Commentary Neurodevelopmental Disorders

NTP study does not support fluoride as a neurotoxin

Publication reviewed:

An Evaluation of Neurotoxicity Following Fluoride Exposure from Gestational Through Adult Ages in Long-Evans Hooded Rats

McPherson CA, Zhang G, Gilliam R et al. — Neurotoxicity Research. 2018 February 5. Epub ahead of print

Section of dorsal lobe from NTP study not supporting fluoride as a neurotoxin

Background on fluoride as a neurotoxin review: The National Toxicology Program (NTP) released a systematic review, the Effects of Fluoride on Learning and Memory in Animal Studies, in 2016 in response to reported association between high levels of naturally occurring fluoride in water and lower IQ. Of 68 studies reviewed in detail, only 32 studies were available for the analyses to generate conclusions due to serious risk of bias and incomparable measurements and designs used across the rest of the studies. These studies showed low-to-moderate confidence for a pattern of findings suggestive of fluoride’s effect on learning and memory. The NTP review group identified the following issues in the available literature and declared an intent to fill data gaps by conducting laboratory studies in rodents in the near future.

  • Very few studies assessed learning and memory effects in experimental animals at exposure levels near 0.7ppm and had information on alternative sources of fluoride (i.e. food, water supply) available, thus relevance of the findings to human exposure levels in the optimally fluoridated communities (0.7ppm fluoride concentration) is unknown.
  • The outcome endpoint in the majority of studies was a simple latency measurement of learning or memory in the final training session rather than an evaluation of the acquisition of the task to demonstrate learning. Thus, interpretation of the data is hindered by inability to exclude alterations from baseline levels or differences in motor-related performance over the training session as contributing factors.
  • In many studies, there was a lack of reporting of 1) randomization and blinding, 2) specification of test methodologies to assess the outcomes, and/or 3) controlling of confounders such as litter effects, sex, life-stage at exposure, and duration of exposure.  Studies also appeared statistically underpowered to detect a <10% or <20% change from controls for most behavioral endpoints.

Methods: The current study led by the Neurotoxicology Group of the NTP Laboratory addressed the previously identified methodological/design issues in the literature successfully. Specifically, the methodological improvements are notable in the following areas:

  • The fluoride exposures simulated human fluoride exposures by the use of equivalent fluoride doses and establishing a separate route of exposures from diet (20.5ppm vs. 3.24ppmF) and drinking water (0, 10, or 20ppm). Fluoride concentration of 20ppm in rat’s drinking water was equivalent to 4ppm, the US EPA’s current maximum contaminant level, based on the conventional wisdom that a 5-fold increase in dose is required in animals to achieve comparable human serum levels. The exposure levels were validated by assessing the fluoride deposition and accumulation in brain and bone (femur) in addition to fluoride levels in plasma and urine.
  • The experiment (exposure to fluoridated food and water, available ad libitum) began on gestational day 6 and continued throughout lactation. Male pups were observed through adulthood (postnatal day >90).
  • The neurobehavioral endpoint in male pups was measured in various domains: Learning, memory, motor, sensory function, depression, and anxiety. Learning and memory were also evaluated across different tests and in reversal trials and demonstrated acquisition over sessions examining a number of different aspects of performance.
  • Additional effects reported for fluoride exposure that may influence behavior were examined (i.e. thyroid hormone levels, kidney, liver, reproductive system histopathology, and neuronal and glia morphology in the hippocampus) to obtain a better understanding of observed effects.
  • To minimize biases, randomizations and blinding are sufficiently implemented and documented along with detail description of test procedures. Many of behavioral tests were video captured for detail analysis.
  • The authors statistically determined group sizes to sufficiently detect significant differences (p<0.05) between experimental and control groups.

Summary Findings and Public Health Implications:

  • Developmental exposure to fluoride from drinking water and diet beginning on gestational day 6 were associated with elevated internal fluoride levels in brain and femur as well as plasma and urine of male rat offspring. A differential absorption of fluoride between water and food was also demonstrated.
  • Fluoride exposure at the levels examined in this study was not found to alter motor performance or learning and memory in the test paradigms assessed or alter thyroid hormone (T3, T4, or TSH) levels or produce neuronal damage or glia reactivity in the hippocampus, or histological damage in heart, kidney, or liver. The only exposure-related effect that they found was mild hyperanalgesia and mild inflammatory response in the prostate.
  • This latest research on fluoride and neurobehavioral health overcame many limitations and weaknesses of previous studies and demonstrated 1) relationships between developmental fluoride exposures from water & diet and fluoride levels in various tissues and specimens in offspring and 2) no exposure-related differences in motor, sensory, or learning and memory performances in rats.
  • When the 2006 NRC report suggested a need for more research on neurotoxicity and neurobehavioral effects of fluoride, the committee was basing this on available data from human studies conducted in fluoride-endemic regions showing high-risk of bias but some consistencies in the findings. Meanwhile data from available molecular and cellular studies could be interpreted to suggest potential changes in nervous system functions but only a few animal studies reported unsubstantial magnitude of alterations in the behavior of rodents after fluoride treatment. Since then, more research (including epidemiological and animal studies) were published, yet the majority of studies still suffer from various sources of risk for bias and the accumulated evidence remains mixed.
  • The findings of this well-controlled animal study directly address previous concerns regarding potential biological plausibility of fluoride as a neurotoxin. The findings provide valuable information and assurance that low-level fluoride exposures from water and diet that are equivalent to the levels allowed in the US does not result in clinically adverse neurobehavioral function or pathological effects in various organs.
Appraisal Neurodevelopmental Disorders

Appraisal of prenatal fluoride IQ study

Publication reviewed:

Prenatal fluoride exposure and cognitive outcomes in children at 4 and 6-12 years of age in Mexico

Morteza Bashash, Deena Thomas, Howard Hu et al. — Environmental Health Perspectives


The authors analyzed data from the Early Life Exposures in Mexico to Environmental Toxicants (ELEMENT) project to examine if prenatal exposure to fluoride is associated with declined childhood intelligence.

Subjects: The ELEMENT project recruited women who were 14 or less weeks pregnant and free of medical, mental disorders, high-risk pregnancy as well as use of recreational alcohol and drugs use at three clinics of the Mexican Institute of Society Security in Mexico City that serve low-to-moderate income populations.

Exposure measure: Prenatal F exposure was measured as an averaged value of maternal creatinine-adjusted urinary fluoride concentrations (maximum three and minimum one spot urine sample[s] were archived for each woman).

Outcome measure: Offspring’s neurocognitive outcomes were measured as the General Cognitive Index (GCI) score at 4 years and IQ score at 6-12 years.

Covariates: Maternal age, education, marital status, birth order, birth weight, gestational age at delivery, maternal smoking, maternal IQ (estimated using selected subtests of the WAIS-Spanish measured at 6-12 months after birth), and cohort ID. The specific-gravity adjusted urinary fluoride values obtained from offspring at 6-12 years of age were included in the model for prenatal F exposure and IQ.

The study found:

  • Significant correlation between GCI and IQ scores.
  • No significant correlation between prenatal creatinine-adjusted urinary fluoride and offspring’s specific-gravity adjusted urinary fluoride levels at 6-12 years of age.
  • Prenatal creatinine-adjusted urinary fluoride level and GCI at 4 years of age showed mild linear relationship: 0.5mg/L increase in prenatal urinary fluoride was associated with 3.15-point drop in GCI scores (p=0.01, N=287).
  • Prenatal urinary fluoride level and IQ at 6-12 years of age showed mild curvilinear relationship: 1) no clear association between prenatal urinary fluoride and IQ scores below approximately 0.8mg/L urinary fluoride levels, and 2) a negative association above prenatal urinary fluoride 0.8mg/L. The authors found 0.5 mg/L increase in prenatal urinary fluoride was associated with -2.5 points in IQ scores (p=0.01, N=211).
  • Sensitivity analyses conducted for the subsets of data (N<200) indicated the following:
  • The negative associations between prenatal urinary fluoride and GCI or IQ persisted with further adjustment for other potential confounders (family possession, maternal bone lead and blood mercury levels). The effect estimates were attenuated when family possession (SES proxy) and maternal blood mercury values were adjusted in the models relative to unadjusted models, while all of the effect estimates were higher in the subset of subjects with available data of SES, maternal bone lead and blood mercury levels.
  • There was no clear, statistically significant, association between contemporaneous children’s urinary fluoride and IQ at 6-12 years of age either unadjusted or adjusted for maternal urinary fluoride during pregnancy.
Prenatal fluoride IQ study plot


  • A – Strong methodology and unbiased, appeared in peer-reviewed in respected science journal
  • B – Strong methodology and unbiased, not in peer-reviewed journal
  • C – Weak methodology and/or biased
  • F – Not a scientific finding


  • High – All the peer-reviewed research to date support these findings, and a significant amount of research has been done in this area.
  • Medium – Most, but not all, peer-reviewed research to date support these findings, and a significant amount of research has been done in this area.
  • Low – Not a lot of research has been done in this area, or some, but not most, other peer-reviewed research supports these findings.
  • Not Supported – No other studies support this study’s conclusions.
  • Contradicted – Most studies contradict this study’s conclusions.


• Data of childhood neurocognitive outcomes collected in the longitudinal birth cohort research project with various maternal and perinatal covariates data including maternal IQ, education, smoking, and birth outcomes.
• Although sample size of subset data was small, the authors were able to check the effect of SES (although proxy), maternal lead and mercury in the investigated association.
• Urinary fluoride data were adjusted by creatinine and specific gravity for variation in urinary dilution.
• The authors report detail methods and results.


• Limitation of urinary fluoride as a biomarker of fluoride exposure: Urinary fluoride level fluctuates during the day and reflects only recent exposures, and it is unknown if fluoride level measured in spot urine samples during pregnancy is a good measure of prenatal fluoride exposure for fluoride’s neurotoxic effect in children.
• No fluoride data other than urinary fluoride levels were collected or available, thus we do not know the source of fluoride exposure (i.e. fluoride in water, salt, toothpaste, environmental or industrial F exposure etc.) or how such external dose of exposures reflected internal F dose (in urine).
• Lack of data on iodine in salt, other nutritional intake and dietary practices that could influence pregnancy, urinary excretion, and fetus-child cognitive development, and environmental neurotoxicants such as arsenic.


This study had an advantage of using the data from the Early Life Exposures in Mexico to Environmental Toxicants (ELEMENT) project, which collected data longitudinally from pregnancy to childhood on the exposures to environmental toxicants such as lead and mercury and childhood neurocognitive outcomes. However, this study on fluoride was not planned prior to the ELEMENT data collection, therefore the authors had limited ability to validate fluoride exposures and relied solely on fluoride concentrations in spot urine samples.

Biomarkers of fluoride exposure such as urinary and serum fluoride are considered a marker of recent fluoride exposures. Urinary fluoride fluctuates, thus the value can be influenced by the timing of exposure and sampling, and we do not know if the level captured in a spot urine sample reasonably reflects the usual and/or long-term exposures to fluoride during the prenatal period. In this study, some of the subjects had three spot (second morning void) urine samples obtained from each of trimesters, but approximately 80% of subjects provided only one or two spot urine samples during pregnancy. While the authors adjusted fluoride concentration in spot urine samples with creatinine and specific gravity for dilution factor, there are a number of factors that affect fluoride uptake, retention, and excretion. It is anticipated that fluoride metabolisms would change with gestation, yet we do not know how it changes during the different phases of pregnancy. The authors reported a mean prenatal urinary fluoride value of 0.9 mg/L among the study subjects and thought the value was within normal range, however there are limited population-based data available to determine the reference value of urinary fluoride concentrations during pregnancy. EPA considers urinary fluoride as Group I biomarker for fluoride-related neurotoxicity because there is a lack of established methodology of sampling (i.e. first morning vs. second morning void, spot urine vs. 24-hour urine sampling), analytic strategies, and established relationship between external dose (i.e. supplemental fluoride dose, fluoride concentration in water), internal dose (i.e. in urine), and biological endpoint (i.e. neurotoxicity).

The negative association between prenatal urinary fluoride level and cognitive ability found at 4 and 6-12 years of age in the offspring, no association found between children’s urinary fluoride and IQ at 6-12 years of age, and no significant effect of prenatal urinary fluoride below 0.8 mg/L on childhood IQ in non-linear relationship found in this study all corroborate with a portion of the published literature. A largely spread scatter plot distribution suggests that prenatal fluoride exposure may be a small portion of variations that explain the relationship. We agree with the authors on that additional studies are needed to examine if the association found in this study are replicated in other study populations and if fluoride exposure during pregnancy is indeed a critical window of susceptibility for population’s neurocognitive health. There are only a few studies of relatively small observational studies from Mexico that looked at the fluoride exposure in pregnant women and its association with neurobehavioral outcomes in their offspring (Bashash et al 2017, Valdez Jiménez et al. 2017, and unpublished thesis of Thomas 2014). We also desperately need to learn more about fluoride metabolism during pregnancy and how prenatal urine fluoride concentrations are related to external fluoride doses such as fluoride in drinking water.

Appraisal Endocrine Disorders

Fluoride thyroid exposure study indicates no association

Publication reviewed:

Fluoride exposure and indicators of thyroid functioning in the Canadian population: implications for community water fluoridation

Barberio AM, Hosein FS, Quiñonez C, McLaren L — Journal of Epidemiology and Community Health

The Fluoride Science editorial board appraised a key fluoride thyroid research study finding no association between fluoride and hypothyroidism indicators in Canada.

Fluoride thyroid study used Canadian Health Measures Survey data


The authors conducted this cross-sectional study using data from the latest (2009-2013) Canadian Health Measures Survey (CHMS) to examine the association between fluoride exposure and thyroid outcomes.

Both thyroid outcome and fluoride exposure were measured at individual level as follows:

Fluoride exposure measures

  • Fluoride level in spot urine sample
  • Fluoride concentration in primary drinking water (mg/L) + self-report on residential history (at least 3 years of consecutive residence)
  • Self-reported use of fluoride containing toothpaste and or mouthwash as well as history of fluoride treatment at dental office

Thyroid outcome measures

  • Self-reported diagnosis of thyroid condition (yes/no)
  • Serum thyroid stimulating hormone (TSH) level (low/normal/high)

No significant association was found between fluoride exposure measured in urine and tap water samples and self-reported diagnosis of a thyroid condition or altered (low or high) TSH levels. Fluoride exposure, in a time and place where multiple sources of fluoride including community water fluoridation exist, is not associated with impaired thyroid functioning in a representative sample of the Canadian population.

Canada has fluoridation program guidelines that are similar to the US, and the findings are relevant to the US and other countries with similar populations and CWF schemes.


  • A – Strong methodology and unbiased, appeared in peer-reviewed in respected science journal
  • B – Strong methodology and unbiased, not in peer-reviewed journal
  • C – Weak methodology and/or biased
  • F – Not a scientific finding


  • High – All the peer-reviewed research to date support these findings, and a significant amount of research has been done in this area.
  • Medium – Most, but not all, peer-reviewed research to date support these findings, and a significant amount of research has been done in this area.
  • Low – Not a lot of research has been done in this area, or some, but not most, other peer-reviewed research supports these findings.
  • Not Supported – No other studies support this study’s conclusions.
  • Contradicted – Most studies contradict this study’s conclusions.


The association between fluoride exposure and thyroid outcomes was examined using the individual-level data collected from the nationally representative sample of Canadian and adjusted for potential demographic confounders (age, gender, household education and income).

In addition to self-reported data of thyroid condition diagnosis and indicators of fluoride exposures along with the sample of tap water to determine fluoride concentration of subjects’ primary drinking water, the authors used biomarkers of contemporary fluoride exposure (urinary fluoride level) and thyroid functions (serum TSH, thyroid stimulating hormone).

Data from the latest Canadian national health surveillance program (2009-2013), which implements extensive data validation and quality control measures


The use of self-reported thyroid condition diagnosis is subject to misclassification. Spot urine samples do not provide cumulative measures of fluoride exposure over time.

Cross-sectional study design, thus causation could not be discerned.

Potential confounders such as iodine intake (although Canada has adopted mandatory iodisation of all food-grade salt since 1949 and reportedly has adequate population iodine status), smoking, and family history of thyroid disease were not adjusted.


As a previous study on this topic by Peckham and colleagues reported an ecological association between fluoride and hypothyroidism (both exposure and outcome measured at medical practice-level in England), the findings of this study, which used data of exposure and outcomes measured at the individual-level, offers a design that overcomes the concern that an ecological fallacy is at play. Although each method of measuring fluoride exposure has some underlying limitations—misclassification due to fluctuation of spot urine sample; reporting/recall bias in self-reported use of fluoride products/fluoride treatment; unknown amount of actual fluoride exposure from primary drinking water—the use of multiple sources of information is a good strategy to identify individual-level fluoride exposure.
The overall prevalence of hypothyroidism or hyperthyroidism at the population-level is low (less than 5%) in Canada, thus the raw number of subjects who had altered serum TSH level or self-reported diagnosis of thyroid condition was small (nearly 95% of subjects had normal TSH level or no diagnosis of thyroid condition).
The study provides sound individual-level evidence that fluoride exposure levels experienced by the general population do not confer risk for disrupted thyroid function.

Commentary Endocrine Disorders

Fluoride and diabetes study is a muddle of models

Publication reviewed:

Community water fluoridation predicts increase in age-adjusted and prevalence of diabetes in 22 states from 2005 and 2010

Fluegge K.  – Journal of Water and Health. 2016;14(5):864-77

A Muddle of Models


The paper about fluoride and diabetes by Fluegge (J Water and Health 2016) examined county level estimates of diabetes prevalence and incidence and their association with community water fluoridation status at the county level in the United States in 2005 and 2010. Several statistical models were presented and the author concluded that the type of chemical used in the fluoridation process had an important role in predicting the incidence and prevalence of diabetes in a county. This is an awkward conclusion because the chemical formulation used to fluoridate a water system to an optimal level does not impact on the bioavailability of fluoride in consumer tap water.1-5 A further look into the methodology that comprised the statistical modeling reveals several key problems.

CDC data used in fluoride and diabetes study

The basic framework of the analysis makes use of generalized estimating equations (GEE). This approach has been devised to address a key assumption in the world of generalized linear modeling: the need for independence of observations. GEE allows an investigator to examine multiple observations that may be correlated and thereby impair the assumption of statistical independence. So in longitudinal studies, GEE allows an investigator to make full use of data that is collected over time for the same study subject, by modeling the correlation within a subject and making an adjustment to the parameter estimate to account for the lack of independence. GEE allows the investigator to use one model to estimate the effect of the independent variables on multiple outcomes. In this case, Fluegge used the GEE approach to model county-level estimates of both incidence and prevalence of diabetes.


The incidence and prevalence estimates for diabetes were developed by the US Centers for Disease Control and Prevention (CDC) based on self-reported information collected in a telephone survey, the Behavioral Risk Factor Surveillance System (BRFSS).6,7 To get county-level estimates of diabetes incidence and prevalence of diabetes for every county in the US, CDC does something known as Small Area Estimation. Because of sampling limitations and costs, not all counties have robust data to make a sound estimate from the BRFSS. Using socio-demographic county data, CDC estimates what incidence and prevalence would be given the socio-demographic profile of counties where robust data exist.  This allows data from the BRFSS to give an estimate to each county based on data from the US Census Bureau on age, sex, race, and Hispanic origin.

Here is how the CDC describes the method: “The county-level estimates for the over 3,200 counties or county equivalents (e.g., parish, borough, municipality) in the 50 US states, Puerto Rico, and the District of Columbia (DC) were based on indirect model-dependent estimates using Bayesian multilevel modeling techniques.8 This model-dependent approach employs a statistical model that “borrows strength” in making an estimate for one county from BRFSS data collected in other counties. Multilevel Poisson regression models with random effects of demographic variables (age 20–44, 45–64, ≥65; race; sex) at the county-level were developed. State was included as a county-level covariate.”8

The accuracy of self-reported diabetes and the ability to estimate incidence using the BRFSS also has limitations. See additional note section below.


Fluegge used several variables to create measures of fluoride exposure. The intent seems to be to create a county-level profile of fluoride exposure for the public water systems in a county, However, the use of multiple variables for fluoride exposure in the statistical model make the regression coefficients difficult to interpret since a given county will be characterized by several variables that are linked together. This is known as effect modification. In the Fluegge model, there are variables for “amount of added fluoride”, “fluoridation chemical” and “years of fluoridation”. To interpret the statistical model one must look at how these variables act together.

Fluegge computed “amount of added fluoride” different ways and showed models for these. Also a separate model was presented for natural fluoride. Most of the models showed that “amount of added fluoride” was associated with a higher prevalence and incidence of diabetes. In all models, the “number of years a system fluoridated” was associated with a lower prevalence and incidence of diabetes.

The “fluoridation chemical” variable revealed that sodium fluoride was associated with higher prevalence and incidence of diabetes, but fluorosilicic acid and sodium fluorosilicate were associated with a lower prevalence and incidence of diabetes in all models. So while one fluoridation chemical was associated with higher prevalence and incidence of diabetes, this effect would be decreased if the water system was fluoridated for a large number of years.

Several perplexing observations arise when one tries to interpret these results. First, diabetes is a chronic disease and one would expect that prolonged exposure to a hypothesized pathogen would be important. In the Fluegge models, number of years of fluoridation is always associated with lower prevalence and incidence of diabetes. Second, the sensitivity analysis (shown in Table 4) reveals evidence that the variable for “Added fluoride” is problematic. In describing the sensitivity analysis, Fluegge states that 32 counties had natural fluoride levels that were above the optimal level. The computation for the variable “Added fluoride” for these counties was therefore a negative value. The sensitivity analysis removed the data from these 32 counties and is presented in Table 4. The table shows model results for exposure in mg and exposure in ppm. The results are very different for these two approaches to the variable “added fluoride”. For exposure in mg, “added fluoride” is associated with a higher prevalence and incidence of diabetes; for exposure in ppm, “added fluoride” is associated with a lower prevalence and incidence of diabetes. This warrants a closer look at how the variable is computed.

The variable for “added fluoride in mg” is based on county-level water delivery data from the US Geological Survey. Fluegge computed per capita fluoride consumption estimates for each county and the derived values are shown in two histograms (one for 2005 and one for 2010) in the top portion of Figure 5. The histograms are not directly comparable because the scales for the x and y axes are different. The range appears to go from -1 to all the way to 4 mg. How did we get here? Fluegge states that USGS estimates that an individual uses 302.8-378.5 L of water per day. Fluegge reasons that an individual drinks 1.9 L of water daily and this means that “Dividing 1.9L by 302.8 (~0.625%) and 378.5 (=0.5%) liters yields an approximate range of the proportion of the per capita supply that is actually ingested.” Fluegge then goes on to use 0.625 and 0.5 to compute the amount of water that a person drinks, given the USGS water delivery data for the county. This means that a person could be estimated to drink more than 1.9 L per day which seems unlikely, and using estimated consumption level to generate mg of fluoride consumed. As a result, Fluegge is creating a measure that has no validity and inserting it into the GEE model as fluoride exposure. This makes the regression model uninterpretable. Alternatively, the “added fluoride in ppm” variable is simply the level of fluoride in the water system measured as parts per million.

Given all the limitations in the data, the model presented in Table 4 listed as M=2 (exposure in ppm) is the most attractive because it is the most simple. It removes the counties that had natural fluoride levels that exceeded the optimal level and it uses the most straightforward measure of amount of fluoride in a water system. That model does not present a basis for concern that adding fluoride to drinking water is associated with a higher prevalence or incidence of diabetes. If anything, it appears that fluoride in drinking water is associated with a lower prevalence and incidence of diabetes.

Bottom line: Garbage in = Garbage out.


Biologic basis for hypothesis that fluoride influences incidence and prevalence of diabetes

The literature cited does not establish a compelling argument for a role of fluoride in the pathophysiology of diabetes.

Behavioral Risk Factor Surveillance System (BRFSS) and Diabetes

To have diagnosed diabetes in BRFSS, respondents answer “yes” to the question “Has a doctor ever told you that you have diabetes?” The diagnosis can be subject to recall bias and misinterpretation and does not distinguish between type 1 and type 2. Furthermore, those who have undiagnosed diabetes are included in the “no diabetes” group. As the author discuss in the Introduction, about 30% of diabetes are reportedly undiagnosed.

While BRFSS has produced additional weights to allow small area estimate for metropolitan and micropolitan statistical areas (MMSA), only 153 and 192 MMSAs met the weighting criteria for the 2005 and 2010 data years, respectively.6,7 County-level estimates are reportedly possible using BRFSS data, but caution is needed in the interpretation as data may not well represent small counties with small number of participants.

Potential for compounding misclassification

Note in table 4, that after removing data from 32 counties to do the sensitivity analysis, the number of counties becomes 759 from 887. This is a reduction of 128. If this is not an error in the manuscript, 32 counties are contributing 128 county-year units to the analysis. This highlights another weakness in the design, that is, CDC derived the estimates with assumption that diabetes patterns can be modeled using a few socio-demographic characteristics in a county. If this assumption is weak for a given county, the data for that county are weak and in this analysis each county contributes two observations for each study year (2005 and 2010). So, up to 4 observations could be flawed for each county that does not fit the assumption.

Additives for fluoridation of public water systems

The author does not provide any background or a rationale of examining the types of fluoridation additives as a confounder of relationship between fluoride in drinking water and diabetes. The type of fluoridation additives is usually determined by various engineering/system characteristics such as system and facility size, feed system used, available installation costs etc. As of 2010, 75%, 10% and 15% of US water systems use fluorosilicic acid (FSA: liquid), sodium fluorosilicate (NaFS or Na2SiF6: powder), and sodium fluoride (NaF: powder/crystalline), and 81%, 13% and 7% of population are served by FSA, NaFS, and NaF, respectively.9 Sodium fluoride is most expensive9 and generally used in a small water system only. According to the information provided in the Result section, more than one additive was used in 19% of counties included in the study. Thus this variable is treated as binary variable (i.e. FSA—yes, no) in regression analyses, which means that one fifth of counties in this study represented more than one category in this variable.

The suggestion that added fluoride and also one type of fluoride additive increase risk for diabetes but natural fluoride or other types of fluoride is protective do not make sense, and there is no plausible biological mechanisms to explain them. If fluoride was a contributing factor to diabetes, one would expect a consistent correlation regardless of the particular form of fluoride. The author makes an argument in Discussion that this finding may imply a future policy change to promote the use of FSA rather than NaF for the prevention of diabetes and the potential cost saving. However, sodium fluoride is the least common fluoridation additive and is used mostly in small US communities.  Thus even if this association was true, the anticipated cost-saving from banning NaF would be limited.


The findings and conclusions on this page are those of the Fluoride Science Editorial Board and do not necessarily represent those of AAPHD. These reviews are not mandates for compliance or spending. Instead, they provide information and options for decision makers and stakeholders to consider when determining which programs, services, and policies best meet the needs, preferences, available resources, and constraints of their constituents.

Document last updated December 16, 2016

  1. Urbansky ET. Fate of fluorosilicate drinking water additives. Chem Rev. 2002;102(8):2837-54
  2. Maguire A, Zohouri FV, Mathers JC et al. Bioavailability of fluoride in drinking water: a human experimental study. J Dent Res. 2005;84(11):989-93
  3. Whitford GM, Sampaio FC, Pinto CS et al. Pharmacokinetics of ingested fluoride: lack of effect of chemical compound. Arch Oral Biol. 2008;53(11):1037-41
  4. McClure FJ. Availability of fluorine in sodium fluoride vs. sodium fluosilicate. Public Health Rep. 1950;65(37):1175-86
  5. Zipkin I, McClure FJ. Complex fluorides: caries reduction and fluorine retention in the bones and teeth of white rats. Public Health Rep. 1951;66(47):1523-32
  6. Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System Summary Data Quality Report. August 25, 2006. Available at
  7. Centers for Disease Control and Prevention. Behavioral Risk Factor Surveillance System. 2010 Summary Data Quality Report. Revised: May 2, 2011. Available at
  8. Center for Disease Control and Prevention. Methodology for County-Level Estimates. Available at
  9. Centers for Disease Control and Prevention. Water Fluoridation Principles and Practices. Water Fluoridation Additives. Atlanta, GA. 2015.
Dental Caries Review

Fluoride mechanism of action

A review of the mechanism of anti-caries action of fluoride

Summary of fluoride mechanism of action: Evidence supports a conclusion that the effects of pre- and post-eruptive fluoride complement each other. Over the lifespan, fluoride inhibits the caries process primarily through its post-eruptive effect on demineralization and remineralizaton. While the attribution of caries resistant teeth and pre-eruptive effect of fluoride in caries prevention is not easily demonstrated, especially in a life-course perspective, fluoride incorporated into developing enamel mineral may offer initial resistance to caries initiation or delay the formation of clinically detectable caries, especially at the surfaces where post-eruptive fluoride is less than effective.

How Dental Caries Develop

It is normal for a wide range of bacteria to be present in the mouth and many are involved with forming biofilm (plaque) on the surfaces of teeth. However, some bacteria can establish levels that can lead to problems. When biofilm bacteria such as mutans streptococci metabolize fermentable carbohydrates (i.e. glucose, sucrose, fructose, or cooked starch), acids are generated as a by-product.1 These acids diffuse through the biofilm and into the porous enamel dissolving calcium phosphate mineral from the tooth surface.1 This process of mineral loss at the tooth surface is what is called “demineralization” and is influenced by multiple risk and protective factors.1 If the demineralization process is not halted or reversed by a “remineralization” process, the carious process creates an enamel lesion and with further progression, eventually results in a cavity.1

How fluoride works in caries prevention and control

Anti-caries mechanisms of fluoride have been elucidated in considerable detail using data from in vitro studies. According to our knowledge base today, fluoride works to prevent and control dental caries through the following two primary mechanisms that affect 1) enamel solubility and 2) reversal of the caries process.

Fluoride mechanism of action with two primary, reduced enamel solubility, and reversal of caries process

Both systemically and topically applied fluoride increase enamel fluoride content as well as ambient fluoride (free fluoride ion presents in saliva and the fluid phase of plaque) in the oral environment. Systemically ingested fluoride, when it is absorbed in the alimentary tract, either is excreted in urine or incorporated into calcified tissues, such as bone and teeth.2 It is well established that fluoride is incorporated into dental apatite crystals during tooth development.3 Fluoride retained during topical application mostly forms calcium fluoride (CaF2) or calcium fluoride-like material,4,5 which is often referred as “loosely bound” fluoride in comparison to fluorapatite or “firmly bound” fluoride, and is the most likely source of fluoride ions during cariogenic challenges.4,5 As fluorapatite has lower solubility than calcium fluoride, firmly bound fluoride is presumably superior to loosely bound fluoride in slowing mineral diffusion within dental tissues.4,5 Laboratory studies found that the levels of enamel fluoride at about 20- 100 ppm in subsurface enamel or at 1,000-2,000 ppm in the outer few micrometers of enamel typically found in optimally fluoridated and non-fluoridated area alone do not provide measurable benefit against acid dissolution.1,6 When fluoride is concentrated into a new crystal surface during remineralization—ambient fluoride bringing calcium and phosphate ions back to partially demineralized crystals and producing fluorapatite-like coating on crystals, Ca10(PO4)6(F)2, — the end-product reportedly has sufficiently high fluoride content (about 30,000 ppm) to reduce enamel solubility against future acid attack.1

Scientific evidence from clinical investigations with regard to a correlation between enamel fluoride content and caries incidence is mixed.5,7-16 Difficulty in demonstrating the effectiveness of tooth-bound fluoride in vivo includes but not limited to the fact that 1) fluoride must be incorporated into those susceptible areas of the teeth (pits and fissures, approximal surfaces, etc.) for the tooth-bound fluoride to effect its caries inhibition,17 while those surfaces are not the candidate for enamel biopsy, 2) presently used fluoride regimens do not deposit significant amounts of firmly bound fluoride into the sound tooth mineral,7 and 3) caries outcomes are usually measured by the presence of visible cavities and dentinal lesions using the DMFT/S index whereas in-vitro studies usually deal with enamel lesions only.5

As cariology and the concept of caries process evolved, the focus of fluoride-induced anti-caries action also has emphasized the enhanced activity of fluoride ion in the oral fluid, specifically in the plaque fluid at the enamel-plaque/biofilm interface, which is more directly related to demineralization and remineralization processes than fluoridated enamel and its solubility.

During the caries process, conditions favoring demineralization of enamel surface occurs at sites that provide an ecological niche where the plaque/biofilm composition gradually adapts to a declining pH environment.3 As previously noted, fluoride present in the oral environment in the biofilm and associated with the surface of teeth acts as a reservoir of free fluoride ion.4,5 Enhanced fluoride ion activity within resting plaque/biofilm under more pH neutral conditions then will increase the driving force for mineral deposition at the porous surface enamel and promote remineralization process.6

Is anti-caries effect of fluoride pre-eruptive or post-eruptive?

Since the discovery of association between fluoride and reduced rate of caries in the population, there has been a question among scientists as well as the public on whether the anti-caries effect of fluoride is preeruptive (“systemic”) or post-eruptive through “topical” fluoride, or both. A few decades after water fluoridation was initiated, researchers found that fluoride may not prevent caries initiation as effectively as caries progression18 and drew attention to fluoride’s anticaries mechanisms during its aqueous phase in order to explain phenomena observed in laboratory and clinical studies.19 When the life-time benefit of fluoride to human health is estimated, the topical, post-eruptive effect may be considered as predominant as preeruptive benefit only applies to children and adolescents. However, these notions do not diminish the importance of a pre-eruptive effect of fluoride, for which we have evidence from in vivo studies.

For example, a pre-eruptive contribution of fluoride may be more important and effective at specific surfaces of the tooth, such as pits and fissures, that are highly susceptible for caries and less likely to receive the benefit of post-eruptive exposure to fluoride compared to other surfaces more exposed to saliva and oral hygiene and less susceptible for caries (i.e. smooth surfaces).19 Clinical investigation conducted in 1970s to determine the effect of fluoride supplementation on caries incidence in children living in non-fluoridated communities documented the formation of a favorable tooth morphology, i.e. shallow and less-retentive pits and fissure on the occlusal surfaces of molar teeth, associated with pre-eruptive fluoride intake,6,11 corroborating previous observations.20

In recent years, Singh and colleagues conducted a study to examine the relative pre- and post-eruption exposure effects of fluoridated water on caries experience among 6-15 year-old Australian children (N=17,773).21-23 Percentage of lifetime exposure to optimally fluoridated water was calculated with respect to the eruption age for the first permanent molars.10-12 They found that maximum caries-preventive effects of fluoridated water were achieved when there were both high pre- and post-eruption exposures.21,22 Furthermore, their data indicated that pre-eruptive exposure to fluoride, especially during the crown completion phase of tooth development, was significantly associated with lower caries in the first permanent molars of children 6-15 years of age.23

The findings of a study conducted by Cho and colleagues on the effect of ceased community water fluoridation (CWF) in South Korea indicated that 11 year-old children who had approximately 4 years of CWF since birth before the CWF cessation had a significantly lower DMFT ratio relative to those children who grew up in the non-fluoridated community (0.68 [95% CI 0.45-0.75]).24 When DMFT ratio was compared between 8 year-olds who had approximately 1 year of CWF since birth before the CWF cessation and those who grew up in a non-fluoridated community, the difference was not significant (0.92 [0.63-1.37]).24

Studies like these that pay attention to estimated length of pre-eruptive exposure to fluoride imply potential benefit of systemic fluoride intake. However, isolating the value of pre-eruptive fluoride from the post-eruptive fluoride effect is not easy in the postfluoridation era of low caries. Furthermore, determining person’s fluoride exposure through residential history can introduce bias. Fluorosis is a condition that clearly indicates that the tooth was exposed to a relatively high level of fluoride during enamel formation. Iida and Kumar used a large national data of US school children (N=16,873) from 1986-1987 to determine the tooth-level association between enamel fluorosis, as a biomarker of pre-eruptive fluoride exposure, and dental caries in first permanent molars.25 The findings indicated that first permanent molar teeth with fluorosis, even including moderate to severe fluorosis, consistently had lower caries experience than did molars without fluorosis both in fluoridated (≥0.7 ppm) and non-fluoridated (<0.7 ppm) communities (adjusted odds ratio [AOR] 0.89, 95% CI 0.74-1.06, and AOR 0.71, 95% CI 0.56-0.89, respectively).25

In summary, evidence supports a conclusion that the effects of pre- and post-eruptive fluoride complement each other. Over the lifespan, fluoride inhibits the process of carious primarily through its post-eruptive effect on demineralization and remineralizaton. While the attribution of caries resistant teeth and pre-eruptive effect of fluoride in caries prevention is not easily demonstrated, especially in a life-course perspective, fluoride incorporated into developing enamel mineral may offer initial resistance to caries initiation or delay the formation of clinically detectable caries, especially at the surfaces where post-eruptive fluoride is less than effective.

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