N = 210 ADHD, N = 158 controls, age: 18–62 years) were assessed five (ADHD) or three (controls) times over two years. We describe cross-sectional and longitudinal group differences, and associations between symptom burden, and behavioral and event-related potential (ERP) components variables by latent growth curve models, including random slopes and intercepts. The ADHD group showed increased reaction time variability, increased commission and omission errors, and attenuated cueP3, CNV, N2d, and P3d amplitudes. We observed a decrease in self-reported symptoms in the ADHD group over the two years. The behavioral measures  did not change over time, whereas the cueP3, P3d, and N2d amplitude attenuated in both groups. There was no evidence for a robust association between symptom burden and behavioral or ERP measures. The changes in the ERP components with stable task performance, potentially indicate more efficient neuronal processing over the two years. Whether the lack of association between symptom burden and behavioral or ERP measures might be due to the low reliability of the ADHD assessment criteria, or the inappropriateness of the objective measures cannot be inferred.This study characterizes a large sample of adults with attention-deficit/hyperactivity disorder (ADHD) and healthy controls regarding their task performance and neurophysiology; cross-sectionally and longitudinally. Self-reported symptoms, behavioral measures, and event-related potentials from a classical cued Go/NoGo task were used to outline the symptom burden, executive function deficits and neurophysiological features, and the associations between these domains. The study participants ( Symptoms listed in the inattention domain are for example: “…is often easily distracted by extraneous stimuli or unrelated thoughts”, “…often does not seem to listen when spoken to directly or fails to follow instructions”. Symptoms listed in the hyperactivity and impulsivity domain are for example: “…often leaves seat in situations when remaining seated is expected”, “…often has difficulty waiting for his or her turn or interrupts on others.” The diagnosis by DSM-5 is based on the number of observable symptoms. The overall symptom burden is mostly quantified by the number of symptoms and their frequency of occurrence in daily life (Attention-deficit/hyperactivity disorder (ADHD) is a neurodevelopmental disorder diagnosed based on the clinical examination. The symptoms are currently assessed and quantified based on the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5) . Accordiily life .In the context of empirical research on ADHD, deficiencies in executive functions have been identified and proposed as potential phenotype of the disorder e.g., . These dCurrently, two models are discussed to describe the underlying neurocognitive functions in ADHD subjects: the maturational lag hypothesis and the trait liability hypothesis. The maturational lag hypothesis states that the observed neurocognitive deficits in children with ADHD derive mainly from neurophysiological immaturity. Accordingly, affected children will partially or fully remit from the impairments and catch up with their peers by adulthood , 2013. TLongitudinal studies describing symptom burden, cognition , neurophThe continuous performance test (CPT) is one of the most frequently used tests examining executive functions in ADHD subjects. A standard variant of CPTs are visually cued Go/NoGo tasks, using a two-stimulus paradigm in which the first stimulus serves as the cue and the second as the target. Some tasks require an active response by the participant. Such tests challenge the core characteristics of ADHD, which are sustained attention, inhibitory control, and stability of cognitive processing. On a behavioral level, the CPTs provide quantitative measures of executive function performance. Difficulties in sustained attention are reflected by increased omission errors , lack ofThe neurophysiological underpinnings during a CPT have been studied extensively in healthy adults using event-related potential (ERPs) components . In addiAmong the most studied ERP components in ADHD research are the positive peak around 300 ms after the cue stimulus (cueP3), the negativity approximately 100 ms before the second stimulus named contingent negative variation (CNV), the negative component around 200 ms (N2), and the positive component around 300 ms (P3) after the target stimulus.d = −0.56  ] for the P3 attenuation in ADHD subjects.The N2 and P3 components, identified after the target stimulus, are related to cognitive and inhibitory control . Early eMany researchers have used the difference waves between the Go and NoGo tasks . These difference waves indicate elegantly the involved neurophysiological and psychological processes. Two prominent components of these difference waves have been proposed: (a) the fronto-central N2d and (b) the fronto-central P3d . Recent r ∼ 0.2–0.45). The large intraindividual difference in perceived and reported symptom burden may explain the low correlation in cross-sectional studies. Association between the within-subject changes over several assessments can control for factors which are challenging to control for, such as perceived cognitive load or stress elicited by the task.ERP data give valuable insights into the deviant cognitive processing of participants diagnosed with ADHD. However, the linkage between the present diagnostic procedure, which relies on symptom report as the determining variable, and the objective measure from the neurophysiological assessment is challenging. A few cross-sectional studies report associations between symptom burden and ERP measures . These sShort-term test-retest reliability  of the ERP components shows moderate to high levels of reliability in terms of intraclass correlation coefficients . This inHowever, these effects of age are present in individuals with ADHD and healthy controls. Longitudinal studies are required to disentangle normal maturation from potential deviations in subjects with ADHD. Only a few studies investigated the developmental effects of ERPs in children and adolescents with and without ADHD using a longitudinal design . DoehnerNonetheless, the existence of adult ADHD emphasizes that the maturational lag hypothesis misses important aspect of the course of ADHD. We therefore used a sample of adults to examine alterations in symptom burden and neurophysiology over time independent of developmental changes.As discussed above, there is a lack of comprehensive investigations of the alteration in neurophysiological processing, task performance, and symptom burden in adults with ADHD over time. Longitudinal analyses adjust for the interindividual variance, which limits the meaningfulness of cross-sectional results. We aim to explore the ADHD symptom burden, executive function performance and neurophysiology among adults with ADHD and healthy controls over two years. This further allows us to investigate if potential changes in perceived symptom burden are associated with alterations in behavioral and/or ERP measures of executive functions.First, we re-evaluate the average group differences between participants with ADHD and controls at baseline in self-reported symptoms, behavioral measures, and ERP components of a cued Go/NoGo task. Second, we describe the change over time in the control group and potential deviations in the ADHD group of those variables (univariate models). Third, within the ADHD group, we investigate the association between the self-reported symptoms and the behavioral and ERP measures, considering person-specific variation (bivariate models).In this observational study with an adult sample, we do not anticipate alleviated symptoms or improved task performance over two years on a group level. Following the trait liability hypothesis, we hypothesize that potential changes in behavioral and neurophysiological measures on an individual level are not associated with the ADHD symptom burden.N = 368 participants , with an age range of 18–62 years. All participants were examined within a period of two years. During this period, the participants with ADHD were assessed five times separated at by a 6-month interval . The control participants were assessed three times with a 12-month interval . At each assessment, all participants completed a questionnaire to assess their symptom burden. EEG data was recorded during resting state (not reported here) and during a cued Go/NoGo task performance. In addition, we administered the Wiener Matrizen-Test 2 (WMT-2) to assess fluid intelligence at baseline and the end of the study . This large database contains information from more than 674 participants. In the current study, we analyzed the data of adults for whom complete baseline demographical information was available. This resulted in a total of he study .t(366) = −2.22, p = 0.027, d = 0.23 , IQ: t(352) = 3.78, p < 0.001, d = −0.41 }. All participants were medication free on the day of the assessment, albeit N = 74 (35%) of the ADHD group indicated use of methylphenidate for their daily routine at the first assessment .The demographic data of the sample at the first assessment, specified within the two groups, is shown in Certified psychiatrists or clinical psychologists confirmed the ADHD diagnosis according to the DSM-5 at the first assessment. The ADHD subtype is determined by the self-reported symptom burden.We addressed the differences in the sex-ratio and age at baseline between groups by including these variables in the structural equation models . The participants described here are part of the total cohort described in a previous publication .N = 15 at t3, N = 10 at t5). In the ADHD group 63 participants (30%) dropped out . In addition, EEG assessment was missing for some participants at individual time points .In the control group, 25 participants (16%) were lost to follow-up  and those who were lost to follow-ups (N = 25). Within the ADHD group, none of the categorical variables, sex, medication intake, and therapy, showed a difference between subjects who completed the trial (N = 148) and those who were lost to follow-ups (N = 62). For the continuous variables, we found evidence for group differences for age and the P3d component. ADHD subjects who were lost to follow-ups, were on average younger t(128) = −3.17, p = 0.002, Cohen’s d = −0.45 , had larger P3d amplitudes t(115) = 2.34, p = 0.021, Cohen’s d = 0.35  and shorter P3d latencies t(115) = −2.65, p = 0.009, Cohen’s d = −0.40  then ADHD subjects which completed the trials. There was no evidence for a difference in IQ, ADHD symptom burden, or any of the behavioral measures of the VCPT measures  between the ADHD subjects who completed the trial and those who dropped out. Details of the analysis for both groups are reported in the To check for potential bias of losing participants to follow-up assessments, we compared the baseline characteristics between subjects who completed the trial and those who dropped out. The categorical variables sex, medication intake and therapy were tested by Fisher’s exact test and the continuous variables were tested by Welch’s The data was collected between 2014 and 2018 at five private clinics in Switzerland. The study was approved by Zurich’s cantonal ethics committee (LeitEKZH_2013-0327/EKNZ_2014_160). All participants gave written informed consent and did not meet any of the exclusion criteria: another primary mental disorder, traumatic brain injury or loss of consciousness in the past, current or past drug abuse, pregnancy, epilepsy, and IQ < 80.Participants in both groups completed a questionnaire based on the DSM-5 to assess the ADHD-related symptom burden at each assessment time point. Each item of the symptom’s list was rated on a five-point Likert scale . The scores were summed separately for the two domains in the DSM-5 (ADHD inattention and ADHD hyperactivity), resulting in an ADHD inattention score and an ADHD hyperactivity score.We followed the DSM-5 to determine the ADHD subtype of the participants in the ADHD group. Items rated with often or very often were dummy coded with 1. If the sum was 5 or higher within one domain, the participant was categorized either as predominantly inattentive presentation (ADHD-inattention) or predominantly hyperactive presentation (ADHD-hyperactivity). A participant who exceeded the threshold of both domains (ADHD-inattention and ADHD-hyperactivity) as classified as a combined ADHD presentation (ADHD-combined).1) and in the last month prior to the final (t5) assessment.Each month, participants were asked to complete an online questionnaire about their general well-being, medication intake and attended therapies. The reported data were obtained in the months directly after the first assessment .Participants could select the medications and therapeutic activities from a list of potential interventions. The report of We applied an established visual continuous performance test (VCPT) , which iAs behavioral measures, we assessed the mean reaction time, reaction time variability (coefficient of variance = mean reaction time/standard deviation of reaction time), the number of omission errors in Go trials (button was not pressed) and the number of commission errors in NoGo trials (button was pressed).As neurophysiological measures, we looked at the amplitudes and latencies of four previously described and commonly used ERP components in ADHD research: cueP3, CNV, N2d, and P3d . The pre® amplifier and a sampling rate of 500 Hz. The impedance was kept below 5 kΩ. The signal was bandpass filtered between 0.5 and 50 Hz and down-sampled to 250 Hz. We changed the reference from linked-earlobe montage to average montage before preprocessing.Electroencephalography (EEG) signals were acquired by a 19-channel tin electrode cap (standard 10–20 placement system) with two reference electrodes attached to the earlobes . The data was registered by the ERPrec software  with a NeuroAmpx23z-score of 6). ERPs averaged over less than 40 valid  trials were not considered  and last (t5) assessments are displayed in The artifact-free data was further analyzed with a custom-built EEGlab plug-in. We applied baseline correction using a 100 ms pre-stimulus period . The peak amplitudes and latencies of the components cueP3, P3d, and N2d were determined within an adjusted time window based on the grand average curve . The windows of the analyzed ERP components are 348–484 ms after S1 onset for cueP3, 220–268 ms after S2 onset for N2d and 308–388 ms after S2 onset for P3d. For the individual peak detection within these windows, we applied self-modeling warping functions  to overc1 to t5. We added group , sex  and age at the first assessment (centered to the average age of the sample) as time invariant covariates on the intercept and slope estimates. For the longitudinal descriptions of the dependent variables, we described the change over time within the control group and the potential distinctive pattern observed in the ADHD group.First, we examined the group differences for the self-reported symptoms, behavioral, and ERP measures between the ADHD and control groups at baseline . Second, we describe the average changes over time . These first two aims were addressed by latent growth curve models. As dependent variables, we used self-reported ADHD symptoms (inattention and hyperactivity), behavioral measures of the Go/NoGo task , and ERP amplitudes and latencies at the assessment time points tThird, we examined potential associations between the symptom burden and the objective measures of the Go/NoGo task. This third aim was addressed by bivariate structural equation models, including the scores of the self-reported symptoms and the behavioral or ERP measures. We were interested in the associations between the person-specific intercepts  as well as between the person-specific slopes (change over time). We used the lavaan package version 0.6-5  in R to We report 95% confidence intervals for the model estimates. The reliability of a measurement is calculated by intraclass correlation coefficients (ICC) and categorized into low (<0.50), moderate (0.5–0.75), good (0.75–0.9), and excellent (>0.9) . The rep1, N = 86 (41%) report inattentive, N = 11 (5%) hyperactive and N = 69 (33%) combined ADHD (inattentive and hyperactive). Data is missing for N = 10 (5%) and N = 34 (16%) did not fulfill the DSM-5 criteria based on their self-report. At the last assessment, t5N = 28 (13%) report inattentive, N = 9 (5%) hyperactive and N = 29 (14%) comorbid ADHD symptoms. Data is missing for N = 80 (38%) and N = 64 (30%) did not fulfill the DSM-5 criteria based on their self-report. Details on the change in ADHD diagnosis and subtype are shown in 1 and t5 (N = 126) and low agreement unweighted Cohen’s kappa .Self-reported symptom burden was used to define the participants’ ADHD subtype. At the first assessment tN = 210). All participants were medication-free at the day of the assessment. However, some took medications for their daily life. The intake of methylphenidate and antidepressants in the ADHD group decreased from the first to the last assessment . Data of medication intake was missing for N = 54 (26%) at t1 and N = 77 (37%) at t5.The data about medication intake and attended therapies was collected only for the ADHD group (1N = 36 (17%), t5: N = 41 (20%), neurofeedback; t1: N = 4 (2%), t5N = 13 (6%) neurofeedback, others; t1: N = 35 (17%), t5: N = 34 (16%)]. Data of therapy attendance was missing for N = 47 (22%) at t1 and N = 76 (36%) at t5.Attended therapies during the study comprised psychotherapy, neurofeedback, and other types of supportive trainings or consultations. The percentage of participants attending the different types of therapies remained constant over the two years  in the control group. The model indicates a non-significant effect of group , indicating comparable decrease per time point, see also The amplitude of the N2d component attenuated by 0.12 μV  in the control group. The non-significant effect of group , indicates comparable decrease in both groups. We did not observe change over time in the CNV amplitude. There is no evidence for change in the cueP3 latency over time for the control group , nor evidence for an effect of group .The latencies of the P3d and N2d component are decreasing in the control group over time . However, there is no evidence for an effect of group for these two components .To fathom the change over time of the P3d amplitude, we investigated the ERP components of the Go and NoGo conditions separately. The intercepts and group differences of the GoP3 and NoGoP3 amplitudes are comparable to those of the P3d component . The slope estimates and its effect of group reveal how these two components change over time within the control and ADHD group. The GoP3 amplitude tends to decrease over time in the control group . Although the negative group effect for ADHD  is non-significant, it is indicating a slightly stronger decrease in the ADHD group.The NoGoP3 amplitude tends to decrease over time in the control group as well . Although the group effect of ADHD for this component is non-significant , it is important to mention that this positive estimate points toward a weaker decrease in the ADHD group over time. To sum up, the total change per assessment in the control group is larger for the NoGoP3 amplitude (−0.34 μV) compared to the GoP3 amplitude (−0.17 μV). Therefore, the amplitude of the difference curve P3d (NoGoP3 minus GoP3) decreases over time in the control group see . In contN = 210 at t1). The correlation coefficients in We fitted bivariate models to investigate the associations between the self-reported symptoms of the ADHD inattention and the ADHD hyperactivity score with the various behavioral and neurophysiological variables. The models were fitted within the ADHD group .The lack of change of the CNV amplitude over time indicates an absence of adaptation in the preparatory processes in both groups.The results with respect of the ERP latencies are mixed. Cross-sectionally, we observed a longer cueP3 latency in the ADHD group compared to the control group. No group effect was seen in the P3d and N2d components. Longitudinally, we observed shorter N2d and P3d latencies at the final measurement in both groups reflecting faster stimulus processing over time.In line with the results of the ERP amplitudes which indicate more efficient processing, the ERP latencies of the N2d and P3d components indicate swifter processing over time. The lack of longitudinal changes in the cueP3 latency indicates a lack of adaption in the speed of alertness related processing.To quantify the reliability of the self-reported symptom burden, behavioral, and ERP measures we computed model-based ICCs. In our sample the reliability of self-reported symptom burden scores, deriving from the DSM-5 items, is moderate to good. Likewise, the reliability of reaction time, reaction time variability, number of commission errors and ERP amplitudes is moderate to good, whereas the reliability of the ERP latencies is poor to moderate.The short-term inter-individual reliability of ERP amplitudes were previously described to be moderate to high and the reliability of the latencies poor to moderate . Our anaEvent-related potential latencies have generally lower ICCs in comparison with ERP amplitudes and behavioral CPT measures as observed previously by others . This isTo sum up, we observed moderate reliability among ADHD symptom scores, reaction time, reaction time variability, number of commission errors and ERP amplitudes.On a cross-sectional level, there is weak evidence for an association of the number of reaction time variability, commission errors and cueP3 with ADHD symptom burden. However, these results should be interpreted with caution as the confidence interval boundaries are close to zero and the estimated correlation coefficients are small.Other cross-sectional studies in adults with ADHD found small correlations between the symptom burden and individual ERP measures. On a longitudinal level, we did not find any evidence for correlations between the individual change in symptom burden and change in the behavioral or ERP measures. To our knowledge this is the first study among adults investigating the longitudinal association between symptom burden and executive functions, using both behavioral and neurophysiological measures. Recent developmental studies in ADHD similarly concluded that symptom alleviation was not associated with neurocognitive development . Our resFurthermore, there are alternative explanations for the lack of evidence for an association between the symptom burden and neurophysiological measures. One possibility is the individually perceived cognitive load or experienced stress level during the task, especially in the cross-sectional analysis. Another explanation is the different levels of specificity that we look at: on one side the ERPs reflect very specific processes and on the other the side symptoms assess rather general difficulties in daily life. Altogether, our results are in line with previous research and corroborate the weak interdependency of the self-reported burden in daily life with the behavioral and neurophysiological measures. Apparently, the degree of symptom burden is not reflected in the magnitude of the objective measures for which research identified robust group differences between participants with ADHD and healthy controls. In addition, the absence of clear associations between the symptom burden and objective measures, both cross-sectionally and longitudinally, highlights the pivotal difficulties in psychiatric research. The field aims to establish more objective neurophysiological markers to describe deficient functioning, while being constrained by the clinical diagnosis that rely on the subjective perception of symptoms.Several limitations apply to the study sample, the assessed clinical and demographical information, and performed statistical analysis.First the dataset is heterogeneous in terms of age and ADHD presentation. Although limited from a strictly research-focused view, such large and heterogeneous samples are important from a clinical perspective. In addition, comorbidities were not assessed thoroughly. Similarly, we have only data available regarding methylphenidate and antidepressants intake. These medications, as well as others not assessed, may affected the results. Especially changes in the two years might have affected perceived symptom burden. Second, the symptom decrease resulted in a considerable number of participants who no longer fulfilled the DSM-5 criteria of ADHD at the end of the study. In addition, symptom burden and ADHD presentation are based on self-report of the individuals, hence are highly subjective and potentially biased. For example, non-credible responders tend to report higher rate of impairment than credible responders . TherefoIn conclusion, we have shown alteration of neuronal processing over two years, whereas neurocognitive performance at the behavioral level was constant. These adaptions are more pronounced in the control than in the ADHD group and potentially reflect improved neuronal efficiency. Using a relatively large sample of adults, we have shown that self-reported ADHD symptoms are not substantially related to objective measures of executive functioning neither cross-sectionally nor longitudinally. Furthermore, the decrease in symptom burden in this observational study and the low agreement on ADHD presentation between the first and last assessment reflects the low reliability of the ADHD diagnosis by clinical presentation.The data tables and R analysis code will be made available on request. The raw EEG data are not publicly available and can only be accessed via collaborations with the Brain and Trauma Foundation Grisons.The studies involving human participants were reviewed and approved by Zurich’s cantonal ethics committee (LeitEKZH_2013 -0327/EKNZ_2014_160). The patients/participants provided their written informed consent to participate in this study.GC, DE-H, and AM contributed to the design and data collection of the dataset. JK and HA-R contributed to the data collection. MM and SS performed the statistical analysis. MM wrote the first draft of the manuscript. MM, SS, GC, and LJ contributed to manuscript revision. All authors approved the submitted version.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.


In livestock, a low birth weight (LBW) has a broad impact on neonatal survival, growth performance, and metabolic health in adult life. The liver plays an important role to regulate lipid metabolism, but the development of hepatic dyslipidemia associated with LBW is still unknown in goats. Herein, we evaluated lipid and metabolic status of LBW livers in contrast to those of newborns with normal birth weight. RNA sequencing was used to screen potential dysregulated functional genes involved in hepatosteatosis. Results showed lower antioxidant capacity, enhanced pro-inflammatory cytokine, and increased hepatic lipid accumulation in LBW goats associated with impaired regulatory machineries. Understanding the knowledge of intrinsic mechanism underlying hepatic dyslipidemia in LBW goats could provide important implications for promoting efficiency of production and health in their later life.Occurrence of low birth weight (LBW) is a major concern in livestock production, resulting in poor postnatal growth, lowered efficiency of feed utilization, and impaired metabolic health in adult life. In the southwest region of China, birth weight of indigenous strains of goats varies seasonally with lower weights in summer and winter, but the metabolic regulation of the LBW offspring is still unknown. In this study, by comparing LBW goats to normal birth weight group, we examined hepatic lipid content in association with regulatory mechanisms. Histological studies showed higher microvesicular morphology in the liver of LBW goats in accompany with a significantly higher level of hepatic free fatty acids, total triglycerides, and cholesterols. Lipid metabolism impairment, increased oxidative stress, and inflammation were observed by transcriptome analysis. Meanwhile, Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation further demonstrated lipid peroxidation, antioxidant pathway, and pro-inflammatory response involved in the hepatic lipid dysregulation from LBW group. Therefore, dysregulations of hepatic lipid metabolism, including fatty acid biosynthesis and degradation, lipid transportation, and oxidative stress, played important roles to contribute the lipid accumulation in LBW goats. Moreover, due to impaired antioxidant capacity, the oxidative damage could interact with persisting pro-inflammatory responses, leading to a higher risk of liver injury and metabolic syndromes in their adult life. Capra hircus) during summer and winter is approximately 20% lower than annual average .,25.CapraFrozen liver tissue was homogenized in RIPA Buffer  with protease inhibitor . After they were separated by SDS-PAGE  and transferred on PVDF membranes , liver proteins were incubated with primary antibodies of tumor necrosis factor alpha , nuclear factor erythroid 2-related factor 2 , OxPhos , and β-actin  overnight. Then, the membranes were incubated with either the goat anti-mouse IgG (H + L)  or the goat anti-rabbit IgG (H + L)  for 2 h. Bands were visualized by Immobilon Western HRP Substrate , obtained by ChemiDoc™ XRS+ Imaging System (Bio-Rad), and analyzed by ImageJ software . https://www.olympus-lifescience.com/en/software/cellsens/#!cms[focus]=cmsContent6017, accessed on 1 February 2022 Olympus).Liver tissues were fixed in 4% paraformaldehyde for 48 h at room temperature. After being consecutively dehydrated in 70%, 90%, and 100% ethanol, liver tissues were embedded in paraffin and cut into slices measuring 5 μm in thickness. The slices were stained by hematoxylin-eosin (HE), according to the manufacturer’s protocol . Histological analysis of steatosis was visualized with the microscope , digitally captured, and analyzed with cellSens software . Statistical analysis was processed by SPSS Statistics 19.0 . Values are given as mean ± SEM, and p < 0.05 was considered significant.All of the data between groups were compared by using paired Student’s p < 0.01). After necropsy, LBW bodyweights were 23% lower than control group, and the carcass weights of LBW goats were still 25% lower than the control group  was significantly less than the NBW group , but a trend of lower Nrf2 (p = 0.064) mRNA expression was observed in LBW kids compared to the control group. No differences in mRNA expression were observed for heme oxygenase 1 (HO-1), superoxide dismutase 2 (SOD2), or GPx2 (Relative mRNA expression of GPx3 was 86% lower in LBW kids ( or GPx2 A. The ra or GPx2 B.p < 0.01, The LBW kids exhibited relatively 63% lower protein abundance of Nrf2, and 1.49-fold higher of TNFα in the liver, compared to the control kids (In the present study, we found that LBW goat kids exhibited preliminary signs of lipid accumulation in liver, including enhanced concentrations of hepatic FFA and TG, as well as the occurrence of vacuole lipid droplets in liver tissue. The key regulatory signaling pathways related to hepatic lipid metabolism, oxidative regulation, and inflammation were impaired in LBW kids compared to NBW kids. The lower antioxidant capacity was expected to play a major role to contribute the excessive ROS induced by lipid peroxidation in the liver. Together with the persistently increased pro-inflammatory cytokine, these metabolic complications could further lead to liver injury in their later life.In clinical studies, LBW is defined as birthweight less than the 10th percentile at gestational delivery . EpidemiIn the liver tissue of LBW goats, we found histological signs of microvesicular steatosis  associatBesides the lipid metabolism, KEGG annotation further indicated dysregulation of oxidative regulation and inflammation in LBW livers. Oxidative stress is one of the main factors in the pathogenesis of metabolic diseases, including hepatic lipid accumulation and dysregulation. On the other hand, hepatic lipid accumulation occurs when fatty acid uptake and synthesis surpass oxidative capacity in liver . As the Oxidative stress is mainly caused by an imbalance between the production of ROS and the corresponding antioxidant-induced protective mechanisms . Nrf2 isIn addition to influencing lipid metabolism directly, chronic ROS exposure could persistently activate pro-inflammatory cytokine generation in liver, and these cytokines, in turn, would stimulate higher ROS production and exacerbate hepatosteatosis . The stuLBW animals are at greater risk for developing oxidative stress-induced hepatosteatosis. The impairment of antioxidant capacity and excessive ROS exposure not only directly damage hepatic lipid regulation, but also trigger hepatic inflammatory responses, leading to irretrievable liver damage with lipid accumulation in the liver in later life . In thes


Objective: To explore the burden associated with childhood ADHD in alarge observational study. Methods: We recruited familes with atleast one child (6-18 years) with ADHD via 15 NHS trusts in the UK, andcollected data from all family members. We made careful adjustments to ensure alike-for-like comparison with two different control groups, and explored theimpact of controlling for a positive parental/carer ADHD screen, employment, andrelationship status. Results: We found significant negative impactsof childhood ADHD on parents’/carers’ hours and quality of sleep, satisfactionwith leisure time, and health-related quality of life (measured by theEuroQol-5D [EQ-5D]). We found a decrement in life satisfaction, mentalwell-being , and satisfaction with intimate relationships, but this was notalways robust across the different control groups. We did not find any decrementin satisfaction with health, self-reported health status, or satisfaction withincome. Conclusion: The study quantifies the impact on the healthand well-being of parents living with a child with ADHD using a survey offamilies attending ADHD clinics in the United Kingdom. The main aim of this study was to identify any differences in health and well-beingoutcomes between parents/carers of a child with ADHD receiving usual treatment andcarefully matched control parents/carers without a child with ADHD. This study alsoaddressed two related questions. First, is the burden on health and well-beingindependent of the potentially confounding impact of parents’ own ADHD symptoms?Second, does parenting a child with ADHD differentially impact on different measuresof health and well-being? By collecting information on a number of differentwell-being outcomes  and health outcomes , wewerDiagnostic andStatistical Manual of Mental Disorders  ADHDthemselves , which mA further weakness in existing work has been the inadequate control for differencesin personal and environmental factors. These vary between households and betweenthose families with and without a child with ADHD. Families with one or morechildren with ADHD commonly face multiple adversities , such asFor policymakers there is increasing interest in the impact of conditions andinterventions on health-related quality of life using generic measures such as theEuroQol-5D EQ-5D; . These mUnderstanding and quantifying the effect of having a child with ADHD on parent/carerwell-being is vital to clinicians providing services and to policymakers. Support toreduce any negative impact on well-being could reduce the need for additional healthand social care input for families, increase the ability of carers to work andimprove their resilience in caring for their child/children.The study obtained ethical approval from the Sheffield NHS Research Ethics Committee,research governance was approved in each research site, and written consent wasobtained from all participants.Families were recruited from 15 centers (NHS trusts), which included routineclinics in Scotland and England  between December 2010 and September2012. Families were invited to participate in the study as a part of theADHD-family group if they had a child (or children) aged 6 to 18 years, with acurrent diagnosis of ADHD and attending one of the centers/clinics. This sampleaimed to be representative of a typical U.K. ADHD-clinic population. Thesampling frame covered a wide geographical area and included both specialistchild and adolescent mental health services (CAMHS) and pediatric clinics. Thechildren had all received a clinical diagnosis of ADHD though centers andclinics varied in their diagnostic protocols. Children with a formal diagnosisof Conduct Disorder were excluded to maintain a tight focus within the study onADHD. Conduct Disorder with ADHD may be etiologically distinct from ADHD alone.To identify the burden imposed upon a family due to a child having ADHD, weideally need to know what life would be like for that family had the child notdeveloped ADHD. We cannot observe this counterfactual life, therefore theidentification and recruitment of an appropriate control is essential. Variousoptions have been used in the past to identify control groups for children withADHD, such as requesting randomly identified individuals from the child’sschool. However, this approach is unlikely to generate a control group which issufficiently matched across the wide range of important householdcharacteristics: parental age, education attainment, socio-economic status,employment status, and household composition. Our study used two differentcontrol groups.generalpopulation sample of adults aged more than 20 and below 70years (1 year above and below the maximum and minimum age of ADHD-familygroup carers) living in a household in England or Scotland, with childrenaged from 6 to 18 years.The first control group was taken from Wave 1 (2009–2010) of the UnitedKingdom’s largest household longitudinal survey “Understanding Society,” amulti-topic survey conducted by the   to ensure a balance in key characteristics. These characteristicsincluded parental education, gender, and age, which were unlikely to havebeen impacted upon by the presence of a child with ADHD. There are strengthsand weaknesses to each of the two control groups. The “UnderstandingSociety” control group offers a very large sample from which to identify alarge well-matched control group, but not all of the outcome measures  are included in this survey, and there is no measure of either childor adult ADHD symptoms. The SYC control is a smaller sample, but it used allof the same health and well-being questionnaires including the adult ADHDrating scale and EQ-5D. The two control groups had different recruitmentmethods, and this is likely to have resulted in some differences between thesamples. Because we rely on observational data, there is a risk ofunobservable differences between our ADHD-family group and the controlnon-ADHD-family group  that couldinfluence the findings. Using two different control groups provides us withan important robustness check that can reduce this risk.worst imaginable health state) and at thetop at 100 .The EQ-5D  is a selnone of the time”) and 5 , giving a maximum total score of 35 . There are no “cutoff” points in the scoringscale. A linear transformation of the raw score is recommended for parametricanalysis to 7 (completely satisfied) scale.Respondents completed four questions about satisfaction with health, satisfactionwith the amount of leisure time, satisfaction with income, and satisfaction withlife overall. Each question uses a 1 . These responses were combined using factor analysis to create asleep problems index. Respondents were also asked about their usual hours ofsleep per night over the last month. These questions were taken from the U.K.survey “Understanding Society.” These were chosen because they had been subjectto rigorous piloting .A question concerning the overall happiness with the carers’ relationship withtheir partner was taken from “Understanding Society.” This has seven responses.To control for the effect of the adult having ADHD symptoms, the Adult SelfReport Scale  to control for important influences on health andwell-being. In particular, comparisons to the SYC control were estimated with andwithout controlling for the adult ADHD screen to explore the impact of possibleparental ADHD on the burden generated by the child with ADHD. The USoc-control groupdid not contain information on adult ADHD; hence, we were not able to include theadult ADHD screen as a control. This may result in an overestimation of the burdenon parents. We addressed this potential bias by making two comparisons with the USocdata, one with the full sample and one with a smaller subsample that excluded thoseadults in our ADHD-family group with a positive ADHD screen or who had previouslybeen diagnosed with ADHD.We also estimated models with and without other variables that may be affected by thepresence of a child with ADHD. Relationship and employment status have a substantialimpact on health and well-being outcomes, but these could be caused in part byliving with a child with ADHD. Those parents/carers who are most impacted by caringfor a child with ADHD are likely also to be those parents who are unable to maintainemployment. Each parent outcome measure was treated as a dependent variable and modeled as afunction of individual and family characteristics and the presence of a child withADHD. We adopted methods suitable to each outcome measure in question, withconsideration given to the nature and distribution of the measure. The S-WEMWBS andthe EQ-VAS reported hours sleep, and the life and domain satisfactions were analyzedusing linear models (ordinary least squares [OLS]), the EQ-5D was analyzed using anonlinear tobit model due to the bounded nature of the scale (it cannot go above 1)and the substantial proportion of responses being at one . WhetherThe sleep problems index was created using factor analysis. Using the Kaisercriterion of retaining factors with an Eigen value of 1 or more, a single factor wasretained (at least 69% of the variance was explained by this factor). Factorloadings were used to create a linear composite variable . The index had an acceptable internal consistency with Cronbach’s alphas ofabove .74. The sleep problems index was analyzed using OLS.Given the multiple testing and large sample size, we rely upon a 1% significancelevel.SD = 10.6). Background details on the samples prior tothe matching are described in online supplement Table S1). The SYC-control group containedparents/carers who were slightly older, more likely to be male, with greatereducation attainment, fewer single parents, with fewer children living inhousehold and more likely to be employed. The USoc-control group shared moresimilar characteristics to the ADHD-family group, although differences inemployment were still present.In total, 549 families with a child with ADHD consented to the study, but despitehaving given consent, no self-completed were obtained for 94 families, leaving455 families with 635 parents/carers . Of these, 428 were primary carers and208 were secondary carers. Data were missing for 31 parents/carers, leaving afinal sample size in the ADHD-family group of 604. In total, 10,718 carers wereidentified from USoc, of which 2,123 had either no outcome data or were missingkey data on covariates, leaving 8,595 eligible participants. In total, 123families from the SYC were recruited, from which we have data on 227parent/carers (123 were primary carers), of which 12 had missing data, leaving215 participants. The children diagnosed with ADHD of the parents/carersincluded in this analysis were mostly boys (83%), aged on average 11.8 years(range 6–18 years), with an average parent completed ADHD rating scale score of41.2 highlight the importance of the matching procedure and the need to includecovariates within the regression models. The matching process resulted indropping 89 of the 604 ADHD-family group carers when making the comparison tothe SYC-control group (with 1 pruned from the SYC). Most of this arises becauseof the lack of adequate matches within the SYC-control group for primary carerswithout any formal education qualifications. For the USoc comparison, none ofthe ADHD-family group carers were pruned and only 35 out of 8,595 USoc caseswere pruned.The matched and adjusted comparisons of ADHD-family group carers to theUSoc-control group for each outcome measure are shown in SD). Controlling for employment and relationship statusreduced the effect to 1.36 lower and using the non-ADHD screen subsample reducedthese effect sizes to 1.19 and 0.97, respectively. Additional analysis(described in the online supplement Table S2) looking at each of the itemresponses found that the ADHD-family group carers report less favorable outcomeson all S-WEMWBS questions.The USoc comparison found that carers of a child with ADHD had S-WEMWBS scoresthat were 1.57 points lower on the 7 to 35 scale for the ADHD-family group. This reduced to –0.33 when the relationship andemployment controls were added. When carers who screened positive for ADHD wereremoved, the effect was reduced to –0.18 and non-significant when employment andrelationship status were included with this subsample (Column 4). TheADHD-family group reported lower health satisfaction ; however, this effect was not robust to the inclusion of employmentand relationship status, nor in the smaller negative-ADHD screen subsample. Nodifferences were identified for income satisfaction in the fullsample, but in the non-ADHD screen subsample, the ADHD-family group had higherlevels of income satisfaction compared with the matched controls. TheADHD-family group, particularly primary carers, report significantly lowersatisfaction with leisure time . This leisure time effect was robust to theinclusion of the additional controls.In the full sample comparison, Carers in the ADHD-family group also reported less sleep (39–48 min a night forprimary carers and 22–31 min for secondary carers) than those in theUSoc-control group, again with the additional controls and non-ADHD screensubsample showing a slightly reduced effect. They also had a higher sleepproblems index: from 0.51 to 0.72 of a standard deviation higher, depending onthe model.There were significantly more single parent/carers in the ADHD-family group thanin the USoc-control group, but this difference was not robust to controlling foremployment status, nor apparent in the non-ADHD screen subsample. Those carersin the ADHD-family group that were in a relationship were significantly lesshappy with their relationship than carers in the USoc-control group: 0.32 lower reducing to 0.29 lower when controlling for employment statusand 0.24 lower when using the subsample that excludes those with a positiveadult ADHD screen.The matched and adjusted comparisons of the ADHD-family group to the SYC-controlgroup are also shown in Table S2 in the online appendix). The EQ-VAS health measure wasunaffected by having a child with ADHD.We found a significant negative impact (–0.071) of the presence of a child withADHD on the EQ-5D . This decreased by approximatelyone third following the inclusion of the carers own ADHD screen (–0.050) and wasnonsignificant when the relationship and employment controls were included.Additional analysis on the individual items of the EQ-5D found that this effectwas driven by differences in the self-care item and the anxiety and depressionitem (see Table S3 in the online appendix), the ADHD-family group carersreported being significantly less relaxed, less optimistic, and less able todeal with their problems, but no differences were identified for the other fouritems.The comparison to the SYC-group matched controls found that the S-WEMWBS was notsignificantly lower for parents/carers of a child with ADHD. Considering theindividual questions within the S-WEMWBS , but this was not robust tothe inclusion of the adult ADHD screen. They reported similar health andincome satisfaction to matched controls in the SYC, with secondarycarers with a child with ADHD expressing higher satisfaction with their healthand income. In line with the USoc comparison, ADHD-family carers also reportedsubstantially lower leisure satisfaction (–0.94 when adult ADHDis controlled for).The ADHD-family group reported lower (at 5% significance) online appendix Table S1).The ADHD-family group reported fewer hours sleep than the SYC-group. This effectsremains even after controlling for the carers own ADHD screen (about 25 min lessper night), though this is no longer significant once the relationship andemployment controls are included. Carers with a child with ADHD reported ahigher sleep problems index, though this was not robust to the inclusion of theadult ADHD screen. Interestingly, carers without employment or without acohabiting partner have notably higher problems with their sleep. Nearly half ofthe ADHD-family group primary carers are woken during the night by their childwith ADHD, with more than 10% being woken three or more times . This difference was robust to the inclusion of the adult ADHD screen andtheir partner’s ADHD screen, but was only significant at the 10% level oncecontrolling for employment status.Having a positive adult ADHD screen had a significantly detrimental impact uponall outcome measures. Not having employment also showed a significantdetrimental impact upon all outcome measures with the exception of leisuresatisfaction and the probability of being in a relationship.p < .01, unless shown as p < .05).When using the USoc-control group, we found that having a child with ADHD reducedparent/carer overall satisfaction with life and mental well-being. We alsoidentified a negative impact on satisfaction with leisure time and happiness withrelationships, sleep hours, and sleep quality. These effects tended to be weakerusing the SYC as a control which may be a consequence of its smaller sample size.When comparing the standard models without the adult ADHD screen, the magnitude ofthe deficit for carers with a child with ADHD is broadly similar between the twocontrol group comparisons .Our key findings are summarized in Our findings reinforce the impact of caring for a child with ADHD on parental healthas measured by the EQ-5D mental well-being and life satisfaction but no significantimpact was identified upon health satisfaction or on the EQ-VAS which was contraryto our expectations. Interestingly, we identified a large and significant deficit inthe EQ-5D (–0.071), which reduced slightly (–0.050) but remained significant at 5%following the inclusion of the adult ADHD screen. This magnitude is substantialrelative to the minimally important difference (MID) for the EQ-5D . This isThe ADHD-family group carers reported fewer hours of sleep in both comparisons whichis not surprising given the sleep problems experienced by many children with ADHD. In the We find some indication of less happy intimate relationships and more single parentswithin the ADHD-family group, but given the potential for reverse causality here, this link is best established through a future analysis oflongitudinal data.satisfactionwith leisure time. This negative impact on leisure time is borne out byclinical experience. Many families report being reluctant to go out for a meal or adaytrip to amusement parks, for example, due to difficult experiences in the past.Children with ADHD find it very difficult to manage their behavior in theseunstructured situations.All models found parents/carers of a child with ADHD had lower income satisfaction. Indeed,the ADHD-family group reported higher income satisfaction than the USoc controls. Wedo not have a good explanation for this, although it could arise from favorablecomparisons the ADHD-family group made to other domains of their life. In theirlongitudinal analysis of the United Kingdom’s Millennium Cohort study, No negative impact was identified on The USoc comparison finds greater impact in sleep and leisure satisfaction forprimary carers than secondary carers. No obvious pattern was detected in the SYCcomparison—which may, in part, result from selection effects into the study, withthose secondary carers most impacted by the ADHD being those more likely toparticipate in the study.The inclusion of the adult ADHD screen as a control in the SYC comparison did notimpact on findings on sleep or leisure satisfaction but did lead to a slightlyreduced effect size for life satisfaction, EQ-5D, and mental well-being. With regardto the USoc comparison, excluding those with a positive ADHD screen resulted in aloss of magnitude in differences in life satisfaction, mental well-being,satisfaction with leisure time, sleep problems, and relationship happiness. Thissuggests that some of the burden in these areas is being driven by symptoms of ADHDexperienced by the adult carer. The adult ADHD screen was negatively related to allof the outcome measures. This is strongly indicative of adult ADHD having adetrimental impact upon health and well-being. However, the inclusion of the adultADHD screen in this study was to act as a control variable. To get a clearer idea ofthe magnitude of the health and well-being burden of adult ADHD would requireclinical confirmation of the presence of adult ADHD.This study has a number of advantages over the existing literature. Foremost isthe size of the study sample and the comparison to two separate control groups.The size of the control groups, particularly the USoc data, means we can affordto prune cases where a comparison would not be based on like for like. Thecontrol groups had extensive background details on the families, includingsocioeconomic information. The weighting of cases within the matching andadditional covariate further controls for observed differences between theADHD-family group and our controls. We have shown that prior to the matching,observed characteristics differ among the groups, and how the inclusion of theadult ADHD screen can alter findings, suggesting the potential for bias withinpast research which does not adequately control for these covariates.The study used a number of different outcome measures, including those which haveparticular policy relevance such as the EQ-5D, not previously reported. Theinclusiveness of outcome measures and adequate adjustments provide a morecomprehensive and accurate estimation of the main impacts of having a child withADHD experienced by parents/carers.The advantages of both large sample size and using a number of different outcomemeasures simultaneously raise the risk of identifying spurious significantfindings. We address this through focusing on a 1% significance level andlooking for consistent findings across different control groups and covariateadjustments while cautious against over-reliance on any single significantfinding.The ADHD-family group contains those who have children that have been diagnosedwith ADHD, are currently receiving some intervention, consent to being in thestudy and complete the required surveys; hence, they may not adequatelyrepresent all families in the United Kingdom with a child with ADHD. The dropoutrate from consent to filling in the questionnaires was fairly high (17%), andthis may be related to the extent of difficulties the families are currentlyexperiencing. The completion rate of those who returned questionnaires was good: inpart due to home visits by research nurses.Our analysis relies on the assumption that children in the control groups did nothave ADHD (diagnosed or undiagnosed). For the SYC-control group, theself-reported presence of a child with ADHD was an exclusion criteria. TheUSoc-control group was a representative sample of parents/carers in the UnitedKingdom, hence potentially includes families with ADHD children. This would leadto an underestimate of the impact of having a child with ADHD, but because theprevalence for childhood ADHD is likely to be around 4% , this shThis analysis was based on observational, cross-sectional data and thereforecannot be used to imply causality. Although the control groups were closelymatched to the ADHD-family group in terms of observable characteristics andfurther model adjustments were implemented, we cannot be certain that there arenot differences in unobserved characteristics which have not been accounted for.A potential risk factor in developing ADHD is the presence of toxins while inthe womb arising from maternal smoking  or alcohor the presence of a different problem . If this was the case then both thecovariate dummy in the SYC regressions and the exclusion of individuals in theUSoc comparison could possibly be over controlling and result in anunderestimate of the true effects. While the adult ADHD control helps usunderstand the impact of caring for a child with ADHD, it is, nevertheless, theactual health and well-being of parents/carers of a child with ADHD that is mostrelevant for clinical practice.In the SYC-control comparison, we control for adult ADHD using the ASRS screeningscale cutoff, and for the USoc comparison, we consider a subsample comparison ofthose carers who do not reach the cutoff on the ASRS screen and have not beendiagnosed with ADHD in the past. However, in both cases, this does not accountfor adults who have experienced (undiagnosed) ADHD symptoms in the past but donot currently do so. Undiagnosed ADHD symptoms in the individual’schildhood/adolescence, even if no longer present, may have had a scarring impactupon health, education, employment, and well-being outcomes. If past ADHD hashad a scarring impact upon health, and is not adequately controlled by the ASRSscreen, this may result in an overestimate of the burden of caring for a childwith ADHD. On the other hand, having a child with ADHD (or associated lack ofsleep) may exacerbate adult ADHD symptoms in which case controlling for adultADHD symptoms may underestimate the burden. Including a dichotomous control forpossible adult ADHD may also risk controlling for something other than adultADHD. Full diagnosis of adult ADHD requires a subsequent clinical interview ofthose individuals with a positive ASRS screen, and the fairly low specificity ofthe screen (0.71 in U.K. primary care ) could mThis analysis has identified important impacts of caring for a child with ADHD uponparent/carers hours of sleep and quality of sleep, satisfaction with leisure time,health , lifesatisfaction, positive mental well-being, and happiness with relationships. Thefindings are not always robust to the inclusion of other variables, critically toemployment status. However, it is not necessarily the case that the effectsidentified in the models which control for adult ADHD screen, employment, andrelationship status are the most appropriate due to the potential inaccuracy of theADHD screen and the potential for the caring role to have had a causal role in thecurrent employment and relationship status. The substantial deficit experienced bythe ADHD-group parents/carers in terms of sleep and leisure satisfaction suggeststhat these are potential areas in which greater support could be targeted. Thiscould include consideration of more joint work between health and social care andthe need for carer respite.Identifying a clear pathway from caring for a child with ADHD to the health andquality of life impacts on the carers is difficult. This analysis clearly shows anegative impact across a range of health and broader quality of life outcomes, yetalso reveals the complexity of isolating this impact from that of parents own ADHD,and shows the complex relationships among potential controls , health and quality of life outcomes, and familymembers ADHD status.Click here for additional data file.Supplemental material, Supplementary_On for What Is the Health and Well-BeingBurden for Parents Living With a Child With ADHD in the United Kingdom? by TessaPeasgood, Anupam Bhardwaj, John E. Brazier, Katie Biggs, David Coghill, DavidDaley, Cindy L. Cooper, Cyril De Silva, Val Harpin, Paul Hodgkins, AmulyaNadkarni, Juliana Setyawan and Edmund J. S. Sonuga-Barke in Journal of AttentionDisorders


Attention Deficit Hyperactivity Disorder (ADHD) and Autism Spectrum Disorders (ASD) are two of the most represented neurodevelopmental conditions in childhood. The diagnostic shift introduced by the DSM-5, allowing a combined diagnosis of ADHD and ASD, poses different clinical challenges related to diagnostic overshadowing, accuracy of clinical judgment and potential delay in an ASD diagnosis in children presenting with ADHD. Here we tried to disentangle the clinical phenotype and specificity of the two co-occurring conditions in relation to autism traits and empathy, by comparing children with ASD with and without comorbid ADHD with children presenting ADHD only and children with typical development. The child versions of the Autism Quotient (C-AQ) and Empathy Quotient (C-EQ) were administered to a total sample of 198 male children between 6 and 14 years old with age appropriate language skills and normal intelligence. Univariate analysis demonstrated no significant differences in the C-AQ total and subscale scores as well as the C-EQ between children with ASD and children with ASD + ADHD, while children with ADHD alone presented an intermediate phenotype between ASD and TD. Furthermore, a receiver operating characteristic (ROC) analysis was applied to discriminate among the different phenotypes. We found that the C-AQ and C-EQ were accurate at distinguishing with satisfactory reliability between: (a) ASD vs. non- ASD (N-ASD) groups comprising both ADHD and TD children (Area Under the Curve AUC 88% for C-AQ and 81% for C-EQ); (b) ASD and TD (AUC 92% for C-AQ and 95% for C-EQ); (c) ASD and ADHD (AUC 80% for C-AQ and 68% for C-EQ). Our data confirm the reliability of the C-AQ and C-EQ as behavioral markers to differentiate ASD (regardless of comorbid ADHD) from an ADHD condition and TD. Interestingly, in our sample an ADHD condition does not increase the severity of the clinical phenotype in terms of autism traits distribution and empathy, suggesting that the psychological measures detected by the two quantitative instruments are independent of ADHD traits. This evidence will contribute to the translational efforts in developing better tailored treatments and preventive strategies. The Diagnostic and Statistical Manual of Mental Disorders fifth edition (DSM-5)  introducThe major changes in the ASD diagnostic category were: (i) the rearrangement of the three core domains down to two core domains ; (ii) the removal of sub-diagnoses  in favor of a dimensional approach of symptoms’ severity; and (iii) the possibility of a comorbid diagnosis of ADHD.Within the autism phenotype, restricted repetitive behaviors have been linked to cognitive rigidity , hyperfoImpairments in EF and self-regulation represent core neuropsychological features of ADHD. Interestingly, poor quality of social perspective’s assumptions, impaired social cognition, lack of pragmatic language and empathy have also been observed in children with ADHD . With rePrevious evidence confirmed the symptoms overlap between ADHD and ASD, with 18–50% of children with ADHD presenting with clinical levels of ASD symptoms  and convDiagnostic accuracy may be even more challenging when ASD and ADHD co-occur. In fact, the neuropsychological and behavioral overlapping between the two conditions poses important questions related to diagnostic overshadowing and need to be in depth investigated to explain possible shared etiologies . FurtherBased on existing literature we aimed to disentangle the clinical phenotype and specificity of the two co-occurring conditions in relation to autistic traits and empathy, by comparing children with mild to moderate ASD, with normal intelligence and functional language, with and without comorbid ADHD, with children presenting with ADHD only and typically developing (TD) children. Specifically, we intend to explore if comorbid ADHD is associated with an increase in the expression of autistic traits and a decrease of empathy as detected by two dimensional measures such as the child versions of the Autism-Spectrum Quotient (C-AQ)  and the SD = 14), assessed using the Wechsler Intelligence Scale for Children-4th edition (WISC-IV).All the children presenting a clinical diagnosis of ADHD and/or ASD were assessed at the clinical facilities of the National Research Council of Italy (IRIB—CNR) and the Polyclinic University Hospital “AOU G. Martino” both located in Messina, Italy, while TD children were recruited in three big mainstream schools in the metropolitan area of Messina. Clinical diagnosis was examined by medical records and was then confirmed by an experienced child neuropsychiatrist  and a chartered clinical psychologist of the team  according to the DSM-5 criteria, with the support of the Autism Diagnostic Observation Schedule-2nd edition  and the parent version of the Conners scale-3rd edition, respectively. All the ASD children included in the study, received a diagnosis of ASD without disorder of intellectual development and with no impairment of functional language (6A02.0) according to ICD-11. All the children had an IQ between 70 and 130  intellectual disability; (2) syndromic (secondary) autism; (3) no fluent language; (4) level 3 (severe) at the DSM-5 severity classification, because for those children many questions on the C-AQ and C-EQ were not applicable or a positive answer would capture difficulties collateral to ASD and not nuclear to it .The study was reviewed and approved by the Ethics Committees of CNR  and parents of the children included in the study provided their written informed consent.The child version of the Autism Spectrum Quotient (C-AQ) and Empathy Quotient (C-EQ) were used to assess Autistic Traits (ATs) and empathy in all the children. The C-AQ and C-EQ were originally developed to study dimensional autistic traits and empathy as a continuum. The C-AQ  is a 50-The C-EQ  is a 27-All statistical analyses were conducted using the SPSS Statistics Release 26.0 . A two-way ANOVA was performed separately for the C-AQ and the C-EQ. We performed a univariate analysis of variance (ANOVA) to verify between-group differences in the total scores of the C-AQ and C-EQ; the four diagnostic groups were used as dependent variables, the C-AQ and C-EQ were used as independent variables. Levene’s Test was used in ANOVA and Box’s Test in MANOVA to test the equality of error variances. Given that the groups size was uneven, in case of unequal variances a robust Bias-corrected accelerated (BCa) Bootstrap with 1,000 samples, stratified by group, will be used . Age andFurthermore, we executed a MANOVA, with the same factors and covariates, to analyze between-group differences in AQ subscales. Multiple comparisons were performed by applying Sidak correction. Pillai’s Trace was used to test the null hypothesis.A receiver operating characteristic (ROC) curve of the C-AQ and C-EQ total scores was calculated to plot sensitivity and 1-specificity in the whole ASD group (ASD+ and ASD−) vs. N-ASD group (ADHD and TD together). The area under the curve (AUC) is a measure of the overall predictive validity, where an AUC = 0.50 indicates a random prediction of the independent variable, AUC > 0.70 indicates fair validity and AUC > 0.90 indicates excellent validity. Potential cut-off scores on the AQ and EQ for differentiating between children with and without ASD were also evaluated using ROC analysis to determine the cut-point corresponding to the best combination of sensitivity and specificity.SD = 2.2) has been recruited and tested in the study. N = 77 children presented a diagnosis of ASD only (ASD−); n = 24 children received a diagnosis of ASD and ADHD (ASD+); n = 33 children had ADHD only and n = 64 children had typical development (TD)  see .F = 60.0, p < 0.001 ηp2 = 0.448] and C-EQ . We found neither the age effect  nor the IQ effect  on C-AQ and C-EQ .Equality of variance conditions were not met for C-AQ. Therefore, we decided to use a stratified BCa for both outcome measures to have more robust error ranges. We detected a main effect of group for both the C-AQ , p = 0.787 for the C-EQ  and the TD group , respectively. Children with ADHD, in turn, displayed, on both the C-AQ and C-EQ, intermediate mean scores, but significantly different than TD children .Furthermore, ASD− scores were significantly different from both the ADHD  = 8.49, p < 0.001, ηp2 = 0.183]. We found no effect of age , while IQ had a significant effect . Univariate analysis led to a main effect of group for communication , social , attention switching , and imagination  subscales. There was no group effect for the attention to details subscale . The effect of IQ was significant for the social skills and attention to details subscales .Equality of variance conditions were not met for the social and the attention to details subscales. As before, we performed a stratified BCa. The main effect of the group, reported on the C-AQ total score, was confirmed by the multivariate analysis  for C-AQ, and 0.806  for C-EQ .We also determined the best threshold able to discriminate between ASD and N-ASD with maximum accuracy. The cut-off was 73  for the C-AQ and 26  for the C-EQ. Using those values, for C-AQ the sensitivity was 82% and the specificity was 79% leading to an overall accuracy of 81%, while for C-EQ the sensitivity was 75% and the specificity was 72%, with an accuracy of 73% .A comparison between ASD and TD, led to an AUC of 0.918  with a cut-off of 73 for AQ-C scores, corresponding to a sensitivity of 82% and a specificity of 89% with an accuracy of 86%. On the C-EQ, AUC was 0.945  with a cut-off of 26, a sensitivity of 75%, a specificity of 100% and an accuracy of 87%.Finally, the direct comparison of the whole ASD group (ASD+ and ASD−) vs. the ADHD group, led, for the C-AQ, to an AUC of 0.795  with a cut-off of 77 corresponding to a sensitivity of 72% and a specificity of 73% with an accuracy of 73%. For the C-EQ, AUC was 0.680  using a cut-off of 18, sensitivity was 36% and specificity was 94% leading to an accuracy of 65%.Recent literature is posing specific attention to the clinical implications of having a comorbid ASD and ADHD with a focus on investigating the extension to which the two co-occurring conditions contribute to a different phenotype expression compared to ASD and ADHD alone. Although clinical data support the presence of a more severe outcome and impairment in the quality of life, evidence from the current standardized clinical measures is still inconsistent in detecting, with sufficient accuracy, the neuropsychological differences between individuals with ASD, individuals with a comorbid ADHD (ASD+) and individuals with an ADHD alone. One of the reasons for this clinical challenge, is that, for example, many individuals with ADHD, as well as individuals with ASD, show significant impairments in the social interaction and communication area, as displayed by higher scores on the social affect domain at the Ados Diagnostic Observation Schedule, second edition (ADOS-2), and the main domain that allows to distinguishing between ASD and ADHD individuals at the ADOS-2 is based on the repetitive and restricted behaviors that remain a specific core domain for ASD . SimilarTo address this gap, in our study, we explored whether two quantitative measures of autism traits and empathy such as the C-AQ and C-EQ were able to distinguish children with ASD with or without ADHD, from children with ADHD alone, and children with typical development. For this reason, we used a ROC analysis to determine the threshold score that maximized classification accuracy among the conditions for each measure . We founConversely, the C-EQ demonstrated good accuracy at distinguishing between ASD and TD (87%) but not a sufficient accuracy in the distinction between ASD and ADHD (65%). In line with previous evidence, one possible explanation for the latter finding is that also children with ADHD may show a specific deficit in empathy  as it has been reported by several studies . FurtherUnivariate analysis , 2 reveapost hoc stratified boot-strapped analysis led to similar results. Secondly, we have not been able to collect ADHD-related specific measures, therefore, for the purpose of the study, we focused on the discriminative ability of quantitative, parent-report, quick screening measures such as the C-AQ and the C-EQ to detect, relatively early, an ASD condition vs. other clinical overlapping conditions such as ADHD. Furthermore, in future larger studies, cluster analysis and single item resolution analysis would be worthy to explore the specificity and transdiagnostic domains of autism traits distribution and empathy in a hybrid dimensional approach.The study has some limitations. The first limitation concerns the demographic characteristics of the sample. Furthermore, average IQ in ASD+ children was higher than in the other groups. Previous studies demonstrated that C-AQ and C-EQ are independent of age and IQ , Also a In our study we found that the C-AQ and C-EQ are reliable and robust instruments to quantify ASD traits and empathy in children with normal intelligence and fluent language with and without comorbid ADHD as compared to children with an ADHD condition alone and TD children. Specifically, within the autism spectrum, the presence of a comorbid ADHD does not influence the severity and distribution of autistic traits and empathy, while children with a diagnosis of ADHD displayed an intermediate phenotype, with higher levels of autistic traits and lower empathy compared to TD children. The results of our study will also contribute to the translational efforts in developing better tailored treatments and preventive strategies.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 Committees of CNR . Written informed consent to participate in this study was provided by the participants’ legal guardian/next of kin.SA contributed to the study design and to the manuscript draft. DV conducted the statistical analysis and statistical data interpretations and participated in the manuscript draft. ACe contributed with statistical data interpretations and to the manuscript draft. EL, CC, FF, ACa, and FlM enrolled and tested the ASD+, ASD−, and TD participants. RS, MA, and EG enrolled and tested the ADHD participants. FrM and GT contributed with statistical analysis and statistical data interpretations. GP contributed to the coordination of the study. AG participated in the study design, coordinated the enrollment of ADHD participants and contributed to the manuscript draft. LR designed and supervised the study and drafted the manuscript. All authors read and approved the final manuscript.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.


Bartonella are gram-negative intracellular bacteria; certain species of Bartonella can cause diseases in mammals and humans. Ticks play a major role in the transmission of Bartonella. Xinjiang is the largest province in China according to land area and has one-third of the tick species in China; the infection rate of Bartonella in ticks in the Xinjiang border areas has not been studied in detail. Therefore, this study investigated tick infections by Bartonella in Xinjiang border areas, and the purpose of the study was to fill in gaps in information regarding the genetic diversity of tick infections by Bartonella in Xinjiang. We tested 1,549 tick samples from domestic animals (sheep and cattle) for Bartonella using ribC-PCR. Positive samples from the ribC-PCR assay for Bartonella spp. were further subjected to PCR assays targeting the ITS, rpoB and gltA genes followed by phylogenetic analyses. Bartonella DNA was detected in 2.19%  of tick samples, and the ITS, rpoB and gltA genes of ribC gene-positive samples were amplified to identify nine samples of Bartonella melophagi. In this study, molecular analysis was used to assess the presence and genetic diversity of B. melophagi in ticks collected from sheep and cattle from Xinjiang, China. This study provides new information on the presence and identity of B. melophagi in ticks from sheep and cattle. Bartonella are obligate gram-negative intracellular bacteria that primarily infect mammalian erythrocytes, macrophages, monocytes, and endothelial and dendritic cells  from Central Europe and northeastern Algeria (Lipoptena cervi) from Norway  in Xinjiang were infected with B. melophagi  until the liquid was clear. DNA was extracted from each sample using a QIAamp DNA mini kit  according to the manufacturer's protocol, and the extracted DNA was stored at −20°C. Initial screening of Bartonella DNA was performed using ribC-PCR. To further characterize the positive samples in this study, positive samples by ribC-PCR assay for Bartonella spp. were further subjected to PCR assays targeting the ITS, rpoB and gltA genes, followed by phylogenetic analyses  and cloned into the pGEM-T Easy vector .Tick samples were placed in 50 mL sterilized centrifuge tubes and washed individually twice with 75% ethanol followed by rinsing with double-distilled water  by SeqMan (www.dnastar.com/); the forward and reverse primers of the sequences after bidirectional sequencing were selected using SeqMan; extra sequences at the ends of the forward and reverse primers were removed, and corrected calibration was performed after manual adjustment as needed. The sequences were aligned with sequences downloaded in GenBank using MAFFT (https://github.com/rambaut/figtree/releases). The confidence values for each branch of the phylogenetic tree were determined by using 1,000 repeat analyses.The nucleotide sequences were confirmed by bidirectional sequencing at TSINGKE Biotech, China. The nucleotide sequences were compared with the reference sequences of GenBank (ng MAFFT . ModelFing MAFFT  was usedng MAFFT  program ng MAFFT . The treDermacentor (n = 608), Hyalomma (n = 511), Rhipicephalus (n = 338), and Haemaphysalis (n = 92)], and ten species  ] .Bartonella in 1,549 tick samples from livestock (cattle and sheep) using ribC-PCR and detected Bartonella DNA in 2.19%  of tick samples. The positive samples had the following proportions: D. nuttalli 3 , D. pavlovskyi 7 , Hy. asiaticum 22 , and R. sanguineus 2 . Meanwhile, the gltA, ITS and rpoB genes were amplified by PCR and sequencing. A total of 17 Bartonella ribC , nine gltA , 12 ITS  and eight rpoB  sequences were obtained.In this study, we assayed rpoB (n = 8), gltA (n = 9), ribC (n = 17), and ITS (n = 12) sequences obtained in this study. The sequence identity between the examined B. melophagi and other known Bartonella species was 84–100% for rpoB, 76.20–99.80% for ribC, 83.10–100% for gltA, and 68.90–96.40% for ITS. The sequence identity between the examined B. melophagi and known B. melophagi was 100% for rpoB (EF605288.1), 99.80% for ribC (99.80), 99.50–100% for gltA , and 96.40% for ITS (JF834886.1)  .gltA and rpoB genes, the gltA and rpoB sequences obtained in this study were clustered into a branch of Bartonella associated with ruminants and supported by 98 and 100% bootstrap analysis. The gltA and rpoB sequences of B. melophagi in this study were closest to AY724768.1 and EF605288.1, respectively  contained Bartonella DNA assayed by ribC-PCR. The gltA, rpoB and ITS genes of the ribC gene-positive samples were selectively amplified, and BLASTn analysis showed that the gltA, rpoB, ITS and ribC genes were all B. melophagi. This may be due to the potential pitfall in this study, because the positive control we selected is Bartonella melophagi. In view of this, more follow-up studies are needed, such as using another Bartonella as a positive control. Previous studies on Bartonella in ticks showed that the positive rates were 2.40% (7/292) in China (Bartonella in ticks from different countries reported positive rates of 1.05% (2/191) in Lithuania (Bartonella in other ruminant-related blood-eating arthropods showed that the positive rates were 78.79% (26/33) in Hippoboscidae flies from Algeria  can be found at: JN and QR performed experiments. HL, MA, YLu, and ZM participated in sample collection. GL, ZC, JinL, and QR identification of tick samples. JN, JG, WL, XX, ZQ, ZW, and YT performed data analysis. JW, YLi, GG, JiaL, HY, and GL revised the manuscript. All authors read and approved the final manuscript.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.


KCNIP1, SLC1A3, MTHFR, ADRA2A, and SLC6A2 genes proved to be associated with the risk of ADHD in the studied population. The COMT gene polymorphism was one that specifically increased the risk of EBW in the ADHD group. Using the whole-exome sequencing technique, we have shown that the ADHD group contains rare and protein-truncating variants in the FBXL17, DBH, MTHFR, PCDH7, RSPH3, SPTBN1, and TNRC6C genes. In turn, variants in the ADRA2A, DYNC1H1, MAP1A, SEMA6D, and ZNF536 genes were specific for ADHD with EBW. In this way, we confirmed, at the molecular level, the existence of genes specifically predisposing to EBW in ADHD patients, which are associated with the biological pathways involved in the regulation of the reward system, intestinal microbiome, and muscle metabolism.Comorbidity studies show that children with ADHD have a higher risk of being overweight and obese than healthy children. This study aimed to assess the genetic alternations that differ between and are shared by ADHD and excessive body weight (EBW). The sample consisted of 743 Polish children aged between 6 and 17 years. We analyzed a unique set of genes and polymorphisms selected for ADHD and/or obesity based on gene prioritization tools. Polymorphisms in the  Researchers have observed higher body weight or body mass index (BMI) among children and adolescents with ADHD than growth charts or control groups of individuals without ADHD suggest. Additionally, a higher percentage of overweight and/or obesity was noted in children and adolescents with ADHD. The association between ADHD and overweight/obesity among children or adolescents has been demonstrated in clinical and epidemiological studies . FTO) seems to be a good candidate gene spanning the biological mechanism responsible for increased food intake in ADHD patients [The basis of biological dysfunction comprises the genetic risk factors, which may be partially shared for ADHD and obesity. Data from different types of research  suggest that shared genes for both diseases are: dopamine D2 and D4 receptor genes , brain-derived neurotrophic factor gene (BDNF), and melanocortin-4-receptor gene (MC4R) . The genpatients ,17. Even-Polymorphisms of a unique set of genes selected based on gene prioritization tools are associated with the risk of ADHD;-There are relationships between the studied polymorphisms and overweight and obesity named excessive body weight (EBW);-The use of whole-exome sequencing allows for the determination of common and unique as well as rare and truncating protein variants between ADHD and EBW.Given the above, the purpose of this study is to investigate whether:We will also try to discuss potential mechanisms linking the identified genetic variants with the biological pathways observed in the ADHD group with EBW.The study used data collected during a project carried out in the years from 2017 to 2020. The study was approved by the Ethics Committee, University of Medical Sciences in Poznan (resolution no. 542/14 from 6 December 2014). All study participants were Caucasians of Polish origin. Parents or legal guardians gave written informed consent for the participation of their children in the study. Before the examination, we explained the purpose and principles of the study to all of the children and asked them to express their opinions.ADHD group: Patients diagnosed or preliminarily diagnosed with ADHD from the western part of Poland were invited to participate in the project through advertisements/information published, i.e., in psychological and pedagogical counseling centers, mental health outpatient clinics for children and youth and the university website. Willing parents and their children were invited to the Children and Adolescents Psychiatry Department, Poznan University of Medical Science (PUMS), where the recruitment took place. A trained group of child psychiatrists and psychologists verified the diagnosis based on the interview, observation and provided medical documentation. The main inclusion criterion was the ADHD diagnosis confirmed by specialist consultation according to ICD-10 and DSM-5 criteria with using polish version of the diagnostic structured interview for ADHD and hyperkinetic disorder , K-SADS-Non-ADHD group: The voluntary non-ADHD control group was recruited in primary schools in Poznan, Poland. The inclusion criterion was a lack of distinct symptoms of ADHD , any somatic disorders and treatment which might affect behavior, cognitive functions or growth. Inclusion and exclusion criteria for both groups are shown in 2). Underweight, healthy weight, overweight, and obesity were diagnosed with the International Obesity Task-Force criteria (IOTF) based on children’s BMI. The reference values of the IOTF were used to take into account the dynamics of children’s development, age, and gender. Criteria were normalized for children aged 2–18 years with an accuracy level of up to half a year [Trained research staff collected the weight and height of participants using standard anthropometric procedures. Obtained data were used to calculate BMI  self-collection kit , following the manufacturer’s instructions. Extraction of DNA from saliva samples was performed using the prepIT® L2P manual protocol to purify DNA from a 4 mL sample [® K3 EDTA collection tubes. The DNA was extracted from 5 mL of EDTA anticoagulated whole blood using the salting-out method [Saliva samples were collected using the Oragene DNAL sample . In patit method .In both methods, the DNA comes from leukocytes. The methods used are equivalent and do not interfere with single nucleotide polymorphism (SNP) and high-throughput analyses .The Nanodrop 2000c Spectrophotometer  was used to quantify and assess the quality of extracted DNA. Purified DNA were stored at −80 °C until further analysis.To select genes predisposing to ADHD and obesity, we used bioinformatics methods of prioritizing genes with the highest probability of their involvement in the development of the studied phenotypes . This stThe polymorphisms have been identified with use of KASP™ genotyping assays provided by the LGC genomic laboratory . In summary, KASP genotyping assays are based on competitive allele-specific multiplex PCR. Allelic discrimination is based on the competitive binding of two allele-specific primers labelled with FAM™ or HEX™ dye. The detailed described protocols can be found in the Biosearchtech producers user guide ,34,35.The SNPs that failed in the KASP multiplex assay were genotyped using TaqMan SNP Genotyping assays  and HOT FIREPol Probe qPCR Mix Plus (ROX)  according to the manufacturer’s instructions. The amplification was done in AbiPrism 7900HT Real-Time PCR System. Data acquisition and analysis were performed using the allelic discrimination analysis module in SDS v2.4 software . As a quality control measure, negative controls and ~5% of samples were genotyped in duplicate to check genotyping accuracy. Information about the analyzed SNPs, primers and assays is shown in http://vassarstats.net/ (accessed on 3th August) and G*Power 3.1  software. The differences in the clinical and sociodemographic features between ADHD and non-ADHD were compared using the non-parametric chi-square test. A multiple linear regression analysis was used to assess the influence of age and sex on other analyzed clinical and molecular variables. Spearman’s rank correlation was used to detect the relationship between variables. Genotype and allele frequencies were compared using the chi-square test and Fisher exact test, respectively. All analyzed polymorphisms were at Hardy–Weinberg equilibrium. All tests were two-tailed, and α was set at 0.05 (as we compared two groups at the same time only).The results were expressed as numbers and percentages, mean ± standard deviation and median, as appropriate. Statistical analyses were conducted in Statistica v13.3 , VassarStats  . The anaThe main objective was to analyze the variants in the genes associated with ADHD and obesity described in the Gene and SNP selection section  and suppFor the list of genes, transcripts were selected for further analysis based on the following databases: Locus Reference Genomic (LRG) , Human GThe clinical characteristics of ADHD and non-ADHD children are presented in The study groups differed in mean age and gender distribution. Patients with ADHD were older than non-ADHD, and boys predominated in this group.Children with ADHD were also characterized by: more frequent living in rural areas, younger age of parents at the time of childbirth, lower level of parents’ education, and lower socioeconomic status. Moreover, in this group, the occurrence of comorbid disorders, preterm labor, assisted delivery, lower birth weight, and Apgar score compared to the group of non-ADHD children were more frequent. The reported features are consistent with previous observations ,57. We dp = 0.550) or sex (p = 0.294) on molecular variables  = 0.630; p = 0.532; R^2 = 0.002). Thus, in further analyses, groups were not subdivided according to age and sex of participants.To control for sex and age as confounding factors for genotype frequencies, we performed the multiple regression analysis. This analysis showed no significant effects of age  were non-polymorphic in studied populations. Two others (rs518147 and rs3813929 in the HTR2C gene) presented deviation from the Hardy–Weinberg equilibrium law. Case-control comparisons between ADHD and non-ADHD groups showed an association as follows:-p = 0.018 for alleles),ADRA2A—adrenoceptor α 2A gene ,KCNIP1—potassium voltage-gated channel interacting protein 1 gene (rs1541665 -p = 0.013 for genotypes),MTHFR—methylenetetrahydrofolate reductase gene ,SLC1A3—solute carrier family 1 member 3 gene .SLC6A2—solute carrier family 6 member 2 gene .The shown nominal associations did not survive the Bonferroni correction taking into account the analyzed number of genes  was indicated: rs12667819 (p = 0.014) in the non-ADHD group; COMT (catechol-O-methyltransferase): rs4680 (p = 0.048) in the ADHD group. For the polymorphisms selected in this way, the frequencies of genotypes and alleles were compared using the ch2 and Fisher test, respectively.In the second stage of the analysis, we attempted to check whether there is an association of the studied polymorphisms with excess body weight in the examined ADHD and non-ADHD subgroups. Overweight and obese children were separated into a subgroup defined as excessive body weight (EBW) compared to the group of children with normal body weight (NBW). The results of Spearman’s rank correlation analysis are presented in p = 0.019) and the G allele (p = 0.005) of the PIK3CG polymorphism: rs12667819 was confirmed in children with EWB in the non-ADHD group. On the other hand, in the ADHD group, the A allele of the COMT polymorphism: rs4680 was more common.More frequent occurrence of the GG genotype (After conducting Whole Exome Sequencing (WES), the search was narrowed down to rare variants, focusing on variants damaging proteins in a unique selected group of genes. The last step was to use Fisher’s exact test to answer the question about the relationship between the occurrence of variants in the selected genes and the phenotype under study, i.e., the diagnosis of ADHD and ADHD and EBW, respectively. Statistical analysis was performed in the R environment using Fishers exact test.ADRA2A, DBH, DYNC1H1, FBXL17, MAP1A, MTHFR, PCDH7, RSPH3, SCN2A, SEMA6D, SPTBN1, TNRC6C, ZNF536). Despite the relatively small study group and the analysis of rare variants in the FBXL17 gene (F-box and leucine rich repeat protein 17), it was possible to confirm a statistically significant difference in the frequency of variants in this gene between ADHD patients and healthy controls (p = 0.033).We looked for variants present in the ADHD group but not present in the non-ADHD group A. We ideADRA2A, DYNC1H1, FTO, MAP1A, SEMA6D, ZNF536) present in the ADHD with EBW group, although without a significant difference compared to the control group.Similarly, we searched for variants in the ADHD with EBW vs. not detected in the non-ADHD with NBW group B. We ideIn order to identify common and distinct genes for ADHD and EBW, we used the Venn diagram .We planned initially to include children aged 6–12 years only. In those children with ADHD, due to the small number willing to participate in the study, we extended the age range to 17 years. We took advantage of the fact that age did not affect the gene polymorphisms that remain unchanged throughout life. Although it is now assumed that the prevalence of ADHD in both sexes is similar, due to the type and severity of symptoms, boys dominate in clinical trials , includiKCNIP1 (rs1541665) [SLC1A3 (rs1049522) [MTHFR (rs1801131) [ADRA2A (rs18005) [SLC6A2 (rs5569) [Classic association studies included a unique set of genes and polymorphisms selected on the basis of the results of gene prioritization studies for ADHD and/or obesity supplemented with GWAS results. Polymorphisms in the 1541665) , SLC1A3 rs18005)  and SLC6rs5569) ,69 genes ,69 geneThe polymorphism of the COMT gene (rs4680) was explicitly associated with EBW in the ADHD group. The study results indicated that, in the ADHD group, there were at least partially separate pathways that increased the risk of excess body weight in the group of ADHD patients. Our results support the hypothesis of the participation of the dopaminergic system in ADHD and reward sensitivity . It was In our search for genes that specifically increase the risk of obesity in the course of ADHD, we found that the polymorphism of the PIK3CG gene (rs12667819) was associated with the risk of excess body weight in the control group. The knowledge available to us shows that this is the first molecular evidence supporting the observations that PI3Kγ activity in leukocytes promotes adipose tissue inflammation and early-onset insulin resistance during obesity. The detailed biochemical mechanism was presented by Breasson et al. .MTHFR and ADRA2A genes were also crucial in our classic SNP association analysis. For the MAP1A, ZNF536, SPTBN1, TNRC6C and SCN2A genes, we replicated the results obtained by Satterstrom et al. [SEMA6D and FBXL17 genes, we replicated the results of Rovira et al. [While searching for rare and PTV variants related to ADHD risk in the studied population, we were able to select a group of 13 genes. The m et al. . In turna et al. . The resADRA2A, DYNC1H1, FTO, MAP1A, SEMA6D, ZNF536). Using a Venn chart, we selected genes common and different for ADHD and EBW. This included seven genes specific for ADHD. Especially intriguing was the FBXL17 gene (F-box and leucine rich repeat protein 17) coding member of the F-box protein family, which acts as protein-ubiquitin ligases [FBXL17 gene was identified in the analysis of interaction networks enriched among genes with SNP coding that correlate with bacterial taxa abundance in humans as presented by Blekhman et al. [FBXL17 gene as a host gene is associated with modulating the composition of the gut microbiome and may influence ADHD susceptibility [FBXL17 gene would influence the composition of the gut microflora and contribute to the development of ADHD risk [In turn, in looking for variants related to EBW, we identified six relevant genes  indicates that, in ADHD, we may have to deal with biological pathways leading to excess body weight not found in the classic and direct causal relationship. An example of this is the research results presented by Amare et al. [The group of genes common for ADHD and EBW . The presented results confirm the need to continue searching for robust biological markers, in both common and rare variants.On the other hand, variants in the