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.
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.