covariance Models With the Composite z Score as the Outcome Variable, Each Trace Element
Measure as the Independent Variable Adjusting for Participants’ Age, Gender, Education, BMI, and APOE e4 Status
ANCOVA Models
Model
Al (mg/L)
Ca (mg/L)
Cd (lg/L)
Cu (mg/L)
Fe (mg/L)
Pb (lg/L)
Zn (mg/L)
0.046
0.01
.0002
0.042
0.012
.0004
0.043
0.011
.0003
0.044
0.012
.0003
0.045
0.012
.0002
0.046
0.012
.0002
0.045
0.012
.0002
0.172
0.105
.1022
0.191
0.099
.0563
0.218
0.104
.0373
0.182
0.103
.0786
0.165
0.105
.1187
0.174
0.105
.0973
0.135
0.107
.2079
0.434
0.108
,.0001
0.403
0.103
.0001
0.398
0.107
.0003
0.407
0.107
.0002
0.442
0.108
,.0001
0.428
0.109
,.0001
0.442
0.108
,.0001
0.295
0.128
.0269
0.247
0.122
.0438
0.270
0.125
.0322
0.277
0.126
.0288
0.279
0.128
.0307
0.282
0.128
.0288
0.273
0.128
.0336
0.016
0.014
.2505
0.015
0.013
.2498
0.022
0.014
.1143
0.018
0.014
.1981
0.017
0.014
.2419
0.015
0.014
.3046
0.014
0.014
.3385
0.136
0.124
.2731
0.023
0.005
,.0001*
0.064
0.022
.0044*
0.407
0.161
.0121*
0.000
0.014
.9971
0.009
0.008
.2496
0.026
0.021
.2074
Age, y
Estimate
SE
p value
Sex (female vs male)
Education (attended school vs no school)
Estimate
SE
p value
APOE e4 (carriers vs noncarriers)
Estimate
SE
p value
BMI
Estimate
SE
p value
Trace element
Estimate
SE
p value
Notes: *Significant at the .05 level.
BMI ¼ body mass index; SE ¼ standard error; APOE ¼ apolipoprotein E; ANCOVA ¼ analysis of covariance.
element and the composite z score adjusting for age, gender,
education, APOE genotype, and BMI. These covariates
were shown to be significantly associated with the
composite z scores in the entire cohort (11). Three trace
elements—calcium, cadmium, and copper—were found to
be significantly related to the composite z score. Increasing
plasma calcium level is associated with higher z score (p ,
.0001). Increasing cadmium and copper, in contrast, were
significantly associated with lower z score ( p ¼ .0044 and
p ¼ .0121, respectively). Other trace elements did not show
significant association with the composite z score.
Because there was a considerable number of participants
reaching the lower detection limit on cadmium, we also
dichotomized cadmium levels by grouping participants into
a low cadmium group (consisting of all participants reaching
the lower detection limit [n ¼ 96]) and a high cadmium
group (consisting of those with levels above the detection
limit). Mean z score in the low cadmium group is 0.34 (SD ¼
0.75), and that in the high cadmium group is 0.16 (SD ¼
0.73), illustrated in Figure 2. ANCOVA models with the
binary cadmium group as the independent variables revealed
a significant difference between the mean z scores of the two
groups while adjusting for age, gender, education, APOE
genotype, and BMI (p ¼ .0247). Additional ANCOVA
models including quadratic terms of trace element or
pairwise interaction among the seven trace elements did
not identify any significant quadratic or interaction effects.
DISCUSSION
In this subsample of a large Chinese cohort of elderly
participants, we found a significant association between
increasing plasma calcium levels and higher cognitive
function scores. We also identified plasma cadmium and
copper levels as factors associated with lower cognitive
scores.
Some of the trace elements examined here had been
previous evaluated for their effect on brain functions. For
example, lead and aluminum were found to be detrimental
to the CNS. Other elements are known to be involved in
major metabolic pathways (iron, zinc). The mechanisms
between the association of trace elements and brain
functions are thought to be through impact on neurotransmitting processes, oxygenation in the cerebral parenchyma, in the synthesis of neurotransmitters, or in the
formation of Ab in the brain.
Previous studies on calcium and cognitive function had
reported positive or quadratic association between calcium
in drinking water and cognitive function (8,24). One
common limitation in these studies is that individual
biological calcium levels were not available and aggregated
analyses have to be conducted looking at mean cognitive
scores among people sharing common water sources. There
is, however, one small study of 60 women that showed that
Alzheimer’s patients had lower serum calcium levels
compared to nondemented women (25). The mechanism
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Estimate
SE
p value
640
GAO ET AL.
for calcium’s impact on brain function is hypothesized to be
that increased calcium level enhances brain dopamine
synthesis through a calmodulin-dependent system, and
increased dopamine levels regulate various brain functions
(3). It has also been proposed that the cellular mechanisms
for maintaining the homeostasis of cellular calcium concentration may play a key role in aging (26).
The association between plasma copper level and
cognitive score we report in this study is consistent with
previous findings. The mechanism for copper’s impact on
cognition is believed to be its interaction with Ab peptide
causing Ab aggregation and the production of hydrogen
peroxide, an oxidant and neurotoxin (4). Dietary copper
intake had been reported to be associated with increased risk
of cognitive decline, but only in individuals with high
saturated or trans fat levels in the large Chicago Health and
Aging Project (10). There were also reports from case–
control studies finding higher plasma copper levels in
Alzheimer’s patients compared to normal individuals
(27,28). In addition, copper-chelating agents reduced oxidative stress in patients with Alzheimer’s disease and slowed
cognitive decline in Phase I and II clinical trials (29).
Previous observational studies on cadmium and cognitive
function in adults and elderly populations had been
inconsistent (30–32), although cadmium’s deteriorative
effect on children’s IQ has been recognized (33). A Swedish
study on elderly persons did not find an association between
blood cadmium level and cognitive function. Previous
studies have also identified a significant zinc–cadmium
interaction in drinking water on cognitive function, where
cadmium level becomes a risk factor for cognitive function
when zinc level was above the population median (24). In
our study using plasma measures, we did not detect
a significant interaction between zinc and cadmium. Instead,
we found a negative relationship between cadmium levels
and cognitive scores regardless of plasma zinc level.
ACKNOWLEDGMENTS
The research is supported by National Institutes of Health (NIH) grants
R01 AG019181, R01 AG09956, and P30 AG10133.
CORRESPONDENCE
Address correspondence to Sujuan Gao, PhD, Department of Medicine,
Indiana University School of Medicine, 410 West 10th Street, Suite 3000,
Indianapolis, IN 46202. E-mail: [email protected]
REFERENCES
1. Goldstein GW. Lead poisoning and brain cell function. Environ Health
Perspect. 1990;89:91–94.
2. Bourre JM. Effects of nutrients (in food) on the structure and function
of the nervous system: update on dietary requirements for brain. Part 1:
Micronutrients. J Nutr Health Aging. 2006;10:377–385.
Downloaded from http://biomedgerontology.oxfordjournals.org/ at Pennsylvania State University on March 4, 2014
Figure 2. Mean composite z score in the low cadmium group (plasma
cadmium level ¼ 0.05 lg/L) and in the high cadmium group (plasma cadmium
level . 0.05 lg/L).
There was a limited number of published studies to
compare trace element levels in our sample. Our sample
appears to have a lower calcium level than a Japanese study
with a sample size of 60 reporting a serum calcium level of
approximately 90 mg/L in normal participants and 85 mg/L
in Alzheimer’s patients (25). Mean copper level in our
sample (0.65 mg/L) is also lower than the mean serum
copper level reported in normal participants (0.86 mg/L) and
Alzheimer’s patients (1.16 mg/L) reported in a case–control
study (27). A study of the Swedish elderly population
reported a mean blood cadmium level of 0.489 lg/L in the
range of 0.059–4.227 lg/L (31), which appeared lower than
the mean cadmium level of 1.75 lg/L and a range of 0.05–
10.96 lg/L in our sample (31).
Our study has a number of strengths. Trace elements were
measured in plasma samples so that measurement errors
from dietary recalls are eliminated. The participants were
mostly lifelong residents in these villages and they were not
taking dietary supplements. Therefore, their biological
measures can be assumed to reflect intake from food and
water sources and remain relatively stable. Our study also
used multiple cognitive tests, so the composite score can
reliably reflect global cognitive function from multiple
cognitive domains.
Our study also has important limitations. Blood samples
were collected from only a small fraction of the study
participants in the cohort. Participants who consented to
blood collection tended to be younger, more educated, and
to have higher BMI values than those who did not donate
blood samples, but they do not differ from the remaining
cohort in other demographic, lifestyle, or medical characteristics. It is also possible that the range in some trace
elements in our cohort could be limited because we
excluded potential sites with high levels of toxic trace
elements, thus limiting the ability to detect significant
relationships in plasma lead levels, for example. Trace
elements were measured in plasma samples, and they may
not be sensitive to tissue-specific changes in trace element
functioning. The reported associations between plasma
calcium, cadmium, and copper level and cognitive function
were based on a cross-sectional examination in a Chinese
population. Longitudinal evaluation of the cohort will help
to establish whether calcium, cadmium, and/or copper levels
affect the rate of cognitive decline associated with aging.
TRACE ELEMENTS AND COGNITION
19. Ding L, Yang Y, Yao X, Qi Q. Measuring multiple trace elements in
blood samples in children using icp-ms. Chinese Journal of Health
Laboratory Technology 2003;13:62–63.
20. Yang M, Hendrie HC, Hall KS, Oluwole OS, Hodes ME, Sahota A.
Improved procedure for eluting DNA from dried blood spots. Clin
Chem. 1996;42:1115–1116.
21. Hixson JE, Vernier DT. Restriction isotyping of human apolipoprotein
E by gene amplification and cleavage with HHAI. J Lipid Res. 1990;
31:545–548.
22. World Health Organization (WHO). Environmental Health Criteria
134 – Cadmium. Geneva, Switzerland: WHO; 1992.
23. Centers for Disease Control and Prevention. Preventing Lead Poisoning
in Young Children: A Statement by the Centers for Disease Control.
U.S. Department of Health and Human Services, Public Health Service,
1991.
24. Emsley CL, Gao S, Li Y, Liang C, et al. Trace element levels in
drinking water and cognitive function among elderly Chinese. Am J
Epidemiol. 2000;151:913–920.
25. Ogihara T, Miya K, Morimoto S. Possible participation of calciumregulating factors in senile dementia in elderly female subjects.
Gerontology. 1990;36(Suppl 1):25–30.
26. Khachaturian ZS. Calcium hypothesis of Alzheimer’s disease and brain
aging. Ann N Y Acad Sci. 1994;747:1–11.
27. Squitti R, Lupoi D, Pasqualetti P, et al. Elevation of serum copper
levels in Alzheimer’s disease. Neurology. 2002;59:1153–1161.
28. Squitti R, Barbati G, Rossi L, et al. Excess of nonceruloplasmin serum
copper in AD correlates with MMSE, CSF (beta)-amyloid, and h-tau.
Neurology. 2006;67:76–82.
29. Squitti R, Rossini PM, Cassetta E, et al. D-penicillamine reduces serum
oxidative stress in Alzheimer’s disease patients. Eur J Clin Invest.
2002;32:51–59.
30. Hart RP, Rose CS, Hamer RM. Neuropsychological effects of
occupational exposure to cadmium. J Clin Exp Neuropsychol. 1989;
11:933–943.
31. Nordberg M, Winblad B, Basun H. Cadmium concentration in blood in
an elderly urban population. Biometals. 2000;13:311–317.
32. Basun H, Lind B, Nordberg M, Nordstrom M, Bjorksten KS, Winblad B.
Cadmium in blood in Alzheimer’s disease and non-demented subjects:
results from a population-based study. Biometals. 1994;7:130–134.
33. Thatcher RW, Lester ML, McAlaster R, Horst R. Effects of low levels
of cadmium and lead on cognitive functioning in children. Arch
Environ Health. 1982;37:159–166.
Received May 17, 2007
Accepted September 21, 2007
Decision Editor: Luigi Ferrucci, MD, PhD
Downloaded from http://biomedgerontology.oxfordjournals.org/ at Pennsylvania State University on March 4, 2014
3. Sutoo D, Akiyama K. Regulation of brain function by exercise.
Neurobiol Dis. 2003;13:1–14.
4. Huang X, Cuajungco MP, Atwood CS, et al. Cu(ii) potentiation of
Alzheimer abeta neurotoxicity. Correlation with cell-free hydrogen
peroxide production and metal reduction. J Biol Chem. 1999;
274:37111–37116.
5. Munoz DG. Is exposure to aluminum a risk factor for the development
of Alzheimer disease?–No. Arch Neurol. 1998;55:737–739.
6. Forbes WF, Hill GB. Is exposure to aluminum a risk factor for the
development of alzheimer disease?–Yes. Arch Neurol. 1998;55:740–
741.
7. Ortega RM, Requejo AM, Andres P, et al. Dietary intake and cognitive
function in a group of elderly people. Am J Clin Nutr. 1997;66:803–
809.
8. Jacqmin H, Commenges D, Letenneur L, Barberger-Gateau P,
Dartigues JF. Components of drinking water and risk of cognitive
impairment in the elderly. Am J Epidemiol. 1994;139:48–57.
9. Jacqmin-Gadda H, Commenges D, Letenneur L, Dartigues JF. Silica
and aluminum in drinking water and cognitive impairment in the
elderly. Epidemiology. 1996;7:281–285.
10. Morris MC, Evans DA, Tangney CC, et al. Dietary copper and high
saturated and trans fat intakes associated with cognitive decline. Arch
Neurol. 2006;63:1085–1088.
11. Gao S, Jin Y, Hall KS, et al. Selenium level and cognitive function in
rural elderly Chinese. Am J Epidemiol. 2007;165:955–965.
12. Morris JC, Heyman A, Mohs RC, et al. The Consortium to Establish
a Registry for Alzheimer’s Disease (CERAD). Part i. Clinical and
neuropsychological assessment of Alzheimer’s disease. Neurology.
1989;39:1159–1165.
13. Isaacs B, Akhtar AJ. The set test: a rapid test of mental function in old
people. Age Ageing. 1972;1:222–226.
14. Yamamoto K, Evans JD, Johnson KE, Unverzagt FW. Clinical utility
of IU token test in the diagnosis of dementia. J Int Neuropsychol Soc.
2003;9:316.
15. Jones SN, Ayers CR. Psychometric properties and factor structure of
an expanded CERAD neuropsychological battery in an elderly VA
sample. Arch Clin Neuropsychol. 2006;21:359–365.
16. Hall KS, Ogunniyi AO, Hendrie HC, et al. A cross-cultural community
based study of dementias: methods and performance of the survey
instrument: Indianapolis, U.S.A. and Ibadan, Nigeria. Int J Methods
Psychiatr Res. 1996;6:129–142.
17. Prince M, Acosta D, Chiu H, Scazufca M, Varghese M. Dementia
diagnosis in developing countries: a cross-cultural validation study.
Lancet. 2003;361:909–917.
18. Yang Y, Ding L, Qi Q, Yao X, Liu J. Determination of 23 trace
elements in whole blood by ICP-MS after microwave digestion for
sample preparation. J Environ Health. 2003;20:240–241.
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