Printer Friendly

Arsenic exposure and cognitive performance in Mexican schoolchildren.

Recent studies indicate that in several regions of the world, arsenic concentration in water is much higher than accepted levels (Smedley and Kinniburgh 2002). The concern with As contamination of drinking water is that consumption and use of this water in cooking can increase As exposure in humans (Del Razo et al. 2002). In Mexico, the amount of this contaminant in ground water varies from 10 to 5,000 [micro]g/L (Del Razo et al. 1990). Del Razo et al. (1990; 1994) reported on the problem of As-contaminated groundwater in the Lagunera Region of northern Mexico, with > 50% of samples having As concentrations > 50 [micro]g/L, which was the former level of reference set by the World Health Organization (International Programme on Chemical Safety 2001). The predominant type of As in 90% of the samples was pentavalent arsenic.

In 1977, the presence of As in potable water was reported in the city of Torreon, the main city in the Region Lagunera and the site of the most important metallurgic complex of Mexico. As concentration in Torreon's water was up to 4-6 mg/L, far above the present 10-[micro]g/L limit (Cebrian et al. 1994; Mandal and Suzuki 2002). Benin et al. (1999) evaluated heavy metal contamination of soil in three residential areas that surround the smelter and found that As levels had a median of 113 [micro]g/g, and ranged from 78 to 287 [micro]g/g. These levels exceeded the level at which the U.S. Environmental Protection Agency (EPA) designated cleanup goals for Superfund sites, 5-65 [micro]g/g (U.S. EPA 1997).

Approximately 60-90% of the soluble inorganic arsenic (InAs) components are absorbed through the gastrointestinal tract (Hall 2002). In humans, InAs metabolism involves at least five metabolites that can exert toxic effects (Valenzuela et al. 2005). The measure of urinary As (UAs) excretion is a good biomarker for chronic exposure via drinking water [Agency for Toxic Substances and Disease Registry (ATSDR) 2000]. As concentration in urine, normally < 10 [micro]g/L, can reach as high as 50 [micro]g/L in adults and children living close to metal foundries (Carrizales et al. 2006; Polissar et al. 1990).

The negative consequences of As exposure in humans include respiratory, gastrointestinal, hematologic, hepatic, renal, dermic, neurologic, and immunologic effects (ASTDR 2000; Garcia-Vargas and Hernandez-Zavala 1996; International Programme on Chemical Safety 2001), many of which continue even after the contaminant source is controlled (Diaz-Barriga et al. 1997). As can also have detrimental effects on the central nervous system and cognitive development in children (International Programme on Chemical Safety 2001). Acute As exposure affects sensory nerves as well as the long axon neurons, which results in the clinical manifestation of numb extremities. Neurologic tests have shown nerve axonophathy and demyelination (Franzblau and Lilis 1989; Rodriguez et al. 2003; Yung 1984). As also affects the content of brain monoamines, and the concentrations of dopamine and serotonin in the hippocampus, hypothalamus, cerebral cortex, and the striatum (Itoh et al. 1990; Mejia et al. 1997; Rodriguez et al. 2003).

Few reports have suggested a detrimental effect of As exposure on cognitive development and function, including disturbed visual perception, problems with visuomotor integration, psychomotor speed, attention, speech, and memory. (Calderon et al. 2001; Rodriguez et al. 2003; Tsai et al. 2003). In this regard, the effects of As are similar to and could be confounded with the effects of other environmental contaminants such as lead. In fact, in many regions of the world, As exposure co-occurs with exposure to other contaminants such as lead (Carrizales et al. 2006; Diawara et al. 2006). Poorer performance on a range of cognitive tests has been reported in children with low to moderate lead exposure in Torreon (Kordas et al. 2004) and other settings (Canfield et al. 2003; De Burbure 2006; Lanphear et al. 2000).

In this study we identified demographic and nutritional factors that are associated with UAs concentration in school-age children. We also investigated the influence of As exposure on cognitive function in these children.


Subjects and design of the study. The study sample consisted of 602 children 6-8 years of age who attended first grade in nine public elementary schools located within 3.5 km of a metallurgic smelter complex in the city of Torreon, Mexico. Parents of all study subjects gave informed written consent before being enrolled. This study area was selected because the foundry contributes with toxic substances that affect a large proportion of the population. The effects of lead exposure in this population have been described previously (Dorea 2004; Flora 2002; Kordas et al. 2004, 2006; Rico et al. 2006; Rosado et al. 2006). Each child was examined to obtain anthropometry, nutritional status by biochemical measurements in serum, levels of blood lead (PbB) and UAs, and cognitive performance. Complete blood and urine samples were available for 591 children. The study was approved by the Human Subjects Research Committee at the Johns Hopkins Bloomberg School of Public Health and the Institute of Medical Sciences and Nutrition in Mexico.

Anthropometry. Measurements were carried out according to standard methods (Habicht 1974). A trained person took all height and weight measurements. Weight was measured to the nearest 10 g using an upright scale (Model Express Plus, Torino, Mexico City, Mexico) before lunchtime to avoid variations due to immediate food ingestion. Height was measured with a 3-m standardized measurement board to the nearest 1 mm. All participants were measured without shoes and wearing only their school uniform without any sweater or jacket.

Biochemical measures. A venous blood sample was collected from each child at the school, after an overnight fast. After blood sample collection, children were given a snack and a juice box. Blood was collected in 5-mL sodium heparin vacutainer trace-metal free tubes (Becton Dickinson, Franklin Lakes, NJ, USA). Hemoglobin (Hb) was analyzed at the school with a HemoCue Photometer (HemoCue Inc., Mission Viejo, CA, USA). Samples were transported in a cooler to be processed in the laboratory on the same day. Serum was obtained and aliquots stored at -80[degrees]C until analysis was done. We analyzed serum ferritin using an immunoradiographic method (Coat-A-count Ferritin IRMA). We analyzed zinc and copper concentration with an atomic absorption spectrophotometer (PerkinElmer Analyst 700, PerkinElmer, Norwalk, CT, USA). PbB and UAs were analyzed at the Center for Research and Advanced Studies in the National Polytechnic Institute in Mexico. This laboratory participates in two quality control programs, the Trace Elements External Quality Assessment Scheme at University of Surrey, United Kingdom, and the Interlaboratory Program of Quality Control at Zaragoza, Spain. For PbB measurement, we analyzed samples by duplicate using atomic absorption spectrophotometry (Zeeman 5100; PerkinElmer, Norwalk, CT, USA) (Miller et al. 1987), and those with a CV > 5% were re-analyzed. Lead in bovine blood (standard reference material 955b; National Institute of Standards and Technology, Gaithersburg, MD, USA) was used as the standard reference. For As measurement, a urine sample was collected in the morning after subjects had fasted overnight. It was collected in a plastic container with 100-mL capacity. Samples were transported with ice to the laboratory and a 25-mL aliquot was frozen at -20[degrees]C until analysis. On the day of analysis samples were unfrozen and warmed to 37[degrees]C in a container with boiling water; 2.5-mL aliquots with urine were placed in glass containers and then 2.5 mL HCl 2 M (6% w/v) was added. Samples were covered with clock glass and heated during 5 hr to 80[degrees]C. Then they were cooled to room temperature and transferred to a volumetric flask of 5 mL using HCl 2 M for dilution (Del Razo et al. 1999). The analysis was done with an atomic absorption spectrophotometer (PerkinElmer 3100; PerkinElmer), according to the procedure reported by Crecelius et al. (1986). UAs analysis included InAs, monomethylarsenic (MMAs), and dimethylarsenic (DMAs) and the sum of all metabolic species of arsenic. Zinc protoporphirin (ZPP) was measured in whole blood with ZP Hematofluorometer (AVIV Biomedical, Lakewood, NJ, USA).

Zinc deficiency was considered when serum zinc was [less than or equal to]65 mg/dL, anemia when Hb was < 12.4 g/dL, and ferritin deficiency [less than or equal to]12 [micro]g/L and copper deficiency when serum copper was < 80 [micro]g/L. Elevated ZPP was considered when [greater than or equal to]70 [micro]mol ZP/mol heme, high As concentration when As in urinary samples was > 50 [micro]g/L, and PbB concentration was considered high when > 10 [micro]g/dL.

Cognitive measures. Cognitive evaluations included tests of memory, attention, problem solving, and vocabulary processes. Each participant required two working sessions to answer 14 pen-and-paper or computer touch-screen tests covering the various aspects of cognitive functioning. All children had previous experience with computers. The first session consisted of the Coding, Digit Span, and Arithmetic subtests of the Weschsler Intelligence Scale for Children Revised Mexican Version (WISCRM) (Wechsler 1974, 1981), a test of number and letter sequencing (Reitan and Wolfson 1992), and the Cognitive Abilities Test (a computerbased test with four tasks: Stimulus discrimination, Sternberg memory, Visual Memory Span, and Visual Search) (Detterman 1988). These tests were all applied in this order on day 1. Day 2 consisted of a curriculumbased Math Achievement Test (MAT), a test of Visual-Spatial Abilities with Figure Design, and the Peabody Picture Vocabulary Test (PPVT-Spanish Edition) (Dunn et al. 1986), applied in this order. The WISC-RM and the PPVT were validated with Mexican-American populations; the rest of the tasks were piloted among 1st and 2nd graders in a public elementary school in Mexico City before the project began. Each of these tests has been described in detail in a previous publication showing the effects of PbB concentrations on cognitive performance of these children (Kordas et al. 2004, 2006; Rico et al. 2006).

Demographics and socioeconomic status. A questionnaire was given to parents or caregivers of all children to identify the sociodemographic characteristics of the families. The questionnaire included questions to determine crowding, housing conditions, family possessions, and parents' education level. These characteristics, except parents' education level, were used to build a socioeconomic status (SES) index by transforming each one into a three-category and ordinal variable, and summing points for each individual to build a scale between 5 and 12 points. Low SES was assigned to a sum of 5-7 points, medium SES level to 8-9 points, and high SES to 10-12 points.

Statistical methods. We performed statistical analysis with Stata version 8 (StataCorp., College Station, TX, USA). Pearson correlations and analysis of variance (ANOVA) between groups of demographic variables were performed to evaluate their association with UAs or the difference between different UAs concentration (50 [micro]g/L cutoff). To evaluate the association of UAs and its metabolites with cognitive performance, we log-transformed the cognitive tests scores when required to fit a normal distribution. Three tests were omitted in the analysis: It was not feasible to analyze the data because variables couldn't be normalized. Those that fitted a normal distribution were evaluated with linear regression models, and those that did not were evaluated with logistic regression at the cutoff points based on the median value. Models were adjusted for variables that were found to be significantly correlated with at least two of the cognitive tests scores at p < 0.05: children's age, children's sex, mother's school education level, Hb concentration, and PbB. The interaction between UAs and PbB was also included in the models when it was significant at p < 0.10. The models adjusted the standard errors for clustering on children's school to correct the intraschool correlation. These analysis were also performed stratified by subjects with UAs concentration [less than or equal to]50 [micro]g/L and > 50 [micro]g/L. Because of the high influence of sex in UAs concentrations, the models were also stratified for boys and girls to investigate individual effects; in these analyses sex was removed from other adjusting variables. Collinearity diagnosis was run for all models to confirm the absence of multicollinearity within independent variables.


Demographic and biochemical characteristics of subjects are shown in Table 1. The mean [+ or -] SD of UAs was 58.1 [+ or -] 33.2 [micro]g/L; 52% of the children had UAs concentrations > 50 [micro]g/L, and 10% had UAs concentrations > 100 [micro]g/L. Mean PbB concentration was 11.5 [+ or -] 6.3 [micro]g/dL, and 50.7% of children had PbB above 10 [micro]g/dL. The percentages of children with Hb, ferritin, and zinc deficiency were 9.8, 11.7, and 27.7%, respectively.
Table 1. Characteristics of study participants.

 Variables Value

No. 591
Percent male 54
Age (months) 83.4 [+ or -]4.4
Socioeconomic level (%)
Low 27.5
Medium 50.1
High 22.4
Mother's school level (%)
Primary or no education 25.6
Junior high school 55
High school or college 19.4
Weight for height: < 2 SD (%) 3
Weight for age: < 2 SD (%) 1.2
Height for age: < 2 SD (%) 2.2
Hb (g/dL) 13.4 [+ or -] 0.8
 < 12.4 (%) 9.8
Ferritin ([mu]g/L) 27.2 [+ or -] 16.1
 < 12 (%) 11.7
ZPP ([mu]mol/mol heme) 65.8 [+ or -]22.2
[greater or equal to] 70 (%) 29.3
Zinc ([mu]g/dL) 80.1 [+ or -]24.7
 < 65 (%) 27.7
Copper ([mu]g/dL) 106.8 [+ or -]21.0
 < 80 (%) 11
PbB concentration ([mu]g/dL) 11.5 [+ or -]6.3
[greater or equal to] 10 (%) 50.7
nAs (ug/L) 8.7 [+ or -]6.1
MMA (ug/L) 7.7 [+ or -]5.2
DMA (ug/L) 41.7 [+ or -]24.1
UAs (ug/L) 58.1 [+ or -]33.2
UAs > 50 (%) 52.3
UAs > 100 (%) 9.8

Values are mean [+ or -] SD or percent.

The association of sociodemographic variables with UAs is shown in Table 2. A significant difference was found between boys and girls in all As compounds: UAs was 11.85 [micro]g/L higher in boys than in girls (p < 0.01). UAs concentration was also associated with SES: UAs in the low SES group was significantly higher than in medium and high SES groups (p < 0.01). Children of parents who had a high school or college education excreted less UAs than children of those who had primary or no education (p < 0.01). Children's age was also associated with UAs concentration; younger children (6 years of age) excreted more UAs and MMAs (p < 0.01) and more InAs and DMAs (p < 0.05) than older children (7-8 years of age). PbB correlated positively with UAs concentration (Pearson R = 0.158, p < 0.01) (Tables 2 & 3). Nutritional status indicators were not related to UAs concentrations (Table 3).
Table 2. Urinary arsenic comparison among sociodemographic, hemoglobin,
and PbB concentration groups [no. (mean [+ or -] SD)].

Variables No InAs MMA DMA UAs

Sex Male 319 9.4 8.5 45.6 63.5
 [+ or -] [+ or -] [+ or -] [+ or -]
 6.9(a) 5.5(a) 26.0(a) 35.9(a)

Female 272 7.8 6.8 37.1 51.7
 [+ or -] [+ or -] [+ or -] [+ or -]
 4.9(b) 4.8(b) 20.7(b) 28.5(b)

Low 154 10.3 9.3 48.6 68.1
 [+ or -] [+ or -] [+ or -] [+ or -]
 6.7(a) 5.8(a) 26.2(a) 36.7(a)

Medium 280 8.1 7.3 39.7 55.6
 [+ or -] [+ or -] [+ or -] [+ or -]
 5.6(b) 5.0(b) 23.8(b) 32.4(b)

High 125 8.2 7.2 37.9 53.3
 [+ or -] [+ or -] [+ or -] [+ or -]
 6.4(b) 4.7(b) 21.4(b) 30.2(b)

school leve

Primary or no 146 9.5 8.5 45.6 63.7
education [+ or -] [+ or -] [+ or -] [+ or -]
 6.8(c) 5.7(c) 25.9(a) 35.6(a)

Junior high 314 8.7 7.7 41.8 58.0
school [+ or -] [+ or -] [+ or -] [+ or -]
 6.2 5.1 24.5 33.8

High school or 111 7.7 7.2 37.6 52.5
 [+ or -] [+ or -] [+ or -] [+ or -]
college 4.8(d) 4.7(d) 20.8(b) 28.7(b)

Age group

6 297 9.3 8.3 44.2 61.7
 [+ or -] [+ or -] [+ or -] [+ or -]
 6.8(c) 5.4(a) 24.9(c) 34.7(a)

7-8 294 8.0 7.2 39.2 54.3
 [+ or -] [+ or -] [+ or -] [+ or -]
 5.3(d) 4.8(b) 23.0(d) 31.3(b)


s 10 293 7.8 7.1 38.5 53.4
[micro]g [+ or -] [+ or -] [+ or -] [+ or -]
/dL 5.1(a) 4.9(a) 22.1(a) 30.3(a)

> 10 297 9.6 8.4 44.7 62.6
[micro]g/dL [+ or -] [+ or -] [+ or -] [+ or -]
 6.9(b) 5.3(b) 25.6(b) 35.4(b)

(a),(b)Different letters represent significant differences among rows
at p < 0.01. (c),(d)Different letters represent significant differences
among rows at p < 0.05.
Table 3. Anthropometric and nutritional variables in children with
high and low total urinary arsenic concentrations.

 UAs a 50 UAs < 50
 [micro]g/L [micro]g/L

Variables No Mean No Mean
 [+ or -]SD [+ or -]SD

Height for age 308 -0.10 281 -0.13
(Z-score) [+ or -]0.92 [+ or -] 1.02

ZPP 308 66.36 282 65.09
([micro]mol/mol [+ or -]24.99 [+ or -] 18.81

Hb (g/dL) 309 13.36 282 13.35r
 [+ or -]0.81 [+ or -]0.83

Ferritin 290 27.31 264 27.13
([mu]g/dL) [+ or -] 16.65 [+ or -] 15.55

Zinc ([mu]g/dL) 299 78.79 267 81.63
 [+ or -]23.09 [+ or -]26.27

Copper 260 107.78 239 105.75
([mu]g/dL) [+ or -]20.88 [+ or -]21.10

Weight (kg) 309 25.28 282 24.78
 [+ or -]5.47 [+ or -]5.69

Height (cm) 309 120.89 282 120.82
 [+ or -]5.22 [+ or -]5.81

Head 309 50.65 282 50.54
circumference [+ or -] 1.47 [+ or -] 1.38

Knee height 309 36.86 282 36.82
(cm) [+ or -]2.05 [+ or -]2.19

No significant differences were found between groups (one-way ANOVA).

The overall unadjusted cognitive scores stratified by UAs concentration levels are shown in Table 4. Children in the group with high UAs concentration presented lower scores in 7 of 11 cognitive tests than children with low UAs concentration.
Table 4. Cognitive scores of children stratified by UAs concentration
[mean [+ or -] SD (minimum-maximum)].

Cognitive tests Overall Children with
 UAs < 50

Math Achievement 31.35 [+ or -] 32.27 [+ or -]
Test 7.50 (3-52) 7.69 (8-52)

Visual-Spatial 18.31 [+ or -] 18.88 [+ or -]
Abilities with 5.15 (2-34) 5.16 (3-34)
Figure Design

WISC-RM Arithmetic 7.41 [+ or -] 7.26 [+ or -]
Subscale 3.62 (1-17) 3.66 (1-17)

Peabody Picture 103.19 [+ or -] 105.20 [+ or -]
Vocabulary Test 15.65 (55-145) 16.11

WISC-RM Digit Span 9.10 [+ or -] 9.46 [+ or -]
Subscale 3.63 (1-19) 3.73 (1-19)

Sternberg Memory 12.14 [+ or -] 12.30 [+ or -]
(correct trials) 2.94 (4-20) 3.01 (4-20)

Visual Memory Span 2 [+ or -]0.52 2.03 [+ or -]
(correct trials) (0.69-3.37) 0.51

Stimulus 0.57 [+ or -] 0.63 [+ or -]
Discrimination 0.50 (0-1) 0.48 (0-1)
(correct trials <
19 vs. [greater
than or equal to]

WISC-RM Coding 2.26 [+ or -] 2.29 [+ or -]
Subscale 0.59 (1-3.71) 0.58

Visual Search 5.03 [+ or -] 5.23 [+ or -]
(correct minus 1.51 (1-10.82) 1.47
incorrect minus (1.14-10.82)
omitted trials)

Letter Sequencing 0.48 [+ or -] 0.55 [+ or -]
(correct trials 0 0.50 (0-1) 0.50 (0-1)
vs. [greater than
or equal to] 1)

Cognitive tests Children with
 UAs > 50

Math Achievement 30.57 [+ or -]
Test 7.20 (3-49)*

Visual-Spatial 17.84 [+ or -]
Abilities with 5.08 (2-31)*
Figure Design

WISC-RM Arithmetic 7.59 [+ or -]
Subscale 3.57 (1-17)

Peabody Picture 101.67 [+ or -]
Vocabulary Test 14.92

WISC-RM Digit Span 8.80 [+ or -]
Subscale 3.55 (2-18)*

Sternberg Memory 11.98 [+ or -]
(correct trials) 2.86 (5-20)

Visual Memory Span 1.97 [+ or -]
(correct trials) 0.53

Stimulus 0.52 [+ or -]
Discrimination 0.50 (0-1)*
(correct trials <
19 vs. [greater
than or equal to]

WISC-RM Coding 2.23 [+ or -]
Subscale 0.61

Visual Search 4.84 [+ or -]
(correct minus 1.50 (1-8.99)*
incorrect minus
omitted trials)

Letter Sequencing 0.41 [+ or -]
(correct trials 0 0.49 (0-1)*
vs. [greater than
or equal to] 1)

Letter Sequencing (correct trials 0 vs. [greater than or equal to]1)
0.48 [+ or -] 0.50 (0-1) 0.55 [+ or -] 0.50 (0-1) 0.41 [+ or -] 0.49
(0-1)* *Difference between children with UAs < 50 and children
UAs > 50 [micro]g/L is significant at p < 0.05.

Table 5 shows the covariate-adjusted relationship between UAs concentration and cognitive performance. The analysis is also presented separately by sex and by UAs below and above 50 [micro]g/L. Overall, a significant inverse association was found between UAs and the Visual-Spatial Abilities with Figure Design, the PPVT, the WISC-RM Digit Span subscale, and the Visual Search and Letter Sequencing Tests (p < 0.05). In the analysis stratified by UAs concentration, UAs was significantly associated with the PPVT, the WISC-RM Digit Span Subscale, the Sternberg Memory Test, and the Visual Memory Span at UAs levels [less than or equal to]50 [micro]g/L. In children with UAs > 50 [micro]g/L, the Visual-Spatial Abilities with Figure Design, the Stimulus Discrimination, and Letter Sequencing Tests were significant at p < 0.05. For boys, the Visual-Spatial Abilities with Figure Design and the PPVT, Visual Search, and Letter Sequencing Tests had an inverse association with UAs (p < 0.05). And for girls, there was a significant negative association of UAs only with the WISC-RM Digit Span Subscale (p < 0.05). The significant associations of each metabolite with the cognitive function tests are also shown in Table 5 (type of arsenic in parentheses). In general, organic forms of As in urine affected more cognitive tests than did the inorganic forms.
Table 5. Covariate-adjusted association between UAs ([micro]g/dL) and
tests of cognitive function.

 Overall Children Children
 with UAs < with UAs >
 50 50
 [micro]g/L [micro]g/L

test (n = 557) (n = 267) (n= 290)

(95% CI)]

and Vocabulary

Math -0.023 -0.098* -0.015
Achievement (-0.052 (-0.199 to (-0.047 to
 to 0.003) 0.018)

 (MMA*) (DMA,*

Visual-Spatial -0.024** -0.018 -0.028**
Abilities with (-0.045 (-0.096 to (-0.053 to
 to 0.061) -0.004)

Figure Design (DMA**) (DMA**)

WISC-RM -0.001 -0.014 -0.006
Arithmetic (-0.016 (-0.051 to (-0.029 to
 0.014) 0.024) 0.016)

Peabody -0.064** -0.185# -0.058*

Vocabulary (-0.115 (-0.293 to (-0.120 to
 to -0.078) 0.004)

Test (InAs,** (DMA,** (DMA*)
 DMA,** MMA*)

WISC-RM Digit -0.014** -0.037** -0.012
Span Subscale (-0.025 (-0.065 to (-0.037 to
 to -0.010) 0.012)

 (DMA,* (DMA**)

Sternberg -0.002 -0.027** 0.002
Memory (-0.007 (-0.053 to (-0.008 to
 to -0.002) 0.012)
(correct (DMA*)

Visual Memory -0.000 -0.003** -0.001
Span (-0.002 (-0.007 to (-0.002 to
 to 0.000) 0.003)
(correct (DMA**)

WISC-RM Coding 0.000 0.000 -0.001
Subscale(a) (-0.001 (-0.005 to (-0.003 to
 to 0.005) 0.002)

Visual Search (DMA*) -0.008 -0.006*
(correct minus -0.007# (-0.022 to (-0.012 to
 0.005) 0.000)
incorrect (InAs,# (DMA**)
omitted DMA,**
trials)(a) MMA**)

[odds ratio
(95% CI)]

Stimulus 0.998 0.982 1.004**

Discrimination (0.993 to (0.957 to (1.000 to
 1.004) 1.008) 1.008)
(correct (DMA*)
< 19 vs.

[greater than or equal to] 19)

Letter 0.992# 0.992 0.993**
Sequencing (0.987 (0.963 to (0.988 to
 to 1.021) 0.999)
(correct (DMA,# (DMA#)

0 vs. [greater than or equal to] 1) MMA#)
 Males Females

test (n = 306) (n = 251)
coefficient (95% CI)]

Problem Solving
and Vocabulary

Math -0.022* -0.019
Achievement (-0.046 to 0.001) (-0.063 to 0.026)


Visual-Spatial -0.026** -0.019
Abilities with (-0.048 to -0.003) (-0.045 to 0.007)

Figure Design (DMA*)

WISC-RM -0.000 -0.003
Arithmetic (-0.020 to 0.020) (-0.021 to 0.015)

Peabody Picture -0.058** -0.070
Vocabulary (-0.105 to -0.010) (-0.169 to 0.029)

Test (DMA,**

WISC-RM Digit -0.008 -0.039#
Span Subscale (-0.025 to 0.008) (-0.062 to -0.016)

 PbB x

Sternberg 0.002 -0.008
Memory (-0.006 to -0.010) (-0.020 to 0.005)

Visual Memory 0.000 -0.001
Span (-0.002 to 0.002) (-0.004 to 0.002)

WISC-RM Coding 0.000 -0.002
Subscale(a) (-0.001 to 0.002) ( 0.001)

Visual Search -0.016** 0.010*
(correct minus (-0.029 to -0.003) (0.000 to 0.019)
incorrect minus (DMA,# (PbB x
omitted MMA,** UAs#)
trials)(a) PbB x

[odds ratio
(95% CI)]


Stimulus 1.002 0.990
Discrimination (0.995 to 1.009) (0.977 to 1.002)
(correct trials
< 19 vs.
[greater than
or equal to]


Letter 0.988# 0.998
Sequencing (0.982 to 0.993) (0.990 to 1.007)
(correct trials (InAs,**
0 vs. [greater DMA,#
than or equal MMA#)
to] 1)

(a)These cognitive tests were log-transformed to achieve the normal
distribution. Beta coefficient significant at
*p < 0.1, **p < 0.05, #p < 0.01.


We found an association of UAs with several cognition tests such as Visual-Spatial Abilities with Figure Design, the PPVT, the WISC-RM Digit Span subscale, and the Visual Search and Letter Sequencing Tests. These associations were independent from sociodemographic variables, nutritional status, and PbB. When analysis was stratified by UAs, some tests were associated with UAs at concentrations < 50 [micro]g/L. However, some tests were not associated with the high UAs group even though the association was significant in the low UAs group and in the total sample; this might be attributed to the power reduction and increased variability in the high UAs group compared with the variability in the low UAs group. Cognitive tests that showed association with UAs represent complex cognitive processes such as memory, problem solving, and attention. Previous studies had shown an adverse relationship between As exposure and IQ (Calderon et al. 1999) and neurobehavioral performance (Rodriguez et al. 2003). Results from our study confirm earlier findings by Calderon et al. (2001) and Tsai et al. (2003) relating As exposure to memory alterations. These associations occurring even in children without elevated As concentrations could be caused by several possible mechanisms. As crosses the blood-brain barrier and has a wide range of effects on the white matter in the brain (Osterberg and Kernohan 1934). Evidence also shows that arsenite inhibits the synthesis and liberation of acetylcholine in brain slices (Kobayashi et al. 1987), and increases the monoamine activity in rat nervous system (Mejia et al. 1997; Tripathi et al. 1997). The increase in one monoamine, 5-hydroxyindole-3 acetic acid, is potentially neurotoxic (Jones et al. 2005; Mejia et al. 1997). Whatever the mechanism, the present investigation shows that exposure to As is associated with deficits in cognitive performance among school-age children, even at low exposure levels, affecting complex cognitive processes such as memory and problem solving, which could potentially interfere with performance at school.

We found different associations between UAs and cognitive tests in boys and girls. Several cognitive tests were negatively associated with UAs only in boys, and the Digit Span subscale, an evaluation of memory, was significantly associated with UAs only in girls. InAs seemed to affect only boys in the Letter Sequencing Test. According to ANOVA to detect differences in cognitive performance between boys and girls (data not shown), differences in associations of cognitive tests with UAs between boys and girls did not seem to be related with differences in sex. Although it has also been reported by others (Chen et al. 2003; Kristiansen et al. 1997; Vahter et al. 2007), it is unclear why boys would excrete more UAs, which thereby affects their cognitive performance.

Values are beta coefficient [95% confidence interval (CI)] of total urinary arsenic from a multiple regression, or odds ratio from a logistic regression, with cognitive test score as the dependent variable. Regression models were adjusted for age in months, sex, mother's school level, Hb concentration, PbB concentration, and for PbB concentration x UAs interaction when it was significant at p < 0.10. CIs were adjusted for clustering on children's school. Significant effect of UAs metabolites on cognition functions or PbB concentration x UAs interaction is shown in parentheses.

SES had an inverse association with UAs in our study. Ahmad et al. (2001) also found an inverse association between arsenicosis and SES and education level, probably due to the lack of access to As-free water. In other studies, As was also related to nutritional status of participants. Sikder et al. (2005) found that most of the arsenicosis patients in their study also suffered from malnutrition. Minamoto et al. (2005) reported that children living in households with As-contaminated tube well water had lower height for their age than children living in noncontaminated households. Similarly, Islam et al. (2004) found higher rates of underweight in individuals with arsenicosis than in controls, and concluded that poor nutritional status increased the complications of arsenicosis. In our study we did not find an association between UAs excretion and height, weight, and iron and other minerals' concentration in serum. The lack of association was probably owing to the fact that our subjects were generally well nourished. Only 2% were stunted and 10% were anemic; and the rate of children with high concentration of UAs was the same among anemic and nonanemic children.

In conclusion, our study of a population of children living in an area contaminated with both As and lead showed that As contamination affected children's cognitive function independent of any effect of lead, even in children with UAs below the safe declared concentration limit of 50 [micro]g/L (ATSDR 2000).


Ahmad SA, Sayed MH, Barua S, Khan MH, Faruquee MH, Jalil A, et al. 2001. Arsenic in drinking water and pregnancy outcomes. Environ Health Perspect 109:629-631.

ATSDR. 2000. Toxicological Profile for Arsenic. Update. Atlanta, GA:Agency for Toxic Substances and Disease Registry.

Benin AL, Sargent JD, Dalton M, Roda S. 1999. High concentrations of heavy metals in neighborhoods near ore smelters in Northern Mexico. Environ Health Perspect 107:279-284.

Calderon J, Navarro ME, Jimenez-Capdeville ME, Santos-Diaz MA, Golden A, Rodriguez-Leyva I, et al. 2001. Exposure to arsenic and lead and neuropsychological development in Mexican children. Environ Res 85(2):69-76.

Calderon RL, Hudgens E, Le XC, Schreinemachers D, Thomas DJ. 1999. Excretion of arsenic in urine as a function of exposure to arsenic in drinking water. Environ Health Perspect 107:663-667.

Canfield RL, Kreher DA, Cornwell C, Henderson CR Jr. 2003. Low-level lead exposure, executive functioning, and learning in early childhood. Child Neuropsychol 9(1):35-53.

Carrizales L, Razo I, Tellez-Hernandez JI, Torres-Nerio R, Torres A, Batres LE, Cubillas AC, et al. 2006. Exposure to arsenic and lead of children living near a copper-smelter in San Luis Potosi, Mexico: importance of soil contamination for exposure of children. Environ Res 101(1):1-10.

Cebrian ME, Albores A, Garcia-Vargas G, Del Razo LM. 1994. Chronic arsenic poisoning in humans: the case of Mexico. In: Arsenic in the Environment, Part II: Human Health and Exocystem Effects (Nriagu JO, ed). John Wiley & Sons, Inc., 93-107.

Chen YC, Guo YL, Su HJ, Hsueh YM, Smith TJ, Ryan LM, et al. 2003. Arsenic methylation and skin cancer risk in southwestern Taiwan. J Occup Environ Med 45:241-248.

Crecelius EA, Bloom NS, Cowan CE, Jenne EA. 1986. Determination of arsenic species in limnological samples by hydride generation atomic absorption spectroscopy. In: Speciation of Selenium and Arsenic in Natural Waters and Sediments, Vol 2: Asenic Speciation. EA-4641.Project 20202. Palo Alto, CA:Electric Power Research Institute, 1-28.

De Burbure C, Buchet JP, Leroyer A, Nisse C, Haguenoer JM, Mutti A, et al. 2006. Renal and neurologic effects of cadmium, lead, mercury, and arsenic in children: evidence of early effects and multiple interactions at environmental exposure levels. Environ Health Perspect 114:584-590.

Del Razo JLM, Hernandez GJL, Garcia-Vargas GG, OstroskyWegman P, Cortinas de Nava C, Cebrian ME. 1994. Urinary excretion of arsenic species in a human population chronically exposed to arsenic via drinking water. A pilot study. In: Arsenic Exposure and Health (Chapell WR, Abernathy CO, Cothern CR, eds). Boca Raton, FL:CRC Press, 91-100.

Del Razo LM, Aguilar A, Sierra-Santoyo A, Cebrian ME. 1999. Interference in the quantitation of methylated arsenic species in human urine. J Anal Toxicol 23:103-107.

Del Razo LM, Arellano MA, Cebrian ME. 1990. The oxidation states of arsenic in well-water from a chronic arsenicism area of northern Mexico. Environ Pollut 64:143-153.

Del Razo LM, Garcia-Vargas GG, Garcia-Salcedo J, Sanmiguel MF, Rivera M, Hernandez MC, et al. 2002. Arsenic levels in cooked food and assessment of adult dietary intake of arsenic in the Region Lagunera, Mexico. Food Chem Toxicol 40:1423-1431. Detterman DK. 1988. Cognitive Abilities Test. Cleveland, OH:Case Western University.

Diawara MM, Litt JS, Unis D, Alfonso N, Martinez L, Crock JG, et al. 2006. Arsenic, cadmium, lead, and mercury in surface soils, Pueblo, Colorado: implications for population health risk. Environ Geochem Health 28(4):297-315.

Diaz-Barriga F, Batres L, Calderon J, Lugo A, Galvao L, Lara I,

Rizo P, et al. 1997. The El Paso smelter 20 years later: residual impact on Mexican children. Environ Res 74:11-16. Dorea JG. 2004. Mercury and lead during breast-feeding. Br J Nutr 92(1):21-40.

Dunn LM, Lugo DE, Padilla ER, Dunn LM. 1986. Test de vocabularioen imagenes Peabody. Circle Pines, MN:American Guidance Service.

Flora SJ. 2002. Lead exposure: health effects, prevention and treatment. J Environ Biol 23(1):25-41. Franzblau A, Lilis R. 1989. Acute arsenic intoxication from environmental arsenic exposure. Arch Environ Health 44(6):385-390. Garcia-Vargas GG, Hernandez-Zavala A. 1996. Urinary porphyrins and heme biosynthetic enzyme activities measured by HPLC in arsenic toxicity. Biomed Chromatogr 10(6):278-284.

Habicht JP. 1974. Standardization of quantitative epidemiologic methods for the field [In Spanish]. Bol Oficina Sant Panam 76:375-384.

Hall AH. 2002. Chronic arsenic poisoning. Toxicol Let 128:69-72.

International Programme on Chemical Safety. 2001. Arsenic and Arsenic Compounds. Environmental Health Criteria 224. 2nd ed. Geneva:World Health Organization.

Islam LN, Nabi AH, Rahman MM, Khan MA, Kazi AI. 2004.

Association of clinical complications with nutritional status and the prevalence of leukopenia among arsenic patients in Bangladesh. Int J Environ Res Public Health 1(2):74-82.

Itoh T, Zhang YF, Murai S, Saito H, Nagahama H, Miyate H, et al. 1990. The effect of arsenic trioxide on brain monoamine metabolism and locomotor activity of mice. Toxicol Lett 54:345-353.

Jones DC, Duvauchelle C, Ikegami A, Olsen CM, Lau SS, de la Torre R, et al. 2005. Serotonergic neurotoxic metabolites of ecstasy identified in rat brain. J Pharmacol Exp Ther 313(1):422-431.

Kobayashi H, Yuyama A, Ishihara M, Matsusaka N. 1987.

Effects of arsenic on cholinergic parameters in brain in vitro. Neuropharmacology 26(12):1707-1713.

Kordas K, Canfield RL, Lopez P, Rosado JL, Vargas GG, Cebrian ME, et al. 2006. Deficits in cognitive function and achievement in Mexican first-graders with low blood lead concentrations. Environ Res 100(3):371-386.

Kordas K, Lopez P, Rosado JL, Garcia Vargas G, Alatorre Rico J, Ronquillo D, et al. 2004. Blood lead, anemia, and short stature are independently associated with cognitive performance in Mexican school children. J Nutr 134(2):363-371.

Kristiansen J, Christensen JM, Iversen BS, Sabbioni E. 1997. Toxic trace element reference levels in blood and urine: influence of gender and lifestyle factors. Sci Total Environ 204(2):147-160.

Lanphear BP, Dietrich K, Auinger P, Cox C. 2000. Cognitive

deficits associated with blood lead concentrations < 10 microg/dL in US children and adolescents. Public Health Rep 115(6):521-529.

Mandal KB, Suzuki KT. 2002. Arsenic round the world: a review. Talanta 58: 201-235.

Mejia JJ, Diaz-Barriga JJ, Calderon J, Rios C, Jimenez Capdeville ME. 1997. Effects of lead-arsenic combined exposure on central, monoaminergic systems. Neurotoxicol Teratol 19:489-497.

Miller DT, Pascal DC, Gunter EW, Stroud PE, D' Angelo J. 1987. Determination of lead in blood using electrothermal atomization absorption spectrometry, a L'vov platform and matrix modifier. Analyst 112:1701-1704.

Minamoto K, Mascie-Taylor CG, Moji K, Karim E, Rahman M. 2005. Arsenic-contaminated water and extent of acute childhood malnutrition (wasting) in rural Bangladesh. Environ Sci 12(5):283-292.

Osterberg AE, Kernohan JW. 1934. The presence of arsenic in the brain and its relation to pericapillary hemorrhages or so-called acute hemorrhagic encephalitis. Am J Pathol 4:362-369.

Polissar L, Lowry-Coble K, Kalman DA, Hughes JP, van Belle G, Covert DS, et al. 1990. Pathways of human exposure to arsenic in a community surrounding a copper smelter. Environ Res 53(1):29-47.

Reitan RM, Wolfson D. 1992. Neuropsychological Evaluation of Older Children. Tucson AZ:Neuropsychology Press.

Rico JA, Kordas K, Lopez P, Rosado JL, Vargas GG, Ronquillo D, et al. 2006. Efficacy of iron and/or zinc supplementation on cognitive performance of lead-exposed Mexican schoolchildren: a randomized, placebo-controlled trial. Pediatrics 117(3):e518-e527.

Rodriguez VM, Jimenez-Capdeville ME, Giordano M. 2003. The effects of arsenic exposure on the nervous system. Toxicol Lett 145:1-18.

Rosado JL, Lopez P, Kordas K, Garcia-Vargas G, Ronquillo D, Alatorre J, et al. 2006. Iron and/or zinc supplementation did not reduce blood lead concentrations in children: results of a randomized placebo controlled trial. J Nutr 136(9):2378-2383.

Sikder MS, Maidul ZM, Ali M, Rahman MH. 2005. Socio-economic status of chronic arsenicosis patients in Bangladesh. Mymensingh Med J 14(1):50-53.

Smedley PL, Kinniburgh DG. 2002. A review of the source, behavior and distribution of arsenic in natural waters. Review. Appl Geochem 17:517-568.

Tripathi N, Kannan GM, Pant BP, Jaiswal DK, Malhotra PR, Flora SJ. 1997. Arsenic-induced changes in certain neurotransmitter levels and their recoveries following chelation in rat whole brain. Toxicol Lett 92(3):201-208.

Tsai SY, Chou HY, The HW, Chen CM, Chen CJ. 2003. The effects of chronic arsenic exposure from drinking water on the neurobehavioral development in adolescence. Neurotoxicology 24:747-753.

U.S. EPA. 1997. Overview of Primary Environmental Regulations Pertinent to BRAC Cleanup Plan Development under the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA). Washington, DC:U.S. Environmental Protection Agency.

Vahter M, [ANGSTROM]kesson A, Liden C, Ceccatelli S, Berglund M. 2007. Gender differences in the disposition and toxicity of metals. Environ Res 104:85-95.

Valenzuela OL, Borja-Aburto VH, Garcia-Vargas GG, Cruz-Gonzalez MB, Garcia-Montalvo EA, Calderon-Aranda ES, et al. 2005. Urinary trivalent methylated arsenic species in a population chronically exposed to inorganic arsenic. Environ Health Perspect 113:250-254.

Wechsler D. 1974. Manual for Wechsler Intelligence Scale for Children--Revised. San Antonio, TX:Psychological Corporation.

Wechsler D. 1981. WISC-R-Espanol. Escala de intelligencia revisiada para el nivel escolar. Mexico City:El Manual Moderno, SA.

Yung CY. 1984. A synopsis on metals in medicine and psychiatry. Pharmacol Biochem Behav 21(suppl 1):41-47.

Address correspondence to J.L. Rosado Apartado Postal No 31, Desarrollo San Pablo, Queretaro Qro. Mexico. Telephone: (51) +442 1921200 Ext. 5351. Fax: (51) +442 234 2958. E-mail: jlrosado@

We thank A. Cebrian, G. Concha, B. Gamez, J. Gavino, M. Gutierrez, G. Leon, A. Luna, F. Marentes, R.I. Morales, C. Sosa, G. Torres, and R. Sanchez for help in data collection, and E. Vera for chemical analyses.

This research was partially supported by funding from the Spencer Foundation, Chicago, IL, USA.

The authors declare they have no competing financial interests.

Received 6 December 2006; accepted 21 May 2007.

Jorge L. Rosado, (1) Dolores Ronquillo,(1) Katarzyna Kordas, (2) Olga Rojas, (3) Javier Alatorre, (3) Patricia Lopez, (4) GonzaloGarcia-Vargas, (5) Maria del CarmenCaamano, (1) Mariano E.Cebrian, (6) and Rebecca J.Stoltzfus (2)

(1) School of Natural Sciences, Universidad Autonoma de Queretaro, Division of Nutritional Sciences, Queretaro, Mexico; (2) Division of Nutritional Science, Cornell University, Ithaca, New York, USA; (3) Department of Psychology, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico; (4) Instituto Nacional de Ciencias Medicas y Nutricion, Mexico City, Mexico; (5) Medical School, Universidad Juarez del Estado de Durango, Gomez Palacio, Mexico; (6) Centro de Investigacion y Estudios Avanzados, National Polytechnic Institute, Mexico City, Mexico
COPYRIGHT 2007 National Institute of Environmental Health Sciences
No portion of this article can be reproduced without the express written permission from the copyright holder.
Copyright 2007, Gale Group. All rights reserved.

Article Details
Printer friendly Cite/link Email Feedback
Title Annotation:Children's Health
Author:Rosado, Jorge L.; Ronquillo, Dolores; Kordas, Katarzyna; Rojas, Olga; Alatorre, Javier; Lopez, Patri
Publication:Environmental Health Perspectives
Geographic Code:1MEX
Date:Sep 1, 2007
Previous Article:Error and bias in determining exposure potential of children at school locations using proximity-based GIS techniques.
Next Article:Workgroup report: developing environmental health indicators for european children: world health organization working group.

Related Articles
Workgroup report: developing environmental health indicators for european children: world health organization working group.
Synergistic effects of traffic-related air pollution and exposure to violence on Urban Asthma Etiology.
Assessing uncertainty in spatial exposure models for air pollution health effects assessment.
Grand rounds: nephrotoxicity in a young child exposed to uranium from contaminated well water.
The relationship between early childhood blood lead levels and performance on end-of-grade tests.
Pesticide urinary metabolite levels of children in eastern North Carolina farmworker households.
Methylmercury and the developing brain.
The outdoor air quality flag program in Central California: a school-based educational intervention to potentially help reduce children's exposure to...
Climate change and disability-adjusted life years.
Research explains how lead exposure produces learning deficits.

Terms of use | Copyright © 2018 Farlex, Inc. | Feedback | For webmasters