Parents completed the Children’s Eating Behaviour Questionnaire (CEBQ) to assess children’s eating behaviour at 4 years and the ADHD Test (ADHDT) and Child Behaviour Checklist for ages 6–18 (CBCL/6–18) to evaluate ADHD symptoms at 4 and 6 years, respectively, as well as measures of BMI. Longitudinal structural equation modeling (SEM) was carried out to evaluate the associations of all variables between 4 and 6 years. Food responsiveness at 4 years was positively associated with hyperactivity at age 6, whereas emotional overeating was negatively associated with hyperactivity. There was no evidence of an association between eating behaviours of preschoolers and BMI at 6 years, or BMI at 4 years and later ADHD symptoms and vice versa. Findings suggest that food responsiveness is an early marker of ADHD symptoms at 6 years of age. In contrast to our hypothesis there was no significant association between ADHD at age 4 and BMI at age 6.Previous evidence suggests a link between attention deficit hyperactivity disorder (ADHD) symptoms and disordered eating behaviours; however, the direction of the causal association remains unclear. Building on our previous research, we aimed to examine the longitudinal association between eating behaviours at 4 years, ADHD symptoms at 6 years of age, and the role of body mass index (BMI). We included children from the RHEA mother–child cohort in Greece, followed up at 4 and 6 years (The online version contains supplementary material available at 10.1007/s00787-021-01720-x. Attention deficit hyperactivity disorder (ADHD) has been identified as one of the most common childhood-onset neurodevelopmental disorders worldwide , with syOur previous work was the first population-based study that investigated the link between ADHD symptoms and eating behaviours in preschoolers , 15. We Starting in February 2007 and within a 12-month period pregnant women were invited to participate in the Rhea birth cohort study in Crete, Greece, . Women wn = 926).Out of 1,363 singleton live births, 872 children were followed up at 4 years and 604 at 6 years of age. In total, 30 children were excluded due to diagnosis of neurodevelopmental disorder , other medical conditions  or incomplete assessments. Analyses included all participants with available data for at least one-time point (Children’s eating behaviour was assessed using the Children’s Eating Behaviour Questionnaire (CEBQ) . PrimaryThe assessment of ADHD symptoms at the 4-year follow-up has been previously described in detail , which iThe Child Behaviour Checklist for ages 6–18 (CBCL/6–18) was used to assess ADHD symptoms at 6 years of age. CBCL is a 113-items parental questionnaire designed to assess behavioural and emotional difficulties in children 6–18 years of age. The CBCL offers two alternative ways to summarize its items, the empirically-based syndrome scales which include the Attention Problems Scale and the DSM-oriented scales which include the Attention deficit/hyperactivity problems scale. In our analysis we used both scales, since both evaluate ADHD-related symptoms. The CBCL has been translated, adapted and standardized for the Greek population .Maternal age (years), maternal education  and maternal origin  at recruitment or delivery.2) and BMI z-scores as a continuous variable at the age of 4 and 6 years. BMI was calculated from height and weight and BMI z-scores have been developed based on the Rhea cohort-specific gender and age-adjusted growth curves. BMI cut-offs were based on International Obesity Task Force (IOTF) definitions . Additional model fit indices, the AIC and BIC confirmed the improved fit of the submodel by assigned lower values (see full fit statistics in Table S4). The submodel is illustrated in Fig. β = − 0.12, 95% CI − 0.18, − 0.06, p < 0.05) but not with attention problems at 6 years. Food responsiveness at 4 years was positively associated with hyperactivity  but not with attention problems at 6 years. As expected ADHD symptoms at 4 years positively contributed to ADHD symptoms at 6 years. Both hyperactivity and impulsivity at 4 years predicted higher hyperactivity  and attention problems  scores at 6 years. BMI at age 4 was not related with later hyperactivity or attention problems, but tracked substantially  to the age of 6 years. There was no evidence of a longitudinal association between food responsiveness and emotional overeating with BMI at 6 years.Results from the full SEM are listed in Table S2, showing standardized beta coefficients, 95% confidence intervals and A subsequent step included a sensitivity analysis and the same model was fitted separately for boys and girls. Results differed slightly between the two groups but were overall in the same direction (see Table S5 for a full list of path estimates).In these sex-stratifed sensitivity analyses, associations seen in whole group analyses remained, but with slight differences between the two groups (Table S5). For boys, food responsiveness was not significantly associated with attention problems at 6 years. Statistical significance was also attenuated between emotional overeating and impulsivity at 4 years and ADHD symptoms at age 6, in the girls group, potentially due to the reduced sample size in the subpopulations.The present study is the first to explore the longitudinal association between food approach behaviours and ADHD symptoms across childhood. Food responsiveness at 4-year predicted hyperactivity at age 6, whereas this association was negative for emotional overeating. Both ADHD symptoms and BMI tracked longitudinally from age 4–6 years. Contrary to our hypothesis, there was no association between ADHD symptoms at 4 years and later BMI or vice versa. Finally, we did not observe any associations between eating behaviours of preschoolers and BMI at 6 years, as well as no evidence for an association between eating behaviours and later attention problems. When testing boys and girls separately, gender did not affect the observed associations, suggesting that similar risk pathways operate across genders.Our prospective findings indicated a positive longitudinal association between food responsiveness  and ADHD symptoms at age 6, even when accounting for their correlation at age 4. In this case, overeating in the form of food responsiveness also known as external eating, might result from a predisposition to impulsivity, which in turn is considered as a central symptom for both ADHD  and eatiAlthough in our previous cross-sectional findings emotional overeating was associated with ADHD symptoms , we founWe found no evidence of an association between eating behaviours at age 4 and later BMI in our study population. Recently Derks et al. were also unable to observe a prospective association between eating behaviours at 4 and BMI at 10 years of age . HoweverInterestingly, we observed that ADHD symptoms at 4 years were not prospectively associated with BMI at 6 years. A recent review of longitudinal studies by Cortese and Tessari supported the causal effect of ADHD on weight gain , 37 maybNotable strengths of the current study include the relatively large sample size, as part of a birth cohort study, allowing us to control for several confounding variables prospectively collected within the cohort. Phenotypes of interest were measured with well-validated instruments, translated and adapted for the Greek population. Furthermore, inclusion of children  from the general population, who do not meet diagnostic criteria for disorder, enables extrapolation of findings to other non-clinical populations. However, extrapolation of the findings might be questioned by the high percentage of non-respondents and their differences with the participating study population.Despite the strengths, there are some limitations that merit consideration. Even though, various phenotypes were measured in this cohort, other factors, potentially involved in the observed associations were not included; such as impulsivity, parental feeding practices, home environment, and family income –41. MoreIn summary, this is the first study that captures the longitudinal association between aberrant eating behaviours and hyperactivity in early childhood. In line with our previous findings, there is evidence that food responsiveness is not only cross-sectionally but also prospectively associated with ADHD symptoms at 6 years of age. In addition, we did not observe any association between ADHD symptoms at 4 years and later BMI or vice versa. We suggest that food responsivenees might be an early sign of possible ADHD symptoms later in life, hence allowing early identification of children who later develop ADHD. Future studies might need to take a broader perspective to better understand if there are causal links between eating behaviours, ADHD symptoms and weight gain, and examine their shared genetic and neurobiological markers.Supplementary file1 (DOCX 28 KB)Below is the link to the electronic supplementary material.


The increasing number of university students seeking diagnosis of attention-deficit/hyperactivity disorder (ADHD), and findings of an increased stimulant misuse among university students, has raised concerns regarding the credibility of the symptoms of those students. However, most of our current knowledge refers to university students in North America and less is known about this issue on European campuses. The present survey aimed to collect opinions on feigning ADHD and to estimate the prevalence of stimulant misuse among 1071 university students in the Netherlands. The majority of students expressed liberal attitudes towards feigning ADHD. Also, a substantial number of respondents considered feigning ADHD themselves or know someone who feigns ADHD. Furthermore, 68% of students assumed benefits of taking stimulants without prescription and 16% have indeed already taken stimulants without prescription. Feigning ADHD and misuse of prescription medication are prevalent issues among Dutch students. The results underline the need for a careful diagnostic evaluation of individuals for ADHD. Furthermore, efforts are required in order to prevent stimulant drug trafficking and misuse among university students. Attention-deficit/hyperactivity disorder (ADHD) is a neuropsychiatric developmental disorder which affects about 1.2–7.3% of adults worldwide . ADHD impacts negatively on multiple aspects of daily living, including learning and achievements in the educational setting . Thus, the remaining sample of 1071 individuals did not contain any students who reported to be diagnosed with ADHD. The vast majority of students were in their first year. Moreover, females took part in the study in larger numbers than males. The self-rated study performance of respondents was mixed ranging from very bad (1) to very good (5). None of the respondents reported to be diagnosed with ADHD.One thousand and seventy-one students from the social sciences of the University of Groningen, the Netherlands, took part in the study and completed the survey Table . Becausethey ever considered to feign ADHD (Yes/No), (2) they know someone who feigns ADHD (Yes/No), (3) they think it is easy to feign ADHD (Yes/No), and (4) they think there are benefits to feign ADHD (Yes/No), including the type of benefits (open response format). Further items were analyzed asking respondents whether (5) they think there are benefits to take stimulants  without prescription (Yes/No), and whether (6) they have ever taken stimulants  without prescription (Yes/No), including frequency of use, source of medication, as well as their motives for either taking (multiple answers in categories possible) or not taking stimulants (open response format).The present study was part of a larger survey on university students. To learn about attitudes towards and experiences with feigning ADHD and stimulant misuse, only a selection of items was used for the present context, i.e. whether (1) The study was approved by the ethical committee psychology (ECP) affiliated to the University of Groningen, the Netherlands. The survey was to be filled in online and took about 20 min to complete. All respondents were informed about the purpose of the study beforehand and gave consent by clicking on a specified button at the introductory page that prompted participants to the start of the survey. It was made explicit to participants that data were stored and processed anonymously. Participation was voluntary and not paid. However, first-year psychology students were awarded with study credits as part of their undergraduate research requirement.n = 170) or for not taking stimulants without prescription (n = 901). The motives for taking stimulants concerned for more than half of the respondents leisure activities and the academic context. The most common motives for not taking stimulants regarded no perceived need  and concerns with health issues . A lower number of students indicated ethical matters in this context Absolute and relative frequencies of respondents who endorsed statements on feigning ADHD and stimulant misuse are presented in Table The present study highlights that about half of the Dutch university students of the present sample assume benefits regarding the feigning of ADHD (52%) and express their confidence in the ease to feign ADHD (57%). The assumed benefits of feigning ADHD are of various types, going beyond getting access to stimulant medication. Such perceived advantages include most often accommodation at university to improve study performance , and benefits in the social interaction with family and friends, such as receiving attention from others, being pitied, or having an excuse for academic failure or occasional misbehavior. The fact that a substantial number of students perceive benefits from an ADHD diagnosis in the social context may indicate a low perceived stigma associated with ADHD, as the diagnosis of ADHD is seemingly not seen as a label that sets individuals apart and may result in depreciation. Instead, the label ADHD may even bring benefits in the way others have understanding for their situation and treat them. The risk of feigned ADHD among university students is not only shown by 2% of the sample who indicated to have ever considered feigning ADHD themselves, but also by the fact that almost 1 in 5 students (17%) knows someone who feigns ADHD.Both, the positive attitudes towards the effects of prescription stimulants for people not diagnosed with ADHD as shown by 68% of the students and the substantial number of students (16%) who indicated to take stimulants without prescription confirm previous research that reported widespread stimulant misuse on university campuses in North America. Previous studies tried to characterize students who take stimulants without prescription. These studies found that in comparison to students not taking stimulants, stimulant misusers are more likely to have neuropsychological dysfunction .Finally, future research needs to determine differences in the opinions and attitudes on this issue across faculties and study years, and is also advised to take special study periods into consideration. For example, it could be speculated that time periods exposing students to acute stress (e.g. exam periods) or revealing academic failure (e.g. post exam periods) may change opinions towards use of stimulants and potentially feigning ADHD.


These findings were further validated by immunohistology. At later stages of the disease process, the right kidneys revealed an increased frequency of focal segmental glomerulosclerosis lesions, interstitial fibrosis and tubular atrophy. Our findings revealed proteome changes in LBW rat kidneys and revealed a strong downregulation of specific mitochondrial respiratory chain proteins, such as UQCR7.The hyperfiltration theory has been used to explain the mechanism of low birth weight (LBW)-related nephropathy. However, the molecular changes in the kidney proteome have not been defined in this disease, and early biomarkers are lacking. We investigated the molecular pathogenesis of LBW rats obtained by intraperitoneal injection of dexamethasone into pregnant animals. Normal-birth-weight (NBW) rats were used as controls. When the rats were four weeks old, the left kidneys were removed and used for comprehensive label-free proteomic studies. Following uninephrectomy, all rats were fed a high-salt diet until 9 weeks of age. Differences in the molecular composition of the kidney cortex were observed at the early step of LBW nephropathy pathogenesis. Untargeted quantitative proteomics showed that proteins involved in energy metabolism, such as oxidative phosphorylation (OXPHOS), the TCA cycle, and glycolysis, were specifically downregulated in the kidneys of LBW rats at four weeks. No pathological changes were detected at this early stage. Pathway analysis identified NEFL2 (NRF2) and RICTOR as potential upstream regulators. The search for biomarkers identified components of the mitochondrial respiratory chain, namely, ubiquinol-cytochrome c reductase complex subunits (UQCR7/11) and ATP5I/L, two components of mitochondrial F Infants born at less than 2500 g are designated with a low birth weight (LBW). LBW individuals have an increased risk of heart disease, diabetes mellitus, and kidney disease. In particular, LBW is associated with an increased risk of chronic kidney disease (CKD) ,2 or endThe most characteristic glomerular change in LBW individuals is focal segmental glomerulosclerosis (FSGS) . We prevTo investigate molecular candidates involved in LBW-related nephropathy, we developed a rat model of human LBW-related nephropathy and performed a comprehensive untargeted proteomic study of the kidneys between LBW and NBW rats at a young age, when neither group had any pathological changes. These studies could reveal the early pathological factors underlying LBW-related nephropathy.We generated a rat model of LBW as previously described  and summThere were no sclerotic lesions or any other pathological changes in glomeruli and tubules of either the LBW or NBW rats at 4 weeks of age . Howeverp < 0.05) between LBW and NBW rats at this time point , as shown in  altered in the LBW rat kidneys were EIF2 signaling, oxidative phosphorylation , TCA cycle, and sirtuin signaling  and ‘Eif4 and P70S6K signaling’ . Lastly, the catabolism of branched chain amino acids (BCAAs) was strongly altered in the LBW kidneys .Analysis of the above-described proteomic data was performed using Ingenuity Pathway Analysis (IPA) software version July 2021. The LBW rat kidney differential proteome composed of 1183 proteins was compared to the IPA Knowledge database to detect enrichment in specific pathways using the ‘core analysis’ module. The results revealed that the main four pathways  in the LBW kidney allowed us to predict putative transcription factors, kinases, proteases, or even microRNAs involved in the observed changes based on the knowledge of the targets associated with specific regulators and the directionality of the expected changes. Such a bioinformatic predictive analysis was performed on the LBW kidney proteome. The results (rapamycin-insensitive companion of mTOR (RICTOR) and NFE2L2 (NRF2), as a large number of their respective targets were found in the LBW differential proteome dataset , which was strongly altered in the LBW kidneys (17 proteins). Abnormal BCAA metabolism can result in BCAA depletion in the plasma, but this has not been verified in the blood of LBW patients. However, a prospective study performed on 15 patients with early stages of chronic kidney disease showed a significant decrease in plasma valine and leucine levels compared with controls . BCAAs cComputer analysis of the untargeted proteomic dataset generated in our work also predicted the alteration of mTOR signaling in LBW. Accordingly, a considerable number of studies have shown the involvement of mTOR and RICTOR in nephropathies ,43. MitoTo conclude, the proteomic and histopathology study of an LBW rat model generated a unique dataset of interest for generating novel hypotheses on the pathogenesis of LBW nephropathy. We unraveled changes in proteins such as Meprin A, UQCR7/11 and Atp5I/L that occurred early in the disease process, suggesting that these elements are potential players in the molecular determinants of this disorder. However, our findings remain limited to the rat model used. Technical and methodological limitations also exist in our study, as only 1200 proteins were found in the rat kidney proteome. This number is low as compared to the reference rat proteome dataset although a recent analysis found a similar number of distinct proteins (1290) in rat kidney . Therefop < 0.001, The rats were treated in accordance with the guidelines of the Committee on Ethical Animal Care and Use of the National Hospital Organization Chiba-Higashi National Hospital. Eight pregnant rats were fed standard chow ad libitum. By intraperitoneally injecting dexamethasone (DEXA) (0.2 mg/kg) into pregnant rats (n = 5) consecutively at 15 and 16 days of gestation, LBW rats were born at high rates, as shown in previous reports . For conNewborn rats remained with their dams until they were 4 weeks old, at which point they were removed from the cages and provided standard chow ad libitum. When the rats were 4 weeks old, the left kidneys were removed. These extracted kidneys were separated for histological and proteomic analyses. The kidneys were horizontally cut, and specimens from the center were used for light microscopic analysis. For the light microscopy studies, kidney specimens were fixed in 10% neutral-buffered formalin followed by paraffin embedding and routine staining with Masson’s trichrome stain, or periodic acid–methenamine–silver (PAM) with hematoxylin and eosin (HE) (PAM-HE) stain. PAM-HE-stained images of paraffin-embedded kidney sections were imported into a vertical slide system. The glomerular size was automatically calculated by manually outlining the glomerular tuft area on the display. The kidney specimens used for the proteomics studies were only obtained from the cortex area. After separating the cortex from the medulla, the isolated cortex specimens were immediately frozen in liquid nitrogen and stored at −80 °C until further analysis. After uninephrectomy, all rats were fed a high-salt diet consisting of 8% NaCl until they were 9 weeks old. After sacrifice, the right kidneys were removed and separated for histological analyses in the same way as those at 4 weeks of age.Primary antibodies were anti-UQCR polyclonal antibody  and anti-ATP5I polyclonal antibody . Antigen retrieval was performed according to the manufacturer’s instructions. Numbers of UQCR7-positive tufts per glomerulus were counted in more than ten glomeruli per kidney section. The relative amounts of UQCR7 staining in vascular poles of glomeruli were also scored semi-quantitatively in more than ten glomeruli per kidney section as follows: 0 = negative staining, 1 = positive in less than 50% of the circumference of vascular pole, 2 = positive in equal to or more than 50% of the circumference 3 = positive in whole circumference. In addition, percentages of UQCR7-positive cells in distal tubular cells were calculated by counting more than fifty distal tubular cells per section. Positive rates of Atp5I in tubular cells were evaluated.Sample preparation and protein digestion, nLC-MS/MS analysis, and database search and results processing were performed as described in .p-value < 0.05, (vii) calculation of protein abundance (sum of corresponding peptide volume), and (viii) ANOVA testing with Benjamini–Hochberg correction for multiple testing at the protein level and filtering for features with values of Adj. p < 0.05. Of note, only nonconflicting features and unique peptides were considered for calculation at the protein level. Additionally, Progenesis performs an arcsinh correction of data before ANOVA calculation. Quantitative data were considered for proteins quantified by a minimum of two peptides. Protein abundance was normalized to the total protein content determined for each sample using in-gel densitometry methods (SDS-PAGE). MS data were also normalized in Progenesis based on feature median ratio. Furthermore, we selected proteins that showed a statistically significant > 20% change in expression levels  for differences between two groups (n = 7 in each group). A global analysis of the data was performed using the computer platform Ingenuity Pathway Analysis . We used the ‘core analysis’ package to identify relationships, mechanisms, functions, and pathways relevant to the dataset of interest. We also used the ‘regulators’ package to identify predicted regulators of the proteomic changes.Raw LC-MS/MS data were imported into the Progenesis LC-MS 4.0 software program . The data processing included the following steps: (i) feature detection, (ii) feature alignment across the samples, (iii) volume integration for two to six charge-state ions, (iv) normalization to the total protein abundance, (v) import of sequence information, (vi) ANOVA procedure at the peptide level, and filtering for features with values of


Relatively little is investigated regarding the neurophysiology of adult attention-deficit/hyperactivity disorder (ADHD). Mismatch negativity (MMN) is an event-related potential component representing pre-attentive auditory processing, which is closely associated with cognitive status. We investigated MMN features as biomarkers to classify drug-naive adult patients with ADHD and healthy controls (HCs). Sensor-level features (amplitude and latency) and source-level features (source activation) of MMN were investigated and compared between the electroencephalograms of 34 patients with ADHD and 45 HCs using a passive auditory oddball paradigm. Correlations between MMN features and ADHD symptoms were analyzed. Finally, we applied machine learning to differentiate the two groups using sensor- and source-level features of MMN. Adult patients with ADHD showed significantly lower MMN amplitudes at the frontocentral electrodes and reduced MMN source activation in the frontal, temporal, and limbic lobes, which were closely associated with MMN generators and ADHD pathophysiology. Source activities were significantly correlated with ADHD symptoms. The best classification performance for adult ADHD patients and HCs showed an 81.01% accuracy, 82.35% sensitivity, and 80.00% specificity based on MMN source activity features. Our results suggest that abnormal MMN reflects the adult ADHD patients’ pathophysiological characteristics and might serve clinically as a neuromarker of adult ADHD. Attention-deficit/hyperactivity disorder (ADHD) has been defined as a neurodevelopmental disorder with symptoms of distracted attention, hyperactivity, and impulsivity . AlthougEvent-related potentials (ERPs) have been widely utilized to study the attentional processes in childhood ADHD . In partSeveral studies have assessed MMN changes and their correlates with attentional problems in children with ADHD –20. A reRecently, an increasing number of researchers have attempted to differentiate patients with ADHD from HCs on the basis of machine learning methods with EEG biomarkers. However, few studies have investigated the classification of adult patients with ADHD and HCs. They have reported accuracies ranging from hardly above chance level (50%) to beyond 90%. For example, Mueller et al. , 24 clasDetection of electrophysiological markers to differentiate adult ADHD patients could support clinicians to provide appropriate diagnosis and treatment for patients suffering from difficulties in directing and maintaining attention in their lives. However, given that there have been limited studies of adult ADHD, further studies are warranted to identify biomarkers for adult ADHD using machine learning techniques. Moreover, research differentiating between adult patients with ADHD and HCs using MMN features has not yet been undertaken.The aim of this study was to investigate changes in MMN features in adult patients with ADHD compared to HCs at both the sensor (amplitude and latency) and source levels (source activation). In addition, we explored the relationships between MMN features and ADHD symptom scores. Finally, we examined the possibility of MMN features serving as biomarkers by differentiating between ADHD patients and HCs using machine learning techniques. We hypothesized that MMN activities at both the sensor and source levels would be attenuated in adult ADHD patients compared to HCs, reflecting the pathophysiology of ADHD. We also hypothesized that these MMN characteristics could differentiate between ADHD patients and HCs with acceptable classification performances. To the best of our knowledge, this study is the first to examine the differences in MMN characteristics between adult patients with ADHD and HCs and to classify them via machine learning measures.n = 34, male: 28, female: 6, mean age: 24.76 ± 7.02 years; range: 19–45 years) and healthy controls . Participants with ADHD were enrolled from the Department of Psychiatry at Soonchunhyang University Cheonan Hospital, Korea. All psychiatric evaluations were conducted by a board-certified psychiatrist specializing in adult ADHD using the full criteria for ADHD in accordance with the DSM-V. Patients who had mental retardation or alcohol abuse, undergone electroconvulsive therapy, or suffered head injury were not included. All patients with ADHD were drug-naïve. Forty-five non-smoking HCs were enrolled by the local community via newspapers and posters. We excluded participants with any axis I or II comorbid psychiatric diagnosis or any history of neurological diseases from the initial screening interviews. Participants had normal hearing ability confirmed by the 512 Hz tuning fork test [A total of 79 subjects aged 18–45 years participated in this study. Subjects included patients with ADHD  . The ASREEG data were recorded by a NeuroScan SynAmps2 amplifier  with 62 Ag-AgCl channels mounted on a Quik Cap, using an extended 10–20 placement scheme. The ground channel was placed on the forehead, and the physically linked reference channel was attached to both mastoids. Vertical electrooculogram (EOG) channels were located above and below the left eye. Horizontal EOG channels were placed at the outer canthus of each eye. The impedance was maintained below 5 kΩ. The EEG data were obtained with a band-pass filter with cutoff frequencies ranging from 0.1 to 100 Hz at a 1000 Hz sampling rate.The acquired EEG data were preprocessed by CURRY 8  and MATLAB R2018b . Gross artifacts were rejected from visual inspection of an experienced person without any prior knowledge concerning the origin of the data. Artifacts related to eye movements or eye blinks were corrected using a covariance- and regression-based mathematical procedure implemented in the preprocessing software . The datEEG recordings and stimulus presentation onset were synchronized by E-prime . The auditory stimuli composed of sounds at 1000 Hz and 85 dB SPL. The participants were instructed to focus their attention on a picture book called “Where’s Wally?” without paying attention to the auditory stimuli. To obtain the MMN wave, the ERP wave derived from standard stimuli was subtracted by the ERP wave from deviant stimuli for each participant.Standard stimuli lasting 50 ms were presented, randomly interspersed with deviant stimuli lasting 100 ms . There were a total of 750 auditory stimuli with a 500 ms inter-stimulus interval. These stimuli were presented by MDR-D777 headphones . The experiment took about 10 min to complete.vs. 609.18 ± 48.56, p = 0.414; deviant stimuli: 66.91 ± 7.46 vs. 67.71 ± 5.14, p = 0.575).MMN amplitude was measured as the peak voltage between 130 and 280 ms at nine channels  according to previous studies revealing that the frontocentral electrodes show larger MMN amplitudes –33. The In order to estimate the cortical distribution of the standardized source current density for MMN activity, standardized low-resolution brain electromagnetic tomography (sLORETA) was employed. sLORETA has been widely utilized as a representative source-imaging method to solve the EEG inverse problem . It assut-test for the 6239 voxels, followed by a randomization test (n = 5000) for correcting multiple comparisons.The MMN source image was analyzed between 130 and 280 ms after stimulus onset. The comparison of sLORETA images between adult patients with ADHD and HCs for MMN was conducted by a statistical non-parametric mapping method (SnPM) carried out in the sLORETA software. The estimated voxel activation was averaged across the calculated time frame and tested with a voxel-by-voxel independent t-tests. A chi-squared test was used to analyze between-group difference in sex ratio. The significance level was p < 0.05 (two-tailed). A multivariate analysis of variance (MANOVA) was performed to evaluate differences in MMN amplitudes and latencies at frontocentral electrodes and MMN source activities between the two groups, with education years as a covariate. An adjusted p-value of 0.05/52 = 0.000962 (18 features from sensor level and 34 features from source level) by Bonferroni corrections were used to control for multiple comparisons. Effect sizes were represented as partial eta squared (η2). A partial Pearson’s correlation analysis was conducted between MMN features from sensor and source levels and ADHD symptom scores, with years of education controlled for. After the correlation analysis, an adjusted p-value of 0.05/(3*27) = 0.000617 (27 significant MMN features with three ASRS scores) by Bonferroni corrections were applied. Statistical analyses were carried out using SPSS 21 .Differences of age, education years, and psychological characteristics (ASRS) between adult patients with ADHD and HCs were compared using independent We discriminated between adult ADHD patients and HCs using the MMN sensor and source activities to check their potential usability as biomarkers. To find optimal features for discriminating the two groups, both MMN features from sensor and source levels were used as follows Table : sensor-Classification was performed using a linear support vector machine classifier with the cost set as 1 –47 and thttp://37steps.com) for the machine learning analyses.In addition, 1000 times permutation test (group label permutation) were performed to assess the statistical significance of our classification accuracy , 57, 58.p < 0.001). In addition, there were significant differences in ASRS scores between the two groups, such that the patients with ADHD showed significantly higher ASRS (p < 0.001) and its subscales, including inattention (p < 0.001) and hyperactivity (p < 0.001).Table p < 0.001), FC4 (p < 0.001), C3 (p < 0.001), Cz (p < 0.001), and C4 (p < 0.001) electrodes. However, there was no significant difference in MMN latency between the two groups. Figure The patients with ADHD showed significantly reduced MMN peak amplitudes compared to HCs at the FCz p < 0.00, FC4 , right anterior cingulate cortex , right inferior frontal gyrus , right superior temporal gyrus , right extra nuclear , left orbital gyrus , right orbital gyrus , left rectal gyrus , right rectal gyrus , left subcallosal gyrus , right subcallosal gyrus , right uncus ; (2) inattention: left anterior cingulate cortex , right anterior cingulate cortex , right inferior frontal gyrus , right extra nuclear , left orbital gyrus , right orbital gyrus , left rectal gyrus , right rectal gyrus , left subcallosal gyrus , right subcallosal gyrus , right uncus ; (3) hyperactivity: left anterior cingulate cortex , right anterior cingulate cortex , right inferior frontal gyrus , right extra nuclear , left orbital gyrus , right orbital gyrus , left rectal gyrus , right rectal gyrus , left subcallosal gyrus , right subcallosal gyrus , right uncus   ASRS: left anterior cingulate cortex .In this study, we investigated MMN abnormalities, indicative of pre-attentive central processing of auditory change detection, in drug-naive adult patients with ADHD and differentiated ADHD patients and HCs based on machine learning methods. Our major findings are as follows. First, the patients with ADHD showed significantly reduced MMN amplitudes at the frontocentral electrodes compared to HCs. Second, the patients exhibited significantly decreased MMN source activities in the frontal, temporal, and limbic regions. Third, MMN source activities were negatively correlated with ADHD symptom scores in the study population. Fourth, the best classification performance for adult ADHD patients and HCs was an 81.01% accuracy, an 82.35% sensitivity, and an 80.00% specificity from the MMN source activity features.The MMN amplitudes were attenuated in adult patients with ADHD compared to HCs at the frontocentral electrodes. Although there has been no previous study exploring MMN in adult patients with ADHD, our results are comparable to those of previous studies showing decreased MMN amplitudes in children and adolescent patients with ADHD , 17, 59.In this study, adult patients with ADHD showed significantly reduced MMN source activities compared to HCs in the frontal lobe , temporal lobe , and limbic lobe . In addition, reduced source activities in the bilateral anterior cingulate cortex, right inferior frontal gyrus, right superior temporal gyrus, right extra nuclear, bilateral orbital gyrus, bilateral rectal gyrus, bilateral subcallosal gyrus, and right uncus were negatively correlated with the ADHD symptom scales in the study population.In general, auditory MMN is produced in the primary auditory cortex and adjacent areas of the superior temporal lobe , 62. TheRegions in which we found dysfunctional MMN source activities and significant symptomatic correlations were areas of focus in several previous ADHD studies. According to previous neuroimaging studies, the development of ADHD was closely associated with impaired frontal lobe function , 71. AltIn addition, several studies have highlighted that the inferior frontal gyrus is crucial for inhibiting behavioral responses related to impulsivity and hyperactivity , 81. OpiFurthermore, the insula is known to play a generic role in updating information and cognitive control, including attention allocation to behaviorally relevant salient stimuli –88. The These abnormal regions belonging to frontal, temporal, and limbic lobes are highly related to the pathophysiology of ADHD. The previous findings support our results regarding altered source activation for MMN in adult ADHD patients and correlations between source activation and ADHD symptom scores. Decreased MMN source activation in adult patients with ADHD could be interpreted as decreased efficiency of automatically regulated attentional processes and increased demand for more effort and resources.We applied machine learning methods to classify adult ADHD patients and HCs using sensor- and source-level features extracted from MMN ERP data. The best classification performance was achieved when source-level features were used . According to our results, the source-level features from MMN might play a more important role in the classification of ADHD patients and HCs than the sensor-level features. This might be due to the notion that the shortcoming of sensor-level features could be supplemented by source-level features such as low spatial resolution originating from volume conduction and poor signal-to-noise ratio –98. ThusMoreover, when we used both sensor- and source-level features for the classification of adult ADHD patients and HCs, we obtained a classification accuracy of 81.01%, which was the same value as that from source-level features. However, the sensitivity was higher for source-level features (82.35%) than for both sensor- and source-level features (79.41%). In the clinical setting, the value of sensitivity, referring to the ability to correctly identify patients with the disease, could be considered more important . Therefop < 0.001). The statistics for the univariate analyses were calculated based on the differences of mean and standard deviation between two groups. Thus, even though the data distribution of the two groups from the features was overlapped, the features could show significant between-group differences with their mean and standard deviation differences. However, to achieve higher classification performances using machine learning, the distribution of data between two groups should be less overlapped. We achieved the classification accuracy of 81.01% between adult ADHD patients and HCs using the source-level feature set. This classification accuracy was acceptable and we found that the accuracy was significantly based on the permutation test. Since our study was the first to investigate MMN changes in adult patients with ADHD, further studies with a larger number of adult patients with ADHD are warranted to verify our findings.In this study, a number of MMN features including sensor and source levels showed significant between-group differences based on the univariate analysis method (This study has several limitations. First, when interpreting the results, the relatively small sample size should be considered. Further studies are warranted to validate the findings with larger samples. Second, the patient group and HCs were not matched for education years. However, the difference in education years between the groups has been accepted in recent studies , and we


Mortality rate in puppies and kittens over the first two months of age is high, estimated at around 20%. Low birth weight has been identified as a major risk factor for neonatal mortality in these species. Using an online questionnaire, we explored perceptions and management practices of breeders regarding newborns at low birth weight. Three different profiles were identified among 649 breeders. The first one included mainly dog and cat breeders who weighed newborns and monitored their suckling by the dam (controlled suckling) but did not warm them. The second group consisted of breeders of both species who did not weigh puppies or kittens to identify low birth weight or to monitor their weight afterwards. The third and final group included mostly cat breeders who routinely weighed neonates as in the first group, but who used artificial feeding rather than controlled suckling. This better knowledge of the management of puppies and kittens at high risk of neonatal mortality in the field provides the basis to establish guidelines to increase their chances of survival.Low birth weight (LBW) has been identified as a major risk factor for neonatal mortality in many species. The aim of this survey was to determine the profiles of canine and feline breeders concerning their perceptions of, and management practices relating to, LBW individuals. An anonymous online survey was addressed to French cat and dog breeders in September 2019 via social networks. Multiple correspondence analysis and hierarchical clustering were used to explore breeders’ profiles. Three clusters were identified among the 649 breeders included in this analysis. Cluster 1 (49%) included dog and cat breeders who weighed newborns (and thus identified LBW) and controlled nursing by the dam (controlled suckling) but did not warm them up. Cluster 2 breeders (21%) of both species did not weigh puppies or kittens to identify LBW or to monitor the evolution of their weight afterwards. Cluster 3 (30%) including mostly cat breeders who weighed neonates routinely as in Cluster 1, but they practiced artificial feeding rather than controlled suckling. This survey provides a basis for better understanding of perceptions and practices regarding LBW puppies and kittens. It will be useful to provide guidelines for neonatal management to increase their chances of survival. Canine and feline newborns face high neonatal mortality (from birth to three weeks of age) with average live-born mortality rates close to 10% during this period ,3. ApproAs in other mammals , low birth weight (LBW) is one of the major risk factors of neonatal mortality in puppies and kittens ,5,6,7. TThe objective of this study was thus, for the first time, to identify canine and feline breeder profiles regarding LBW management  based on data collected through a large online survey.n = 3743 and n = 910, respectively). This network is made up of breeders who have voluntarily signed up to receive newsletters about the work of the team. The survey was also shared without our control. At the beginning of the questionnaire, participants were informed about the anonymity and confidentiality of the data they provide, and that by completing the questionnaire they were giving their consent for their answers to be analysed.A collaborative meeting was first organised between five veterinarians specialized in carnivore neonatology and 15 French canine and/or feline breeders to explore the management of newborns in their facilities. An online questionnaire was then drafted in French using the software Sphinx iQ 2  and pretested by 10 people. The survey was launched in September 2019: the link was sent to dog and cat breeders belonging to the NeoCare network via Facebook and an internal mailing list  was performed to summarize the information contained in the selected set of categorical variables and evaluate the pattern of their relationships. MCA reduces the dimensions of these multivariate data by constructing a small number of uncorrelated synthetic factors (components or dimensions) accounting for most data variability . This deFor the second stage, the results obtained from MCA were used in a hierarchical cluster analysis (HCA) to place breeders into different classes. The “Euclidean” distance was calculated between the individual breeders based on the first five components selected from the MCA. Then, using Ward’s minimum variance method consolidated with the K-means method , HCA wasStatistical analyses were performed using R software, version 4.0.1  and the n = 514/649, 79%, n = 10; Switzerland, n = 8; Canada, n = 1). The country of the 116 remaining respondents (18%) could not be identified. Among the 649 participants, 48% were cat breeders, 46% were dog breeders and the remaining 6% were both dog and cat breeders. Six hundred and forty-nine of the 674 breeders who participated in the survey were retained for this study . Most of them were located in France . In total, three profiles of breeders regarding LBW identification, management and perception were identified and are represented in Breeders who gave a score of 1 or 2 on the frequency of LBW and the difficulty of managing it were significantly more numerous in Cluster 1 than in the other two . Thus, bCluster 2 included breeders who estimated that LBW is a risk factor for neonatal mortality and who considered LBW as frequent. Moreover, the majority of breeders in this cluster did not use weighing, either at birth to identify LBW newborns or to monitor their weight evolution, were in Cluster 2. Mixed breeders were over-represented in this group.Breeders in Cluster 3 considered LBW to be moderately frequent and difficult to manage. As in Cluster 1, they weighed LBW routinely. Among the three clusters, they practiced artificial feeding and warming most frequently, but never controlled suckling. Cat breeders were more likely to be represented in Cluster 3 . The respondents were recruited on a voluntary basis, mostly via Internet. Thus, a selection bias cannot be ruled out and younger breeders (more familiar with the Internet) and/or with more efficient management of LBW and/or already aware of LBW as an issue regarding neonatal mortality, were potentially more likely to respond to the survey. As this survey was written in French, the respondents were mostly French or from French-speaking countries. Further studies are needed to explore the differences in management and perceptions in other countries.Data were collected from 674 breeders. This population represents less than 2% of the total number of active breeders referenced in France . FigurePerception regarding LBW neonates and their management varied between the different clusters  but, surWith regard to management practices, the current analysis revealed three clusters all of which implement actions to manage LBW . Canine Furthermore, it would be very useful to identify which practice is more or less effective in terms of managing LBW neonates, by comparing the clusters on the basis of their mortality rates. Unfortunately, the figures provided by the respondents were inconsistent and unusable for analysis, indicating that neonatal mortality is poorly estimated by canine and feline breeders; likewise for other domesticated species ,33. The This survey allowed us to identify three clusters of canine and feline breeders in terms of current LBW management practices. The management practices described by breeders were complementary in the three clusters and may have a beneficial impact on LBW survival, although the relationship between those practices and the survival of newborns remains to be established. Indeed, further studies are needed to develop practical guidelines to deal with LBW (to prevent and manage them) and thus improve puppies’ and kittens’ survival.


These friendship features (W1) were investigated as potential mediators of the prospective association between teacher-rated ADHD symptoms (W1) and self-rated depressive symptoms (W2) seven months later. Parent–child relationship quality (W1) was tested as a moderator of any indirect effects of ADHD on depression via friendship. ADHD symptoms were inversely associated with friendship presence, friendship quality and positive characteristics of classroom friendship groups. Depressive symptoms were inversely associated with presence and quality of friendships. Friendship quality had indirect effects in the association between ADHD and subsequent depressive symptoms. There was some evidence of moderated mediation, whereby indirect effects via friendship quality attenuated slightly as children reported warmer parent–child relationships. This highlights the importance of considering the quality of friendships and parent–child relationships in children with ADHD symptoms. Fostering good quality relationships may help disrupt the link between ADHD symptomology and subsequent depression risk.Attention deficit/hyperactivity disorder (ADHD) is associated with friendship difficulties. This may partly account for the increasingly recognised association between ADHD and subsequent depression. Little is known about the types of friendship difficulties that could contribute to the association between ADHD and depressive symptoms and whether other relationships, such as parent–child relationships, can mitigate against potential adverse effects of friendship difficulties. In a representative UK school sample (The online version contains supplementary material available at 10.1007/s10802-021-00798-w. Attention Deficit/Hyperactivity Disorder (ADHD) is a DSM-5  are important in increasing vulnerability to depressive symptoms in those with elevated ADHD symptoms. A secondary aim of this study was to investigate whether any indirect effects via elements of friendship were moderated by mother–child or father-child relationship quality. A follow-up exploratory research question was whether indirect effects differed according to sex. The study included children in the first year of secondary school followed over a seven-month timespan.n = 1372). For 1020 of these 1372 children (74.3%), teachers completed the section rating the children’s baseline ADHD symptoms. Of the 1020 children, self-rated depressive symptom data at follow-up were available for 901. Of these, there were 752  for whom data were additionally available on friendship and sociodemographic variables. To address bias that might be caused by missingness, predictors of missingness were investigated  in the last month completed a measure assessing their perception of each parent’s warmth and hostility towards them using the Iowa family interaction rating scale . All analyses presented are adjusted for confounders by including these variables in the models as covariates.ADHD symptoms were standardised meaning a point increase in SDQ hyperactivity score was equivalent to a standard deviation unit increase. Linear regression was used to test the association between ADHD symptoms and depressive symptoms 7 months later. Separate linear and logistic regressions were used as appropriate to test associations between ADHD symptoms and indicators of three friendship elements , and to test associations of these friendship indicators (standardised if continuous) with depressive symptoms. Regressions included the school class as a random effect to account for potential hierarchical data structure or clustering.To test whether the different features of friendship contributed to the association of ADHD and depressive symptoms, indirect effects were tested separately using the [sureg] STATA command. Sureg conducts Seemingly Unrelated Regressions Zellner, , from whZ-tests) of the difference between the observed indirect effect at the mean level of the moderator compared to the mean level of the moderator -1SD (for hostility) or +1SD (for warmth) were conducted. To investigate which path(s) in the indirect effects were being moderated, we additionally tested models with an interaction effect on the path between ADHD symptoms and friendship or the path between friendship and depression symptoms only.Moderated mediation was tested using [sureg] to investigate whether any indirect effects via friendship varied according to the warmth or hostility of parent–child relationships. The model used tested combined moderation of the pathway from ADHD symptoms to friendship and the pathway from friendship to depression symptoms  p = 0.002).ADHD symptoms at baseline were associated with naming fewer friends and lower friendship quality at baseline, both of which were associated with increased depressive symptoms at follow-up. ADHD symptoms were also associated with being part of a classroom friendship group that had higher total difficulties and was rated as less cooperative and more disruptive at baseline. Stability of the best friendship from baseline to follow-up was inversely associated with depressive symptoms Table . ADHD syADHD and depressive symptoms were associated directly and indirectly via friendship quality, both for best friend and top three friends, when tested separately Table . They shThe indirect effect of ADHD symptoms on depressive symptoms via friendship quality was larger for females than males (Supplement p = 0.040) as mother–child relationship warmth increased   children reported that they had been in contact with their mother in the last month and 92.5% with their father. Indirect effects via top three friendships quality attenuated  Fig. .Fig. 1PlAll results were very similar before and after imputation Below is the link to the electronic supplementary material.


Prospective studies have shown differences in some disease risks between vegetarians and nonvegetarians, but the potential biological pathways are not well understood.We aimed to assess differences in concentrations of biomarkers related to disease pathways in people with varying degrees of animal foods exclusion.The UK Biobank recruited 500,000 participants aged 40–69 y (54.4% women) throughout the United Kingdom in 2006–2010. Blood and urine were collected at recruitment and assayed for more than 30 biomarkers related to cardiovascular diseases, bone and joint health, cancer, diabetes, renal disease, and liver health. In cross-sectional analyses, we estimated adjusted geometric means of these biomarkers by 6 diet groups  in 466,058 white British participants and 2 diet groups  in 5535 British Indian participants.We observed differences in the concentrations of most biomarkers, with many biomarkers showing a gradient effect from meat eaters to vegetarians/vegans. Of the largest differences, compared with white British regular meat eaters, white British vegans had lower C-reactive protein [adjusted geometric mean (95% CI): 1.13  compared with 1.43  mg/L], lower low-density lipoprotein cholesterol [3.13  compared with 3.65  mmol/L], lower vitamin D [34.4  compared with 44.5  nmol/L], lower serum urea [4.21  compared with 5.36  mmol/L], lower urinary creatinine [5440  compared with 7280  μmol/L], and lower γ-glutamyltransferase [23.5  compared with 29.6  U/L]. Patterns were mostly similar in British Indians, and results were consistent between women and men.The observed differences in biomarker concentrations, including lower C-reactive protein, lower LDL cholesterol, lower vitamin D, lower creatinine, and lower γ-glutamyltransferase, in vegetarians and vegans may relate to differences in future disease risk. Previous epidemiologic studies have reported differences in disease risks between vegetarians and nonvegetarians. Compared with meat eaters, vegetarians have been shown to have a lower risk of ischemic heart disease , diabeteThe aim of this study is to provide a detailed description of biomarker concentrations relevant for 6 groups of disease outcomes  across white British and British Indian participants with varying degrees of animal-sourced food exclusion, using data from a large population-based cohort in the United Kingdom.The UK Biobank is a prospective cohort of 500,000 people aged 40–69 y, recruited from across the United Kingdom between 2006 and 2010 .At recruitment, all participants also completed a computer-assisted personal interview and had physical measurements and blood samples taken. Permission for access to patient records for recruitment was approved by the Patient Information Advisory Group  in England and Wales and the Community Health Index Advisory Group in Scotland, and all participants gave informed consent to participate in UK Biobank using a signature capture device at the baseline visit .n = 23,858). Participants were also excluded if they did not provide the relevant information to be classified into one of the prespecified diet groups (n = 3591), had no data on biomarkers (n = 3416), or had missing information on fasting time  (n = 30). A participant flowchart of the inclusion and exclusion criteria of this study is shown in Supplemental Figure 1.On the touchscreen questionnaire, participants were asked to self-identify their ethnicity from options of “white,” “mixed,” “Asian or Asian British,” “Black or Black British,” “Chinese,” “Other ethnic group,” “Do not know,” or “Prefer not to answer,” with further subcategories under each option. Participants were included for our analyses if they self-identified as “white” or as “Asian or Asian British” and subsequently as “Indian,” hereafter referred to as “white British” and “British Indian.” The white British population was included as it made up the majority of the UK Biobank population (∼94%), and the British Indian population was included due to the large proportion of vegetarians in this population group . The number of vegetarians in the other ethnic groups was small and did not allow comparisons by diet groups, and therefore other ethnic groups were excluded from these analyses  who declined, were deemed unable to, or where the attempt was abandoned for either technical or health reasons. Nonfasting blood samples were taken from a vein in the inner elbow using a 18-gauge vacutainer needle and barrel or, if that appeared unsuitable, from a vein on the back of the hand using a 21-gauge Safety Lok (BD Vacutainer) butterfly needle connected to a vacutainer barrel . In totaIn total, 34 biomarkers were assayed, which were grouped as cardiovascular related , bone and joint related , cancer related , diabetes related , renal related , and liver related  biomarkers. Although these biomarkers may not be associated with all disease subtypes within the outcome group and may also be associated with outcomes other than the one labeled, they were grouped as such to allow broad interpretation in relation to risks of the several major chronic diseases by diet group.All measured biomarkers, with the exception of lipoprotein(a), rheumatoid factor, estradiol, and urinary microalbumin, were assessed here as potential biomarkers of interest. Lipoprotein(a) was excluded because concentrations are almost entirely genetically determined and therefore unlikely to be influenced by diet or lifestyle . RheumatBaseline characteristics of UK Biobank participants were tabulated by 6 diet groups in the white British population and by 2 diet groups in the British Indian population, as described above. The primary outcomes were 32 biomarker measures, including ratios . All biomarker variables, with exception of the ratios, were log-transformed to approximate a normal distribution. Adjusted geometric (or arithmetic for the ratios) mean concentrations (95% CIs) of each biomarker of interest were calculated using predictive values from linear regressions adjusted for the relevant covariates. For cardiovascular biomarkers, the analyses were restricted to participants not taking lipid-lowering medications; for diabetes biomarkers, the analyses were restricted to participants not taking diabetes medications.2; <20,  20.0–22.4, 22.5–24.9, 25.0–27.4, 27.5–29.9, 30.0–32.4, 32.5–34.9, ≥35, unknown); and model 3 was also adjusted for alcohol consumption  and smoking status . Owing to the large proportion of missing data  for the physical activity variable, physical activity  , fasting status , and, in the case of vitamin D, for month of recruitment; model 2 was also adjusted for BMI .In addition, we also estimated the adjusted arithmetic mean  of all biomarkers, based on model 3. All analyses were repeated separately for women and men in both ethnicities . For each baseline characteristic and each biomarker of interest, Wald tests were used to assess heterogeneity between the 6 diet groups in the white population and the 2 diet groups in the Indian population. For the white British population, Bonferroni-corrected pairwise comparisons between the 6 diet groups were also reported. All statistical analyses were performed using Stata release 15.1 (StataCorp), and 2-sided Supplemental Tables 1–3. On average, compared with meat eaters in the same ethnic group, white British low-meat, poultry, or non–meat eaters  were slightly younger, whereas British Indian vegetarians were slightly older. Generally, low-meat, poultry, or non–meat eaters also had lower BMI (not in British Indians) and were less likely to report heavy drinking , heavy smoking (≥15 cigarettes per day), and lipid-lowering or diabetes medication use.After the prespecified exclusions , the curSupplemental Tables 4–27. For the white British population, all results described below represent significant differences after Bonferroni correction for pairwise comparisons across the 6 diet groups and by using regular meat eaters as the reference group, unless otherwise stated.Geometric mean biomarker concentrations or arithmetic mean of biomarker ratios by diet groups and ethnicities based on model 3 are shown as For cardiovascular-related biomarkers, compared with regular meat eaters, white British low-meat, poultry, and non–meat eaters generally had lower serum concentrations of total cholesterol, LDL cholesterol, ApoB, C-reactive protein, total to HDL cholesterol ratio, and ApoB to ApoA1 ratio . The vegFor bone and joint–related biomarkers, both white British vegetarians and vegans  and BritFor cancer-related biomarkers, in white British participants, all other diet groups had higher serum SHBG than regular meat eaters, whereas vegetarians and vegans had lower IGF-I concentrations . In men,For diabetes-related biomarkers, in the white British population, there were minimal differences in HbA1c concentrations and no significant difference in glucose concentrations ; no signFor renal-related biomarkers, compared with the respective meat eaters in both ethnicities, the other diet groups had lower concentrations of serum and urinary creatinine, lower urea, but higher urinary potassium to creatinine ratio, whereas the vegetarians had higher cystatin C concentrations and higher urinary sodium to creatinine ratio . In the For liver-related biomarkers, the low-meat, poultry, and non–meat eaters in both ethnic groups had lower GGT and ALT concentrations than meat eaters . In whitFor most biomarkers, differences between diet groups were similar across all levels of covariate adjustment . Of the In this large British cohort, we observed differences in the concentrations of many biomarkers by diet group. Of the largest differences, vegetarians and vegans had lower average concentrations of blood cholesterol, C-reactive protein, vitamin D, urea, creatinine (in both blood and urine), and some liver enzymes (GGT and ALT) but higher potassium to creatinine ratios compared with meat eaters. The differences in these disease-related biomarkers may reflect physiologic differences and/or differences in underlying disease pathology and therefore future risk of the relevant health outcomes.We saw clear differences in the concentrations of many blood lipids by diet group, consistent with previous smaller observational studies and small randomized trials that also reported lower total and LDL cholesterol (or non-HDL cholesterol)  or lowerPrevious studies have suggested that vegetarians or vegans might have poorer bone health than meat eaters , 10, 26.Our observation of higher SHBG concentrations in the white British low to non–meat eaters was consistent with some previous studies that reported slightly higher concentrations in vegans , althougPrior epidemiologic studies have reported a lower risk of diabetes in vegetarians than nonvegetarians, at least partly explained by differences in BMI , 48, 49.There has been limited evidence on comparisons of renal biomarkers between vegetarians and nonvegetarians, although several small studies have reported lower serum  or urinaOf the very few studies that reported on liver-related biomarkers associated with vegetarian diets, 1 Chinese study reported lower GGT concentrations in vegetarians than omnivores , which wThe current study included close to 500,000 white British and British Indian participants, as well as examined >30 biomarker measures, and is therefore the largest comprehensive study on biomarker concentrations by diet groups (up to 6 groups in the white British population), as well as the first report, to our knowledge, on differences in several biomarkers by diet group. We also presented results with varying levels of adjustment; although BMI was shown to be an important factor for explaining at least partly the differences in several biomarker concentrations by diet group, BMI was derived based on objectively measured height and weight in the current study, and therefore residual confounding by BMI should be minimal, although residual confounding from other measures of adiposity cannot be ruled out. Of the limitations, some self-selection bias might be present in the cohort, which limits generalizability of the findings to the wider population. Because the study is cross-sectional, causality cannot be determined. As with any studies involving biomarker assays, some degree of laboratory drift might also be present, although this is expected to be nondifferential by diet group. Finally, our analyses were focused on broad classifications of habitual diet groups with varying degrees of animal-sourced food exclusion; the extent to which any differences might be driven by individual foods therefore requires further examination.We observed differences in the concentrations of many biomarkers by vegetarian diet group in both white British and British Indian participants in this large UK population. Our study confirmed previous observations of lower blood lipids and, in some settings, lower serum vitamin D in the vegetarians and vegans, but it also provided the first comprehensive data, to our knowledge, on differences in many other biomarkers, including lower concentrations of C-reactive protein (in the white British population only), urea, blood and urinary creatinine, GGT, and ALT but higher potassium to creatinine ratio, among other differences, in the vegetarians and vegans compared with meat eaters. It is likely that many of these differences in biomarkers are due to differences in dietary composition and nutritional status between the diet groups, but it is also possible that some of the differences are due to residual confounding by adiposity and other aspects of lifestyle. Because these biomarkers are known to be associated with disease risk, the observed differences might be indicative of differences in physiology and/or underlying disease pathology and, in turn, future risks of diabetes and cardiovascular, bone and joint, cancer, renal, and liver diseases. Future work should aim to replicate these findings in other settings, and prospective studies should investigate whether possible differences in disease risk are present by diet group.nxab192_Supplemental_FileClick here for additional data file.


Stimulants are the most effective treatment for Attention Deficit/ Hyperactivity disorder (ADHD). In addition, studies have shown that nicotine dependence in patients with ADHD is probably best explained by self-medication. The question is whether this is also true for caffeine use and caffeine dependence. The aim of our study was, therefore, to examine the relationship of ADHD symptoms, caffeine consumption, caffeine use disorder (CUD) and well-being. We hypothesized that those who have more ADHD symptoms and regularly consume caffeine have higher psychological well-being than those who have more ADHD symptoms, but do not consume caffeine.N = 2,259, 70.5% male, mean age 34.0) filled out the 10-item Caffeine Use Disorder Questionnaire (CUDQ), the Adult ADHD Self-report Scale (ASRS) and the WHO-5 Well-Being Index (WHO-5) and were asked about their caffeine consumption habits in an online survey.A general population sample  and negatively with the WHO-5 (β = −0.259).Caffeine consumption was not associated with ADHD symptom severity and thus not likely to represent self-medication. On the contrary, caffeine use disorder severity is associated with more ADHD symptoms and both caffeine use disorder and ADHD are associated with lower well-being. Attention-deficit/hyperactivity disorder (ADHD) can be characterized by a pattern of attention deficit, hyperactivity and/or impulsivity, which interferes with development or daily functioning . The preStimulant medication  is an evidence based and accepted treatment option for ADHD , 9. TherThe effects of caffeine on ADHD symptoms have been studied in several animal studies, comparing spontaneous hypertensive (SHR) rats with Wistar (WIS) rats , 14 or WA review of studies from the 1970–80s  found thGeneral caffeine consumption patterns and ADHD have been investigated in cross-sectional studies with adults (mainly students) and children. Martin et al.  found thIn general, these results suggest that habitual caffeine consumption has a positive correlation with the presence of ADHD symptoms/diagnosis. It is important to note that research in this field is extremely heterogeneous regarding the age of participants as well as the measurement of ADHD and caffeine consumption. The majority of studies did not separate the consumption of various caffeinated products and there were also differences in the main focus of the studies: some of them focused mainly on caffeine consumption, and ADHD was a more peripheral topic , while in other studies ADHD was the main focus instead of caffeine consumption . It is also important to note that, due to its effects on different neurotransmitter systems, caffeine can have both beneficial and detrimental effects not only on ADHD, but on several other mental disorders as well, such as mood disorders, anxiety disorders and schizophrenia, and besides the possible self-medication motives, some patients may consume it to counteract the side effects of their medication . The freThe possibility that caffeine is used by people with ADHD– or ADHD-like symptoms–as a kind of self-medication strategy has been raised by several authors , 19, 20,N = 2,259) was asked about its caffeine consumption habits, ADHD symptoms and well-being in a cross-sectional online survey using convenience sampling. The questionnaire was presented on one of the biggest and most visited news website of Hungary (www.444.hu) and adults (above 18 years) who consume caffeine at least weekly were invited to participate.A sample from the adult general population , energy drink, cola and caffeine pill consumption on an eight-point scale . Total daily caffeine consumption was computed from the daily use of different caffeinated beverages. The consumption of coffee, tea, energy drink and cola was dichotomized (consumes it daily or not). The method of calculation of caffeine content was published elsewhere .Participants filled out the 10-item Caffeine Use Disorder Questionnaire (CUDQ), which aims to assess the presence of caffeine use disorder symptoms during the last 12 months. The answers are scored on a four-point Likert scale , but the items were transformed into binary answers by combing the last three answering options into one “yes” answer. The discriminative value and severity of the various items of CUDQ were reported in another article . InternaWe used the Adult ADHD Self-Report Scale-V1.1 (ASRS-V1.1) Part A  for the The five-item WHO Well-Being Index (WHO-5) was used for the evaluation of psychological well-being. The one-factor WHO-5 had excellent internal consistency in a previous study on a representative sample in Hungary (α = 0.85)  and in tADHD symptoms were used as an independent categorical variable in the Chi-square tests and ANOVAs. The ADHD categories were based on the recommendations of Kessler et al.  (see in post hoc tests. The bivariate associations of the variables were examined by Pearson correlations.The probability of the consumption of each caffeinated beverage (coffee/tea/energy drink/cola) in the four ADHD groups was compared by using Chi-square tests. We also compared the four ADHD groups regarding the magnitude of daily caffeine consumption and caffeine use disorder symptoms by using ANOVAs with Games-Howell Two path models with observed variables were used to explore the relationship between self-reported ADHD symptoms, caffeine consumption, caffeine use disorder symptoms and psychological well-being. We used the total score on the ASRS-V1.1 as a continuous independent variable, the score on WHO-5 as a continuous dependent variable and the CUDQ total score as a continuous mediator variable in both models. We used the total daily caffeine consumption as a continuous mediator variable in the first model and coffee, tea, cola and energy drink consumption as dichotomous mediator variables (consumes it daily/does not consume it daily) in the second model. In the first model, we used the maximum likelihood estimation method (ML) because all variables were continuous. In the second path model we used probit regression with WLSMV estimation and delta parameterization because of the dichotomous mediator variables. We used the STDYX output of Mplus to determine the standardized regression coefficients (β) and the indirect effects.2-test statistic, the Comparative Fit Index (CFI)  (Model fit was investigated by examining χve 0.95) –44, the ve 0.95) –45, the ve 0.95) , 44, 46 ve 0.95) .The two path analyses were performed with MPLUS 6.0  and the Most participants (70.5%) were male, and the mean age was 34.0 years (SD = 9.3 years). This is a generally well-educated group with 73.5% having a college degree or higher, 24.9% with a high school diploma, and only 1.6% with elementary school or vocational school as the highest educational attainment. Most of the participants were employed with 77.5% having a full-time job, 10.2% having less than full-time job and “only” 12.3% being unemployed. The sample was mainly urban with 63.1% of the participants living in Budapest, 31.1% living in other cities, and only 5.8% living in a town or village.Almost all participants (92.1%) were daily caffeine users. The average daily caffeine consumption was 255.40 mg (SD = 145.36) for males  and 223.35 mg (SD = 125.61) for females , which is higher than the average consumption: 121.70 mg/day for males (1.2 cups/day) and 123.1 mg/day for females (1.2 cups/day) in Hungary in 2009 . Particin = 1,351) belonged to the low negative category, 25.8% (n = 583) to the high negative category, 9.3% (n = 210) to the low positive category, and 2.3% (n = 52) to the high positive category .The mean score for the WHO-5 was 8.46 (SD = 2.86) and for the ASRS-V1.1. 8.21 (SD = 4.48). The average age of ADHD symptom detection was 16.8 years (SD = 10.4). Of all 2,259 participants, 59.8%  = 0.276, p = 0.843] but not for CUDQ scores . There was no difference between the four ADHD groups in daily caffeine consumption , but the groups had significantly different CUDQ scores . The post-hoc test showed that each group significantly differed from the others: the low negative ADHD group had the lowest CUDQ score compared to the other three ADHD groups (p < 0.001), the high negative ADHD group significantly differed from the low positive (p = 0.014) and high positive ADHD groups (p < 0.001), and the low positive ADHD group also differed from the high positive ADHD group (p = 0.015)  .The correlations of the variables are presented in ADHD → CUD → well-being , where more ADHD symptoms predict more CUD symptoms and more CUD symptoms predict lower well-being, and (2) total caffeine consumption → CUD → well-being , where higher total daily caffeine consumption predicts more CUD symptoms and more CUD symptoms predict lower well-being.The first path analysis, which included ADHD symptoms, total caffeine consumption, and caffeine use disorder symptoms resulted in a saturated model. The unstandardized and standardized regression coefficients of the first path analysis are depicted on 2 = 122.246, df = 6, p < 0.001; CFI = 0.854; TLI = 0.488; RMSEA = 0.094 (CI: 0.080–0.109)]. Based on the examination of the modification indices, the covariances between the four caffeinated beverages were introduced to the model. This modified path analysis was a saturated model. The unstandardized and standardized regression coefficients of the second path analysis are depicted on ADHD → CUD → well-being , which also appeared in the first path model, (2) coffee → CUD → well-being , where coffee consumption was associated with more CUD symptoms and more CUD symptoms with lower well-being, and (3) energy drink → CUD → well-being , where energy drink consumption was associated with more CUD symptoms and more CUD symptoms predicted lower well-being.The second path analysis, which included ADHD symptoms, coffee, tea, cola and energy drink consumption, CUD symptoms and well-being had poor fit indices [χper se, but rather the problematic use of caffeine that is related to self-reported ADHD symptoms. Looking at the differences between the four ADHD groups, the relationship seems to be linear: an increased probability of ADHD is associated with an increased number of caffeine use disorder symptoms. Caffeine consumption did not mediate the relationship between ADHD symptoms and well-being, but caffeine use disorder symptoms were a significant mediator in both path analyses with reduced well-being in participant with more caffeine use disorder symptoms. Together, these findings suggest that the hypothesis of (successful) self-medication does not apply to ADHD symptoms and caffeine consumption, but it seems that those who have more ADHD symptoms may be more prone to develop caffeine use disorder regardless of the magnitude of caffeine consumption. This result is partly contradicting and partly in line with the findings of Cipollone et al. . Zsolt Horváth was supported by the ÚNKP-21-4 New National Excellence Program of the Ministry for Innovation and Technology from the source of the National Research, Development and Innovation Fund.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.


ADHD is a neurodevelopmental disorder with a long trajectory into adulthood where it is often comorbid with depression, substance use disorder (SUD) or obesity. Previous studies described a dysregulated dopaminergic system, reflected by abnormal reward processing, both in ADHD as well as in depression, SUD or obesity. No study so far however tested systematically whether pathologies in the brain’s reward system explain the frequent comorbidity in adult ADHD. To test this, we acquired MRI scans from 137 participants probing the reward system by a monetary incentive delay task (MIDT) as well as assessing resting-state connectivity with ventral striatum as a seed mask. No differences were found between comorbid disorders, but a significant linear effect pointed toward less left intrastriatal connectivity in patients depending on the number of comorbidities. This points towards a neurobiologically impaired reward- and decision-making ability in patients with more comorbid disorders. This suggests that less intrastriatal connectivity parallels disorder severity but not disorder specificity, while MIDT abnormalities seem mainly to be driven by ADHD. ADHD is a neurodevelopmental disorder, primarily characterized by inattentiveness, impulsiveness and hyperactivity . PrevaleOne of the functional brain networks that potentially links the emergence of ADHD and comorbid disorders is the reward system. Extensive literature demonstrates subjects with ADHD show altered behavior and neural activation patterns during both the anticipation and reward of receipts , 10, in An important feature of the comorbid disorders mentioned above is that each of them is also associated with altered reward processing, as recently discussed . Hence tTherefore, In the current study, we investigate the reward network in subjects suffering from ADHD and various comorbid disorders where alterations in the reward network have been suggested to play a role, namely major depressive disorder (MDD), substance use disorder (SUD), and obesity. Previous neuroimaging research in MDD, obesity, and SUD point to the reward system as common denominator . For SUDWe will investigate both functional brain activation and functional connectivity. For the former, we will use the Monetary Incentive Delay task , 21 to mWe address three research questions. (1) Is there a general effect of comorbidities on MID task performance and neural activation and connectivity? (2) Is there a dose effect of comorbidities? (3) Is there a specificity effect of comorbidities? We hypothesize that subjects with ADHD and comorbid disorders show a larger impairment on MID performance and altered neural activation patterns than patients suffering from ADHD alone, in particular in the NAcc. We also expect altered functional connectivity with the NAcc region in subjects with comorbidities. We further expect that multiple comorbidities will be associated with larger impairments. We hypothesize that subjects with comorbid MDD will show decreased reward-related speeding/sensitivity to reward, and hypoactivation during reward anticipation, while for subjects with SUD and obesity we expect stronger reward sensitivity and hypoactivation during reward receipt.n = 137 physically healthy subjects (for demographics see Table The study includes 2). As we had mainly access to patients who used alcohol and amphetamine as primary drug of abuse, included these as major inclusion criteria. Other patients who were screened were excluded because of polytoxicomania.Inclusion criteria were (1) age between 18 and 50 years; (2) sufficient understanding of the Dutch/German language, (3) established childhood diagnosis of ADHD made by a specialist (DSM-IV criteria) and additional validated ADHD-questionnaires , and (4) additional comorbid condition of depression (according to DSM-IV), alcohol- or amphetamine-dependence (DSM-IV), or overweight , serious acute or chronic physical diseases, pregnancy, as well as exclusion criteria of the MRI examination. Only patients with at least 4 weeks of stable medication regimen were included. Stimulants, alcohol, and nicotine were stopped on the day of the scan. Patients with antipsychotic medication were excluded. Participants were examined by a registered psychiatrist in Frankfurt in a specialized ADHD-outpatient clinic. In Nijmegen selection and diagnostic procedures were conducted by trained psychiatrists or psychologists. No inpatients participated in the study. No inpatients did participate in the study.For assessment of psychosocial functioning, a blinded and trained psychiatrist rated in the Frankfurt dataset the global assessment of functioning (GAF) and the clinical global impression (CGI) score by clinical interview.https://apps.who.int/trialsearch/). Subjects gave written informed consent. The subjects received 10€ per hour for participation. In addition, the monetary gain of the Monetary Incentive Delay Task of all three measurement dates was paid out to the volunteers.The project was carried out in accordance with the provisions of the Declaration of Helsinki  and the European guidelines on Good Clinical Practice and was approved by the Ethics Committee of the Medical Faculty of the J.W. Goethe University Frankfurt am Main (reg.no. 256/16) and in Nijmegen by the Radboud University (reg.no. ABR64162). The study was registered in the German register of clinical studies on 16/11/2016 with the study ID DRKS00011248, indexed in the WHO clinical trial search platform  or miss . In order to increase the expectation of rewards, the participants were informed beforehand that money won in the MID task will be paid out in cash directly after the measurement.The behavioral measures  were tested for the same models as the fMRI analysis in an ANOVA model in SPSS 25. First, we corrected for age, sex, and site and compared with and without comorbidity. Second, we defined the number of comorbid disorders as ordinal variable and included it as a factor , and third, we defined a factor for all combinations of different comorbid disorders.Images from participants were realigned, slice-time corrected, spatially normalized to standard stereotactic space , resampled to 3 mm isotropic voxels, and smoothed with 8 mm full-width at half-maximum Gaussian kernel. A band-pass filtering was used in the frequency band frequency bands to 0.01–0.1 Hz to get rid of non-neural signals for the resting-state data. Further noise correction was done by regressing out motion parameters derived from the realignment procedure, the 1st order derivative of movement parameters. Data analysis was done with SPM12 in addition to CONN toolbox for preprocessing of resting-state data. A band-pass filter reduced frequency bands to 0.01–0.1 Hz, additional noise correction of rs-fMRI was done by regressing out motion parameters (from rigid-body transformation), their 1st order derivative and correcting for cerebrospinal-fluid signal and white-matter-signal  .For seed-voxel connectivity see Fig. , we usedd = 0.58). For an alpha error of 0.05 and a power of 0.8, a sample size of n = 136 was calculated. For the simpler comparison ADHD with versus ADHD without comorbid disorders, a sample size of n = 76 was calculated. For the regression model with the number of comorbid disorders, a sample size of n = 81 was calculated. Sample size calculations were done with GPower Version 3.1 [Sample size was calculated for a ANOVA analysis with four groups and at least one covariate for site. We estimated effect sizes from a meta-analysis of the MID task 7  [First-level correlation maps were calculated by extracting the residual BOLD-time course from the seed ROIs and correlating these with the other voxels within the brain. These correlation coefficient maps were then converted via a normally distributed z-score (Fisher transformation). Transformed correlation maps were used for directed paired < 0.001) .t-tests, the third one as F-test.We defined three hypotheses to test for the effects of comorbidity. First, we compared patients with and without comorbidity. Second, we looked at a linear effect of comorbidity . Third, we compared via F-test contrast all 8 subgroups of different combinations of depression, SUD, or obesity. All tests included age, gender, and site as covariates. The first two tests were defined as We calculated differences in medication between the comorbidity subgroups with an univariate general linear model in SPSS. The comorbidity  was used as dependent variable, whereas medication type  was used as independent variable with age, sex, and site as covariates.t-test revealed a trend-wise non-significant difference for the number of ADHD symptoms in only ADHD vs. ADHD with comorbidity with a p-value of p = 0.09 (t(130) = 1.69) for the MID-sample, and p = 0.09 (t(135) = 1.67) for the rs-fMRI-sample.A two-sample p < 0.001, F = 14.69), stimulants and other medication did not differ among groups.Depressive disorders were significantly associated with more antidepressant use .Neither the direct comparison between “pure” ADHD and ADHD with comorbidity, the test of linear effects on the number of comorbidities nor the comparison among groups yielded any significant result for the analysis of the activation in the ROI for the main contrast anticipation WIN > anticipation CONTROL. The behavioral data did not reveal any significant differences for the hypotheses mentioned above either , but CGI  was significantly correlated with the extracted task-betas.We extracted the mean beta from the accumbens-ROI-mask and correlated it to GAF and CGI in p = 0.9, r = −0.013) nor hyperactivity  were significantly correlated with the mean extracted beta.To test whether a certain ADHD dimension specifically influenced our results, we extracted the number of symptoms and correlated these with the number of symptoms with extracted mean beta from the nucleus accumbens for the MID task. Neither inattention . However, we did not use p = 0.005 for cluster-threshold definition because of recent critique and controversy [The difference between ADHD without and with comorbid disorder did reach FDR-corrected significance when using a lower troversy .k = 433, MNI −30/−6/+14, T(130) = 3.37).The comparison of accumbens seed connectivity in dependence of the number of comorbidities showed a significant decrease in FC between accumbens and parts of the left putamen and the left insula  nor CGI  were significantly correlated with the extracted task-betas.We extracted the mean beta from the significant FC-cluster and correlated it to GAF and CGI in p = 0.97, r = −0.004) or hyperactivity  were significantly correlated with the mean extracted beta value.In order to test whether a particular ADHD dimension specifically influenced our results, we extracted the number of symptoms. We correlated the number of symptoms with the extracted mean beta value of the significant cluster in the left putamen in the rs-fMRI analysis. None of inattention  when not accounting for the number of two tests, so it suggests that a lower activation and connectivity goes together with a lower CGI. In keeping with this observation, CGI but not GAF was correlated with the number of comorbid disorders. We were not able to find a correlation between a specific symptom dimension of the DSM-scale in our sample. While this points toward an effect non-specific for ADHD, a limitation is the fact that in an all ADHD sample the range of symptoms is necessarily low as per definition five or symptoms have to be met for diagnosis. The fact that we find a difference in CGI but not ADHD symptom scores between comorbid groups emphasizes the need to take comorbid disorders into consideration when studying ADHD, as their presence may influence the severity of a disorder even when the base number of symptoms remains similar.A general limitation of our study is its focus on categorical categories. Other studies like the ABCD-study or the IMAGEN-cohort looked longitudinally at more fine-grained psychopathological dimensions. However, the absolute number of cases with full-blown comorbid diagnoses are low and SUD diagnosis is rare in children and adolescents. Studies which draw samples from the general population need a much higher sample size to sample enough participants in order to have participants with a high load of psychopathology. In contrast to this, our study may not be representative, but was able to recruit ADHD patients with a complex comorbidity pattern. Another strength of the current study is that we can combine multiple imaging measures in the same well-defined sample. This allowed us to look at the neural response of the reward network both from a task-based and resting-state perspective. Our results indicate that subtle alterations of the reward network may be visible in resting state but not picked up by reward-related functional imaging tasks, which is an important caveat to take into consideration when researching reward network functioning in patient groups.More specific limitations are the lack of a healthy control group. While our paradigms have been established in ADHD , 15, 33 However, the most interesting conclusion comes from the lack of specific diagnosis-related diagnosis-related groups. This is countered by—at least in the accumbens-connectivity analysis—a linear decrease of intrastriatal connectivity with the numbers of comorbidity. This points to some interesting transdiagnostic effects: Is number of comorbidity related to a general drop in psychosocial functioning and general severity of illness? In our study, we did not have access to individual data which would allow to answer this question empirically. So far, studies with more fine-grained dimensional measures in contrast to categorial disorders did not demonstrate decisive advantages or differences when trying to understand the underlying neurobiology: Some of these issues were discussed in more detail in a recent review . Since iIn conclusion, we demonstrate functional connectivity alterations with the NAcc in subjects with ADHD and comorbid MDD, SUD, or obesity, as a function of the number of comorbidities. No specific effects of any one comorbidity were detected, and neither performance nor neural activation during the MID task was influenced by comorbidities. Our study is more in line with a general dysregulation of the reward network, linked to disease severity which is independent of a specific disorder. This was found for accumbens-connectivity but not for the reward anticipation task. While this task has been able to detect blunted reward anticipation before , other ssupplementary table