Different studies have suggested that fluoride is related to neurological disorders in children and adolescents, but clinical evidences of which neurological parameters associated to fluoride exposure are, in fact, still controversial. In this way, this systematic review and meta-analysis aimed to show if there is an association between fluoride exposure from different sources, doses and neurological disorders. Terms related to "Humans"; "Central nervous system"; "Fluorides"; and "Neurologic manifestations" were searched in a systematic way on PubMed, Scopus, Web of Science, Lilacs, Cochrane and Google Scholar. All studies performed on humans exposed to fluoride were included on the final assessment. A meta-analysis was then performed and the quality level of evidence was performed using the GRADE approach. Our search retrieved 4,024 studies, among which 27 fulfilled the eligibility criteria. The main source of fluoride was naturally fluoridated water. Twenty-six studies showed alterations related to Intelligence Quotient (IQ) while only one has evaluated headache, insomnia, lethargy, polydipsia and polyuria. Ten studies were included on the meta-analysis, which showed IQ impairment only for individuals under high fluoride exposure considering the World Health Organization criteria, without evidences of association between low levels and any neurological disorder. However, the high heterogeneity observed compromise the final conclusions obtained by the quantitative analyses regarding such high levels. Furthermore, this association was classified as very low-level evidence. At this time, the current evidence does not allow us to state that fluoride is associated with neurological damage, indicating the need for new epidemiological studies that could provide further evidences regarding this possible association.
Fluoride (F) has been used as preventive and therapeutic agent in dentistry for over eight decades. It is widely known that its main side-effect (i.e., dental fluorosis) was reported decades prior to the accidental discovery of its caries-preventive effects[
As for community-based methods, water fluoridation is by far the most widely used worldwide, covering ~ 400 million people in 25 countries[
Despite the body of evidence attesting the efficacy and safety of water fluoridation, this method has been the subject of heated debate in several parts of the world, questioning legal aspects of the compulsory nature and potential harmful effects. Within this context, a recent systematic review with meta-analysis attempted to demonstrate the relationship between F exposure from the drinking water and intelligence quotient (IQ) impairment, concluding that exposure to water containing high F levels interferes with the child's intelligence development[
Considering the above, the present systematic review and meta-analysis aimed to investigate the impact of environmental exposure to F from different sources on neurological disorders in humans. For studies that assessed F exposure from water, this review adopted the WHO guidelines to dichotomize between low (0.5 to 1.0 mg F/L) and high (above 2 mg F/L) exposure, allowing the discussion of doses safety of water fluoridation.
This systematic review was registered in PROSPERO database, under CRD number 42017067234. A review was performed according to Moher, Liberati[
This review was designed using the PECO strategy and based on it, observational studies in humans (P) exposed to high concentrations of F (E) and low concentrations (C) in which the associations between F and neurological damage (O) were investigated. Case reports, descriptive studies, review articles, opinion articles, technical articles, guidelines, as well as animal and in vitro studies were disregarded.
The study was based on the question: "Can chronic F exposure be associated with neurological damage?" The searches were performed in January 2021, with no restrictions on the date of publication and the language of the studies. The electronic databases used were: PubMed, Scopus, Web of Science, Lilacs, Cochrane and Google Schoolar. The MeSH terms used were: "Humans"; "Central nervous system"; "Nervous system"; "Fluorine"; "Fluorides"; "Fluorine Compounds"; "Fluoride Poisoning"; "Neurobehavioral manifestations"; "Nervous System Disease"; "Neurologic manifestations"; "Intelligence". All MeSH keywords and search strategy were adapted according to the specifics of each database, as represented in Table A.1.
After the search stage, an alert was registered in each database for weekly notification of new studies that fit the vested strategy. All citations were entered into a bibliographic reference manager and duplicate studies were excluded, either automatically or manually (EndNote®, v. X7, Thomson Reuters, Philadelphia, USA). The search, study selection, risk of bias and data extraction stages were performed independently by two evaluators (G.H.N.M; M.O.P.A.) and checked by a third evaluator in case of disagreement (R.R.L).
Then, the study selection was made based on the title and abstract of articles and then by full-text analysis according to the recommended eligibility requirements. Reference lists of included studies were also evaluated for study selection.
From the included articles, data regarding the year of publication, study design, participant characteristics (origin and sample size), mean age, F concentration measurement parameters, diagnostic criteria for assessment of cognitive performance, results and statistical analysis were extracted and tabulated. In case of doubts about the methodology, lack of data in the studies and inability to find full articles, the authors were contacted via email with a weekly message for three consecutive weeks.
To assess the methodological quality and risk of bias, the checklist of Fowkes and Fulton[
After evaluating each criterion, a (++) sign was assigned for major study problems or (+) for minor problems to assess whether the methods are adequate to produce consistent and valid information, as well as whether the results offered the expected effects. In items where the question was not applicable to the type of study, it was assigned the acronym NA (not applicable). "No problem" has been assigned the sign (0). The evaluation for each domain was standardized by the examiners and is described in Table A.2.
After detailed evaluation of the methods and results, the studies were analyzed to verify the possibility of "skewed results", "confusions" and "random occurrence". To determine the value of the study, three summary questions were answered: "Were the results biased?"; "Are factors of confusion or distortion present?" and "Is there a possibility that the results came about by chance?" "YES" and "NO" answers were given. If the answer is NO in the three questions, the article is considered reliable, with low risk of bias.
The studies data were analyzed using Review Manager software (Review Manager v. 5.3, The Cochrane Collaboration; Copenhagen, Denmark) to evaluate if Chronic exposure to F is associated with neurological deficit. In all analyses, only studies with low risk of bias were included.
A meta-analysis was performed to compare the percentage of low IQ with high and low chronic exposure to F. Previously, each study classified the F levels as low or high with heterogeneous concentrations. Then, for the meta-analysis we decided to classify the studies according to the WHO guidelines that consider optimal levels between 0.5–1.0 mg/L (low levels) and > 2 mg/L, as higher levels for water fluoridation[
The heterogeneity among studies was tested using I
The publication bias was assessed through a comprehensive analysis of Egger's test, and Funnel Plot Visual interpretation[
A sensitivity analyses was used to explore the influence of each study in the pooled meta-analysis or publication bias results. This analysis was adopted in case of substantial or considerable (50 to 100%) heterogeneity, or significant publication bias (p < 0.05). This evaluation was performed by manually omitting one study at time, one by one, and verifying its impact in the final results[
The level of evidence was determined using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) approach. This tool provides a structured process for developing and presenting evidence summaries that measure the quality of evidence to confirm or reject hypotheses in systematic reviews[
GRADE has four levels of evidence –decreasing from low to very low, moderate, and high; depending on whether issues such as risk of bias, inconsistency, inaccuracy and publication bias are severe, very serious or not serious. Although, observational studies begin as poor-quality evidence, the level can increase from low to high if the magnitude of the effect is large or very large[
All the authors are in accordance with the publication.
Based on the database searches, 4,024 studies were found. Three studies were included after manually searching in the reference lists[
Graph: Figure 1 Flow diagram of databases searched according to PRISMA guidelines. PRISMA, Preferred Reporting Items for Systematic Review and Meta-Analysis.
The 27 included studies were characterized as observational, cross-sectional type, among which 26 were analytical studies, and one was descriptive[
The F concentrations in drinking water categorized as low exposure in the selected studies ranged from 0.19 ppm[
Regarding the source of sample used for the estimation of F exposure, most of the studies evaluated the drinking water alone[
In relation to the parameters of cognitive assessment, in 26 studies the IQ was used to estimate a comparative intellectual and stabilizing capacity between the high and low groups, whereas one study[
In the analysis of results, 23 studies showed a statistical difference between exposure to high and low doses of F. In three studies a comparison of intellectual skill among the groups exposed to high and low F concentrations was not statistically significant[
Table 1 Data extraction from included studies.
Author, (year) Study design Participants Case evaluation Statistical analysis Results Risk of bias Source of sample Sample size and levels of fluoride exposure Age (years) Neurological assessment Fluoride levels measurement Aravind et al., (2016) Cross-sectional Mastihalli, Banavara and Virajpet, Hassan, India (n = 180) 60: High (> 3 ppm) 60: Medium (1.2–2 ppm) 60: Low (< 1.2 ppm) 10–12 Raven's Standard Progressive Matrices test Evaluation by ion selective electrode method in water samples Analysis of variance (ANOVA), Student's t-test, Kruskal–Wallis ANOVA and Spearman's rank correlation coefficient The mean IQ level was more in the region with medium fluoride concentration in drinking water (56.68 ± 14.51) compared to areas with low fluoride concentration (41.03 ± 16.36) and high fluoride concentration (31.59 ± 16.81); p < 0.0001 Low Chen et al., (1991) Cross-sectional Biji village and Jiaobei village, Linyi County, Shanxi Province, China (n = 640) 320: High (4.55 ppm) 320: Low (0.89 ppm) 7–14 Chinese Standardized Raven Test N/I t‑test The average IQ of children in lower fluoride área (104.03 ± 14.96) was significantly higher than that of in the higher fluoride (100.24 ± 14.52); p < 0.01 Low Eswar et al., (2011) Cross-sectional Davangere district, Karnataka, India (n = 133) 68: High (2.45 ppm) 65: Low (0.29 ppm) 12–14 Raven's Standard Progressive Matrices test Evaluation by ion selective electrode method in water samples Chi-square and Z tests There were no significant differences in IQ score of children living in high drinking water fluoride region (86.3 ± 12.8) and children living in low drinking water fluoride region (88.8 ± 15.3); p = 0.30 High Guo et al. (1991) Cross-sectional Xinshao County, Hunan Province, China (n = 121) 60: High (0.0298 mg/m3) 61: Low (N/I) 7–13 Chinese Binet IQ Test N/I Correlation analysis In the high fluoride area, the correlation co-efficient r = –0.25 (p<0.05), and for the control area r = –0.07 (p>0.05), for the two combined r = –0.205 (p<0.05). These results indicate that there is a negative correlation between serum fluoride and IQ, and that the correlation is greater within the high fluoride group. The average IQ of the endemic area children was 76.7, and the control group children had average IQs of 81.4; when compared, the difference is statistically significant; p < 0.05 Low Hong et al. (2001) Cross-sectional Wukang, Boxing, Zouping, Shangdong Province, China (n = 117) 85: High (2.90 ppm) 32: Low (0.75 ppm) 8–14 Chinese Standardized Raven Test Evaluation by conventional chemical assay methods t-test and Chi-squared There is no significant difference between the high fluoride (80.58 ± 2.28) and control areas (82.79 ± 8.98); p > 0.05 Low Karimzade et al., (2014) Cross-sectional West Azerbaijan, Iran (n = 39) 19: High (3.94 ppm) 20: Low (0.25 ppm) 9–12 Raymond B Cattell test Evaluation by SPADNS colorimetric method in water samples Unpaired t test and chi-squared testing The mean IQ of children living in high drinking water fluoride region (81.21 ± 16.17) was lower than that of children living in low drinking water fluoride region (104.25 ± 20.73); p=0.0004 Low Khan et al., (2015) Cross-sectional Asoha block in district Unnao and Tiwariganj block in district Lucknow of Uttar Pradesh, India (n = 429) 214: High (2.41 ppm) 215: Low (0.19 ppm) 6–12 Raven's Coloured Progressive Matrices (RCPM) Evaluation by ion selective electrode method in water samples Chi-squared test, ANOVA, Post-Hoc and Spearman's rank correlation Difference in IQ grade of children from different locations was found to be statistically significant (p < 0.001). Low Kundu et al., (2015) Cross-sectional Najafgarh and Defence Colony, Delhi, India (n = 200) 100: High (N/I) 100: Low (N/I) 8–12 Ravens Standardized Progressive Matrices Test Evaluation by ion selective electrode method in water samples Independent Comparison of mean IQ of children in high (76.20 ± 19.10) and low F (85.80 ± 18.85) areas showed a significant difference; p = 0.013 High Lu et al., (2000) Cross-sectional Tianjin Xiqing District, China (n = 118) 60: High (3.15 ± 0.61 ppm) 58: Low (0.37 ± 0.04 ppm) 10–12 Chinese Combined Raven's Test, Copyright 2 (CTR-C2) Evaluation by ion selective electrode method in water and urine samples Fisher's exact test, Welch's alternate t-test, the rank sum test, and multiple regression analysis The IQ of high fluoride area was significantly lower (92.27 ± 20.45) than that of the children in the low fluoride area (103.05 ± 13.86); p < 0.005 Low Nagarajappa et al., (2013) Cross-sectional Mundra and Bhuj, Kutch District, Gujarat, India (n = 100) 50: High (2.4–3.5 ppm) 50: Low (0.5 ppm) 8–10 Seguin Form Board Test Based on Water and Sanitation Management Organization, Gujarat Independent student Mean IQ scores were found to be significantly higher among children living in low fluoride region (30.45 ± 4.97) than those living in high fluoride region (23.20 ± 6.21); p < 0.05 Low Poureslami et al., (2011) Cross-sectional Koohbanan and Baft, Kerman Province, Iran (n = 120) 60: High (2.38 ppm) 60: Low (0.41 ppm) 7–9 Raven's Progressive Matrices Intelligence Test Evaluation by ion selective electrode method in water and urine samples t test and Mann–Whitney test The mean IQ of children living in high fluoride region (91.37 ± 16,63) was significantly lower than the average IQ of children living in low fluoride region (97.80 ± 15.95); p = 0.028 Low Qin et al., (2008) Cross-sectional Jing County, Hubei Province, China (n = 447) 141: High (2.1–4.0 ppm) 159: Medium (0.5–1.0 ppm) 147: Low (0.1–0.2 ppm) 9–10 Raven's Standard Progressive Matrices test Evaluation by ion selective electrode method in water samples N/I The difference between the high and low groups exposed was not statistically significant; p > 0.05 High Razdan et al., (2017) Cross-sectional Raya, Farah and Charora; Mathura district, Uttar Pradesh, India (n = 219) 69: High (2.99 ppm) 75: Medium (1.70 ppm) 75: Low (0.60 ppm) 12–14 Raven's Progressive Matrices Test Evaluation by ion selective electrode method in water samples Independent t test, One way analysis of variance, and post hoc analysis and Chi-square test Comparison between all the groups showed the mean IQ scores in low (38.60 ± 6.33), medium (18.94 ± 4.38), and high (13.94 ± 5.13) fluoride regions a statistically significant difference; p < 0.001 Low Saxena et al., (2012) Cross-sectional Karera Block, Shivpuri district and Parwaliya village, Bhopal district, Madhya Pradesh state, India (n = 170) 120: High (≥ 1.5 ppm) 50: Low (< 1.5 ppm) 12 Raven's Standard Progressive Matrices Evaluation by ion selective electrode method in water and urine samples ANOVA One Way Comparison of mean IQ of children in high (4.17) and low (3.16) fluoride area showed a significant difference; p = 0.000 Low Sebastian et al., (2015) Cross-sectional Nerale, Belavadi, Naganahall, Mysore district; Carnataca, India (n = 405) 135: High (2.20 ppm) 135: Medium (1.20 ppm) 135: Low (0.40 ppm) 10–12 Raven's Coloured Progressive Matrices (RCPM) Based on Rajiv Gandhi National Rural Drinking Water Program (RGNRDWP) Analysis of variance (ANOVA), post-hoc test and binary logistic regression The mean IQ scores for children with normal (88.6 ± 14.01) and low (86.37 ± 13.58) fluoride content were significantly higher than high fluoride level (80.49 ± 12.67); p < 0.01 Low Seraj et al., (2012) Cross-sectional Babur, Panjarlu, Dizaj, Small Donalau and Large Donalau; Makoo, Iran (n = 293) 91:High (5.2 ± 1.1 ppm) 106: Medium (3.1 ± 0.9 ppm) 96:Low (0.8 ± 0.3 ppm) 6–11 Raven's Color Progressive Matrices (RCPM) Evaluation by SPADNS colorimetric method in water samples ANOVA, Post Hoc test and Kruscal-Wallis IQ scores for children with low fluoride (97.77 ± 18.91) were significantly higher than the medium (89.03 ± 12.99) and high (88.58 ± 16.01) fluoride level; p = 0.001 Low Sharma et al., (2009) Cross-sectional Sanganer Tehsil, India (n = 1145) 418: High (1.5–6.4 ppm) 355: Medium (1.0–1.5 ppm) 372: Low (< 1.0 ppm) 12–18 Interviewed (questionnaire) for neurological manifestations (Headache Insomnia Lethargy Polyuria Polydipsia) N/I Descriptive analysis There were no neurological manifestations in children in the low and medium F villages, whereas, in the high F villages, 9.48% of the children had headache, 1.21% had insomnia, and 3.23% exhibited lethargy. There were no cases of polyuria or polydipsia among the children in any of the villages High Shivaprakash et al., (2011) Cross-sectional Bagalkot taluk and Hungund taluk, India (n = 160) 80: High (2.5–3.5 ppm) 80: Low (< 0.5 ppm) 7–11 Raven's Coloured Progressive Matrices Based on indiawaterportal.org The average IQ of children in lower fluoride area (76.3625 ± 20.8431) was significantly higher than that of in the higher fluoride (66.6250 ± 18.0908); p = 0.0019 Low Sudhir et al. (2009) Cross-sectional Nalgonda District, Andhra Pradesh, India (n = 1000) 247: Level 1 (< 0.7 ppm) 243: Level 2 (0.7–1.2 ppm) 267: Level 3 (1.3–4.0 ppm) 243: Level 4 (> 4.0 ppm) 13–15 Raven's standard progressive matrices Evaluation by ion selective electrode method in water samples Chi-square test and Spearmen's rank correlation Chi-aquare test was used to test the association among the different fluoride levels with IQ scores, and the Spearman's rank correlation was used to measure the relationship between the two variables. The results showed a statistically significant inverse association between both variables (p < 0.001). Low Trivedi et al., (2007) Cross-sectional Chandlodia, Ahmedabad and Sachana, Sanand district of Gujarat, India (n = 190) 89:High (5.55 ± 0.41 ppm) 101:Low(2.01 ± 0.09 ppm) 12–13 Questionnaire standardized with 97% reliability rate in relation to the Stanford-Binet Intelligence Scale Evaluation by ion selective electrode method in water and urine samples Student's t test The mean IQ score of the high F area was significantly lower (91.72 ± 1.13) than that of the lower F area (104.44 ± 1.23). p < 0.001 Low Trivedi et al., (2012) Cross-sectional Baroi, Chhasara, Gundala, Mundra, Pragpar, and Zarpara; Kachchh, Gujarat, India (n = 84) 34:High (2.3 ± 0.87 ppm) 50:Low (0.84 ± 0.38 ppm) 11–13 Questionnaire standardized with 97% reliability rate in relation to the Stanford-Binet Intelligence Scale Evaluation by ion selective electrode method in water and urine samples Paired sample T test The average IQ level of schoolchildren from the low F villages was (97.17 ± 2.54), which is significantly higher (p ≤ 0.001) than (92.53 ± 3.13) of schoolchildren from the high F villages; p ≤ 0.001 High Wang et al. (2007) Cross-sectional Shanxi Province, China (n = 449) 253: High (8.3 ± 1.9 ppm) 196: Low (0.5 ± 0.2 ppm) 8–12 Combined Raven's Test The Rural in China (CRT-RC) Evaluation by ion selective electrode method in water and urine samples Comparison of mean IQ of children in high (100.5 ± 15.8) and low F (104.8 ± 14.7) areas showed a significant difference; p < 0.05 Low Wang et al., (2008) Cross-sectional Shehezi, Xinjiang Province, China (n = 230) 147: High (> 1.0 ppm) 83: Low (≤ 1.0 ppm) 4–7 Wechsler Preschool and Primary Scale of Intelligence (WPPSI) guidelines Evaluation by ion selective electrode method in water samples There was a significant difference in IQ in the endemic area of fluoride concentration (95.64 ± 14.34) compared to the control area (101.22 ± 15.84); p < 0.05 High Wang et al., (2006) Cross-sectional Yuncheng City, Shanxi, China (n = 368) 202: High (5.54 ± 3.88 ppm) 166: Low (0.73 ± 0.28 ppm) 8–12 Combined Raven's Test for Rural China (CRT-RC) Evaluation by ion selective electrode method in water and urine samples The IQ in the control group (111.55 ± 15.19) were higher than those of the high fluoride area (107.46 ± 15.38), and the difference was statistically significant, p < 0.01 High Xiang et al., (2003) Cross-sectional Wamiao, Xinhuai, Jiangsu Province, China (n = 512) 222: High (2.47 ± 0.79 ppm) 290: Low (0.36 ± 0.15 ppm) 8–13 Combined Raven's Test for Rural China (CRT-RC) Evaluation by ion selective electrode method in water and urine samples The mean QI score of high F village (92.02 ± 13.00) was found to be lower than the mean QI score of low F village (100.41 ± 13.21); p < 0.01 Low Yu et al., (2018) Cross-sectional Tianjin, China (n = 2886) 1250: High (2.00 ± 0.75 ppm) 1636: Low (0.50 ± 0.27 ppm) 7–13 Combined Raven's Test–The Rural in China (CRT-RC2) Evaluation by ion selective electrode method in water and urine samples Student's t-test or Wilcoxon test was used to compare the difference of continuous variables, and Chi-square test was applied to compare the difference of categorical variables The average IQ score was 107.4 ± 13.0 in the normal-fluoride exposure group, which was statistically higher than the mean level of 106.4 ± 12.3 in the high fluoride exposure group; p = 0.036 Low Zhao et al., (1996) Cross-sectional Sima village, Shanxi and Xinghua village, China (n = 320) 160: High (4.12 ppm) 160: Low (0.91 ppm) 7–14 Rui Wen Test Manual N/I N/I There was a significant difference in IQ in the endemic area of fluoride concentration (97.32 ± 13.00) compared to the control area (105.21 ± 14.99); p < 0.02 High
IQ, Intelligence Quotient; F, fluoride; N/I, no information; SPADNS (sulfo phenylazo dihydroxy naphthalene disulfonic acid).
The quality of the studies was assessed based on risk of bias, confounding factors, and random occurrence. Eight studies were considered of low methodological quality and were classified as high risk of bias[
For "Control group acceptable", the item "Definition of controls" presented two articles with minor problems (+) because they did not report the F concentration of the control group. Regarding "Matching/Randomization", nine studies did not mention randomization, but did the matching, being considered as a minor problem (+). However, two articles did not mention randomization or pairing, being considered as a major problem (++).
The domain "Quality of measurements and outcomes", the item with the most serious issues was the "Blindness", as 18 studies did not adopt any kind of blinding, followed by "Quality control", with eight studies that did not describe the measurement method. Table 2 presents the risk assessment of bias of the 27 eligible articles.
Table 2 Quality assessment of the studies included in the review.
Guideline Checklist Aravind et al., 2016 Chen et al., 1991 Eswar et al., 2011 Guo et al., 1991 Hong et al., 2001 Karimzade et al., 2014 Khan et al., 2015 Kundu et al., 2015 Lu et al., 2000 Nagarajappa et al., 2013 Poureslami et al., 2011 Qin et al., 2008 Razdan et al., 2017 Saxena et al., 2012 Sebastian and Sunitha, 2015 Seraj et al., 2012 Sharma et al., 2009 Shivaprakash et al., 2011 Sudhir et al., 2009 Trivedi et al., 2007 Trivedi et al., 2012 Wang et al., 2007 Wang et al., 2008 Wang et al., 2006 Xiang et al., 2003 Yu et al., 2018 Zhao et al., 1996 Study design appropriate to objectives? Objective common design Prevalence Cross-sectional Prognosis Cohort Treatment Controled trial Cause Cohort, case-control, cross-sectional 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Study sample representative? Source of sample 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 + 0 0 0 Sampling method 0 0 ++ 0 0 0 ++ ++ 0 0 0 0 0 0 ++ ++ ++ 0 0 ++ ++ 0 ++ 0 0 0 0 Sample size 0 + + 0 + ++ + + + + 0 0 0 + + + + + 0 + ++ 0 + + 0 0 + Entry criteria/exclusion 0 0 0 0 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 + 0 0 0 0 0 Non-respondents 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Control group acceptable? Definition of controls 0 0 0 + 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 + 0 0 0 Source of controls 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Matching/randomization + 0 + 0 + 0 0 + 0 0 0 + 0 0 ++ + + 0 0 + + 0 ++ 0 0 0 0 Comparable characteristics 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Quality of measurements and outcomes? Validity 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 ++ 0 0 0 0 0 0 0 0 0 0 Reproducibility 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 + 0 0 0 0 0 0 0 0 0 0 Blindness ++ ++ ++ 0 ++ ++ ++ ++ 0 ++ 0 0 0 ++ 0 0 ++ ++ ++ 0 ++ ++ ++ ++ 0 ++ ++ Quality control 0 + 0 + 0 0 0 + 0 + + ++ 0 0 0 0 + + 0 0 0 0 0 0 0 0 ++ Completeness Compliance 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Drop outs 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Deaths NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA Missing data 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 + 0 0 0 Distorting influences? Extraneous treatments 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Contamination NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA Changes over time 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Confounding factors 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Distortion reduced by analysis 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 Summary questions Are the results erroneously biased in certain direction? No No Yes No No No No Yes No No No Yes No No No No Yes No No No Yes No Yes Yes No No Yes Confounding: Are there any serious confusing or other distorting influences? No No Yes No No No No Yes No No No Yes No No No No Yes No No No Yes No Yes Yes No No Yes Chance: Is it likely that the results occurred by chance? No No Yes No No No No Yes No No No Yes No No No No Yes No No No Yes No Yes Yes No No Yes
The assessment of the level of certainty of the evidence was conducted through a narrative synthesis following the GRADE parameters for systematic reviews. The level of evidence of the studies was very low, both for studies evaluating IQ impairment and for the only study assessing other neurological manifestations, due to observational nature of the study protocol, as well as due to methodological inaccuracy. For the studies that evaluated IQ impairment, a serious risk of bias was observed. Regarding the study evaluating neurological manifestations other than IQ impairment, it also presented a highly suspicious publication bias, given that the measurement of these manifestations was done by the application of a questionnaire with unknown information about validation and without precise details for their reproduction.
Although, a narrative synthesis does not provide precise estimates, nor measure of effects, it was concluded that the level of evidence of the studies taken together is not strong enough to affirm that the high F exposure may produce a neurological damage in children. Results are represented in Table 3.
Table 3 GRADE evidence profile table.
Certainty assessment Impact Certainty Importance No. of studies Study design Risk of bias Inconsistency Indirectness Imprecision Other considerations 26 Cross-sectional Not serious Not serious Not serious Seriousa None The IQ was assessed in 9930 patients. Three studies did not present significant differences between the group exposed to high fluoride and the control group; 24 studies showed significant changes for the IQ score (Lower IQ scores for High Fluoride Exposures—1.5 to 8.3 ppm) ⨁◯◯◯ VERY LOW CRITICAL 1 Cross-sectional Not serious Not serious Not serious Seriousa Publication bias strongly suspectedb The neurological manifestation was assessment in 1145 patients. There were no neurological manifestations in children living in villages with low fluoride exposure; in villages with high exposure, 9.48% of the children had headache, 1.21% insomnia and 3.23% lethargy ⨁◯◯◯ VERY LOW CRITICAL
Ten studies[
Graph: Figure 2 Forest plot of meta-analysis for ten studies (I2 = 77%). The association between chronic exposure to fluoride and cognitive deficit. CI, confidence interval; M-H, Mantel–Haenszel method. The figure was created using Review Manager v. 5.3 software (https://training.cochrane.org).
Graph: Figure 3 Funnel plot of meta-analysis for ten studies (I2 = 77%). The association between chronic exposure to fluoride and cognitive deficit (p < 0.001). The figure was created using Review Manager v. 5.3 software (https://training.cochrane.org).
After performing the sensitivity analysis, three studies were identified as a possible cause of publication bias[
This systematic review and meta-analysis gathered evidence showing that, following the WHO classification of low and high levels in the drinking water, exposure to low/adequate water F levels is not associated with any neurological damage, while exposure to high levels is. The level of evidence for this association, however, was considered very low. Furthermore, the IQ deficit was reported in the marjority of the primary studies identified, and only one article reported others neurological manifestations.
Systematic reviews aim to gather all the available evidence in the literature to answer a guiding question according to predefined eligibility criteria. It uses a well-designed, explicit and systematic methodology to minimize bias, generating reliable results, answers to raised questions and conclusions about certain problems, thus helping in decision making[
Combined with qualitative synthesis, the meta-analysis reunites the quantitative data of the elected studies, thus being able to estimate the effects of the evidence, whether or not it can confirm the individual results of the elected studies of the systematic review[
Despite some variations in the literature on the F concentrations in the drinking water regarded as both effective and safe, it has been often reported that 1 mg/L is the "optimum level"[
The mechanisms by which F can interfere with child neurodevelopment are associated with damage to nervous cells. Evidences suggest that chronic exposure to F in the prenatal and neonatal periods is potentially toxic to the metabolism and physiology of neuronal and glial cells, which leads to changes in processes related to memory and learning[
According to the WHO, neurological disorders are multifactorial clinical conditions that may be characterized by signs and symptoms with different aspects, as physical functioning limitations, behavioral problems, psychosocial limitations, communicative and cognitive impairments[
In this context, aiming to evaluate cognitive functions of people exposed to F, the researchers from the elected studies used IQ test varieties as previously mentioned and due to that, different abilities of cognitive functions are evaluated, not having standardized and homogeneous parameters among the tests. Matzel and Sauce[
The studies included individuals with ages ranging from 6 to 18 years of age. From epidemiological point of view, this is not interesting, because intelligence tests were applied to participants with very different degrees of neurodevelopment. Data extraction indicates that all eligible studies were concentrated in the Asian continent. These data reflect the remarkable influence of the geographical aspect on the epidemiology of clinical manifestations resulting from F exposure. The availability of naturally occurring high concentration fluoridated compounds in drinking water used by rural communities increases their susceptibility to the adverse effects of F. Considering this aspect, a systematic review proposed to evaluate the neurotoxic effects of F from studies conducted specifically in the Chinese territory[
The methodological quality analyses of the studies detected serious problems related to the quality of sample, measurements and outcomes. There were also problems related to the absence of randomization, sample size calculation and blinding, which increase the risk of bias and limit the inference capacity of studies on the neurotoxic effects of F.
Most studies did not assess the individual level of exposure to F, i.e., by urinary F samples. The F concentration in drinking water in regions with high and low F levels was the most reported method. However, there were also studies that used secondary data or did not report the F content in water, which significantly compromises the findings of these investigations. Furthermore, it should be considered that some studies used creatinine-adjusted urinary F concentrations to account for urinary dilution which may cause an additional bias[
Another point worth mentioning is the increased risk of water contamination by other substances in the areas of naturally occurring F. Although some authors consider it unlikely that the effects attributed to F neurotoxicity can be triggered by other contaminants[
Following the parameters of GRADE, the level of evidence was considered as very low even for individuals exposed to high doses of F, due to imprecision problems (Table 3). This result is related to the types of studies included in this systematic review, as the level of evidence in observational studies starts at a very low level, which can only increase if the study meets the other criteria of this evaluation. Despite the large numbers of participants in the analysis, detected problems of inaccuracy can be elucidated by possible methodological disparities in the studies that might interfere in the intelligence quotient (IQ) analysis and neurological manifestations.
Another important limitation to be considered is the predominance of cross-sectional studies in this systematic review. Cross-sectional and ecological studies do not allow the establishment of cause-and-effect relationships. They are useful for investigating the effect of environmental exposures related to acute processes, as the time interval between exposure and measurement of physiological parameters is close. Therefore, cross-sectional studies are not the ideal model to assess the effect of chronic F exposure on a parameter such as human intelligence[
To sum up, despite the elected studies showed an association between F exposure and IQ deficit, this association was only observed for individuals exposed to levels above those regarded as safe, and the evidence certainty for this association is very low. Within the above-mentioned limitations, the results of the present systematic review demonstrated that exposure to fluoridated water at levels recommended by the WHO can be considered as safe, as it is not associated with IQ impairment.
Although the findings of this meta-analysis indicated that IQ damage can be triggered only by exposure to F at levels that exceed those recommended as a public health measure, the high heterogeneity observed compromise the final conclusions obtained by quantitative analyses. Thus, based on the evidence available on the topic, it is not possible to state neither any association or the lack of an association between F exposure and any neurological disorder.
R.R.L. and N.C.F.F.: Conceptualization and Supervision; N.C.F.F.: Software and Data curation; G.HN.M., M.O.P.A., M.K.M.F., B.P. and L.O.B.: Writing—Original draft; J.P.P. and M.A.R.B.: Validation; J.P.P., M.A.R.B. and R.R.L.: Writing—Reviewing and Editing.
This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Code 001. The funder was not involved in the design of the study, data collection, analysis, and interpretation of the data and the writing of the manuscript. The APC was funded by Pró-Reitoria de Pesquisa e Pós-graduação da Universidade Federal do Pará (PROPESP-UFPA).
All the data is available within the article and on the supplementary materials.
The authors declare no competing interests.
Graph: Supplementary Information 1.
Graph: Supplementary Information 2.
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Graph: Supplementary Information 5.
• F
- Fluoride
• WHO
- World Health Organization
• IQ
- Intelligence Quotient
• PRISMA
- Preferred reporting items for systematic reviews and meta-analyses
• NA
- Not Applicable
• CI
- Confidence Interval
• GRADE
- Grading of Recommendations Assessment, Development and Evaluation
• WPPSI
- Wechsler Preschool Guidelines and Primary Intelligence Scale
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By Giza Hellen Nonato Miranda; Maria Olímpia Paz Alvarenga; Maria Karolina Martins Ferreira; Bruna Puty; Leonardo Oliveira Bittencourt; Nathalia Carolina Fernandes Fagundes; Juliano Pelim Pessan; Marília Afonso Rabelo Buzalaf and Rafael Rodrigues Lima
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