Attention deficit/hyperactivity disorder (ADHD) is a highly heritable neurodevelopmental disorder. We performed a transcriptome-wide association study (TWAS) using the latest genome-wide association study (GWAS) meta-analysis, in 38,691 individuals with ADHD and 186,843 controls, and 14 gene-expression reference panels across multiple brain tissues and whole blood. Based on TWAS results, we selected subsets of genes and constructed transcriptomic risk scores (TRSs) for the disorder in peripheral blood mononuclear cells of individuals with ADHD and controls. We found evidence of association between ADHD and TRSs constructed using expression profiles from multiple brain areas, with individuals with ADHD carrying a higher burden of TRSs than controls. TRSs were uncorrelated with the polygenic risk score (PRS) for ADHD and, in combination with PRS, improved significantly the proportion of variance explained over the PRS-only model. These results support the complementary predictive potential of genetic and transcriptomic profiles in blood and underscore the potential utility of gene expression for risk prediction and deeper insight in molecular mechanisms underlying ADHD. Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by inappropriate levels of inattentiveness, hyperactivity, or impulsivity that affects around 2.6% of persistent adult ADHD and 6.8% of symptomatic adult ADHD . ADHD inTwin and family studies show a strong genetic component underlying the disorder, with a heritability of 76% , 4. RecePPP1R16A and B4GALT2 as top genes [The SNP-based heritability for ADHD estimated so far is 0.14  and the op genes .Given that a substantial proportion of GWAS association signals demonstrate gene regulation effects , risk scIn the present study, we ran a multi-tissue TWAS on the latest GWAS-MA on ADHD performed so far , and forhttps://github.com/hakyimlab/MetaXcan) [https://gtexportal.org/home/samplingSitePage) both cortex and frontal cortex correspond to the same brain area, right cerebral frontal pole cortex, sampled and collected using different techniques. We considered default settings in S-Predixcan and linkage disequilibrium (LD) estimates from the European subset of the 1000 Genomes Phase 3 reference sample with the precalculated covariances. As TWAS results from different brain areas were highly correlated , we applied Bonferroni correction considering the number of genes tested within each of the 14 expression reference panels separately to account for multiple testing.TWAS was performed with S-PrediXcan (etaXcan)  using suetaXcan) , and SNPetaXcan) . We usedetaXcan) . AccordiSummary statistics from TWAS in DLPFC described in Demontis et al. 2022 were also used in the TRS analysis . In brieEnrichment analyses on gene-sets from the Molecular Signatures Database (MSigDB v6.2), including Gene ontology (GO), KEGG, Reactome, miRNA targets and GWAS Catalog, were performed on genes nominally associated with ADHD in each TWAS using a hypergeometric test with the GENE2FUNC module of FUMA and considering all genes from the TWAS as background . EnrichmP < 0.05 in the GWAS-MA of Demontis et al. [https://github.com/mulinlab/CAUSALdb-finemapping-pip#4; [torus software [https://github.com/xqwen/fastenloc.Gene locus-level colocalization probability (GLCP) for significant genes identified in TWAS was performed using fastENLOC and only genes with a GLCP ≥ 0.5 were considered further , 34. Firg-pip#4; ) which ig-pip#4; , PAINTORg-pip#4;  and CAVIg-pip#4; . We usedsoftware . Finallysoftware . ColocalTRSs and PRS were constructed in an in-house sample of 222 medication-naïve adult ADHD cases  and 269 controls . All subjects were from European ancestry, which was confirmed through principal component analysis (PCA) using genetic data. Clinical assessment was conducted by structured interviews and self-reported questionnaires as previously described , based iThe control sample consisted of unrelated blood donors matched by sex with the clinical group. Individuals with ADHD symptomatology were excluded retrospectively from the control sample under the following criteria: (1) diagnosed with ADHD previously and (2) answering positively to the life-time presence of the following ADHD symptoms: (a) often has trouble in keeping attention on tasks, (b) usually loses things needed for tasks, (c) often fidgets with hands or feet or squirms in seat, and (d) often gets up from seat when remaining in seat is expected. The study was approved by the Clinical Research Ethics Committee (CREC) of Hospital Universitari Vall d’Hebron, methods were performed in accordance with the relevant guidelines and regulations and written informed consent was obtained from all subjects before inclusion in the study.OligoR [arrayQualityMetrics [empiricalBayesLM algorithm included in WGCNA R package [TRSs were constructed from transcriptomic profiles in PBMCs separated by a Ficoll density gradient method immediately after blood extraction. Total RNA was isolated using Qiazol Lysis reagent and the RNAeasy Midi Kit . RNA integrity and concentration were assayed by 2100 Bioanalyzer . RNA was retrotranscribed using the Ambion WT Expression Kit . The cDNA was subsequently fragmented, labelled, and hybridized with the GeneChip WT Terminal Labelling and Hybridization Kit . Samples were hybridized to the GeneChip Human Gene 1.1 ST 96-Array plate (Affymetrix), covering a total of 36,079 transcripts that correspond to 21,014 genes. The array processing and data generation were assessed using the Gene Titan Affymetrix microarray platform. Raw data were pre-processed as previously described . In brieOligoR , sample yMetrics  and tran package . Raw datP-value thresholds  and tested for association with ADHD using a logistic regression model in R, with sex, age, GWAS wave and the 10 first principal components based on GWAS data as covariates. For the best P-value threshold in each tissue, the empirical P-value was calculated by permuting the target phenotype 10,000 times and repeating the TRS analysis on each set of permuted phenotypes [seudo-R2 were calculated using the Lee’s formula [P-value threshold at each tissue, which resulted in 11 independent tissues out of 14. To account for multiple testing, we used the Sidák correction  for 11 independent tests. To discard an artificial inflation of the results due to the inclusion of different genes at the same genomic loci under the control of the same eQTL in the TRS construction, a sensitivity analysis was performed by calculating TRSs considering a single gene per locus: the one showing the lowest P-value in the TWAS at each genomic loci (defined by genes < 500 kb apart). Colocalization analyses were conducted using the same strategy described in the TWAS section, selecting genetic variants within a genomic window of 1 Mb upstream and 500 kb downstream from each of the genes in the best P-value threshold of TRSs associated with ADHD after multiple comparison corrections and sensitivity analyses.TRSs were calculated as the sum of the standardized expression of each gene weighted by its signed Z-score value from TWAS results on the different expression reference panels. TRSs per tissue were constructed by selecting genes under several TWAS enotypes . Pseudo- formula  and cons formula  considern = 163) and Infinium™ Global Screening Array-24 v2.0 (n = 328) Illumina arrays. Polygenic scoring was conducted using the summary statistics from the largest GWAS-MA on ADHD in 38,691 individuals with ADHD and 186,843 controls [https://github.com/getian107/PRScs) [pseudo-R2 was calculated using the Lee’s formula [lmtest R-package was used to compare the goodness of fit of the model that includes the PRS and covariates with the model that also includes the TRS.DNA samples were genotyped in two genotyping waves using Omni2.5 (7/PRScs) . The Eur7/PRScs)  and it w formula  and consP < 0.05) with ADHD in at least one tissue, including 2234 that were significant in more than one and 94 in all of them. These genes were enriched for genes previously associated with social interaction , psychiatric disorders  and body fat distribution, among others  showed transcriptome-wide significance, of which 28 were significant in more than one tissue, all of them showing consistent direction of the effect Fig. . Of themues Fig. . From thues Fig. .Fig. 1ObHELZ, GIGYF2, SLC25A22 and PNPLA2), with PNPLA2 and HELZ also differentially expressed in the whole blood TWAS and with consistent direction of effect , followed by REELD1 in 8 tissues (GCLP range: 0.615–0.818), and LSM6 in 7 tissues  being cortex the most significant one , frontal cortex , putamen , caudate basal ganglia  and amygdala , with subjects with ADHD having a significantly higher ADHD-TRS than controls in all of them (Table P < 7.1e-04 and 0.22≤ r  ≤ 0.62) , with TRSs from cortex showing the best results and reaching a pseudo-R2 of 0.052 in the combined model  for the TRSs significantly associated with ADHD after multiple comparison corrections and sensitivity analyses. We found that, despite reducing the number of genes included, the association signal remained in all four tissues and that the predictive performance improved for TRScol in three of them, amygdala, caudate basal ganglia and frontal cortex , remained significant in the sensitivity analysis considering only the most significant gene per locus (Supplementary Table P = 6.9e-05), reaching a pseudo-R2 of 0.046 in the combined model  have been associated with psychiatric disorders , cognitive function  or ADHD comorbid somatic traits like obesity or extreme body mass index, suggesting a potential importance of these genes in the context of ADHD and its comorbid conditions.Genes included in the best-performing TRSs provide additional information to prioritize candidates for further investigation of biological mechanisms underlying ADHD. For example, all TRSs associated with ADHD include three genes, isorders , 60–62. lifespan , which cntiation , which cP-value thresholds showed clear evidence of better performance and stronger associations with ADHD, a pattern similar to the one observed for TRS in amyotrophic lateral sclerosis based on observed expression data [P-value thresholds are used [For most of the brain tissues, the TRSs constructed under stricter TWAS ion data . This coare used , 26. Theare used , 30 , orare used .In agreement with a previous study in depression , TRSs weApart from TWAS, other methods have been designed to prioritize likely causal genes by combining genomic, transcriptomic, and other regulatory and functional information including colocalization methods, that use a Bayesian framework to infer whether a regulatory SNP is also responsible for the association with a trait of interest, or summary-based Mendelian randomization (SMR), that combines GWAS and eQTL data to prioritize target genes with evidence for causal or pleiotropic effects. In order to narrow down the number of genes identified by TWAS and included in the TRS analyses, we assessed colocalization and found that the signal for 14 out of the 56 genes identified in the TWAS was supported by the colocalization analyses. This low convergence between TWAS and colocalization signals is consistent with other studies  and may The results of the present study, however, should be interpreted in the context of several strengths and limitations: (i) Due to linkage disequilibrium, a single genetic variant might point to several TWAS associations in the same locus. For that reason, sensitivity analysis using only the most significant gene in each locus was performed to discard artefactual inflation in the TRS analysis. However, considering that genes located in the same region are not necessarily involved in the same biological processes and given the difficulty to determine which ones really contribute to the phenotype, enrichment analysis were performed including all significant genes from TWAS, which could have potentially biased these results. (ii) In this study, the PRS failed to approach the performance of the two best-performing TRSs , which suggests that TRSs may potentially outperform PRSs and provide a closer physiological picture of the disorder; (iii) While TRSs differences may reflect distinct molecular pathways captured by each of the tissues considered, the variability in sample size between the expression reference panels may limit our ability to compare TRSs results across tissues. Besides, in the present study we used multiple-tissue TWAS. Although this method shows improved prediction over single tissue approaches and it underscores specific genes overlapping between tissues , additioIn conclusion, we found association between ADHD and TRSs in PBMCs constructed using TWAS results from multiple brain areas implicated in the disorder, showing that individuals with ADHD carry a higher burden of TRSs than controls. TRSs combined with PRS increased significantly the proportion of variance explained of ADHD over genome-wide PRS alone, which points to the complementary predictive potential of genetic and transcriptomic signatures and support that integrating biological information may benefit standard PRS prediction approaches. Through this approach that leverages GWAS summary statistics, multi-tissue cis-eQTL reference panels and target sample gene expression data we underscore the potential of utilizing transcriptomic information to improve risk prediction and provide deeper insight into the molecular mechanisms underlying ADHD.Supplementary FiguresSupplementary Tables


Specifically, biased interpretation of social information can result in interpersonal difficulties. Cognitive biases are prevalent in psychosis, but no previous study has investigated whether the type and severity of cognitive biases differ between subjects experiencing first-episode psychosis (FEP) with . Cognitive biases were assessed by the Cognitive Biases Questionnaire for Psychosis (CBQp). CBQp scores of FEPs groups were compared with those of healthy controls (HCs) with an analysis of covariance. Spearman correlation analysis explored associations between CBQp scores and psychopathology.− [42 (37–48)] and HCs [38 (35.5–43)]. CBQp scores did not differ between FEP-ADHD-and HCs when adjusted for age and sex. After controlling for clinical differences, Intentionalising  and Emotional Reasoning biases  were more strongly associated with FEP-ADHD+ than FEP-ADHD−. Cognitive biases were significantly correlated with positive psychotic symptoms in both groups but only with depressive symptoms in FEP-ADHD−  and with poor functioning in FEP-ADHD+ .Thirty-one FEPs met the criteria for childhood ADHD and reported significantly more cognitive bias [median (interquartile range): 47 (38–56)] than FEP-ADHD+ patients. FEP-ADHD+ patients may be a particularly vulnerable group in which metacognitive targeted interventions are needed.Cognitive bias severity increased from HCs to FEP-ADHD-patients to FEP-ADHD The cognitive model of psychosis suggests that psychotic symptoms may arise because of biased information processing . In accoAttention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder affecting 3–7% of school-age children . It is c+ patients) being the most impaired. Compared to FEP-ADHD−, FEP-ADHD+ were more frequently men, showed a worse antipsychotic response and had a higher risk of drug consumption. The present study builds on this previous study by aiming to determine whether the type and severity of different cognitive biases (measured with the CBQp) differ between FEP patients with and without c-ADHD relative to a control group. In addition, we aimed to investigate the relationship between cognitive bias and psychopathological symptoms in both FEP-ADHD+ and FEP-ADHD-patients.In addition to the genetic overlap between some risk alleles , psychot“onset of full psychotic symptoms within the last 12 months”; and less than 6 months of antipsychotic treatment. The exclusion criteria were as follows: psychosis induced by substances or other medical conditions, intellectual disability, severe head injury or a lack of fluency in Spanish. During the target period, 152 FEP subjects were referred to the EIP. Six subjects refused to participate and 15 did not fulfil the inclusion criteria. Of the 133 FEP subjects included in our previous study, 11 did not complete the CBQp. Therefore, the final sample consisted of 122 FEP subjects, 31 FEP-ADHD+ subjects and 91 FEP-ADHD−subjects. The sample of HCs (N = 26) was drawn from our previous validation study of the CBQp in the Spanish language  at the University Hospital Institut Pere Mata of Reus, Spain, from January 2015 to July 2019 fulfilling the following inclusion criteria: age between 14 and 35 years; FEP, defined as the language .Ethical approval was obtained by the Committee for Ethical Clinical and Pharmacological Investigation of the Pere Virgili Research Institute (IISPV). After a complete description of the study was given to the subjects, written informed consent was obtained.“at least several times a week.” The severity of psychotic symptoms was assessed using the Positive and Negative Syndrome Scale (PANSS)  equivalents in mg/day . We defi (PANSS)  and the  (PANSS)  and the  (PANSS) . Finally (PANSS) .The Spanish version of the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, 4th edition (DSM-IV) for Axis I disorders (SCID-I)  confirme+ otherwise; they were categorized as FEP-ADHD−.A child and adolescent psychiatrist blind to clinical assessments administered the Spanish version of the Diagnostic Interview for ADHD in Adults (DIVA) . AdditioThe CBQp  has been+, FEP-ADHD-and HC groups using the chi-squared test with Yates’ correction or Fisher’s exact test for discrete variables and one-way analysis of variance (ANOVA) followed by post hoc Tukey pairwise comparisons. Differences in clinical variables between the two FEP groups were explored by the chi-squared test with Yates’ correction or Fisher’s exact test for discrete variables and Student’s t test or the Mann–Whitney U test for continuous variables.Demographic data were compared among the FEP-ADHDpost hoc Tukey pairwise comparisons. Significant group differences (at p < 0.05) were controlled by age and sex by subsequent univariate analysis of covariance (ANCOVA).First, differences in cognitive bias  between the three groups were analysed by between-subject univariate analysis of variance (ANOVA) followed by + and FEP-ADHD-groups differed at p < 0.10. Finally, Spearman correlation analyses were used to separately explore associations between the CBQp total score, TE and SA theme scores, cognitive bias scores and psychopathological symptoms in each clinical group.Second, differences in cognitive bias scores between clinical groups were analysed while controlling for the effects of clinical variables on which FEP-ADHDAll analyses were conducted using IBM SPSS for Windows, version 20.0 .Thirty-one FEP subjects (25.4%) fulfilled the criteria for c-ADHD: 35.4% as the inattentive subtype, 25.8% as the hyperactive–impulsive subtype, and 38.8% as the combined subtype. Ten out of 31 ADHD subjects (32.25%) had been previously diagnosed with c-ADHD in a child and adolescent mental health unit, but only three were taking treatment for ADHD: one was on methylphenidate, one on guanfacine and one on atomoxetine.+ group (90.3%). The severity of clinical variables did not differ between the FEP groups, but FEP-ADHD+ subjects used tobacco and cannabis more frequently and were treated with a higher dose and a greater number of antipsychotics than FEP-ADHD−subjects.As it can be seen in Post hoc pairwise comparisons showed that FEP-ADHD+ patients scored significantly higher than HCs on all scores. After adjusting for age and sex, these differences remained significant except for the C bias. FEP-ADHD-patients exhibited significantly higher scores than HCs on the CBQp total score, the TE theme and the ER bias; however, after adjusting for age and sex, these differences were no longer significant.As shown in p < 0.10, that is, after adjusting for sex, years of education, tobacco and cannabis use and antipsychotic dose , TE  and AP  themes and Int  and RE  biases.The two clinical groups were further directly compared after adjusting for sociodemographic and clinical differences at a threshold of dose See . Compare− group. In the FEP-ADHD+ group, the CBQp total score was also correlated with lower GAF scores. Regarding the TE and AP themes, TE was correlated with the PANSS-P scores in both clinical groups and with the PANSS-G and CDSS scores in the FEP-ADHD− group. The AP theme score was associated with PANSS-P and PANSS-G scores in only the FEP-ADHD− group.In the correlation analyses , the CBQ+ patients. Positive correlations were found between the C and DT bias scores and the PANSS-P score in both clinical groups and between the CDSS score in the FEP-ADHD− group. The DT bias score also correlated with the PANSS-G score in the FEP-ADHD− group. JTC correlated with PANSS-P score in FEP-ADHD+ patients and with general PANSS-G scores, and CDSS score in FEP-ADHD-patients. Finally, the ER bias score was associated with positive PANSS-P and PANSS-G scores in only in the FEP-ADHD− group.Regarding cognitive biases, Int was positively correlated with the PANSS-P score in FEP-ADHD-patients and with worse GAF scores in FEP-ADHD+ and FEP-ADHD− subjects relative to HCs. Our results therefore must be considered preliminary and caution is required when interpreting the subsequent findings.As far as we know, this is the first study to assess and compare the severity of cognitive bias (measured by the CBQp) between FEP-ADHD+ group: [median (interquartile range) 47 (38–56)], to the FEP-ADHD− group [42 (37–48)] to the HC group [38 (35.5–43)]. The FEP-ADHD+ group presented significantly higher scores not only on the total score but also in the CBQp themes of TE and AP than both the FEP-ADHD-and HC groups. Regarding specific cognitive biases, FEP-ADHD+ patients exhibited greater Int, DT, C and ER bias scores than HCs, while FEP-ADHD-patients presented only greater ER bias scores than HCs. The FEP-ADHD+ group therefore showed the most marked differences from HCs. However, the HC group used as a control group was not specifically recruited for the present study but was used in our previous study to validate the questionnaire in the Spanish population  (We found a gradient of cognitive bias severity from the FEP-ADHDpulation . Consequds task) , JTC biads task)  and FEP ds task)  as well ds task) . Howeverds task) . One posds task) . Therefods task) . Unfortu+ patients showed more severe cognitive biases than FEP-ADHD-patients, even after controlling for clinical and sociodemographic differences. Apart from scoring higher on the two themes (AP and TE), it is particularly interesting that the FEP-ADHD+ group exhibited more DT and significantly more Int and ER than the FEP-ADHD− group. Consistent with these findings, in their original validation report comparing subjects with psychosis with HCs and depressed subjects, Peters and colleagues . The Int bias refers to the implicit and automatic inclination to interpret human actions as intentional and to think that negative actions toward oneself were committed on purpose . In the ADHD literature, research on interpretation bias is very scarce. There is inconsistent evidence that hostile attribution bias (HAB) with ambiguous situations and ambiguous faces occurs more frequently in children and adults with ADHD than in HCs  was developed by Moritz S and colleagues two decades ago to address problems related to cognitive biases and social cognition in psychosis . Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.VS-G designed the current study. AC, MS, VS-G, and LC contributed to the acquisition of clinical data. NM was blinded to the results of the clinical assessments and performed the ADHD interviews. VS-G, AG-Z, and EV undertook the statistical analysis and all authors took part in the interpretation of the results. VS-G wrote the first draft of the article. All authors critically revised the first draft and provided their contributions and have approved the final manuscript.This work was supported by the AGAUR Agència de Gestió d’Ajuts Universitaris i de Recerca (SGR2017_444).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.


The HR group exhibited a more severe symptom profile, including higher mania and dysregulation scores, compared to the LR group. For subcortical volumes, the HR group exhibited smaller bilateral thalamic, hippocampal, and left caudate nucleus volumes compared to both LR and HC, and smaller right caudate nucleus compared with LR. No differences were found between LR and HC groups. For cortical surface area, the HR group exhibited lower parietal and temporal surface area compared with HC and LR, and lower orbitofrontal and superior frontal surface area compared to LR. The HR group exhibited lower left anterior cingulate surface area compared with HC. LR participants exhibited greater right pars opercularis surface area compared with the HC. Some cortical alterations correlated with symptom severity ratings. These findings suggest that ADHD in youth with a BD family history is associated with a more a severe symptom profile and a neuroanatomical phenotype that distinguishes it from ADHD without a BD family history.Although attention-deficit/hyperactivity disorder (ADHD) and a family history of bipolar I disorder (BD) are associated with increased risk for developing BD, their neuroanatomical substrates remain poorly understood. This study compared cortical and subcortical gray matter morphology in psychostimulant-free ADHD youth with and without a first-degree relative with BD and typically developing healthy controls. ADHD youth (ages 10-18 years) with  or without  a first-degree relative with BD and healthy comparison youth (HC) were enrolled. High-resolution 3D T1-weighted images were acquired using a Philips 3.0 T MR scanner. The FreeSurfer image analysis suite was used to measure cortical thickness, surface area, and subcortical volumes. A general linear model evaluated group differences in MRI features with age and sex as covariates, and exploratory correlational analyses evaluated associations with symptom ratings. A total of  The onset of bipolar I disorder (BD) often occurs during late childhood and adolescence , and is The initial onset of both ADHD and BD commonly occurs during a developmental period associated with progressive cortical and subcortical structural maturational changes –19. ConsPrevious studies have linked genetic risk for BD to variations in brain structure , 32, andThree groups of psychostimulant-free youth (10-18 years) were recruited: 1) youth with ADHD and at least one biological parent or sibling with BD , 2) youth with ADHD and no first- or second-degree relative with a mood or psychotic disorder , and 3) typically developing healthy comparison youth (HC) with no personal or family history of a DSM-5 Axis I psychiatric disorder. Family Interview for Genetics Studies (FIGS)  was usedAll participants and their legal guardians provided written informed consent/assent. The research protocol was approved by the Institutional Review Board of the University of Cincinnati, and the study was registered on clinicaltrials.gov (NCT02478788).ADHD symptom ratings were obtained using the clinician-administered Attention-Deficit Hyperactivity Disorder Rating Scale (ADHD-RS) , and ina3, number of axial slices = 160, gap between slices = 0. After the MR scan, data with excessive head movements, brain lesions or obvious artifacts were discarded. Images were inspected by neuroradiologists who made decisions about excessive motion artifact for scan inclusion.High-resolution 3D T1-weighted images were collected using a Philips Ingenia 3 T MRI scanner with a 32-channel head coil. Sequence parameters were as follows: TR = 8.1 ms, TE = 3.7 ms, flip angle = 8°, field of view = 256 × 224, matrix = 256 × 224, voxel size = 1 × 1 × 1mmhttp://surfer.nmr.mgh.harvard.edu/) to obtain unbiased estimates of morphometric measures, including surface area, cortical thickness, and subcortical volumes. Briefly, the procedure includes intensity normalization; removal of non-brain tissue; segmentation of cortical grey, subcortical white and deep grey matter structures; and triangular tessellation of the grey/white matter interface and white matter/cerebrospinal fluid (CSF) boundary . All surface reconstructions were visually inspected and, where necessary, corrected manually using editing tools provided by FreeSurfer, including corrections of erroneous skull stripping and white matter and grey matter segmentations. Individual reconstructed surfaces were smoothed, transformed and resampled onto a common standard space. Regions based on the Desikan-Killiany atlas [All structural MRI scans were processed on the same workstation using FreeSurfer version 6.0.0 . Extracted values for cortical thickness, surface area and subcortical volumes of all participants were fitted into three independent analysis of covariance (ANCOVA) models as independent variables, with group  as a fixed factor. As age and gender are known to impact brain morphometry, they were used as covariates. All group differences were deemed significant with a threshold level of the false discovery rate (FDR) corrected p < 0.05. Post-hoc two-sample comparisons were performed if ANCOVA was significant. For all morphological measures that differed statistically between the LR and/or HR groups and HC, we fit a regression model including each morphological measures as outcome, and each clinical rating, group and their interaction term  as predictors. Whether the clinical association between two ADHD groups differed significantly was also determined by the significance level of regression coefficients of the interaction term (p < 0.01 uncorrected). Additionally, we performed partial correlation analyses (correcting for age and sex) to examine correlations between morphological measures and clinical scores. A two-sided p value < 0.05 was used as the criterion to indicate a statistically significant difference.Tests for group differences in demographic and descriptive variables were performed using SPSS (IBM SPSS Statistics V23.0). We conducted one-way analysis of variance (ANOVA) and chi-square tests to compare continuous and categorical variables across groups. Independent-sample t-tests and chi-square tests were performed for pairwise comparisons. Statistical analyses of the morphometric measures were performed using n = 142 adolescents  were included in the analysis . Sociodemographic and clinical characteristics are presented in Table ps < 0.0001). Compared with the LR group, the HR group had higher ADHD-RS hyperactivity/impulsivity subscale scores (p = 0.032), YMRS (p = 0.004), and CGI-S (p = 0.014) total scores, and higher CBCL total score (p = 0.002) and internalization (p = 0.010), externalization (p = 0.002), and dysregulation (p = 0.047) subscale scores.A total of p < 0.05, FDR corrected). The LR group exhibited a larger right pars opercularis compared with the HC group . Group differences in cortical thickness did not survive correction for multiple comparisons. For subcortical volumes, the HR group exhibited smaller bilateral thalamus, caudate nuclei, and hippocampal volumes compared with the LR group as well as the HC group with the exception of the right caudate, and there were no significant differences between LR and HC groups . In an exploratory follow-up analysis, we examined associations among regional measurements with significant differences between the LR and HR groups. This involved computing the correlation matrices of these relations separately for volume and surface area measurements. These matrix plots are presented for heuristic purposes in the For cortical surface area, the HR group exhibited smaller left postcentral, bilateral inferior parietal, right precuneus extending to the right PCC, right left temporal pole, right fusiform, and left ACC surface area compared with the HC group, and smaller bilateral lateral orbitofrontal, left superior frontal, left postcentral, bilateral inferior parietal, right precuneus extending to the right PCC, left temporal pole, right fusiform, and right parahippocampal cortical surface area compared with the LR group , YMRS total scores correlated negatively with the surface area both left inferior parietal  and right precuneus extending to the right PCC , and ADHD-RS inattention subscale scores correlated positively with left lateral orbitofrontal surface area     Fig. , CBCL toed) Fig. , and CBCed) Fig. . In eachThe primary aim of this study was to compare neuroanatomical features in psychostimulant-free ADHD youth with (high-risk) and without (low-risk) a BD family history. In agreement with our hypothesis, we found that HR youth exhibited regional subcortical and cortical deficits compared with LR youth as well as healthy youth. Furthermore, the LR group did not exhibit any significant regional subcortical and cortical deficits compared with HC. The HR group also exhibited a more severe symptom profile, including higher ADHD hyperactivity/impulsivity subscale scores, mania scores, and dysregulation subscale scores, compared with the LR group. Among both LR and HR groups, regional cortical morphological differences correlated with some symptom ratings including mania and dysregulation. Taken collectively, these findings suggest that ADHD in conjunction with BD family history is associated with a more a severe symptom profile and a more pervasive pattern of morphological deficits than are ADHD participants without a BD family history.In contrast to prior meta-analyses , 21, lowConsistent with our hypothesis, our findings demonstrate that ADHD youth with, but not without, a BD family history exhibit widespread cortical and subcortical gray matter deficits compared with healthy comparison youth. Specifically, ADHD youth with a BD family history exhibited smaller bilateral thalamic, hippocampal, and left caudate volumes, and lower parietal, temporal, and ACC surface area compared with HC. Some of these regional deficits have previously been reported in youth with ADHD, including smaller caudate and hippocampal volumes , 21, as Compared with ADHD youth without a BD family history, ADHD youth with a BD family history exhibited robust cortical and subcortical gray matter deficits in regions that contribute to the default mode network (DMN) , 59, incCompared with the LR group, the HR group also exhibited a more severe symptom profile, including higher ADHD hyperactivity/impulsivity subscale scores, mania scores and parent-rated externalization and dysregulation scores. Among both LR and HR groups, mania scores correlated negatively with surface area of both left inferior parietal and right precuneus extending to the right PCC, and ADHD inattention subscale scores correlated positively with left lateral orbitofrontal surface area. No significant correlations were observed between subcortical volumes and symptom ratings after controlling for age and sex. Within the LR group, negative correlations were observed between mania total scores and left superior frontal surface area, and CBCL total scores and dysregulation subscale scores with right lateral orbitofrontal surface area. In contrast, these correlations were not significant in HR. These findings suggest that regions that exhibit morphological differences in LR and HR ADHD groups are associated with symptom measures that are relevant to BD risk progression, including mania and dysregulation , 64.The present study has several notable limitations. First, the sample size may be too small to detect more subtle structural differences after controlling for multiple comparisons. Second, the study was cross-sectional and prospective longitudinal studies are required to determine the relevance of these findings to BD risk progression. Third, this structural study does not inform on potential associations with cortical and subcortical function measures. Strengths of this study include a well-characterized cohort of psychostimulant-free ADHD youth with and without BD family history with similar group demographics, a healthy comparison group, stringent correction for multiple comparisons, and assessment of both cortical and subcortical morphology.The present cross-sectional findings demonstrate for the first time that psychostimulant-free ADHD youth with familial risk for BD exhibit robust regional cortical and subcortical morphological deficits compared with ADHD youth without familial risk for BD and healthy comparison youth. No significant reduction in cortical surface area or subcortical volumes was seen in the LR group. Gray matter deficits are consistent with a more pervasive disruption of peripubertal neurodevelopmental trajectories, and contributing heritable and illness-related biological features warrant additional investigation. Associations between regional morphological measures and symptom ratings including mania and dysregulation suggest potential relevance to BD risk progression in youth with ADHD and familial risk for BD.Cortical and Subcortical Structural Differences in Psychostimulant-Free ADHD Youth With and Without a Family History of Bipolar I Disorder: A Cross-Sectional Morphometric Comparison


Weighted median results indicated that genus Butyricicoccus (p = 0.018) was negatively correlated with the risk of ADHD. At genome-wide statistical significance level (p < 5 × 10−8), Wald ratio results demonstrated that genus Ruminococcustorquesgroup (p = 0.003) was a risk factor for ADHD, while genus Romboutsia (p = 0.006) and family Peptostreptococcaceae (p = 0.006) had a negative correlation with the risk of ADHD. In reverse MR analysis, IVW results showed that ADHD may lead to an increase in the abundance of genus Roseburia (p = 0.020). Analysis of heterogeneity (p > 0.05) and pleiotropy (p > 0.05) confirmed the robustness of MR results. We demonstrated that there was a potential causal relationship between gut microbiota and ADHD. Our research provides a foundation for understanding the causal relationship between gut microbiota and ADHD, and the several gut bacteria found in this study that may reduce the occurrence of ADHD may have potential in the prevention and treatment of ADHD.Previous research has suggested a link between gut microbiota and attention deficit hyperactivity disorder (ADHD), but their causal relationship has not been elucidated. Aiming to comprehensively investigate their causal relationship and to identify specific causal microbe taxa for ADHD, we conducted a two-sample Mendelian randomization (MR) analysis. Instrumental variables of 211 gut microbiota taxa were obtained from gene wide association study (GWAS), and Mendelian randomization study was carried out to estimate their effects on ADHD risk from PGC GWAS  and FinnGen GWAS . Wald ratio (WR), inverse variance weighted (IVW), MR-Egger, and weighted median were the main methods to analyze causality, and MR results are verified by several sensitivity analysis analyses. At locus-wide significance level ( Several randomized controlled trials and cross-sectional studies have indicated that gut microbiota played a critical role in the pathogenesis, prognosis, and treatment of ADHD. However, conventional observational studies are usually susceptible to confounding factors. New methods for investigating the relationship between gut microbiota and ADHD are urgently needed (Attention deficit hyperactivity disorder (ADHD) is a neuropsychiatric developmental disease commonly found in children and adolescents. The worldwide prevalence of ADHD among children is estimated to be around 7%. Its core symptoms include inattention, hyperactivity and impulsivity, severely impacting children’s life quality and learning ability . The ADHy needed .Mendelian randomization (MR) studies estimate the causal effect of a risk factor on an outcome using genetic variants as instrumental variables . BenefitIn this study, we applied the two-sample MR analysis to evaluate the causal relationship between the gut microbiome and ADHD. We hope this research would refresh our understanding of the mutual interaction between gut microbiota and ADHD and be helpful in future investigations.2.2.1.2.2.Genome-wide association studies summary statistics of both ADHD and microbiota were used for the MR analysis. Each GWAS involved in this study is publicly available through the original research and ethically approved by the respective institutions.2.3.Genome-wide association data in gut microbiota were obtained from the MiBioGen study , the larGenome-wide association studies summary statistics for ADHD were extracted from the Psychiatric Genomics Consortium (PGC) and FinnGen cohort. In the discovery stage, the outcome samples for ADHD were obtained from PGC, including 20,183 cases and 35,191 controls, which identified 12 independent genome-wide loci associated with ADHD. The vast majority of the participants are of European ancestry . All the ADHD cases were diagnosed by psychiatrists at a psychiatric hospital according to ICD10 (F90.0) and genotyped using Illumina PsychChip . Replica2.4.p < 5 × 10−8). Due to the small number of eligible IVs (p < 5 × 10−8), a relatively more comprehensive threshold (p < 1 × 10−5) was also applied to obtain a more comprehensive result (r2 = 0.3). In this study, we employed the Wald ratio model for analyzing data when there was only one instrumental variable, and the IVW method was used as the primary analysis method when the number of instrumental variables was two or more. We considered a causal relationship between bacterial taxa and ADHD to exist if the result was significant in the IVW model. Additionally, we used the MR-Egger and weighted median models as references, and if the positive results were replicated in these models, we deemed them to be more robust.The two-sample Mendelian randomization analysis was performed using the R package “TwoSampleMR” (version 0.5.6). A total of four models were utilized in our Mendelian randomization analysis, including Wald ratio (WR), inverse variance weighted (IVW), MR-Egger, and weighted median. To ensure data robustness and the accuracy of results, the choice of instrumental variables (IVs) included in our models needed to meet three crucial principles: (1) IVs were associated with GM taxa (e result . (2) The2.5.p < 5 × 10−8). (2) There was no association between IVs and bacterial taxa. (3) The IVs were not associated with confounders. Finally, SNPs with linkage disequilibrium were excluded . We used the inverse variance weighted (IVW) model as the primary analysis model, and if the result was positive in the IVW model, we determined that there was a causal relationship between ADHD and bacterial taxa. We also referred to the results of other models. If the positive result was replicated in the weighted median and MR-Egger models, we considered it to be more robust.To investigate whether there is a reverse causal relationship between the screened bacterial taxa and ADHD, we conducted a reverse Mendelian randomization analysis using ADHD as the exposure and the significant bacterial taxa identified in the discovery stage as the outcome. Similar to forward Mendelian randomization, the selection of instrumental variables needs to follow three fundamental principles: (1) IVs were associated with ADHD . To reduce false-positive results, we performed FDR correction on the p-values using the Benjamini–Hochberg method. We computed Cochran’s Q statistic to quantify the heterogeneity effects among the selected SNPs. The MR-Egger regression was conducted to assess whether the included SNPs had potential horizontal pleiotropic effects. We also conducted Steiger’s directional test to examine the consistency of the SNP’s effect direction on exposure and outcome. F statistics used to measure the strength of IVs was calculated using the following equation  and 18 unique SNPs  associated with 211 bacterial taxa were selected as IVs in the discovery stage. In the replication stage, 1937 unique SNPs (p < 1 × 10−5) and 24 unique SNPs (p < 5 × 10−8) were selected as IVs  and RuminococcaceaeUCG013  were positively correlated with the risk of ADHD and Butyricicoccus , Roseburia , Desulfovibrio , LachnospiraceaeNC2004group , Romboutsia  were negatively correlated with the risk of ADHD. We also found that the family Oxalobacteraceae  had a negative correlation with the risk of ADHD  served as a protective factor for ADHD. The detailed statistical results of the 211 intestinal flora samples can be found in p-values. However, none of the eight features were able to be reproduced in the replication phase. The complete results of the replication MR analysis can be seen in  was positively correlated with the risk of ADHD, while genus Romboutsia  and family Peptostreptococcaceae  had a negative correlation with the risk of ADHD  had a reverse causal relationship with ADHD, suggesting that ADHD may lead to an increase in the abundance of genus Roseburia. MR-Egger regression analysis revealed no horizontal pleiotropy between instrumental variables and outcome. Additionally, the Cochrane Q statistics indicated no significant heterogeneity, and the F statistics were above 10. The complete results of the reverse MR analysis are shown in  identified in the discovery stage. The IVW model showed that the genus shown in .4.Eubacteriumhalliigroup, genus RuminococcaceaeUCG013, genus Butyricicoccus, genus Roseburia, genes Desulfovibrio, genus LachnospiraceaeNC2004group, genus Romboutsia, and family Oxalobacteraceae had a causal relationship with ADHD at the locus-wide significance level (p < 1 × 10−5), while genus Ruminococcustorquesgroup, genus Romboutsia, and family Peptostreptococcaceae had a causal relationship with ADHD at the genome-wide statistical significance level (p < 5 × 10−8). However, in the replication stage, none of the aforementioned features were replicated. Results of reverse MR analysis showed that genus Roseburia had a reverse causal relationship with ADHD.In this study, we conducted a two-sample MR analysis to investigate the relationship between gut microbiota and ADHD. In the discovery stage, we found that the genus Neisseria and Bacteroides genera were increased in adolescents with ADHD. Another study found that the relative abundance of Bacteroides coprocola was reduced in the ADHD group compared to healthy controls, while the relative abundance of Bacteroides uniformis, Bacteroides ovatus, and Sutterella stercoricanis increased in the ADHD group , bipolar disorder, schizophrenia, and major depressive disorder  analysis. Consistent with previous studies, we found that the genus disorder . A recenol group . The genttention . Dimu Ni and ASD . In addiand ADHD . Althougophrenia . Reductipression . Similarophrenia . The reloccaceae  and Rumi torques  is relatF statistics. (5) Due to significant differences in gut microbiota among children of different ages, an ideal experimental design should include stratified analyses based on age. However, the GWAS data we obtained lacked age information, which limited our ability to explore the causal relationship between gut microbiota and ADHD in different age groups. Nevertheless, the GWAS summary data used in our analysis has been corrected for age, which helped to eliminate the interference of age on the causal relationships we obtained through MR analysis. (6) Studies have shown that ADHD is often accompanied by many comorbidities, including anxiety disorders and behavioral disorders, with prevalence rates as high as 37.9 and 31%, respectively  We analyzed genetic data obtained from a large sample population, making the results more reliable compared to small observational studies. (2) MR analysis prevents the interference of confounders on causal relationships, and the identified causal relationships in our study may provide candidate bacteria for future functional studies. At the same time, some limitations of this study should be fully considered: (1) The GWAS data included in the analysis were mainly from European ancestry subjects, and the results of this study may not apply to other races. (2) 16S rRNA gene sequencing only allows for resolution from the level of phylum to genus, so this study cannot analyze the causal relationship between the gut microbiota and ADHD at a more specific species level. (3) The causal relationship determined in the discovery stage is not replicated in the replication stage. We consider this to be due to the low number of ADHD cases included in the FinnGen study , which will affect the statistical power of the MR analysis. (4) We cannot determine whether there are overlapping participants in both the exposure and outcome data used in the two sample MR analysis, but bias from participant overlap can be minimized through the application of ectively . Many ofectively . TherefoIn summary, we evaluated the causal relationship between the gut microbiota and ADHD, using publicly available GWAS summary statistics, and determined specific bacterial groups that may affect the development of ADHD through two-sample MR analysis. Our study provides a foundation for understanding the causal relationship between the gut microbiota and ADHD, and several of the gut bacteria found to potentially reduce the occurrence of ADHD in this study may have potential for use in the prevention and treatment of ADHD. It should be noted that further observational or laboratory-based research is needed to validate these findings. One commonly used approach is to transplant the studied microbial species into germ-free animals to observe their effects. Using this approach, we can transplant specific microorganisms into animal models of ADHD and observe their therapeutic effects. However, the germ-free environment may induce permanent neurodevelopmental defects in the experimental animals, which may interfere with the results . AnotherThe original contributions presented in the study are included in the article/JML designed the research. LW, GlL, and GyL collected the data. LW analyzed the data and drafted the early version of the manuscript. ZHX collected and organized relevant literature and participated in the subsequent revision of the article. All authors were involved in writing the manuscript and had final approval of the submitted and published versions.This work was supported by the National Natural and Science Foundation of China (Nos. 82271509 and 81771396), the Foundation of Jilin Provincial Key Laboratory of Pediatric Neurology (No. YDZJ202102CXJD021), the Project of Jilin Provincial Science and Technology Development Plan (No. YDZJ202201ZYTS676), and the Project of Jilin Medical and Health Talents (No. JLSWSRCZX2021053).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.


To assess ADHD in both childhood and adulthood as well as current psychopathological impairments, we used self-report instruments and corresponding observer reports. We examined group differences between age groups and between ADHD and non-ADHD groups. The prevalence of ADHD in the total sample was 2.6% with no significant differences between the two age groups (40–59 years: 3.1% vs. 60–80 years: 2.1%). Although differences emerged in impulsivity/emotional lability and self-concept problems, overall ADHD symptom ratings did not differ between the age groups. The ADHD group showed more psychopathological peculiarities compared to individuals without ADHD with medium-to-large effect sizes. Self-reports and observer reports showed good concordance in the assessment of ADHD and comorbid psychopathological symptoms. Regarding current ADHD symptomatology, in 92.1%, self-report was corroborated by observer's information. Our findings underline that ADHD symptoms are relevant across the lifespan. Augmenting self-reports with observer reports could increase the assessment quality of ADHD. For successful treatment, clinicians should also focus on additional psychopathological impairments and comorbidities in older adults with ADHD.Attention deficit hyperactivity disorder (ADHD) is still a neglected disorder in older adults. The aim of the present study was to examine the prevalence and symptomatology of ADHD and associated psychopathology in adults aged 40–80 years in a German community sample. We examined 539 participants in two age groups: (1) 40–59 years old ( For a considerable period of time, it had been assumed that attention deficit hyperactivity disorder (ADHD) was just a childhood disorder; however, meanwhile, it is mostly conceptualized as a neurodevelopmental disorder with early onset that can persist into adulthood with both psychopathological symptoms and functional impairments  than girls , r = 0.52–0.70 (Hyperactivity/restlessness), r = 0.45–0.61 , and r = 0.38–0.52 (problems with self-concept) . Information from close persons who know the assessed person well is often only collected if self-ratings are questionable, e.g., in cases of severe psychiatric disorders like psychosis, cognitive limitations, or if the assessed person is interested in distorting the information, e.g., in forensic contexts. As information from different people displays different perspectives, assessments for clinical and research purposes benefit by augmenting self-ratings with observer ratings  diagnosed with ADHD, a knowledge gap remains in the research focusing on this population. The prevalence of ADHD in older adults and psychiatric comorbidities have been previously studied. Nevertheless, the state of research on this topic is still insufficient, especially compared to what we know in younger people with ADHD. The present study aimed to replicate previous studies and enlarge the knowledge of ADHD in older adults. We examined a large community-based sample of adults at the age of 40–80 years for ADHD symptomatology and additional psychopathological impairments. We used self-report information as well as information from close persons who knew the assessed person well. Based on the current literature, we expected a prevalence rate of adult ADHD around 3% with a slightly lower rate in the group of the 60–80 years old compared to the 40–59 years old. We further hypothesized that ADHD symptomatology would differ in the two age groups, e.g., in terms of lower impulsivity and hyperactivity in the older age group. Besides ADHD, we expected several additional psychopathological impairments, especially depressive and anxiety symptoms, and hypothesized that ADHD groups would show a higher load of psychopathological symptoms in general than non-ADHD participants. Furthermore, we expected a good concordance of self-ratings and observer ratings, but with differences in intrapsychic aspects such as self-concept problems.N = 539 participants between 40 and 80 years  in two age groups, 40 and 59 years (n = 256) and 60 and 80 years (n = 283); thus, there was a participation rate of 23.0%.Participants for the present study were recruited from the sample of the Gutenberg Brain Study (GBS). The GBS is ongoing and started 2014. It primarily aimed at studying the genetic and environmental determinants of brain health. The GBS sample is a population-based, non-clinical sample of adults living in Mainz, the capital city of Rhineland-Palatinate, Germany. Only participants with sufficient knowledge of the German language, without learning disabilities, and without severe mental or neurological disorders were included. Within the present study, an invitation letter including the study information, a consent form to sign, and the study materials was sent out twice via mail to all participants of the GBS who agreed to be contacted for future studies. The participants were asked to fill out different paper–pencil self-report questionnaires at home. Additionally, included participants were asked to hand over the paper–pencil observer-report forms to a close relative or friend. Filled-out questionnaires were sent back to the study authors via mail. The ethics committee at the Rhineland-Palatinate state chamber of physicians approved our study protocol . Altogether, 2342 individuals from the initial GBS sample were asked to participate in the present study. Participants who sent back incomplete questionnaires were excluded. The present study included data of α = 0.91 : Childhood ADHD was retrospectively assessed by the German short version of the WURS : Current ADHD was assessed by the German version of the CAARS short form : The OASR is a self-report instrument to measure psychopathology in adults aged 60 years and above. The older adults rate their cognitive, emotional, behavioral, and social problems on 113 problem items. Each item is rated on a three-point Likert scale . The questionnaire provides a total problems score , a personal strengths score, seven syndrome scales , and six DSM-oriented scales . We considered only those scales with counterparts for the group aged 18–59 years. T values ≥ 60 or ≥ 65 indicate current clinically relevant symptomatology. The ASR show good psychometric properties. For the DSM-oriented scales, the mean test–retest reliability was r = 0.83, and for the empirically based scales, it was r = 0.88 : The ASR is a 126-item self-report questionnaire assessing psychopathology in adults (18–59 years). It provides a total problems score, the personal strengths score, scores for eight syndrome scales , and for seven DSM-oriented scales . To ensure comparability, we applied only those scales with counterparts for groups aged 60 years and above. The items are rated on a three-point Likert scale . χ2 analyses for categorical variables. Missing values were excluded from the calculation. Cramér’s V effect size values of 0.10, 0.30, and 0.50 represented small, medium, and large effects, respectively .All statistical analyses were conducted using IBM SPSS version 26.0. Descriptive group differences between the 40–59 years old and the 60–80 years old were investigated using ly Cohen . The levly Cohen . Therebyly Cohen . To compts Cohen . In addiN = 539, 53.1% females) was 59.63 years . Regarding female–male ratio, the two age groups did not differ (χ2(1) = 1.40, p = 0.24, V = 0.05). No significant differences were found between the two age groups for relationship status (χ2(1) = 1.48, p = 0.22, V = 0.05). Approximately four out of five older adults lived in a solid partnership (40–49 years: 83.2% vs. 60–80 years: 79.2%). However, the younger age group showed higher educational levels (χ2(1) = 5.42, p = 0.02, V = 0.10). In the age group of 40–59 years old (n = 256), 14.5% of the participants showed low education (≤ 10 school years) versus 22.3% in the age group of 60–80 years old (n = 283). Descriptive statistics of the study sample are presented in Table The mean age of the total sample (N = 539), the prevalence of self-reported clinically relevant ADHD symptoms in childhood (WURS-K score ≥ 30) was 8.9% (95% CI 6.5%–11.5%). The prevalence of self-reported clinically relevant ADHD symptoms in adulthood  was 7.4% (95% CI: 5.2%–9.8%). Considerably less individuals reported about ADHD symptomatology in both childhood and adulthood . Neither the ADHD prevalence in childhood nor the prevalence in adulthood significantly differed between the two age groups  and 11.1% of the males (n = 253) reported clinically relevant ADHD symptomatology in childhood. No statistical significant gender effect was found (χ2(1) = 2.78, p = 0.10, V = 0.07). In adulthood, 7.7% of the women and 7.1% of the men reported clinically relevant ADHD symptoms, also with no significant difference (χ2(1) = 0.07, p = 0.80, V = 0.01). Both ADHD symptomatology in childhood and adulthood have been found in 2.4% of the examined women and 2.8% of the men; again, differences were not statistically significant (χ2(1) = 0.05, p = 0.82, V = 0.01). However, when comparing the two age groups, a significant gender difference was found in the group of the 40–59 year old adults (χ2(1) = 5.99, p = 0.01, V = 0.15). In this younger age group, 6.2% of the females but 15.7% of males reported childhood ADHD symptoms. In the age group of the 60–80 years old , no significant difference was found (χ2(1) = 0.18, p = 0.67, V = 0.03). With regard to adult ADHD, 6.2% females versus 11.8% males of the 40–59 year old adults reported current clinical relevant ADHD symptoms, with no significant gender difference (χ2(1) = 2.46, p = 0.12, V = 0.10). In the older age group (60–80 years) the proportion of females with self-rated current ADHD symptoms (8.9%) differed significantly from the proportion of males (3%) (χ2(1) = 5.29, p = 0.02, V = 0.14). In the group of the 40–59 years old adults, both clinical relevant ADHD symptomatology in childhood and in adulthood were found significantly more often in males than in females (5.5% vs. 0.8%) (χ2(1) = 4.74, p = 0.03, V = 0.14). In the group of the 60–80 year old adults, the gender ratio also differed significantly (χ2(1) = 4.92, p = 0.03, V = 0.13) and the proportion of women who met the criteria (3.8%) was higher than the proportion of men (0.0%).With regard to gender differences, 7.0% of the examined females  and the non-ADHD childhood group (n = 491)  and on all included subscales between the childhood ADHD group n = 8 and theThe same accounted for the ADHD adult group. Adults with ADHD symptomatology reported about significantly more psychopathological symptoms on all included subscales compared to adults without ADHD symptomatology during adulthood. Effect sizes were again medium to large , i.e., assessments of the participant’s ADHD symptomatology by a close person like their partner. According to our hypothesis, the comparison of 466 self-report/observer report dyads showed good concordance with medium-to-large correlations. This finding fits well to correlation coefficients found in the validation study of the German version of the CAARS . There is considerable concern about the ability of adults to accurately recall childhood conditions (Fayyad et al. Moreover, we compared two adult age groups. A prospective longitudinal design from childhood to old age would have allowed statements about the development of ADHD across the lifespan and into old age. We collected data from a community-based sample in Germany, which was quite large, but still too small to be a representative. It is not possible to generalize the outcomes into other societies without limitations. We recruited our sample from subjects who had given their interest in studying brain health. This may influence the results. Of these subjects, only those who also had an interest in studying mental health completed our questionnaires, which may have introduced further bias. Considering this, it is more likely that psychopathological abnormalities were overestimated in our sample. The study was not administered specifically to participants with clinical adult ADHD, so the ADHD group is relatively small and so the interpretation of the comparison of the ADHD versus the non-ADHD group is limited. For this reason, we have refrained several analyses. Future studies should investigate the difference in psychopathological distress between those patients who received treatment in childhood compared to those who did not. Because not all individuals with adult ADHD present additional psychopathological impairments in adulthood, it should also be examined which risk factors increase the likelihood of comorbid psychiatric disorders.In conclusion, the current findings provide further evidence of the prevalence rate of adult ADHD of around 3%, even in the group of adults aged 40 to 80 years. Between elderly and middle-aged adults, there are more similarities than differences in the symptomatology of ADHD with no significant gender effects. ADHD is often accompanied by additional psychopathological symptoms and life impairments. To offer sufficient and effective treatment to older adults with ADHD, it is important to assess symptoms well and thoroughly. Augmenting observer ratings to self-ratings can make a valuable contribution. Further well-designed prospective studies in older adults with ADHD need to be conducted to broaden the current knowledge in this area.