In children with ADHD with poor symptom control, the assessment of family functioning could be helpful. Developing a more specific intervention for caregivers that promotes good family roles and behavioral control may be beneficial.Poor family functioning is linked with poor child ADHD symptoms. However, there are many dimensions of family functioning. Thus, this study aims to find the association between each dimension of family functioning and controlled ADHD symptoms in an Asian culture. This cross-sectional study involved caregivers of 113 Thai children with ADHD ages 4–16 years old who visited the Outpatient Department at Maharaj Nakorn Chiang Mai Hospital between October 2017 and March 2018. The caregivers completed the Chulalongkorn Family Inventory and the SNAP-IV Thai version. Logistic regression was used to examine each dimension of family functioning adjusting for potential confounders. In univariate analyses, six of the seven dimensions of family functioning were associated with controlled ADHD symptoms. In multivariate analyses, findings revealed that good family roles (aOR 7.48, 95% CI = 1.56 to 35.85,  ADHD is one of the most common neurodevelopmental disorders of childhood. In 2016, the National Survey of Children’s Health (NSCH) estimated the number of children and adolescents in the United States ever diagnosed with ADHD to be 9.4% (approximately 6.1 million children) . The symThe McMaster Model of Family Functioning was proposed by Epstein in 1987. He regards the process of the family system as the core and assumes that the basic function of the family is to provide appropriate environmental conditions for family members to develop in physical, psychological, social, and other aspects [Previous research has found that more negative family functioning is linked with more child ADHD symptoms . HoweverThus, this study aims to find the association between each dimension of family functioning and controlled ADHD symptoms in Thailand, which can lead to better ways of taking care of children with ADHD and their families, especially in the Asian context.This cross-sectional study involved caregivers of 113 ADHD children ages 4–16 years old who visited the Outpatient Department of Child and Adolescent Psychiatry, Maharaj Nakorn Chiang Mai Hospital, Thailand , between October 2017 and March 2018. Self-administered questionnaires were completed by caregivers, including on demographic characteristics .The caregiver’s stress was assessed using the Suan Prung Stress Test (SPST). The SPST is the standard questionnaire recommended by the Department of Mental Health, Ministry of Public Health. Cronbach’s alpha reliability coefficient was more than 0.7. The SPST consists of 20 questions, each with a score of 1 to 5 (higher scores reflect severe stress). The range of overall scores was divided into two levels . The cut-off point for severe stress was more than 43 .(1)Family functioning was assessed using the Chulalongkorn Family Inventory (CFI), which assessed the six dimensions as proposed by the McMaster Model of Family Functioning . The CFI(2)Child inattention and hyperactivity/impulsivity symptoms were assessed using the Thai version of SNAP-IV . CronbacBased on a published finding that the prevalence of controlled ADHD symptoms was approximately 24%  and assut-test. Fisher’s exact test and chi-square were used to explore independency in the categorical variables between the two groups. The t-test was used to compare means between groups and explore factors associated with controlled ADHD symptoms. For multivariate analyses, logistic regression was used to identify the associations between family functioning and ADHD symptoms adjusting for predefined potential confounders as identified in the literature [p-value, and p-value of less than 0.05 is considered to be statistically significant. A sensitivity analysis was also conducted to explore the associations between family functioning and symptom control for each of the two subscales of ADHD (inattention and hyperactivity/impulsivity) separately.We used STATA version 16  for data analyses. Characteristics of children with ADHD, their families, and family functioning were reported as numbers and percentages for the categorical data and as means and standard deviations or medians and interquartile ranges as appropriate for the continuous data. For the univariate analyses, we used Fisher’s exact test, chi-square, and terature ,17,18,19The mean age of the caregivers was 43 years, most were female (81%), 49% held a bachelor’s degree, and 26% reported severe stress . The meap = 0.01) and behavior control  were individually associated with controlled ADHD symptoms (In symptoms .p = 0.003) were associated with controlled ADHD symptoms on the inattention subscale. However, there were no associations between family functioning and controlled ADHD symptoms on the hyperactivity/ impulsivity subscale.The associations between family functioning and controlled ADHD symptoms for each subscale  are summarized in Among 113 ADHD children, 29% of them have controlled ADHD symptoms. While most dimensions of family functioning were associated with controlled ADHD symptoms, the study revealed that family roles and behavior control dimensions have been a key factor in the better control of ADHD symptoms.This study revealed that 29% of children with ADHD had controlled ADHD symptoms. This finding was similar to the result from research in Eastern Thailand which reported that 24% of children with ADHD had controlled behavior . MoreoveSpecifically, good family roles refer to the provision of resources, supporting personal development, and maintaining and managing the family systems . This diWhen considering the associations between family functioning and controlled ADHD symptoms for each subscale, a positive association between good family roles and controlled ADHD symptoms inattention subscale was observed, but no association was found for the ADHD hyperactivity/impulsivity subscale. Good family roles are likely more specific to the core symptoms of ADHD inattention type, which are characterized by difficulties sustaining attention in tasks, following through on instructions, finishing chores, and organizing tasks . On the Overall, our findings support that family functioning is an important factor for achieving the control of ADHD symptoms. They provide further insights that family roles and behavior control are the important dimension of family functioning, which supports the control of ADHD symptoms in an Asian context. These findings are consistent and help support the theory that well-established behavioral treatment for children with ADHD and training for parents in behavior management should be applied and is appropriate to Asian settings ,24. For A limitation of this study is that ADHD symptoms were reported from only one source, namely one caregiver of children with ADHD, which may cause bias. A previous study showed that mothers tended to report more symptoms than fathers . The queFamily functioning was associated with controlled ADHD symptoms. In children with ADHD with poor symptom control, assessment of family functioning could be helpful. Developing a more specific intervention for caregivers that promotes good family roles and behavioral control may be beneficial in the Thai setting.


Attention deficit hyperactivity disorder (ADHD) frequently co-occurs with other neurodevelopmental diagnoses, such as autism spectrum disorder (autism), which can make clinical decision making around symptom management challenging for clinicians. There is a paucity of research examining pharmacotherapeutic management of children who have ADHD with co-occurring diagnoses. We aimed to report on the co-occurring diagnoses and symptom profile of children, and report on medication use, stratified by ADHD, autism and ADHD + autism diagnoses.n = 239), autism (n = 117), and co-occurring ADHD + autism (n = 149) completed a questionnaire on current medication use and clinical rating scales about their child's symptoms, as part of a broader project investigating diagnosis and management of symptoms in children with ADHD or autism.Caregivers of 505 children (2–18 years) with ADHD (The parents of the ADHD group reported a higher proportion of their children had learning disorders (17.15%) and speech and language disorders (4.60%) compared to the parents of the autism and ADHD + autism groups. Parents of the ADHD + autism group reported higher proportions of intellectual disability (5.37%), oppositional defiant disorder (20.13%), anxiety (38.93%), depression (6.71%) and genetic conditions (3.36%) in their children, in comparison to the parents of the ADHD and autism groups. Children with ADHD were reported to be taking a higher proportion of psychotropic medication (90%), followed by ADHD + autism (86%) and autism (39%). The parents of children with ADHD + autism reported a higher proportion of non-stimulant ADHD medication (25.5%), antipsychotic (18.79%), antidepressant (22.15%) and melatonin (31.54%) use by their children, compared to the parents of the ADHD and autism groups.A similar proportion of children with ADHD + autism and ADHD were reported to be taking medication. However, the types of medication taken were different, as expected with reported co-occurring diagnoses. The complexity of symptoms and diagnoses in ADHD + autism warrants targeted research to optimize management and therapeutic outcomes. Attention-deficit/hyperactivity disorder (ADHD) is a prevalent neurodevelopmental disorder, affecting approximately 5–7% of children , 2. ADHDOptions for management of symptoms associated with ADHD encompass both psychotherapeutic and behavioral interventions –12. StimIn addition to within-disorder heterogeneity, ADHD often co-occurs with other conditions. Research shows that approximately 66% of children with ADHD have at least one co-occurring condition, including anxiety, depression and/or sleep conditions , 17. AutIn contrast to ADHD, there are no pharmacological treatments that currently target the core social communication difficulties or repetitive behaviors of autism . HoweverSleep problems are prevalent in both children with ADHD and autism, and are particularly prevalent in children with ADHD + autism , 32, witn = 32), and there was no sample of children with ADHD without comorbid autism.Despite the high proportion of ADHD and co-occurring autism, there is little research on medication use in children with ADHD, autism and ADHD + autism. Factors that may be associated with different proportion of pharmacotherapy in these clinical populations include demographic variables , 34 and A key limitation of previous studies of medication use relates to changes in diagnostic criteria that occurred with the introduction of DSM-5 in 2013 , 15, 35.To better characterize the medications currently used in symptom management among children with ADHD and co-occurring diagnoses, this study examines psychotropic medication use among children with ADHD, autism and ADHD + autism. This study included medications not previously surveyed in this context such as lisdexamfetamine, guanfacine, clonidine, and melatonin. Parents reported current medication use of their child and completed clinical rating scales to measure current symptom severity. The aims were: 1) to report on the co-occurring conditions and symptom profile of children, stratified by ADHD, autism and ADHD + autism diagnoses; and 2) to report on medication use of children with ADHD, autism and ADHD + autism.M = 9.64 years, SD = 3.12 years), diagnosed with ADHD , autism , or ADHD-autism  within the last 6 years. Participants were recruited using convenience sampling via physical and electronic flyers, advertising on social media, direct contact with schools, and via existing databases, see Participants were the primary caregivers of 505 children  = 133.57, p = 0.102] was <5% and was missing completely at random (MCAR). In accordance with Little's MCAR test, a p-value of > 0.05 is interpreted as the data is missing completely at random. This indicated that analyses were unlikely to converge on biased estimates due to systematically missing values from some respondents related to extraneous variables, and missingness was ignorable  of whom completed the ASD and ADHD symptom rating scales between June 2019 and November 2020. Missing data for both the overall sample of 701 . REWe sought to determine whether parental reported diagnoses of ADHD, autism and ADHD + autism were corroborated by symptom ratings collected at the time of survey on both the CPRS Inattentive and Hyperactive scales, and the SRS RRB and SCI scales. Significant group differences in the expected directions were observed, such that the ADHD and ADHD + autism groups had significantly elevated scores on the CPRS Inattentive and Hyperactive scales, while the autism and ADHD + autism groups showed significantly elevated scores on SRS RRB and SCI scales (see S1 for significance testing).Descriptive statistics including frequencies were reported for co-occurring diagnoses and symptom profile stratified by ADHD, autism and ADHD + autism diagnosis. Frequencies were reported for medication use stratified by ADHD, autism and ADHD + autism diagnosis as well as biological sex. All calculations were performed using SPSS 26 and Microsoft Excel 2019.We sought to determine whether parental reported diagnoses of ADHD, autism and ADHD-autism were supported by symptom ratings collected at the time of survey on both the CPRS Inattentive and Hyperactive scales, and the SRS RRB and SCI scales. Section 1 of the The final sample included 505 children, of which 47% (239) had a parent-reported ADHD diagnosis, 23% (117) an autism diagnosis and 29.5% (149) had a co-occurring ADHD + autism diagnosis. When including the children who had a co-occurring ADHD + autism diagnosis in the ADHD group, 77% of the children had an ADHD diagnosis, with 39% of children with ADHD also having an autism diagnosis.The majority of children across the ADHD (59%), autism (44%) and ADHD + autism groups (67%) were in the 6–10 years age range, however a large percentage (27%) of the autism sample were under 5 years age, compared to the ADHD (4%) and ADHD + autism (3%) group.When stratifying by group, 50% of the ADHD group reported additional co-occurring diagnoses, while 44% of the autism group and 53% of the ADHD + autism group reported additional diagnoses.The ADHD group had the lowest rate of reported intellectual disability (0.84%), compared to the autism (5%) and the comorbid ADHD + autism (2%) groups. In contrast, the ADHD group reported the highest rate of learning disorders 17%, followed by the ADHD + autism group at 8% and the autism group 2%. Similarly, the ADHD group reported the most speech and language disorders (4%) compared to the autism (4%) and the ADHD + autism group (2%).Caregivers of the ADHD + autism group reported the highest proportion of co-occurring oppositional defiant disorder  in their children, followed by the ADHD group (16%) and the autism group (2%). Additionally, caregivers of the ADHD + autism group reported the highest rates of anxiety (38%) and depression (6%) in their children. The autism group had the highest proportion of other conditions (21%), including conditions as different as congenital heart disease to verbal dyspraxia.The socio-economic status (SES) varied little across the ADHD, autism and ADHD + autism groups and with 49%, 48 and 52% across the middle SES, respectively.Caregivers of all three groups reported high levels of inattention and hyperactive symptoms in their children. As expected, there were less frequent reports by the autism group  of very elevated inattention and hyperactive symptoms compared to the ADHD  and the ADHD + autism group . The autism  and ADHD + autism  groups had high levels of severe restrictive and repetitive behaviors (RRB) and social communication and interaction (SCI) behaviors, while the ADHD group did not. Stimulant use was most frequently reported in the ADHD group, with 81% taking at least one stimulant medication, with methylphenidate being the reported stimulant (46%) taken. The ADHD + autism group reported less stimulant use (66%) while the autism group reported least frequent use again (4%). The most frequently reported stimulant medications in the ADHD + autism group were lisdexamfetamine (19%) and methylphenidate .The ADHD + autism group had the highest use of non-stimulant, antipsychotic, antidepressant and melatonin classes. The ADHD and ADHD+autism groups had similar frequency of use of non-stimulant ADHD medication  with less frequent use in the ASD group (11%). Clonidine was the most used non-stimulant across all three groups, followed by Guanfacine. The ADHD + autism group reported the most use of antipsychotics (19%) and antidepressants (22%), followed by the autism group  and the ADHD group . Risperidone was the most reported antipsychotic and fluoxetine the most reported antidepressant by all three groups.All three groups reported frequent use of melatonin, with the ADHD + autism group reporting the highest use (32%), followed by the autism group (22%) and then the ADHD group (21%).The range of pharmacotherapeutic options for treatment and support of ADHD has expanded over the past decade, as has our understanding of the complex presentation of ADHD. Co-occurring diagnosis of ADHD and autism can represent a significant challenge for clinicians in deciding appropriate pharmacological therapies for individuals. The purpose of this study was to better understand the range of co-occurring diagnoses and symptoms and pharmacological management for ADHD, autism and ADHD + autism presentations.The first aim of this study was to understand the range of co-occurring diagnoses and the symptom profiles of children stratified by ADHD, autism and ADHD + autism diagnoses, to capture the breadth of complexity in symptom presentation when considering pharmacotherapeutic treatment options.In the present study, over a third of children with ADHD had a co-occurring autism diagnosis. Children with co-occurring ADHD + autism are more likely to present with a broader range of clinical symptoms, and greater symptom severity , 24, 49.This study found that 54% of children with ADHD + autism reported at least one additional co-occurring condition, compared to 51% of ADHD children and 44% of autistic children. The ADHD + autism group reported higher proportions of intellectual disability, ODD, anxiety, depression and genetic conditions, which is consistent with previous findings , 53–55. Previous research reported that children with autism 57%) were more likely to have at least one co-occurring condition, compared to children with ADHD . A possible explanation is that Steensel et al. % were mo. An alteIn terms of support and therapeutic options, learning disorders and speech and language disorders are often supported by speech pathology, and educational remediation and intervention –61, howeOur findings show that children with ADHD + autism had higher prevalence of anxiety (39%) and depression (7%), compared to the autism only group  and the ADHD group . The ADHD (17%) and the ADHD+autism (20%) group also reported higher prevalence of ODD compared to the autism (3%) group. In Icelandic children with ADHD, 19% of the children met cut-off criteria for ODD, 42% for anxiety, and 21% for depression . A UK stGreater symptom severity and complexity can make selecting the appropriate support or intervention for each child difficult for both clinicians and caregivers , 23, 24.Children with ADHD (90%) and children with ADHD+autism (86%) had higher prevalence of reported medication use, compared to children with autism (39%). Children with ADHD+autism predominantly reported non-stimulant ADHD medication, antipsychotic, antidepressant, and melatonin use. Parents of children with ADHD + autism reported a higher proportion of polypharmacy , compared to children With ADHD or autism.Similar to our study, in North America 82% of children diagnosed with ADHD and 25–40% of those with autism were prescribed psychotropic medications . This isOur findings for current medication use are slightly higher than previous reports from North America , 38, AusThe most commonly used medications in the ADHD group were the stimulant and non-stimulant ADHD medications, which is consistent with current international treatment guidelines that suggest stimulant therapy as one of the first-line treatments for ADHD in children and adolescents , 25. TheOverall, a quarter of the sample were taking melatonin. Previous research has found that sleep problems are prevalent in children with ADHD (61%), autism (65%) and ADHD + autism (66%) , 32. In The reliance of our study on parental report is both a strength and a limitation. Previous studies have utilized or merged parental reports with healthcare data, examining prescription and dispensing of medications by health professionals, often resulting in higher sample sizes and more objective data , 15, 26.Our study had more survey completions by parents of children with ADHD and ADHD + autism than parents of children with autism. This is likely due to the sampling strategy used, where parents self-selected to participate in the study. Our findings may be non-representative, however the description of medication and polypharmacy use within our cohort is in keeping with other reports and adds to information indicating the complexity of co-occurring ADHD and autism, and the way they are managed .Overall, the ADHD + autism group reported the highest proportion of comorbidities, including intellectual disability, oppositional defiant disorder, anxiety and depression. The severity of ADHD and autism symptoms was as expected for diagnoses reported by parents. Medication use overall was highest in children with ADHD, however, polypharmacy was highest in children with ADHD + autism.Our findings are similar to others reporting the prevalence of medication use, with new knowledge about children diagnosed with ADHD + autism. The complexity of ADHD and autism presentations and their co-occurrence should be the subject of future research to investigate the efficacy of the medications used, in a way that allows clinicians, parents and young people to make decision about first line and additional medication that is suited to their symptoms, needs and priorities.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 Monash University Human Research Ethics Committee. The patients/participants provided their written informed consent to participate in this study.OM conceptualized and designed the study, designed the data collection instruments, collected data, carried out the analyses, drafted the initial manuscript, and reviewed and revised the manuscript for important intellectual content. RK conceptualized and designed the study, designed the data collection instruments, collected data, and reviewed and revised the manuscript for important intellectual content. JT and KW contributed to the analyses and interpretation of data and critically reviewed the manuscript for important intellectual content. KK conceptualized and designed the study and reviewed and revised the manuscript for important intellectual content. MB and BJ conceptualized and designed the study, coordinated and supervised data collection, and critically reviewed the manuscript for important intellectual content. All authors approved the final manuscript as submitted and agree to be accountable for all aspects of the work.This work is supported by a grant from the Medical Research Future Fund (MRFF) to Prof. Bellgrove and Dr. Johnson, EPCD000002. Prof. Bellgrove is supported by a Senior Research Fellowship from the National Health and Medical Research Council (NHMRC) of Australia, 1154378. BJ is supported by a Peter Doherty Fellowship from the NHMRC APP1112348. JT was supported by NHMRC Project Grants 1002458 and 1046054. The NHMRC and MRFF had no role in the design and conduct of the study.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.


Maternal polycystic ovary syndrome (PCOS) may increase the risk of attention-deficit/hyperactivity disorder (ADHD) in offspring; however, their association remains unexplored in Asian populations. Hence, this study aimed to investigate the association between maternal PCOS and ADHD in offspring aged 3–6 years and whether it differed by offspring sex.This was a district-wide population-based study of 87,081 preschoolers from 234 kindergartens in Longhua District, Shenzhen, China. The parents were invited to complete a self-administrated questionnaire covering information on socio-demographics, maternal disease history, and child behavior. ADHD symptoms were measured with the parent-rating 26-item Swanson, Nolan, and Pelham Rating Scale (SNAP-IV). Logistic regression was performed to examine the associations between maternal PCOS and ADHD symptoms in offspring.P for interaction = 0.391). Treatment of PCOS tended to be associated with a lower risk of ADHD symptoms than untreated PCOS albeit risk confidence intervals were overlapped .The response rate was 80% and 63,390 mother-child pairs were included. Of the mothers, 1,667 (2.6%) reported PCOS diagnoses. The mean age of children at ADHD assessment was 4.86 ± 0.84[SD] years, and 53.6% were boys. Children with maternal PCOS had a higher risk of developing ADHD symptoms than other children . The risk estimate was significant in boys  but not in girls (adjusted OR = 1.23, 95% CI: 0.94–1.57, Maternal PCOS increases the risk of developing ADHD in offspring, especially boys. Further studies are warranted to confirm our findings, and early neurodevelopmental screening may be needed in children born to mothers with PCOS. Attention-deficit/hyperactivity disorder (ADHD) is the most common neurodevelopmental disorder in children, with an estimated prevalence of 2–7% globally , 2. ApprPolycystic ovary syndrome (PCOS) is the most common endocrine disorder, affecting 6–20% of women of reproductive age . PCOS isAlthough ADHD predominantly occurs in males , it remaTo this end, this study aimed to investigate the association between maternal self-reported PCOS and offspring ADHD among 3- to 6-year-old Chinese preschoolers in a population-based study. We also performed a stratified analysis to test if the association differed according to offspring sex and PCOS treatment status.The Longhua Child Cohort Study (LCCS) is an ongoing district-wide population-based study to evaluate the impact of early life family and school environments on preschoolers' psycho-behavioral development. First initiated in 2014, the LCCS has been performed annually among kindergarten children in the Longhua District of Shenzhen in mainland China. This study was based on a 2021 wave survey involving 234 kindergartens conducted between November and December 2021. Parents were invited to complete a structured questionnaire assigned through a mobile app. The questionnaire includes information on socio-demographics, maternal condition during pregnancy , children's characteristics, and early-life exposure at age 0–1 and age 1–3 years , as well as children's psycho-behavioral conditions assessed by validated questionnaires. The details of the study design and implementation procedures of the LCCS have been described in previous studies –21.During the study period, 87,081 preschoolers were attending 234 kindergartens in Longhua District. Among them, 69,633 mother-child pairs completed the questionnaires, with a response rate of 80%. After excluding children under 3 years and above 6 years or those with missing or invalid data on maternal PCOS and covariates, 63,390 mother-child pairs were included in the current analysis. The data selection procedure is illustrated in History of PCOS was assessed using a self-reported question, “have you ever been diagnosed with PCOS before this pregnancy?” The answers to this question were “no,” “yes, but not treated,” “yes, treated,” and “uncertain.” A mother was considered as having a history of PCOS if she had reported “yes” regardless of treatment status. In the current study, participants who reported “uncertain” were excluded from the analysis.The widely used 26-item Parent-rating Swanson, Nolan, and Pelham (SNAP) revision 4 (SNAP-IV) scale was adopted to assess ADHD symptoms , 23. Thet-tests and χ2 tests were used to compare the maternal, paternal, and child characteristics of those with or without maternal PCOS exposure. A logistic regression model was used to evaluate the association between maternal PCOS history and offspring ADHD borderline symptoms. Covariates related to socio-demographics and environmental exposures suggested to play important roles in ADHD development were included in the logistic regression models. These include maternal age at childbirth, education, marital status, household income, passive smoking during pregnancy, and pre-pregnant body mass index [BMI]); paternal age at childbirth, education; and child sex and age at ADHD assessment , and a P-value of <0.05 (two-sided) was considered statistically significant.The The mean maternal age of childbirth was 29.1 ± 4.4 years, and more than 65.2% of parents received at least college education. The mean child age at ADHD assessment was 4.86 ± 0.84 years, and 53.6% of children were boys. Overall, 1,667 (2.6%) mothers reported a history of PCOS, of which 1,149 (68.9%) were treated for PCOS. Mothers with self-reported PCOS were older at childbirth, more educated, and had higher household income and higher pre-pregnant BMI than their counterparts. Additionally, children born to mothers with PCOS had a shorter gestational age and lower birthweight and were more likely to be born with fertility treatment and cesarean delivery. The characteristics of the study participants are presented in P < 0.001). Specifically, 7.8, 8.6, and 31.3% of children with maternal PCOS exposure were identified as having borderline symptoms of inattention, hyperactivity/impulsivity, and opposition/defiance, respectively, compared to 5.4, 6.7, and 23.3% of those without PCOS exposure , inattention , hyperactivity/impulsivity , and opposition/defiance  compared to those without PCOS exposure. Even after controlling for confounding variables, maternal PCOS was still associated with an increased risk of ADHD  and borderline symptoms of inattention , hyperactivity/impulsivity , and opposition/defiance  . SimilarP-values for interaction >0.05). Similarly, PCOS exposure was associated with higher SNAP scores with a stronger association in boys  than in girls  , 1.73 (95% CI: 1.38–2.15), 1.35 (95% CI: 1.08–1.66), and 1.55 (95% CI: 1.34–1.78) for ADHD, inattention, hyperactivity/impulsivity, and opposition/defiance symptoms, respectively, in boys after controlling for confounders. Nevertheless, the odds ratio for the girls was attenuated. Although maternal PCOS exposure was still associated with greater odds for opposition/defiance symptoms , the odds for ADHD , inattention symptoms , and hyperactive symptoms  became insignificant . However= 0.063) .To determine the impact of PCOS treatment on ADHD risk in children, we performed further analysis by dividing PCOS mothers into treated and untreated groups. As shown in This population-based study investigated the association between maternal PCOS and ADHD symptoms in Chinese preschoolers. We confirmed that maternal PCOS was independently associated with ADHD symptoms in offspring aged 3–6 years. Moreover, male offspring tended to be more susceptible to maternal PCOS exposure than female offspring.Significant associations between maternal PCOS and ADHD symptoms in offspring have been reported –16. For Although boys are two-three times more likely to have ADHD, it remains unclear whether boys are more vulnerable to maternal PCOS exposure than girls. Two Swedish registry-based studies reported a stronger association between maternal PCOS exposure and ADHD in female offspring than in male offspring , 13, wheThe effect of treatment on the association between maternal PCOS and offspring ADHD remains unknown. The Upstate New York study involving 1915 mother-child pairs reported a lower risk estimate for ADHD in treated PCOS than in untreated PCOS cases; however, the association between PCOS and ADHD was not significant, probably due to the small sample size . LikewisThe major strengths of this study are its population-based design and large sample size. However, there are still some limitations that should be considered when interpreting these findings. First, this study was retrospective and relied on self-reported information; therefore, the results should be treated with caution. Second, the lack of parental history of psychiatric disorders data prevented us from investigating its confounding effects, which might bias our findings . Third, In conclusion, maternal PCOS is associated with a higher risk of ADHD in offspring, with boys being more susceptible to the exposure than girls. Further studies are needed to confirm our findings, and early neurodevelopmental screening may be needed in children born to mothers with PCOS.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 Shenzhen Longhua Maternity and Child Healthcare Hospital. Written informed consent to participate in this study was provided by the participants' legal guardian/next of kin.YuZ and DL researched data, wrote, and edited the manuscript. VG, SQ, JZ, and YaZ contributed to the data collection and review of the manuscript. YW and WC contributed to the discussion, reviewed, and edited the manuscript. BW and WY conceptualized the study, contributed to the discussion, and reviewed and finalized the manuscript. All authors have read and approved the final manuscript.


Gossypium hirsutum) started in the northwest of the Yucatán Peninsula, from where it spread to other regions inside and outside of Mexico. We tested this hypothesis by assembling chloroplast genomes of 23 wild, landraces, and breeding lines . The phylogenetic analysis showed that the evolutionary history of cotton in Mexico involves multiple events of introgression and genetic divergence. From this, we conclude that Mexican landraces arose from multiple wild populations. Our results also revealed that their structural and functional chloroplast organizations had been preserved. However, genetic diversity decreases as a consequence of domestication, mainly in transgene‐introgressed (TI) individuals . We identified homologous regions that differentiate wild from domesticated plants and indicate a relationship among the samples. A decrease in genetic diversity associated with transgene introgression in cotton was identified for the first time, and our outcomes are therefore relevant to both biosecurity and agrobiodiversity conservation.Several Mesoamerican crops constitute wild‐to‐domesticated complexes generated by multiple initial domestication events, and continuous gene flow among crop populations and between these populations and their wild relatives. It has been suggested that the domestication of cotton ( Domestication of cotton landraces in Mexico. Changes in chloroplast genetic diversity related to cotton domestication and management. These events have often occurred in Mesoamerica, a region recognized as the main center of origin of crop domestication, mainly induced by the diffusion of seeds and vegetative propagules carried out by humans  is a cotton species with high biological, economical, and cultural importance worldwide. Mexico has been recognized as its center of origin, due to the occurrence of wild populations along its coastal dunes , green, and white cotton, which remain important to the cultural and economic activities of indigenous peoples and local communities  with transgenic pumpkin varieties (Cucurbita pepo Virus Resistant Transgenic). The result showed that germination and survival rates were significantly higher in the parental lines than in hybrids, while the germination time was higher and more variable in hybrids compared with parental lines . Because Mexico is the center of origin, domestication, and diversity of cotton, we can raise the problem of cotton in this country as an important issue concerning biosafety. The possible loss or modification of diversity related to the presence of transgenes, coupled with the antecedents observed in bean landraces, motivated us to include landraces within the priority genetic resources that must be conserved similarly to wild populations.The present study is the first report about the presence of transgenes in cotton landraces. The loss or modification of genetic diversity in landraces due to transgene introgression has been scarcely studied. However, concern about genetic erosion associated with the use of modern or transgenic cultivars has been raised indirectly, since the use of modern cultivars and the tendency to homogenize diversity in monocultures has caused disuse and reduction in the number of landraces  showing that, eventually, the transgenes are transferred to the nuclear genome, where the regulatory machinery is more effective. This study, proved that DNA escapes from the chloroplast and that its integration into the nuclear genome occurs more frequently than generally considered, therefore providing a mechanism that not only causes intraspecific but also intraorganism genetic variation , G. hirsutum Guatemala TX‐0231 (SRR1536369), G. hirsutum TM‐1 (SRR1534688), G. hirsutum Stoneville474 (SRR1536367), G. hirsutum Coker‐312 (SRR1536365), G. hirsutum Fibermax832 (SRR1536364), and G. hirsutum Acala Maxxa (SRR617482).Wild and landrace samples of 19 Table . Sequenc2.2Cry1Ab/Ac, Cry2Ab, and CP4EPSPS), were verified using the following primers from Eurofins Scientific:To characterize the presence of transgenic samples among the collection, we performed a Polymerase Chain Reaction (PCR) assay. Transgenic events released in Mexico, related to constructions of lepidopteran resistance and one of herbicide tolerance .(1) Cry2Ab .(2) CP4EPSPS .(3) Furthermore, we searched for transgenes in all 23 samples with blastn task from BLAST+ V 2.9.0–2 , and NGSEP 3.3.0 , Indels, and short tandem repeats (STRs) we aligned the raw reads with the reference genome of 2.4Gossypium's chloroplast genomes from NCBI . Furthermore, we developed a phylogenetic network using SplitsTree V 5.0.20 with 1000 bootstraps and samples of the wild‐to‐domesticated complex; we also included representative samples of each genetic group described as a kind of Gossypium as an outgroup.For the phylogenetic inferences, we downloaded BI Table  and usedG. herbaceum, G. logicalyx, G. stockii, and G. thurberi as external groups. We performed two independent runs of 400,000 chain length sampling of each 1000 chains. We used the gamma site with the GTR evolutionary model and estimated the nucleotide substitution rate using JModeltest , large single copy , and small single copy , with an identical arrangement and 37% of CG content. The same gene composition of 131 genes was preserved, just as in the available G. hirsutum plastomes, although the assemblies featured an inversion in the SSC region with respect to reference genome Coker 310 in all samples  was homologous in the rrn16S gene. Within the domesticated genomes, we found particular regions, such as the mint green block , shared by the landraces of Guerrero and Oaxaca (green variety cotton), which correspond to the trnG‐GCC gene , −0.24844 , and 1.22474 , respectively . In addition to the ycf1 gene, we identified rpoC2, ycf2, clpP, cemA, rps11, and accD genes under selection through the Tajima’ D test, and they were therefore candidates to test the ratio of nonsynonymous to synonymous substitutions (dN/dS) with PAML. The complete results obtained in each comparison are shown in Table ycf1, which was under positive selection in genome landraces (dN/dS > 1).The selection effects in wild, landrace, and BL genomes were different according to the Tajima's D values: −1.74084  represent 44.39% in wild genomes, 34.27% in local varieties, and 21.33% in BL genomes, of the total genomic diversity of the wild‐to‐domesticated complex of cotton. It is worth mentioning that genomes of transgene‐introgressed samples showed less diversity than the genomes from samples of the same variety or the samples obtained from the same wild population without transgenes. Particularly, brown landraces genomes from Oaxaca with transgenes showed less variations than Yucatán's genomes with transgenes: 21.85%–51.27% of variation reduction in wild genomes of TI samples regarding chloroplast genome without transgene sample; and 85.16%–89.85% reduction in landraces genomes of TI samples regarding chloroplast genome without transgene sample  were ndhD, matk, petN, psbZ, and rps14 in wild; atpA, atpF, psbC, and psbL in landraces; and rpl16 and ndhG in BL , 0.50000 (2), 0.40000 (2), 0.00000 (1), 0.60000 (2) of wild, TI‐wild, landraces, TI‐landraces, and breeding lines, respectively; the number of haplotypes is indicated in brackets. In the haplotype network, it is possible to see which haplotypes are shared between the groups with different degrees of management , but landraces exhibited the highest diversity (π = 0.00018), followed by wild populations (π = 0.00011), while BL showed the lowest Pi value (π = 0). When the chloroplast from TI individuals was separated, the values of nucleotide diversity were 0.00020, 0.00001, 0.00016, 0, and 0, for wild, TI‐wild, landraces, TI‐landraces, and BL, respectively. The BL haplotype diversity was equal to 0.6 ± 0.175 with 2 haplotypes; the landraces´ was ST approach, the higher differentiation was found between wild populations and BL (NST = 0.75070), while the pairwise values for landraces generally showed low differentiation compared to the differences between other groups: . The calculation of the NST value distinguishing the TI samples shown in Table Using the N3.2Gossypium genus, clustered by genome type: A + AD, F, D, B, and C + G + K. Our samples were grouped within the A + AD genome node  , G. herbaceum subsp. Africanum (A), and G. thurberi (D), which have been considered as the possible chloroplast donor species. Our samples were grouped into four different phylogenetic clusters with high support values: (1) BL + TI‐brown and TI‐white landraces from Oaxaca + Hutchinson's varieties; (2) wild samples; (3) landraces; (4) wild + landraces . Both the SSC and the whole chloroplast tree showed the same topology: two main polyphyletic clusters with high support formed by different genomic types, caused by the high diversity in the SSC region , while the time between W_banderasbay and coyuchi from Oaxaca was less ample: 0.0036 mya . On the contrary, BL showed signs of a balancing selection where the most common phenotype tends to be preserved .It is possible to identify different selection signals among crops and wild relatives associated with variable environments and domestication, since (1) high productivity may be related to an increase in energy demand, as observed in wheat crops; (2) wild and domesticated plants are commonly under different management; (3) the chloroplast of wild relatives must meet the energy demand to survive various environmental conditions Aliyev, . The negrpoC2 partakes in the transcription process  are genes with unknown functions in the chloroplast genome, but they are essential to protein import into chloroplast stroma  in wild and landraces can be considered as markers that exhibit the recent and continuous gene flow and introgression.Human selection operating through plant domestication commonly leads to a reduction in genetic diversity  were used in the development of BL, and that landraces played an important role in the domestication process. Unlike trees, the phylogenetic network suggested more than one evolutionary history. Although the divergence between wild and BL was lower than BL and landraces, alternate branches did not reject any alternative topology. The position of W_banderasbay and L_oaxbrown as the basal node in the tree shown in Figure The genetic divergence and the evidence of introgression observed match with the archeological, historical, and ethnobotanical records. Regarding the multiple origins of landraces, the women weavers from Guerrero corroborated our results; they consider that the fiber obtained from cotton from Guerrero, Oaxaca, and Veracruz have different characteristics among them, which is why they need to undergo different procedures before spinning them. Another evidence of multiple domestication events that helps calculate the time in which the cotton domestication began in Mexico is the finding of cotton archeological remains. These have been found in Tehuacán, Puebla and dated between 5500–4300 years BP; in the Valley of Oaxaca in a context dated 1300 years BP ; data curation (lead); formal analysis (lead); investigation (lead); methodology (lead); project administration (lead); software (lead); supervision (lead); validation ; visualization ; writing – original draft (lead); writing – review and editing (lead). Christian Quintero‐Corrales: Data curation (supporting); formal analysis (supporting); methodology (supporting); software (lead); validation (supporting); visualization (supporting); writing – original draft ; writing – review and editing . Alicia Mastretta‐Yanes: Conceptualization (supporting); investigation (supporting); supervision ; writing – original draft (supporting); writing – review and editing . Alejandro Casas: Conceptualization (supporting); investigation (supporting); supervision ; writing – original draft (supporting); writing – review and editing . Victorina López‐Hilario: Investigation (supporting); supervision ; writing – review and editing . Ana Wegier: Conceptualization (lead); funding acquisition (lead); investigation ; methodology ; project administration (lead); resources (lead); supervision (lead); validation ; visualization ; writing – original draft ; writing – review and editing .The benefits of this research include the generation of information on local varieties requested by local communities. The results were communicated to collaborators in the local communities, and their comments have been included in the discussion. In addition, they are recognized in the document as authors or in the acknowledgments according to their personal decision.Data S1Click here for additional data file.


Various genetic polymorphisms have been associated with attention-deficit/hyperactivity disorder (ADHD), and some of these have also been implicated in individual differences in affective processing. Yet, no studies to date have examined the complex interrelations across these genetic polymorphisms, ADHD, and affective processing. Several variables  have been shown to affect whether a given genetic variant confers risk. Our aim was to examine whether relevant genetic variants differentially confer risk for negative affectivity (NA) and/or emotion dysregulation (ED), depending on ADHD status.n = 297 adolescents  with (n = 83) and without (DSM-5) ADHD. ADHD- and affective processing-related dopaminergic and serotonergic polymorphisms were genotyped , dopamine receptor DRD4 exon-3 48 bp VNTR, and serotonin transporter linked polymorphic region 5-HTTLPR including the rs25531). Affectivity and ED were measured via parent- and/or self-report.Participants were r to z-transformed) values between with and without ADHD groups. There were no correlations that were significant – but several differed – across groups. In youth without ADHD, carrying the DRD2 rs1800497 T-allele was negatively associated both with negative affectivity (pcorr=.033) and with self-rated ED (pcorr=0.039). In youth with ADHD, carrying the DRD4 VNTR 7-repeat allele was positively associated with self-rated ED (pcorr=0.008), and carrying the L'L’ relative to the low-expression S’ serotonergic allele was also positively associated with parent-rated ED (pcorr=.042)We first calculated bivariate correlations between polymorphisms, affectivity, and ED then compared the obtained  suggest these results may be robust.


Multiple studies support the relationship between ADHD and overweight/obesity in youth. Different mechanisms may be involved, such as temperamental and psychopathological factors. The aim of this study was to test the hypothesis that specific temperamental and psychopathological dimensions could mediate the relationship between ADHD and obesity. The sample included 100 children and adolescents . The assessment procedure included Conners’ Parent Rating Scale—Long (CPRS-R:L) as the inclusion criterion for ADHD diagnosis, the Child Behavior Checklist (CBCL), a dimensional measure for psychopathology, and the Junior Temperament and Character Inventory, which describes four temperamental dimensions: novelty seeking (NS), harm avoidance (HA), reward dependence (RD), and persistence (P). While in the whole ADHD sample, the highest scores were found in NS and the lowest in P, ADHD with overweight/obesity, compared to ADHD with normal weight, showed higher HA and RD, lower NS, and higher CBCL Internalizing scores. These findings suggest that ADHD youth with overweight/obesity present specific temperamental and psychopathological features compared to those without overweight/obesity. If confirmed in larger samples, using a control group without ADHD, these temperamental and psychological features may be helpful for an earlier recognition of ADHD patients at higher risk for obesity, and may represent possible targets for temperament-based preventive interventions and tailored treatment programs. These features should be included in the routine assessment of children and adolescents with ADHD and/or are overweight/obese. Attention-deficit/hyperactivity disorder (ADHD) is characterized by age-inappropriate symptoms of inattention, hyperactivity/impulsivity, or both, which negatively impact academic, social, and domestic relationships . ADHD isThe multifaceted nature of ADHD is expressed by the heterogeneity of its clinical presentations, namely the co-occurrence of emotional dysregulation in at least 40% of the patients, with implications not only in clinical expression but also in comorbidities (both psychiatric and non-psychiatric) and in response to treatments . One of Overweight/obesity are major health problems, a sort of global epidemic, contributing significantly to both morbidity and mortality. According to the Centers for Disease Control and Prevention’s (CDC), a body mass index (BMI) at or above the 84th percentile is considered overweight, instead of a BMI at or above the 95th percentile.Pathophysiological, environmental, and psychological mechanisms are usually mutually involved in pediatric obesity, and sometimes psychiatric disorders are co-occurring . MultiplConsistently, ADHD was found to be significantly more prevalent in clinically ascertained obese children ,19,20, aThe relationship between ADHD and overweight can be primarily interpreted in the light of impulsivity and emotional dysregulation , with loTemperament and personality traits frequently associated with ADHD like ‘neuroticism’ or ‘early negativity’, ‘impulsivity’, and ‘sensitivity to reward’ are possible risk factors for obesity, while ‘conscientiousness’  and ‘self-control’, which are poorly expressed in ADHD patients, have been shown to have a protective function in relation to weight gain . An asseA comprehensive model to assess temperamental features of personality is Cloninger’s biopsychosocial theory of personality, based on the assumption that personality involves four temperament dimensions, novelty-seeking (NS); harm-avoidance (HA); reward dependence (RD), and persistence (P), and three character dimensions, self-directedness (SD), cooperativeness (C), and self-transcendence (ST) . The fouA “typical ADHD temperament profile” in children and adolescents with ADHD is characterized by high NS in combination with low RD, SD, C, and P ,38,39,40NS represents the tendency to respond with excitement to novel stimuli or cues for potential rewards, which leads to frequent exploratory activities in pursuing potential rewards as well as an active avoidance of monotony . Some stHA, instead, is a heritable tendency to respond intensely to signals of aversive stimuli, thereby learning to inhibit behavior to avoid punishment, novelty, and frustrative non-reward. HA is related to personality-associated traits of negative affect such as anxiety, depression, fear, shyness, constant concern, neuroticism, and negative affectivity . SubjectConsistent with these reports, according to Cloninger’s model of personality, obese individuals, compared with normal-weight subjects, should present a distinctive temperament profile, characterized by low SD and C and high NS and HA. In obese individuals with associated binge eating disorder, this profile is even more pronounced ,45. ThesThe aim of our study was to explore the role of both temperament and psychopathological dimensions in youth with ADHD. Our hypothesis is that specific temperaments, such as RD, and psychopathological dimensions, such as internalizing symptoms, may mediate the relationship between ADHD and obesity. If these features differentiate ADHD patients with and without overweight/obesity, they may represent possible targets for the diagnostic assessment and the treatment strategies.Our sample included all the 100 drug-naïve children and adolescents with ADHD referred and consecutively diagnosed at the Child and Adolescent Psychiatric Unit of Tor Vergata Hospital from 2018 to 2020. The sample consisted of 78 males and 22 females, ranging in age from 6 to 18 years, with a mean age of 9.9 ± 2.5 years, 67 with combined presentation, 29 with inattentive presentation, and 4 with hyperactive/impulsive presentation. Socio-economic status (SES) was assessed with the Barratt Simplified Measure of Social Status (BSMSS). The diagnosis was based on the DSM-5 criteria and the Conners’ Parent Rating Scales and Teacher  as part of a comprehensive clinical assessment by a trained multidisciplinary team. All the patients taking any meditations or with metabolic or endocrinological diseases were excluded, as were patients with intellectual disability, autism spectrum disorder, schizophrenic spectrum disorder, bipolar disorder, personality disorder, and substance use disorder.All the participants received a comprehensive assessment, including also the Kiddie Schedule for Affective Disorders and Schizophrenia for school-age children (K-SADS-PL)  for cateThe methodology of the study was clearly presented to patients and parents, and a written informed consent to participate was obtained by the parents of all the participants.The study protocol was approved and registered (R.S. #216.20) by the local institutional review board .https://www.who.int/news/item/27-04-2006-world-health-organization-releases-new-child-growth-standards/). The WHO defines “normal weight” as ranging between the 3rd to 84th percentile, “overweight” as ranging between the 85th to 96th percentile, and “obesity” as equal to or greater than the 97th percentile.Children’s height and weight  were measured during their first visit. The indicators of BMI (weight in kilograms divided by height in meters squared) percentiles and BMI z-score were used to determine the individuals’ growth. BMI percentiles and z-scores were considered based on the World Health Organization (WHO) age/gender references available on the WHO website , ItalianThe Child Behavior Checklist 6–18 (CBCL) , ItalianThe Junior Temperament and Character Inventory (JTCI) , a 107-iStatistical analysis included one-way analysis of variance (ANOVA) and covariance (ANCOVA). To minimize the likelihood of a type-I error, univariate ANOVAs were preceded by an overall multivariate analysis of variance (MANOVA) using all the JTCI or CBLC scales as dependent variables. The significance of the MANOVA effect was assessed employing the F-test in association with Wilk’s lambda. Hierarchical regression analysis was used to identify the significant predictors of BMI in the entire sample. Collinearity diagnostics based on the eigenvalues of the scaled and uncentered cross-products matrix, variation inflation factors (VIF) and tolerances for individual variables were used to exclude multicollinearity among the independent variables. The software we used was SPSS version 25.0. The algorithms used by the software SPSS 25.0 for computing the multivariate analysis are proprietary algorithms reported in the software manual.All the variables of the study and their descriptive statistics are shown in Preliminary analyses did not show any significant association between demographic variables and children’s severity of ADHD symptoms or BMI z-scores.Based on WHO-age norms 35, children whose BMI was above the ≥85th percentile were classified as overweight/obese, while children between the 3rd and 84th percentile were classified as normal weight.The prevalence of overweight/obesity was 71/100 . None in the sample had a BMI < 3rd percentile.Regarding the temperament profile in the whole ADHD sample, according to the JTCI, the highest scores were found in NS and the lowest in P and SD. Specifically, the NS trait was present in 86% of the sample, and the P trait in 11%.The role of temperament in influencing the BMI of ADHD children was assessed with a hierarchical stepwise regression analysis using the three scales of the JTCI, formerly included by Cloninger in the Tridimensional Personality Questionnaire (TPQ) and more closely related to ADHD, that is, novelty seeking, harm avoidance, and reward dependence, as predictors. Persistence was excluded to reduce predictors’ numbers and increase the validity of the multivariate analysis, considering the sample size.p = 0.03, R squared = 0.09) and the HA scale of the JTCI emerged as a significant predictor of the BMI . See The variables entered in the first block were age and gender. The three scales of the JTCI comprised the second block. The multivariate model was significant  and the internalizing scale of the CBCL emerged as a significant predictor of the BMI . See The relationship between BMI and psychopathological dimensions was assessed using a hierarchical stepwise regression analysis with the three broad-band scales of the CBCL, internalizing, externalizing, and total problems, as predictors. The variables entered in the first block were age and gender. The three scales of the CBCL entered the second block. The multivariate model was significant  based on the WHO growth reference data for children and adolescents.p = 0.03). Compared to non-overweight subjects, overweight/obesity subjects scored higher on the RD  and lower on the NS scale .The role of temperament was assessed by conducting a MANOVA with the three scales of the JTCI  as dependent variables. The omnibus test was significant .The role of behavioral problems was assessed by conducting a MANOVA with the three broad-band scales of the CBCL, internalizing, externalizing, and total problems, as dependent variables. The omnibus test fell short of statistical significance , while the least represented were persistence (P) and self-directedness ,37,38. RSubjects with high RD tend to strongly respond to signals of reward, and when this feature is particularly high, it may increase the development of psychopathological traits like the risk of addictive behavior or overeating, as in our sample. These results are consistent with the notion that dysregulated brain reward pathways may be contributing not only to drug addiction but also to increased intake of palatable foods and ultimately obesity . ObesityIn comparison to the personality variables that influence later substance use, such as high NS and RD, low HA and negative affectivity, our patients with obesity have low NS and high HA, as well as prevalent internalizing disorders. In other words, both ADHD subjects with obesity and those with substance abuse may present high RD, but while subjects prone to substance abuse may present high NS and low HA , patients with obesity are characterized by low NS and high HA , with neurotic introversion more closely related to the typical features of binge- and over-eating ,45. ThisOur study should be considered in the light of several limitations, mainly the small sample size, the lack of differentiation of ADHD’s subtypes and their different temperament profiles, and the lack of a control group of patients without ADHD, to ascertain whether the psychopathological and temperamental features may be related to the ADHD diagnosis. Given the small sample size, gender differences were not considered, as the girls were under-represented, consistent with the ADHD gender ratio. Furthermore, in our study, we focused on the three broad-band scales of the CBCL, without a closer consideration of specific categorical diagnoses. The main strength of the study is that it included an unselected sample of drug-naïve ADHD youth in routine clinical conditions; thus, the results may more closely reflect the real-life context.Our preliminary study suggests that ADHD children and adolescents, with or without obesity significantly differ according to their temperamental and psychopathological profiles. Further research with larger samples, a longitudinal design, and different measures may support these preliminary results. In clinical settings, diagnostic and treatment implications may be drawn more consistently. A comparison between overweight/obese children and adolescents with and without ADHD may further support the specificity of our findings. Among the new measures, a body shape index (ABSI) may replace the old BMI as an index of obesity, which is becoming outdated as a result of different medical fields ,61,62.ADHD subjects with lower BMI have a predominant NS temperament and externalizing symptoms, whereas ADHD subjects with higher BMI have a high RD and HA temperament and associated internalizing symptoms. This profile, markedly different from that of patients with other addictive behaviors, such as substance abuse (except for the shared RD), may represent a possible marker of vulnerability in ADHD patients. If confirmed in larger, prospective studies, this finding may underline the need to include a dimensional approach to temperament and psychopathology in a systematic assessment of children with ADHD, to improve our ability to identify more at-risk subjects, and to tailor interventions to temperament-based prevention programs (such as mindfulness-based programs) , accordi


Air pollution data were based on daily data from the Environmental Protection Administration Executive Yuan, R.O.C. (Taiwan). These included particulate matter (PM2.5 and PM10). The two databases were merged according to the living area of the insured and the location of the air quality monitoring station; (3) Results: The highest levels of air pollutants, including PM2.5 (adjusted hazard ratio (aHR) = 1.79; 95% confidence interval (CI) = 1.58–2.02) and PM10 , had a significantly higher risk of ADHD; (4) Conclusions: As such, measures for air quality control that meet the WHO air quality guidelines should be strictly and uniformly implemented by Taiwanese government authorities.(1) Background: Recently, a growing number of studies have provided evidence to suggest a strong correlation between air pollution exposure and attention-deficit/hyperactivity disorder (ADHD). In this study, we assessed the relationship between early-life exposure to particulate matter (PM) Attention-deficit/hyperactivity disorder (ADHD) is a common neurodevelopmental spectrum disorder that has shown a notable increase in incidence over most recent decades. Current epidemiological figures show that ADHD is the most common behavioral disorder in children, affecting approximately 2–7% of children globally . HoweverAlthough the exact pathogenic mechanisms leading to ADHD are still poorly understood, several genetic and environmental risk factors have been implicated in the development of ADHD-related behavioral abnormalities ,9. Genom2.5 and PM10, classified according to their aerodynamic diameter. PM10 is defined as PM with a diameter of less than or equal to 10 μm; this pollution can be absorbed by alveoli into the bloodstream and distributed to several organs. PM2.5 is defined as PM with a diameter of less than or equal to 2.5 μm; this pollution can induce cardiopulmonary impairments and/or disorders, and, thus, poses a significant risk to health. A cumulative body of evidence has shown that PM (PM2.5 or PM10) significantly contributes to the development of a wide range of human diseases, including cardiovascular disorders, chronic obstructive pulmonary disease, pollen allergy, cognitive impairment, malignancy, and all-cause mortality [PM, defined as “a complex mixture of extremely small solid particles and liquid droplets that enter air. Once inhaled, these particles can affect the heart and lungs and cause serious health effects”, is a major component of indoor and outdoor air pollution . Along wortality ,20,21.10 or PM2.5) and ADHD [10 and PM2.5) in early life has been reported in the literature. In addition, most studies were conducted in Europe, which has a relatively lower PM2.5 concentration than Asia, and these studies did not investigate the dose–response relationship between PM10 or PM2.5 and ADHD. Thus, in this population-based study, we assessed the relationship between early-life exposure to PM10 or PM2.5 and ADHD.To date, several reports have investigated the association between PM exposure . The TAQMD includes 74 air quality monitoring stations, which were established based on population density in Taiwan. The two databases were merged according to the living area of the insured and the location of the air quality monitoring station. Each participant was assigned pollutant exposure concentrations based on the data collected from the monitoring station located in their living area.The National Health Insurance Research Database (NHIRD) contains the medical records, drug information, inspection data, etc., of the people of Taiwan, and, thus, can serve as an important research resource. Disease coding in this database follows the International Classification of Diseases, Ninth Revision, Tenth Revision, and Clinical Modification (ICD-9-CM and ICD-10-CM). The present study used data from the NHIRD, which includes a population of two million. The Taiwan Air Quality-Monitoring Database (TAQMD) contains daily concentrations of PMThis study was approved by the institutional review board of the Research Ethics Committee of the China Medical University Hospital (CMUH109-REC2-031(CR-2)).2.5  and PM10 . Related comorbidities, such as asthma, atopic dermatitis, and allergic rhinitis, were considered covariates.We investigated the population under the age of 18 in 2003 and living in areas with air quality monitoring stations. The study period began in 2003 and ended in 2017. Attention-deficit hyperactivity disorder (ADHD)  was defined in participants having two or more outpatient diagnoses or one admission record. We calculated the annual average amount of air pollutants each subject was exposed to during the follow-up period. The quartiles of each air pollutant were: PM2.5 and PM10, were expressed as the mean and standard deviations. The incidence of ADHD per 1000 person-years was calculated. To reveal the association between air pollutants and ADHD in children aged younger than 18 years, the Cox proportional hazards model was used to estimate the risk of ADHD. The incidence rate of ADHD was estimated according to different pollutant levels.The variables sex, age, urbanization level, comorbidities, and ADHD were presented as counts and percentages. Variables related to environmental factors, including temperature and air pollutants PMA multivariable model adjusted for covariates was used to estimate the adjusted hazard ratio (aHR) and 95% confidence interval (CI) of the association between the risk of ADHD and PM exposure. Cumulative incidence curves of ADHD were further drawn using the Kaplan–Meier method. Curves were evaluated using the log-rank test. All statistical analyses were performed using SAS software . The statistical significance was set at 0.05.2.5 and PM10 exposures during the participation period were 29.3 (±7.15) μg/m3 and 53.6 (±12.1) μg/m3, respectively. Ultimately, 2856 participants developed ADHD. The mean follow-up time was 14.7 (±1.86) years.As shown in 2.5 and PM10 stratified into quartiles. The incidence rates of ADHD for the four quartiles of the PM2.5 level were 1.15 (Q1), 0.22 (Q2), 2.25 (Q3), and 2.03 (Q4). The crude HR (95% CI) of ADHD in Q2 of PM2.5 exposure was 0.19 . Patients in Q3  and Q4  had a higher risk of ADHD. The incidence rates of ADHD in the four quartiles of the PM10 level were 1.45 (Q1), 1.39 (Q2), 2.87 (Q3), and 2.30 (Q4). For PM10 pollutants, the subjects in the Q3 level had a 1.96-fold risk of ADHD  and the subjects in the Q4 level had a 1.58-fold risk of ADHD .2.5 and the other one for PM10. The greater the age of the participants, the lower the risk of ADHD. In the first model with PM2.5, the risk factors of ADHD included male gender , asthma , AD , the Q3 group of PM2.5 , and the Q4 group of PM2.5 . Children in the Q2 level of PM2.5  had a lower risk of ADHD than those in the Q1 level. For the other model with PM10, male gender , asthma , and exposure to high levels of PM10  all increased the risk of ADHD. The aHR of ADHD for patients with allergic rhinitis (AR) relative to AR-free patients was 0.84 .2.5 had a relatively high cumulative incidence of ADHD, and the Q2 level of PM2.5 had a relatively low cumulative incidence of ADHD when considering the Q1 level of PM2.5 as a reference . A similar cumulative incidence of ADHD for pollutants PM10 was shown.As shown in 10 and PM2.5) exposure and human diseases is well-established in the literature [10 and PM2.5. Thus, in this population-based study, we assessed the relationship between early-life exposure to PM10 or PM2.5 and ADHD in a Taiwanese population.The association between airborne PM  and nitro-substituted PAHs (nitro-PAHs), which both enhance inflammatory responses [10 can trigger the release of proinflammatory cytokines [10 and PM2.5 can both damage dopaminergic cells [10 and PM2.5, which can be distributed to various organs, including the brain, via the nasal olfactory mucosa [The exact mechanistic approaches underpinning the association between PM exposure and ADHD remain unclear. PMe stress , transdue stress , and pene stress . PM2.5 gesponses . PM10 caytokines , while Pic cells ,38. Thery mucosa , trigemiy mucosa , and ciry mucosa , directl2.5 exposure during pregnancy and ADHD [10 [2.5 and PM10 concentrations than Asia; thus, the difference in the exposure to PM between ADHD and non-ADHD individuals may be too small to detect statistical differences. Methodologically, published reports have utilized different exposure assessment tools and diagnostic criteria for ADHD, leading to substantial variability in the estimation or results. On the other hand, ADHD is a multifactorial disorder, and several confounders may contribute to disease development. It is challenging to control for all potential confounders using population-based registries that affect the association between PM exposure and ADHD. Furthermore, the components of air pollution are extraordinarily heterogeneous; hence, the association between PM and ADHD may be attributed to the synergistic effect of the mixture of pollutants, such as NO2, PAHs, and nitro-PAHs, rather than solely to PM2.5 and PM10 concentrations.However, it is worth noting that the current literature shows conflicting results regarding the association between PM exposure and the risk of ADHD. In a recent systematic review by Zhang et al., PM exposure was not significantly associated with a significant increase in the risk of ADHD; the pooled analysis showed substantial heterogeneity . Likewisand ADHD . A cohorADHD [10 . In our 2.5 and PM10 with ADHD. This study collected data from a prospectively collected population-based database, which enhanced the generalizability of our findings. We further explored the association between PM exposure and ADHD after controlling for potential confounders, such as age and socioeconomic status. More importantly, we assessed PM exposure using both temporal and spatial resolution to reduce the risk of exposure misclassification. Additionally, patients were followed-up with for an average of 14.7 years.To our knowledge, only a few studies have investigated the dose-dependent relationship between early-life exposure to PM2, were not captured; thus, we could not exclude possible interactions with other factors that may have influenced the association between ADHD and PM exposure. Moreover, data regarding relocation during the follow-up period were unavailable; hence, we could not account for changes in PM exposure according to residential relocation. Consequently, we enrolled 98,177 children in this nationwide study. The residential address was not available from the NHIRD. However, the nonresidential geographic contexts (where air pollution affects people) could lead to erroneous exposure assessments [Despite its several strengths, we acknowledge the limitations of this study. Firstly, data were retrieved from electronic medical records, which increased the risk of misclassification bias, particularly with a spectrum disorder such as ADHD, in which the diagnosis is challenging due to the substantial variations in presenting symptoms and the extent of impairment. Although we collected data on potential confounders, we could not obtain data regarding ADHD phenotypes and genetic risk factors, precluding the assessment of gene–phenotype–environment interactions. In addition, data regarding other pollutants, such as noise pollution and NOessments ,45,46. M2.5 and PM10 significantly increases the risk of developing ADHD even after controlling for common confounders, such as age, socioeconomic status, and comorbidities. These results align with previous toxicological and epidemiologic research showing a negative impact of PM on the behavioral and cognitive development of the population. We further demonstrated a novel finding of the dose–response relationship between PM2.5 and PM10 and ADHD.The present population-based study provides further evidence that early-life exposure to PMAlthough regulatory actions and policies to mitigate exposure to air pollution have been implemented in Taiwan, the application of these policies is still suboptimal, and the hazardous health impacts of PM are still relevant. Therefore, measures for air quality control that meet the WHO air quality guidelines should be strictly and uniformly implemented by the Taiwanese government authorities. Community measures that aim to reduce indoor pollution may include preventing indoor smoking, limiting the use of open fireplaces, and providing houses with effective electrostatic pleated air filters or high-quality air purifiers. Previous reports have shown that air filters can reduce PM levels by 60% . HoweverFurther, well-designed and controlled prospective studies are needed to study the association between PM exposure and ADHD using standard ADHD diagnosis and exposure assessment methods.


At 5 years of age, the children (n = 957) were classified as ADHD-positive or -negative using Conners’ Parent Rating Scale. Relying solely on parent-rated ADHD or impairment at age 3 years did moderately well in identifying children with persistent elevation of ADHD symptoms, but gave many false positives : .40–.57). A small group of children  scored above cut-off on both parent-rated ADHD and impairment, and teacher-rated ADHD symptoms, although adding teacher-rated ADHD symptoms slightly weakened the predictive power for girls. For this small group, PPVs were .76 for boys and .64 for girls. Limiting follow-up to these few children will miss many children at risk for ADHD. Therefore, we recommend close monitoring also of children with parent-reported ADHD symptoms and/or impairment to avoid delay in providing interventions. Clinicians should also be aware that teachers may miss ADHD symptoms in preschool girls.We investigated to what extent parent-rated attention-deficit/hyperactivity disorder (ADHD) and impairment at age 3 years predicted elevated ADHD symptoms at age 5 years, and whether teacher-rated ADHD symptoms improved these predictions. This study is part of the longitudinal, population-based Norwegian Mother, Father and Child Cohort Study. Parents of 3-year-old children ( Diagnostic and Statistical Manual of Mental Disorders (DSM-5) states that ADHD symptoms are difficult to distinguish from normative behaviours before this age [Attention-deficit/hyperactivity disorder (ADHD) is characterized by age-inappropriate and impairing hyperactivity-impulsivity and/or inattention that is pervasive across settings . ADHD ofthis age .n = 11). Another community study with 170 hyperactive 3-year-old children found significant continuity between early hyperactivity and ADHD after 14–25 years, but only in boys [n = 168), found that 58% of the children with behavioural problems continued to meet diagnostic criteria 3 years later [n = 96) or two (n = 29) settings (according to parent or teacher reports) annually for 3 years [n = 104, baseline mean age = 4.4 years) found that the predictive utility of parent-reported ADHD symptoms  was significantly improved by the addition of teacher reports [Still, the few longitudinal studies that begin before the age of 4 years indicate that ADHD symptoms at this early age may be important to identify . One comrs later . None ofrs later , 16. How reports . Again,  reports . As ment reports .n = 957). Based on the diagnostic criteria [In the present study, we followed a cohort of 3-year-old children until they were 5 years old (criteria  and datan = 2798) were invited to participate, along with randomly selected children from MoBa (n = 654). Of these, 35% agreed to participate, and 1195 children took part in a 1-day clinical assessment that included a diagnostic interview with parents  from 2007 to 2011. Ninety-five percent of Norwegian 3-year-olds attend preschool. When the children were 3 years old, their parents received the screening questionnaires by mail and gave theirs to the teachers, who mailed their responses to the study administrator. By the 5-year follow-up, 6 mothers withdrew from the MoBa-study, leaving 1,189 participants. Eighty percent  of the mothers who participated when their child was 3 years old also completed questionnaires when their child was 5 years old.The Norwegian Mother, Father and Child Cohort Study (MoBa) is an ongoing prospective population-based cohort study of Norwegian-speaking pregnant women conducted by the Norwegian Institute of Public Health (41% participation rate) . About 8Length of parental (mothers’ + fathers’) education was obtained at the first MoBa assessment (about Week 17 of pregnancy) and reported in mean number of years.To check how many children were referred for treatment, we included results on one item asked to mothers at child age 3 and 5 years: “Has your child been referred to Child and Adolescent Mental Health Services (CAMHS)? To check for comorbidities at age 5 years, we included two questions: (1) “Has your child ever had behavioural problems ?” (2) “Has your child ever had emotional problems (sadness or anxieties)?”Birth date and child gender were obtained from the Norwegian Medical Birth Registry.The preschool age psychiatric assessment (PAPA)  was deven = 131, 50 girls), 5% inattentive , and 19% combined presentation .In line with our earlier studies , 24, we The ADHD section of the PAPA concluded with an impairment section . WheneveOppositional Defiant Disorder (ODD) was defined by the presence of ≥ 4 DSM-IV ODD symptoms according to PAPA.p < 0.001) [The first 44% of the sample received the Norwegian version of the Strengths and Difficulties Questionnaire (SDQ) rated on a three-point Likert scale  . The psy< 0.001) . We used< 0.001) –34.p < 0.001) [ n = 1). In line with the ECI-4 manual, the cut-off score was set to the minimum number of symptoms necessary for the DSM-IV ADHD diagnosis  [p < 0.001).The rest of the sample received the Norwegian version of the Early Child Inventory-4 (ECI-4) that contains 108 items corresponding to the symptom lists of the child psychiatric disorders in the DSM-IV . The inv< 0.001) . We usedbscales) . CronbacThus, for the entire sample, teacher ADHD was classified as present by an SDQ HI score ≥ 7 (first subsample) or by an ECI-4 H-I or IA symptom count score ≥ 6 (last subsample).n = 32,633, 49% girls), we created cut-offs for ADHD to be present at the 95th percentile of the CPRS-R:S ADHD index scores: > 14.2 for boys and > 11 for girls. In our sample, 215 children  reached the ADHD classification by these cut-offs, giving a 22.5% (215/957) occurrence rate.The mothers received the revised short form of the Conners’ Parent Rating Scale (CPRS-R:S) including the 12 ADHD items comprising the ADHD index . The CPRn = 957) completed the CPRS-R:S ADHD index when their child was 5 years old, we therefore checked for differences in rates of ADHD among responders and non-responders. Of the missing participants (n = 232), 22% of children fulfilled symptom criteria for ADHD at 3 years old, which is nearly the same as for those who continued to participate (22.5%). However, the non-responders had significantly higher parent-reported ADHD symptom scores at 3 age years old  than the responders , while there was no significant difference in teacher-reported ADHD symptoms. The parents of the responders were significantly better educated  than the parents of non-responders . There were more boys (54%) than girls among the responders, and vice versa among the non-responders (45% boys), a significant difference . At age 3 years, only 16 children (1.3%) had been referred to CAHMS. All inter-correlation coefficients between the parent and teacher variables when the child was 3 years old and CPRS-R:S when the child was 5 years old were low to moderate (range 0.27–0.61) (Table S1).As mentioned, only eighty percent , we estimated sensitivity (the probability that a measure correctly classified a child with ADHD at age 5 years) and specificity (the probability that a measure correctly identified a child without ADHD at age 5 years). We calculated the positive predictive values , negative predictive values , positive likelihood ratios , and negative LRs . LRs between 0 and 1 argue against the presence of the disorder [Internal consistencies were analysed using Cronbach’s α, differences between means of continuous variables with disorder . Becausedisorder .Children classified with ADHD at 5 years had significantly higher mean parent and teacher symptom scores at 3 years than those who were not classified with ADHD Table .Table 1tThe parent-classified ADHD and impairment at 3 years gave similar sensitivity (43–45%) and specificity values (81–91%) for detecting elevated ADHD symptoms at 5 years old in boys and girls. In boys, the teacher-classified ADHD gave sensitivity and specificity values of 27% and 88%, respectively, while the sensitivity of the teacher classification was particularly low (14%) in girls. For boys, the highest + LR of being classified with ADHD at 5 years was found for the children with parent-rated ADHD, impairment, and teacher-rated ADHD (+ LR = 10.79), but the sensitivity was low (12%) for this combination. For girls, the combination of the parent-rated ADHD and impairment gave a + LR of 7.98 and adding teacher-rated ADHD symptoms slightly weakened the predictive power . In the small group of 3-year-old children  who fulfilled ADHD criteria according to both parent and teacher reports, and were impaired, the probability of finding children categorized with ADHD at 5 years was high . For girls, the highest PPV (0.69) was reached by combining information about parent-rated ADHD and impairment.Our finding of increased risk for persistent elevation in ADHD symptoms even in children with only parent-rated symptoms and/or impairment is in accordance with a community study that found that children diagnosed with ADHD by parent interview at 3 years old were highly likely to meet ADHD criteria at 6 (OR = 17.96), 9 (OR = 5.53), and 12 years old (OR = 6.80) , 41. ThaThe moderate diagnostic stability of ADHD when relying on one source of information is not unique for 3-year-old children. As pointed out by Law et al., community samples using checklists to ascertain ADHD diagnoses have reported about 50% stability in < 7-year-old children after a 2-year period , 15. ThiImpairment in the present study had similar sensitivity and specificity values to the ADHD symptoms measures, in accordance with a study of school children that found impairment of ADHD at baseline significantly predicted persistence of ADHD 11 years later . Our finAs in previous studies –46, we fn = 31) had been referred to CAHMS, significantly more than among the children not classified with ADHD at age 5, of whom 2% (n = 17) had been referred. Although we cannot entirely rule out that treatments received may have influenced whether the children continued to fulfil criteria at age 5 years, few had been referred, particularly for those who were not classified with ADHD at 5 years. Also, according to the Norwegian prescription database, very few children in Norway are medicated for ADHD before age 5 years, with a variation in medication rate from 0.02/1000 to 0.1/1000 during a fifteen-year period (2004—2019) (http://www.norpd.no/). These rates are in line with a study from the five Nordic countries reporting annual prevalence of ADHD drug use under age 6 years in 1/1000 boys and 0.3/1000 girls [p < 0.001); emotional: 12% versus 3% (p < 0.001), in line with studies reporting high comorbidity rates to ADHD [The strengths of our study included the population-based cohort design and the use of a parent diagnostic interview when the children were 3 years old, but it also had limitations. There were selection biases due to attrition , 48, how00 girls . About 9 to ADHD , 52. Our to ADHD . We crea to ADHD . UnfortuThere was a larger proportion of girls than boys with ADHD inattentive presentation at age three (6 girls of 8 children), but most of the children had hyperactive/impulsive or combined presentations (91% girls and 98% boys). Therefore, this gender difference can hardly explain why teachers were poor at identifying girls later categorized with ADHD. Because most of our sample had the hyperactive/impulsive presentation at age 3 years (76%), and we only had CPRS ADHD index at age 5, we were unable to investigate ADHD subtype stability/instability. Likewise, we did not fully address the role of comorbidity for this stability/instability, but we found that ODD, the most common comorbid disorder to ADHD, did not improve prediction of ADHD classified at age five years. Our at-risk sample had a higher proportion of children classified with ADHD at 5 years (22.5%) than did the general population, increasing the probability of finding true cases given a positive screen result. However, this should not affect LR-estimates, which may generalize to other samples.In the present study, children where only the parent-reported child ADHD symptoms or impairment at age 3 years constitute a high-risk group, as about 50% continued to be categorized with ADHD at age 5 (according to parent report). We also identified a few children with combined parent- and teacher-reported ADHD and impairment with high probability of persistent elevation of ADHD symptoms to age 5 years. However, limiting follow-up to these few children will miss many children at risk for ADHD. Therefore, we recommend close monitoring also of children with parent-reported ADHD symptoms and/or impairment to avoid delay in providing interventions, in line with ADHD guidelines . Clinici