Participants were asked to optimally extract and combine information about stimulus salience and value to maximize rewards. While results of this pilot study are ambiguous due to the small sample size and limited number of task trials, data suggest that neither participants with ADHD nor typically developing participants performed optimally to maximize rewards, though typically developing participants were somewhat more successful at the task  regardless of task condition. Further, the manuscript examines several follow-up questions regarding group differences in task response times and group differences in task performance as related to sustained attention across the duration of the task. Finally, the manuscript examines follow-up questions related to heterogeneity in the ADHD group  in predicting task performance.Although inattention is a key symptom subdomain of attention-deficit/hyperactivity disorder (ADHD), the mechanisms underlying this subdomain and related symptoms remain unclear. There is a need for more granular approaches that allow for greater specificity in linking disruptions in specific domains of cognitive performance  with behavioral manifestations of ADHD. Such approaches may inform the development of more targeted therapeutic interventions. Here, we describe the results of a pilot study of elementary-aged children (ages 6–12years) with ADHD ( Attention-deficit/hyperactivity disorder (ADHD) is a complex neurodevelopmental disorder with a worldwide prevalence of 11% . Althougd=0.46–0.69; d=0.36–0.45) and substantial heterogeneity among individuals with ADHD  and/or reward processing . This makes it difficult to discern whether specific manifestations of ADHD behavioral symptomology result from disruptions in reward processing, selective attention, or a complex interaction of reward processing and selective attention. These limitations of the prior literature result in reduced understanding of ADHD etiology, as well as limitations in the identification of potential treatment targets. Thus, there is an urgent need for more granular approaches that will allow researchers to link disruptions in specific RDoC domains with different ADHD behavioral symptoms and presentations. A greater understanding of these links would enable enhanced diagnostic accuracy and could inform the development of more targeted therapeutic interventions.Motivated by this need, the current study was designed to probe links between the domains of executive function and reward processing in a sample of children diagnosed with ADHD, as well as typically developing children. Following developments in the cognitive neuroscience literature e.g., , we takeHumans need to make rapid choices among multiple targets embedded within noisy perceptual environments. Consider a student in a classroom deciding which visual stimuli to attend to. Valuable stimuli  may be scarce or camouflaged by the other visual stimuli in the room, while less-valuable stimuli  may be more plentiful and easier to find. To optimize the probability of taking in the lesson, the student must consider information about the salience and value of different targets. This raises a fundamental question: are rapid decisions among stimuli in cluttered environments dominated by sensory information , value information , or a mixture of both?via a selective deficit in the processing of salience  or via a selective deficit in the processing of value .Bayesian optimality provides a useful benchmark for examining the deployment of selective attention among children with ADHD. For example, a study design including manipulations of both salience and value would allow for the examination of deviations from optimal search behavior  among children with ADHD, as well as for a more fine-grained characterization of the disorder rooted in cognitive science. This study represents a first step toward this goal. We hypothesized that typically developing children will exhibit optimal search behavior that maximizes rewards over trials while considering both salience and value of stimuli. In contrast, participants with ADHD will deviate from optimal search behavior either M=9.09, SD=1.77), completed the present study. Fifty met Diagnostic and Statistical Manual of Mental Disorders, fifth edition (DSM 5) criteria for ADHD . Exclusion criteria included as: estimated Full-Scale Intellectual Quotient (IQ)<80; diagnosis of autism spectrum, cardiovascular, developmental, neurological, psychotic, or mood disorders; and use of psychotropic medication for disorders other than ADHD within the previous 6months. Additionally, participants were excluded if they had four to five symptoms of ADHD, as to be designated as typically developing, participants were required to have three or fewer inattentive or hyperactivity symptoms. In contrast, participants with ADHD were required to have six or more symptoms within either the inattentive or hyperactivity/impulsivity domains.To determine eligibility, parents and teachers of participants with ADHD completed the Disruptive Behavior Disorders Rating Scale DBD-RS; . TeacherBest-practice methods were utilized in identifying ADHD diagnoses . As suchAll children were required to be medication free at the time of testing. Stimulant medication underwent a 24–48h washout, dependent on the type of stimulant preparation, which included 32 children with ADHD .via a keypress. Specifically, participants could earn 15 points by correctly reporting the location of the HVT on a given trial, or five points by correctly reporting the location of the LVT on a given trial. Participants received no points for reporting the location of an oblique distractor. To manipulate the relative salience of the HVT and LVT, we manipulated the orientation of the four non-target items (hereafter referred to as distractors) across trials. Specifically, on each trial, the four distractors were assigned orientations from the set {15, 30, 45, 60, 75}°. Thus, a horizontal (0°) LVT would appear conspicuous during 75° distractor trials but not during 15° trials, while the converse would be true for a vertical (90°) HVT.Our experimental approach is based on a method devised by The visual search display was rendered for 500ms and followed by a mask/response screen for 2,000ms. Participants were instructed to report the location of either target  by pressing the appropriate number on a computer keyboard see . ParticiPrior to beginning the task, participants completed five practice trials to establish understanding of the task demands. Each participant completed a single block of 90 trials lasting approximately 15–20min. We quantified task performance by plotting the proportion of HVT responses as a function of feature contrast :H, L, and D are the HVT, LVT, and distractor orientations on each trial.where F=3.25, p=0.07, η2=0.033], with control participants reporting the HVT slightly more frequently than ADHD participants . There was also a marginal effect of feature contrast , but no interaction between diagnosis and feature contrast . An ANOVA revealed a marginal effect of diagnosis , but did observe a significant main effect of feature contrast, , and a significant interaction of group by feature contrast . Specifically, typically developing youth displayed slower response times when feature contrast was low and faster response times when feature contrast was high. In contrast, youth with ADHD appeared to have somewhat less consistent response times in relation to feature contrast (see Slow and variable response times (RTs) are prominent features of ADHD and have been used as an index of overall ability on tasks which require speed-accuracy tradeoffs, as prior work has shown that youth with ADHD display slower, more variable response times than typically developing youth even when accuracy is not compromised . As suchrast see .F=1.53, p=0.22, η2=0.303], feature contrast , nor an interaction . We also examined performance on the first vs. second half of the task by diagnostic group and identified a non-significant three-way interaction of group, feature contrast, and time .Prior work has also demonstrated that youth with ADHD tend to perform worse than their typically developing peers during the latter half of tasks requiring sustained attention, such as a continuous performance task . As suchF=26.43, p<0.0001, η2=0.36]; i.e., older children performed more optimally than younger children; however, there was no significant main effect of feature contrast  or interaction .We also acknowledge that task performance may be associated with development, and thus, participant age. Thus, we conducted an additional analysis examining task performance according to child age wherein we found a main effect of age . Overall, the inattentive presentation was slightly more likely to choose the HVT than the either the control or combined presentation groups. However, we observed no significant main effect of feature contrast  or interaction, .Prior studies have demonstrated heterogeneous performance among youth with ADHD, even when no group differences are observed between youth with ADHD and typically developing youth . With reF=3.79, p=0.025, η2=0.074] such that youth with ADHD and comorbid ODD/CD performed worse than either the ADHD only or control groups, but no significant ME of contrast  or interaction .In terms of the contribution of comorbid ODD and/or CD diagnoses, a 3 (group – control vs. ADHD only vs. ADHD with comorbid ODD/CD)×5 (feature contrast) mixed ANOVA revealed a significant main effect of group .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.


Trial registration: This study was registered on PROSPERO (registration ID: CRD42020213219).Studies in the field of neuroscience and psychology have hypothesized that a causal association exists between atopic diseases and attention-deficit/hyperactivity disorder (ADHD). Previous systematic reviews and meta-analyses have reported a higher risk of ADHD in children with atopic diseases; however, the relationship between ADHD symptoms and atopic diseases remains unclear. We systematically reviewed observational cross-sectional and longitudinal studies to investigate the relationship between atopic diseases and ADHD symptom severity (hyperactivity/impulsivity and inattention). The majority of studies showed a statistically significant association between atopic diseases and both ADHD symptoms, with substantial heterogeneity in the outcome of hyperactivity/impulsivity. Remarkably decreased heterogeneity and statistical significance were observed in the second meta-analysis of ADHD-related behavior symptoms in atopic patients without ADHD. Our study indicated that atopic diseases not only associated with ADHD but also ADHD symptoms severity. This association was even observed in children with subthreshold ADHD, indicating that atopic diseases may play a role in the spectrum of ADHD symptom severity.  Atopic march, defined as the development of atopic dermatitis in infancy and subsequent allergic rhinitis and asthma in later childhood, can cause fatigue; attention, learning, and memory deficits; and depression. Moreover, atopic diseases have been reported to considerably affect children’s sleep, school performance, development, and quality of life4. In addition, recent studies have reported that the parents or main caregivers of children with atopic diseases, particularly those with multiple atopic diseases, experience considerable stress and psychosocial burden8 Thus, increasing public attention has been focused on the management of atopic diseases8.In the past decade, atopic diseases have been a cause of increased concern because of their substantially high prevalence (approximately 20 to 40%) worldwide10. ADHD can be caused by multiple factors . Moreover, studies have indicated that ADHD occurs due to immature brain development, and it may also be accompanied by other neurobehavioral developmental delays and even intellectual disability11. Many studies have reported that ADHD symptoms were still observed in adulthood in a majority of patients with ADHD, even in those with adult ADHD who did not meet the criteria for ADHD in their childhood14. Adult patients with ADHD experience a financial burden and may even require considerable support14. These findings indicate that ADHD is a matter of considerable concern in child and adolescent psychiatry.Attention-deficit/hyperactivity disorder (ADHD) is among the most common neuropsychiatric disorders in children and adolescents, with a prevalence of approximately 5%. ADHD is characterized by hyperactivity, impulsivity, and inattention that lead to not only functional impairment but also executive function impairment and emotional dysregulation15. A higher risk of ADHD was noted in patients with all three atopic diseases, namely atopic dermatitis, allergic rhinitis, and asthma 17. In addition, children with both atopic diseases and ADHD had an increased risk of developing more severe ADHD symptoms compared with those with only ADHD18. Also, the association between atopic diseases and ADHD-related behavioral symptoms19, which some researches mentioned as subthreshold ADHD20, has been investigated recently. Although several meta-analyses and systematic reviews have strongly supported the relationship between atopic diseases and ADHD22, they still had many limitations. The heterogeneity observed in some systematic reviews might have affected their findings; for instance, some reviews did not include studies with standard inclusion and exclusion criteria, and whether the atopic and unexposed groups included patients diagnosed as having ADHD remained unclear21. In addition, few studies have reported a relationship between atopic diseases and subthreshold ADHD. Moreover, no systematic review and meta-analysis has examined whether atopic diseases affect the severity of both hyperactivity/impulsivity and inattention—the two main symptoms and diagnostic criteria related to ADHD.A previous study investigated the relationship between atopic diseases and ADHDIn this systematic review and meta-analysis, we reviewed studies examining the relationship between atopic diseases and the severity of ADHD symptoms including hyperactivity/impulsivity and inattention. In addition, we explored more precise value of this relationship by defining inclusion and exclusion criteria for groups. By doing so, we could exclude some confounding effects and provide additional information regarding the relationship between atopic diseases and subthreshold ADHD; this involved examining the data of children without a previous diagnosis of ADHD but who had an association of developing more severe ADHD symptoms.Through our literature search, we identified 4722 studies . Fifteen additional studies were identified through other sources. Using EndNote, we found that 671 studies were duplicates and we thus excluded them. Subsequently, we screened the titles and abstract for eligibility of the remaining 4066 studies. During title and abstract screening, we removed 630 because they did not include a relevant unexposed group and were letters or replies. Additionally, we excluded 1077 studies because they were not original research. Moreover, 1222 studies were excluded because they were not relevant to our research. Subsequently, after reviewing the full texts of the remaining 1136 studies, we excluded 1098 studies, 78 of which had missing data or full texts, 811 had nonrelevance, and 210 were not case–control, cross-sectional, or cohort studies. Finally, we included the remaining 38 studies in this systematic review for qualitative synthesis, and 16 of them were included in the quantitative review and meta-analysis. The procedure for study inclusion is shown in Appendix B in the Supplementary Materials.25. Most of the studies investigated the effect of confounding factors, including age, sex, multiple atopic diseases, the comorbidity with ADHD, severity of atopic diseases, persistent atopic diseases, and stressful parenting. Nineteen of the twenty studies examining the outcome of total ADHD symptoms reported a significant relationship between the severity of ADHD symptoms and atopic diseases. Furthermore, 9 of the 15 studies examining the outcome of hyperactivity and 13 of the 16 studies examining the outcome of inattention demonstrated a significant relationship between the severity of the examined ADHD symptoms and atopic diseases.In 38 studies including 117,181,049 participants in our qualitative systemic review, the researchers examined the relationship between atopic diseases and ADHD symptoms severity by displaying prevalence of ADHD in patients with atopy, odds ratios (OR) of Attention deficit disorder (ADD)/ADHD in children with atopy, or calculating scores for hyperactivity/impulsivity, inattention, or total ADHD symptoms. Table 26 performed structural equation modeling to determine interactions among eczema, asthma, rhinitis, sleep, and behavior disorders. In three studies, the computerized comprehensive attention test (CAT) was performed, a more objective and precise measurement tool, to assess attention deficit, and they obtained results similar to those of other studies, thus supporting our hypothesis that atopy increased ADHD symptoms severity. In addition, eight studies examined treatment modalities for atopic diseases; however, additional randomized controlled trials are required to obtain more consistent results and causal relationships. The included studies evaluated several confounding factors such as age, sex, and atopic disease severity. Additional detailed information is provided in Table Three of the four studies reported a significant association between the comorbidity of ADHD and atopic diseases in patients with ADHD. Ten studies reported a significantly positive correlation among atopic diseases, sleep disturbance, and ADHD or an increased risk of ADHD in children with atopic diseases. One research groupSixteen studies including 25,337 participants examined the relationship between at least one type of atopic disease and the severity of total ADHD symptoms, hyperactivity/impulsivity or inattention, by including unexposed groups consisting of participants without atopic diseases. Table p value < 0.01  of 0.35, 95% CI of 0.23–0.48, and p value of 0.003 . The funnel plot  showed no significant publication bias.In terms of the outcome of hyperactivity/impulsivity, the overall effect showed a statistically significant association with atopic diseases, with SMD of 0.51, 95% CI: 0.23–0.79, and p value of 0.0002  was observed for the outcome of inattention. The funnel plot  showed no statistically significant publication bias.The overall effect of the outcome of inattention showed a significant and positive correlation with atopic diseases, with SMD of 0.5, 95% CI: 0.32–0.68, and 002 Fig. e. Differlot Fig. f and Eggp value: 0.0212 .Five, four, and four studies included an atopic group without previous ADHD diagnosis and a healthy unexposed group to examine the outcomes of ADHD-related behavioral problems, which indicated total ADHD symptoms, hyperactivity/impulsivity, and inattention, respectively. All studies investigating total ADHD symptoms, hyperactivity/impulsivity, and inattention exhibited a statistically significant association, with SMD of 0.35 and hyperactivity/impulsivity , with low to moderate heterogeneity , hyperactivity/impulsivity , and inattention , with moderate to substantial heterogeneity , with low to substantial heterogeneity , whereas allergic rhinitis subgroup showed low to considerable heterogeneity.As sensitivity analysis, we observed decreased heterogeneity in meta-analysis of ADHD-related behavioral problems in children without previous ADHD diagnosis. However, in subgroups were categorized according to the types of atopic diseases, only the atopic dermatitis and asthma subgroup showed decreased heterogeneity . Allergic rhinitis was significantly associated with the severity of total ADHD symptoms, hyperactivity/impulsivity, and inattention, with moderate to considerable heterogeneity (I2 = 59–91%). Asthma was significantly associated only with the severity of total ADHD symptoms, with low to substantial heterogeneity (I2 = 0–55%).We performed a second meta-analysis of ADHD-related behavioral problems in atopic children without previous ADHD diagnosis, which not only consisted of studies with more strict exclusion criteria for diagnosed ADHD, but also reflected on the association between atopic diseases and subthreshold ADHD. The result showed statistical significance with low heterogeneity, indicating that atopic children was associated with subthreshold ADHD, as ADHD-related behavioral problems, despite of lack of diagnosed ADHD. The subgroup analysis of different types of atopic diseases showed that atopic dermatitis was significantly and positively correlated with the severity of total ADHD symptoms and hyperactivity/impulsivity, with low to moderate heterogeneity . The results of the subgroup analysis of different types of atopic diseases indicated that all atopic diseases were significantly associated with the severity of total ADHD symptoms; however, inconsistent heterogeneity was observed in the association between atopic diseases and the severity of hyperactivity/impulsivity and inattention. These findings suggest that different types of atopic diseases may have distinct interactions with ADHD symptoms, although our results demonstrated that any type of atopic disease was associated with the severity of ADHD symptoms.We also observed low to considerable heterogeneity in the subgroup analysis of different types of atopic diseases. Asthma had a significant association only with the severity of total ADHD symptoms, not with inattention. Considering that previous studies have reported that children with asthma had an increased risk of developing severe ADHD symptoms30) . One of the more commonly accepted hypotheses is that atopic diseases induce inflammation, including Th1, Th2, and Th17 immune responses, which lead to the downstream hypersecretion of IgE33 . These inflammatory factors may affect the neuroactivity of the prefrontal cortex (PFC) and anterior cingulate cortex (ACC) that have been reported to be strongly associated with ADHD pathology36. This process affects the maturation of the PFC and ACC, which usually occurs in early life when the brain is undergoing profound changes and is important for the development of cognitive function37. In addition to the direct effect, cytokines may indirectly affect these brain areas by disturbing the hypothalamic–pituitary–adrenal axis (HPA) axis38, altering the central metabolism of neurotransmitters including norepinephrine and dopamine39.Although the causal relationship and mechanism underlying the association between atopic diseases and ADHD remain unclear, many studies have provided several hypotheses41. An unsatisfactory parent–child relationship characterized by overprotection, anxiety, low support, and poor sleep quality can lead to a decline in psychosocial and cognitive performance due to tiredness; emotional problems were reported to be associated with negative outcomes in a previous study42. Although the relationship between these effects and brain development in early life has not yet been well studied, stress in early childhood is believed to affect the balance between neurotransmitters and neuroendocrine systems, including norepinephrine, dopamine, and the HPA axis43. This eventually resulted in increased vulnerability to psychological diseases, such as ADHD, because of altered neuropsychologic pathways leading to unsuccessful brain development and maturation44.In addition to neuroimmunological pathways, psychological mechanisms should also be considered. Atopic diseases cause psychological stress in not only patients but also their main caregivers since early childhood when the onset of atopic diseases occurs45. Although these mechanisms were obscure, studies have found that stress may induce deficits in skin barriers, and a similar inflammatory cascade with atopy-associated immune responses was observed in patients with atopic diseases; some patients exhibited an increased tendency to exhibit Th1 responses46. These findings indicate that stress can exacerbate atopic diseases. ADHD was reported to be related to psychosocial stress because poor family support, school performance, and peer relationships can all be the sources of stress47. Therefore, ADHD-related stress can exacerbate atopic diseases. Recently, genetic factors and prenatal stress have been indicated as common risk factors for atopic diseases and ADHD. Although few studies have evaluated genetic susceptibility and other associated interaction factors leading to epigenetic reactions49, a twin study supported the hypothesis that common genetic factors between atopy and ADHD exist50. Moreover, studies reporting a relationship between maternal stress and symptoms of atopic diseases and ADHD have indicated that dysregulation of the HPA axis results in delayed brain development and a shift in Th1/Th2 balance, resulting in an atopic disease–prone immune profile52.An increased risk of the development and progression of atopic diseases was observed in children with ADHD because atopic diseases are attributed to stress53, and this association existed in subthreshold ADHD group in our result as well.Previous systematic reviews and meta-analyses had discussed about increased risk of diagnosed ADHD in atopic group; however, the evidence of whether atopic diseases associated with increased severity of ADHD symptoms in participants with subthreshold ADHD remained unclear. This association could be observed in the meta-analysis of ADHD-related behavioral problems in atopic children without previously diagnosed ADHD in the present study, provided an evidence that atopic diseases are associated with “spectrum of ADHD symptom severity”, the term which we quoted following a previous study to explain how atopic diseases associated with ADHD symptoms54. However, potential systematic errors may still occur. Forth, differences in exclusion criteria, including the exclusion of patients with ADHD and other neuropsychiatric disorders and development problems, caused significant heterogeneity in our study. Finally, some studies included children with multiple atopic diseases, which may be potential confounding factors in our study.This study has several limitations that should be considered. First, although more than 25,000 participants were included, the outcome of hyperactivity was examined in only 1700 participants, which may reduce the strength of evidence. Second, Atopic diseases were diagnosed based on physicians’ decisions or by using some questionnaires in the included studies; these might have caused heterogeneity. Third, we chose to combine scores of hyperactivity/impulsivity and inattention as the primary outcome, and this method has been used in several studiesTo minimize the effect of those limitations, we conducted sensitivity analysis and subgroup analysis to assess the effects of potential confounding factors, namely different types of atopic diseases and exclusion criteria for previous diagnosed ADHD and other neuropsychiatric disorders; this considerably reduced heterogeneity in sensitivity analysis and most subgroup analyses. Therefore, standardized and more strictly defined exclusion and inclusion criteria should be included in future studies. Moreover, additional studies investigating the relationship between different types of atopic diseases and ADHD symptoms should be conducted because we could not conduct a complete subgroup analysis of different atopic diseases owing to the limited number of studies.Our study results indicated that atopic diseases not only increased the odds of ADHD but also were associated with more severe core symptoms of ADHD. We observed increased severity of ADHD-related behavioral symptoms in children with atopic diseases without previously diagnosed ADHD, indicating that atopic diseases may also associated with spectrum of ADHD symptom severity in participants with subthreshold ADHD, which was never been investigated in previous researches. According to the results of this study, while treating children with ADHD, clinicians should consider the possibility of comorbid atopic diseases. On the other hand, the comorbidity of ADHD should be considered when treating children with atopic diseases. Moreover, clinicians should be aware of the increased ADHD-related behavioral symptoms in children with atopic diseases. Additional studies including more strictly defined criteria, studies investigating the mechanism underlying this association, and randomized controlled trials of related therapeutic strategies should be conducted.55. Related checklists are provided in the Supplementary Materials. Two researchers searched for and pooled observational studies examining atopic diseases and ADHD symptom severity to examine the association between them.This systematic review and meta-analysis was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelinesTo reduce selection bias, we defined eligibility criteria before the inclusion of studies. We included the following studies: (1) observational studies investigating the association between one of the three major types of atopic diseases  and the severity of ADHD symptoms, irrespective of whether they included patients with ADHD in the exposed group; (2) studies recruiting children and adolescents; (3) studies including any of the three atopic diseases as the exposure variable and ADHD symptom severity  as the outcome variable; (4) studies investigating the severity of ADHD symptoms in patients with at least one type of atopic disease by using any type of assessment method; (5) studies including a matched or an unmatched unexposed group for comparison with the exposed group; and (6) cross-sectional, case–control, or cohort studies. The following studies were excluded: (1) studies that did not provide adequate information regarding the relationship between atopic diseases and ADHD symptom severity, including the crude data of exposed cases and the outcome of the association between at least one type of atopic disease and the severity of hyperactivity/impulsivity, inattention, and/or total ADHD symptoms (hyperactivity/impulsivity + inattention) determined on the basis of the scores of a behavior rating scale; (2) studies including only outcomes measured after interventions or those not including a separate outcome of total ADHD symptoms, hyperactivity/impulsivity, and inattention; (3) studies using the same database in different published articles , Embase, and Psycinfo for studies published up to December 18, 2021. The following search terms were combined and adjusted to fulfill the demand of the database.A three-step screening strategy was adopted. In the title screening step, studies that met the inclusion criteria and were not letters or replies were included. In the abstract screening step, studies that were original research and included relevant unexposed groups were included. In the full-text screening step, studies for which full texts and data were available and those that did not meet the exclusion criteria were finally included. A third author resolved any disagreement between the two authors in terms of the eligibility and inclusion of studies through discussion.By using a customized data form, the first author extracted the following information from eligible studies: study title, study design, study characteristics , and assessment methods and criteria for diagnosing atopic diseases and ADHD. Subsequently, the second author reviewed the extracted data. Any disagreement regarding data extraction was resolved by the third author through discussion. If required, data were calculated from the available data of included studies . The main outcome variables were the scores of the behavior rating scales used to evaluate hyperactivity/impulsivity and inattention in the atopic disease and unexposed groups. Although the included studies used different outcome scales, they were all based on the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV) criteria. In addition, to minimize the potential confounding effects, we analyzed standardized data. Because of the limited availability of studies, we combined the three types of atopic diseases in the meta-analysis and conducted a subgroup analysis. The authors individually recorded abstracted data in Microsoft Excel.56. The random effects model with the Hartung–Knapp–Sidik–Jonkman method was used to determine the estimated variance for pooling effect sizes in our meta-analysis, with standardized mean difference as effect size. The I2 value was calculated to assess statistical heterogeneity, and this value represents the total variance of pooled data explained by the heterogeneity. According to the Cochrane handbook for systematic reviews of interventions, the I2 values of 0–40%, 30–60%, 50–90%, and 75–100% indicated low, moderate, substantial, and considerable heterogeneity57. We used a funnel plot to assess the potential publication bias.The relationship between one of the three main types of atopic diseases with the severity of hyperactivity/impulsivity, inattention, or total ADHD symptoms was independently investigated. To exclude confounding factors, any study including outcomes measured after interventions were excluded. For each study, the relative weight of outcome data to the whole data set was calculated using the generic inverse variance method along with the 95% confidence interval (95% CI) by using Rstudio software, Version 1.2.5042 Rstudio57.For the outcome “severity of total ADHD symptoms,” if a study reported ADHD symptoms in “severity of hyperactivity/impulsivity” and “severity of inattention” separately, we calculated weighted mean and pooled standard deviation based on relevant formula before meta-analysisFor studies including a subgroup analysis, we pooled effect sizes two times. First, we combined subgroups within studies to pool effect sizes and calculated 95% CIs and standard errors. Subsequently, we included the calculated standard errors into our meta-analysis and pooled effect sizes a second time.To assess the strength of the association between atopic diseases and ADHD in different scenarios, we performed a sensitivity analysis by investigating patients with strictly defined exclusion criteria for previous ADHD diagnosis. We included studies researching both exposed and unexposed groups with exclusion criteria for previous ADHD diagnosis for a second meta-analysis, which was mentioned as meta-analysis of ADHD-related behavioral problems in atopic children without previous ADHD diagnosis. Detailed information on the risk of bias assessment could be found in Table We conducted subgroup analysis of different type of atopic diseases in three outcomes: total ADHD symptoms, hyperactivity, and inattention, using a random effect model. If there were less than 3 studies included in each subgroup, we would not perform analysis.Supplementary Information.


BackgroundAttention deficit hyperactivity disorder (ADHD) is a neuro-developmental ailment diagnosed with inattention and hyperactivity-impulsivity. It is one of the most prevalent neurodevelopmental disorders and has complex aetiology, both genetic and environmental. There is a perceived decrease in skill acquirement, leading to insufficient income and job opportunities as adults, which drives them towards poor physical and mental outcomes compared to their contemporaries without ADHD. The impact of heavy metals on ADHD is a topic of interest but is much less studied. Copper has been implicated as a pro-oxidant and in the metal accelerated production of free radicals that may affect oxidative stress. Zinc also serves as an antioxidant, and changes in its concentrations may impact the homeostasis of oxidative stress.MethodsTwenty-four children diagnosed with ADHD were taken as cases and matched with 24 healthy controls. Hair and urine samples were collected from all the study participants. The samples were collected in sterile containers according to established protocols. Acid digestion of hair samples was done using 65% nitric acid and 30% hydrogen peroxide. Urine samples were extracted by a solution of 0.1% Triton-X-100 and 1% ultrapure nitric acid. The levels of zinc and copper were determined in both samples by inductively coupled plasma optical emission spectrometry (ICP-OES). The copper/zinc ratio (Cu/Zn) was calculated from these values. Mann Whitney U Test and receiver operating characteristic (ROC) analysis were done to estimate statistical significance.Results-1 of hair) was significantly lower than the control group (527.05 µg g-1 of hair). However, hair copper levels were increased significantly in the ADHD children (14.01 µg g-1 of hair) compared to the controls (7.43 µg g-1 of hair). Urine zinc levels were significantly lower in cases than controls (525.7 µg g-1 of spot urine creatinine vs 1374.09 µg g-1 of spot urine creatinine). However, copper levels in urine were higher in the ADHD children (17.01 µg g-1 of spot urine creatinine compared to 7.26 µg g-1 of spot urine creatinine in controls). Both hair and urine copper to zinc ratio was significantly higher in the ADHD group. On ROC analysis, the hair Cu/Zn ratio had an area under the curve (AUC) of 0.920 , and the urine Cu/Zn ratio had an AUC of 0.967 . When used as a diagnostic classifier for ADHD based on the cut-off value determined by ROC, both hair and urine Cu/Zn ratio had high sensitivity and specificity.The median age of the study population was eight years. Overall, 34 male and 14 female subjects participated. There was no significant difference in height, weight and BMI between the cases and controls. Hair zinc levels in the ADHD group (198.49 µg gConclusionLow zinc levels in the urine and hair of children and higher levels of copper may impact the aetiology of ADHD in these children. At an early stage, the Cu/Zn ratio in both hair and urine samples may be used as a precise biomarker to identify and monitor such children. According to the World Health Organization (WHO), neurodevelopmental diseases are today's most significant public health challenges accounting for a significant component of this category, impacting morbidity, mortality, disability, and quality of life . AccordiSeveral studies highlight the role of oxidative stress as an important participating agent in the aetiology of ADHD. An increase in the production of free radicals and oxidative stress may be affected by and affect the homeostasis of certain trace elements . Trace eCopper is required for the catalytic activity of many enzymes involved in the neurophysiology of this disorder, including Cu/Zn superoxide dismutase (antioxidant protection of cells), tyrosinase and dopamine-hydroxylase , monoamine oxidase (catecholamine degradation), and ceruloplasmin (iron homeostasis in the brain). In several studies, copper has been implicated as a pro-oxidant and in the metal accelerated production of free radicals. As a result, high Cu levels and copper-mediated neurotoxicity may be linked to the creation of a copper-dopamine complex, which is followed by dopamine oxidation. This condition has been linked to physical and mental exhaustion, depression, and other mental illnesses like schizophrenia, learning impairments, ADHD, and general behavioural issues ,10.Zinc is a critical component in the metabolism of neurotransmitters, prostaglandins, and melatonin and has an indirect effect on dopamine metabolism. It is required to form a variety of metalloenzymes and metal-protein complexes, most notably in the central nervous system, and hence contributes to the brain's structure and function. Additionally, the dopamine transport system contains a zinc-binding site required for the brain's transport systems . Zinc alHair trace elements may have a relationship with these elements' storage in the body. Hair analysis has been used to determine exposure to several environmentally hazardous substances based on this concept . Urine aThe aim of the study was to emphasize the levels of copper and zinc in hair and urine of ADHD children. We hypothesized that there is an existent difference in these levels when compared to healthy children and thus the study was conducted.Study designThis was a case control study carried out at All India Institute of Medical Sciences, Bhubaneswar , between August 2019 and February 2021. Ethical clearance for the study was duly permitted by the Institutional Ethical Committee of All India Institute of Medical Sciences Bhubaneswar vide letter no. IEC/AIIMS BBSR/PG Thesis/2019-20/01, dated August 05, 2019. The requisite Written Informed Consent and Written Informed Assent were taken from the subjects participating in the study following the Indian Council of Medical Research, National Ethical Guidelines for Biomedical Research involving Children. All participants and parents were provided with a written Participants' Information Sheet, that contained the details of the study hypothesis, design, sample collection methodology and a clear statement about the confidentiality and anonymity of their data. The children were aged between three and 16 years and were diagnosed by a medical psychiatrist following DSM-5 Criteria for ADHD. Age- and sex-matched controls were recruited. Any children with intellectual disability, any neurological or behavioural disorder, or suffering from any chronic illness were duly excluded. Sample size was calculated, on OpenEpi Sample Size Calculator for Case Control Study, by the method for rates and proportions as prescribed by Fleiss with continuity correction, from existing data, to be 48 . Sample collection and processingConvenience sampling was done to get the samples for the study. Those subjects who were enrolled as ADHD subjects in the hospital registers were contacted and samples were collected.0C for extraction. Hair samples were digested in a solution of 6mL of 65% nitric acid and 2mL of 30% hydrogen peroxide by heating at 1800C for 15 minutes, followed by cooling at room temperature for 15 minutes [0C. The extraction of heavy metals from urine samples was done using a solution containing 0.1% (V/V) Triton-X-100  and 1% ultrapure concentrated nitric acid [Hair samples were collected from the nape of the children by alcohol-washed stainless steel scissors, approximately amounting to 500mg . These w minutes . Urine s-1 of hair) and urine heavy metals as microgram per gram of spot urine creatinine (µg g-1 of spot urine creatinine).The measurement of heavy metal levels was done by Avio™ 200 dual-view instrument equipped with Syngistix™ software for inductively coupled plasma optical emission spectrometry (ICP-OES) . Both axial and radial views were used depending on the concentration of the heavy metal to be analysed. Calibration standards and blanks were used for analysis for samples for each heavy metal. Urine creatinine was measured from the collected sample by Jaffe's Method in a Beckman Coulter  AU480 autoanalyser. Hair heavy metal levels were expressed as microgram per gram of hair . Independent sample's t-test and Mann Whitney U test were used to determine the difference between the groups. Receiver operating characteristic (ROC) and area under the curve (AUC) analysis were done to check for diagnostic and predictive values of the heavy metals. All data analysis was done using Statistical Package for Social Sciences (SPSS) version 19.0  and JASP v0.13. A p-value of <0.05 was considered statistically significant.The data was found not to have a normal distribution, hence non-parametric methods were used to express the statistical analysis. The median age of the study population was eight years of age. Most of the subjects were male (72.92%) and had a median BMI of 20.46. There was no significant difference in the children's weight, height, and BMI in the two groups. The demographic data have been summarised in Table -1 and 527.05 µg g-1. However, copper levels were significantly increased in the ADHD group compared to . Urinary concentration of zinc was also found to be significantly lower in the ADHD group. Median urine zinc levels were 848.9 µg g-1 of spot urine creatinine lesser in the ADHD cases as compared to the healthy controls. However, urine copper levels were significantly higher in ADHD. The data has been summarised in Table The hair zinc levels were lower in ADHD cases as compared to the controls. The median level in cases was 198.49 µg gThe copper to zinc ratio in hair significantly differed between the groups p<0.001), as mentioned in Figure , as mentThe classification accuracy of heavy metals was determined by ROC analysis. Cu/Zn ratio in hair samples had a very high classification accuracy  as compared to zinc  and copper . Similarly, in the urine sample also Cu/Zn Ratio had a very high classification accuracy . Urine zinc was insignificant in classification  whereas copper had a comparatively lower efficacy  as compared to Cu/Zn Ratio. The ROC curves have been depicted in Figure From the ROC analysis with the aid of the Youden Index, cut-off values were estimated for the Cu/Zn ratio for both hair and urine samples. For hair Cu/Zn ratio the cut-off was determined to be 0.0274 . A value of 0.0164 was determined as the cut-off for urine Cu/Zn ratio . The classification was used to determine the predictive probability of the Cu/Zn ratio for ADHD cases. Hair Cu/Zn Ratio had a Positive Predictive Value of 92% and Negative Predictive Value of 95.65%. Similarly, the urine Cu/Zn Ratio had a Positive Predictive Value of 95.45% and a Negative Predictive Value of 88.46%. Our study aimed at studying the levels of zinc and copper in children diagnosed with ADHD and determining if there was any significant difference compared to the healthy children who have not been diagnosed with ADHD. We found out that there exists a significant difference in the levels of zinc as well as copper in the hair and urine samples of the studied children.In studies, copper deposition has been linked to localised brain oedema, neurodegeneration, nerve fibre demyelination, and glial cell proliferation. Necrosis, cystic transformation, interstitial focus, and cavernous vacuolation are all possible risk factors for the development of these illnesses and may contribute to the origin of ADHD in children . StudiesZinc is required for brain and neural tissue formation and growth since it transforms dietary pyridoxine to its active form . Additionally, pyridoxine is required for the conversion of tryptophan to 5-hydroxytryptamine, which is associated with ADHD. If prior literature is to be believed, the evidence for zinc and ADHD is rather mixed. Skalny et al. and Tippairote et al. examined hair zinc levels and discovered that they were significantly higher in the ADHD group ,24. HoweFrom this study, it can be considered that Cu/Zn ratio is an important parameter in assessing a child with ADHD. A similar manifestation was determined by Viktorinova et al. in a population of Russian children, in which they had found significantly higher levels of Cu/Zn levels in serum . A deranIt has been suggested that disruption of the dopaminergic, serotonergic, and noradrenergic neurotransmitter systems may contribute to the symptoms of a number of mental and neurological illnesses . Copper Zinc deficiency has been linked to DNA damage, which may be caused by a combination of oxidative stress, impaired antioxidant defences, and defective DNA repair . Zinc deThere were several limitations associated with our study. The study was focused on the eastern Indian population; however, due to the ongoing coronavirus disease 2019 (COVID-19) pandemic, the subjects recruited got strictly confined to only one state and clustered around the city of the tertiary care hospital. Multiple environmental factors are potent risks for the aetiology of ADHD in children, and studying each confounder is a cumbersome process. Hence, this study concentrated only on the two heavy metals. Detailed antenatal, postnatal, and other family and socioeconomic histories were not obtained due to the constraints of time and contact, which might affect the diagnosis of ADHD in the children.Results have suggested a lower zinc level in hair and urine of ADHD children as compared to the healthy control group. However, the median copper levels are higher in both samples of ADHD children. Cu/Zn ratio for hair and urine is significantly higher in the ADHD group. Cu/Zn ratio can be effectively used as an effective predictor of ADHD diagnosis in children and thus can potentially be used as a tool for assessing ADHD children and monitoring their progression of management. Also effective use of zinc supplementation and copper chelation can be further studied as treatment modalities for ADHD.


Are there associations among attention-deficit/hyperactivity disorder (ADHD) pharmacotherapy, externalizing symptoms, and childhood suicidality ?In this cohort study of 11 878 children aged 9 to 10 years at baseline, externalizing symptoms showed expected associations with suicidality. ADHD pharmacotherapy was associated with less suicidality in children with externalizing symptoms, specifically among those with substantial symptoms burden, both at the baseline and 1-year follow-up assessment.These findings suggest that in children with substantial externalizing symptoms, ADHD medication use may be associated with less suicidality, in addition to its known clinical benefits in treating externalizing symptoms. This cohort study investigates the associations of attention-deficit/hyperactivity disorder (ADHD) medication with externalizing symptoms and childhood suicidality. Childhood suicidality  rates are increasing, and attention-deficit/hyperactivity disorder (ADHD) and externalizing symptoms are common risk factors associated with suicidality. More data are needed to describe associations of ADHD pharmacotherapy with childhood suicidality.To investigate the associations of ADHD pharmacotherapy with externalizing symptoms and childhood suicidality.In this cohort study, cross-sectional and 1-year-longitudinal associations were examined using data (collected during 2016-2019) from the Adolescent Brain Cognitive Development (ABCD) Study, a large, diverse US sample of children aged 9 to 11 years. Data analysis was performed from November to December 2020.Main and interaction associations of externalizing symptoms  and ADHD medication treatment  at baseline assessment.Child-reported suicidality . Covariates were age, sex, race/ethnicity, parents’ education, marital status, and concomitant child psychiatric pharmacotherapy (antidepressants and antipsychotics).P < .001), as was ADHD medication treatment . ADHD medication use was associated with less suicidality in children with more externalizing symptoms , such that for children who were not receiving ADHD medications, there was an association between more externalizing symptoms and suicidality ; however, for children who were receiving ADHD medication, there was no such association . The association with medication remained even when covarying for multiple confounders, including risk and protective factors for suicidality in ABCD, and was replicated in 1-year longitudinal follow-up. Sensitivity analyses matching participants with high numbers of externalizing symptoms taking and not taking ADHD medication treatment confirmed its association with less suicidality.Among 11 878 children at baseline assessment , 1006 (8.5%) were treated with ADHD medication and 1040 (8.8%) reported past or current suicidality. Externalizing symptoms  were associated with suicidality (for a change of 1 SD in symptoms, odds ratio [OR], 1.34; 95% CI, 1.26-1.42; These findings suggest that ADHD medication treatment is associated with less suicidality in children with substantial externalizing symptoms and may be used to inform childhood suicide prevention strategies. Large-scale population-based studies27 from Sweden, Taiwan, and the US indicated that psychostimulants, particularly methylphenidate, may be associated with less suicidal behavior in patients with ADHD. However, these studies relied on national registries or insurance claims with limited granularity in clinical phenotypes, including symptom severity and other confounders that are associated with medication treatment and suicidality, such as socioeconomic status and home and school environment.27 Thus, more data are needed on the potential relevance of ADHD medications use in children, which is increasing globally,29 to suicide prevention.Recent studies30 Our hypotheses were that externalizing symptoms and ADHD medications would be independently associated with higher likelihood of reporting suicidality at baseline and at 1-year follow-up. Furthermore, because ADHD medications are established as effective for treating externalizing (ADHD-related) symptoms, we hypothesized an interaction, such that ADHD pharmacotherapy would moderate the association between externalizing symptoms and risk of suicidality both at baseline and 1-year follow-up assessments .Our main objective was to determine the association of ADHD pharmacotherapy with child suicidal ideation or attempts  in a large, diverse longitudinal cohort from the Adolescent Brain Cognitive Development (ABCD) Study (11 878 children recruited at ages 9-10 years).31 Participants were enrolled at 21 sites, with a catchment area encompassing more than 20% of the entire US population in this age group. We included data from the ABCD Study data release 3.0. All included measures in the current analyses were collected at baseline assessment, with 2 variables collected at 1-year follow-up . All participants gave assent. Parents and/or caregivers provided written informed consent. The ABCD Study protocol was approved by the University of California, San Diego, institutional review board and was exempted from a full review by University of Pennsylvania institutional review board. This study follows the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline for reporting observational studies.The ABCD Study sample includes a cohort of 11 878 children aged 9 to 10 years at baseline, recruited through school systems.Diagnostic and Statistical Manual of Mental Disorders (Fifth Edition) (KSADS-5)32 and included ADHD hyperactivity symptoms, ODD symptoms, and CD symptoms (eTable 1 in the 33 Caregivers were asked to bring the youth’s prescription medication (used in the last 2 weeks) to the in-person assessment.30 We considered any of the following classes of medication as ADHD medications: methylphenidate derivatives, amphetamine derivatives, α-2-agonists, and atomoxetine  was based on the parent and/or caregiver–validated and computerized Kiddie-Structured Assessment for Affective Disorders and Schizophrenia for 34 Items relating to self-injurious behavior without suicidal intent were not included. Because the proportion of suicidal attempts was low, and to mitigate risk of type I error caused by multiple testing, we combined suicidal ideation and attempt, consistent with previous analyses.34 Thus, suicidal outcomes in the baseline assessment (past or current) were collapsed into a single binary measure termed suicidality. Prevalence of suicidal ideation and attempts is detailed in eTable 3 in the 37 (including in the ABCD Study) showed poor youth-caregiver agreement on suicidality, we focused on child-report in the main analyses.The KSADS-5 assessed suicidal ideation and attempt (past or current).other), Hispanic ethnicity, parent education (mean duration), and marital status . To address confounding associations with other psychiatric medications, we included a binary variable indicating treatment with any antidepressant (AD) or antipsychotic (AP) medication  using KSADS-5–derived diagnoses .The analytical plan and hypotheses were preregistered on Open Science Framework on October 2020, and analyses were conducted in November to December 2020 following the ABCD Study data release 3.0, which was the first release of 1-year suicidality measures for the entire sample. We used the SPSS statistical package version 26.0 (IBM) and R statistical software version 3.6.1  for our data analyses. Mean (SD) and frequency (percentage) were reported for descriptive purposes. Univariable comparisons were made using z score of sum) and ADHD medication (binary variable). To allow easier interpretation of main associations, we first tested the main associations of externalizing symptoms and ADHD medication without the interaction term. ADHD medication use was regressed out of the symptom count to address collinearity . We also ran a model with externalizing symptoms as the independent variable separately in children taking or not taking ADHD medication. Covariates were age, sex, race/ethnicity, parents’ education and marital status, and receipt of other psychiatric medications (AD or AP). The longitudinal model also included time between assessments (in months) and baseline suicidality. We used 2-tailed tests for all cross-sectional models and 1-tailed tests in confirmatory longitudinal models and case-matched sensitivity analyses.To address the study’s question of the moderating association of ADHD medications on the association between externalizing symptoms and suicidality (referred to later as main models), we performed binary logistic regression where the dependent variable was baseline child-reported suicidality  or current 1-year follow-up suicidality , and the independent variable of interest was the interaction term of externalizing symptoms  and addressing potential associations with family relatedness. Finally, to better estimate clinical presentations where ADHD medication is especially associated with less suicidality, we matched participants receiving ADHD medications with high externalizing symptom loads at 3 thresholds  with controls not receiving ADHD medications and compared suicidality rates.We conducted several sensitivity analyses , 1006 (8.5%) were treated with ADHD medication and 1040 (8.8%) reported past or current suicidality . More boP < .001) and ADHD medication  with suicidality, covarying for demographic characteristics and use of other psychiatric medications (AD or AP). These main associations were qualified by a significant interaction between externalizing symptoms and ADHD medication  (P < .001). However, for children who were receiving ADHD medication, there was no association .Multivariable logistic regression models revealed main associations of externalizing symptoms  , such thP = .64), and was observed similarly across sexes (eTable 6 in the P = .84).The association of ADHD medication was similar in both the population of 11 161 children not receiving additional psychiatric medication , panel AP = .03) (P < .001). However, for children who were receiving ADHD medication, there was no such association .We then tested whether the ADHD medication treatment at baseline would moderate the association of externalizing symptoms (at baseline) with suicidality reported at 1-year follow-up assessment. Longitudinal 1-year follow-up data were available for 11 014 participants, who had similar baseline ADHD medication use and suicidality and slightly fewer externalizing symptoms compared with the 790 participants (7.1%) who were lost to follow-up (eTable 7 in the P = .03) , with thP = .04) (P = .06)  . HoweverP = .06) .P = .04).Because children receiving ADHD medication differed from those not receiving ADHD medication  controlling for baseline history of depression or anxiety (eTable 9 in the P < .001) (eTable 15 in the We tested the associations of methylphenidate medications (541 participants), amphetamine salts (363 participants), α-2-agonists (243 participants), and atomoxetine (47 participants) in separate models. All classes showed negative B values for the interaction term of externalizing symptoms and medication, but only methylphenidate showed a statistically significant interaction . The observed difference in suicidality between children receiving and not receiving ADHD medication was greatest among children with the most externalizing symptoms. Findings were replicated in a 1-year longitudinal analysis and were robust to multiple confounders. As the reported prevalence of ADHD medication use and suicidality are consistent with recent literature,8 First, childhood suicidality has rapidly increased over the last decade.7 Second, the results offer a straightforward, actionable target to optimize prevention plans and intervention strategies in elementary school–age children with externalizing problems. Early diagnosis and treatment with ADHD medication, particularly among children with severe externalizing symptoms, may serve not only to improve learning and behavior problems,40 but also to decrease suicidality risk. Third, childhood suicidality is associated with adult psychiatric morbidity and mortality and, thus, may represent an early marker for lifelong vulnerability to poor mental health.42 Thus, better and more thorough screening of school-age children for externalizing psychopathology, which is a modifiable developmental risk factor, has strong potential to prevent and mitigate serious psychopathology later in the life span.Our findings address a knowledge gap on childhood suicidality that is critical for several reasons.43 Observing externalizing disorders along a continuum holds promise for elucidating more precise associations with other forms of psychopathology, including suicidality. Notably, interpretation of the dimensional approach in the context of medication treatment should be done cautiously, because, to our knowledge, there are no clinical guidelines for treatment of externalizing symptoms alone with pharmacotherapy, but rather, these medications are used to treat children with an ADHD diagnosis. The fact that the associations were similar among boys and girls is also noteworthy, especially in light of data showing that girls are less likely to receive ADHD treatment compared with boys.44This study supports a dimensional approach to ADHD.45 confounding their associations by indication. Our findings for methylphenidate are in line with prior evidence supporting the effectiveness of methylphenidate in reducing impulsivity,46 which contributes to the suicide risk–bearing association of ADHD in youth.49 Further studies are needed to better understand the mechanisms underlying the potential protective association of methylphenidate vs other medications with youth suicidality.An exploratory analysis of the associations of different classes of ADHD medication with suicidality found a significant association with methylphenidate, but not for atomoxetine, amphetamine salts, or α-2-agonists. Given the limited sample sizes of these other drug classes, it is possible that lack of statistical power underlies these findings, but smaller or different associations with these medications cannot be ruled out. In addition, amphetamines and α-2-agonists may be prescribed differently than methylphenidate ,50 thereby complementing the evidence derived from randomized, placebo-controlled trials, which themselves are not free from limitations.51 Thus, by leveraging the ABCD Study cohort, we bridge a critical knowledge gap regarding modifiable pediatric suicide risk factors in children. We suggest that the replication of our main finding in the longitudinal 1-year assessment and the matched sensitivity analyses support the applicability of findings to real-life clinical practice.This study has immediate clinical implications that should be viewed in the context of the observational nature of the study, which was not designed to test causal effects of treatment. Although the reference standard for establishing protective effects of medications are randomized, placebo-controlled trials, it is unlikely that psychotropic intervention trials for suicidality can be used in the general pediatric population, as rates of childhood suicidality are low (approximately 8% lifetime and <1.5% current in the ABCD Study). A major challenge is to draw causal inferences from observational data to better inform clinical decision-making,4 and more power to study modifiable risk factors. Despite these limitations, the size, representativeness, and systematic in-depth assessment of the ABCD Study cohort are unique strengths, allowing us to quantify the associations of ADHD pharmacotherapy with child suicidality, over and above the outcomes of established risk and protective factors and other potential confounders.Our findings should be interpreted considering several limitations. First, we cannot rule out the role of confounders (measured and unmeasured) that are associated with ADHD medication treatment and suicidality, such as other modalities of mental health interventions, which may have affected the results. Specifically, it is possible that higher-risk children were more likely to be prescribed medications, creating a confounding-by-indication problem. However, we included other psychiatric medication treatment (AD or AP) and multiple established risk and protective factors for suicidality that may somewhat mitigate this concern. Second, the association of medication in the longitudinal and matched analyses was tested using a 1-tailed test (in line with our preregistered hypothesis). Third, the participants lost to follow-up (7.1%) reported slightly more externalizing symptoms, which might have affected the longitudinal findings. Fourth, longitudinal models did not reflect the outcomes of ongoing ADHD medication treatment or of changes in externalizing symptoms over time, as both medication use and externalizing symptoms were assessed at baseline. Future analyses of the ABCD Study cohort and other longitudinal data sets are warranted to examine suicidality trajectories throughout adolescence, which heralds higher rates of suicidalityIn conclusion, this study found that ADHD medication was associated with less suicidality in children with high externalizing symptom burden, equally for both sexes. The findings reported here may provide immediate and practical implications to potentially reduce childhood suicidality. Given the high prevalence of externalizing disorders among children, including ADHD, ODD, and CD, it is critical that we optimize both psychoeducation and pharmacotherapy interventions to shift otherwise adverse developmental trajectories and improve functional and clinical outcomes.


Although Attention Deficit Hyperactivity Disorder (ADHD) has been related to an increased risk for behavioral addictions, the relationship between ADHD and Internet Gaming Disorder (IGD) is still debated. The aim of this study is to address this topic by exploring the prevalence of IGD in a consecutive sample of ADHD youth, compared to a normal control group, and by assessing selected psychopathological and cognitive features in ADHD patients with and without IGD. One hundred and eight patients with ADHD  and 147 normal controls (NC)  were included in the study and received structured measures for IGD. In the ADHD group, 44% of the sample were above the IGD cut-off, compared to 9.5% in the NC group. ADHD patients with IGD presented with greater severity and impairment, more severe ADHD symptomatology, more internalizing symptoms, particularly withdrawal/depression and socialization problems, and more prominence of addiction and evasion dimensions. A binary logistic regression showed that the degree of inattention presented a greater weight in determining IGD. These findings may be helpful for identifying, among ADHD patients, those at higher risk for developing a superimposed IGD. The increasing diffusion of Internet use in youth has determined, in the last decade, a high rate of potentially addictive behaviors, with negative implications on social and psychological functioning  and acadMore recently, a specific characterization led to the new category of Internet Gaming Disorder (IGD), included in the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), under Section 3 (“Emerging Measures and Models”) . SimilarThere are two conceptualizations of IGD, not necessarily alternative, either as a primary addictive disorder or an epiphenomenon of other psychiatric conditions. To date, available empirical evidence suggests that IGD is prevalently associated with various mental disorders, particularly with Depression, Anxiety, Obsessive-Compulsive Disorder, Social Anxiety Disorder, and Attention Deficit Hyperactive Disorder (ADHD) . HoweverRegarding ADHD, a neurodevelopmental disorder characterized by impulsivity, inattention, and hyperactivity, evidence from the literature suggests that this disorder may be a significant predictor of Substance Use Disorder  and behaThe aims of this study are the following: (a) To explore the prevalence of IGD in Italian drug-naïve youth with ADHD with respect to a normal control group); (b) to compare two reliable measures (IGDS9-SF and IAT) for the diagnosis of IGD in ADHD patients; and (c) to describe a possible psychopathological and cognitive profile of youth with ADHD and IGD, according to IGDS9-SF or IAT, compared to patients with ADHD without IGD.N = 96, 89%). The diagnoses were based on the DSM-4 diagnostic criteria, according to the historical information and a structured clinical interview, the Schedule for Affective Disorders and Schizophrenia for School-Age Children-Present and Lifetime Version (K-SADS-PL)  iThe Child Behavior Checklist for Ages 6–18 (CBCL) , a 118-iThe Conners’ Parent Rating Scale—Revised: Short Form  is a 27-Use, Abuse, Dependence to Internet (UADI)  is a valWechsler Intelligence Scale for Children—Fourth Edition (WISC-IV)  is a staSkewness and Kurtosis were calculated to verify the normality of the distribution of data. For all variables, the skew values were <2 and kurtosis values < 3. These values indicated a normal distribution for the current data .The power of the sample size was calculated using G*Power , and we settled on an ES of 0.40 and found a power > 0.90 for all analyses.2), depending on the types of variables considered ; Cohen’s ES was calculated for variables with significant differences between groups. Finally, the characteristics associated with ADHD and IGD were explored using binary logistic regression models, using the IAT or IGDS9-SF cut-off as dependent variable and variables that had significant differences in the means as independent variables. Tests to see if the data met the assumption of collinearity indicated that multicollinearity was not a concern: Tolerance values ranged between 0.43 and 0.79, and variance inflation factors were between 1.21 and 1.78. Statistical analyses were carried out using the SPSS 19.0 program for Windows. The level of significance adopted is 0.05 (p < 0.05).Statistical analyses were performed with parametric tests (univariate ANOVA test) and non-parametric tests . Similarly, according to the IGDS9-SF questionnaire, 48 subjects in the ADHD group (44.4%), compared to 14 (9.5%) in the normal control group , were above the cut-off.According to the IAT score, among the 108 ADHD patients assessed with the IAT test, 45 (42.1%) scored higher than 50, while in the control group, only 30 out 147 subjects (20.4%) presented a score ≥ 50 , hyperactivity , and ADHD index . They also presented higher scores on CBCL internalizing problems  and on the following CBCL syndrome subscales: Withdrawal/depression , socialization problems , attention problems , and rule-breaking behavior  [Patients with higher IAT scores showed greater inattention  . Of noteMeans and standard deviations are reported in p < 0.001, ES = 0.95), “addiction” subscale , “escape” subscale , “dissociation” subscale , and, to a lesser degree, on the “impact” subscale  [According to the UADI, ADHD patients with IAT higher than 50 scored higher on the “experimentation” subscale  .p = 0.001; ES = 0.65), greater clinical severity on the CGI-S , and higher scores in inattention , hyperactivity , and ADHD Index score . Furthermore, they scored higher in both CBCL internalizing  and externalizing  problems, as well in the following syndromic CBCL subscales: Withdrawal/depression , socialization problems , aggressive behavior , anxious/depressed , attention problems , thought problems , and rule-breaking behavior  . Means and standard deviations are reported in ADHD patients with an IGDS9-SF score above the cut-off presented greater functional impairment according to the C-GAS ( = 0.71) . No diffp < 0.001, ES = 0.94), “addiction” subscale , “evasion” subscale , and “dissociation” subscale , while the “impact” subscale  only approached clinical significance [A comparison of patients with IGDS9-SF scores above and below the cut-off according to the UADI subscales showed that ADHD patients with higher scores also presented higher scores in the UADI “experimentation” subscale , while no variables were associated with ADHD and IGDS9-SF conditions.Two binary logistic regression models were tested, using the IAT and the IGDS9-SF cut-offs as dependent variables and variables that had significant differences in the means (see results reported above) as independent variables. The degree of inattention  was the only variable associated with ADHD and IAT conditions , is a relatively new nosographic entity, frequently associated with other psychiatric disorders. ADHD has been considered a clinical disorder possibly associated with a higher risk of developing an IGD, but clinical features of ADHD patients with or without IGD are less explored. Our aim was to compare rates of IGD in a consecutive sample of referred drug-naïve ADHD patients and a normal control group, using structured measures. Furthermore, we aimed to compare ADHD patients with or without IGD, in order to explore putative clinical features in ADHD patients at higher risk for IGD. Consistently with other reports , our stuIn order to better clarify this association, we explored clinical and cognitive features of ADHD patients presenting with IAT or IGCS9-ST scores above the cut-offs. Overall, features of ADHD patients scoring above the cut-off at the IAT and IGDS9-SF are largely overlapping. Both present more severe ADHD, according to hyperactive/impulsive and inattentive components, based on Conners’ subscales and the CBCL “inattentive” subscale. This finding may further support the above-mentioned hypothesis that more severe core symptoms of ADHD increase the risk for IGD . Interestingly, ADHD patients with IGD also show higher CBCL internalizing problems, including anxiety/depression, withdrawal, and socialization problems. Of note, all the UADI dimensions are highly represented in patients with both higher IAT and IGDS9-SF scores. This finding suggests that ADHD patients more prone to escape from the real world and dissociate from reality, as well as those with addictive and sensation-seeking tendencies according to the UADI, may be at higher risk for developing IGD. However, the two profiles  may discriminate different phenotypes of ADHD plus IGD patients. More specifically, ADHD patients with CBCL internalizing problems may use Internet addiction as a way of escaping from daily difficulties deriving from low-esteem and social anxieties. Of note, no differences in the WISC-IV profile were found between ADHD with or without higher scores on the two measures, suggesting that, notwithstanding the lower working memory and processing speed indices, this cognitive profile of our ADHD patients fails to influence the proneness to IGD. Regarding the differences between patients with either IAT or IGDS9-SF scores above the cut-off, only in patients with a higher IGDS9-SF score were clinical severity and functional impairment higher, compared to patients below the cut-off. Furthermore, they presented both higher CBCL externalizing and externalizing problems, particularly aggression, rule-breaking, and thought problems. Of note, our data from the predictive analysis shows that inattention, but not hyperactivity and impulsivity, more closely predicts a higher IAT, but not IGDS9-SF, score. This result may suggest that the type of stimuli provided by electronic games  not only interferes with the attentional deficits of ADHD, but on the contrary, may fit with these attentional deficits. Similarities between the two groups with either IAT or IGDS9-SF scores above the cut-off suggest that patients with severe ADHD, namely with internalizing problems , and with higher UADI scores in all dimensions may be at higher risk of developing IGD. Discrepancies between the two groups may suggest that IGDS9-SF characterizes a more severe group of patients, with a “mixed”  presentation. Our previous study  showed tOur findings in ADHD patients are consistent with these previous data, supporting the notion that even in ADHD, IGD patients may represent a heterogeneous group. Patients presenting externalizing problems, with prominent aggression and rule-breaking behaviors, may represent a specific phenotype of ADHD plus IGD patients, with impulsivity and sensation-seeking, while the presence of only internalizing problems may discriminate isolated and fearful patients, with low self-esteem and poor social strategies. Further studies with larger samples may support this distinction, which may have relevant implications for implementing personalized therapeutic approaches. Our findings should be interpreted in light of several limitations, namely the small sample size and the self-report questionnaires describing the Internet and Gaming addiction, as well as the UADI dimensions. The paucity of females in the clinical sample did not allow for closer comprehension of possible gender specificities, which certainly deserve specific research. Finally, we did not explore the possible effect of pharmacological treatment with stimulants in the affecting rates, severity, and course of IGD ,36,37. T


Background: Adverse pregnancy outcomes (APOs) are associated with periodontal disease owing to the induction of a chronic systemic inflammatory response. Hence, knowledge of periodontal status during pregnancy is important in order to reduce the risk of APOs. The aim of this study was to compare the risk of APOs in women with and without periodontal disease to ascertain whether regular scaling performed prior to pregnancy improves the risk of APOs. Method: This case-control study enrolled1,386,887 pregnant women from the National Health Insurance Research Database who gave birth to their first child between 1 January 2004 and 31 December 2014. The study population included mothers who gave birth to low birth weight (LBW) and non-LBW newborns, totaling 86,958 and 1,299,929, respectively. Scaling and periodontal emergency treatment during and before pregnancy were assessed. Univariable and multivariable logistic regression analyses were performed to identify the associations between periodontal treatment and LBW risk. Results: Compared with the comparison cohort, the pregnant women who didnot have periodontal emergency treatment or scaling treatment during pregnancy exhibited a significantly increased risk of LBW than those who had treatment. Women who underwent scaling within the2 years before pregnancy or during pregnancy had a lower risk of delivering a LBW baby (odds ratio (OR), 0.93; 95% confidence interval (CI), 0.91–0.94). In the normal group, the mothers who had periodontal emergency treatment within the2 years before pregnancy or during pregnancy had a higher risk of delivering a LBW baby . In those who had scaling treatment, a lower risk of delivering a LBW baby was noted . Conclusion: The risk of LBW was significantly increased in women who underwent periodontal treatment, and our findings suggested that periodontal disease is an important risk factor for preterm LBW babies in an East Asian population. Periodontal disease is a serious infection of the gums that is responsible for a chronic inflammatory response in the body. It is mainly the result of infection and inflammation of the gums, which may lead to immunoinflammatory response activation . AccordiAlthough some studies have demonstrated an association between periodontal disease and APOs, studies of Asian populations are rare. The purpose of this study was to determine the association between periodontal disease and APOs in a large Taiwanese national sample, obtained from the Taiwan National Health Insurance Research Database (NHIRD), to ascertain whether regular scaling before pregnancy or during pregnancy reduces the risk of APOs.This retrospective case-control study was approved by the Institutional Review Board of Taichung Veterans General Hospital, Taiwan (IRB number CE19166A-2), and the study protocol was conducted in accordance with the Declaration of Helsinki. All available data were obtained from the Taiwan National Health Insurance Research Database (NHIRD), which collects information in a standardized format to fit researchers’ needs in different fields. Informed consent was not required, given that the claim database used in the present study contained only de-identified data. This study followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) reporting guideline.This study was conducted using data from the NHIRD, which is maintained by the National Health Insurance program in Taiwan, which has operated since 1 March 1995, and enrolls 99.9% of Taiwan’s population. The program was established by Taiwan’s Ministry of Health and Welfare (MOHW) and collects information in a complete and standardized format to fit researchers’ needs in different fields.n = 1,411,304); 24,417 women who had a maternal age of <18 years or a multiple pregnancy were excluded. The final study population included 1,386,887 women who gave birth to 86,958 and 1,299,929 LBW (birth weight < 2500 g) and non-LBW newborns, respectively. The non-LBW cohort was matched to the LBW cohort according to maternal age and year of birth at a ratio of 1 to 10. In total, 869,580 women who gave birth to non-LBW newborns comprised the comparison group. As periodontitis in the mother may affect birth weight, we attempted to confirm cases by identifying those who underwent scaling and periodontal emergency treatment during and before pregnancy.As shown in The covariates in our analysis included maternal age, income, urbanization of residence, model of delivery, maternal comorbidities and pregnancy-related complications. Diagnostic and procedural codes were classified according to the International Classification of Diseases, 9th revision, Clinical Modification (ICD-9-CM) coding system. Diabetes mellitus , hypertension (ICD-9-CM code 401-405), hyperlipidemia (ICD-9-CM code 272), gestational DM , gestational hypertension (ICD-9-CM code 642.3), pre-eclampsia , and placenta previa and abruptio placentae  were recorded. Hypertension, hyperlipidemia and DM were diagnosed before pregnancy.p = 0.05.The demographic information is presented as mean ± standard deviation for continuous variables and number (percent) for categorical variables, and the Chi-squared test for categorical variables was used to compare data between the LBW group and the non-LBW control group. Univariable and multivariable logistic regression analyses were performed to estimate the associations between periodontal treatments and the risk of LBW. The odds ratios (ORs) and 95% confidence intervals (95% CIs) of variables in the two groups were calculated by logistic regression and adjusted according to the number of medical visits within two years prior to the index date. The association of periodontal treatment with the risk of LBW in all mothers was calculated and stratified analysis by maternal age was performed. All data were analyzed using SAS 9.4 software . Statistical significance was set at A total of 956,538 pregnant women who were ≥18 years of age were identified in the present study; these women gave birth to 86,954 LBW and 869,540 normal weight babies . The demographic characteristics are reported by categories of maternal age and maternal comorbidities in p < 0.001) in income, urbanization of residence, model of delivery, maternal comorbidities, and pregnancy-related complications. Higher rates of cesarean section, diabetes mellitus, hypertension, and hyperlipidemia were observed in the LBW group.In contrast to the comparison cohort, the pregnant women who did not have periodontal emergency treatment or scaling treatment during pregnancy exhibited a significantly increased risk of LBW than those who had treatment. There were significant differences  from those who experienced a preterm birth (<37 weeks). As preterm birth and periodontal emergency treatment are important causes of LBW, we examined the risk of LBW in these groups. In the normal delivery group, the mothers who had periodontal emergency treatment within the2 years before pregnancy or during pregnancy had a higher risk of delivering a LBW baby . For those who had scaling, a lower risk of delivering a LBW baby was noted  .In our study, we observed an association between periodontal treatment and LBW. Pregnant women who did not have periodontal emergency treatment or scaling treatment during pregnancy exhibited a significantly increased risk of LBW than those who had treatment. These results indicated an association between the periodontal status of the mother and the risk of delivering a baby of LBW. Regular dental visits for scaling and oral hygiene education among pregnant woman were shown to be effective in reducing the rate of LBW, and consequently led to a better neonatal outcome . The treThere are several strengths of our study that are worthy of note. First, up to 99.99% of Taiwan’s population is enrolled under the National Health Insurance program . Thus, tHowever, there were some limitations of our study. First of all, we may have underestimated the prevalence of periodontitis in the pregnant women as we only collected data on those who attended for therapy; therefore, the data we gathered only represented patients who underwent treatment during or before their pregnancy. There may have been patients with periodontitis who did not attend for dental care. Second, the information was obtained from the Taiwan NHIRD; the results therefore may not apply to the entire Asian population, or to the whole world, as Taiwan is a developed country with a sound health insurance system. Third, subjects with current use or a history of tobacco and alcohol use were not excluded from this study. Smoking and alcohol intake are known risk factors for PLBW; however, the prevalence of smoking and alcohol-drinking is relatively low among Taiwanese pregnant woman.In conclusion, we conducted a retrospective case-control study using a population-based database of Taiwanese subjects, and determined that periodontal disease is an additional important risk factor for preterm LBW babies our findings suggested that regular dental visits for scaling treatment and oral hygiene education in pregnant woman reduced the rate of LBW, and consequently resulted in a better neonatal outcome.