To estimate prevalence and incidence of attention deficit hyperactivity disorder (ADHD) in children and adolescents in Slovenia using different epidemiological models.Data from the National Institute of Public Health of the Republic of Slovenia for the period 1997-2012 were analyzed. The database includes the annual number of newly diagnosed outpatients with ADHD in Slovenia. The evaluation for ADHD diagnoses was done in accordance with the Tenth Revision of the International Classification of Diseases (ICD-10) outpatient data codes. In model 1, a linear increase was proposed to fit the data in the period from 1997 to 2003 in order to extrapolate the data before 1997. In model 2 and 3, an exponential increase in the annual incidence rate was proposed.The incidence rate of ADHD diagnosis in 1997 was 0.032% and in 2012 it increased to 0.082%. Mean prevalence rate was 750  per 100 000 children and adolescents. It was estimated that the prevalence rate in 2020 would be 1% , which is 6.3-fold higher than in 1997.ADHD is a common mental health disorder among Slovenian children and adolescents, but it remained underdiagnosed compared with Western countries. Our results indicated a need for improved timely interventions in Slovenia, not only in child and adolescent psychiatry but also in primary settings and adult psychiatry, where ADHD should be more efficiently recognized. Attention deficit hyperactivity disorder (ADHD) is one of the most common psychiatric disorders in childhood and adolescence, characterized by developmentally inappropriate inattention, hyperactivity, and/or impulsivity . It entaChildren exhibiting symptoms of inattention, hyperactivity, and impulsivity have been described previously . In 1980ADHD affects 3% to 9% of children worldwide . In EuroWhile the popular press across Europe frequently comments on increased rates of ADHD diagnosis, questioning whether ADHD is overdiagnosed and overtreated, reviews of clinical practice suggest that in Europe ADHD is underdiagnosed and undertreated ,14. ADHDThe national epidemiological data were obtained from database of the National Institute of Public Health of the Republic of Slovenia (NIPH) . The datThis study included the data regarding the following diagnoses: hyperkinetic disorders (F90), disturbance of activity and attention (F90.0), hyperkinetic conduct disorder (F90.1), other hyperkinetic disorders (F90.8), and unspecified hyperkinetic disorder (F90.9). The participants’ selection was limited to those younger than 19 years. Ethical approval was received from the National Medical Ethics Committee of the Republic of Slovenia.The estimate of annual incidence rate was calculated as the ratio of the number of newly diagnosed children and adolescents with ADHD and the total number of people younger than 19 years. As the data regarding the number of newly diagnosed patients with ADHD had been collected only from 1997 onward, the incidence rate of ADHD diagnosis was modeled in order to estimate the incidence rate before 1997. The annual incidence rate was increasing in the period from 1997 to 2003, reaching a plateau from 2004 to 2012.b), presenting the increase in annual incidence rate (I) per one year. The intercept (a) represents the estimated annual incidence rate in 1997 as the variable t (years) in Eq 1 was set to zero for the year 1997:In model 1, in order to extrapolate the data before 1997, it was assumed that in the period from 1997 to 2003 there was a linear increase in incidence . A linear regression model (Eq.1) was used to calculate the slope of the line (Eq [1]k) of incidence rate increase and the intercept A, which represents the estimated annual incidence rate for 1997:In the model 2 and 3, an exponential increase was assumed. An exponential regression model was used to estimate the rate constant . In this approach it was assumed that in Slovenia no ADHD diagnosis existed before 1997.P) of ADHD diagnosis among children and adolescents was calculated on the basis of the estimate of annual incidence rate (I) and the proportion of patients who reached the age of 19 each year (G), using a recursive formula:Estimate of prevalence rate . It was assumed that the percentage of patients with ADHD who annually reached the age of 19 equaled to the ratio of the number of all 19-year-olds and the total number of all people aged 19 or younger. The required population data for the period from 1997 to 2013 were obtained from the Statistical Office of the Republic of Slovenia . The incidence rate was assumed to be constant for the years 2013 to 2020, corresponding to the value of the incidence rate plateau reached in the period from 2004 to 2012. Additionally, we estimated the number of people aged 19 or younger for the year 2020 on the basis of a geometric growth model, where the geometric growth rate was constant and equaled to the mean growth rate in the previous three years (2011-2013). Tables, and graphs were made in Microsoft Excel® 2010 .2 = 0.875; exponential model: A = 30.36, k = 0.141, and r2 = 0.931.The annual incidence rates of ADHD diagnosis in Slovenia increased from 1997 to 2003 (2.4-fold) . Mean vaThe point estimates of the prevalence rate calculated on the basis of 4 different models were used for calculation of the annual incidence rate . The devThe prevalence rate of ADHD diagnosis in Slovenia increased 4.8-fold from 1997 to 2012. In this period, the prevalence rate increased almost linearly.The prevalence of ADHD diagnosis in 2020 was predicted to be 6.3-fold higher than in 1997, 1.3-fold higher than in 2012, and 1.8-fold higher than in 2007. All values were calculated using the exponential model with the starting point at 1980, because the differences within this model were smallest, although the differences among all the models were very small.In the observed period, the annual incidence rates of ADHD diagnosis in Slovenia increased logarithmically and the prevalence rates increased linearly. The annual incidence increased 1.5-fold from 1997 to 2000 and 2.5-fold from 1997 to 2003. The prevalence of ADHD diagnosis increased 3.5-fold from 2000 to 2012 and 2.5-fold from 2000 to 2006. The increase in the incidence and prevalence in Slovenia is similar to that in Western countries  and coulUnderdiagnosis in Slovenia might be attributable to the fact that only few psychiatrists diagnose ADHD and prescribe ADHD medicines. Also, there are no national guidelines for ADHD treatment and diagnosis, which would integrate the existing knowledge about ADHD into the Slovenian health care system ,26. PareIn many countries, the main barrier to providing treatment of psychiatric disorders is the limited treatment in primary care settings . In SlovThe incidence rates calculated in our four models show the models’ usefulness in the calculation of incidence data from the NIPH database as well as their wide applicability. The incidence rate of ADHD diagnosis obtained from the model 1 was deviated, but primarily because of inaccuracies in the database. In prevalence calculation, there were minimal differences among the models, which showed the usefulness and appropriateness of these models to estimate the future ADHD prevalence rate. These models can be used in planning resources to cope with the burden of ADHD .Although data from western countries show a fast increase in the number of ADHD patients in the last decade, in Slovenia this trend is observed with a big delay. However, this increase could be a result of improved diagnostics and awareness, rather than an increase in real prevalence. Therefore, this issue needs to be investigated in a new study including regional data.There are several advantages of using claims data for an epidemiological study of ADHD, for example using longitudinal data from a large national population-based sample to calculate the annual incidence . HoweverIn this study the annual incidence and prevalence rate of ADHD diagnosis were evaluated using four different approaches. All four approaches gave were very similar the results. The incidence and prevalence rate of children and adolescents with ADHD in Slovenia were increasing, but it was still 2 to 10 times slower than in some comparable countries. According to the calculated prevalence of ADHD in Slovenia, the diagnosis rates in children and adolescents did not exceeded those expected according to the rates in Western countries (less than 1% in children and adolescents). All models showed similar results, which indicated that they were appropriate for calculating ADHD incidence and prevalence using national database data. The epidemiological models could be used to assist ADHD epidemiology calculation in other countries in the region. Finally, these data indicated an urgent need for further research of health care utilization and quality of care in the field of ADHD in Slovenia.


Twin studies and genome-wide complex trait analysis (GCTA) are not in agreement regarding heritability estimates for behavioral traits in children from the general population. This has sparked a debate on the possible difference in genetic architecture between behavioral traits and psychiatric disorders. In this study, we test whether polygenic risk scores associated with variation in attention-deficit/hyperactivity disorder (ADHD) trait levels in children from the general population predict ADHD diagnostic status and severity in an independent clinical sample.p < .5 from a genome-wide association study of ADHD traits in 4,546 children  from the Avon Longitudinal Study of Parents and Children  were selected to calculate polygenic risk scores in 508 children with an ADHD diagnosis  and 5,081 control participants. Polygenic scores were tested for association with case-control status and severity of disorder in the clinical sample.Single nucleotide polymorphisms (SNPs) with p = .0003), higher ADHD symptom severity , and symptom domain severity in the clinical sample.Increased polygenic score for ADHD traits predicted ADHD case-control status (odds ratio = 1.17 [95% CI = 1.08–1.28], This study highlights the relevance of additive genetic variance in ADHD, and provides evidence that shared genetic factors contribute to both behavioral traits in the general population and psychiatric disorders at least in the case of ADHD. Traditional behavioral genetic studies have shown that psychiatric disorders, whether defined categorically as diagnoses or viewed as trait measures, are moderately to highly heritable.Additive effects of common gene variants (single nucleotide polymorphisms [SNPs]), when jointly considered in terms of estimated heritability or polygenic risk scores, have been found to contribute to psychiatric disorders: specifically ADHD, autism, depression, bipolar disorder, and schizophrenia.Previous research suggests that polygenic risk scores can distinguish children with clinically diagnosed ADHD from control participants.In the present study, we derived polygenic risk scores from a discovery genome-wide association study (GWAS) of ADHD traits in a general population sample to test whether common genetic risk alleles associated with variation in ADHD trait levels predict ADHD diagnostic status and also the severity of disorder in an independent clinical case-control ADHD sample.The polygenic risk score method described by the International Schizophrenia Consortium (ISC) was used in this analysis.http://www.bris.ac.uk/alspac/researchers/data-access/data-dictionary/). Ethical approval was obtained from the ALSPAC Law and Ethics Committee and the local ethics committees.ALSPAC, which was used as the discovery sample for this study, is a prospective birth cohort that recruited pregnant women with expected delivery dates between April 1991 and December 1992 from Bristol, United Kingdom. A total of 14,541 pregnant women were initially enrolled, with 14,062 children born. Detailed information on health and development of children and their parents was collected from regular clinic visits and completion of questionnaires. A detailed description of the cohort has been published previously.A total of 9,912 research participants were genotyped using the Illumina HumanHap550 quad genome-wide SNP genotyping platform by the Wellcome Trust Sanger Institute  and the Laboratory Corporation of America  using support from 23andMe. Details on the quality control (QC) procedure have been published previously.ADHD traits were assessed in ALSPAC when the participants were 7 years 7 months of age using the parent-completed Development and Well-Being Assessment (DAWBA).DSM-IV research diagnosis of ADHD (N = 508) recruited from community child and adolescent mental health and child health clinics. Trained interviewers used the Child and Adolescent Psychiatric Assessment—Parent Version,DSM-IV ADHD and symptom domain severity were calculated by summing scores on ADHD items. Pervasiveness of ADHD symptoms (in school) was assessed using the Child Attention-Deficit Hyperactivity Disorder Teacher Telephone InterviewThe target sample consisted of British children with a confirmed IQ was assessed using the WISC-IV.DNA samples from children with ADHD were genotyped on the Illumina Human660W-Quad BeadChip, and control participants were genotyped by the Wellcome Trust Case Control Consortium–Phase2 using the Illumina Human 1.2M BeadChip.The target sample was selected for this study due to its similarity to the ALSPAC study both in terms of ethnicity and geographic location, as well as its robust diagnostic assessment process. Research participants in the target and discovery samples were recruited from geographically nearby regions . Therefore, an identity by descent (IBD) analysis was conducted using PLINKp < 0.5, in line with previous studies.To identify risk alleles in the ALSPAC discovery sample (quantitative GWAS of ADHD traits), we selected SNPs with a threshold of 2) exceeded 0.2.SNPs in linkage equilibrium in the Cardiff clinical sample were identified using PLINKDSM-IV ADHD inattentive and hyperactive/impulsive symptoms, using linear regression with sex as a covariate. The amount of variance explained was calculated as the difference of Nagelkerke’s pseudo-R2 in the full model as compared to the null model, which included sex but not polygenic score. p Values were determined from likelihood ratio tests, which compare the full model to the null model. In addition, conditional analysis was performed with hyperactive/impulsive symptoms included as a covariate in the model for inattentive symptoms and inattentive symptoms included as a covariate in the model for hyperactive/impulsive symptoms to test whether any particular ADHD symptom domain was contributing more to the association. Statistical analyses were performed using Stata Statistical Software Release 13.The SNPs that were present in both the list of risk alleles from ALSPAC and the LD-pruned list from Cardiff  were used to calculate a polygenic score for each individual in the Cardiff clinical sample, which was a sum of all risk alleles weighted by β coefficient using the PLINK command (–score) with imputation of missing genotypes. The resulting polygenic scores were standardized using z-score transformation and tested for association with ADHD case-control status in the Cardiff sample using logistic regression with sex as a covariate. They were also tested for association with total number of In this study, we used risk alleles from a GWAS of ADHD traits in the general population to calculate polygenic risk scores in a clinical sample of children with ADHD and population cohorts. p = .0003, pseudo-R2 = 0.004).Polygenic risk scores were calculated in the Cardiff cases and controls based on the results of the GWAS of ALSPAC ADHD traits. To test whether increased polygenic score for ADHD traits was associated with case-control status in the clinical study, logistic regression of polygenic risk scores for ADHD traits on 508 individuals with ADHD and 5,081 control participants was used. Results show that population ADHD trait polygenic scores significantly distinguish participants with ADHD from controls , as well as symptom domain severity. The association with inattentive symptoms  was stronger than that for hyperactive/impulsive symptoms , but the direction of effect was as expected.The present study is the first to show, using molecular genetics, that the same risk alleles contributing to ADHD trait levels in the general population also confer risk of ADHD diagnosis and increased symptom severity. ADHD trait polygenic risk scores distinguished case individuals with an ADHD diagnosis from general population controls and were associated with a higher number of ADHD symptoms in individuals with an ADHD diagnosis.Polygenic risk score analysis is well suited for phenotypes such as ADHD. ADHD is a heritable disorder with twin heritability estimates at 76%These findings have important implications for ADHD research. First, they provide further support to the notion that ADHD as a disorder lies on the spectrum of normal trait variation. Family and twin studies have already shown that a clinical diagnosis of ADHD in one sibling is associated with increased ADHD trait scores in the unaffected sibling.More importantly, these results are relevant to the debate of whether the genetic architecture of behavioral traits differs from that of other quantitative traits. A recent molecular genetic study of twins reported a discrepancy of heritability estimates for behavioral traits between twin estimates and GCTA in the same sample of children at age 12 years.DSM-5, and they serve only as descriptive specifiers. Despite the lack of distinct genetic influences for ADHD symptom domains, inattentive symptoms are developmentally more persistent than hyperactive/impulsive symptoms.Adjustment of the associations with ADHD symptom domains suggests that the association of ADHD trait scores with the number of ADHD symptoms is driven by inattentive symptoms rather than hyperactive/impulsive symptoms. This could indicate that the genetic component of inattentive symptoms is stronger than that for hyperactive/impulsive symptoms. An alternative explanation could be that genetic influences on hyperactive/impulsive symptoms that do not overlap with genetic influences on inattentive symptoms may contribute more to ADHD traits in the general population. Although some twin studies have found distinct genetic influences on symptom domains, the evidence on the whole is not compelling.Our study benefits from a large discovery population sample that is not only ancestrally similar to the well-characterized clinical sample used but also geographically adjacent . Both samples are homogeneous in terms of their ancestry and also diagnostic assessment. Although each study used different diagnostic instruments, the assessment within each sample was standardized across all individuals.A limitation of the study is that there is no phenotypic information available on the controls in the case-control ADHD study. This means that there could be individuals with ADHD in the control sample that we were unable to identify. However, this would only reduce our power to distinguish between individuals with ADHD and controls. In a future study, it would be interesting to test whether polygenic scores were associated with ADHD trait levels in individuals from another general population cohort. Unfortunately this was not possible in this study because of the lack of ADHD trait scores in the control sample.As a longitudinal study, the ALSPAC cohort suffers from attrition that could be associated with behavioral problems, such as ADHD. In this case, the predictive power of the polygenic scores would be reduced and the associations with ADHD would be weaker. However, the prevalence of ADHD in the ALSPAC sample was 3.1% for boys and 0.3% for girls, which is very similar to the UK prevalence for ADHD during childhood (2.7% for boys and 0.4% for girls).Another potential issue that should be discussed is that because the target sample consists of children who already have a diagnosis of ADHD, a significant proportion of them were likely to be on medication that reduces ADHD symptom severity. Although this would have an impact on the power to detect an association with the number of ADHD symptoms, it would not have an impact on our ability to distinguish between individuals with ADHD and controls, because children with ADHD had a lifetime diagnosis based on symptoms present before initiation of medication.•Genetic risk factors for subthreshold ADHD traits present in individuals from the general population and those with the clinical disorder overlap.•There is further support to the notion that ADHD as a disorder lies on the spectrum of normal trait variation.•Additive genetic variance is relevant to the genetic architecture of behavioral traits not unlike other quantitative traits.•More research is required to investigate whether recognizing ADHD symptoms, even subthreshold symptoms, at an early stage and offering appropriate support could reduce the risk of educational underachievement and problematic behaviors.In summary, polygenic scores for ADHD trait levels in the general population are associated with an ADHD diagnosis and with symptom severity for those with the disorder. This study highlights the importance of common genetic variants for ADHD traits and the dimensionality of the ADHD phenotype. It also counters suggestions that the genetic architecture of behavioral traits is different from that of other complex traits.


Objective: Attention-Deficit/Hyperactivity Disorder (ADHD) is one of the most common behavioral disorders in children effecting the families and society. This systematic review examined the literature on the role of lead exposure in children with ADHD symptoms. Articles were analytically compared, focusing on the methodology used to assess exposure and adverse effects on children with ADHD. Method: Using the search strategy from six databases , hand searching in key journals, list of references of selected articles and gray literature, without time and language limitation, articles up to May 2014 were entered into this review. In this review, 1,387 articles were acquired at the primary search. Study selection and quality assessment processes were done based on Cochrane library guidelines. After assessing the quality and inclusion and exclusion criteria, 18 articles were selected and entered into the data synthesis.Results: Blood Lead level (BLL) of less than 10µg/dL in children has been attributed to at least one type of ADHD i.e., Combined / Inattentive / Hyperactive-Impulsive. The results of this study revealed that in 16 out of the 18 studies, a significant association was found between BLL and one of the types of ADHD.Conclusion: Based on the findings of this study, even the BLL of less than the action level of 10µg/dL, chosen by Centers for Disease Control and Prevention (CDC), may affect children with ADHD. Attention deficit / hyperactivity disorder (ADHD) is one of the most commonly diagnosed psychiatric disorders in children. In addition, it is probably the most common chronic condition undiagnosed in adults , 2. AccoBiological and environmental factors are the pathogenesis of this disorder, including head injury, a decrease in the prefrontal cortex, and toxins and chemicals found in the environment . AlthougLead (Pb) metal as a neurotoxin has been causing abnormal behavior in children, and many studies have examined the relationship between exposure to heavy metals and other harmful environmental factors in the pathogenesis -21. SincA systematic review was conducted to have a clear answer and deep understanding of the topic of concern. Among all the heavy metals, Lead is one of the significant heavy metals causing mental illness. In order to have a clear and deep understanding of the effects of lead on the mental health of children, lead was selected as the main element for the systematic review in this paper. The aim of this study was to systematically review all the studies showing the relationship between ADHD symptoms and blood lead levels below 10µg/dL in children. The findings of these studies also account for the gaps and research needs. Synthesizing the evidence related to ADHD symptoms in children with BLLs less than 10µg/dL will help determine whether the current BLL is still appropriate for children or not.Search Strategy:National and international databases, such as PubMed, PsycINFO, Web of Science, SID, IRAN Medex, IRAN DOC, were searched. In addition, hand searching in key journals, list of references of the selected articles in both English and Persian, and gray literature were investigated. Moreover, Studies working on the age group of younger than 18 were selected to be included into the systematic review. As per the CDC and government agencies around the world, individuals who are younger than 18 years of age are considered as children. The databases were thoroughly searched for articles with no time limit, until May 2014. Language limitation was not set as inclusion criteria. Animal studies were not included in our review. In order to ensure that no relevant papers are lost, the lists of review articles were also fully investigated.Keywords:The key words used for the search are divided into three categories as follows:"Attention deficit disorder*";"Attention Deficit Disorder with Hyperactivity*"; Hyperactivity Disorder*;"Attention Deficit Hyperactivity Disorder"*; Attention-deficit*; Attention Deficit Disorder with Hyperactivity Risk Factor*; Inattention*; ADHDThe key words on lead metal were as follows: “Hazardous material*”, “Hazardous exposure*”, “Toxic Metal Exposure*”“Toxic material*”, “Chemical hazard*”, Lead*, “Environmental toxicant*”Key words on children: Infant*, Child*, Childhood*The search terms with similar meanings were combined using the OR logic, and the search terms were coupled using the AND logic. At the end, the search terms used for searching the databases are as follows: (ADHD OR Hyperactivity disorder* OR Attention Deficit Disorder with Hyperactivity Risk Factor* OR Attention Deficit Hyperactivity Disorder* OR Attention-deficit* OR Attention Deficit Disorder with Hyperactivity* OR Attention deficit disorder* OR Inattention*) AND  AND (Infant* OR Child* OR Childhood*) The search strategy was modified and customized for every database.Criteria for Inclusion and Exclusion:An article was included in our systematic review if it was An original article An article with the main subject of lead exposure in children (birth to 18 years)An article studied on one of the three types of ADHD in children At first, we evaluated the titles and abstracts of the retrieved articles to determine the initial eligibility; and if necessary, the full papers were studied in detail in order to be selected for the review.To check the eligibility, those studies conducted on children under the age of 18, and those on the relationship between blood lead levels and symptoms of ADHD  or types of ADHD with measured BLL<10µg/dL were studied. Articles in which lead exposure was examined using urine, hair, nails and teeth were excluded. Two researchers separately conducted the initial check on the titles and abstracts of the papers and excluded the articles without the specific criteria. After a detailed study, the remaining articles were included. Assessment of the Quality of Articles:The quality assessment of the included articles was also a necessary task. There are many international standards for quality measurement of articles like CASP, OTTAWA, NEWCASEL, JADAD, CONSORT and STROBE . We usedThe Checklist for STORBE is a list of 22 items; of which, eight items are related to quality assessment and selected to be used in our study. Each of the quality assessment questions was equivalent to 1 point, and the articles were divided into the following groups depending on the points they obtained as per our Quality assessment Scale: 0-2 Points: Low quality 3-5 Points: Medium quality6-8 Points: High quality Methods of Data Extraction:After screening databases and available resources, the initial articles were selected and their data were extracted uniformly. The data extract form consisted of the author's name, year of publication, place of study, the number of subjects examined, type of study, the amount measured and the method of measuring BLLs and blood lead sample in the subjects, and the technique used to determine ADHD. For establishing common grounds in all the studies, the units of the measured BLLs were converted to microgram per deciliter (µg/dL).After eliminating the duplicate articles and reviewing the titles and abstracts, 993 articles were obtained for this review. After removing 826 unrelated records, 167 full texts were assessed for eligibility . After rThe selected articles (n = 18) consisted of five case-control studies, six cohort studies and seven cross-sectional studies. The number of subjects examined in these articles ranged from a minimum of 61 to a maximum of 4,704 subjects. Overall, in the 18 articles, 12,195 subjects were examined that included both male and female subjects. The youngest subject was 30 months old and the oldest was 17years old. The methods of diagnosis  used were different in the articles. The Conner’s Continuous Performance Test (CPT) was used in five articles, KITAP in two articles, a new Korean Version of SPECS in three articles, and DSM-IV in four articles. Three of the studies used questionnaires from the parents and teachers report forms, and in the remaining two, a fusion of the above techniques was used for diagnosis.The results of the STROBE checklist with strengths and weaknesses of the examined articles are reported in Due to the potential negative consequences of ADHD and increased prevalence in the United States and other countries, for ADHD diagnosis, it is crucial to determine all the toxic environmental elements that cause this disorder. Lead, as a neurotoxin, can cause the formation of abnormal behavior by interfering with neurotransmitter . ExperimIn this systematic review, five of the articles reported the mean blood lead concentrations from 5-10µg/dL; of which, four articles showed a significant relationship between lead exposure and at least one type of ADHD. CDC reduced the standard BLL from 10µg/dL to 5µg/dL from 2012 to 2016. However, in 12 of the selected articles, even BLLs below 5µg/dL level were reported to have a significant positive relationship with at least one type of ADHD. In the study by Wang et al. (2008) , minimumIn six of the studies, the relationship between blood lead levels and hyperactivity disorder was investigated ; of which, five reported a positive association. Five articles focused on impulsivity disorder; of which, three showed positive effects. .In some cases, confounding factors such as family economic status, parent’s education level, smoking habits, alcohol use, and other factors associated with blood lead levels were also considered. Some statistical models were used to determine the relationship between the concentration of lead and ADHD. A positive and statistically significant relationship was observed in both unadjusted and adjusted models. There were some exceptions due to the small sample size of the studies.Most of the articles in the systematic review underlined the symptoms of ADHD, but they did not concentrate on the diagnosis of ADHD, meaning that having the symptoms may not necessarily disclose the actual ADHD case.In addition, the tests used to determine the type of ADHD are not uniformed and standardized in all studies, and this may lead to differences in data interpretation.Exposure to lead has negative consequences, which can be reduced by addressing the gaps in the knowledge. The systematic review reveals that lead levels of less than 10µg/dL and even less than 5µg/dL have significant effects on ADHD, Hyperactivity-Impulsive and Inattentive disorder. To ensure the health and safety of children, the present permissive blood lead levels need to be revised in the context of the recent evidence.


Recent research has demonstrated that deficits in basic, self-regulatory processes, or executive function (EF), may be related to symptoms of attention-deficit/hyperactivity disorder (ADHD) already during the preschool period. As the majority of studies investigating these relations in young children have been based primarily on clinically administered tests, it is not clear how early symptoms of ADHD may be related to observations of EF in an everyday context. The preschool version of the Behavior Rating Inventory of Executive Function (BRIEF-P) was developed to provide information about EF through observable, behavioral manifestations of self-regulation, and is the most commonly used rating scale for EF assessment in children.Relations between symptoms of ADHD reported in the Preschool Age Psychiatric Assessment interview (PAPA), and EF as measured by the BRIEF-P (parent form), were investigated in a large, nonreferred sample of preschool children  recruited from the Norwegian Mother and Child Cohort Study (MoBa) at the Norwegian Institute of Public Health. The inventory’s discriminative ability was examined in a subsample consisting of children who met the diagnostic criteria for either ADHD, oppositional defiant disorder (ODD) or anxiety disorder, and typically developing controls (n = 308). The four groups were also compared with regard to patterns of EF difficulties reported in the BRIEF-P.Of the five BRIEF-P subscales, Inhibit and Working Memory were the two most closely related to ADHD symptoms, together explaining 38.5% of the variance in PAPA symptom ratings. Based on their scores on the Inhibit and Working Memory subscales (combined), 86.4% of the children in the ADHD and TD groups were correctly classified. ADHD symptoms were associated with more severe difficulties across EF domains, and a different EF profile in comparison to children with other symptoms  and to typically developing controls.Early symptoms of ADHD were linked to parent-reported difficulties primarily within inhibition and working memory, suggesting that deficiencies within these two EF domains characterize early forms of ADHD. Our findings support the clinical utility of the BRIEF-P as a measure of EF in young preschool children with symptoms of ADHD. Deficiencies in basic self-regulatory processes- collectively referred to as executive function (EF), are frequent among children with attention-deficit/hyperactivity disorder (ADHD). The association is well-documented in school-aged samples , and durThe Behavior Rating Inventory of Executive Function (BRIEF)  was specGiven the limited knowledge about relations between everyday executive functioning and early signs of ADHD at the point in development when fundamental EF processes are thought to emerge, the present study’s main objective was to investigate associations between EF as measured by the BRIEF-P, and ADHD symptoms in young preschool children. Relations between ADHD symptoms and behavioral ratings of EF were studied in a large, nonreferred sample of 3-year -old preschoolers. We first examined the amount of variance in ADHD symptoms predicted by each of the five BRIEF-P subscales. The inventory’s ability to discriminate between children who met diagnostic criteria for ADHD and typically developing controls was then assessed in a subsample. In a last set of analyses, we compared patterns of EF difficulties across four groups: three with children who met diagnostic criteria for ADHD, ODD and anxiety respectively, and one consisting of typically developing children.Executive functions are commonly referred to as higher-order cognitive abilities necessary for goal-directed behavior . The prePreschool children with symptoms of ADHD resemble their school-aged counterparts with regard to EF, with deficits primarily in inhibition and, to a lesser degree, in WM . HoweverEF difficulties have also been described in children with internalizing problems, such as anxiety. Links between anxiety and reduced mental flexibility (shifting) have been demonstrated both in preschool- and school-aged samples ,32. AccoAlthough most frequently described in children with ODD, recent research suggests that emotional dysregulation contributes to the symptomatology of all three of the abovementioned disorders -40. In aTo the best of our knowledge, only one previous study has investigated parent ratings of children with elevated levels of ADHD symptoms using the complete BRIEF-P. Comparing parents’ ratings of EF in 25 children aged 3–5 years who were diagnosed with ADHD, with 25 normally developing, matched controls, Mahone and Hoffman found that children with ADHD were rated as more impaired than children without ADHD on all five BRIEF-P subscales. The strongest effect was found for the Working Memory subscale . HoweverIn the present study, our first aim was to investigate possible links between early symptoms of ADHD and parent ratings of EF on the BRIEF-P’s five clinical subscales. In our young preschool sample, we expected symptoms of ADHD to be related primarily to the two basic EF domains inhibition and WM. Secondly, we investigated our hypothesis that the BRIEF-P Inhibit and Working Memory subscales would discriminate accurately between children in our sample who met the diagnostic criteria for ADHD, and typically developing controls. Thirdly, we aimed to extend previous research on EF in young children with ADHD by examining and comparing their BRIEF-P profile in relation to profiles of children with symptoms of ODD, anxiety, and typically developing controls. All three clinical groups were expected to exhibit higher overall BRIEF-P problem scores than did typically developing children. We expected that externalizing problems  would be associated with higher problem ratings primarily on the Inhibit scale relative to typically developing children, but to differ with regard to scores on the Working Memory scale. We further hypothesized that anxious children would present with fewer inhibitory problems than the other two clinical groups, but would show poorer mental flexibility as reflected in the BRIEF-P Shift subscale. As empirical findings with regard to WM in this clinical group have been inconsistent, the question with regard to anxiety-related deficiencies in WM was considered to be open.The present study used data from a longitudinal prospective study of ADHD (The ADHD study) with participants recruited from The Norwegian Mother and Child Cohort Study (MoBa), a population based birth cohort study managed by the Norwegian Institute of Public Health . When eaNone of the participating children had been or was receiving psychopharmacological treatment for ADHD at the time of assessment. Parents gave informed consent to the research and to the publication of the results. The MoBa and the present substudy were approved by the Norwegian Regional Committee of Ethics in Medical Research and The Norwegian Data Inspectorate.Upon accepting the invitation to participate in the ADHD prospective study, parents were requested to fill out the BRIEF-P and return it by the time of assessment. This request was made approximately four weeks prior to the one- day clinical assessment, which included a structured clinical interview with one of the parents (see below), a short medical screening, and a neuropsychological examination. All information from the clinical assessments and the parent interview was reviewed by senior specialists, who assessed symptom quality and severity, and the validity of test results.Inhibit , Shift , Emotional Control , Working Memory  and Plan/Organize . The subscales are then summarized into three broad indexes: Inhibitory Self-Control , Flexibility , and Emergent Metacognition . Data collection in the prospective study commenced in 2007, using an existing Norwegian translation developed for research purposes [The BRIEF-P consists of 63 items that describe children’s everyday executive function behaviors. Parents respond regarding whether their child exhibits problems with specific behaviors; Never (1), Sometimes (2) or Often (3). Thus, higher scores are associated with poorer executive functioning. Ratings are summed across items within each of the five theoretically and empirically derived subscales: purposes . A new BPsychiatric symptoms were assessed using an adapted version of the Preschool Age Psychiatric Assessment interview (PAPA) . The PAPFor the categorical analyses, participants were selected to one of four groups based on information from the PAPA interview: ADHD (n = 104), ODD (n = 39), anxiety  or typically developing controls . Children were assigned to the ADHD group if they were reported to have at least six of nine DSM-IV-TR criteria of inattentive subtype and/or hyperactive/impulsive subtype ADHD. The ODD group comprised children with at least four of eight symptoms of ODD according to DSM-IV criteria. Inclusion in either the ADHD or the ODD group required impairment, and symptom duration of three months or more. Children with co-occurring ODD/ADHD were excluded from the ADHD and the ODD groups. Children exhibiting symptoms of one or more of the most frequent DSM-IV anxiety subtypes  were assigned to the ANX group if their anxiety symptoms were inappropriate and excessive, and caused impairment. The TD group consisted of children randomly drawn from the MoBa cohort, who did not meet criteria for any psychiatric condition.Child gender and mother’s educational status were included as covariates, as these variables are likely to be related to the severity and type of ADHD symptoms -50. ADHDData analyses were conducted using PASW 21.0, except for the comparison of the two BRIEF-P translations, which was performed in Mplus 7.11. Descriptive data were computed for child characteristics, maternal education, symptom ratings from the PAPA interview, and BRIEF-P scores. Pearson’s correlation coefficient was used to study relations between parent-reported symptoms of ADHD from the PAPA, maternal education, general intellectual ability, and BRIEF-P ratings. Missing analyses revealed no significant differences between children included in the data set, and those excluded because of an incomplete BRIEF-P form (n = 38) with regard to any of these variables .Dimensional analyses were performed in the entire sample in order to assess the amount of variance in ADHD symptom load  explained by the BRIEF-P. The first set of linear regression analyses (univariate) assessed the amount of variance in ADHD symptom load, explained by each of the five BRIEF-P subscales. Gender, maternal education, and IQ were entered as predictors in step one, and each subscale in five separate analyses in step two. Addressing our hypothesis of the two subscales Inhibit and Working Memory as primary contributors to variance in ADHD symptom load, the contribution to explained variance in ADHD symptoms by the Inhibit and Working Memory subscales relative to the remaining three scales was further explored by use of hierarchical regression analysis.The categorical analyses were conducted in a subsample. BRIEF-P subscale scores and indexes in the four groups were first compared in a multiple analysis of variance (MANOVA). The Inhibit and Working Memory subscales’ ability to predict group adherence correctly among children with ADHD and typically developing controls was then estimated by use of a discriminant function analysis. The four groups  then served as the between-group variable, and each of the five BRIEF-P subscale scores as within-subject variables in a profile analysis which allowed for the comparison of subscale profiles among the four groups, asking three questions: 1) Do the profiles show the same level of scores ? 2) Do any of the profiles exhibit flatness across subscales ? and 3) Are the scoring profiles from the four groups parallel ?Means and standard deviations for age, child IQ, maternal education, and number of parent-reported psychiatric symptoms in the total sample are displayed in Table F change  = 11.88, p < .001). Each of the five BRIEF-P subscales contributed significantly to the model in the univariate analyses (Δ R2s ranging from .06 to .37). The relative contribution of the subscales to variance in the dependent variable was examined in the multiple analyses: Entered simultaneously in step two, the five subscales explained 38.9% of the variance in ADHD symptom load; F change  = 147.68, p < .001. Three subscales contributed significantly to the model as a whole; Inhibit, Working Memory and Shift. A further investigation of the first two predictors’ contribution to explained variance in ADHD symptoms revealed that of the 38.9% explained by all five subscales, Inhibit and Working Memory accounted for 38.5%. The addition of Shift, Emotional Control and Plan/Organize scales to the model led to a marginal change in this estimate .Results from the univariate and multiple hierarchical regression analyses performed in the full sample are presented in Table Means and standard deviations for BRIEF-P subscales and indexes for the four comparison groups are shown in Table To assess the accuracy of the BRIEF-P subscales Inhibit and Working Memory in predicting group membership (ADHD or TD), we conducted discriminant function analyses with the two subscales entered simultaneously as predictors. Of the 221 children in the ADHD and TD groups, 86.4%  were correctly classified on the basis of their scores on these two subscales. In the ADHD group, 80.1% of the children were correctly classified; the corresponding percentage in the TD group was 91.5%. In a second discriminant analysis, with the same two subscales as predictors but with all four groups included, the percentage of correctly classified children was 55.8.2 = .23) and indexes  = 26.4, p < .001, Pillai’s Trace = .62; η2 = .21) which remained significant after controlling for gender. Group differences across BRIEF-P subscales were further explored in a profile analysis , indicating differences in levels of parent-reported EF difficulties. A significant within-groups main effect was found, showing different scale elevations across the BRIEF-P subscales  = 23.1, p < .001, η2 = .07). Finally, the profiles of scale elevations varied between our four groups  = 19.0, p < .001, η2 = .16). Post hoc comparisons conducted with a conservative probability level (p < .01) indicated that children in the ADHD group scored significantly higher than typically developing controls (TD) in all five EF domains. This was also the case for the two other clinical groups . The ADHD group exhibited more problems on two of the five BRIEF-P subscales, Inhibit and Working Memory, than children in the ODD and ANX groups. In terms of absolute scores, anxious children presented with the highest problem scores of all four groups on the Shift subscale; but the only difference to reach significance was between the ANX group and typically developing controls.MANOVA results Table  revealedes  and teacher ratings of EF in 3-year old children, using the BRIEF-P . It is aFindings have so far been less consistent with regard to the role of WM in early ADHD, with only a handful of preschool studies reporting ADHD-related difficulties in WM as measured by neuropsychological tests  for any of the three clinical groups when the original US norms were applied. Contrary to previous findings in clinical samples, this may be related to characteristics of the present sample- including possible cultural differences- or perhaps to a somewhat stronger reluctance among parents to describe certain behaviors as problematic at this early point in development ,63. In lAll five BRIEF-P subscale scores were clearly elevated for children with symptoms of ADHD, ODD or anxiety in comparison with their typically developing peers. Overall, 3-year old children with externalizing problems  were reported to have more pronounced difficulties as measured by the BRIEF-P than children with internalizing problems . While the TD group exhibited a relatively flat EF profile, indicating no particular problems in any domain, the three symptom groups differed in several ways, both from each other and relative to controls. Children in the ADHD group were rated as having more problems within the inhibition and WM domains than the ODD and ANX groups, and the controls. The observed pattern is consistent with what was predicted on the basis of previous research, and it suggests that difficulties within the two EF domains may be characteristic for ADHD at this early developmental stage. It is noteworthy that DSM-IV criteria for the ADHD diagnosis and formulations in some of the BRIEF-P items, particularly in the Inhibit and Working Memory scales, are different in content but similar in concept . This isThe ADHD and the ODD group had in common a peak on the Inhibit problem scale; however, the second peak for the ADHD group was found in the Working Memory scale, while the second peak for the ODD group was on the Emotional Control scale. The closer relationship between difficulties in emotional control and early symptoms of ODD relative to other symptoms in the present study is consistent with previous research on older children, which describes emotional dysregulation as a core feature of ODD ,66. AlthWe found support for the assumption that children with anxiety disorders would present with a pattern of EF difficulties different from the two other clinical groups in our sample. Anxiety symptoms were linked to relatively fewer inhibitory problems, but to the highest absolute score on the Shift scale among the three symptom groups. Although nonsignificant, the difference relative to the ADHD and ODD groups on the Shift scale may represent a first indication of an emerging, specific link between anxiety and reduced mental flexibility. Anxious children did not differ significantly from the two other clinical groups in the Working Memory scale, contrary to what could have been predicted on a theoretical basis . To the The study’s most important strengths include a large sample with a fairly equal gender representation, and a narrow age range that is rarely studied with regard to ADHD and to the BRIEF-P. Psychiatric symptoms were assessed using a well-validated psychiatric interview for preschoolers, and ADHD was characterized both categorically and dimensionally.Among limitations, it must be noted that both the MoBa cohort and the longitudinal study had relatively low participation rates (approximately 35- 40%). This has led to an underrepresentation of children from high-risk families , smoking during pregnancy, and possibly children with the most severe behavioral and cognitive problems ,69. In tThe exclusion of children with co-occurring ADHD and ODD offered the opportunity to study possibly diagnosis-specific patterns of EF deficiencies. This precluded investigations of possible additive or interaction effects associated with this frequently occurring comorbidity. The narrow age range also limited the generalization of findings to the youngest preschoolers. As only parent ratings were included here, findings may not apply to the use of the inventory by teachers. Finally, estimates of common variance reported in the present study must be interpreted bearing in mind that assessment of psychiatric symptoms and executive behavior were both based on parent reports.The current study is one of very few to investigate relations between ADHD symptoms and behavioral ratings of EF at age 3, when basic EF skills are thought to emerge. Our findings support the use of the BRIEF-P in the identification and description of EF difficulties in young preschool children with symptoms of ADHD. The comparison of ratings within the BRIEF-P’s five clinical scales proved useful in distinguishing ADHD from the two other most commonly occurring disorders in the preschool population- ODD and Anxiety- , and from typically developing controls. ADHD-related difficulties were identified primarily in inhibition and WM at age 3 years, suggesting that deficiencies within these two EF domains contribute to the development of ADHD. These relations should be further addressed in follow-up studies of children first assessed as young preschoolers. Developed to describe patterns of behavior associated with different aspects of EF, the BRIEF-P may contribute important, ecologically valid information about early, self-regulatory capacities. Behavior ratings of EF in everyday situations are likely to tap into aspects of EF other than those measured by clinically administered tests, thus constituting an important supplemental source of information in the assessment of EF across childhood. Identification of deviancies within specific areas of everyday EF at an early point in development may aid the development of targeted interventions for use with young children.


This study is the first to evaluate an assessment aid for attention-deficit/hyperactivity disorder (ADHD) according to both Class-I evidence standards of American Academy of Neurology and De Novo requirements of US Food and Drug Administration. The assessment aid involves a method to integrate an electroencephalographic (EEG) biomarker, theta/beta ratio (TBR), with a clinician's ADHD evaluation. The integration method is intended as a step to help improve certainty with criterion E .To evaluate the assessment aid, investigators conducted a prospective, triple-blinded, 13-site, clinical cohort study. Comprehensive clinical evaluation data were obtained from 275 children and adolescents presenting with attentional and behavioral concerns. A qualified clinician at each site performed differential diagnosis. EEG was collected by separate teams. The reference standard was consensus diagnosis by an independent, multidisciplinary team , which is well-suited to evaluate criterion E in a complex clinical population.93/275). Of those 93, 91% were also identified by EEG, showing a relatively lower TBR (85/93). Further, the integration method was in 97% agreement with the multidisciplinary team in the resolution of a clinician's uncertain cases (35/36). TBR showed statistical power specific to supporting certainty of criterion E per the multidisciplinary team . Patients with relatively lower TBR were more likely to have other conditions that could affect criterion E certainty . Integration of this information with a clinician's ADHD evaluation could help improve diagnostic accuracy from 61% to 88%.Of 209 patients meeting ADHD criteria per a site clinician's judgment, 93 were separately found by the multidisciplinary team to be less likely to meet criterion E, implying possible overdiagnosis by clinicians in 34% of the total clinical sample (The EEG-based assessment aid may help improve accuracy of ADHD diagnosis by supporting greater criterion E certainty. Attention-deficit/hyperactivity disorder (ADHD) is characterized by the presence of developmentally inappropriate attentional and behavioral symptoms according to the criteria outlined in the DSM-IV-TR and DSM-V  APA , 2013. TTo address the challenges of ADHD diagnosis, professional guidelines recommend a comprehensive evaluation of available clinical information including that from assessment aids AACAP . AlthougFor biomarkers to be of clinical utility, integration into the clinic may require a strategy other than noting an association with ADHD Bush . One posTherefore, we proposed a method to integrate an electroencephalographic (EEG) biomarker  in series with a clinician's regular ADHD evaluation for the purpose of improving criterion E certainty to assist in determining whether ADHD is the primary diagnosis. We developed an EEG integration method intended to distinguish pediatric subgroups that vary in their likelihood of having conditions that may account for ADHD symptoms or otherwise impact ADHD diagnosis and management. Clinically, the capacity to identify a subgroup that is less likely to have such complicating conditions  would enhance clinician confidence in rendering a primary diagnosis of ADHD. Likewise, the ability to identify a subgroup that is more likely to have such conditions  would provide support for clinicians in suggesting further examination of the child prior to making a diagnostic conclusion of ADHD and/or proceeding with treatment planning, increasing the efficiency of the diagnostic process.To validate the EEG integration method, we examined concordance with a reference standard defined as consensus best estimate diagnosis of an independent multidisciplinary clinical team . Previous clinical findings have shown that when a multidisciplinary model is applied, a significant number of patients presenting with ADHD-like concerns may be determined as having other primary diagnoses  is applied as a next step intended to improve certainty with criterion E. Relatively lower TBR is used to note cases more likely to have complicating conditions such as medical mimics . The clinician ultimately would resolve those cases with their own judgment, performing further testing for other conditions as they see fit on a patient-by-patient basis.To function, the integration method requires input from a clinician's ADHD evaluation and from a standardized TBR measurement, and yields recommendations based on a predefined decision-algorithm Table . After tThe validation study met Class I evidence requirements for ADHD assessment aids per the American Academy of Neurology and the American Clinical Neurophysiology Society Nuwer . The stuInstitutional review boards for each site approved the protocol. The study including informed consent was conducted in accord with US Food and Drug Administration regulations, guidelines for Good Clinical Practice, and the Declaration of Helsinki. Diagnostic methods were modeled according to the American Academy of Child and Adolescent Psychiatry (AACAP) practice parameter for ADHD AACAP . The stuThe recruited sample was intended to be representative of patients for whom the next step after clinical presentation would be full ADHD evaluation, including application of assessment tools such as the proposed biomarker integration method. Recruits were 364 children and adolescents ages 6.00–17.99 years consecutively presenting with attentional and behavior concerns to 13 geographically distinct clinics  in United States from December 11th, 2007 to June 16th, 2008. For inclusion in the study, subjects needed to be willing to stop: (1) psychiatric medications, (2) prescription or nonprescription medications with psychoactive properties, and (3) any medication that might affect EEG. Exclusion criteria were: (1) previous diagnosis of mental retardation or intelligence quotient (IQ) <70; (2) history of seizure disorder, EEG abnormalities, or anticonvulsant use for seizure control; (3) metal plate or device in the head; (4) suicidal ideation or gesture and/or homicidal ideation or gesture; and (5) known serious medical problems .Of the 364 recruits, three were excluded due to IQ<70, 1 was excluded due to suicidal ideation, 1 was removed by the investigator to restart an asthma medication, and 1 was withdrawn by the parent to restart an antipsychotic medication. Sixteen recruits did not complete the study, 35 had incomplete EEG recordings related to external issues with computer power supply and impedance meter, and 32 did not receive a complete EEG recording addressed below in Results: Missing Data Analysis. As such, there were 275 subjects who met protocol criteria, completed the study, and had complete EEG recordings. All 275 subjects were included in analysis of diagnostic accuracy.n = 275): (1) adolescents 27%; children 73%; (2) female 36%; male 64%; (3) African American 17%; Asian American 1%; Caucasian 73%; Hispanic/Latino 4%; Native American 2%; Other 4%; (4) upper/upper-middle class 16%; lower middle class 33%; working class 33%; lower class 18%. Clinical characteristics by mean (standard deviation) include: (1) age in years 10.1 (2.9); (2) Children's Global Assessment Scale (CGAS) 56 (9); (3) Clinical Global Impression-Severity subscale (CGI-S) 3.7 (1.0); (4) Investigator ADHD-IV Rating Scales (ADHD-IV RS) percentile: Inattention 94 (13); Hyperactivity 86 (21); Total 93 (12); (5) Teacher ADHD-IV RS percentile: Inattention 74 (24); Hyperactivity 69 (28); Total 74 (23); (6) Wechsler Abbreviated Scale for Intelligence-long version (WASI), estimated full scale IQ 102 (14); (7) Wide Range Achievement Test-4 (WRAT-4) Standard Score: Spelling 99 (15); Math 98 (15); Reading 100 (14); Comprehension 98 (16); Reading Composite 98 (15). Socioeconomic status was estimated by an academic class model  psychostimulants, at least 1 week prior to study entry, (2) other psychiatric medications, at least 2 weeks prior to study entry, and (3) fluoxetine, 28 days prior to study entry. Of the 275 subjects included in final analysis, 13 (5%) required medication washout prior to study entry. No adverse effects related to medication withdrawal were reported over the course of the study.All data collection was conducted over visits on four different days to minimize subject fatigue. Investigators collected clinical evaluation data from patients by administering: (1) Physical examinations, including vital signs, vision/hearing screens, and medical/neurological/medication histories, (2) Clinician interviews, with initial impressions and reference to DSM-IV-TR criteria APA , (3) KidReviewing the above-listed data, a qualified clinician  at each site performed and recorded differential diagnosis for ADHD and other disorders and conditions. Extra focus was recommended for disorders and conditions that could account for ADHD-like symptoms. Diagnostic evaluations were guided by the DSM-IV-TR criteria and AACAP practice parameter.In addition, the clinician summarized their own impressions and judgments for: (1) primary diagnosis, (2) certainty regarding presence of ADHD, and (3) certainty regarding primary diagnosis. Impressions were based on the clinician's whole evaluation of the patient, in which the clinician could describe suspicions beyond DSM-IV-TR criteria. Certainty included addressing whether the diagnosis should be a clinical concern based on symptoms, impairment, and overall coherence of the clinical profile.To implement these diagnostic results as part of the study analysis of the proposed EEG integration method, the clinician's diagnostic conclusions were summarized as “positive”, “negative” or “uncertain” for ADHD. ADHD was listed as “positive” for the analysis if the clinician's primary diagnosis was ADHD  with definite certainty. ADHD was considered as “negative” for the analysis if the clinician's primary diagnosis was for another condition, and the clinician listed ADHD as absent or secondary. ADHD was considered as “uncertain” in the remaining cases that involved different reported levels of uncertainty.At each clinical site on two different subject visits , a data collection technician or clinician (who each had received training standardized to the collection system) recorded digital EEG data . The standardized EEG collection protocol included recommendations for the subject regarding optimal EEG collection such as for sleep prior to the day of recording. The planned EEG analysis required a single recording electrode (CZ) placed in accordance with the International 10–20 system using an electrode headband . Electro-oculography was used to monitor eye blinks and movement. Electrode impedances were to be adjusted (≤10 kΩ). EEG specifications included 256 Hz sampling rate and corner frequencies at 0.5 and 36 Hz. EEG data were collected for ten minutes while patients were seated in a straight-back chair with eyes open and fixed with attention to a point at eye level on the wall.μV threshold followed by visual recognition per standardized methods; NEBA Health). Analysis of EEG data required at least 15 epochs (30 sec) of data with minimal to no artifact. Analysis was conducted using Fast Fourier Transform analysis (frequency resolution: 0.5 Hz) with data resampled at 128 Hz. TBR was examined using a standardized EEG collection and processing system .To limit noise in the EEG data, artifacting and processing technicians at a central EEG processing center screened out epochs containing the most artifact . First, an interval width for a zone now labeled “moderate TBR” was developed to account for measurement error due to variability from artifacting, data collection, and data processing that might not be fully addressed by standardization and calibration, as determined by analyses of the development data, as well as by engineering evaluation of systems and methods. Second, consideration of risk of false results in the context of use according to expert consultation and other applicable resources  supported development of an integration method that could reduce risk by favoring specificity of TBR in cutoff development . Third, development data sets from children and adolescents, who all had ADHD-like symptoms but only a portion had received a clinician's ADHD diagnosis, were used to produce age-based distributions of: (1) subjects with ADHD-like symptoms due to ADHD, and (2) subjects with ADHD-like symptoms better accounted for by other conditions. Fourth, as informed by distribution analyses, the measurement error-based interval (“moderate TBR”) was positioned within the ADHD/non-ADHD overlap to favor specificity in a manner considered optimal for lowering risk per the integration method. As a view of the ultimate distribution of the development data (with ADHD presence determined per the reference standard of the previous study), 75% of subjects with a clinician's ADHD diagnosis were in the high TBR range , 6% were in the moderate range, and 19% were in the low range . In addition, 13% of those who had ADHD-like symptoms better accounted for by other conditions were in the high TBR range, 11% in the moderate, and 76% in the low. Finally, it should be noted that between-site variation in application of the previous study's reference standard – particularly with respect to differential diagnosis – supported the insight that many “low to moderate TBR” cases receiving an ADHD diagnosis per individual clinicians might have benefited from further rigorous consideration of criterion E, as addressed now in the design of the integration method.After blind-break in the current study, the integration method was used to parse subjects into test-result subgroups based on the clinician's ADHD diagnostic result and the EEG result, which in turn designated predefined recommendations regarding criterion E certainty and primary diagnosis . Team qualifications included over 25 years of ADHD specialization by the team leader, and regular application of multidisciplinary model in research and clinical practice. Patient files included all clinical evaluation data, except for blinding to EEG and site clinician's diagnoses . Team members independently reviewed patient files and recorded initial impressions. Then, the multidisciplinary team met and determined consensus best estimate diagnosis for ADHD, as well as differential diagnosis for other disorders and conditions, guided by DSM-IV-TR criteria. Consensus was reached per standard practice of the multidisciplinary clinic, which included a team discussion of each patient with contributions from each member based on perspectives and insights from their specializations. With the understanding gained from the multidisciplinary discussion, each member reconsidered prior opinions and formed a team consensus diagnosis. The team also described recommendations for further testing, in particular to address criterion E certainty. The team summarized diagnostic results by referring to their DSM-IV-TR guided diagnoses and then applying their impressions and clinical judgment regarding: (1) primary diagnosis, (2) certainty regarding presence of ADHD, and 3) certainty regarding primary diagnosis.We hypothesized that the proposed method to integrate an EEG biomarker in series with a clinician's ADHD evaluation would improve diagnostic accuracy. To test the hypothesis, diagnostic accuracies associated with “clinician's regular evaluation plus EEG” were compared with estimates of those achieved by “clinician alone”. Because one outcome of the integration method is “less likely to meet criterion E” leading to a “recommendation for further testing for other conditions”, this outcome was evaluated in the study as follows. During the comprehensive clinical data collection, clinicians were free to use judgment to apply any further testing on a patient-by-patient basis. After the clinician's initial evaluation and diagnosis were completed, the integration method designated potential cases less likely to meet criterion E in which even more “further testing” may have been needed than the clinician initially provided. To evaluate both clinician judgment and EEG-based feedback regarding further testing and criterion E certainty, an independent multidisciplinary team reviewed the clinician's collected data and determined whether even more “further testing” was indeed needed in particular regarding conditions that could impact the primary diagnosis. To evaluate diagnostic accuracy results in terms of criterion E certainty as produced by the integration method, sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy were analyzed with reference to: (1) best estimate diagnosis results of multidisciplinary team, (2) presence of complications from predefined study list determined per patient files and multidisciplinary team, (3) suggestion by multidisciplinary team for further testing for conditions on predefined study list, and (4) suggestion by multidisciplinary team that further information may be needed related to differential diagnosis. Confidence intervals (95% CI) were reported with all accuracy results . Because comparisons between subgroups were complementary and few in number, a multiple testing correction was performed to control for false discovery rate . TBR measures were ordered by 1st and 2nd recordings, which could produce an ordering effect because of increased familiarity of subject with EEG at 2nd recording. Therefore, the ICC model chosen was two-way, random, single-measure, consistency: ICC. In the current report to address concerns of the journal peer review regarding TBR effect size and age effects, further analyses were included applying Cohen's d calculations, one-way ANOVA , and linear fits as per previous studies  analysis was performed on TBR repeated measures, collected on two different visits  site: clinical data collection and clinician's diagnoses, (2) EEG: site collection and off-site processing, (3) multidisciplinary team: diagnoses. Prior to blind-break, clinical data, EEG, and diagnostic results were locked in databases by third-party agencies independent of study sponsor. After blind-break, all analyses were performed on data from the locked and controlled databases per predefined statistical analysis plans or regulatory agency guidance. Study data were submitted to regulatory agencies which repeated analyses and confirmed results.In the proposed integration method, the clinician first performs their regular ADHD evaluation. EEG is applied as a next step intended to improve certainty with criterion E. Relatively lower TBR is used to note cases less likely to meet criterion E. Relatively higher TBR is used to confirm ADHD Table .To validate, the reference standard was consensus diagnosis by a multidisciplinary team, which is well-suited to evaluate criterion E in a complex clinical population in which all subjects presented with attentional and behavioral concerns but not all had ADHD. The diagnostic evaluation of the multidisciplinary team included collection of information and conclusions to address the outcomes of the integration model. For patients meeting ADHD criteria per a site clinician's judgment, integration method outcomes were: (1) ADHD confirmed, or 2) Less likely to meet criterion E. Table  Less lik93/275). Of those 93, 91% were also identified by EEG, showing a relatively lower TBR (85/93).Per results in Table 21/275).Of the 116 ADHD cases confirmed by the multidisciplinary team, 21 were identified by the integration method as less likely to meet criterion E, implying an unnecessary prompt for further clinical testing by the integration method in 8% of the total clinical sample ((116 + 27)/(116 + 93 + 3 + 27)]. And, overall accuracy for the integration method was 87% [(95 + 85 + 27)/(95 + 8 + 21 + 85 + 3 + 27)].Finally, it should be noted that by the integration method, ADHD-negative cases are the same by definition for clinician and for “clinician + EEG” . Relatively lower TBR is used to note cases less likely to meet criterion E Table . And, re(11 + 24)/(11 + 25)]. Taken together with the results of Table 11 + 24 + 95 + 85 + 27)/(11 + 24 + 1 + 95 + 8 + 21 + 85 + 3 + 27)].Per results in Table 11 + 4/, 0.83).Results in Tables 10 significant results; P ≤ 0.05), as shown in Tables The proposed integration method is intended to help improve certainty with criterion E, as supported by results of Tables less likely to meet criterion E… recommend resolution by further clinical testing for other conditions”. These subjects are much more likely to have other conditions that could affect the clinician's decision on criterion E.Results show that ADHD and uncertain cases (per site clinicians) with relatively lower TBR were significantly more likely to have complicating conditions including: (1) medical or neurological conditions that could mimic ADHD Table a, (2) anRelated TBR results have been provided in the supplement . In addition, results support that the observed TBR effect size can vary based on: (1) application of TBR (criterion E certainty vs. ADHD diagnosis) and (2) reference standard . In other words, the TBR results show sufficient statistical power to improve certainty of criterion E (per multidisciplinary team), but not to diagnose ADHD .In Table d values  have been plotted relative to ADHD prevalence (per CDC estimates at each study publication date) (●). Linear fit (- - - -) shows an inverse relationship . Additional inclusion of the current TBR results in the plot demonstrates support that the rapid change in ADHD prevalence may be due in part to a less stringent approach to criterion E by regular ADHD evaluations. As shown in FigureThe current observation of variation in TBR effect size may provide insights into previous TBR studies. FigureThe current observation of variation in TBR effect size may also provide insights into age effects observed in previous TBR studies. In FigureP = 0.006), had higher CGI-S severity scores  and lower CGAS functioning scores . Of the 32 excluded data sets, 29 had between 1 and 14 epochs. Further analysis with inclusion of data sets with 1–14 epochs was shown to have minimal effect on accuracy results. Accuracy was 86%  with missing data included, compared to 88%  observed per planned study analyses.Missing data analysis was conducted to evaluate the 32 subjects who did not have “complete” EEG recordings, by which “complete” refers to a quality standard set prior to study initiation requiring at least 15 epochs (30 sec) with minimal to no artifact. One-way ANOVA analysis  showed the 32 subjects with incomplete EEG recordings were younger than those with complete EEG recordings . Of those 93, 91% were also identified by EEG, showing a relatively lower TBR (85/93) (Table 35/36)   (Table The proposed assessment aid involves a method to integrate an EEG biomarker (TBR) with a clinician's regular ADHD evaluation. To evaluate the assessment aid, we conducted a prospective, triple-blinded, multi-site, clinical cohort study with a reference standard based on an independent multidisciplinary team. Results support that the EEG-based assessment aid may help improve accuracy of ADHD diagnosis by supporting greater criterion E certainty. Of 209 patients meeting ADHD criteria per a site clinician's judgment, 93 were separately found by the multidisciplinary team to be less likely to meet criterion E, implying possible overdiagnosis by clinicians in 34% of the total clinical sample 93/27. Of thosThe current approach for integrating an EEG biomarker with a clinician's ADHD evaluation represents a divergence from methods used in previous EEG studies. Prior studies examined methods that directly identify ADHD. In contrast, the current method is intended to identify cases with ADHD symptoms that are either more or less likely to meet criterion E. In other words, prior studies used EEG variable cutoffs that were optimized to differentiate ADHD patients from controls , but not to diagnose ADHD  Table . This fiR2, 0.89). Per the current study, applying TBR to ADHD diagnosis per an individual clinician does have reduced statistical power as predicted by the trend. However, applying TBR to improve certainty of criterion E per a multidisciplinary team restores statistical power to prior levels. One possible implication is that the rapid change in ADHD prevalence from 1999 to 2012 may be due in part to a more inclusive approach to ADHD by regular evaluations .The current observation of variation in TBR effect size may also provide insights into a recent meta-analysis which observed a trend of decline over recent years in statistical power of TBR applied to ADHD diagnosis   with relatively lower TBR were more likely to have had a history of nonresponse and adverse events on ADHD medications Table b. PrevioThe current results are consistent with previous studies recognizing differences between neuropsychological test-result subgroups of patients with ADHD. Neuropsychological testing of executive functions has been reported to separate patients with ADHD into two subgroups differing in academic outcomes: grade retention and academic achievement . Of those 93, 91% were also identified by EEG, showing a relatively lower TBR (85/93). Further, the integration method was in 97% agreement with the multidisciplinary team in the resolution of a clinician's uncertain cases (35/36).A risk/benefit analysis underscores the main clinical implication of the proposed approach, which is that integration of the biomarker may increase specificity in ADHD diagnosis Table . This ou21/275). In addition, it is important to note that in the cases in which ADHD is determined to be less likely to be the primary diagnosis, it may still be necessary to treat ADHD-related symptoms, in particular in the cases in which these symptoms have not been resolved after successful treatment and management of the primary condition.The accompanying risk is that some patients may be delayed before receiving ADHD treatment while they are receiving unnecessary further testing due to incorrect recommendations by the integration method. Of the 116 ADHD cases confirmed by the multidisciplinary team, 21 were identified by the integration method as less likely to meet criterion E, implying an unnecessary prompt for further clinical testing by the integration method in 8% of the total clinical sample  with minimal to no artifact. Further analysis with inclusion of missing data  was shown to have minimal effect on accuracy results . Therefore, implementation of the proposed EEG biomarker with these subjects is viable, but rate of acquiring acceptable epochs needs to be improved. The main cause of epoch rejection in younger children with more severe symptoms is motion artifact, which can be reduced by modifications including: (1) use of standard calming methods with children during EEG collection, and (2) use of electrode setup that reduces movement of individual electrodes by application of elastic band, cap, or tape.The report of current results includes comparisons with previous EEG studies, however, the comparisons may have limitations due to possible differences in EEG materials and methods between studies. In the standardization of the current study's EEG materials and methods, areas of possible variation were examined and optimized specifically to support consistent TBR determination. Areas that were standardized included: artifacting methods , device characteristics , TBR analysis , and other EEG collection factors in the study protocol , minimized visit requirements prior to EEG collection to limit fatigue, impedance range allowed, accounting for electromagnetic interference in the recording area including computer power supply and local interference). Also of note is the implementation of a blinding protocol between EEG methods and ADHD evaluation. Because of the potential for variation as evaluated in the standardization process, it cannot be ruled out that differences of EEG materials and methods between studies may have contributed to differences observed in results between studies.By meeting De Novo requirements of US Food and Drug Administration as well as Class I evidence requirements of American Academy of Neurology and American Clinical Neurophysiology Society, the current assessment aid has been held to higher validation standards than commonly used assessment aids. In addition, the current assessment aid is novel in providing a validated method for integration into clinical practice and addressing an important issue in ADHD evaluation, namely sufficient determination of criterion E.10 significant results; P ≤ 0.05). And, ADHD cases with higher theta/beta ratio were less likely to have those conditions. Integration of this information with a clinician's regular ADHD evaluation could improve diagnostic accuracy from 61% to 88%, in particular when addressing criterion E in a complex clinical population in which all patients have ADHD-like symptoms but not all have ADHD. Results support that the EEG-based assessment aid may help the clinician to determine whether symptoms are better explained by another condition.Study results showed that in patients meeting ADHD criteria per an individual clinician, those with a relatively lower theta/beta ratio were more likely to have conditions that may account for ADHD symptoms or otherwise impact the ADHD evaluation (Application of EEG in a clinical integration model produced results that are consistent with those of prior studies of neuroimaging, neuropsychology, and medication effects. The current results also support that a source of heterogeneity in ADHD research may be diagnostic variation in the clinical evaluation. Future validation studies of a refined ADHD phenotype based upon biomarkers may include neuropsychological testing, genomic measures, or differential response to treatment, and lead to more objective diagnostic procedures and more personalized ADHD treatment.


MAOB variants were analyzed in ADHD probands (N = 150) and ethnically matched controls (N = 150) recruited following the Diagnostic and Statistical Manual for Mental Disorders-4th edition (DSM-IV). Appropriate scales were used for measuring the behavioural attributes. Gene variants were analyzed by amplification of target sites followed by DNA sequencing and data obtained were analyzed by population based statistical methods.Attention deficit hyperactivity disorder (ADHD) is characterized by symptoms of inattention, excessive motor activity and impulsivity detected mostly during childhood. These traits are known to be controlled by monoamine neurotransmitters, chiefly dopamine, serotonin and norepinephrine. Monoamine oxidase A (MAOA) and B (MAOB), two isoenzymes bound to the outer membrane of mitochondria, are involved in the degradation of monoamines and were explored for association with ADHD in different ethnic groups. In the present study, few exonic as well as intronic ‘C’ (P = 3.45e-006) and rs3027440 ‘T’ (P = 0.02) alleles showed higher frequencies in ADHD probands as compared to controls. rs56220155 ‘A’ (P = 0.04) allele and ‘GA’ (P = 0.04) genotype showed higher frequencies in the male and female ADHD probands respectively as compared to sex-matched controls. Analysis of pairwise linkage disequilibrium revealed striking differences between probands and controls. Haplotype analysis revealed significantly higher occurrence of different haplotypes in the ADHD probands while some haplotypes were detected in the controls only. Higher scores for conduct problems were found to be associated with rs56220155 ‘A’ (P = 0.05) allele in the male ADHD probands. Multifactor dimensionality reduction analysis showed independent as well as interactive effects of polymorphic variants which were more robust in the male probands.Out of 34 variants present in the analyzed sites, only seven functional variants, rs4824562, rs56220155, rs2283728, rs2283727, rs3027441, rs6324 and rs3027440, were found to be polymorphic. rs2283728 MAOB gene variants are contributing to the etiology of ADHD in the Indo-Caucasoid population from eastern India which merits further in depth analysis.Since all the polymorphic variants analyzed were functional, it may be inferred that The online version of this article (doi:10.1186/s12863-016-0401-6) contains supplementary material, which is available to authorized users. Attention deficit hyperactivity disorder (ADHD) is an etiologically complex behavioural disorder. Major symptoms include persistent age-inappropriate hyperactivity and impulsivity, sometimes in association with inattention , leadingBeing a multi-factorial genetic disorder with around 76 % heritability , 12, genMAOB knockout mice showed high level of PEA in the brain as well as an increased reactivity to stress and other behavioural alterations [Monoamine oxidase A (MAOA) and B (MAOB) are mitochondrial outer membrane bound isoenzymes, catalyzing oxidative deamination of neurotransmitters like dopamine, serotonin, norepinephrine, and other neuromodulators like benzylamine, phenylethylamine (PEA), tyramine and tryptamine in the brain as well as peripheral tissues , 20. Theerations , 28. Plaerations .MAOA variants have showed association with ADHD in various populations, including the Indo-Caucasoid population [MAOB gene variants have also been studied, though the numbers of variants investigated were few and the data obtained were inconsistent [MAOA variants to the male probands [MAOB variants were explored for association with ADHD and its associated phenotypic traits in the Indo-Caucasoid population.Several pulation , 31. MAOnsistent , 32–37. nsistent , we hypoprobands . In thisth edition (DSM-IV) criteria [Sample size of 150 was determined statistically  considercriteria  and Wechcriteria /Developmcriteria  for chilThe control group comprised of 150 ethnically matched healthy individuals  assessed by the same psychometric evaluation procedure. Mean age of the control individuals was 18.41 ± 8.78 years (range 3 to 28 years).in silico using the is-rSNP [Peripheral blood collected from the study participants was used for genomic DNA preparation using the standard protocol . The tar is-rSNP .Allelic and genotypic frequencies of African , American , East Asian , European (CEU: Utah Residents (CEPH) with Northern and Western European Ancestry; FIN: Finnish in Finland; GBR: British in England and Scotland; IBS: Iberian Population in Spain; TSI: Toscani in Italia); South Asian  ancestry were retrieved from the 1000 Genomes Project Phase 3 (32) database  and compMAOB gene is X-linked [To test the Hardy-Weinberg equilibrium , genotypX-linked  and it iX-linked . AllelicX-linked  and corrX-linked  was usedX-linked  was usedX-linked ., an interaction circle graph, using the entropy estimates from step 1, was depicted by the program. Interaction circle graph comprised of a node for each attribute (i.e. polymorphism) with pairwise connections between them. The percentage of entropy (i.e. information gain or IG) by each polymorphism was visualized on each node, while the IG for each pairwise combination of polymorphisms was visualized on each connection. Thus, the independent main effects of each polymorphism were quickly compared to the interaction effect between them. Positive entropy values indicated synergy, while negative entropy values indicated redundancy [Interaction between the variants or epistasis was analyzed by the Multifactor Dimensionality Reduction (MDR) program  through dundancy . All theBased on the CPRS-R, ‘T scores’, ranging between 38 and 90, were obtained for ADHD probands. DSM-IV scores (ranging between 0 and 36) for assessing ODD trait and PACS scores (ranging between 0 and 90) for assessing conduct problems were also obtained. Male probands were sub-grouped based on the presence/absence of the derived allele for each variant and distribution pattern of behavioural scores in each of the two comparing groups was checked using the Kolmogorov-Smirnov normality test . EqualitIn silico analysis revealed that all these variants have potential regulatory function , chiefly due to an increase in the minor allele frequency in the IND. rs2283727 also exhibited significant differences in the derived ‘A’ allele frequency as compared to all other populations except BEB, ITU, PJL, STU with South Asian ancestry  and rs3027440 ‘T’  alleles in the probands , rs3027440 ‘T’  and rs56220155 ‘A’  alleles in the male probands in comparison to the male controls  = 1.28–11.95; power = 62 %) was also observed in the female probands as compared to the female controls , exhibited significant higher occurrence  in the probands  was also noticed in the male probands  in the probands   in the male probands  and nominally significant higher occurrence of the A-G-C-C-T-C-T haplotype in the female probands  as compared to sex-matched controls  followed by rs2283728, rs2283727, rs4824562, and rs3027440 . High degree of synergistic effects of rs4824562 with rs2283728 and rs2283727, along with moderate synergistic effect with rs56220155, and minimal synergistic effect with the rest of the variants were also noticed  observed for a few association analyses could also be attributed to the limitation in sample number. We may infer from the data obtained that further investigation on a large cohort of samples belonging to different ethnic groups is warranted to validate our observation in the Indo-Caucasoid population from the eastern India.Our investigation for the first time revealed association of rs56220155 and rs2283728 with ADHD. rs3027440, previously reported to have no association in the Euro-Caucasoid ADHD subjects , also reth edition; EAS, East Asian; ESN, Esan in Nigeria; EUR, European; FIN, finnish in Finland; GBR, British in England and Scotland; GIH, Gujarati Indian from Houston, Texas; HWE, Hardy-Weinberg equilibrium; IBS, Iberian Population in Spain; IG, information gain; IND, Indo-Caucasoid control population; IQ, intelligent quotient; is-rSNP, in silico regulatory single nucleotide polymorphism; ITU, Indian Telugu from the UK; JPT, Japanese in Tokyo, Japan; KHW, Kinh in Ho Chi Minh City, Vietnam; LD, Linkage disequilibrium; LWK, Luhya in Webuye, Kenya; MAG, Mandinka in The Gambia; MAOA, Monoamine oxidase A; MAOB, Monoamine oxidase B; MDR, Multifactor Dimensionality Reduction; MSL, Mende in Sierra Leone; MXL, Mexican Ancestry from Los Angeles USA; ODD, oppositional defiant disorder; OR, odds ratio; PACS, Parental Account of Children’s Symptoms; PEA, phenylethylamine; PEL, Peruvians from Lima, Peru; PJL, Punjabi from Lahore, Pakistan; PUR, Puerto Ricans from Puerto Rico; SAS, South Asian; STU, Sri Lankan Tamil from the UK; TSI, Toscani in Italia; YRI, Yoruba in Ibadan, NigeriaACB, African Caribbeans in Barbados; ADHD, attention deficit hyperactivity disorder; AFR, African; AMR, American; ASW, Americans of African Ancestry in SW USA; BEB, Bengali from Bangladesh; CDX, Chinese Dai in Xishuangbanna, China; CEU, Utah Residents (CEPH) with Northern and Western European Ancestry; CHB, Han Chinese in Bejing, China; CHS, Southern Han Chinese; CI, confidence interval; CLM, Colombians from Medellin, Colombia; CPRS-R, Conners’ Parent Rating Scale-revised; DNA, deoxyribonucleic acid; DSM-IV, Diagnostic and Statistical Manual of Mental Disorders-4