PSSXXX10.1177/0956797615618365Nigg et al.Attention-Deficit/Hyperactivity Disorder and Blood Lead research-article6183652015

Research Article

Psychological Science 2016, Vol. 27(2) 257­–269 Variation in an Iron © The Author(s) 2015 Reprints and permissions: sagepub.com/journalsPermissions.nav Moderates the Association Between DOI: 10.1177/0956797615618365 Blood Lead Levels and Attention-Deficit/ pss.sagepub.com Hyperactivity Disorder in Children

Joel T. Nigg1,2, Alexis L. Elmore3, Neil Natarajan1, Karen H. Friderici4, and Molly A. Nikolas4 1Department of Psychiatry, Oregon Health & Science University; 2Department of Behavioral Neuroscience, Oregon Health & Science University; 3Department of Psychological & Brain Sciences, University of Iowa; and 4Department of Microbiology and Molecular Genetics, Michigan State University

Abstract Although attention-deficit/hyperactivity disorder (ADHD) is a heritable neurodevelopmental condition, there is also considerable scientific and public interest in environmental modulators of its etiology. Exposure to neurotoxins is one potential source of perturbation of neural, and hence psychological, development. Exposure to lead in particular has been widely investigated and is correlated with neurodevelopmental outcomes, including ADHD. To investigate whether this effect is likely to be causal, we used a Mendelian randomization design with a functional gene variant. In a case-control study, we examined the association between ADHD symptoms in children and blood lead level as moderated by variants in the hemochromatosis (HFE) gene. The HFE gene regulates iron uptake and secondarily modulates lead metabolism. Statistical moderation was observed: The magnitude of the association of blood lead with symptoms of ADHD was altered by functional HFE genotype, which is consistent with a causal hypothesis.

Keywords attention, childhood development, psychopathology

Received 3/26/15; Revision accepted 10/29/15

Attention-deficit/hyperactivity disorder (ADHD) is a Regulation of lead in the 1970s resulted in substantial neurodevelopmental condition with complex etiology reductions in average body burden in the United States. that includes effects of genetics, early environment, and However, lead remains ubiquitous in the environment, their interplay. Because of the extensive literature on the and children continue to ingest it in small amounts neural effects of lead (Bellinger, 2011; Hu et al., 2011; through normal hand-to-mouth contact from house and Senut et al., 2014) and its established epidemiological yard dust (i.e., residuals from prior use of leaded paint associations with ADHD and other behavioral and cog- and gasoline). It leaches into tap water from aging water nitive outcomes, lead exposure provides an attractive pipes, is found in some toys and jewelry, and occurs in “case study” of potential toxicological influences on industrial air pollution (Holstege, Huff, Rowden, & neurodevelopmental conditions. Lead easily crosses the O’Malley, 2013; World Health Organization, 2015). blood-brain barrier, and among the affected brain In recent years, it has become apparent that even at regions and tissues are those of long-standing interest in the current historically low levels typical in the United ADHD research, including prefrontal cortex, hippocam- pus, basal ganglia, cerebellum, and white matter in gen- eral (Costa, Aschner, Vitalone, Syversen, & Soldin, 2004). Corresponding Author: Joel T. Nigg, Departments of Psychiatry and Behavioral Neuroscience, Its effects therefore provide a biologically plausible Oregon Health & Science University, 3550 SW U.S. Veterans Hospital route to ADHD via subtle environmental perturbation of Rd., DC7P, Portland, OR 97239 neurodevelopment. E-mail: [email protected]

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States, lead burden in the body is correlated with alter- position 82 (C282Y) and histidine to aspartic acid at posi- ations in human neural development (Brubaker et al., tion 63 (H63D)—cause a shortage of the HFE , 2009; Cecil et al., 2011) and with behavioral and cogni- resulting in up-regulation of receptors and tive outcomes in children, including ADHD, conduct divalent metal transporters and increased iron uptake in problems, reductions in executive functioning, reduc- the gut. Homozygous mutation can cause adult-onset tions in IQ, and learning problems (Canfield, Kreher, hemochromatosis, an autosomal recessive disease of iron Cornwell, & Henderson, 2003; Goodlad, Marcus, & overload (Hanson, Imperatore, & Burke, 2001; Santos, Fulton, 2013; Lanphear, 2015; Marcus, Fulton, & Clarke, Krieger, & Pereira, 2012). 2010; Nigg et al., 2008). The effect sizes after control- Because lead interacts with iron metabolically, these ling for covariates are medium to small (on the order of mutations are believed to alter lead’s effects (Gundacker, r = .15) but are similar for ADHD, IQ, and conduct Gencik, & Hengstschlager, 2010). There is no consensus problems (a developmental outgrowth of some cases of on the mechanism of HFE’s effects on lead metabolism, ADHD). These correlations appear to be nonlinear, but one hypothesis is that HFE variants alter lead’s health such that even at low doses, lead has clinically signifi- effects via increased oxidative stress (Park et al., 2009). cant associations with outcomes (Lanphear, 2015). That hypothesis is supported by laboratory work demon- Although these effects are statistically modest, they may strating increased iron-related lipid oxidation in the pres- have substantial population importance because of the ence of lead (Adonaylo & Oteiza, 1999). Under that near-universal exposure of children to lead (see model, the HFE variants are not required to alter blood Lanphear, 2015). lead level per se; rather, the variants alter the biochemical Although many potential confounders have been sta- reactions involving lead and iron at a given blood lead tistically covaried in this research, and animal studies level and thus change the . have demonstrated a causal effect of lead exposure on Accordingly, HFE variants appear to modulate lead’s activity level (Luo et al., 2014), what appear to be causal association with several phenotypic outcomes. For exam- effects of lead on ADHD in humans could still be due to ple, in a series of studies of older men, Wright and his unmeasured confounders. One approach to clarify cau- colleagues reported that the association between lead sality is Mendelian randomization (Lewis, Relton, Zammit, level and cognitive decline was stronger for men who & Smith, 2013), which is a type of natural experiment. were carriers of HFE mutations (Wang et al., 2007) and This approach entails dividing groups of people by geno- that the association between blood lead level and dis- type for a functional gene that metabolically or physio- turbed cardiac function was greater for those with the logically influences the presumed causal input (e.g., C282Y variant of the HFE gene (Park et al., 2009). The lead). The genotype must vary in the population at ran- C282Y variant also altered the association between lead dom and must be independent of both measured con- and amyotrophic lateral sclerosis (Eum et al., 2014) and founders (i.e., lower socioeconomic status or parental altered the rate of placental lead transfer (Karwowski IQ); therefore, it can be assumed to be independent of et al., 2014); H63D variants altered lead’s association with unmeasured confounders. In particular, the absence of a low birth weight (Cantonwine et al., 2010). These find- correlation between the genotype and the phenotype (in ings are quite consistent, even though the strength of this case, ADHD) sharply reduces the likelihood of a association among HFE variants and absolute blood lead reverse causality (i.e., the probability that increased level is modest and inconsistent in these studies. hyperactivity is causing more lead exposure). Therefore, Our governing hypothesis, therefore, was that carriers if this change in functional metabolism moderates the of specific HFE mutations (C282Y and H63D) are more clinical endpoint, a causal pathway is supported. susceptible than noncarriers to developmental damage as Mendelian randomization has been a valuable strategy a result of low-level lead body burden. Thus, if lead influ- for other disorders, including alcoholism (Irons, McGue, ences ADHD symptoms, HFE variants will statistically Iacono, & Oetting, 2007; Ritchie et al., 2014); however, it moderate the magnitude of the association between is underused in research concerning most psychiatric blood lead level and ADHD. conditions and has not been previously used to study ADHD in children. Figure 1 schematizes the predictive logic of this design as implemented in the current study. Method Among the most widely studied functional gene vari- Participants ants thought to influence lead metabolism is the human hemochromatosis gene (HFE) located at 6p21.3. Two We recruited physically healthy children with normative somewhat common missense mutations (i.e., mutations lead exposure for a study with a case-control design. To in which a change in one nucleotide results in a different ensure that effects on ADHD symptoms within the amino acid) within the HFE gene—cysteine to tyrosine at clinical range would be adequately represented, we

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Genetic Putative Biological Exposure Variation Change Outcome

Normal Effect of Relatively Small Wild-Type Lead on Iron Effect on ADHD HFE Oxidation Symptoms

Lead

Accelerated Relatively Large HFE Mutation Effect of Lead on Effect on ADHD Iron Oxidation Symptoms

Fig. 1. Diagram of the hypothesized interplay between blood lead level and hemochromato- sis (HFE) genotype in influencing attention-deficit/hyperactivity disorder (ADHD) symptoms. overselected youths with ADHD. Our participants were (American Psychiatric Association, 2000), interrater agree- from Michigan, a U.S. region in which generalizability of ment r > .80. allele frequencies could be compared with those found The experienced clinicians’ agreement rates for diag- in a randomized population study published previously nostic assignment were acceptable for ADHD, conduct (Barry, Derhammer, & Elsea, 2005). All children were disorder (CD), and oppositional defiant disorder (ODD), recruited via mailings to parents in regional school dis- all κs > .80, as well as for all other disorders with a base tricts, public advertisements, and outreach to local clinics. rate of greater than 5% in the sample, all κs > .75. Parents provided written informed consent, and children Disagreements were resolved by discussion. The clini- provided written informed assent. All procedures were cians assigned a primary diagnosis of ADHD when they approved by the university’s institutional review board saw evidence of impairment, cross-situational symptoms, and complied with all applicable guidelines for protec- and onset by the age of 7 years and concluded that a tion of human participants. Data collection proceeded diagnosis of ADHD best accounted for symptoms; this until funding was exhausted and sample size was suffi- practice was consistent with ADHD criteria from the cient to meet power targets. Diagnostic and Statistical Manual of Mental Disorders Families entered a multistage screening process that (4th ed., text rev.; American Psychiatric Association, established diagnostic groupings for the case-control 2000), which was current at the time of data collection. design. For each child, a parent and a teacher completed Youths with situational ADHD symptoms (i.e., symptoms standardized clinical rating scales, and a trained clinician noticeable only at home or school) were coded as “not completed a semistructured clinical interview with the otherwise specified.” Interviewers coded youths judged parent about the child (Kiddie Schedule for Affective to have five symptoms of one dimension (i.e., either inat- Disorders and Schizophrenia–epidemiologic version, or tention or hyperactivity) but fewer than five of the other K-SADS-E; Puig-Antich & Ryan, 1986). All interviewers dimension as subthreshold. This step was intended to had master’s degrees in clinical psychology or social minimize diagnostic assignment errors around the six- work. To establish reliability of the coder KSAD diagno- symptom cutoff. These youths (n = 24) were excluded ses and fidelity to procedures, we video-recorded 20 from ADHD-control group comparisons but included in interviews conducted by each interviewer. These inter- regression analysis of symptom severity effects. Sibling views were then double-coded by the primary inter- pairs were allowed into the study if they met our inclu- viewer and by an expert criterion coder (for all disorders sion criteria, because we were also conducting ancillary mentioned in this report, κs > .80). studies of sibling effects in ADHD; nonindependence of Using all available data, two experienced clinicians (a sibling data was handled statistically because sample size board-certified child psychiatrist and a licensed clinical was not large enough to use sibling differences as a child psychologist) blind to the study’s hypotheses and to causal lever. the children’s blood lead levels arrived independently at Children were excluded at the intake stage if they a best-estimate diagnostic decision for ADHD and comor- were taking long-acting psychotropic medication (includ- bid disorders, for each child. Impairment was required, ing antidepressants or long-acting ADHD-related medica- and so the two clinicians quantified their impairment rat- tion, such as atomoxetine) because it was unlikely that ings using the Global Assessment of Functioning Scale they could complete a medication washout for the

Downloaded from pss.sagepub.com at The University of Iowa Libraries on March 21, 2016 260 Nigg et al. ancillary studies of neurocognitive function in ADHD Barry et al. (2005), but the number of cycles was (Martel, Nikolas, Jernigan, Friderici, & Nigg, 2012; Nikolas increased from 30 to 35 for a more robust amplification & Nigg, 2015). Children whose parents reported a history product. Genomic DNA (40–60 ng) was amplified using of seizure (except a single febrile seizure), neurological 0.5 units of Taq DNA polymerase (Invitrogen Corp., impairments, prior diagnosis of intellectual disability or Carlsbad, CA) in standard PCR buffer consisting of 20 autism spectrum disorder, head injury with loss of con- mmol/L tris(hydroxymethyl)aminomethane hydrochlo- sciousness beyond 30 s, sensorimotor handicap, or other ride, 50 mmol/L potassium chloride, 1.5 mmol/L magne- major medical conditions were excluded. If the research sium chloride, 0.2 mmol/L deoxynucleoside triphosphates,­ diagnostic evaluation revealed substance addiction, and 0.7 µmol/L concentrations of each primer— autism spectrum disorder, bipolar disorder, history of for C282Y: 5′-TCCCAAGGGTAAACAGATCC-3′ (forward) psychosis, sleep disorder, a medical or neurological con- and 5′-TACCTCCTCAGGCACTCCTC-3′ (reverse); for dition, or an estimated IQ of less than 75 (evaluated by a H63D: 5′-CTTCATGGGTGCCTCAGAGC-3′ (forward) and short form of the Wechsler Intelligence Scale for Children– 5′-CCCTTGCTGTGGTTGTGATT-3′ (reverse). 4th Edition; Wechsler, 2003), children were excluded. Reaction conditions consisted of an initial denaturing These criteria were intended to minimize diagnostic step at 94 °C for 3 min, followed by 35 cycles at 94 °C for assignment errors for ADHD. After exclusion, the sample 30 s each, annealing at 63 °C for 30 s, and an extension at comprised 386 children, ages 6 to 17 years, from 267 72 °C for 30 s, followed by a final extension step at 72 °C families (148 singletons, 119 sibling pairs). for 5 min. Expected product sizes for C282Y and H63D were 393 and 243 base pairs, respectively. Digestion of Measures the C282Y PCR product with the restriction endonuclease RsaI (New England Biolabs, Ipswich, MA) for 1 to 2 hr Blood lead. Researchers drew 2 ml of whole blood yielded the fragment sizes of 251 and 142 base pairs (bp) from each child’s arm into a 2-ml Vacutainer tube (BD for the wild type and 251, 113, and 29 bp for the mutation. Diagnostics, Franklin Lakes, NJ) containing ethylenedi- Digestion of the H63D PCR product with the restriction aminetetraacetic acid (tubes were lot checked for lead by endonuclease DpnII (New England Biolabs) yielded frag- lab before use). Blood samples were labeled with a study ment sizes of 100, 85, and 58 bp for the wild type and 185 number, frozen, and stored at −20 °C before analysis. and 58 bp for the mutation. Both were analyzed on a 3.0% Samples were assayed using the process of inductively agarose gel. Genotyping failed for 3 children, meaning coupled plasma mass spectrometry. This method’s detec- that 383 were included in the final analyses. tion limit for lead is 0.3 µg/dl; interrun precision was 5.8% (coefficient of variation) at a lead value of 2.9 µg/dl. Measures of ADHD and comorbid externalizing The process began with bringing each whole-blood sam- symptoms. For data-analysis purposes, ADHD symp- ple to room temperature and vortexing it so that no par- toms were assessed by parental and teacher ratings on ticulate matter remained at the bottom. Samples were three widely used rating scales: (a) the ADHD Rating diluted 1:50 with a diluent composed of 1.0% tetrameth- Scale (DuPaul, Power, Anastopoulos, & Reid, 1998) inat- ylammonium hydroxide, internal standard (iridium), tention and hyperactivity-impulsivity symptom scores; 1.0% isopropyl alcohol, 0.01% ammonium pyrrolidine (b) Conners’ Rating Scale–Revised (Conners, 1997), spe- dithiocarbamate, and 0.05% wetting solution (Triton cifically the subscales for cognitive problems (i.e., inat- X-100). Samples were then mixed by inverting them tention) and hyperactivity problems; and (c) the Strengths three or four times. The analysis then entailed quantitat- and Weaknesses of ADHD Symptoms and Normal Behav- ing total lead by summing the signal intensities for three ior Scale (SWAN; Swanson et al., 2012) inattention and isotopic masses of lead (206, 207, and 208), using three hyperactivity symptom scores (from parents but not replicates per sample on an Elan DRC Plus inductively teachers), on which ADHD symptoms are listed on a nor- coupled plasma mass spectrometer (PerkinElmer, malized scale and worded to reflect normal variation, to Waltham, MA). avoid floor effects often seen on symptom scales. Teach- ers also completed ratings of ODD and CD behaviors as Genotyping. Samples were obtained from buccal part of the ADHD Rating Scale; parents’ ratings of ODD swabs (43%) and salivary DNA (57%). Buccal samples and CD symptoms were derived from their reports on the were purified using a method described previously K-SADS-E. In this sample, all scales exhibited adequate (Meulenbelt, Droog, Trommelen, Boomsma, & Slagboom, internal consistency (αs from .83 to .94), and their manu- 1995). Oragene samples were processed according to als provide adequate test-retest reliabilities. For children the manufacturer’s instructions (DNAgenotek, Ottawa, taking prescribed medication, raters were asked to rate, Ontario, Canada). Polymerase chain reaction (PCR) to the extent possible, the children’s behavior as observed was performed according to the method used by when they were not taking medication.

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Data analysis Iron hemoglobin level was assayed by standard meth- ods from blood obtained by venipuncture and was a Statistical power. A review of prior studies of Mende- covariate in all analyses to protect against the possibility lian designs, studies of Gene × Environment in ADHD, of attributing to lead an effect that was actually attribut- and HFE interaction studies suggested that we needed to ing to iron. The reference range for iron hemoglobin in be able to detect a small to medium interaction effect. children is 11 to 13 g/dl; in adolescents, the reference Power analysis was conducted in G*Power (Faul, Erd- range is 12 to 16 g/dl for girls and 14 to 18 g/dl for boys. felder, Lang, & Buchner, 2007) using assumptions regard- Values for the current sample were within the reference ing allele frequencies and correlational structure. With range (11.0–15.6 g/dl). more than 350 participants, as we were able to achieve, 2 IQ was not covaried because reduction in IQ may power was .80 to detect an interaction effect size (ΔR ) of be part of ADHD as a neurodevelopmental condition .037 at an α level of .05. When α was adjusted to .0125, or may even be one of its consequences (Dennis et al., power was .80 for an effect size of .04. These effect sizes 2 2 2009; Miller & Chapman, 2001). Medication and treat- are between small, R = .01, and medium, R = .09, by ment history were not covaried because they were Cohen’s conventions. substantively correlated with symptom severity; greater symptom severity was positively correlated with past Handling of covariates. Total gross annual income in or current medication or other treatment, rs ranging the child’s primary household was reported by parents as from .49 for lifetime treatment to .71 for current treat- a proxy for socioeconomic status and was included as a ment, ps < .001. covariate. Because of the relatively wide age range of the A substantial literature suggests that boys are more children, we included age as a covariate even though it vulnerable than girls to early-life neurodevelopmental was not reliably correlated with blood lead level. perturbations (Jacquemont et al., 2014), which perhaps HFE mutations are more common among White par- accounts for the fact that nearly all neurodevelopmental ticipants (Hanson et al., 2001). In our sample, White conditions affect more males than females (Martel, 2013). youths were more likely to have one or two copies of the We therefore handled the sex variable in two ways. First, H63D mutation, p = .004, whereas the C282Y mutation our primary analyses included sex as a covariate to pre- was not significantly higher among White youths relative serve statistical power for detecting our primary interac- to non-White youths (p = .29). tion of interest (Blood Lead × Genotype). Because of Race is an important potential confound in any genetic boys’ greater vulnerability, we also conducted secondary study. Table 1 reports in detail the racial makeup of the analyses to explore the lower-powered but potentially sample. We controlled for race in two ways, following the important interactions between sex and lead exposure in method of Keller (2014). For our main results, we simply predicting ADHD symptoms as reported by parents and used a single race code (White vs. non-White) as a covari- teachers. ate. We then conducted secondary checks in which we entered all the interactions (one for each group listed in Data reduction Table 1) simultaneously, so that all effects of race were controlled. Mixed-race individuals were coded separately ADHD symptom composite scores as dependent vari- as biracial and treated as one group. ables. To maximize the reliability and validity of our out- To rule out artifact, we created a comparison variable come measures while minimizing the number of statistical that was not expected to yield an interaction with HFE tests required, we used structural equation modeling to genotype. Specifically, we obtained measures of parenting create latent factors for inattention and hyperactivity- behavior using the Alabama Parenting Questionnaire impulsivity for each informant (i.e., each parent and (Shelton, Frick, & Wooton, 1996). Because there is no evi- teacher). Thus, two separate two-factor models were fitted dence or theory suggesting that HFE genotype moderates to the data for each informant. The models were based on the association of parenting behavior with ADHD symp- reports from each informant for (a) composite inattention- toms, we used this check to rule out the possibility that disorganization, and (b) composite hyperactivity-impulsiv- our analyses overidentified interactions with genotype. ity. Each composite was created from the relevant scales Testing for ODD and CD is important to clarify whether on the ADHD Rating Scale, the Conners’ Rating Scale– any effects found are specific to ADHD or are related to Revised, and the parent-reported SWAN. Each model pro- externalizing problems. We report secondary data checks vided a good fit to the data—parental reports: χ2(7, N = involving a composite ODD-CD outcome variable (based 383) = 51.3, comparative fit index = .97, Tucker-Lewis on parental reports on the K-SADS-E and teacher reports index = .94, root-mean-square error of approximation = on the ODD and CD items added to their ADHD Rating 0.04; teacher reports: χ2(3, N = 369) = 12.5, comparative fit Scale). index = .98, Tucker-Lewis index = .97, root-mean-

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Table 1. Demographic and Clinical Differences Between the Attention-Deficit/Hyperactivity Disorder (ADHD) and Non-ADHD Groups

Non-ADHD ADHD group Difference Characteristic group (n = 147) (n = 122) between groups: p Male (%) 49.7 68.4 < .001 Age (years) 12.5 (3.1) 11.5 (2.8) .003 Race White (non-Hispanic) 80.3%, n = 118 75.0%, n = 159 .51 African American (non-White) 8.2%, n = 14 8.5%, n = 18 .91 Latino-Hispanic (non-White) 1.4%, n = 2 3.8%, n = 8 .17 Asian American (non-White) 1.3%, n = 2 0%, n = 0 .28 Native American (non-White) 1.4%, n = 2 1.9%, n = 4 .66 Mixed race 5.4%, n = 8 10.3%, n = 22 .44 Pacific Islander 0.68%, n = 1 0.0%, n = 0 .49 Middle Eastern 0%, n = 0 0.47%, n = 1 .51 Total household income (U.S. $) 87,300 (45,800) 68,000 (40,100) < .001 Global Assessment Functioning score 81.2 (9.4) 67.5 (9.1) < .001 Estimated Full Scale IQ 111.5 (12.6) 103.8 (14.6) < .001 Inattention symptoms 1.7 (2.0) 8.5 (1.4) < .001 Hyperactivity-impulsivity symptoms 1.0 (1.5) 5.9 (3.1) < .001 Oppositional defiant disorder (%) 2.7 26.5 < .001 Conduct disorder (%) 0.0 4.7 .008 Blood lead level (µg/dl) 0.74 (0.35) 0.94 (0.52) < .001 Iron hemoglobin level (g/dl) 14.1 (1.2) 14.0 (1.2) .23 HFE C282Y genotype (%) 11.0 11.4 .89 HFE H63D genotype (%) 24.0 31.0 .15 Conners’ Rating Scale–Parent Inattention (cognitive) T score 46.8 (6.7) 71.1 (10.3) < .001 Hyperactivity T score 48.2 (6.9) 66.5 (15.1) < .001 Conners’ Rating Scale–Teacher Inattention (cognitive) T score 49.3 (9.0) 57.7 (13.0) < .001 Hyperactivity T score 48.0 (7.1) 59.1 (10.2) < .001

Note: Values in parentheses are standard deviations. Twenty-four children with a diagnosis of ADHD not otherwise specified were excluded from these group comparisons. Parental and teacher ratings were collected using the Conners’ Rating Scale–Revised (Conners, 1997), and Full Scale IQ was estimated with the Wechsler Intelligence Scale for Children–4th edition (Wechsler, 2003). HFE = hemochromatosis gene. square error of approximation = 0.08. To ease replication Lead nondetection. Three children’s lead levels were by other researchers, we created composite scores from below the limit of detection of 0.3 µg/dl. Following con- the factor indicators for further analysis (results were vention, those levels were scored as 0.2 (i.e., 0.3/ 2 ). unchanged using factor scores). These composite scores Also following a frequent convention in the literature, we had satisfactory internal reliability—inattention: α = .89 for log10-transformed the blood lead score to reduce the parental reports, α = .92 for teacher reports; hyperactivity- influence of outliers (posttransformation skew < 1.0). impulsivity: α = .91 for parental reports, α = .93 for teacher reports. Handling gene-environment correlation effects. To account for potential gene-environment correlation ODD and CD. A sum total of ODD symptoms reported effects, we regressed blood lead level on HFE genotype by parents was computed for each child. To create a par- to remove variance in lead scores associated with geno- allel sum score for teacher ratings, we counted the num- type. These residuals were standardized and used for all ber of teacher-rated ODD and CD symptoms present analyses. (i.e., items rated as occurring “often” or “very often”). These totals were transformed (to correct skewness) and Multiple testing. We provide 95% confidence intervals mean-centered. (CIs), but we also provide modified Bonferroni-corrected

Downloaded from pss.sagepub.com at The University of Iowa Libraries on March 21, 2016 Attention-Deficit/Hyperactivity Disorder and Blood Lead 263 p values for the tests of interaction reliability, using an α Table 2. Correlations Among Blood Lead Level, Genotype, level of .0125 for both parental and teacher data. and Demographic Indicators Blood C282Y H63D Data-analytic strategy Characteristic lead genotype genotype Unless otherwise noted, analyses were conducted in Sex –.24** .07 .06 Mplus Version 7.0 (Muthén & Muthén, 2015). The Age .08 –.08 –.04 CLUSTER option was used to account for nonindepen- Household income –.22** .01 –.11* dence issues stemming from sibling data. Missing data Estimated Full Scale IQ –.11* .02 –.05 Global assessment functioning –.09 –.03 –.09 were handled using full-information maximum-likelihood Parental ratings (composite) procedures in Mplus; missing data were minimal except Inattention .29** .04 .05 for income (7% missing). Linear regression was the pri- Hyperactivity-impulsivity .34** –.02 .06 mary method of examining Gene × Environment effects. Teacher ratings (composite) All models included the main effects of the respective Inattention .22** .03 –.01 HFE genotypes (examined separately), blood lead level, Hyperactivity-impulsivity .26** .02 –.04 and their interaction. All continuous variables were stan- Blood lead level — –.12* –.06 dardized so they would be centered on zero. Effect cod- HFE C282Y genotype –.12* — –.11* ing was used to designate genotype groups, centered on HFE H63D genotype –.05 –.11* — 0 (wild type = −1, one or two mutation copies = 1). Iron hemoglobin .11* –.02 .08 Standardized parameter estimates were computed and are presented along with their 95% CIs. Note: Parental and teacher inattention and hyperactivity-impulsivity scores are factor scores based on the two-factor latent-variable model constructed to capture variance across the parents’ and teachers’ reports on the ADHD measures, as described in the Method section. Results N = 386, including all children with a diagnosis of attention-deficit/ hyperactivity disorder (ADHD) not otherwise specified. HFE = Descriptive statistics and bivariate hemochromatosis gene. correlations *p < .05. **p < .01. Table 1 presents demographic and clinical data and the percentage of children in our sample who were homozy- lead and hemoglobin levels, HFE genotype, teacher and gous and heterozygous for the mutation. As we reported parental ratings of ADHD symptoms, and selected demo- previously for an almost identical sample, blood lead level graphic indicators. Sex was associated with ADHD symp- was higher in the ADHD group than in the non-ADHD toms (i.e., boys were overrepresented in the ADHD group, d = 0.45, 95% CI = [0.24, 0.66]. Iron hemoglobin group) and was also associated with lead level, such that levels did not differ reliably by group. In a random popu- males had higher mean levels of both, further justifying lation sample of more than 3,500 White individuals in our decision to covary sex and to secondarily examine Michigan, Barry et al. (2005) found an HFE C282Y allele interactions with sex. frequency of 5.7% (compared with 5.6% for our overall As reported previously (Nigg et al., 2008), blood lead sample), and an HFE H63D allele frequency of 14.0% level correlated with scores calculated from both parental (compared with 14.6% for our overall sample). This means and teacher reports of the inattention and hyperactivity- that 11.4% of the Barry et al. sample were either homozy- impulsivity factor, even at these low, population-typical gous or heterozygous for the C282Y mutation, compared blood lead levels. Contrary to expectations, blood lead with 10.9% of our sample (10.8% in the non-ADHD group level was only weakly positively correlated with iron and 11.4% in the ADHD group). In their study, by exten- hemoglobin level. However, as expected, higher blood sion 28.0% were either homozygous or heterozygous for lead level was associated with lower income, lower full- the HFE H63D mutation, compared with our 28.5% (24.4% scale IQ, and the HFE C282Y variant genotype, whereas non-ADHD group, 31.1% ADHD group). the association between blood lead level and the H63D The average blood lead level in our sample was almost variant genotype was not statistically reliable. identical to the national average reported by the U.S. Centers for Disease Control and Prevention at the time of Primary tests of Gene × Environment data collection (for discussion, see Nigg et al., 2008; Nigg, effects Nikolas, Mark Knottnerus, Cavanagh, & Friderici, 2010). Thus, as intended, our sample provided a reasonable Parental reports. The interaction between HFE C282Y representation of allele frequency and lead exposure and genotype and blood lead level was a statistically reliable allowed us to take advantage of a case-control design. predictor of parental reports of hyperactivity-impulsivity, Table 2 shows the bivariate correlations among blood β = 0.22, 95% CI = [0.11, 0.40], ΔR2 = .02, total R2 = .28,

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but not parental reports of inattention, β = 0.16, 95% CI = hyperactivity-impulsivity was stronger for boys with the [−0.004, 0.32], ΔR2 = .005, total R2 = .14. The association HFE C282Y mutation, β = 0.50, 95% CI = [0.10, 0.90], than between blood lead level and hyperactivity was signifi- for boys with the wild-type genotype, β = 0.21, 95% CI = cantly stronger for youths with the HFE C282Y mutation, [0.06, 0.27]. In the smaller sample of females, the interac- β = 0.74, 95% CI = [0.52, 0.96], than for those with the tion between HFE C282Y mutation and blood lead level wild-type genotype, β = 0.28, 95% CI = [0.15, 0.41]. Fig- was not reliable, β = 0.09, 95% CI = [–0.16, 0.34], and the ure 2 illustrates this effect, which passed our modified association between blood lead level and hyperactivity Bonferroni threshold. The interactions between the HFE did not differ much between girls with the HFE C282Y H63D genotype and blood lead level as a predictor of mutation, β = 0.22, 95% CI = [–0.02, 0.46], and those with parental and teacher reports of ADHD symptoms were the wild-type genotype, β = 0.17, 95% CI = [–0.10, 0.54]. very small and not statistically reliable, ΔR2s < .01, ps > .3. In teacher reports for boys, the interaction between HFE C282Y genotype and blood lead level was a reliable Teacher reports. HFE C282Y genotype and blood lead predictor of reports of hyperactivity-impulsivity, β = 0.19, level also interacted in predicting teacher reports of 95% CI = [0.07, 0.31], ΔR2 = .016, total R2 = .15. Once again, hyperactivity-impulsivity, although the 95% CI did the association between blood lead level and ADHD include zero, β = 0.19, 95% CI = [–0.002, 0.41], ΔR2 = symptoms was stronger for boys with the HFE C282Y .015, total R2 = .22. The association between blood lead mutation, β = 0.46, 95% CI = [0.23, 0.69], than for boys with and hyperactivity was qualitatively stronger for youths the wild-type genotype, β = 0.15, 95% CI = [0.02, 0.28]. In with the HFE C282Y mutation, β = 0.47, 95% CI = [0.22, the smaller sample of girls, again, the interaction was not 0.72], than for those with the wild-type genotype, β = reliable, β = 0.11, 95% CI = [–0.03, 0.25]. The associations 0.29, 95% CI = [0.18, 0.40]. The interaction of HFE C282Y between blood lead level and hyperactivity-impulsivity genotype and blood lead level was not a statistically reli- were similar for girls with the HFE C282Y mutation, β = able predictor of teacher-reported inattention, β = 0.06, 0.20, 95% CI = [0.04, 0.36], and those with the wild-type 95% CI = [–0.04, 0.12], ΔR2 = .002, total R2 = .11, just as it genotype, β = 0.16, 95% CI = [0.07, 0.25]. was not for parent-reported inattention. As with parental reports, the interaction effects of HFE H63D genotype Race. Following the method of Keller (2014), we also and blood lead level were small and not reliably differ- created a model that included an interaction term for ent from zero. For secondary analyses, we focused on HFE C282Y genotype and each of the seven racial groups the C282Y genotype. shown in Table 1 to determine whether these interactions accounted for the interactive effect of C282Y genotype Secondary analyses and blood lead level on ADHD symptoms. This was repeated for H63D. The results for this model were not Sex. Males and females differed significantly in mean discernibly different from the results already reported. level of ADHD symptoms and blood lead level, and there is a substantial theoretical literature on biological sex as Other interactions. Also following the method of a moderator of ADHD vulnerability (Martel, 2013). There- Keller (2014), we created models with blood lead level fore, we conducted secondary analyses to determine and terms for the interaction between HFE genotype and whether sex moderated the impact of lead exposure on all other covariates (i.e., age, ethnicity, income, iron ADHD symptoms. Interaction terms were not reliably dif- hemoglobin). Again, the results for this model were ferent from zero for analyses of either parental or teacher essentially the same as those reported earlier. reports of inattention symptoms. The Sex × Blood Lead interaction term was reliably different from zero in Comorbidity. When ODD-CD score was included as a the model predicting teacher reports of hyperactivity- covariate, results were essentially the same. The interac- impulsivity, β = 0.26, 95% CI = [0.11, 0.41], but not the tion between C282Y genotype and blood lead level was model predicting parental reports of hyperactivity- still a significant predictor of parental reports of hyperac- impulsivity, β = 0.10, 95% CI = [–0.04, 0.24]. The direction tivity on the K-SADS-E, β = 0.20, 95% CI = [0.04, 0.36], of the effect for teacher reports indicated that the effect ΔR2 = .020, total R2 = .28; the interaction between C282Y was larger in boys than in girls. We then examined effects genotype and blood lead level was not a reliable predic- separately within sex. For completeness, we report results tor of parental reports of inattention, β = 0.15, 95% CI = for both teacher and parental reports. [–0.003, 0.31], ΔR2 = .005, total R2 = .14. When teacher- In parental reports for boys, the Blood Lead × C282Y reported ODD-CD sum score was included as a covari- Genotype interaction was associated with hyperactivity- ate, the interaction between C282Y genotype and blood impulsivity, β = 0.31, 95% CI = [0.14, 0.48], ΔR2 = .033, total lead level was not reliably different from zero for either R2 = .24. The association between blood lead level and hyperactivity, β = 0.17, 95% CI = [–0.003, 0.43], ΔR2 = .018,

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3 Children With Wild-Type HFE Genotype Children With HFE C282Y Mutation 2 β = 0.74, p < .001

1 β = 0.28, p = .006 0

–1

–2 Hyperactivity-Impulsivity Parental Composite

–3 –3 –2 –1 0123 Blood Lead Level (z score)

Fig. 2. Parental reports of hyperactive-impulsive symptoms as a function of blood lead level (z-scored), plot- ted separately for youths with the hemochromatosis (HFE) C282Y mutation and youths with the HFE wild-type genotype. total R2 = .22, or for inattention, β = 0.07, 95% CI = [–0.05, is partially distinct (Sonuga-Barke, 2005; Willcutt et al., 0.19], ΔR2 = .003, total R2 = .11. Conversely, in a model 2012). Lead may preferentially act in DA-related reward predicting ODD-CD score (controlling for ADHD symp- circuits, for example, and thus preferentially affect the toms), the interaction of C282Y genotype and blood lead hyperactivity-impulsivity symptom domain. level was small and not reliably different from zero, β = In this school-age sample, the association between the 0.08, 95% CI = [–0.07, 0.23], ΔR2 = .006, total R2 = .17. HFE C282Y mutation and lead level itself was rather weak. Although similar associations were seen in prior Specificity. As a cross-check on specificity of effects, studies of U.S. adults (Wright et al., 2004), the direction of we conducted analyses examining HFE C282Y genotype association was reversed in a study of Mexican preschool- as a moderator of the impact of parental monitoring, an ers (Hopkins et al., 2008). Aside from markedly greater environment not expected to be related to ADHD or to lead exposure and different allele frequencies in the interact with HFE genotype (Ellis & Nigg, 2009). Parental Mexican preschoolers compared with the U.S. adults, the monitoring was evaluated by parental self-report using functional effect of HFE mutations on lead level may vary the Alabama Parenting Questionnaire (Shelton et al., across development in relation to developmental changes 1996). As expected, there was no hint that the interaction in iron demand in the body (Delatycki, Powell, & Allen, between parental monitoring and genotype was a reli- 2004; Gundacker et al., 2010). Park et al. (2009) proposed able predictor of parental or teacher reports of hyperac- that if the C282Y effect primarily alters lead’s influence tivity or inattention, all R2s < .01, all ps > .50. on secondary metabolic activity (rather than lead uptake per se), then C282Y could alter lead’s effect on hyperac- Discussion tivity without markedly altering blood lead readings. Although their specific hypothesis remains unproven, it We used Mendelian randomization to evaluate effects of would be consistent with the effects seen here. blood lead level in childhood ADHD. Consistent with the The same may hold for iron level. In this sample, HFE proposition that lead plays a causal role in the develop- variants were not associated with increased iron hemo- ment of some cases of ADHD, the HFE C282Y mutation globin level. Perhaps a different measure, such as serum heightened the association between lead and ADHD, iron, would have given different results (Zimmermann, principally for hyperactivity-impulsivity symptoms. The 2008). More likely, this result is related to greater iron reliability of effects for hyperactivity but not for inatten- demand during development, which is known to attenu- tion echoes the literature, which suggests that the neuro- ate the HFE association with absolute iron stores in the biology and etiology of the two ADHD symptom domains body. Nonetheless, it is clear that confirmation of the

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hypothesis proposed here will require a better under- This study highlights lead as an exemplar environ- standing of how absolute iron and lead levels contribute mental exposure for understanding the interplay among to altered metabolism or cell damage in the presence of environmental, genetic, and (potentially) epigenetic particular genotypes, particularly in children. We reported inputs to the development of psychopathology in the previously that lead’s association with ADHD was medi- case of ADHD. The results enable speculation on the ated by effects on cognitive control (Nigg et al., 2008; larger question of Gene × Environment etiology of Nigg et al., 2010). To better evaluate this theory and its ADHD, although this larger Gene × Environment ques- pathophysiological meaning, future researchers could tion was not our focus. Twin studies demonstrate sub- consider intermediate , such as cognitive con- stantial heritability for ADHD (Nikolas & Burt, 2010). trol and, in particular, reward processing related to Genetic factors have been shown to influence the uptake hyperactivity-impulsivity. of toxic elements, including lead (Whitfield et al., 2007). Effects were reliable for C282Y but not for H63D. Widespread and common environmental exposures Although some past work has indicated moderation of (such as that to lead) likely function as shared environ- lead’s clinical effects by H63D, effects have been more ments (Nigg, 2006), which means that lead exposure is consistent for C282Y. Evidence suggests that C282Y has a correlated among siblings within the same family and more powerful effect on iron-related metabolism than serves to increase their similarity. However, given that that of H63D. The HFE C282Y mutation causes a greater shared environmental effects may influence ADHD within loss of HFE protein function than does the H63D muta- the context of genetic risk, gene-by-environment interac- tion and confers a greater risk for development of hemo- tions have been proposed (Nigg, 2006; Nigg et al., 2010). chromatosis and in adulthood (Hanson Shared environment is of specific interest in the current et al., 2001). study, because the Gene × Shared Environment interac- Findings were most robust for boys. Boys are more tion is subsumed within the additive genetic variance in vulnerable than girls are to early neurodevelopmental twin studies, inflating the typical heritability estimate insult, and ADHD is more common in boys, possibly (Purcell, 2002). Thus, Nigg (2006) proposed that studies because of hormone-mediated effects (Martel, 2013). It of environment and ADHD should emphasize common may be that additional biochemical pathways interact environmental exposures, among which low-level lead with lead and iron handling in boys. The boys in this exposure is salient. sample also had higher lead levels than the girls, so it is Our results add to the growing picture of how ADHD also possible that C282Y’s moderating effects are stronger and related disorders involve dynamic, genetically modi- at slightly higher lead levels. fied responses to common environments, and they The timing of the exposure to lead in this cross-sec- should stimulate development of appropriate theoretical tional sample is unknown; obviously, if lead exposure is conceptualizations. At the same time, this work should causal, it should precede development of ADHD. In encourage further research on other neurotoxicants and keeping with this idea, results from broader population neurodevelopmental disorders. For example, even low- studies have shown that peak lead exposure in children level lead exposure may result in epigenetic changes dur- occurs in the toddler years, whereas ADHD is typically ing development (e.g., histone modification), and such not observed or diagnosed until later in preschool or changes can mediate increased hyperactive behavior school age. Even so, it is unclear whether early exposure (Luo et al., 2014). Thus, future work could specifically or sustained exposure drove the effects we observed. examine changes in expression in ADHD-relevant The size of the Blood Lead Level × HFE interaction related to lead, as well as other toxicants. effect on ADHD was modest. Although this effect passed Studies of specific genes in specific environments multiple-testing correction and tests of specificity, and move the field toward biology-based nosology and per- therefore is reliably different from zero and indicates a sonalized treatments for developmental psychopathol- causal association, it is also likely that HFE genotype is ogy. Although achievement of those goals remains some only one of the elements moderating the strength of distance away, the present findings suggest that further association between blood lead level and ADHD. investigation of environmental toxicants may provide Lead is associated with conduct problems and delin- clues regarding causal pathways for neurodevelopmental quency as well as ADHD. In the current study, effects disorders. were specific to ADHD. Many youths with ODD will not develop CD or delinquency, and few youths with Author Contributions CD were included. In addition, many of these youths J. T. Nigg and K. H. Friderici developed the study concept and were not old enough to display conduct problems. design. Data collection, including genotyping of samples ana- ADHD is a typical precursor to CD, particularly for lyzing blood lead levels, was overseen by J. T. Nigg, K. H. boys. Thus, as these youths mature, an effect with CD Friderici, and M. A. Nikolas. M. A. Nikolas, A. L. Elmore, and N. might emerge. Natarajan performed the data analysis with supervision and

Downloaded from pss.sagepub.com at The University of Iowa Libraries on March 21, 2016 Attention-Deficit/Hyperactivity Disorder and Blood Lead 267 guidance by J. T. Nigg. J. T. Nigg, A. L. Elmore, and M. A. Costa, L. G., Aschner, M., Vitalone, A., Syversen, T., & Soldin, Nikolas drafted the manuscript, and all authors provided critical O. P. (2004). Developmental neuropathology of envi- revisions. All authors approved the final manuscript before ronmental agents. Annual Review of Pharmacology and submission. Toxicology, 44, 87–110. doi:10.1146/annurev.pharmtox .44.101802.121424 Declaration of Conflicting Interests Delatycki, M. B., Powell, L. W., & Allen, K. J. (2004). Hereditary hemochromatosis genetic testing of at-risk children: The authors declared that they had no conflicts of interest with What is the appropriate age? Genetic Testing, 8, 98–103. respect to their authorship or the publication of this article. doi:10.1089/1090657041797248 Dennis, M., Francis, D. J., Cirino, P. T., Schachar, R., Barnes, Funding M. A., & Fletcher, J. M. (2009). Why IQ is not a covari- This work was supported by National Institute of Mental Health ate in cognitive studies of neurodevelopmental disorders. Grants R01-MH070004, R01-MH099064, and R37-MH59015. Journal of the International Neuropsychological Society, 15, 331–343. doi:10.1017/S1355617709090481 DuPaul, G. J., Power, T. J., Anastopoulos, A. D., & Reid, R. References (1998). ADHD Rating Scale-IV: Checklists, norms, and clini- Adonaylo, V. N., & Oteiza, P. I. (1999). Pb2+ promotes lipid cal interpretation. New York, NY: Guilford Press. oxidation and alterations in membrane physical properties. Ellis, B., & Nigg, J. (2009). Parenting practices and attention- Toxicology, 132, 19–32. doi:10.1016/S0300-483X(98)00134-6 deficit/hyperactivity disorder: New findings suggest partial American Psychiatric Association. (2000). Diagnostic and sta- specificity of effects. 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