Low-to-moderate fluoride exposure in relation to overweight and obesity among school-age children in China

Ecotoxicology and Environmental Safety 183 (2019) 109558

Contents lists available at ScienceDirect

Ecotoxicology and Environmental Safety journal homepage: www.elsevier.com/locate/ecoenv

Low-to-moderate fluoride exposure in relation to overweight and obesity among school-age children in China

T

Ling Liua, Mengwei Wanga, Yonggang Lib, Hongliang Liuc, Changchun Houc, Qiang Zengc, Pei Lia, Qian Zhaoa, Lixin Donga, Xingchen Yud, Li Liud, Shun Zhanga,∗, Aiguo Wanga,∗∗ a

Department of Occupational and Environmental Health, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China b Tianjin Baodi District Centers for Disease Control and Prevention, Tianjin, PR China c Tianjin Centers for Disease Control and Prevention, Tianjin, PR China d Department of Epidemiology and Biostatistics, Ministry of Education Key Lab of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China

ARTICLE INFO

ABSTRACT

Keywords: Water fluoride Urinary fluoride Body mass Overweight and obesity School-age children

High fluoride exposure has been related to harmful health effects, but the impacts of low-to-moderate fluoride on child growth and obesity-related outcomes remain unclear. We performed a large-scale cross-sectional study to examine the association between low-to-moderate fluoride in drinking water and anthropometric measures among Chinese school-age children. We recruited 2430 resident children 7–13 years of age, randomly from lowto-moderate fluorosis areas of Baodi District in Tianjin, China. We analyzed the fluoride contents in drinking water and urine samples using the national standardized ion selective electrode method. Multivariable linear and logistic analyses were used to assess the relationships between fluoride exposure and age- and sex-standardized height, weight and body mass index (BMI) z-scores, and childhood overweight/obesity (BMI z-score > 1). In adjusted models, each log unit (roughly 10-fold) increase in urinary fluoride concentration was associated with a 0.136 unit increase in weight z-score (95% CI: 0.039, 0.233), a 0.186 unit increase in BMI z-score (95% CI: 0.058, 0.314), and a 1.304-fold increased odds of overweight/obesity (95% CI: 1.062, 1.602). These associations were stronger in girls than in boys (Pinteraction = 0.016), and children of fathers with lower education levels were more vulnerable to fluoride (Pinteraction = 0.056). Each log unit (roughly 10-fold) increase in water fluoride concentration was associated with a 0.129 unit increase in height z-score (95% CI: 0.005, 0.254), but not with other anthropometric measures. Our results suggest low-to-moderate fluoride exposure is associated with overweight and obesity in children. Gender and paternal education level may modify the relationship.

1. Introduction Fluoride is a naturally occurring trace element of earth's crust and humans are exposed to the contaminant through several major sources, including drinking water, fluoridated foodstuffs, agricultural fertilizers, as well as living near an industrial area seriously polluted by this element (Ghosh et al., 2012). Excessive intake and long-term accumulation of fluoride may lead to the fluorosis, which is characterized by the damage of calcified tissues, principally the dental mottling and skeletal manifestations, and non-skeletal phase damage such as liver, kidneys, heart and brain (Xiong et al., 2007). To minimize the harmful effect of fluoride, the World Health Organization (WHO) guideline value of

fluoride in drinking water is 1.5 mg/L (World Health Organization, 2017), while the guideline is stricter in China, at a fluoride value of 1.0 mg/L (Ministry of Health of China, 2007). Millions of people worldwide suffered from fluorosis due to drinking high-fluoride water, especially in Africa, Asia and the United States (Ali et al., 2016). In China, it is presently estimated that approximately 87 millions of people remain to be affected by high fluoride drinking water, and the water-drinking endemic fluorosis covers areas of more than 2.2 million km2 (Li et al., 2009). Although defluoridation of drinking water has been proposed in high water fluoride areas over time, many rural children remain to be exposed at low to moderate levels (Huang et al., 2017; Pei et al., 2015; Zhu et al., 2006).

*

Corresponding author. Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, PR China. ** Corresponding author. E-mail addresses: [email protected] (S. Zhang), [email protected] (A. Wang). https://doi.org/10.1016/j.ecoenv.2019.109558 Received 1 May 2019; Received in revised form 27 July 2019; Accepted 8 August 2019 Available online 26 August 2019 0147-6513/ © 2019 Elsevier Inc. All rights reserved.

Ecotoxicology and Environmental Safety 183 (2019) 109558

L. Liu, et al.

In addition to dental and skeletal fluorosis, there is evidence that fluoride can cause direct disorder of lipid metabolism and adipogenesis (Lin et al., 2001; Silva et al., 2016). For example, in vivo, the adult male rats of Wistar strain orally exposed to fluoride indicated a significant increase in total plasma lipids (Miltonprabu and Thangapandiyan, 2015; in vitro, fluoride induced increased levels of lipid peroxidation in murine hepatocytes with concentration-dependent manners (Ghosh et al., 2008). Moreover, fluoride's potential influences on body weight may also be related to the alterations in thyroid hormones, estrogen and androgen levels, or glucose tolerance and insulin resistance (AbdElhakim et al., 2018; Al-Raddadi et al., 2012; Garcã A-Montalvo et al., 2009; Hu et al., 2009; Liu et al., 2016; Ozsvath, 2009; Trivedi et al., 1993). Only a few human studies relate fluoride exposure to obesityrelated outcomes in children, and results remain inconclusive: some report a positive association with overweight following exposure to fluoride (Das and Mondal, 2016), while others report no association, or decreases in body weight (Wang et al., 2007; Yousefi et al., 2018). The diverging effects seem to be associated to race, gender, age, exposure levels, and the small sample size. Most studies investigating the influence of fluoride exposure on the damages to children tend to focus on relatively high fluoride levels (more than 3 mg/L) (Seraj et al., 2012; Wondwossen et al., 2004). In China, however, the majority of inhabitants living in endemic fluorosis areas are exposed to lower fluoride levels in their everyday life since the water defluoridation projects have been widely implemented (Zhao et al., 2016). Although a lot of research has been devoted to the adverse impact of high fluoride exposure, it is still unclear the implications of low-to-moderate fluoride exposure on growth in school-age children. Thus, we performed a large population-based study to investigate the association of low-to-moderate fluoride exposure with anthropometric measures, especially the measures of overweight and obesity in a Chinese population of school-age children, and to assess whether certain groups are more susceptible than others to low-to-moderate fluoride.

selected using the simple random sampling (SRS) method, three of which were historical high fluoride areas, and four, non-endemic fluorosis areas. Then, twenty-four villages were further selected from each chosen town using SRS. Finally, children were selected from each chosen village using the cluster sampling method. Inclusion criteria were: 1) residing since birth in the study area; 2) living in villages supplied by groundwater; 3) being between 7 and 13 years old. Those children with history of chronic medical illness (e.g. renal, hepatic, and endocrine disorders), or long-term medication related to overweight and obesity were not included in the study. The water supplies for these villages were made up of dug-wells and tube wells with handy pumps. During the investigation, water samples were collected randomly from the public water supply wells in each village. All participants were invited to provide first morning urine samples and complete structured questionnaires with the help of parents. Questionnaires elicited information on participants’ characteristics including age, gender, residential addresses, basic health status, parental education, occupational background, household income, maternal exposures occurring and medical history during pregnancy (e.g., diabetes, undernourishment, anaemia, drinking, smoking and exposure to tobacco smoke), and adverse birth outcome (e.g., dystocia, hypoxia, premature birth, low birth weight, and post-term birth). 2.3. Measurement of fluoride concentrations In total, we collected and analyzed 168 water samples and 2430 first morning urine samples in this study. All samples were stored in sterile polypropylene containers at −80 °C until use. In a test phase, the concentrations of F− [mg/L] were measured by ion selective electrode (PF-202-CF, INESA, Shanghai) using the national standardized method in China (WS/T 89–2006) (Wu et al., 2015; Yu et al., 2018). The limit of detection for F− was 0.01 mg/L. After further dilution with fresh stock solution, calibration standards were obtained: 0.1, 0.5, 2.0, 5.0 and 20.0 mg/L. During the measurement, the fluoride concentration in the samples was determined directly after dilution with equal volumes of total ionic strength adjustment buffer solution (pH 5–5.5). Double-distilled deionized water was used to prepare all solutions. Each experiment was performed thrice in each sample (urine and water) to reduce measurement error, and the average was used for analysis. For most samples, there was less than 10% variation between the measurements. All the chemicals used were of either analytical reagent or guaranteed reagent grade purity.

2. Materials and methods 2.1. Study area Baodi District (117°30′N, 39°72′E) is in Tianjin City in the northeast of China, with an area of 1509 km2. The annual rainfall is 603.6 mm, while annual evaporation is 1612.0 mm. The water resources per capita is only 160 m3, ranked the worst in China (Zhang et al., 2008). Up to 70% of the rural population uses groundwater from privately or community-owned wells for domestic use and agriculture. However, groundwaters with high fluoride concentration above 1 mg/L were found in many parts of the region (Wang, 2014). The prevalence of dental fluorosis was over 70% among children and the epidemic of drinking water type of endemic fluorosis is still serious in Baodi District (Tianjin Health Committee, 2017). Regional hydrogeological survey indicated that the saltwater intrusion, hydrolysis of fluoride-rich minerals, and evaporation may be important factors for the occurrence of high fluoride groundwater (Dong et al., 2011; Wen et al., 2013; Zhu and Xu, 2009). The rural area of Baodi District was selected in the present study to investigate fluoride contamination (Fig. S1).

2.4. Anthropometric measurements At study entry, a standardized anthropometric survey was conducted by a single trained investigator (OS) and without knowledge of the children's fluoride levels. The children wore light clothing and took off their shoes for weight measurement. Height was measured to the nearest 0.1 cm using a stadiometer, and weight was measured to the nearest 0.1 kg using a standard dual reading scale. Age- and sex-standardized specific z-scores were calculated for height and body mass index (BMI; kilograms per square meter) using the World Health Organization's (WHO) Child Growth standards (World Health Organization, 2011), and for weight using the Centers for Disease Control and Prevention (CDC) reference standards (Centers for Disease Control and Prevention, 2009) because WHO standards are unavailable for this age group. Measurements that corresponded to zscore > 5 or < −5 were considered implausible and were excluded from analyses (n = 1 observation). Because our main study outcome was overweight (including obesity; hereafter described as “overweight/ obesity”), defined as a BMI z-score > 1 according to the WHO reference (De et al., 2007).

2.2. Study population and data collection From May to October 2015, we conducted this cross-sectional study in the rural areas of Baodi District in Tianjin City, China, following procedures approved by the Institutional Ethical Committee of Tongji Medical College of Huazhong University of Science and Technology. The monitoring data have shown that the levels of fluoride remain relatively stable in the whole region over time. At our study location, a two-stage cluster sample of children was randomly selected to achieve a gradient of fluoride. The primary sampling units were towns, and secondary units were the children in villages. First, seven towns were 2

Ecotoxicology and Environmental Safety 183 (2019) 109558

L. Liu, et al.

2.5. Statistical analysis

Table 1 General and baseline characteristics of the study participants (n = 2430).

Standard descriptive analyses were performed, including calculation of means and standard deviations for continuous variables and frequencies for categorical variables. Fluoride concentrations in water and urine were either categorized in quartiles or were log10 transformed when used as continuous measures to make their distributions more normal. Spearman rank correlation coefficients (rs) were calculated to evaluate the closeness of the relationship between fluoride contents in water and urine. Multiple linear regression models were used to examine the association between fluoride concentrations and continuous outcomes (height, weight and BMI z-scores) and fluoride measurements adjusted for potential confounders. Logistic regression models were used to calculate odds ratios (ORs) of overweight/obesity associated with fluoride concentrations in water and urine. Tests for trend were performed using the Wald test and used the midpoint value of each exposure category treated as a continuous variable in regression models. Nonlinear relationships between fluoride and body mass indexes were fitted using restricted cubic splines with three knots at the 5th, 50th and 95th percentiles of fluoride. Potential confounding variables were selected a priori based on the childhood anthropometric measurements and overweight or obesity literature (Baheiraei et al., 2015; Behl et al., 2013; Krebs et al., 2007; Monasta et al., 2010; Wang and Lim, 2012). All models in children were adjusted for maternal age at delivery (< 25 vs. ≥ 25 years), secondhand tobacco smoke (SHS) (yes/no), maternal education level (middle school and ≥ high school vs. ≤ primary school), paternal education level (middle school and ≥ high school vs. ≤ primary school), household incomes (10,000 – 30,000 and > 30,000 vs. < 10,000 yuan/year), child age, gender (boys vs. girls), and low birth weight (yes/no). To explored potential modification of the relationship between fluoride exposure and anthropometric indicators by demographic characteristics of children, we introduced interaction terms into the multiple regressions model. Main effects and interactions were considered statistically significant at P < 0.05 and P < 0.10 based on two-sided tests, respectively. All statistical analyses were performed using SPSS version 25.0 (SPSS Inc., Chicago, IL, USA) and SAS version 9.4 (SAS Institute Inc., Cary, NC, USA). In sensitivity analyses, we examined whether the results differed after excluding children born to women with smoking, drinking, diabetes, undernourishment and anaemia at pregnancy, and children with dystocia, hypoxia, premature birth and post-term birth because these behaviors and disorders may be associated with child's adiposity distribution and rates of growth (Black et al., 2013; Casey, 2008; Mourtakos et al., 2015; Reilly and Kelly, 2011; Toschke et al., 2002).

Characteristic

n

Percent or mean ± SD

Age (years) Gender Boys Girls Height (cm) Weight (kg) Body mass index (kg/m2) Years of residence Household incomes (RMB/year) < 10000 10000-30000 > 30000 Paternal education Primary school and below Middle school High school and above Maternal education Primary school and below Middle school High school and above Maternal age at delivery (years) Secondhand tobacco smoke (SHS) Maternal disease history during pregnancy Diabetes Undernourishment Anaemia Delivery conditions Hypoxia Dystocia Premature birth Post-term birth Low birth weight

2430

9.8 ± 1.2

1242 1188 2430 2430 2430 2256

51.1 48.9 142.8 ± 8.9 36.6 ± 10.8 17.7 ± 3.7 9.6 ± 1.6

215 441 1625

9.4 19.3 71.3

290 1693 375

12.3 71.8 15.9

404 1667 303 2379 370

17.0 70.2 12.8 26.9 ± 5.0 15.2

12 54 70

0.5 2.2 2.9

118 135 149 169 144

4.9 5.6 6.1 7.0 6.0

Table 2 Summary of anthropometric measurements (mean ± SD) among study participants. Group

All

Boys

Girls

n (%) Height z-score Weight z-score BMI z-score Normal weight/underweight (n, %) Overweight/obesity (n, %)

2430 0.80 ± 0.99 0.42 ± 1.08 0.21 ± 1.44 1721 (70.8) 709 (29.2)

1242 (51.1) 0.84 ± 0.97 0.54 ± 1.07 0.35 ± 1.52 832 (67.0) 410 (33.0)

1188 (48.9) 0.75 ± 1.00 0.30 ± 1.08 0.06 ± 1.34 889 (74.8) 299 (25.2)

Abbreviations: BMI, body mass index; Overweight/obesity, Age- and sex-specific BMI z-score > 1.

3.2. Exposure characteristics

3. Results

Descriptions of the exposure variables are presented in Table 3. Urinary fluoride concentrations were significantly correlated with water fluoride levels (rs = 0.48, P < 0.001). When comparing the distribution of fluoride concentrations by overweight/obesity status, children with overweight/obesity, compared with those normal/underweight ones, tended to have slightly higher urinary fluoride concentrations (P = 0.003), although the estimate for water concentrations did not reach statistical significance.

3.1. Study population characteristics Table 1 shows the demographic characteristics of the 2430 study children. There were slightly more boys than girls (51.1% vs. 48.9%), and the average age was 9.8 years. Low birth weight (< 2,500 g) comprised 6.0% of total births. Most mothers did not smoke during pregnancy (99.6%) but were exposed to SHS either at home or work (15.2%). Most of the parents reported having a medium educational level or below, with an average income of approximately 30000 Yuan RMB per year. Table 2 shows the results of the children's anthropometric measurements. The mean BMI z-score among the school-age children was 0.21 standard deviation scores (SD = 1.44); Based on BMI z-score, the prevalence of overweight/obesity in our study was 29.2%. When stratified by gender, children's height and weight z-scores were higher in boys than in girls, and similar patterns were observed for BMI z-score and the prevalence of overweight/obesity (33.0% for boys and 25.2% for girls).

3.3. Water fluoride concentrations and anthropometric measurements We observed a linear dose-dependent positive association between water fluoride levels and height z-score, as indicated by the trend across fluoride quartiles (Ptrend = 0.022). Children in the third fluoride quartile had, on average, a 0.131-unit (95% CI: −0.002, 0.264) taller than children in the first quartile with borderline significance. When modeled as a continuous variable, each 10-fold increase in water fluoride was associated with a 0.129-unit increase in height z-score, but 3

Ecotoxicology and Environmental Safety 183 (2019) 109558

L. Liu, et al.

Table 3 Distribution of fluoride concentrations (F) among study participants. Fluoride exposure (mg/L)

Geometric Mean (95% CI)

Min.

25th percentile

50th percentile

75th percentile

Max.

Correlation with F in water

Water fluoride Growth status Normal weight/underweight Overweight/obesity Urinary fluoride Growth status Normal weight/underweight Overweight/obesity

0.83 (0.81, 0.86)

0.20

0.40

1.00

1.53

3.50

–

0.84 (0.81, 0.87) 0.82 (0.77, 0.87) 0.43 (0.41, 0.46)

0.20 0.20 0.01

0.40 0.40 0.18

1.00 1.00 0.44

1.60 1.50 1.10

3.50 3.50 4.74

– – 0.48**

0.41 (0.39, 0.44) 0.49 (0.45, 0.53)

0.02 0.01

0.17 0.20

0.42 0.50

1.05 1.24

4.63 4.74

0.51** 0.43**

P

a

0.414

0.003

Abbreviations: Min., minimum; Max., maximum. **P < 0.001 statistically significant. a Mann-Whitney U test. Table 4 Associations between concentrations of fluoride (log10) and child anthropometric measures. Fluoride exposure (mg/L)

Height-z-score β (95% CI)

Weight-z-score P

BMI-z-score

β (95% CI)

P

Overweight/obesity

β (95% CI)

P

OR (95% CI)

P

a

Water fluoride Quartile1 (lowest) Quartile2 Quartile3 Quartile4 (highest) Ptrend b Continuous c Urinary fluoride a Quartile1 (lowest) Quartile2 Quartile3 Quartile4 (highest) Ptrend b Continuous c

Reference −0.026 (−0.158, 0.106) 0.131 (−0.002, 0.264) 0.114 (−0.019, 0.247) 0.129 (0.005, 0.254) Reference 0.004 (−0.128, 0.135) 0.006 (−0.124, 0.136) 0.077 (−0.053, 0.208) 0.059 (−0.030, 0.148)

0.703 0.054 0.092 0.022 0.041 0.954 0.929 0.246 0.172 0.195

Reference 0.031 (−0.113, 0.176) 0.102 (−0.044, 0.247) 0.074 (−0.071, 0.220) 0.066 (−0.071, 0.202) Reference 0.032 (−0.112, 0.176) 0.070 (−0.072, 0.213) 0.179 (0.036, 0.321) 0.136 (0.039, 0.233)

0.670 0.173 0.316 0.321 0.345 0.662 0.331 0.014 0.008 0.006

Reference 0.070 (−.121, 0.262) 0.093 (−0.100, 0.286) 0.028 (−0.164, 0.220) 0.015 (−0.165, 0.195) Reference 0.052 (−0.138, 0.241) 0.104 (−0.084, 0.292) 0.247 (0.059, 0.435) 0.186 (0.058, 0.314)

0.470 0.343 0.778 0.965 0.871 0.595 0.278 0.010 0.007 0.005

Reference 0.852 (0.632, 1.149) 0.988 (0.732, 1.333) 0.840 (0.620, 1.137) 0.864 (0.650, 1.148) Reference 1.147 (0.843, 1.438) 1.204 (0.889, 1632) 1.472 (1.089, 1.990) 1.304 (1.062, 1.602)

0.293 0.938 0.258 0.839 0.313 0.382 0.230 0.012 0.013 0.011

Abbreviations: BMI, body mass index; Overweight/obesity, Age- and sex-specific BMI z-score > 1. Adjusted model includes maternal age at delivery, secondhand tobacco smoke, maternal education, paternal education, household incomes; child age, gender and low birth weight. a Water fluoride exposure quartiles (Q1-Q4, mg/L): Q1 0.20–0.39; Q2 0.40–0.99; Q3 1.00–1.52; Q4 1.53–3.50; Urinary fluoride exposure quartiles (Q1-Q4, mg/L): Q1 0.01–0.18; Q2 0.19–0.44; Q3 0.45–1.10; Q4 1.11–4.74. b P for trend across fluoride quartiles. c log10-transformed fluoride concentration. Fig. 1. Nonlinear relationships between urinary fluoride levels and child anthropometric measures. The solid and dashed lines represent the estimations and 95% CIs, respectively. Associations between urinary fluoride levels (log10-transformed, x-axis) and child anthropometric measures (y-axis) were fitted by restricted cubic spline models with three knots at the 5th, 50th and 95th percentiles. CI, confidence interval.

4

Ecotoxicology and Environmental Safety 183 (2019) 109558

L. Liu, et al.

with non-significant increase in weight z-score (βadjusted = 0.066; 95% CI: −0.071, 0.202), BMI z-score (βadjusted = 0.015; 95% CI: −0.165, 0.195), or the odds of overweight/obesity (ORadjusted = 0.864; 95% CI: 0.650, 1.148) (Table 4). Also, we found no evidence that any associations were modified by child gender, age, household income and parents’ education (all Pinteraction were > 0.1) (see Supplemental Material, Table S2).

concentrations and anthropometric measurements by paternal education for height, weight and BMI z-scores and the odds of overweight/ obesity, with higher estimates for children of fathers with lower education levels (Pinteraction between 0.001 and 0.056) (Table 5). The effect modification by maternal education was similar but did not reach statistical significance. Nonetheless, we did not find effect modification for other covariates (age and household incomes).

3.4. Urinary fluoride concentrations and anthropometric measurements

3.5. Sensitivity analysis

When urinary fluoride concentrations were categorized into quartiles, estimates for exposure in the second and third quartiles versus the first quartile were non-significant in all models. However, the highest quartile of urinary fluoride levels was positively associated with weight z-score (βadjusted = 0.179; 95% CI: 0.036, 0.321), BMI z-score (βadjusted = 0.247; 95% CI: 0.059, 0.435) and odds of overweight/obesity (ORadjusted = 1.472, 95% CI: 1.089, 1.990). When modeled as a continuous variable, urinary fluoride concentrations were similarly associated with significant increases in weight z-score (βadjusted = 0.136; 95% CI: 0.039, 0.233), BMI z-score (βadjusted = 0.186; 95% CI: 0.058, 0.314), and odds of overweight/ obesity (ORadjusted = 1.304; 95% CI: 1.062, 1.602). Further analysis showed that the association between urinary fluoride levels and BMI zscore was driven entirely by weight: there was a significant increase of 0.136-unit in weight z-score for each 10-fold increase in urinary fluoride concentrations, but no associations between urinary fluoride and height z-score (βadjusted = 0.059; 95% CI: −0.030, 0.148) (Table 4). A restricted cubic spline analysis also indicated that weight and BMI zscores and the odds of overweight/obesity increased throughout the range of fluoride concentrations with significant linear trend (P for overall association < 0.05, P for nonlinear association ≥ 0.05) (Fig. 1). Moreover, we found evidence of effect modification by gender, with stronger associations between urinary fluoride levels and BMI z-score and overweight/obesity in girls than in boys (e.g., for overweight/ obesity, ORadjusted = 1.672; 95% CI: 1.248, 2.239; ORadjusted = 1.003, 95% CI: 0.748, 1.345, respectively, Pinteraction = 0.016) (Table 5). We also observed significant effect modification between urinary fluoride

Sensitivity analysis showed that when we excluded children born to women with smoking, drinking, diabetes, undernourishment and anaemia at pregnancy, and children with dystocia, hypoxia, premature birth and post-term birth, the associations of fluoride exposure with anthropometric measures were kept robust (Tables S3–S11). 4. Discussion In this large-scale population study, we found that exposure of school-age children to low-to-moderate level of fluoride was positively associated with BMI z-score and overweight/obesity prevalence. The elevated BMI z-score was due to weight but not height and corresponded to an increased odds of overweight/obesity. Our results also suggest that individual sociodemographic factors (e.g., gender and paternal education) may modify the association between fluoride exposure and adiposity effects in Chinese school-age children. Specifically, this association differed by gender and appeared to be present in girls but not in boys, and lower paternal education may intensify the adiposity effects of fluoride. The median concentration of water fluoride in our study is equal to the maximum permissible limit for drinking water in China (1 mg/L) (Ministry of Health of China, 2007) and within the WHO recommended limit (1.5 mg/L) (World Health Organization, 2017), suggesting the residents are exposed to fluoride in drinking water at low-to-moderate levels. Fluoride is mainly absorbed via the gastrointestinal tract and mainly excreted in urine accounting for around 35%–45% of the fluoride intake in children (Villa et al., 2010; World Health Organization, 2014). Therefore, the fluoride concentration in urine is

Table 5 Modification of associations between concentrations of urinary fluoride (log10) and child anthropometric measures. Potential effect modifier/covariate level Gender Boys Girls Pinteraction Age ˂10 ≥10 Pinteraction Household incomes (RMB/year) ˂10000 10000-30000 > 30000 Pinteraction Maternal education Primary school and below Middle school High school and above Pinteraction Paternal education Primary school and below Middle school High school and above Pinteraction

n

Height-z-score β (95% CI)

Weight-z-score β (95% CI)

BMI-z-score β (95% CI)

Overweight/obesity OR (95% CI)

1242 1188

0.067 (−0.062, 0.196) 0.055 (−0.068, 0.178) 0.656

0.045 (−0.098, 0.187) 0.212 (0.078, 0.345) 0.134

0.025 (−0.175, 0.226) 0.312 (0.148, 0.476) 0.028

1.003 (0.748, 1.345) 1.672 (1.248, 2.239) 0.016

1296 1134

0.052 (−0.069, 0.172) 0.085 (−0.049, 0.219) 0.914

0.082 (−0.048, 0.213) 0.195 (0.050, 0.341) 0.327

0.101 (−0.073, 0.276) 0.259 (0.070, 0.449) 0.226

1.154 (0.871, 1.531) 1.454 (1.074, 1.967) 0.339

215 441 1625

0.055 (−0.275, 0.385) 0.084 (−0.138, 0.305) 0.046 (−0.060, 0.153) 0.661

0.096 (−0.256, 0.448) 0.072 (−0.158, 0.302) 0.155 (0.036, 0.273) 0.632

0.118 (−0.336, 0.572) 0.055 (−0.250, 0.360) 0.227 (0.071, 0.383) 0.375

1.741 (0.709, 4.277) 0.929 (0.582, 1.484) 1.409 (1.107, 1.795) 0.318

404 1667 303

0.218 (−0.007, 0.443) 0.024 (−0.081, 0.129) 0.015 (−0.238, 0.268) 0.344

0.350 (0.100, 0.601) 0.083 (−0.031, 0.196) 0.108 (−0.182, 0.397) 0.199

0.443 (0.111, 0.776) 0.126 (−0.023, 0.275) 0.127 (−0.258, 0.512) 0.183

1.761 (1.016, 3.052) 1.227 (0.964, 1.563) 1.167 (0.627, 2.173) 0.289

290 1693 375

0.150 (−0.114, 0.415) 0.105 (0.003, 0.207) −0.227 (−0.480, 0.025) 0.042

0.363 (0.064, 0.661) 0.172 (0.060, 0.284) −0.249 (−0.515, 0.017) 0.001

0.509 (0.108, 0.910) 0.214 (0.066, 0.363) −0.259 (−0.601, 0.084) 0.001

1.879 (0.977, 3.614) 1.370 (1.077, 1.743) 0.812 (0.470, 1.404) 0.056

Adjusted model includes maternal age at delivery, secondhand tobacco smoke, maternal education, paternal education, household incomes; child age, gender and low birth weight. P for interaction [potential effect modifier/covariate level × fluoride exposure (continuous log10-transformed fluoride concentration)]. 5

Ecotoxicology and Environmental Safety 183 (2019) 109558

L. Liu, et al.

generally accepted as a reliable internal exposure indicator (Rango et al., 2017; Singh et al., 2007; Zhou et al., 2019). The Chinese guideline value for fluoride in urine sets at 1.6 mg/L (Ministry of Health of China, 2006). Drinking water is the major contributor of fluoride intake for the residents of the rural areas in China (Wang et al., 2004). In our sample population, urinary fluoride concentrations have been shown to correlate well with water fluoride levels (with a correlation coefficient of 0.48), indicating that fluoride from drinking water plays a predominant role in urinary fluoride. Furthermore, several studies point that the other forms of ingested fluoride may also make some contribution, including dietary sources (food grown in soil containing fluoride and drinks made with fluoridated water), fluoride tablets and drops and other supplements, and inadvertent ingestion of fluoridated toothpaste (especially by children) or oral fluoride rinses (Harrison, 2005; Jackson et al., 2010). While we collected both data on the fluoride concentrations in water and urine, we have no way of establishing the pattern of consumption or more importantly, the “dose” of external exposure—how much fluoride was consumed from water, so the research of fluoride exposure in urine produced more convincing results. The effects of long-term exposure to fluoride on dental mottling and skeletal manifestations have been studied extensively, but studies of body size outcomes are less common. An Indian research including 149 school-age children observed a positive correlation between BMI and drinking water fluoride levels in girls aged 6–10 years (Das and Mondal, 2016), which would be similar to that found in our population. However, Rębaczmaron et al., 2013 did not observe any associations between fluoride exposure in the hair and BMI outcomes among 52 boys aged 12–18 years in Tanzania (Rębaczmaron et al., 2013). Furthermore, in an additional study from the Hyderabad that included 203 adults aged 20–35 years, urinary fluoride levels were significantly higher in the overweight (BMI > 25) females than normal-weight females, but not in males (Amarendra Reddy et al., 2009). Although Wang et al. (2007) found association of fluoride with reduced height in 720 rural children aged 8–12 years in China (Wang et al., 2007), several other studies have shown that there is no significant association between fluoride exposure levels and BMI in the children (Khandare et al., 2017; Tiwari et al., 2010; Zohoori et al., 2013). Notably, we used BMI z-score as endpoints to control the various levels of age- and sex-specific adiposity, whereas most of these earlier studies used the BMI without standardization, so the inconsistent findings may be due to differences in sex and age distributions. Furthermore, the discrepancy among studies may be attributed to the differences in the sample size, fluoride exposure levels, population susceptibility and vulnerability, as well as long-term cumulative effect. Although some animal studies have reported weight gain with fluoride exposure (Zhang et al., 2015), others have indicated null or inverse associations (Chen et al., 2013; Chioca et al., 2008; Jiang et al., 2014; Perera et al., 2018). One possibility is that the direction of the association between fluoride and growth measures differs between species (Arner, 2005; James et al., 1979; Spurlock and Gabler, 2008). So far the mechanism by which fluoride affects overweight and obesity have not been well understood, but studies conducted in rodents have shown that fluoride can cause disorder of lipid metabolism and adipogenesis (Xiong et al., 2007). Very little studies have been performed on the effect of fluoride exposure on adipocyte functions. Emerging evidence has documented an increase in serum triglycerides, cholesterol and total lipids associated with exposure to fluoride in rat (Chiba et al., 2015; Miltonprabu and Thangapandiyan, 2015; Umarani et al., 2015). It has also been proposed that chemical liver injury caused by fluoride can lead to damage, steatosis, eosinophilic and inflammatory responses of hepatocytes, resulting in abnormal catabolism of blood lipids and lipoproteins and elevating blood lipids (Lin et al., 2001). Additionally, previous studies have reported that fluoride exposure can affect the expression of reproduction-related genes in the hypothalamicpituitary-testicular (HPT) axis in male rats and generative functions in

female rats (Han et al., 2015; Zhou et al., 2013), which may disrupt energy balance and body fat distribution. Moreover, in a number of rat studies, fluoride exposure has consistently been shown to cause endocrinal disorders like hypothyroidism (decrease thyroid hormones and thyroid stimulating hormone) and hyperparathyroidism (increase parathyroid hormone) (Abd-Elhakim et al., 2018; Basha et al., 2011; Liu et al., 2016), which could regulate a modest weight gain by decreasing the basal metabolic rate and lipid metabolism. This study was not designed to elucidate mechanisms of action. However, some sex-specific association differences were seen in the present study and most significant association only presented in girls (the results of interaction analyses were significant). This was consistent with results from two previous epidemiological studies, although these studies were distinctly different from our research in exposure levels and age distributions (Amarendra Reddy et al., 2009; Das and Mondal, 2016). We speculate that our findings may be compatible with the HPT axis mechanism of action of fluoride. Moreover, the gender differences in the accumulation, mobilization and metabolism of fat, at different stages of growth and development, should be considered in the sex-specific obesogenic effects of fluoride (Heindel et al., 2017). In addition, we also found associations between fluoride exposure and anthropometric measurements to be higher among children of fathers with lower education levels. Our results provide the first evidence in Chinese school-age children that lower paternal education may compose a risk factor for fluoride pollution–related adiposity effects. Our study has several strengths. We first made a thorough and systematic analysis of the association between low-to-moderate fluoride exposure on growth among Chinese school-age children. Our analysis was based on a large sample with a high response rate, using structured questionnaires and quality assurance protocols, which ensured sufficient statistical power to detect modest effects. Moreover, compared with most previous studies, our study provided more information with respect to the impacts of fluoride exposure at low-to-moderate levels, which could further support the epidemiological evidence on the adverse impact of fluoride across different levels. In addition, we accounted for many potential confounders, including sociodemographic factors, parental education standard, maternal medical history, and birth outcome, making our results more reliable. Among the limitations of our study is the cross-sectional design, which precludes causal statements about the effects of fluoride exposure on body size of children, and results should, thus, be confirmed using a longitudinal design with repeated measures. Residual confounding may also be a concern because we did not have information on diet and physical activity of the children. As dietary and physical activity behaviors are important factors of overweight and obesity in children (An, 2015; Janssen et al., 2005), future studies should further investigate the roles of diet and physical activity whether both elements operate as confounders or effect modifiers of the observed associations. However, in our study, all children were currently attending school and lived in rural areas with similar economic, geographic and cultural characteristics, and there may also be a certain similarity in dietary and physical activity patterns. Although many occupational and environmental exposure investigations have used the first-morning urine because of its close correlation with 24-h void (Scher et al., 2007; Zohouri et al., 2006), monitoring the urinary fluoride concentration for a full day may be more accurate and reliable. A further limitation is that we considered only fluoride exposure; therefore, we were unable to examine whether other groups of unmeasured environmental pollutants may have partially contributed to the observed associations. 5. Conclusions In summary, we found that low-to-moderate fluoride exposure, mainly from drinking water, was associated with the increased BMI zscore and odds of overweight/obesity in Chinese school-age children. Moreover, our results suggest that gender and paternal educational 6

Ecotoxicology and Environmental Safety 183 (2019) 109558

L. Liu, et al.

level may modify the relationship. Due to the cross-sectional design, longitudinal and experimental studies are needed to confirm our findings, and to determine whether the associations persist as children age since altered growth in early life has long-term health implications.

Ghosh, J., et al., 2008. Cytoprotective effect of arjunolic acid in response to sodium fluoride mediated oxidative stress and cell death via necrotic pathway. Toxicol. In Vitro 22, 1918–1926. Han, H., et al., 2015. Fluoride exposure changed the structure and the expressions of reproductive related genes in the hypothalamus-pituitary-testicular axis of male mice. Chemosphere 135, 297–303. Harrison, P.T.C., 2005. Fluoride in water: a UK perspective. J. Fluorine Chem. 126, 1448–1456. Heindel, J.J., et al., 2017. Metabolism disrupting chemicals and metabolic disorders. Reprod. Toxicol. 68, 3–33. Hu, L., et al., 2009. Fluoride toxicity in the male reproductive system. Fluoride 42, 260–276. Huang, D., et al., 2017. Probabilistic risk assessment of Chinese residents' exposure to fluoride in improved drinking water in endemic fluorosis areas. Environ. Pollut. 222, 118–125. Jackson, R.D., et al., 2010. The fluoride content of foods and beverages from negligibly and optimally fluoridated communities. Community Dent. Oral Epidemiol. 30, 382–391. James, W., et al., 1979. Comparison of Genetic Models of Obesity in Animals with Obesity in Man. Animal Models of Obesity. Springer, pp. 221–235. Janssen, I., et al., 2005. Comparison of overweight and obesity prevalence in school-aged youth from 34 countries and their relationships with physical activity and dietary patterns. Obes. Rev. 6, 123–132. Jiang, C., et al., 2014. Low glucose utilization and neurodegenerative changes caused by sodium fluoride exposure in rat's developmental brain. NeuroMolecular Med. 16, 94–105. Khandare, A.L., et al., 2017. Dental fluorosis, nutritional status, kidney damage, and thyroid function along with bone metabolic indicators in school-going children living in fluoride-affected hilly areas of Doda district, Jammu and Kashmir, India. Environ. Monit. Assess. 189, 579. Krebs, N.F., et al., 2007. Assessment of child and adolescent overweight and obesity. Pediatrics 120 (Suppl. 4), S193–S228. Li, H.R., et al., 2009. Fluoride in drinking water, brick tea infusion and human urine in two counties in inner Mongolia, China. J. Hazard Mater. 167, 892–895. Lin, K.Y., Tang, J.J., Bao, C.Z., 2001. Observation on change of lipidemia and lipoprotein in wistar rats with fluorosis. Chin. J. Endemiol. 20, 111–112. Liu, H., et al., 2016. Role of endoplasmic reticulum stress-induced apoptosis in rat thyroid toxicity caused by excess fluoride and/or iodide. Environ. Toxicol. Pharmacol. 46, 277–285. Miltonprabu, S., Thangapandiyan, S., 2015. Epigallocatechin gallate potentially attenuates Fluoride induced oxidative stress mediated cardiotoxicity and dyslipidemia in rats. J. Trace Elem. Med. Biol. 29, 321–335. Ministry of Health of China, 2006. Health Industry Standard of the People's Republic of China WS/T 256-2005: the Normal Urinary Fluoride Level of Chinese Population. People’s Medical Publishing House, Beijing, pp. 1–3. Ministry of Health of China, 2007. Standardization Administration of the People's Republic of China, Standards for Drinking Water Quality GB5749-2006. Standards Press of China, Beijing. Monasta, L., et al., 2010. Early-life determinants of overweight and obesity: a review of systematic reviews. Obes. Rev. : Off J Int. Assoc. Study Obes. 11, 695–708. Mourtakos, S.P., Panagiotakos, D.B., Tambalis, K.D., et al., 2015. Maternal lifestyle characteristics during pregnancy, and the risk of obesity in the offspring: a study of 5,125 children. BMC Pregnancy Childbirth 15(1), 66. Ozsvath, D.L., 2009. Fluoride and environmental health: a review. Rev. Environ. Sci. Biotechnol. 8, 59–79. Pei, J., et al., 2015. An annual national monitoring report on drinking-water-borne endemic fluorosis in 2012. Chin. J. Endemiol. 34, 680–684. Perera, T., et al., 2018. Effect of fluoride on major organs with the different time of exposure in rats. Environ. Health Prev. Med. 23, 17. Rango, T., et al., 2017. Biomarkers of chronic fluoride exposure in groundwater in a highly exposed population. Sci. Total Environ. 596, 1–11. Rębaczmaron, E., et al., 2013. The content of fluoride, calcium and magnesium in the hair of young men of the Bantu language group from Tanzania versus social conditioning. Biol. Trace Elem. Res. 156, 91–95. Reilly, J.J., Kelly, J., 2011. Long-term impact of overweight and obesity in childhood and adolescence on morbidity and premature mortality in adulthood: systematic review. Int. J. Obes. 35, 891–898. Scher, D.P., et al., 2007. Agreement of pesticide biomarkers between morning void and 24-h urine samples from farmers and their children. J. Expo. Sci. Environ. Epidemiol. 17, 350. Seraj, B., et al., 2012. Effect of high water fluoride concentration on the intellectual development of children in makoo/Iran. J. Dent. 9, 221. Silva, P.H.A.B., et al., 2016. Fluoride intensifies hypercaloric diet-induced ER oxidative stress and alters lipid metabolism. PLoS One 11, e0158121. Singh, B., et al., 2007. Fluoride in drinking water and human urine in Southern Haryana, India. J. Hazard Mater. 144, 147–151. Spurlock, M.E., Gabler, N.K., 2008. The development of porcine models of obesity and the metabolic syndrome. J. Nutr. 138, 397–402. Tianjin Health Committee, 2017. In: Tianjin Residents’ Health Status Report (2016). Tianjin Health Committee. Tiwari, P., et al., 2010. Dental fluorosis and its association with the use of fluoridated toothpaste among middle school students of Delhi. Indian J. Med. Sci. 64, 1–6. Toschke, A., et al., 2002. Childhood obesity is associated with maternal smoking in pregnancy. Eur. J. Pediatr. 161, 445–448. Trivedi, N., et al., 1993. Reversible impairment of glucose tolerance in patients with endemic fluorosis. Fluoride Collaborative Study Group. Diabetologia 36, 826–828.

Declarations of interest None. Acknowledgement We sincerely thank all the participants in this study and the Tianjin Center for Disease Control and Prevention for its assistance for epidemiological investigation and sample collection. This work was supported by the State Key Program of National Natural Science of China (Grant No. 81430076), the National Natural Science Foundation of China (Grants No. 81773388 and No. 81502785) and the Fundamental Research Funds for the Central Universities (HUST 2016YXMS221 and HUST 2015ZDTD052). The study design was approved by the Review Board of Huazhong University of Science and Technology and Ethical Committee of Tianjin Center for Disease Control and Prevention. Appendix A. Supplementary data Supplementary data to this article can be found online at https:// doi.org/10.1016/j.ecoenv.2019.109558. References Abd-Elhakim, Y.M., et al., 2018. Impact of subchronic exposure to triclosan and/or fluoride on estrogenic activity in immature female rats: the expression pattern of calbindin-D9k and estrogen receptor α genes. J. Biochem. Mol. Toxicol. 32, e22027. Ali, S., et al., 2016. Worldwide contamination of water by fluoride. Environ. Chem. Lett. 14, 291–315. Al-Raddadi, R.M., Bahijri, S.M., Al-Khateeb, T., 2012. 1026 excessive fluoride intake is associated with hyperparathyroidism and hypothyroidism in children and adolescent, Jeddah- Saudi Arabia. Arch. Dis. Child. 97, A294. Amarendra Reddy, G., et al., 2009. Bone mass of overweight affluent Indian youth and its sex-specific association with body composition. Arch. osteoporos. 4, 31–39. An, R., 2015. Diet quality and physical activity in relation to childhood obesity. Int. J. Adolesc. Med. Health 29, 1–9. Arner, P., 2005. Resistin: yet another adipokine tells us that men are not mice. Diabetologia 48, 2203–2205. Baheiraei, A., et al., 2015. The effects of secondhand smoke exposure on infant growth: a prospective cohort study. Acta Med. Iran. 53, 39–45. Basha, P.M., et al., 2011. Fluoride toxicity and status of serum thyroid hormones, brain histopathology, and learning memory in rats: a multigenerational assessment. Biol. Trace Elem. Res. 144, 1083–1094. Behl, M., et al., 2013. Evaluation of the association between maternal smoking, childhood obesity, and metabolic disorders: a national toxicology program workshop review. Environ. Health Perspect. 121, 170–180. Black, R.E., et al., 2013. Maternal and child undernutrition and overweight in low-income and middle-income countries. Lancet 382, 427–451. Casey, P.H., 2008. Growth of low birth weight preterm children. Semin. Perinatol. 32, 20–27. Centers for Disease Control and Prevention, 2009. Growth charts. http://www.cdc.gov/ growthcharts/zscore.htm. Chen, J., et al., 2013. Effects of fluoride on growth, body composition, and serum biochemical profile in a freshwater teleost, Cyprinus carpio. Environ. Toxicol. Chem. 32, 2315–2321. Chiba, F.Y., et al., 2015. Chronic treatment with a mild dose of NaF promotes dyslipidemia in rats. Fluoride 48, 205. Chioca, L.R., et al., 2008. Subchronic fluoride intake induces impairment in habituation and active avoidance tasks in rats. Eur. J. Pharmacol. 579, 196–201. Das, K., Mondal, N.K., 2016. Dental fluorosis and urinary fluoride concentration as a reflection of fluoride exposure and its impact on IQ level and BMI of children of Laxmisagar, Simlapal Block of Bankura District, W.B., India. Environ. Monit. Assess. 188, 1–14. De, O.M., et al., 2007. Development of a WHO growth reference for school-aged children and adolescents. Bull. World Health Organ. 85, 660–667. Dong, D., et al., 2011. Study on infiltration of Tianjin river systems in different typical years based on Mapinfo. In: Second International Conference on Mechanic Automation & Control Engineering. Garcã A-Montalvo, E.A., et al., 2009. Fluoride exposure impairs glucose tolerance via decreased insulin expression and oxidative stress. Toxicology 263, 75–83. Ghosh, A., et al., 2012. Sources and toxicity of fluoride in the environment. Res. Chem. Intermed. 39, 2881–2915.

7

Ecotoxicology and Environmental Safety 183 (2019) 109558

L. Liu, et al. Umarani, V., et al., 2015. Rutin potentially attenuates fluoride-induced oxidative stressmediated cardiotoxicity, blood toxicity and dyslipidemia in rats. Toxicol. Mech. Methods 25, 143–149. Villa, A., et al., 2010. Relationships between fluoride intake, urinary fluoride excretion and fluoride retention in children and adults: an analysis of available data. Caries Res. 44, 60. Wang, B.B., et al., 2004. Relationship between fluorine in drinking water and dental health of residents in some large cities in China. Environ. Int. 30, 1067–1073. Wang, S.X., et al., 2007. Arsenic and fluoride exposure in drinking water: children's IQ and growth in Shanyin county, Shanxi province, China. Environ. Health Perspect. 115, 643–647. Wang, L., 2014. Evaluation of Underground Water Resources in Baodi of Tianjin School of Water Resources & Environment Master China University of Geosciences. Wang, Y., Lim, H., 2012. The global childhood obesity epidemic and the association between socio-economic status and childhood obesity. Int. Rev. Psychiatry 24, 176–188. Wen, D., et al., 2013. Arsenic, fluoride and iodine in groundwater of China. J. Geochem. Explor. 135, 1–21. Wondwossen, F., et al., 2004. The relationship between dental caries and dental fluorosis in areas with moderate- and high-fluoride drinking water in Ethiopia. Community Dent. Oral Epidemiol. 32, 337–344. World Health Organization, 2011. Growth reference data for 5-19 years. https://www. who.int/growthref/en/. World Health Organization, 2014. Basic Methods for Assessment of Renal Fluoride Excretion in Community Prevention Programmes for Oral Health. WHO, Geneva, Switzerland. World Health Organization, 2017. Guidelines for Drinking-Water Quality. Fourth Edition Incorporating the First Addendum. WHO, Geneva, Switzerland. Wu, J., et al., 2015. Modifying role of GSTP1 polymorphism on the association between

tea fluoride exposure and the brick-tea type fluorosis. PLoS One 10, e0128280. Xiong, X., et al., 2007. Dose–effect relationship between drinking water fluoride levels and damage to liver and kidney functions in children. Environ. Res. 103, 112–116. Yousefi, M., Yaseri, M., Nabizadeh, R., et al., 2018. Association of hypertension, body mass index, and waist circumference with fluoride intake; water drinking in residents of fluoride endemic areas, Iran. Biol. Trace Elem. Res. 185(2), 282–288. Yu, X., et al., 2018. Threshold effects of moderately excessive fluoride exposure on children's health: a potential association between dental fluorosis and loss of excellent intelligence. Environ. Int. 118, 116–124. Zhang, K.-L., et al., 2015. Activation of the AGE/RAGE system in the brains of rats and in SH-SY5Y cells exposed to high level of fluoride might connect to oxidative stress. Neurotoxicol. Teratol. 48, 49–55. Zhang, X., et al., 2008. Water resources planning based on complex system dynamics: a case study of Tianjin city. Commun. Nonlinear Sci. Numer. Simul. 13, 2328–2336. Zhou, G., et al., 2019. Low-to-moderate fluoride exposure, relative mitochondrial DNA levels, and dental fluorosis in Chinese children. Environ. Int. 127, 70–77. Zhao, L.J., Pei, J.R., Zhang, W., et al., 2016. Surveillance on drinking-water-born endemic fluorosis in China, 2013. Chin. J. Epidemiol. 37, 816–820. Zhou, Y., et al., 2013. The toxicity mechanism of sodium fluoride on fertility in female rats. Food Chem. Toxicol. 62, 566–572. Zhu, C., et al., 2006. Screening high-fluoride and high-arsenic drinking waters and surveying endemic fluorosis and arsenism in Shaanxi province in western China. Water Res. 40, 3015–3022. Zhu, Q.S., Xu, G.Q., 2009. The current situation and research progress of ground water fluorine pollution, in China. Environ. Sci. Manag. 34(1), 42–44. Zohoori, F.V., et al., 2013. Total fluoride intake and excretion in children up to 4 years of age living in fluoridated and non-fluoridated areas. Eur. J. Oral Sci. 121, 457–464. Zohouri, F., et al., 2006. Is the fluoride/creatinine ratio of a spot urine sample indicative of 24-h urinary fluoride? Community Dent. Oral Epidemiol. 34, 130–138.

8