U test. In total, 29,929 patients met study criteria . ASD was associated with increased procedures , including intubation and ventilation ; and outpatient specialty care, including physical therapy  and ophthalmology . ADHD was associated with increased procedures , including blood transfusion ; hospital admission ; and ED visits . Median length of stay was increased after birth in ASD  and ADHD , and after non-birth admission in ADHD  and ASD + ADHD . Each condition was associated with increased health system utilization and distinctive patterns of utilization before age 1. Recognizing these patterns may contribute to earlier detection and intervention.Children with autism spectrum disorder (ASD) or attention deficit hyperactivity disorder (ADHD) have 2–3 times increased healthcare utilization and annual costs once diagnosed, but little is known about their utilization patterns early in life. Quantifying their early health system utilization could uncover condition-specific health trajectories to facilitate earlier detection and intervention. Patients born 10/1/2006–10/1/2016 with ≥ 2 well-child visits within the Duke University Health System before age 1 were grouped as ASD, ADHD, ASD + ADHD, or No Diagnosis using retrospective billing codes. An additional comparison group was defined by later upper respiratory infection diagnosis. Adjusted odds ratios (AOR) for hospital admissions, procedures, emergency department (ED) visits, and outpatient clinic encounters before age 1 were compared between groups via logistic regression models. Length of hospital encounters were compared between groups via Mann–Whitney  Both diagnoses lead to higher rates of healthcare utilization9 and place considerable financial burden on affected individuals and their families17. Rates of hospitalization and outpatient clinic visits among children with ASD are approximately twice that of the average child, contributing to a two- to threefold increase in annual costs18 comparable to other chronic medical diagnoses13. Children with ADHD visit the emergency department (ED) approximately twice as often as those without7, with even higher rates reported among those with comorbid psychiatric conditions19. These factors are compounded in children with co-occurring ASD and ADHD, who incur higher healthcare costs than children with either disorder alone20.Autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) affect approximately 1.5%21. More recently, analysis of nationwide registry data from Denmark demonstrated that rates of medical service use were 1.8 times higher in children later diagnosed with ADHD, and co-occurring ASD further increased this difference22. These results suggest that increased utilization may be characteristic of ASD and/or ADHD, and not simply a consequence of recognizing and treating them. However, similar findings have not yet been reported in the United States, and specific health services more commonly used by children later diagnosed with ASD and ADHD have not yet been identified.These findings demonstrate that individuals with ASD and ADHD utilize greater healthcare resources once diagnosed, but little is known about their health system utilization early in life, prior to diagnosis. Emerging evidence suggests that diagnosis is preceded by distinct patterns of increased utilization. For example, a study of newly diagnosed ADHD patients in Germany found that healthcare costs were elevated in the year before diagnosis25, preterm birth, low APGAR scores, and other perinatal complications27 that may lead to procedures and increase patients’ length of stay. Early ASD-related comorbidities28, such as postnatal hyperbilirubinemia29 and respiratory infections30, also appear in the EHR as possible indicators of increased risk. Problems with crying, sleeping, and feeding more common in infants with ADHD31 and ASD32 may lead parents to seek additional support, leading to higher use of specific health services. Utilization patterns may be unique among children with co-occurring ASD and ADHD, who are often diagnosed with ASD much later than children without ADHD symptoms34. While some risk factors for ASD and ADHD are based on physiologic or diagnostic findings that are not always available, others relate to health interactions readily accessed through most EHR systems.Based on known early risk factors for both disorders, distinctive patterns of utilization are likely to be present in the electronic health record (EHR) from an early age. Both ASD and ADHD are associated with lower birth weight40 and ADHD43 and reduced healthcare costs44. Multiple studies have illustrated the potential of EHR-based prediction models45, which can utilize existing EHR data standards to analyze a very large number of predictors46. This approach can also uncover unknown risk factors to further illuminate the early trajectories and biological bases of neurodevelopmental disorders and other pediatric conditions. Unlike data from Medicaid claims or health maintenance organization registries47, EHR data can be immediately analyzed within the hospital system itself to inform provider decision-making during routine care.Identifying EHR-based risk factors is a critical step toward earlier referral, diagnosis, and treatment, which in turn lead to markedly improved outcomes in both ASD50; and (c) distinct patterns of interactions were present in each disorder. Our primary aim was to improve our understanding of the early trajectories of children with ASD and ADHD, contributing to earlier, EHR-based risk stratification. A secondary aim was to illustrate a framework that could be used to analyze early healthcare trajectories in other pediatric conditions.In this study, we retrospectively analyzed EHR data collected over a 12.5-year period in a large U.S. health system to compare health system utilization before age 1 between children later diagnosed with ASD and/or ADHD and other children. Analysis focused on hospital admissions, emergency department (ED) visits, outpatient clinic encounters, and procedures across multiple health services. We hypothesized that (a) children later diagnosed ASD and/or ADHD interacted with the health system at higher rates, including increased rates of admissions, ED visits, and non-routine procedures; (b) these children visited specific medical specialties at higher rates, including neurology and gastroenterology in ASDAll results are based on retrospective data analysis approved by the Duke Health Institutional Review Board and conducted at the Duke University School of Medicine (2/1/2019–10/1/2019). Analyses were executed within the Duke Protected Analytics Computing Environment (PACE), a highly protected virtual network space designed for protected health information. Participant consent was waived due to the minimal risk posed by study procedures and infeasibility of obtaining consent in a large retrospective cohort.Analyses were based on inpatient and outpatient encounters within the Duke University Health System (DUHS), a large academic medical center based in Durham, North Carolina. DUHS is comprised of Duke University Hospital, a large (957 bed) facility that includes a regional emergency/trauma center and major (51 operating rooms) surgery suite; Duke Regional Hospital, a medium (369 bed) facility serving Durham and 5 surrounding counties; Duke Raleigh Hospital, a medium (189 bed) facility serving Raleigh and Wake County; and a large network of outpatient facilities, including 30 Duke Primary Care locations. The surrounding region, called the Research Triangle, includes the cities of Raleigh, Durham, and Chapel Hill, and has an estimated population of 2,079,687 (2019 census). Records were extracted from the current (2013—present) DUHS EHR, which is based on the platform developed by Epic , as well as several EHR platforms operating prior to 2013.Study inclusion criteria were (1) date of birth between 10/1/2006 and 10/1/2016; and (2) ≥ 2 outpatient well-child visits within the DUHS before age 1. Criterion (2) was designed to limit analyses to patients likely to have received routine care through DUHS before age 1. Well-child visits were identified using visit type fields present in current and legacy EHR systems. DUHS encounters between 10/1/2006 and 2/1/2019 were extracted for patients meeting criteria. Children diagnosed with ASD or ADHD before age 1 were excluded to ensure that observed health system interactions were not a consequence of an existing diagnosis.Five mutually exclusive diagnosis groups were defined based on billing codes from the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) and Tenth Revision, Clinical Modification (ICD-10-CM). Problem list-based diagnoses were excluded due to concerns regarding data quality and availability.51; or ICD-10-CM code F84.0, F84.8, or F84.9. ADHD was defined as ICD-9-CM code 314.0, 314.01, 314.1, 314.2, 314.8, or 314.9; or ICD-10-CM code F90.0, F90.1, F90.2, F90.8, or F90.9. Code selection was guided by literature review and the authors’ knowledge of billing practices.Patients with an ASD or ADHD diagnosis were grouped as ASD , ADHD , or ASD + ADHD. ASD was defined as ICD-9-CM code 299.00, 299.80, or 299.9052.Patients without an ASD or ADHD diagnosis were grouped as upper respiratory infection (URI) or No Diagnosis. URI was chosen to serve as a comparison group due to its status as a common but non-chronic diagnosis. URI was defined as any ICD-9-CM or ICD-10-CM code categorized as URI by Clinical Classification Software (CCS) developed by the Agency for Healthcare Research and QualityAnalyses focused on rates of hospital admissions, outpatient clinic visits, ED encounters, and procedures among patients in the five previously defined groups. Hospital admissions were further categorized by discharge service rather than admission service, which was not reported prior to 2013. Similarly, outpatient clinic visits were further categorized by clinic service or specialty. Discharge and clinic services were grouped into clinically meaningful and interpretable service types eTables . ED encoBirth encounters  were analyzed separately from other admissions. Encounter length was calculated as the time difference between arrival and discharge (non-birth encounters) or birth and discharge (birth encounters).Medicaid status was determined based on documented insurance status associated with all encounters before age 1. Patients whose status was most commonly documented as “Medicaid”, “NC Medicaid”, or “Medicaid Pending” were grouped as “Medicaid”. This group includes patients receiving assistance through the Innovations Waiver program offered to children with documented developmental disabilities by the Division of Mental Health, Developmental Disabilities and Substance Abuse Services within the North Carolina Department of Health and Human Services. Patients whose insurance status was not documented in any encounter were grouped as “Unknown”. Other patients were grouped as “Non-Medicaid”.U test, and (b) comparing the proportion of patients with at least one occurrence (i.e. rate of occurrence) by two-tailed two-sample proportion test. Differences in length of stay were assessed by two-tailed Mann–Whitney U test. Mann–Whitney U tests (rather than parametric tests) were selected because count and length of stay data are non-negative and long-tailed.Group differences in demographic variables  were assessed via chi-square test. Encounters and procedures before age 1 in each category described previously were counted for each patient. Group differences in encounters and procedures were then assessed by (a) comparing counts by pairwise two-tailed Mann–Whitney p < 0.0056 were considered statistically significant.Raw p-values have been reported throughout, but statistical significance was determined by applying Bonferroni correction with an initial threshold of 53 with Python 3.654. An additional binary variable  was included to control for differences in encounter and procedure reporting between the current, Epic-based EHR platform and legacy platforms prior to 2013. Two-tailed t-tests were applied to all model parameters, and those with Logistic regression was used to control for sex, race , ethnicity , and Medicaid status when comparing encounter and procedure rates between groups. Models were implemented in Statsmodels v0.11All methods were carried out in accordance with the relevant guidelines and regulations. Study procedures were approved by the Duke Health Institutional Review Board and comply with institutional policies and federal regulations. A waiver of participant consent was approved due to the minimal risk posed by study procedures and infeasibility of obtaining consent in a large retrospective cohort. Analyses were executed within the Duke Protected Analytics Computing Environment (PACE), a highly protected virtual network space designed for protected health information.Records for 200,423 unique patients born 10/1/2006–10/1/2016 were initially extracted, and 29,931 had ≥ 2 well-child visits before age 1. T wo patients were excluded due to ADHD diagnosis before age 1, leaving 29,929 patients who met study criteria. Demographics by group and age at diagnosis are presented in Table Among patients meeting criteria, unadjusted ASD and ADHD prevalence were 1.6% and 4.4%, respectively. Co-occurring ASD + ADHD (N = 140) was identified in 29.0% of patients with ASD and 10.6% of patients with ADHD. Mean age at ASD diagnosis was 1.4 years higher in patients with co-occurring ADHD (5.5y vs. 4.1y), whereas mean age at ADHD diagnosis was 1.3 years lower in patients with co-occurring ASD (5.9y and 7.2y).Χ2 = 133.6, dof = 6, p < 0.001) and ethnic status , but ASD diagnosis was not . Both diagnoses were associated with Medicaid status . Among patients with known insurance status, Medicaid rates were 52.9% and 60.5% in the ASD and ADHD groups, respectively, compared to 42.3% in the No Diagnosis group.Male to female ratios were 3.6 for ASD, 2.5 for ADHD, and 7.2 for ASD + ADHD. ADHD diagnosis was strongly associated with racial , although only the latter reached statistical significance, and in ADHD . In contrast, ED visits were more likely in ADHD , but not in ASD . Hospital admission, procedures, and ED visits were increased in ASD + ADHD but not statistically significant due to smaller group size . These rates were not increased in URI  were born within DUHS. Before age 1, 8.8% were admitted (non-birth) to a DUHS hospital, 8.8% underwent a procedure, and 30.2% visited an ED. Hospital admission and procedures were more likely in ASD , but procedures were not . Rates of hospital admissions, procedures, and ED visits were also higher among African American, Hispanic/Latino, and Medicaid patients and lower among White and Asian patients , ADHD , and ASD + ADHD  compared to No Diagnosis (12.5), whereas it was lower in URI .All patients had ≥ 2 outpatient clinic encounters for inclusion, but the mean number of these encounters was higher in ASD . ASD and ADHD groups were both more likely to visit neonatology , speech pathology , and ophthalmology , but these increases were more pronounced in ASD.Distinct patterns of outpatient clinic encounters were observed in ASD versus ADHD. Visits to medical and surgical specialists were increased Fig. , with thp < 0.0001) and twofold in ADHD , although only the former was statistically significant. In contrast, adjusted odds of visits related to child abuse and neglect were increased over fivefold in ADHD only . Adjusted odds of diagnostic visits  were also higher in ADHD .Adjusted odds of physical therapy were increased almost threefold in ASD , whereas endocrinology, gastroenterology, and neurology visits were over threefold more likely in ASD  , whereas ear, nose, and throat visits were twice as likely in ASD  , African American , and Medicaid  patients, higher rates of cardiology visits among African American , Hispanic/Latino , and Medicaid  patients, and higher rates of audiology  and speech pathology visits  among Hispanic/Latino patients (eTable p < 0.0001), labs and imaging , and urgent care .Statistically significant differences between demographic groups included higher rates of general surgery visits among male , although not statistically significant; ADHD ; and ASD + ADHD . Enteral and parental nutrition were also more likely in all three groups, although not statistically significant in ASD + ADHD .Distinct patterns of procedure occurrence before age 1 were also observed in ASD versus ADHD , but adjusted odds were also increased in ADHD . Blood transfusions were over four times more likely in ADHD  and in ASD + ADHD , but not in ASD .Intubation and ventilation were twice as likely in ASD  and statistically significant in ADHD , higher rates of discharge by Neonatology were found only in ADHD , and in ASD + ADHD .Patients were discharged by Pediatrics in 79.7% of all non-birth hospital admissions, therefore adjusted odds of discharge by Pediatrics eFigure  are simip < 0.0001) and over two days longer in ASD + ADHD  compared to those in the No Diagnosis group (median = 2.67d). The typical hospital stay after birth was 6.5 h longer in ASD , 3.8 h longer in ADHD , and 5.8 h longer in ASD + ADHD  compared to those in the No Diagnosis group (median = 2.28 days)  was over a day longer in ADHD  than either disorder alone. More broadly, this work demonstrates that ASD and ADHD have profound effects on health and health system resources prior to diagnosis. Thus, EHR platforms provide an opportunity to monitor health interactions early in life to stratify patients’ risk of developing ASD and ADHD, and may offer a unique window into the early trajectory and biological bases of these conditions.This is the first study reporting health system utilization among individuals with ASD, ADHD, and co-occurring ASD + ADHD in their first year of life, prior to diagnosis. Analysis drew on records from over 200,000 unique patients visiting DUHS over a 12.5-year period, almost 30,000 of whom met study criteria. Results corroborate studies from Europe demonstrating increased utilization prior to ADHD diagnosis, extend this finding to ASD and co-occurring ASD + ADHD, and highlight distinct interaction patterns underlying the increased rates of utilization observed in both disorders. We also highlight the demographics of co-occurring ASD + ADHD, which was associated with a 1.4 year delay in ASD diagnosis27, for example, but our results show that these associations have measurable impact on a health system level. Additionally, many other findings were unexpected. In ASD, increased visits to gastroenterology and neurology were expected, but increased visits to ophthalmology and endocrinology were not; this finding suggests that these comorbidities28 also have substantial impact on early utilization. In ADHD, increased odds of blood transfusion and visits related to child abuse and neglect are consistent with known associations with ADHD55, including problems in infancy31 and increased parental stress previously observed in infants with ADHD12, but are striking nevertheless.Many of these findings are not surprising given current knowledge of characteristics and comorbidities associated with ASD and ADHD. Higher rates of clinic follow-up, hospital admission, and intubation may result from the association with perinatal complications found in both disordersThe size and number of statistically significant effects suggest that early health interactions contain valuable information about ASD and ADHD risk, implying that health services data from the EHR could inform risk stratification. Given the importance of prompt ASD and ADHD diagnosis, further work is needed to determine how this information might be leveraged to inform clinician decision-making and/or health system policy. Risk monitoring systems could be implemented within the EHR itself, for example, in order to automatically alert the families and/or providers of children whose ASD or ADHD risk is high. Follow-up work should not only implement and validate risk monitoring, but also explore the effectiveness of a range of interventions aimed at families, individual providers, and the health system as a whole. Moreover, this paradigm could be applied to promote earlier identification and intervention in a wide range of pediatric conditions.The current findings demonstrate that patterns of health system utilization associated with ASD and ADHD, such as longer hospital stays after birth, emerge in the first few days of life, and many more emerge within the first year. Increases in specific forms of utilization, such as ED visits, are not limited to ASD or ADHD, therefore additional work is needed to determine which utilization patterns—or combinations of patterns—have high predictive value. Subsequent work will focus on the development of predictive models that can be deployed and iteratively evaluated in clinical workflow.., asthma, congenital heart disease), and on understanding differences between demographic groups, including disadvantaged groups, in greater detail.Results are observational and associative, and should be interpreted with a degree of caution due to possible confounding effects. Analyses controlled for sex, race, ethnicity, insurance status, and an institutional transition between EHR systems that affected the reporting of some encounter types. Socioeconomic status and other potentially relevant covariates, such as family characteristics, were not consistently available. Importantly, our analysis did account for a potentially impactful confounder, namely, the presence of a diagnosis after age 1. By including the URI comparison group, we demonstrate that selecting children with a non-chronic diagnosis who remain engaged with the health system does not produce similar effects to those observed in ASD and ADHD. Future work should focus on comparisons to other chronic health conditions  implies that patients’ ages ranged from 2.3 to 12.3 years at the end of the observation. Patients in our cohort may have been (or still be) diagnosed with ASD or ADHD subsequent to observation, or at facilities outside of DUHS. Thus, analyses should be viewed as comparing patients with known ASD and ADHD diagnoses to all other patients, including those with other medical and psychiatric conditions, rather than a cohort of typically developing children. Results demonstrate that children later diagnosed with ASD and ADHD utilize health system resources at higher rates than the average child.56. Further, analysis was based on DUHS encounters only, whereas patients may have received additional care outside of DUHS. Study criteria  were designed to identify patients receiving routine care within DUHS, but their effectiveness is uncertain. These limitations are consistent with our overarching goal to identify EHR-based predictors of ASD and ADHD, but results may not generalize to health systems and/or geographic regions with different demographic makeup compared to DUHS and its surrounding area; or to other sources of health utilization data, particularly non-EHR data sources.EHR-based analyses are affected by known data quality issues, including unreliable race and ethnicity reporting. Our analysis groups do not reflect gold standard diagnoses, and identification of ADHD by diagnosis codes was 70–75% accurate across 10 institutionsASD and ADHD are associated with increased health system utilization in the first year of life, prior to diagnosis. Moreover, the two disorders are associated with distinct patterns of early health interactions that could be monitored through the EHR to stratify patients’ risk of developing ASD and ADHD. Subsequent work will focus on developing predictive models that could be deployed within a health system to inform provider decision-making, contributing to earlier diagnosis and treatment.Supplementary information


Ferritin status during prenatal brain development may influence the risk of attention deficit and hyperactivity disorder (ADHD) symptoms in childhood. We investigated the association of maternal ferritin in pregnancy and ADHD-like symptoms in offspring. A total of 1095 mother-child pairs from three birth cohorts of the INMA Project (Spain) were studied. Maternal plasma ferritin in pregnancy was measured at 11.57 weeks of gestation. Children′s ADHD-like symptoms at ages 4–5 years were assessed using the ADHD Rating Scale-IV. The count model of the zero-inflated Poisson regression model showed a significant inverse association between ferritin (continuous variable) and inattention, β = −0.19 , for boys. Comparing ferritin level by tertiles, significant differences were observed between the first tertile  and the second  and third tertiles  (mg/L).The number of symptoms was lower for those in the third tertile, β = −0.3 , and for those in the second one, β = −0.37 . The model stratification by sex also showed this inverse association for boys only, β = −0.21 . No associations were found between ferritin level and hyperactivity or total ADHD symptoms. High ferritin levels during pregnancy show a protective association with child inattentive-type ADHD symptoms. Attention deficit/hyperactivity disorder (ADHD) is the most frequent childhood-onset neuropsychiatric condition, with an estimated worldwide prevalence of approximately 5% in school-aged children . ADHD syDespite ADHD being the most studied neuropsychiatric condition in child psychiatry worldwide , the etiThe human brain is highly sensitive to environmental exposure occurring during particular periods of vulnerability ,9. In eaIn recent years, it has been suggested that iron deficiency (ID) may contribute to behavioral and cognitive dysfunctions . Iron isFurther evidence supports the hypothesis that a lack of brain iron might contribute to the pathophysiology of ADHD ,17. FirsThe role of serum ferritin levels as a reliable measure of iron stores in body tissues, including the brain, in children with ADHD has been studied in recent years. A meta-analysis  and a syTherefore, the objective of the present study was to evaluate the relationship between prenatal ferritin levels with infant ADHD symptoms. For that, the association between ID during pregnancy measured by ferritin levels in maternal serum and ADHD symptoms in children aged 4–5 years was assessed. Findings might have important implications for clinical practice, regarding prenatal iron supplementation.The INMA  Project , a multiMaternal whole blood samples were collected during pregnancy (mean [SD] 13.3 [1.5] weeks of gestation) by venipuncture under fasting conditions and stored between −70 and −80 °C until analysis. Maternal plasma ferritin concentrations were quantified in samples from Gipuzkoa and Sabadell cohorts by time-resolved fluorescence immunoassay (DELFIA Ferritin kit A069–101), at the Gipuzkoa Public Health Laboratory, and in samples from the Valencia cohort by immunoturbidimetry  at La Fe hospital.2) and breastfeeding (weeks of any breastfeeding). Anthropometric measures at birth and the sex of the infant were obtained from the child’s medical records. Deliveries before 37 weeks of gestation were defined as preterm births. Gestational age was calculated from the date of the last menstrual period reported at recruitment and confirmed using ultrasound examination at week 12 of gestation. Whole blood samples were collected by venipuncture of cord vessels before the placenta was delivered for the measurement of mercury in the newborn. At 14 months of age, various types of data related to the children were collected including daycare attendance, birth order among siblings (first/not first), whether they were living with their parents (both/only one) and the number of people in the household. The exact age of the child was also recorded at the time of the assessment of ADHD-like symptoms.Data on maternal and child characteristics were collected through two questionnaires administered during face-to-face interviews at different points in the first and third trimester of pregnancy, at birth, at 14 months after birth and when children reached 4–5 years of age. We used data on maternal country of origin (Spain/other), maternal social class , maternal education , maternal age, maternal mental health , maternal intelligence quotient, parity, smoking during pregnancy (yes/no), alcohol intake during pregnancy (yes/no), pre-pregnancy body mass index (BMI) . The difference in the mean number of symptoms was analyzed using the Student’s t-test (for two samples) and one-way analysis of variance (more than two samples). p-value ≤ 0.20 in the bivariate analysis were added to the base models (ferritin level and ADHD symptoms). These variables were maintained in the models if they changed the magnitude of the main effects by more than 10% or were significant. The final model was adjusted for: cohort , maternal smoking during pregnancy (yes/no), alcohol intake during pregnancy (g/day), pre-pregnancy BMI and social class , the child’s sex, age at the time of the test and whether they were living with parents (both/only one) and the week of sample collection for ferritin analysis. For the count model of the association of inattention symptoms with ferritin levels, the interaction between ferritin and sex was significant, and hence results are shown separately for boys and girls. The final model was also stratified by cohort and sex of the child. Statistical analyses were conducted using R version 3.6.1. Due to the excess of zeros in symptom ratings, the association between ferritin level and symptoms was analyzed using zero-inflated Poisson regression models. This type of model has two parts, a Poisson count model and a logit model for predicting excess zeros. The level of ferritin was entered in the models in two ways: as a continuous variable and categorized in tertiles ) to study the trend across the tertiles. Initially, variables with a Overall, 1095 eligible mother-child pairs were included in this study . The socThe mean maternal ferritin level was 35.9 mg/L (standard deviation: 26.81 mg/L). In the univariate analyses, the total ADHD symptom score was higher in boys than girls. Further, total ADHD symptom scores were higher in children of mothers with higher BMI, lower alcohol intake, worse mental health and lower education and social class, who smoked and did not breastfeed . p = 0.002). In contrast, no significant association was found between ferritin level and hyperactivity/impulsivity or total ADHD symptom scores , in boys. In addition, comparing ferritin levels by tertiles for boys, there were significant differences between the first and second tertile, β = −0.3 , and the first and third tertile, β = −0.37 , with a significant trend , and also when comparing the individuals by tertiles: the expected change in the number of symptoms was lower for those in the second tertile, β = −0.32 , and for those in the third one, β = −0.4  (p for trend 0.001). In contrast, the count model for inattention showed a positive association for the girls in the third tertile of ferritin levels, β = 0.58 , compared with those in the first one (p for trend 0.012). Similarly, when considering the total number of ADHD symptoms, a negative association was observed for the boys in the third tertile, β = −0.22 . The zero-inflation model for ADHD symptoms showed a positive association, β = 0.6 , for girls in the second tertile and a positive association was also observed when taking ferritin level as a continuous variable, β = 0.39  .The present study assessed the association between maternal ferritin levels during pregnancy and ADHD symptoms in their offspring when the children reached 4–5 years of age. Significant inverse associations were observed between maternal ferritin level during pregnancy and symptoms of inattention in boys. The stratification by sex of the count model also showed this negative association between inattention symptoms in boys and ferritin levels, both analyzed continuously and by tertiles. In contrast, in girls, a positive association was observed with ferritin levels in the third tertile compared with the first tertile. This positive association was also found for the zero-inflated model of total ADHD symptoms in girls.There is a large body of evidence about iron deficiency during pregnancy and maternal and fetal outcomes . Two recThe involvement of iron in several biological processes has been described previously . NeverthSerum ferritin reflects the status of iron stores in the body: bone marrow stores are only depleted when the level falls below 10 ng/mL and then anemia develops. The role of iron deficiency with and without anemia and developmental/cognitive deficits has been demonstrated in various neurologic and developmental disorders in both laboratory and clinical studies . As ironHuman studies have shown that iron deficiency and iron-deficiency anemia in infants are associated with behavioral impairment ,46, but ADHD is a complex multifactorial condition in which multiple genes and environmental factors act together to affect individual risk and contribute to an ADHD phenotype and clinical presentation. The heritability of ADHD was estimated at 72% based on 1316 child and adolescent twin studies . On the Various models have indicated that dopamine is a key element of ADHD pathophysiology. First, dopamine is associated with the modulation of psychomotor activity and executive functions, which are core features in ADHD. Second, molecular genetic studies of ADHD have focused on the genes involved in dopaminergic function, especially the dopamine D4 receptor gene and the dopamine transporter gene (DAT1) . Third, In our study, total ADHD and inattention symptom scores were also predicted by maternal years of study, social class, maternal smoking during pregnancy, maternal BMI and breastfeeding. ADHD has traditionally been associated with a range of indicators of social and economic disadvantage including poverty, housing tenure, maternal education, income, lone parenthood and younger motherhood; the association of ADHD with socioeconomic disadvantage has found several potential explanatory pathways often operating through differential exposure . Such exFinally, in our study, breastfeeding was not a predictor of total ADHD symptoms or inattention in 4 to 5-year-old children according to the multivariate model, though it was significantly correlated with ADHD in the bivariate analysis. A robust body of research literature has reported on the short-term medical, developmental and emotional benefits of breastfeeding for infants and toddlers ,60. A reIn accordance with previous studies, we observed sex differences in our results regarding inattention, hyperactivity/impulsivity and ADHD symptoms. Differences have been reported previously ,62,63, wOverall, some of these differences may be partly explained by diagnostic and ascertainment biases. Nonetheless, it is likely that most are due to sex biological differences . There iA hypothesis that could explain the sex-specific association of ferritin and ADHD symptoms is related to acetylcholinesterase (AChE) activity. AChE activity has been associated with deficits in neurodevelopment, particularly attention, inhibition and memory, in boys but not in girls . In laboAnother explanation could be related to mitochondrial function in brain cells, since mitochondrial proteins have been related to neurodevelopmental abnormalities and to the pathophysiology of ADHD . Iron ovFinally, in our study, ferritin levels in maternal serum at week 12 of pregnancy predicted symptoms of inattention; interestingly, maternal ferritin level was not a predictor of hyperactive and impulsivity symptoms. Previous studies have already suggested how different ADHD presentations or subtypes may correlate with different etiopathological models . Our resThis study has a number of limitations which should be recognized. First, the study explored ADHD symptoms assessed using a rating scale based on DSM-IV criteria; however, a confirmed clinical diagnosis was not available. In addition, we only have a single measurement of ferritin during pregnancy, and it might not be a representative measure of ferritin levels throughout pregnancy. Moreover, ferritin levels in children were not measured, and therefore the study relies solely on maternal ferritin levels in the first trimester of pregnancy. On the other hand, this study has considerable strengths such as the large sample size covering different geographical regions, which increases the external validity, the prospective design and the inclusion of several confounding variables in the statistical models.In summary, this study makes a new contribution to understanding the association between ferritin levels in mothers during pregnancy and ADHD symptoms in their children. There is a need for more research to clarify the mechanisms of action of iron and neurotransmitters in relation to cognitive function. Maternal ferritin levels during pregnancy are independently associated with ADHD symptoms in 4 to 5-year-old offspring. More specifically, maternal ferritin levels seem to influence inattention symptoms and specifically in boys, whereas girls seem to be more protected. Other maternal variables including BMI and education level are also associated with ADHD and inattention symptoms.


Objective: The objective of this study was to examine the association between parent mental health (ADHD and depression) and offspring performance on neurocognitive tasks in children with ADHD. Method: The clinical sample consisted of 570 children  with ADHD who completed neurocognitive tasks measuring working memory, attention set-shifting, and motivational deficits. Questionnaire measures were used to assess ADHD and depression symptom presence in parents. Results: Controlling for ADHD severity, children of parents with ADHD had poorer working memory  and increased errors on the extra dimensional shift stage of the set-shifting task . Parent depression was not associated with offspring performance on any of the assessed neurocognitive tasks. Conclusion: Children with ADHD who have a parent with ADHD symptom presence are a subgroup of children who may have additional neurocognitive impairments that have potential implications when implementing interventions that target cognition and learning. ADHD is a common childhood neurodevelopmental disorder characterized by impairing levels of inattention and hyperactivity-impulsiveness. ADHD is a genetically influenced and highly familial disorder, although noninherited factors also contribute . PsychopPrevious research, including our own, has shown that parental ADHD is associated with a more severe clinical presentation of the same disorder in offspring, including higher ADHD symptom severity and elevated rates of comorbid conduct symptoms and disorder . There iADHD is characterized by neurocognitive deficits as well as by its core clinical features . ChildreThe few studies to date that have undertaken this type of investigation have somewhat mixed findings. A number of studies report that children with a parent or first-degree relative with ADHD show poorer performance on inhibition, set-shifting, and verbal cognitive ability  while otGiven the somewhat inconsistent as well as limited body of evidence to date, this study will explore the associations between parental ADHD and depression and neurocognitive performance in a sample of children with a clinical diagnosis of ADHD. General cognitive ability and three domains of neurocognition, previously shown to be associated with ADHD , were chDiagnostic and Statistical Manual of Mental Disorders  participated in the study, only one child (the oldest) was included in analysis. Parents and children gave informed written consent and assent before taking part in the study. Ethical approval for the study was obtained from the Wales Multicentre Research Ethics Committee.This sample of children with ADHD and their parents was recruited from child and adolescent psychiatry and pediatric services in the United Kingdom and has been described previously . All chiSM-III-R  . Parent ADHD symptom presence was defined as either mother or father having a positive ADHD status. Cronbach’s alpha reliability for parent ADHD measures ranged from 0.91 to 0.94.Parent psychopathology was assessed using questionnaire measures. Biological mothers and fathers completed a questionnaire regarding ADHD symptoms in themselves at age 7 to 11 years (childhood) and in the last 6 months (current), using an 18-item checklist of To assess parent depression, biological mothers and fathers completed the Hospital Anxiety and Depression Scale HADS; . As in pDSM-5 criteria. Individual symptoms of CD and ODD were counted as present when endorsed by either the parent or child and summed to calculate severity scores.Child psychopathology was assessed using the Child and Adolescent Psychiatric Assessment (CAPA), a semi-structured research diagnostic interview . The parAll cognitive assessments were performed by trained psychologists. Children were requested to be off their stimulant medication 24 hr prior to testing. Cognitive ability was assessed using the Wechsler Intelligence Scale for Children IV (WISC-IV) where a measure of full scale IQ was obtained . The DigThe Intra-Extra Dimensional Set Shift (IED) task is taken from the Cambridge Neurocognitive Test Automated Battery (CANTAB), a computerized battery of nonverbal visually presented neurocognitive tests . It is aquality of decision making which looks at the proportion of trials where the majority color was chosen (a higher score is favorable), delay aversion which is difference in percentage bets on the descending versus ascending trials (higher scores indicate impulsivity and intolerance of waiting), risk adjustment that is the rate at which subjects increase the bet proportion in response to more favorable ratios (low scores are unfavorable), and risk taking that is the mean proportion of points bet on trials where the most likely outcome was chosen (The Cambridge Gambling Task (CGT), also part of the Cambridge Neurocognitive Battery (CANTAB) assesses decision making and delay aversion . On eachs chosen .Information on demographics and family background was obtained from each family using a self-report parent questionnaire. Social class status was classified according to the occupation of the main family wage earner, using the U.K. Standard Occupational Classification 2000 . FamilieLinear regressions were used to examine association between predictors (parent ADHD/parent depression symptom presence) and outcomes (child scores on the neurocognitive tasks). All neurocognitive outcome scores were standardized for ease of interpretation and comparison across different tasks. Estimates were then further adjusted for child age, low social class, and low parent education to test whether associations found were explained by parent level of education (as a proxy measure of parent IQ) and by social class status. Child IQ was included as a covariate in the subsequent model but not for the analysis looking at Digit Span as it is one of the subtests used to assess full scale IQ. In the final model, child ADHD and CD severity was included as a covariate to determine whether associations between parent psychopathology and child neurocognitive performance were independent of child psychopathology. Post estimation tests identified two outliers within the digit span scores. As these outliers had higher than average leverage and residual points, it was decided that these two individuals would be excluded from the analyses. All analyses were performed using STATA (version13). The analyses in this study were exploratory without any correction for multiple testing.SD = 3.01). All children had a clinical and research diagnosis of ADHD  Hyperkinetic Disorder or DSM ADHD). Rates of ADHD subtypes and comorbidities are reported in more detail in This sample consisted of 568 children aged 6 to 18 years, 480 (85%) males with a mean age of 10.77 years (n = 186) of children in the sample had a parent meeting symptom criteria for DSM-5 ADHD. There were only a few children where both mothers and fathers had ADHD in the same family , and there were no significant correlations between mother and father ADHD total symptom scores (for either current or childhood symptoms). Looking at parent depression, 23.8% (n = 133) of children had a parent who met the cut point for depression based on the HADS. Only 1.4% (n = 8) of children had both parents meeting study criteria for depression.ADHD and depression symptoms were available from completed questionnaire for 546 mothers and 280 fathers. The sample consisted of many single parent families; 58.7% (mostly mothers) of which many had fathers without questionnaire data available (50.9 %). Overall, 33% (2 = 6.69 (1), p = .01). There were no significant differences in terms of social class for parents with and without depression, but there was weak evidence that families with a parent with depression were more likely to be classified in the lower social class compared with families without a parent with depression , p = .081). There were 452 (84%) children who were not on stimulant medication 24 hr before cognitive testing and 84 (16%) who were on medication at time of assessment. Medication taken at the time of assessment was not significantly associated with any cognitive test scores.Child age, gender, ADHD medication status of the child, and parent education level did not differ between those with and without a parent with ADHD or depression symptom presence. We found that 62% of families with a parent with ADHD were classified as being of low social class compared with 50% of families without an ADHD parent (χr = .17 and .26. SD = 0.96). Most children in this sample had completed the intra dimensional shift stage (ID—Stage 6), but just less than half the sample (49%) couldn’t complete the extra dimensional (ED) shift stage of the task (Stage 8). While not matched to the sample in this study, normative data on the set-shifting tasks reported mean number of stages passed ranging from 8.60 (SD = 0.97) to 8.68 (SD = 0.85) for children between ages 8 and 19 years  and higher scores on total number of errors made in the ED shift stage . We found that only 44% of children with a parent with ADHD completed Stage 8/9 of the set-shifting task compared with 55% in the group of children without a parent with ADHD (χ2 = 3.32 (1), p = .07).These results are presented in Parent ADHD symptom presence was not associated with total errors made on the set-shifting task and any of the measures from the gambling task , p = .13).We did not observe associations between parent depression symptom presence and any of the offspring neurocognitive outcome scores, apart from weak evidence of association with the delay aversion score This study aimed to build upon previous findings that parental ADHD and depression symptom presence are associated with a clinically more severe presentation of ADHD as defined by reported symptoms . As prevThe findings indicated that children who had a parent with ADHD symptom presence performed more poorly in measures of working memory (the digit span task) and set-shifting ability (number of errors in the ED shift stage and completion of stage 8/9 of the set shifting task). The associations remained even after controlling for ADHD and CD severity. However, no differences were found in the domains of general cognitive ability  or motivational deficits in decision making (measured by the Cambridge Gambling Task). These findings for set-shifting ability are similar to those reported by Seidman and colleagues , 1997 whOur finding of association between parent ADHD symptom presence and poorer offspring working memory differ from those of Thissen and colleagues who found no association in their study of 259 adolescents with ADHD . This diNonetheless, there has been recent evidence which found composite ADHD molecular genetic risk scores are associated with lower IQ and working memory performance as well as ADHD symptom levels in children in the general population . This suIn contrast to the findings for children with parents who have ADHD, no associations were found between parent depression status and offspring neurocognitive performance. These findings support and extend those of a much smaller pilot study  which foThis study is one of the first studies to investigate the links between parent psychopathology and variation in offspring neurocognitive performance in a large clinical sample of children with ADHD. It includes the analysis of both parent ADHD and depression symptom presence within the same sample and explores associations with variation in offspring neurocognitive functions implicated as being affected in ADHD including delay aversion and decision making, which have not been examined previously. Overall, the results of this study highlight that children with ADHD who already have neurocognitive deficits relative to the general population, and who have a parent with ADHD symptom presence may experience even greater neurocognitive problems, which underscores the importance of considering parent mental health during clinical assessment. Parent mental health problems appear to be linked to both cognitive as well as clinical indices of ADHD severity in clinic children; this is in the context of elevated social adversity that commonly accompanies parent psychopathology. Mechanisms that account for these cross-generational links likely include genetic, biological, and social ones.DSM-5 diagnosis of major depressive disorder. However, the HADS has been reported to have good validity and performs well in predicting caseness of anxiety disorder and depression in both psychiatric and primary care patients as well as the general population , p = .43) and parent depression (χ2 = 1.68 (1), p = .19) status. However, sensitivity analyses were also conducted to examine association for mother and father psychopathology separately, and weak evidence of associations was found for working memory .Thissen and colleagues found that there may also be different influences of mother and father ADHD on the child’s neurocognitive task performance , typical of referrals to many child and adolescent mental health services in the United Kingdom where health care is free of charge so those from high-risk backgrounds are well represented in clinics. Therefore, information on a substantial number of fathers was missing, and the rates of parent ADHD and depression likely will have been underestimated. Sensitivity analyses were conducted in the sample, and we found that there were no differences in associations for children with data available from both parents and those from single-parent families.Finally, the findings of this study are in need of replication considering that we did not perform any corrections for multiple testing. The outcomes for each task in this study are correlated with each other and some have suggested that traditional methods of correcting for multiple testing, such as the Bonferroni method, would be overly conservative in situations like this . This waThere have been a few advances in the development of intervention strategies for children with ADHD that target neuropsychological impairments . These iIn conclusion, the results of this study suggest that parent ADHD symptom presence is related to poorer performance in set-shifting and working memory in their offspring with ADHD, but that parent depression symptom presence is not associated with impaired offspring neurocognitive functioning measured in this study. This further extends findings that parental ADHD is associated with offspring ADHD severity and again highlights the importance of considering parental mental health when assessing child ADHD.


IIIOOH with intercalated OH−/CO32− transpired that served as a highly active structure as shown by various ex situ methods and quasi in situ Raman spectroscopy.Identifying novel classes of precatalysts for the oxygen evolution reaction (OER by water oxidation) with enhanced catalytic activity and stability is a key strategy to enable chemical energy conversion. The vast chemical space of intermetallic phases offers plenty of opportunities to discover OER electrocatalysts with improved performance. Herein we report intermetallic nickel germanide (NiGe) acting as a superior activity and durable Ni‐based electro(pre)catalyst for OER. It is produced from a molecular bis(germylene)‐Ni precursor. The ultra‐small NiGe nanocrystals deposited on both nickel foam and fluorinated tin oxide (FTO) electrodes showed lower overpotentials and a durability of over three weeks (505 h) in comparison to the state‐of‐the‐art Ni‐, Co‐, Fe‐, and benchmark NiFe‐based electrocatalysts under identical alkaline OER conditions. In contrast to other Ni‐based intermetallic precatalysts under alkaline OER conditions, an unexpected electroconversion of NiGe into γ‐Ni )‐Ni complex is used as a molecular precursor for the synthesis of ultra‐small NiGe nanostructures, which can generate a γ‐NiOOH structure with intercalated species. This displays exceptional catalytic activity and long‐term durability for the oxygen evolution reaction (OER).A xanthene‐based bis(germylene The electrocatalytic oxygen evolution reaction (OER) by water oxidation plays a decisive role in the development of clean renewable energy conversion and storage systems, as it provides electrons that can be used for critical reactions such as to reduce water to hydrogen, carbon dioxide to carbonaceous fuels, as well as metal ions to the corresponding metals in recharging of metal‐air batteries.In the last few years, nickel‐based catalysts have drawn enormous attention not only because of their promising activity and stability in alkaline electrolytes but also due to their function as anodes in large‐scale commercial alkaline electrolyzers.x phases, nickel monogermanide (NiGe) has been regarded as a highly promising material due to its low electric resistivity , enhanced stability in a wide temperature range, low‐temperature synthetic approach, and facile chemical processing.Intermetallic nickel phases such as nickel germanides are an important class of metal semiconductors that are well‐known for their high thermal stability as well as their applications in the areas of microelectronics, photovoltaics, magnetism, and thermoelectrics.Considering NiGe is a stable intermetallic phase with high intrinsic conductivity with the active catalytic center, we aimed to address in this report the following five research questions: (i) Can we adopt a rational approach to synthesize well defined ultra‐small NiGe nanostructures with high surface area from a molecular precursor?, (ii) will the structure of this intermetallic phase remain stable or is it merely a precursor  in electrochemical alkaline OER?, (iii) what is the structural and functional role of Ge?, (iv) what type of transformation (active structure) can be expected and how does it influence the electrocatalytic OER activity and durability?, and (v) can we provide a guideline for access to other transition‐metal germanides towards OER based on our soft molecular approach?In the last few years, significant progress has been made in developing various synthetic routes for the preparation of intermetallic compounds towards applications in electrocatalysis.4a, 132 ‐Ni complex 2 Scheme  and its II atom with π‐donor interactions with the neighbor N atoms and/or from an intramolecular Lewis donor, are promising steering ligands akin to the N‐heterocyclic silylene (NHSi) and carbene (NHC) congeners.0 complex could act as a suitable single‐source molecular precursor for a low‐temperature synthesis of intermetallic nickel germanides.Germylenes represent heavy analogues of carbenes that possess a divalent Ge atom with a lone pair of electrons; they can serve as potential ligands towards transition‐metals.1, the starting material of molecular bis(NHGe)Ni complex 2, was prepared in analogy to its silicon homologue .1 with one molar equivalent of Ni(cod)2  in toluene at room temperature resulted in the formation of the bis(NHGe)Ni(cod) complex 2 in 87 % isolated yields as a dark‐brown solid (Scheme The xanthene‐based bis(NHGe) 2 is diamagnetic and has been unambiguously characterized by 1H and 13C NMR spectroscopy, high‐resolution mass spectrometry (HRMS), elemental analysis, and IR‐spectroscopy (synthesis of complex 2 in Supporting Information). The molecular structure of 2 established by an single‐crystal X‐ray diffraction analysis is depicted in Figure 2 features a 1,5‐cod ligand. Besides the coordination of both C=C bonds of cod, the nickel atom is coordinated by the two germanium(II) centers of the bis(germylene) 1 with a Ge‐Ni‐Ge angle of 101.6(2)°. The Ni1‐Ge1 [2.315(4) Å] and Ni1‐Ge2 [2.311(4) Å] distances are similar to the sum of theoretically predicted covalent radii for Ni‐Ge single bonds [2.31 Å].2 are given in Tables S3 and S4.Compound 2 was subjected to thermolysis under hot‐injection conditions at 250 °C in oleylamine to produce black powdered NiGe with ≈75 % yields (see Supporting Information). Note that a NiGe2 phase was expected from the precursor Ni:Ge ratio, however, NiGe2 is a metastable phase and does not exist in the Ni‐Ge phase diagram .The molecular complex 2) were also synthesized  and deposited through EPD on both NF and FTO under identical conditions with the same mass loading .The OER catalytic performance of the prepared NiGe materials was evaluated in aqueous 1 M KOH solutions using a three‐electrode cell. Prior to the OER measurements, the NiGe was deposited on the high surface area, low‐cost, conductive, and 3D open porous nickel foam (NF) as well as on fluorinated tin oxide (FTO) by electrophoretic deposition (EPD) that directly served as working electrodes. Subsequently, the as‐deposited films were also characterized extensively which confirmed preservation of the chemical identity (stability) of NiGe on the electrode substrates . To have a fair comparison of its catalytic OER activity, the literature reported highly active Ni‐based catalysts (NiOOH and Ni(OH)2/NF and NiOOH/NF while the bare NF (subjected to the same EPD protocol) exhibited a limited activity. Remarkably, the overpotential of the NiGe/NF electrode was only 228±3 mV at a current density of 10 mA cm−2, which is lower than those of Ni(OH)2/NF (292±5 mV), NiOOH/NF (308±5 mV) and bare NF (485±9 mV). Prior to the onset of OER, all measured Ni materials showed distinct redox peaks between 1.1–1.5 V vs. reversible hydrogen electrode (RHE) associated with the oxidation of NiII → NiIII [Ni(OH)2 + OH− → NiOOH + H2O + e−] that has widely been regarded as the catalytically active site for OER.[−1 compared to Ni(OH)2/NF (79±1 mV dec−1) and NiOOH/NF (83±1 mV dec−1) suggesting a more effective electron transfer, highly efficient reaction kinetics, and efficient catalytic OER activity of nanostructured NiGe/NF.As shown in Figure  for OER.4a, 21Rct) across the electrode/electrolyte interface was obtained from the Nyquist plot (Table S6). As shown in Figure Rct of NiGe/NF was lower than those of the other three Ni‐electrodes, demonstrating its faster charge transport leading to an improved electrocatalytic OER activity. One of the most important criteria to evaluate the performance of the catalysts for practical applications in water splitting is their long‐term durability under strongly alkaline operating conditions.−2 current density at a potential of 1.48 V vs. RHE for a period of 24 h . Motivated by this, we further extended the durability measurements over three weeks that showed unceasing stability and sustainability of the NiGe/NF without any drop in the current density signifying its capability as an efficiently stable anode for alkaline water electrolysis  during electrolysis that matched well with the theoretically calculated values (Table S7). As the large‐scale alkaline electrolyzers are operated at elevated temperature,−2 at 65 °C for 20 h that exhibited a stable potential curve supporting its usability at higher current densities .Electrochemical impedance spectroscopy (EIS) was conducted to offer insights into the electrode kinetics during the OER process.s Figure . BesidesCdl) was determined, which is proportional to the electrochemically active surface area (ECSA).Cdl. A Cdl value of 1.36±0.04 mF cm−2 was attained for NiGe/NF while much lower values, 0.85±0.02, 0.79±0.01, and 0.64±0,01 mF cm−2 were obtained for Ni(OH)2/NF, NiOOH/NF and NF, respectively , suggesting that more catalytically active sites of NiGe/NF are available for the OER process. It is known from previous OER studies that non‐oxide phases can undergo severe corrosion in strongly alkaline conditions leading to a partial or complete loss of metal/metalloid.[Cdl of NiGe/NF after 24 h of OER CA giving rise to a value of 2.82±0.04 mF cm−2, which is twice larger than the as‐deposited NiGe/NF . The increase in Cdl (or ECSA) is an indication of the rapid loss of Ge into the electrolyte with significant structural transformation. Furthermore, the currents of NiGe/NF, Ni(OH)2/NF, and NiOOH/NF catalysts were normalized by (i) geometric area, (ii) mass, (iii) Cdl (ECSA), and (iv) BET surface area and presented in Figure S27 that supported the derived conclusions (Table S8). Most importantly, it is well known that the comparison of overpotentials is only meaningful if the catalysts are tested in identical conditions with optimized parameters .x as well as commercial IrO2 and RuO2 for the alkaline OER in identical conditions . Remarkably, the obtained OER overpotentials of these catalysts were still substantially higher than the one of NiGe/NF 2/FTO (380±6 mV) and NiOOH/FTO (444±6 mV). As observed in the case of NiGe/NF, the CV of NiGe/FTO also featured a redox pair evidencing that NiII is reversibly converted to NiIII (NiOOH) which serves as the active site for OER .2/FTO, and NiOOH/FTO exhibited accelerated charge transfer and higher density of active sites for NiGe/FTO compared to the other investigated reference catalysts . The OER CA response of NiGe/FTO was examined at 1.58 V vs. RHE for 24 h, which displayed excellent stability of the electrocatalyst . Furthermore, the normalized current densities followed the same trend as that of deposited catalysts on NF . Finally, the superiority of NiGe/FTO was illustrated by comparing the overpotentials of the as‐deposited non‐noble and noble‐metal‐containing catalysts on FTO as well as literature reported Ni‐based catalysts .After achieving encouraging results on NF, we further investigated the electrocatalytic OER performance of the same materials deposited on FTO, primarily to understand the inherent role of the electrode substrates influencing the catalytic activity. The LSV profiles of NiGe/FTO, Ni(OH)xHy structure . The percentage of distribution of the elements derived by EDX mapping is also consistent with the result acquired from the ICP‐AES and XPS analysis . As observed for SEM, the TEM images showed a rigorous transformation of initial NiGe into a hollow nanostructure  NiGe/FTO electrode was extensively characterized through diffraction, microscopic, spectroscopic, and analytical methods. The PXRD pattern of NiGe/FTO after OER exhibited a broad diffraction pattern similar to as‐prepared material indicating the small particle size . Interestingly, the SEM images of NiGe after OER showed porous‐type morphology with an agglomeration of particles . The EDX mapping of the film exhibited an almost total loss of Ge (over 95 %) and concomitant incorporation of O into the structure that shows homogeneous dispersion of Ni, suggesting complete corrosion of the NiGe phase to an OER active NiOe Figure , inset. 3/2 and Ni 2p1/2 spectrum displayed sharp peaks at the binding energy of 856.0 and 873.7 eV  that can only be ascribed to NiIII of the structure, while the peaks responsible for Ni0 were absent, confirming the complete electrooxidation of NiGe surface to γ‐NiOOH under alkaline OER .The surface valence states of fully converted NiGe after OER was obtained by XPS analysis . The Ni 2pR Figure .[21b, l Figure . The decIII throughout the bulk . Thus, from the ex situ characterizations at various time intervals of OER, it was apparent that the NiGe suffers spontaneous restructuring under an applied bias finally forming a γ‐NiIIIOOH as the catalytically active structure. According to the Pourbaix diagrams,32−), and subsequent oxidation of nickel [from Niδ+ to NiIII, Eq. To address the question on the dissolution rate of Ge into the electrolyte and extent of phase transformation in OER conditions, we also examined the NiGe/FTO film after initial CV  measurements  by SEM, EDX, elemental mapping, ICP‐AES, FT‐IR, and XPS analysis. Surprisingly, the OER CV characterization results matched exactly with that of OER CA demonstrating that the electroconversion of NiGe to γ‐NiOOH is an instantaneous process. Besides, OER CV and CA, the NiGe/NF electrodes were also characterized after three weeks (505 h) of durability tests. As anticipated, the obtained SEM, EDX, elemental mapping, and XPS mapping results were found to be similar to that of CV and CA experiments further confirming the porous nature of electrodes, the absence of Ge in the structure as well as the formation of Ni −1 were observed.[−1 is clearly associated with the depolarized Eg mode (bending) whereas the band at 554 cm−1 can be assigned to the polarized A1g mode (stretching).[g mode the oxygen atoms vibrate along the plane while in the A1g mode they vibrate perpendicular to the plane.−1 and 1200 cm−1 also appeared that has been previously attributed to ν(O‐O) of an active oxygen species (NiOO−) in oxyhydroxide structure .[−1 that can be attributed to the presence of oxygen vacancies.To uncover the active structure under quasi in situ conditions, we performed (resonance) Raman spectroscopy of NiGe, which is a very sensitive probe to the surface structure of the catalysts. The as‐prepared NiGe powder and the as‐deposited NiGe film did not show any distinguishable features indicating that NiGe does not possess any Raman allowed bands. We then performed quasi in situ Raman measurements on the NiGe film treated for 24 h of OER CA where Raman bands at 481 and 554 cmobserved.5c, 362, β‐Ni(OH)2, β‐NiOOH, and γ‐NiOOH have widely been utilized as cathodes in primary and secondary alkaline batteries as well as supercapacitors.2 or overcharging of β‐NiOOH .[− or CO32− (from the dissolved electrolyte) anions to expose a large number of active sites for the evolution of oxygen hydroxides, in particularly, α‐Ni(OH)ure S66).32, 422 evolution. It could be expected that the large interplanar spacing of γ‐NiOOH provides the ionic intercalation of OH−/CO32−, which is favored for the OER process as reflected in higher ECSA values. We believe that the insights offered in this study can easily be generalized to the other transition metal germanides that are also expected to follow the same transformation in alkaline media as that of NiGe. The presented molecular approach might be referenced to investigate and explore a large number of unexplored class of solid‐state intermetallic materials for water splitting that otherwise are impossible to attain in their nano‐form.We have successfully addressed the research questions (i)–(v) as mentioned above in the introduction. With respect to question (i), a rational protocol to synthesize well‐defined ultra‐small NiGe nanoparticles could be achieved, starting from a new Ni‐Ge molecular precursor. To answer the questions (ii)–(v), the as‐prepared NiGe was electrophoretically deposited on NF and FTO and investigated for the alkaline OER for the first time. The catalytic activity and stability of the NiGe was found to be strikingly higher than that of benchmarked Ni‐, Fe‐, Co‐, and NiFe‐based catalysts in identical conditions. Under electrochemical conditions, a vigorous electroconversion of NiGe occurred indicating that the NiGe is indeed a precatalyst for OER. The dissolution of Ge further confirmed the insignificant structural or functional role of Ge for OER, however, it is essential to direct the structure to form the most active catalyst. From the combination of advanced ex situ and quasi in situ Raman spectroscopy, it is demonstrated that the transformation of NiGe is instantaneous, forming a highly‐active γ‐NiOOH phase that acts as the most active structure to facilitate OThe authors declare no conflict of interest.As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.SupplementaryClick here for additional data file.


P = 1.39 × 10−07) and with the combined ADHD/ASD status , but not with ASD status . No associations for the ASD and SCZ PRS were observed. In sum, the PRS of ADHD is significantly associated with the combined ADHD/ASD status. Yet, this association is primarily driven by ADHD status, suggesting disorder specific genetic effects of the ADHD PRS.Neurodevelopmental disorders such as attention deficit/hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are highly heritable and influenced by many single nucleotide polymorphisms (SNPs). SNPs can be used to calculate individual polygenic risk scores (PRS) for a disorder. We aim to explore the association between the PRS for ADHD, ASD and for Schizophrenia (SCZ), and ADHD and ASD diagnoses in a clinical child and adolescent population. Based on the most recent genome wide association studies of ADHD, ASD and SCZ, PRS of each disorder were calculated for individuals of a clinical child and adolescent target sample (N = 688) and for adult controls (N = 943). We tested with logistic regression analyses for an association with (1) a single diagnosis of ADHD (N = 280), (2) a single diagnosis of ASD (N = 295), and (3) combining the two diagnoses, thus subjects with either ASD, ADHD or both (N = 688). Our results showed a significant association of the ADHD PRS with ADHD status  contains supplementary material, which is available to authorized users. Psychiatric disorders are heritable complex traits with varying heritability estimates. At the top end of the heritability range, reported heritabilities vary from 74% for ASD  approach. PRS are the sum of risk alleles weighted by their estimated effect size as determined in an independent GWAS sample, and can serve as such as an estimation of an individual’s polygenic risk , and the prevalence of ASD in an SCZ samples ranges between 3.4 and 52% compared to 1% in the general population  diagnoses as well as continuous rating scale scores on behavioral/emotional problems. We aim to investigate whether PRS for ADHD   previously described by Polderman et al. . NESCOG https://www.illumina.com/products/by-type/microarray-kits/infinium-psycharray.html). Genetic variants in the clinical sample were filtered based on minor allele frequency (MAF < 1%), Hardy–Weinberg disequilibrium (P < 1 × 10−6) and SNP call rate (< 95%). Individuals were subsequently filtered based on relatedness (pairwise Identity-By-Descent (IBD) > 0.185), genotype and phenotypic sex mismatch, outlying heterozygosity and non-European ancestry ) resulting in a clinical sample of 812 patients of which 688 are diagnosed with ADHD, ASD or both. The remaining part of the children in this sample (N = 124) are diagnosed with either Rett syndrome, Anorexia Nervosa or other eating disorders, Tourette Disorder, or other disorders. Another subset of the sample is currently being genotyped and includes children diagnosed with Anxiety Disorder, Affective Disorder or other disorders. In the control sample, SNP filtering was based on MAF (< 1%) Hardy–Weinberg disequilibrium (P < 0.00001) and SNP call rate (< 95%). Individual QC was based on missingness (> 5%), ancestry (within the range of 1000G CEU population on first PCs), relatedness (pairwise IBD > 0.185), gender mismatch, outlying heterozygosity and missing phenotypes.Genotyping of the cases and controls was performed on the same Illumina PsychChip array. The PsychChip SNP array contains HumanCore, Human Exome and custom content to accurately capture genetic variants previously linked with psychiatric disorders (www.nlgenome.nl for more information), by means of correlation. The Pearson correlation coefficient between the male and female allele frequencies is 0.99, removing concerns of different allele frequencies in the two samples due to sex differences.The case and control samples differed in sex distribution . Therefore, we compared allele frequencies between males and females in an independent sample, GoNL . Starting from a low P-value threshold moving up to P-value 1, an optimal P-value threshold with the highest explained variance was identified, including the most truly associated positives. After this threshold more false positives will be included dampening the true signal.  to remove variants in LD. Polygenic scoring was performed with the software package PRSice . Parents score each problem on a three-point scale . This follow up analysis included children with a CBCL 6–18 report, completed by the parent less than a year before receiving the diagnosis. If a CBCL from within a year before diagnosis was not present the person was excluded from this part of the analysis. In all analyses, sum scores on the CBCL syndrome scales were used.Child emotional and behavioral problems were assessed using the Dutch version of the Child Behavior Checklist/6–18 (CBCL) , three PRS , eight PRS thresholds  per disorder). To account for population stratification we included eight principal components (PCs). The PCs were calculated based on the pruned data with Eigensoft  with a diagnosis of ADHD, ASD not permitted as co-diagnosis , (2) with a diagnosis of ASD, ADHD not permitted as co-diagnosis , and (3) combining the first two samples, thus subjects with either ASD, ADHD or both . For each PRS, eight different SNP inclusion thresholds were tested. All We aim to provide additional evidence for the significant association of the PRS and the disorders as measured by the CBCL score severity. Given statistical power, we tested the association with symptom severity only in the combined ADHD/ASD sample by calculating the correlation between the significantly associated PRS  and the syndrome scales of the CBCL. Age was added as a covariate in addition to the previously used eight PCs and sex. All analyses were performed in IBM SPSS statistics 21.P-value thresholds remained significant after Bonferroni multiple testing correction in both the combined ADHD/ASD and ADHD sample, but not the ASD sample. The most stringent P-value threshold of 0.01 generated a positive association in the ADHD/ASD sample OR 1.28 (P = 1.3 × 10−3), and ADHD sample OR 1.4 (P = 3.6 × 10−4). The most optimal P-value threshold as defined by explained variance, OR and P-value was 0.3 for the ADHD/ASD sample , and 0.4 for the ADHD sample .The ADHD PRS showed significant associations before multiple testing correction with disorder status in all three samples Table . As showP-value threshold of P < 1 had a significant association in the ADHD/ASD sample, OR 1.35 (P = 1.9 × 10−5), and also in the ADHD sample OR 1.62 (P = 4.73 × 10−8). In the ASD sample none of the results remained significant after Bonferroni correction.The most lenient P-value threshold P < 1) showed a trend towards association in the ADHD/ASD sample  .The ASD and SCZ PRS were not significantly associated with the ADHD, ASD, or combined ADHD/ASD status. The SCZ PRS including all SNPs  to warrant the linear regression analysis. Mean scores and standard deviations for the CBCL syndrome scale scores for the ADHD/ASD sample are provided in Supplementary Table 5.Based on the correlational structure in the ADHD/ASD sample  between the CBCL syndrome scale scores and the ADHD PRS P-value thresholds, showing that in addition to the GWAS significant hits, the non-significant SNPs in the ADHD GWAS also contribute to the associations with diagnostic status. Given the comorbidity between ADHD and ASD, and previously reported genetic correlations, we expected that the ADHD PRS would also be associated with ASD status, however, this association was not observed in our data. In contrast, the current results suggest a disorder specific effect of ADHD associated SNPs instead of a shared common genetic mechanism with ASD. The ADHD PRS is based on the most recent GWAS results, and explained variance up to 4.5% in our sample, which is in line with the results from the initial GWAS , which might differ from the discovery sample. Moreover, about one-third of the discovery sample were trio data (i.e. case pseudo control design), of which it has been suggested that the un-transmitted chromosomes contain increased polygenic burden, and as such the genetic signal based on these data might be decreased  2.08–6.05, P < 0.001) Supplementary material 2 (DOCX 80 kb)Supplementary material 3 (DOCX 11 kb)Supplementary material 4 (DOCX 15 kb)Supplementary material 5 (DOCX 15 kb)Supplementary material 6 (DOCX 16 kb)Supplementary material 7 (DOCX 12 kb)Below is the link to the electronic supplementary material.


Amphidinium klebsii, possesses potent antifungal activity. AM3 is known to interact directly with membrane sterols and permeabilize membranes by forming pores. Because AM3 binds to sterols such as cholesterol and ergosterol, it can be assumed that AM3 has some impact on lipid rafts, which are membrane domains rich in sphingolipids and cholesterol. Hence, we first examined the effect of AM3 on phase-separated liposomes, in which raft-like ordered and non-raft-like disordered domains are segregated. Consequently, AM3 disrupted the phase separation at 22 μM, as in the case of methyl-β-cyclodextrin, a well-known raft-disrupter that extracts sterol from membranes. The surface plasmon resonance measurements and dye leakage assays show that AM3 preferentially recognizes cholesterol in the disordered membrane, which may reflect a weaker lipid-cholesterol interaction in disordered membrane than in ordered membrane. Finally, to gain insight into the AM3-induced coalescence of membrane phases, we measured membrane fluidity using fluorescence correlation spectroscopy, demonstrating that AM3 significantly increases the order of disordered phase. Together, AM3 preferentially binds to the disordered phase rather than the ordered phase, and enhances the order of the disordered phase, consequently blending the separated phases.Amphidinol 3 (AM3), a polyhydroxy-polyene metabolite from the dinoflagellate   •Amphidinol 3 (AM3) is an antifungal agent isolated from a marine dinoflagellate.•AM3 merged the coexistent ordered and disordered membrane domains.•AM3 preferentially interacted with cholesterol in disordered domain.•Pore formation of AM3 was also more prominent in disordered membrane domain.•AM3 reduces the fluidity of disordered domain, blending the separated phases. In 1991, amphidinol 1, the very first member of the polyhydroxy-polyene metabolite family, was isolated from Amphidinium klebsii  and immobilized on the CM5 sensor chip surface, as previously described . To compprotocol .2.53 in a round-bottom flask. The solvent was removed by nitrogen gas flow and further dried in vacuo for 12 h. The lipid film obtained was rehydrated with 1 mL of 60 mM calcein in HBS-N  and subjected to two cycles of vortexing (1 min) and warming (65 °C) followed by five cycles of freezing (−20 °C) and thawing (65 °C) to obtain multilamellar vesicles (MLVs). Then, the suspension was passed through a polycarbonate membrane filter  19 times using a Mini-Extruder  to prepare LUVs of homogenous size. Excess calcein was removed by passing the suspension through a Sephadex G-75 column (Sigma-Aldrich) with HBS-N buffer. The lipid concentration in the LUV fraction was quantified using phospholipid C-Test Wako . Resulting stock solution was stored at 4 °C under nitrogen gas. Measurement of calcein leakage was performed on a JASCO FP 8300 spectrofluorometer  with an excitation wavelength of 490 nm and an emission wavelength of 517 nm. To monitor calcein leakage, the LUV suspension was diluted to 980 μL with the HBS-N buffer, and a 20-μL aliquot of AM3 in HBS-N buffer was then added to give the final AM3 concentration of 0, 1, 5, or 10 μM. Subsequently, 20 μL of 10% Triton X-100 (v/v)  was added to obtain the condition of 100% leakage. All measurements were performed at room temperature with a final lipid concentration of 27 μM.The extent of calcein leakage from liposomes was assessed as reported previously . Large u2.6D, was obtained by fitting the autocorrelation function of the time-dependent changes of the signal intensities of fluorescent probe molecules in diffraction-limited spots, G(τ), with the following equation for two-dimensional simple Brownian diffusion:N is the average number of fluorescent particles in the detection area. The beam waist (radius) in the focal plane w0  was calibrated with Rhodamine 6G, for which the diffusion coefficient is known. D is the diffusion coefficient, and τ is the delay time.The GUVs for FCS measurements were prepared in the manner as described above. For the observation of 33.1To examine the effect of AM3 on Lo-Ld phase-separated membranes which mimic lipid rafts, we prepared giant unilamelar vesicles (GUVs) composed of SM, DOPC, and Chol . In GUVs3.2To gain insight into the mechanism underlying the AM3-induced disruption of phase separation, we performed surface plasmon resonance (SPR) experiments using liposomes that mimic ordered and disordered phases. A dodecylamine-modified CM5 sensor chip was prepared to immobilize the liposomes on the sensorchip as we previously reported ,32. The ka1 and kd1 are association and dissociation rate constants of the first step, while ka2 and kd2 are association and dissociation rate constants for the second step. Accordingly, KD1, KD2, and KD represent the dissociation constants for the first step, the second step, and overall equilibriums, respectively. Notably, the overall dissociation constant KD for DOPC/Chol is remarkably smaller than that for SM/Chol, in line with the above conclusion that AM3 recognizes Chol in DOPC membrane better than the one in SM membrane. The kinetic data further demonstrate that although Chol promotes both the first and second steps of AM3 binding in both the DOPC and SM membranes, the Chol's effect is more prominent in the DOPC membrane than in SM membrane, which will be discussed later.To evaluate the SPR data more quantitatively, we extracted kinetic parameters from the sensorgrams using the two-state reaction model, which was proved to be suitable for analysis of the binding of AM3 on membranes . In this3.3To examine the channel formation of AM3 in ordered and disordered membranes, we next performed calcein leakage assays using SM or DOPC membranes in the presence and absence of Chol . Althoug3.4Finally, to investigate the ordering effect of AM3 on ordered and disordered membranes, we measured diffusion coefficients of lipids using fluorescence correlation spectroscopy (FCS) experiments. First, we prepared SM/Chol and DOPC/Chol membranes, which mimic the ordered and disordered phases, and labeled those membranes with 0.002 mol% 594neg-SM and 594neg-DOPC, respectively. These fluorescent lipids, which we recently developed, were shown to reproduce the diffusion properties of native lipids . As a reWe further examined the effect of AM3 on the order of phase-separated membranes. To do this, we prepared Lo-Ld phase-coexistent GUVs composed of SM/DOPC/Chol (1:1:1 in molar ratio) and treated the GUVs with AM3 at a concentration lower than that it disrupts the phase separation. The Lo and Ld phases in the GUVs were labeled with 594neg-SM and 594neg-DOPC, respectively, and the respective diffusion coefficient was obtained by the FCS measurements . As a re4It was reported that AM3's pore formation is mediated by the direct interaction between AM3 and sterol in membranes . This acHere, the difference in the Chol content in the Lo and Ld phases should be taken into account. The Chol content in the Lo phase of the phase-separated membranes is more than 30 mol%, while that in the Ld phase is less than 20 mol% . This meTo evaluate the SPR data, we adopted two-reaction model following our previous report . In thisAnother interesting finding in this study is that AM3 disrupts the Lo-Ld phase-separation of membranes . The defIn conclusion, we found that AM3 disrupts membrane phase separation and disclosed its mechanism of action. As mentioned in the Introduction, lipid rafts are believed to play significant functions in signal transduction, and therefore it is not far-fetched to consider that the disruption of lipid rafts induced by reagents would provoke serious biological responses. Although the biological activities of AM3 such as antifungal, hemolysis, and cytotoxicity can be mostly accounted for by its pore formation in membrane, the raft-disrupting activity of AM3 may be involved in its known and unknown activities. Since numerous sterol- and lipid-binding reagents are known to date , the impManami Hieda: Investigation. Akira Sorada: Investigation, Visualization. Masanao Kinoshita: Visualization, Investigation. Nobuaki Matsumori: Conceptualization, Supervision, Writing.We do not have any conflict of interest.


We then examined whether the phenotypes of impulsivity and BMI, and the PRS scores of ADHD and BMI, shared common associations with whole-brain grey matter and the Monetary Incentive Delay fMRI task, which associates with reward-related impulsivity. A sparse partial least squared analysis (sPLS) revealed a shared neural substrate that associated with both the phenotypes and PRS scores. In a last step, we conducted a bias corrected bootstrapped mediation analysis with the neural substrate score from the sPLS as the mediator. The ADHD PRS associated with impulsivity symptoms  and BMI  via the neuroimaging substrate. The BMI PRS associated with BMI  and impulsivity symptoms  via the neuroimaging substrate. A common neural substrate may (in part) underpin shared genetic liability for ADHD and BMI and the manifestation of their (observable) phenotypic association.There is an extensive body of literature linking ADHD to overweight and obesity. Research indicates that impulsivity features of ADHD account for a degree of this overlap. The neural and polygenic correlates of this association have not been thoroughly examined. In participants of the IMAGEN study, we found that impulsivity symptoms and body mass index (BMI) were associated ( Previou obesity , 3—condi obesity , 5. Rese obesity . The bul obesity , 3. Impo obesity .n = 20,183) compared to controls  in the accumbens, amygdala, caudate nucleus, hippocampus and putamen. Importantly, some of these brain areas are involved in inhibitory control and reward-related pathways [With regard to neural correlates, a recent ADHD ENIGMA mega-analysis  reportedpathways . Neuroim n = 35,11 in the pathways  and obespathways  showed apathways ), waist-pathways  using thpathways ). Indeedpathways  have beepathways .PRS approaches have been applied to brain imaging studies. Although this has yet to be done with ADHD and obesity simultaneously, a PRS based on the recent ADHD GWAS  associatThese findings suggest that a better understanding of the relationship between ADHD and overweight/obesity may be gained by using an approach that incorporates both genetic and neural variation. We used the rich dataset of the ongoing IMAGEN study, comprising impulsivity symptoms, BMI, genome-wide loci and whole brain structural and functional neuroimaging measures at age 19. Firstly, we aimed to examine associations between PRS scores and phenotypes for ADHD and BMI, to assess genetic and phenotypic relationships between these traits. Secondly, we aimed to assess the associations between the PRS scores for ADHD and BMI, whole-brain structural variation, as well as a functional neuroimaging measure that assesses reward-related impulsivity. We were also interested in examining the degree to which the PRS scores of ADHD and BMI associated with the phenotypic measurements of impulsivity symptoms and BMI via the identified neural imaging substrate . We had no a priori hypotheses about associations between the PRS scores and brain regions; we ran the analyses in an exploratory manner.www.imagen-europe.com), a large-scale imaging genetic study aimed at identifying genetic, neuroimaging, and behavioural bases of individual variability in psychiatric disorders. Genetic data was derived from blood samples collected at the age of 14, while clinical, physical and neuroimaging data analysed in the present study were collected at the age of 19. Psytools software  was used to conduct the behavioural characterization via its internet-based platform. The assessment battery of questionnaires and cognitive tasks was self-administered both in participants’ homes and at the neuroimaging facilities. The study was approved by local ethics research committees at each site. Written informed consent was obtained from all participants as well as from their legal guardians. Measures.We conducted all analyses on individuals drawn from the IMAGEN study  [le (BIS) . The BISle (BIS) , neuroimle (BIS) , personale (BIS) , over-eale (BIS)  and in rle (BIS) . At the 2). BMI values were recorded for 1347 subjects at the time the study was conducted. We excluded 114 participants who either were underweight (BMI < 18.5) extremely overweight (BMI > 50).BMI at age 19 was derived from height and weight measurements. Height was measured using a standard protocol to the last complete centimeter, weight was measured to the nearest 100 g. BMI was calculated as weight (kg)/height task, to examine neural responses to reward anticipation and reward outcome. Full details on the MRI acquisitions [In this investigation, we used voxel-based morphometry measures of grey matter derived from Tisitions  pre-procisitions  and confA Task Based fMRI Acquisition of the Monetary Incentive Delay (MID) task was used to examine neural responses to reward anticipation and reward outcome [ outcome . The tasThe analyses proceeded in three steps . In Step 1, PRS scores were created for ADHD and BMI and we examined associations between the scores and phenotypic measures. We also calculated associations between the ADHD and BMI PRS scores themselves, in order to investigate a potential genetic comorbidity between ADHD and BMI.http://www.med.unc.edu/pgc/results-and-downloads; 20,183 cases and 35,191 controls, all European descent). To calculate the BMI PRS, GWAS summary statistics from 339,224 European descents were downloaded from the GIANT Consortium . Deriving a PRS necessitates the use of a significance threshold for inclusion of SNPs in the calculation of the score . In investigations where the researcher is only interested in the relation between the score and the phenotype, it is possible to vary that threshold, and select that which results in the highest variance explained between the PRS and the phenotype [p = 0.05 for the inclusion of SNPs in the calculation of the score. This approach avoids both overfitting and multiple testing. All genetic data processing and analyses were performed using the R package PRSice [To derive the ADHD PRS, summary statistics were downloaded from the Psychiatric Genomics Consortium  for this purpose , 19. Thi1 penalty, applied under resampling [Although PLS methods are very powerful, results can be difficult to interpret, as all variables contribute to associations between data-views. This is particularly problematic with neuroimaging data, which is high dimensional. It would be useful to know which neuroimaging features were associated with PRS scores and phenotypes of impulsivity and BMI. For this reason, we used a method that induces sparsity by setting some PLS weights to zero through the application of an Lsampling , 34. Thrsampling . Signifisampling .to brain BY brain to phenotype). There were four overall possible effects: 1) ADHD PRS → brain regions → impulsivity symptoms; 2) ADHD PRS → brain regions → BMI; 3) BMI PRS →brain regions → BMI; and 4) BMI PRS →brain regions → impulsivity. Because standard errors underlying mediation pathways  are known to be skewed, we bootstrapped all indirect effects 10,000 times with bias corrected (90% and 95%) confidence intervals. The mediation pathways reported here are based on the bootstrapped variability around the product of non-standardized path coefficient estimates . Mediation pathways were programmed in Laavan [In Step 3, we examined the degree to which the set of brain regions identified by sPLS may act as an intermediary endophenotype between the PRS scores and the impulsivity and BMI phenotypes. In other words, we examined if the brain regions could help explain (or mediate) the observable association between ADHD and BMI genetic vulnerabilities and the impulsivity and BMI phenotypes. The mediation (or indirect) pathways were defined by the product term of the pathways of interest  and the PRS score for ADHD was significantly associated with impulsivity symptoms . We also note that impulsivity symptoms were associated with BMI , and that the PRS scores for ADHD and BMI were significantly cross-correlated . Of interest, using Steiger’s test for dependent correlations [p = 0.036 FWE corrected) than the correlation between the phenotypic measures of impulsivity and BMI.At the time the study was conducted, elations , we foun1 and fMRI data that passed QC. Using sPLS, we found that both the ADHD and BMI phenotypes, and their respective polygenic risk scores, are significantly associated with a common neural substrate, constructed from T1 and fMRI data, which we term the ‘neural endophenotype’. The PRS for BMI was associated with the neural endophenotype at r = 0.12, n = 604, p = 9.5 × 10−3, FWE corrected, whilst the ADHD PRS was significant at r = 0.087, n = 604, p = 0.036 FWE corrected. ADHD and BMI phenotypes were also associated with this set of brain regions, with ADHD associated at r = 0.091, n = 604, p = 0.035 FWE corrected, and BMI associated at r = 0.15, n = 604, p = 9.0 × 10−4 FWE corrected respectively. These correlations are summarized in a matrix and path diagram in Fig. Of the 874 subjects who had both complete genetic and phenotypic data, 604 had both TThe neural endophenotype is made up of grey matter regions and regions of activation derived from the MID task. Grey matter regions contributing to this neural endophenotype were located bilaterally in the cerebellum, amygdala, hippocampus, para-hippocampus and orbital frontal cortex, which all present bilaterally. There was also a largely left lateralised grey matter cluster in the inferotemporal cortex. sPLS weights in each of these brain regions were negative, implying an inverse association between PRS scores/phenotypes and grey matter. Neural endophenotype clusters that came up in the MID task were largely left lateralised and included the fusiform gyrus and para-hippocampus, postcentral and parietal inferior, calcarine and occipital superior and frontal superior medial cortex. These results are displayed in Fig. b = 0.006, 90% CIs = 0.001, 0.019); ADHD PRS → neuroimaging score → BMI ; 3) BMI PRS → neuroimaging score → BMI ; and 4) BMI PRS → neuroimaging score → impulsivity .In Step 3, we saved the neuroimaging factor score from the sPLS and conducted the multivariate bias corrected bootstrapped mediation analyses. The four mediation pathways were observed, albeit at a 90% CIs for ADHD and 95% CIs BMI, suggesting that the neural endophenotype may act as an intermediary variable in the link between the PRS scores and the impulsivity and BMI phenotypes. These four mediation pathways were: 1) ADHD PRS → neuroimaging score → impulsivity  and BMI  phenotypFirstly, using previously established genome-wide evidence, and a multi-modal whole-brain approach, this study showed that the PRS scores and phenotypes of ADHD and BMI simultaneously associated with decreases in grey matter in similar brain areas as reported by the case-control ADHD ENIGMA mega-analysis  as well Third, we examined a multivariate mediation model. Here, the associations between the ADHD and BMI PRS scores and the impulsivity and BMI phenotypes were partially carried by the brain areas identified by sPLS. We therefore suggest that these brain areas  may act as a neural endophenotype  between How might the results of the present study bear on future research? Are there  clinical implications? It is important to note that our findings of genetic influence on ADHD and BMI are non-deterministic and can be context dependent . Indeed,n = 46,350) is smaller than BMI , which can affect the relative predictive power of these PRS scores [Our findings should be considered in light of a number of limitations. Firstly, our models were statistically driven—although theoretically informed. The models are therefore dependent on the properties of the measures included. While our sPLS model identified simultaneous association between the PRS scores, the two neuroimaging modalities and the phenotypic measurements of ADHD and BMI, this does not mean that this model—or the study variables contained within it—is the best (or only) solution. Secondly, it is also important to note that PRS and neural correlates only explain a small proportion of the liability for many psychiatric difficulties and other traits. Thirdly, the current findings differ from reward anticipation in case-control ADHD designs , 66 and S scores . In addiOur results indicate that ADHD and BMI PRS scores are significantly correlated, suggesting shared genetic risk between the traits. In a data-driven model, both the PRS scores were associated with the phenotypic measures of ADHD and BMI, as well as shared neural correlates of impairments in inhibitory control and reward processing.Supplemental Material