N = 11,748), and then examined shared and non-shared neural correlates of response inhibition and error processing during the Stop Signal Task in a subset of sample with quality neuroimaging data . Latent class analysis (LCA) revealed four phenotypic groups, i.e., high ADHD with co-occurring irritability symptoms , moderate ADHD with low irritability symptoms , high irritability with no ADHD symptoms , and typically developing peers with low ADHD and low irritability symptoms . Latent variable modeling revealed group differences in the neural coactivation network supporting response inhibition in the fronto-parietal regions, but limited differences in error processing across frontal and posterior regions. These neural differences were marked by decreased coactivation in the irritability only group relative to youths with ADHD and co-occurring irritability symptoms and typically developing peers during response inhibition. Together, this study provided initial evidence for differential neural mechanisms of response inhibition associated with ADHD, irritability, and their co-occurrence. Precision medicine attending to individual differences in ADHD and irritability symptoms and the underlying mechanisms are warranted when treating affected children and families.Attention-deficit/hyperactivity disorder (ADHD), characterized by symptoms of inattention and/or hyperactivity and impulsivity, is a neurodevelopmental disorder associated with executive dysfunctions, including response inhibition and error processing. Research has documented a common co-occurrence between ADHD and pediatric irritability. The latter is more characterized by affective symptoms, specifically frequent temper outbursts and low frustration tolerance relative to typically developing peers. Shared and non-shared neural correlates of youths with varied profiles of ADHD and irritability symptoms during childhood remain largely unknown. This study first classified a large sample of youths in the Adolescent Brain Cognitive Development (ABCD) study at baseline into distinct phenotypic groups based on ADHD and irritability symptoms ( Attention-deficit/hyperactivity disorder (ADHD), characterized by symptoms of inattention and/or hyperactivity and impulsivity, is a common neurodevelopmental disorder in youths . IrritabA large body of literature demonstrates deficits in executive functions , i.e., Compared to the large body of literature in ADHD, the neural underpinnings of cognitive control, particularly response inhibition, in irritability have received less attention. A growing body of work using fMRI and functional near infrared spectroscopy (fNIRS) with tasks involving inhibitory and cognitive control suggests that higher irritability symptoms are associated with altered activation in the prefrontal regions –22 and rN < 100), and almost none directly investigated the neural correlates underlying the co-occurrence of irritability and ADHD vs. either phenotype presenting alone. One exception is the study by Pagliaccio et al.  from the Adolescent Brain Cognitive Development (ABCD) study at baseline  to derive distinct phenotypic groups based on ADHD and irritability symptoms using LCA. Our rationale of using the baseline sample of N = 11,748 in the LCA was to obtain the largest possible sample, which allowed us to assay a diverse spectrum of ADHD and irritability symptoms, thereby generating more accurate community prevalence estimates of different clinical phenotypes , Standardized Root Mean Square Residual (SRMR) = 0.03.Irritability symptoms were measured using four items across relevant diagnostic categories from the parent-reported K-SADS , CFI = 1.00, TLI = 1.00, RMSEA = 0.05 , SRMR = 0.03.ADHD symptoms were measured using 18 items from the ADHD module of the K-SADS. Items included “Difficulty sustaining attention,” “Often makes careless mistakes,” and “Runs or climbs excessively.” Consistent with past research assessing child ADHD via the K-SADS , confirThe SST was used to index cognitive control-related brain functions and behavioral performance during fMRI in the ABCD study , 30, 36.In brief, the SST asks participants to indicate the direction of left- or right-facing arrows  as quickly and accurately as possible, but also not to respond following an upward-pointing arrow that indicated a “Stop” signal. The SST used in the ABCD study was in an event-related design comprised of two runs, each having 180 trials inclusive of 30 “Stop” trials; hence, there were 150 × 2 = 300 “Go” trials and 30 × 2 = 60 “Stop” trials in total. Each trial lasted 1 s. An algorithm caused the time between the “Go” and “Stop” signals to vary depending on the participant's accuracy in the previous trial, thus achieving an overall success rate of 50%. The behavioral task measure of inhibition ability was Stop Signal reaction time (SSRT). To address experimental issues of SST noted previously , we estiTask-dependent functional brain activations were indexed by average beta weights. Data were pre-processed by the ABCD Data Analysis and Informatics Center . To empiLatent variable modeling was conducted in Mplus version 8.3 , with moN = 11,748). A one-class solution was first fitted to the data, followed by successive solutions with one additional class of the previous solution until the best fitting solution was identified. The various solutions were compared based on model fit indexed by the Akaike Information Criterion (AIC), Bayesian Information Criteria (BIC), sample-size adjusted BIC (ABIC), entropy, Vuong-Lo-Mendell-Rubin likelihood ratio test (VLMR LRT), Lo-Mendell-Rubin adjusted likelihood ratio test (LMR adjusted LRT), and parametric bootstrapped likelihood ratio test (PB LRT). Following previous research  as assessed by the K-SADS in the full sample at baseline  across latent classes.We included various quality control parameters to screen out cases with potentially problematic neuroimaging data , 35, 36.Methodological and programming issues regarding the SST in the ABCD study were acknowledged and addressed in the current study , 64. BriFinally, inspection of the SST neuroimaging data suggested that ~25% of brain regions showed extreme outliers, defined as ±3 standard deviations and skewness >2 of the average beta weight of that brain region , 35. We Four sets of supplementary analyses were conducted.Since Korucuoglu et al.  studied Taking advantage of the large sample, we also explored sex by latent class interactions in each of the constituent brain regions of the response inhibition and error processing coactivation networks. Because the comparison of latent variables within a multiple hierarchical structure is not available in the current version of Mplus, the analyses were performed at the regional level.post-hoc group comparisons using family as a clustering variable in Mplus to account for the interdependent, nested data within family. The same modeling procedures were used for the group comparisons using a priori selected regions and all available brain regions on the SST.We also conducted post-hoc basis.Given that various socio-demographic differences between the LCA groups were of small to medium effect sizes see , we re-rN = 11,748). A four-class solution was identified as the best fitting solution, AIC = 56111.24, BIC = 56782.04, ABIC = 56492.85, entropy =0.97, VLMR LRT p < 0.001, LMR adjusted LRT p < 0.001, PB LRT p < 0.001, smallest class proportion = 2.4%. Compared to the five-class solution, there was a drop in statistical significance in the PB LRT, suggesting that the four-class solution was superior ; (2) youths with high ADHD symptoms and co-occurring irritability ; (3) youths with moderate ADHD symptoms and low irritability ; and (4) youths with high irritability and low ADHD symptoms . To provide a more concrete overview of the differences in symptom profiles, ps < 0.001. Youths with moderate ADHD symptoms  also showed a mild level of irritability symptoms  relative to typically developing peers , p < 0.001.rior see . As depiN = 5,948; 52.9% female) and the respective LCA-derived groups based on irritability and ADHD symptoms. The mean age of the total sample was 9.9 years (SD = 0.6 years). Most youths were white (78.4%) and had caregivers attaining bachelor's degrees (29.9%). Most caregivers were married (71.7%) and had a total combined family income of $100,000–$199,999.p < 0.001]. Specifically, relative to the typically developing group (45.4%), more boys were in the high ADHD symptoms with co-occurring irritability group (66.1%), [p < 0.001], and the moderate ADHD symptoms but low irritability group (56.0%), [p < 0.001]. The proportion of boys and girls in the high irritability but low ADHD group were comparable to typically developing peers, [p = 0.78], with the two groups having a slightly higher proportion of girls .Comparisons of sex proportions across the latent classes suggested that there were more boys than girls in the two ADHD groups, [p < 0.001]. There was also a significant group difference in caregiver education, in which most caregivers of youths with moderate ADHD symptoms but low irritability attained some college or associate degrees (34.6%), while most caregivers of typically developing youths attained bachelor's degrees (30.1%), [p < 0.001].Additionally, there were more youths whose ethnicity was reported as not Hispanic or Latinx in the group with high ADHD symptoms with co-occurring irritability (83.9%) than in the typically developing group (78.7%), .The mean SSRT was 262.29 ms (SD = 65.98 ms). Linear mixed model revealed no significant group differences in SSRT , SRMR = 0.01. The measurement models of the neural coactivation network of response inhibition achieved measurement invariance across the latent classes, all ΔCFI <0.01 and ΔRMSEA < 0.015 (ps < 0.001) in the left inferior parietal cortex (0.91), left supramarginal gyrus (0.86), left lateral occipital cortex (0.68), and left pars orbitalis (0.51).Exploratory factor analysis and subsequent confirmatory factor analysis confirmed a neural coactivation network consisting of a single latent response inhibition factor with excellent model fit, , SRMR = 0.01. This error processing coactivation network also showed measurement invariance among the latent classes, all ΔCFI < 0.01 and ΔRMSEA < 0.015 (ps < 0.001) in the bilateral lateral orbital frontal cortices  and left pars orbitalis (0.79). The second latent error processing factor was also indicated by three regions, characterized by significant factor loadings  in the bilateral superior parietal cortices  and lateral occipital cortex (0.68).Exploratory factor analysis and subsequent confirmatory factor analysis suggested a neural coactivation network consisting of two latent error processing factors with excellent model fit, , the ADNo behavioral differences  were observed between the latent groups, suggesting that the current neural differences may not necessarily reflect response inhibition performance. However, it is premature to conclude that behavioral inhibitory deficits are not present in the ADHD and/or irritability phenotypes as inhibitory control was only indexed by SSRT. One should interpret these SSRT results with caution as it has been shown that the current SST violated the key assumption of context independence, which might compromise the interpretability of the integration estimates of SSRT. Alternative models accounting for context independence with less biased SSRT estimates are being developed .post-hoc comparisons were not adjusted for any sociodemographic covariates and should therefore be interpreted with caution. The second constituent network of error processing was primarily within the parietal regions, represented by the bilateral superior parietal cortices and the left occipital cortex. The superior parietal cortices support manipulation of information stored in working memory  that contributed to the response inhibition and error processing coactivation networks. Intriguingly, the conventional regional analysis revealed no significant group difference in each of the response inhibition regions, with one exception the left pars orbitalis  during error processing. That is, relative to typically developing peers, youths with ADHD and occurring irritability showed heightened activation in the left pars orbitalis, whereas youths with ADHD only and with irritability only showed reduced activation in this region. The current lack of regional evidence might seem at odds with the previous studies that found an association between irritability severity and aberrant cognitive control-related neural responses in various frontal and subcortical regions , 24. HowStill, the functional significance of the pars orbitalis subregion of the inferior frontal gyrus has been proposed as a hub for attention control during error detection , 98 and The latent classification findings and the shared and non-shared neural correlates of ADHD and/or irritability symptoms have important clinical implications. The current study identified a significant proportion of youths with ADHD and co-occurring irritability at baseline in the ABCD study, comprising almost half of the youths with at least moderate levels of ADHD symptoms. Despite the high prevalence of ADHD plus irritability and greater impairment, most interventions for youths with ADHD target ADHD symptoms and executive functions such as attention control and motor control, with less focuses on improving emotion regulation or mood symptoms , althougDifferential group differences in the neural coactivation networks provided a more nuanced understanding that the neural mechanisms vary subtly between ADHD, irritability, and their co-occurring conditions, and are more associated with response inhibition than error processing at the systems neuroscience level. These neural differences are of large effect sizes, which may be clinically meaningful. Given that ADHD symptoms  and irritability (frustration and temper outbursts) are linked to dysfunction in inhibitory control, targeting response inhibition and its underlying circuitry may therefore be a promising intervention approach for youths with ADHD and co-occurring irritability symptoms. Effortful neural processing (manifested as hyper-activation) in the ADHD + irritability group may imply that developing adaptive inhibitory control strategies could be prioritized. Conversely, neural inefficiency (manifested as hypo-activation) in the irritability only group may suggest that consistent training that promotes one's efficiency to inhibit temper outbursts may be more beneficial to youths with primarily irritable symptoms. Taken together, this study identified distinct phenotypic profiles based on ADHD and irritability symptoms and found dissociable neural correlates during inhibitory control across these phenotypes. These provide novel insights into the potential neural mechanisms for change that are specific to unique symptom profiles, paving the way for transdiagnostic interventions and precision medicine , 106.We note several limitations. First, the current study is cross-sectional, with data from a narrow development window . It is likely that cognitive control-related brain functions undergo rapid development at later developmental stages , and theThis study is the first to use the ABCD dataset to examine shared and non-shared neural correlates of response inhibition and error processing across distinct phenotypes of ADHD, irritability, and their co-occurrence using data-driven, latent variable modeling techniques. This is the largest study to date on irritability and neuroimaging. Taking a systems neuroscience and multivariate approach, differential group differences emerged in the neural coactivation network associated with response inhibition but not error processing, providing novel evidence for differential neural mechanisms among these common and frequently co-occurring clinical phenotypes. Findings may inform a precision medicine approach in which novel treatments for ADHD, irritability, and their comorbidity can be developed and tailored to each individual patient based on the extent to which the treatment targets a specific form of neural dysfunction.https://nda.nih.gov/abcd). An NDA study detailing related information for this study may also be accessed at the following https://doi.org/10.15154/1524665 further inquiries can be directed to the corresponding author/s.All data analyzed in this study are accessible through an official request to the NIH Data Archive . W-LT was supported by a research grant from the National Institute of Mental Health (R00MH110570) and the Fund to Retain Clinical Scientists from the Yale School of Medicine and the Yale Center for Clinical Investigation. The ABCD Study® is supported by the National Institutes of Health and Additional Federal Partners under award numbers U01DA041048, U01DA050989, U01DA051016, U01DA041022, U01DA051018, U01DA051037, U01DA050987, U01DA041174, U01DA041106, U01DA041117, U01DA041028, U01DA041134, U01DA050988, U01DA051039, U01DA041156, U01DA041025, U01DA041120, U01DA051038, U01DA041148, U01DA041093, U01DA041089, U24DA041123, U24DA041147. A full list of supporters is available at This study reflects the views of the authors and may not reflect the opinion or views of the funders and NIH or the ABCD consortium investigators.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. The reviewer PV-R declared a shared affiliation, with no collaboration, with the authors EL at the time of the review.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.


Depression is highly heterogeneous in its clinical presentation. Those with attention deficit/hyperactivity disorder (ADHD) may be at risk of a more chronic and impairing depression compared to those with depression alone according to studies of young people. However, no studies to date have examined ADHD in recurrently depressed adults in mid-life.In a sample of women in mid-life (n=148) taken from a UK based prospective cohort of adults with a history of recurrent depression, we investigated the prevalence of ADHD and the association of ADHD with clinical features of depression.12.8% of the recurrently depressed women had elevated ADHD symptoms and 3.4% met DSM-5 diagnostic criteria for ADHD. None of the women reported having a diagnosis of ADHD from a medical professional. ADHD symptoms were associated with earlier age of depression onset, higher depression associated impairment, a greater recurrence of depressive episodes and increased persistence of subthreshold depression symptoms over the study period, higher levels of irritability and increased risk of self-harm or suicide attempt. ADHD symptoms were associated with increased risk of hospitalisation and receiving non-first-line antidepressant medication.ADHD was measured using a questionnaire measure. We focussed on mothers in a longitudinal study of recurrent depression, so the findings may not apply to males or other groups.Higher ADHD symptoms appear to index a worse clinical presentation for depression. Clinical implications include that in women with early onset, impairing and recurrent depression, the possibility of underlying ADHD masked by depression needs to be considered. StudiesThere is evidence to suggest that there is a neurodevelopmental genetic contribution to some forms of depression, including ADHD and schizophrenia genetic risk . For insIn this UK based longitudinal study spanning 13 years, we aim to investigate ADHD in women with recurrent depression. First, we investigate the prevalence of ADHD in this group. Next, we investigate a number of clinical features of depression potentially associated with ADHD including age of onset, severity, episode recurrence, subthreshold symptom persistence, suicide and self-harm attempts, psychotic affective symptoms and irritability symptoms. Additionally, we investigate the association of ADHD symptoms with aspects of the clinical management of depression, including hospitalisation and use of first-line versus non-first-line antidepressant medication, as an indicator of poor treatment response or a complex clinical presentation.22.1Data came from the Early Prediction of Adolescent Depression (EPAD) study – a prospective longitudinal study of recurrently depressed parents and their offspring based in the UK . The bas2.2The sample was recruited mainly from general practice surgeries in South Wales (78%). Additional participants were recruited using a database of individuals who had been identified as having previous unipolar depression, via community mental health teams and advertisements in local media and primary care centres . The oriAt each assessment wave (1 through 4), the Schedule for Clinical Assessment (SCAN)  was used2.32.3.1Participants completed the Adult ADHD Investigator Symptom Rating Scale (AISRS)  via quesPredictor variables were 1) a total ADHD symptom score (possible range: 0-54) and 2) a binary variable of “elevated ADHD symptoms”. Those meeting the AISRS clinical cut point by scoring 24 or higher  were claDiagnosis: Participants were also asked to complete a further set of questions in the questionnaire if they reported ADHD symptoms: at what age they could recall these symptoms starting in approximate years and to indicate whether the symptoms impacted on functioning in a number of areas of life. For descriptive purposes, participants were classed as meeting DSM-5 criteria for adult ADHD diagnosis if they reported at least 5 current symptoms, an age of onset of these problems prior to 12 years old and associated impairment in home, work or social life .During the interview at assessment wave 4, participants were also asked to report on their recent and current service use and any diagnoses they had received from a medical professional using a questionnaire adapted from the Children's Services Interview , which w2.3.22.3.2.1Participants were asked at baseline interview to report the age of onset (in years) of their first MDD episode. Age of onset was dichotomised so that onset at 25 years old or earlier was classed as early-onset depression based on previous studies .2.3.2.2Impairment associated with depression was measured using the Global Assessment of Functioning (GAF) , which w2.3.2.3A count variable of the recurrence of depressive episodes across the study period was derived. This included a count of the number of assessment points at which participants met clinical criteria for MDD plus the number of times participants reported being depressed since the last assessment wave (possible range=0-7).2.3.2.4In addition to recurrence of depression episodes meeting diagnostic criteria, chronicity of subthreshold symptoms over time is common and impairing . Thus, a2.3.2.5The SCAN  was used2.3.2.6A binary variable capturing whether participants had ever endorsed psychotic affective symptoms during the SCAN  at waves2.3.2.7Participants completed the Affective Reactivity Index (ARI)  via ques2.3.2.8Participants reported the number of times they had ever been hospitalised due to depression at baseline, and the number of times they had been hospitalised since the last assessment wave at waves 2 and 3. At wave 4, participants were not asked explicitly to report the number of hospitalisations, but they were asked to give details of what happened during any depressive episodes experienced since the last wave, which gave an opportunity to disclose hospitalisations. From this information, a binary variable was derived capturing whether participants had ever been hospitalised due to depression.2.3.2.9At wave 4, participants reported what medication they were currently taking, including medication for depression. Based upon UK National Institute for Health and Care Excellence (NICE) clinical guidelines for management of adult depression and the British National Formulary (BNF) , a binar2.3.3All regression analyses presented were adjusted for sociodemographic factors associated with depression that might confound associations . The con2.42.4.1A series of linear and logistic regression analyses were conducted as appropriate to test the association of ADHD symptoms and clinical features of depression. Continuous ADHD symptoms were standardised (z-scored) so that a one-unit increase was equivalent to one standard deviation increase. All regression analyses were then repeated for ADHD defined as elevated symptoms, entered as a binary variable instead of a continuous variable. This was to test whether the presentation of depression was significantly different for those above the AISRS clinical cut point for ADHD (≥24) compared to those below the cut-point.2.4.2Regression analyses were repeated with Inverse Probability Weights (IPW) applied to assess possible bias arising from potential non-random missing data. IPW is a reliable approach to handling missing data, particularly in longitudinal cohorts where participants can have missing data for multiple variables . Weights3In the primary sample of recurrently depressed females (n=148), 12.8% (n=19) had elevated ADHD symptoms, indicated by having an AISRS score above the validated clinical cut point (>24), and 3.4% (n=5) met DSM-5 diagnostic criteria for adult ADHD. The lower number of those who met diagnostic criteria was explained by participants not meeting the age of onset criterion specified in the DSM-5 (<12 years). None of the women in the study however, reported having been diagnosed with ADHD by a medical professional or receiving medication for ADHD. Descriptive statistics are shown in 3.1Standardised ADHD symptoms were associated with an earlier age of depression onset (≤ 25 years) and associated severe impairment (GAF ≤ 50) . ADHD syIn addition, ADHD symptoms were associated with increased odds of ever being hospitalised and treatment with a non-first-line antidepressant medication, which may be indicative of poor response to first-line antidepressants or a complex clinical presentation. The frequencies of different psychotropic medication types used are reported in Supplement 3, which includes depression medication and other neuropsychiatric medications. Of the primary sample (n=148), 63.5% (n=94) reported taking psychotropic medication and 19.6% (n=29) reported taking more than one psychotropic medication.The same associations were tested with ADHD as a binary variable: those above the AISRS clinical cut-point for ADHD vs. those below the cut-point. Elevated ADHD symptoms were associated with increased recurrence of depressive episodes, increased irritability symptoms, increased risk of hospitalisation and taking non-first-line depression medication. ADHD symptoms above the clinical cut point were also associated with a 2-fold increase in odds of early onset depression, but this did not reach conventional thresholds for significance. The association of elevated ADHD symptoms with severe impairment could not be tested, as all participants with elevated ADHD symptoms had had a GAF score below or equal to 50, and the association with suicide and self-harm attempts could not be tested due to small cell sizes.Results remained similar with IPW applied (Supplement 4). However, the association between total ADHD symptoms and subthreshold persistence attenuated after IPW was applied.4In a prospective, longitudinal study of recurrently depressed women, we investigated the prevalence of ADHD and the effect of comorbid ADHD on the clinical phenotype of depression. In this sample, 12.8% had elevated ADHD symptoms and 3.4% met DSM-5 diagnostic criteria for ADHD. These are higher than estimates reported in general population samples of adults (2.5%) . None ofOur findings suggest that a proportion of women in mid-life with a history of early-onset, recurrent depression that has been severely impairing may have undetected ADHD. Previous studies have shown associations of ADHD in childhood and adolescence with more severe depressive outcomes in young adulthood, such as depression recurrence, suicide and hospitalisation . There iThere is much heterogeneity observed in the presentation of depression , includiOur findings have important clinical implications. They suggest that in women with early onset, recurrent depression in adulthood, the possibility of depression masking underlying ADHD needs to be considered. Effective treatment of ADHD along with depression could improve functioning and associated depression symptoms . Even foLimitations of this work include the measurement of ADHD was based on a questionnaire measure completed at one assessment wave. Also, in this study we focussed on women with adolescent children who had participated in a longitudinal study of recurrent depression so the findings may not apply to males or other groups, for example, hospitalised patients. The rate of ADHD observed may be influenced by specific characteristics of the present sample. Rates of ADHD may be higher in this sample because individuals all had recurrent depression. However, given that ADHD is associated with multiple social and educational impairments , those wTo conclude, we found that in a sample of recurrently depressed adults, ADHD was associated with an earlier onset, more impairing and recurrent depression presentation, in addition to increased risk of self-harm, hospitalisation and receiving non-first-line antidepressant medication. Higher ADHD symptoms appear to index a worse clinical presentation for depression. Findings suggest that in women with early onset, recurrent depression, the possibility of underlying ADHD masked by depression needs to be considered. It is also possible that specifically treating ADHD symptoms in women with recurrent depression will improve long-term clinical outcomes.10.13039/501100000265Medical Research Council (MR/R004609/1). The cohort was established with funding from the 10.13039/501100000282Jules Thorn Charitable Trust (JTA/06). The work was also supproted by the Wolfson Centre for Young People's Mental Health - established with support from the Wolfson Foundation.The work was supported by the VP and FR conceptualised the study. VP conducted the analysis and drafted the manuscript. DS, JA and RP conducted clinical consensus meetings. AS, BW and JL carried out data collection during the most recent assessment wave. All authors provided critical revision to the manuscript, approved the final version and consented to its publication.The data that support the findings of this study are available on reasonable request from the corresponding author, Victoria Powell. The data are not publicly available due to their containing information that could compromise the privacy of research participants.The authors declare that they have no conflicts of interest.


Background: Patients with congenital heart defects have a well-established risk of neuropsychiatric comorbidities. Inattention and hyperactivity are three to four times more frequent in children with complex congenital heart defects. We have previously shown a higher burden of overall attention deficit/hyperactivity disorder (ADHD) symptoms in adults with simple congenital heart defects as well. However, it is unknown whether the higher burden of ADHD symptoms is mainly driven by hyperactivity, inattention, or both.Methods: The participants  and a close relative for each (n = 107) responded to the long version of the Conners' Adults ADHD Rating Scales questionnaire. Our primary and secondary outcomes are mean T-scores in the ADHD scores and symptom sub-scores.Results: Patients with simple congenital heart defects reported a higher mean T-score at all three DSM-IV ADHD scores  and in all four ADHD symptom sub-scores . The results were maintained after the removal of outliers (incongruent responses), albeit the hyperactivity/restlessness ADHD symptom sub-score lost significance. Self- and informant ratings differed significantly on the ADHD—inattention score for the congenital heart defect group, where informants rated the ADHD—inattention scores better than the congenital heart defect patients rated themselves.Conclusions: Patients with a simple congenital heart defect have a higher symptom burden across all ADHD scores and all symptom sub-scores. The higher burden of ADHD is driven by both inattention and hyperactivity symptoms, though the inattention symptoms seem more prominent. Close relatives were less aware of the inattention symptoms than the congenital heart defect patients themselves. Routine screening for ADHD symptoms may be warranted to facilitate adequate help and guidance as these symptoms are easily overlooked.Clinical Trial Registration:www.ClinicalTrials.gov, identifier: NCT03871881. Congenital heart defects (CHDs) are the most common birth defect found in neonates  . With advice versa in regard to ADHD—hyperactivity. In adults, ADHD has a negative impact on everyday life: ADHD increases the risk of unemployment and thereby lowers the overall income for the affected patient (Attention deficit/hyperactivity disorder (ADHD) symptoms, such as inattention and hyperactivity, are found three to four times more frequent in children with complex CHDs . The sev patient –19. More patient .We have previously found increased ADHD symptoms in patients with simple CHDs, looking at the overall ADHD score . It is uIn this study, we aimed to evaluate the self- and informant-reported burden of ADHD symptoms in young adults with simple CHDs and investigate whether the symptoms are driven by sub-scores of inattention, hyperactivity, or both.The study complies with the Declaration of Helsinki . It is approved by the Danish Central Regional's Committee on Biomedical Research Ethics (chart: 1-10-72-233-17) and the Danish Data Protection Agency (chart: 2012-58-006). It is registered in clinicaltrials.gov (identifier: NCT03871881). All participants provided written informed consent prior to enrollment. The data that support the findings of this study are available from the corresponding author upon reasonable request.We included patients age >18 years with either an isolated atrial septal defect (ASD) or an isolated ventricular septal defect (VSD). We excluded patients with known syndromes , who had a previous stroke, with recent head trauma, who are pregnant, or who lack sufficient Danish language skills.All patients had been diagnosed and treated for their CHD by a homogeneous group of anesthetists, cardiologists, and cardiac surgeons at Aarhus University Hospital, a tertiary referral hospital, in the years 1990 to 2000.The study group was composed of 32 surgically closed VSDs, 34 surgically closed ASDs, 10 percutaneously closed ASDs, and four still open ASDs. The control group is composed of 39 healthy peers matched on age, gender, and educational level .Each participant provided a close relative (informant) to participate in the study as well.Both the participants and their close relatives (informant) were asked to respond to the long version of the Conners' Adult ADHD Rating Scales (CAARS) questionnaire .Most of the participants in this study (except the participants with percutaneously closed ASDs and with open ASDs) have participated in a previous study by Asschenfeldt et al. . Please The CAARS questionnaire consists of 66 questions, and it is designed to screen for ADHD symptoms. As an overall parameter, it provides an ADHD index score which is composed of the questions most likely to differentiate a person with ADHD from a person without ADHD. It is further composed of ADHD scores that differentiate between the DSM-IV diagnosis of the three presentations of ADHD:(1) ADHD—combined, (2) ADHD—inattention, and (3) ADHD—hyperactivity.It also includes four ADHD symptom sub-scores related to the ADHD diagnosis:(1) inattention/memory problems, (2) hyperactivity/restlessness, (3) impulsivity/emotional lability, and (4) self-esteem problems.The CAARS questionnaire is validated, and all ADHD scores and ADHD symptom sub-scores can be standardized to an age- and gender-corrected T-score  based on American normative data. Higher scores indicate a higher presence of symptoms.An incongruent score for each participant can be calculated. A high incongruent score (>8) is most likely due to an unwilling participant, random answering, or a participant answering to make a specific impression. Exclusion of responses with an incongruent score >8 increases the validity of the answers.Though the CAARS questionnaire is based on DSM-IV, the definition of ADHD has remained the same from DSM-IV and DSM-V.Like most other psychiatric questionnaires, CAARS does not, by itself, provide a diagnosis. In this study, we will use the results to provide an estimate of a given burden of symptoms.The percentage of T-scores above 65 (corresponding to the 93rd percentile), representing a great likelihood for a clinical problem, was calculated for each group.n (%) where appropriate. Comparisons between all three groups were done with ANOVA, and when appropriate, post-hoc comparisons between two groups were made with either Welch T-test or Student's T-test.Data are presented as mean (±SD) or as Chi-square test was used to analyze a dichotomous outcome.Our primary outcome was between group difference in the mean T-score of the four DSM-IV ADHD scores: ADHD—index, ADHD—combined presentation, ADHD—inattention presentation, and ADHD—hyperactivity presentation.Post-hoc sub-analyses were done to compare the T-scores of the ADHD symptom sub-scores and to compare the primary outcome between subgroups of the surgically closed ASDs and the percutaneously closed ASDs.Statistical analysis was performed on a with/without-outlier basis. Analysis of all participants was initially performed regardless of their incongruence score; however, in order to control for potential bias of outliers, patients with an incongruent score above 8 were excluded, and all of the above-mentioned analyses were rerun to test the robustness of the results.The Benjamini–Hochberg method was used to correct false discovery rates caused by multiple testing.p-value of 0.05 is considered statistically significant.A The sample size was calculated as in the study by Asschenfeldt et al.  and estiThe groups (CHD vs. controls) were comparable on sex/gender, age, body mass index, and ISCED education level .The participants gave a self-reported status of prior psychiatric diagnosis . More ofAs there were no differences in the mean T-scores between the VSD and the ASD group in any of the ADHD scores or symptom sub-scores, we analyzed these two groups combined as a CHD group .p < 0.001] , < 0.001] . If a pa< 0.001] .post-hoc analysis of the different ADHD scores between the CHD and the control groups to characterize this difference in continuous T-scores.We performed a The CHD group reported significantly higher means across all ADHD scores than the control group .N = 10) compared to ASD patients treated with open surgery (N = 34)  (We found no difference in the ADHD scores between the subgroup of ASD patients treated with percutaneous ASD closure (p = 0.5) .The CHD group reported a higher mean T-score in all of the four ADHD symptom sub-scores than the control group .g effect size was calculated, and a medium effect size between groups on all ADHD scores and three out of four ADHD symptom sub-scores  were found. Only a small effect size was found on hyperactivity/restlessness symptoms , leaving a total number of 36 controls, 28 VSDs, and 41 ASDs .p = 0.50, ADHD—combined: 51.4 vs. 48.4, p = 0.7, ADHD—inattention: 53.1 vs. 53.3, p = 1.0, and ADHD—hyperactivity: 49.5 vs. 45.1, p = 0.5).We again found no difference in the DSM-IV ADHD scores between the subgroup of ASD patients treated with open surgery compared to ASD patients treated with percutaneous ASD closure  and 46.7 (SD 6.1), ADHD-combined score: 62.5 (SD 11.8) and 53.3 (SD 7.0), ADHD—inattention: 65.3 (SD 13.8) and 54.7 (SD 5.5), and ADHD—hyperactivity: 57.0 (SD 8.1) and 50.3 (8.1), inattention/memory problems: 60.6 (SD 8.1) and 47.3 (SD 3.1), impulsivity/emotional lability: 56.7 (SD 10.1) and 44.7 (SD 7.8), self-esteem problems: 62.9 (SD 9.5), and 47.7 (SD 8.4), hyperactivity/restlessness: 57.6 (SD 8.6) and 52.0 (SD 8.0)]. Moreover, 34% of T-scores in the excluded CHD group were above 65 vs. 0% in the excluded control group.The CHD group reported themselves as having more problems at the ADHD—combined score and at the ADHD—inattention score, but not at the ADHD—hyperactivity score, compared to the informant report. There were no differences between self- and informant-reported scores in the control group .The aim of this study was to investigate the distribution in ADHD symptoms in young adults with simple CHD and investigate whether such a burden is mostly driven by inattention, hyperactivity, or both.We found that the CHD group had a higher burden of ADHD symptoms in general  and all related symptoms  compared to healthy controls.Nevertheless, this burden seems somewhat more dominated by inattention and less by hyperactivity symptoms, as the hyperactivity sub-scores reveal smaller effect sizes and did not reach significance after removing the incongruent responses. Typically, inattention symptoms continue into adulthood, whereas hyperactivity seems to burn out with age. This could be why we only find a small effect size in this group of young adults. In accordance to our findings, a higher burden of inattention symptoms, but not hyperactivity, has previously been documented in a combined group of complex (TGA) and simple (VSD) CHD children (age 10–17) when compared to a matched control group . We founThe informants reported the CHD patients better  than the CHD patients reported themselves at the scores for ADHD—inattention. There are different possibilities as to why we find this discrepancy between participants and informants. It could be that the participants over-report their problems because of symptoms of depression or general low self-worth. The discrepancy could also be due to informants being defensive in their responses (“things are better than I expected”), or it could be that the informants are not aware of the struggling at school or work because inattention problems are more “silent” than hyperactivity. Since the discrepancy is only seen with regards to ADHD—inattention, we think the latter to be most likely in this study. This corresponded well with another study that found that parents are less aware of the social difficulties experienced by children with ADHD—inattention . ADHD—inADHD—combined and ADHD—inattention are associated with problems in social performance. ADHD—inattention is associated with social performance problems linked to more passive behaviors. ADHD—combined is associated with more aggressive behavior problems, which, in turn, are more visible to the outside world . InattenBoth patients with complex and simple CHDs have neurocognitive and psychiatric comorbidities compared to the general population –15. Our N = 34 and ASD percutaneously corrected: N = 10). These findings are in line with the above-mentioned notion that other factors than surgical complications may play a bigger role in the development of neurocognitive and psychiatric morbidities in simple CHDs.Different pre-, peri-, and post-surgical complications are known risk factors for early and late neuropsychiatric comorbidities in patients with complex CHD , 29. Resin utero have shown delayed brain maturation, decreased white and gray volumes, immature cortical gyrification, and affected brain metabolism  must be interpreted with caution since symptom criterions tend to overestimate . If a paWe did not include information on socioeconomic status. We deal with young adults who have not yet established their own socioeconomic status, but they are most likely not adequately defined by the status of their parents. Therefore, we chose to match the control group to the CHD group by the educational level of the patients in the belief that the educational level would be the most accurate reflection of a lifetime socioeconomic status. Moreover, in a large meta-analysis, no correlation was found between socioeconomic status and the prevalence of ADHD , but theThe observations made by the participant informants are surrogates for a proper psychiatric evaluation that would lead to a definitive ADHD diagnosis. In this light, we avoid extrapolating the results beyond statements about the difference in burden of ADHD and of related symptoms. As the participants in the CHD group are relatively well functioning, the burden of ADHD and symptoms and the quality of this burden were, in fact, what we wanted to explore.A notable strength of this study is the control group of healthy peers matched on gender, age, and educational level instead of only the normative data provided in the CAARS manual. The CHD group is exclusively simple CHD (ASD and VSD) and not a mixture as most previous studies are. Another strength is that we had evaluations from both the participants themselves and a close relative, adding an extra layer to the understanding. Despite this being a relatively small study, a clinically significant difference was demonstrated.Our control group had fewer high T-scores (above the 93rd percentile) than expected (4 vs. 7%). In general, the mean T-score for our control group was lower than the normative data provided by the CAARS manual . This is an expected finding as this is the case in many European and especially Scandinavian normative datasets –47. ThisYoung adults with simple CHD have a higher burden of ADHD compared with healthy peers. The difference is driven both by introvert symptoms, like inattention, and also extrovert symptoms , like hyperactivity, though the inattention symptoms seem more prominent, and these are more likely to be overlooked by close relatives. Awareness of potential inattention is the first step to managing these difficulties in a child or an adolescent. In this aspect, the treating physician has a special and important role in making the parent or other close relatives aware of these difficulties and their effect on academic achievements.This study emphasizes the long-term neurodevelopmental struggles of patients with simple CHDs and underlines the importance of raising awareness of these hitherto unrecognized life-changing burdens—struggles that we, as healthcare providers (be it nurses or doctors), need to discuss with our patients and their families.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Danish Central Regional's Committee on Biomedical Research Ethics (chart: 1-10-72-233-17). The patients/participants provided their written informed consent to participate in this study.SL-J has contributed with data management, data analyzes and all manuscript writing. BA has conceptualised and designed the study and contributed with funding applications, data collection, input regarding data analyzes, supervision regarding interpretation of results and supervision with manuscript writing. LE and VH have conceptualised and designed the study, they have contributed with general and detailed supervision in all aspects of the data management and manuscript writing.SL-J was funded by The Danish Heart Foundation. The funding payed the salary for the SL-J, but the funder was not included in any aspect of data management, analysis, interpretation or manuscript writing.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.


N = 10,610–766,345). Our results show that EA-related genetic variation is shared across ASD and ADHD architectures, involving identical marker alleles. However, the polygenic association profile with EA, across shared marker alleles, is discordant for ASD versus ADHD risk, indicating independent effects. At the single-variant level, our results suggest either biological pleiotropy or co-localisation of different risk variants, implicating MIR19A/19B microRNA mechanisms. At the polygenic level, they point to a polygenic form of pleiotropy that contributes to the detectable genome-wide correlation between ASD and ADHD and is consistent with effect cancellation across EA-related regions.Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD) are complex co-occurring neurodevelopmental conditions. Their genetic architectures reveal striking similarities but also differences, including strong, discordant polygenic associations with educational attainment (EA). To study genetic mechanisms that present as ASD-related positive and ADHD-related negative genetic correlations with EA, we carry out multivariable regression analyses using genome-wide summary statistics ( Autism Spectrum Disorder and Attention-Deficit/Hyperactivity Disorder are co-occurring neurodevelopmental conditions displaying strong, discordant polygenic associations with educational attainment. Here, the authors study genetic mechanisms underlying genome-wide correlation patterns across these traits. Approximately 15–25% of individuals with ADHD show ASD symptoms, and ~40–70% of individuals with ASD have a comorbid ADHD symptomatology3, although knowledge of shared aetiological mechanisms is scarce.Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD) are genetically complex childhood-onset neurodevelopmental conditions4. There is increasing evidence from twin and genome-wide association studies (GWAS)6 suggesting genetic links between ASD and ADHD symptoms, both throughout population variation13 and at the clinical level14. The largest and most recent cross-disorder GWAS reported at least a hundred loci (as tagged by single variants) with pleiotropic effects on more than one disorder, including ASD and ADHD4. A model of single-nucleotide polymorphism (SNP)-based genetic correlations among multiple psychiatric disorders, using exploratory factor analyses and genomic structural equation models, showed that both ASD and ADHD are part of the same cluster of early-onset neurodevelopmental disorders4. The existence of genetic links between these disorders is further strengthened by the familial co-aggregation of both clinical disorders in large register-based studies15 and the identification of shared copy number variations, suggesting similar biological pathways16.Like many other complex psychiatric disorders, ASD and ADHD are highly polygenic, and the majority of genetic influences can be attributed to common genetic variation17 in molecular studies to 0.87(95%-CI: 0.77–1.0)18 in twin analyses19. Evidence for genetic links between ASD and ADHD symptom co-occurrence can be even stronger in population-based samples8. However, when both, clinical ASD and clinical ADHD, are investigated with respect to a third genetically complex trait, some differences in the genetic architecture become apparent. Each disorder, when predicted with GWAS variants, reveals an opposite genetic correlation with cognitive functioning and educational attainment (EA). While increased polygenic ADHD risk has been linked to lower cognitive abilities and EA21, increased polygenic ASD risk has been associated with higher cognitive functionality and EA22. This discordant association pattern is most discernible for measures of years of schooling and college completion21. Observational research in ADHD strongly confirms the associations with lower school performance and educational outcomes23. Reports of academic achievement in ASD are more variable24, although high-functioning individuals can obtain higher-order qualifications, despite disadvantages in the labour market25.Estimates of genetic correlations between ASD and ADHD diagnosis range from 0.36: 0.26–0.46)26, we define biological pleiotropy as processes where the same gene has a direct biological influence on more than one phenotype. In contrast, spurious pleiotropy involves multiple sources of bias that cause a false association between a gene and multiple phenotypes26. Different causal risk variants in high linkage disequilibrium (LD) with the same marker are described as co-localising variants26. We do not consider mechanisms of mediated pleiotropy, i.e. an indirect association between a genetic variant and a further phenotype that arises due to causal associations between phenotypes26, as this mechanism would imply concordant associations between EA and both ASD and ADHD risk. An overview of candidate mechanisms underlying discordant genetic associations is shown in Fig. The mechanisms underlying the discordant polygenic association pattern with EA are not yet known and may involve different biological effects, including pleiotropy. Following Solofiev and colleagues27. In a large population-based study in Sweden, where free ASD services are available to all regardless of SES, an association between lower SES and higher ASD risk28 has been observed. In contrast, children in low SES families are consistently more likely to receive a diagnosis of ADHD than children in high SES families29. Third (scenario III), opposite GWAS marker alleles may tag opposite causal ASD and ADHD risk alleles at the same risk variant , identical GWAS marker alleles may tag independent ASD and ADHD risk alleles due to high LD26, either at the same locus , risk alleles might be shared across ASD and ADHD liability but exert, because of biological pleiotropy, different genetic effects. Here, different single-variant genetic effects may lead to different polygenic associations with EA , the set of underlying causal variants linking ASD to EA might be independent of the set of causal variants linking ADHD to EA. Here, independent GWAS marker alleles may tag independent ASD and ADHD risk alleles, either residing within regions of low LD at the same gene locus  study evidence for genetic mechanisms presenting as discordant polygenic association pattern with EA, specifically with respect to ASD and ADHD risk, and (ii) identify and annotate underlying ASD and ADHD risk variants. We model polygenic relationships between ASD, ADHD and EA using a multivariable regression (MVR) technique, a multivariate methodology based on summary statistics adopted from a causal modelling approachHD Table  using a 32, studying sets of independent subthreshold GWAS markers as selected for polygenic scoring analyses34. Given that disorder-related GWAS markers can be identified with respect to both ASD and ADHD risk, we adopted a bidirectional approach with two complementary MVR designs:i , across different P-value selection thresholds. Each ASD-increasing marker allele was aligned to an ASD (ASD), ADHD (ADHD) and EA (EA) SNP estimate, and the aggregate association effect with EA across all alleles was simultaneously estimated for ASD (ASD) and ADHD (*ADHD) risk. The “*” symbol indicates the estimation of a polygenic risk effect with EA, here (*ADHD), using alleles that were selected to increase liability for ASD, but are shared by position, here with ADHD risk.For an ASD-MVR model Fig. , we selej , also generated across different P-value selection thresholds. Similarly, each ADHD-increasing marker allele was aligned to an ASD (ASD), ADHD (ADHD) and EA (EA) SNP estimate and their joint association with EA was simultaneously estimated for ASD (#ASD) and ADHD risk (ADHD). The ‘#’ symbol indicates the estimation of a polygenic risk effect with EA, here (#ASD), using alleles that were selected to increase liability for ADHD, but are shared by position, here, with ASD risk.For an analogous ADHD-MVR model Fig. , we usedWe investigated polygenic relationships between ASD, ADHD and EA with a weighted MVR framework , analogous to Mendelian randomization (MR) approachesASD), ADHD (ADHD) and EA (EA) SNP estimates were extracted from GWAS summary statistics based on non-overlapping samples or cases, as provided by large consortia  and ADHD (Gj) variants sets as selected from ASD and ADHD(iPSYCH) GWAS summary statistics, respectively ; shown for simplicity at Pthr < 0.05 and Pthr < 0.0015). These bidirectional MVR analyses, based on SNP estimates from ASD and ADHD(iPSYCH) and EA(SSGAC) summary statistics, showed that inverse polygenic associations with EA can be captured for both, ASD and ADHD risk, using the same set of genetic variants, irrespective of the selection of Gi and Gj variants sets , P = 0.002). Simultaneously, the same ASD-related risk alleles captured a negative association between ADHD and EA with a 0.029 decrease in years of schooling per log odds in ADHD liability (ASD-MVR *ADHD = −0.029 (SE = 0.004), P < 1 × 10−10). ADHD-MVR findings revealed a complementary association profile , this corresponds to an 0.012 decrease in years of schooling per log odds in ADHD liability (ADHD-MVR ADHD = −0.012(SE = 0.003), P = 4 × 10−5), and an increase in 0.022 years of schooling per log odds in ASD liability (ADHD-MVR #ASD = 0.022(SE = 0.003), P < 1 × 10−10)  boosted the statistical power  and ADHD (Gj) variant sets to markers carrying the same risk-increasing allele for both disorders . MVR analyses with concordant variants confirmed the robustness of identified MVR effects, with little evidence for attenuation  and ADHD (ADHD) risk allele effects, but their inverse association with EA (EA), consistent with a positive LDSC genetic correlation between both disorders  from ASD with SNP estimates from the independent ASD(PGC) sample ) and confirmed the discordant genetic association pattern with EA at Pthr < 0.05  captured a positive association between ASD and EA (#ASD = 0.003(SE = 4 × 10−4), P < 1 × 10−10), despite zero genetic correlations between ADHD(iPSYCH) and ASD(PGC)(Supplementary Table i at Pthr < 0.05: ASD = 0.005(SE = 0.001), P < 1 × 10−10, Gi at Pthr < 0.0015: ASD = 0.01(SE = 0.003), P = 0.003). Association patterns remained largely unchanged when the analyses were repeated with concordant variant sets, confirming the robustness of our findings (~50%) compared to the ASD(~80%) sample  and ADHD(iPSYCH) statistics, we identified evidence for shared genetic effects assuming a model of biological pleiotropy/high-LD co-localisation at three independent genomic regions , chromosome 5q14.3  and chromosome 20p11.22-23 (1524 kb: PLK1S1(KIZ)17, BC042893, BC034426, XRN217, NKX2-2, NKX2-417, Nkx2_2as, PAX137, LOC100270679, CR627206, LOC284788)  and ADHD-related (NSNPs = 481) variants from each LD block (LD-r2 < 0.25 within ±500 kb), beyond the assumption of a single causal locus, and repeated ASD-MVR and ADHD-MVR models without applying a P-value threshold  and ADHD(iPSYCH) GWAS summary statistics did not result in the identification of genomic regions with pleiotropic/high-LD co-localisation signals  and ADHD (Gj) variant sets, based on ASD and ADHD(iPSYCH) summary statistics, by creating a grid of marker subsets , we, conditionally, filtered for joint association with ADHD risk at six different thresholds (0.0015 ≤ Pthr < 0.5), resulting in six Gi|j subsets , based on joint association with ASD risk, representing 3.1–37.5% of the discovery ADHD variant set Gj , we estimated ASD effects of 0.15(SE = 0.025)(P = 1 × 10−7) and ADHD effects of −0.15(SE = 0.025) (P = 1 × 10−7) years of schooling, per log odds in ASD and ADHD liability, respectively P = 1 × −7 and ADPthr < 0.0015 for both, ASD and ADHD risk, Gi|j (4.2% of Gi) and Gj|i (3.1% of Gj) comprise the same 83 loci, based on 30 identical and 53 tagged proxy SNPs , of which 99% carry the same risk-increasing allele for both disorders (Supplementary Data P < 2 × 10−4), and suggests locus specificity. Nine of 83 loci represent previously reported GWAS signals , WikiPathways (v20191010) and reported genes from the GWAS Catalog (e96_2019-09-24), rendering patterns of spurious pleiotropy of unconnected genes less likely and strengthening support for the regulatory role of micro RNAs in disorder and behaviour. The most strongly enriched genetic feature includes MIR19A/19B targets -adjusted P-value = 7.7 × 10−4) at genes such as CACNAC1 and ERBB4  Fig.  and 16 fNext, we investigated whether MVR findings for EA (dependent variable) extend to general intelligence, using summary statistics from intelligence(CTG) instead of EA  replicated at Pthr < 0.05 using ASD(PGC) summary statistics, (iii) consistent with the previously reported genetic overlap between EA, ASD and ADHD21, and (iv) independent of the harmonisation of GWAS marker alleles according to ASD or ADHD risk.Using a multivariate analysis approach, we investigated genetic mechanisms embedded in ASD and ADHD genetic architectures that present as discordant polygenic association pattern with EA. We found strong evidence that EA-related genetic variation is shared across ASD and ADHD architectures, consistent with biological pleiotropy or high-LD co-localisation of genetic effects at the same subthreshold ASD- or ADHD-risk associated marker alleles. Discordant EA-related association patterns for ASD and ADHD genetic effects were (i) reciprocally detectable with MVR using either ASD- or ADHD-related risk alleles as selected for polygenic scoring approaches41  passed a joint ASD and ADHD risk variant selection threshold (Pthr < 0.0015) and captured considerably larger polygenic effects than observed in discovery analyses. This set of loci encompasses variants from genomic regions on chromosome 1, 5 and 20 that were identified with high posterior probability for pleiotropy or high-LD co-localisation using gwas-pw36 and harbours known GWAS signals for ASD17, ADHD21 and EA44. Biological annotation of the 83 loci suggested a role for microRNA genes. One of these loci is rs4916723, the single variant with the highest posterior probability for pleiotropy/high-LD co-localization as identified by gwas-pw, which resides near/within LINC00461, pri-miR-34b/c and MIR9-2 genes. miR-9 couples brain neurogenesis and angiogenesis in vertebrates, and is involved in a conserved transcriptional cascade that is critical for brain development45. Consistently, mapping the full set of 83 loci to at least 52 genes, the strongest enrichment was found for MIR19A/19B target genes. miRNAs are key regulators of many biological processes in neurodevelopment46 and involve post-transcriptional regulation of gene expression (often through gene silencing), while their expression can also be shaped by environmental signals46. The identified enriched miRNA targets encode, for example, biological signalling proteins such as the calcium voltage-gated channel subunit alpha1C (CACNA1C) and the tyrosinkinase ERBB4, which have been previously associated with both ASD and ADHD as well as other disorders48.Against the shared polygenic background involving several thousands of subthreshold ASD- and/or ADHD-risk associated variants, a small fraction . This suggests that our findings are robust and unlikely to be affected by diagnostic classification systems for clinical ASD, routes of patient ascertainment or association analysis designs. Moreover, we show here that patterns of polygenic pleiotropy become detectable once SNP estimates from multiple disorders are modelled simultaneously, using multivariate approaches that can detect and de-stratify polygenic signals. For example, across iPSYCH samples discordant ASD and ADHD association effects with EA increased in strength and size once modelled in a multivariate compared to a univariate regression framework  scale44 and analysed as a quantitative variable defined as an individual’s years of schooling. Participants were >30 years of age at the time of assessment and of European ancestry. The meta-analysis consisted primarily of population-based cohorts, but also included family-based and case-control samples. 55.2% of participants were female. For most cohorts, genome-wide data were imputed to a 1000 genomes project version 3 reference template44.GWAS summary statistics on years of schooling (excluding 23andMe) were obtained from the Social Science Genetic Association Consortium  lab , 51.2% were female and all of them were of European descent. Genome-wide data were predominantly imputed to the Haplotype Reference Consortium (HRC) reference panel20.GWAS summary statistics on intelligencehttp://ipsych.au.dk/) using samples from the Danish Neonatal Screening Biobank hosted by Statens Serum Institute54 , ADHD(iPSYCH), Table 17, 21.6% female ADHD-cases21) with shared controls 21, all of European ancestry with age ranges spanning infancy to adulthood21. ASD samples were restricted to ASD-cases without (wo) an additional ADHD diagnosis , Table GWAS summary statistics for ASD and ADHD were accessed through the Danish Lundbeck Foundation Initiative for Integrative Psychiatric Research 54. The median age at first diagnosis of ASD was 10 years. Genotyping was performed using the Illumina Infinium PsychArray BeadChip and genotypes were imputed to a 1000 Genomes template . Genotyping, quality control, imputation and genetic association analysis were carried out using the Ricopili pipeline with standard PGC settings21.ASD cases and ADHD cases were diagnosed according to ICD-10www.med.unc.edu/pgc/). They were based on a case-control/pseudo-control design and all individuals were ≥3 years of age and of European ancestry (ASD(PGC), Table 35. A consensus ASD diagnosis was made using research standard diagnoses and expert clinical consensus diagnoses. The majority of ASD-cases (94.1%) also had a clinical diagnosis based on the Autism Diagnostic Interview-Revised57 or the Autism Diagnostic Observation Schedule58. Genome-wide data were imputed to a 1000 Genomes reference template (Phase1 v3). Note that the sample size for ADHD(iPSYCH) is about three times compared to ASD(PGC).Independent ASD GWAS summary statistics were obtained from the Psychiatric Genomics Consortium 17 and ADHD(iPSYCH)21 (Table 44 and intelligence20. ASD(PGC), ADHD(iPSYCH) and ASD17 GWAS statistics have independent case samples; iPSYCH controls were shared across reported ASD and ADHD(iPSYCH) summary statistics.GWAS summary statistics for ASD(PGC)52, schizophrenia (SCZ)53 and BD53. Cases were identified based on international consensus criteria. For MDD, cases were identified based on a lifetime diagnosis of MDD, established using DSM-III, DSM-IV, ICD-9 and/or ICD-10 criteria or self-report52. For SCZ, the majority of cases were diagnosed using DSM-III, DSM-III-R, DSM-IV, ICD-10, and SCID criteria59. BD cases were diagnosed according to DSM-III, DSM-IV-TR, DSM-IV, SCID, ICD-10 or RDC criteria60. For all three data sets, genotype imputation was performed using the IMPUTE2/SHAPEIT pipeline against the 1000 Genomes Project (v3) template. Summary data were obtained from the PGC  ASD and ADHD(iPSYCH); (ii) ASD(PGC) and ADHD(iPSYCH); for specificity analyses only, we also analysed (iii) ASD and MDD (PGC); and (iv) ADHD(iPSYCH) and BD(PGC), as integrated within METACARPA software2, the proportion of phenotypic or liability variance tagged by SNPs on genotyping arrays, was estimated for EA, intelligence and psychiatric disorders using linkage disequilibrium score (LDSC) regression62  among psychiatric disorders, between psychiatric disorders  and EA, as well as between psychiatric disorders and intelligence  and based on the set of well-imputed HapMap3 SNPs and a European reference panel of LD scores67.All analyses were performed with LDSC software32. This approach was implemented using GWAS summary statistics, often described as Egger regression32. Here, MVR analyses do not infer causality as we allow for biological pleiotropy. We apply this method to simultaneously estimate genetic ASD and ADHD risk associations with EA. We control for collider bias that may arise when adjusting for heritable covariates33 by studying relationships between genetically predicted phenotypes only.We adopted a bidirectional inverse-variance weighted regression framework, MVR, analogous to statistical models proposed for multivariable MRi) and ADHD-related (Gj) variant sets, according to guidelines for polygenic scoring methods34. ASD-related variant sets Gi  and ADHD-related variant sets Gj  were selected from ASD and ADHD(iPSYCH) GWAS statistics respectively, using 11 different P-value thresholds . All variant sets were restricted to common , independent (LD-r2 < 0.25 within ±500 kb) and well-imputed  > 0.7) SNPs. Next, corresponding SNP estimates for ASD  ADHD , resulting in a multiple-testing-adjusted significance threshold of 0.0023.Multivariable MR Egger-related approaches with intercept terms, including MVR analyses applied in this study, are sensitive to allelic alignment. It has been recommended to orient all variants with respect to the genetic association with the independent variable of primary interestPthr < 0.0015 and Pthr < 0.05) were created by restricting ASD variant sets Gi and ADHD variant sets Gj to variants with the same risk-increasing allele for both ASD and ADHD risk. Consequently, by design, SNP estimates were aligned to increase both ASD and ADHD risk in these analyses. As all variant sets were nested within the variant sets used for discovery analysis, the same multiple-testing-adjusted significance threshold was applied (0.0023).To assess whether EA-association patterns with ASD and ADHD were driven by variants that encoded opposite risk alleles for each disorder  from ASD(PGC), instead of ASD, for ASD and ADHD variant sets from the discovery analyses selected at Pthr < 0.0015 and Pthr < 0.05. Within ASD-MVR, SNP estimates were aligned to increase ASD risk as observed in ASD(PGC). In total, four tests were performed (two MVR models across two variant sets), resulting in a multiple-testing-adjusted significance threshold of 0.0125.To replicate MVR findings, ASD-MVR and ADHD-MVR were conducted using ASD SNP estimates (i) and ADHD (Gj) variant sets to variants with the same risk-increasing allele for ASD and ADHD risk (multiple-testing-adjusted significance threshold 0.0125).As for discovery analyses, concordant variant sets were created by restricting ASD (G36 (v0.21) analyses were based on summary statistics for (i) ASD and ADHD(iPSYCH), and (ii) ASD(PGC) and ADHD(iPSYCH), without applying P-value selection criteria. After dividing the genome into approximately independent LD blocks, gwas-pw estimates the posterior probability that a given LD block contains: (model 1) a genetic variant associated with ASD, (model 2) a genetic variant associated with ADHD, (model 3) a genetic variant associated with both disorders, reflecting biological pleiotropy , and carried out ASD-MVR and ADHD-MVR as described for discovery analyses above  and ADHD (Gj) variant sets. Overlapping independent SNPs associated with both ASD and ADHD risk were identified using PLINK , starting with ASD and ADHD variant sets from discovery analyses at Pthr < 0.0015. For each variant set  and ADHD ), we identified SNPs that were, conditionally, also associated with the other disorder across six different P-value thresholds . This resulted in six subsets for ASD-related variants (Gi|j) and six subsets for ADHD-related variants (Gj|i). As conditionally selected variant sets are nested within each discovery set Gi and Gj, respectively, the multiple-testing-adjusted threshold for discovery analyses was applied (0.0023). Note that the notation of conditional variant sets has no mathematical meaning.To identify variants underlying the discordant EA-related association patterns for ASD and ADHD genetic risk, we systematically assessed overlap between ASD  may occur by chance, we performed a permutation analysis. For each permutation, 83 independent SNPs were randomly selected from Gi ) for ASD-MVR, and, likewise, from Gj  GWAS) for ADHD-MVR. Corresponding SNP estimates for ASD (ASD), ADHD (ADHD) and EA (EA) were extracted from ASD, ADHD(iPSYCH) and EA(SSGAC) GWAS statistics, respectively. In total, 10,000 permutations were carried out for both, empirical ASD-MVR and ADHD-MVR analyses.To assess whether association with EA across the set of 83 identified loci  and ADHD-MVR models  were fitted to genetically predictable general intelligence as outcome (Intelligence) instead of EA (EA), using Intelligence(CTG) GWAS summary statistics. For these four additional tests (two MVR models across two variant sets), we applied a multiple-testing-adjusted significance threshold of 0.0125.ASD-MVR (Gi) and ADHD-related variant sets (Gj), encode also discordant EA-related association patterns for adult-onset neuropsychiatric disorders , we carried out sensitivity analyses. For risk-related alleles within the discovery sets Gi and Gj (at Pthr < 0.0015 and Pthr < 0.05), we extracted SNP estimates for MDD (MDD), SCZ (SCZ) or BD (BD) from GWAS summary statistics for MDD(PGC), SCZ(PGC), BD(PGC), respectively. Using ASD-MVR, polygenic ASD effects with EA (θASD) were estimated simultaneously with the polygenic effect of another psychiatric disorder . Likewise, using ADHD MVR (θADHD), polygenic ADHD effects on EA were simultaneously estimated with risk effects for other psychiatric disorders . In total, 12 sensitivity analyses were performed, leading to a multiple-testing-adjusted P-value threshold of 0.0042.To assess whether ASD-  were annotated (Supplementary Data 68 and GWAS hits (P < 5 × 10−8), as recorded in the GWAS Catalog, were identified using the UCSC Genome Browser data integrator tool, build 37 (https://genome.ucsc.edu/cgi-bin/hgIntegrator). Finally, SNPs were mapped to 52 RefSeq gene IDs (genome build 37) based on positional mapping using PLINK software , similar to the default options in MAGMA gene-enrichment software69.The 83 loci identified using a conditional thresholding approach  was conducted with respect to pre-defined gene-sets derived from the Molecular Signature Database (v7.0), WikiPathways (v.20191010), or the GWAS Catalog (v.e96_r2019-09-24). Enrichment analysis was carried out with MAGMA69 (v1.08) using a one-sided hypergeometric test, as implemented in FUMA40 software , by mapping identified genes for the 83 loci (see above) to unique Ensembl IDs (v92). The False Discovery Rate (FDR) was controlled using the Benjamini–Hochberg procedure .Gene-set enrichment (>5 overlapping genes, FDR-adjusted All used web resources are listed in Supplementary Note Further information on research design is available in the Supplementary InformationDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Reporting Summary


Background: Beside the core symptoms, patients with attention-deficit/hyperactivity disorder (ADHD) frequently show relevant difficulty in developing relationships with peers. Although ADHD symptoms may account for social impairment, deficits in cognitive and/or affective empathy have also been involved. Our aim was to investigate the effect of methylphenidate (MPH) treatment on affective and cognitive empathy. Methods: Sixty-one drug-naïve youths with ADHD  naturalistically treated with MPH monotherapy were followed up for 6 months for ADHD symptoms and empathy, measured with the Basic Empathy Scale. Results: After being treated with MPH, the patients showed a significant improvement in affective and cognitive empathy scores. Linear regression models showed that changes in inattention symptoms predicted changes in affective but not in cognitive empathy, while changes in the hyperactivity/impulsivity symptoms did not predict changes in affective or cognitive empathy. Conclusions: Our study provides a further contribution for a better understanding of the possible effects of the MPH on youth’s characteristics. Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder with onset before 12 years of age, with a worldwide prevalence of around 7%, characterized by persistent inattention and/or hyperactivity/impulsivity, with significant social and academic impairment  describeADHD symptoms, per se, may interfere with adequate social interactions. Disinhibition of motor, verbal and behavioral decisions can lead to fewer opportunities for social interaction due to peer rejection. Impulsivity involves inappropriately intruding upon conversations or play , while iRegarding the overlap between ADHD and ASD symptoms, until DSM-5 , the posChildren with ADHD showed impaired AE, compared to normal controls ,18,19, eAttention is a key component of empathic responsiveness in normal developmental processes . InattenPsychostimulants, including methylphenidate (MPH) and amphetamines, the gold-standard for the pharmacological treatment of ADHD ,27, haveThe aim of our study was to investigate the effect of MPH on AE and CE in a group of youths with a primary diagnosis of ADHD, all drug-naïve, after 6 months treatment. We assumed that the MPH treatment would be associated with an improvement in the children’s attention abilities and that this improvement would be associated with an improvement of both the components of empathy. Considering that ASD is a neurodevelopmental disorder often associated with a lack of empathy, we decided to exclude patients with the ASD comorbidity in this pilot study.N = 14; 23%), oppositional defiant disorder , mood disorders , language disorder , anxiety disorders , tics , and verbal dyspraxia . All the subjects were diagnosed according to the DSM-5 criteria [Sixty-six subjects with ADHD as main reason for referral participated in the present study, five of which with ASD comorbidity. The final clinical group included 61 drug-naïve youths aged between 6 and 17 years  with ADHD, 50 with ADHD-C (82%) and 11 with ADHD-I (18%), recruited in our third-level Department of Child and Adolescent Psychiatry from January 2018 to April 2019. None of the patients were undergoing psychological or educational treatment at the time of the recruitment and they all attended school regularly. The comorbidities were: specific learning disorders (criteria . The twocriteria . The inccriteria ) of 80 oAll the 61 patients were treated in monotherapy with MPH during the follow-up. At the baseline, they received a dose-test of MPH . After one week, the starting dose of MPH was increased, with subsequent titrations of 5–10 mg no more frequently than at 5-day intervals, with flexible titration, depending on age and weight, clinical outcome, and occurrence of side effects, based on weekly monitoring visits during the first month, then monthly. The final MPH dosage was 31.6 ± 15.1 mg/day (dose range 5 to 60 mg/day). All the patients were followed-up for 6 months. At baseline and at endpoint, parents completed the ADHD-Rating Scale-IV , and youCategorical diagnosis: The K-SADS-PL [k Cohen ≥ 0.80). Both parents and children participating in the study completed the K-SADS interview independently. The rate of child−parent K-SADS diagnosis agreement was 0.87 (k Cohen).-SADS-PL  was usedChildren’s Intelligence Quotient: Cognitive abilities were assessed with the Wechsler Intelligence Scales for Children—4th Ed. [—4th Ed. .Cognitive and affective empathy: The Basic Empathy Scale (BES) [le (BES)  is a selADHD severity: The ADHD-Rating Scale-IV (ADHD-RS) [ADHD-RS)  is an 18p-value fixed at <0.05. The third step was to test the effect of treatment on all measures, using paired sample t-test. Finally, to test whether changes in inattention and hyperactivity symptoms were associated with changes in children’s AE and CE, we ran four linear regression models. We used the residualized change, which is the difference between the observed score at postintervention and the predicted score at postintervention, in a two-wave model to measure the change in children’s inattention, hyperactivity symptoms, and both AE and CE [All the statistical tests were run on IBM SPSS Statistics 23. The first step of the statistical analysis plan involved missing data handling. In total, 9.32% of the values were incomplete. Missing data were imputed using multiple imputations. Incomplete variables were imputed under fully conditional specification, using the default setting “Impute Missing Data Values (Multiple Imputation)” available on SPSS 23. As a preliminary analysis, we computed descriptive statistics and bivariate correlations of the study variables. The second step was to calculate a post hoc power analysis using the * Power 3.1.9 ; resultsE and CE .p-values was applied across all correlations and regression analysis [To evaluate changes in the AI, HI, and BES scores, we decided to use the residual changes analysis instead of the simple difference scores . We preferred the residualized change scores to the difference scores because they adjust for baseline differences and overcome some of the issues with the reliability of different scores. To obtain the residual change scores, first, we performed a series of linear regressions, using the baseline (T0) scores as independent variables and the follow-up (T1) measures as dependent variables. This allowed us to control for differences in the baseline assessment. Unstandardized residuals  were then saved and used as change scores. The means of all the change scores are equal to zero because they were calculated as residual changes, and the mean of residuals in OLS regressions is always equal to zero. In the first model, we used changes in AE as the dependent variable and changes in inattention symptoms as the independent variable. In the second model, we tested whether changes in hyperactivity symptoms predicted changes in AE. Then, we tested if changes in CE were predicted by changes in inattention or hyperactivity symptoms. In all the regression analyses, we controlled for children’s age and IQ. False discovery rate  correction of the analysis . r = −0.320, p < 0.05) and positively associated with changes in CE . The score of the ADHD-RS-Inattention subscale was positively associated with the score of the ADHD-RS-Hyperactivity-Impulsivity subscale at T0  and at T1 . At the same time, the score of BES-affective subscale was positively associated with the score of BES-cognitive subscale at T0  and at T1 . During the treatment, the inattention symptoms showed a significant decrease , as well as the hyperactivity/impulsivity symptoms . Besides, cognitive empathy improved , as well as affective empathy .Descriptive statistics and bivariate correlations between the study variables are shown in p = 0.143). Furthermore, changes in hyperactivity symptoms did not predict changes in AE  or CE . The only significant model was the one with changes in inattention symptoms predicting changes in AE after controlling for children’s age and IQ   found thIn a meta-analysis, Bora and Pantelis (2016)  found thOur findings suggest that an effective therapeutic intervention on attention in ADHD patients may be associated with an improvement of social cognition and, in particular, of AE. Future studies could explore the effects of MPH on AE also in patients with co-occurring ASD. Some studies demonstrated the positive correlation between empathic competences and executive functions (EFs) in healthy subjects  and in cAt the pharmacological level, the first effect of MPH is related to increased central dopaminergic and noradrenergic activity in the brain regions that include the cortex and striatum, regions involved in the regulation of executive and attentional functions . InteresOur preliminary findings must be viewed in light of several limitations, first, the relatively small sample size. Second, we did not have a healthy control group, or a control group receiving a placebo in a double-blind design. Third, the information obtained from the BES questionnaires presents the limits of all the self-report measures, and it should be integrated with parent reports or experimental paradigms. Fourth, no measures were used to find the socioeconomic level of participants and it should be considered in future studies. Finally, the present study does not consider the effects on empathy of other variables, such as gender  or parenIn conclusion, our study provides a further contribution for a better understanding of the possible effects of the MPH. This evidence supports the notion that the timely and affective treatment of ADHD symptoms may have beneficial effects not only on core symptoms of ADHD, but also on the social difficulties of youths with ADHD. Furthermore, our findings suggest that attention may be a primary target of a pharmacological or psychological or psychoeducational intervention that aims to improve AE in youths with ADHD. Future studies on the association of several measures of empathy with comorbid disorders, such as ASD and disruptive behavioral problems, are warranted. At the same time, considering the absence of a healthy control group, we could not exclude the possibility that the improvement of AE in our patients could be due to learning; about this, a placebo-controlled study might be useful.


Viral infections lead to expeditious activation of the host’s innate immune responses, most importantly the interferon (IFN) response, which manifests a network of interferon-stimulated genes (ISGs) that constrain escalating virus replication by fashioning an ill-disposed environment. Interestingly, most viruses, including rotavirus, have evolved numerous strategies to evade or subvert host immune responses to establish successful infection. Several studies have documented the induction of ISGs during rotavirus infection. In this study, we evaluated the induction and antiviral potential of viperin, an ISG, during rotavirus infection. We observed that rotavirus infection, in a stain independent manner, resulted in progressive upregulation of viperin at increasing time points post-infection. Knockdown of viperin had no significant consequence on the production of total infectious virus particles. Interestingly, substantial escalation in progeny virus release was observed upon viperin knockdown, suggesting the antagonistic role of viperin in rotavirus release. Subsequent studies unveiled that RV-NSP4 triggered relocalization of viperin from the ER, the normal residence of viperin, to mitochondria during infection. Furthermore, mitochondrial translocation of NSP4 was found to be impeded by viperin, leading to abridged cytosolic release of Cyt c and subsequent inhibition of intrinsic apoptosis. Additionally, co-immunoprecipitation studies revealed that viperin associated with NSP4 through regions including both its radical SAM domain and its C-terminal domain. Collectively, the present study demonstrated the role of viperin in restricting rotavirus egress from infected host cells by modulating NSP4 mediated apoptosis, highlighting a novel mechanism behind viperin’s antiviral action in addition to the intricacy of viperin–virus interaction. Rotaviruses (RVs) are the foremost etiologic agents of severe diarrhea  among infants and children <5 years) around the world . It is a years ar2+ concentration by forming aqueous pores in the ER membrane through its viroporin domain [2+] regulates the formation of the viroplasm and also initiates the early stages of autophagy. NSP5, a key component of the viroplasm, has two pseudo-EF-hand Ca2+ binding sites. Binding of Ca2+ to these sites triggers the aggregation of soluble NSP5 into viroplasm-like components  cluster, while the GGE, SNG and ISCDS motifs all appear to function in the binding of SAM. This radical SAM domain catalyzes the conversion of SAM to methionine and a 5′ deoxyadenosyl radical, (5′-dAdo·) which subsequently perform a variety of radical mediated enzymatic reactions such as sulfur insertions, hydrogen abstractions, rearrangements, methylation reactions, DNA repair and cofactor biosynthesis )) )) .The ER fraction of infected or transfected HT-29 cells was isolated by the Endoplasmic Reticulum Isolation Kit , using the manufacturer’s instructions.g at 4 °C) for 10 min. Supernatants were carefully collected in a separate tube and further centrifuged at 7000× g for 15 min at 4 °C. The final supernatant was saved as the cytosolic fraction. The pellets representing the mitochondrial fraction were washed with cold wash buffer , followed by centrifugation at 7000× g for 10 min. The pellets were stored at −80 °C in a freezer unless used immediately. Mitochondrial proteins were extracted by re-suspending the mitochondrial pellets in a buffer containing 7 M urea, 2 M thiourea, 4% CHAPS, 120 mM dithiothreitol (DTT), 2% ampholytes (pH 3–10) and 40 mM tris HCl (pH 5). Pure mitochondrial fractions were isolated by ultracentrifugation using iodixanol as described previously [Mitochondria were isolated from infected or transfected HT-29 cells by the differential centrifugation method. Cells were trypsinized and washed with cold PBS. Cells were re-suspended in freshly prepared cold isolation buffer  and homogenized on ice followed by centrifugation  and pre-cleared by incubating with protein A-Sepharose at 4 °C for 2 h. Next, cell lysates were incubated with specific antibodies overnight at 4 °C, followed by incubation with protein A-Sepharose beads  for 4 h. The beads were washed five times with 1 X lysis buffer, and bound proteins were separated by SDS-PAGE (10%) and transferred to a PVDF membrane. Subsequently, western blot analysis was performed to detect the presence of specific proteins in the immunoprecipitate.Sample-ODBlank) × 100/(ODControl-ODBlank). Data was represented as “cell viability (%)”, considering the cellular viability of untreated control cells as 100%.After specific treatment (mentioned in the results section), HT-29/HEK-293 cells were incubated for 72 h and subsequently, cell viability was measured by MTT assay. Briefly, MTT solution was added to the cells at a final concentration of 0.5 mg/mL and incubated at 37 °C for 4 h. The purple formazan crystals formed after incubation were completely dissolved in 200 μL MTT solvent . Subsequently, the optical density (OD) of the solution was measured at 570 nm, and cell viability was calculated using the formula (ODn ≥ 3). Statistical significance was analyzed by the unpaired Student’s t test. In all experiments, p ≤ 0.05 was considered statistically significant.Data were expressed as mean ± the standard deviation (SD) of at least three independent experiments  at different hours post-infection . Parallelly, the lysate of HT-29 cells treated with IFN-β for 6 h was used as a positive control for viperin expression. Results showed null expression of viperin in mock-infected HT-29 cells a, whereaThe antiviral activity of viperin against diverse groups of viral infections has been well-established. To assess the relevance of viperin induction in RV replication, HT-29 cells, transfected with either cont-shRNA or vip-shRNA, were infected with RV-SA11 at a MOI of 3, and total infectious virus (both extracellular and intracellular) production was measured at 24 hpi by plaque assay. Estimation of virus yield by plaque assay revealed viperin knockdown had no significant impact on the production of total infectious virus particles at 24 hpi a. IntereIn order to identify the antiviral domain of viperin, FLAG-tagged, wild-type viperin and four deletion mutants, namely ∆N-42 , ∆N-210 , ∆C-151  and ∆C-319  were constructed a. ExpresAs an initial approach to identify the potential antiviral mechanism of viperin, we questioned the specific intra-cytosolic location of viperin during rotavirus infection. Viperin has previously been reported to relocalize from the ER to the mitochondria during HCMV infection . With thOn the grounds of previous observations c,e, we hGiven the role of viperin in restricting rotavirus release, we hypothesized that the localization of viperin to the mitochondria might affect the activation of intrinsic apoptosis, previously reported to have a role in rotavirus release . To addr2+ that sequentially activates Bax-mediated intrinsic apoptosis [2+ into the cytosol, HT-29 cells, transfected with either cont-shRNA or vip-shRNA, were infected with RV-SA11 in the presence of cell-permeant Ca2+ chelator BAPTA-AM. Immunoblot analysis of cell lysates revealed increased cleavage of caspase-3 and caspase-9 in viperin-knocked-down cells compared to viperin-expressing cells in the presence of BAPTA-AM (10 µM) at 12 hpi  into the surrounding environment. Recognition of pathogen-associated molecular patterns (PAMPs) by the host pattern recognition receptors (PRRs), notably the retinoic acid-inducible gene I (RIG-I)-like receptors (RLRs), toll-like receptors (TLRs) and nucleotide-oligomerization domain (NOD)-like receptors (NLRs), elicits intricate signaling cascades that result in the expression of IFNs and other early antiviral proteins. Among the three types of IFN, type I IFNs (IFN-α and IFN-β) principally play key roles to establish an antiviral milieu through the activation of the JAK-STAT signaling pathway, which drives the expression of more than 300 interferon-stimulated genes (ISGs). Despite the well-established knowledge regarding the antiviral action of the IFN response, the function of most ISGs remains undetermined. Through in vitro cell culture studies, only a handful of ISGs such as protein kinase R (PKR) , 2′-5′-oTo date, the fundamental molecular mechanism that underprops viperin-arbitrated antiviral action varies among different viruses. Viperin inhibits productive HCMV replication by prohibiting the expression of early–late (pp65), late (gB) and true late (pp28) proteins that are requisite for virus assembly and maturation . ViperinThe specific domain of viperin indispensable for antiviral action differs among viruses and remains undefined for many viruses. Anti-CHKIV activity of viperin is dependent on its ER localization through the N-terminal amphipathic α-helical domain . The cenViperin is generally localized to the cytosolic face of the ER through its N-terminal amphipathic helix embedded in the ER membrane, where it is believed to play an antiviral role against SINV ,34, inflThe role of intrinsic apoptosis in the release of rotavirus from the infected host cells is well-established ,75. Give2+ into the cytosol, which subsequently stimulates Bax-dependent activation of the intrinsic apoptosis pathway [2+ into the cytosol (In rotavirus-infected cells, NSP4 can trigger intrinsic apoptosis by two different pathways. First, NSP4, by using its viroporin domain, creates a transmembrane aqueous pore in the ER membrane, leading to the release of Ca pathway ,75,76. I cytosol . Second, cytosol e,f. ThusIn conclusion, the study highlights the underlying molecular mechanism by which viperin exerts its antiviral action during rotavirus infection. We propose that rotaviral NSP4 drives the relocalization of viperin from the ER to the mitochondria, where viperin, in turn, limits the release of Cyt c into the cytosol, resulting in the inhibition of intrinsic apoptosis to restrict rotavirus release. Therefore, our study delineates a novel stratagem adopted by the host to restrict rotavirus egress and uncovers a new dynamic of virus–viperin interaction, which sheds light on the intricate relationship between host and viral proteins for successful infection.