The latest research accumulates information to explore the correlation between gut microbiota and neurodevelopmental disorders, which may lead to new approaches to treat diseases such as attention deficit/hyperactivity disorder (ADHD). However, the conclusions of previous studies are not completely consistent. The objective of the systematic review and meta-analysis was to identify evidence on the dysbiosis of gut microbiota in ADHD and find potential distinctive traits compared to healthy controls.Electronic databases, including PubMed, Embase, Web of Science, Cochrane Library, and PsycINFO, were searched up to August 24, 2021, using predetermined terms. Meta-analysis was performed to estimate the comparison of microbiota profiles  and the relative abundance of gut microbiota in ADHD patients and healthy controls.P = 0.02; 2I = 96%), but the significance vanished after sensitivity analysis because of high heterogeneity. No significant differences in other alpha diversity indexes were found. Regarding the relative abundance of gut microbiota, the genus Blautia was significantly elevated in ADHD patients compared with controls .A total of eight studies were included in the meta-analysis, containing 316 ADHD patients and 359 healthy controls. There was a higher Shannon index in ADHD patients than in healthy controls .[ ADHD is one of the most common neurodevelopmental disorders and onset in early childhood, with a prevalence of 5.9% worldwide . It is aADHD patients usually have gastrointestinal symptoms  such as Currently, the major therapeutic interventions for ADHD are medications, behavioral therapy, and cognitive training. While the efficacy of stimulant medications is validated by powered clinical trials, side effects, including decreased appetite, slight sleep delay, and cardiovascular risks, remain a cause for concern. In recent years, researchers have emphasized the importance of environmental factors such as the gut microbiota to investigate novel therapeutic approaches, including probiotics and prebiotics.To date, several systematic reviews have shown the correlation between ADHD and gut microbiota, but no meta-analysis has been conducted. Thus, we performed this systematic review and meta-analysis to investigate the relationship between ADHD and gut microbiota and find potential distinctive traits in ADHD.The study was registered in PROSPERO (CRD42021273993) and strictly followed the PRISMA guidelines .Studies were included based on the following PICOS criteria.Participants with confirmed ADHD were selected for the review, irrespective of age, gender, race, the existence of co-morbidities, and the use of medication. Animal studies were excluded in the review.No specific exposure was required. We were not interested in interventional studies.Comparator group was healthy controls (HCs) without ADHD diagnosis.Studies were eligible if they report the differences between ADHD patients and HCs in gut microbiota diversity indices  and relative or absolute abundance of microbial taxa.Studies were included if they were observational studies or controlled trials. Studies were excluded if they met any of the following criteria: case reports, conference presentations, reviews, expert opinions, or study protocol.The most commonly used databases, including PubMed, Embase, Web of Science, Cochrane Library, and PsycINFO, were searched up to August 24, 2021, using the predetermined terms. The search strategy used is available in Two individual reviewers  screened the titles and abstracts independently for possible articles. If there was an agreement between the two reviewers regarding a particular study, it was selected for further analysis; however, if there was disagreement, a third reviewer (LY) would determine whether the study qualifies for inclusion. The full texts of these potentially eligible studies were independently evaluated for eligibility by three reviewers . Any disagreement between them was resolved by discussion or by a third reviewer (LY) when required.If studies met the criteria mentioned above, then the data were extracted by one independent reviewer (NW) using a standardized extraction form. The second author (LY) will review all the extracted data with the team to resolve disputes, and the group  will finalize the data.For all eligible studies, the following information was extracted: first author; year of publication; country; number, age and sex of ADHD patients as well as healthy controls; definition of ADHD; alpha diversity , observed species, Shannon diversity, Chao1 diversity, Simpson diversity); beta diversity ; data on microbiota ; dietary assessment; probiotics usage assessment.The quality of eligible studies was assessed using the Newcastle–Ottawa Quality Assessment Scale (NOS)  and evalDifferent studies have investigated the gut microbiota’s taxonomic composition at different levels, such as phylum, order, family, genus and species, with a large number and limited overlap of findings. We excluded results if they were reported only in one study.https://automeris.io/WebPlotDigitizer/) to extract these parameters from the graphs. Most data are expressed as the means ± standard deviations, and the others are presented as medians and interquartile ranges. We standardized all the data into the form of means ± standard deviations for subsequent analyses using a web-based tool (https://www.math.hkbu.edu.hk/~tongt/papers/median2mean.html).Studies included in this meta-analysis reported the comparison of gut microbiota between ADHD patients and controls, including alpha diversity and the relative abundance of bacteria of different phyla, families, and genera. These data were extracted from texts, figures, and supplementary materials. If only figures were given, we used Webplot-digitizer software . Heterogeneity was measured using Up to August 24, 2021, 593 records were found after searching the five databases, and 502 were retained after duplicate manual removal. After screening the title and abstract, 488 studies were removed because of dissatisfaction with the inclusion criteria. After reviewing the full texts of the remaining articles, three were excluded because of a lack of insufficient data, and one was excluded because the data of microbiota is not for gut microbiota. Finally, eight eligible studies were included in this systematic review and meta-analysis Figure 1,For the clinical diagnosis of ADHD, six studies were assessed according to DSM-IV , 15, 16,We also take care of ADHD medication because it may cause gut microbiota disorders. Three of the included records consisted of medication-naïve participants to compare ADHD patients and HCs , 13, 16,Another aspect to highlight was the preparation of fecal samples. Most studies sequenced each sample of all participants separately. Nevertheless, Zhou et al.  made mixAll included studies were assessed for quality using the NOS Table 2.P = 0.31; 2I = 97%)      of Chao1 index, Shannon index, and Simpson index, we wanted to perform subgroup analyses and meta-regression but gave up because of limited literature. Then, we found that the heterogeneity was skewed by the results from two outlier studies Wang et al. and Zhou et al.  of Firmicutes and Actinobacteria, sensitivity analyses excluded the study of Zhou et al. . For other genera, no significant difference was found.As shown in the forest plot Figure 9P = 0.050), but not in Chao1 index (P = 0.218).Potential publication biases were observed in funnel plots of Chao1 index and Shannon index which were presented in Blautia was significantly elevated in ADHD patients.To our knowledge, this is the first meta-analysis to identify evidence on the dysbiosis of gut microbiota in ADHD. We searched five important databases to accumulate evidence on whether ADHD patients have a different gut microbial composition than healthy controls. A total of eight studies with high quality were included, including 316 ADHD patients and 359 healthy controls. Then, we investigated the diversity and relative abundance of the gut microbiota, more specifically at the 5 phyla, 8 families and 14 genera. Our findings are as follows. First, for the alpha diversity of ADHD patients and HCs, we only found a higher Shannon index in ADHD, but the significance vanished after sensitivity analysis because of high heterogeneity. Second, at the phylum level, no significant difference was found. And at the family level, there was no difference between ADHD and HCs. Finally, at the genus level, It is worth noting that several systematic reviews , 19, 20 Regarding the alpha diversity of gut microbiota, we found that the Shannon index, which provides information on richness and evenness of gut microbiota, was elevated in ADHD patients, which meant that the within-group diversity was higher in the ADHD group. The result of Shannon index was consistent with reports drawn by Wang et al. , 18 and Blautia was significantly higher in ADHD patients, which may serve as a biomarker for ADHD. But there still needs more evidence to verify because of the limited number of studies currently.For specific gut microbiota taxa, we selected bacteria that had two or more studies with sufficient data in the meta-analysis. Our findings that there were no significant differences in bacterial phyla and families were not entirely in tune with previous studies , 20. SomBlautia belongs to the Lachnospiraceae family, Firmicutes phylum, and contains 20 kinds of species as of now , Beijing Municipal Science and Technology Commission (Z181100001518005).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder diagnosed during adolescence and adulthood. Assessment of the long-term risks of the current drugs for ADHD treatment has been insufficient, and little is known concerning the long-term therapeutic effects of psychostimulants. Commercially available traditional Chinese medicine compound oral preparations  have been widely used in the clinical treatment of ADHD, but their influence on the interaction between gut microbes and potential metabolomes remains inconclusive.We used a series of behavioral experiments to evaluate the behavioral effects of DMNS on adolescent and adult ADHD rats and used 16S rDNA sequencing of gut microbes and nontarget metabolomics to evaluate the potential pathogenesis of ADHD and explore the biological mechanism of DMNS in ADHD treatment.Ruminococcaceae_NK4A214_group, Ruminococcus_2, and Eubacterium_nodatum_group. Simultaneously, DMNS increased the circulating levels of peripheral monoamine neurotransmitter precursors  and reduced the circulating levels of peripheral fatty acid amides . Finally, the changes in the ADHD serum metabolites were strongly correlated with the gut microbiota.For the first time, DMNS was shown to reduce the excessive activity of adult and adolescent ADHD rats and improve the attention deficit of adult ADHD rats. DMNS improved the structural composition of the ADHD gut microbiota and reduced the abundance of DMNS has a good effect in treating ADHD, and it may exert this effect by regulating the gut microbiota and affecting metabolites in the peripheral circulation.The online version contains supplementary material available at 10.1186/s12906-022-03512-5. Attention deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder in adolescents and adults that presents with inattention, hyperactivity, and impulsivity and affects more than 190 million people globally . With thExtensive evidence indicates that the gut microbiota plays an essential role in the pathophysiology of ADHD , 9. ChanMetabolomics is recognized as a powerful approach to characterize the exposome because it can measure thousands of small molecules in biospecimens , 16. Thi. Dimu Ningshen (DMNS) is an oral preparation of a traditional Chinese medicine compound that has been on the market and has been used in clinical research to treat children with ADHD [We are seeking to identify another class of drugs that are safe and effective to treat ADHD in addition to neurostimulants, and we are focusing on botanicals. Many herbal drugs or herbal compounds have been investigated for their nootropic and neuroprotective effects on different central nervous system disorders, revealing that changes in the gut microbiota play a key role in the pharmacological effects of drugs . Dimu Niith ADHD –24. Howen = 6): SD rats were administered a specific amount of pure water; II) SHR group (n = 6): SHRs were administered a specific amount of pure water; III) SHR + MH group: SHRs were administered methylphenidate hydrochloride ; IV) SHR + DMNS (n = 6): SHRs were administered DMNS (4.05 mg/kg).Specific pathogen-free (SPF) male Sprague–Dawley (SD) rats (3 weeks old) and SPF male spontaneously hypertensive rats (SHRs) (3 weeks old) were obtained from Vital River Laboratory Animal Technology Co., Ltd. . The study was conducted at the formula-pattern research center of Jinan University  and strictly abided by all applicable institution and government regulations regarding the ethical use of animals. The animals were maintained at a controlled temperature (20 ± 2 °C) and humidity (60 ± 10%) with a 12 h light/12 h dark cycle. After 1 week of adaptive feeding, the rats were randomly divided into four groups. I) SD group  was purchased from Xi’an Janssen Pharmaceutical Co., Ltd. .DMNS  was purchased from Yantai Juxian Pharmaceutical Co., Ltd. . DMNS contains the following 11 Chinese medicines: Di Huang, Gou Qizi, Nv Zhenzi, Shan Zhuyu, Wu Weizi, Shan Yao, Zhi Mu, Xuan Shen, Gan Cao, Mu Li and Long Gu. The specific information is shown in Table This study involved four behavioral experiments: the open field test (OFT), Y maze task, novel object recognition test (NORT) and 5-choice serial reaction time task (5-CSRTT). The specific behavioral operation process is described in the materials and methods section of the supplementary materials. Behavioral results were analyzed using professional behavioral analysis software .After completing the last behavioral test, the rats were anesthetized with isoflurane using a small animal breathing anesthesia machine . Blood samples were collected from the abdominal aorta for blood biochemical analysis, and stool samples were collected from the cecum in a sterile environment. The stool samples were immediately stored at 20 °C until DNA extraction.After extracting genomic DNA from the sample, the V3 + V4 region of 16S rDNA was amplified using barcode-specific primers. The primer sequences were as follows: 341F: CCTACGGGNGGCWGCAG; 806R: GGACTACHVGGGTATCTAAT. The purified amplified product was connected to the sequencing adapter to construct a sequencing library and was sequenced on Illumina . After the raw reads were obtained by sequencing, Usearch software was used to filter the low-quality reads first, the double-ended reads were spliced into tags, and then low-quality tags were filtered out. The resulting data were called clean tags. Next, based on the clean tags, clustering was performed using Usearch software to remove the chimera tags detected during the clustering process and obtain the abundance of OTUs and representative sequences of OTUs. The bioinformatics analysis process of 16S rDNA sequencing is described in the materials and methods section of the A 100 μl serum sample was placed in an Eppendorf (EP) tube and resuspended in prechilled 80% methanol and 0.1% formic acid. The samples were then incubated on ice for 5 min and centrifuged at 15000 g for 20 min at 4 °C. The sample was then transferred to a fresh EP tube and centrifuged at 15000 g at 4 °C for 20 min. Finally, the supernatant was injected into a liquid chromatography–tandem mass spectrometry (LC–MS/MS) system for analysis. Subsequent ultrahigh performance liquid chromatography–MS/MS analysis and metabolite matching analysis are described in the materials and methods section of the The alanine aminotransferase (ALT), total bile acid (TBA), L-lactate dehydrogenase (LDH-L), creatinine (Cre) and triglyceride (TG) levels in serum were detected using an automatic biochemical analyzer .One milliliter of DMNS oral solution was added to 3 times the volume of methanol for dilution, the mixture was ultrasonicated for 30 min and centrifuged at 13,000 rpm for 10 min, and 2 μl of the supernatant was subjected to ultra-performance liquid chromatography/quadrupole time-of-flight mass spectrometry (UPLC-Q/TOF-MS) analysis. Subsequent UPLC-Q/TOF-MS analysis of DMNS is described in the materials and methods section of the P value < 0.05 indicated significant differences for all statistical tests. Drawing was performed using GraphPad Prism 7.0 software  and Adobe Illustrator CC 2018 software .The data were expressed as means ± SEM and were tested for normality and homogeneity of variance using SPSS 22.0 software . In multiple group experiments using parametric data, the differences between groups were assessed using one-way analysis of variance to determine overall significance. Because many statistical methods are used in the 16S rDNA sequencing of the gut microbiota, nontarget metabolomics, and the combined analysis of the two, the statistical methods were marked in the annotations below the figure. A P < 0.01), while the level of ALT was higher than that in the SD group (P < 0.05). However, no study has shown that this difference was due to the rat breed or onset of ADHD. The circulating levels of TBA, LDH-L, and Cre in the serum of the SHR group were not significantly different from those in the SD group  . However, neither drug reduced the residence time of SHRs in the central area. To evaluate the learning and memory abilities of rats quickly and effectively [P < 0.05), indicating that the learning and memory abilities of SHRs in adolescence decreased. DMNS and MH increased the preference of SHRs in adolescence for novel objects and improved their learning and memory abilities. The Y maze experiment can reflect the spatial recognition ability of rats [In the 6th week, the adolescent SHRs showed significant hyperactivity behavioral performance in the open field experiment, and their stay in the central area during the same time was significantly higher than that of the SD rats (01) Fig. B. After ectively , we perf of rats . The spaP < 0.01). The accuracy of caving in the SD group was 75.65%, while that of caving in the SHR group was only 44.56%. SHRs in adulthood exhibited the characteristics of inattention. After long-term administration, both DMNS (P < 0.05) and MH (P < 0.01) improved the accuracy of burrowing in adult SHRs by 59.79 and 74.41%, respectively, and the improvement ratios were 34.18 and 66.99%, respectively. At the same time, using open field experiments, the total movement distance of SHRs in adulthood was not significantly different from that of SDs, but the residence time of SHRs in the central area was still greater than that of the SD rats (P < 0.05) . In the novel object recognition experiment and Y maze experiment, no significant difference was found in the distinguishing ability or spontaneous alternation rate of SHRs in adulthood compared with that observed in the SD rats  Fig. F. After ats Fig. G and H. To explore the possible biological mechanism of the therapeutic effect of DMNS, we performed 16S DNA sequencing on the cecum contents of the rats. Principal coordinates analysis (PCoA) revealed that the structure of the SD gut microbiota is significantly different from that of the other three groups. The SHR and SHR + DMNS groups had fewer overlapping areas, indicating that DMNS affects the structure of the gut microbiota of the SHRs Fig. A. The raFirmicutes/Bacteroidetes (F/B) ratio were observed between groups  and SHR + MH group (8.10%), but there was no significant difference. The proportion of Patescibacteria in the SHR group (0.29%) was lower than that in the SHR + DMNS group (0.71%) and SHR + MH group , as shown in Fig. Ruminococcaceae and Christensenellaceae in the SHR group  were significantly higher than those in the SD group , while DMNS downregulated the proportion of Christensenellaceae in the SHR + DMNS group   were higher than those in the SD group , while DMNS downregulated the proportion of Christensenellaceae_R-7_group in the SHR + DMNS group   and Clostridiales (order), strongly implying that the high abundance of Clostridiales may be closely related to the onset of ADHD. Additionally, we analyzed the metabolic pathways corresponding to the gut microbiota of each group. The SHR group was mainly enriched in D-alanine, D-glutamine and D-glutamate metabolism and fatty acid biosynthesis, while the SHR + DMNS group showed low enrichment in these pathways  as the experimental group and SD rats as the normal control group . After 4Based on the development of omics methods, the synergistic effect of botanicals and gut microbes significantly affects the health of the host . StudiesChristensenellaceae accounted for a higher proportion in the SHR group than in the SD group. After DMNS treatment, the proportion of Christensenellaceae decreased. Considering that Christensenellaceae is a universal and heritable strain closely related to obesity, diabetes and other metabolic-related diseases [Christensenellaceae may participate in the pathogenesis of ADHD by influencing metabolic pathways. We screened out different microorganisms using Welch’s t test and found that 3 microorganisms  had high abundance in the SHR group but low abundance in the SD and SHR + DMNS groups. This finding is consistent with that of a Dutch research team investigating ADHD, implying that these three microorganisms may have a special effect on neurodevelopment, a key to brain development and several mental diseases, including ADHD [Our study showed that the abundance of the gut microbiota is not significantly related to the pathogenesis of ADHD, but differences exist in the structure of the gut microbiota between ADHD and SD rats. At the family and genus levels, diseases , we specing ADHD .Ruminococcaceae_NK4A214_group, Ruminococcus_2, and Eubacterium_nodatum_group. Thus, we speculate that the metabolic activity of these three bacteria will affect the level of amino acids related to monoamine neurotransmitter synthesis in the peripheral circulation and produce ADHD-related behaviors.The brain-gut axis is critical for the gut microbiota to act on the central nervous system of the host. It has a two-way regulatory effect, involving mechanisms including immunity, short-chain fatty acids, neurotransmitter production, the vagus nerve, serotonin, and the endocannabinoid system , 40. OnePrimary fatty acid amides (PFAMs) are a group of lipids that serve as bioactive lipid signaling molecules . Since tIn summary, the present study demonstrated for the first time that DMNS protected against ADHD in rats. The mechanism underlying the protective effect of DMNS on ADHD may involve, at least in part, the modulation of gut microbiota and its derived metabolites. Hence, our findings may offer information about the application of DMNS to prevent ADHD in rats, but further studies are needed to verify the mechanism of action of DMNS on the “brain-gut” axis of ADHD.Additional file 1.


There was a significant difference in EQ between children with and without ADHD, the score being lower in children with ADHD. There was also a significant difference in SQ among girls with and without ADHD, but not in boys. The brain types in both groups were not significantly different. The results indicate that children with ADHD have a lower ability to empathize compared to children without ADHD. Systemizing abilities were significantly lower in girls with ADHD than in girls without. Therefore, an intervention program focusing on improving empathy and systemizing ability needs to be developed in the community.This study compares the Empathy Quotient (EQ) and Systemizing Quotient (SQ) scores of elementary school children with and without ADHD. The study also examined their brain types and, because sex plays a big role in empathy and systemizing ability, compared the results of the boys and girls. This cross-sectional study involved 122 participants, including 61 parents of children with ADHD and 61 parents of children without ADHD. The EQ, SQ and brain types were obtained using the Empathy and Systemizing Quotient in children (EQ-/SQ-C), validated in the Indonesian language. Data was analyzed using the SPSS program version 20 for Windows, with a  Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder that has the main characteristics of persistent failure in sustaining attention and/or hyperactive or impulsive behavior. These characteristics are significant in at least two situations and have an onset before the age of 12 years old ,4,5.The social interaction is an important milieu for children especially for their empathy and psychosocial development. During the preschool age, children tend to think egocentrically and may not see things from others’ perspectives. However, as language and cognition develop, they gradually learn to express more complex emotions , share things, and try to understand about other people. At the end of preschool age, children have the capacity for empathy and learn that others may think and feel differently from them. These abilities help them through their interaction with others during school age to adulthood . Peer reEmpathy, according to Goldenfeld, Baron-Cohen and Wheelwright , is reciBaron-Cohen, Richler, Bisarya, Gurunathan and Wheelwright  developeBoth empathy and systemizing abilities are relatively stable in life. Thus, individual differences in these cognitive styles should have been observed since childhood ,11. So fp-value < 0.05 to determined the statistical significancy. The number of participants included in this study was accordant to the sample size calculation for comparison study. The Ethics Committee of the Faculty of Medicine Universitas Indonesia has approved the study protocol (KET-387/UN2.F1/ETIK/PPM.00.02/2020 on 6 April 2020).This was a cross-sectional study. A total of 122 elementary school-aged children and their parents participated in this study. They were categorized into two groups: with ADHD group (61 pairs) and without ADHD group (61 pairs). Children with ADHD were recruited from Child and Adolescent Psychiatry outpatient clinic at dr. Cipto Mangunkusumo General Hospital, Central Jakarta. Meanwhile, children without ADHD were selected from one public primary school at Central Jakarta. All research subjects joined the study consecutively based on parents and children willingness to participate into the study. The inclusion criteria for children without ADHD were those with Conners’ Abbreviated Teacher/Parent Rating Scale (CATPRS) score of less than 12 and never been diagnosed as ADHD based on parent report. Furthermore, the inclusion criteria for children in the ADHD group were children with diagnosed ADHD by a child and adolescent psychiatrist from the Dr. Cipto Mangunkusumo General Hospital, Child and Adolescent Psychiatry outpatient clinic. All parents had at least a junior high school education and agreed to participate in this study by signing informed consent. The diagnosis of ADHD was based on the International Classification of Disease-10 (ICD-10) diagnostic criteria for ADHD. Furthermore, children with ADHD have been medicated by using methylphenidate hydrochloride immediate release for several months with dose range 5–20 mg per day in divided dose. The exclusion criteria for this study were children with severe psychiatric disorder  and physical disability. The statistical power for this study was set 80% and The socio-demographic data of both parents and children were collected using a questionnaire that was specifically developed for this study. Conners’ Abbreviated Teacher/Parent Rating Scale (CATPRS), which is an abbreviated version from Conners’ Teacher Rating Scale and Conners’ Parent Rating Scale with 10-statements related to children’ behavior, was also used to screen out the children with ADHD from the elementary school. The rating of the instruments was on a Likert scale, with scores ranging from zero  to three  for each statement. The instrument was valid and reliable for use, with a cut-off score of 12 . The MINThe Empathy and Systemizing Quotient for Children (EQ- & SQ-C), which is a parent-rating scale to assess empathy and systemizing quotients, was also used in this study. The Indonesian version of the EQ- & SQ-C was assessed for reliability with an internal consistency of Cronbach’s alpha 0.957 for EQ-C, 0.962 for SQ-C, and 0.979 for overall EQ- & SQ-C. Each point of this instrument also has good construct and content validity. The Indonesian version of this questionnaire has 38 items in total, with 18 statements for EQ-C and 20 statements for SQ-C. Parents have four different choices for each statement: “strongly agree,” “slightly agree,” “slightly disagree,” and “strongly disagree.” For the empathy quotient, statements 9, 11, 15, 18, 19, 28, 29, and 31 will be given a score of 2 points if “strongly agree” is chosen and 1 point if “slightly agree” is chosen. For statements 1, 3, 4, 6, 12, 20, 23, 26, 37, and 38, the 2 points are given if the parents choose “strongly disagree” and 1 point for “slightly disagree.” For systemizing quotients, the scoring for statements 5, 7, 8, 13, 17, 21, 22, 24, 25, 27, 30, 32, 34, and 35 is 2 points for those who answered “strongly agree” and 1 point for “slightly agree.” For statement numbers 2, 10, 14, 16, 33, and 36, the 2 points are given for a “strongly disagree” answer and 1 point for “slightly disagree.” The rest of the choices were given a score of 0 points. The maximum total points were 36 for EQ and 40 for SQ. If there is no answer in a minimum of five statements, then the result will be considered invalid, and the data will not be analyzed [After the EQ and SQ scores were calculated, the D score (difference between standardized EQ and SQ) and C score (combined score of standardized EQ and SQ) were calculated. The formulas used were:Standardized EQ or SQ is obtained by subtracting mean EQ or SQ of typical population from observed EQ or SQ score, then divide it with the possible maximum score of EQ or SQ. The D score indicates the differences between empathy and systemizing ability, which can be used to determine the brain types of children. The brain types were extreme empathy (with percentile of D score < 2.5), empathy (percentile of D score between 2.5 to 35), balance (percentile of D score between 35 to 65), systemizing (percentile of D score between 65 to 97.5), and extreme systemizing (percentile of D score > 97.5). The C score indicates the accumulation between empathy and systemizing ability, which can be used to see whether the two abilities are reciprocal to each other or not.All of the data were analyzed using SPSS version 20.0. Descriptive data for socio-demographics and brain types according to EQ and SQ are shown in proportion. EQ and SQ scores are shown in the form of mean and standard deviation if the data were distributed normally, and median and range if not normally distributed. The relationship between ADHD and brain type was analyzed with chi-square or alternative Kolmogorov-Smirnov tests. Furthermore, the mean difference of EQ and SQ, and the D and C scores between groups was analyzed using an independent t-test for normally distributed data, and the Mann-Whitney U test if not.Between May 2020 and February 2021, 122 participants completed the questionnaire. The participants’ characteristics are listed below. p = 0.010 in boys and p = 0.002 in girls), and a statistically significant difference in SQ scores between both groups (with and without ADHD) in the girls subgroup (p = 0.008). However, the SQ scores in the boy subgroup did not differ significantly (p = 0.288). The effect of ADHD that may influence EQ and SQ scores in this study was small to large difference effect size (ranged from 0.29 to 1.29)  were distributed normally and were analyzed using an independent t-test. As some of the data were not distributed normally, this study used the mean, standard deviation (SD), median, and range (minimum-maximum) for descriptive purposes. The results are presented in to 1.29) .p = 0.385).The mean (SD) of the EQ scores in the without ADHD group, regardless of the sex-differences, is 17.80 (4.29), while the mean (SD) of the SQ scores is 15.30 (4.72) . Using tp = 0.184 in boys and p = 0.334 in girls).For the boys, the mean (SD) D scores of the ADHD group and the without ADHD group were 0.019 (0.063) and 0.000 (0.052), respectively. For the girls, the mean (SD) D scores of the ADHD group and the without ADHD group were 0.022 (0.077) and 0.000 (0.064), respectively. p = 0.099). However, with regard to the girls, the results were significantly different (p = 0.001).The mean (SD) C score was also calculated. For the boys, the mean (SD) of the C score of the ADHD group was −0.050 (0.137) and the group without ADHD’s was 0.000 (0.106). For the girls, the mean (SD) of the C score of the group with ADHD was −0.139 (0.165), and the group without ADHD’s was 0.009 (0.110). The C score distribution shown in In this study, the Empathy Quotient and the Systemizing Quotient were determined using the EQ- & SQ-C questionnaire that was completed by 122 parents of elementary school children. To our knowledge, this is the first study to compare the EQ, SQ, and brain types according to EQ and SQ in children with and without ADHD. This study reports the results in boys and girls subgroup, taking into account the role of gender in empathy and systemizing ability. Some studies have found that boys have lower empathy than girls. Chaidir et al., 2019 observed that boys tend to have a more systemizing brain type, while girls tend to have more empathizing brain type . Auyeungp = 0.010). A significant difference was also observed in girls (p = 0.002). In both boys and girls, a lower EQ score was found in the ADHD group. The results indicate that in children with ADHD, regardless of gender, there is a difference in empathy. Lower empathy means that children with ADHD have difficulties in identifying others’ mental states and responding appropriately. These results are concordant with the study conducted by Groen et al. [The results of the EQ in boys with and without ADHD were significantly different , while for the girls, the difference was significant (p = 0.008). However, in both boys and girls, children with ADHD had lower SQ scores than those without ADHD. Consistent with our study, a study by Aviles et al. (2014) also showed that the SQ score in adults with ADHD was lower than that in adults without ADHD, with a mean score of 29.5. Aviles et al., 2014 used a version of the instrument with a maximum score of 80. [As for the SQ, the results for boys did not differ significantly between the groups . This result may be influenced by the medication being administered to children with ADHD. Most of the participants in the ADHD group had previously received methylphenidate as a medication for their ADHD symptoms. In addition, the data were collected during the Covid-19 pandemic, when children were stayed mostly in their houses. Empathy and systemizing skills may not be rated accurately by parents because both skills are clearer when observed in the school situation, when they are faced with more friends, more complex interactions, and more tasks that may need their systemizing skills. Another factor that may be taken into consideration is that in this study, the questionnaire was filled out by the parents. Parents usually interact with children one-on-one. As stated by Deschamps, Schutter, Kenemans, and Matthys (2015), when interacting with a friend in a quiet environment, children with ADHD are not associated with decreased empathic responses to other people’s sadness and difficulties [In this study, there were no significant differences found in brain types based on EQ and SQ between children with and without ADHD . However, for the girls, the results were significantly different (p = 0.001). The significant difference means that empathy and systemizing abilities are not in a reciprocal relationship with each other in the girls subgroup. Reduced empathy is not always followed by higher systemizing abilities, and vice versa; reduced systemizing ability is not always followed by higher empathy in girls with ADHD. Although some studies found that empathy is reciprocal with systemizing, Wakabayashi, Baron-Cohen, Wheelwright, Goldenfeld, Delaney, Fine, Smith, and Weil (2006) stated that SQ and EQ were almost unrelated to each other, with the correlation between EQ and SQ being close to zero [The C score in this study in the boys subgroup did not differ significantly p = 0.09. Howeve to zero . Groen e to zero . These rNeuroanatomical factors may also play a role in the independency between EQ and SQ, as the brain part included is different. Lai, Lombardo, Chakrabarti, Ecker, Sadek, Wheelwright, Murphy, Suckling, Bullmore, MRC AIMS Consortium, and Baron-Cohen  used strDue to the role of executive function in empathy and systemizing abilities, this study considered IQ, which may have influenced the results. However, in several studies that have been conducted, it was found that while systemizing was related to mental rotation, it was not related to intelligence ,33. The This study found significant differences in EQ (in both boys and girls) and SQ (in girls); however, there was no significant difference in brain types between elementary school children with and without ADHD. Further studies are needed that included more factors that may influence EQ and SQ in children with ADHD. Thus, better understandings of the brain types in this population, according to EQ and SQ can be generated. This study also suggested a concept for future research on the relationship between EQ, SQ and peer problems in the community, along with academic difficulties that are usually experienced by children with ADHD. Moreover, this study may add to the understanding of empathy and systemizing abilities in children with ADHD. Therefore, a more comprehensive therapy to target these abilities in clinical practice is needed with regard to this population, rather than targeting only the symptoms of inattention, hyperactivity, and/or impulsive behavior.


Genetic variants associated with ADHD have a partial genetic causality increasing the risk for chronic obstructive pulmonary disease and carpal tunnel syndrome. Protective factors for ADHD risk included genetic variants associated with the likelihood of participating in socially supportive and interactive activities. Our results show that genetic liability to multiple complex traits influences a higher risk for ADHD, highlighting the potential role of cardiometabolic phenotypes and physical pain in ADHD’s aetiology. These findings have the potential to inform future clinical studies and development of interventions.Attention Deficit-Hyperactivity Disorder (ADHD) is a complex psychiatric and neurodevelopmental disorder that develops during childhood and spans into adulthood. ADHD’s aetiology is complex, and evidence about its cause and risk factors is limited. We leveraged genetic data from genome-wide association studies (GWAS) and performed latent causal variable analyses using a hypothesis-free approach to infer causal associations between 1387 complex traits and ADHD. We identified 37 inferred potential causal associations with ADHD risk. Our results reveal that genetic variants associated with  It typically develops during childhood with symptoms often persisting to adulthood3, affecting around 5% of children and 2.5% of adults4.Attention Deficit-Hyperactivity Disorder (ADHD) is a complex neurodevelopmental and psychiatric disorder characterised by impulsivity, hyperactivity, and attention and concentration impairment5, positioning it among the most heritable psychiatric conditions. Genome-wide association studies (GWAS) have identified 304 genetic variants across 12 independent loci associated with ADHD6. Similarly, genetic correlations have been identified between ADHD and smoking, obesity-related phenotypes, and psychiatric disorders such as major depressive disorder and schizophrenia6.ADHD is heritable, and its aetiology involves a variety of genetic and environmental factors. Twin and family studies have estimated ADHD heritability between 0.72 and 0.887. The heterogeneity and multifactorial causal architecture of ADHD, combined with the relatively small sample sizes of ADHD studies, pose challenges to identify potentially modifiable risk factors for ADHD4. Previous studies have identified shared genetic pathways between ADHD and other phenotypes6; however, a genetic correlation between two polygenic phenotypes is not sufficient evidence for a causal association. For instance, a genetic correlation could be explained by horizontal pleiotropic effects , which in turn may produce spurious findings in genetic epidemiological studies investigating causality with traditional methods such as Mendelian randomisation8. Likewise, sample overlap between GWAS can bias results increasing false-positive findings in Mendelian randomisation studies9.The causal architecture of a polygenic phenotype pinpoints potential exposures and outcomes in putative causal pathways underlying the aetiology of a complex trait8 method, which is less susceptible to bias by horizontal pleiotropy and sample overlap than Mendelian randomisation8, to conduct a large-scale genetic investigation of the causal architecture of ADHD. To this end, we conducted a hypothesis-free phenome-wide screening of traits causally associated with ADHD using the ADHD GWAS summary statistics from the Psychiatric Genomics Consortium (PGC) and an extensive collection of GWAS summary statistics for 1,387 other traits. Our results should be interpreted as a set of testable hypotheses for future epidemiological and interventional studies.In the present study, we use the latent causal variable (LCV)We identified 578 significant genetic correlations between ADHD risk and other complex traits  , having a blue badge disability allowance  and presenting carpal tunnel syndrome  as an adult.Genetic variants associated with ADHD risk displayed positive genetic correlations and significant causal proportion on three complex traits Fig. . Specifihigh-density lipoprotein (HDL) , being a school teacher or teaching professional  and doing unpaid or voluntary work  as an adult displayed negative genetic correlations and significant genetic causal proportion with a high ADHD risk as a child (Table Genetic liability for anaemia (ICD10) , peripheral artery disease , self-reported type 2 diabetes  and obesity . Musculoskeletal-related traits, included synovitis and tenosynovitis (ICD10) , neck or shoulder pain for more than three months , polyarthrosis (ICD10)  and fibroblastic disorders . Finally, we observed a similar effect for genetic components associated with eye diseases such as entropion and trichiasis of the eyelid .We observed that genetic liability for a number of physical health conditions, psychiatric-related traits, and lifestyle-related phenotypes may increase ADHD risk as a child Table . In partEnalapril  and Felodipine  which are typically prescribed for treating hypertension, Gabapentin  prescribed for seizures and epilepsy, and Pantoprazole  commonly prescribed for gastro-oesophageal reflux disease.Genetic variants associated with the frequent use of various medications as an adult were identified to contribute to a higher ADHD risk as a child. These medications included 11, we note that both LCV and Mendelian randomisation methods test the effect of the genetic liability for a given phenotype on the outcome. For instance, potential genetic evidence of causal associations in the present study indicates the inferred causal effect between genetic variants that influence complex traits as an adult and ADHD risk as a child. Our results reveal that the genetic liability to multiple complex traits, notably cardiometabolic and pain-related phenotypes as an adult, increases the risk for ADHD as a child.In the present study, we performed a large-scale genetic investigation of the potential causal architecture of ADHD risk. We used GWAS summary data to conduct bivariate LCV analyses between ADHD and 1,387 other phenotypes. As it has been previously stressedIt is important to triangulate findings from studies with different study designs, of which at least one should be an interventional study such as a randomised controlled trial. However, interventional studies are known to be expensive, time-consuming, and sometimes unfeasible due to ethical concerns . Thus, using methods in statistical genetics to identify the potential causal effect of genetic liability to a disease on an outcome could be the best option available, particularly for young- and late-onset phenotypes. Our findings contribute to elucidate the complex aetiology of ADHD and should be interpreted as a set of testable hypotheses for future studies.self-reported COPD was causally influenced by genetic variants associated with ADHD. Previous studies have reported an association between ADHD and COPD13, and some suggest that genetic influences could partially explain this relationship14. However, it is well established that smoking is a risk factor for COPD16, and youth diagnosed with ADHD are two to three times more likely to engage in smoking behaviour17. We hypothesise that the association between ADHD and COPD could be influenced by potential vertical pleiotropic effects and may also be mediated by susceptibility to lifestyle factors.A higher risk for 20, suggesting that iron deficiency may contribute to the physiopathology of ADHD by influencing dopaminergic dysfunction21. Our results show the potential causal effect of the genetic liability for iron deficiency anaemia (ICD10) on a higher risk for ADHD. Thus, we provide convergent evidence with previous studies suggesting that children with ADHD (or with a high ADHD predisposition) and low iron levels could benefit from iron supplementation. Nonetheless, an interventional, and preferably, a randomised clinical trial is required to confirm this hypothesis. For example, case–control studies could screen children with ADHD and iron deficiency to investigate if and to what extent iron supplementation in those with iron deficiency could prevent or ameliorate ADHD symptoms in the longer term.Previous studies have assessed the relationship between iron deficiency and ADHD22, and previous studies suggest that this relationship is not influenced by confounding factors23. Some genetic studies show a predominant one-way causal association in which obesity is potentially causal for ADHD25, while others suggest that there may be plausible bidirectional effects between ADHD and obesity26. In addition, it has been reported that children with diabetes are more likely to develop ADHD, even after accounting for possible confounding factors such as obesity27. However, the biological mechanisms behind this association remain unclear28. Similarly, some children with high blood pressure might have a comorbid diagnosis of ADHD30. In the present study, genetic liability for obesity appeared to lead to a higher risk for ADHD, as did genetic variants associated with other cardiometabolic traits such as peripheral artery disease, self-reported type 2 diabetes, and the use of Enalapril and Felodipine, which could be used as a proxy for hypertension. Consistently, we identified that genetic variants associated with low HDL cholesterol levels in adulthood increase the risk for ADHD in childhood; this is most likely explained by the effect of obesity-related variants, which are known to influence a decrease in HDL cholesterol32. Although we cannot rule out a potential influence of obesity in the potential causal association between cardiometabolic phenotypes and ADHD, our results suggest that genetic variants influencing the likelihood of peripheral artery disease, hypertension, and type 2 diabetes as an adult could also be partly responsible for a higher ADHD risk as a child.It has been observed that obesity can be comorbid with ADHD34, and previous studies report that ADHD symptoms correlate with severe pain that hinders an individual’s capacity to work34. In our study, genetic liability for musculoskeletal pain-related phenotypes as an adult contributed to a higher ADHD risk as a child, whereas genetic susceptibility to ADHD increases the risk for carpal tunnel syndrome, which is commonly due to high levels of gaming/computer use in poor ergonomic setups36. Our results add up to the evidence suggesting that genetic risk variants for pain and poor musculoskeletal system health increase the risk for ADHD.Widespread musculoskeletal pain and motor inhibition problems have been observed among adults with ADHD37. ADHD’s prevalence among children with epilepsy ranges from 20 to 50%38. In this study, we showed genetic variants potentially influencing the use of Gabapentin, which could be used as a proxy for epilepsy39, and syncope and collapse (ICD10), posing a potential causal genetic effect increasing the risk for ADHD. Thus, our results support putative vertical pleiotropic effects between genetic susceptibility to epilepsy and ADHD.It has been reported that children with epilepsy are more likely to experience concentration problems40. Also, it has been suggested that individuals with ADHD may be more sensitive to experience impairment effects of alcohol, particularly for inhibitory control41. Similarly, previous studies have identified associations between poor social skills and ADHD43, suggesting that interventions to refine social skills among individuals with ADHD could be of benefit44. In the present study, genetic evidence suggested that alcohol misuse as an adult may increase the risk for ADHD as a child. Consistently, genetic variants influencing the likelihood of never being injured or having injured someone else through drinking alcohol were negatively correlated with ADHD risk, as were those for doing unpaid or voluntary work. Therefore, our results suggest that children with ADHD may be more likely to engage in substance use  as adults.The relationship between substance use and ADHD has become a focal point of interest in identifying risk factors for ADHD. Previous studies have reported that alcohol abuse is associated with the worsening of ADHD symptoms45. Here, we observed that genetic correlations between ADHD and bipolar disorder, schizophrenia, anxiety and depression can be explained by horizontal pleiotropic effects rather than a potential causal pathway. Although psychiatric phenotypes may share considerable common variant genetic risk, we speculate that the spectral nature of psychiatric phenotypes may influence phenotypic overlap between them45, which in turn would contribute to the shared genetic risk among them.ADHD, bipolar disorder, major depressive disorder and schizophrenia are known to share common genetic risk47, (b) it can cope with sample overlap47 and (c) it increases statistical power by using information across the whole genome47, which in turn enables researchers to test potential causality with phenotypes that would be considered “underpowered” with other statistical methods.The LCV method is known to have advantages over other traditional methods used in genetic epidemiology to investigate potential causal associations. These include that (a) it is less susceptible to bias due to horizontal pleiotropy49. (ii) Although more than 1,300 phenotypes were included in our analysis, potential causal genetic effects with other traits may exist. (iii) Many GWAS used in this study may proxy other complex traits complicating the interpretability of the results. For instance, medication use GWAS were interpreted as a proxy for the phenotype they are most commonly prescribed for. (iv) The LCV method identifies the predominant causal pathway between a pair of correlated phenotypes and it is unable to test for bidirectional causality8. (v) Despite increased statistical power due to the use of aggregated genetic information throughout the genome, the LCV method still depends on the statistical power of the original GWAS8, and the capacity to identify potential causal genetic effects could be limited for some phenotypes, particularly for those with small sample sizes. Related to this is the inconsistency between obesity-related phenotypes. For example, Diagnoses—main ICD10: E66 Obesity refers to unspecified obesity diagnosed in the International Classification of Diseases. In contrast, Obesity refers to the combination of several ICD10 codes for obesity, including drug-induced obesity, morbid obesity with alveolar hypoventilation and obesity due to excess calories, among others. In this study, genetic susceptibility to both obesity phenotypes contributed to a higher risk of ADHD  If several latent factors mediate a causal association between two traits, the LCV method may produce spurious findings, and GCP estimates could be biased towards the null due to a reduction of statistical power8.Limitations of the present study include: (i) the generalisability of our results across ethnicities may be limited, given reports of ethnic differences in ADHD manifestationsIt is important to note the temporality difference of phenotype measurements between ADHD and the phenotypes included in this large-scale study. ADHD symptoms are known to begin during childhood and are estimated to continue in adulthood for around 50% of those affected. We note that our results reflect the potential causal effect of genetic liability for a number of adult phenotypes on higher ADHD risk in childhood. Therefore, our results should be interpreted as a set of testable hypotheses that need to be validated in future investigations in both children and adults. For instance, longitudinal studies could monitor participants from childhood to adulthood to assess whether adults who had an ADHD diagnosis during childhood are more likely to present a clinical manifestation for a phenotype whose genetic liability was identified to increase the risk for ADHD in the present study. Also, given that the genetic liability for participating in socially supportive and interactive activities  in adulthood is inversely correlated with high ADHD risk, future studies could investigate if and to what extent could these activities help refine social skills or manage ADHD symptoms in adults who had an ADHD diagnosis in childhood.Here, we assessed the evidence for potential causal genetic effects between ADHD and more than 1,300 phenotypes. Our findings uncovered the potential role of iron metabolism in ADHD’s aetiology, supporting the hypothesis that iron supplementation could benefit children at high ADHD risk. Similarly, we identified the putative causal effect of the genetic susceptibility to substance use behaviours as an adult on a higher risk for ADHD as a child. Further, we show the probable influence of cardiometabolic phenotypes and poor musculoskeletal health as an adult on an increased risk for ADHD. Although the mechanisms are unclear, we highlight a possible role of genetic susceptibility to ADHD as a contributor to COPD. Altogether, our results contribute to our understanding of ADHD’s aetiology while supporting evidence from several previous observational studies and providing a set of novel testable hypotheses that need further validation.6. Briefly, an inverse variance weighted GWAS meta-analysis of childhood ADHD classified under DSM-IV was performed on samples from the PGC and the Lundbeck Foundation Initiative for Integrative Psychiatric Research (iPSYCH), including a total of 55,374 participants . The Ricopilli pipeline50, developed by the PGC, was used to conduct stringent quality control and imputation procedures6. For each cohort, principal components were included as covariates in the model to control for population stratification6.We leveraged a large and publicly available GWAS summary statistics dataset for ADHD from the PGC. A detailed description of these summary statistics is available in their corresponding publicationhttps://genoma.io/)51. Details for these summary statistics are described directly in the CTG-VL. Briefly, summary statistics available in the CTG-VL include objective laboratory measurements and self-reported phenotypes from Neale’s Lab second wave of GWAS results from the UK Biobank cohort 52 and several other GWAS consortia. Most GWAS are derived from European ancestry samples, mitigating potential biases due to population differences in linkage-disequilibrium and allele frequencies. The CTG-VL’s inclusion criteria require a nominally significant heritability derived from LD score regression51.A compilation of GWAS summary statistics for 1,387 polygenic traits and diseases are publicly available in The Complex Traits Genomics Virtual Lab  analysis between ADHD and 1,387 phenotypes to assess whether a genetic correlation (rG) could be explained by vertical pleiotropic effects . Briefly, we loaded the ADHD GWAS summary statistics onto the CTG-VL and estimated genetic correlations and potential causal associations with 1387 other traits using the MASSIVE phenome-wide analysis pipeline. Then, we generated causal architecture plots to visualise the results. A detailed and illustrated description of this approach is available in previous studies47.We employed the phenome-wide analysis pipeline in the CTG-VL as described in previous studies8 method made available in a GitHub repository (https://github.com/lukejoconnor/LCV) to implement the phenome-wide analysis pipeline in R 4.0.046. The LD score script munge_sumstats.py was used to format data and ensure consistency of alleles and variants across GWAS summary statistics. HapMap 3 SNPs were extracted with the list of SNPs (w_hm3.snplist) (https://github.com/bulik/ldsc/wiki).The CTG-VL uses the same scripts that the original authors of the LCVL, representing the causal component between both traits, and estimates the genetic causal proportion parameter (GCP)8. A GCP value equal to zero indicates that horizontal pleiotropic effects mediate the genetic correlation and thus, provides no evidence for genetic causality between the phenotypes8. In contrast, an absolute GCP value equal to one represents full genetic causality8. An absolute GCP value below 0.60 represents a weak causal association and indicates limited partial genetic causality8. In the present study, LD score regression53 was used to estimate genetic correlations between ADHD and 1,387 other phenotypes. Bivariate LCV analyses were performed between ADHD and 578 traits with a significant genetic correlation. We applied Benjamini-Hochberg’s False Discovery Rate (FDR < 5%) adjustment to define statistical significance (adjusted p < 0.05) at both the genetic correlation and LCV steps.The LCV method mediates the relationship between two genetically correlated phenotypes with a latent variable This study was approved by the Human Research Ethics Committee of the QIMR Berghofer Medical Research Institute. We confirm that all methods were performed in accordance with relevant guidelines and regulations.Informed consent was obtained from all individual participants included in the study.All participants provided informed consent for the publication of study results.Supplementary Legends.Supplementary Information.


Previous research investigating language in attention-deficit hyperactivity disorder (ADHD) has demonstrated several deficits in many aspects. However, no previous study employed quantitative methodology providing objective measures that could be compared among different studies with diverse samples. To fill this gap, we used network analysis to investigate how ADHD symptomatology impacts narrative discourse, a complex linguistic task considered to be an ecological measure of language. Fifty-eight adults  with a mean age of 26 years old and a mean of 17 years of educational level were administered the Adult Self-Rating Scale for ADHD symptomatology. They also completed the State-Trait Anxiety Inventory, the Beck Depression Inventory and the Urgency, Premeditation, Perseverance, Sensation Seeking Behavior Scale. Intelligence quotient was calculated. Individuals were asked to tell a story based on a wordless picture book. Speech was recorded and transcribed as an input to SpeechGraphs software. Parameters were total number of words (TNW), number of loops of one node (L1), repeated edges (RE), largest strongly connected component (LSC) and average shortest path (ASP). Verbosity was controlled. Statistical analysis was corrected for multiples comparisons and partial correlations were performed for confounding variables. After controlling for anxiety, depression, IQ, and impulsiveness ADHD symptomatology was positively correlated with L1 and negatively correlated with LSC. TNW was positively correlated with ADHD symptoms. In a subdomain analysis, both inattention and hyperactivity-impulsivity were negatively correlated with LSC. Only hyperactivity-impulsivity positively correlated with TNW and L1. Results indicated a correlation between ADHD symptoms and lower connectedness in narrative discourse (as indicated by higher L1 and lower LSC), as well as higher total number of words (TNW). Our results suggest that the higher the number of ADHD symptoms, the less connectivity among words, and a higher number of words in narrative discourse. Attention-Deficit Hyperactivity Disorder is defined by the core symptoms of inattention, hyperactivity and impulsivity , but theAmong the phenotypic dimensions of ADHD, language problems may encompass a large set of discourse comprehension and production difficulties. A myriad of ADHD-related deficits has already been documented: poor comprehension of main plotline, ambiguous reference, event sequencing errors, incomplete clauses, discourse interruptions and embellishment. In addition—or possibly as a consequence of the aforementioned difficulties—their discourse often displays pragmatic problems like excessive speech, poor turn-taking and fail in maintaining appropriate topics in conversations. Finally, deficits were demonstrated in comprehension and elaboration of the main plot, monitorization and autocorrection of speech, online monitorization and organization of discourse and story resolution; such deficits could be a linguistic expression of executive deficits often seen in ADHD –12.Narrative discourse (ND) refers to the ability of verbally reporting real or imaginary events by translating them into comprehensive structured sequences of logically-linked ideas ,14. It iPrevious studies investigating language in children with ADHD, reported a wide range of language problems such as: 1) less overall recall of story units, more production of ambiguous references, semantically inappropriate word substitutions, more inaccurate information and more sequence errors ,7; 2) miThe two main methods to assess language production are story retelling and conversational samples. Both methods may employ either qualitative or quantitative analyses and have given heterogeneous and sometimes discordant results according to the literature. Most studies investigating ADHD and language included samples with children and adolescents. Although there are very few previous studies investigating adult ADHD, they have either used instruments with uncertain ecological validity or qualitative analyses, which are prone to interpretation biases. The main contribution of this study is that we have used a novel quantitative approach  to investigate Narrative discourse, a complex linguistic skill considered to be an ecological measure –32.Network analysis of discourse  has been proposed as an useful method to investigate ND, providing quantitative indices of many elements, such as long and short-range recurrences of nodes ; the former being a proxy of connectedness –33. SuchThe present study aimed to investigate the structure of ND in adults considering their ADHD symptomatology using network analysis. For this, individuals were asked to tell a story based on a wordless picture book. We hypothesized based on previous findings that network analysis would reveal differences in ND attributes in individuals with ADHD symptoms . SpecifiThe study was conducted in Brazil, with Brazilian young adults subjects. All participants volunteered and signed an informed consent approved by the Ethics Committee of Copa D’Or Hospital (Submission Certification for Ethics Appreciations (CAAE) number: 38000614.1.0000.5249). Participants (n = 71) were primarily volunteers recruited among university students (graduate and undergraduate); a small number (10%) was referred by their mental health professionals aware of the study. Inclusion criteria were a) age between 18 and 40 years old, and b) current or past diagnosis of ADHD. Exclusion criteria were: a) IQ lower than 80  ,35; b) cNeuropsychological measure of IQ was used as screening measure for eligible criteria attendance. It was obtained using the Vocabulary and Blocks subtests of Wechsler Adult Intelligence Scale (WAIS-III) ,37.Individuals with known ADHD (n = 6), who were already under pharmacological treatment, were asked to undergo a 48-hour washout of psychostimulants before the assessment.Our final sample comprised 34 females and 24 males with a mean age of 26 years old and a mean educational level of 17 years of scholarship. Their mean IQ was 119 . Table 2Participants were given the Adult Self Rating Scale (ASRS) in Portuguese  to accesThe Brazilian-Portuguese version of State-Trait Anxiety Inventory is composed of two 20-item scales; with each question scored from 1 to 4; measures situational anxiety  and anxious trait . Shows high internal consistency estimated as Cronbach’s alpha coefficient of 0.89 .The Brazilian-Portuguese version of Beck Depression Inventory composed of 21 questions; each item being scored on a scale value of 0 to 3; final score is a measure of depression symptom severity. BDI also shows high internal consistency estimated as Cronbach’s alpha coefficient of 0.89 ,39.The Brazilian-Portuguese version of Urgency, Premeditation, Perseverance, Sensation Seeking Behavior Scale composed of 45 items that address four personality factors related to impulsive behavior; each item is scored on a scale from 1 to 4. UPPS provides a total score of impulsivity, and besides that, also provides subscale scores of each impulsivity subtype: lack of premeditation, urgency, sensation seeking and lack of perseverance; each one with the respective Cronbach’s alpha coefficient of 0.87 (lack of premeditation), 0.85 (urgency), 0.84 (sensation seeking), and 0.76 (lack of perseverance) ,41.The book “Frog, where are you?”  previousThe book was presented to the individual with the following instructions: “Here is a picture book. It is about a boy and his pets, a frog, and a dog. You should look at each page, and then tell me the story. I will record the story you produce. You can look through the whole book as many times as you want before we start. It is not necessary to memorize the book; you will keep it with you while you tell me the story. You should try telling the best possible story, pretending that I don’t know it”.Network analysis of discourse is the attempt to apply small-world network theory in assessment and objective description of linguistic properties. This method analyzes the networks (graphs) of transcripted discourse . As words relation in discourse is a complex system that could be represented as a network (graph), those network structures provide intuitive and useful representations for language modeling knowledge and inference ,31,48,49SpeechGraphs software, created by the same group that proposed the analysis [general characteristics  and Edges (E), and total of words–Word Count (WC)); recurrence characteristics (Repetitive Edges (RE), Parallel Edges (PE), and Loops of one, two and three nodes ; connectivity characteristics (number of nodes on the Largest Connected Component (LCC), number of nodes on the Largest Strongly connected Component (LSC), and Average Total Degree (ATD)); four global measures (Density (D) or the amount of edges in a given graph divided by the potential number of edges given the number of nodes, Diameter (DI) or the shortest distance measured by the number of edges between the pair of nodes with the highest distance in the graph, Average Shortest Path (ASP) or the average shortest distance measured by the average number of edges between all pair of nodes in the graph, and the clustering coefficient (CC) or, considering a node, CC of this node is the estimation of how the direct linked nodes are also linked to each other, and CC of a graph is the average number of CC considering all nodes in a graph [The analysis automatization generated the analysis . It auto a graph .SpeechGraphs software [Narratives were represented as word graphs using the software  Fig 1)..SpeechGrsoftware  was not software .In this present paper we chose to apply a widely used method for network analysis, where each node represents a word, different from our previous paper where each node corresponded to a different nucleus of the narrative plot. In order to control SGAs for verbosity, which has a strong influence on them according to previous findings ,32,33,52We investigated elements already shown to be relevant in ND using network analysis ; they arAll the statistical analysis used the SPSS software. Normality and variance homogeneity were assessed by the Kolmogorov-Smirnov test. Spearman correlations were calculated for SG attributes  and ADHD symptoms assessed by ASRS scale. Partial correlation to control for confounding factors were performed with SPSS software. Statistical analysis was corrected for multiples comparisons .When the correlation between ADHD and SGA parameters were controlled for IQ, educational level, UPPS, BDI and STAI, the total number of words (TNW), loops of one node (L1) and largest strongly connected component (LSC) persisted in positive correlation with ADHD symptoms . L1 posiIn this study, the first to address ADHD and narrative discourse in young adults using network analysis, we provided a different framework to investigate ADHD-related language problems previously demonstrated in different languages across the world. Our findings from this quantitative methodology provide strong support for previous research in the field, however using mainly qualitative methodologies which strongly rely on the examiners’ expertise and may vary depending on the tasks employed for language assessment. Our study may provide the basis for more studies using this strategy in ADHD.The small sample size should be considered a main limitation to this study, as well the IQ and educational level of the sample. Further studies should investigate narrative discourse in larger samples of subjects. Besides that would try to correlate with neuropsychological variables (as working memory and executive function). Notwithstanding, we used a task addressing an ecological language measure—narrative discourse (ND). The use of network analysis of narrative discourse  allowed structural quantification of language characteristics of young adults with objective and unbiased measures. We chose parameters theoretically and functionally important for narrative discourse analysis, such as attributes of short-range and long-range recurrences from SGA graph networks –33,52.As we hypothesized, ADHD symptoms positively correlated with short-range recurrence  and the opposite was found for long-range recurrence , where a negative correlation with ADHD symptoms was found. ADHD symptomatology was associated to narrative discourses with less connectivity among words (lower LSC); our results are in accordance to a large study with children where poor language skills have been associated higher inattention or hyperactivity symptoms in primary school . A genomThose results suggest that the more ADHD symptoms the individual has, the more he presents short-range recurrences and fewer long-range recurrences. In typical children, less long-range recurrences  correlated with lower IQ and lower verbal memory as well as worse performance on tasks addressing theory of mind ,54; incrBecause ADHD may be associated with comorbid anxious and depressive disorders  we contrTotal number of words was positively correlated with Hyperactivity-Impulsivity. Of note, this result is in accordance to DSM-5 criterion “talks excessively”. Our finding may add further understanding of previous descriptions of verbosity in adult ADHD discourse, however using a different methodology  ADHD assIn summary, our study has demonstrated that ADHD symptoms are associated with narrative discourse problems, in particular the degree of connectedness and number of words used to convey the story. From a clinical perspective, our results suggest that whenever individuals with ADHD symptomatology are requested to narrate a story based on visual stimuli, they are more verbose, repeat more words or words pairs and produce a discourse with less cohesion among the words they have chosen to tell the plot.Of note, in a study employing different tasks addressing ND in adults with mild dementia, it was shown that the same profile of impairment was seen across all modalities  . There aS1 File(XLSX)Click here for additional data file.


Attention-deficit/hyperactivity disorder (ADHD) often persists into adolescence and adulthood, but the processes underlying persistence and remission remain poorly understood. We previously found that reaction time variability and event-related potentials of preparation-vigilance processes were impaired in ADHD persisters and represented markers of remission, as ADHD remitters were indistinguishable from controls but differed from persisters. Here, we aimed to further clarify the nature of the cognitive-neurophysiological impairments in ADHD and of markers of remission by examining the finer-grained ex-Gaussian reaction-time distribution and electroencephalographic (EEG) brain-oscillatory measures in ADHD persisters, remitters and controls.A total of 110 adolescents and young adults with childhood ADHD  and 169 age-matched controls were compared on ex-Gaussian  indices and time-frequency EEG measures of power and phase consistency from a reaction-time task with slow-unrewarded baseline and fast-incentive conditions (‘Fast task’).Compared to controls, ADHD persisters showed significantly greater mu, sigma, tau, and lower theta power and phase consistency across conditions. Relative to ADHD persisters, remitters showed significantly lower tau and theta power and phase consistency across conditions, as well as lower mu in the fast-incentive condition, with no difference in the baseline condition. Remitters did not significantly differ from controls on any measure.We found widespread impairments in ADHD persisters in reaction-time distribution and brain-oscillatory measures. Event-related theta power, theta phase consistency and tau across conditions, as well as mu in the more engaging fast-incentive condition, emerged as novel markers of ADHD remission, potentially representing compensatory mechanisms in individuals with remitted ADHD. Attention-deficit/hyperactivity disorder (ADHD) often persists into adolescence and adulthood  and lower-level processes [e.g. attentional lapses measured by reaction-time variability (RTV)], tend to remain impaired in individuals whose ADHD persist (‘persisters’) , sophisticated ex-Gaussian analyses can decompose the reaction times (RTs) and separate extremely slow responses  from the mean (mu) and s.d. (sigma) of the normal RT distribution . Based on previous studies in ADHD samples, including our previous ex-Gaussian and time-frequency analyses using this task in a smaller-scale adult ADHD sample  after baseline: 110 had a diagnosis of combined-type ADHD based on DSM-IV in childhood (10 sibling pairs and 90 singletons) and 169 were control participants (76 sibling pairs and 17 singletons). Participants with ADHD were recruited from specialized ADHD clinics , we excluded six controls who met DSM-IV ADHD criteria based on the parent-reported Barkley Informant Rating Scale  and functional impairment (p < 0.01) than those not medicated. The proportion of participants on medication did not differ between ADHD persisters and remitters . A 48-h ADHD medication-free period was required prior to assessments. All participants and parents provided informed consent. Study procedures were approved by the London-Surrey Borders Research Ethics Committee (09/H0806/58).Among those with childhood ADHD, 87 (79%) continued to meet clinical (DSM-IV) levels of ADHD symptoms and impairment (ADHD persisters), whereas 23 (21%) were below the clinical cut-off (ADHD remitters). Fourteen ADHD remitters displayed ⩾5 symptoms of inattention or hyperactivity/impulsivity but no functional impairment. Groups were age-matched . Of the total, 84% and 82% of participants in the persisters and control groups were males, while 100% of remitted participants were male, as there were no females among ADHD remitters (online Supplementary Table S1). Childhood ADHD participants on medication at follow-up (47%) showed higher ADHD symptoms (The Diagnostic Interview for ADHD in Adults (DIVA) , using a 500-Hz sampling rate, impedances under 10 kΩ, and FCz as the recording reference. The electro-oculograms were recorded from electrodes above and below the left eye and at the outer canthi. EEG data were pre-processed using Brain Vision Analyzer 2.0 . EEG recordings were down-sampled to 256 Hz, re-referenced to the average of all electrodes (turning FCz into an active channel) and filtered using Butterworth band-pass filters . Electrical and movement artefacts were removed manually. Trials containing artefacts exceeding ± 100 μV or with a voltage step >50 μV were automatically rejected. Ocular artefacts were corrected using independent component analysis  and ITC (phase consistency) were measured in time windows and at scalp locations where they were maximal, following our previous study , condition (baseline v. fast-incentive) and group-by-condition interactions. For measures showing significant (p < 0.05) group-by-condition effects, we report pair-wise group comparisons in baseline and fast-incentive conditions separately. For measures showing significant main group effects but non-significant group-by-condition effects, we report pair-wise group comparisons collapsed across conditions. Additional tests followed up significant condition effects to examine within-group changes between conditions, and significant group-by-condition interactions to examine group differences on the change between conditions. Since theta and alpha ERSP indices were measured, respectively, at two scalp regions and two-time windows, we also tested three-way interactions with these additional factors. All models controlled for age and participants at the family level by including random effects to model the non-independence of observations of siblings within families in multilevel random-intercept models  investigating main effects of group . Because ADHD persisters had a lower IQ than remitters and controls (online Supplementary Table S1), all analyses were rerun controlling for IQ. As groups were not matched on sex, group analyses were further rerun excluding females . For between-group comparisons, we report both t Cohen, .We further examined ex-Gaussian and time-frequency measures in relation to ADHD remission with dimensional analyses (aim 2b). Random-intercept linear models were run in all participants with childhood ADHD to investigate the associations of ex-Gaussian and EEG measures significant in aim 1 (dependent variables) with parent-reported ADHD symptoms and functional impairment (independent variables). These models included symptoms-by-condition or impairment-by-condition interactions to test whether associations changed in the two conditions, and three-way interactions as appropriate for measures included in these analyses. Analyses were run clustering for family status and controlling, firstly, for age and sex and, secondly, also for IQ.Additional random-intercept linear models examined the associations between the ex-Gaussian (dependent variables) and EEG time-frequency measures (independent variables) that emerged as markers of remission from categorical analyses (aim 3). These analyses were run in the full sample and included an interaction between group and EEG measures to investigate if the strength of the associations differed between groups.In analyses comparing ADHD persisters and controls on all measures (aim 1), we applied multiple testing correction using the false discovery rate (FDR) to reduce type I errors. Analyses for aim 2 and 3 were only run on a restricted set of measures respectively, surviving multiple-testing correction in aim 1 and emerging as markers of remission in aim 2. We, therefore, did not apply further FDR correction and used a nominal significance level (0.05).Statistical analyses were run in Stata 15 . With the exception of beta , all other variables showed skewed distributions and were transformed to normal with a logarithmic transformation. Due to technical issues during data collection, RT and EEG data were not available for one ADHD persister and three controls. All participants with RT data had sufficient responses for ex-Gaussian analyses. Six ADHD persisters and five controls were excluded from EEG analyses in the baseline condition, and one control from both conditions, due to having <20 clean EEG segments.p-value threshold of p < 0.043 (see p > 0.1). All RT measures showed within-group decreases from the baseline to the fast-incentive condition (p < 0.001), while theta ERSP in both regions and theta phase consistency did not . Among measures showing the significant within-group change between condition, only mu showed a significant difference between groups in the degree of change between conditions (p < 0.001), with persisters changing less than controls (online Supplementary Table S2). Further details on condition and group-by-condition effects are reported in online Supplementary material.FDR corrections indicated a .043 see . A signi.043 see . Sigma, .043 see . No signADHD persister-control differences in mu became non-significant in both conditions when controlling for IQ (online Supplementary Table S3) and in the baseline condition in the male-only sample (online Supplementary Table S4).p < 0.05) but not in theta ERSP and phase consistency measures  Full details on condition and group-by-condition effects are reported in online Supplementary material.Analyses were restricted to measures that survived multiple testing corrections in the analysis of aim 1. In categorical analyses aim 2a) on ADHD remitters, persisters and controls, remitters did not significantly differ from controls on any other measure . Mu, whia on ADHDThe ADHD remitter-persister differences in mu in the fast-incentive condition became non-significant when controlling for IQ (online Supplementary Table S3) and in the male-only sample (online Supplementary Table S4).p > 0.1). Mu showed a significant interaction between functional impairment and condition (p = 0.024): functional impairment was associated with mu in the fast-incentive condition but not in the baseline condition (p < 0.1). When also controlling for IQ, the association of functional impairment with mu and tau became non-significant (online Supplementary Table S5).Dimensional analyses (aim 2b) in participants with childhood ADHD, controlling for sex and age, showed non-significant associations of ADHD symptoms with all ex-Gaussian and time-frequency measures . These aondition . Functioondition , as the We examined the association of mu in the baseline and fast-incentive condition separately and tau across conditions with centro-parietal theta ERSP and phase consistency, as these measures emerged as markers of remission in categorical analyses. Mu showed a significant negative association with theta ERSP and theta phase consistency in both conditions (online Supplementary Table S6), while the interactions between group and theta ERSP or theta phase consistency were non-significant, indicating that the groups did not differ on the strength of these associations. Similarly, tau across conditions showed a significant negative association with theta ERSP and phase consistency, while the interactions with the group were non-significant (online Supplementary Table S6).In a first large-scale investigation to examine ex-Gaussian and EEG time-frequency markers in adolescents and adults with childhood ADHD, we observed widespread impairments in ADHD persisters, compared to controls, in ex-Gaussian measures of response variability (sigma and tau) and response speed (mu), and in neurophysiological markers of neural variability (theta phase consistency) and attention allocation (theta ERSP). We further identified several potential new markers of remission, on which ADHD remitters were comparable to controls but significantly different from persisters: mu, tau, centro-parietal theta ERSP and theta phase consistency. The ex-Gaussian and EEG markers of remission were significantly associated with each other, indicating they may reflect partly overlapping processes. The measures emerging as potential markers of remission represent possible compensatory mechanisms in ADHD remitters, extending our previous findings on more traditional cognitive-performance and ERP measures , consistent with our hypotheses and previous ex-Gaussian studies in individuals with ADHD  level, ADHD remitters were comparable to controls but showed significantly higher centro-parietal theta power and theta phase consistency compared to persisters, suggesting that these variables are potential markers of remission. Yet, they were not dimensionally associated with ADHD symptoms or functional impairment, suggesting that the pattern of remission for these variables should be investigated further in future research. In further analyses controlling for IQ, results for tau and centro-parietal theta power were unchanged, indicating they are markers of remission independently of IQ, while results for other measures became non-significant. Taken together, the current results provide novel evidence that markers of attention-vigilance processes, including ex-Gaussian measures of response speed (mu), variability of long responses (tau) and EEG power and phase consistency in theta oscillations, may be implicated in ADHD remission, consistent with previous findings on RTV measured as In examining the association between the identified ex-Gaussian and EEG markers of remission, we found a significant association of evoked theta power and theta phase consistency with mu and tau. These results indicate that alterations in theta oscillations may partly underlie atypical response speed and variability of long responses. Future studies should replicate these associations and further investigate their possible underlying etiological processes. Of note, while all groups showed significant improvements in ex-Gaussian measures from the baseline to the fast-incentive condition, in line with previous findings on RTV   conditions, while measures of neural markers of phase variability (i.e. lower theta phase consistency) and attention allocation , as well as attentional lapses (tau), emerged as markers of remission independently of the condition. These measures may point to potential compensatory mechanisms linked to remission of ADHD from childhood to adulthood, extending our previous findings on more traditional measures of attention-vigilance processes (Cheung et al.,


This study aimed to examine social gradients in ADHD during late childhood (age 9–11 years) using absolute and relative relationships with socioeconomic status exposure  during early childhood (<5 years) in seven cohorts from six industrialised countries .Secondary analyses were conducted for each birth cohort. Risk ratios, pooled risk estimates, and absolute inequality, measured by the Slope Index of Inequality (SII), were estimated to quantify social gradients in ADHD during late childhood by household income and maternal education measured during early childhood. Estimates were adjusted for child sex, mother age at birth, mother ethnicity, and multiple births.All cohorts demonstrated social gradients by household income and maternal education in early childhood, except for maternal education in Quebec. Pooled risk estimates, relating to 44,925 children, yielded expected gradients , middle 1.42, high (reference); maternal education: low 2.13, middle 1.42). Estimates of absolute inequality using SII showed that the largest differences in ADHD prevalence between the highest and lowest levels of maternal education were observed in Australia (4% lower) and Sweden (3% lower); for household income, the largest differences were observed in Quebec (6% lower) and Canada .Findings indicate that children in families with high household income or maternal education are less likely to have ADHD at age 9–11. Absolute inequality, in combination with relative inequality, provides a more complete account of the socioeconomic status and ADHD relationship in different high-income countries. While the study design precludes causal inference, the linear relation between early childhood social circumstances and later ADHD suggests a potential role for policies that promote high levels of education, especially among women, and adequate levels of household income over children’s early years in reducing risk of later ADHD. Attention-Deficit/Hyperactivity Disorder (ADHD) is amongst the most common mental health disorders in childhood. Early studies of the worldwide prevalence of ADHD reported rates between 8–12% . ADHD caabsolute measures of inequality in ADHD. Indeed, few studies compare the association of ADHD with SES using compatible measures in more than one country; one cross-sectional study reported on the association in five European countries . Researchers reviewed and considered large, population-based birth cohorts from their respective countries that had comparable data aligned with the UNICEF child well-being outcomes. We acknowledge potential bias in selection of participating countries due to data accessibility; however, all included countries are high income with contrasting social policies and distributed along the gradient of country-level measures of income inequality. In addition, the birth cohort enrollments were largely conducted during the same time period , with the exception of the US cohort . The impact of low socioeconomic status in early childhood on adult health has been extensively studied [The Elucidating Pathways Of Child Health inequalities (EPOCH) study draws on data from seven birth cohort studies from six countries to explore the pathways from early SES exposure to child health outcomes at age 9–11 years. The EPOCH study, funded by the Canadian Institutes of Health Research (CIHR), was conceived and planned by researchers in the International Network for Research on Inequalities in Child Health (INRICH) [ studied , but les studied . The EPOData were extracted from seven birth cohorts participating in the EPOCH study: UK Millennium Cohort Study (MCS); Alla Barn i Sydöstra Sverige ; Quebec Longitudinal Study of Child Development (QLSCD); Longitudinal Study of Australian Children B cohort (LSAC B); Generation R, Rotterdam, The Netherlands (GenR); National Longitudinal Study of Children and Youth, Canada (NLSCY); and National Longitudinal Study of Youth, USA (US NLSY). Concordia University Human Research Ethics Committee certified the ethical acceptability for EPOCH’s secondary data use (#2011028). All original birth cohorts complied with the ethical standards of their relevant institutional and/or national committees and with the Helsinki Declaration of 1964, and its later amendments. As part of the original cohort methodology, information summarizing what participation would involve was provided in both oral and written form; explicit informed written consent was obtained for all participants . Cohort profiles and cohort technical report references are shown in th percentile) and the timeframe was limited to past 6 months. The US cohort (US NLSY) recorded ‘limiting’ hyperkinesis/hyperactivity/attention deficit disorder, whereas all other cohorts recorded ‘ADHD ever diagnosed’.Attention Deficit Hyperactivity Disorder (ADHD) was measured in late childhood. Household income and maternal education are two of the most common measures of SES in observational studies . HousehoCohort surveys defined ethnicity using ‘Majority / Minority’ or ‘Born inside country / Born outside country’ designations; Mother Ethnicity was dichotomized using these designations. Aboriginal  and First Nation (Quebec) mothers were classified as ‘Born in country’ in these cohorts. Additional confounding variables included maternal age at child’s birth, child sex, and multiple births. The Quebec cohort did not include multiple births as it had singleton births only.We estimated unweighted frequencies for all variables. We estimated unadjusted and adjusted Risk Ratios (RRs) using a generalized linear model with a log link and robust variance estimation  was estimated for each population. SII estimates the absolute advantage of higher income or education, interpreted as the absolute difference in percentage of ADHD from the highest to the lowest level in the social hierarchy. SII is a regression-based index that accounts for the socioeconomic distribution of the population, excluding the size of socioeconomic groups as a source of variability in estimating the magnitude of inequalities in health [After Risk Ratios were estimated, they were pooled using the Meta for package in R . We estin health . DiffereSamples for all cohorts were drawn from whole populations and were broadly representative of the target populations . The desUnadjusted Relative Ratios  of ADHD by income tertiles, the three categories of maternal education, and confounding variables  are shown in 2 middle income 47.9%, middle education 47.9%, low income 51.3%, low education 79.8%).  and maternal education . Heterogeneity ranged from moderate to high ; while improvement in household income would lead to the largest reductions in Quebec and Canada . These findings may seem to contradict the impression given by Relative Risks that the most severe inequalities by income are found in the UK and The Netherlands . However, while the poorest in these countries are at higher risk of ADHD compared to the wealthiest, the lower prevalence of ADHD means lower absolute risk at the population level. On the other hand, income changes would result in larger reductions in Canada where the prevalence of ADHD is higher. It should also be noted that while Relative Risks were adjusted for confounders , the SIIs were not. Click here for additional data file.S1 Table(CSV)Click here for additional data file.