Children's HealthOpen Access

Attention Deficit/Hyperactivity Disorder and Childhood Autism in Association with Prenatal Exposure to Perfluoroalkyl Substances: A Nested Case–Control Study in the Danish National Birth Cohort

    Published:https://doi.org/10.1289/ehp.1408412Cited by:15

    Abstract

    Background:

    Perfluoroalkyl substances (PFASs) are persistent pollutants found to be endocrine disruptive and neurotoxic in animals. Positive correlations between PFASs and neurobehavioral problems in children were reported in cross-sectional data, but findings from prospective studies are limited.

    Objectives:

    We investigated whether prenatal exposure to PFASs is associated with attention deficit/hyperactivity disorder (ADHD) or childhood autism in children.

    Methods:

    Among 83,389 mother–child pairs enrolled in the Danish National Birth Cohort during 1996–2002, we identified 890 ADHD cases and 301 childhood autism cases from the Danish National Hospital Registry and the Danish Psychiatric Central Registry. From this cohort, we randomly selected 220 cases each of ADHD and autism, and we also randomly selected 550 controls frequency matched by child’s sex. Sixteen PFASs were measured in maternal plasma collected in early or mid-pregnancy. We calculated risk ratios (RRs) using generalized linear models, taking into account sampling weights.

    Results:

    Perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA) were detected in all samples; four other PFASs were quantified in ≥ 90% of the samples. We did not find consistent evidence of associations between mother’s PFAS plasma levels and ADHD [per natural log nanograms per milliliter increase: PFOS RR = 0.87 (95% CI: 0.74, 1.02); PFOA RR = 0.98 (95% CI: 0.82, 1.16)] or autism [per natural log nanograms per milliliter increase: PFOS RR = 0.92 (95% CI: 0.69, 1.22); PFOA RR = 0.98 (95% CI: 0.73, 1.31)]. We found positive as well as negative associations between higher PFAS quartiles and ADHD in models that simultaneously adjusted for all PFASs, but these estimates were imprecise.

    Conclusions:

    In this study we found no consistent evidence to suggest that prenatal PFAS exposure increases the risk of ADHD or childhood autism in children.

    Citation:

    Liew Z, Ritz B, von Ehrenstein OS, Bech BH, Nohr EA, Fei CY, Bossi R, Henriksen TB, Bonefeld-Jørgensen EC, Olsen J. 2015. Attention deficit/hyperactivity disorder and childhood autism in association with prenatal exposure to perfluoroalkyl substances: a nested case–control study in the Danish National Birth Cohort. Environ Health Perspect 123:367–373;  http://dx.doi.org/10.1289/ehp.1408412

    Introduction

    Perfluoroalkyl substances (PFASs) are a group of human-made fluorine-containing compounds with unique properties that make materials resistant to stains, oil, and water (Buck et al. 2011). PFASs have been used widely in commercial products since the 1950s, and they are persistent in the environment and in living organisms throughout the globe (Houde et al. 2006). Human exposure routes include contamination of food from packaging, bioaccumulation in the food chain, and household dust (D’eon and Mabury 2011). Perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA) are the two most frequently used PFASs; they have estimated biological half-lives in humans between 4 and 5 years (Olsen et al. 2007). PFOS and PFOA concentrations in humans were reported to be decreasing in some countries following a drop in production since 2000 (Kato et al. 2011), but exposure to other short-chain compounds such as perfluorobutane sulfonate (PFBS) and perfluorohexane sulfonate (PFHxS) and long-chain perfluorononanoic acid (PFNA) and perfluorodecanoic acid (PFDA) are reported to be increasing (Glynn et al. 2012).

    PFASs can cross the placental barrier and expose the fetus during the most vulnerable period of development (Fei et al. 2007). Experimental data suggest that PFASs may be developmental neurotoxicants that can affect neuronal cell development (Slotkin et al. 2008), alter cognitive function, and reduce habituation and learning ability in mice (Johansson et al. 2008, 2009; Viberg et al. 2013). PFASs also have endocrine-disruptive properties (Kjeldsen and Bonefeld-Jørgensen 2013) and might interfere with thyroid hormone function (Lau et al. 2003; Lin et al. 2013; Long et al. 2013; Wang et al. 2014), which is essential in regulating fetal brain development (Porterfield 2000).

    Attention deficit/hyperactivity disorder (ADHD) is considered one of the most common neurobehavioral disorders worldwide, and is characterized by inattention, hyperactivity, increased impulsivity, and motivational/emotional dysregulation (Polanczyk et al. 2007). Autism is a neurodevelopmental disorder characterized by impairments in communication and reciprocal social interaction, coupled with repetitive behavior (Pickett and London 2005). The incidence of ADHD and autism has increased over the past decades, and it has been suggested that the rise is not attributable solely to changes in diagnostic practices or parental awareness (Faraone et al. 2003; Hertz-Picciotto and Delwiche 2009; Møller et al. 2007). The etiologies are not well understood, but both environmental and genetic factors are thought to contribute to ADHD and autism (Lyall et al. 2014; Millichap 2008). ADHD and autism disproportionately affect boys (Arnold 1996), and studies suggest that prenatal exposure to endocrine-disrupting chemicals may be associated with the occurrence of both diseases (de Cock et al. 2012).

    A limited number of epidemiologic studies have evaluated the potential neurobehavioral or neurocognitive impact of PFASs and findings were inconclusive. Several cross-sectional studies have reported positive associations between serum levels of some PFASs with impulsivity (Gump et al. 2011) and ADHD in children (Hoffman et al. 2010; Stein and Savitz 2011). Reverse causality, however, is a concern for studies that measure PFAS levels in children already diagnosed with ADHD at time of blood draw. Little evidence of associations was found for prenatal exposures to PFOS or PFOA and behavioral problems in 7-year-old children assessed with the Strengths and Difficulties Questionnaires in the prospective Danish birth cohort (Fei and Olsen 2011). A study conducted in a community with high long-term exposure to PFOA in contaminated drinking water reported that in utero PFOA levels were associated with higher Full-Scale IQ and decreased ADHD characteristics among children 6–12 years of age (Stein et al. 2013). However, prenatal PFOA exposures were estimated based on exposure modeling. A recent study examined the associations between several endocrine-disrupting chemicals, including PFASs, and autistic behaviors in children but no conclusive evidence was found, perhaps due to small sample size (175 mother–child pairs) and low statistical power (Braun et al. 2014).

    We conducted a nested case–control study within the framework of the Danish National Birth Cohort (DNBC) to examine whether prenatal exposure to PFASs is associated with ADHD or autism in children.

    Methods

    The DNBC is a nationwide cohort study of pregnancies and health-related outcomes in the children; details have been described elsewhere (Olsen et al. 2001). Briefly, pregnant women were recruited through their general practitioners during early gestation (weeks 6–12) from 1996 to 2002. About 50% of all general practitioners in Denmark participated in the study, and 60% of the women invited agreed to participate. Women were ineligible if they did not speak sufficient Danish for interviews or intended not to carry their pregnancy to term. Information was collected during four computer-assisted telephone interviews (twice during pregnancy and twice postpartum). Two prenatal maternal blood samples were collected and stored, one each in the first and second trimester. English versions of questionnaires are available online (Statens Serum Institut 2013).

    Written informed consent was obtained from all participants at recruitment. Study procedures have been approved by the Danish Data Protection Agency and the Institutional Review Board at University of California, Los Angeles.

    Source population. The source population for this study consisted of live-born singletons along with mothers who participated in the first telephone interview, conducted approximately during the 12th gestational week, and had provided a blood sample at least once either during the first or second pregnancy trimesters. This resulted in 83,389 mother–child pairs, with 42,737 boys and 40,652 girls; we excluded from the original DNBC those with an unsuccessful pregnancy (n = 6,207), non-singleton births (n = 2,080), births with unknown birth outcomes (n = 25) or missing dates of birth (n = 99), mothers who emigrated (n = 51) or died (n = 3), and women who did not participate in the first telephone interview (n = 4,578) or did not provide a prenatal blood sample (n = 4,609).

    Selection of cases and controls. We identified children who were diagnosed with ADHD and autism, respectively, by linking DNBC records to the Danish National Hospital Registry (Andersen et al. 1999) that contains the nationwide data for all admissions for somatic illnesses, and also to the Danish Psychiatric Central Registry (Munk-Jørgensen et al. 1993), which covers admissions to all psychiatric hospitals in Denmark. The record linkage relied on the unique civil registration numbers given to all Danish citizens at birth. All diagnoses are based on the International Classification of Diseases, 10th Revision (ICD-10 codes F90.0 for ADHD and F84.0 for childhood autism) and included inpatient and outpatient records. A total of 890 ADHD cases and 301 autism cases were identified in the cohort during an average of 10.7 years of follow-up (record linkage was conducted on 1 August 2011). Because of the high costs of measuring PFASs, we randomly selected 220 cases of ADHD and autism each for inclusion in this study.

    We used a case-cohort sampling strategy and randomly selected 550 children (440 males and 110 females) as controls from the source population, frequency matched to cases by sex. The flow chart of subject selection and sampling fractions of cases and controls is shown in Figure 1.

    Figure 1 Flow chart of study population selection in the Danish National Birth Cohort. Sampling fraction of ADHD cases is 0.2472. Sampling fraction of autism cases is 0.7309. Sampling fractions of control group are 0.0103 for male and 0.0027 for female.

    PFAS measurements. Details about analytic methods for PFASs have been described previously (Liew et al. 2014). Briefly, the collected maternal blood samples were sent by mail to Statens Serum Institut in Copenhagen, and separated and stored in freezers at –20°C or –80°C or in liquid nitrogen. We used 0.1 mL stored maternal plasma, and the samples were analyzed at the Department of Environmental Science at Aarhus University. Most of the samples (87%) for both cases and controls were collected during the first trimester; if the first sample was not available, we used the second sample collected in the second trimester instead. Solid-phase extraction technique was used for extraction and purification, and PFAS concentrations were measured by liquid chromatography–tandem mass spectrometry. Measurements were performed in a random sequence for cases and controls by laboratory personnel blinded to diagnoses and any other participant information. Seventeen maternal samples (5 ADHD, 7 autism, 5 controls) were either not available from the biobank or failed the PFAS extraction and purification process, and hence were excluded. For 21 samples included in our current study, PFOA and PFOS values previously had been analyzed at the 3M Toxicology Laboratory for an earlier study in the DNBC (Fei et al. 2007). For quality control we compared the PFOA and PFOS value measured from the two laboratories in these samples and found high correlations (Pearson r = 0.94 for PFOS and r = 0.95 for PFOA).

    Of the 16 different PFASs detected in maternal plasma, we focused on the six PFASs for which at least 90% of all samples were above the lower limit of quantitation (LLOQ): PFOS, 100%; PFOA, 100%; PFHxS, 98%; PFHpS (perfluoroheptane sulfonate), 96%; PFNA, 92%; and PFDA, 90%. The LLOQ for the six PFASs are as follows: PFOS, 0.28 ng/mL; PFOA, 0.20 ng/mL; PFHxS, 0.08 ng/mL; PFHpS, 0.11 ng/mL; PFNA, 0.27 ng/mL; and PFDA, 0.09 ng/mL. The full panel for the LLOQ and distribution of all PFASs was reported previously (Liew et al. 2014).

    Statistical analysis. We used generalized linear models and accounted for the sampling fractions of cases and controls to estimate risk ratios (RR) and 95% confidence intervals (CIs) for prenatal PFAS exposures and ADHD or autism. We also performed unconditional logistic regression to estimate odds ratios (ORs) without applying the sampling weights. PFAS concentrations were first analyzed as continuous variables (per natural-log unit increase). The PFAS values were natural-log transformed to reduce the influence of outliers, to improve the model fit, and to make interpretation simpler and more consistent across different PFASs that vary in their ranges of concentration. We also categorized PFAS values into quartiles according to the distribution among controls, using the lowest quartile as the reference group. Moreover, we fitted generalized additive models with a smoothing function of natural-log PFAS values to examine potential nonlinear relations. Five knots were set as the upper limit of number of degrees of freedom, and we compared model fit and visually inspected plots of the smoothed data. We did not find evidence for nonlinearity between natural-log PFAS values and ADHD or autism (data not shown).

    Potential confounders were chosen a priori considering variables that may influence PFAS exposures and previously suggested risk factors of ADHD or autism. We included maternal age at delivery (≤ 24, 25–29, 30–34, ≥ 35 years), parity (1, > 1), socioeconomic status (low/medium or high), maternal smoking (never, ≤ 9 cigarette/day, > 9 cigarettes/day) and alcohol drinking (yes, no) during pregnancy, mother’s self-reported psychiatric illnesses (yes, no), gestational week of blood draw (4–8, > 8 week), child’s birth year (1998–2000, 2001–2003), and the matching factor child’s sex in the final model. Socioeconomic status was determined based on self-reported maternal and paternal education and occupation using three categories (high, medium, and low): Higher education (4 years beyond high school) or work in management were classified as high; skilled workers and middle-range education as medium; and unskilled workers and unemployed as low (Bech et al. 2005). To determine maternal psychiatric illnesses, we asked women to report whether they had ever seen a doctor or psychologist because of depression, anxiety, childhood psychiatric disorder, family problems/life crisis, or other mental health problems. Additionally, other potential confounders such as father’s age at child’s birth, mother’s prepregnancy body mass index, whether the pregnancy was planned, and season of conception were evaluated but not included in final models because they changed effect estimates of interest minimally (< 1%).

    To account for PFAS values below the LLOQ when PFASs were analyzed as continuous variables, we used multiple imputation (Lubin et al. 2004) with the procedure PROC MI in SAS (SAS Institute Inc.) with all six PFASs and all covariates included in the model. Ten simulated complete data sets were generated via imputation, and we employed standard analytical procedures to combine the results (Yuan 2001).

    A Pearson correlation matrix for the considered PFASs is presented in Supplemental Material, Table S1. We constructed a “multiple PFAS” model where we simultaneously included all PFASs in one model to examine whether any single PFAS may be of particular importance. We also evaluated potential effect measure modification by child’s sex; we compared the sex-stratified estimates and examined the p-value for the PFAS–sex interaction term. For ADHD we also conducted analyses in which we excluded children born after 2000 because the duration of follow-up may not have been long enough to identify children with this diagnosis. For these stratified analyses we used logistic regression without applying sampling weights because the weighted analyses may underestimate uncertainty in our data when the number of actual measured samples is small. Finally, in sensitivity analyses we excluded PFAS values that were greater than three times the 75th percentile (n = 2 PFOA, n = 7 PFHxS, n = 1 PFNA, n = 2 PFHpS, n = 1 PFDA) to ensure that individuals with extreme exposure values did not disproportionately influence our results. Analyses were performed using SAS version 9.2.

    Results

    Table 1 presents the demographic characteristics of cases and controls. Table 2 shows the median and interquartile range distribution of maternal PFAS concentrations during pregnancy in cases and controls.

    Table 1 Characteristics of study participants [n (%)].

    CharacteristicADHD(n = 220)Childhood autism(n = 220)Controls (n = 550)
    Child’s sex
    Male179 (81.4)187 (85.0)440 (80.0)
    Female41 (18.6)33 (15.0)110 (20.0)
    Mother’s age at delivery (years)
    ≤ 2437 (16.8)28 (12.7)42 (7.6)
    25–2983 (37.7)81 (36.8)235 (42.7)
    30–3472 (32.7)75 (34.1)201 (36.5)
    ≥ 3528 (12.7)36 (16.4)72 (13.1)
    Socioeconomic status
    Low/medium112 (50.9)74 (33.6)209 (38.0)
    High106 (48.2)144 (65.5)339 (61.6)
    Parity
    1107 (48.6)119 (54.1)247 (44.9)
    > 1100 (45.5)96 (43.6)288 (52.4)
    Maternal drinking during pregnancy
    No79 (35.9)79 (35.9)161 (29.3)
    Yes 141 (64.1)141 (64.1)389 (70.7)
    Maternal smoking during pregnancy
    Never139 (63.2)142 (64.5)409 (74.4)
    ≤ 9 cigarettes/day32 (14.5)33 (15.0)64 (11.6)
    > 9 cigarettes/day49 (22.3)45 (20.5)77 (14.0)
    Mother’s self-reported psychiatric illnesses
    No167 (75.9)173 (78.6)469 (85.3)
    Yes 53 (24.1)47 (21.4)81 (14.7)
    Child’s birth year
    1998–2000133 (60.5)114 (51.8)322 (58.5)
    2001–200387 (39.5)106 (48.2)228 (41.5)
    Gestational weeks at blood draw
    4–8 weeks87 (39.5)88 (40.0)216 (39.3)
    > 8 weeks119 (54.1)115 (52.3)305 (55.5)
    The missing values for socioeconomic status, parity, and gestational weeks at blood draw are about 1%, 4%, and 7%, respectively.

    Table 2 Distribution of maternal plasma PFAS concentrations in cases and controls.

    Perfluoroalkyl substanceAbbreviationCarbon chain lengthaPercent quantifiable in all samplesPFAS concentration (ng/mL) [median (25th, 75th percentile)]b
    ADHD (n = 215)Childhood autism (n = 213)Controls (n = 545)
    Perfluorooctane sulfonatePFOS810026.80 (19.20, 35.00)25.40 (18.73, 32.40)27.40 (20.40, 35.60)
    Perfluorooctanoic acidPFOA81004.06 (3.08, 5.50)3.88 (3.08, 5.28)4.00 (3.01, 5.42)
    Perfluorohexane sulfonatePFHxS6980.84 (0.61, 1.15)0.92 (0.70, 1.17)0.92 (0.68, 1.23)
    Perfluoroheptane sulfonatePFHpS7960.30 (0.20, 0.40)0.28 (0.19, 0.38)0.30 (0.21, 0.41)
    Perfluorononanoic acidPFNA9920.42 (0.34, 0.52)0.41 (0.33, 0.51)0.43 (0.35, 0.56)
    Perfluorodecanoic acidPFDA10900.15 (0.11, 0.20)0.15 (0.11, 0.20)0.17 (0.12, 0.23)
    aThe number of carbons in the fully fluorinated alkyl chain. bConcentrations for 17 samples (5 ADHD, 7 autism, and 5 controls) were missing because the samples were either not available from the biobank or failed the extraction process.

    We generally found no association between ADHD or autism in children and PFAS levels in maternal plasma (modeled as natural-log units) (Table 3). We did not detect apparent effect modification by child’s sex (all PFASs and sex interaction p-values ≥ 0.25), but because both diagnoses were more prevalent in boys, estimates for girls were less precise (see Supplemental Material, Table S2).

    Table 3 Risks ratiosa for ADHD and childhood autism in children according to maternal plasma concentrations of PFASb in pregnancy.

    Prenatal exposureADHDcChildhood autismc
    Adjusted RRd (95% CI)Adjusted RRe (95% CI)Adjusted RRd (95% CI)Adjusted RRe (95% CI)
    PFOS 0.87 (0.74, 1.02)1.04 (0.70, 1.56)0.92 (0.69, 1.22)1.21 (0.69, 2.13)
    PFOA 0.98 (0.82, 1.16)1.21 (0.84, 1.74)0.98 (0.73, 1.31)1.15 (0.68, 1.93)
    PFHxS 0.97 (0.88, 1.08)1.05 (0.91, 1.20)1.10 (0.92, 1.33)1.26 (1.00, 1.58)
    PFNA 0.80 (0.62, 1.03)0.99 (0.58, 1.70)0.80 (0.58, 1.11)0.84 (0.48, 1.49)
    PFHpS 0.91 (0.79, 1.05)0.93 (0.64, 1.36)0.91 (0.74, 1.12)0.82 (0.56, 1.22)
    PFDA 0.76 (0.64, 0.91)0.80 (0.58, 1.11)0.79 (0.63, 1.01)0.82 (0.53, 1.28)
    aInverse probability weights derived from sampling fractions of cases and controls were applied in analyses. bPer 1 natural-log unit (ng/mL) increase. cWe used 215 ADHD cases, 213 autism cases, and 545 controls in analyses. dAdjusted for maternal age at delivery, socioeconomic status, parity, smoking and drinking during pregnancy, psychiatric illnesses, gestational week of blood drawn, and child’s sex and birth year. eAdditionally adjusted including all PFASs in the model.

    When we categorized PFAS values, mothers in the highest quartile of PFOS, PFHxS, PFHpS, and PFDA were less likely to have a child diagnosed with ADHD than mothers in the lowest quartile, after adjustment for potential confounders (Table 4). When all PFASs were simultaneously entered into the model, PFOA and PFNA levels were positively associated with ADHD, whereas negative associations with the other compounds persisted, with most showing monotonic trends. There was some evidence of a positive association between PFHxS and autism, though RRs for the highest quartile were closer to the null than RRs for the second and third quartiles. Similar patterns were found with lower precision of the estimates when we used logistic regression without applying sampling weights (see Supplemental Material, Table S3).

    Table 4 Risks ratiosa for ADHD and childhood autism in children according to maternal plasma concentrations of PFAS (in quartiles) in pregnancy.

    Prenatal exposurebADHDChildhood autism
    Crude RRAdjusted RRc (95% CI) Adjusted RRd (95% CI) Crude RRAdjusted RRc (95% CI) Adjusted RRd (95% CI)
    PFOS (ng/mL)
    3.85–20.401.001.00 (reference)1.00 (reference)1.001.00 (reference)1.00 (reference)
    20.41–27.400.830.95 (0.79, 1.15)0.93 (0.75, 1.15)0.720.91 (0.66, 1.25)1.05 (0.73, 1.50)
    27.41–35.600.900.93 (0.76, 1.13)0.86 (0.65, 1.12)0.801.01 (0.73, 1.40)1.20 (0.77, 1.89)
    ≥ 35.610.780.79 (0.64, 0.98)0.65 (0.47, 0.91)0.600.86 (0.59, 1.25)1.16 (0.65, 2.09)
    PFOA (ng/mL)
    0.57–3.011.001.00 (reference)1.00 (reference)1.001.00 (reference)1.00 (reference)
    3.02–4.001.001.02 (0.84, 1.23)1.24 (0.99, 1.55)1.051.13 (0.82, 1.56)1.11 (0.76, 1.60)
    4.01–5.421.131.09 (0.90, 1.33)1.46 (1.14, 1.88)1.031.05 (0.74, 1.47)0.97 (0.63, 1.48)
    ≥ 5.431.071.14 (0.92, 1.40)2.02 (1.49, 2.75)0.780.95 (0.65, 1.38)0.93 (0.54, 1.59)
    PFHxS (ng/mL)
    < LLOQ–0.681.001.00 (reference)1.00 (reference)1.001.00 (reference)1.00 (reference)
    0.69–0.920.971.05 (0.88, 1.26)0.94 (0.76, 1.15)1.261.33 (0.95, 1.87)1.55 (1.06, 2.28)
    0.93–1.230.900.94 (0.78, 1.14)0.82 (0.65, 1.02)1.381.50 (1.08, 2.10)1.86 (1.25, 2.76)
    ≥ 1.240.640.67 (0.54, 0.83)0.56 (0.43, 0.73)0.941.07 (0.73, 1.56)1.33 (0.84, 2.11)
    PFNA (ng/mL)
    < LLOQ–0.351.001.00 (reference)1.00 (reference)1.001.00 (reference)1.00 (reference)
    0.36–0.431.071.08 (0.90, 1.30)1.29 (1.05, 1.59)1.061.06 (0.78, 1.44)0.94 (0.66, 1.34)
    0.44–0.561.281.12 (0.93, 1.33)1.48 (1.18, 1.86)1.030.81 (0.59, 1.11)0.73 (0.49, 1.08)
    ≥ 0.570.750.85 (0.69, 1.04)1.58 (1.17, 2.13)0.700.80 (0.56, 1.12)0.98 (0.59, 1.63)
    PFHpS (ng/mL)
    < LLOQ–0.211.001.00 (reference)1.00 (reference)1.001.00 (reference)1.00 (reference)
    0.21–0.300.740.70 (0.58, 0.84)0.67 (0.54, 0.83)0.830.82 (0.60, 1.12)0.70 (0.49, 1.01)
    0.31–0.410.910.87 (0.72, 1.05)0.86 (0.65, 1.13)0.820.92 (0.66, 1.29)0.83 (0.53, 1.31)
    ≥ 0.420.750.71 (0.58, 0.87)0.81 (0.57, 1.15)0.660.82 (0.57, 1.19)0.80 (0.44, 1.48)
    PFDA (ng/mL)
    < LLOQ–0.121.001.00 (reference)1.00 (reference)1.001.00 (reference)1.00 (reference)
    0.13–0.170.910.82 (0.69, 0.97)0.80 (0.66, 0.96)1.040.93 (0.69, 1.25)0.99 (0.72, 1.37)
    0.18–0.230.830.87 (0.72, 1.05)0.91 (0.73, 1.14)0.981.07 (0.77, 1.47)1.34 (0.92, 1.95)
    ≥ 0.240.510.53 (0.43, 0.66)0.53 (0.40, 0.72)0.500.52 (0.35, 0.77)0.73 (0.43, 1.24)
    aInverse probability weights derived from sampling fractions of cases and controls were applied in analyses. bPFAS values below the LLOQ were grouped in the lowest quartile. cAdjusted for maternal age at delivery, socioeconomic status, parity, smoking and drinking during pregnancy, psychiatric illnesses, gestational week of blood drawn, and child’s sex and birth year. dAdditionally adjusted including all PFASs in the model.

    Results were similar to those from the primary models when we performed additional sensitivity analyses restricting the analyses to children born before 2001 (see Supplemental Material, Table S4), and excluding extreme PFAS values (results not shown).

    Discussion

    Overall, our results do not suggest that prenatal exposure to PFASs increases the risk of ADHD or childhood autism in children. We observed some inverse associations between several PFASs and ADHD after controlling for potential confounders. In the “multiple PFAS” model, we found some positive as well as negative associations between PFASs and ADHD but these might be subject to multicollinearity or sparse data bias. Results were mostly close to null for autism in both single and multiple PFAS models.

    Toxicology studies have raised concerns that PFASs are neurotoxic and hormone disruptive and can impair fetal brain development (Johansson et al. 2008; Lau et al. 2003; Long et al. 2013). However, some neurotoxic effects in rats were observed at doses several orders of magnitude higher than the PFAS levels found in the U.S. and Danish general populations (Butenhoff et al. 2009; Fei et al. 2007). Several epidemiologic studies have investigated associations between PFASs and hyperactivity or behavioral problems in children, but the findings have been inconclusive (Braun et al. 2014; Fei and Olsen 2011; Hoffman et al. 2010; Stein and Savitz 2011; Stein et al. 2013). A previous study based on a subset of children from the Danish National Birth Cohort found some inverse associations between prenatal PFOA, but not PFOS, and behavioral problems in 7-year-old children measured by (parent-reported) items in the Strength and Difficulty Questionnaire (Fei and Olsen 2011). Another study also suggested a lower prevalence of ADHD characteristics in children associated with higher estimated in utero PFOA exposures based on the Clinical Confidence Index (Stein et al. 2013). There is, however, no biologic explanation for PFASs protecting the developing brain from ADHD, and potential biases such as uncontrolled confounding or selection bias might have driven these unexpected findings. No apparent associations were found between PFASs and autism in current and a previous small study (Braun et al. 2014).

    Because several PFASs are moderately to highly correlated, it is difficult to disentangle mixture effects from compound-specific effects. A recent in vitro assay reported an additive or more than additive antagonistic effect for a mixture of compounds (PFHxS, PFOS, PFOA, PFNA, and PFDA) on androgen receptor function (Kjeldsen and Bonefeld-Jørgensen 2013). Unfortunately, our sample is too small to allow for interaction analyses between different PFASs. Further experimental studies are needed to determine mechanisms of action for PFAS mixtures on biologic targets that could better inform our population-based studies in terms of the most biologically relevant exposure model to be employed.

    It has previously been shown that prenatal exposure to PFASs can increase the incidence of fetal resorption and neonatal deaths in animal models (Abbott et al. 2007; Lau et al. 2007; Luebker et al. 2005). PFASs may interfere with sex and thyroid hormone homeostasis (Kjeldsen and Bonefeld-Jørgensen 2013; Lin et al. 2013; Wang et al. 2014), and it has been suggested that higher PFAS levels are associated with reduced fecundity in women (Buck Louis et al. 2013; Fei et al. 2009) and with an increased risk for miscarriage (Darrow et al. 2014). It is therefore possible that PFAS exposure at a level that reduces fetal or neonatal survival, especially in high-risk fetuses and infants susceptible to neurological disorders such as ADHD and autism, could appear to have null or even protective effects on adverse neurobehavioral outcomes in children based on observational studies, because only live-born children can be followed up and examined.

    There are several strengths in our study. First, the PFAS measures were obtained from maternal plasma samples collected in pregnancy before the assessment of the outcomes in the children. Previous studies have shown that PFASs are stable in human serum, and measurements obtained from serum or plasma samples gave comparable results (Ehresman et al. 2007). High correlations between maternal and cord blood PFAS measures were also reported, and these suggested that PFASs measured in maternal plasma can be used as a reasonable surrogate for fetal exposure levels throughout gestations (Fei et al. 2007). Furthermore, the maternal PFAS levels in our study are similar to those previously measured during the same time period in the U.S. general population (Calafat et al. 2007). Study participants were selected from a well-defined nationwide pregnancy cohort with an average of 10.7 years of follow-up, sufficiently long to assess the outcomes of interest. The outcome measures were clinical diagnoses using standardized diagnostic criteria from both the general and psychiatric hospital registries in Denmark, a country with high-quality health care and universal coverage for its population. Diagnoses of childhood autism recorded in the psychiatric registry have previously been shown to have high validity: A study extracted and reviewed the medical records of 499 childhood autism cases from the registry and confirmed the diagnoses for 94% of the cases (Lauritsen et al. 2010). Follow-up was conducted through record linkage that did not require subjects’ responses, thus minimizing chances for selection bias due to subject’s nonresponse.

    Our study also has some limitations. Both ADHD and autism are about four times more prevalent in boys, and because of cost limitations we were required to sample no more than 220 cases for each diagnostic group, resulting in few female cases (n = 41 with ADHD; n = 33 with autism). Thus, our subgroup analyses by sex were relatively imprecise for girls, resulting in effect estimates with wide CIs. For autism, the cases were limited to children diagnosed with childhood autism. Although this is the most severe disorder of the autism spectrum, it constitutes only a part of autistic spectrum disorders; specifically children with Asperger’s syndrome and other pervasive development disorders were not studied. Moreover, we have no data for other endocrine-disrupting chemicals, preventing us from evaluating possible correlations or interactions of PFASs with other ubiquitous environmental chemicals with these properties such as polychlorinated biphenyls (PCBs), organophosphates, bisphenol A, and phthalates (de Cock et al. 2012; Polanska et al. 2012). Further, our blood samples had to be transported to the laboratory by ordinary mail before being processed, which may have induced some random measurement errors.

    In summary, we found no consistent evidence that prenatal PFAS exposures were associated with ADHD or childhood autism in children in the Danish National Birth Cohort. Both weak negative associations as well as some positive associations between PFASs and ADHD that we observed in multiple PFAS models should be further explored. We recommend that future studies analyze a larger sample, consider both prenatal exposure and exposure during first year of life, assess the potential mixture effects of exposures to different co-occurring endocrine disruptors, and examine more sensitive indicators such as neuropsychological functioning in children.

    Supplemental Material

    (273 KB) PDF Click here for additional data file.

    The study is part of the FETOTOX program ( http://www.fetotox.au.dk) supported by the Danish Strategic Research Council (10-092818). The Danish National Research Foundation established the Danish Epidemiology Science Centre, which initiated and created the Danish National Birth Cohort. The cohort is furthermore a result of a major grant from this foundation. Additional support for the Danish National Birth Cohort was obtained from the Pharmacy Foundation, the Egmont Foundation, the March of Dimes Birth Defects Foundation, and the Augustinus Foundation.

    C.Y.F. is employed by AbbVie Inc. The authors declare they have no actual or potential competing financial interests.

    Reference

    • Abbott BD, Wolf CJ, Schmid JE, Das KP, Zehr RD, Helfant Let al.. 2007. Perfluorooctanoic acid–induced developmental toxicity in the mouse is dependent on expression of peroxisome proliferator–activated receptor-alpha.Toxicol Sci 98:571-58117488742. Crossref, MedlineGoogle Scholar
    • Andersen TF, Madsen M, Jørgensen J, Mellemkjoer L, Olsen JH. 1999. The Danish National Hospital Register. A valuable source of data for modern health sciences.Dan Med Bull 46:263-26810421985. MedlineGoogle Scholar
    • Arnold LE. 1996. Sex differences in ADHD: conference summary.J Abnorm Child Psychol 24:555-5698956084. Crossref, MedlineGoogle Scholar
    • Braun JM, Kalkbrenner AE, Just AC, Yolton K, Calafat AM, Sjödin Aet al.. 2014. Gestational exposure to endocrine-disrupting chemicals and reciprocal social, repetitive, and stereotypic behaviors in 4- and 5-year-old children: the HOME study.Environ Health Perspect 122:513-520; doi:10.1289/ehp.130726124622245. LinkGoogle Scholar
    • Bech BH, Nohr EA, Vaeth M, Henriksen TB, Olsen J. 2005. Coffee and fetal death: a cohort study with prospective data.Am J Epidemiol 162:983-99016207803. Crossref, MedlineGoogle Scholar
    • Buck RC, Franklin J, Berger U, Conder JM, Cousins IT, de Voogt Pet al.. 2011. Perfluoroalkyl and polyfluoroalkyl substances in the environment: terminology, classification, and origins.Integr Environ Assess Manag 7:513-54121793199. Crossref, MedlineGoogle Scholar
    • Buck Louis GM, Sundaram R, Schisterman EF, Sweeney AM, Lynch CD, Gore-Langton REet al.. 2013. Persistent environmental pollutants and couple fecundity: the LIFE study.Environ Health Perspect 121:231-236; doi:10.1289/ehp.120530123151773. LinkGoogle Scholar
    • Butenhoff JL, Ehresman DJ, Chang SC, Parker GA, Stump DG. 2009. Gestational and lactational exposure to potassium perfluorooctanesulfonate (K+PFOS) in rats: developmental neurotoxicity.Reprod toxicol 27:319-33019162172. Crossref, MedlineGoogle Scholar
    • Calafat AM, Kuklenyik Z, Reidy JA, Caudill SP, Tully JS, Needham LL. 2007. Serum concentrations of 11 polyfluoroalkyl compounds in the U.S. population: data from the National Health and Nutrition Examination Survey (NHANES) 1999–2000.Environ Sci Technol 41:2237-224217438769. Crossref, MedlineGoogle Scholar
    • Darrow LA, Howards PP, Winquist A, Steenland K. 2014. PFOA and PFOS serum levels and miscarriage risk.Epidemiology 25:505-51224807698. Crossref, MedlineGoogle Scholar
    • de Cock M, Maas YG, van de Bor M. 2012. Does perinatal exposure to endocrine disruptors induce autism spectrum and attention deficit hyperactivity disorders? Review.Acta Paediatr 101:811-81822458970. Crossref, MedlineGoogle Scholar
    • D’eon JC, Mabury SA. 2011. Is indirect exposure a significant contributor to the burden of perfluorinated acids observed in humans?Environ Sci Technol 45:7974-798421630688. Crossref, MedlineGoogle Scholar
    • Ehresman DJ, Froehlich JW, Olsen GW, Chang SC, Butenhoff JL. 2007. Comparison of human whole blood, plasma, and serum matrices for the determination of perfluorooctanesulfonate (PFOS), perfluorooctanoate (PFOA), and other fluorochemicals.Environ Res 103:176-18416893538. Crossref, MedlineGoogle Scholar
    • Faraone SV, Sergeant J, Gillberg C, Biederman J. 2003. The worldwide prevalence of ADHD: is it an American condition?World Psychiatry 2:104-11316946911. MedlineGoogle Scholar
    • Fei CY, McLaughlin JK, Lipworth L, Olsen J. 2009. Maternal levels of perfluorinated chemicals and subfecundity.Hum Reprod 24:1200-120519176540. Crossref, MedlineGoogle Scholar
    • Fei CY, McLaughlin JK, Tarone RE, Olsen J. 2007. Perfluorinated chemicals and fetal growth: a study within the Danish National Birth Cohort.Environ Health Perspect 115:1677-1682; doi:10.1289/ehp.1050618008003. LinkGoogle Scholar
    • Fei CY, Olsen J. 2011. Prenatal exposure to perfluorinated chemicals and behavioral or coordination problems at age 7 years.Environ Health Perspect 119:573-578; doi:10.1289/ehp.100202621062688. LinkGoogle Scholar
    • Glynn A, Berger U, Bignert A, Ullah S, Aune M, Lignell Set al.. 2012. Perfluorinated alkyl acids in blood serum from primiparous women in Sweden: serial sampling during pregnancy and nursing, and temporal trends 1996–2010.Environ Sci Technol 46:9071-907922770559. Crossref, MedlineGoogle Scholar
    • Gump BB, Wu Q, Dumas AK, Kannan K. 2011. Perfluorochemical (PFC) exposure in children: associations with impaired response inhibition.Environ Sci Technol 45:8151-815921682250. Crossref, MedlineGoogle Scholar
    • Hertz-Picciotto I, Delwiche L. 2009. The rise in autism and the role of age at diagnosis.Epidemiology 20:84-9019234401. Crossref, MedlineGoogle Scholar
    • Hoffman K, Webster TF, Weisskopf MG, Weinberg J, Vieira VM. 2010. Exposure to polyfluoroalkyl chemicals and attention deficit/hyperactivity disorder in U.S. children 12–15 years of age.Environ Health Perspect 118:1762-1767; doi:10.1289/ehp.100189820551004. LinkGoogle Scholar
    • Houde M, Martin JW, Letcher RJ, Solomon KR, Muir DC. 2006. Biological monitoring of polyfluoroalkyl substances: a review.Environ Sci Technol 40:3463-347316786681. Crossref, MedlineGoogle Scholar
    • 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 108:412-41819211617. Crossref, MedlineGoogle Scholar
    • Johansson N, Fredriksson A, Eriksson P. 2008. Neonatal exposure to perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA) causes neurobehavioural defects in adult mice.Neurotoxicology 29:160-16918063051. Crossref, MedlineGoogle Scholar
    • 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 45:8037-804521469664. Crossref, MedlineGoogle Scholar
    • Kjeldsen LS, Bonefeld-Jørgensen EC. 2013. Perfluorinated compounds affect the function of sex hormone receptors.Environ Sci Pollut Res Int 20:8031-804423764977. Crossref, MedlineGoogle Scholar
    • Lau C, Anitole K, Hodes C, Lai D, Pfahles-Hutchens A, Seed J. 2007. Perfluoroalkyl acids: a review of monitoring and toxicological findings.Toxicol Sci 99:366-39417519394. Crossref, MedlineGoogle Scholar
    • Lau C, Thibodeaux JR, Hanson RG, Rogers JM, Grey BE, Stanton MEet al.. 2003. Exposure to perfluorooctane sulfonate during pregnancy in rat and mouse. II: Postnatal evaluation.Toxicol Sci 74:382-39212773772. Crossref, MedlineGoogle Scholar
    • Lauritsen MB, Jørgensen M, Madsen KM, Lemcke S, Toft S, Grove Jet al.. 2010. Validity of childhood autism in the Danish Psychiatric Central Register: findings from a cohort sample born 1990–1999.J Autism Dev Disord 40:139-14819728067. Crossref, MedlineGoogle Scholar
    • Liew Z, Ritz B, Bonefeld-Jørgensen EC, Henriksen TB, Nohr EA, Bech BHet al.. 2014. Prenatal exposure to perfluoroalkyl substances and the risk of congenital cerebral palsy in children.Am J Epidemiol 180:574-58125139206. Crossref, MedlineGoogle Scholar
    • Lin CY, Wen LL, Lin LY, Wen TW, Lien GW, Hsu SHJet al.. 2013. The associations between serum perfluorinated chemicals and thyroid function in adolescents and young adults.J Hazard Mater 244–245:637-644. Crossref, MedlineGoogle Scholar
    • Long M, Ghisari M, Bonefeld-Jørgensen EC. 2013. Effects of perfluoroalkyl acids on the function of the thyroid hormone and the aryl hydrocarbon receptor.Environ Sci Pollut Res Int 20:8045-805623539207. Crossref, MedlineGoogle Scholar
    • Lubin JH, Colt JS, Camann D, Davis S, Cerhan JR, Severson RKet al.. 2004. Epidemiologic evaluation of measurement data in the presence of detection limits.Environ Health Perspect 112:1691-1696; doi:10.1289/ehp.719915579415. LinkGoogle Scholar
    • Luebker DJ, York RG, Hansen KJ, Moore JA, Butenhoff JL. 2005. Neonatal mortality from in utero exposure to perfluorooctanesulfonate (PFOS) in Sprague-Dawley rats: dose–response, and biochemical and pharamacokinetic parameters.Toxicology 215:149-16916129535. Crossref, MedlineGoogle Scholar
    • Lyall K, Schmidt RJ, Hertz-Picciotto I. 2014. Maternal lifestyle and environmental risk factors for autism spectrum disorders.Int J Epidemiol 43:443-464; doi:10.1093/ije/dyt28224518932. Crossref, MedlineGoogle Scholar
    • Millichap JG. 2008. Etiologic classification of attention-deficit/hyperactivity disorder.Pediatrics 121:e358-e36518245408. Crossref, MedlineGoogle Scholar
    • Møller LR, Sørensen MJ, Thomsen PH. 2007. ICD-10 classification in Danish child and adolescent psychiatry—have diagnoses changed after the introduction of ICD-10?Nord J Psychiat 61:71-78. Crossref, MedlineGoogle Scholar
    • Munk-Jørgensen P, Kastrup M, Mortensen PB. 1993. The Danish Psychiatric Register as a tool in epidemiology.Acta Psychiatr Scand Suppl 370:27-328452052. Crossref, MedlineGoogle Scholar
    • Olsen GW, Burris JM, Ehresman DJ, Froehlich JW, Seacat AM, Butenhoff JLet al.. 2007. Half-life of serum elimination of perfluorooctanesulfonate, perfluorohexanesulfonate, and perfluorooctanoate in retired fluorochemical production workers.Environ Health Perspect 115:1298-1305; doi:10.1289/ehp.1000917805419. LinkGoogle Scholar
    • Olsen J, Melbye M, Olsen SF, Sørensen TI, Aaby P, Andersen AMet al.. 2001. The Danish National Birth Cohort—its background, structure and aim.Scand J Public Health 29:300-30711775787. Crossref, MedlineGoogle Scholar
    • Pickett J, London E. 2005. The neuropathology of autism: a review.J Neuropathol Exp Neurol 64:925-93516254487. Crossref, MedlineGoogle Scholar
    • Polanczyk G, de Lima MS, Horta BL, Biederman J, Rohde LA. 2007. The worldwide prevalence of ADHD: a systematic review and metaregression analysis.Am J Psychiatry 164:942-94817541055. Crossref, MedlineGoogle Scholar
    • Polanska K, Jurewicz J, Hanke W. 2012. Exposure to environmental and lifestyle factors and attention-deficit/hyperactivity disorder in children—a review of epidemiological studies.Int J Occup Med Environ Health 25:330-35523086631. Crossref, MedlineGoogle Scholar
    • Porterfield SP. 2000. Thyroidal dysfunction and environmental chemicals—potential impact on brain development.Environ Health Perspect 108(suppl 3):433-43810852841. LinkGoogle Scholar
    • Slotkin TA, MacKillop EA, Melnick RL, Thayer KA, Seidler FJ. 2008. Developmental neurotoxicity of perfluorinated chemicals modeled in vitro.Environ Health Perspect 116:716-722; doi:10.1289/ehp.1125318560525. LinkGoogle Scholar
    • Statens Serum Institut.2013. Access to DNBC (Danish National Birth Cohort) data.Available: http://www.ssi.dk/English/RandD/Research%20areas/Epidemiology/DNBC/For%20researchers.aspx [accessed 1 April 2013]. Google Scholar
    • Stein CR, Savitz DA. 2011. Serum perfluorinated compound concentration and attention deficit/hyperactivity disorder in children 5–18 years of age.Environ Health Perspect 119:1466-1471; doi:10.1289/ehp.100353821665566. LinkGoogle Scholar
    • Stein CR, Savitz DA, Bellinger DC. 2013. Perfluorooctanoate and neuropsychological outcomes in children.Epidemiology 24:590-59923680941. Crossref, MedlineGoogle Scholar
    • Viberg H, Lee I, Eriksson P. 2013. Adult dose-dependent behavioral and cognitive disturbances after a single neonatal PFHxS dose.Toxicology 304:185-19123287389. Crossref, MedlineGoogle Scholar
    • Wang Y, Rogan WJ, Chen PC, Lien GW, Chen HY, Tseng YCet al.. 2014. Association between maternal serum perfluoroalkyl substances during pregnancy and maternal and cord thyroid hormones: Taiwan Maternal and Infant Cohort Study.Environ Health Perspect 122:529-534; doi:10.1289/ehp.130692524577800. LinkGoogle Scholar
    • Yuan Y.2001. Multiple Imputation for Missing Data: Concepts and New Development. Rockville, MD:SAS Institute Inc.Available: https://support.sas.com/rnd/app/stat/papers/multipleimputation.pdf [accessed 18 March 2015]. Google Scholar