Skip to main content

Per- and polyfluoroalkyl substances (PFAS) and neurobehavioral function and cognition in adolescents (2010–2011) and elderly people (2014): results from the Flanders Environment and Health Studies (FLEHS)

Abstract

Background

PFAS are persistent, bioaccumulative compounds repelling water, oil and stains which are widely used. There is mounting evidence linking exposure to a range of adverse health outcomes including renal, hepatic, immunotoxic, reproductive, endocrine disrupting and carcinogenic effects. PFAS possibly also induce neurobehavioral and developmental effects. Within Flanders Environment and Health Studies (FLEHS) internal exposure to PFAS and relevant health effects are assessed since 2008.

Results

Adolescents 14–15 y (2010–2011) living in an industrially contaminated area (without known PFAS contamination) and adults 50–65 y (2014) randomly sampled from the general Flemish population using a stratified clustered multi-stage design, were recruited. For the adolescents perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA) were measured in serum, for the adults PFOS, PFOA, perfluorohexane sulfonate (PFHxS), perfluorononanoic acid (PFNA) and perfluorobutane sulfonate (PFBS). In adolescents the Neurobehavioral Evaluation System (NES3) computerized battery of tests developed to study the neurological effects of an exposure to environmental agents was applied. The adults did the Stroop test, the NES3 Continuous Performance Test and the NES3 Digit Span Test. In adolescents sleepiness, masculinity and femininity were assessed via the Epworth Sleepiness Scale and Personal Attributes Questionnaires, respectively. In adolescents PFOA was associated with significantly increased somnolence, and PFOS with a significant inverse association with boys’ femininity and with girls’ masculinity. In adolescents, PFAS were also associated with a marginal decrease in sustained attention (PFOS) and cognitive performance (PFOA) and a significant decrease in short-term memory (PFOS). However, in older adults PFOS was associated with a significant increase in the capacity to pay attention and PFHxS with a significant increase in sustained attention.

Conclusion

Our observations point to neurobehavioral and cognitive effects of PFAS. The neurobehavioral effects might in part result from the changes in sex hormone levels that have been reported to be associated with internal exposure to PFAS. Interestingly, whereas in relation to cognition some adverse effects were recorded for adolescents, for elderly persons our observations rather suggest possible weak positive effects with respect to cognition. Our observations might be in line with the view that PFAS have many, sometimes contrasting health effects.

Background

PFAS were synthesized over 60 years ago as a new class of surfactants repelling water, oil and stains [1]. This allowed production of innovative consumer products such textile coatings, non-stick cookware, electronics, mist suppressants, and firefighting foams [1]. Emissions during its manufacture, usage, and disposal, resulted in the widespread abundance in the environment of this group of chemicals. From a regulatory and toxicological perspective, two substances of this chemical group have received past attention, PFOS and PFOA. Nowadays also much data are published on PFHxS and PFNA.

While being considered as persistent, PFAS are fundamentally different from traditional persistent organic pollutants such as polychlorinated biphenyls (PCBs). They have no aromatic ring in their structures, do not accumulate in fatty tissues and their solubility in aqueous solution is much greater than that of polycyclic aromatic hydrocarbons (PAHs) and polybrominated diphenyl ethers (PBDEs) [2]. PFAS are thermally, chemically, and biologically inert in many circumstances or give rise to terminally persistent degradation products [3], therefore rendering them very useful for certain industrial purposes, but also non-biodegradable and bioaccumulative in the environment and food chains. Many PFAS that leach through the soil are highly mobile in groundwater systems, while others may evaporate and disseminate via the atmosphere [4]. PFAS have been detected in serum of populations in many countries including Belgium (Flanders) [5].

As well as in the environment, PFAS can persist for a long time in the human body, posing a likely threat to human health [6]. The average half-life in humans is estimated to be 5.4 years for PFOS, 3.8 years for PFOA and 8.5 years for PFHxS [7], 3.2 years for PFNA [8]. Lower half-life values (1.77 years for PFOA, 2.87 years for PFHxS and 2.93 years for linear PFOS) have been found in a study on Swedish airport employees [9]. Concerning exposure during the susceptible stage of early life, it is known that PFAS can transfer through the human placenta and via human milk [10].

There is mounting evidence linking exposure to a range of adverse outcomes including renal, hepatic, immunotoxic, reproductive, and endocrine disrupting effects [11,12,13,14,15]. PFAS have carcinogenic properties [6, 16,17,18,19,20,21] and possibly induce neurobehavioral and developmental effects [14]. There is concern that fetal and childhood periods are sensitive exposure windows for adverse health outcomes of PFAS [14, 22].

In Flanders, the northern part of Belgium, the successive Flanders Environment and Health Study (FLEHS) campaigns provide data on internal exposure to pollutants and associated early biological and health effects in participants of different age groups randomly sampled from the general population. Data on the determinants of exposure to PFAS and on the concentrations measured in umbilical cord plasma and in adult serum samples from FLESHS-2 (2007–2011) and FLEHS-3 (2012–2015) and in serum samples from adolescents living in an industrially contaminated site (2010–2011) with no specific records of PFAS production were reported by Colles et al. [5]. Here we present, for adolescents from FLESHS-2 and adults from FLEHS-3, data concerning the association of internal exposure to PFAS with cognitive and neurobehavioral parameters.

Methods

Participants

Adults from the Flemish reference study FLEHS 3

In the FLEHS-3 (2012–2015) campaign, adults were recruited in all five Flemish provinces to establish reference values for Flanders [23]. PFOS, PFOA, PFHxS, PFNA and PFBS were analyzed in 209 adults (50–65 years old) sampled between May 2014 and November 2014 (FLEHS-3 ADU). A stratified clustered multi-stage design was used to obtain a sample representative of the Flemish population taking into account population density, sex, socioeconomic status and age (in the selected age range 50–65). Sampling took place in three steps: first by study area, secondly by entities, the primary sampling units (PSUs), for access to participants, and thirdly by random selection of participants in accordance with the inclusion criteria. These PSUs consisted of general practitioner offices. Within each PSU, individuals were randomly selected. The study design aimed to obtain representative study populations in terms of social class, an equal distribution of males and females and of participants over age classes. Inclusion criteria were: (1) residing in Flanders for at least 10 years, (2) giving written informed consent, and (3) being able to complete an extensive Dutch questionnaire. For the adults, exclusion criteria were: severe kidney disease (glomerular filtration rate < 60 mL/min) and active anti-cancer therapy (chemotherapy or radiotherapy). A map of Flanders (Fig. 1) shows in which area’s adult participants resided.

Fig. 1
figure 1

Map of Flanders showing the administrative borders of municipalities. Between the most western and most eastern point: about 230 km. In red the industrial “Grensland” area (without known PFAS contamination) where the 199 adolescents participating in FLEHS 2 resided. In blue, the areas where the 209 adults participating in the FLEHS-3 campaign resided, as a random sample of the Flemish population. In yellow, the agglomeration of Brussels which is not part of Flanders and is not involved in this study

Adolescents from the FLEHS-2 study

During the FLEHS-2 study PFOS and PFOA were measured in 199 adolescents aged 14–15 years, recruited in the municipalities Menen and Wevelgem in the “Grensland” industrial area (FL2-ADO). See Fig. 1 for a map. They were sampled from May 2010 to February 2011. The industrial contaminated area had no specific records of PFAS production. It was considered for assessment of internal exposure to PFAS because it was home to a large-scale recycling plant of ferrous and non-ferrous metals, timber industry, pigment industry, several incinerators and, until 2005, a large-scale incinerator for municipal waste. The study area was delineated according to the predominant wind direction. More details about study design and recruitment can be found elsewhere [23,24,25,26,27]. Our results presented on our web site show that the adolescents participating in the present study have a higher internal exposure to some industrial pollutants (https://www.milieu-en-gezondheid.be/sites/default/files/atoms/files/Brochure%20Menen%20steunpunt%202.pdf).

The FLEHS studies were approved by the Ethics Committee of the University of Antwerp and the Antwerp University Hospital (UZA), Belgium. The dossier numbers for the different studies were, respectively, UA A08 09 (FLEHS-2 adolescents of “Grensland” industrial area) and B300201419843 for FLEHS-3 adults.

Field work and sampling

Adult and adolescent participants were examined by trained research nurses and this included administration of the neurological tests (see further), collection of blood/urine samples, measurement of the height and the weight and filling out of a questionnaire. It took about an hour. Examination of the adolescents from the “Grensland” area in FLEHS 2 occurred from the 18th of May 2010 to the 12th of February 2011. Examination of the adults participating in FLEHS 3 occurred from the 26th of May 2014 to the 18th of November 2014. Forty mL peripheral blood was collected from each adult or adolescent participant and used for several biomarker measurements included in the respective studies. Blood samples were centrifuged and/or fractionated at the local sampling center and afterwards transported to the central laboratory where they were stored at − 80 °C in a biobank within 12 h after sampling. Plasma and serum were conserved at − 80 °C. All laboratory analyses were performed on coded samples, in laboratories that met quality-control standards.

Data derived from questionnaires

Participants completed a self-reporting questionnaire on personal and lifestyle factors, including: age, country of birth of their parents, weight, height, housing, residence history, occurrence of in-house structural modifications or painting, family composition, density of nearby traffic, in-house use of pesticides, in-house exposures to pollutants and chemicals, sports, hobbies, contact with animals, smoking and consumption of alcohol, health status and disease experience, occurrence of asthma or eczema or allergies among relatives.

To assess socioeconomic status (SES), two different variables were evaluated separately: equivalized household income (total monthly household income standardized according to the number of household members) and highest educational attainment of the mothers in the case of neonates, of parents in the case of the adolescents and the household in the case of the adults.

Participants also completed food frequency questionnaires in order to assess the consumption of food items as described by Colles et al. [5]. The consumption of locally produced food was also recorded. Additionally, a short questionnaire on recent exposure during the past 3 days was filled out [28, 29].

Measurement of poly- and perfluoroalkyl substances (PFAS)

PFOS, PFHxS, PFOA, PFNA and PFBS were determined in peripheral serum and cord plasma using procedures as described by Kato et al. [30]. Blank bovine serum was spiked at levels of 0.4 ng/mL for PFBS, PFHxS and PFNA as well as 4.0 ng/mL for PFOA and PFOS for calculation of the recovery. As internal standards for quantification sodium perfluoro-1-hexane [18O2]sulfonate (MPFHxS) as well as sodium perfluoro-1-[1,2,3,4-13C4]-octane sulfonate (MPFOS) and perfluoro-n-[13C8]-octanoic acid (MPFOA) and perfluoro-n-[13C9]-nonanoic acid (MPFNA) were used. The analytical method consisted of an offline protein precipitation with acetonitrile, followed by separation by HPLC and MS/MS detection. Liquid chromatography was carried out on an Agilent 1100 Series HPLC apparatus. The Agilent G 1310A was used to load the processed sample (100 ul) on a restricted access material (RAM) phase, a LiChrospher RP-8ADS (25 um) 24 × 4 mm RAM from Merck (Darmstadt, Germany) using a solution of 2 mM ammonium acetate buffer pH 4 in water (solvent A) and 2 mM ammonium acetate buffer pH 4 in acetonitrile (solvent B) (80:20,v/v) as the mobile phase and a flow rate of 0.3 mL/min. After this clean-up and enrichment step, we transferred the analytes after 2 min to a reversed-phase HPLC column (Luna C8 (2) 150 × 4.6 mm, 3 um particle size from Phenomenex, Aschaffenburg, Germany) in back-flush mode. Tandem mass spectrometric detection was performed on a Sciex Applied Biosystems API 3000 triple quadrupole mass spectrometer (Foster City, CA, USA) in negative ionization MS/MS mode with multiple reaction monitoring. Two specific mass transitions to determine the analytes were used. Quality controls included reagent methods blanks comprising bovine serum as well as calibration standards and quality-control samples in bovine serum (ACILA AG, Weiterstadt, Germany). Reproducibility was checked by analyzing spiked bovine serum and a native human plasma sample. Recovery rates were 102% for PFOS, 99% for PFOA, 104% for PFHxS, 95% for PFNA and 91% for PFBS. Detection limits (LOD) were calculated as three times the signal/noise ratio of the analytical background noise in the temporal vicinity of the analyte signal. The limit of quantification (LOQ) was determined as twice the LOD and was 0.3 µg/L for PFOS and PFOA in FLEHS-2 (2007–2011) and in FLEHS-3 (2012–2015) LOQ was 0.2 µg/L for PFOS, PFOA, PFHxS and PFBS, and 0.1 µg/L for PFNA. In terms of PFOS and PFOA, the only PFAS for which a round robin was available at G-EQUAS (GERMAN EXTERNAL QUALITY ASSESSMENT SCHEME for Analyses in Biological Materials www.g-equas.de), participation in the German round robin G-EQUAS was successful with deviation percentages ranging from 11.7% to 15.9% for PFOS and 0.6% to 8.2% for PFOA. To assess the comparability of measurements performed during FLEHS-2 (2007–2011) with those performed during FLEHS-3 (2012–2015), PFOS and PFOA levels were re-measured in 3 samples from the biobank of FLEHS-2 together with the samples of FLEHS-3. Deviation percentages ranged from − 7.8 to − 29.4% for PFOS and + 5.0% to + 21.5% for PFOA. So, despite being measured at points in time differing by more than 3 years, the data from FLEHS 2 and FLEHS 3 are quite comparable.

Neurobehavioral tests

In adolescents of the industrial contaminated site of FLEHS-2 sleepiness, masculinity and femininity were assessed via a questionnaire as described below. The Neurobehavioral Evaluation System (NES3) computerized battery of tests developed to study the neurological effects of an exposure to environmental agents [31, 32] were performed in the adolescents of FLEHS-2 and in the FLEHS-3 adult reference population. Lack of time to perform the Stroop test in the adolescent study and other circumstances resulted in the fact that different sets of tests were applied in the adolescent and adult studies. The adolescents performed the NES3 Continuous Performance Test, NES3 Digit–Symbol Substitution Test, NES3 Digit Span Test and NES3 Finger Tapping Test. The adults in FLEHS-3 did the NES3 Continuous Performance Test and NES3 Digit Span Test, aside from the Stroop test.

In the Continuous Performance Test, a series of letters is displayed on the screen, one at a time, and each for approximately 200 ms. The task is to immediately respond to the letter S, and not to other letters, by pressing the spacebar. A new letter appears each 1000 ms. In total, the letter S appears 60 times. The mean reaction time for responding to the target letter in ms (CPTmean) and the number of incorrect (CPTincor) reactions were used as the measure of performance. The test evaluates sustained attention. It showed a good test–retest reliability in a group of patients directed to a neuropsychological examination [32]. In the Digit–Symbol Substitution Test (DSST), used to measure general cognitive performance, a row of 9 symbols paired with 9 digits is displayed at the top of the screen. The same 9 symbols but in a different order are shown at the bottom. When a digit is displayed, the task is to indicate the symbol, which is paired with this digit, from the bottom row. A new digit appears only after the correct symbol has been indicated. In total, 27 digits are displayed. The total time needed to complete the test measured in seconds (DSSTlat) describes the performance. The number of errors (DSSTnerr) was recorded [Letz R.: NES3 user's manual. Atlanta (GA): Neurobehavoral Systems Inc, 2000]. The Digit Span Test consists of two parts. In the first part, a subject hears a sequence of digits. The task is to reproduce them. In case of a correct answer, a one digit longer sequence is presented. In case of a mistake, a sequence of the same length is presented. When two incorrect answers in a row are given, the first part of the test finishes. The second part is the same as the first one, but the sequences are reproduced in the reverse order. Digit Span Forward (DSF) is the maximum span reproduced in the first part. Digit Span Backward (DSB) is the maximum span reproduced in the reverse order. The first part of the test assesses the working memory span. Good performance in the second part requires both the ability to hold and manipulate information. For adolescents in the FLEHS-2 industrial area, in a sub-population, the Digit Span Test was administered using computers with touch screens instead of a keyboard. In order to account for a possible effect of the way the test was administered, an indicator variable was included in the regression models for this test. In the Finger Tapping Test (FTT) a subject presses the spacebar as many times as possible during a trial of 10 s. The first part of the test consists of 4 trials with the preferred hand. The second part consists of 4 trials with the non-preferred hand. The summary measures are the total number taps with the preferred hand (FTTpref) and the total number of taps with the non-preferred hand (FTTnpref). The test measures the manual motor speed.

The Stroop test assesses the person’s capacity to pay attention by changing colors and the way colors are written (selective attention domain). In this test, four buttons are displayed on the screen (yellow, red, blue, and green). During the test, the name of one of these colors appears on the screen printed in a different color than the name. The task is to touch as fast as possible the button that has the same color as the name, ignoring the color of the printed name. Before the test, eight practice trials take place followed by 48 test trials. The mean reaction time is the average time that passed between the appearance of the name and touching the correct button, expressed in ms (Strooptime). The number of errors made was also recorded (Strooper).

Additionally, in the FLEHS-2 adolescents, sleepiness during the day was evaluated based on 8 questions (high to no chance of somnolence during activities such as reading, TV watching, inactivity). The scoring was done using the Epworth Sleepiness Scale, a scale between 0 and 24 [33]. The extent to which male and female identity (masculinity or femininity) were present in the test persons was evaluated using the Personal Attributes Questionnaire as proposed by Spence and Helmreich [34], with a scoring between 0 (completely agree) and 4 (not agree at all) on 24 questions on attitudes, such as aggression, independence, emotionality, passiveness, decisiveness, self-confidence.

Statistical analysis

Biomarker values were reported if they were above a quantifiable level as determined in the laboratory. Taking into account that, for the PFASs for which associations with biological or health effects could be studied, the percentage of measurements below the LOQ was low, values below the LOQ were replaced by LOQ/2. Associations between internal exposure to PFAS compounds and parameters of biological or health effects were assessed using multiple linear regression or multiple logistic regression, taking into account predetermined confounding factors. Confounders were selected a priori based on experience in previous studies and a literature search. Potential covariates were included on the basis of mechanistic considerations or because they showed, in one of the cohorts in our study, an association (p < 0.25) with a dependent variable of interest. Confounders and covariates were included as continuous variables whenever continuous data were available. These potential covariates are listed in the additional file 1 Tables S1, and S2. Stepwise multiple linear or logistic regression analyses were done using R version 3.3.0 and RStudio version 1.0.136, and logistf for Firth logistic regression for associations where exposure variables showed quasi separation in the normal logistic regression. In the models the PFAS concentrations were included on the original scale, without transformation. To limit the number of independent variables in the final models, confounders were removed from the model if they had a p > 0.5 and subsequently covariates were removed if they had a p  > 0.05. For each association the confounding factors and significant covariates included in the final model are mentioned in the additional file 1 Tables S1 and S2. Continuous effect markers were ln-transformed if this normalized the distribution of the regression residuals. For linear and logistic regression, estimates and odds ratios (OR) were calculated, respectively. Estimates, OR and their 95% confidence intervals were reported for interquartile (IQR) increases in exposure.

Estimates for IQR differences in exposure, were either calculated as ‘ratios’ or ‘differences’ in the levels of effect markers, respectively, in case of ln-transformed or non-transformed effect markers, used in the regression models. To put this estimate into context, this change in the effect marker was also expressed in percentage of the observed difference in effect marker between the 25th and 75th percentile within the respective study population. Associations with a p  < 0.05 or a p  < 0.1 are, respectively, designated as significant or marginally significant.

Regression analyses are sensitive to influential cases, whose deletion from the dataset would noticeably change the result of the calculation. Outliers are cases with y values—in this case effect marker values—deviating from the trend. Cases have high leverage if they have extreme or unusual combinations of predictor values, here exposures, confounders or covariates. Although these are not necessarily faulty data, they lie ‘far’ from the other cases and are too few to develop robust models. We are aware that the exclusion of influential cases, in particular cases with high exposure values, can potentially have implications in terms of environmental justice [35], but the number of subjects in our studies was too low to address this problem. In order to get a model that was correct for the general population, it was decided to test exposure–response associations without influential outliers, employing two methods for their identification. The first was to identify outliers as cases with Studentized residuals with an absolute value larger than 3, and check which of these also had high leverage by determining if their hat value was higher than twice the average hat value of all cases in the regression. The second method was to plot the Cook’s distance of each case. We decided not to work with a cut-off value for the Cook’s distance since the traditional cut-off values identified either no influential cases or too many (often 5% or more). Instead, cases that had a Cook’s distance clearly higher than the rest of the cases and/or higher than one of the influential cases identified using the first method were also identified as influential cases [36]. Combining these metrics allows identification of influential cases, and although the exact cut-offs to be used are as usual controversial [37], we picked conservative values that seemed to produce at most a few influential cases in our models. We present results after exclusion of influential cases. For most associations there were no or only one influential case, the maximum number of influential cases was 4 (this occurred in two associations as shown in Additional file 1: Tables S3 and S4 show results in which as well data with as without influential cases are described.

Results

PFAS serum concentrations

PFAS concentrations in cord plasma or serum of the subjects participating in the FLEHS-2 and -3 studies have been published in detail by Colles et al. [5]. A summary is provided in Table 1. At least 99.5% of the PFOA or PFOS values were above LOQ in the FLEHS-2 and FLEHS-3 studies. The same was true for PFHxS and PFNA measured in adults of FLEHS-3. For PFBS measured in adults of FLEHS-3, the detection rate was below 5%, so we did not attempt to study associations between PFBS and cognitive parameters.

Table 1 Summary of PFAS exposure levels measured in the adolescents (FLEHS2) and adults (FLEHS 3)

Results of the neurocognitive tests

The results of the neurocognitive tests are summarized in Table 2.

Table 2 Scores of neurocognitive and neurobehavioral tests performed for adolescents or adults

Associations between PFAS serum concentrations and neurobehavioral and cognitive parameters

Several neurobehavioral or cognitive parameters were assessed on adolescents in FLEHS-2 and on adults in FLEHS-3 (see methods). Associations after removal of any influential cases are shown in Figs. 2, 3. Statistical data, also comprising all cases, can be found in the Additional file 1 Table S3 and Table S4. Below the main findings are summarized.

Fig. 2
figure 2

Forest plot of the estimates of neurocognitive tests (NES3 tests, Stroop test) and behavior (somnolence score and femininity/masculinity score) in relation to the measured PFAS in the different FLEHS studies (with FL2 = FLEHS-2, FL3 = FLEHS-3, ADO = adolescents, ADU = adults). The estimate (95% confidence interval) is expressed as an increase or decrease in effect marker, associated with an IQR increase in exposure. Calculations based on effect marker values without logarithmic transformation. Confounders and covariates that were included at the start of the stepwise multiple regressions are mentioned in Additional file 1 Tables S1 and S2

Fig. 3
figure 3

Forest plot of the estimates of outcomes of the neurocognitive tests (NES3 tests, Stroop test) in relation to the measured PFAS in the different FLEHS studies (with FL2 = FLEHS-2, FL3 = FLEHS-3, ADO = adolescents, ADU = adults).). The estimate (95% confidence interval) is expressed for an interquartile increase in exposure (calculations on ln-transformed effect marker values). Confounders and covariates that were included at the start of the stepwise multiple regressions are mentioned in Additional file 1 Tables S1 and S2

NES-battery of neurological tests

For the NES-battery of neurological tests, we observed only a few significant or marginally significant associations with the PFAS levels. In adolescents (of FLEHS-2) some of these associations pointed in the direction of decreased cognitive performance. Serum PFOA concentrations were marginally significantly associated with an increase in the number of errors (DSSTnerr) in the Digit–Symbol Substitution Test (p = 0.059) (Fig. 2), with an interquartile increase in PFOA associated with an increase of 0.182 in the number of errors, corresponding to 18.2% of the difference between the p75 and p25 values in the number of errors observed among all the adolescents tested in FLEHS-2. PFOS concentrations were significantly associated with a decrease in the maximum span reproduced in the reverse order (DSB) in the Digit Span Test (p = 0.02), with an interquartile increase in PFOS concentration associated with a decrease of 0.139 digits in the number of digits that could be reproduced, corresponding to 13.9% of the difference between the p75 and p25 values in the number of digits observed among all the adolescents tested in FLEHS-2 (Fig. 2), suggesting a decrease in short-term memory and capacity to hold and manipulate information. Also, PFOS concentrations were marginally significantly associated with an increase in the time needed to react (CPTmean) in the Continuous Performance Test (p = 0.073), with an interquartile increase in PFOS concentration associated with an increases of 0.7% in the mean reaction time, corresponding to 4,6% of the increase in reaction time at P75 compared to P25 among all adolescents tested in FLEHS-2 (Fig. 3), suggesting a possible decrease in the capacity to concentrate and maintain a sustained attention.

Contrasting with the findings for adolescents, for the older adults participating in FLEHS-3, 21 of the 24 associations between PFAS serum concentrations and NES tests results pointed, non-significantly or significantly, in the direction of increased cognitive performance. Favorable associations pointing in the direction of increased cognitive performance were observed for all associations studied, except for the association of PFNA with DSF and DSB and of PFOA with DSF which were in an unfavorable direction (Fig. 2). Some of the associations were significant. Indeed, a higher PFOS serum concentration showed an inverse, thus favorable, association with the Strooptime, (p = 0.049). An interquartile increase in PFOS internal exposure was associated with a decrease of 2.4% in the Strooptime (i.e., quicker reaction time), corresponding to 11.1% of the decrease in reaction time at P25 compared to P75 among all adults tested in FLEHS-3, suggesting an increase in the capacity to concentrate (Fig. 3). The adults of FLEHS-3 also showed a significant inverse, favorable, association between higher PFHxS serum concentration and the CPTincor in the Continuous Performance Test (p = 0.029), with an interquartile increase in PFHxS concentration associated with a decrease of 0.377 errors corresponding to 12.6% of the decrease in errors between the p25 compared to the p75 values in the number of errors observed among all the adults tested in FLEHS-3, suggesting an increase in the capacity to concentrate and in sustained attention (Fig. 2).

Neurobehavioral parameters assessed for adolescents

Sleepiness during the day was assessed in adolescents of FLEHS-2. A higher PFOA serum concentration showed a significant positive association with sleepiness during the day (p = 0.007), with an interquartile increase in PFOA concentration associated with an increase in somnolence of 0.786 points in the Epworth Sleepiness Scale, corresponding to 15.7% of the increase between the p75 value compared to the p25 value in somnolence score observed among all the adolescents tested in FLEHS-2. For PFOS, this association was non-significant (Fig. 2). Furthermore, masculinity and femininity were evaluated using the Personal Attributes Questionnaire in FLEHS-2 adolescents. All 8 associations with PFOA or PFOS serum concentrations were non-significantly or significantly inverse (Fig. 2). For males the PFOS concentration showed a significant inverse association with femininity, with an interquartile increase in PFOS concentration associated with a decrease of 0.046 points in the Personal Attributes Questionnaire score (p = 0.006), corresponding to 7.36% of the difference between the p75 value and the p25 value in femininity score observed among all male adolescents participating in FLEHS-2. For female adolescents the PFOS serum concentration showed a significant inverse association with masculinity, with an interquartile increase in PFOS concentration associated with a decrease in masculinity of 0.178 points in the Personal Attributes Questionnaire score (p = 0.015), corresponding to 23.73% of the difference between the p75 value and the p25 value in the masculinity score observed among all female adolescents participating in FLEHS-2 (Fig. 2).

Discussion

Internal exposure to PFAS in Flanders in the period of 2010–2014, covered by the studies included in this paper, were in the middle or low range compared to concentrations observed in other Western countries [5]. Differences in PFAS serum levels between our adolescent and adult cohorts were described in detail and discussed by Colles et al. [5]. As described under Methods the measurements on adolescents and adults were comparable in spite of being done at different points in time. So, in spite of the time trend towards decreasing internal exposure to PFAS in Flanders reported and discussed by Schoeters et al. [38], the serum levels measured in our adult cohort in 2014 were still higher than those measured in our adolescent cohort in 2010–2011. This is consistent with the increasing serum concentrations of PFAS with increasing age reported in several publications [39,40,41]. Differences between associations observed here for older adults and adolescents cannot rest on the fact that real internal exposure to PFAS of the older adults would have been lower than that of the adolescents.

Our results on the associations of PFAS levels in relation to neurobehavioral tests are in line with other contrasting and variable observations that are reported in the literature concerning the biological and health effects of PFAS.

Somnolence and gender identity

For adolescents of FLEHS-2, we observed a significant positive association between PFOA (but not PFOS) blood concentrations and somnolence during the day. In terms of gender identity as assessed through the Personal Attributes Questionnaire, a significant inverse association was seen for PFOS with femininity for boys and with masculinity for girls (Fig. 2) using the Personal Attributes Questionnaire as proposed by Spence and Helmreich [34]. We are, however, aware of the fact that this questionnaire is not able to measure the complexity of global masculinity or femininity [42] and of limitations affecting this questionnaire in terms of societal changes around gender identities [43]. We are not aware of any observations concerning PFAS and somnolence. Speculatively, it might be argued that induction of somnolence by PFAS may involve the possibility that PFAS share some physicochemical properties with fluorinated organics (such as fluorinated oxolanes and oxetanes) that were reported to produce somnolence or anesthesia [44]. To our knowledge, no associations between internal exposure to PFAS and masculinity or femininity have been reported in the literature. However, PFAS exposure associated to sex-, age- and compound-specific changes in sex hormone concentrations has been reported by Xie et al. [45]. These changes in sex hormone concentrations might well explain the changes in gender-specific behavior we observed. In particular, a decrease in testosterone levels associated with PFOS as observed in Taiwanese girls 12–17 years of age [46], American girls 6–9 years of age [47] and American Girls aged 12–19 [45] might contribute to the quite important decrease in masculinity observed for girls. Also for boys changes in oestradiol and testosterone concentrations were reported by Nordström-Joensen et al. [48], Lopez-Espinoza et al. [47] and Xie et al. [45], which might contribute to the observed decrease in boys’ femininity.

Other functions and cognition

Neonatal exposure to PFOS or PFOA caused neurobehavioral defects in adult mice [49]. Unfavorable developmental or neurobehavioral effects on humans of prenatal exposure to PFAS were reported by Donauer et al. [50] (hypotonic babies at 5 weeks of age) and Hoyer et al. [51] (hyperactivity and behavioral problems in children). However, not all studies observed adverse effects. Fei et al. [52] found, at concentrations higher than in our study, no convincing associations between developmental milestones in early childhood and levels of PFOA or PFOS measured in maternal plasma early in pregnancy. PFAS measured in cord plasma in the Netherlands, at concentrations lower than in our study, had no impact on 18-month-old children examined using the Child Behavior Checklist 1.5–5, in ‘Attention Deficit Hyperactivity Disorder’ scores but showed a significant inverse association with externalizing problem behavior [53]. Harris et al. [54] reported that, at concentrations higher than in our study, higher prenatal levels of some PFAS were associated with a better non-verbal IQ in children with a mean age of 7.7 years. Stein & Savitz [55] observed, in 5–18 years old children, an inverted J-shaped association between PFOA and attention deficit/hyperactivity disorder (ADHD) (small increase in prevalence for the second quartile of exposure compared with the lowest, and a decrease for the highest versus lowest quartile). Forns et al. [56] observed no association between PFOS and PFOA measured in breast milk samples one month after delivery and cognitive and psychomotor development at 6 and at 24 months and behavioral development at 12 and at 24 months. A systematic review by Roth & Wilks [57] concluded that the epidemiological evidence did not support a strong causal association between PFAS and adverse neurodevelopmental and neurobehavioral outcomes in infants and children.

Considering neurocognitive effects, rather opposite findings were seen for adolescents vs. older adults. Our results concerning unfavorable effects on cognition in adolescents—unfavorable effects on sustained attention (CPTmean) (Fig. 3), short-term memory (DSB) (Fig. 2) and cognitive performance (DSSTnerr) (Fig. 2)—are consistent with several published reports. In the Hokkaido Birth Cohort Study [58] PFOA, but not PFOS, adversely affected mental development in girls at 6 months of age. The association between maternal PFAS concentrations and early communication development in British girls at 15 and 38 months of age varied by maternal age at delivery. In daughters of younger mothers (< 25 years of age), every 1 ng/mL of PFOS was associated with a 3.82 point (95% confidence interval (CI) − 6.18, − 1.47) lower vocabulary score at 15 months and a 0.80 point (95% CI − 1.74, 0.14) lower language score at 38 months [59].

The molecular mechanisms which might contribute to neurobehavioral or cognitive effects were studied in cell lines [60] (different mechanisms for different PFAS). As reviewed by Wang et al. [61], PFAS could affect neurotransmitter concentrations, expression of neurotransmitter receptors, synaptogenesis and synaptic plasticity, induce apoptosis of neuronal cells, and alter the expression of microRNAs. As to the basic mechanisms intervening in the biological activity of PFAS, activation of PPARα, a ligand-activated transcription factor intervening in lipid homeostasis and inflammation, probably plays a role [62]. In addition, other putative mechanisms have been proposed, such as gap junctional inhibition to disrupt cell–cell communication, mitochondrial dysfunction, interference of protein binding, partitioning into lipid bilayers, oxidative stress, alterations in calcium homeostasis and related signaling pathways [63] and changes in DNA methylation [64, 65].

Our results concerning favorable effects on cognition in older individuals of 50–65 y—favorable effects on the capacity to concentrate (Strooptime) (Fig. 3) and to sustain attention (CPTincor) (Fig. 2)—were consistent with several publications. A cross-sectional study using data from participants from the 1999–2000 and 2003–2008 National Health and Nutrition Examination Surveys (NHANES) (n = 1766, age 60–85 years) reported non-significant inverse associations between perfluoroalkyls and self-reported cognitive limitation consisting in difficulty remembering or periods of confusion [66]. Another cross-sectional study with 21,024 older adults (aged ≥ 50 years) who were exposed to high levels of PFOA from a chemical facility in the Mid-Ohio Valley, West Virginia, and participated in the C8 Health Project, reported statistically significant inverse associations of self-reported short-term memory loss with PFOS, PFOA, and PFHxS [67]. A study of 126 older adults aged 55–74 years and living in upper Hudson River communities which utilized various neuropsychological assessment tools (e.g., the California Verbal Learning Test, the Wechsler Memory Scale, the Wisconsin Card Sorting Test) also showed that higher PFOA and PFOS concentrations were associated with better performance in memory and learning, executive function and visuospatial function [68]. Park et al.[69], in a study on 903 adults aged ≥ 60 years from NHANES 2011–2014, found, after substantial adjustment, significant positive associations for PFOA and PFNA serum concentrations, but for PFOS a non-significant negative association, with a composite z-score for global cognition. After excluding persons suffering from chronic kidney disease, the positive associations for PFOA and PFNA were no longer significant and the negative association for PFOS became significant [69].In a study on 777 individuals aged > / = 60 from the National Health and Nutrition Examination Survey (NHANES) 2011–2014, Weng et al. [70] found that PFOA was significantly inversely associated with cognitive decline after multivariable adjustment and, in the Bayesian kernel machine regression, mixtures of 5 PFAS were significantly protective on cognitive decline in the Immediate Recall Test. Weng et al. [70] concluded that low-dose (essentially below the median value observed in their cohort) mixed PFAS was inversely associated with the risk of cognitive decline and that no significant interaction between PFAS was observed for cognitive function.

Difference in impact between adolescents and older adults

For the neurocognitive effects, it was hypothesized that the difference in impact, seen between elderly and adolescents, may have been due to the time frame difference in which they were born, implicating a higher prenatal PFAS exposure in the adolescents (born in 1995–1997) compared to the elderly group (born between 1943 and 1964). It is also likely that adverse effects on the brain might result predominantly from early exposures. The effects of endocrine disrupting agents can differ importantly in function of the time window during which exposure takes place. That is well documented [71, 72]. Mechanisms leading to adverse effects of PFAS prenatal exposure on the nervous system later in life were shown in animal experiments: Johansson et al. [73] (proteins important for neuronal growth and synaptogenesis), Lee et al. [74] (more lipid oxidation and oxidative stress in fetuses than in dams), Wang et al. [75] (reduced spatial learning and memory abilities of the offspring on postnatal day 35), Zhang et al. [76] (inhibition of long-term potentiation and changes in receptors after exposure starting in utero), and Zhang et al. [77] (tau hyperphosphorylation and beta-amyloid aggregation in adult rats after pre/postnatal PFOS exposure). Disruption of thyroid function might be involved, as thyroid hormones play an important role in the development of the brain [78]. Pedersen et al. [79] observed changes in neurochemical signaling in association with PFAS concentrations in the brain of polar bears. Additionally, exposure to PFAS mainly during later life of the adults might have induced protective effects. As proposed by Power et al. [66] and discussed by Quaak et al. [53], PFAS may have neuroprotective effects. It is known that PFAS are agonist of PPAR receptors [80]. As reviewed by Kapadia et al. [81] PPAR agonists have both neuroprotective as well as central nervous system anti-inflammatory characteristics. As PFASs are known to interfere with the immune function [11, 12, 82) and as neurodegenerative diseases are accompanied by inflammation, it may be speculated that exposure to PFASs may have a beneficial impact on brain health in older human adults. It is conceivable that increased leptin levels also play a role in possible positive cognitive effects of PFAS, especially on older people. Leptin levels were not measured in the adolescents participating in FLEHS 2 nor in adults participating in FLEHS 3. However, a weak positive association with Leptin was observed for PFAS cord plasma concentrations in mothers participating in the FLEHS 2 and FLEHS 3 campaigns, significant only for PFHxS in FLEHS 3 mothers (unpublished results of the Flemish biomonitoring, see Note on Leptin in Additional Materials). Although some animal experiments [83] and observations in humans [84, 85] found negative associations between PFAS and Leptin concentrations, often positive associations were observed. Experimental evidence for induction of higher leptin concentrations by PFAS was observed in human cells in vitro [86] and in animals [87,88,89,90] and also observations on humans showed positive associations between internal exposure to PFAS and leptin serum concentrations [91,92,93]. Specially relevant in relation to our findings concerning cognition in elderly people is the observation of Ding et al. [94] who found that, in women aged 45–56, higher PFAS concentrations were associated with higher leptin and free leptin values. Leptin was observed to improve memory processing in AMP8 mice, which developed elevated amyloid-beta and memory deficits with advancing age [95], and this effect was more pronounced in older AMP8 mice. Studies in transgenic mouse models of Alzheimer's disease have shown that chronic administration of leptin could ameliorate brain pathology and improve cognitive performance [96].

Strengths and limitations

As to strengths and limitations of the study, PFAS blood levels were analyzed in the same lab during the different surveys performed in a 4 year time frame. Furthermore, well trained research nurses with much experience were responsible for the contact and interactions with the participants, including the administration of the neurobehavioral tests. Uniform study protocols facilitated comparability of the results.

Weaknesses in our study comprise that the questionnaire used to study gender identity is too simple to take into account the complexity of this issue and the societal changes involved. Many statistical associations were tested, increasing the likelihood of chance findings. However, in accordance with the views of the epidemiologist Kenneth Rothman [97], and in view of the known endocrine disrupting properties of PFAS—which render observed exposure–effect relations biologically plausible—we did not apply corrections for multiple testing. Since the studies were cross-sectional, since we could not exclude reverse causality due to reuptake of PFAS in the kidneys and through the enterohepatic circulation, this study on its own does not provide evidence for causality. However, by confirming the results of other research, it contributes to the knowledge and evidence concerning the diverse effects of PFAS.

Conclusion

Our observations point to neurobehavioral and cognitive effects of PFAS. The neurobehavioral effects might in part result from the changes in sex hormone levels that have been reported to be associated with internal exposure to PFAS. Interestingly, whereas in relation to cognition some adverse effects were recorded for adolescents, for elderly persons our observations rather suggest possible weak positive effects with respect to cognition. Our observations might be in line with the view that PFAS have many, sometimes contrasting health effects.

Availability of data and materials

The aggregated data are publically available via the IPCHEM data platform. (https://ipchem.jrc.ec.europa.eu/RDSIdiscovery/ipchem/index.html#discovery). The individual records can be requested via the procedures that are available on this portal (https://ipchem.jrc.ec.europa.eu/RDSIdiscovery/ipchem/index.html#showmetadata/FLEHS1REFNB).

Abbreviations

AMGC:

Department of analytical, environmental and geochemistry

AML:

Algemeen Medisch Laboratorium

ATSDR:

Agency for toxic substances and disease registry

BELAC:

Belgische Accreditatie-Instelling

BMI:

Body mass index

CPTincor:

Number of incorrect reactions in the continuous performance test

CPTmean:

Mean reaction time for responding to the target letter in milliseconds in the continuous performance test

DSB:

Digit span backward, the maximum span (number of digits) reproduced in the in the reverse order in the digit span test

DSF:

Digit span forward, the maximum span (number of digits) reproduced in the forward part of the digit span test

DSST:

Digit–symbol substitution test

DSSTlat:

Total time needed to complete the digit–symbol substitution test measured in seconds

DSSTnerr:

Number of errors in the digit–symbol substitution test

EQAS:

External quality assessment scheme

FLEHS:

Flanders environment and health studies

FTT:

Finger Tapping Test

FTTnpref:

Total number of taps with the non-preferred hand in the finger tapping test

FTTpref:

Total number taps with the preferred-hand in the finger tapping test

G-EQUAS:

German external quality assessment scheme for Analyses in Biological Materials

HPLC:

High-performance liquid chromatography

IQR:

Interquartile

Ln-transformed:

Natural logarithm has been taken, with as base the number “e”

LOD:

Limit of detection

LOQ:

Limit of quantification

MS/MS:

Tandem mass spectrometry

NES3:

Neurobehavioral evaluation system 3

OR:

Odds ratio

p25:

Percentile 25

p75:

Percentile 75

p90:

Percentile 90

PAHs:

Polycyclic aromatic hydrocarbons

PBDEs:

Polybrominated diphenyl ethers

PCBs:

Polychlorinated biphenyls

PFAS:

Poly- and perfluoroalkyl substances

PFBS:

Perfluorobutane sulfonate

PFDA:

Perfluorodecanoic acid

PFDoDA:

Perfluorododecanoic acid

PFHxS:

Perfluorohexane sulfonate

PFNA:

Perfluorononanoic acid

PFOA:

Perfluorooctanoic acid

PFOS:

Perfluorooctane sulfonate

PFUnDA:

Perfluoroundecanoic acid

PPAR-alpha:

Peroxisome proliferator-activated receptor (PPAR)-alpha

PSUs:

Primary sampling units

SES:

Socioeconomic status

Strooper:

Number of errors made in the Stroop test

Strooptime:

Average time that passed between the appearance of the name and touching the correct button in the Stroop test, expressed in milliseconds

UZA:

Antwerp University Hospital

VITO:

Vlaams Instituut voor Technologisch Onderzoek

VUB:

Vrije Universiteit Brussel

References

  1. Buck RC, Franklin J, Berger U, Conder JM, Cousins IT, de Voogt P, Jensen A, Kannan K, Mabury SA, van Leeuwen SP (2011) Perfluoroalkyl and polyfluoroalkyl substances in the environment: terminology, classification, and origins. Integr Environ Assess Manag 7:513–541

    Article  CAS  Google Scholar 

  2. Benford D, de Boer J, Carere A, di Domenico A, Johansson N, Schrenk D, Schoeters G, de Voogt P, Dellatte E (2008) Opinion of the Scientific Panel on Contaminants in the Food chain on perfluorooctane sulfonate (PFOS), perfluorooctanoic acid (PFOA) and their salts. EFSA 653:1–131

    Google Scholar 

  3. Ahrens L, Bundschuh M (2014) Fate and effects of poly—and perfluoroalkyl substances in the aquatic environment: a review. Environ Toxicol Chem 33(9):1921–1929

    Article  CAS  Google Scholar 

  4. Agency for toxic substances and disease registry (2009) Draft toxicological profile for perfluoroalkyls. Agency for toxic substances and disease registry, Atlanta, GA. https://stacks.cdc.gov/view/cdc/5391

  5. Colles A, Bruckers L, Hond E, Govarts DE, Morrens E, Schettgen B, Buekers T, Coertjens J, Nawrot D, Loots T, Nelen I, De Henauw V, Schoeters S, Baeyens G, van Larebeke WN (2020) Perfluorinated substances in the Flemish population (Belgium) Levels and determinants of variability in exposure. Chemosphere 242:12525

    Article  Google Scholar 

  6. Grandjean P, Clapp R (2015) Perfluorinated alkyl substances: emerging insights into health risks. New Solut 25(2):147–163

    Article  Google Scholar 

  7. Olsen GW, Burris JM, Ehresman DJ, Froehlich JW, Seacat AM, Butenhoff JL, Zobel LR (2007) Half-life of serum elimination of perfluorooctanesulfonate, perfluorohexanesulfonate, and perfluorooctanoate in retired fluorochemical production workers. Environ Health Perspect 115:1298–1305

    Article  CAS  Google Scholar 

  8. Zhang Y, Beesoon S, Zhu L, Martin JW (2013) Biomonitoring of perfluoroalkyl acids in human urine and estimates of biological half-life. Environ Sci Technol 47:10619–10627

    Article  CAS  Google Scholar 

  9. Xu Y, Fletcher T, Pineda D, Lindh C, Nilsson C, Glynn A, Vogs C, Norström K, Lilja K, Jakobson K, Li Y (2020) Serum half-lives for short- and long-chain perfluoroalkyl acids after ceasing exposure from drinking water contaminated by firefighting foam. Environ Health Perspect 128(7):077004

    Article  CAS  Google Scholar 

  10. Needham LL, Grandjean P, Heinzow B, Jorgensen PJ, Nielsen F, Patterson DG Jr, Sjodin A, Turner WE, Weihe P (2011) Partition of environmental chemicals between maternal and fetal blood and tissues. Environ Sci Technol 45:1121–1126

    Article  CAS  Google Scholar 

  11. DeWitt JC, Peden-Adams MM, Keller JM, Germolec DR (2012) Immunotoxicity of perfluorinated compounds: recent developments. Toxicol Pathol 40:300–311

    Article  CAS  Google Scholar 

  12. Corsini E, Luebke RW, Germolec DR, DeWitt JC (2014) Perfluorinated compounds: emerging POPs with potential immunotoxicity. Toxicol Lett 230:263–270

    Article  CAS  Google Scholar 

  13. Hansmeier N, Chao TC, Herbstman JB, Goldman LR, Witter FR, Halden RU (2015) Elucidating the molecular basis of adverse health effects from exposure to anthropogenic polyfluorinated compounds using toxicoproteomic approaches. J Proteome Res 14:51–58

    Article  CAS  Google Scholar 

  14. Rappazzo KM, Coffman E, Hines EP (2017) Exposure to perfluorinated alkyl substances and health outcomes in children a systematic review of the epidemiologic literature. Int J Environ Res Public Health 14(7):691. https://doi.org/10.3390/ijerph14070691

    Article  CAS  Google Scholar 

  15. EFSA Panel on Contaminants in the Food Chain (2020) Risk to human health related to the presence of perfluoroalkyl substances in food. EFSA J 18(9):6223

    Google Scholar 

  16. Barry V, Winquist A, Steenland K (2013) Perfluorooctanoic acid (PFOA) exposures and incident cancers among adults living near a chemical plant. Environ Health Perspect 121(11–12):1313–1318

    Article  Google Scholar 

  17. Mancini FR, Go CS, Gambaretti J, Marchand P, Boutron-Ruault MC, Severi G, Arveux P, Antignac JP, Kvaskoff M (2020) Perfluorinated alkylated substances serum concentration and breast cancer risk Evidence from a nested case control study in the French E3N cohort. Int J Cancer 146(4):917–928

    Article  CAS  Google Scholar 

  18. Tsai MS, Chang SH, Kuo WH, Kuo CH, Li SY, Wang MY, Chang DY, Lu YS, Huang CS, Cheng AL, Lin CH, Chen PC (2020) A case-control study of perfluoroalkyl substances and the risk of breast cancer in Taiwanese women. Environ Int 142:105850

    Article  CAS  Google Scholar 

  19. Omoike OE, Pack RP, Mamudu HM, Liu Y, Wang L (2021) A cross-sectional study of the association between perfluorinated chemical exposure and cancers related to deregulation of estrogen receptors. Environ Res 196:110329

    Article  CAS  Google Scholar 

  20. Li H, Hammarstrand S, Midberg B, Xu Y, Li Y, Olsson DS, Fletcher T, Jakobsson K, Andersson EM (2022) Cancer incidence in a swedish cohort with high exposure to perfluoroalkyl substances in drinking water. Environ Res 204(Pt C):112217

    Article  CAS  Google Scholar 

  21. Messmer MF, Salloway J, Shara N, Locwin B, Harvey MW, Traviss N (2022) Risk of cancer in a community exposed to per—and poly-fluoroalkyl substances. Environ Health Insights 16:11786302221076708

    Google Scholar 

  22. Guerrero-Preston R, Goldman LR, Brebi-Mieville P, Ili-Gangas C, Lebron C, Witter FR, Apelberg BJ, Hernandez-Roystacher M, Jaffe A, Halden RU, Sidransky D (2010) Global DNA hypomethylation is associated with in utero exposure to cotinine and perfluorinated alkyl compounds. Epigenetics 5:539–546

    Article  CAS  Google Scholar 

  23. Schoeters G, Den Hond E, Colles A, Loots I, Morrens B, Keune H, Bruckers L, Nawrot T, Sioen I, De Coster S, Van Larebeke N, Nelen V, Van de Mieroop E, Vrijens J, Croes K, Goeyens K, Baeyens W (2012) Concept of the Flemish human biomonitoring programme. Int J Hyg Environ Health 215:102–108

    Article  CAS  Google Scholar 

  24. Den Hond E, Chovanova H, Dumez B, Keune H, Schoeters G, Teughels C, Van Campenhout K (2009). Human biomonitoring in Flanders: some aspects related to study design, future, communication and ethics. Bulletin épidémiologique hebdomadaire Special edition, 9–13.

  25. Schoeters G, Colles A, Den Hond E, Croes K, Vrijens J, Baeyens W, Nelen V, van de Mieroop E, Covaci A, Bruckers L, Van Larebeke N, Sioens I, Morrens B, Loots I (2012) The Flemish environment and health study (FLEHS): second survey (2007–2011): establishing reference values for biomarkers of exposure in the Flemish population. In: Knudsen Lisbeth, Merlo Domenico Franco (eds) Biomarkers and human biomonitoring, vol 1. Royal Society of Chemistry, Cambridge

    Google Scholar 

  26. Croes K, Den Hond E, Bruckers L, Loots I, Morrens B, Nelen V, Colles A, Schoeters G, Sioen I, Covaci A, Van VandermarkenLarebeke TN, Baeyens W (2014) Monitoring chlorinated persistent organic pollutants in adolescents in Flanders (Belgium): concentrations, trends and dose-effect relationships (FLEHS II). Environ Int 71:20–28

    Article  CAS  Google Scholar 

  27. Colles A, Coertjens D, Morrens B, Den Hond E, Paulussen M, Bruckers L, Govarts E, Covaci A, Koppen G, Croes K, Nelen V, Van Larebeke N, De Henauw S, Fierens T, Van Gestel G, Chovanova H, Mampaey M, Van Campenhout K, Loots I, Baeyens W, Schoeters G (2021) Human biomonitoring data enables evidence-informed policy to reduce internal exposure to persistent organic compounds: a case study. Int J Environ Res Public Health 18(11):5559. https://doi.org/10.3390/ijerph18115559.PMID:34067438,PMCID:PMC8196998

    Article  CAS  Google Scholar 

  28. Den Hond E, Paulussen M, Geens T, Bruckers L, Baeyens W, David F, Dumont E, Loots I, Morrens B, de Bellevaux BN, Nelen V, Schoeters G, van Larebeke N, Covaci A (2013) Biomarkers of human exposure to personal care products: results from the Flemish environment and health study (FLEHS 2007–2011). Sci Total Environ 463–464:102–110

    Article  Google Scholar 

  29. Vrijens J, Leermakers M, Stalpaert M, Schoeters G, DenHond E, Bruckers L, Colles A, Nelen V, Van Den Mieroop E, van Larebeke N, Loots I, Baeyens W (2014) Trace metal concentrations measured in blood and urine of adolescents in flanders, belgium: reference population and case studies Genk-Zuid and Menen. Int J Hyg Environ Health 217(4–5):515–527

    Article  CAS  Google Scholar 

  30. Kato K, Wong LY, Jia LT, Kuklenyik Z, Calafat AM (2011) Trends in exposure to polyfluoroalkyl chemicals in the U.S. Population: 1999–2008. Environ Sci Technol 45:8037–8045

    Article  CAS  Google Scholar 

  31. Baker EL, Letz RE, Fidler AT, Shalat S, Plantamura D, Lyndon M (1985) A computer-based neurobehavioral evaluation system for occupational and environmental epidemiology: methodology and validation studies. Neurobehav Toxicol Teratol 7:369–377

    CAS  Google Scholar 

  32. White RF, James KE, Vasterling JJ, Letz R, Marans K, Delaney R, Krengel M, Rose F, Kraemer HC (2003) Neuropsychological screening for cognitive impairment using computer-assisted tasks. Assessment 10(1):86–101

    Article  Google Scholar 

  33. Johns MW (1991) A new method for measuring daytime sleepiness: the Epworth sleepiness scale. Sleep 14(6):540–545

    Article  CAS  Google Scholar 

  34. Spence JT, Helmreich RL (1979) Comparison of masculine and feminine personality attributes and sex-role attitudes across age groups. Dev Psychol 15(5):583–584. https://doi.org/10.1037/h0078091

    Article  Google Scholar 

  35. Gochfeld M, Burger J (2011) Disproportionate exposures in environmental justice and other populations: the importance of outliers. Am J Public Health 101:S53–S63. https://doi.org/10.2105/AJPH.2011.300121

    Article  Google Scholar 

  36. Chatterjee S, Hadi AS (1986) Influential observations, high leverage points, and outliers in linear regression. Statist Sci 1(3):379–393

    Google Scholar 

  37. Zhang Z (2016) Residuals and regression diagnostics focusing on logistic regression. Ann Transl Med 4(10):195

    Article  Google Scholar 

  38. Schoeters G, Govarts E, Brucker L, Den Hond E, Nelen V, De Henauw S, Sioen I, Nawrot TS, Plusquin M, Vriens A, Covaci A, Loots I, Morrens B, Coertjens D, Van Larebeke N, De Craemer S, Croes K, Lambrechts N, Colles A, Baeyens W (2017) Three cycles of human biomonitoring in Flanders-time trends observed in the Flemish environment and health study. Int J Hyg Environ Health 220:36e45. https://doi.org/10.1016/j.ijheh.2016.11.006

    Article  Google Scholar 

  39. Rylander C, Brustad M, Falk H, Sandanger TM (2009) Dietary predictors and plasma concentrations of perfluorinated compounds in a coastal population from northern Norway. J Environ Public Health 2009:268219

    Article  Google Scholar 

  40. Berg V, Nost TH, Huber S, Rylander C, Hansen S, Veyhe AS, Fuskevag OM, Odland JO, Sandanger TM (2014) Maternal serum concentrations of per- and polyfluoroalkyl substances and their predictors in years with reduced production and use. Environ Int 69:58–66

    Article  CAS  Google Scholar 

  41. Bjerregaard-Olesen C, Bach CC, Long M, Ghisari M, Bech BH, Nohr EA, Henriksen TB, Olsen J, Bonefeld-Jorgensen EC (2016) Determinants of serum levels of perfluorinated alkyl acids in Danish pregnant women. Int J Hyg Environ Health 219(8):867–875

    Article  CAS  Google Scholar 

  42. Choi N, Fuqua D (2003) The structure of the bem sex role inventory: a summary report of 23 validation studies. Educ Psychol Measur 63(5):872–887. https://doi.org/10.1177/0013164403258235

    Article  Google Scholar 

  43. Vantieghem W, Vermeersch H, Van Houtte M (2014) Why “gender” disappeared from the gender gap:(re-) introducing gender identity theory to educational gender gap research. Soc Psychol Edu 17(3):357–381

    Article  Google Scholar 

  44. Eger E, Lemal D, Laster M, Liao M, Jankowska K, Raghavanpillai A, Popov A, Gan Y, Lou Y (2007) Anesthetic properties of some fluorinated oxolanes and oxetanes. Anesth Analg 104(5):1090–1097

    Article  CAS  Google Scholar 

  45. Xie X, Weng X, Liu S, Chen J, Guo X, Gao X, Fei Q, Hao G, JingC FL (2021) Perfluoroalkyl and polyfluoroalkyl substance exposure and association with sex hormone concentrations: results from the NHANES 2015–2016. Environ Sci Eur 33:69

    Article  CAS  Google Scholar 

  46. Tsaia M-S, Chien-YuLinbc C-C, Mei-HueiChend SHJ, Hsue K-L, Fung-ChangSungh P-C, Ta-ChenSugj, (2015) Association between perfluoroalkyl substances and reproductive hormones in adolescents and young adults. Int J Hyg Environ Health 218(5):437–443

    Article  Google Scholar 

  47. Lopez-Espinosa MJ, Mondal D, Armstrong BG, Eskenazi B, Fletcher T (2016) Perfluoroalkyl substances, sex hormones, and insulin-like growth factor-1 at 6–9 years of age: a cross-sectional analysis within the C8 health project. Environ Health Perspect 124:1269–1275. https://doi.org/10.1289/ehp.1509869

    Article  CAS  Google Scholar 

  48. Nordström Joensen U, Veyrand B, Antignac JP, Blomberg Jensen M, Holm Petersen J, Marchand Ph, Skakkebæk NE, Andersson AM, Le Bizec B, Jørgensen N (2013) PFOS (perfluorooctanesulfonate) in serum is negatively associated with testosterone levels, but not with semen quality, in healthy men. Hum Reprod 28(3):599–608

    Article  Google Scholar 

  49. Johansson N, Fredriksson A, Eriksson P (2008) Neonatal exposure to perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA) causes neurobehavioural defects in adult mice. Neurotoxicology 29(1):160–169

    Article  CAS  Google Scholar 

  50. Donauer S, Chen A, Xu Y, Calafat AM, Sjodin A, Yolton K (2015) Prenatal exposure to polybrominated diphenyl ethers and polyfluoroalkyl chemicals and infant neurobehavior. J Pediatr 166(3):736–742

    Article  CAS  Google Scholar 

  51. Hoyer BB, Ramlau-Hansen CH, Obel C, PedersenHS HA, Ogniev V, Jonsson BA, Lindh CH, Rylander L, Rignell-Hydbom A, Bonde JP, Toft G (2015) Pregnancy serum concentrations of perfluorinated alkyl substances and offspring behaviour and motor development at age 5–9 years–a prospective study. Environ Health 14:2

    Article  Google Scholar 

  52. Fei C, McLaughlin JK, Lipworth L, Olsen J (2008) Prenatal exposure to perfluorooctanoate (PFOA) and perfluorooctanesulfonate (PFOS) and maternally reported developmental milestones in infancy. Environ Health Perspect 116(10):1391–1395

    Article  CAS  Google Scholar 

  53. Quaak I, de Cock M, de Boer M, Lamoree M, Leonards P, van de Bor M (2016) Prenatal exposure to perfluoroalkyl substances and behavioral development in children. Int J Enviro Res Publ Health 13(5):511

    Article  Google Scholar 

  54. Harris MH, Oken E, Rifas-Shiman SL, Calafat AM, Ye X, Bellinger DC (2018) Prenatal and childhood exposure to per-and polyfluoroalkyl substances (PFASs) and child cognition. Environ Int 115:358–369

    Article  CAS  Google Scholar 

  55. Stein CR, Savitz DA (2011) Serum perfluorinated compound concentration and attention deficit/hyperactivity disorder in children 5–18 years of age. Environ Health Perspect 119(10):1466–1471

    Article  CAS  Google Scholar 

  56. Forns J, Iszatt N, White RA, Mandal S, Sabaredzovic A, Lamoree M, Thomsen C, Haug LS, Stigum H, Eggesbo M (2015) Perfluoroalkyl substances measured in breast milk and child neuropsychological development in a Norwegian birth cohort study. Environ Int 83:176–182

    Article  CAS  Google Scholar 

  57. Roth N, Wilks MF (2014) Neurodevelopmental and neurobehavioural effects of polybrominated and perfluorinated chemicals: a systematic review of the epidemiological literature using a quality assessment scheme. Toxicol Lett 230(2):271–281

    Article  CAS  Google Scholar 

  58. Kishi R, Araki A, Minatoya M, Hanaoka T, Miyashita C, Itoh S, Kobayashi S, Ait B, Yamazaki K, Miura R, Tamura N, Ito K, Goudarzi H (2017) The hokkaido birth cohort study on environment and children’s health: cohort profile-updated 2017. Environ Health Prev Med 22(1):46

    Article  Google Scholar 

  59. Jeddy Z, HartmanTJ TEV, Poteete C, Kordas K (2017) Prenatal concentrations of perfluoroalkyl substances and early communication development in british girls. Early Hum Dev 109:15–20

    Article  CAS  Google Scholar 

  60. Slotkin TA, MacKillop EA, Melnick RL, Thayer KA, Seidler FJ (2008) Developmental neurotoxicity of perfluorinated chemicals modeled in vitro. Environ Health Perspect 116(6):716–722

    Article  CAS  Google Scholar 

  61. Wang Y, Wang L, Chang W, Zhang Y, Yuan Z, Liu W (2019) Neurotoxic effects of perfluoroalkyl acids: neurobehavioral deficit and its molecular mechanism. Toxicol Lett. https://doi.org/10.1016/j.toxlet.2019.01.012

    Article  Google Scholar 

  62. Ren H, Vallanat B, Nelson DM, Yeung LWY, Guruge KS, Lam PKS, Lehman-McKeeman LD, Corton JC (2009) Evidence for the involvement of xenobiotic-responsive nuclear receptors in transcriptional effects upon perfluoroalkyl acid exposure in diverse species. Reprod Toxicol 27(3–4):266–277

    Article  CAS  Google Scholar 

  63. Lau C (2015) Perfluorinated Compounds An Overview. In: DeWitt J (ed) Toxicological Effects of Perfluoroalkyl and Polyfluoroalkyl Substances Molecular and Integrative Toxicology Humana Press. Cham

  64. Kobayashi S, Azumi K, Goudarzi H, Araki A, Miyashita C, Kobayashi S (2017) Effects of prenatal perfluoroalkylacid exposure on cordblood IGF2/H19 methylation and ponderal index:the hokkaidostudy. J Expo SciEnviron Epidemiol 27(3):251–259. https://doi.org/10.1038/jes.2016.50

    Article  CAS  Google Scholar 

  65. Starling P, Liu C, Shen G, Yang IV, Kechris K, Borengasser SJ, Boyle KE, Zhang W, Smith HA, Calafat AM, Hamman RF, Adgat JL, Dabelea D (2020) prenatal exposure to per- and polyfluoroalkyl substances, umbilical cord blood dna methylation, and cardio-metabolic indicators in newborns: the healthy start study. Environ Health Perspect 128(12):127014

    Article  CAS  Google Scholar 

  66. Power MC, Webster TF, Baccarelli AA, Weisskopf MG (2013) Cross-sectional association between polyfluoroalkyl chemicals and cognitive limitation in the national health and nutrition examination survey. Neuroepidemiology 40(2):125–132

    Article  Google Scholar 

  67. Gallo V, Leonardi G, Brayne C, Armstrong B, Fletcher T (2013) Serum perfluoroalkyl acids concentrations and memory impairment in a large cross-sectional study. BMJ Open 3(6):e002414

    Article  Google Scholar 

  68. Shrestha S, Bloom MS, Yucel R, Seegal RF, Rej R, McCaffrey RJ, Wu Q, Kannan K, Fitzgerald EF (2017) Perfluoroalkyl substances, thyroid hormones, and neuropsychological status in older adults. Int J Hyg Environ Health 220(4):679–685

    Article  CAS  Google Scholar 

  69. Park SK, DingN HD (2021) Perfluoroalkyl substances and cognitive function in older adults: Should we consider non-monotonic dose-responses and chronic kidney disease? Environ Res 192:110346

    Article  CAS  Google Scholar 

  70. Wenig X, Liang H, Tan Y, Chen J, Fei Q, Liu S, Guo X, Wen L, Wu Y, Jing C (2022) Mixed effects of perfluoroalkyl and polyfluoroalkyl substances exposure on cognitive function among people over 60 years old from NHANES. Environ Sci Pollut Res Int 29(21):32093–32104

    Article  Google Scholar 

  71. Gore AC, Chappell VA, Fenton Flaw SE, Nadal JA, Prins A, Toppari GS, Zoeller JRT (2015) EDC-2: the endocrine society’s second scientific statement on endocrine-disrupting chemicals. Endocr Rev 36(6):150

    Article  Google Scholar 

  72. Petzold S, Averbeck M, Simon JC, Lehmann I, Polte T (2014) Lifetime-dependent effects of bisphenol a on asthma development in an experimental mouse model. PLoS ONE 9(6):e100468

    Article  Google Scholar 

  73. Johansson N, Eriksson P, Viberg H (2009) Neonatal exposure to PFOS and PFOA in mice results in changes in proteins which are important for neuronal growth and synaptogenesis in the developing brain. Toxicol Sci 108(2):412–418

    Article  CAS  Google Scholar 

  74. Lee YY, Wong CK, Oger C, Durand T, Galano JM, Lee JC (2015) Prenatal exposure to the contaminant perfluorooctane sulfonate elevates lipid peroxidation during mouse fetal development but not in the pregnant dam. Free Radic Res 49(8):1015–1025

    Article  CAS  Google Scholar 

  75. Wang WL, Zhang Q, Zhao H, Quan X (2015) Effects of developmental perfluorooctane sulfonate exposure on spatial learning and memory ability of rats and mechanism associated with synaptic plasticity. Food Chem Toxicol 76:70–76

    Article  Google Scholar 

  76. Zhang Q, Liu W, Zhao H, Zhang Z, Qin LHF, Niu Q (2018) Developmental perfluorooctane sulfonate exposure inhibits long-term potentiation by affecting AMPA receptor trafficking. Toxicology 412:55–62

    Article  Google Scholar 

  77. Zhang Q, Zhao H, Liu W, Zhang Z, Qin H, Luo F, Leng S (2016) Developmental perfluorooctane sulfonate exposure results in tau hyperphosphorylation and beta-amyloid aggregation in adults rats: Incidence for link to Alzheimer’s disease. Toxicology 347–349:40–46

    Article  Google Scholar 

  78. Rovet J (2014) The role of thyroid hormones for brain development and cognitive function. Endocr Dev 26:26–43. https://doi.org/10.1159/000363153

    Article  Google Scholar 

  79. Pedersen K, Eggers N, Basu R, Letcher AK, Greaves C, Sonne RD, Styrishave B (2015) Brain region-specific perfluoroalkylated sulfonate (PFSA) and carboxylic acid (PFCA) accumulation and neurochemical biomarker responses in east greenland polar bears (Ursus maritimus). Environ Res 138:22–31

    Article  Google Scholar 

  80. Vanden Heuvel JP, Thompson JT, Frame SR, Gillies PJ (2006) Differential activation of nuclear receptors by perfluorinated fatty acid analogs and natural fatty acids: a comparison of human, mouse, and rat peroxisome proliferator-activated receptor-alpha, -beta, and -gamma, liver X receptor-beta, and retinoid X receptor-alpha. Toxicol Sci 92:476–489

    Article  CAS  Google Scholar 

  81. Kapadia R, Yi JH, Vemuganti R (2008) Mechanisms of anti-inflammatory and neuroprotective actions of PPAR-gamma agonists. Front Biosci 13:1813–1826

    Article  CAS  Google Scholar 

  82. Dalsager L, Christensen N, Halekoh U, Timmermann CAG, Nielsen F, Kyhl H, Husby S, Grandjean P, Jensen TK, Andersen HR (2021) Exposure to perfluoroalkyl substances during fetal life and hospitalization for infectious disease in childhood: a study among 1,503 children from the odense child cohort. Environ Int 149:106395

    Article  CAS  Google Scholar 

  83. Austin ME, Kasturi BS, Barber M, Kannan K, MohanKumar PS, MohanKumar SM (2003) Neuroendocrine effects of perfluorooctane sulfonate in rats. Environ Health Perspect 111(12):1485–1489

    Article  CAS  Google Scholar 

  84. Shelly C, Grandjean P, Oulhote Y, Plomgaard P, Frikke-Schmidt R, Nielsen F, Zmirou-Navier D, Weihe P, Valvi D (2019) early life exposures to perfluoroalkyl substances in relation to adipokine hormone levels at birth and during childhood. J Clin Endocrinol Metab 104(11):5338–5348

    Article  Google Scholar 

  85. Domazet SL, Jensen TK, Wedderkopp N, Nielsen F, Andersen LB, Grontved A (2020) Exposure to perfluoroalkylated substances (PFAS) in relation to fitness, physical activity, and adipokine levels in childhood: The european youth heart study. Environ Res 191:110110

    Article  CAS  Google Scholar 

  86. Liu W, Qin H, Pan Y, Luo F, Zhang Z (2019) Low concentrations of perfluorooctane sulfonate repress osteogenic and enhance adipogenic differentiation of human mesenchymal stem cells. Toxicol Appl Pharmac 367:82–91

    Article  CAS  Google Scholar 

  87. CuiLy Liu YJ, Nie S, Chen J, Dong Q, Huang C, Yang D (2017) Chronic perfluorooctanesulfonic acid exposure disrupts lipid metabolism in zebrafish. Hum Exp Toxicol 36(3):207–217

    Article  Google Scholar 

  88. Du G, Sun J, Zhang Y (2018) Perfluorooctanoic acid impaired glucose homeostasis through affecting adipose AKT pathway. Cytotechnology 70(1):479–487

    Article  CAS  Google Scholar 

  89. Hines EP, White SS, Stanko JP, Gibbs-Flournoy EA, Lau C, Fenton SE (2009) Phenotypic dichotomy following developmental exposure to perfluorooctanoic acid (PFOA) in female CD-1 mice: Low doses induce elevated serum leptin and insulin, and overweight in mid-life. Mol Cell Endocrinol 304(1–2):97–105

    Article  CAS  Google Scholar 

  90. Lv Z, Li G, Li Y, Ying C, Chen J, Chen T, Wei J, LinJiang YY, Wang Y, Shu B, Xu B, Xu S (2013) Glucose and lipid homeostasis in adult rat is impaired by early-life exposure to perfluorooctane sulfonate. Environ Toxicol 28(9):532–542

    Article  CAS  Google Scholar 

  91. Buck CO, Eliot MN, Kelsey KT, Calafat AM, Chen A, Ehrlich S, Lanphear P, Braun JM (2018) Prenatal exposure to perfluoroalkyl substances and adipocytokines: the HOME Study. Pediatr Res 84(6):854–860

    Article  CAS  Google Scholar 

  92. Halldorsson T, Rytter D, Haug LS, Bech BH, Danielsen I, Becher G, Henriksen TB, Olsen SF (2012) Prenatal exposure to perfluorooctanoate and risk of overweight at 20 years of age: a prospective cohort study. Environ Health Perspect 120(5):668–673

    Article  CAS  Google Scholar 

  93. Shih YH, Blomberg A, Jorgensen LH, Weihe P, Grandjean P (2022) Early-life exposure to perfluoroalkyl substances in relation to serum adipokines in a longitudinal birth cohort. Environ Res 204(Pt A):111905

    Article  CAS  Google Scholar 

  94. Ding N, Karvonen-Gutierrez CA, Herman WH, Calafat AM, Mukherjee B, Park SK (2021) Associations of perfluoroalkyl and polyfluoroalkyl substances (PFAS) and PFAS mixtures with adipokines in midlife women. Int J Hyg Environ Health 235:113777

    Article  CAS  Google Scholar 

  95. Farr SA, Banks WA, Morley JE (2006) Effects of leptin on memory processing. Peptides 27(6):1420–1425. https://doi.org/10.1016/j.peptides.2005.10.006.PMID16293343.S2CID42496027

    Article  CAS  Google Scholar 

  96. Greco SJ, Bryan KJ, Sarkar S, Zhu X, Smith MA, Ashford JW, Johnston JM, Tezapsidis N, Casadesus G (2010) Leptin reduces pathology and improves memory in a transgenic mouse model of Alzheimer’s disease". J Alzheimers Dis 19(4):1155–1167. https://doi.org/10.3233/JAD-2010-1308.PMC2862270.PMID20308782

    Article  CAS  Google Scholar 

  97. Rothman KJ (1986) Modern epidemiology. Little Brown And Company, Boston

    Google Scholar 

Download references

Acknowledgements

We thank the families, mothers, adults and adolescents who participated in FLEHS 2 and 3 and the hospitals, doctors and nurses who were involved in this study. Without their effort, this study would not have been possible. We thank the field workers from the Provincial Institute of Hygiene and VITO for the sample and data collection and the “Algemeen Medisch Laboratorium” for certain measurements. The FLEHS-studies were carried out by the Flemish Center of Expertise on Environment and Health. The studies of the Center were commissioned by the Ministry of the Flemish Community. We thank in particular Karen Van Campenhout and her collaborators of the Flemish Ministry, Department of Environment, for years of constructive collaboration in the administration of the FLEHS campaigns. The views expressed herein are those of the authors and are not necessarily endorsed by the Flemish government.

Funding

This study was commissioned and financed by the Ministry of the Flemish Community (Department of Economics, Science and Innovation; Flemish Agency for Care and Health; and Department of Environment, Nature and Energy). The sponsor was not involved in analysis and interpretation of the data, nor in the decision to publish the data.

Author information

Authors and Affiliations

Authors

Contributions

NVL participated in the setting up of the studies, was the major contributor in writing the manuscript and was the spokesperson for the FLEHS 2 studies. GK participated in the setting up of the studies, and in the writing of the manuscript. SD performed the statistical analysis. AC participated in the setting up of the studies and in the practical organization; LB participated in the setting up of the studies and assisted in the statistical analysis. EDH participated in the setting up of the studies and in the organization of the field work. EG participated in the setting up of the studies and assisted in the statistical analysis and in the practical organization. BM participated in the setting up of the studies, in the field work and had a major role in the sociological aspects of the studies. TS performed the chemical analyses on PFAS. SR gave advice concerning the statistical analysis. DC participated in the practical organization of the studies and in the sociological aspects of the studies. TN participated in the setting up of the studies and guided the neurobehavioral and cognition aspects of the studies. VN participated in the setting up of the studies, in the organization of the field work and was the spokesperson for the FLEHS 3 studies. WB participated in the setting up of the studies and was responsible for the general coordination of part of the studies. GS participated in the setting up of the studies, was responsible for the general coordination of part of the studies and was coordinator of the field work committee. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Nicolas van Larebeke.

Ethics declarations

Ethics approval and consent to participate

In order to participate all subjects had to give written informed consent. The FLEHS studies were approved by the Ethics Committees of the University of Antwerp and the Antwerp University Hospital (UZA), Belgium. The dossier numbers for the different studies were, respectively, UA A08 09 (FLEHS-2, neonates, adults and adolescents of industrial contaminated site) and B300201419843 for FLEHS-3 adults. All methods were carried out in accordance with relevant guidelines and regulations (Declaration of Helsinki).

Consent for publication

Not applicable.

Competing interests

‘‘The authors declare that they have no competing interests’’.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1

: Table S1. FLEHS-2 campaign on adolescents. List of confounders and potential covariates included in stepwise multiple or logistic regressions. Table S2. FLESH-3 campaign on adults. List of confounders and potential covariates included in stepwise multiple or logistic regressions. Table S3. Association of blood concentrations of perfluoro compounds with ln-transformed cognitive and neuropsychological parameters. Table S4. Association of blood concentrations of perfluoro compounds with non-transformed cognitive and neuropsychological parameters.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

van Larebeke, N., Koppen, G., Decraemer, S. et al. Per- and polyfluoroalkyl substances (PFAS) and neurobehavioral function and cognition in adolescents (2010–2011) and elderly people (2014): results from the Flanders Environment and Health Studies (FLEHS). Environ Sci Eur 34, 98 (2022). https://doi.org/10.1186/s12302-022-00675-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s12302-022-00675-3

Keywords

  • PFAS
  • Perfluorooctane sulfonate
  • Perfluorohexane sulfonate
  • Perfluorooctanoic acid
  • Perfluorononanoic acid
  • Cognition
  • Somnolence
  • Femininity
  • Masculinity