Multivariate Analysis of the Relationship Between Dental Fluorosis Severity and Intelligence in Peruvian SchoolchildrenABSTRACT
Objective: To explore the correlation between dental fluorosis (DF) and intelligence quotient (IQ) in 1063 schoolchildren from two districts in Arequipa, Peru, one located near a mining center. Sociodemographic and contextual variables were also analyzed.
Material and Methods: DF was assessed using Dean's index, and IQ was measured with the Toni-2 Test. Maternal education, age, sex, and district of residence were recorded. Fluoride levels in water were determined using ion-selective potentiometry. Statistical analysis included chi-square, Kruskal-Wallis, Spearman's correlation, and multiple linear regression models at a 5% significance level.
Results: Most children without or with mild DF exhibited very superior IQ levels, while those with moderate to severe DF had average IQs. In the district near the mining center, higher fluoride levels coincided with average IQ scores and cases of low IQ. In contrast, the district with lower fluoride levels showed a predominance of very superior IQs and no low IQ cases. A weak negative correlation was observed between DF severity and IQ (ρ = -0.131). However, multivariate analysis revealed that maternal education and district of residence had stronger associations with IQ than fluorosis severity.
Conclusion: While a slight inverse pattern between DF severity and IQ was observed, the adjusted analysis highlights the greater influence of contextual and sociodemographic factors, suggesting the need for more comprehensive evaluations in future studies.
Keywords:
Tooth Abnormalities; Intelligence; Fluorides; Students; Cross-Sectional Studies.
Introduction
Dental fluorosis (DF) is a developmental enamel defect characterized by homogeneous mineral loss and altered crystallinity, primarily affecting the outer enamel layer [1]. This condition results from excessive fluoride intake during dental development, leading to structural changes in the enamel [2,3]. In mild cases, DF presents as faint white lines or spots on the enamel, while more severe cases can progress to cavities and deep structural defects [4]. These alterations affect both the aesthetics and function, highlighting the need for preventive measures, particularly in regions with high fluoride exposure [3].
Epidemiological studies have suggested that in regions with endemic fluorosis, DF may be associated with cognitive impairments, particularly in domains such as verbal intelligence [5,6]. This complex relationship may be influenced not only by the levels of fluoride exposure but also by the timing of exposure during early childhood development [5,7]. Additionally, genetic factors may predispose certain individuals to greater susceptibility to fluoride’s effects [8].
While several studies have reported an association between fluoride exposure and lower intelligence quotient (IQ) scores [6-9], it is important to note that some studies have not identified a significant relationship, suggesting the potential involvement of other factors, such as sociodemographic or environmental variables that could also affect cognitive outcomes [10]. In particular, maternal educational level has been consistently associated with children’s cognitive performance, as it reflects key aspects of early stimulation, home environment, and access to health and educational resources [11].
In Arequipa, Peru, DF poses a significant public health concern, mainly due to the variability in fluoride levels across water sources and their impact on oral health [12]. Despite relatively low fluoride concentrations in the city's water supply (0.193 mg/L) [13], the prevalence of DF among the young population is alarmingly high [12]. This raises the need to investigate the potential effects of DF on cognitive development, particularly in schoolchildren from areas with higher fluoride exposure.
This study aimed to examine the relationship between dental fluorosis (DF) and intelligence quotient (IQ) among schoolchildren in two districts of Arequipa with contrasting fluoride exposure levels. In addition, the study explored the influence of contextual and sociodemographic factors - including district of residence, sex, age, and maternal educational level - on IQ. The null hypothesis tested was that there is no significant correlation between DF severity and the IQ of the schoolchildren evaluated.
Material and Methods
Study Design and Ethical Clearance
An observational, cross-sectional, and correlational study with a double-blind design was conducted and approved by the Institutional Human Research Ethics Committee of the Universidad Católica de Santa María (UCSM) in Arequipa, Peru (Ethics Committee Code No. 010-2023 UCSM). Informed consent was obtained from the parents of all participating schoolchildren, and assent was given by the children themselves.
Participants
Both the dental clinicians and psychologists involved in the assessments were blinded to the identities of the schoolchildren to minimize bias. Participants were drawn from the districts of Uchumayo, where students consume groundwater in a rural area near a copper mine, and Yanahuara, where students consume potable water in an urban area 2 km from downtown Arequipa, Peru.
Inclusion criteria required schoolchildren of both sexes, born and residing in Uchumayo or Yanahuara, who had not received dental treatment in the past three months, and whose parents provided informed consent. Exclusion criteria included schoolchildren with systemic diseases, mental, motor, sensory, or psychological disabilities, as well as those with dental defects or undergoing fixed orthodontic treatment.
A pilot test was conducted at a school in Uchumayo with 50 students, using a double-blind design. Data collection occurred between March and July 2023. Oral examinations were performed in school settings using LED flashlights and natural light, and the most severe fluorotic lesions were recorded according to Dean's Index. The sample was proportionally selected by education level among students aged 8 to 16, with a total sample of 1.063 schoolchildren (559 from Uchumayo and 504 from Yanahuara), ensuring a 99% confidence interval and a 1% margin of error.
Sociodemographic data were collected through a structured questionnaire completed by parents or guardians, including information on the child’s sex, age, district of residence, and maternal level of education. The latter was recorded using an ordinal scale ranging from 0 (no schooling) to 7 (postgraduate education), and subsequently recategorized into three levels for analysis: low (levels 0-2), medium (3-4), and high (5-7), onsistent with previous epidemiological studies that grouped maternal education into three levels for analysis [11].
Dental fluorosis was assessed using Dean's Index [14] by a team of 10 calibrated dental clinicians, achieving inter-examiner Kappa values between 0.8 and 0.9, indicating near-perfect agreement. The modified Dean’s Index codes and criteria were as follows: 0 - Normal (translucent, polished enamel); 1 - Questionable (slight variations in translucency with whitish spots); 2 - Very mild (opacities affecting no more than 25% of the enamel surface); 3 - Mild (opacity affecting up to 50% of the enamel surface); 4 - Moderate (entire enamel affected, with brown spots); 5 - Severe (generalized hypomineralization with depressions and spots); and 9 - No information (when dental fluorosis cannot be evaluated, such as in cases of dentures).
IQ was assessed using the Toni-2 test, administered by six calibrated psychologists with an inter-examiner Kappa value of 0.9, indicating near-perfect agreement. The Toni-2 test measures intellectual functioning and the ability to solve abstract, nonverbal problems, eliminating the influence of language, motor skills, and cultural factors, thus providing a cross-cultural assessment [15]. The test booklets, which contained 55 items organized by increasing difficulty, were used following fixed and concrete guidelines. Students were asked to identify patterns among abstract figures and select the correct answer from four or six options. Instructions were given in an appropriate classroom free of noise and distractions, with each test taking approximately 15 minutes to complete. Results were evaluated according to age-appropriate cognitive development.
Fluoride concentrations in the drinking water of Uchumayo and Yanahuara were measured using two samples collected from each district over a three-month period. The samples were preserved and analyzed using the ion-selective potentiometric method with a Seven Direct TM SD50 potentiometer (Mettler Toledo, Greifensee, Switzerland) and a fluoride ion-selective electrode (ISE), previously calibrated [16]. Readings were expressed in ppm/L, and the average value was calculated. If variability was detected between the two initial samples, a third collection was conducted.
Data Analysis
Statistical analysis was carried out using Jamovi software (Melbourne, Australia), version 2.3.18.0. The statistical tools used included mean, standard deviation, chi-square test, Kruskal-Wallis, and post-hoc analysis. Spearman's rank correlation was used to assess the correlation between the variables under study. A significance level of 5% was adopted to all tests. Additionally, a multiple linear regression analysis was performed to examine the association between dental fluorosis severity and intelligence quotient (IQ), adjusting for age, sex, maternal education level, and district of residence. The regression model included evaluation of collinearity, normality of residuals, and global model fit indicators (R2, RMSE, F-test). Stratified regression models were also conducted for each district to explore context-specific interactions.
Results
The final sample comprised 1063 participants (559 from Uchumayo and 504 from Yanahuara). In Yanahuara, a higher percentage of females were evaluated (67.46%), while in Uchumayo, gender distribution was more balanced. The average age of the schoolchildren was 11.5 ± 2.47 years, with no significant age difference between the districts (p = 0.057). Notably, significant differences were also found in maternal educational level between the districts. While most mothers in Yanahuara had a low educational level (91.1%), Uchumayo showed a more diverse distribution, with a higher proportion of mothers classified as having medium (64.4%) or high (13.8%) educational levels. Table 1 presents the demographic profile of the schoolchildren from both districts, including the number of students and their distribution by gender, age, and maternal education level.
Demographic profile of schoolchildren in the districts by gender, age, and maternal education level.
In Uchumayo, the majority of schoolchildren (35.60%) had average IQs, while 8 had low or very low IQs. In contrast, in Yanahuara, most schoolchildren (32.94%) had a very superior IQ, and no student had a low or very low IQ. The difference in IQs between the districts was statistically significant (p < 0.001). Table 2 shows the comparison of schoolchildren’s IQs across the districts.
Moderate and severe DF were more prevalent in Uchumayo (82.46%) compared to Yanahuara (67.46%). Conversely, healthy students or those with very mild DF were more common in Yanahuara (17.06%) than in Uchumayo (3.94%). The difference in DF severity between the districts was statistically significant (p < 0.001). Table 3 presents a comparison of DF severity across districts. Among the 1.063 schoolchildren participating in the study, 267 had severe DF with an average IQ of 116 ± 15.5, while 75 schoolchildren had very mild DF with an average IQ of 124 ± 14.6. The difference in IQ between these groups was statistically significant (p < 0.01). Table 4 shows the association between DF and the students' IQ.
Among healthy schoolchildren and those with mild or very mild DF, the majority showed a very superior IQ (7th level). In contrast, most schoolchildren with moderate and severe DF exhibited average IQs (4th level). As DF severity increased, a decrease in IQ was observed. A negative correlation between DF severity and IQ was observed, although it was weak (Spearman’s ρ = -0.131). Table 5 shows the correlation between DF severity and IQs. Although the chi-square test did not show a statistically significant association (p = 0.120), a visual inspection of the data revealed a slight trend: very superior IQs were more frequent in children without or with very mild DF, whereas average IQs were more frequent in those with moderate to severe DF.
A multiple linear regression analysis was conducted to examine the association between dental fluorosis severity and intelligence quotient (IQ), adjusting for age, sex, maternal educational level, and district. The overall model was statistically significant (F(6, 1056) = 14.8, p < 0.001), explaining 7.8% of the variance in IQ scores (R2 = 0.0775; adjusted R2 = 0.0722; RMSE = 15.1). As shown in Table 6, the district of residence was the strongest predictor: schoolchildren from Yanahuara had IQ scores 9.16 points higher on average than those from Uchumayo (p < 0.001). Additionally, male students had significantly higher IQ scores than females (+2.09 points, p = 0.030), and children whose mothers had a high educational level also scored significantly higher (+3.98 points, p = 0.029) than those with low maternal education. In contrast, fluorosis severity was not significantly associated with IQ in the adjusted model (β = -0.484, p = 0.280), suggesting that the observed bivariate association may be influenced by sociodemographic or contextual variables.
Multiple linear regression model evaluating the association between dental fluorosis severity and intelligence quotient, adjusted for covariates.
To further explore whether this association varied across districts, stratified multiple linear regression models were conducted. In Uchumayo, maternal educational level was the only significant predictor: children of mothers with higher education had IQ scores 4.60 points higher than those of mothers with lower education (p = 0.049), while fluorosis severity showed no association with IQ (p = 0.976). In Yanahuara, a negative but non-significant trend was observed between fluorosis severity and IQ (β = -0.871, p = 0.115). Age also approached significance in this district (p = 0.061), and male students tended to score higher than females (p = 0.096), though neither association reached statistical significance. These findings suggest that district-level factors and maternal education may have a stronger influence on IQ than fluorosis severity alone. Table 7 summarizes the regression coefficients for both districts.
Finally, Table 8 shows the average fluoride concentrations in drinking water of both districts, measured over the three-month data collection period. A third verification test was conducted in Uchumayo due to variability detected in the initial two samples. The results indicated that the average fluoride concentration in Uchumayo's drinking water was significantly higher than in Yanahuara.
Discussion
Our results showed that IQ levels and DF severity varied between districts with different fluoride exposures. In Yanahuara, where fluoride levels are low, cases of superior intelligence and very mild DF predominated. In contrast, in Uchumayo - a semi-rural district with higher fluoride levels - moderate to severe DF and average IQ scores predominated. Although we observed a trend suggesting that greater DF severity may be associated with lower IQ scores, the correlation was weak and not statistically significant, suggesting the possible influence of other factors, such as socioeconomic status [17], family environment [18], and environmental pollutants [19]. This interpretation is supported by the adjusted regression models, which show that district of residence and maternal education were more strongly associated with IQ than fluorosis severity. As a result, we do not reject the null hypothesis.
The differences observed between districts may reflect broader contextual factors influencing cognitive outcomes, beyond fluoride exposure alone. The contrasting residential settings - urban in Yanahuara and semi-rural in Uchumayo - likely involve disparities in infrastructure, access to health and educational resources, and types of environmental exposures. These district-level conditions may play a central role in shaping early childhood development trajectories [20,21]. Similar findings have been reported in studies showing that children's cognitive performance is affected not only by chemical agents, but also by the cumulative burden of socioeconomic disadvantages, nutritional deficiencies, and geographic proximity to industrial zones [11]. From this perspective, geographic context may act as a modifier or mediator in the relationship between environmental exposures and neurodevelopment [11,20].
Although the caries-preventive benefits of fluoride are well-established, several studies have associated fluoride exposure to adverse health outcomes, including muscular and skeletal disorders, and possible neurotoxic effects during early development [22-24]. High concentrations of fluoride in drinking water have been associated with DF and skeletal fluorosis, with severity proportional to both fluoride concentration and exposure duration [25,26]. Consistent with these findings, our results in Uchumayo indicate a high prevalence of moderate to severe DF and average IQ scores.
In addition to environmental context, sociodemographic factors such as maternal education can exert a significant influence on children's cognitive development [27,28]. Scientific literature has consistently shown that maternal educational attainment is associated with children’s intellectual performance, acting as a protective factor against adverse environmental conditions [28]. This effect may be mediated by greater home stimulation, improved health and nutritional practices, and increased resilience to environmental risks. In our study, maternal education was significantly associated with IQ, particularly in Uchumayo, reinforcing the importance of considering family and social variables when interpreting the impact of fluoride exposure on cognition.
Environmental factors such as climate change, increased pollution, and social inequity have significantly affected communities like Uchumayo, where groundwater consumption is common [12], particularly among its predominantly lowto middle-income agricultural residents. The district is also located near one of Peru’s largest open-pit copper mines. During our water sampling in Uchumayo, fluoride concentrations ranged from 0.59 to 0.90 ppm/L, with an average of 0.76 ± 0.16 ppm/L. These values are lower than those reported in a previous study, which recorded fluoride concentrations between 0.88 ± 0.02 ppm/L and 1.41 ± 0.03 ppm/L [12]. Although these values are below the threshold associated with cognitive damage (>1.5 ppm/L) [29], some studies have suggested that IQ can be affected by fluoride levels as low as 1 ppm/L [9], raising concerns that concentrations close to this threshold may pose a risk to the population.
In Yanahuara, where fluoride levels were lower (0.19 ± 0.03 ppm/L), schoolchildren showed no signs of DF or presented with only very mild cases, registering higher IQ scores, and no cases of low intelligence. This suggests that reduced fluoride concentrations in drinking water significantly decrease the incidence of DF [30] without negatively affecting cognitive function [31]. These findings support the hypothesis of a relationship between DF severity and IQ in individuals exposed to higher fluoride concentrations [32].
Analyzing the entire sample, we found that severe DF cases were associated with lower IQ scores, suggesting a potential relationship between DF severity and cognitive decline [6]. This relationship could be influenced by high fluoride exposure in endemic areas like Uchumayo, although genetic factors may also play a role [8]. While mild DF cases were associated with higher IQs and severe DF cases with lower IQs, the correlation between DF severity and IQ was weak. This may be due to the uneven distribution of DF cases in our sample, with a predominance of moderate and severe cases.
Although dental fluorosis showed a negative association with IQ in bivariate analyses, this relationship did not persist in models stratified by district. When analyzing schoolchildren from Uchumayo and Yanahuara separately, fluorosis severity was not a significant predictor of IQ, suggesting that this association may be confounded by other contextual or sociodemographic variables [33]. This finding supports the hypothesis that while dental fluorosis may serve as a clinical marker of fluoride exposure, it does not necessarily reflect the full neurotoxic burden to which a child may be exposed [30]. The interaction between environmental exposure, social vulnerability, and family factors may be more decisive than fluorosis itself in modulating the neurodevelopmental impact [34-36].
The association between DF and IQ has been explored in various studies, with conflicting results. Some studies have found a correlation between high fluoride exposure and reduced cognitive function [11,24,32], while others have not identified a significant association [5,20]. Among the most relevant studies are three major cohort investigations. The ELEMENT study [21] from 2019, involving 224 participants, suggested that maternal fluoride exposure may more significantly affect visuospatial and perceptual domains than verbal ones, with a decrease of 2 IQ points for every 0.5 mg/L increase in maternal urinary fluoride concentration. The MIREC study [20] from 2024 evaluated pregnant women and measured fluoride exposure through water, beverages, and urine. While a slight decrease in children's IQ was observed in fluoridated communities, no definitive conclusions were drawn. In an additional analysis of 596 mother-child pairs, the association between fluoride and performance IQ was stronger during the prenatal period in boys and in childhood for girls, with no impact on verbal IQ [5]. The MADRES study [11] from 2024, involving 229 mother-child pairs, found that prenatal fluoride exposure (0.68 mg/L in urine) doubled the risk of neurobehavioral issues in children, suggesting the need to reconsider current fluoride exposure recommendations during pregnancy.
While evidence indicates a correlation between high fluoride exposure and reduced IQ, particularly in areas with endemic fluorosis, this association is not universal. In our study, mild DF cases were associated with higher IQs, whereas severe DF cases were associated with lower IQs. However, the correlation was weak, likely due to the uneven distribution of DF cases and the influence of other variables. Importantly, when controlling for district and maternal educational level in the adjusted analysis, dental fluorosis was no longer significantly associated with IQ, reinforcing the hypothesis that broader contextual and social factors may play a more decisive role in cognitive outcomes than fluorosis severity alone.
Our study included schoolchildren aged 8 to 16 years from Uchumayo, who consume groundwater with fluoride levels within the limits recommended by the WHO. Nonetheless, moderate to severe DF was associated with lower IQ scores in unadjusted analyses, suggesting that other environmental factors may contribute. In addition to fluoride levels in water, the natural release of fluoride from soil minerals may contribute to DF and cognitive impairment [24]. The proximity of Uchumayo to a major copper mine and its location in a volcanic region may further affect the cognitive health of its residents.
This study has limitations, including the uneven distribution of DF cases and the lack of control over factors influencing IQ, such as education and nutrition. However, a key point is the large representative sample from two districts with different fluoride exposure levels. Future research should focus on more detailed analyses of socio-environmental factors and on longitudinal studies to explore the long-term effects of fluoride exposure on cognitive development.
Conclusion
Although a slight inverse trend was observed between dental fluorosis (DF) severity and intelligence quotient (IQ), this relationship was not significant after adjusting for key variables. Although higher fluoride exposure may affect schoolchildren's IQ, our findings suggest that contextual factors, such as district of residence and maternal educational level, may play a more substantial role in cognitive outcomes. This underscores the importance of considering broader socio-environmental determinants when evaluating the neurodevelopmental impact of fluoride exposure.
-
Financial Support
This work was supported by the Vice-Rectorate of Research of the Catholic University of Santa María (UCSM) (Finance Code 1264-VRINV-2022).
Acknowledgments
The authors would like to thank the Vice-Rectorate of Research of the Catholic University of Santa María (UCSM) for their support in the development of the study.
Data Availability
The data used to support the findings of this study can be made available upon request to the corresponding author.
References
-
[1] Houari S, DeRocher K, Thuy TT, Coradin T, Srot V, van Aken PA, et al. Multi-scale characterization of Developmental Defects of Enamel and their clinical significance for diagnosis and treatment. Acta Biomater 2023; 169:155-167. https://doi.org/10.1016/j.actbio.2023.08.011
» https://doi.org/10.1016/j.actbio.2023.08.011 -
[2] Aulestia FJ, Groeling J, Bomfim GHS, Costiniti V, Manikandan V, Chaloemtoem A, et al. Fluoride exposure alters Ca signaling and mitochondrial function in enamel cells. Sci Signal 2020; 13(619):eaay0086. https://doi.org/10.1126/scisignal.aay0086
» https://doi.org/10.1126/scisignal.aay0086 -
[3] Wu S, Wang Y, Iqbal M, Mehmood K, Li Y, Tang Z, et al. Challenges of fluoride pollution in environment: Mechanisms and pathological significance of toxicity - A review. Environ Pollut 2022; 304:119241. https://doi.org/10.1016/j.envpol.2022.119241
» https://doi.org/10.1016/j.envpol.2022.119241 -
[4] Kashirtsev F, Tressel J, Simon JC, Fried D. High contrast imaging of dental fluorosis in the short wavelength infrared. J Biophotonics 2021; 14(10):e202100145. https://doi.org/10.1002/jbio.202100145
» https://doi.org/10.1002/jbio.202100145 -
[5] Farmus L, Till C, Green R, Hornung R, Martinez Mier EA, Ayotte P, et al. Critical windows of fluoride neurotoxicity in Canadian children. Environ Res 2021; 200:111315. https://doi.org/10.1016/j.envres.2021.111315
» https://doi.org/10.1016/j.envres.2021.111315 -
[6] Lou D, Luo Y, Liu J, Zheng D, Ma R, Chen F, et al. Refinement impairments of verbal-performance intelligent quotient in children exposed to fluoride produced by coal burning. Biol Trace Elem Res 2021; 199(2):482-489. https://doi.org/10.1007/s12011-020-02174-z
» https://doi.org/10.1007/s12011-020-02174-z -
[7] Green R, Lanphear B, Hornung R, Flora D, Martinez-Mier EA, Neufeld R, et al. Association between maternal fluoride exposure during pregnancy and IQ scores in Offspring in Canada. JAMA Pediatr 2019; 173(10):940-948. https://doi.org/10.1001/jamapediatrics.2019.1729
» https://doi.org/10.1001/jamapediatrics.2019.1729 -
[8] Zhao L, Yu C, Lv J, Cui Y, Wang Y, Hou C, et al. Fluoride exposure, dopamine relative gene polymorphism and intelligence: A cross-sectional study in China. Ecotoxicol Environ Saf 2021; 209:111826. https://doi.org/10.1016/j.ecoenv.2020.111826
» https://doi.org/10.1016/j.ecoenv.2020.111826 -
[9] Veneri F, Vinceti M, Generali L, Giannone ME, Mazzoleni E, Birnbaum LS, et al. Fluoride exposure and cognitive neurodevelopment: Systematic review and dose-response meta-analysis. Environ Res 2023; 221:115239. https://doi.org/10.1016/j.envres.2023.115239
» https://doi.org/10.1016/j.envres.2023.115239 -
[10] Kumar JV, Moss ME, Liu H, Fisher-Owens S. Association between low fluoride exposure and children’s intelligence: A meta-analysis relevant to community water fluoridation. Public Health 2023; 219:73-84. https://doi.org/10.1016/j.puhe.2023.03.011
» https://doi.org/10.1016/j.puhe.2023.03.011 -
[11] Malin AJ, Eckel SP, Hu H, Martinez-Mier EA, Hernandez-Castro I, Yang T, et al. Maternal urinary fluoride and child neurobehavior at age 36 months. JAMA Netw Open 2024; 7(5):e2411987. https://doi.org/10.1001/jamanetworkopen.2024.11987
» https://doi.org/10.1001/jamanetworkopen.2024.11987 -
[12] Calderón ZM de, Abanto J, Sá Oliveira G, Haddad AE, Bönecker M. Does fluorosis have an impact on Peruvian children’s oral health related quality of life? RGO 2021; 69:e2021002. https://doi.org/10.1590/1981-8637202100023412
» https://doi.org/10.1590/1981-8637202100023412 -
[13] Johnson D, Walczak M. Dental fluorosis and fluoride in Willoq and Cuzco: Assumptions and reality in Peru. Int J Med Sci Public Health 2015; 4(9):1269. https://doi.org/10.5455/ijmsph.2015.10032015262
» https://doi.org/10.5455/ijmsph.2015.10032015262 -
[14] Dean HT, Elvove E, Poston RF. Mottled enamel in South Dakota. Public Health Rep 1939; 54(6):212. https://doi.org/10.2307/4582787
» https://doi.org/10.2307/4582787 - [15] Brown L, Sherbenou RJ, Johnsen SK. TONI-2. Test de Inteligencia no verbal: Apreciación de la habilidad cognitiva sin influencia del lenguaje. TEA Ediciones; 2009. [In Spanish].
-
[16] Yokoyama Y, Kano K, Kondo Y, Miyahara Y, Miyazaki K, Abe T. Fluoride ion-selective electrode for organic solutions. Anal Chem 2021; 93(45):15058-15062. https://doi.org/10.1021/acs.analchem.1c03064
» https://doi.org/10.1021/acs.analchem.1c03064 -
[17] Ilardi D, Alexander N, Xiang Y, Figueroa J, Blackwell L. Social-environmental factors as mediators of IQ and achievement differences across race groups in adolescents with high risk congenital heart disease. Child Neuropsychol 2023; 29(7):1003-1020. https://doi.org/10.1080/09297049.2022.2117798
» https://doi.org/10.1080/09297049.2022.2117798 -
[18] Liu YY, Lu HJ, Zhu N, Chang L. Environmental harshness, life history, and crystallized intelligence of Chinese adolescents. Evol Psychol 2023; 21(3):14747049231190051. https://doi.org/10.1177/14747049231190051
» https://doi.org/10.1177/14747049231190051 -
[19] Vermeir G, Viaene M, Staessen J, Hond ED, Roels HA. Neurobehavioural investigations in adolescents exposed to environmental pollutants. Environ Toxicol Pharmacol 2005; 19(3):707-713. https://doi.org/10.1016/j.etap.2004.12.041
» https://doi.org/10.1016/j.etap.2004.12.041 -
[20] Guichon JR, Cooper C, Rugg-Gunn A, Dickinson JA. Flawed MIREC fluoride and intelligence quotient publications: A failed attempt to undermine community water fluoridation. Community Dent Oral Epidemiol 2024; 52(4):365-374. https://doi.org/10.1111/cdoe.12954
» https://doi.org/10.1111/cdoe.12954 -
[21] Perng W, Tamayo-Ortiz M, Tang L, Sánchez BN, Cantoral A, Meeker JD, et al. Early life exposure in Mexico to ENvironmental Toxicants (ELEMENT) project. BMJ Open 2019; 9(8):e030427. https://doi.org/10.1136/bmjopen-2019-030427
» https://doi.org/10.1136/bmjopen-2019-030427 -
[22] Yazıcı Karabulut B, Derin P, Demir Yetiş A, Yeşilnacar MIR. Health risk assessment in an area of dental fluorosis disease from high fluoride drinking water: A case study from southeastern Türkiye. Int J Environ Health Res 2024; 34(5):2299-2314. https://doi.org/10.1080/09603123.2023.2243848
» https://doi.org/10.1080/09603123.2023.2243848 -
[23] Emenike CP, Tenebe IT, Jarvis P. Fluoride contamination in groundwater sources in Southwestern Nigeria: Assessment using multivariate statistical approach and human health risk. Ecotoxicol Environ Saf 2018; 156:391-402. https://doi.org/10.1016/j.ecoenv.2018.03.022
» https://doi.org/10.1016/j.ecoenv.2018.03.022 -
[24] Grandjean P. Developmental fluoride neurotoxicity: An updated review. Environ Health 2019; 18(1):110. https://doi.org/10.1186/s12940-019-0551-x
» https://doi.org/10.1186/s12940-019-0551-x -
[25] Taher MK, Momoli F, Go J, Hagiwara S, Ramoju S, Hu X, et al. Systematic review of epidemiological and toxicological evidence on health effects of fluoride in drinking water. Crit Rev Toxicol 2024; 54(1):2-34. https://doi.org/10.1080/10408444.2023.2295338
» https://doi.org/10.1080/10408444.2023.2295338 -
[26] Tiwari KK, Raghav R, Pandey R. Recent advancements in fluoride impact on human health: A critical review. Environ Sustain Indic 2023; 20(100305):100305. https://doi.org/10.1016/j.indic.2023.100305
» https://doi.org/10.1016/j.indic.2023.100305 -
[27] Sentenac M, Benhammou V, Aden U, Ancel PY, Bakker LA, Bakoy H, et al. Maternal education and cognitive development in 15 European very-preterm birth cohorts from the RECAP Preterm platform. Int J Epidemiol 2021; 50(6):1824-1839. https://doi.org/10.1093/ije/dyab170
» https://doi.org/10.1093/ije/dyab170 -
[28] Joseph RM, O’Shea TM, Allred EN, Heeren T, Kuban KK. Maternal educational status at birth, maternal educational advancement, and neurocognitive outcomes at age 10 years among children born extremely preterm. Pediatr Res 2018; 83(4):767-777. https://doi.org/10.1038/pr.2017.267
» https://doi.org/10.1038/pr.2017.267 -
[29] National Toxicology Program. NTP monograph on the state of the science concerning fluoride exposure and neurodevelopment and cognition: A systematic review. NTP Monogr 2024; (8):NTP-MGRAPH-8. https://doi.org/10.22427/NTP-MGRAPH-8
» https://doi.org/10.22427/NTP-MGRAPH-8 -
[30] Wang F, Li Y, Tang D, Zhao J, Yang B, Zhang C, et al. Epidemiological analysis of drinking water-type fluorosis areas and the impact of fluorosis on children’s health in the past 40 years in China. Environ Geochem Health 2023; 45(12):9925-9940. https://doi.org/10.1007/s10653-023-01772-9
» https://doi.org/10.1007/s10653-023-01772-9 -
[31] Jin T, Huang T, Zhang T, Li Q, Yan C, Wang Q, et al. A Bayesian benchmark concentration analysis for urinary fluoride and intelligence in adults in Guizhou, China. Sci Total Environ 2024; 925(171326):171326. https://doi.org/10.1016/j.scitotenv.2024.171326
» https://doi.org/10.1016/j.scitotenv.2024.171326 -
[32] Prabhakar A, Abdulkhayarkutty K, Cheruvallil SV, Sudhakaran P. Effect of endemic fluorosis on cognitive function of school children in Alappuzha District, Kerala: A cross sectional study. Ann Indian Acad Neurol 2021; 24(5):715-720. https://doi.org/10.4103/aian.AIAN_850_20
» https://doi.org/10.4103/aian.AIAN_850_20 -
[33] Do LG, Sawyer A, John Spencer A, Leary S, Kuring JK, Jones AL, et al. Early childhood exposures to fluorides and cognitive neurodevelopment: A population-based longitudinal study. J Dent Res 2025; 104(3):243-250. https://doi.org/10.1177/00220345241299352
» https://doi.org/10.1177/00220345241299352 -
[34] Dickerson AS. Invited perspective: Still beating the drum-Environmental health disparities and neurodevelopment. Environ Health Perspect 2023; 131(9):91302. https://doi.org/10.1289/EHP13425
» https://doi.org/10.1289/EHP13425 -
[35] Park CH, Blaisdell CJ, Arteaga SS, Mash C, Laessig S, Hanspal M, et al. How the Environmental Influences on Child Health Outcome (ECHO) cohort can spur discoveries in environmental epidemiology. Am J Epidemiol 2024; 193(9):1219-1223. https://doi.org/10.1093/aje/kwae073
» https://doi.org/10.1093/aje/kwae073 -
[36] Miguel PM, Pereira LO, Silveira PP, Meaney MJ. Early environmental influences on the development of children’s brain structure and function. Dev Med Child Neurol 2019; 61(10):1127-1133. https://doi.org/10.1111/dmcn.14182
» https://doi.org/10.1111/dmcn.14182
Edited by
-
Academic Editor:
Alessandro Leite Cavalcanti
