Assessing the Intelligence Quotient of 5–10-year-old Children in Salem District, India with Varying Fluoride Levels: A Cross-sectional Study.

Introduction

Fluoride is a naturally occurring mineral in rocks, soils, and water. Globally, 2.5 billion people rely on groundwater for daily needs. The WHO recommended 1 ppm of fluoride in drinking water as optimal for preventing dental caries. However, in tropical regions like India, where water intake and natural fluoride levels are higher, concentrations below 1.0 ppm are considered safe.¹ While fluoride is crucial for dental health, its excessive consumption is linked to severe health issues. These include skeletal and dental fluorosis, conditions that can also impair a child’s cognitive development.

The neurotoxic potential of excess fluoride exposure is well-documented, and there are studies showing that high levels may reduce intelligence quotient because of the effects of fluoride on the developing brain.^2,3 As fluoride can readily cross the placental and blood–brain barriers, fetuses are particularly vulnerable to its effects, which can lead to disturbed brain development resulting in postbirth cognitive impairments. At higher concentrations, fluoride inhibits the enzymatic process, disturbing metabolism and physiological processes, which may have direct consequences on the nervous system and neurodevelopment.⁴ Fluorosis represents a severe public-health challenge in India, mainly in its rural settings, where the major source of drinking water comes from groundwater. Dental fluorosis represents an acute problem in the state of Tamil Nadu, while high prevalence was documented in districts such as Dharmapuri, Salem, Namakkal, and Krishnagiri. The effects of fluoride exposure are exacerbated by poor sanitation and hygiene practices, as well as malnutrition.⁵

The wide range of fluoride exposure in this region creates a unique opportunity to investigate the direct relationship between fluoride intake and children’s intelligence quotient (IQ) scores. The current study aims to apply Raven’s Colored Progressive Matrices (RCPM), a nonverbal test designed for children aged 5 through 11 years, to measure cognitive abilities and provide insights into the public health implications of fluoride exposure in this region. The study focuses on Salem District, where groundwater fluoride content varies widely, to investigate the potential relationship between fluoride exposure and IQ in children.

Materials and Methods

This cross-sectional study was conducted prospectively in the Department of Pediatric and Preventive Dentistry, Vinayaka Mission’s Sankarachariyar Dental College, Vinayaka Mission’s Research Foundation (Deemed to be University), Salem, Tamil Nadu, India. The study was carried out between June 2023 and October 2023, after obtaining the approval from Institutional Research Ethical Committee (VMSDC/IEC/Approval No. 253).

Results

A total of 258 children aged 5–10 years were included in the study. The mean age of the participants was 8.21 years. The distribution of age groups is presented in Table 1, with the highest representation from the 10-year-old group (31%) and the lowest from the 7-year-old group (9.3%). The fluoride levels in the water samples collected from the two study areas indicated that Ayothiyapattinam exhibited the highest fluoride concentrations, with well water measuring 6.50 ppm and borewell water measuring 3.16 ppm. In contrast, Attayampatti had the lowest fluoride concentrations, with well water containing 0.112 ppm and borewell water containing 0.734 ppm (Table 1). A comparative evaluation of dental fluorosis prevalence and fluoride content in water across different areas revealed a positive correlation in Ayothiyapattinam, where 64.6% of children were affected by fluorosis. IQ distribution across all areas, categorized according to cumulative exposure from various water sources and expressed in frequency and percentage, was found to be highly statistically significant based on a chi-square test, with a probability value (p) < 0.05 set as the threshold for significance (Fig. 1). Multiple linear regression analysis was conducted to assess the relationship between IQ scores (dependent variable) and several independent variables, including socioeconomic status (SES) scores and fluoride concentration (in parts per million) from two water sources (borewell and well water). The analysis revealed that as fluoride levels increased, IQ scores tended to decrease, a statistically significant inverse relationship. (Table 2). Further correlation analysis using Spearman’s correlation coefficients to explore the relationships between IQ scores, SES, and fluoride levels in water demonstrated a weak negative monotonic connection between fluoride concentrations and IQ scores that was statistically significant (Table 3).

Discussion

Fluorosis is a major public health concern in India, where an estimated 62 million individuals, six million of whom are children, are exposed to excessive fluoride levels in their drinking water. Fluoride’s strong electronegativity causes it to bind with positively charged calcium in teeth and bones, resulting in dental fluorosis, skeletal fluorosis, and bone deformation in both children and adults. Groundwater, which sustains 80% of India’s population, is heavily contaminated, particularly with elevated fluoride levels.¹² According to the World Health Organization (1971) and Indian standards (1975), the permissible limit for fluoride in drinking water is 1.0 mg/L and 1.5 mg/L, respectively. The Indian Council of Medical Research (ICMR) and Public Health Engineering (PHE) committees also recommend a maximum fluoride concentration of 1.0 mg/L, given the high water consumption in tropical regions such as India. In Tamil Nadu, the districts of Dharmapuri and Salem have the highest fluoride levels in groundwater, followed by Coimbatore, Madurai, Trichy, Dindukal, and Chidambaram.¹³ Data from the Tamil Nadu Water Supply and Drainage Board indicates that 62% of water sources in Krishnagiri have fluoride levels above 1.0 ppm, followed by Vellore (60%), Salem (56%), Dharmapuri (53%), and Erode (26%).¹⁴

Early brain development is highly susceptible to imbalances in multiple elements, such as fluoride, particularly in a fetus or young child. According to a study by Li et al., the brain develops most rapidly before age six, and basic structure development is mostly complete by ages seven to eight. The brain is vulnerable to a high amount of fluoride intake throughout this time. Overconsumption of fluoride by a pregnant woman may cause an increased risk, as fluoride can pass the placenta and potentially disrupt normal neurological growth. If a child lives in an endemic environment from birth, the child will ingest more fluoride than needed. This is a particularly important matter between birth and age eight, a time when fluoride is able to cross the blood–cerebrospinal fluid barrier, possibly changing the brain development of a child through many stages and potentially having a negative effect on the child’s IQ and nervous system function.¹⁵ Various standard tests measure IQ, including the Raymond B Cattell test, the Stanford–Binet Intelligence Scale, and the Seguin Form Board, though their practical applications are limited. RCPM, a nonverbal test widely used in educational settings, was initially designed for studies on environmental and genetic influences on cognition. It comes in three formats: Standard, Colored, and Advanced Progressive Matrices. This study utilizes the Colored Progressive Matrices, designed for children aged 5–11 and individuals with mental or physical impairments. The test assesses mental development by evaluating logical reasoning and is culture-free, with established validity and reliability across multiple countries.¹⁶

The current study found average fluoride concentrations in well and borewell water samples from Yercaud, Ayothiyapattinam, Veerapandi, Shevapet, and Attayampatti to be 1.06 ppm, 4.80 ppm, 2.21 ppm, 0.7 ppm, and 0.3 ppm, respectively. These findings align with Periakali et al.’s study on groundwater in Salem district, where fluoride levels ranged from 0.8 to 14.7 ppm, with an average of 3.7 ppm. Consistent with both studies, Ayothiyapattinam showed the highest fluoride concentration, with well water at 6.50 ppm and borewell water at 3.16 ppm.⁶ Additionally, according to Ramesh et al., the District of Salem’s government-supplied drinking water had a fluoride content ranging from 0 to 3 ppm.¹⁷ Fluoride levels were confirmed once again using a Fluoride A214 PH/Ion Selective Electrode meter (Thermo-Scientific Orion 4-star) at the Central Research Laboratory, Vinayaka Mission’s Sankarachariyar Dental College. This method provides more accurate results by distinguishing between organic and inorganic fluoride and resisting interference from other ions like phosphates and sulfates.

In the present study, 64.6% of fluorosis cases were found in the area with the greatest fluoride content, Ayothiyapattinam, where average fluoride levels were 4.83 ppm. In contrast, 52.5% of children in Attayampatti, a low-fluoride area, with fluoride levels of 0.42 ppm, were free of fluorosis. Dental fluorosis was used as an indirect marker of chronic fluoride exposure, reinforcing the link between fluoride intake and cognitive outcomes. These were in line with findings from a study by Gopalakrishnan et al. that showed that, in comparison to the reference group, which had water fluoride content below 1 ppm, a water fluoride concentration above 1 ppm was associated with a 1.85-fold greater risk of dental fluorosis prevalence.¹⁸

The distribution of IQ with respect to area in this study revealed that children in low-fluoride areas had a higher probability of falling into the superior IQ group (5.6%) compared to children in high-fluoride areas (1.5%), a finding that was highly statistically significant. Ren et al. were the first to discover that 160 children between the ages of 8 and 14 who were born and raised in a high-fluoridated village in Shandong Province, China, had an average IQ that was lower than that of a low-fluoridated village in the same province.¹⁹ This was consistent with the study by Kundu et al. on two hundred 8–12-year-old children in the state of Delhi, where they discovered a substantial difference in the mean IQ of children in high fluoride areas (76.20 ± 19.10) compared to low fluoride areas (85.80 ± 18.85).²⁰ Conversely, a cross-sectional study conducted by Eswar et al. on 12–14-year-olds in the Davengere district of Karnataka revealed no statistically significant differences in the IQ scores of children residing in the high and low fluoride region.²¹

According to this study’s findings, a child’s IQ was not influenced by their age, gender, or socioeconomic status. Socioeconomic status (SES) was analyzed to rule out its confounding effect on IQ scores, confirming that fluoride exposure played a more significant role. These findings align with previous studies that have identified fluoride exposure as a key factor affecting neurodevelopment, independent of socioeconomic variations. This finding is corroborated by Sebastian et al., who similarly did not find a significant statistical link between children’s IQ scores and factors such as parental education level or family income. He suggested that this may be because the occupation and education levels were nearly equal in all domains in this study, as neurobehavioral development can be influenced by genetic, socioeconomic, and regional factors.²² Seraj et al. also agreed with these findings and clarified that there was no correlation between the educational levels of the parents and the IQ scores of their children. This is because the fetal and early childhood stages of brain development are more vulnerable to neurological impairment, and this is independent of the parents’ income or educational attainment.²³ On the other hand, studies conducted by Chen et al. found that children born into “employed” households had higher IQs and that these IQs increased in tandem with the parents’ educational attainment. This suggests that a family’s favorable educational effect is beneficial to a child’s intellectual growth.⁴

The limitations of this study should be acknowledged. First, the research was conducted on a local scale with a small sample size of 258 due to time constraints, which may limit the generalizability of the findings. However, the study provides valuable insights into fluoride exposure and its potential neurotoxic effects, particularly in regions with high groundwater fluoride levels. While conducted in a specific geographic area, the methodology aligns with similar studies worldwide, making the results applicable to other fluoride-endemic regions. Nonetheless, further large-scale, multiregional studies are needed to validate these findings and draw more definitive conclusions. As a cross-sectional study, this research identifies a significant association between fluoride exposure and cognitive impairment but does not establish causation. The lack of temporal sequencing limits definitive conclusions about causality. However, the findings align with existing literature on fluoride neurotoxicity, reinforcing the need for well-controlled longitudinal studies to track cognitive changes over time and confirm a direct causal relationship. While this study evaluated the impact of fluoride concentration on children’s IQ, the potential influence of other trace elements in drinking water on neurological outcomes cannot be ruled out. Additionally, confounding factors such as diet were not analyzed in depth, although the study controlled for major variables such as fluoride exposure, SES, and water source. The absence of a significant correlation between SES and IQ aligns with previous research, suggesting that fluoride exposure plays a more dominant role. However, the Modified Kuppuswamy Scale, though widely used, may not fully capture nuanced socioeconomic differences in rural populations. Future studies should incorporate more granular SES assessment tools to ensure a comprehensive analysis of potential confounders.

Conclusion

The primary conclusion of this study is the significant association observed between higher fluoride levels in drinking water and lower IQ scores in the pediatric population studied. Ayothiyapattinam recorded the highest fluoride concentrations in both well and borewell water, while Attayampatti had the lowest levels. The prevalence of dental fluorosis was notably high, affecting 58.9% of the children assessed. IQ scores were inversely related to fluoride exposure, with children consuming well water, particularly with higher fluoride content, showing a greater proportion of extremely low IQ scores compared to those consuming borewell water. Interestingly, there was no significant association between socioeconomic status and IQ scores, indicating that fluoride exposure, rather than socioeconomic factors, may play a more critical role in cognitive outcomes in this population. Ultimately, our findings emphasize that effective regulation of fluoride in drinking water is a critical public health priority for preventing adverse neurodevelopmental outcomes in children. Further research is warranted to deepen our understanding of the long-term impacts of fluoride on children’s cognitive development.

Availability of Data and Materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing Interests

The authors declare that they have no competing interests.

Acknowledgment

None.

Authors’ Contributions

RER conducted the design, literature search, data acquisition, and analysis of the study; VA defined the intellectual content and helped with manuscript editing. SG proposed the concept and helped with manuscript preparation and editing; VD and PDG conducted the statistical analysis and manuscript review; YL conducted the statistical analysis; and JBJ conducted the manuscript review.

AI Disclosure

ChatGPT (OpenAI, GPT-5.5) was used to assist with language editing and improvement of manuscript readability. All scientific content, data interpretation, revisions, and final approval were performed by the authors, who take full responsibility for the content of the manuscript.

Orcid

Reshma Elizabeth Rajan https://orcid.org/0000-0002-0769-1524

Veena Arali https://orcid.org/0000-0002-6614-3405

Sowndarya Gunasekaran https://orcid.org/0000-0001-9828-7561

Vinola Duraisamy https://orcid.org/0000-0002-1422-6268

Pradeep D Gainneos https://orcid.org/0000-0001-5921-9902

Yash Latkar https://orcid.org/0000-0003-0595-2548

J Baby John https://orcid.org/0000-0002-9540-9364