Here we examined whether and what structural alterations could be elicited in the dendritic trees of the same cell populations in young adult rats after being exposed for 2 months to an enriched environment (EE), and how these changes evolved when animals were returned to standard housing for an additional 2 months. Neurons were retrogradely labeled with BDA delivered to, respectively, the ventral posteromedial thalamic nucleus or Pr5. Fully labeled cells were digitally reconstructed with Neurolucida and analyzed with NeuroExplorer. EE gave rise to increases in dendritic length, number of trees and branching nodes, spatial expansion of the trees, and dendritic spines, which were less pronounced in Sp5C than in Pr5 and differed between sides. In Pr5, these parameters returned, but only partially, to control values after EE withdrawal. These results underscore a ubiquity of experience-dependent changes that should not be overlooked when interpreting neuroplasticity and developing plasticity-based therapeutic strategies.Nervous systems respond with structural changes to environmental changes even in adulthood. In recent years, experience-dependent structural plasticity was shown not to be restricted to the cerebral cortex, as it also occurs at subcortical and even peripheral levels. We have previously shown that two populations of trigeminal nuclei neurons, trigeminothalamic The online version contains supplementary material available at 10.1007/s00429-021-02424-3. Sustained alterations in sensory experience induce structural changes in the brain not only during development and at early maturational stages, but also throughout life. This property started to be explored soon after Hebb formulated his influential hypothesis that lasting structural changes, consisting of increases in number and/or size of interneuronal contacts, should occur whenever a cell repeatedly excites another cell Cooper . For manEnvironmental enrichment (EE) is a widely used strategy to increase the exposure of experimental subjects to sensory stimulation and promote voluntary social interactions and exploratory behaviors , which are the main source of the lemniscal pathway to the thalamus . One population consists of the n = 44) from our own colony, originating from Harlan  were used. All animal procedures were approved in advance by the Ethics in Research Committee of the Autónoma University of Madrid, in accordance with European Community’s Council Directive 2010/63/UE. Every effort was made to minimize the suffering of the animals, as well as the number of animals used. After weaning, at P21, rats were tagged and housed under standard colony conditions [4 rats/cage (28 × 48 × 28 cm)] and maintained on a reversed 12:12-h dark/light cycle with ad libitum access to food and water. The animals were divided into three groups: Control group , rats subjected to minimal handling for routine cleaning; enriched environment group , rats daily exposed to an EE for 2 months; and Enrichment Withdrawal group , animals that after 2 months of EE were returned for an additional 2 months to standard housing. For enrichment, animals that reached the age at which they are considered young adults (between 63 and 70 days old) were placed 3 h per day, 5 days a week during the activity cycle (dark conditions), in groups of eight animals in an enrichment and socialization methacrylate cage (80 × 100 × 60 cm) with a metal wire mesh cover. This cage had platforms and ramps at different heights, and contained various toys with different textures: tubes, boxes, platforms and ramps as "non-natural" elements, and pinecones, stones, sticks and bowls with water as “natural” elements. Every day, rewards  were placed in different places in the cage and the position of platforms and ramps as well as toys are changed twice a week. Running wheels were not included, to avoid the well-known interfering factors that wheel running introduces in rats’ energy balance and behavior , rats were anesthetized with ketamine (55 mg/kg)–xylazine (15 mg/kg)–atropine (0.2 mg/kg) and their heads were positioned in a stereotaxic frame. Bilateral microiontophoretic injections of biotinylated dextran amine  were made with a Midgard constant current source . Injections were guided by the presence of short-latency, high-amplitude single- and multiunit responses to gentle mechanical stimulation of the vibrissal pad, recorded through the glass micropipettes . The targets were the ventral posteromedial nucleus of the thalamus (VPM) and the principal trigeminal nucleus (Pr5), to retrogradely label Pr5 trigeminothalamic barrelette neurons and trigeminal intersubnuclear neurons in the caudal division of the spinal trigeminal nucleus that project to Pr5 (Sp5C-Pr5), respectively . Alternate thalamic and Pr5 series were Nissl-stained with 0.1% Cresyl Violet, or reacted for cytochrome oxidase  and perfused through the ascending aorta with 0.9% NaCl , followed by 4% paraformaldehyde in 0.1 M phosphate buffer . The brains were removed and blocked depending on whether injections were made in the thalamus or in Pr5. In the first case two blocks were obtained, one containing the thalamic region that received the tracer deposits and the other containing the whole rostrocaudal extent of Pr5. In cases injected in Pr5 only one block was prepared that included the whole rostrocaudal extent of the brainstem, from the rostral tip of Pr5 to segment C1 of the spinal cord. All blocks were postfixed in the same fixative overnight at 4 ºC, subsequently cryoprotected for 2 days in 30% sucrose in 0.1 M PB, and then frozen and serially cut at 50 µm in the coronal plane in a sliding microtome. For precisely assessing the injection location and extent, one-third of the sections of the thalamus and one-third of the sections of Pr5 which included the area of injection, were incubated in avidin–biotin peroxidase  and revealed with diaminobenzidine  with Hz-axis, using Neurolucida system with a 40 × dry objective  and NeuroExplorer software , path length, and, only for Sp5C, spine number. Because of their low numbers, spines were not reconstructed in Pr5. Also, we performed 3D Sholl analyses  we prepared dendrograms that included measures of node number (node: branching point of a dendrite), tree degree , segment order  or nonparametric tests (Mann–Whitney), after testing for normality and homogeneity of variances. To assess differences in the distribution of morphometric events at selected distances from the soma in Sholl analyses, two-way analysis of variance (ANOVA) with post hoc tests were used, and the Mann–Whitney test was applied pointwise to bootstrapped distributions. The distributions of dendritic trees according to their maximum Euclidean distance were plotted in Excel, and were compared between groups using the two-sample Kolmogorov–Smirnov D test (Kirkman p < 0.05), and very significant (**p < 0.01); for other symbols, see legends.Statistical testing was performed using Statgraphics plus (ver. 4.0 for Windows) and SPSS (ver.15.0 for Windows). Point graphs were generated with Graphpad Prism ver. 5.03 . For graphic rendering of Sholl data, curves and pointwise bootstrap confidence intervals were generated with Real Statistics Resource Pack software , as shown by digital 3D reconstructions and polar histograms. Intersubnuclear neurons in Sp5C projecting ipsilaterally to Pr5 have multipolar ovoid or fusiform bodies that appear more often, but not exclusively in lamina III. From 4–5  primary dendrites emerge dendritic arbors that ramify mainly in the same lamina as the cell body. These arbors lack any definite polarization, and vary in size, occasionally reaching high complexity (up to degree 25), but in most cases being restricted to degrees 1–12.The location of BDA injections and the retrogradely labeled neurons in control animals were described in detail in our previous studies  in the left Pr5 barrelette neurons, compared to the corresponding sides in controls Table . One colA more detailed analysis of the trees showed that the increase in primary dendrites on the right Pr5 occurred without altering the distribution of trees according to their branching complexity. This did not happen on the left side, where a significant decrease of lower degree trees was counterbalanced by an increase of more complex trees, with no changes in the mean number of primary dendrites Fig. . Sp5C neMoreover, not all segment types were likewise affected by enrichment. In Pr5, intermediate segments of any order barely changed on the left side, but those of lower order increased in length on the right. Terminal segments, however, showed marked and significant reductions in length on the left side, an effect which failed to appear only in the highest order segments Fig. . In Sp5CThe comparisons between groups benefited from the Sholl analysis, which allowed to determine the spatial distributions of morphometric and topological parameters of the dendritic trees at fixed intervals from the neuronal somata. These intervals were set at 20 μm for Pr5 neurons and at 30 μm for the more extensive Sp5C neurons. The distribution curves of segment length and number of intersections were similar in all cases, as were the number of dendritic nodes and endings, hence only data for segment length and endings will be presented in the following Figures. The overall effect of enrichment on Pr5 neurons was the disappearance of the notable asymmetries observed in controls Fig. . This efIntergroup comparisons by side with controls are summarized in the color contour maps shown in Figs. Two months after animals were no longer exposed to an EE, the dendritic trees of Pr5 neurons (Sp5C neurons were not studied using this paradigm) returned to values more similar to those in control cases, but not in all parameters. In particular, total dendritic length remained elevated on the right, and increased even further on the left, where dendrites reached significantly greater Euclidean distances than in C and E groups Table , Fig. 5.The present study shows that discontinuous periods of exposure to a sensory-enriched environment during 2 months bring forth metric and topological changes in the dendritic trees of different populations of neurons in trigeminal nuclei of young adult male rats. Pr5 barrelette neurons, which receive primary afferents from vibrissae and project to the ventroposterior thalamic nucleus in a topologically consistent fashion  Pr5 Below is the link to the electronic supplementary material.


There have been various therapies for attention-deficit/hyperactivity disorder (ADHD), but the previous meta-analysis of ADHD efficacy remains unclear. This study aims to systemically meta-regress the effect sizes (ES) of psychostimulant pharmacotherapy (methylphenidate and lisdexamfetamine), non-stimulant pharmacotherapy , psychosocial therapy , combination therapy (psychostimulant plus PBT), and alternative/complementary interventions to determine the right treatment for ADHD.We searched various ADHD interventions from the MEDLINE and PubMed databases  between January 1, 1980, and July 30, 2018. Following the meta-analysis of random effects, the meta-regression analyses were used to explore factors potentially influencing treatment efficacy. The confounding variables included type of treatment, type of study, age, type of symptom scale used, and year of publication.P = .004) and −0.419 (P = .028), respectively. However, compared with psychostimulant, PBT  and the combination of psychostimulant and PBT  did not differ significantly.A total of 107 trials (n = 9883 participants) were included. After adjustment, compared with the psychostimulant therapy , non-stimulant pharmacotherapy  and alternative/complement intervention  were less effective by the ES of −0.384 (Psychostimulant therapy surpassed non-stimulant pharmacotherapy and alternative/complement intervention. Psychostimulant therapy, PBT, and the combination of psychostimulant therapy and PBT appear to be similar in efficacy according to this meta-regression. Attention-deficit hyperactivity disorder (ADHD) is a common neurodevelopmental disorder among children and adolescents. The right choice of ADHD treatment is always a major concern of parents. However, previous network meta-analysis of ADHD efficacy by indirectly estimating effect size remains unclear in comparing the effects of pharmacotherapy, psychosocial intervention, and alternative/complementary approaches. Understanding the treatment efficacy of ADHD by new meta-regression analysis is an effective and exploratory analyses for heterogeneity according to the studies of cross-level interactions. This meta-regression is eligible in directly comparing the effects of ADHD treatments by systemically reviewing the “interaction analysis” of pharmacotherapy (psychostimulant and non-stimulant pharmacotherapy), psychosocial intervention, and alternative/complementary approaches. In conclusion, this meta-regression indicates that a combination therapy of psychopharmacotherapy and parental behavioral therapy provides an evidence-based ADHD treatment model.Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental disorder . The posIn developed countries, the first choice of ADHD treatment is pharmacotherapy, as indicated by ADHD treatment guidance from the UK National Institute for Health and Care Excellence , the BriMPH has been reported to be effective for enhancing cognition  and reduNon-stimulant pharmacotherapy, ATX, also improves overall ADHD symptoms  and secoADHD treatment guidelines established by The Multisite Multimodal Treatment Study of Children with ADHD  and the Alternative/complementary treatments interventions of ADHD have not yet been accepted by the US Food and Drug Administration. However, some parents refuse regular treatments  and instA recent network meta-analysis showed that psychostimulant treatment was more effective than placebo by indirectly estimating the relative effects or a single pooled treatment effect estimate of various interventions . InsteadMeta-regression is an effective tool for exploratory analyses of heterogeneity and for studies of cross-level interactions . The hetImportantly, meta-regression of randomized controlled trials can provide clinicians with the confidence to choose the right treatment option for ADHD . This meInclusion criteria included the following: a formal diagnosis of ADHD, attention-deficit disorder, or hyperkinetic disorder with any subtype being diagnosed in accordance with either DSM-IV or ICD-10 criteria. Exclusion criteria included the following: patients with ADHD and a major neurological impairment, psychosis, major depressive disorder, or history of substance abuse disorder.Two authors (Y.C.C. and R.F.T.) independently searched the MEDLINE and PubMed databases  from January 1980 to July 2018 for studies evaluating the efficacy and clinical outcomes of pharmacotherapeutic and non-pharmacotherapeutic interventions for children and adolescents with ADHD.The following keywords were used to identify relevant articles:  AND (attention deficit OR attention-deficit OR “attention-deficit disorder with hyperactivity” OR ADHD OR ADD OR inattentive OR “hyperactive*” OR hyperkinetic OR impulsivity*) AND (“treat*” OR “intervention*” OR “therapy*” OR “psychotherapy*” OR “training*” OR “program*” OR “workshop*”).Papers that satisfied the inclusion criteria were selected for comparing the treatment effects of pharmacotherapy, PBT, combined intervention, and complementary and alternative therapies for ADHD.The following information was extracted from each study: the last name of the first author, publication year, treatment type , primary outcome measurement, baseline and endpoint mean and SD of primary efficacy measures, mean age of total number of participants in the study, type of symptom measurement scale, and study quality.The authors were contacted if data were missing, incomplete, or unclear. Only the available data were analyzed, without imputing the missing data. Studies with insufficient data were excluded .We included and re-coded the following interventions: (1) Treat_1, pharmacotherapy with stimulant: MPH, lisdexamfetamine; (2) Treat_2, pharmacotherapy with non-stimulant: ATX, alpha-2 agonist (clonidine or guanfacine); (3) Treat_3, PBT; (4) Treat_4, combined intervention: psychostimulant + PBT; and (5) Treat_5, other : cognition training, hippotherapy, fluoxetine hydrochloride, cinnamon aromatherapy, EEG biofeedback, and sandplay therapy.g to express the size of the intervention effect in each study relative to the variability observed in that study.The standardized mean difference (SMD) is scaleless in statistics. The current meta-regression therefore created a model to describe the linear relationship between  study-level covariates and the ES. We applied the particular definition of SMD used in Cochrane reviews for the ES known in social science as Hedges’ (adjusted) ES can be also influenced by the measurements used. To explore potentially influential factors and reduce heterogeneity, the primary scales used to evaluate the ES were classified and coded as follows: (1) Scale_1: Swanson, Nolan, and Pelham–IV, IOWA Conners Rating Scale hyperactivity; (2) Scale_2: Conners Parent (or Teacher) Rating Scale; (3) Scale_3: ADHD Rating Scale-IV, ADHD Rating Scale, Daily Parent Rating of Evening and Morning Behavior Scale; (4) Scale_4: Disruptive Behavior Disorder Rating Scale, Conduct Disorder Score, Irritability Scale, Turgay DSM-IV-Based Child and Adolescent Behavior Disorders Screening and Rating Scale, Child Behavior Check List Chinese, Aberrant Behavior Checklist, Barkley Home Situations Questionnaire, Barkley School Situations Questionnaire, Child Behavior Checklist, Difficulties in Emotion Regulation Scale, Swanson, Nolan, and Pelham–IV Oppositional Defiant Disorder, Eyberg Child Behavior Inventory; (5) Scale_5: Other . SMD is used as a summary statistic in meta-analysis when the studies assess the same outcome in a variety of ways . As described previously, there were 5 types of coded scales for evaluating the treatment effects. In this circumstance, it was necessary to standardize their measurements to a uniform scale before their combination.The SMD has been the most commonly used ES for evaluating treatment effect in randomized control trials. However, SMD is significantly influenced by the choice of the control group, for example, an active  or placebo control. In the study designs that used 2 or more treatments and compared with either an active or placebo control group, the data were extracted by dividing the individual studies into several appropriate and comparable groups.g was calculated using the change-related information (from pre-test to post-test) in both treatment and control groups. We treated active control studies as having 2 independent groups with pre- and post-test information available and calculated each group’s unbiased SMD of Hedges’ g according to StudyType_2. (2) StudyType_2: single group with pre- and post-test information available. We followed the recommendation by Rosenthal and used a conservative estimate of r = .70 (g. (3) StudyType_3: 2 groups with only post-test information available. The unbiased SMD of Hedges’ g was calculated in terms of SD of change of the means and SDs of post-test from both treatment and control groups. (4) StudyType_4: a meta-analysis paper with only an unbiased SMD of Hedges’ g and its standard errors. In this study, we treated the types of study as a potential confounding variable.To enable comparison, we classified the types of studies as follows: (1) StudyType_1: for placebo control studies with both pre- and post-tests in both groups, the SMD of Hedges’  r = .70 . We thenAccording to the Jadad scale guidelines , the quality of the randomized controlled trials can be shown by Jadad scale. A score of 1 represents that it is easy to use, 2 means it contains many of the important elements that have been empirically shown to correlate with bias, and 3 indicates that it has known reliability and external validity . Here, 2The risk of bias was evaluated by funnel plots and Egger’s regression intercept tests. The goodness-of-fit indices of the fitted model were presented by 2 values and 1 plot; I-squared residual , adjusted R-squared (the proportion of between-study variance explained by the model), and meta-regression plot.Meta-regression is an extension to subgroup analyses that allows the effect of continuous, as well as categorical, characteristics to be investigated and in principle allows the effects of multiple factors to be investigated simultaneously. Therefore, even the continuous, as well as categorical, characteristics were investigated to explore their unbiased efficacy.g, where a larger ES value represented more improvement. Such conversions aided interpretation of the meta-regression.The primary outcomes of all included studies contained various types of evaluation scales. As described previously, there were 5 types of coded scales used to evaluate treatment effects. Each scale type featured its unique format, but all had the same evaluation purpose for ADHD treatment. SMD is used as a summary statistic in meta-analysis when the studies all assess the same outcome but measure it in a variety of ways. ES obtained from different evaluation scales were transferred into the Hedges’ P < .05.The independent variables were publication year, mean age, and the abovementioned re-coded treatment types (Treat_1 to Treat_5), scale types , and study design (StudyType_1 to StudyType_4). The level of significance was set at All meta-analyses were conducted using STATA v.13.0 software . We first used the fixed effects meta-analysis to evaluate the pooled ES. Random effects meta-analysis followed if inter-study heterogeneity was highly significant.g, assessed according to the aforementioned 4 types of study design. The independent variables were publication year, mean age, and abovementioned re-coded treatment types (Treat_1 to Treat_5), scale types , and type of study design (StudyType_1 to StudyType_4). The level of significance was set at P < .05.Following the meta-analysis of random effects, the meta-regression analyses were used to explore and compare factors potentially influencing treatment efficacy. The dependent variable was the SMD of Hedges’ d.f. = 186, P < .001). The index of variation in ES attributable to heterogeneity, I-squared, was 88.3%. To account for the heterogeneity among the studies, the random effect’s meta-analysis was used to estimate the pooled ES. The estimated pooled ES was equal to 0.642 with 95% confidence interval =  and the estimate of between-study variance, tau-squared = 0.2826. The results of the funnel plot showed that there was a mild-moderate publication bias (P < .001).A total of 107 trials  met the inclusion criteria and had data amenable to analysis. The mean age ranged from 3 to 17.33 years. Owing to various treatment methodologies and/or treatment effect assessments included, the results were unsurprisingly heterogeneous. The heterogeneity of the meta-analysis was found to be highly significant ; (2) the ES decreased gradually with respect to the mean age of ADHD children, although it only reached borderline significance ; (3) the ES of Treat_2, Treat_3, and Treat_5 were, on average, 0.092, 0.030, and 0.151 units lower than that of Treat_1, respectively, although all of these results were not statistically significant. On the other hand, the ES of Treat_4 was 0.256 units higher than that of Treat_1, with P = .140 (insignificant).We further employed the meta-regression analysis to investigate the possible sources of heterogeneity among the 107 included trials. The results of univariate meta-regression analysis are presented in P values of all 3 Fisher’s exact tests < .001, not shown). Therefore, to evaluate the impact of any 1 of these 3 factors on treatment efficacy, we simultaneously adjusted for the effects of other 2 factors.The impacts of types of treatment, types of evaluation scale, and types of study on treatments efficacy were mutually affected . It is worth mentioning that the PBT alone  and combined treatment  were 0.308 and 0.196 units less effective, respectively, than Treat_1, although the results were insignificant .Accordingly, to compare the treatment effects among all collected intervention methods after adjusting for the effects of other potential factors , the multiple meta-regression analysis was used . After at = 4.48, P < .001)  . In otheTo date, clinicians struggle to balance benefits and costs of various interventions of ADHD , 2019. TPrevious studies on ADHD treatments sometimes met difficulties, such as heterogeneity across outcome measures and tests . Catala-P = .004) and complementary and alternative intervention (P = .028) (P = .024). Therefore, the current meta regression adjusted the confounder of publication year to obtain an unbiased ES.The findings of the current study appear clinically instructive. After adjusting for confounding variables, psychostimulant medication was significantly more effective than non-stimulant treatment ( = .028) . However = .028) . Also, d = .028) . Likewis = .028) . We alsoIn addition to psychostimulants , parent-Since the remission rate from pharmacotherapy by osmotic-release oral system-MPH remains between 44%  and 66.1There were some limitations in this study. The mean age of the study population ranged from 5.2 to 17.7 years. There may have been informant rater problems affecting the treatment effects . We planAlthough there are many therapies available for ADHD, comparative efficacy for different treatments remains unclear based on previous meta-analyses. The current study systematically meta-regressed the ES of various treatments for ADHD and overcame heterogeneity among ADHD studies. It is also the first, to our knowledge, to attempt to examine all published meta-analyses and randomized control trials on multitudinous treatments for ADHD. The results showed that the psychostimulant surpassed non-stimulant pharmacotherapy and alternative/complementary interventions. Psychostimulant therapy, PBT, and a combination of psychostimulant and PBT appeared to be similar in their efficacy according to this meta-regression. Our findings will help clinicians, healthcare providers, parents, and caregivers in choosing treatment for ADHD in children or adolescents.pyab034_suppl_Supplementary_MatrialsClick here for additional data file.


CDH13 is associated with a wide spectrum of neuropsychiatric disorders, including attention-deficit/hyperactivity disorder (ADHD), autism, and major depression. CDH13 regulates axonal outgrowth and synapse formation, substantiating its relevance for neurodevelopmental processes. Several studies support the influence of CDH13 on personality traits, behavior, and executive functions. However, evidence for functional effects of common gene variation in the CDH13 gene in humans is sparse. Therefore, we tested for association of a functional intronic CDH13 SNP rs2199430 with ADHD in a sample of 998 adult patients and 884 healthy controls. The Big Five personality traits were assessed by the NEO-PI-R questionnaire. Assuming that altered neural correlates of working memory and cognitive response inhibition show genotype-dependent alterations, task performance and electroencephalographic event-related potentials were measured by n-back and continuous performance (Go/NoGo) tasks. The rs2199430 genotype was not associated with adult ADHD on the categorical diagnosis level. However, rs2199430 was significantly associated with agreeableness, with minor G allele homozygotes scoring lower than A allele carriers. Whereas task performance was not affected by genotype, a significant heterosis effect limited to the ADHD group was identified for the n-back task. Heterozygotes (AG) exhibited significantly higher N200 amplitudes during both the 1-back and 2-back condition in the central electrode position Cz. Consequently, the common genetic variation of CDH13 is associated with personality traits and impacts neural processing during working memory tasks. Thus, CDH13 might contribute to symptomatic core dysfunctions of social and cognitive impairment in ADHD.The cell—cell signaling gene  CDH13) was found among the top hits in several genome-wide association studies (GWAS) for attention-deficit/hyperactivity disorder (ADHD) [CDH13 in neurodevelopmental processes.The gene coding for Cadherin 13 (r (ADHD) ,3. Additr (ADHD) . Its recr (ADHD) ,6, majorr (ADHD) ,8, bipolr (ADHD) , and subr (ADHD) ,11,12, pCDH13 SNP variation impacts CDH13 expression. Drgonova et al. (2016) reported four CDH13 SNPs in intron 2 within 8 kb, which were associated with CDH13 mRNA expression levels in the human frontal cortex postmortem tissue. The greatest effect was detected for rs2199430 with an 80% increase in CDH13 expression levels in samples derived from GG allele carriers [CDH13 with temporal lobe volumes [CDH13 expression levels, brain structure, and brain function by analyzing genotypes on different endophenotypic levels in a large cohort of adult ADHD (aADHD) patients. volumes , and the volumes . We are CDH13 is a crucial driver for neurodevelopmental processes [CDH13 for axonal guidance in migrating motor neurons by temporal and spatial changes in CDH13 expression [CDH13 for the development of serotonergic projections, as CDH13−/− mice showed an increased density of serotonergic neurons in the dorsal raphe during brain maturation and adulthood [CDH13−/− mice exhibit locomotor hyperactivity, learning deficits with decreased cognitive flexibility, and memory impairments, hinting at behavioral and executive dysfunctions resembling ADHD symptomatology [CDH13 transcripts found in prominent nuclear brain regions, such as the locus coeruleus, the raphe nuclei, and—to a lesser extent—in the ventral tegmental area, underpins the importance of CDH13 for the modulation of monoaminergic pathways [rocesses . Early spression . A recendulthood . Phenotymatology ,20. A repathways ,21.CDH13 moderates not only rodent but also human behavior. Genetic variation was associated with extremely violent offenses in prisoners [CDH13 with common personality traits. A CDH13 SNP was among the best hits for extraversion and agreeableness in a GWAS which investigated the Big Five  in a population-based sample [CDH13 variation in ADHD should consider dimensional personality traits.risoners . Besidesd sample . As persd sample ,25, studCDH13 is related to executive functioning in childhood ADHD. Arias-Vasquez et al. (2011) showed an association of a SNP in intron 6 with verbal working memory measurements in a case-control sample on a gene-wide level. Furthermore, there was a 72 kb haploblock only 12 kb upstream of rs2199430 with suggestive, though not gene-wide significant, association with verbal memory [CDH13 variation in humans so far.l memory . This stCDH13 with neurodevelopmental processes, which might be due to altered neuronal proliferation, synaptic plasticity, and cellular migration during axonal outgrowth. It is unknown whether and how this is reflected by the functional integrity of neural circuitry in humans. Preliminary evidence points to the effect of CDH13 on personality traits [CDH13 with these dimensional endophenotypes in adult ADHD patients. Therefore, we investigated the effect of functional CDH13 SNP rs2199430 in aADHD on various levels to challenge the hypothesis that common SNP variation in CDH13 is associated with ADHD beyond the categorical syndrome. First, we performed a case-control association study on the categorical disease level of ADHD. Subsequently, we tested for association with personality traits, as assessed by the NEO-PI-R. Finally, we investigated rs2199430 with regard to task performance in a working memory (n-back) and a continuous performance test (CPT) assessing response inhibition by means of a Go-NoGo task, and measured brain activation as reflected by electroencephalographic (EEG) event-related potentials (ERPs) in response to these tasks in aADHD and control probands.Consequently, there is promising evidence for an association of y traits  and execy traits ,26, but DSM-IV criteria with retrospective confirmation of an onset before the age of 7. Exclusion criteria were IQ below 80, diagnosis of bipolar affective disorder , and better explanation of symptoms by another psychiatric disorder. Control subjects consisted of 884 healthy individuals  of which 739 volunteers were recruited in the context of the Collaborative Research Center SFB-TRR58 subproject Z02 wave 2 [A sample of 998 adults with an ADHD diagnosis  was recruited from the in- and outpatient units of the Department of Psychiatry, Psychosomatics, and Psychotherapy, University of Würzburg, Germany. Part of the sample (372 patients recruited between 2003 and 2005) represents the German IMpACT cohort, which has been described previously ,28. The 2 wave 2 ,31, and https://sites.google.com/a/broadinstitute.org/ricopili/, accessed on 26 August 2021), excluding SNPs with call rate (CR) < 99%, MAF < 1% and SNPs that deviate from HWE (p < 1 × 10−10) as well as individuals with CR < 95%, outlier from principal component analyses (>3-fold SD from PC1-PC3), gender discrepancies and related or duplicated individuals. To ensure accordance with the genotyping results of our PCR method and the chip array data, 50 samples with available chip data were additionally genotyped by PCR with a 100% match of genotyping results.DNA was extracted from blood leukocytes by a slightly modified method based on the protocol of Miller et al. . Rs21994Personality traits were measured by means of self-ratings with the NEO-PI-R. The NEO-PI-R questionnaire is a questionnaire that was constructed to reliably assess personality dimensions of the Big Five . For eacN-back and CPT tasks are well-established methods for the investigation of working memory capacity and the execution or inhibition of prepared motor responses as a measure of sustained attention and impulsivity, respectively ,35. Both2-Test, p = 0.89) nor in the ADHD group . Genotyping call rate was > 99% for both the PCR method and the chip array. χ2-tests were performed to analyze associations between rs2199430 genotype and categorical disease states. To account for possible gender effects, χ2-tests were carried out separately for female and male participants. As only brain samples from probands homozygous for the minor (G) allele showed increased CDH13 mRNA levels in a previous study [Minor allele (G) frequency was 0.333 in the patient and 0.348 in the healthy control group. Rs2199430 genotype distribution did not significantly differ from the Hardy-Weinberg equilibrium, neither in the control (χOne-way ANCOVAs were conducted to compare the influence of rs2199430 genotype (AA vs. AG vs. GG) on dimensional personality traits as indicated by the Big Five main domains  reflected by respective NEO-PI-R raw scores. After exclusion of only the most extreme values with −3 ≥ Z-scores ≥ +3, test assumptions for the general linear model were met in terms of normally distributed residuals (Shapiro-Wilk test) and homogeneity of variances (Levene’s test). A previous study of the same aADHD cohort showed a strong impact of gender on the NEO-PI-R measures of neuroticism, openness, and agreeableness . FurtherStatistical analyses of the functional EEG data were performed using IBM SPSS Statistics (version 27). For the n-back task (N200 and P300 amplitudes), repeated-measures ANOVAs were conducted separately for ADHD patients and controls for the 1-back and 2-back task, with anterior-posterior location , side  and condition (target vs. non-target) as within-subject variables and rs2199430 genotype  as between-subject factor. For the CPT, NGA values—as well as the so-called “centroids”  underlying this difference measure—were compared between genotypes using univariate ANOVAs. Post hoc analyses were conducted using univariate ANOVAs and two-tailed t-tests for paired or independent samples, as appropriate.p-values are nominal, unless stated otherwise.The significance threshold was set at 0.05, and all reported 2-Test, p = 0.47) nor in the female or male subgroup . Also, the recessive model  revealed no significant association with ADHD diagnosis .Although there was a nominally lower percentage of GG carriers in the disease group  in the aADHD group. None of the four other personality domains  showed a statistically significant association with rs2199430 genotype  and AA allele carriers  as depicted in t-tests performed separately in the female and male subgroup revealed that in both females and males the rs2199430 GG carrier group had significantly lower mean agreeableness scores than AA or AG allele carriers  between genotype  and ADHD subgroup (inattentive vs. combined vs. hyperactive). Bonferroni-adjusted post hoc t-tests revealed that the GG genotype is associated with lower agreeableness in patients with inattentive , but not in patients with combined , and predominantly hyperactive subtype  leads to an association of the rs2199430 GG genotype with lower agreeableness scores in normal populations we performed a one-sided t-test showing a significant association with lower agreeableness scores in GG carriers  than in AA or AG allele carriers  in the healthy control group .As indicated by univariate ANOVAs carried out separately for ADHD patients and healthy controls, genotype (AA vs. AG vs. GG) did not significantly impact any of the behavioral data  and 2-back condition . Post hoc analyses (univariate ANOVAs) of this interaction showed significant genotype effects only for the more posterior  row, more specifically the middle central electrodes : N200 amplitudes were consistently higher  for the heterozygous genotype (AG carriers) compared to AA  or GG genotype carriers  × 2 (condition: target vs. non-target) × 2  × 3  ANOVA for the n-back data revealed a significant interaction between all four factors for both the 1-back . Post hoc analyses of the interaction further revealed no significant genotype effects (univariate ANOVAs) for either target or non-target trials at either left, right or central positions ; however, depending on the genotype, healthy controls differed in their P300 lateralization pattern: While AA genotype carriers had a clear topographical maximum of P300 amplitudes over central electrode positions (Cz/Pz) as indicated by significant t-comparisons with both left  and right sites , carriers of at least one G allele did not show this clear central distribution, especially for target trials where amplitudes were equally high for central and right-sided electrode positions, i.e., the topography showed a shift toward the right as indicated by significant t-contrasts of Cz/Pz and C4/P4 as compared to C3/P3: all t > 3.2, p < 0.01, but no significant differences between Cz/Pz and C4/P4  × lateralization  × genotype (AA/AG/GG) was found for the 2-back condition of the n-back task .For general comparisons of ADHD patients and healthy controls, see Ziegler et al. (2020). Regarding genetic effects, rs2199430 genotype did not significantly impact any of the behavioral data ; neither were the Go  and NoGo centroids  underlying this difference measure.The NoGo anteriorization (NGA) is a topographical ERP parameter quantifying a frontalization of the brain electrical field of the P300 during inhibitory processes in the context of Go-NoGo tasks  that wasCDH13 intron 2 SNP rs2199430 is associated with frontal cortex CDH13 expression levels [n levels ,40,45,46CDH13 with ADHD and the association of rs2199430 with CDH13 expression levels [Our study revealed no general association of rs2199430 genotype with ADHD on the categorical level. Given the replicated association of CDH13 with agreeableness and extraversion [The association of aversion , and theaversion ,25. Encoaversion . ADHD paaversion ,48. Theraversion .CDH13 [CDH13 expression goes hand in hand with lower prefrontal serotonergic innervation [CDH13 on social behavior. The GG genotype, which is associated with higher frontal cortex CDH13 expression levels [CDH13−/− mice with Golgi cell-specific knockout show increased reciprocal social interactions [CDH13 gene [CDH13-dependent alterations in social behavior. Future studies that address the functional effects of CDH13 in ADHD should consider paradigms of social reciprocity and empathy, for example, by functional imaging studies to test for an association of CDH13 with pro- and anti-social behavior at a neural system level.Low agreeableness has been replicably associated with higher aggression ,50 and, CDH13  fits ourCDH13 . As highervation , the finervation ,53. The ervation ,55. Thusractions . Low agrractions ,58, and ractions . Thus, tCDH13 influences working memory performance in childhood ADHD [CDH13 SNP in aADHD. To this end, we assessed working memory performance and EEG neural response patterns during an n-back paradigm. While rs2199430 genotype was not associated with task performance as measured by the number of omission/commission errors and reaction time, we found a significant impact on N200 amplitudes in central electrode positions for both the 1-back and the 2-back condition. This effect only occurred in the aADHD group, but not in the group of healthy controls, possibly indicating disease specificity. Heterozygotes (AG) exhibited significantly higher N200 amplitudes than homozygotes in the central electrode positions. Even if unexpected at first sight, there is a vast amount of literature describing heterosis effects for candidate genes of complex polygenic neuropsychiatric disorders, e.g., for the BDNF Val66Met polymorphism [CDH13. The N200 amplitude is generally associated with conflict processing and cognitive inhibition during mentally demanding tasks [ood ADHD . In contmorphism ,61,62, tmorphism . Our datng tasks ,65. HencCDH13 SNP variation in neural processing involved in verbal memory and attentional control. As a previous study hints at an impact of CDH13 SNP variation on verbal memory in children with ADHD [CDH13 is an important neural moderator for working memory and sustained attention in ADHD. This is in line with the pathophysiological concept of ADHD as a dysexecutive neurodevelopmental disorder with alterations in attentional brain networks with a focus on the connectivity between frontal brain areas, such as the dorsolateral prefrontal cortex and striatal circuits [We did not find any genotype-dependent quantitative alterations of P300 amplitudes. However, there was a notable rightward lateralization of P300 amplitudes in rs2199430 G allele carriers, which occurred only in the healthy control but not in the aADHD group. The P300 is thought to reflect stimulus recognition, memory updating, and decision-making during n-back tasks on a higher hierarchical level than the N200, which seems to be more closely related to immediate decision-making ,67. Regaith ADHD , we are circuits .CDH13−/− mice are predominantly impaired regarding working memory and learning while displaying only subtly increased impulsivity [CDH13 in neurodevelopmental disorders should concentrate on the role of CDH13-mediated neuronal outgrowth in attentional networks, such as frontostriatal circuits and hippocampal regions, in both rodent models and human studies. For the former, a conditional knockout mouse is already under investigation [CDH13+/− and CDH13−/− human-induced pluripotent cell lines have been created recently [CDH13 on the excitatory-inhibitory balance in neuronal cell models [Behavioral measures and neural responses were not altered by rs2199430 genotype in the CPT (Go-NoGo task). As the CPT mainly assesses prefrontal functions of cognitive response control that are disturbed in highly impulsive individuals, our result is in accordance with reports that ulsivity . Therefotigation ; to addrrecently  and can l models .CDH13—besides its categorical association with ADHD—influences intermediate phenotypes of dimensional personality traits and executive functions. Thereby, CDH13 might contribute to important symptomatic domains of cognitive impairment and disturbed social relationships in ADHD. If and how differential brain CDH13 expression is related to functional alterations in monoaminergic neurotransmission, especially the serotonergic system in this context, should be the focus of future studies.As a limitation, it has to be stressed that the effects of the significant results in this study were only in the small- to medium-sized range and would not survive correction on a gene-wide level, an immanent problem when investigating common SNPs in complex polygenetic neuropsychiatric disorders. Nevertheless, this study provides further evidence that


Attention deficit hyperactivity disorder (ADHD) is a highly heritable, neurodevelopmental disorder known to associate with more than double the risk of death compared with people without ADHD. Because most research on ADHD has focused on children and adolescents, among whom death rates are relatively low, the impact of a high polygenic predisposition to ADHD on accelerating mortality risk in older adults is unknown. Thus, the aim of the study was to investigate if a high polygenetic predisposition to ADHD exacerbates the risk of all-cause mortality in older adults from the general population in the UK.single) and (2) chronic obstructive pulmonary disease and younger age of giving first birth, which were shown to have a strong genetic correlation with ADHD using the multi-trait analysis of genome-wide association summary statistics; this polygenic score was referred to as PGS-ADHDmulti-trait. All-cause mortality was ascertained from the National Health Service central register that captures all deaths occurring in the UK.Utilising data from the English Longitudinal Study of Ageing, which is an ongoing multidisciplinary study of the English population aged ≥ 50 years, polygenetic scores for ADHD were calculated using summary statistics for (1) ADHD ; further analyses showed this relationship was significant in men . Risk of all-cause mortality increased by an approximate 11% for one standard deviation increase in PGS-ADHDmulti-trait . When the model was run separately for men and women, the association between PGS-ADHDmulti-trait and an increased risk of all-cause mortality was significant in men  and women .The sample comprised 7133 participants with a mean age of 64.7 years ; of these, 1778 (24.9%) died during a period of 11.2 years. PGS-ADHDA high polygenetic predisposition to ADHD is a risk factor for all-cause mortality in older adults. This risk is better captured when incorporating genetic information from correlated traits.The online version contains supplementary material available at 10.1186/s12916-022-02279-3. Attention deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder, characterized by age-inappropriate inattention, overactivity or impulsiveness . One of ADHD is a highly heritable disorder , with a However, the impact of having a higher polygenic predisposition to ADHD extends beyond ADHD diagnosis. Indeed, a high polygenic predisposition to ADHD was shown to associate with higher levels of symptoms related to ADHD diagnosis, such as inattention, hyperactivity and impulsivity, in children and adolescence . SimilarPGSs can further be used to assess predisposition to a condition that may never be expressed phenotypically, highlighting a shared genetic risk between traits and health conditions , 14. ForTherefore, drawing on a large, phenotypically well-defined sample of population-representative older adults without a known diagnosis of ADHD, the aim of the present study was to investigate the extent to which the polygenic predisposition to ADHD is associated with all-cause mortality in general population in the UK. We further investigated if utilising a measure of polygenic predisposition to ADHD based on multiple traits that have an overlapping genetic make-up with ADHD provides a stronger predictor of the nexus of ADHD and mortality. We hypothesised that there would be a significant association between polygenic score for ADHD and all-cause mortality during the 11-year follow-up period in general population in the UK. We further hypothesised that a polygenic predisposition to ADHD based on multiple overlapping traits will provide a significantly stronger predictor of the nexus of ADHD and mortality compared to polygenic score capturing polygenic predisposition to ADHD diagnosis only.We utilised data from the English Longitudinal Study of Ageing (ELSA), which is an ongoing large, multidisciplinary study of a nationally representative sample of the English population aged ≥ 50 years . The ELSThe outcome was all-cause mortality that occurred from baseline till the end of wave 8 2016–2017) encompassing an average follow-up period of 11.2 years . The rate of mortality recorded at each year of follow-up is presented in Additional file 6–2017 enThe genetic data were extracted from the blood draws taken during home visits. The genome-wide genotyping was performed at University College London Genomics in 2013–2014 using the Illumina HumanOmni2.5 BeadChips , which measures ~ 2.5 million markers that capture the genomic variation down to 2.5% minor allele frequency (MAF).p < 10−4. Samples were removed based on call rate (< 0.99), suspected non-European ancestry, heterozygosity and relatedness and if the recorded sex phenotype was inconsistent with genetic sex  were excluded if they were non-autosomal, MAF was < 1%, if more than 2% of genotype data were missing and if the Hardy-Weinberg Equilibrium single.To calculate PGS for ADHD (PGS-ADHD), summary statistics from several large genome-wide association studies (GWAS) of ADHD including the one conducted in the 23andMe with a combined sample of 117754 participants , 25 werer2 = 0.65) and age at birth of first child (r2 = − 0.62) [multi-trait using LDpred as described above.To calculate PGS for ADHD based on multiple traits that have an overlapping genetic make-up with ADHD, the Multi-Trait Analysis of GWAS (MTAG) approach was employed . MTAG wa − 0.62) . MTAG thp-value thresholds (pTs)  weighted according to the strength of effect estimate. Because a PGS at p-value thresholds pT = 1 was recommended as the optimal threshold for further analyses [pT = 1 assuming all genetic markers contributed to ADHD diagnosis; we refer to this PGS as PGS-ADHDPT = 1. Nonetheless, the results related to the remaining pTs are provided in the Additional file single, PGS-ADHDmulti-trait, and PGS-ADHDPT = 1 was standardized to a mean of 0 (SD = 1).Because calculating PGS based on clumping and thresholding method  as implemented in PRSice  is oftenanalyses , we utilp > 0.05)  to a regression that additionally included PGS as an independent variable; this procedure was repeated for each PGS separately. All analyses were carried out in RStudio version 4.0.3 [p ≤ 0.05 were considered statistically significant.Considering ADHD affects life expectancy differently in men and women , we ran on 4.0.3 ; all tesn = 3294) were men and 84.9% (n = 6059) were ≤ 75 years old at baseline. Of the entire sample, 1778 (24.9%) died during the 11-year follow-up with an average length of survival of 134.3 months . Compared to those who remained alive at the end of follow-up, a higher proportion of responders who died during the follow-up period was smokers  and not married ; the latter group further tended to report severe depressive symptoms , low accumulated wealth  and a lower educational attainment .Baseline sample characteristics of ELSA participants are presented in Table single was associated with a greater risk for all-cause mortality during follow-up in the entire sample   , whereas the relationship of PGS-ADHDsingle and all-cause mortality remained insignificant in women.PGS-ADHDmulti-trait   and women   and 13% in women .Risk for all-cause mortality increased by an approximate 11% for each increase in SD in PGS-ADHD%) Table . When thPT = 1, the results were closely aligned with those observed for PGS-ADHDsingle , which was shown to be significant in men  and not women . When analyses were limited to adults who were aged 50–75 years old, for every SD increase in PGS-ADHDPT = 1, the risk for all-cause mortality in men increased by 11% , though the relationship remained insignificant in women.When we repeated the cox regression analyses using PGS-ADHDTo our knowledge, this is the first study to investigate the relationships of a polygenic predisposition to ADHD, as measured with a single trait and multi-trait approaches, with a risk of all-cause mortality among the nationally representative adults aged ≥ 50 years without known diagnosis of ADHD, who were followed over an average period of 11 years. Cumulatively, our results contribute to a better understanding of the role a higher polygenic predisposition to ADHD plays in accelerating risk of death in the general population in the UK, for which knowledge is currently lacking.single was built using results from large-scale biomedical databases, such as UK Biobank and 23andMe, the use of this PGS may be limited as these data sources do not tend to be publicly available. When we repeated the analyses using PGS-ADHDPT = 1 which was calculated using the commonly available summary statistics of individuals diagnosed with ADHD and controls [Assuming a variation in risk for mortality is a function of the degree of a polygenic predisposition to ADHD, our results showed that the risk of all-cause mortality is amplified by a higher loading of common genetic markers associated with ADHD diagnosis, with a greater genetic liability indicating a greater risk of all-cause mortality. This finding supports the results from epidemiological studies suggesting a link between ADHD and an increased mortality risk among adults , 3. Thiscontrols , these rcontrols , these dsingle was associated with an increased risk of all-cause mortality by an average 6%; the observed risk doubled among men who were age ≤ 75 years old. The significant association between the PGS-ADHD and all-cause mortality risk observed in men, which did not reach the standard level of significance in women, may imply that men may particularly be vulnerable to the negative impact of having a higher aggregate of loci for ADHD in relation in the increased risk for all-cause mortality. In epidemiological research, the nexus of ADHD diagnosis and mortality has been linked to an increased risk of criminal and antisocial activities [Our further analyses revealed that this relationship of a polygenic predisposition to ADHD with a higher risk of all-cause mortality was specific to men, where one standard deviation increase in PGS-ADHDtivities , 31, subtivities , 33. OurNonetheless, mortality has been shown to have shared polygenic contributions with several other traits and conditions beyond ADHD, including educational attainment , coronarmulti-trait) showed that the nexus of ADHD diagnosis and all-cause mortality was equally strong in men and women. Thus, it may be argued that by focusing on a single trait, the standard methods for calculating PGS do not capture the full impact of genetic predisposition to ADHD [multi-trait were higher compared to the ADHDsingle, the predictive power of each polygenic score, as was measured by an incremental R2 value, was the same for ADHDmulti-trait and ADHDsingle highlighting that increase in predictive power in the ADHDmulti-trait was not significant. Nonetheless, this mapping of aetiological sources of cross-disorder overlap can guide future research aiming to identify specific mechanisms contributing to risk of ADHD.Indeed, utilising the genetic data from these traits into a polygenic score (i.e. PGS-ADHD to ADHD , which iWe analysed a large, longitudinal and population-based cohort of nationally representative adults residing in the UK. Our study included a relatively equal proportion of women and men from socio-economically diverse backgrounds. Because we did not rely on the clinical diagnosis to identify people who were at an increased risk for ADHD, but instead used a continuous measure of polygenetic liability to ADHD, our results are unlikely to be affected by sex biases in the referral practices, delaying diagnosing females with ADHD, which consequently may be translated into research . AlthougEven though no participants reported a diagnosis of ADHD, the condition is known to be difficult to diagnose. Given there is evidence that around 4% of adults have ADHD , the absPolygenic predisposition to ADHD confers increased risk for all-cause mortality in the general population of adults aged 50 years old and onwards. Our results further suggest that to fully capture the genetic risk of ADHD, it is imperative to incorporate genetic information contained in traits with an overlapping molecular basis.Additional file 1: Figure S1. Depicts distribution of 10 principal components once 65 individuals with ancestral admixture were removed from the sample. Figure S2. Depicts every step of quality control and assurance that was undertaken in preparation of the genetic data for the analyses in the ELLSA study. Table S1. Comparisons between the ELSA participants who were included in the analyses and those who were excluded. Table S2. The rate of mortality at each year of follow-up. Table S3. An overview of the summary of full QC procedure employed in the ELSA study and how many variants and/or participants were lost at each step. Table S4. Cox regression analyses highlighting associations between PGS-ADHDPT=1 and risk for all-cause mortality during a follow-up period. Table S5. Results of a global test for violation of proportional hazards assumption for the fully adjusted Cox models.


Research on the quantification of hyperactivity in youth with attention-deficit/hyperactivity disorder (ADHD) has been limited and inconsistent. The purpose of this study was to test the discriminative value of impulse-radio ultra-wideband (IR-UWB) radar for monitoring hyperactive individuals with ADHD and healthy controls (HCs). A total of 10 ADHD patients and 15 HCs underwent hyperactivity assessment using IR-UWB radar during a 22-min continuous performance test. We applied functional ANOVA to compare the mean functions of activity level between the 2 groups. We found that the mean function of activity over time was significantly different and that the activity level of the ADHD group slightly increased over time with high dispersion after approximately 7 min, which means that the difference in activity level between the two groups became evident at this period. Further studies with larger sample sizes and longer test times are warranted to investigate the effect of age, sex, and ADHD subtype on activity level function. Early recognition of hyperactivity remains a challenge as young children are considered generally active and it is difficult to determine a deviate activity level from the norm2. The severity of hyperactivity is regarded to change over the life span, as the gross motor hyperactivity in children is assumed to change towards fidgeting and a more subtle sense of restlessness with increasing age3. ADHD children have been known to display hyperactivity in specific contexts, such as academic classes4. The activity levels of ADHD children according to different situations over time have not been well studied5.Attention-deficit/hyperactivity disorder (ADHD) is a childhood-onset neurodevelopmental disorder that is characterized by three cardinal symptom domains: inattention (IA), impulsivity and hyperactivity. Hyperactivity is considered a core and ubiquitous characteristic of ADHD6, but these measurements are inherently limited by informant reporting bias, which can lead to discrepancies in the diagnosis of ADHD7. The most frequently used research techniques have been infrared motion analysis and accelerometer-based devices ), but both methods have produced controversial results.There is no consensus on the best method to quantify and assess activity level. Currently, the determination of excess gross motor activity in clinical settings mostly relies on subjective tools, such as clinicians’ observations, reports by caregivers or questionnaires8, are available. Among these, the most well-studied tool is the Qb test, which was approved by the Food and Drug Administration (FDA) as a diagnostic aid and treatment monitoring system9. Head movement is measured with an infrared camera that tracks a reflective marker attached to a headband worn by the participant while undergoing a 20-min continuous performance test (CPT). Despite its good psychometric properties, recent studies have shown that the Qb test cannot differentiate ADHD from other neurodevelopmental disorders11. Notably, the Qb test would likely fail to detect movements if the participant were to deviate from the test area and is not sensitive to the movements of body parts other than the head.Several commercial devices incorporating infrared motion tracking cameras with computerized attention evaluations, such as OPTAx, the McLean Motion and Attention Test System (MMAT) and the quantified behavior test (Qb test)12. They can also detect movement of the arms or legs if worn on the wrist or ankle. The strength of accelerometer devices lies in their portability, making the assessment of activity in naturalistic setting possible. Despite numerous studies on its clinical utility13, no gold standard protocol has been established for actigraphic measurements14. Time periods of assessment vary widely, and study results vary according to the position of the devices12. Moreover, certain movements (such as fidgeting-like activities involving the upper body) are unlikely to be assessed adequately15.Accelerometer devices can record fine-grain activity information  that may be particularly relevant for the assessment of activity16. The radar’s ability to detect every change in the environment through electromagnetic waves enables it to capture a wide range of body movements as well as the movement of specific body parts. Based on these characteristics, we suggest that the IR-UWB radar can be a useful tool to objectively evaluate hyperactivity in ADHD patients. Another merit of IR-UWB radar is that it can be applied in various situations, such as during a CPT or in a naturalistic setting.Impulse-radio ultra-wideband (IR-UWB) radar combines the advantages of the previous infrared motion tracking devices and accelerometer-based devices, as both the quantification of movements and tracking of spatial movements are possible. Due to the very low power required to transmit and receive signals to comply with Federal Communications Commission (FCC) standards, IR-UWB radar can detect a person's movements continuously in an indoor environment. Our previous study results demonstrated the superiority of IR-UWB radar in detecting changes in spatial position and sedentary micromovements compared to actigraphic sensorsThe purpose of this study was to compare the activity level of youth with ADHD with that of healthy controls (HCs) using four IR-UWB radar sensors during a 22-min CPT. The median value of the signals detected by the four radars during one minute was called the QAR (quantified assessment of hyperactivity using IR-UWB radar) score, and total activity during the 22 min were calculated by the sum of the 22 QAR scores. We compared the total activity level during the 22-min period, and we also assessed the temporal variance in activity by applying functional ANOVA (fANOVA), which is a statistical method used to analyze continuous-time monitoring processes whose final outputs are samples of functions. Functional data is data providing information about curves, surfaces or anything else varying over a continuum. Analysis of longitudinal curve data is challenging, and previous researchers focused on simple summary measures, thereby discarding potentially important information. FANOVA implements interpolation, smoothing and derivation to effectively highlight the characteristics and identify patterns of variation. In fANOVA, we compared the mean function and velocity of total movement between the two groups. The mean function of movement indicates the representative function of the movements from all subjects in a group, which is calculated by the arithmetic mean values at a given time point. The velocity of total movement refers to the rate of change in movement within a group during a given time point.8, we hypothesized that (1) the ADHD group would have a higher average activity level than the HC group. Based on results of a previous study showing that hyperactivity levels increases with time17, and also results of studies showing that the time-on-task affects task performance in ADHD patients18, we hypothesized that (2) the activity level would increase over time in the ADHD group, whereas it would remain relatively stable in the HC group.As previous meta-analyses observed significantly greater activity in individuals with ADHD on both actigraphy and motion tracking data with medium to large effect sizesThe demographic and clinical characteristics of the two groups are presented in Table The functional analysis of variance results, which compared the temporal variance in activity between the two groups, are summarized in Table 6. Further studies using indices reflecting the temporal variance in activity are warranted.This is the first study to use IR-UWB radar for the quantification of activity in ADHD patients and to apply fANOVA to investigate the temporal variance in activity levels during a CPT. Although there were no differences in the total activity level between the 2 groups, the patterns of activity level across time were useful in discriminating the ADHD and HC groups. When we separately investigated the activity levels across time according to task, only the results of the auditory task reached significance. This may be due to the order (thus the time effect) of the tasks, as the auditory tasks was always conducted after the visual task. The null results could be due to the small sample size, so further studies are warranted, but currently, our study results implicate that the temporal variance in activity could be more sensitive than total movement in hyperactivity assessment. Furthermore, the increase in activity in ADHD patients was evident in only a short period of time (7 min). The most frequently used parameters include movement frequency, intensity, duration, and distance, but no studies have considered the effect of time19. An actigraphy study also found a significant group x time effect on activity when testing activity levels over 2 days5. Another study analyzed the 24-h activity circadian rhythm by dividing the timeline into 1-h intervals20. A previous study found a significant group x time effect on activity during the Qb test when dividing the whole test period into 3 phases and calculating the parameters separately according to phase17. Hyperactivity was more prominent towards the end of the task; the motor activity of the ADHD patients was 3.5 times higher than that observed in the HCs. However, previous studies have been methodologically limited with regard to statistical analyses, as they analyzed the variance in mean activity across a certain interval of time . The activity level of participants should be treated as functional data, which are represented by functions or curves that reflect observations of a random variable over a continuous interval or in large discretizations. We suggest that future studies on movement or activity levels should implement fANOVA methods.The temporal variance in activity levels in ADHD patients has been underinvestigated. According to previous studies, ADHD patients show significant decreases in activity and performance scores over time21, but the results of the effect of age and ADHD subtype have been inconsistent. Some studies reported obvious hyperactivity decline from childhood to adulthood22, whereas other studies found no difference in the activity level between children and adults6. In contrast to the prevailing DSM-5 clinical view, some studies suggest that hyperactivity may be a cross-subtype and relatively homogeneous feature of ADHD despite clear differences in subjective perceptions regarding its presence/severity6. Although the majority of our ADHD population was predominantly inattentive, temporal variance in activity still differentiated the ADHD and HC groups, thus providing supporting evidence for these previous assumptions.Our small sample size prohibited further analyses on the effect of sex, age and ADHD subtype on hyperactivity. Studies have consistently reported that boys exhibit more hyperactivity than girls6 suggested that the presence and magnitude of hyperactive behavior is context-dependent. Hyperactivity becomes prominent during high cognitive load, under high executive functioning demands and in low-stimulation environments6. Given the high within- and between-day variability in the classroom behavior of children with ADHD24, hyperactivity differences are likely to become increasingly large over a prolonged period6. Therefore, hyperactivity during a CPT and hyperactivity during other cognitive tests or in every-day situations may differ. This suggestion is supported by the minimal correlation between hyperactivity measured by the IR-UWB radar during the CPT task and the parent-rated hyperactivity level measured by questionnaires in our study. IR-UWB radar measured hyperactivity during a short CPT, which reflects a very specific moment in a laboratory setting, whereas parent-rated hyperactivity reflects the accumulation of behavior. Further studies in naturalistic settings, such as in the classroom, could be conducted to explore the nature of hyperactivity in real-life situations.Our study was conducted during a CPT, but these results may vary according to test situations, as a meta-analysis by Kofler et al.A strong advantage of IR-UWB radar is that it is a no contact method, causes minimal discomfort and is small and unnoticeable. It can be placed in various naturalistic settings to continuously monitor movement for long periods of time. Therefore, it could be used to collect data from normal-activity children, which is needed to establish a baseline for activity according to age and sex. IR-UWB radar may be used as a screening and treatment monitoring device for ADHD patients not only in the hospital but also in the classrooms and in the home.This study has some notable limitations. First, the small sample size limited the study power. Comorbidity with tic disorders was not considered. There is a possibility that the table or chair used in the study restricted the participants’ movement or affected the detection of signals of the sensors. We minimized these confounding effects by using four radars from four directions and also used the median, rather than the mean, value of the signal detected from the four radars to quantify whole body movement. Future studies are warranted to determine the degree of signal reduction caused by surrounding objects. The concurrent validity was not examined; therefore, future studies should compare the results of IR-UWB radar and other devices, such as actigraphy or the Qb test. Finally, the majority of our ADHD patients were already taking medication; therefore, future drug-naïve or recent-onset studies are required.25. The exclusion criteria for ADHD were as follows: an IQ < 70; a hereditary genetic disorder; a current or past history of brain trauma, an organic brain disorder, a seizure or any other neurological disorder; autism spectrum disorder, communication disorder or learning disorder; schizophrenia or any other childhood-onset psychotic disorder; major depressive disorder or bipolar disorder; Tourette’s syndrome or a chronic motor/vocal tic disorder; and obsessive compulsive disorder. The HC group was defined as community-dwelling children free of any psychiatric diagnosis according to the K-SADS-PL interview. The exclusion criteria for the HC group were the same as those for the ADHD group, with the addition of a diagnosis of ADHD.A total of 10 young individuals with ADHD and 15 HCs were recruited from the Hanyang University Medical Center psychiatry outpatient clinic and the Seongdong-gu Community Mental Health Center from September 2019 to May 2020. The ADHD participants who were taking ADHD medication underwent assessment after a 1-week wash-out period. ADHD and other psychiatric comorbidities were confirmed according to the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) criteria by a board-certified child and adolescent psychiatrist using the Korean Kiddie Schedule for Affective Disorders and Schizophrenia—Present and Lifetime version (K-SADS-PL)This study was approved by the institutional review board of Hanyang University Medical Center (No. 2017-09-046-002). All methods were carried out in accordance with standard human research ethics guidelines (Declaration of Helsinki) and regulations. Written informed consent was obtained from the parents of the participants.26. Full-scale IQ (FSIQ) scores were normalized to a mean of 100 and standard deviation of 15 based on Korean population-based reference data27. Higher scores indicate better cognitive performance.The level of intelligence was assessed using the Wechsler Intelligence Scale for Children IV (WISC-IV)28. It consists of 18 items rated on a scale from 0–3, with potential responses of “never or rarely”, “sometimes”, “often”, and “very often”. The total score ranges from 0–54, with 9 items reflecting IA symptoms (IA scores range from 0–27) and 9 items rating hyperactivity-impulsivity (HI) symptoms (HI scores range from 0–27). Higher scores indicate greater severity.ADHD symptoms were assessed by using a parent-report questionnaire called the ADHD Rating Scale IV (ARS)29. We used the visual selective attention task and auditory selective attention task, which are each 11 min long. Performance was assessed considering four variables: (1) omission errors , (2) commission errors , (3) response time , and (4) response time standard deviation . All scores were transformed into attention quotients (AQs), which were adjusted for age and sex by comparison with a normal population with an average AQ of 100 and standard deviation of 15.A Korean version of a computerized CPT called the comprehensive attention test, which has established reliability and validity, was administeredHyperactivity assessment was conducted in a 3.0 × 2.4 × 2.4 m wide space inside Hanyang University Hospital’s psychiatry outpatient clinic  was used to quantify hyperactivity. XK-340 sensors can utilize a variety of center frequencies, but 8.748 GHz was chosen according to local regulations. The bandwidth was -10 db at approximately 1.5 GHz, and there was no interference between each sensor and between all multiple IR-UWB radar sensors. The radar sampled the received signals at 23.328 GS/s, and the sampled signals were transmitted to a PC in sequence via the USB interface. The radar's radiation power was 68.85 µW, which is lower than the radiation power used by communications such as Wi-Fi and Bluetooth. Each sensor was sealed inside a white plastic case to avoid attracting the attention of participants as much as possible. Due to the relatively small indoor environment, the sensors were installed radially because the volume of the human body could cause errors in distance or detection parameters. As other people's movements can interfere with those of the observed subjects during the test, the subjects were left alone during the test. Signals received from the sensors were loaded as digital values in MATLAB 2020a to run the signal processing algorithm. The PC received 20 signals per second from each sensor, and the PC's operating system was Windows 10.The impulse signal 30. Since radar sensors were installed indoors, x comprises a target signal as well as a background signal generated from a wall or static object. As these background signals have a large amplitudes and interfere with the target signal, it is necessary to remove the background signals so that only human motion can be extracted and observed. A background removal algorithm was used to observe only the moving target in the indoor environment31. The signal The sampled time index If there is no movement by the target indoors, the coefficient of the radar signal reflected and returned will not change. Conversely, if the target moves, the received signal x value will change as the electromagnetic wave path changes. Additionally, if the target moves, 32. Then, the envelope of Since the radar emits impulse signals 33. To quantify movement, the movement of each subject is detected by the received signal and the threshold, and it can be expressed as follows:Equation  is also mentclass2pt{minim35. If the subject's movement is substantial, the difference in signals 36 and F-max test, with 1,000 bootstrap replications37, which maintained the preassigned α-level26. We also tested whether the velocities of the total movements between the two groups were significantly different over time; the velocity was defined as the change rate of the total movements per minute. In addition, we also observed the sample distributional differences between the two groups at 3, 7, 9, 11 and 22 min by comparing the density distributions. We nonparametrically examined the correlations between selective attention tests and total movements based on Spearman’s correlation coefficient, with statistical significance of the correlation coefficients considered at α = 0.05. The total movement was quantified as a single value for each subject by approximately integrating the area under the functional curve through the end of the test time. All of the statistical analyses and figure generations were performed using R software .The demographic and clinical characteristics of the 2 groups were compared using the Wilcoxon rank sum test for continuous variables and chi-square test for categorical variables. The study tested the difference in the mean functions between the two groups using one-way fANOVA under the null hypothesis of We found that IR-UWB radar can be useful in discriminating the mean functions of activity level during a CPT in ADHD and HC groups when applying fANOVA as a statistical method. Further studies with larger sample sizes are warranted to elucidate the effects of age, sex, subtype and situation.Supplementary Information.


Attention deficit hyperactivity disorder (ADHD) is the most common childhood-onset neurodevelopmental disorder. Currently, increasing amounts of attention have been focused on the epidemiologic profiling of ADHD in children, viewed as a continuously distributed risk dimension throughout the whole lifespan. This study aimed to identify and characterize potential influential factors susceptible to ADHD-related symptoms among preschool-aged children. A comprehensive questionnaire was self-designed for both children and their parents or guardians and was distributed to 30 kindergartens from Beijing and Hebei, collecting potential influential factors in susceptibility to ADHD. ADHD was assessed by the Conner’s Abbreviated Symptom Questionnaire (C-ASQ), and 7,938 children were analyzed. Least absolute shrinkage and selection operator (LASSO) regression and hierarchical degree of adjustment were used to control possible covariates. Five factors, namely, children’s secondhand smoking exposure, breastfeeding duration, sleep mode, maternal pregnancy smoking exposure, and parental self-rating for patience, were identified to be independently and significantly associated with ADHD susceptibility. Meanwhile, dose–response relationships were observed between breastfeeding duration, parental self-rating for patience, and ADHD-related symptoms. Finally, a nomogram model was created for predicting ADHD susceptibility based on significant and conventional attributes under each criterion. Attention deficit hyperactivity disorder (ADHD) is the most common childhood-onset neurodevelopmental disorder, and it is characterized by developmentally inappropriate levels of overactivity, inattention, and impulsivity . CurrentLongitudinal data have shown that early onset ADHD is consistently associated with emotional problems throughout childhood , and it The exploration of risk factors has proven to be beneficial for the development of preventive and intervention strategies to curb this global burden. It is widely recognized that ADHD is a highly heritable and multifactorial disorder to which multiple inherited and non-inherited factors contribute individually and interactively . EnvironTo shed some light on this issue and yield more information for further studies, we conducted a cross-sectional investigation among preschool-aged children enrolled from 20 kindergartens in Beijing and 10 kindergartens in Tangshan city to identify promising factors that can predict the risk of childhood ADHD, and to enhance the magnitude of predicting ADHD susceptibility, and further we established a nomogram model by regressing individual attributes that were significantly associated with ADHD.A population-based cross-sectional survey, in accordance with the principles of the Declaration of Helsinki, was conducted in Beijing and Tangshan city (Hebei province) between September and December 2020. This survey was reviewed and approved by the Ethics Committee of China-Japan Friendship Hospital.Preschool-aged children (3–7 years old) who attended junior to senior kindergarten classes were surveyed in this study. Utilizing a stratified cluster random sampling strategy, 5 out of 16 districts in Beijing and 1 city (Tangshan) in Hebei, a total of 30 public kindergartens were randomly selected.To collect potential influential factors for ADHD susceptibility, a comprehensive electronic questionnaire was designed for both children and their parents or guardians.For the children, basic characteristics including age, sex, region, birth weight, ABO blood type, delivery mode, breastfeeding duration, daily sleep duration, daily sleep initiation time, secondhand smoke exposure, vitamin D supplement duration, probiotics supplementary, and average daily screen time were recorded. Children’s body weight (to the nearest 0.1 kg) and height (to the nearest 0.1 cm) were measured by trained health physicians. The hyperactivity and inattention domains of the children were assessed using the Conner’s Abbreviated Symptom Questionnaire (C-ASQ).For the parents or guardians, sex and self-reported data, including age, weight, height, education, family income, maternal prepregnancy weight, gestational weight gain (GWG), maternal pregnancy smoking exposure, and parental self-rating for their patience with their children, which ranged from 0 to 10 scores, were recorded.R2cs) value is 0.33. Assuming that the new model can explain 15% of the variability, the anticipated R2cs value should be 0.15 × 0.33, equivalent to 0.05. On the basis of the above parameters, the sample size is estimated to be 4,199, corresponding to 210 events and an events per candidate predictor parameter of 8.75.According to the method proposed by n = 10,230) returned the questionnaires within the scheduled time. After excluding 1,810 questionnaires that had no C-ASQ, 8,420 valid questionnaires were obtained within the specified time frame, the number exceeding the estimated sample size 4,199.In the present study, the aforementioned self-designed questionnaires were sent to the parents or guardians of 10,441 children, who read and signed an informed consent form prior to participation, and 98% of them , a global measure of psychopathology, is not a specific indicator of ADHD diagnosis , yet it Daily sleep duration was calculated as the sum of both break time during the day and sleep time at night, combining the time on working days × 5 and the corresponding time on weekends × 2 divided by 7. Daily sleep initiation time was grouped into before and after 23:00. Sleep mode was defined according to both sleep duration and daily sleep initiation time and was divided into 4 stages. Sleep mode was encoded as the number “4” if the sleep duration was less than 10 h per day and fall asleep time was after 23:00, as the number “3” if sleep duration was more than 10 h per day and the daily sleep initiation time was after 23:00, as the number “2” if sleep duration was less than 10 h and the daily sleep initiation time was before 23:00, and as the number “1” if the sleep duration was more than 10 h and the daily sleep initiation time was before 23:00. The children’s average daily screen time was calculated as for the daily sleep duration. Secondhand smoking exposure meant that guardians living with the child were long-time smokers, and it was divided into 1–5 cigarettes per day, 5–10 cigarettes per day, and > 10 cigarettes per day. Vitamin D supplement duration was classified into ≤ 3 months, 3–6 months, 6–12 months, and > 12 months.GWG was calculated from antepartum weight minus the prepregnancy weight, and it included three classes: adequate GWG , inadequate GWG (less than the lower limits of adequate levels), and excessive GWG (greater than the upper limits of adequate levels). All reference criteria were based on the recommendations of the Institute of Medicine (2009) .Family income (RMB per year) was classified into ≤ 100,000, 100,000–300,000, 300,000–600,000, 600,000–900,000, and >1,000,000. Parent education was defined as a high school degree or below, college degree, master’s degree, and doctor’s degree and above. Smoking exposure during maternal pregnancy included secondhand smoke exposure on the mother and the mother smoking. Scores of parent self-rating about their patience with their children ranged from 0 to 10. A score of 0 indicates no patience. Stages of parent self-rating for the patience were classified into four stages based on the parent self-rating scores: stage 1 (scores = 0–3); stage 2 (scores = 4–6); stage 3 (scores = 7–9); and stage 4 (score = 10).The STATA software  and the R programming environment (version 4.1.0) were used for statistical analyses. All continuous variables were tested for normality and were log-transformed as appropriate, and the mean (SD) was calculated from the raw/non-log-transformed data. Skewed continuous variables were expressed as median (interquartile range). The intraclass correlation coefficient (ICC) was calculated to assess the possibility of non-random measurement error for factors under study collected from 30 public kindergartens. The ICC ranges from 0 to 1, and an ICC of 0 indicates that the variance in factors under study is not due to variation between the kindergartens.t-test or rank-sum test for continuous variables and the chi-square for discrete variables, respectively, were performed for baseline characteristics’ comparison. All statistical tests were two-sided with 0.05 significance levels. Dose–response relationships between children’s breastfeeding duration and C-ASQ-defined ADHD susceptibility were investigated using restricted cubic spline models.Univariate analyses, specifically the To identify the contribution of all possible factors, the least absolute shrinkage and selection operator (LASSO) model , a regreWe searched for predictors of ADHD symptoms that were repeatedly reported in studies or systematic reviews, which included family income, screen time, low birth weight, probiotic supplementation, and parental education . These fThe R programming environment (version 4.1.0) “rms” package was used to create a prediction nomogram model for the early identification of C-ASQ-defined ADHD susceptibility. The concordance index or C-index, defined as the AUROC curve, was used to quantify the model predictive accuracy.Sample size was estimated using the “pmsampsize” package in the STATA software , which was developed by The flow diagram to selection of study children could be found in In addition, as study children were from 30 public kindergartens, the potential bias arising from different kindergartens was evaluated using the ICC. For 24 factors under study, ICC values ranged from 0.001 to 0.02, indicating a low likelihood of potential bias.p < 0.001), breastfeeding duration , sleep mode , maternal pregnancy smoking exposure , and parental self-rating for patience , were independently identified by the multiple adjustment to be significantly associated with C-ASQ-defined ADHD susceptibility  compared to those who fell asleep before 23:00 combined with sleep duration of more than 10 h. Likewise, serious trends were observed for a shorter sleep duration and a later sleep time  ] . These rp = 0.08), while for those exposed to 5–10 and > 10 cigarettes per day, the risk ratio increased .Meanwhile, the risk of having ADHD-related symptoms was proportional to cigarette exposure of the children. Compared to never exposure, the risk ratio of children’s exposure to 1–5 cigarettes per day was 1.20, with a 95% CI ranging from 0.98 to 1.46 (p = 0.02) in stage 2, 0.35  in stage 3, and 0.14  in stage 4.For parental self-rating for patience, we concluded that children had ADHD-related symptoms when decreasing the stages of the self-rating. Compared with stage 1, the odds ratio was 0.62 , and good predictive accuracy  aged 5 years old (18 points), who falls asleep after 23:00 each day (24 points), with a sleep duration of 10 h (34 points), exposed to secondhand smoking 5–10 cigarettes per day (17 points), with a breastfeeding duration of 5 months (20 points), maternal pregnancy smoking exposed (20 points), and stage 4 of parental self-rating for practice being 1–3 (100 points). Since the total point is 258, the probability of developing ADHD-related symptoms is estimated to be approximately 64%.Using a cross-sectional representative survey, annexed with robust statistical techniques, we aimed to identify and characterize prenatal and perinatal factors and environmental-related factors for C-ASQ-defined ADHD susceptibility among 7,938 preschool-aged children. The key point of our study is that five significant factors, namely, children’s secondhand smoking exposure, breastfeeding duration, sleep mode, maternal pregnancy smoking exposure, and parental self-rating for patients, were independently associated with ADHD susceptibility assessed by Conner’s ASQ. To our knowledge, this study is the first investigation about screening for ADHD-related symptoms in preschool-aged children in China.In China, great efforts are being made to improve children’s mental health, and the prevalence estimates of ADHD in mainland China, Hong Kong, and Taiwan were 6.5, 6.4, and 4.2%, respectively , which wA growing number of studies have examined the risk factors associated with ADHD-related symptoms, yet the results are often not reproducible. For example, some studies have shown a significant association between maternal exposure to smoking and ADHD , whereasThe present study accounted for this aspect and undertook logistic LASSO regression and a hierarchical degree of adjustment for possible covariates, and added to existing evidence that co-exposure to prenatal and perinatal factors and environmental related factors significantly influenced ADHD-related outcomes. First, we found that smoking exposure was a severe risk factor, including maternal pregnancy exposure to smoking and children directly exposed to secondhand smoking. Many studies, including umbrella reviews, have proposed a relationship between nicotine exposure during pregnancy and offspring ADHD and have found a dose–response relationship . MeanwhiSecond, in terms of sleep mode, many studies have concluded that a short sleep duration is significantly linked to ADHD compared with an average sleep duration , especiaProtective factors were also prominent in our study. A breastfeeding duration over 6 months is associated with lower conduct disorders. In early 1998, the World Health Organization (WHO) Expert Consultation recommended that infants be exclusively breastfed during the first 6 months of life . ProlongFinally, a risk prediction nomogram model for C-ASQ-defined ADHD susceptibility was created to enhance the practical applications of our findings. This model has excellent forecast precision and will be useful for conducting preliminary screening. However, several limitations of our study merit special consideration. First, since it is a cross-sectional study, it precludes further comments on the cause–effect relationship. We could not ascertain whether short sleep duration was a risk factor or a consequence of ADHD in this study, but we argued from an epidemiological perspective that sleep duration has an effect on preschool-aged children’s ADHD susceptibility. Second, ADHD is a clinical diagnosis requiring a detailed evaluation of current and previous symptoms and a functional impairment, and full family, gestational, and developmental history should be taken . The keyvia an analysis of survey data from 7,938 preschool-aged children and their parents or guardians, we identified five factors independently and consistently associated with children’s C-ASQ-defined ADHD susceptibility, and a risk prediction nomogram model was established.In summary, In general, five factors, namely, children’s secondhand smoking exposure, breastfeeding duration, sleep mode, maternal pregnancy smoking exposure, and parental patience self-rating, were independently associated with ADHD susceptibility. These findings will help with developing intervention strategies for preventing ADHD susceptibility by promoting parental patience and encouraging a longer sleep duration and an earlier bedtime, a sufficient breastfeeding duration, and no secondhand smoking exposure.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by the Ethics Committee of China-Japan Friendship Hospital. Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.KW, ZZ, and WN conceived and designed the experiments. XD and MY performed the experiments. XD and WN analyzed the data and wrote the manuscript. XD, MY, and BZ contributed materials and analysis tools. All authors read and approved the final manuscript prior to submission.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.